Abstract: The present disclosure relates to a positive electrode material for a lithium ion battery and its preparation. The positive electrode material in accordance with the present disclosure has an intrinsic specific surface area of 5-13 m2/g. The positive electrode material in accordance with the present disclosure has an intrinsic specific surface area and an intrinsic pore size within the required ranges. In this regard, the positive electrode material in accordance with the present disclosure has excellent particle strength, excellent Li ion transference ability, and good resistance to electrolyte erosion. When used in lithium batteries, it may impart the batteries with excellent rate performance and cycle performance. The present disclosure also relates to a method for preparing the positive electrode material.

Abstract: The present invention discloses a perceptual representation learning method for protein conformations based on a pre-trained language model, including: obtaining a protein made up of an amino acid sequence, building different data sets according to protein conformations, and defining a prompt for each type of protein conformation; building, based on a pre-trained language model, a representation learning module for fusing an embedding representation of each type of the prompt into an embedding representation of the protein, so as to obtain a protein embedding representation under a prompt identifier; building a task module for performing task prediction on a task corresponding to each type of protein conformation based on the protein embedding representation under the prompt identifier; building a loss function for each type of task based on a task prediction result and a tag, and updating model parameters of the representation learning module and the task module in combination with loss functions of all type

Abstract: A positive electrode material, a preparation method therefor, and an application thereof are disclosed. The cathode material is composed of secondary particles agglomerated by primary particles; wherein individual secondary particle contains an inner core structure, a middle layer, and a shell layer, in this order, along a direction from a center to a surface of the secondary particle; wherein the middle layer is distributed in a circular ring shape; and wherein the secondary particle has a structure of close packing of an inner core structure, loose and porous middle layer, and close packing of the shell layer.

Abstract: A machine learning application method, a device, an electronic apparatus, and a storage medium, used to directly link service scenarios, aggregate data related to the service scenarios, accordingly explore modeling schemes, and ensure that data used in offline modeling scheme exploration and data used in an online model prediction service have the same source, thereby realizing consistency of source of offline and online data. Directly deploying an offline model to an online environment results in data inconsistency between online feature computation and offline feature computation, which in turn causes poor prediction performance; therefore, only a modeling scheme is deployed online, and the offline model is not deployed. After a modeling scheme is deployed online, sample data having a feature and feedback can be obtained by receiving a prediction request, thereby enabling model self-learning by means of the sample data.

Abstract: A data exchange method, a data exchange device, and a computing device for data exchange between a provider and a recipient for machine learning, the method including: (a) receiving a machine learning model from the provider (S1100); (b) respectively transforming output data samples into corresponding output eigenvectors by utilizing the machine learning model from the provider (S1200); (c) after transformation, combining the output eigenvectors with corresponding identifiers to form exchange samples (S1300). According to the data exchange method, original data is transformed into vector information which cannot be restored but can be applied to machine learning, for use in exchange, so as to, on one hand, enable efficient use of data for machine learning and, on the other hand, prevent unauthorized use, sale or disclosure of the original data.

Abstract: A method and device for presenting a prediction model, and a method and device for adjusting a prediction model. The method for presenting a prediction model includes: obtaining at least one prediction result of a prediction model for at least one prediction sample; obtaining at least one decision-making tree training sample for training a decision-making tree model according to the at least one prediction sample and the at least one prediction result, the decision-making tree model being used for fitting the prediction model; training the decision-making tree model by using at least one decision-making tree training sample; and visually presenting the trained decision-making tree model. By means of the method, a prediction model hard to understand can be approximated to a decision-making tree model, and the approximated decision-making tree model is presented, so that a user better understands the prediction model according to the presented decision-making tree model.

Abstract: A method for performing a machine learning process implementation is provided. The method comprises includes the following. Data is obtained. A labelling result of the data is obtained. A model framework matching a requirement of a user and/or a model matching a predicted target of the user is selected. Model training is performed using the data and the labelling result of the data based on the selected model framework and/or the selected model. The model framework is used to perform the model training on the basis of a machine learning algorithm.

