Abstract: Various aspects of the present disclosure relate to training a node of a two-sided model. When a new user equipment (UE) side node is added to a two-sided model, training information representing a trained model is received (e.g., from the network side). An encoder model for the UE is trained based at least in part on the training information and, once trained, the UE transmits data encoded at the UE using the trained encoder model. When a new network (e.g., base station) side node is added to the two-sided model, training information associated with one or more UEs is received. A decoder model for the base station is trained based at least in part on the training information and, once trained, the base station uses the trained decoder model to decode data encoded at and received from UE.

Abstract: The present disclosure provides compositions, methods, systems, and devices for polynucleotide processing and analyte characterization. Such polynucleotide processing may be useful for a variety of applications, including analyte characterization by polynucleotide sequencing. The compositions, methods, systems, and devices disclosed herein generally describe barcoded oligonucleotides, which can be bound to a bead, such as a gel bead, useful for characterizing one or more analytes including, for example, protein (e.g., cell surface or intracellular proteins), genomic DNA, and RNA (e.g., mRNA or CRISPR guide RNAs). Also described herein, are barcoded labelling agents and oligonucleotide molecules useful for “tagging” analytes for characterization.

Abstract: Various aspects of the present disclosure relate to training dataset updates. A training dataset is partitioned into multiple dataset groups and each dataset group includes one or more training datapoints. Each dataset group is associated with a first label and a second label. The first label corresponds to a temporal or time-domain related parameter, such as a time stamp or a time duration. The second label is at least one of a weight or a value associated with a characteristic of the dataset. The training dataset is updated based on at least one of the first label or the second label, such as by updating a subset of values of the second label, removing a dataset group, or adding a new dataset group to the training dataset. Updated information corresponding to the updated training dataset can then be sent from one device to another.

Abstract: An algorithmic-level optimization technique based on kernel segregation mechanisms for efficient transpose convolution implementation without requiring an upsampling layer. Experimental results showed that the proposed approach showed an average of 3.7×(3.4×) faster computation than conventional methods known in the art. The method further provides significant improvement in computation speed and substantial memory savings from the obtained results.

Abstract: A vision transformer is provided featuring improved computational efficiency. A plurality of image vectors corresponding to an input image is provided to a sequence of neural network layers configured to generate an image classification. The neural network layers comprise at least one scatter layer coupled to at least one attention layer, the scatter layer configured to receive the plurality of image patch vectors and to generate low-frequency tokens and high-frequency tokens by applying a dual-tree complex wavelet transform and applying tensor and Einstein mixing to each set of tokens, respectively. The tokens are thereafter transformed back to the physical domain by an inverse scatter network, provided to a multi-layer perceptron (MLP) layer which in turn provides an output to the at least one attention layer that includes multi-head self-attention and MLP layers further coupled to a classifier head configured to generate a classification of the input image.

Abstract: Methods, systems, apparatuses, and computer-readable storage mediums are descried for identifying a similarity between queries. An intermediate representation generator receives a set of queries from a repository, each query in the set of queries having generated a corresponding set of data stored in a data store. An intermediate representation is generated for each query, where the intermediate representation is characterized by a feature associated with text specified in the query. A similarity determiner determines similarity scores between pairs of intermediate representations. A pair of intermediate representations with a similarity score above a threshold is identified. An indication is generated that sets of data corresponding to queries corresponding to the intermediate representations are overlapping.

Abstract: Methods, systems, apparatuses, and computer-readable storage mediums are descried for identifying a similarity between queries. An intermediate representation generator receives a set of queries from a repository, each query in the set of queries having generated a corresponding set of data stored in a data store. An intermediate representation is generated for each query, where the intermediate representation is characterized by a feature associated with text specified in the query. A similarity determiner determines similarity scores between pairs of intermediate representations. A pair of intermediate representations with a similarity score above a threshold is identified. An indication is generated that sets of data corresponding to queries corresponding to the intermediate representations are overlapping.

Abstract: Methods, systems, apparatuses, and computer-readable storage mediums are descried for identifying a similarity between queries. An intermediate representation generator receives a set of queries from a repository, each query in the set of queries having generated a corresponding set of data stored in a data store. An intermediate representation is generated for each query, where the intermediate representation is characterized by a feature associated with text specified in the query. A similarity determiner determines similarity scores between pairs of intermediate representations. A pair of intermediate representations with a similarity score above a threshold is identified. An indication is generated that sets of data corresponding to queries corresponding to the intermediate representations are overlapping.

