A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.

A system for implementing an algorithmic market maker is configured to: receive a transaction including a request to redeem an amount of stablecoins for a reserve asset; retrieve initial states of a current block of a blockchain; determine a region on a curve function associating the stablecoin and the reserve asset, the curve function including at least two regions; determine an anchor reserve value based at least in part on the determined region; and provide a redemption amount of the reserve asset in exchange for the amount of stablecoins in the request based at least in part on the anchor reserve value.

A method for re-entry prediction of an uncontrolled artificial space object includes: calculating an average semi-major axis and an argument of latitude by inputting two-line elements or osculating elements of an artificial space object at two different time points; calculating an average semi-major axis, argument of latitude, and atmospheric drag at a second time point; estimating an optimum drag scale factor while changing the drag scale factor; predicting the time and place of re-entry of an artificial space object into the atmosphere by applying the estimated drag scale factor. Here, orbit prediction is performed by using a Cowell's high-precision orbital propagator using numerical integration from the second time point to a re-entry time point.

An optimization apparatus includes an output data acquirer to acquire output data expressing a result of experiment or simulation based input parameters, input/output data storage to store the input parameters and the output data corresponding to the input parameters, as a pair, an evaluation value calculator to calculate evaluation values of the output data, an input parameter converter to generate conversion parameters of a dimension number changed from the dimension number of the input parameters, a next-input parameter decider to decide next input parameters based on pairs of the conversion parameters and the evaluation values corresponding to the conversion parameters, and a repetition determiner to repeat operations of the output data acquirer, the input/output data storage, the evaluation value calculator, the input parameter converter, and the next-input parameter decider, until satisfying a predetermined condition.

An outlier detection device sets a weight for a kernel center of a grid cell based on a distribution of the data disposed on the grid cell region, calculates a cumulative change of a weight for each corresponding kernel center, sets a stationary region in the grid cell region based on the cumulative change, maintains a density of a kernel center of the stationary region as a previous density, calculates a density of a kernel center excluding the stationary region to update the calculated density, estimates a density of multidimensional data at the current time, and detects an arbitrary number of outliers based on a relative difference between the density of the multidimensional data and a density of a kernel center nearest to the multidimensional data.

Interpolation logic described herein provides a good approximation to a bicubic interpolation, which is generally smoother than bilinear interpolation, without performing all the calculations normally needed for a bicubic interpolation. This allows an approximation of smooth bicubic interpolation to be performed on devices (e.g. mobile devices) which have limited processing resources. At each of a set of predetermined interpolation positions within an array of data points, a set of predetermined weights represent a bicubic interpolation which can be applied to the data points. For a plurality of the predetermined interpolation positions which surround the sampling position, the corresponding sets of predetermined weights and the data points are used to determine a plurality of surrounding interpolated values which represent results of performing the bicubic interpolation at the surrounding predetermined interpolation positions. A linear interpolation is then performed on the surrounding interpolated values to determine an interpolated value at the sampling position.

A method of additively manufacturing an object may include defining an interlace path for a plurality of energy beams from an energy beam system based at least in part on a route-finding algorithm. The interlace path may delineate a first contour zone of a build plane assigned to a first one of the plurality of energy beams from a second contour zone of the build plane assigned to a second one of the plurality of energy beams. An exemplary method may additionally or alternatively include outputting a control command based at least in part on the interlace path. The control command may be configured to cause the energy beam system to irradiate a layer of a powder bed with the plurality of energy beams.

This application discloses a time series data management method, device, and apparatus. A device receives a data query request, wherein the data query request includes an identifier of the first object. The device obtains first time series data corresponding to the first object in a target data table according to the identifier of the first object. The device determines a second storage location that is in a location index table and that is stored in a first storage location corresponding to the first object. Further, the device obtains second time series data of the first object and a third storage location corresponding to the second time series data that are stored in the second storage location, The device also obtains other time series data corresponding to the first object according to the third storage location.

A method, computer system, and computer program product to detect anomalies in a multivariate or multidimensional time series data set. The time series data set is retrieved from a monitored device. A pair of neural networks are trained simultaneously using the retrieved time series data set by implementing an adversarial training process, to generate a generative neural network and a discriminative neural network. The anomalies in the time series data set of the monitored device are detected by implementing one or both of the generative neural network and the discriminative neural network to monitor the time series data set.

According to a disclosed embodiment, data analysis is secured with a microservice architecture and data anonymization in a multitenant application. Tenant data is received by a first microservice in a multitenant application. The tenant data is isolated from other tenant data in the first microservice and stored separately from other tenant data in a tenant database. The tenant data is anonymized in the first microservice and thereafter provided to a second microservice. The second microservice stores the anonymized tenant data in an analytics database. The second microservice, upon request, analyzes anonymized tenant data from a plurality of tenants from the analytics database and provides an analytics result to the first microservice.