Abstract: A gas-liquid separation device includes a first cylinder, a second cylinder, a heat exchange assembly and a gas-liquid separation assembly. The gas-liquid separation device defines a first cavity and a second cavity. The first cavity includes a space between the first cylinder and the second cylinder. The second cavity includes an inner cavity of the second cylinder. The gas-liquid separation assembly is at least partially located in the second cavity. An inner cavity of the gas-liquid separation assembly is in communication with the first cavity and the second cavity. At least part of the heat exchange assembly is located in the first cavity. The gas-liquid separation device includes a first pipe portion. A pipe cavity of the first pipe portion communicates with the second cavity and an outer space of the first cylinder. A thermal management system having the gas-liquid separation device is also disclosed.

Abstract: A prompt reinforcement tool (“tool”) compares benign sample prompts to determine common and differing content across the prompts. The tool prompts a LLM to identify system prompt components from the common prompt content and identify user inputs and additional inputs from the differing prompt content. The tool also prompts the LLM to extract prompt components based on the response samples. The prompt reinforcement tool updates an application agnostic prompt template and generates an application tailored prompt template. To update, the tool removes irrelevant sections and populates remaining/preserved sections of the application agnostic prompt template that correspond to the identified system prompt components and the extracted prompt components. The tool then inserts, adjacent to template sections for user input and additional input, placeholders for a secure wrapper and inserts a guardrail instruction to isolate any commands or instructions that may be in the user input or additional input.

Abstract: A security feed normalizer aggregates and normalizes threat intelligence data across security feeds and extracts threat descriptors of cybersecurity threats from the aggregated/normalized data. A first large language model (LLM) determines whether each threat descriptor is informative, i.e., comprises sufficient information for reproducing/generating network traffic of the corresponding cybersecurity threat. For informative threat descriptors, a second LLM generates network traffic for the corresponding cybersecurity threats. The generated network traffic is used for subsequent remediation of corresponding threats.

Abstract: A system facilitates multi-session, multi-aspect analysis of conversations between users and applications that leverage language models (“generative AI applications”). The system retrieves conversation logs for conversations carried out between users and a generative AI application(s) and sorts the conversation logs by conversation. The system analyzes the conversations in multiple aspects for various use cases: security, analytics, and organizational compliance. In the security analysis, the system analyzes the queries and responses of each conversation to determine if the conversation includes a multi-turn attack. For the analytics and organization compliance use cases, the system evaluates how end users are using the generative AI application(s) across conversations to provide customers with additional insights, such as the spread of subject matter areas for which employees are using the generative AI application(s).

Abstract: A genetic algorithm is implemented to generate prompts that evade content filters of generative artificial intelligence (AI) systems. The genetic algorithm applies grammar operations to mutate candidate prompts, communicates the candidate prompts to generative AI systems, and selects candidate prompts that successfully evade content filters according to corresponding responses. A disambiguation model that corrects grammar in prompts is tested on the selected prompts to determine if grammar is properly corrected. Once tested, the disambiguation model is deployed in an ensemble with a classifier that outputs verdicts for prompts with grammar corrected by the disambiguation model.

Abstract: Techniques for detecting anomalous network behavior in operational technology (OT) protocols are disclosed. A system, process, and/or computer program product for detecting anomalous network behavior in OT protocols include monitoring network traffic to perform automated OT malware detection analysis of OT related network traffic, extracting one or more features from the OT related network traffic, inputting the one or more extracted features into a model for malware detection analysis, and performing an action based on a result of the model.

Abstract: An air conditioner control method, an air conditioner, and a storage medium. The air conditioner is provided with the detection apparatus group. The method includes controlling the detection apparatus group to be in different detection modes to detect a state of a target object; and controlling operation of the air conditioner based on the state of the target object.

Abstract: Provided herein are compounds of formula A-B, where A is selected from a monosaccharide or a disaccharide and B is a therapeutic moiety (e.g. selected from an anti-inflammatory moiety or chemotherapeutic moiety); as well as a pharmaceutically acceptable salt, stereoisomer, acid, or solvate thereof. Such a compound may be a substrate for glucosylceramidase beta 2 (GBA2), such that the compound is a prodrug that provides an active compound after cleavage of the monosaccharide or disaccharide by GBA2. Also provided are pharmaceutical compositions comprising the compounds, as well as various medical and non-medical uses of the compounds and compositions.

Abstract: The present invention provides a VOPRF protocol for post-quantum security, and a method and system for anonymous token authentication. The VOPRF protocol includes: a public parameter setting phase, an encoding and request phase, a re-encoding and response phase, and a decoding phase. The method for anonymous token authentication includes: an initialization phase: generating, by a trusted third-party, a common reference string; a key generation phase; a signing phase, executing jointly, by a client and a server, the VOPRF protocol to generate an anonymous token; and a token redemption phase: extracting, by the client, a token from a local list, and sending the token to the server for verification. The present invention implements the VOPRF protocol for post-quantum security and the method and system for anonymous token authentication, to still ensure security of an algorithm and a protocol under the threat of strong computing power of a quantum computer.

