Abstract

Embodiments are directed to Molecular Guidance System (MGS) peptides, MGS compounds comprising an MGS peptide, MGS-cargo conjugates comprising an MGS peptide conjugated to a cargo, and methods associated therewith. An example MGS peptide is selected from SEQ ID NOs: 2-6 and 8-26. In some embodiments, the MGS peptide may selectively bind to cardiomyocytes.

IPC Classes  ?

• A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent
• A61K 47/60 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an organic macromolecular compound, e.g. an oligomeric, polymeric or dendrimeric molecule obtained otherwise than by reactions only involving carbon-to-carbon unsaturated bonds, e.g. polyureas or polyurethanes the organic macromolecular compound being a polyoxyalkylene oligomer, polymer or dendrimer, e.g. PEG, PPG, PEO or polyglycerol
• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides

Abstract

Embodiments are directed to systems, devices, and methods for altering midbrain dopamine signals. An example system comprises stimulation circuitry configured to output a neuromodulation signal to a nerve target of a subject, and processor circuitry configured to cause the stimulation circuitry to output the neuromodulation signal to the nerve target as timed with an event, and in response, cause alteration to midbrain dopamine signals to the subject.

IPC Classes  ?

• A61N 1/36 - Applying electric currents by contact electrodes alternating or intermittent currents for stimulation, e.g. heart pace-makers
• A61N 1/04 - Electrodes

Abstract

A vapor cell includes a cell body and a cell window anodically bonded to the cell body. The cell window and the cell body define a vapor cavity configured to contain a vapor comprising a plurality of alkali atoms.

Abstract

An example genetically engineered effector cell comprises a cell carrying an exogenous polynucleotide sequence that includes a receptor element that encodes a chimeric antigen receptor (CAR) including an extracellular antigen binding domain operably linked to a transmembrane domain, and an intracellular signaling domain, an immune-regulator element that encodes an immune-regulator complex including an immune-regulator and a membrane-bound trafficking molecule, an actuator element that encodes a transcription factor binding site that upregulates synthesis of an effector protein and the immune-regulator complex in response to the extracellular antigen binding domain of the CAR binding to an antigen of a target cell, and an effector element that encodes the effector protein, wherein, in response to the extracellular antigen binding domain of the CAR binding to the antigen of the target cell, the genetically engineered effector cell is configured to activate and to synthesize the effector protein and the immune-regulator complex.

IPC Classes  ?

• C12N 5/078 - Cells from blood or from the immune system
• C07K 14/54 - Interleukins [IL]
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C12N 15/11 - DNA or RNA fragmentsModified forms thereof
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells

Abstract

The present invention relates to compositions for effective targeted delivery of a gene editing agent or transcriptional modulator to a target cell, as well as methods of use thereof for the treatment of diseases including cancer and genetic diseases. In some embodiments, the invention relates to a composition for targeted delivery of a genome editing agent or transcriptional modulator, wherein the composition comprises a delivery vehicle comprising a targeting moiety specific for binding to a cell or tissue of interest. In some embodiments, the genome editing agent comprises a ribonucleoprotein (RNP) complex comprising a CRISPR Cas protein and a guide RNA.

IPC Classes  ?

• C12N 15/11 - DNA or RNA fragmentsModified forms thereof
• A61K 9/51 - Nanocapsules
• A61K 48/00 - Medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseasesGene therapy
• C07K 19/00 - Hybrid peptides
• C12N 9/10 - Transferases (2.)
• C12N 9/22 - Ribonucleases
• C12N 15/54 - Transferases (2)

Abstract

The disclosure provides pharmaceutical preparations containing (2R,6R)-hydroxynorketamine, or (R)- or (S)-dehydronorketamine, or other stereoisomeric dehydro or hydroxylated ketamine metabolite. The disclosure provides pharmaceutical preparations containing (2R,6R)-hydroxynorketamine, or (R)- or (S)-dehydronorketamine, or other stereoisomeric dehydro or hydroxylated ketamine metabolite. The disclosure provides pharmaceutical preparations containing (2R,6R)-hydroxynorketamine, or (R)- or (S)-dehydronorketamine, or other stereoisomeric dehydro or hydroxylated ketamine metabolite. The disclosure also provides novel ketamine metabolite prodrugs. The disclosure provides methods of treating, bipolar depression, major depressive disorder, neuropathic and chronic pain, including complex regional pain synthetic pain disorder (CRPD) by administering a purified ketamine metabolite or a ketamine metabolite prodrug directly to patients in need of such treatment.

IPC Classes  ?

• A61K 31/5375 - 1,4-Oxazines, e.g. morpholine
• A61K 31/137 - Arylalkylamines, e.g. amphetamine, epinephrine, salbutamol, ephedrine
• A61K 31/165 - Amides, e.g. hydroxamic acids having aromatic rings, e.g. colchicine, atenolol, progabide
• A61K 31/222 - Esters, e.g. nitroglycerine, selenocyanates of carboxylic acids of acyclic acids, e.g. pravastatin with compounds having aromatic groups, e.g. dipivefrine, ibopamine
• A61K 31/24 - Esters, e.g. nitroglycerine, selenocyanates of carboxylic acids having an aromatic ring attached to a carboxyl group having an amino or nitro group
• A61K 31/265 - Esters, e.g. nitroglycerine, selenocyanates of carbonic, thiocarbonic or thiocarboxylic acids, e.g. thioacetic acid, xanthogenic acid, trithiocarbonic acid
• A61K 31/4453 - Non-condensed piperidines, e.g. piperocaine only substituted in position 1, e.g. propipocaine, diperodon
• C07C 225/20 - Compounds containing amino groups and doubly-bound oxygen atoms bound to the same carbon skeleton, at least one of the doubly-bound oxygen atoms not being part of a —CHO group, e.g. amino ketones having amino groups bound to carbon atoms of rings other than six-membered aromatic rings of the carbon skeleton
• C07C 229/48 - Compounds containing amino and carboxyl groups bound to the same carbon skeleton having amino or carboxyl groups bound to carbon atoms of rings other than six-membered aromatic rings of the same carbon skeleton with amino groups and carboxyl groups bound to carbon atoms of the same non-condensed ring
• C07C 237/04 - Carboxylic acid amides, the carbon skeleton of the acid part being further substituted by amino groups having the carbon atoms of the carboxamide groups bound to acyclic carbon atoms of the carbon skeleton the carbon skeleton being acyclic and saturated
• C07C 237/20 - Carboxylic acid amides, the carbon skeleton of the acid part being further substituted by amino groups having the carbon atoms of the carboxamide groups bound to acyclic carbon atoms of the carbon skeleton the carbon skeleton containing six-membered aromatic rings
• C07D 295/108 - Heterocyclic compounds containing polymethylene-imine rings with at least five ring members, 3-azabicyclo [3.2.2] nonane, piperazine, morpholine or thiomorpholine rings, having only hydrogen atoms directly attached to the ring carbon atoms with substituted hydrocarbon radicals attached to ring nitrogen atoms substituted by doubly bound oxygen or sulfur atoms with the ring nitrogen atoms and the doubly bound oxygen or sulfur atoms attached to the same carbon chain, which is not interrupted by carbocyclic rings to an acyclic saturated chain
• C07D 295/112 - Heterocyclic compounds containing polymethylene-imine rings with at least five ring members, 3-azabicyclo [3.2.2] nonane, piperazine, morpholine or thiomorpholine rings, having only hydrogen atoms directly attached to the ring carbon atoms with substituted hydrocarbon radicals attached to ring nitrogen atoms substituted by doubly bound oxygen or sulfur atoms with the ring nitrogen atoms and the doubly bound oxygen or sulfur atoms separated by carbocyclic rings or by carbon chains interrupted by carbocyclic rings
• C07D 295/15 - Heterocyclic compounds containing polymethylene-imine rings with at least five ring members, 3-azabicyclo [3.2.2] nonane, piperazine, morpholine or thiomorpholine rings, having only hydrogen atoms directly attached to the ring carbon atoms with substituted hydrocarbon radicals attached to ring nitrogen atoms substituted by carbon atoms having three bonds to hetero atoms with at the most one bond to halogen, e.g. ester or nitrile radicals with the ring nitrogen atoms and the carbon atoms with three bonds to hetero atoms attached to the same carbon chain, which is not interrupted by carbocyclic rings to an acyclic saturated chain
• C07D 295/155 - Heterocyclic compounds containing polymethylene-imine rings with at least five ring members, 3-azabicyclo [3.2.2] nonane, piperazine, morpholine or thiomorpholine rings, having only hydrogen atoms directly attached to the ring carbon atoms with substituted hydrocarbon radicals attached to ring nitrogen atoms substituted by carbon atoms having three bonds to hetero atoms with at the most one bond to halogen, e.g. ester or nitrile radicals with the ring nitrogen atoms and the carbon atoms with three bonds to hetero atoms separated by carbocyclic rings or by carbon chains interrupted by carbocyclic rings

Abstract

A method, apparatus and system for preventing hallucinations in a language model include monitoring a generation of a token by the language model, determining a measure of uncertainty for the generated token, comparing the determined measure of uncertainty with an expected measure of uncertainty, such as a predetermined threshold, generating at least one think token if the determined measure of uncertainty does not comply with the expected measure of uncertainty, and communicating the at least one generated think token to the language model to cause the language model to perform at least one additional computation for determining the token.

IPC Classes  ?

• G06F 40/40 - Processing or translation of natural language

Abstract

The disclosure provides a method for modulating gene expression in a cell-specific manner in response to an intracellular or extracellular stimulus. The disclosure provides a platform entitled the PROTEGE platform, which comprises a DNA binding module and a transcription factor binding module, referred to herein as PGM. The PGM binds the promoter region of a target gene with sequence specificity through the DNA binding module and also binds a TF through the TF-binding module. When the TF is activated in response to an intracellular or extracellular stimulus, it binds the PGM and, due to its close proximity to the promoter of the gene, modulates expression of a target gene in response to the stimulus.

IPC Classes  ?

• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• A61K 48/00 - Medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseasesGene therapy
• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• C12N 7/00 - Viruses, e.g. bacteriophagesCompositions thereofPreparation or purification thereof
• C12N 9/22 - Ribonucleases

Abstract

An example sensor includes an upconversion layer comprising a plurality of crystals configured to convert electromagnetic radiation comprising a first range of wavelengths greater than 1100 nm to electromagnetic radiation comprising a second range of wavelengths less than or equal to 1100 nm. The sensor also includes a photo-sensitive silicon substrate configured to detect the electromagnetic radiation comprising the second range of wavelengths. The sensor also includes a light source configured to emit electromagnetic radiation comprising the first range of wavelengths to the upconversion layer.

IPC Classes  ?

• G01J 1/58 - Photometry, e.g. photographic exposure meter using luminescence generated by light

Abstract

A method, apparatus and system for determining question-answer pairs for finetuning a language model includes, for at least two layers of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, and determining at least one question-answer pair intended to increase a semantic understanding of content based on a question generated using at least one word of the set of words and the content to which the question-answer pair is applied. A language model can then be finetuned using the determined question-answer pairs.

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06F 16/242 - Query formulation

Abstract

An example light detection and ranging (lidar) system includes a sensor comprising a plurality of photodetectors and a metalens configured to focus light onto the plurality of photodetectors of the sensor. The metalens is disposed on a sensor surface.

IPC Classes  ?

• G01S 17/00 - Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G02B 1/00 - Optical elements characterised by the material of which they are madeOptical coatings for optical elements
• G01S 17/88 - Lidar systems, specially adapted for specific applications
• G01S 17/48 - Active triangulation systems, i.e. using the transmission and reflection of electromagnetic waves other than radio waves

Abstract

In general, the disclosure describes techniques for matching, by a computing system, an interpretation, written in an intermediate language, of an equation descriptive of one or more physical phenomena to one or more templates for a target platform, wherein each of the templates comprises syntax in the intermediate language; and compiling, by the computing system, the one or more templates to generate code for a solver, the code targeted to the target platform.

IPC Classes  ?

• G06F 17/11 - Complex mathematical operations for solving equations
• G06F 17/10 - Complex mathematical operations
• G06F 7/64 - Digital differential analysers, i.e. computing devices for differentiation, integration or solving differential or integral equations, using pulses representing incrementsOther incremental computing devices for solving difference equations
• G06F 30/327 - Logic synthesisBehaviour synthesis, e.g. mapping logic, HDL to netlist, high-level language to RTL or netlist
• G06F 15/00 - Digital computers in generalData processing equipment in general

Abstract

In an example, a method for estimation of the probability of rare events includes determining a control function that guides an Artificial Intelligence (AI) agent towards a rare region of state space; modifying, using the control function, dynamics of the behavior of the AI agent to generate modified dynamics; simulating behavior of the AI agent using the modified dynamics to generate one or more samples that are more likely to enter the rare region of the state space; assigning a weight to each of the one or more generated samples; and estimating probability of one or more rare events in the behavior of the AI agent by fitting a distribution describing behavior of the one or more rare events to the one or more weighted samples.

IPC Classes  ?

• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06N 20/00 - Machine learning

Abstract

Method and apparatus for deidentifying a document comprising a field analyzer for identifying at least one structured field and at least one unstructured field within a document. An attribute analyzer, using a first machine learning model and the identified at least on structured field and at least one unstructured field, identifies at least one attribute within the document. An unstructured field analyzer, using a second machine learning model and the identified at least one attribute from the at least one structured field, identifies at least one attribute within the at least one unstructured field. A redactor redacts the identified at least one attribute in the at least one structured field and the at least one unstructured field to form a deidentified document.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

In an example, a connection header includes a substrate and a plurality of electrical traces. The substrate defines a contact end configured to face an external substrate. The plurality of electrical traces are coupled to an upper surface of the substrate. Each electrical trace includes a micro-spring extending beyond the contact end of the substrate. Each micro-spring includes a stress-engineered thin film configured to impart a curvature profile on the micro-spring. Each micro-spring is configured to electrically contact a contact pad of the external substrate and flex in response to the contact with the contact pad.

IPC Classes  ?

• H01R 12/69 - Fixed connections for flexible printed circuits, flat or ribbon cables or like structures characterised by the terminal deformable terminals e.g. crimping terminals
• H01R 12/73 - Coupling devices for rigid printing circuits or like structures coupling with the edge of the rigid printed circuits or like structures connecting to other rigid printed circuits or like structures
• H05K 3/36 - Assembling printed circuits with other printed circuits

Abstract

A method, apparatus and system configured to provide automated dialog engagement through the use of at least one dialog playbook to guide a dialog conversation between an automated dialog engagement system and an automated dialog engagement system user. The automated dialog engagement system utilizes a large language model (LLM) in conjunction with the at least one dialog playbook to guide the dialog conversation. The LLM provides responses to the automated dialog system user when a response is not available from the at least one playbook.

IPC Classes  ?

• G06F 40/35 - Discourse or dialogue representation
• G06F 40/40 - Processing or translation of natural language

Abstract

Embodiments of the present principles generally relate to methods, apparatuses, and systems for improved long video retrieval by training video language models (VLM) using diverse captions. In some embodiments, a method for improved long video retrieval may include generating a plurality of captions of varying dimensions using one or more Large Language Models (LLM); associating the plurality of captions of varying dimensions to one or more videos in one or more video data sets to generate one or more enhanced video data sets; generating an enhanced VLM by finetuning a pretrained video language model using the generated one or more enhanced video data sets; and retrieving one or more videos with a query using the enhanced VLM having a R@K rank.

IPC Classes  ?

• G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations

Abstract

The present invention provides novel compositions for treating malarial infections comprising prodrugs of ELQ-300 and an oil, wherein the composition can further comprise benzyl alcohol and/or benzyl benzoate, and methods of use thereof.

IPC Classes  ?

• A61K 31/47 - QuinolinesIsoquinolines
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61K 47/44 - Oils, fats or waxes according to two or more groups of Natural or modified natural oils, fats or waxes, e.g. castor oil, polyethoxylated castor oil, montan wax, lignite, shellac, rosin, beeswax or lanolin
• A61K 9/00 - Medicinal preparations characterised by special physical form
• C07D 215/22 - Oxygen atoms attached in position 2 or 4
• A61P 33/06 - Antimalarials

Abstract

Example embodiments relate to improved near-infrared. (NIR) / short-wave infrared (SWIR) quantum efficiency for a silicon image sensor. An example embodiment includes a. light-sensitive pixel configured to detect light signals that illuminate a top side of the light- sensitive pixel is provided, lire light-sensitive pixel includes an absorption region configured to absorb photons and generate electrical charge carriers. The light-sensitive pixel also includes a barrier region configured to block the electrical charge carriers generated within tire absorption region. Additionally, the light-sensitive pixel includes a drain region configured to collect electrical charge earners arising as a. result of dark current or overflow charge from the absorption region. Further, the light-sensitive pixel includes a mirror region located on an opposite side of the absorption region from the top side of the light-sensitive pixel. The mirror region is configured to reflect photons back toward the absorption region.

IPC Classes  ?

• H10F 39/18 - Complementary metal-oxide-semiconductor [CMOS] image sensorsPhotodiode array image sensors

Abstract

A method and system for generating mineral potential maps (MPM) is described in embodiments consistent with the present disclosure. In some embodiments, a method for generating an MPM includes extracting features from mineral mapping data (MMD) from a plurality of data source modalities using one or more feature extraction networks; fusing the features extracted by each of the one or more feature extraction networks to produce fused multimodal features; projecting the fused multimodal features into an embedding space that is trained to classify the features' mineral deposit potential; and generating mineral potential data indicating a spatial output of mineral deposit potential.

IPC Classes  ?

• G01V 20/00 - Geomodelling in general

Abstract

In an example, a method for decision-making by an Artificial Intelligence (AI) agent based on risk attitude includes processing an explored state space of an environment to identify one or more potential outcomes for each of a plurality of potential decisions; assigning, based on a utility function, a utility value to each of the one or more potential outcomes for each of the plurality of potential decisions, wherein the utility function depends on a risk parameter indicative of a specified risk preference of the AI agent; determining, based on a predefined sigma algebra defining a set of events that may occur in the environment, a probability of each of the one or more potential outcomes occurring for each of the plurality of potential decisions; selecting a decision from the plurality of potential decisions based on the utility values and the probabilities; and outputting an indication of the decision.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

A hyperdexterous surgical system can include one or more surgical arms coupleable to a fixture and configured to support one or more surgical tools. The system can include an electronic control system configured to communicate electronically with the one or more surgical tools. The control system can electronically control the operation of the one or more surgical tools. The system can include one or more portable handheld controllers actuatable by a surgeon to communicate one or more control signals to the one or more surgical tools via the electronic control system to operate the one or more surgical tools. The one or more portable handheld controllers can provide said one or more control signals from a plurality of locations of an operating arena, allowing a surgeon to be mobile during a surgical procedure and to remotely operate the one or more surgical tools from different locations of the operating arena.

IPC Classes  ?

• A61B 90/50 - Supports for surgical instruments, e.g. articulated arms
• A61B 17/00 - Surgical instruments, devices or methods
• A61B 17/32 - Surgical cutting instruments
• A61B 34/00 - Computer-aided surgeryManipulators or robots specially adapted for use in surgery
• A61B 34/30 - Surgical robots
• A61B 34/37 - Leader-follower robots
• A61B 90/00 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges
• A61B 90/10 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges for stereotaxic surgery, e.g. frame-based stereotaxis
• A61B 90/11 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges for stereotaxic surgery, e.g. frame-based stereotaxis with guides for needles or instruments, e.g. arcuate slides or ball joints
• A61B 90/57 - Accessory clamps
• A61B 90/60 - Supports for surgeons, e.g. chairs or hand supports

Abstract

A silicon-based image sensor has an upconversion layer of crystals. The silicon-based image sensor receives light from a common beam splitter with a second image sensor. An optical band pass filter cooperates with the common beam splitter to pass some of the light to be incident on the upconversion layer of crystals in a second range of wavelengths, which will be absorbed and converted by the upconversion layer of crystals into a third range of wavelengths, and then the third range of wavelengths is transmitted onto the pixels in the silicon-based image sensor. A pulse repetition frequency decoder cooperates with the upconversion layer of crystals to decode a pulse repetition frequency of a laser flash in the second range of wavelengths passed by the optical band pass filter and subsequently upconverted by the upconversion layer of crystals and then captured by the pixels of the silicon-based image sensor.

