Abstract: Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N?M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N?M+P) feature columns; and training the model based on the training data including the (N?M+P) feature columns.

Abstract: The present disclosure provides methods and an apparatuses for building a data identification model. One exemplary method for building a data identification model includes: performing logistic regression training using training samples to obtain a first model, the training samples comprising positive and negative samples; sampling the training samples proportionally to obtain a first training sample set; identifying the positive samples using the first model, and selecting a second training sample set from positive samples that have identification results after being identified using the first model; and performing Deep Neural Networks (DNN) training using the first training sample set and the second training sample set to obtain a final data identification model. The methods and the apparatuses of the present disclosure improve the stability of data identification models.

Abstract: Methods and apparatuses for training model based on random forest are provided. The method includes: dividing worker nodes into one or more groups; performing random sampling, by worker nodes in each group, in the preset sample data to obtain the target sample data; and training, by the worker nodes in each group, one or more decision tree objects using the target sample data. Example embodiments of the present disclosure do not need to scan the complete sample data for once, thereby greatly reducing the amount of data to be read, the time cost, and further the iterative update time of the model. The efficiency of training is improved.

Abstract: A high-pressure fuel pump includes a drive shaft, and a vane pump and a plunger pump which are driven by the drive shaft. The vane pump is configured to supply pre-pressurized fuel to the plunger pump. The drive shaft includes a cam configured to drive a piston rod of the plunger pump such that the plunger pump alternately executes a fuel suction stroke and a fuel discharge stroke. The drive shaft further includes a shaft portion for driving a rotor of the vane pump. The vane pump is configured such that for each fuel suction stroke of the plunger pump the vane pump provides a fuel supply cycle that is advanced by a phase angle relative to the fuel suction stroke.

Abstract: Provided herein are combination therapies for treating blood cancer, in particular, acute myeloid leukemia, by concurrently targeting Axl and BCL-2.

Abstract: The present disclosure provides feature data processing methods and devices. One exemplary feature data processing method comprises: classifying features into an important feature set and an auxiliary feature set according to information attribute values of the features; converting features in the auxiliary feature set to hash features; and combining the hash features with features in the important feature set, and setting the combined features as fingerprint features. Training and prediction of to-be-processed data can be performed based on the fingerprint features. With the embodiments of the present disclosure, training dimensions can be more controllable and the amount training data amount can be reduced. Therefore, the efficiency of data processing can be improved.

Abstract: There is provided an association analysis method and apparatus. An original database is divided into projection databases, each not contributing to a support count of a frequent item set of another. The projection databases are used for sequential-pattern association analysis performed respectively by nodes corresponding to the projection databases. Local frequent item sets and corresponding support counts obtained by the nodes are combined. Since an established projection database does not contribute a support count of a frequent item set of another projection database, different nodes can perform association mining, including pruning, on different projection databases respectively.

Abstract: A model that closely resembles human excessive myopia can be prepared by mounting a minus lens (2) and a protector (4) to a juvenile mouse, the minus lens having an angle and a width adjustable in response to growth of the mouse. Further, this model analysis shows that myopia induction causes endoplasmic reticulum stress in a sclera and the endoplasmic reticulum stress induces myopia. Furthermore, it is revealed that an endoplasmic reticulum stress suppressant, particularly, phenylbutyrate and tauroursodeoxycholic acid act as a myopia prevention/suppression agent.

Abstract: There is provided an association analysis method and apparatus. An original database is divided into projection databases, each not contributing to a support count of a frequent item set of another. The projection databases are used for sequential-pattern association analysis performed respectively by nodes corresponding to the projection databases. Local frequent item sets and corresponding support counts obtained by the nodes are combined. Since an established projection database does not contribute a support count of a frequent item set of another projection database, different nodes can perform association mining, including pruning, on different projection databases respectively.

Abstract: The disclosed embodiments provide a method and an apparatus for acquiring an evaluation index. The method comprises the steps of inputting samples into a classification model for classification training and acquiring output data of the classification model; acquiring probability statistics by performing probability distribution on the output data, wherein the probability statistics comprise probability intervals and a number of true positive samples and a number of true negative samples in each probability interval; and calculating the evaluation index of the classification model according to a threshold set and the acquired probability statistics. In the disclosed embodiments, probability statistics is performed for the output data of the classification model; and the evaluation index is calculated based on the acquired probability statistics, thereby solving the problem of scanning output data multiple times during the calculation of evaluation index.

