Abstract: A small-file storage optimization system based on a virtual file system in a KUBERNETES user-mode application which is applied to a target file system includes a network file system including a network file system server and a network file system client, a user-mode application mounted on a shared directory exposed by the network file system server through the network file system client, and a virtual file system including a virtual file system client and a virtual file system server. A file block creating and formatting module in the virtual file system server creates a virtual file block for storing small files on one or more object storage target devices of the target file system, and the virtual file block is mounted on the shared directory exposed by the network file system server.

Abstract: Claim verification is facilitated by identifying a selection of textual content within a user interface, accessing a claim detection module, inputting the textual content into the claim detection module to detect one or more claims within the selection of textual content, accessing an evidence extraction module, using the evidence extraction module to automatically search one or more reference repositories for one or more related references that have content that is related to the detected one or more claims, automatically determining whether the content in the one or more related references supports or refutes the one or more claims using a claim verification module, and, in response to determining a set of references of the one or more related references supports or refutes the one or more claims, presenting a support indicator or a refute indicator within the user interface in association with the one or more claims.

Abstract: Procedural optimization is facilitated by receiving user input for creating or modifying a body of text comprising a procedure, detecting one or more procedural steps associated with the procedure using a procedural step detection module, automatically searching within a corpus of references for one or more related procedural steps using a related procedural step extraction module, automatically identifying one or more outcomes within the corpus of references associated with the one or more related procedural steps using an outcome extraction module, automatically determining whether the one or more outcomes comprise detrimental results using an outcome analysis module, and, in response to determining a set of detrimental outcomes from the one or more outcomes that comprise detrimental results, presenting a detriment indicator within the user interface in association with the one or more procedural steps.

Abstract: A relevance system ranks a set of medical studies based on a relevance of each medical study in the set of medical studies to a patient profile. The relevance system includes a relevance model. The relevance model determines a relevance of each medical study to the patient profile based on a semantic relationship score, a concept relationship score, and a term-occurrence score. The semantic relationship score is a measure of a similarity in semantic meaning of a medical study and a patient profile. The concept relationship score is a measure of the closeness of medical concepts in a medical study to medical concepts in a patient profile. The term-occurrence score is a measure of occurrences of terms in a medical study that also appear in a patient profile and the statistical significances of the terms.

Abstract: Provided are an anti-CLDN18.2 antibody or antigen-binding fragment thereof, a nucleic acid molecule encoding the same, an immunoconjugate, bispecific molecule, chimeric antigen receptor and pharmaceutical composition comprising the same. The antibody or antigen-binding fragment thereof is used for preventing and/or treating a tumor.

Abstract: A small-file storage optimization system based on a virtual file system in a KUBERNETES user-mode application which is applied to a target file system includes a network file system including a network file system server and a network file system client, a user-mode application mounted on a shared directory exposed by the network file system server through the network file system client, and a virtual file system including a virtual file system client and a virtual file system server.

Abstract: This application relates to a file loading method performed at an electronic device, and a non-transitory computer-readable storage medium thereof. The method including: receiving, in response to a user operation, an instruction for loading a target file; determining an associated feature of at least one piece of resource information in the target file; determining a type of the resource information according to the associated feature of the resource information; and loading the resource information by using a loading algorithm corresponding to the type of the resource information.

Abstract: A method (900) includes delivering a first bias current (IGAIN) to an anode of a gain-section diode (590a) and delivering a second bias current (IPH) to an anode of a phase-section diode (590b). The method also includes receiving a burst mode signal (514) indicative of a burst-on state or a burst-on state, and sinking a first sink current (ISINK) away from the first bias current when the burst mode signal is indicative of the burst-off state. When the burst mode signal transitions to be indicative of the burst-on state from the burst-off state, the method also includes sinking a second sink current away from the second bias current at the anode of the phase-section diode and ceasing the sinking of the first sink current away from the first bias current at the anode of the gain section diode.

Abstract: A method (900) includes a gain current (IGAIN) to an anode of a gain-section diode (D0) disposed on a shared substrate of a tunable laser (310), delivering a modulation signal to an anode of an Electro-absorption section diode (D2) disposed on the shared substrate of the tunable laser, and receiving a burst mode signal (330) indicative of a burst-on state or a burst-off state. When the burst mode signal is indicative of the burst-off state, the method includes sinking a sink current (ISINK) away from the gain current at the anode of the gain-section diode. When the burst mode signal transitions to be indicative of the burst-on state from the burst-off state, the method includes ceasing the sinking of the sink current away from the gain current and delivering an overshoot current (IOVER) to the anode of the gain-section diode.

