Abstract: The invention discloses a bamboo chip integrated material. The bamboo chip integrated material is formed by a plurality of lengthened bamboo chips which are glued and overlaid; each lengthened bamboo chip is formed by a plurality of bamboo chip units which sequentially and continuously mesh and are butted; sharp teeth and grooves are formed in the two ends in the length direction of the bamboo chip units, wherein the sharp tooth of each bamboo chip unit and the groove of the corresponding bamboo sheet unit are matched to form a meshing butt-joint part, and the meshing butt-joint parts of the adjacent lengthened bamboo chips are arranged in a staggered mode; and each bamboo chip unit has a thickness of 4-12 mm and a width of 15-50 mm.

Abstract: Certain aspects of the present disclosure provide techniques for generating a user interface to prompt users of a software application to perform an action in the software application. The method generally includes generating historical transaction time gap data for transactions in the account. A probability distribution is generated based on the historical time gap data. The probability distribution represents a probability that a transaction related to the account has been performed after an elapsed time from a previous transaction. A probability that an unrecorded transaction exists for an account based on the probability distribution and a time difference between a most recent transaction and a current time. The probability that an unrecorded transaction exists is determined to exceed a threshold probability, and a user interface is generated and displayed to a user of the software application including a prompt for the user to enter new transactions for the account.

Abstract: Aspects of the present disclosure provide techniques for efficiently utilizing physical computing resources for non-convex function approximation. Embodiments include determining a model formulation for determining a target value based on one or more constraints, wherein the model formulation comprises a non-convex function. Embodiments include converting the model formulation into a mixed integer programming (MIP) model, wherein the converting involves determining, based on the non-convex function, a first convex function and a second convex function. Embodiments include solving the MIP model, including computing a first piecewise linear approximation of the first convex function, computing a second piecewise linear approximation of the second convex function, and determining a difference between the first piecewise linear approximation and the second piecewise linear approximation to produce an approximated result of the non-convex function.

Abstract: Certain aspects of the present disclosure provide techniques for training predictive risk models based on user transaction history. An example method generally includes extracting, from a transaction history data set for a plurality of users of a software application, a plurality of features for each user of the plurality of users having records in the transaction history data set. A training data set is generated based on the extracted plurality of features for each user of the plurality of users. A plurality of predictive risk models is trained to generate a risk propensity score indicating a likelihood that a specified event will occur based on the training data set. Generally, monotonicity of one or more constraints is implemented in the model.

Abstract: The present invention discloses a reconstituted bamboo board integrating an embossed texture decoration and protective functions, comprising a reconstituted bamboo layer and a heat-pressed and cured film layer with an embossed texture decoration located on a surface of the reconstituted bamboo layer.

Abstract: A method may include obtaining a cluster. The cluster may include a subset of reference entities. The method may further include calculating distances between features of a target entity and features of the subset of reference entities, selecting, based on the distances, peer entities from the subset, and generating an estimated value of a metric. The generating may include applying, to the features of the target entity, a machine learning model trained using training data including values of the features for the peer entities labeled with a value of the metric. The method may further include presenting the estimated value of the metric.

Abstract: Certain aspects of the present disclosure provide techniques for generating a human readable summary explanation to a user for an outcome generated by a complex machine learning model. In one embodiment, a risk assessment service can receive a request from a user in which a risk model of the risk assessment service performs a specific task (e.g., determining the level of risk associated with the user). Once the risk model determines the risk associated with the user, in order to comply with regulations from a compliance system, the risk model can provide a user with an explanation as to the outcome for transparency purposes.

Abstract: Systems and methods for segmenting a group of objects concurrently based on two or more sets of metrics are disclosed. A system is configured to obtain a set of first metrics for the group of objects, with the set of first metrics including, for each object, a first metric associated with the object. The system is also configured to obtain a set of second metrics for the group of objects, with the set of second metrics including, for each object, a second metric associated with the object. The system is also configured to segment the group of objects into one or more segments concurrently based on the set of first metrics and the set of second metrics and to generate a data set including the one or more segments. For example, entities may be segmented concurrently based on a first credit score and a second credit score of each entity.