IPC Classes  ?

• G01J 1/42 - Photometry, e.g. photographic exposure meter using electric radiation detectors
• G01J 1/04 - Optical or mechanical part
• G01J 1/44 - Electric circuits
• H10F 39/00 - Integrated devices, or assemblies of multiple devices, comprising at least one element covered by group , e.g. radiation detectors comprising photodiode arrays

Abstract

Embodiments are directed to Molecular Guidance System (MGS) peptides, MGS compounds comprising at least two MGS peptides, MGS-cargo conjugates comprising an MGS peptide conjugated to a cargo, directly or indirectly through a linker, and methods of using such MGS peptides, MGS compounds, and MGS-cargo conjugates. An example Molecular Guidance System (MGS) peptide is selected from SEQ ID NO: 1-29.

IPC Classes  ?

• C07K 7/08 - Linear peptides containing only normal peptide links having 12 to 20 amino acids
• A61K 38/10 - Peptides having 12 to 20 amino acids
• A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent

Abstract

Improved systems and methods for optical communications are provided that have reduced size, weight, and power use. Such system and methods can find, use between satellites or other distant systems that are in significant relative motion. Such embodiments include the use of a diamagnetically levitated magnetic actuator or other actuator to control the orientation of an optical phased array (OPA), thereby aiming the array at distant target systems. An OPA has reduced size and mass relative to traditional telescopes, providing similar beam apertures while having reduced size, weight, and cost of the OPA and associated actuators. The actuator allows the OPA to omit, or to include fewer, phase shifter elements than previous steerable OPA telescopes, reducing complexity', cost, and power requirements.

IPC Classes  ?

• H04B 10/118 - Arrangements specific to free-space transmission, i.e. transmission through air or vacuum specially adapted for satellite communication
• G02B 6/293 - Optical coupling means having data bus means, i.e. plural waveguides interconnected and providing an inherently bidirectional system by mixing and splitting signals with wavelength selective means
• H04B 10/50 - Transmitters

Abstract

Embodiments are directed to non-transitory computer-readable storage medium, methods, and systems. An example non-transitory computer-readable storage medium comprises instructions that when executed cause a processor circuitry to: identify a biorhythm associated with a time series of biosignal data obtained from a subject which is continuously collected over a time period associated with a cycle of the biorhythm; provide a first set of biorhythm metrics including a presence of an oscillation in the time series of biosignal data and characteristics of the oscillation; and output a health indicator based on the first set of biorhythm metrics.

IPC Classes  ?

• A61N 1/00 - ElectrotherapyCircuits therefor
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G16H 50/00 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics

Abstract

Variable and non-variable transmission mechanisms are provided that include 'radially inverted' pulleys and/or sprockets that are nested, within each other or otherwise overlap in order to exert 'inward' forces onto a compressive belt that transmits power between the pulleys/sprockets. By exerting forces 'inward' onto the belt, the belt can be subjected to net compression everywhere along its length, allowing the belt to be less complex and to have a lower cost, and allowing the transmissions to exhibit reduced size and cost and increased power capacity compared to transmissions that employ 'non-radially inverted' pulleys/sprockets. Two or more 'inner' pulleys or sprockets may be nested within a single 'outer' pulley or sprocket, exerting 'inward' forces onto respective two or more belts in a planetary arrangement, balancing the rotating mass of an inner planet carrier and increasing power rating by distributing transmission from input to output across two or more belts.

IPC Classes  ?

• B62M 9/08 - Transmissions characterised by use of an endless chain, belt, or the like of changeable ratio using a single chain, belt, or the like involving eccentrically-mounted or elliptically-shaped driving or driven wheelTransmissions characterised by use of an endless chain, belt, or the like of changeable ratio using a single chain, belt, or the like with expansible driving or driven wheel
• F16G 13/02 - Driving-chains
• F16H 9/16 - Gearings for conveying rotary motion with variable gear ratio, or for reversing rotary motion, by endless flexible members without members having orbital motion using belts, V-belts, or ropes engaging a pulley built-up out of relatively axially-adjustable parts in which the belt engages the opposite flanges of the pulley directly without interposed belt-supporting members using two pulleys, both built-up out of adjustable conical parts
• F16H 37/02 - Combinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings
• F16H 55/17 - Toothed wheels
• F16H 55/32 - Friction members
• F16H 37/08 - Combinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings with a plurality of driving or driven shaftsCombinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings with arrangements for dividing torque between two or more intermediate shafts with differential gearing
• F16H 37/10 - Combinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings with a plurality of driving or driven shaftsCombinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings with arrangements for dividing torque between two or more intermediate shafts with differential gearing at both ends of intermediate shafts

Abstract

A method, apparatus, and system for efficient machine learning with query-based knowledge assistance includes determining a state of data captured by a sensor in communication with a first edge device to determine if the captured data includes data that is out of distribution based on a trained inference model of the first edge device, if it is identified that an amount of out of distribution data in the captured data is preventing the trained inference model from making an accurate prediction, communicating a request for resources to a second edge device or a server to elicit a response from the second edge device or the server including resources required to update the trained inference model, receiving the requested resources, updating the trained inference model using the received resources, and making a prediction for the received captured data using the updated, trained inference model.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

In a CMOS image sensor, a bias connection is made of a first semiconductor material and arranged to be reversed biased by a voltage source opposite in polarity to a polarity of the first semiconductor material. A pixel can include a photosensitive element that includes a first well made of a second semiconductor material and a second well made of the first semiconductor material. The implanted or epitaxial growth buried layer is made of the second semiconductor material and extends continuously across a region making up the pixel array beneath the first and the second well of the pixels. The implanted buried layer made with the second semiconductor material is arranged to cooperate with the reverse bias applied to the bias connection to reduce a parasitic current path between the second well and a substrate.

IPC Classes  ?

• H01L 27/146 - Imager structures
• H01L 27/02 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier

Abstract

22232222 gas stream.

IPC Classes  ?

• B01D 53/18 - Absorbing unitsLiquid distributors therefor
• B01D 53/62 - Carbon oxides
• C01B 32/50 - Carbon dioxide

Abstract

In some examples, a computing system includes a storage device configured to store a front-end neural network and a back-end model; and processing circuitry. The processing circuitry is configured to: receive a test audio data sample; process, by executing the front-end neural network, the test audio data sample to extract one or more embeddings from the front-end neural network; process, by executing the back-end model, the one or more embeddings to determine a likelihood that indicates whether the test audio data sample represents speech by a particular human; and output an indication as to whether the test audio data sample represents genuine speech by the particular human.

IPC Classes  ?

• G10L 17/18 - Artificial neural networksConnectionist approaches
• G06N 3/047 - Probabilistic or stochastic networks
• G10L 17/04 - Training, enrolment or model building

Abstract

In an example, a method for generating responses by a Machine Learning (ML) system includes processing, by a first language model, a natural language instruction to generate an instruction representation based on a meaning of the natural language instruction; translating, by a translation module comprising an interface between the first language model and a second language model, the instruction representation into data indicating an intent of the natural language instruction, wherein the second language model is trained with domain specific knowledge; providing, by the translation module, the natural language instruction and the data indicating the intent of the natural language instruction to the second language model; and generating, by the second language model, a response based on the natural language instruction and the data indicating the intent of the natural language instruction.

IPC Classes  ?

• G06F 40/56 - Natural language generation

Abstract

In an example, a method for fine-tuning a Large Visual Language Model (LVLM) includes providing visual queries, each of the visual queries comprises at least an image and a textual query related to the image; processing, by the LVLM, the visual queries to extract visual embeddings from the visual queries, wherein the LVLM comprises a Visual Language Model (VLM), a first Large Language Model (LLM), and a linear projection layer interconnecting the VLM and the LLM; for visual queries: i) generating, by the LVLM, a response to the corresponding visual query based on the corresponding visual embedding; ii) evaluating, by a second LLM, the generated response to verify that the generated response satisfies predefined criteria; and iii) providing, by the second LLM, a feedback to the LVLM, in response to the evaluating the generated response; and fine-tuning the LVLM using aggregated feedback provided by the second LLM for the visual queries.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/334 - Query execution
• G06F 16/338 - Presentation of query results
• G06F 16/532 - Query formulation, e.g. graphical querying

Abstract

Techniques are described for a machine learning system configured to generate respective sample embeddings for a plurality of sample statements. The machine learning system may further be configured to generate a statement embedding for a statement. The machine learning system may further be configured to determine, based on the sample embedding and the statement embedding, respective similarity scores for the sample embeddings. The machine learning system may further be configured to select, based on the respective similarity scores for the sample embeddings, one or more sample statements from the plurality of sample statements. The machine learning system may further be configured to generate a prompt including the one or more sample statements, the statement, and at least one of respective ground-truth information or respective paraphrases for the selected one or more sample statements. The machine learning system may further be configured to provide the prompt to a machine learning model.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

The invention includes compositions comprising altered immune mast (AIM) cells modified to reduce expression of at least one gene that promotes an allergic response and/or increase expression of at least one gene for preventing or reducing an allergic response, as well as methods of producing AIM cells. The invention also provides methods of preventing or reducing an allergic response or allergic disease comprising administering to a subject an AIM cell.

IPC Classes  ?

