Abstract: Provided is a fully-spherical radiation therapy system. The fully-spherical radiation therapy system includes a multi-degree-of-freedom robot, a linear accelerator and a double-image-guided positioning mechanism. The double-image-guided positioning mechanism includes four ray sources and two ray detectors, and the four ray sources include a first ray source, a second ray source, a third ray source and a fourth ray source. An intersection of two beams emitted by the first ray source and the second ray source is a low-level treatment center, an intersection of two beams emitted by the third ray source and the fourth ray source is a high-level treatment center, and the low-level treatment center and the high-level treatment center form a fully-spherical treatment space for multiple treatment nodes of a spherical center.

Abstract: Systems and methods for dynamically executing sparse neural networks are provided. In one implementation, a system for providing dynamic sparsity in a neural network may include at least one memory storing instructions and at least one processor configured to execute the instructions to: reduce an input vector and a set of weights of the neural network, execute an input layer of the neural network using the reduced input vector and set of weights to generate a reduced output vector; expand the reduced output vector to a full output vector using first predictable output neurons (PONs); using a PON map, reduce a dimension of the full output vector; execute subsequent layers of the neural network using the reduced full output vector to produce a second reduced output vector; and expand the second reduced output vector to a second full output vector using second PONs.

Abstract: Provided is a fully-spherical radiation therapy system. The fully-spherical radiation therapy system includes a multi-degree-of-freedom robot, a linear accelerator and a double-image-guided positioning mechanism. The double-image-guided positioning mechanism includes four ray sources and two ray detectors, and the four ray sources include a first ray source, a second ray source, a third ray source and a fourth ray source. An intersection of two beams emitted by the first ray source and the second ray source is a low-level treatment center, an intersection of two beams emitted by the third ray source and the fourth ray source is a high-level treatment center, and the low-level treatment center and the high-level treatment center form a fully-spherical treatment space for multiple treatment nodes of a spherical center.

Abstract: The present disclosure relates to systems and methods for providing block-wise sparsity in neural networks. In one implementation, a system for providing block-wise sparsity in a neural network may include at least one memory storing instructions and at least one processor configured to execute the instructions to: divide a matrix of weights associated with a neural network into a plurality of blocks; extract non-zero elements from one or more of the plurality of blocks; re-encode the extracted non-zero elements as vectors with associated coordinates of the extracted non-zero elements within the one or more blocks; enforce input sparsity in the neural network corresponding to the associated coordinates; and execute the neural network using the vectors and the enforced input sparsity.

Abstract: The present disclosure provides a medicine box, including a housing, a door, a first clamping member, and a second clamping member, a cavity is formed inside the housing, the housing has an opening on a top surface; an end of the door is rotatably connected with one side of the housing at the opening, an end of the first clamping member is connected with the other end of the door, and the other end of the first clamping member includes a first magnetic portion; an end of the second clamping member is connected with the other side of the housing at the opening, and the other end of the second clamping member includes a second magnetic portion; and a clamping area with a changeable size is formed between the first magnetic portion and the second magnetic portion.

Abstract: A configurable semiconductor device (“CSD”) is organized in four (4) quadrants able to perform user-defined logic functions via a clock fabric. The first quadrant, in one embodiment, includes a first serializer and deserializer (“SerDes”) region and a bank0 region for data processing. The second quadrant includes a second SerDes region and a bank5 region and the third quadrant contains a bank3 region and a bank4 region. The fourth quadrant includes a bank1 region and a bank2 region. The clock fabric is configured to provide a set of programmable or selectable clock signals with different clock speeds to various regions within the CSD.

Abstract: A configurable semiconductor device (“CSD”) is organized in four (4) quadrants able to perform user-defined logic functions via a clock fabric. The first quadrant, in one embodiment, includes a first serializer and deserializer (“SerDes”) region and a bank0 region for data processing. The second quadrant includes a second SerDes region and a bank5 region and the third quadrant contains a bank3 region and a bank4 region. The fourth quadrant includes a bank1 region and a bank2 region. The clock fabric is configured to provide a set of programmable or selectable clock signals with different clock speeds to various regions within the CSD.

Abstract: This application provides a data processing method applied to a GPU, including: reading a to-be-processed matrix from memory corresponding to a target GPU into registers in a target streaming multiprocessor, where the target streaming multiprocessor is a streaming multiprocessor adapted to perform a matrix computation on the to-be-processed matrix; in a process of reading the to-be-processed matrix from the registers into shared memory corresponding to the registers, performing a first preset operation on the to-be-processed matrix to obtain an initial matrix corresponding to the to-be-processed matrix; and in a process of reading the initial matrix from the shared memory into the registers, performing a second preset operation on the initial matrix to obtain a target matrix meeting a matrix multiplication operation requirement of a matrix-dedicated computation unit.

Abstract: In one aspect, antibodies that specifically bind to a human triggering receptor expressed on myeloid cells 2 (TREM2) protein are provided. In some embodiments, the antibody decreases levels of soluble TREM2 (sTREM2). In some embodiments, the antibody enhances TREM2 activity.

