Abstract: There is disclosed a method of and a system for generating a database query. A natural language input for requesting data from a database is received. A first portion of the natural language input is input to a first machine learning algorithm (MLA). A second portion of the natural language input is input to a second MLA. The first MLA outputs a first probability distribution. The second MLA outputs a second probability distribution. One or more sets of classes from the first probability distribution and the second probability distribution that satisfy a plurality of constraints are determined. A predicted probability is determined for each of the one or more sets of classes. A set of classes having a highest predicted probability from the one or more sets of classes is selected. A database query is generated based on the set of classes.

Abstract: In one embodiment, a device obtains network telemetry and environmental telemetry associated with a physical environment. The device forecasts, based on the network telemetry and environmental telemetry, future values of the network telemetry and environmental telemetry. The device predicts, based on the future values, quality of experience metrics for an online application for different locations within the physical environment. The device provides, based on the quality of experience metrics, a recommendation to a user interface that recommends a user of the online application navigate to a particular location from among the different locations to access the online application at a future point in time.

Abstract: In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

Abstract: A flame-holder device for a turbojet afterburner comprises an annular row of flame-holder arms, each having an inner branch having a free end and another end, and two outer branches which extend from the other end and diverge from one another in a direction extending from the free end to the other end, such that the inner branch transitions radially outward into the two outer branches which diverge radially outward from one another in two opposing circumferential directions, thereby forming mutually approaching areas between consecutive flame-holder arms for enabling the flame to spread from arm to arm.

Abstract: A method is disclosed for reduction of peak-to-average power ratio (PA-PR) of transmission using multiple-input multiple-output (MIMO) from a transmitter. The method comprises defining a rank-extended MIMO transmission towards one or more intended receivers and one or more virtual receivers (wherein the one or more virtual receivers reside in a null space of a MIMO channel between the transmitter and the one or more intended receivers), generating a rank-extended MIMO signal for the rank-extended MIMO transmission (wherein the rank-extended MIMO signal comprises an intended receiver signal portion and a virtual receiver signal portion), determining a clipping signal for the rank-extended MIMO signal, and generating a PAPR reduced MIMO signal by combining the rank-extended MIMO signal with a projection of the clipping signal onto the null space of the MIMO channel. In some embodiments, the method further comprises transmitting the PAPR reduced MIMO signal over the MIMO channel.

Abstract: Quantum device formed from a substrate, the substrate being covered: with at least one semiconductor region forming a quantum dot, a detection structure with Coulomb blockade for detecting a state of charge of the quantum dot, said detection structure with Coulomb blockade including a detection island disposed above and facing the quantum dot and able to be coupled to the quantum dot by electrostatic coupling, said detection structure furthermore including at least one first tunnel junction between said detection island and a first gate block, the first gate block being juxtaposed with said detection island.

Abstract: In one implementation, a device uses a large language model-based agent to complete a task with respect to a computer network. The device makes an evaluation of an action taken by the large language model-based agent to complete the task. The device selects, based in part on the evaluation, a set of allowed actions that the large language model-based agent is allowed to perform to complete the task. The device updates the large language model-based agent with an indication of the set of allowed actions that it is allowed to perform to complete the task.

Abstract: In one implementation, a device uses a large language model-based agent to identify a task to correct an issue in a network. The device makes a determination that the large language model-based agent cannot complete the task. The device identifies, based on the determination, a subject matter expert to help complete the task. The device sends a request to the subject matter expert to complete the task.

Abstract: In one implementation, a device may determine how well a large language model-based troubleshooting agent for a network was able to perform during a first test having a first difficulty. The device may update the large language model-based troubleshooting agent using reinforcement learning based on how well the large language model-based troubleshooting agent was able to perform during the first test. The device may select a second difficulty for a second test based on how well the large language model-based troubleshooting agent was able to perform during the first test. The device may initiate the second test to assess how well the large language model-based troubleshooting agent is able to perform.

