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: Disclosed herein is a model flow that generates eligibility criteria for a clinical trial based on eligibility criteria associated with a protocol title of the trial. Unlike standard black-box generation models, the techniques disclosed herein leverage existing knowledge to enhance the title. The enhanced title also acts as an intermediate between the title and the generated criteria clauses, enabling explicit control of the generated content as well as an explanation of why the generated content is relevant. The resulting workflow is knowledge-grounded, controllable, transparent, and interpretable.

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: The methods and systems may automatically generate criteria for the different sections of the protocol document for a clinical study. The methods and systems use machine learning models to identify medical articles that are associated with the clinical study of a protocol document. The machine learning models analyze the medical articles and generate recommended criteria for the different sections of the protocol document based on the analysis.

Abstract: Provided are a preparation method and system for a recombinant adeno-associated virus (rAAV) and a recombinant bacmid. The method comprises: first reconstructing a recombinant bacmid containing a recombinant baculovirus genome that produces essential functional elements for an rAAV, at least one of the essential functional elements being inserted into the N-terminal or C-terminal of a locus of an essential gene of the recombinant baculovirus genome; and then transfecting the obtained recombinant bacmid containing the recombinant baculovirus genome that produces the rAAV into a host cell line for culturing to prepare an rAAV. Compared with recombinant baculoviruses obtained by conventional Tn7 recombinant preparations of recombinant bacmid, the recombinant baculovirus obtained by inserting a core element containing Cap, Rep and ITR into two sides of a baculovirus essential gene has a more stable rAAV serial passage production level in a cell and has a higher rAAV yield.

Abstract: A computer implemented method includes receiving an image that includes a type of object, segmenting the object into multiple segments via a trained segmentation machine learning model, and inputting the segments into multiple different attribute extraction models to extract different types of attributes from each of the multiple segments.

Abstract: Expanding search engine functionality using AI models. A method includes, as part of a search session, receiving user input at a search engine. One or more searches on a set of data using the user input. Search results are provided from the one or more searches to a user. Based on a history of the search session, suggestions are provided in a user interface of AI models that could be applied to expand potential search results for the search session. User input is received at the user interface selecting one or more of the suggested AI model. The one or more selected AI models are applied to expand the set of data. Search results to the user based on searching the expanded set of data.

Abstract: Machine learning to predict a layout type that each of a plurality of portions of a document appears in. This is done even though the computer-readable representation of the document does not contain information at the granularity of the prediction to be made that identifies which layout type that each of the plurality of document portions belongs in. For each of a plurality of the portions, the machine-learning system predicts the layout type that the respective portion appears in, and indexes the document using the predictions so as to result in a computer-readable index. The index represents a predicted layout type associated with each of the plurality of portions of the document. Thus, the index can be used to search based on position of a searched term within the document.

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 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: Systems and methods are provided for building and training machine learning models configured to generate in-domain embeddings and perform multimodal analysis inside the same domain. The models include a first encoder trained to receive input from one or more entities represented in a first modality and to encode the one or more entities in the first modality, such that the first encoder is configured to output a first set of embeddings. The models also include a second encoder trained to receive input from one or more entities represented in the second modality and to encode the one or more entities in the second modality, such that the second encoder is configured to output a second set of embeddings. The models also include a projection layer configured to project the first set of embeddings and the second set of embeddings to a shared contrastive space.

Abstract: A method (900) includes delivering a first bias current (IGAIN) to an anode of 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: An integrated pressure condensing boiler is provided which relates to the technical field of boilers. The integrated pressure condensing boiler includes a pressure-bearing housing, a heat-exchange furnace arranged in the pressure-bearing housing, a combustion chamber communicating with the heat-exchange furnace and cooling tube groups fixed in the heat-exchange furnace. Heat-exchange medium flows from bottom to top in the pressure-bearing housing and in the cooling tube groups, and exchanges heat with high-temperature flue gas flowing from top to bottom in the heat-exchange furnace, thus achieving a counterflow heat exchanging. The heat-exchange furnace includes a multi-stage heat-exchange chamber with each heat-exchange chamber being cylindrical. The heat-exchange chambers are arranged in sequence from top to bottom to achieve a flue gas diffusing manner that high-temperature flue gas diffuses from center part to periphery and then gathers from periphery to center part.

Abstract: The improved exercise of artificial intelligence by providing a systematic way for a computing system to interface with output from AI models. To do this, the computing system obtains results of an input data set being applied to an AI model. The results are then refined based upon characteristic(s) of the AI model and perhaps the input data set. Based upon characteristic(s) of the AI model and perhaps the input data set, interface element(s) are identified that can be used to interface with the refined results. The interface element(s) are then communicated to an interface element that interfaces with the refined results. The interface element(s) may include, for instance, operator(s) or term(s) that may be used to query against the refined results and/or an identification of visualization(s) that may be used to present to a user results of queries against the refined results.

Abstract: The present disclosure relates to generating a complex entity index based on a combination of atomic and deep learned attributes associated with instances of a complex entity. For example, systems described herein generate a multi-dimensional representation of entity instances based on evaluation of digital content associated with the respective entity instances. Systems described herein further generate an index representation in which similarity of entity instances are illustrated and presented via an interactive presentation that enables a user to traverse instances of an entity to observe similarities and differences between instances of an entity that have similar embeddings to one another within a multi-dimensional index space.

Abstract: The disclosure discloses a method for detecting an intrusion in parallel based on an unbalanced data Deep Belief Network, which reads an unbalanced data set DS; under-samples the unbalanced data set using the improved NCR algorithm to reduce the ratio of the majority type samples and make the data distribution of the data set balanced; the improved differential evolution algorithm is used on the distributed memory computing platform Spark to optimize the parameters of the deep belief network model to obtain the optimal model parameters; extract the feature of data of the data set, and then classify the intrusion detection by the weighted nuclear extreme learning machine, and finally train multiple weighted nuclear extreme learning machines of different structures in parallel by multithreading as the base classifier, and establish a multi-classifier intrusion detection model based on adaptive weighted voting for detecting the intrusion in parallel.

Abstract: The methods and systems may improve the development of protocol documents used for clinical trials. The methods and systems may automatically estimate the likelihood of success or failure of executing a protocol document for a clinical study using a machine learning model that leverages several hundred thousand of past protocol documents and the outcomes of the clinical studies. The methods and systems may highlight sections of the protocol document that may increase a likelihood of an unsuccessful execution of the protocol document and may provide one or more recommendations to improve the highlighted sections of the protocol document.

Abstract: An antibody specifically binding to BCMA and an antigen binding fragment thereof, a chimeric antigen receptor (CAR) containing the antigen binding fragment, a nucleic acid molecule encoding the CAR, an immune effect cell expressing the CAR, a method for preparing the immune effect cell, use of the CAR and the immune effect cell for preventing and/or treating a B cell-related disease (for example, a B-cell malignancy and an autoimmune disease), and a method for preventing and/or treating the B cell-related disease.

Abstract: The improved exercise of artificial intelligence. Raw output data is obtained by applying an input data set to an artificial intelligence (AI). Such raw output data is sometimes difficult to interpret. The principles defined herein provide a systematic way to refine the output for a wide variety of AI models. An AI model collection characterization structure is utilized for purpose of refining AI model output so as to be more useful. The characterization structure represents, for each of multiple and perhaps numerous AI models, a refinement of output data that resulted from application of an AI model to input data. Upon obtaining output data from the AI model, the appropriate refinement may then be applied. The refined data may then be semantically indexed to provide a semantic index. The characterization structure may also provide tailored information to allow for intuitive querying against the semantic index.