Abstract: A configurable semiconductor device (“CSD”) is organized in four (4) quadrants able to perform user-defined logic functions via a clock fabric. The first quadrant, in one embodiment, includes a first serializer and deserializer (“SerDes”) region and a bank0 region for data processing. The second quadrant includes a second SerDes region and a bank5 region and the third quadrant contains a bank3 region and a bank4 region. The fourth quadrant includes a bank 1 region and a bank2 region. The clock fabric is configured to provide a set of programmable or selectable clock signals with different clock speeds to various regions within the CSD.

Abstract: A field-programmable gate array (“FPGA”) contains a configurable semiconductor organized in multiple clock regions with a clock fabric for facilitating user-defined logic functions. The clock fabric provides a set of regional clock signals (“RCSs”) generated from a clock source with a high clock signal quality (“CSQ”) for clocking logic blocks in a clock region. Also, a set of neighboring clock signals (“NCSs”) or inter-regional clock signals are generated from a neighboring clock source(s) for clocking logic blocks in two neighboring regions. In addition, the clock fabric is operable to provide secondary clock signals (“SCSs”) generated from the RCSs with a low CSQ for clocking logic blocks with less time-sensitive logic operations.

Abstract: The present disclosure provides a medicine box, including a housing, a door, a first clamping member, and a second clamping member, a cavity is formed inside the housing, the housing has an opening on a top surface; an end of the door is rotatably connected with one side of the housing at the opening, an end of the first clamping member is connected with the other end of the door, and the other end of the first clamping member includes a first magnetic portion; an end of the second clamping member is connected with the other side of the housing at the opening, and the other end of the second clamping member includes a second magnetic portion; and a clamping area with a changeable size is formed between the first magnetic portion and the second magnetic portion.

Abstract: In one aspect, antibodies that specifically bind to a human triggering receptor expressed on myeloid cells 2 (TREM2) protein are provided. In some embodiments, the antibody decreases levels of soluble TREM2 (sTREM2). In some embodiments, the antibody enhances TREM2 activity.

Abstract: In one aspect, antibodies that specifically bind to a human triggering receptor expressed on myeloid cells 2 (TREM2) protein are provided. In some embodiments, the antibody decreases levels of soluble TREM2 (sTREM2). In some embodiments, the antibody enhances TREM2 activity.

Abstract: The present disclosure relates to systems and methods for providing vector-wise sparsity in neural networks. In some embodiments, an exemplary method for providing vector-wise sparsity in a neural network, comprises: dividing a matrix associated with the neural network into a plurality of vectors; selecting a first subset of non-zero elements from the plurality of vectors to form a pruned matrix; and outputting the pruned matrix for executing the neural network using the pruned matrix.

Abstract: The present disclosure relates to systems and methods for providing block-wise sparsity in neural networks. In one implementation, a system for providing block-wise sparsity in a neural network may include at least one memory storing instructions and at least one processor configured to execute the instructions to: divide a matrix of weights associated with a neural network into a plurality of blocks; extract non-zero elements from one or more of the plurality of blocks; re-encode the extracted non-zero elements as vectors with associated coordinates of the extracted non-zero elements within the one or more blocks; enforce input sparsity in the neural network corresponding to the associated coordinates; and execute the neural network using the vectors and the enforced input sparsity.

Abstract: The present disclosure relates to systems and methods for dynamically executing sparse neural networks. In one implementation, a system for providing dynamic sparsity in a neural network may include at least one memory storing instructions and at least one processor configured to execute the instructions to: reduce an input vector and a set of weights of the neural network, execute an input layer of the neural network using the reduced input vector and set of weights to generate a reduced output vector; expand the reduced output vector to a full output vector using first predictable output neurons (PONs); using a PON map, reduce a dimension of the full output vector; execute subsequent layers of the neural network using the reduced full output vector to produce a second reduced output vector; and expand the second reduced output vector to a second full output vector using second PONs.

Abstract: A method for generating one or more push notifications to a user device is described. The method comprises: obtaining history data representing a history of online activities of a user and candidate data representing a set of candidate information; generating, based on the history data and the candidate data, user profile vectors representing a user profile associated with the user and content vectors representing a set of content profiles associated with the set of candidate information; generating, based on a machine learning model trained with a history of online activities, embedding user feature vectors and embedding content feature vectors based on the history data and the candidate data; and providing for transmission information for one or more push notifications including first candidate information of to a user device associated with the user, the first candidate information being determined from the set of candidate information based on the aforementioned vectors.

Abstract: A method for generating one or more push notifications to a user device is described. The method comprises: obtaining history data representing a history of online activities of a user and candidate data representing a set of candidate information; generating, based on the history data and the candidate data, user profile vectors representing a user profile associated with the user and content vectors representing a set of content profiles associated with the set of candidate information; generating, based on a machine learning model trained with a history of online activities, embedding user feature vectors and embedding content feature vectors based on the history data and the candidate data; and providing for transmission information for one or more push notifications including first candidate information of to a user device associated with the user, the first candidate information being determined from the set of candidate information based on the aforementioned vectors.

Abstract: An entity may be related to multiple related entities by one or more properties, and the entity may also be associated with one or more entity types. A system for providing sorted results may include identifying the entity, related entities, and types. The system may also determine related entity scores for each respective related entity, relative to the entity. For each property, the related entity scores of the related entities related to the entity by that property are combined to generate a property score. The properties are then sorted based on their property scores. The sorting may occur for properties associated with an entity type, and sorted search results may be provided as output for one or more entity types of interest.