Abstract: According to examples, an apparatus may include a processor that may, responsive to a secondary display being detected, cause a window including an option for a user to proceed with a location detection operation of the secondary display to be displayed on a primary display. The processor may also cause an icon to be displayed on the secondary display, track a movement of a cursor from the primary display to the icon displayed on the secondary display, and determine a position of the secondary display with respect to the primary display based on the tracked movement of the cursor. The processor may further store the determined position of the secondary display with respect to the primary display.

Abstract: A semiconductor package includes: a carrier having an electrically insulative body and a first contact structure at a first side of the electrically insulative body; and a semiconductor die having a first pad attached to the first contact structure of the carrier, the first pad being at source or emitter potential. The first pad is spaced inward from an edge of the semiconductor die by a first distance. The semiconductor die has an edge termination region between the edge and the first pad. The first contact structure of the carrier is spaced inward from the edge of the semiconductor die by a second distance greater than the first distance such that an electric field that emanates from the edge termination region in a direction of the carrier during normal operation of the semiconductor die does not reach the first contact structure of the carrier. Methods of production are also provided.

Abstract: Techniques for using enhanced logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system and inputting the utterance into a machine-learning model including a series of network layers. A final network layer of the series of network layers can include a logit function. The machine-learning model can map a first probability for a resolvable class to a first logit value using the logit function. The machine-learning model can map a second probability for a unresolvable class to an enhanced logit value. The method can also include the chatbot system classifying the utterance as the resolvable class or the unresolvable class based on the first logit value and the enhanced logit value.

Abstract: Techniques for noise data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes receiving a training set of utterances for training an intent classifier to identify one or more intents for one or more utterances; augmenting the training set of utterances with noise text to generate an augmented training set of utterances; and training the intent classifier using the augmented training set of utterances. The augmenting includes: obtaining the noise text from a list of words, a text corpus, a publication, a dictionary, or any combination thereof irrelevant of original text within the utterances of the training set of utterances, and incorporating the noise text within the utterances relative to the original text in the utterances of the training set of utterances at a predefined augmentation ratio to generate augmented utterances.

Abstract: Provided are methods for the isolation of phorbol from seed sources and the use of the phorbol for the generation of tigliane tiglate and derivatives thereof.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: Disclosed herein are MEMS devices and systems and methods of manufacturing or operating the MEMS devices and systems. In some embodiments, the MEMS devices and systems are used in imaging applications.

Abstract: Techniques regarding calibrating one or more quantum decoder algorithms are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a correlation inversion decoder component that can calibrate a quantum decoder algorithm for decoding a quantum error-correcting code by estimating hyperedge probabilities of a decoding hypergraph that are consistent with a syndrome dataset.

Abstract: Techniques regarding compiling quantum circuits with parallelized entangled measurements are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a circuit compilation component that can compile one or more quantum circuits for a hybrid quantum-classical algorithm. The one or more quantum circuits can include a mid-circuit operation to parallelize entangled measurements.

Abstract: Techniques regarding calibrating one or more quantum decoder algorithms are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a correlation inversion decoder component that can calibrate a quantum decoder algorithm for decoding a quantum error-correcting code by estimating hyperedge probabilities of a decoding hypergraph that are consistent with a syndrome dataset.

Abstract: A motion compensation module can be used in a video encoder for encoding a video input signal that includes a sequence of images that are segmented into a plurality of macroblocks. The motion compensation module includes a motion search module that generates a motion search motion vector for a plurality of subblocks for a plurality of partitionings of a macroblock of a plurality of macroblocks and generates a selected group of the plurality of partitionings based on a group selection signal. A motion refinement module—generates a refined motion vector for the plurality of subblocks for the selected group of the plurality of partitionings of the macroblock of the plurality of macroblocks, based on the motion search motion vector for each of the plurality of subblocks of the macroblock of the plurality of macroblocks.

