Abstract: The present invention provides a bio-based succinic acid composition, a preparation method therefor, and use thereof. The bio-based succinic acid composition includes bio-based succinic acid and a readily oxidizable substance; and a content of the readily oxidizable substance in the bio-based succinic acid composition is ?0.32 mL/g. In the present invention, the bio-based succinic acid composition has a long shelf life, a small change in b value after aging, and good aging stability. Moreover, a polyester prepared from the bio-based succinic acid composition has a lower acid number, a whiter color, and a higher molecular weight.

Abstract: The present invention provides a bio-based succinic acid composition, a preparation method therefor, and a polyester prepared therefrom. The bio-based succinic acid composition includes bio-based succinic acid and a metal ion; and a mass content of the metal ion in the bio-based succinic acid composition is 10-350 ppm. In the present invention, by adopting the bio-based succinic acid composition containing the metal ion as a raw material for the polyester and controlling the content of the metal ion in the bio-based succinic acid composition within a specific range, the obtained polyester has better mechanical properties and heat retention stability.

Abstract: The present invention provides a succinic acid composition, a polyester prepared therefrom, and a polyester composite material. The succinic acid composition includes succinic acid, a compound containing a sulfate radical, and a phosphorus compound; a mass content of the sulfate radical in the succinic acid composition is 240-500 ppm; and a mass content of a phosphorus element in the succinic acid composition is 0.5-40 ppm.

Abstract: A method and apparatus for adapting a pretrained machine learning model using active learning for improved task performance in a target domain includes embedding a vector representation of at least one unlabeled target class data in an embedding space associated with the pretrained machine learning model, analyzing the embedding space to select, for labeling, at least one unlabeled class data representation based on a distance measurement in the embedding space that identifies an unlabeled class data representation that, if labeled, improves a coverage for at least one unlabeled target class data representation in the embedding space, labeling the selected at least one unlabeled class data representation, and adapting the pretrained machine learning model for improved task performance in a domain of the at least one unlabeled target class data for which coverage was improved by retraining the pretrained machine learning model using the labeled unlabeled class data representation.

Abstract: Techniques are described for neural networks based on Progressive Neural ODEs (PODEs). In an example, a method to progressively train a neural ordinary differential equation (NODE) model comprises processing, by a machine learning system executed by a computing system, first training data, the first training data having a first complexity, to perform training of a first layer for the NODE model; and after performing the first training, processing second training data, the second training data having a second complexity that is higher than the first complexity, to perform training of a second layer for the NODE model.

Abstract: A secondary battery positive electrode material, relating to the field of battery materials. The secondary battery positive electrode material comprises large particles with particle size of ?2 ?m and small particles with particle size of ?1 ?m. The surfaces of some of the small particles are provided with a carbon coating layer; and the surfaces of some of the large particles are not provided with a carbon coating layer. According to the positive electrode material, by means of gradation design of the large and small particles, the compaction density is improved. Moreover, the surfaces of the small particles are coated with a carbon layer to provide sufficient electron transport paths. Additionally, the surfaces of the large particles are not hindered by a carbon coating layer, so that the impedance in a charge and discharge process can be reduced.

Abstract: In general, various aspects of the techniques are directed to causal analysis using large scale time series data. A computing system may convert large scale time series data to first time period records and second time period records according to a multi-scale time resolution. The computing system may implement a hierarchical machine learning model to generate embeddings that capture temporal characteristics of features of the large scale time series data. The computing system may generate a graph data structure indicating cause and effect correlations between features of the large scale time series data based on temporal dynamics captured in the cause and second time period records and/or the embeddings.

Abstract: A ternary positive electrode material, a method for preparing the same, a positive electrode sheet and a lithium ion battery in which the ternary positive electrode material has a chemical composition of Lia(NixCoyM1-x-y)1-bM?bO2-cAc, wherein 0.75?a?1.2, 0.5?x<1, 0<y?0.1, 0?b?0.01, 0?c?0.2; M is at least one selected from the group consisting of Mn and Al; M? is at least one selected from the group consisting of Al, Zr, Ti, Y, Sr, W and Mg; A is at least one selected from the group consisting of S, F and N; and 2%?CCo1?CCo, 5%?CAl?CAl1. The lithium ion battery shows better short-term kinetic performances and long-term kinetic performances, and it also exhibits excellent stability in long-term cycles.

Abstract: A biodegradable polyester composition, a preparation method therefor and use thereof are provided. The biodegradable polyester composition of the present invention includes a biodegradable polyester and a titanium element. Polymerizable monomers of the biodegradable polyester include: an aromatic dicarboxylic acid or an esterified product thereof, or a mixture thereof; an aliphatic dicarboxylic acid or an esterified product thereof, or a mixture thereof; a dihydroxy compound; and a chain extender. A content of the titanium element in the biodegradable polyester composition is 55-86 ppm; and an acid number of the biodegradable polyester composition is 1.10-1.96 mg KOH/g.

Abstract: An aliphatic-aromatic polyester composition, a polyester fiber, a preparation method therefor and use thereof are provided. The aliphatic-aromatic polyester composition includes the following components: i) an aliphatic-aromatic polyester based on aliphatic and aromatic dicarboxylic acids and an aliphatic dihydroxy compound; and ii) a titanium element, wherein calculated based on a weight of the aliphatic-aromatic polyester composition, a content of the titanium element is 55-88 ppm, and an acid number of the aliphatic-aromatic polyester composition is ?0.84 mg KOH/g according to the standard DIN EN 12634-1998.