• C12N 5/0787 - Granulocytes, e.g. basophils, eosinophils, neutrophils or mast cells
• A61K 35/15 - Cells of the myeloid line, e.g. granulocytes, basophils, eosinophils, neutrophils, leucocytes, monocytes, macrophages or mast cellsMyeloid precursor cellsAntigen-presenting cells, e.g. dendritic cells
• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides

Abstract

An example Geiger mode avalanche photodiode includes a first semiconductor alloy forming a compositionally graded gain region configured to form a conduction band having free electrons, a valence band having free holes, and a bandgap between the valence band and the conduction band that varies in size across the graded gain region; a second semiconductor alloy forming an absorber region; and a semiconductor substrate.

IPC Classes  ?

• H10F 30/225 - Individual radiation-sensitive semiconductor devices in which radiation controls the flow of current through the devices, e.g. photodetectors the devices having potential barriers, e.g. phototransistors the devices being sensitive to infrared, visible or ultraviolet radiation the devices having only one potential barrier, e.g. photodiodes the potential barrier working in avalanche mode, e.g. avalanche photodiodes
• G01S 7/481 - Constructional features, e.g. arrangements of optical elements
• H10F 71/00 - Manufacture or treatment of devices covered by this subclass
• H10F 77/124 - Active materials comprising only Group III-V materials, e.g. GaAs
• H10F 77/14 - Shape of semiconductor bodiesShapes, relative sizes or dispositions of semiconductor regions within semiconductor bodies

Abstract

Disclosed herein are methods and systems that involve an aiding signal that can be used to acquire a long code in a. wireless signal, such as a ranging code in a wireless navigation signal transmitted by a satellite. A receiver may receive a first wireless signal and a first aiding signal transmitted by a first transmitter of a plurality of transmitters. The first wireless signal comprises a first, long code. The first aiding signal comprises repeating instances of a sequence of short codes, wherein the sequence has a predetermined time relationship with the first long code and is specific to the first transmitter. The receiver may use the first aiding signal to acquire the first long code by determining the sequence of short codes in the first aiding signal and using the predetermined time relationship to synchronize with the first long code.

IPC Classes  ?

• G01S 19/30 - Acquisition or tracking of signals transmitted by the system code related
• G01S 19/02 - Details of the space or ground control segments
• H04B 1/7073 - Synchronisation aspects
• H04L 9/40 - Network security protocols

Abstract

In general, various aspects of the techniques are directed to causal analysis using large scale time series data. A computing system may convert large scale time series data to first time period records and second time period records according to a multi-scale time resolution. The computing system may implement a hierarchical machine learning model to generate embeddings that capture temporal characteristics of features of the large scale time series data. The computing system may generate a graph data structure indicating cause and effect correlations between features of the large scale time series data based on temporal dynamics captured in the cause and second time period records and/or the embeddings.

IPC Classes  ?

• G06F 16/901 - IndexingData structures thereforStorage structures

Abstract

12 2 are independently selected from H, O, phenyl, allyl, alkenyl, carbonyl, and a combination thereof. In some embodiments, the synthetic compounds can bind to a spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or to fentanyl.

IPC Classes  ?

• C07D 217/24 - Oxygen atoms
• A61P 25/30 - Drugs for disorders of the nervous system for treating abuse or dependence
• A61P 31/14 - Antivirals for RNA viruses

Abstract

Method and apparatus for processing input information using an adaptable and continually learning neural network architecture comprising an encoder, at least one adaptor and at least one reconfigurator. The encoder, at least one reconfigurator and at least one adaptor determine whether the input information is out-of-distribution or in-distribution. If the input information is in distribution, the architecture extracts features from the input information, creates hyperdimensional vectors representing the features and classifies the hyperdimensional vectors. If the input information is out of distribution, the architecture creates at least one adaptor to operate with the encoder and the at least one reconfigurator to extract features from the input information, create hyperdimensional vectors representing the features and classify the hyperdimensional vectors.

IPC Classes  ?

• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

A method, apparatus, and system for developing an understanding of at least one perceived environment includes determining semantic features and respective positional information of the semantic features from received data related to images and respective depth-related content of the at least one perceived environment on the fly as changes in the received data occur, for each perceived environment, combining information of the determined semantic features with the respective positional information to determine a compact representation of the perceived environment which provides information regarding positions of the semantic features in the perceived environment and at least spatial relationships among the sematic features, for each of the at least one perceived environments, combining information from the determined intermediate representation with information stored in a foundational model to determine a respective understanding of the perceived environment, and outputting an indication of the determined respective understanding.

IPC Classes  ?

• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads

Abstract

An encryption module and the decryption module cooperate with an identity-based key generator of users in a communication system in order to use an identity-based and certificate-based construction using two or more encryption schemes. An encrypted message is communicated between users of the communication system such that the encrypted message received by the device of the user needs keys from each of the two or more encryption schemes to be able to decrypt the encrypted message. The validation module cooperates with a limited-time of validity certificate issued from a certificate authority platform to decrypt the encrypted message via the limited-time of validity certificate. The validation module allows the decryption module to decrypt the encrypted message with the limited-time of validity certificate corresponding to an identity of the user when the user is determined to be actually validated for a period of time specified for the limited-time of validity certificate. The certificate authority platform grants the users validation.

IPC Classes  ?

• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems

Abstract

Techniques are disclosed for a soft logic block that can provide visual primitives to soft logic in coordination with a learned attention mechanism. In an example, computing system for object detection, the computing comprising processing circuitry and a storage device, wherein the processing circuitry has access to the storage device and is configured to execute a machine learning system comprising a placement neural network configured to process a patch of image data to generate local placement parameters for aligning a footprint in the patch to a template footprint; and a template comprising a backend network and the template footprint, the template configured to process a transformed footprint comprising the footprint in the patch transformed according to the local placement parameters, to generate a probability value quantifying a likelihood that a particular pattern is present in the footprint.

IPC Classes  ?

• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
• G06V 10/24 - Aligning, centring, orientation detection or correction of the image
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/776 - ValidationPerformance evaluation
• G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations

Abstract

A computing system is configured to obtain a plurality of media files that each includes speech of one or more speakers. The computing system is further configured to process the plurality of media files to generate indexed data, wherein the indexed data includes a corresponding embedding for each speaker of the one or more speakers identified in the media file and a corresponding one or more keywords identified in the speech in the media file. The computing system is further configured to receive an indication at least one of a selection of a particular speaker from the one or more speakers or a selection of a particular keyword from a plurality of keywords. The computing system is further configured to generate one or more correlations based on the indexed data. The computing system is further configured to output an alert regarding the one or more correlations.

IPC Classes  ?

• G06F 16/683 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06F 16/61 - IndexingData structures thereforStorage structures
• G10L 15/00 - Speech recognition

Abstract

A method, apparatus, and system for object detection on an edge device include projecting a hyperdimensional vector of a query request for an image received at the edge device into a hyperdimensional embedding space to identify at least one exemplar in the hyperdimensional embedding space having a predetermined measure of similarity to the query request using a network trained to: generate a respective hyperdimensional image vector and a respective hyperdimensional text vector for the image and received text descriptions of the image, generate a hyperdimensional query text vector of the query request, combine and embed respective ones of the hyperdimensional image vectors and the hyperdimensional text vectors into a hyperdimensional embedding space to generate respective exemplars, project the hyperdimensional query text vector into the hyperdimensional embedding space, and determine a similarity measure between the hyperdimensional query text vector and at least one of the respective exemplars.

IPC Classes  ?

• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces

Abstract

A silicon-based image sensor can have i) a pixel array with one or more pixels and ii) an upconversion layer of crystals on at least one of a front side and a backside of the silicon-based image sensor. A pulse repetition frequency decoder cooperates with the upconversion layer of crystals to decode a pulse repetition frequency of a laser flash captured by one or more of the pixels of the silicon-based image sensor. The pulse repetition frequency decoder can use a known frame rate of the silicon-based image sensor and a decay time of an upconverting emission from the upconversion layer of crystals to decode the pulse repetition frequency of the laser flash.

IPC Classes  ?

• H04N 23/11 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
• H01L 27/146 - Imager structures
• H04N 25/773 - Pixel circuitry, e.g. memories, A/D converters, pixel amplifiers, shared circuits or shared components comprising A/D, V/T, V/F, I/T or I/F converters comprising photon counting circuits, e.g. single photon detection [SPD] or single photon avalanche diodes [SPAD]

Abstract

Embodiments are directed to systems, devices, and methods for perturbing a biosystem to quantify responsiveness. An example system comprises stimulation circuitry configured to output a plurality of biostimulation signals to a target of a subject, sensor circuitry configured to obtain measures of a biosignal from the subject, and processor circuitry. The processor circuitry is configured to cause the stimulation circuitry to output the plurality of biostimulation signals to perturb a biosystem of the subject, and quantify responsiveness of the biosystem to the perturbation based on the plurality of biostimulation signals and measures of the biosignal responsive to the plurality of biostimulation signals applied to the target.

IPC Classes  ?

• A61N 1/36 - Applying electric currents by contact electrodes alternating or intermittent currents for stimulation, e.g. heart pace-makers

Abstract

In an example, a method includes obtaining data indicating a plurality of trajectories representing a behavior of a team comprising a plurality of agents; obtaining a plurality of baseline profiles, wherein each of the plurality of baseline profiles encodes at least one of a preference and/or a goal that is relevant to a task performed by the team; generating a probability distribution of each agent of the plurality of agents over the plurality of baseline profiles, wherein the probability distribution of each agent describes a behavior of the agent; updating the corresponding probability distribution of each agent of the plurality of agents; and generating, based on the updated probability distributions of the plurality of agents, reward functions that explain the observed joint actions performed by the team, wherein each of the reward functions describes the behavior of a corresponding one of the plurality of agents.

IPC Classes  ?

• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06N 5/043 - Distributed expert systemsBlackboards

Abstract

Disclosed herein are formulation embodiments comprising levonorgestrel butanoate (“LNGB”) particles having particle sizes that facilitate administering a higher concentration of LNGB at lower volumes. The disclosed formulation embodiments exhibit long-lasting contraceptive effects and can be administered subcutaneously, which lends to their utility in acting as self-administrable contraceptive formulations that do not result in side effects associated with other contraceptive agents.

IPC Classes  ?