Abstract: This application relates to systems and methods for automatically correcting labels in untrusted data based on a small sample of trusted data in a training database. In some examples, training data may be divided into a trusted dataset and an untrusted dataset using stratified sampling. An adversarial algorithm may be used to reassign labels in the data samples associated with the untrusted data based on a set of features in the data and labels in the trusted dataset. The untrusted dataset with the reassigned labels may then be used to train a machine learning model.

Abstract: Described are examples for generating a model for forecasting time series data. For a timeseries data set, one or more layers can be provided, where each layer in the one or more layers includes, for each timeseries data input in at least a portion of multiple timeseries data inputs, generating, for the timeseries data input, a short range output from a causal convolution process that is based on timeseries data inputs from the timeseries data set that are associated with timestamps within a threshold time before the timestamp of the timeseries data input, and generating, for the timeseries data input, a long range output from a transformer process based on the short range outputs from the causal convolution process for each timeseries data input from at least the portion of the multiple timeseries data inputs that are associated with timestamps before the timestamp of the timeseries data input.

Abstract: Example aspects include techniques for anomaly detection via sparse judgmental samples. These techniques may include generating, via lexical analysis, a plurality of tokens from a textual representation of a machine learning (ML) model and generating, via a parser, based on the plurality of tokens, an abstract syntax tree (AST) corresponding to the ML model. In addition, the techniques may include identifying a data dependency of the ML model based on an AST node within the AST, the AST node corresponding to a data source and the data dependency indicating the ML model depends on the data source. Further, the techniques may include detecting a potential issue associated with the data source, and transmitting, based on the data dependency, an alert notification in response to the potential issue.

Abstract: In various examples, a system can obtain a first time series data set, the first time series data set including a plurality of data elements. Each data element can include value data and corresponding time data. Based on the first time series data set, the system can generate a second data set and a third dataset. The second dataset can indicate one or more data elements with missing value data and the third dataset can include extremeness data. The extremeness data can indicate an extremeness score for each data element of the plurality of data elements. Additionally, based on the first time series data set, the second data set and a third dataset, the system can implement a set of operations that generate a substitute value data for each data element of the one or more data elements that is missing value data.

Abstract: The invention relates to a composition comprising: at least one alkane-sulphonic acid R—SO3H wherein R represents a saturated, linear or branched, hydrocarbon chain comprising from 1 to 4 carbon atoms, which can or cannot be substituted by at least one halogen atom; sulphuric acid; and optionally at least one solvent; of which the proportions are defined in the description. The invention also relates to the use of the composition as a fatty acid esterification catalyst.

Abstract: This application relates to apparatus and methods for automatically predicting values for a future time period based on time series data of a previous time period. In some examples, a computing device employs multiple algorithms or predictions models to determine baseline predictions and bias predictions accounting for both normal and surge-induced events in the future time period. Accuracy of the algorithms and exogenous variables, such as holidays, events, temporal indicators, are leveraged to accurately predict future values. Baseline predictions using baseline algorithms are aggregated with bias predictions associated with surge events to determine final predictions for the future time period without compromising on the accuracy and efficiency of the predictions for both normal and surge-induced events.

Abstract: This application relates to systems and methods for automatically correcting labels in untrusted data based on a small sample of trusted data in a training database. In some examples, training data may be divided into a trusted dataset and an untrusted dataset using stratified sampling. An adversarial algorithm may be used to reassign labels in the data samples associated with the untrusted data based on a set of features in the data and labels in the trusted dataset. The untrusted dataset with the reassigned labels may then be used to train a machine learning model.

Abstract: An on-demand learning system provides an enhanced leaning environment capable of delivering relevant content on virtually any topic to specific learners. The learning system implements technical features that facilitate curation and subject matter validation of many different types of content. The technical architecture of the learning system also supports intelligent matching of learners to subject matter areas, creation of specific subject matter boards, and resilient maintenance of the boards.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.

Abstract: An Artificial Intelligence (AI) based resource identification system enables identifying available resources in response to receiving a request for resources. The request attributes are mapped to the attributes of the resources in a resource pool. Matching index scores are calculated and resources that match the request can be selected based on the matching index scores. If no resources are available suitable alternate resources with lower matching index scores are suggested so that a user has the choice to filter this alternate resources based on a threshold score. Particular suggestions to change one or more of the request attributes can be provided based on analysis of the mapping of the request and the resource attributes which enables the resource identification system to suggest changes to one or more of the skill attributes, level attributes, time attributes or location attributes of the request.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.