Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.

Abstract: The present disclosure relates to genetically modified strains of Salmonella, engineered to be tumor navigating, self-eradicating, and armed with decoy polypeptides that bind endogenous ligand and block activation of signal transduction cascades associated with cancer cell proliferation and tumor growth. Also provided herein are methods of producing and methods of using such genetically modified Salmonella strains to treat cancer.

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.

Abstract: The present disclosure relates to genetically modified strains of Salmonella, engineered to be tumor navigating and to alter tumor metabolism in the tumor microenvironment. Also provided herein are methods of producing and methods of using such genetically modified Salmonella strains to treat cancer.

Abstract: An electronic-cigarette cartridge and an electronic cigarette are provided. The electronic-cigarette cartridge includes: a first contact point and a second contact point which are connected to a base part of an electronic cigarette, wherein the first contact point is configured to connect a control signal, and the second contact point is configured to input a reference voltage signal; an authentication module, wherein one end of the authentication module is connected to the first contact point, another end is connected to the second contact point, and is configured to interact with the base part of the electronic cigarette to conduct anti-counterfeiting authentication; and a heating module which is connected in parallel with the authentication module and is configured to performing heating after the anti-counterfeiting authentication is passed; wherein a working frequency of the authentication module is at least 10 times that of the heating module.

Abstract: The present disclosure relates to genetically modified strains of Salmonella, engineered to be tumor navigating, self-eradicating, and armed with decoy polypeptides that bind endogenous ligand and block activation of signal transduction cascades associated with cancer cell proliferation and tumor growth. Also provided herein are methods of producing and methods of using such genetically modified Salmonella strains to treat cancer.

Abstract: The present invention discloses a wire rod for an ultrahigh-strength steel cord and a manufacturing method thereof. The manufacturing method includes: smelting molten steel where inclusions in sizes ?5 ?m are at a number density ?0.5/mm2 and sizes of inclusions are ?30 ?m; casting the molten steel into an ingot blank with a center carbon segregation value of 0.92-1.08; cogging the ingot blank into an intermediate blank with a center carbon segregation value of 0.95-1.05; rolling the intermediate blank into a wire rod; and performing temperature control cooling on the wire rod to obtain a wire rod with high purity, high homogeneity and tensile strength ?1,150 MPa. The wire rod may be used for an ultrahigh-strength steel cord with single tensile strength ?3,600 MPa.

Abstract: The present disclosure relates to genetically modified strains of Salmonella, engineered to be tumor navigating, self-eradicating, and armed with decoy polypeptides that bind endogenous ligand and block activation of signal transduction cascades associated with cancer cell proliferation and tumor growth. Also provided herein are methods of producing and methods of using such genetically modified Salmonella strains to treat cancer.

Abstract: The disclosure provides a method and an apparatus for processing visual data of a deep model, and an electronic device. The method includes: determining a user identifier in a request of displaying a workspace in response to acquiring the request; acquiring a set of historical visual data associated with the user identifier; and displaying the set of historical visual data in a page of a workspace of a user corresponding to the user identifier.

Abstract: The present disclosure relates to genetically modified strains of Salmonella, engineered to be tumor navigating and to alter tumor metabolism in the tumor microenvironment. Also provided herein are methods of producing and methods of using such genetically modified Salmonella strains to treat cancer.

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.

Abstract: An electronic-cigarette cartridge and an electronic cigarette are provided. The electronic-cigarette cartridge includes: a first contact point and a second contact point which are connected to a base part of an electronic cigarette, wherein the first contact point is configured to connect a control signal, and the second contact point is configured to input a reference voltage signal; an authentication module, wherein one end of the authentication module is connected to the first contact point, another end is connected to the second contact point, and is configured to interact with the base part of the electronic cigarette to conduct anti-counterfeiting authentication; and a heating module which is connected in parallel with the authentication module and is configured to performing heating after the anti-counterfeiting authentication is passed; wherein a working frequency of the authentication module is at least 10 times that of the heating module.

