Abstract: A method for generating training datasets for training an evaluation algorithm using which an alignment of two map datasets can be evaluated to determine navigation information for a mobile device that is moving or is to move in an environment. The method includes, for each of a plurality of training datasets: providing two input feature datasets, wherein underlying map datasets each contain environmental information that has been acquired using a sensor of the mobile device; providing a transformation dataset generated during an alignment of the two input feature datasets; providing a reference transformation dataset as ground truth; determining a correlation dataset based on the two input feature datasets and/or the transformation dataset; determining a quality measure depending on an accuracy of a match between the transformation dataset and the reference transformation dataset; and providing the training dataset which includes the correlation dataset and the quality measure.

Abstract: A fuel cell comprising a stack of a plurality of cells sandwiched between a first clamping plate and a second clamping plate, the plurality having a first cell at a first end and a last cell at a second end, the stack having a distributor plate and an interface plate interposed between the first clamping plate and the distributor plate, the interface plate having an outer face facing the first clamping plate and an inner face, the interface plate comprising a first dispensing opening, a first section of which opens onto the outer face and a second section of which opens onto the inner face, the first section and the second section having distinct shapes.

Abstract: The present application relates to the use of 3,3?-(1,3-phenylenebis(oxy))bis(1-(piperidin-1-yl)propan-2-ol (VE607) or a pharmaceutically acceptable salt thereof for the inhibition of SARS-CoV-2 and/or management of COVID-19.

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: A well 1 is drilled or exists that passes through the earth's surface 2 and underlying rocks 3 to connect with a subterranean hydrocarbon reservoir 4 that contains hydrocarbons 5 and commonly brine 6 (which can include formation, interstitial, connate and injected water). Well 1, (there may be a plurality of well 1's) allows the contents of reservoir 4, either hydrocarbons 5 or brine 6, to flow to the surface.

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 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: A method and apparatuses for full sender-side rate control include receiving a data stream at a forwarding unit, determining a bandwidth estimation for communicating the data stream between the forwarding unit and a sender of the data stream, determining respective bandwidth estimations for communicating the data stream between the forwarding unit and at least two receivers, aggregating the bandwidth estimations determined for communicating the data stream between the forwarding unit and the at least two receivers, and limiting the bandwidth estimation determined for communicating the data stream between the forwarding unit and the sender of the data stream based on the aggregated bandwidth estimations. In some embodiments, the bandwidth estimation determined for communicating the data stream between the forwarding unit and the sender of the data stream is limited by communicating a limitation message regarding the aggregated bandwidth estimation of the receivers to the sender.

Abstract: The present invention relates to a method of manufacturing a pellet or film, wherein the pellet or film comprises at least 30 wt % seaweed material, wherein the seaweed material comprises seaweed material derived from macro algae, and wherein the at least one plasticiser comprises water or at least one C—H bond.

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: A mobile surgical control console is provided and includes a base housing, a lower frame assembly, a plurality of vertical supports, a monitor, and an upper frame assembly. The lower frame assembly further includes a proximal end, a distal end and an opening defined therethrough which extends through the proximal end thereof. The lower frame assembly is coupled to the base housing. The plurality of vertical supports each have an inferior end and a superior end, and each vertical support is attached to the base housing. The monitor is attached to the superior end of each vertical support. The upper frame assembly includes an upper frame and a mobile section. The upper frame includes a proximal end, a distal end and an opening defined therethrough. The upper frame is supported on each vertical support. The mobile section is supported by the upper frame.

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: A method for plasma cutting of workpieces, in which use is made of at least one plasma cutting torch having at least one plasma torch body, one electrode and one nozzle, through the nozzle opening of which at least one plasma gas or plasma gas mixture flows and which constricts the plasma jet, wherein, before the plunge cutting of the plasma jet into and through the workpiece, a washout is formed by the workpiece being exposed to the plasma jet from the workpiece surface at least for a duration t2 such that material of the workpiece is removed from the workpiece surface and the washout is produced.

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: AR glasses comprise a frame with a front frame part (1), mounting means (2, 3, 8) and an optical unit (5). The optical unit (5) is held in the front frame part (1), wherein the optical unit (5) forms a module mountable to the eyeglass frame as a single component. The optical unit (5) comprises at least a display source (56), at least one refractive device, such as triplet lens (54), and a beamsplitter (51, 52). These AR glasses are robust, compact and affordable.

Abstract: Methods and apparatus are provided. In an example aspect, a method in a first access point calculating interference suppression weights is provided. The method includes receiving, for each of at least one other access point, channel information for channels between the other access point and at least one User Equipment (UE), calculating, for each of the at least one other access point, the interference suppression weights based on the channel information received for the at least one other access point, wherein the interference suppression weights are for signals transmitted between the at least one other access point and the at least one UE, and sending, to each of the at least one other access point, the interference suppression weights for the other access point.

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: The present invention is applied in the field of heat exchange and temperature regulation and, more specifically, relates to an outer cooling assembly for structures with inner refractory lining, the outer cooling assembly comprising: a distributor arranged on the outer side and around an upper section of a structure with inner refractory lining; and a collector arranged below the distributor and on the outer side and around a lower section of a structure with inner refractory lining; wherein the distributor comprises a fixing plate and cooling fluid injection members arranged on the outer side and around the fixing plate and configured to eject cooling fluid onto the outer surface of the structure.

Abstract: The present invention relates 2,2-difluoro-5H-[1,3]dioxolo[4,5-f]isoindol-7-one derivatives of the formula (I) wherein Q is Qa, Qb or Qc. An exemplary compound is e.g. 6-[3-ethylsulfonyl-6-(trifluoromethyl) imidazo[1,2-a]pyridin-2-yl]-2,2-difluoro-5H-[1,3]dioxolo[4,5-f]isoindol-7-one (compound P1). Furthermore, the present invention relates to agrochemical compositions which comprise compounds of formula (I), to preparation of these compositions, and to the use of the compounds or compositions in agriculture or horticulture for combating, preventing or controlling animal pests, including arthropods and in particular insects, molluscs, nematodes or representatives of the order Acarina.

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.