Embodiments described herein are related to systems and methods for forecasting demands for a transportation service. In one aspect, a set of neural network models may be implemented, where each neural network model can be configured to predict a booking status of a category of carriers on a corresponding date from a range of dates before a departure date. In one aspect, for each neural network model, a corresponding set of configuration values can be determined. Examples of the corresponding set of configuration values includes at least one of a number of layers, a number of neurons, and an activation function of the each neural network model. The set of neural network models can be constructed, according to corresponding sets of configuration values, and the constructed neural network models can be trained.

Detection of command and control malware is disclosed. A network traffic session is monitored. Automatic feature identification for real-time malicious command and control traffic detection based on a request header of the monitored network traffic session using a deep learning model is performed.

Methods, systems and wearable devices for real-time mental stress assessment are provided. The methods and systems employ deep learning using a Gated Recurrent Unit (GRU) gating mechanism in a recurrent neural network with a sliding window approach applied to raw EEG data.

Techniques for operating a computing system to perform neural network operations are disclosed. In one example, a method comprises receiving a neural network model, determining a sequence of neural network operations based on data dependency in the neural network model, and determining a set of instructions to map the sequence of neural network operations to the processing resources of the neural network processor. The method further comprises determining, based on a set of memory access operations included in the set of instructions, a first set of memory references associated with a first location of an external memory to store the input data and a second set of memory references associated with a second location of the external memory to store the output data, and generating an instruction file including the set of instructions, the first set of memory references and the second set of memory references.

Accuracy of a model extracting a graph structure as an intermediate representation from input data is improved. An encoding unit (100) extracts a feature amount of each of a plurality of vertices included in a graph structure (Tr) from input data (10), and calculates a likelihood that an edge is connected to the vertex. A sampling unit (130) determines the graph structure (Tr) based on a conversion result of a Gumbel-Softmax function for the likelihood. A learning unit (150) optimizes a decoding unit (140) and the encoding unit (100) by back propagation using a loss function including an error (L.sub.P) between output data (20) generated from the graph structure (Tr) and correct data.

Disclosed embodiments may include a method and system for automated incremental payments. The system may identify recurring charges from historical account data. Based on the recurring charges and an incremental period, the system may calculate an incremental amount and expected amount. At each iteration of the incremental period, the incremental amount may be assigned to a savings bucket. The value of the savings bucket may be subtracted from an actual account balance to calculate a reduced account balance. The system may generate and transmit a graphical user interface to a user device showing the reduced account balance. The system may receive current data containing a charge that corresponds to the recurring charges. The system may reduce the value of the savings bucket by the amount of the current data charge. If the current data charge is different from the expected amount, the system may adjust the incremental amount accordingly.

An apparatus for processing a narrowband speech input signal by conducting bandwidth extension of the narrowband speech input signal to obtain a wideband speech output signal according to an embodiment is provided. The apparatus includes a signal envelope extrapolator including a first neural network, wherein the first neural network is configured to receive as input values of the first neural network a plurality of samples of a signal envelope of the narrowband speech input signal, and configured to determine as output values of the first neural network a plurality of extrapolated signal envelope samples. Moreover, the apparatus includes an excitation signal extrapolator configured to receive a plurality of samples of an excitation signal of the narrowband speech input signal, and configured to determine a plurality of extrapolated excitation signal samples. Furthermore, the apparatus includes a combiner configured to generate the wideband speech output signal such that the wideband speech output signal is bandwidth extended with respect to the narrowband speech input signal depending on the plurality of extrapolated signal envelope samples and depending on the plurality of extrapolated excitation signal samples.

A method for implementing a targeted medical outputs is disclosed. The method may comprise: receiving first user data associated with a user from an external server; receiving second user data associated with the user from an internal server; processing the first user data and the second user data at a data fabric structure to generate domains; receiving the domains as an input at a machine learning model trained to generate a machine learning output; receiving at a database cube the machine learning output; performing one or more database cube processing operations on the machine learning output to generate a database cube output; transmitting the database cube output to one or more outputs, identified based on the one or more markers.

Efficient communication between processing elements of a configurable processor for implementing CNNs are provided. One such configurable processor includes a first processing element coupled to an image sensor, and a second processing element coupled to the first processing element via a serial communication link. The first processing element is configured to generate preselected data to be communicated using the serial communication link, receive image data from the image sensor, the image data including a first image data including multiple rows of data, send, via the serial communication link, a first row of the first image data to the second processing element, send, via the serial communication link, a portion of the preselected data to the second processing element, and send, via the serial communication link, a second row of the first image data to the second processing element.

A computerized method includes obtaining a set of entities. The method also includes, for each entity, obtaining data specific to the entity, generating a feature vector based on the data, and processing the feature vector to generate an entity fall likelihood that indicates a likelihood that the entity will experience a fall based on the feature vector. The method further includes determining a subset of entities having entity fall likelihoods that satisfy a threshold. For each entity in the subset, the method includes determining impact scores for parameters of the feature vector associated with the entity and generating a feature list based on the determined impact scores. Each impact score is indicative of an effect of the parameter on the entity fall likelihood for the entity. The feature list is specific to the entity and includes a parameter having the highest impact score.