Abstract: A system and method for generating hard training data from easy training data. Training data including visual data with synthetic semantic implants (“VSSI”) having at least one cue is received. An annotator identifies at least one cue in the VSSI and annotates the VSSI to indicate the cue to create a modified training data set. A data scrambler removes at least one cue from the VSSI to create the tagged training data, which can then be used to train a classifier to identify transitions between segments when the cues are not present.

Abstract: A system is provided for managing an infrastructure. An extraction engine is in communication with a managed infrastructure that includes physical hardware. A signalizer engine includes one or more of an NMF engine (Non-negative matrix factorization), a k-means clustering engine (a method of vector quantization), and a topology proximity engine. The signalizer engine determines one or more common characteristics of events and produces clusters of events relating to the failure or errors in the infrastructure. The signalizer engine uses graph coordinates and optionally a subset of attributes assigned to each event to generate one or more clusters to bring together events whose characteristics are similar. One or more interactive displays provide a collaborative interface coupled to the extraction and the signalizer engine with a collaborative interface (UI) for decomposing events from the infrastructure.

Abstract: The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on some models, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. A second of the plurality of types of sensor data are obtained from a second type of the plurality of sensors and are used to generate validation base data. Some of the one or more items are labeled, automatically, via validation base data to generate labeled at least some item, which is to be used to generate model updated information for updating the at least one model.

Abstract: In an aspect, an apparatus for bias eliminated performance determination is presented. An Apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory includes instructions configuring at least a processor to receive, through a sensing device, biological feedback of a user. At least a processor is configured to compare biological feedback to a performance parameter of a task. At least a processor is configured to generate, as a function of a comparison, a performance determination through a performance determination machine learning model. At least a processor is configured to classify a performance determination to a bias category as a function of a bias classifier. At least a processor is configured to train a performance determination machine learning model with biological feedback and a bias classification of a performance determination.

Abstract: Disclosed is a neural network enabled interface server and blockchain interface establishing a blockchain network implementing event detection, tracking and management for rule based compliance, with significant implications for anomaly detection, resolution and safety and compliance reporting.

Abstract: A method including setting an initial lookback path length for a current path in a directed acyclic graph. The current path includes a subset of the nodes connected by a sequence of the edges. The method also includes querying, for a current lookback path length, whether a matching key is present in a transition probability dictionary (TPD). The method also includes querying, responsive to the matching key being present in the TPD for the current lookback path length, whether a matching value is present for the matching key. The matching value includes a sample path in the TPD that matches the current path. Responsive to the matching value being present in the TPD for the matching key, a next node associated with the matching value is returned. The next node is connectable in a valid operational relationship to a last node in the current path.

Abstract: Methods and systems for predicting the susceptibility of bacterial pathogens to antibiotics using genomic data sets. Various embodiments described herein receive a genomic dataset and a set of labels and run principal variance component analysis thereon to determine the effect sizes of the labels. One or more labels are then selected based on their effect sizes and used in a machine learning model to make predictions on future datasets.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: Disclosed is a new location threat monitoring solution that leverages deep learning (DL) to process data from data sources on the Internet, including social media and the dark web. Data containing textual information relating to a brand is fed to a DL model having a DL neural network trained to recognize or infer whether a piece of natural language input data from a data source references an address or location of interest to the brand, regardless of whether the piece of natural language input data actually contains the address or location. A DL module can determine, based on an outcome from the neural network, whether the data is to be classified for potential location threats. If so, the data is provided to location threat classifiers for identifying a location threat with respect to the address or location referenced in the data from the data source.

Abstract: The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on some models, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. A second of the plurality of types of sensor data are obtained from a second type of the plurality of sensors and are used to generate validation base data. Some of the one or more items are labeled, automatically, via validation base data to generate labeled at least some item, which is to be used to generate model updated information for updating the at least one model.

Abstract: An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.

Abstract: Techniques and systems are provided for training a machine learning model using different datasets to perform one or more tasks. The machine learning model can include a first sub-module configured to perform a first task and a second sub-module configured to perform a second task. The first sub-module can be selected for training using a first training dataset based on a format of the first training dataset. The first sub-module can then be trained using the first training dataset to perform the first task. The second sub-module can be selected for training using a second training dataset based on a format of the second training dataset. The second sub-module can then be trained using the second training dataset to perform the second task.

