A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

Examples described herein provide a method that includes performing a first scan of an object to generate first scan data. The method further includes detecting a defect on a surface of the object by analyzing the first scan data to identify a region of interest containing the defect by comparing the first scan data to reference scan data. The method further includes performing a second scan of the region of interest containing the defect to generate second scan data, the second scan data being higher resolution scan data than the first scan data. The method further includes combining the first scan data and the second scan data to generate a point cloud of the object.

Examples of methods are described. In some examples, a method includes determining, using a variational autoencoder model, a latent space representation. In some examples, the latent space representation is of object model data. In some examples, the method includes predicting manufacturing powder degradation. In some examples, predicting the manufacturing powder degradation is based on the latent space representation.

A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.

There is provided a magnetoresistance effect element includes: a channel layer that extends in a first direction; a recording layer which includes a film formed from a ferromagnetic material, of which a magnetization state is changed to one of two or greater magnetization states, and which is formed on the channel layer; a non-magnetic layer that is provided on a surface of the recording layer; a reference layer which is provided on a surface of the non-magnetic layer, which includes a film formed from a ferromagnetic material, and of which a magnetization direction is fixed; a terminal pair that includes a first terminal and a second terminal which are electrically connected to the channel layer with an interval in the first direction, and to which a current pulse for bringing the recording layer to any one magnetization state with a plurality of pulses is input by flowing a current to the channel layer between the first terminal and the second terminal; and a third terminal that is electrically connected to the reference layer.

A computer-implemented method for transferring data is provided. In an illustrative embodiment, the method includes retrieving, by a computer, an original dataset to be sent from a sender to a receiver. The method also includes generating, by the computer, a model based on at least a subset of the original dataset. The model generates a predicted dataset. The model is selected from a plurality of model types based on data complexity of the original dataset and a desired level of approximation of the predicted dataset to the original dataset. The method also includes transferring, by the computer, the model to the receiver. The receiver uses the model to generate the predicted dataset, wherein the predicted dataset matches the original dataset to a selected degree of approximation. Transfer of the model is quicker than transfer of the original dataset.

The example embodiments are directed to a continuously expanding cross-domain featuring engineering system. In one example, a method may include one or more of storing predictive features in a cross-domain data store, the predictive features previously used in machine learning modeling in a plurality of different domains, receiving data of an asset included in a target domain and information about an evaluation attribute associated with the asset in the target domain, determining a predictive feature in the received data based on a previously used predictive feature stored in the cross-domain data store which is associated with a machine learning model in a different domain and the evaluation attribute, and outputting the determined predictive feature for display via a user interface.

A system monitors an individual for conditions indicating a possibility of occurrence of irregular heart events. A database includes a plurality of combinations of at least a first signature and a second signature. A first portion of the plurality of combinations is associated with a normal heartbeat and a second portion of the plurality of combinations is associated with an irregular heart event. A wearable heart monitor that is worn on a body of the patient includes a heart sensor for generating a heart signal responsive to monitoring a beating of a heart of the individual. The monitor further includes a processor for receiving the heart signal from the heart sensor. The processor is configured to analyze the heart signal using a plurality of different processes. Each of the plurality of different processes generates at least one of the first signature and the second signature. The plurality of different processes provide a unique combination including at least the first signature and the second signature for the generated heart signal. The processor compares the unique combination with the plurality of combinations in the database, locates a combination of the plurality of combinations that substantially matches the unique combination and generates a first indication if the unique combination substantially matches one of the first portion of the plurality of combinations and a second indication if the unique combination substantially matches one of the second portion of the plurality of combinations.

Techniques for improved neural network modeling are provided. In one embodiment, a system comprises a memory that stores computer-executable components and a processor that executes the components. The computer-executable components can comprise a loss function logic component that determines a penalty based on a training term, the training term being a function of a relationship between an output scalar value of a first neuron of a plurality of neurons of a neural network model, a plurality of input values from the first neuron, and one or more tunable weights of connections between the plurality of neurons; an optimizer component that receives the penalty from the loss function component, and changes one or more of the tunable weights based on the penalty; and an output component that generates one or more output values indicating whether a defined pattern is detected in unprocessed input values received at the neural network evaluation component.

A computer-implemented method for designing a 3D modeled object via user-interaction. The method includes obtaining the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further includes defining a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further includes applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.