Abstract: A method, system, and computer program product, include recording high-frequency wave data of local arid adjacent fault curves of a power grid to compare the high-frequency wave data with historical fault data to detect an actual fault region in the power grid, the power grid having a plurality of sensors distributed in the power grid, each sensor being upstream of one adjacent sensor and downstream of an other adjacent sensor.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: A method for performing disaster recovery in a clustered environment comprises identifying, at a master device, a first indexer from a set of indexers to serve as a primary indexer for responding to queries pertaining to a subset of data. The method also comprises assigning, at the master device, a generation identifier indicating that the first indexer is the primary indexer for the subset of data. Responsive to an event prompting a change in a primary indexer designation for the subset of data, the method comprises identifying, at the master device, a second indexer from the set of indexers to serve as the primary indexer for responding to queries pertaining to the subset of data. Further, the method comprises assigning, at the master device, a new generation identifier indicating that the second indexer is the primary indexer for the subset of data.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: Techniques and mechanisms are disclosed to execute data searches using generation identifiers. In general, a method of executing the searches comprises broadcasting, from a search head, a first query to a plurality of indexers in a cluster, wherein a portion of the first query is directed to a set of data, and wherein the set of data comprises time-stamps within a particular time frame. The method further comprises providing, with the first query, a first generation identifier for the set of data, wherein the first generation identifier identifies a first indexer from the plurality of indexers to serve as a primary indexer for responding to queries that comprise the first generation identifier and that pertain to the set of data, wherein one or more indexers in the cluster other than the first indexer are designated as secondary indexers, wherein the secondary indexers are configured to ignore queries that pertain to the set of data and that comprise the first generation identifier.

Abstract: System and method for generating a ranked list are disclosed. A plurality of prior interactions for a first customer are received by a computing device. Each of the prior interactions includes a product interaction and time. A ranked list of item recommendations is generated based on the plurality of prior interactions. The ranked list of item recommendations is generated by a trained prediction model trained using temporal information embedded into a finite-dimensional vector space. The ranked list of item recommendations is output by the computing device.

Abstract: The present disclosure relates to a detection panel, a manufacturing method thereof and a detection device. The detection panel comprises: a base substrate and a photoelectric conversion structure located on the base substrate, and a display structure located on a side of the photoelectric conversion structure facing away from the base substrate and electrically connected to the photoelectric conversion structure; wherein the photoelectric conversion structure is configured to convert an optical signal into an electrical signal, and the display structure is configured to perform image display according to the electrical signal.

Abstract: A smart vending apparatus and a control method thereof. The apparatus includes: a cabinet (10) provided with a product shelf (102) used to display items for sale, the items carrying item information tags. A main control system is provided within the cabinet (10) and has a tag scanner (304) and a communication device (306) being respectively connected to a central processor (302) thereof. Upon receiving a door-opening instruction sent by the server after the server authenticates the identity information, the central processor (302) controls the door (20) to switch from a closed state to an open state. After the door (20) switches from the open state to the closed state, the tag scanner (304) scans all item information tags in the cabinet (10) to determine current inventory information.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: The present disclosure provides a GPU-based third-order low-rank tensor completion method. Operation steps of the method includes: (1) transmitting, by a CPU, input data DATA1 to a GPU, and initializing the loop count t=1; (2) obtaining, by the GPU, a third-order tensor Yt of a current loop t based on the least squares method; (3) obtaining, by the GPU, a third-order tensor Xt of the current loop t based on the least squares method; (4) checking, by the CPU, whether an end condition is met; and if the end condition is met, turning to (5); otherwise, increasing the loop count t by 1 and turning to (2) to continue the loop; and (5) outputting, by the GPU, output data DATA2 to the CPU. In the present disclosure, in the third-order low-rank tensor completion, a computational task with high concurrent processes is accelerated by using the GPU to improve computational efficiency.