Abstract: The disclosure provides a collaborative deep learning method and a collaborative deep learning apparatus. The method includes: sending an instruction for downloading a global model to a plurality of user terminals; receiving a set of changes from each user terminal; storing the set of changes; recording a hash value of the set of changes into a blockchain; obtaining a storage transaction number from the blockchain for the hash value of the set of changes; sending the set of changes and the storage transaction number to the plurality of user terminals; receiving the set of target user terminals from the blockchain; updating the current parameters of the global model based on sets of changes corresponding to the set of target user terminals; and returning the sending the instruction, to update the global model until the global model meets a preset condition.

Abstract: Disclosed is a system and a method for engagement of human agents for decision-making in a dynamically changing environment. An information request related to a problem requiring a decision is received. Further, problem data comprising metadata associated to the problem, and decision-making data is received. Then, an information type is determined for the information request. Subsequently a set of human agents from a list of one or more human agents is determined using an engagement model. Further, a request elicitation type is determined for the set of human agents using an elicitation model. Further, an input is received from the set of human agents. Further, the input is used to retrain the engagement model and the elicitation model. Finally, the decision-making data is continuously enhanced based on the input received, the request elicitation type, and the information type.

Abstract: A parallel convolutional neural network is provided. The CNN is implemented by a plurality of convolutional neural networks each on a respective processing node. Each CNN has a plurality of layers. A subset of the layers are interconnected between processing nodes such that activations are fed forward across nodes. The remaining subset is not so interconnected.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for multi-modal machine learning medical assessment. A source module is configured to receive multiple types of data for a user. A machine learning module is configured to analyze the multiple types of data using machine learning to determine multiple predictions of likelihoods of the user getting a neurological disease. A multi-modal result module configured to determine a single result indicating a likelihood of the user getting the neurological disease based on the multiple predictions.

Abstract: A system for selecting an ameliorative output using artificial intelligence includes at least a server configured to receive at least a prognostic output. At least a server is configured to generate a plurality of ameliorative outputs as a function of at least a prognostic output wherein the plurality of ameliorative outputs include at least a short-term indicator and at least a long-term indicator. At least a server is configured to receive at least a user life element datum wherein the at least a user life element datum includes at least a user life quality response. At least a server is configured to generate a loss function of the plurality of short-term indicators and the plurality of long-term indicators using at least a user life element datum. At least a server is configured to select at least an ameliorative output from a plurality of ameliorative outputs to minimize the loss function.

Abstract: The disclosure discloses a graph optimization method and apparatus for neural network computation. The graph optimization method includes the following steps: S1: converting a computation graph; S2: allocating a register; S3: defining a route selector for a redefined variable; S4: solving the route selector for the redefined variable; S5: defining a criterion of inserting the route selector for the redefined variable into a node; S6: analyzing a dominating edge set of the node for the redefined variable; S7: inserting the route selector for the redefined variable; and S8: renaming the redefined variable. The disclosure solves the problem of the corresponding route selection on a correct definition of the redefined variable when a node including the redefined variable in a computation graph in the compiling period flows through multiple paths of computation flow, reduces the memory cost and promotes the development of implementation application of a deep neural network model.

Abstract: Artificial neural networks (ANNs) are a distributed computing model in which computation is accomplished with many simple processing units, called neurons, with data embodied by the connections between neurons, called synapses, and by the strength of these connections, the synaptic weights. An attractive implementation of ANNs uses the conductance of non-volatile memory (NVM) elements to record the synaptic weight, with the important multiply—accumulate step performed in place, at the data. In this application, the non-idealities in the response of the NVM such as nonlinearity, saturation, stochasticity and asymmetry in response to programming pulses lead to reduced network performance compared to an ideal network implementation.

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for generating a convolutional neural network (CNN) are described. In one aspect, a minimal CNN having, e.g., three or more layers is trained. Cascade training may be performed on the trained CNN to insert one or more intermediate layers until a training error is less than a threshold. When cascade training is complete, cascade network trimming of the CNN output from the cascade training may be performed to improve computational efficiency. To further reduce network parameters, convolutional filters may be replaced with dilated convolutional filters with the same receptive field, followed by additional training/fine-tuning.