Abstract: An online system selects advertisements for presentation a user based on characteristics of the user. The online system monitors performance of advertisements based on a goal for the advertisement and a time interval for achieving the goal. During a time period within the time interval, the online system determines an actual performance of the advertisement and compares the actual performance to a portion of the goal associated with the time period. If the actual performance does not satisfy the portion of the goal associated with the time period, the online system expands targeting criteria of the advertisement to increase a number of users eligible to be presented with the advertisement.

Abstract: There is provided an IC board and a display apparatus. Switching components (01; 02) are added between the internal interfaces (J1, J2 . . . Jn; j1, j2 . . . jn) corresponding to the backend data processing chips (U2; U3) and the frontend data processing chip (U1), or a switching component (02) is added between the internal interfaces (j1, j2 . . . jn) corresponding to the backend data processing chip (U2) and another backend data processing chip (U3). The switching components (01; 02) can ensure normal signal transmission between the backend data processing chips (U2; U3) and the frontend data processing chip (U1) or between the backend data processing chips (U2; U2) when no external test signal is input into the internal interfaces, i.e., when the IC board operates normally; and interrupt the signal transmission between the backend data processing chips (U2; U3) and the frontend data processing chip (U1) or between the backend data processing chips (U2; U3) when the internal interfaces j1, j2 . . .

Abstract: Aspects for vector comparison in neural network are described herein. The aspects may include a direct memory access unit configured to receive a first vector and a second vector from a storage device. The first vector may include one or more first elements and the second vector may include one or more second elements. The aspects may further include a computation module that includes one or more comparers respectively configured to generate a comparison result by comparing one of the one or more first elements to a corresponding one of the one or more second elements in accordance with an instruction.

Abstract: Aspects for matrix multiplication in neural network are described herein. The aspects may include a controller unit configured to receive a matrix-addition instruction. The aspects may further include a computation module configured to receive a first matrix and a second matrix. The first matrix may include one or more first elements and the second matrix includes one or more second elements. The one or more first elements and the one or more second elements may be arranged in accordance with a two-dimensional data structure. The computation module may be further configured to respectively add each of the first elements to each of the second elements based on a correspondence in the two-dimensional data structure to generate one or more third elements for a third matrix.

Abstract: Aspects for generating a dot product for two vectors in neural network are described herein. The aspects may include a controller unit configured to receive a transcendental function instruction that includes an address of a vector and an operation code that identifies a transcendental function. The aspects may further include a CORDIC processor configured to receive the vector that includes one or more elements based on the address of the vector in response to the transcendental function instruction. The CORDIC processor may be further configured to apply the transcendental function to each element of the vector to generate an output vector.

Abstract: A network computer system operates to arrange transport for delivery orders in a given region, in accordance with a set of provisioning parameters, including a service value parameter, and at least one of a service batching parameter or a service range parameter. For an upcoming time interval, the network computer system determines a forecast for at least one of a number of service requests or a number of available service providers. A provisioning level determination may be made for the given region during the upcoming time interval, based at least in part on the forecast. The set of provisioning parameters may be adjusted to adjust the provisioning level to a desired target.

Abstract: A network computing system estimates a provisioning level for a given time interval for one or more regions. The provisioning level determination may be based on an estimated number of order requests, and a number of available service providers in the given region that are available to provide transport in fulfilling the order requests. For individual requesters within the given region, the computer system selects a set of multiple suppliers for a respective supplier menu that is displayed on a mobile device of the requester. The selection of suppliers for individual requesters may be based at least in part on a current location of the requesters, and at least one of the estimated number of order requests or the estimated number of available service providers.

Abstract: A reflective display includes a first substrate and a second substrate arranged oppositely, a first electrode provided on the first substrate, a transparent dielectric layer arranged on the side of the first substrate opposite to the second substrate, a second electrode provided on the second substrate, and immiscible electrostriction light-absorbing material and transparent liquid filled between the first substrate and the second substrate. The light incident into the reflective display can be totally reflected on the side of the transparent liquid next to the first substrate; the electrostriction light-absorbing material deforms under action of an electric field formed by the first electrode and the second electrode, which enables a spreading area of the side of the transparent liquid next to the first substrate change.

Abstract: Aspects for vector circular shifting in neural network are described herein. The aspects may include a direct memory access unit configured to receive a vector that includes multiple elements. The multiple elements are stored in a one-dimensional data structure. The direct memory access unit may store the vector in a vector caching unit. The aspects may further include an instruction caching unit configured to receive a vector shifting instruction that includes a step length for shifting the elements in the vector. Further still, the aspects may include a computation module configured to shift the elements of the vector toward one direction by the step length.

Abstract: The present disclosure relates to a light-emitting diode (LED), including: a grating layer; and a light reflecting layer, wherein a light-emitting component is disposed between the grating layer and the light reflecting layer; and wherein the grating layer is configured to let linearly polarized light perpendicular to a grating direction of the grating layer in light emitted from the light-emitting component transmit through the grating layer, and reflect linearly polarized light parallel to the grating direction of the grating layer in the light emitted from the light-emitting component.