Abstract: Systems and methods for flexible, multi-constraint risk segmentation.

Abstract: A method including training, using accepted data having known labels, an untrained prediction model to generate a trained prediction model. The method also includes generating, using the trained prediction model, predicted labels for unknown data. The method also includes generating, using a confidence model, confidence scores for the predicted labels. The method also includes selecting, using the confidence scores, a subset of the unknown data and confident labels. The confident labels include ones of the predicted labels for the subset of the unknown data. The method also includes training, using the subset of the unknown data and the confident labels, the trained prediction model to generate an updated prediction model.

Abstract: The present invention discloses an assembled bamboo sleeper, which is obtained by using a bamboo unit as a raw material, dried and modified at the temperature of 110-180° C., undergone coating treatment using a dopamine solution, adhesive dipping, curing and solidifying, assembling and gluing, further solidifying, further treatment using a dopamine solution, and anti-mildew and/or anti-corrosion and/or anti-insect treatment, and then fastened. The present invention further provides a preparation method for the foregoing bamboo sleeper. The bamboo sleeper prepared in the present invention is green and environmentally friendly, and applicable for ballasted tracks of railways and urban rail transit systems.

Abstract: The present invention discloses a one-step integrally-formed bamboo sleeper. For the one-step integrally-formed bamboo sleeper, a bamboo unit is used as a raw material, to be dried and modified at the temperature of 110-180° C., and then subject to adhesive dipping, adhesive throwing, solidification, dopamine solution treatment, anti-mildew and/or anti-corrosion and/or anti-insect treatment, and fastening, to obtain the one-step integrally-formed bamboo sleeper with a density of 0.9-1.5 g/cm3. The present invention further provides a preparation method for the foregoing bamboo sleeper. The bamboo sleeper prepared in the present invention has a suitable elastic modulus, and applicable for ballasted tracks of railways and urban rail transit systems.

Abstract: Certain aspects of the present disclosure provide techniques for generating a human readable summary explanation to a user for an outcome generated by a complex machine learning model. In one embodiment, a risk assessment service can receive a request from a user in which a risk model of the risk assessment service performs a specific task (e.g., determining the level of risk associated with the user). Once the risk model determines the risk associated with the user, in order to comply with regulations from a compliance system, the risk model can provide a user with an explanation as to the outcome for transparency purposes.

Abstract: The invention relates to a method for characterizing time series. The method includes obtaining a time series that includes data points for consecutive time intervals, decomposing the time series into a trend component, a seasonal component, and a residual component, and clustering the seasonal component into a number of clusters. Each of the consecutive time intervals is assigned to one of the clusters based on a level of the data points of the seasonality component in the time interval. The method further includes labeling one of the clusters as baseline with the baseline cluster containing more time intervals than any one of the other clusters, determining cluster labels for the other clusters based on the level of the data points of the time intervals assigned to the other clusters; and for each time interval, storing the corresponding cluster label in a vector.

Abstract: The invention discloses an infinite-arbitrary-length bamboo chip integrated material. The bamboo chip integrated material is formed by a plurality of lengthened bamboo chips which are glued and overlaid; each lengthened bamboo chip is formed by a plurality of bamboo chip units which sequentially and continuously mesh and are butted; sharp teeth and grooves are formed in the two ends in the length direction of the bamboo chip units, wherein the sharp tooth of each bamboo chip unit and the groove of the corresponding bamboo sheet unit are matched to form a meshing butt-joint part, and the meshing butt-joint parts of the adjacent lengthened bamboo chips are arranged in a staggered mode; and each bamboo chip unit has a thickness of 4-12 mm and a width of 15-50 mm. The invention further provides a manufacturing method of the bamboo chip integrated material, comprising the steps of preparation of the bamboo chip units, meshing and butting, hot pressing and gluing and the like.