Abstract: In accordance with some embodiments, systems, methods, and media for generating and using neural networks having improved efficiency for analyzing video are provided. In some embodiments, the method comprises: providing image data to a trained neural network; receiving, at a neuron, a delta-based input ?in from a previous layer; generating an output g(?in) of a linear transform g; generating an updated state variable a based on g(?in) and a current a; generating an output f(a) of an activation function f based on updated a; generating an updated state variable d based on a current d, a state variable b, and f(a); generating an updated b based on output f(a); transmitting d to a next layer based on a transmission policy and subtracting the value from d; and receiving an output from the trained neural network that represents a prediction based on the image data.

Abstract: Presented are user-friendly battery powered touchless identity card emulator systems and methods that allow existing ID management installations, such as physical card reader systems, to securely operate without requiring a physical key and irrespective of type, model, shape, and size of reader and card format. Various embodiments integrate wireless functionality to existing systems to enable mobile access to provide advanced user/identity management capabilities for access control systems.

Abstract: Systems and methods are provided for interpreting a correlation model that predicts a correlation between a pair of data corresponding to a pair of incident tickets using an interpreter model. The correlation model includes a Siamese Network including a plurality of neural networks. The interpreter model, trained by using training data, represents a student model (a glass-box model) while the correlation model, trained using the training data, represents a more complex teacher model (a black-box mode) of a teacher-student model. The present disclosure generates global feature importance scores based on the trained interpreter model, which indicates a degree of influence of a feature compared to other features in incident data in determining correlations, to generate additional training data emphasizing influential features and to retrain the correlation model.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining true signals from the raw mass spectrometry data, determining intensities corresponding to the true signals, adjusting the determined intensities, and based on the adjusted intensities, determining concentrations of one or more constituents corresponding to the true signals.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, generating an image representation from the raw mass spectrometry data, selecting a portion of the signals corresponding to the image representation, inputting the selected portion into a machine learning model to determine or infer an existence or an absence of signals within respective retention time windows, obtaining a retention time window within which a subset of the signals exist, determining whether to expand the retention time window, determining or receiving an indication of a retention time window within which a subset of the signals are located, and determining whether to expand the retention time window. The systems and methods may selectively expand the retention time window based on the determination, and retrieve information within the expanded retention time window or the retention time window.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining signals present across the samples, and separating the raw mass spectrometry data into discrete intervals in each of the samples. At each interval of the discrete intervals of the raw mass spectrometry data, a local highest intensity signal, relative to any other signal within each interval, is determined, and a frequency of occurrence of each local highest intensity signal across the samples is determined. A subset of local highest intensity signals is retrieved based on respective frequencies of occurrence of the local highest intensity signals. The subset of the local highest intensity signals is ingested into a machine learning model.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive sensor data indicating an obstacle, formulate a control barrier function for a vehicle and the obstacle based on the sensor data, determine a control input based on the control barrier function and a combination function, and actuate a component of the vehicle according to the control input. The combination function is a sum of a first function weighted by a first weight and a second function weighted by a second weight, and the first weight and the second weight are based on a kinematic state of the obstacle.

Abstract: An electronic device has a frame configured to be worn on a user's head, where the frame includes a body and a temple coupled to the body. The body is configured to support a lens. The electronic device includes a processor in the frame and an RF antenna positioned in the body and in data communication with the processor.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, extracting a subset of the raw mass spectrometry data based on storage characteristics of the computing system, storing the subset of the raw mass spectrometry data within a storage of the computing system, and transmitting the stored subset to a machine learning component.

Abstract: A processor may initialize a fetch of a first instruction. The processor may determine whether there is an icache miss for the first instruction. The processor may fetch the next instruction from a cache.

Abstract: Multi-radio wireless network devices are capable of transmitting and/or receiving data from multiple radiofrequency (RF) networks at different bands. Total transmission power limitations may be in place due to, for example, safety reasons. As a result, active management of transmission power may be performed during simultaneous transmission in different bands and/or networks. In some embodiments, the management may take place on group-by-group basis and a network-by-network basis. Antennas may be grouped based on their relative positions and impact on radiation emitted by the devices.

Abstract: Implementations are directed to obtaining a plurality of item profiles for a plurality of new items, each item profile comprising a set of attributes for a respective new item; for each new item: selecting one or more existing items that are similar to the new item based on item attributes of the existing items and the set of attributes of the new item, and executing a collaborative filtering model to determine a first score for the new item based on historical user interactions with the one or more existing items; determining a second score for each new item using an adaptive model; and outputting a first set of new items based on the first score, and a second set of new items based on the second score, an initial ratio between the first set of new items and the second set of new items is a predetermine value.