Abstract: The present disclosure relates to a detection panel, a manufacturing method thereof and a detection device. The detection panel comprises: a base substrate and a photoelectric conversion structure located on the base substrate, and a display structure located on a side of the photoelectric conversion structure facing away from the base substrate and electrically connected to the photoelectric conversion structure; wherein the photoelectric conversion structure is configured to convert an optical signal into an electrical signal, and the display structure is configured to perform image display according to the electrical signal.

Abstract: A screen assembly of a mobile device which can be expanded for a larger viewing area includes a body and a back cover. The body has a main screen and a slot to one side. The back cover is detachably mounted on a rear surface of the body. The back cover has a mounting board with auxiliary screen thereon and an insertion board. The back cover is mounted on the rear surface of the body when the auxiliary screen is not deployed, and the insertion board of the back cover is inserted into the slot of the body when the auxiliary screen is deployed.

Abstract: Techniques and mechanisms are disclosed to execute data searches using generation identifiers. In general, a method of executing the searches comprises broadcasting, from a search head, a first query to a plurality of indexers in a cluster, wherein a portion of the first query is directed to a set of data, and wherein the set of data comprises time-stamps within a particular time frame. The method further comprises providing, with the first query, a first generation identifier for the set of data, wherein the first generation identifier identifies a first indexer from the plurality of indexers to serve as a primary indexer for responding to queries that comprise the first generation identifier and that pertain to the set of data, wherein one or more indexers in the cluster other than the first indexer are designated as secondary indexers, wherein the secondary indexers are configured to ignore queries that pertain to the set of data and that comprise the first generation identifier.

Abstract: Techniques and mechanisms are disclosed to increase the availability of summary data within a clustered data intake and query system by replicating the summary data within the cluster. In general, summary data may store “pre-computed” results for one or more search queries and can be used by indexers of a cluster to process subsequent instances of the same search queries. At a high level, replication of summary data within a cluster may include ensuring that each instance of summary data created by an indexer of a cluster is replicated to other indexers within the cluster that store copies of the same grouped subset(s) of data to which the summary data relates. In this manner, if one or more indexers of an indexer cluster fail, other indexers of the cluster can make immediate use of replicated copies of the summary data without re-creating it.

Abstract: A screen assembly of a mobile device which can be expanded for a larger viewing area includes a body and a back cover. The body has a main screen and a slot to one side. The back cover is detachably mounted on a rear surface of the body. The back cover has a mounting board with auxiliary screen thereon and an insertion board. The back cover is mounted on the rear surface of the body when the auxiliary screen is not deployed, and the insertion board of the back cover is inserted into the slot of the body when the auxiliary screen is deployed.

Abstract: Methods are presented for determining pulse transit time (PTT) and/or pulse wave velocity (PWV) of a subject by application of parametric system identification to proximal and distal arterial waveforms. The two waveforms are measured from the subject. A system is defined that relates the proximal arterial waveform to the distal arterial waveform (or vice versa) in terms of the unknown parameters of a parametric mathematical model. The model parameters are determined from the measured waveforms using system identification. PTT between the proximal and distal arterial sites is then determined from the system model. PWV may also be determined by dividing the distance between measurement sites (D) by PTT.

Abstract: A computer-implemented method comprises identifying a computer task; determining a hardware exception source associated with the computer task; determining an exception unit associated with the hardware exception source; determining a parallelization factor associated with the hardware exception source; and determining a parallel execution scenario associated with the computer task based on the exception unit and the parallelization factor. A corresponding computer program product and computer system are also disclosed.

Abstract: A method, system, and computer program product, include recording high-frequency wave data of local arid adjacent fault curves of a power grid to compare the high-frequency wave data with historical fault data to detect an actual fault region in the power grid, the power grid having a plurality of sensors distributed in the power grid, each sensor being upstream of one adjacent sensor and downstream of an other adjacent sensor.

Abstract: A method, system, and computer program product, include obtaining a dataset related to a power grid collected by a plurality of sensors distributed in the power grid, identifying a region as a candidate fault region based on first data in the dataset, the first data being collected by a first sensor from among the plurality of sensors that is located in the region, and verifying the candidate fault region based on second data in the dataset, the second data being collected by a second sensor from among the plurality of sensors that is adjacent to the first sensor.