Abstract: Disclosed are a three-dimensional modeling system and modeling method based on multimodal fusion. The method includes: separately collecting feedback data of an electroencephalogram sensor, an electromyography sensor, an eye movement sensor, a gesture sensor, and a voice sensor, conducting multimodal fusion on the feedback data, obtaining multimodal-fused model data, matching the model data with a database instruction, obtaining and analyzing an instruction set, obtaining and identifying a relevant modeling parameter, obtaining a modeling method, automatically conducting modeling according to the modeling method, and obtaining a visual entity model.

Abstract: Various techniques for LLM powered detection reasoning solutions are disclosed. In some embodiments, a system, a process, and/or a computer program product for an LLM powered detection reasoning solution includes monitoring network traffic at a security platform, wherein the security platform generates a sample based on the monitored network traffic; sending the sample to a security service to generate a Large Language Model (LLM) powered detection and reason, wherein the LLM is prompted to automatically generate a malware or benign verdict and a reason for explaining the verdict; and reporting the LLM powered detection and reason.

Abstract: A security feed normalizer aggregates and normalizes threat intelligence data across security feeds and extracts threat descriptors of cybersecurity threats from the aggregated/normalized data. A first large language model (LLM) determines whether each threat descriptor is informative, i.e., comprises sufficient information for reproducing/generating network traffic of the corresponding cybersecurity threat. For informative threat descriptors, a second LLM generates network traffic for the corresponding cybersecurity threats. The generated network traffic is used for subsequent remediation of corresponding threats.

Abstract: A prompt generator receives descriptive data for a plug-in of a custom or open-source security testing tool. The prompt generator chooses a prompt template corresponding to the type of descriptive data and the security testing tool. For an open-source security testing tool, the prompt generator populates the prompt template with the descriptive data and prompts an open-source large language model (LLM) with the populated prompt to generate the plug-in for the open-source security testing tool. For a custom security testing tool, the prompt generator populates the prompt template with the descriptive data or an existing open source plugin, and prompts a custom LLM with the populated prompt to generate the plug-in for the custom security testing tool.

Abstract: According to one aspect of the present disclosure, a memory apparatus is provided. The memory apparatus may include a first memory die and a die group stacked in a first direction. The die group may include M second memory dies stacked in the first direction. Each of the second memory dies may be connected to a third memory die and configured to perform data exchange at a bandwidth of (N*X) bits with the third memory die through N data channels. The first memory die may be connected to the third memory die. The first memory die may be configured to perform data exchange at a bandwidth of (N*X) bits with the third memory die through N data channels. The first memory die may be configured to replace a first data channel of the die group when a storage portion corresponding to the first data channel fails.

Abstract: Disclosed is an intelligent interactive decision-making method for a discrete manufacturing system. The method includes the following steps: step 1, establishing a production scheduling optimization model and strategy for discrete manufacturing for an actual application scene; step 2, training the scheduling strategy with existing production data on the basis of a deep reinforcement learning algorithm, and storing a state having a high reward in a training process in a memory; step 3, updating the state according to prior knowledge in the memory; step 4, inputting the updated state into a deep reinforcement learning network, obtaining a corresponding reward, and updating the memory according to the reward; and step 5, repeating step 4 until model parameters converge, and saving and putting the model into an actual production scene.

Abstract: A system, method, and device for detecting Command and Control (C2) traffic is disclosed. The method includes (i) converting a monitored traffic sample to an image representation, (ii) querying a classifier based at least in part on the image representation to obtain a traffic classification, (iii) automatically detecting C2 traffic based at least in part on the traffic classification, and (iv) handling the network traffic based at least in part on the traffic classification.

Abstract: The present invention provides a VOPRF protocol for post-quantum security, and a method and system for anonymous token authentication. The VOPRF protocol includes: a public parameter setting phase, an encoding and request phase, a re-encoding and response phase, and a decoding phase. The method for anonymous token authentication includes: an initialization phase: generating, by a trusted third-party, a common reference string; a key generation phase; a signing phase, executing jointly, by a client and a server, the VOPRF protocol to generate an anonymous token; and a token redemption phase: extracting, by the client, a token from a local list, and sending the token to the server for verification. The present invention implements the VOPRF protocol for post-quantum security and the method and system for anonymous token authentication, to still ensure security of an algorithm and a protocol under the threat of strong computing power of a quantum computer.

Abstract: The present disclosure discloses a silicon wafer side edge detection device, comprising a first detection unit and a second detection unit, wherein the first detection unit and the second detection unit are of the same structure and are respectively configured to detect two mutually parallel side edges of a silicon wafer; and the first detection unit and the second detection unit each comprises a side edge detection assembly, the side edge detection assembly comprising a first point light source, a first optical path defining element, and a camera, wherein the first optical path defining element comprises a reflective structure and a slit, the reflective structure is configured to reflect light emitted by the first point light source and reflect the light to a side edge of a silicon wafer to be detected, and the light is reflected by the side edge of the silicon wafer.

Abstract: A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data/metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.

Abstract: An AI-based application uses AI models to expeditiously identify a product or service most relevant to an issue input to the application instead of a user navigating a platform or catalogue of products/services. The application prompts a first language model to convert the input description into a structured description of the issue. The application then, for each product/service category, prompts a second language model to identify a product/service most relevant to the structured description within each product/service category. The application then returns an identification of a product/service determined to be most relevant to the issue as represented by the structured description, confidence in the identification, and a reason the product/service was identified.