• A61K 31/565 - Compounds containing cyclopenta[a]hydrophenanthrene ring systemsDerivatives thereof, e.g. steroids not substituted in position 17 beta by a carbon atom, e.g. oestrane, oestradiol
• A61K 9/16 - AgglomeratesGranulatesMicrobeadlets

Abstract

Techniques are disclosed for storing bits of information in composite classes of valid data. An example method includes determining, by processing circuitry and based on a bitstring format, a first set of bitstrings for a first transcoder, determining, by the processing circuitry and based on the bitstring format, a second set of bitstrings for a second transcoder, wherein no bitstring of the first set of bitstrings matches a bitstring of the second set of bitstrings, and outputting, by the processing circuitry, an indication of a first bit value assigned to the first transcoder and a second bit value assigned to the second transcoder.

IPC Classes  ?

• H03M 7/30 - CompressionExpansionSuppression of unnecessary data, e.g. redundancy reduction

Abstract

In general, the disclosure describes a computing system to automatically identify and classify audio input, including non-speech audio signals. The computing system may also add new classes based on only a limited number of examples of the new classes, to identify classes of sounds for which the system had not been trained.

IPC Classes  ?

• G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks

Abstract

In an example, a method for training a machine learning model to transpile quantum circuits includes generating, by a quantum circuit generator, a first plurality of quantum circuits according to a general quantum circuit design language, wherein each of the first plurality of quantum circuits comprises a sequence of instructions comprising one or more gates and one or more gate operations; obtaining a second plurality of quantum circuits, wherein each of the second plurality of quantum circuits is transpiled for a target quantum device from a corresponding one of the first plurality of quantum circuits; and training, using the first plurality of quantum circuits and the second plurality of quantum circuits, a machine learning model to transpile a quantum circuit according to the general quantum circuit design language to a quantum circuit for the target quantum device.

IPC Classes  ?

• G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
• G06N 10/40 - Physical realisations or architectures of quantum processors or components for manipulating qubits, e.g. qubit coupling or qubit control

Abstract

In general, the disclosure describes sensor including an intermediate band layer including a plurality of dopant particles, wherein the intermediate band layer is configured to absorb a portion of incident electromagnetic radiation comprising a first range of wavelengths greater than 1100 nm and form optically induced minority carriers. The sensor also includes a photo-sensitive silicon substrate configured to detect the electromagnetic radiation comprising a second range of wavelengths less than or equal to 1100 nm.

IPC Classes  ?

• H01L 27/146 - Imager structures
• H01L 31/101 - Devices sensitive to infrared, visible or ultraviolet radiation

Abstract

In general, the disclosure describes techniques for detecting synthetic speech of a speaker. In an example, a machine learning system may be configured to generate, using a deep learning model trained to distinguish between synthetic speech and authentic speech, reference embeddings for the speaker that characterize a first set of acoustic features and a first set of phonetic features associated with the speaker. The machine learning system may further be configured to generate, using the deep learning model, a test embedding for an audio clip that characterizes a second set of acoustic features and a second set of phonetic features associated with the audio clip. The machine learning system may further be configured to compute a score based on the test embedding and the reference embeddings. The machine learning system may further be configured to output, based on the score, an indication of whether the audio clip includes synthetic speech.

IPC Classes  ?

• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/32 - Multiple recognisers used in sequence or in parallelScore combination systems therefor, e.g. voting systems

Abstract

A method, apparatus, and system for training a language model for enhanced consistency include selecting at least a portion of the content data of the language model, generating reasoning statements in the form of natural language relevant to the selected portion of the content data, and training the language model using the generated reasoning statements such that a logical inference of the trained language model in response to a prompt directed to the selected portion of the content data is increased as compared with the logical inference of the language model in response to the same or similar prompt before the training of the language model to enhance the consistency of the language model with respect to the selected portion of the content data. The trained language model can be used to generate a logical inference having enhanced consistency for at least a portion of content data.

IPC Classes  ?

• G06N 3/098 - Distributed learning, e.g. federated learning
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06F 40/40 - Processing or translation of natural language
• G06N 3/0475 - Generative networks

Abstract

In an example, a method for a method for training a Machine Learning (ML) model using arbitrarily sized training data files, to selectively identify informative portions of one or more training data files for improving the ML model includes automatically selectively identifying, by a computing system, one or more informative portions of one or more training data files; calculating, by the computing system, gradients for the identified one or more informative portions; and updating, by the computing system, weights of a ML model using the calculated gradients.

IPC Classes  ?

• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

Disclosed are MGS peptides, MTS peptides, linkers and combinations combining any combination thereof. Disclosed are MGS peptides comprising the amino acid sequence of any of the sequences set forth in SEQ ID NOs: 1-8 Disclosed are MTS peptides comprising the amino acid sequence of any of the sequences set forth in SEQ ID NOS: 9-14. Disclosed are compositions comprising at least one MGS peptide conjugated to at least one MTS peptide, wherein the MGS peptide comprises the amino acid sequence of GFHNVYPYTWGGFSDIDLMADEI (SEQ ID NO: 1); EQRWVQMLHILQTRYAGEWPG (SEQ ID NO: 2); FQHINPFPYTYSMEDTDVEIK (SEQ ID NO: 3); or YAAWPASGAWT (SEQ ID NO: 4). In some aspects, the disclosed compositions can further comprise one or more linkers.

IPC Classes  ?

• C07K 14/00 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof

Abstract

The disclosure relates to the discovery of Neu5Gc-Lewisaas a biomarker for cancer cells and tissues and provides methods for diagnosis, treatment, and drug targeting based on the same, as well as molecules that specifically bind to Neu5Gc-Lewisa.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• A61K 38/17 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans
• A61K 38/08 - Peptides having 5 to 11 amino acids
• G01N 33/548 - Carbohydrates, e.g. dextran
• A61K 9/36 - Organic coatings containing carbohydrates or derivatives thereof

Abstract

An example method for identifying one or more potential malicious activities in a software-defined open radio access network includes detecting, by a trusted monitoring device, a communication flow from a sender component to a receiver component via an intermediate component. The method also includes, in response to the detecting of the communication flow, generating, by the trusted monitoring device and utilizing an intermediate identifier associated with the intermediate component, a flow record based on one or more parameters associated with the communication flow. The method further includes providing, by the trusted monitoring device and based on the flow record, an indication of the one or more potential malicious activities in the software-defined open radio access network.

IPC Classes  ?

• H04W 12/122 - Counter-measures against attacksProtection against rogue devices

Abstract

A computing system is configured to obtain a video that includes text elements and visual elements. The computing system is further configured to generate a plurality of text tokens representative of audio spoken in the video and a plurality of frame tokens representative of one or more frames of the video. The computing system is further configured to generate a set of features that includes a text feature, a frame feature, and a multi-modal feature, wherein the multi-modal feature is representative of multi-modal elements of the video, and wherein generating the set of features is based on the plurality of text tokens and the plurality of frame tokens. The computing system is further configured to associate the set of features with one or more labels to generate a multi-label classification of the video. The computing system is further configured to output an indication of the multi-label classification of the video.

IPC Classes  ?

• H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
• H04N 21/439 - Processing of audio elementary streams

Abstract

In general, techniques are described that address the limitations of existing conformal prediction methods for cascaded models. In an example, a method includes receiving a first validation data set for validating performance of an upstream model of the two or more cascaded models and receiving a second validation data set for validating performance of a downstream model of the two or more cascaded models wherein the second validation data set is different than the first validation set; estimating system-level errors caused by predictions of the upstream model based on the first validation data set; estimating system-level errors caused by predictions of the downstream model based on the second validation data set; and generating a prediction confidence interval that indicates a confidence for the system based on the system-level errors caused by predictions of the upstream model and based on the system-level errors caused by predictions of the downstream model.

IPC Classes  ?

• G06N 20/20 - Ensemble learning
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks

Abstract

This disclosure is in the field of cardiac diseases. For example, the disclosure provides a new platform comprising iPSC and cardiomyocytes carrying one or more gene mutations, and models to study the effect of those gene mutations on cardiomyocytes, and on drug toxicity. This platform is identified herein as PREDICT PLATFORM.

IPC Classes  ?

• C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
• C12M 1/32 - Inoculator or sampler multiple field or continuous type
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing

Abstract

A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.

IPC Classes  ?

• G06T 7/174 - SegmentationEdge detection involving the use of two or more images
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 7/38 - Registration of image sequences
• G06T 7/50 - Depth or shape recovery

Abstract

In an example, a system includes processing circuitry in communication with storage media. The processing circuitry is configured to execute a machine learning system including at least a first module, a second module and a third module. The machine learning system is configured to train one or more machine learning models. The first module is configured to generate augmented input data based on the streaming input data. The second module includes a machine learning model configured to perform a specific task based at least in part on the augmented input data. The third module configured to adapt a network architecture of the one or more machine learning models based on changes in the streaming input data.

IPC Classes  ?

• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning

Abstract

A method, apparatus, and system for determining an uncertainty estimation of at least one layer of a neural network includes identifying a neural network to be analyzed, representing values of each layer of the neural network as respective variable nodes in a graphical representation of the neural network, and modeling connections among each of the layers of the neural network as different respective factors across the variable nodes in the graphical representation, the graphical representation to be used to determine the uncertainty estimation of at least one layer of the neural network. The method, apparatus, and system can further include propagating data through the graphical representation to determine the uncertainty estimation of the neural network.

IPC Classes  ?

• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks

Abstract

Examples are directed a plurality of particles comprising transparent conductive oxide particles. Example transparent conductive oxide particles have a median diameter of about 200 nanometer (nm) to about 500 nm in the particle size distribution, and a plasma wavelength of about less than 2000 nm. Additionally, the transparent conductive oxide particles may comprise a primary component and about 2 weight percent to about 20 weight percent of a secondary component. Also disclosed are methods of producing a plurality of particles comprising transparent conductive oxide particles and compositions comprising a plurality of particles comprising transparent conductive oxide particles and a binder.

IPC Classes  ?