Abstract: A method or apparatus for monitoring a system by detecting misbehaving components in the system is presented. A computing device receives historical data points based on a set of monitored signals of a system. The system has components that are monitored through the set of monitored signals. For each monitored component, the computing device performs unsupervised machine learning based on the historical data points to identify expected states and state transitions for the component. The computing device identifies one or more steady components based on the identified states of the monitored components. The computing device also receives real-time data points based on monitoring the set of signals from the system. For each identified steady component, the computing device examines the received real-time data points for deviation from the expected state and state transitions of the steady component. The computing device reports anomaly in the system based on the detected deviations.

Abstract: A control-plane component of a virtual network interface (VNI) multiplexing service assigns one or more VNIs as members of a first interface group. A first VNI of the interface group is attached to a first compute instance. Network traffic directed to a particular endpoint address associated with the first interface group is to be distributed among members of the first interface group by client-side components of the service. The control-plane component propagates membership metadata of the first interface group to the client-side components. In response to a detection of an unhealthy state of the first compute instance, the first VNI is attached to a different compute instance by the control-plane component.

Abstract: The present disclosure relates to a display panel, a display device and a smart watch, wherein, the display panel includes a liquid crystal display panel; and a light-exiting surface of the liquid crystal display panel is provided with a cholesteric liquid crystal display film layer; wherein, the cholesteric liquid crystal display film layer is switchable between a scattering state and a transparent state depending on a voltage applied thereto, and is used for displaying by means of its scattering state when the liquid crystal display panel is powered off.

Abstract: Aspects for generating a dot product for two vectors in neural network are described herein. The aspects may include a controller unit configured to receive a transcendental function instruction that includes an address of a vector and an operation code that identifies a transcendental function. The aspects may further include a CORDIC processor configured to receive the vector that includes one or more elements based on the address of the vector in response to the transcendental function instruction. The CORDIC processor may be further configured to apply the transcendental function to each element of the vector to generate an output vector.

Abstract: Aspects for vector operations in neural network are described herein. The aspects may include a controller unit configured to receive an instruction to generate a random vector that includes one or more elements. The instruction may include a predetermined distribution, a count of the elements, and an address of the random vector. The aspects may further include a computation module configured to generate the one or more elements, wherein the one or more elements are subject to the predetermined distribution.

Abstract: Aspects for matrix multiplication in neural network are described herein. The aspects may include a controller unit configured to receive a matrix-addition instruction. The aspects may further include a computation module configured to receive a first matrix and a second matrix. The first matrix may include one or more first elements and the second matrix includes one or more second elements. The one or more first elements and the one or more second elements may be arranged in accordance with a two-dimensional data structure. The computation module may be further configured to respectively add each of the first elements to each of the second elements based on a correspondence in the two-dimensional data structure to generate one or more third elements for a third matrix.

Abstract: Aspects for vector comparison in neural network are described herein. The aspects may include a direct memory access unit configured to receive a first vector and a second vector from a storage device. The first vector may include one or more first elements and the second vector may include one or more second elements. The aspects may further include a computation module that includes one or more comparers respectively configured to generate a comparison result by comparing one of the one or more first elements to a corresponding one of the one or more second elements in accordance with an instruction.

Abstract: Aspects for matrix multiplication in neural network are described herein. The aspects may include a controller unit configured to receive a matrix-addition instruction. The aspects may further include a computation module configured to receive a first matrix and a second matrix. The first matrix may include one or more first elements and the second matrix includes one or more second elements. The one or more first elements and the one or more second elements may be arranged in accordance with a two-dimensional data structure. The computation module may be further configured to respectively add each of the first elements to each of the second elements based on a correspondence in the two-dimensional data structure to generate one or more third elements for a third matrix.

Abstract: Aspects for vector combination in neural network are described herein. The aspects may include a direct memory access unit configured to receive aa first vector, a second vector, and a controller vector. The first vector, the second vector, and the controller vector may each include one or more elements indexed in accordance with a same one-dimensional data structure. The aspects may further include a computation module configured to select one of the one or more control values, determine that the selected control value satisfies a predetermined condition, and select one of the one or more first elements that corresponds to the selected control value in the one-dimensional data structure as an output element based on a determination that the selected control value satisfies the predetermined condition.

Abstract: Aspects for matrix multiplication in neural network are described herein. The aspects may include a master computation module configured to receive a first matrix and transmit a row vector of the first matrix. In addition, the aspects may include one or more slave computation modules respectively configured to store a column vector of a second matrix, receive the row vector of the first matrix, and multiply the row vector of the first matrix with the stored column vector of the second matrix to generate a result element. Further, the aspects may include an interconnection unit configured to combine the one or more result elements generated respectively by the one or more slave computation modules to generate a row vector of a result matrix and transmit the row vector of the result matrix to the master computation module.