Abstract: In one implementation, a device receives feedback from a user regarding an output of a large language model-based troubleshooting agent for a network that the large language model-based troubleshooting agent generates in response to an input request from the user. The device determines a network scenario associated with the input request and the output. The device causes, based on the feedback, the network scenario to be replicated in a test network. The device updates the large language model-based troubleshooting agent in part by using the large language model-based troubleshooting agent to assess the network scenario in the test network.

Abstract: In one implementation, a device receives a prompt for input to a large language model of a troubleshooting agent for a computer network. The device generates, using the troubleshooting agent, an intermediate answer by inputting the prompt to the large language model. The device identifies a hyperlink to a view associated with the intermediate answer and provided by an online application associated with administration of the computer network. The device provides the hyperlink in conjunction with the intermediate answer for display.

Abstract: In one implementation, a device performs, using a using a large language model-based troubleshooting agent, troubleshooting of a plurality of issues in a network indicated by a plurality of support tickets opened by users of the network. The device aggregates, based on results of the troubleshooting, the plurality of support tickets into a master support ticket. The device obtains a resolution to the master support ticket from a support engineer. The device uses the resolution to the master support ticket in conjunction with the large language model-based troubleshooting agent to troubleshoot a new support ticket.

Abstract: In one implementation, a device receives an input request for a large language model-based troubleshooting agent for a network. The device selects an optimization criterion for the large language model-based troubleshooting agent based on the input request. The device provides the optimization criterion to the large language model-based troubleshooting agent to cause the large language model-based troubleshooting agent to select a particular large language model to process the input request based on the optimization criterion. The device sends, to a user interface, an indication of a result of the particular large language model processing the input request.

Abstract: In one implementation, a device uses a large language model-based agent to perform a task in a network, to obtain a first result. The device executes code in the network to perform the task, to obtain a second result. The device makes a comparison between the first result and the second result. The device provides, based in part on the comparison, one or more performance metrics for the large language model-based agent for display.

Abstract: In one implementation, a device receives an input request for a large language model-based network troubleshooting agent regarding an issue in a network. The large language model-based network troubleshooting agent performs a lookup of a recipe based on the input request, wherein the recipe comprises contextual information for the issue. The device generates, by the large language model-based network troubleshooting agent, a prompt for a large language model based on the input request and on the recipe. The device provides, by the large language model-based network troubleshooting agent, the prompt to the large language model to troubleshoot the issue in the network.

Abstract: In one implementation, a device makes a determination as to whether an issue identified in a network is eligible for self-healing. The device uses, based on the determination, a large language model-based agent to determine a root cause of the issue. The device identifies, using the large language model-based agent, a set of one or more self-healing actions to address the root cause of the issue. The device causes, using the large language model-based agent, performance of the set of one or more self-healing actions in the network.

Abstract: In one implementation, a device configures an impairment scenario in a computer network. The device performs an evaluation of the impairment scenario by a language model-based troubleshooting agent. The device makes a comparison between a root cause associated with the impairment scenario and the evaluation of the impairment scenario by the language model-based troubleshooting agent. The device provides an indication of the comparison.

Abstract: A method and network node for forward distortion correction for time domain beam forming are provided. According to one aspect, a method in a network node includes filtering cach signal output by the time domain beamformer by a band stop filter, the band stop filter including a stop band overlapping a pass band of the signal output by the time domain beamformer. The method also includes filtering each signal output by the time domain beamformer by a group delay filter in electrical parallel with a band stop filter, the group delay filter providing a group delay to the signal output by the time domain beamformer. The method also includes subtracting an output of each band stop filter from an output of a corresponding group delay filter.

Abstract: In one implementation, a device generates a response to user input using a first language model. The device determines that the response is an erroneous response. The device generates a resolution to the erroneous response using a teacher language model. The device updates the first language model using the resolution from the teacher language model.

Abstract: In one implementation, a device obtains a prompt from a source for input to a language model. The device performs a search for similar prompts to that of the prompt in a prompt database. The device determines, based on results of the search, whether the prompt represents a potential attack. The device prevents a response to the prompt from being returned from the language model to the source, when the device determines that the prompt represents a potential attack.