• C09D 7/61 - Additives non-macromolecular inorganic
• C01G 19/02 - Oxides
• C08K 3/22 - OxidesHydroxides of metals
• C09D 5/24 - Electrically-conducting paints
• C09D 5/33 - Radiation-reflecting paints
• C09D 133/12 - Homopolymers or copolymers of methyl methacrylate

Abstract

In general, the disclosure describes techniques for detecting synthetic speech in an audio clip. In an example, a computing system may include processing circuitry and memory for executing a machine learning system. The machine learning system may be configured to process an audio clip to generate a plurality of speech artifact embeddings based on a plurality of synthetic speech artifact features. The machine learning system may further be configured to compute one or more scores based on the plurality of speech artifact embeddings. The machine learning system may further be configured to determine, based on the one or more scores, whether one or more frames of the audio clip include synthetic speech. The machine learning system may further be configured to output an indication of whether the one or more frames of the audio clip include synthetic speech.

IPC Classes  ?

• G10L 17/26 - Recognition of special voice characteristics, e.g. for use in lie detectorsRecognition of animal voices
• G10L 17/02 - Preprocessing operations, e.g. segment selectionPattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal componentsFeature selection or extraction
• G10L 17/04 - Training, enrolment or model building

Abstract

An example photonic integrated circuit includes a wavelength selective switch (WSS), the WSS including an input port configured to receive a plurality of optical signals. Each optical signal has a center wavelength and a signal bandwidth, and the center wavelength of each optical signal is different from the center wavelengths of the other optical signals. The WSS also includes an output port and a spectral filter configured to switch an optical signal of the plurality of optical signals to the output port based on the center wavelength of the optical signal.

IPC Classes  ?

• H04J 14/02 - Wavelength-division multiplex systems
• G02B 6/12 - Light guidesStructural details of arrangements comprising light guides and other optical elements, e.g. couplings of the optical waveguide type of the integrated circuit kind

Abstract

In an example, a method includes, generating, by a machine learning system, one or more formal queries based on data contained in a database repository; generating, by the machine learning system, a natural language query for each formal query of the one or more formal queries to generate pairs of formal queries and corresponding natural language queries by applying a general grammar for a language of each formal query; and training, by the machine learning system, a neural network configured to translate natural language queries into formal queries using the pairs of the formal queries and corresponding natural language queries generated by the machine learning system.

IPC Classes  ?

• G06F 16/2452 - Query translation
• G06F 16/242 - Query formulation
• G06F 40/253 - Grammatical analysisStyle critique

Abstract

Embodiments provided herein include rotatable magnets (e.g., spherical dipole magnets) disposed within sets of coils that can be used to operate the magnets as reaction/momentum “spheres” and/or as control moment gyroscopes. The coils are able to exert three-dimensional torques onto the magnet in order to effect attitude control of a satellite or other system. The coils can also optionally exert translational forces onto the magnet in order to maintain the magnet in position and avoid contact with static components. Diamagnetic materials can also be included to provide stabilizing repulsive magnetic forces to maintain the magnet in position and/or to reduce the necessary performance of the coils with respect to applying stabilizing translational forces.

IPC Classes  ?

• H02N 15/00 - Holding or levitation devices using magnetic attraction or repulsion, not otherwise provided for
• B64G 1/28 - Guiding or controlling apparatus, e.g. for attitude control using inertia or gyro effect
• G05D 1/49 - Control of attitude, i.e. control of roll, pitch or yaw
• H01F 7/06 - ElectromagnetsActuators including electromagnets

Abstract

A robotic arm according to various implementations includes: a tool driver configured to hold a surgical tool; a first section comprising a first end coupled to a base, a second end distal from first end; a first link that includes a motor configured to rotate at least a portion of the first section around a pitch axis; a second link coupled to the first link, the second link including a motor configured to rotate at least a portion of the first section around a roll axis; and a second section comprising: a first end coupled to the second end of the first section, a second end distal from the first end, a first link that includes a motor configured to rotate at least a portion of the second section around a roll axis, a second link coupled to the first link.

IPC Classes  ?

• A61B 34/30 - Surgical robots
• A61B 17/00 - Surgical instruments, devices or methods
• A61B 17/29 - Forceps for use in minimally invasive surgery
• A61B 17/34 - TrocarsPuncturing needles
• A61B 34/00 - Computer-aided surgeryManipulators or robots specially adapted for use in surgery
• A61B 34/37 - Leader-follower robots
• A61B 46/10 - Surgical drapes specially adapted for instruments
• B25J 3/04 - Manipulators of leader-follower type, i.e. both controlling unit and controlled unit perform corresponding spatial movements involving servo mechanisms
• B25J 9/00 - Programme-controlled manipulators
• B25J 9/06 - Programme-controlled manipulators characterised by multi-articulated arms
• B25J 9/10 - Programme-controlled manipulators characterised by positioning means for manipulator elements
• B25J 9/12 - Programme-controlled manipulators characterised by positioning means for manipulator elements electric
• B25J 13/02 - Hand grip control means
• B25J 13/08 - Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
• B25J 17/02 - Wrist joints

Abstract

A computing system that receives an audio waveform representing speech from an individual and produces as output a modified version of the audio waveform that maintains the speaker's speech characteristics as well as prosody for specific utterances (e.g., voice timbre, intonation, timing, intensity). The system uses a bottleneck-based autoencoder with speech spectrograms as input and output. To produce the output audio waveform, the system includes a reconstruction error-based loss function with two additional loss functions. The second loss function is speaker “real vs fake” discriminator that penalizes for the output not sounding like the speaker. The third loss function is a speech intelligibility scorer that penalizes the output for speech that is difficult for the target population to understand. The produced modified audio waveform is an enhanced speech output that delivers speech m a target accent without sacrificing the personality of the speaker.

IPC Classes  ?

• G10L 21/013 - Adapting to target pitch
• G10L 17/02 - Preprocessing operations, e.g. segment selectionPattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal componentsFeature selection or extraction
• G10L 17/04 - Training, enrolment or model building
• G10L 17/26 - Recognition of special voice characteristics, e.g. for use in lie detectorsRecognition of animal voices

Abstract

In an example, a method includes, collecting interaction data comprising one or more interactions between one or more Reinforcement Learning (RL) agents and an environment; analyzing interestingness of the interaction data along one or more interestingness dimensions; determining competency of the one or more RL agents along the one or more interestingness dimensions based on the interestingness of the interaction data; and outputting an indication of the competency of the one or more RL agents.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning

Abstract

A method, apparatus and system for adapting a language model for understanding domain-specific multimodal content include acquiring domain-specific multimodal content for at least one content domain and applying question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content for the at least one domain. As such, the trained language model can be implemented to answer questions directed to the domain-specific multimodal content for the at least one domain.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

Disclosed is a temperature-actuated optical switch comprising a. material having a ferroelectric to paraelectric transition at a Curie temperature, wherein the material has a first transmissivity to infrared light below the Curie temperature and has a second transmissivity to the infrared light above the Curie temperature, and wherein the second transmissivity is higher than the first transmissivity. Also disclosed are optical systems that comprise such a temperature-actuated optical switch and methods for using such a temperature-actuated optical switch.

IPC Classes  ?

• G02F 1/035 - Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulatingNon-linear optics for the control of the intensity, phase, polarisation or colour based on ceramics or electro-optical crystals, e.g. exhibiting Pockels or Kerr effect in an optical waveguide structure
• G02F 1/05 - Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulatingNon-linear optics for the control of the intensity, phase, polarisation or colour based on ceramics or electro-optical crystals, e.g. exhibiting Pockels or Kerr effect with ferro-electric properties

Abstract

An image sensor with a pixel array that has two or more pixels is discussed. The defective pixel detector detects a defective pixel. A dynamic threshold range is based on amplitude levels of neighboring pixels. The defective pixel detector detects the defective pixel with the dynamic threshold range to detect and determine a defective status for the pixel under analysis based on an amplitude level of the pixel under analysis relative to the amplitude levels of neighboring pixels. The defective pixel detector applies algorithms to create candidate pixel patterns for the neighboring pixels used to create the dynamic threshold range. The value of the dynamic threshold range will change based on the amplitude levels of the neighboring pixels.

IPC Classes  ?

• H04N 25/683 - Noise processing, e.g. detecting, correcting, reducing or removing noise applied to defects by defect estimation performed on the scene signal, e.g. real time or on the fly detection
• H04N 25/63 - Noise processing, e.g. detecting, correcting, reducing or removing noise applied to dark current

Abstract

In one example, a sensor comprising a vapor cell including a vapor of alkali atoms is disclosed. The sensor further comprises a system configured to direct electromagnetic (EM) radiation of one or more frequencies into the vapor cell and incident on the vapor of alkali atoms. The EM radiation of one or more frequencies is configured to prepare the alkali atoms from a first quantum state to a Rydberg state. The alkali atoms prepared in the Rydberg state comprise an orbital angular momentum quantum number that is at least the number of quanta of the one or more frequencies. The sensor further comprises a detector configured to detect a response of the alkali atoms to incident electromagnetic radiation after the alkali atoms are prepared in the Rydberg state.

IPC Classes  ?

• G01R 29/08 - Measuring electromagnetic field characteristics

Abstract

In an example, a method for adapting a machine learning model includes receiving a digital representation of a three-dimensional (3D) object; learning, using a surrogate model, relationships between a plurality of points on a surface of the 3D object; and generating, using the surrogate model, one or more predictions about fluid properties along the surface of the 3D object.

IPC Classes  ?

• G06T 17/00 - 3D modelling for computer graphics
• G06V 10/426 - Graphical representations

Abstract

In general, techniques are described for unsupervised domain adaptation of models with pseudo-label curation. In an example, a method includes generating a plurality of pseudo-labels for a dataset of unlabeled data using a source machine learning model; estimating a reliability of each pseudo-label of the plurality of pseudo-labels using one or more reliability measures; selecting a subset of the plurality of pseudo-labels having estimated reliabilities that satisfy a reliability threshold; and training, using one or more curriculum learning techniques, a target machine learning model starting with the selected subset of the plurality of pseudo-labels and the corresponding unlabeled data.

