Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

A method, system, and computer program product for IO leveling comprising receiving an IO, determining if there is a delay for processing IO because of pending IO, based on a positive determination there is a delay for processing IO, determining a priority for the IO, and based on the priority of IO determining whether to process the IO.

Non-parametric transforms such as t-distributed stochastic neighbor embedding (tSNE) are used to analyze multi-parametric data such as data derived from flow cytometry or other particle analysis systems and methods. These transforms may be included for dimensionality reduction and identification of subpopulations (e.g., gating). By nature, non-parametric transforms cannot transform new observations without training a new transformation based on the entire dataset including the new observations. The features described parameterize non-parametric transforms using a neural network thereby allowing a small training dataset to be transformed using non-parametric techniques. The training dataset may then be used to generate an accurate parametric model for assessing additional events in a manner consistent with the initial events.

Technical solutions are described to accelerate training of a multi-layer convolutional neural network. According to one aspect, a computer implemented method is described. A convolutional layer includes input maps, convolutional kernels, and output maps. The method includes a forward pass, a backward pass, and an update pass that each include convolution calculations. The described method performs the convolutional operations involved in the forward, the backward, and the update passes based on a first, a second, and a third perforation map respectively. The perforation maps are stochastically generated, and distinct from each other. The method further includes interpolating results of the selective convolution operations to obtain remaining results. The method includes iteratively repeating the forward pass, the backward pass, and the update pass until the convolutional neural network is trained. Other aspects such as a system, apparatus, and computer program product are also described.

The present invention provides a disease condition information research method and system. The system includes: an information collection module, configured to collect clinical manifestation information in different forms; an information analysis module, configured to establish logical association among the cause-pathophysiology-clinical manifestation information by using the information collected by the information collection module according to the objective laws of medical knowledge so as to form a disease condition information analysis model; and a result display module, configured to display pathophysiological and anatomical location lesion information after the information analysis module performs disease condition analysis. The present invention can help the medical students to more deeply understand the teaching contents of physiology, anatomy, pathology, pathophysiology, clinical diagnostics, surgery and internal medicine, thus improving the teaching quality.

The invention provides a control arrangement where the controller is arranged to drive the actuator utilizing automatically the explanation values. The control arrangement has a controller, which is arranged to drive an actuator. The control arrangement comprises also a setpoint controller, which is arranged to utilize deviations between explanation values of machine learning and normal explanation values of machine learning. The setpoint controller forms a setpoint value for the controller.

One embodiment of the present invention provides a computer implemented method for generating a training set to train a convolutional neural network comprising the steps of providing prediction space data to a General Logic Gate Module (GLGM). Prediction space expert judgement is also provided to the GLGM and to a sensitivity and importance module. The GLGM determines or outputs state possibilities. The state possibilities are provided to the sensitivity and importance module and to the feature extraction module. Feature extraction algorithms are applied to the state possibilities within the feature extraction module to produce a training possibility set that is a virtual training possibility set. The training possibility set is provided to a state inferential module and to a final training set. From the state inferential module a possibility ranking is generated that is independent of the convolutional neural network and further the output from the state inferential module is provided to a sensitivity and importance module for analysis. A sensitivity parameter and an importance parameter is determined from the output from the sensitivity and importance module. The state possibility ranking is provided to the final training set. The sensitivity parameter and importance parameter are provided to a final training set and a training set structure metric. A convolutional neural network input layer is generated from the final training set informed by one or more of the state possibility ranking, the sensitivity parameter, the importance parameter and the training possibility set. A convolutional neural network layer design is generated from the training set structure metric.

A system for monitoring a power system and controlling an operation is provided. The system includes: a process setting unit receiving a process modeling file from a user and setting, as a process setting model, a process modeling file on which process verification is completed; a process verification unit performing the verification of preset process modeling items on the process modeling files received from the user; and a data storage unit storing file information for the operation of a system, the process modeling file, process modeling verification results, and the process setting file.

A system for monitoring a power system and controlling an operation is provided. The system includes: a process setting unit receiving a process modeling file from a user and setting, as a process setting model, a process modeling file on which process verification is completed; a process verification unit performing the verification of preset process modeling items on the process modeling files received from the user; and a data storage unit storing file information for the operation of a system, the process modeling file, process modeling verification results, and the process setting file.