Abstract: In some embodiments, a machine learning model may be accessed and used to generate a likelihood score related to a condition. In some embodiments, pre-computed vectors may be derived from a training dataset used to build the machine learning model, and the pre-computed vectors may be used to generate processed data from target data derived from a target sample. The machine learning model may then be used on the processed data to generate the likelihood score related to the condition. As an example, subsets of the training dataset may be randomly selected, and the pre-computed vectors may be derived from the randomly-selected subsets of the training dataset. The pre-computed vectors may be applied to the target data to generate the processed data. In one use case, for example, the target data may be normalized using the pre-computed vectors.

Abstract: An apparatus for generating a generalized linear model structure definition by generating a gradient boosted tree model and separating each decision tree into a plurality of indicator variables upon which a dependent variable of the generalized linear model depends.

Abstract: A peer-to-peer signal node may include a communication interface in communication with a plurality of client machines via a network. The communication interface may receive a client request message identifying a designated computing model and input data on which to execute the designated computing model. The peer-to-peer signal node may also include a computation node registry maintaining access information for a plurality of distributed computation nodes accessible via the network. The peer-to-peer signal node may also include a work scheduler, which may select a distributed computation node and transmit a computation request message to the selected node identifying the designated computing model and the input data and establishing a communication session between the distributed computation node and the client machine through which the distributed computation node transmits a result obtained based on executing the computing model using the input data.

Abstract: A system for training machine learning models with unlabeled electrocardiogram signals, the system including a memory containing instructions configurating a processor to receive a plurality of electrocardiogram (ECG) data in a textual format, create one or more overlapping temporal patches from the plurality of ECG data, mask at least one temporal patch from the one or more overlapping temporal patches, pretrain an ECG machine learning model to predict the at least one masked temporal patch from the one or more overlapping temporal patches, adjust one or more parameter values of the ECG machine learning model as a function of the at least one predicted masked temporal patch and the at least one masked temporal patch and train the ECG machine learning model as a function of the one or more parameter values and a labeled set of ECG training data.

Abstract: An Applicant Assistant System may receive, from a user device, a user input indicating a query or task associated with information displayed in a user interface. The device may determine one or more components associated with the user interface and access component metadata of the determined one or more components, the component metadata for each of the one or more components indicating one or more of a fact, a data link, or an action. A prompt comprising at least some of the component metadata, at least some context information, and an indication of one or more available response elements may be provided to a large language model (LLM). The LLM may response with one or more response elements that may be processed by the System, such as to cause updates to the user interface on the user device.

Abstract: A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.

Abstract: The method for processing experimental data can include: determining experimental data (e.g., mass spectrometry spectra) and processing the experimental data. In variants, processing the experimental data can include: identifying one or more molecules, comparing experimental samples, determining a quantification, evaluating a quality of the experimental data, and/or otherwise processing the experimental data. The method can optionally include determining supplemental information, determining a set of candidate molecules, training a model, and/any other suitable steps.

Abstract: Embodiments of the present disclosure implement a stochastic neural network (SNN) where nodes are selectively activated depending on the inputs and which can be trained on multiple objectives. A system and/or network can include one or more nodes and one or more synapses, wherein each synapse connects a respective pair of the plurality of nodes. The system and/or network can further include one or more processing elements, wherein each of the processing elements is embedded in a respective synapse, and wherein each of the processing elements is adapted to receive an input and generate an output based on the input. In various embodiments, a super-imposable stochastic graph is employed with training, regularization and load balancing.

Abstract: Provided is a system and method for identifying and recommending the best hierarchical levels for training a predictive model such as a time-series forecasting model. In one example, the method may include receiving an identification of a measure of multidimensional data, generating a plurality of training data sets that comprise different combinations of hierarchical dimension granularities of aggregation, training a plurality of instances of a machine learning model based on the plurality of training data sets, respectively, and determining and outputting predictive accuracy values of the plurality of instances of the trained machine learning model.

Abstract: According to one embodiment, a method, computer system, and computer program product for hard negative training is provided. The embodiment may include a computer receiving a training set, where the training set comprises one or more training samples. The computer trains a deep neural network (DNN) with the training set. The embodiment may also include determining, using the DNN, information for each of the one or more training samples, where the information includes one or more scores associated with the one or more training samples. The embodiment may further include generating a training epoch from the one or more training samples based on the information and updates the information based on using the training epoch with the DNN.