IPC Classes  ?

• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 10/771 - Feature selection, e.g. selecting representative features from a multi-dimensional feature space
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/776 - ValidationPerformance evaluation

Abstract

Techniques for a machine learning system configured to obtain a dataset of a plurality of sample speech clips; generate a plurality of sequence; initialize a plurality of speaker embeddings and a plurality of accent embeddings; update the plurality of speaker embeddings; update the plurality of accent embeddings; generate a plurality of augmented embeddings based on the plurality of sequence embeddings, the plurality of speaker embeddings, and the plurality of accent embeddings; and generate a plurality of synthetic speech clips based on the plurality of augmented embeddings. The machine learning system may further be configured to obtain an audio waveform; decompose the audio waveform into first magnitude spectral slices and an original phase; process the first magnitude spectral slices to map the first magnitude spectral slices to second magnitude spectral slices; and generate a modified audio waveform in part by combining the second magnitude spectral slices and the original phase.

IPC Classes  ?

• G10L 13/047 - Architecture of speech synthesisers
• G10L 15/16 - Speech classification or search using artificial neural networks

Abstract

In general, the disclosure describes techniques for estimating a relative position of a receiver in a global navigation satellite system (GNSS) using time domain recursive filtering applied to GNSS data for an additional plurality of receivers. For example, multiple receivers each obtains GNSS data that indicates the raw position information for the receiver. A system applying techniques described herein may use the GNSS data, obtained for each receiver of the additional plurality of receivers, to improve position accuracy for a particular receiver using time domain recursive filtering.

IPC Classes  ?

• G01S 19/24 - Acquisition or tracking of signals transmitted by the system

Abstract

A method, apparatus, and system for providing orientation and location estimates for a query ground image include determining spatial-aware features of a ground image and applying a model to the determined spatial-aware features to determine orientation and location estimates of the ground image. The model can be trained by collecting a set of ground images, determining spatial-aware features for the ground images, collecting a set of geo-referenced images, determining spatial-aware features for the geo-referenced images, determining a similarity of the spatial-aware features of the ground images and the geo-referenced images, pairing ground images and geo-referenced images based on the determined similarity, determining a loss function that jointly evaluates orientation and location information, creating a training set including the paired ground images and geo-referenced images and the loss function, and training the neural network to determine orientation and location estimates of ground images without the use of 3D data.

IPC Classes  ?

• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods

Abstract

Example embodiments relate to techniques and systems for optimizing the topology of antennas. A computing device initially obtains a set of parameters specifying a model and a design domain for an antenna and identifies a Figure of Merit for use when generating the antenna. The computing device then generates an initial design for the antenna based on the set of parameters and the Figure of Merit and modifies, using up to a total of the design domain, the initial design for the antenna iteratively until a final design for the antenna i s generated that maximizes performance of the antenna for the Figure of Merit. Tire computing device provides an output representing the final design for the antenna, which can be used to manufacture the antenna. The computing device can use disclosed techniques to generate different types of antenna designs that optimize one or more Figure of Merits.

IPC Classes  ?

• G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computerOutput arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model

Abstract

The machine learning in the neural networks module can analyze an annotation and its metadata on the annotation made by a first user on a first computing device to make an embedding regarding the annotation and then cooperate with the persistence knowledge store to store the embedding of the machine learning's understanding of the annotation and its metadata. The delivery module can proactively push a notice regarding a potentially related embedding out to a second user on a second computing device based on a threshold amount of relatedness between one or more factors of i) a first task undertaken by the first user and a second task undertaken by the second user, ii) a role of the first user and a role of the second user, and iii) a subject matter of the embedding to a subject matter of a task undertaken by the second user.

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06Q 10/101 - Collaborative creation, e.g. joint development of products or services
• H04L 67/55 - Push-based network services

Abstract

In general, techniques are described for coordinating actions of a plurality of agents or subsystems using a machine learning system that implements a Capability Graph Network (CGN). In an example, a method includes generating a control policy model comprising a plurality of nodes and a plurality of edges interconnecting the plurality of nodes, wherein the plurality of nodes represents a plurality of agents or subsystems and the plurality of edges represent information exchange between the plurality of agents or subsystems; and encoding agent behavior control policy within the control policy model for executing to coordinate a plurality of the actions of the plurality of agents or subsystems.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks

Abstract

In general, techniques are described for generating counterfactuals using a machine learning system that implements a generative model. In an example, a method includes receiving, by a trained generative machine learning model, an input query, wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations; generating, by the trained generative machine learning model, latent representation of the input query; and transforming, by the trained generative machine learning system, the latent representation of the input query to generate a counterfactual related to the received input query, wherein the generated counterfactual meets a predefined outcome criteria.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/0475 - Generative networks

Abstract

A method, apparatus, and system for creating a script for rendering audio and/or video streams include identifying at least one prosodic speech feature in a received audio stream and/or a received language model, creating a respective prosodic speech symbol for each of the at least one identified prosodic speech features, converting the received audio stream and/or the received language model into a text stream, temporally inserting the created at least one prosodic speech symbol into the text stream, identifying in a received video stream at least one prosodic gesture of at least a portion of a body of a speaker of the received audio stream, creating at least one respective gesture symbol for each of the at least one identified prosodic gestures, and temporally inserting the created at least one gesture symbol into the text stream along with the at least one prosodic speech symbol to create a prosodic script.

IPC Classes  ?

• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
• G10L 15/06 - Creation of reference templatesTraining of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G10L 15/183 - Speech classification or search using natural language modelling using context dependencies, e.g. language models
• G10L 15/25 - Speech recognition using non-acoustical features using position of the lips, movement of the lips or face analysis
• G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band

Abstract

Retrosynthetic methods are described for determining one or more optimal synthetic routes to generate a target compound.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G16C 10/00 - Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
• G16C 20/80 - Data visualisation

Abstract

A computer-implemented method for defending, preventing, and/or mitigating short message service (SMS) based activities is provided. The method includes monitoring, by a security component in a radio interface layer (RIL) of a computing device configured to access a radio network, data related to SMS interactions over the radio network. The method also includes, based on the monitoring, detecting a potential activity associated with an SMS interaction. The method additionally includes providing an indication of the potential activity.

IPC Classes  ?

• H04W 12/122 - Counter-measures against attacksProtection against rogue devices
• H04W 4/14 - Short messaging services, e.g. short message service [SMS] or unstructured supplementary service data [USSD]

Abstract

Disclosed herein are methods and systems for object recognition utilizing reflective light blocking. Further disclosed herein are systems and methods for recognition of at least one fluorescent object being present in a scene by using a light source including at least one illuminant and a bandpass filter for each illuminant of the light source, a sensor array including at least one light sensitive sensor and at least one filter selectively blocking the reflected light originating from illuminating the scene with the light source and allowing passage of luminescence originating from illuminating the scene with the light source into the at least one color sensitive sensor, and a processing unit for identifying the at least one object based on the data detected by the sensory array and known data on luminescence properties associated with known objects.

IPC Classes  ?

• G06V 10/145 - Illumination specially adapted for pattern recognition, e.g. using gratings
• G06V 10/141 - Control of illumination
• G06V 10/143 - Sensing or illuminating at different wavelengths
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
• G06V 10/88 - Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
• G06V 20/80 - Recognising image objects characterised by unique random patterns

Abstract

Embodiments of the present principles generally relate to methods, apparatuses and systems for determining a measure of conceptual consistency in large language models for understanding of relevant concepts. In some embodiments, a method for measuring conceptual consistency may include prompting an LLM in order to extract answers to background queries and anchor tasks. The method also includes comparing background knowledge facts for a given anchor task associated with known answers with facts extracted from the LLM to determine an LLM performance. The method also includes determining a background knowledge score and an anchor task score based on the LLM's performance. The method also includes determining a conceptual may include score for the LLM by predicting the anchor task score from the background knowledge score. The method also includes outputting an indication of the conceptual may include score.

IPC Classes  ?