Abstract: Methods and apparatuses for training model based on random forest are provided. The method includes: dividing worker nodes into one or more groups; performing random sampling, by worker nodes in each group, in the preset sample data to obtain the target sample data; and training, by the worker nodes in each group, one or more decision tree objects using the target sample data. Example embodiments of the present disclosure do not need to scan the complete sample data for once, thereby greatly reducing the amount of data to be read, the time cost, and further the iterative update time of the model. The efficiency of training is improved.

Abstract: The present disclosure provides methods and an apparatuses for building a data identification model. One exemplary method for building a data identification model includes: performing logistic regression training using training samples to obtain a first model, the training samples comprising positive and negative samples; sampling the training samples proportionally to obtain a first training sample set; identifying the positive samples using the first model, and selecting a second training sample set from positive samples that have identification results after being identified using the first model; and performing Deep Neural Networks (DNN) training using the first training sample set and the second training sample set to obtain a final data identification model. The methods and the apparatuses of the present disclosure improve the stability of data identification models.

Abstract: Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N?M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N?M+P) feature columns; and training the model based on the training data including the (N?M+P) feature columns.

Abstract: The present disclosure provides feature data processing methods and devices. One exemplary feature data processing method comprises: classifying features into an important feature set and an auxiliary feature set according to information attribute values of the features; converting features in the auxiliary feature set to hash features; and combining the hash features with features in the important feature set, and setting the combined features as fingerprint features. Training and prediction of to-be-processed data can be performed based on the fingerprint features. With the embodiments of the present disclosure, training dimensions can be more controllable and the amount training data amount can be reduced. Therefore, the efficiency of data processing can be improved.

Abstract: The present disclosure provides question recommendation methods and devices. One exemplary method comprises: acquiring questions and question features corresponding to the questions; processing the question features, the processed question features being in a preset numerical range; and determining a to-be-recommended question according to the questions, a second probability of each question among the questions, and a specified recommendation threshold, wherein the second probability of each question among the questions is obtained by using the processed question features and first probabilities, the first probabilities being obtained based on the question features. By using the methods and devices in the present disclosure, a question to be recommended to a user can be obtained by performing calculation on historical question features, thereby improving accuracy of question recommendation to the user.

Abstract: Described are methods for reducing the proliferation cancer cells by modulating the expression or activity of TOX such as by use of a TOX inhibitor. Inhibiting TOX expression with antisense nucleic acids is shown to reduce the proliferation of malignant T cells. Also described are methods for the treatment of cancer in a subject in need thereof comprising administering to the subject a TOX inhibitor. Optionally, the cancer is a T cell malignancy such as Cutaneous T cell Lymphoma (CTCL).

Abstract: An improved grid for a nuclear reactor fuel assembly that has an egg-crate base grid as the primary support structure with each support cell of the base grid that supports a fuel rod having a lock-support sleeve that is rotatable within the support cell between a first and second orientation. In the first orientation the lock-support sleeve fits loosely within the support cell of the base grid and respectively, loosely receives the fuel rods that are loaded therein. The lock-support sleeves are then rotated to a second orientation that locks the fuel rods axially within the support cells.

Abstract: A support grid for a nuclear fuel assembly, the fuel rod assembly having a generally cylindrical fuel rod with a diameter, wherein the support grid includes a frame assembly having a plurality of generally uniform cells, each the cell having at least one wall and a width and at least one generally cylindrical tubular member having a cell contact portion with a greater diameter and at least one helical fuel rod contact portion with a lesser diameter, the cell contact portion and the fuel rod contact portion joined by a transition portion, the greater diameter being generally equivalent to the cell width, and the lesser diameter being generally equivalent to the fuel rod diameter such that a fuel rod disposed in the tubular member would engage the inner diameter. Wherein the least one tubular member disposed in one cell of the plurality of generally square cells so that the cell contact portion engages the at least one cell sidewall.

Abstract: An improved grid for a nuclear reactor fuel assembly that has an egg-crate base grid as the primary support structure with each support cell of the base grid that supports a fuel rod having a lock-support sleeve that is rotatable within the support cell between a first and second orientation. In the first orientation the lock-support sleeve fits loosely within the support cell of the base grid and respectively, loosely receives the fuel rods that are loaded therein. The lock-support sleeves are then rotated to a second orientation that locks the fuel rods axially within the support cells.