Abstract: The present application discloses a method, system, and computer system for providing access to data. The method includes receiving, by a data manager service from a data requesting service, a request using an identifier for a high-level data object to access a set of data associated with the high-level data object, determining, by the data manager service, low-level data object(s) corresponding to the set of data based on the identifier for the high-level data object, determining whether a user associated with the request has permission to access at least a subset of the low-level data object(s), and in response to determining that the user associated has permission to access the at least the subset of the low-level data object(s), generating, by the data manager service, a uniform resource locator (URL) via which the at least the subset of the one or more low-level data objects is accessible by the user.

Abstract: The present application discloses a method, system, and computer system for providing access to information stored on system for data storage. The method includes receiving a data request from a user, determining data corresponding to the data request, determining whether the user has requisite permissions to access the data, and in response to determining that the user has requisite permissions to access the data: determining a manner by which to provide access to the data, wherein the data comprises a filtered subset of stored data, and generating a token based at least in part on the user and the manner by which access to the data is to be provided.

Abstract: Methods and systems for compressing a neural network (NN) which performs an inference task and for performing computations of a Kronecker layer of a Kronecker NN are described. Data samples are obtained from a training dataset. The input data of the data samples are inputted into a trained NN to generate NN predictions for the input data. Further, the input data are inputted into a Kronecker NN to generate Kronecker NN predictions for the input data. Two losses are computed: a knowledge distillation loss, based on outputs generated by a layer of the NN and a corresponding Kronecker layer of the Kronecker NN and a loss for Kronecker layer, based on the Kronecker NN predictions and ground-truth labels of the data samples. The two losses are combined into a total loss, which is propagated through the Kronecker NN to adjust values of learnable parameters of the Kronecker NN.

Abstract: Methods and processors for using a Neural Network (NN) are disclosed. The method includes, during a first iteration, determining a first sub-network of the NN and training the first sub-network. The method includes during a second iteration a second sub-network of the NN and training the second sub-network. The method includes during an inference iteration of the NN, selecting a target sub-network amongst the first sub-network and the second sub-network, and generating an inference output by employing only the target sub-network of the NN on inference data for reducing computational resources of the processor for generating the inference output.

Abstract: The present application discloses a method, system, and computer system for providing access to data. The method includes receiving, by a data manager service from a data requesting service, a request using an identifier for a high-level data object to access a set of data associated with the high-level data object, determining, by the data manager service, low-level data object(s) corresponding to the set of data based on the identifier for the high-level data object, determining whether a user associated with the request has permission to access at least a subset of the low-level data object(s), and in response to determining that the user associated has permission to access the at least the subset of the low-level data object(s), generating, by the data manager service, a uniform resource locator (URL) via which the at least the subset of the one or more low-level data objects is accessible by the user.

Abstract: The present application discloses a method, system, and computer system for providing access to data. The method includes receiving, by a data manager service from a data requesting service, a request using an identifier for a high-level data object to access a set of data associated with the high-level data object, determining, by the data manager service, low-level data object(s) corresponding to the set of data based on the identifier for the high-level data object, determining whether a user associated with the request has permission to access at least a subset of the low-level data object(s), and in response to determining that the user associated has permission to access the at least the subset of the low-level data object(s), generating, by the data manager service, a uniform resource locator (URL) via which the at least the subset of the one or more low-level data objects is accessible by the user.

Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Abstract: Method and system of training a student neural network (SNN) model. A first training phase is performed over a plurality of epochs during which a smoothing factor to teacher neural network (TNN) model outputs to generate smoothed TNN model outputs, a first loss is computed based on the SNN model outputs and the smoothed TNN model outputs, and an updated set of the SNN model parameters is computed with an objective of reducing the first loss in a following epoch of the first training phase. The soothing factor is adjusted over the plurality of epochs of the first training phase to reduce a smoothing effect on the generated smoothed TNN model outputs. A second training phase is performed based on the SNN model outputs and a set of predefined expected outputs for the plurality of input data samples.