Abstract: An ore content prediction system is provided. The system receives structured geological data that is derived based on spatial geological information that is associated with an input region. The received structured geological data includes a plurality of multidimensional tensors that are derived from spatial geological information of a plurality of sub-regions of the input region. The spatial geological information includes one or more types of data. The system trains a prediction model to produce a prediction output based on an average grade of an ore of a target mineral type at a target region by using the received structured geological data. The system identifies a relationship of the structured geological data to the prediction output and determines a revised input region based on the identified relationship.

Abstract: A system and method for generating a decision tree having a plurality of nodes, arranged hierarchically as parent nodes and child nodes, comprising: generating a node including: receiving i) training data including data instances, each data instance having a plurality of attributes and a corresponding label, ii) instance weightings, iii) a valid domain for each attribute generated, and iv) an accumulated weighted sum of predictions for a branch of the decision tree; and associating one of a plurality of binary prediction of an attribute with each node including selecting the one of the plurality of binary predictions having a least amount of error; in accordance with a determination that the node includes child nodes, repeat the generating the node step for the child nodes; and in accordance with a determination that the node is a terminal node, associating the terminal node with an outcome classifier; and displaying the decision tree including the plurality of nodes arranged hierarchically.

Abstract: Techniques are described for generating parallel data for real-time speech form conversion. In an embodiment, based at least in part on input speech data of an original form, a speech machine learning (ML) model generates parallel speech data. The parallel speech data includes the input speech data of the original form and temporally aligned output speech data of a target form different than the original form. Each frame of the input speech data temporally corresponds to the corresponding output speech frame of the target speech form and contains a same portion of the particular content. The techniques further include training a teacher machine learning model that is offline and is substantially larger than a student machine learning model for converting speech form. Transferring “knowledge” from the trained Teacher model for training the Production Student Model that performs the speech form conversion on an end-user computing device.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: A system, or platform, for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results. The platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine. A core engine, which includes a number of machine learning modules, such as a feature extraction engine, analyzes the data stream and produces data fed back to the clients via various interfaces. A learning engine incrementally and dynamically updates the training data for the machine learning by consuming and processing validation or feedback data. The platform includes a data viewer and a services layer that exposes the enriched data results. Integrated domain modeling allows the system to adapt and scale to different domains to support a wide range of enterprises.

Abstract: A method begins by agriculture equipment collecting current on-site gathered agriculture data regarding an agriculture region and sending at least a representation of the current on-site gathered agriculture data to a host device. The method continues with the host device processing one or more of the at least a representation of the current on-site gathered agriculture data, current off-site gathered agriculture data, historical on-site gathered agriculture data, historical off-site gathered agriculture data, and historical analysis of agriculture predictions regarding the agriculture region to produce a current agriculture prediction for the agriculture region. The method continues with the host device generating an agriculture prescription regarding at least a portion of the agriculture region based on the current agriculture prediction and sending the agriculture prescription to one or more of the agriculture equipment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a data entity that causes a processing unit to process a computational graph. In one aspect, a method includes the actions of receiving data identifying a computational graph, the computational graph including a plurality of nodes representing operations; obtaining compilation artifacts for processing the computational graph on a processing unit; and generating a data entity from the compilation artifacts, wherein the data entity, when invoked, causes the processing unit to process the computational graph by executing the operations represented by the plurality of nodes.

Abstract: A method and apparatus for generating a temporal knowledge graph, a device and a medium. An embodiment comprises: acquiring corpus including time information; performing multivariate data extraction on the corpus, multivariate data including an entity pair, an entity relationship and a target time interval of the entity relationship, the target time interval being used to indicate a valid period of the entity relationship; and generating a temporal knowledge graph based on the entity pair, the entity relationship and the target time interval of the entity relationship.

Abstract: Systems and methods for performing a quantization of artificial neural networks (ANNs) are provided. An example method may include receiving a description of an ANN and sets of inputs to neurons of the ANN, the description including sets of weights of the inputs, the weights being of a first data type, determining a first interval of the first data type to be mapped to a second interval of a second data type; performing computations of sums of products of the weights and the inputs to obtain a set of sum results, wherein the computations are performed using at least one number within the second interval, the number being a result of mapping of a number of the first interval to a number of the second interval, determining a measure of saturations in sum results, and adjusting, based on the measure of saturations, one of the first and second intervals.

Abstract: Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that strongly correlate with response of colorectal cancer patients to FOLFOX. Described herein are data structures, data processing, and machine learning models to predict effectiveness of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising administration of 5-fluorouracil/leucovorin combined with oxaliplatin (FOLFOX) or with irinotecan (FOLFIRI).