Abstract: A network device, system-on-a-chip, and method of performing packet handling are described. A packet is received, and data associated with the packet is processed, using a configurable artificial intelligence engine, to generate a size classification for a flow associated with the packet. An action is performed based, at least in part, on the size classification for the flow associated with the packet.

Abstract: A method, system and product including obtaining offline user information at an end device, wherein the offline user information is obtained from offline sensors of the end device; based on the offline user information, generating a user profile indicating that a user of the end device matches at least one micro-segment, wherein the at least one micro-segment comprises at least one detailed population category; based on the at least one micro-segment, selecting a campaign from a set of one or more campaigns retained at a server, wherein the campaign comprises one or more rules for displaying at least one content item; monitoring the offline sensors of the end device to identify real time user activities; and upon identifying, based on the real time user activities, that a rule of the one or more rules for displaying a content item is complied with, displaying the content item in the end device.

Abstract: In one embodiment, a system includes a memory, and a processor coupled to the memory, where the processor is to explore a plurality of control policies during exploration phases, wherein the control policies are associated with different probabilities of being selected for exploration, measure system performance during the exploration phases, and control a resource during an exploitation phase based on a policy selected to maximize expected reward based on the measured system performance.

Abstract: In one embodiment, a system includes a processor to control a resource according to policies selected by a multi-armed bandit machine learning agent in exploration phases and in exploitation phases, and execute the multi-armed bandit machine learning agent to select from the policies to control the resource in the exploration phases according to probabilities to explore corresponding one of the policies, wherein the probabilities include different probabilities, perform measurements on the system during execution of the multi-armed bandit machine learning agent, and execute the multi-armed bandit machine learning agent to select from the policies to maximize potential rewards from controlling the resource in exploitation phases based on the performed measurements, and a memory to store data used by the processor.

Abstract: A method, system, computerized apparatus and computer program product for training a specific model in a computerized environment with multiple user devices. Each user device executes a generic model that is configured to classify a user thereof into segments based on data obtained at the user device, each segment representing audience characterized by attributes. Data obtained from a specific application forms a training set for the specific model, comprising pairs of a ground truth label used by the application and a respective set of segments of a user device for which the ground truth label is deemed applicable by the application. The specific model may be trained on this training set, and applied in concatenation with the generic model, to predict a label for a user device based on classification determined by the generic model without being privy to the data obtained at the user device.

Abstract: Pelvic implant having a frame with two arms and a sheet are disclosed. The sheet may be held under tension in the assembled implant. The implant may be planar (flat) or may be non-planar. The implant may be contoured laterally. A curved non-planar implant may be convex or concave in a rostro-caudal direction. The implants may (optionally) include two additional arms and a second sheet for supporting a urethra and/or a bladder neck. Methods for constructing and assembling the implants are disclosed. A method ultrasonically welding the frame of the implant without thermally damaging the sheet is disclosed. Methods of implantation of the implants are disclosed.

Abstract: A method and system for mapping vulnerabilities to a private network identifier are provided. The method includes generating a request addressed to a public network identifier of a server, wherein the request includes a first beacon; transmitting the generated request to the server, wherein upon receiving the request by the server, the request causes a registry of a log file of the server, wherein the log includes at least the first beacon; in response to a query, receiving a response from the server, wherein the response includes a second beacon, and a private network identifier of the server; associating the public network identifier with the private network identifier, when the first beacon matches the second beacon; and mapping vulnerabilities identified with the public network identifier to associate the identified vulnerabilities with the associated public network identifier and the private network identifier.

Abstract: In one embodiment, a network device includes a plurality of ports to receive packets, and processing circuitry to determine adaptive routing profile classifications of the received packets, make adaptive routing decisions for the packets based on different adaptive routing profiles associated with the adaptive routing profile classifications of the packets, and forward the received packets to the ports according to the adaptive routing decisions.

Abstract: A method, system and product for enhancing AI generative systems. The method comprises obtaining a query and obtaining at least one detail related to a user of a client device. The query is automatically enhanced to include the at least one detail related to the user, thereby obtaining an enhanced query adapted to the user. Said enhancing is performed by the client device, thereby protecting a privacy of the user. The enhanced query is submitted to a generative Artificial Intelligence (AI) model. A response from the generative AI model is received and an output that is based on the response is provided.

