Abstract: Methods and apparatus disclosed herein introduce a novel integration of sensing and neuromorphic computation that addresses the energy, latency, and complexity challenges of conventional event-based vision pipelines. A monolithic neuromorphic sensor fuses sensing and computation into a single neuro-transducer cell, including pixels that contain a photonic transducer, a membrane capacitor, and a Leaky Integrate-and-Fire (LIF) neuron whose membrane potential is driven directly by a raw physical stimulus rather than by an injected current. Adjacent neuro-transducers are linked by non-volatile, programmable RRAM synapses that store multi-bit weights and are updated locally via Spike-Timing-Dependent Plasticity (STDP)-compatible write pulses.

Abstract: The present disclosure relates to a water splitting catalyst including a porous carbon layer, a bimetallic metal alloy core dispersed on the porous carbon layer, and a single-atom precious metal dispersed on the bimetallic metal alloy core, in which oxygen is adsorbed on the surface of the bimetallic metal alloy core.

Abstract: The present description relates to press extractors for processing municipal solid waste or source separated organics, and can have large perforations for separation of a wet fraction from a liquid depleted fraction. The extractor can also be operated based on a target plug thickness of the liquid-depleted plug left over in the perforated chamber after compression. The extractors can facilitate good organics extraction yields, and lower operating pressures, among other benefits.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: The present disclosure relates to traffic monitoring through one or more access control servers configured for (i) routing server resource request messages to resource server(s), (ii) extracting information identifying a target server resource from data packets corresponding to one or more received server resource request messages, and (iii) selectively transmitting the received server resource request message to a resource server. The security server(s) is configured to receive a server resource request message data extracted from a server resource request message and initiate a first security response, wherein the initiated first security response is dependent on analysis of the server resource request message data.

Abstract: The present disclosure relates to traffic monitoring through one or more access control servers configured for (i) routing server resource request messages to resource server(s), (ii) extracting information identifying a target server resource from data packets corresponding to one or more received server resource request messages, and (iii) selectively transmitting the received server resource request message to a resource server. The security server(s) is configured to receive a server resource request message data extracted from a server resource request message and initiate a first security response, wherein the initiated first security response is dependent on analysis of the server resource request message data.

Abstract: A method for automatically converting note-to-self to action reminders in an electronic device is provided. The method includes receiving an input comprising at least one word from a user of the electronic device, analyzing an anaphora representation or a deictic representation for each of the at least one word, and generating a reminder based on a context from the anaphora representation or the deictic representation.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: The present disclosure relates to traffic monitoring through one or more access control servers configured configured for (i) routing server resource request messages to resource server(s), (ii) extracting information identifying a target server resource from data packets corresponding to one or more received server resource request messages, and (iii) selectively transmitting the received server resource request message to a resource server. The security server(s) is configured to receive a server resource request message data extracted from a server resource request message and initiate a first security response, wherein the initiated first security response is dependent on analysis of the server resource request message data.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: The present disclosure relates to a water splitting catalyst including a porous carbon layer, a bimetallic metal alloy core dispersed on the porous carbon layer, and a single-atom precious metal dispersed on the bimetallic metal alloy core, in which oxygen is adsorbed on the surface of the bimetallic metal alloy core.

Abstract: The present invention relates to an in-vitro method of detecting Mtb infection by using SAXS profile of hair sample. The invention is an approach to overcome the problem of non-invasively and cost-effectively yet reliably diagnosing presence of active tuberculosis in the patient. There would be no risk to the sample handlers getting infected from the sample, cross-contamination of samples, low cost and quick turn-around time of diagnosis.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: The present disclosure relates to traffic monitoring through one or more access control servers configured configured for (i) routing server resource request messages to resource server(s), (ii) extracting information identifying a target server resource from data packets corresponding to one or more received server resource request messages, and (iii) selectively transmitting the received server resource request message to a resource server. The security server(s) is configured to receive a server resource request message data extracted from a server resource request message and initiate a first security response, wherein the initiated first security response is dependent on analysis of the server resource request message data.

Abstract: Disclosed is an optical coherence tomography scanner and a method for recording sub-surface scans of an object, wherein a position encoder is arranged in the path of the probing beam of an interferometric system. The encoder pattern is detected in a sequence of A scans at generated for different probing beam positions on the scanned object, the probing beam position and/or inclination for at least one A scan of said sequence of A scans is deducing based on the detected encoder pattern, and the sub-surface scan of the object is generated based on the sequence of A scans taking into account the deduced probing beam position and/or inclination.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.