Abstract: Disclosed is a lithium ion battery. The lithium ion battery comprises: a positive electrode piece, comprising a positive electrode coating area and a positive electrode empty foil area, herein the positive electrode coating area has macro-pores and micro-mesopores, the specific surface area of the macro-pores of the positive electrode coating area is 3.0˜7.0 m2/g, and the specific surface area of the micro-mesopores of the positive electrode coating is 2˜5 m2/g; and a negative electrode piece, comprising a negative electrode coating area and a negative electrode empty foil area, herein the negative electrode coating area has macro-pores and micro-mesopores, the specific surface area of the macro-pores of the positive electrode coating area is 0.8˜2.0 m2/g, and the specific surface area of the micro-mesopores of the positive electrode coating is 0.6˜1.7 m2/g.

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: Styling shampoo compositions which comprise (a) a cationic polymer with charge density of from 2 to 10 meq/gram; (b) at least one anionic surfactant selected from sulfate anionic surfactants and amino acid anionic surfactants; and (c) optionally an amphoteric or non-ionic surfactant or combination thereof, wherein the amount of (a) is from 0.3 to 10 wt %, based on the weight of the styling shampoo compositions.

Abstract: A compensation method for bed surface drop before and after accelerator radiotherapy bed includes the following contents: the weight G is decomposed into N weights on average, the length L is decomposed into M positions on average, and then an experiment is carried out. The drop matrix HN×M of the couch top is measured by the experiment, by performing cubic spline interpolation on the rows of the matrix HN×M, and then performing cubic spline interpolation on the columns, the resulting cubic spline interpolation matrix SN×N1, M×M1 of HN×M has N*N1 rows and M*M1 columns, and then applies it to practice. The drop amount H obtained by the compensation method for bed surface drop before and after accelerator radiotherapy bed of the present invention is the optimal estimated value with the Least Sum of Square Error.

Abstract: Embodiments of the present disclosure provide a real-time microseismic magnitude calculation method based on deep learning and a corresponding device. The method includes: constructing a DAS-based horizontal well microseismic monitoring system; constructing a training data set; constructing a magnitude calculation module, wherein the magnitude calculation module comprises two input branches of frequency spectrum and time waveform, the two input branches use a 3-layer convolution structure to extract frequency characteristic and waveform characteristic of a microseismic event, and then a model fusion is performed, and then 2 fully connected layers are used, and finally a calculated magnitude is outputted; training the magnitude calculation module; and analyzing and processing field data.

Abstract: Techniques are disclosed for identifying multimodal subevents within an event having spatially-related and temporally-related features. In one example, a system receives a Spatio-Temporal Graph (STG) comprising (1) a plurality of nodes, each node having a feature descriptor that describes a feature present in the event, (2) a plurality of spatial edges, each spatial edge describing a spatial relationship between two of the plurality of nodes, and (3) a plurality of temporal edges, each temporal edge describing a temporal relationship between two of the plurality of nodes. Furthermore, the STG comprises at least one of: (1) variable-length descriptors for the feature descriptors or (2) temporal edges that span multiple time steps for the event. A machine learning system processes the STG to identify the multimodal subevents for the event. In some examples, the machine learning system comprises stacked Spatio-Temporal Graph Convolutional Networks (STGCNs), each comprising a plurality of STGCN layers.

Abstract: A current collector has a pore-forming functional coating layer, an electrode sheet and a battery. The current collector having a pore-forming functional coating layer comprises an electrically conductive substrate layer and a functional coating layer applied on at least one surface of the substrate layer, wherein the functional coating layer comprises a gas-generating compound which has a decomposition temperature of 250° C. or less and is capable of producing gas. A compound capable of generating gas by decomposition is applied in the functional layer of the current collector. Hence, through holes extending through the coating layer can be produced by decomposing the pore-forming compound at the bottom of the active coating layer to generate gas with no need to change the existing coating process, thereby improving the kinetic performance of battery products. The method is simple, practicable, cost efficient, suitable for popularization, and has high application value.

Abstract: Embodiments of the present disclosure provide a real-time microseismic magnitude calculation method based on deep learning and a corresponding device. The method includes: constructing a DAS-based horizontal well microseismic monitoring system; constructing a training data set; constructing a magnitude calculation module, wherein the magnitude calculation module comprises two input branches of frequency spectrum and time waveform, the two input branches use a 3-layer convolution structure to extract frequency characteristic and waveform characteristic of a microseismic event, and then a model fusion is performed, and then 2 fully connected layers are used, and finally a calculated magnitude is outputted; training the magnitude calculation module; and analyzing and processing field data.

Abstract: Embodiments of the present disclosure provide a DAS same-well monitoring real-time microseismic effective event identification method based on deep learning, comprising: constructing a DAS-based horizontal well microseismic monitoring system; constructing a training data set, comprising microseismic event data, pipe wave data and background noise data with different types of labels; constructing a signal identification module; training the signal identification module by using the training data set; preprocessing actual monitoring data, inputting the preprocessed data into the signal identification module to obtain an output result; marking microseismic events identified in the output result, and updating the marked microseismic events into the training data set; and adjusting and updating the signal identification module. The identification method according to the present disclosure can identify microseismic events in DAS same-well monitoring data in real time and efficiently.