Abstract: In various examples, systems and methods are disclosed relating to data center scale prediction-based power reservation steering. One or more circuits can receive, from a plurality of components of a data center, power consumption data for a first time period. The one or more circuits can generate, using at least one prediction model and based at least on the power consumption data, a predicted power consumption of the plurality of components for a second time period following the first time period. The one or more circuits can determine a power policy for the plurality of components based at least on the predicted power consumption and a state of the data center and cause the plurality of components to limit power consumption for the second time period according to the power policy.

Abstract: In one embodiment, a method includes receiving data of a set of configurations of preprocessor engines, receiving measurements of performance of a device executing benchmark applications while changing a configuration of preprocessor engines selected from the set of configurations of preprocessor engines, defining an order of at least some of the configurations based on the measurements, and providing a pruned set of configurations based on the defined order of the at least some configurations.

Abstract: Network devices, systems, and methods are provided. In one example, a network device includes one or more packet classification circuits and one or more hash selection circuits that cooperate with the one or more packet classification circuits to provide the one or more packet classification circuits with a hash table selection order. The hash table selection order may be determined for a packet to be classified with the support of machine learning by the one or more packet classification circuits based on information contained in the packet.

Abstract: A method and system for evaluating cyber risk of an entity comprising a risk evaluation module configured to collect risk data on risks of cyber-attacks connected to SaaS, infrastructure, and legal regulations classified by geolocation, industry type, and size of the victim organization, an entity evaluation module for collecting vulnerability data on assets of the entity classified by industry type, geolocation, size and cyber threat vector vulnerabilities and a monetization engine configured to make an assessment of expected financial loss from a specified cyber-attack to an entity classified by geolocation, industry type, and size, based on the risk data.

Abstract: A network device, system-on-a-chip, and method of performing packet handling are described. A packet is received, and a lookup based on one or more packet header fields of the packet is performed using a lookup generator action handler. An artificial intelligence engine processes packet header data associated with the packet, and a smart indication is generated. A forwarding decision is made for the packet based on the lookup performed by the lookup generator action handler as well as based on the smart indication generated with the artificial intelligence engine.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to control the prefetcher engines according to configurations of the prefetcher engines selected by a machine learning agent in exploration phases and in exploitation phases during execution of the software application, and execute the machine learning agent to select from a pruned set of configurations to control the prefetcher engines in the exploration phases, perform measurements on the system during execution of the machine learning agent, and execute the machine learning agent to select from the configurations to maximize potential rewards from controlling the prefetcher engines in the exploitation phases based on the performed measurements.

Abstract: A network device includes a memory and a memory management circuit. The memory is to store a shared buffer. The memory management circuit is to estimate respective bandwidth measures for one or more queues used in processing packets in the network device, and to allocate and deallocate segments of the shared buffer to at least one of the queues based on the bandwidth measures.

Abstract: In one embodiment, a system includes a processor to reduce a number of cache replacement precedence value policies available for selection by a machine learning agent, each cache replacement precedence value policy including an array of predefined cache replacement precedence values for corresponding different combinations of (a) prefetcher engines that loaded cache lines, and (b) event-types of events that have been performed on the cache lines, and a memory to store data used by the processor.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to assign cache replacement precedence values to cache lines based on the prefetcher engines that loaded the cache lines, and evict the cache lines from the cache based on the cache replacement precedence values of the cache lines.

Abstract: In one embodiment, a system includes a processor to reduce a number of cache replacement precedence value policies available for selection by a machine learning agent, each cache replacement precedence value policy including an array of predefined cache replacement precedence values for corresponding different combinations of (a) prefetcher engines that loaded cache lines, and (b) event-types of events that have been performed on the cache lines, and a memory to store data used by the processor.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to assign cache replacement precedence values to cache lines based on the prefetcher engines that loaded the cache lines, and evict the cache lines from the cache based on the cache replacement precedence values of the cache lines.