Abstract: A method, apparatus and system for adapting a pre-trained network for application to a different dataset includes arranging at least two different types of active adaptation modules in a pipeline configuration, wherein an output of a previous active adaptation module produces an input for a next active adaptation module in the pipeline in the form of adapted network data until a last active adaptation module, and wherein each of the at least two different types of adaptation modules can be switched on or off, determining at least one respective hyperparameter for each of the at least two different types of active adaptation modules, and applying the at least one respective determined hyperparameter to each of the at least two different types of active adaptation modules for processing received data from the pretrained network to determine an adapted network.

Abstract: A method, apparatus and system for object detection in sensor data having at least two modalities using a common embedding space includes creating first modality vector representations of features of sensor data having a first modality and second modality vector representations of features of sensor data having a second modality, projecting the first and second modality vector representations into the common embedding space such that related embedded modality vectors are closer together in the common embedding space than unrelated modality vectors, combining the projected first and second modality vector representations, and determining a similarity between the combined modality vector representations and respective embedded vector representations of features of objects in the common embedding space to identify at least one object depicted by the captured sensor data. In some instances, data manipulation of the method, apparatus and system can be guided by physics properties of a sensor and/or sensor data.

Abstract: A composition comprising ES2 prevents ACE2 translocation to the cell surface and susceptibility to SARS-CoV-2 infection. Treating a subject with a composition comprising the compound ES2 may reduce the risk of severe COVID-19. The prognosis of COVID-19 in a subject can be predicted by measuring ACE2 and inflammatory and immunosuppressive phenotypes on isolated peripheral blood mononuclear cells by flow cytometry.

Abstract: Disclosed are a system and a method for achieving system cross-site scripting (XSS) prevention in a componentized form. The system includes: a request message receiving module, configured to receive a request message; a thread data enable judgment module, configured to enable thread data to acquire a thread data enable state when judging that interception on the request message is required; an annotation retrieval and storage module, configured to store a preset filter annotation in a class of the request message in the thread data to acquire final thread data; a component creation module, configured to generate a XSS prevention component based on the thread data enable state and the final thread data; an intercept data determination module, configured to acquire intercepted request thread data based on the XSS prevention component; and an intercept prevention execution module, configured to delete the intercepted request thread data and return a request result.

Abstract: A computing system comprising a memory configured to store an artificial intelligence (AI) model and an image, and a computation engine executing one or more processors may be configured to perform the techniques for error-based explanations for AI behavior. The computation engine may execute the AI model to analyze the image to output a result. The AI model may, when analyzing the image to output the result, process, based on data indicative of the result, the image to assign an error score to each image feature extracted from the image, and obtain, based on the error scores, an error map. The AI model may next update, based on the error map and to obtain a first updated image, the image to visually indicate the error score assigned to each of the image features, and output one or more of the error scores, the error map, and the first updated image.

Abstract: A method, apparatus and system for determining user-content associations for determining and providing user-preferred content using multimodal embeddings include creating an embedding space for multimodal content by creating a first modality vector representation of the multimodal content having a first modality, creating a second modality vector representation of the multimodal content having a second modality, creating a user vector representation, as a third modality, for each user associated with at least a portion of the multimodal content, and embedding the first and the second modality vector representations and the user vector representations in the common embedding space using at least a mixture of loss functions for each modality pair of the first, the at least second and the third modalities that pushes closer co-occurring pairs of multimodal content.

Abstract: In an example, a method for generating responses by a Machine Learning (ML) system includes processing, by a first language model, a natural language instruction to generate an instruction representation based on a meaning of the natural language instruction; translating, by a translation module comprising an interface between the first language model and a second language model, the instruction representation into data indicating an intent of the natural language instruction, wherein the second language model is trained with domain specific knowledge; providing, by the translation module, the natural language instruction and the data indicating the intent of the natural language instruction to the second language model; and generating, by the second language model, a response based on the natural language instruction and the data indicating the intent of the natural language instruction.

Abstract: In general, various aspects of the techniques are directed to causal analysis using large scale time series data. A computing system may convert large scale time series data to first time period records and second time period records according to a multi-scale time resolution. The computing system may implement a hierarchical machine learning model to generate embeddings that capture temporal characteristics of features of the large scale time series data. The computing system may generate a graph data structure indicating cause and effect correlations between features of the large scale time series data based on temporal dynamics captured in the cause and second time period records and/or the embeddings.

Abstract: A computer-implemented method is provided that includes transmitting, by a master node to a plurality of computing nodes, definition information about an initial medical validation model (410): performing, by the master node, a federated learning process together with the plurality of computing nodes (420), to jointly train the initial medical validation model using respective processed local training datasets available at the plurality of computing nodes, the respective local training datasets being processed by the plurality of computing nodes based on the definition information; and determining, by the master node, a final medical validation model based on a result of the federated learning process (430). Through the solution, by means of federated learning, it addresses the data security and privacy concerns from local sites owning.

Abstract: An an energy storage apparatus is provided. the energy storage apparatus includes: a plurality of single batteries and a bipolar plate, each of the plurality of single batteries including a battery cell, the battery cell including a first electrode and a second electrode with opposite polarities and being insulated from each other; the bipolar plate is arranged between two adjacent single batteries, the first electrode or the second electrode of one of the adjacent single batteries forms an electrical connection with the bipolar plate through contacting, and the second electrode or the first electrode of the other of the adjacent single batteries forms an electrical connection with the bipolar plate through contacting.