Abstract: Techniques for achieving application high availability via application-transparent battery-backed replication of persistent data are provided. In one set of embodiments, a computer system can detect a failure that causes an application of the computer system to stop running. In response to detecting the failure, the computer system can copy persistent data written by the application and maintained locally at the computer system to one or more remote destinations, where the copying is performed in a manner that is transparent to the application and while the computer system runs on battery power. The application can then be restarted on another computer system using the copied data.

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%?CCol?CCo, 5%?CAl?CAll. 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: The invention discloses an orthogonal dual-layer grating dynamic intensity modulation segmentation method based on quadrant, specifically include the following steps: S1: the fluence distribution under each beam is calculated through the radiation treatment planning system; S2: use orthogonal double-layer collimator for fluence segmentation; S3: divide the quadrant, divide the field surrounded by the upper, lower, left and right leaves into at least two quadrants, to obtain the fluence distribution and the corresponding leaf sequence of each quadrant; S4: perform regional planning of the fluence in each quadrant to obtain multiple different regions and determine the segmentation mode of different regions; S5: for any quadrant, use two mutually orthogonal leaf groups for segmentation.

Abstract: Disclosed are a fast-charging graphite and a battery. The graphite has a graphitization degree of 90-97% and a lithium-ion diffusion coefficient of 2.3×10?14?8.7×10?12 cm2/s at 25° C. and a state of charge (SOC) of 10%. The battery includes a cathode plate, an anode plate, a separator arranged between the cathode plate and the anode plate, and an electrolyte. The anode plate includes an anode current collector and an anode coating coated on at least one side of the anode current collector. The anode coating includes an anode active material, and the anode active material includes the fast-charging graphite.

Abstract: Technologies are provided for reporting engaged impressions of directed content. Some embodiments include a computing device that can initiate a screensaver session. The computing device also can cause presentation of a directed content asset on a display device functionally coupled to the computing device. The computing device can then update a queue to add a record of an impression of the directed content asset, where the queue is retained in a memory device of the computing device. The computing device can receive a signal indicative of user activity. The computing device can send, to an attribution server device, first data identifying one or more directed content assets corresponding to one or more records of the queue.

Abstract: The application discloses an AI real-time microseism monitoring node, which includes a processor and a data acquisition device, an AI calculation device, and a communication device connected to the processor, wherein the AI calculation device is provided with pre-trained microseism data analysis Device, and the processor is configured to perform the following operations: controlling the data acquisition equipment to acquire microseism data; turning on the AI calculation device to calculate the acquired microseism data by means of the microseism data analysis device to determine the valid event data associated with the microseism; and sending the valid event data to the remote data center through the communication device.

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 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: It relates to a cosmetic composition in the form of a transparent or semi-transparent gel, comprising (a) an oil phase; (b) a surfactant portion comprising a first non-ionic surfactant and a second non-ionic surfactant; (c) an aqueous phase; and (d) at least one C2-C6 polyol; wherein the first nonionic surfactant comprises at least one alkyl polyglucoside and the second nonionic surfactant comprises at least one having the following general formula (I) or (II): R(X)nOR? (I) wherein, R represents a linear or branched C8-C30 alkyl group or C8-C30 alkenyl group; X represents —OCH2CH2— or —OCH2—CHCH3—; n represents an integer from 2 to 100; and R? represents H, or a linear or branched C8-C30 alkyl group or C8-C30 alkenyl group; (II) wherein, the sum of x+y+z represents an integer from 2 to 100; and R1, R2, and R3, are the same or different and are each independently represents OH, or a linear or branched C8-C30 fatty acid ester group, with the proviso that at least one of R1, R2 and R3 is not OH.

Abstract: Provided is a composite insole structure including: an insole body being oblong, having a front segment, a middle segment and a rear segment, being made of foam by foaming, and having hardness of 50˜80 OO; and a heel element made of plastic and disposed at the rear segment of the insole body. The middle area of the heel element is of greater thickness than the peripheral area of the heel element; hence, the middle area of the heel element is arched to be centrally raised and thus thinned toward the edge of the middle area radially. The heel element is of hardness of 35˜75 OO.

Abstract: An artificial intelligence calculation method and apparatus for monitoring an earthquake in real time based on edge cloud cooperation is applied to a micro-earthquake data processing system. The micro-earthquake data processing system includes an edge calculation device and a remote server in communication connection with the edge calculation device. The remote server deploys a micro-earthquake data analyzing model based on an artificial intelligence to the edge calculation device in advance. The method includes steps of receiving, by the remote server, effective event data related to the micro-earthquake from the edge calculation device; performing a transfer training to the micro-earthquake data analyzing model by the remote server according to the effective event data; and updating the model after the micro-earthquake data analyzing model that has been transfer-trained is transmitted to the edge calculation device by the remote server.