Abstract: A method (600) for tuning a tunable laser (310) includes delivering a bias current (IDBR) to an anode of a distributed Bragg reflector (DBR) section diode (D2) disposed on a shared substrate of the tunable laser and receiving a burst mode signal (440) indicative of a burst-on state or a burst-off state. When the burst mode signal is indicative of the burst-off state, the method includes offsetting the bias current at the anode of the DBR section diode by one of sourcing a push current with the bias current to the anode of the DBR section diode or sinking a pull current away from the bias current at the anode of the DBR section diode. When the burst mode signal is indicative of the burst-on state, the method also includes ceasing any offsetting of the bias current at the anode of the DBR section diode.

Abstract: A method (700) of biasing a tunable laser (310) during burst-on and burst-off states includes receiving a burst mode signal (514) indicative of the burst-on state or the burst-off state and when the burst mode signal is indicative of the burst-on state: delivering a first bias current (IGAIN) to an anode of a gain-section diode (590a) disposed on a shared substrate of the tunable laser; and delivering a second bias current (IPH) to an anode of phase-section diode (590b) disposed on the shared substrate. The second bias current is less than the first bias current. When the burst mode signal transitions to be indicative of the burst-off state, the method also includes delivering the first bias current to the anode of the gain-section diode; and delivering the second bias current to the anode of the phase-section diode wherein the first bias current is less than the second bias current.

Abstract: A method of combining optical signals from a plurality of optical fibers into a single optical signal includes receiving, at corresponding optical-signal receivers optically coupled to corresponding trunk fibers, respective optical signals. The method further includes determining, by the corresponding optical-signal receivers, when each respective optical signal is received. When the respective optical signal is received, the method includes performing the following steps: converting, by the corresponding optical-signal receiver, the respective optical signal to a corresponding electrical signal; transmitting, by the corresponding optical-signal receiver, the corresponding electrical signal to a corresponding input channel of an electrical-multiplexing device; and configuring the electrical-multiplexing device to select the corresponding input channel.

Abstract: A method of combining optical signals from a plurality of optical fibers into a single optical signal includes receiving, at corresponding optical-signal receivers optically coupled to corresponding trunk fibers, respective optical signals. The method further includes determining, by the corresponding optical-signal receivers, when each respective optical signal is received. When the respective optical signal is received, the method includes performing the following steps: converting, by the corresponding optical-signal receiver, the respective optical signal to a corresponding electrical signal; transmitting, by the corresponding optical-signal receiver, the corresponding electrical signal to a corresponding input channel of an electrical-multiplexing device; and configuring the electrical-multiplexing device to select the corresponding input channel.

Abstract: The improved exercise of artificial intelligence by systematically refining and semantically indexing the output from AI models, so that the semantic index is highly relevant. To do this, the computing system obtains results of an input data set being applied to an AI model. The computing system then determines a refinement to apply to the obtained results. This determination may be based on one or more characteristics of the AI model and/or input data set. The determination may also be based on hints associated with that AI model, and/or learned behavior regarding how that AI model is typically used. The obtained results are then refined using the determined refinement. It is then this more relevant refined results that are semantically indexed to generate the semantic index. Thus, the semantic index represents, the more useful output from an AI model, which is semantically exposed so as to provide meaning.

Abstract: A monitoring system operable to monitor an oilfield additive system having multiple components. The oilfield additive system is operable to transfer an additive-containing substance for injection into a wellbore. The monitoring system includes sensors each associated with, and operable to generate information related to an operational parameter of, a corresponding one of the oilfield additive system components. The monitoring system also includes a monitoring device in communication with the sensors and operable to record the information generated by the sensors to generate a database. The database includes information indicative of maintenance aspects of the oilfield additive system and/or the oilfield additive system components.

Abstract: Methods and apparatus for monitoring and controlling wellsite emissions. A mobile emissions monitoring device is operated in airspace above a wellsite to generate and transmit a monitor signal related to emissions in the airspace. The emissions may be associated with wellsite equipment at the wellsite. A processing system in receipt of the monitor signal operates to cause a change in an operational parameter of the wellsite equipment based on information conveyed by the monitor signal.

Abstract: Applying AI models to a search using a search engine for a user. A method includes receiving user search input at a search engine user interface. The search input is used with the search engine to obtain first search results. One or more AI models are applied to the first search results to obtain additional search data. The additional search data is searched to identify additional search results. Using the additional search results, a subset of second search results are identified from the first search results while filtering out other search results from the first search results. At least a portion of the second search results are provided to the user in the user interface while preventing the other search results that were filtered from being displayed in the user interface, such that a user at the user interface has the second search results returned as results.