Abstract: A multi-format video decoder includes an entropy decoding device that generates entropy decoded (EDC) data from an encoded video signal. A multi-format video decoding device includes a memory module that stores a plurality of operational instructions including at least one matrix multiply instruction that includes matrix input configuration data. A plurality of vector processor units generate a decoded video signal from the EDC data, wherein at least one of the plurality of vector processors include a matrix multiplier that generates output data based on a multiplication of first input data and second input data in accordance with the matrix input configuration data, wherein the matrix input configuration data indicates the dimensionality of the first input data and the second input data.

Abstract: A video processing device operates in an encoding mode when a mode selection signal has a first value and operates in a decoding mode when the mode selection signal has a second value. The video processing device utilizes an interpolation filter to perform an encoding function in the encoding mode and to perform a decoding function in a decoding mode.

Abstract: A video processing device includes a video processing unit that decodes a video input signal into a decoded video signal in accordance with a video compression protocol, based on uncompressed video frame data. A tile engine includes a tile accumulation module that accumulates the uncompressed video frame data into a plurality of tile units, wherein each of the plurality of tile units includes a plurality of video span units. A tile compression/decompression module generates compressed video frame data for storage in a compressed video frame buffer by compressing the plurality of video span units into a plurality of compressed video span units and further that retrieves the compressed video frame data from the compressed video frame buffer by retrieving the plurality of compressed video span units and generating the uncompressed video frame data by decompressing the plurality of compressed video span units.

Abstract: A video decoder includes an entropy decoding device that generates entropy decoded (EDC) data from an encoded video signal. A multi-format video decoding device includes a plurality of vector processor units that generate a decoded video signal from the EDC data. The plurality of vector processing units are programmed via VPU instructions formatted to include a vector instruction portion, a scalar instruction portion, and a branching instruction portion.

Abstract: A motion refinement engine can be used in a video encoder for encoding a video input signal that includes a sequence of images that are segmented into a plurality of macroblocks. The motion refinement engine includes a motion search module, that generates at least one motion search motion vector for a macroblock of the plurality of macroblocks based on a first plurality of directions. A motion refinement module generates at least one refined motion vector for the macroblock of the plurality of macroblocks, based on a second plurality of directions, wherein the second plurality of directions differ from the first plurality of directions. In a further embodiment, motion search, motion refinement, and motion compensation are selectively performed in a single pass.

Abstract: A motion compensation module can be used in a video encoder for encoding a video input signal that includes a sequence of images that are segmented into a plurality of macroblocks. The motion compensation module includes a shared memory and a motion search module that generates a motion search motion vector for a plurality of subblocks for a plurality of partitionings of a macroblock of a plurality of macroblocks and generates a selected group of the plurality of partitionings and stores the selected group of partitionings and the corresponding motion search motion vectors in the shared memory.

Abstract: A video decoder includes an entropy decoding device that generates entropy decoded (EDC) data from an encoded video signal. A multi-format video decoding device includes a plurality of vector processor units that generate a decoded video signal from the EDC data. The plurality of vector processing units includes at least one filter vector processor that operates in conjunction with a plurality of programmable filter parameters.

Abstract: A multi-format video decoder includes an entropy decoding device that generates entropy decoded (EDC) data from an encoded video signal. A multi-format video decoding device includes a memory module that stores format configuration data corresponding to a plurality of video coding formats. A plurality of vector processor units generate a decoded video signal from the EDC data, wherein at least one of the plurality of vector processors include a vector function module that generates vector function data based on a vector function of a first input vector and a second input vector. A selection module selects each element of a vector output as one of: a corresponding element of the vector function data, and a corresponding element of a third input vector.

Abstract: A video processing device operates in an encoding mode when a mode selection signal has a first value and operates in a decoding mode when the mode selection signal has a second value. The encoding mode utilizes a plurality of function specific hardware engines that each perform a specific coding function and the decoding module utilizes at least one of the plurality of hardware engines.