Abstract: Fibers, yarns, woven fabric including the yarns and fibers, and methods of manufacturing the same are disclosed. Fibers can include base material staple fibers and dissolvable or water-soluble fibers that are mixed together to define an ultra-homogenous yarn comprising base material and dissolvable material, which is provided in at least the warp direction to form a woven fabric having a 7-end, 8-end or 10-end sateen weave. A processing step provides for the removal of the dissolvable fibers to produce a yarn defining a plurality of pores that are uniformly distributed throughout the structure of the yarn. The woven fabric has a thread count between 450-1200. The woven fabric is thermally-insulative, breathable and moisture-wicking.

Abstract: A door includes an inner wall, an outer wall, and a window assembly including a window. The inner wall is configured to engage with a cab of a vehicle. The outer wall is coupled to the inner wall and spaced apart from the inner wall to form an interior cavity. The door is configured to move between a closed position in which the inner wall is engaged with the cab and an open position in which the inner wall is separated from the cab. In the closed position, at least a portion of the interior cavity is configured to be disposed outboard from the cab in a lateral direction. The window is slidably engaged to the door and configured to move between an extended position and a retracted position. In the retracted position, the window is at least partially disposed in the portion of the interior cavity.

Abstract: A Bloom filter is used to track contents of a cache. A system checks the Bloom filter before deciding whether to prefetch an address (by hashing the address and checking a value of the Bloom filter at an index based on the hash). This allows the system to utilize more aggressive prefetching schemes by reducing the risk of wasteful redundant prefetch operations.

Abstract: A first network device may receive first traffic of a session that involves a service. The first network device may identify that the service is configured for distributed node processing. The first network device may identify a second network device that is configured for distributed node processing. The first network device may identify a state machine that is associated with the service. The first network device may determine, based on the state machine, a first function and a second function, wherein the first function is identified by a first label and the second function is identified by a second label. The first network device may process the first traffic based on the first function. The first network device may provide, to the second network device, the first traffic and the second label to permit the second network device to process second traffic in association with the second function.

Abstract: A user space driver for input/output traffic distribution and packet processing is provided. A device can establish a driver in user space with access to a memory mapped region shared with a kernel of the device. The driver can access a packet stored to the memory mapped region by a network interface of the device responsive to receipt of the packet. The driver can provide the packet to a selected application of a plurality of applications for processing by the selected application.

Abstract: For connection establishment, a system allocates memory that will be occupied by the data and handshake sub-protocol infrastructure that facilitates establishing a TLS connection. After connection establishment, the system allocates memory space for the data and record sub-protocol infrastructure that facilitates the asynchronous communication of application traffic. The memory space for the TLS session (i.e., the communication information separate from the handshake) has a substantially smaller footprint than the memory space for the TLS handshake. The TLS handshake memory space can be released and recycled for other connections while application communications use the smaller memory space allocated and populated with the TLS session data and infrastructure.

Abstract: Described are techniques for linking generating a skill-stored user profile, and linking same with a natural language processing (NLP) system-stored user profile. In at least some examples, a user may provide a natural language input to a NLP system. The NLP system may determine a skill is to process to perform an action responsive to the natural language input. To perform the action, the skill may require the user have a user profile stored by the skill, but the user may not have such a user profile. However, the NLP system may store a user profile for the user. The NLP system may determine the user profile stored thereby and may send, with user permission, information in the user profile to the skill. The skill may use the received information to generate and store a user profile for the user. Thereafter, the skill may provide the NLP system with a user profile identifier that the skill may use to identify the user's profile stored thereby.

Abstract: A configuration system may generate synthetic datasets based on machine learning, and may provide the synthetic datasets in order to configure, train, test, and/or otherwise modify operation of network equipment and/or other devices. The configuration system may receive a sampling of data used by the particular network equipment, and may determine parameters that define values for the variables represented in the sampling. The configuration system may generate different datasets based on the parameters, and may determine an accuracy of each dataset to the sampling based on the values of each dataset conforming by different amounts to the parameters that define the values for the variables represented in the sampling by different amounts. The configuration system may modify the network equipment operation using the values from a particular dataset in response to determining that the accuracy of the particular dataset is greatest to the sampling.