Abstract: A spatially sparse convolutional neural network (CNN) framework is introduced to that leverages high sparsity of input data to significantly reduce the computational cost of applications that employ CNNs (e.g., inking applications and others) by avoiding unnecessary floating point mathematical operations. The framework, which is compatible with parallelized operations, includes (1) a data structure for sparse tensors that both (a) reduces storage burden and (b) speeds computations; (2) a set of sparse tensor operations that accelerate convolution computations; and (3) the merging of pooling and convolutional layers. Practical applications involving handwriting recognition and/or stroke analysis demonstrate a notable reduction in storage and computational burdens.

Abstract: An ink parsing system receives ink strokes at an inking device input and render the received ink strokes into an image in a pixel space. Writing strokes are detected in the image and labeled. Pixels corresponding to the labeled writing strokes are removed from the image. Drawing strokes in the image having the removed pixels are detected using and labeled. Writing objects and drawing objects corresponding, respectively, to the labeled writing strokes and the labeled drawing strokes are output. A digital ink parsing pipeline is thereby provided having accurate ink stroke detection and segmentation.

Abstract: An ink parsing system receives ink strokes at an inking device input and render the received ink strokes into an image in a pixel space. Writing strokes are detected in the image and labeled. Pixels corresponding to the labeled writing strokes are removed from the image. Drawing strokes in the image having the removed pixels are detected using and labeled. Writing objects and drawing objects corresponding, respectively, to the labeled writing strokes and the labeled drawing strokes are output. A digital ink parsing pipeline is thereby provided having accurate ink stroke detection and segmentation.

Abstract: A spatially sparse convolutional neural network (CNN) framework is introduced to that leverages high sparsity of input data to significantly reduce the computational cost of applications that employ CNNs (e.g., inking applications and others) by avoiding unnecessary floating point mathematical operations. The framework, which is compatible with parallelized operations, includes (1) a data structure for sparse tensors that both (a) reduces storage burden and (b) speeds computations; (2) a set of sparse tensor operations that accelerate convolution computations; and (3) the merging of pooling and convolutional layers. Practical applications involving handwriting recognition and/or stroke analysis demonstrate a notable reduction in storage and computational burdens.

Abstract: A contactless communication device comprises a contactless front chip, a security chip concentrator and a security chipset. Wherein, said security chipset contains more than two security chips for storing and processing the contactless application data; said contactless front chip is used for implementing the interaction between said security chip and the external contactless field; said security chip concentrator, which is connected with the contactless front chip and with the security chip of the security chipset, is used for selecting a security chip based on the contactless application and implementing the connection between the selected security chip and the contactless front chip.

Abstract: A contactless communication device comprises a contactless front chip, a security chip concentrator and a security chipset. Wherein, said security chipset contains more than two security chips for storing and processing the contactless application data; said contactless front chip is used for implementing the interaction between said security chip and the external contactless field; said security chip concentrator, which is connected with the contactless front chip and with the security chip of the security chipset, is used for selecting a security chip based on the contactless application and implementing the connection between the selected security chip and the contactless front chip.

Abstract: A contactless communication device comprises a contactless front chip, a security chip concentrator and a security chipset. Wherein, said security chipset contains more than two security chips for storing and processing the contactless application data; said contactless front chip is used for implementing the interaction between said security chip and the external contactless field; said security chip concentrator, which is connected with the contactless front chip and with the security chip of the security chipset, is used for selecting a security chip based on the contactless application and implementing the connection between the selected security chip and the contactless front chip.

Abstract: Provided are a central processing unit (CPU) and method for executing a branch instruction of a CPU, which can protect user's data by preventing an error due to a computer virus and a hacker is provided. The CPU includes: a branch instruction verification unit which verifies whether a branch instruction is valid; and a branch instruction execution unit which executes the branch instruction when the branch instruction is valid. The method includes: verifying whether the branch instruction is valid; and not executing the branch instruction when the branch instruction is invalid.