Abstract: The present systems and methods are directed to de novo identification of peptide sequences from tandem mass spectrometry data. The systems and methods uses unconverted mass spectrometry data from which features are extracted. Using unconverted mass spectrometry data reduces the loss of information and provides more accurate sequencing of peptides. The systems and methods combine deep learning and neural networks to sequencing of peptides.

Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Abstract: The present application discloses a method, system, and computer system for providing access to information stored on system for data storage. The method includes receiving a data request from a user, determining data corresponding to the data request, determining whether the user has requisite permissions to access the data, and in response to determining that the user has requisite permissions to access the data: determining a manner by which to provide access to the data, wherein the data comprises a filtered subset of stored data, and generating a token based at least in part on the user and the manner by which access to the data is to be provided.

Abstract: Methods and systems for compressing a neural network which performs an inference task and for performing computations of a Kronecker layer of a Kroenke NN are described. A batch of data samples are obtained from a training dataset. The input data of the data samples are inputted into a trained neural network to forward propagate the input data through the trained neural network and generate neural network predictions for the input data. Further, the input data are inputted into a Kronecker neural network to forward propagate the input data through the Kronecker neural network to generate Kronecker neural network predictions for the input data. Afterwards, two losses are computed: a knowledge distillation loss and a loss for Kronecker layer. The knowledge distillation loss is based on outputs generated by a layer of the neural network and a corresponding Kronecker layer of the Kronecker neural network.

Abstract: A method of and system for compressing a deep neural network model using knowledge distillation.

Abstract: The present systems and methods are directed to de novo identification of peptide sequences from tandem mass spectrometry data. The systems and methods uses unconverted mass spectrometry data from which features are extracted. Using unconverted mass spectrometry data reduces the loss of information and provides more accurate sequencing of peptides. The systems and methods combine deep learning and neural networks to sequencing of peptides.

Abstract: A system for processing a notebook includes an input interface and a processor. The input interface is to receive a first notebook. The notebook comprises code for interactively querying and viewing data. The processor is to load the first notebook into a shell. The shell receives one or more parameters associated with the first notebook. The shell executes the first notebook using a cluster.

Abstract: A system for multiple views for a notebook includes an input interface and a processor. The input interface to receive a notebook. The processor is to load the notebook into a shell, wherein the shell executes the notebook using a cluster, to receive an indication to view a dashboard associated with the notebook, and to provide dashboard display information. The dashboard includes a page layout display.

Abstract: A system for cluster resource allocation includes an interface and a processor. The interface is configured to receive a process and input data. The processor is configured to determine an estimate for resources required for the process to process the input data; determine existing available resources in a cluster for running the process; determine whether the existing available resources are sufficient for running the process; in the event it is determined that the existing available resources are not sufficient for running the process, indicate to add new resources; determine an allocated share of resources in the cluster for running the process; and cause execution of the process using the share of resources.

Abstract: A system for processing a notebook includes an input interface and a processor. The input interface is to receive a first notebook. The notebook comprises code for interactively querying and viewing data. The processor is to load the first notebook into a shell. The shell receives one or more parameters associated with the first notebook. The shell executes the first notebook using a cluster.

Abstract: A system for cluster management comprises a status monitor and an instance replacement manager. The status monitor is for monitoring status of an instance of a set of instances on a cluster provider. The instance replacement manager is for determining a replacement strategy for the instance in the event the instance does not respond. The replacement strategy for the instance is based at least in part on a management criteria for on-demand instances and spot instances on the cluster provider.

Abstract: A system for processing a notebook includes an input interface and a processor. The input interface is to receive a first notebook. The notebook comprises code for interactively querying and viewing data. The processor is to load the first notebook into a shell. The shell receives one or more parameters associated with the first notebook. The shell executes the first notebook using a cluster.