• G06F 40/40 - Processing or translation of natural language

Goods & Services

Video cameras; multi-purpose cameras; image sensors for cameras; downloadable and recorded computer software for video test tools; computer hardware for video test tools; tactical robots; downloadable speech recognition software; downloadable and recorded computer vision software, namely, downloadable and recorded software for image- and video-based indexing and search, object recognition, event detection and recognition, tracking, 3D scene modeling, computational imaging, embedded processing, video archiving and retrieval, video text recognition and extraction; downloadable and recorded computer software for artificial intelligence, namely, downloadable and recorded software for intelligent personal assistants, planning and scheduling, robotic control, text processing and analytics, process automation and discovery, human-machine dialog, forecasting, intelligent user interfaces, analytic assistants, game playing, intelligent control systems, intelligent design, autonomous and multi-agent systems, ontologies and semantic technologies, intelligent training systems, personalization and recommender systems, question-answering systems, bioinformatics, intelligent drug design, hybrid symbolic neural reasoning and learning, expert systems, biometrics, image and video understanding, automated theorem proving; downloadable and recorded computer software for artificial intelligence for conceptual prototyping; downloadable and recorded computer software for networking and security; downloadable and recorded computer software for cyber security analytics; downloadable and recorded computer software for training simulation; downloadable and recorded software for augmented reality for operating and controlling augmented reality helmet and binocular system for military training, weapon shooting system for military training and gaming, training system assisting in maintenance and repair tasks of complex machinery, namely, vehicles, appliances, and industrial machinery; downloadable and recorded augmented reality software for integrating electronic data with real world environments for the purpose of education and training; downloadable and recorded augmented reality software for transmitting data and electronic communications for conceptual prototyping and collaborative design; downloadable and recorded computer software, namely, software development tools for use in collaborative software development; downloadable and recorded computer software for control systems, namely, computer software used in the field of robotics, artificial intelligence, computer vision, bioscience and medical systems, high-assurance systems, training systems, defense and security, client-sponsored research and development and commercialization programs; downloadable and recorded computer software for robotics for robotic programming; downloadable and recorded software for use in biological modeling; downloadable and recorded computer software, namely, a software platform for pharmaceutical applications; downloadable and recorded computer software for use in synthesis of pharmaceuticals; downloadable and recorded software for use in analysis and development of pharmaceuticals and medicinal chemicals; microchips; downloadable electronic publications in the nature of articles, journal articles, posts, technical reports, and conference papers in the fields of science, technology, education, healthcare, biotechnology, and pharmaceuticals; downloadable electronic publications in the nature of research reports featuring information and findings in the fields of science, technology, education, healthcare, biotechnology, and pharmaceuticals. Printed publications in the nature of articles, journal articles, posts, technical reports, and conference papers in the fields of science, technology, education, healthcare, biotechnology, defense and security, and pharmaceuticals; research reports featuring information and findings in the fields of science, technology, education, healthcare, biotechnology, defense and security, and pharmaceuticals; posters. Business management and organization consultancy; assistance, advisory services and consultancy with regard to innovation planning (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulation), business planning, business analysis, business management, business organization, marketing and customer analysis; public policy research services (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulation); public policy consultancy services (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulation); research and analysis in the fields of economics and economic policy; management consulting and advisory services in the areas of corporate growth strategy, innovation and growth processes, organizational transformation, and talent management and development strategies (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulation); providing information in the fields of business innovation process, business management and business opportunities. Financial consultancy services in the nature of advising and consulting regarding investments and funding to support innovation. Foundry services in the nature of manufacturing services for others in the field of microcircuits and integrated circuits (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulation); foundry services in the nature of custom manufacturing of microcircuit emulation services; custom manufacturing services for others for microcircuits and integrated circuits. Research services in the field of education; consultation services in the field of K-12 and higher education systems; educational consultancy services, namely, providing consultation and data analysis for educators and education administrators, government agencies, and foundations; providing online non-downloadable publications in the nature of articles, journal articles, technical reports, conference papers, and online posts in the fields of science, technology, education, healthcare, biotechnology, defense and security, and pharmaceuticals; providing online non-downloadable publications in the nature of research reports featuring information and findings in the fields of science, technology, education, healthcare, biotechnology, defense and security, and pharmaceuticals; consulting services in the field of education. Scientific research and consulting services in the fields of physical and life sciences, engineering, economics, management, social and computer sciences; pharmaceutical research services; pharmaceutical drug development services; consulting services in the fields of medicinal chemistry, biotechnology, and pharmaceutical research and development; software as a service (SAAS) services featuring software for speech recognition, namely, software for speech and audio enhancement and noise cancellation, RF signal processing and identification of signals of interest, audio segmentation based on speech presence, speaker changes and other audio events of interest, word recognition for multiple languages and multiple genres, identification of speakers, identification of language, detection of keywords and phrases of interest, identification of speaker state and trait, speech and language translation for multiple languages and genres, natural language understanding, dialog systems, topic detection from audio and text, and information extraction and retrieval; advanced product research in the field of artificial intelligence; innovation consulting services, namely, advising others in the areas of product development; design for others in the field of computer hardware; design for others in the field of software; consultation services in the field of development and commercializing of new technology, technology processes and technology services (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulation); consultation services in the field of process redesign and change management (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulation); consultation services in the field of research and development of information systems, communications, chemical, energy, financial service and healthcare technologies; consultation to management of technology and technological developments; consultation services in the field of information systems, project and technology management, technology portfolio and strategic business planning, and product and market assessment for the computing, chemical, energy, engineering, pharmaceutical and medical health care fields; review, assessment, and consulting with regard to technology development strategy and technology development roadmaps, namely, technology development in field of biosciences, health and medical research, computing and information technology, defense and security, ocean and space technology, robotics and sensing technology. Licensing of intellectual property rights.

Abstract

Disclosed are compositions comprising an antibody conjugated to one or more molecular guidance system (MGS) peptides. Disclosed are methods of treating a subject in need thereof comprising administering to the subject in need thereof an effective amount of an antibody conjugated to one or more MGS peptides, wherein the antibody targets an intracellular target involved in the disease process. Disclosed are methods of targeting an intracellular target comprising administering an antibody conjugated to one or more MGS peptides, wherein the antibody targets an intracellular target.

IPC Classes  ?

• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent
• A61P 35/00 - Antineoplastic agents
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 38/00 - Medicinal preparations containing peptides

Abstract

A variety of transmission mechanisms are provided that include ‘radially inverted’ pulleys that are nested within each other or otherwise overlap in order to exert ‘inward’ forces onto a compressive belt that transmits power between the pulleys. By exerted forces ‘inward’ onto the belt, the belt can be subjected to net compression everywhere along its length. This allows the belt to be less complex and to have a lower cost than belts of transmissions that exert forces ‘outward’ from pulleys onto a belt, thus requiring expensive and technically challenging belt packs or other elements to sustain the longitudinal tensions experienced by such belts, even when such belts are operated as “push” belts. Transmissions that include such ‘radially inverted’ pulleys can exhibit reduced size and cost and increased power capacity compared to transmissions that employ ‘non-radially inverted’ pulleys.

IPC Classes  ?

• F16H 9/20 - Gearings for conveying rotary motion with variable gear ratio, or for reversing rotary motion, by endless flexible members without members having orbital motion using belts, V-belts, or ropes engaging a pulley built-up out of relatively axially-adjustable parts in which the belt engages the opposite flanges of the pulley directly without interposed belt-supporting members using two pulleys, both built-up out of adjustable conical parts both flanges of the pulleys being adjustable
• F16G 1/22 - Driving-belts consisting of several parts
• F16H 37/02 - Combinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings

Abstract

In general, the disclosure describes techniques for joint spatiotemporal Artificial Intelligence (AI) models that can encompass multiple space and time resolutions through self-supervised learning. In an example, a method includes for each of a plurality of multimodal data, generating, by a computing system, using a first machine learning model, a respective modality feature vector representative of content of the multimodal data, wherein each of the generated modality feature vectors has a different modality; processing, by the computing system, each of generated modality feature vectors with a second machine learning model comprising an encoder model to generate event data comprising a plurality of events and/or activities of interest; and analyzing, by the computing system, the event data to generate anomaly data indicative of detected anomalies in the multimodal data.

IPC Classes  ?

• G06V 20/40 - ScenesScene-specific elements in video content
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• H04N 23/21 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from infrared radiation only from near infrared [NIR] radiation

Abstract

In general, various aspects of the techniques are directed to a hierarchical framing transformer for activity detection. A computing system comprising a memory and processing circuitry may implement the techniques. The memory may store a plurality of input vectors representative of time-series data. The processing circuitry may implement an unsupervised machine learning transformer, where the unsupervised machine learning transformer is configured to process the plurality of input vectors to obtain a sequence of time ordered segments that maintain a time order of the plurality of input vectors. The unsupervised machine learning transformer may also encode the sequence of time ordered segments to obtain a single semantic embedding vector that identifies an activity occurring over at least a portion of the time-series data represented by the plurality of input vectors, and output an indication of the activity detected based on the semantic embedding vector.

IPC Classes  ?

• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/088 - Non-supervised learning, e.g. competitive learning

Abstract

A robotic manipulator is provided that exhibits improved grip strength and dexterity. The manipulator includes multiple fingers, each of which includes a grip surface formed by a belt running along a. compliant member. Actuation of the fingers about axes of rotation that are not parallel to the grip surfaces can result in exertion of forces from the compliant member into an object, resulting in deformation of the compliant member, This deformation can improve the contact area and grip strength with which the object is held. The belt can be driven along the grip surface, allowing the object to also be rotated around an axis that is not parallel to the direction along the grip surface while still being held securely. Each finger may also include a compliant joint to allow the fingers to rotate passively when an object is held, orienting the grip surfaces toward the object to improve grip strength.

IPC Classes  ?

• B25J 15/00 - Gripping heads
• B25J 15/10 - Gripping heads having finger members with three or more finger members
• B25J 15/12 - Gripping heads having finger members with flexible finger members
• B25J 9/10 - Programme-controlled manipulators characterised by positioning means for manipulator elements
• B25J 13/02 - Hand grip control means
• B25J 13/08 - Controls for manipulators by means of sensing devices, e.g. viewing or touching devices

Abstract

A method, apparatus and system for lifelong reinforcement learning include receiving features of a task, communicating the task features to a learning system, where the learning system learns or performs a task related to the features based on learning or performing similar previous tasks, determining from the features if the task has changed and if so, communicating the features of the changed task to the learning system, where the learning system learns or performs the changed task based on learning or performing similar previous tasks, automatically annotating feature characteristics of received features including differences between the features of the original task and the features of the changed task to enable the learning system to more efficiently learn or perform at least the changed task, and if the task has not changed, processing the task features of a current task by the learning system to learn or perform the current task.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning

Abstract

Techniques are disclosed for searching audio recordings in a second language with a key phrase in a first language. For example, a system as described herein receives a first key phrase in the first language and an audio recording in the second language. The system converts the first key phrase into a second key phrase in the second language. The system processes the second key phrase to produce a second key phrase variant. The system identifies, from a graph of words in the second language generated from the audio recording, instances of the second key phrase or the second key phrase variant within the audio recording. The system displays the identified instances of the second key phrase or the second key phrase variant within the audio recording to enhance searchability of the audio recording in the second language.

IPC Classes  ?

• G06F 16/632 - Query formulation
• G06F 16/683 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G10L 15/00 - Speech recognition
• G10L 15/08 - Speech classification or search

Abstract

Various embodiments of the present disclosure are directed to compounds having Formula (I), Formula (II), Formula (III), and/or pharmaceutically acceptable salts thereof. The compounds can be suitable for providing antimicrobial activity and/or inhibiting lipoxygenases. In some embodiments, the compounds may be administered to a patient as part of a pharmaceutical formulation.

IPC Classes  ?

• A61K 31/395 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
• A61K 31/505 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim
• A61K 31/13 - Amines, e.g. amantadine
• A61K 31/33 - Heterocyclic compounds