Abstract: The disclosure provides a training method and system of a neural network model including a three-level model, and a prediction method and system. The training method comprises: acquiring a training data record; generating features of a training sample based on attribute information of the training data record, and using a label of the training data record as a label of the training sample; training the neural network model using a set of the training samples, learning an interaction representation between corresponding input items respectively by a plurality of intermediate models comprised in a second-level model of the neural network model, learning a prediction result at least based on the interaction representations output by the second-level model by a third-level model of the neural network model, and adjusting the neural network model at least based on a difference between the prediction result and the label.

Abstract: A visual interpretation method and a device for a logistic regression model, relating to the computer technology field. The method includes: receiving an interpretation request for a logistic regression model (S11); obtaining, according to the interpretation request, model parameters of the logistic regression model, the model parameters comprising each feature in the logistic regression model and a weight value of each feature (S12); aggregating each feature in the obtained model parameters according to a feature name (S13); obtaining feature statistics for each feature name to obtain feature statistics information for each feature name, wherein the feature statistics information indicates distribution information of weight values of each feature under the same feature name and/or dimension information of each feature under the same feature name (S14); and displaying the feature name and the corresponding feature statistics information using a graphical interface (S15).

Abstract: Provided are method, apparatus and system for performing machine learning by using data to be exchanged. The apparatus includes: at least one computing device and at least one storage device storing instructions.

Abstract: A data exchange method, a data exchange device, and a computing device for data exchange between a provider and a recipient for machine learning, the method including: (a) receiving a machine learning model from the provider (S1100); (b) respectively transforming output data samples into corresponding output eigenvectors by utilizing the machine learning model from the provider (S1200); (c) after transformation, combining the output eigenvectors with corresponding identifiers to form exchange samples (S1300). According to the data exchange method, original data is transformed into vector information which cannot be restored but can be applied to machine learning, for use in exchange, so as to, on one hand, enable efficient use of data for machine learning and, on the other hand, prevent unauthorized use, sale or disclosure of the original data.

Abstract: A method and device for presenting a prediction model, and a method and device for adjusting a prediction model. The method for presenting a prediction model includes: obtaining at least one prediction result of a prediction model for at least one prediction sample; obtaining at least one decision-making tree training sample for training a decision-making tree model according to the at least one prediction sample and the at least one prediction result, the decision-making tree model being used for fitting the prediction model; training the decision-making tree model by using at least one decision-making tree training sample; and visually presenting the trained decision-making tree model. By means of the method, a prediction model hard to understand can be approximated to a decision-making tree model, and the approximated decision-making tree model is presented, so that a user better understands the prediction model according to the presented decision-making tree model.

Abstract: A bamboo-surfaced laminated louver curtain slat and a method of manufacturing the same. The bamboo-surfaced laminated louver curtain slat comprises two peeled bamboo veneer layers (21,22) and at least one filler layer (23) sandwiched between the two bamboo veneer layers. The peeled bamboo veneer layers and the filler layer(s) are bond together in a glued manner. The production method comprises follow steps: thin peeled bamboo veneer layers are peeled from a massive bamboo board and after being cut into a specified width, the peeled bamboo veneer layers, together with filler material, are put into moulds for hot-pressing and gluing; or thin peeled bamboo veneer layers are peeled from a massive bamboo board made by gluing and pressing bamboo material, a first peeled bamboo veneer layer, filler material, and a second peeled bamboo veneer layer are glued and press-formed in sequence into a piece of ply-bamboo, then, laminated louver curtain slats of a specified width are cut from the ply-bamboo.

Abstract: A bamboo-surfaced laminated louver curtain slat and a method of manufacturing the same. The bamboo-surfaced laminated louver curtain slat comprises two peeled bamboo veneer layers and at least one filler layer sandwiched between the two bamboo veneer layers. The peeled bamboo veneer layers and the filler layer(s) are bond together in a glued manner. The production method comprises follow steps: thin peeled bamboo veneer layers are peeled from a massive bamboo board and after being cut into a specified width, the peeled bamboo veneer layers, together with filler material, are put into moulds for hot-pressing and gluing; or thin peeled bamboo veneer layers are peeled from a massive bamboo board made by gluing and pressing bamboo material, a first peeled bamboo veneer layer, filler material, and a second peeled bamboo veneer layer are glued and press-formed in sequence into a piece of ply-bamboo, then, laminated louver curtain slats of a specified width are cut from the ply-bamboo.