Abstract: An electronic device includes a housing and two electronic modules. The housing has an accommodation space. The accommodation space has a first cross-sectional shape perpendicular to a first direction. The two electronic modules are disposed in the accommodation space. Each of the electronic modules has a concave-convex surface. The concave-convex surfaces of the two electronic modules are arranged oppositely and cooperate with each other, so that the two electronic modules together form a second cross-sectional shape perpendicular to the first direction, and the second cross-sectional shape matches the first cross-sectional shape.

Abstract: A chassis includes a fixing base, an operating member, a tray and a cover. The operating member is pivotally connected to the fixing base. The operating member includes a first linkage portion. The tray is disposed on the fixing base. The tray includes a second linkage portion corresponding to the first linkage portion. The cover is disposed on the fixing base and covers the tray. When the operating member is located at a closed position, the operating member and the fixing base are locked, and the first linkage portion blocks the second linkage portion, so as to fix the tray on the fixing base. When the operating member rotates from the closed position to an opened position, the first linkage portion pushes the second linkage portion to drive the tray to move toward outside of the fixing base.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: Methods, systems, and storage media for outputting a synchronized arrangement of a plurality of video streams are disclosed. Exemplary implementations may: select a stage status for a subset of the plurality of client devices; determine, based on the stage status, that a client device of the plurality of client devices is assigned a stage state; send, based on the stage status and the stage state being assigned to the client device, a server state to each client device of the plurality of client devices; determine, based on the server state, a graphical layout comprising a position and size of each video stream of the plurality of video streams to output to a graphical user interface; and provide, to each client device of the plurality of client devices, instructions to display the synchronized arrangement of the plurality of video streams according to the graphical layout.

Abstract: A computer-implemented method, a system, and a computer program product include program code that obtains, from a controller, temporal objectives and configuration specifications. The program code adjusts weighting for multiple concurrent modes. The program code optimizes each mode of the multiple concurrent modes. The program code discovers conflicts exist between the optimized multiple concurrent modes and resolves the conflicts. The program code allocates resources to the optimized multiple concurrent modes.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: A computer-implemented method, a computer program product, and a system include a processor(s) that produces a set of beamforming vectors, each vector produced based on a distinct set of parameters. To produce vector of the vectors, the processor(s) obtains, from receive antennas, data comprising a waveform comprising slots with boundaries, from a first collection window. The processor(s) determines the parameters (e.g., silent portion length and signal portion length) and determines which slots of the waveform contain signal operating instructions. The processor(s) aligns the slot boundaries, utilizing the data, based on determining which slots of the waveform contain signal operating instructions. The processor(s) extracts the signal portion and the silent portion, determines a covariance for each, and determines a vector and eigenvalue.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: There is set forth herein, in one embodiment, obtaining with a radiofrequency receiver a combined radiofrequency signal having a first stream combined with a second stream, wherein the first stream has a first power level, and wherein the second stream has a second power level, the first power level of the first stream being greater than the second power level of the second stream, wherein the radiofrequency receiver includes one or more antenna; processing the combined radiofrequency signal to recover a first digital bitstream associated to the first stream; and recovering a digital bitstream associated to the second stream using the first digital bitstream.

Abstract: Embodiments of the present invention include a system, method and computer program product for mitigating interference in data received by a multiple antenna array. Processor(s) executing program code identify signals from users, including active users, and by identifying these signals, mitigate interference and jamming in the received data, overall.

Abstract: Embodiments of the present invention include a system, method and computer program product for mitigating interference in data received by a multiple antenna array. Processor(s) executing program code identify signals from users, including active users, and by identifying these signals, mitigate interference and jamming in the received data, overall.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: A datacenter has more computing power than a personal computer. The personal computer sends a request to perform an operation on a data set to the datacenter. The datacenter evaluates various inputs to determine if, despite the datacenter's computing power, the personal computer is likely to complete the operation faster. Based on the determination, the datacenter may perform the operation, send the data set to the personal computer for the personal computer to process, or start a competitive computation. As a result, a user interface can be more responsive. Machine learning processes can be used to improve predictions.

Abstract: Provided are processes for suppressing jamming signals that may include use of a signal processing circuit. A signal processing circuit can be configured to obtain a jamming signal and a feedback signal, process the jamming signal and the feedback signal to determine a cancellation signal for use in suppressing the jamming signal, and output the cancellation signal to a radio-frequency transmitter. The signal processing circuit may be further configured to obtain a transmission signal, determine a jamming channel from the jamming signal and a feedback channel from the feedback signal, and combine the transmission channel, jamming channel, and feedback channel to determine a transfer function, where the transfer function is configured to determine the cancellation signal.

Abstract: Provided are processes for suppressing jamming signals that may include use of a signal processing circuit. A signal processing circuit can be configured to obtain a jamming signal and a feedback signal, process the jamming signal and the feedback signal to determine a cancellation signal for use in suppressing the jamming signal, and output the cancellation signal to a radio-frequency transmitter. The signal processing circuit may be further configured to obtain a transmission signal, determine a jamming channel from the jamming signal and a feedback channel from the feedback signal, and combine the transmission channel, jamming channel, and feedback channel to determine a transfer function, where the transfer function is configured to determine the cancellation signal.

Abstract: Embodiments described herein relate generally to devices, systems and methods for transplanting and growing human fetal organs within non-human animal hosts. In some embodiments, a method for transplanting an organ from a human fetus in a non-human animal host includes fluidically coupling the fetal organ to the blood circulation system of an immunocompromised non-human animal host such that the organ receives arterial blood flow from the non-human animal host. The blood pressure of the arterial blood flow entering the fetal organ is controlled to be compatible with the blood pressure of the organ from the human fetus. In some embodiments, the blood pressure of the blood flow to the fetal organ is controlled using a blood flow control device that includes an inflatable cuff in pressure contact with a blood vessel of the non-human animal host.