Abstract: The present disclosure relates to a NiTi alloy surface cutting process and a roughness adjustment method. Aiming at defects of difficult machining and unsatisfactory machining performance of Ni—Ti alloys, the present disclosure provides a surface cutting process of a difficult-to-machine nickel-titanium alloy, in which firstly, the nickel-titanium alloy is transformed from a two-phase state to a single-phase state using its transformation characteristic, and electrochemical nickel removal treatment is carried out on a surface of the alloy in the single-phase state so as to obtain a porous surface layer, and then a surface material with the porous surface layer is cut. The method provided by the present disclosure can effectively reduce machining difficulty of alloy materials, and can also obtain alloy workpieces with adjustable roughness, which is of great significance for NiTi alloy machining.

Abstract: A skin layer of a superconducting tape has a woven mesh structure and is disposed on a surface of a superconducting tape. The skin layer of a superconducting tape solves the problem where a vapor layer generated when a superconductor is in a normal resistive state greatly reduces the efficiency of a heat exchange between the superconductor and liquid nitrogen. Further provided are the superconducting tape and a superconducting coil.

Abstract: An example method of virtualizing a hardware accelerator in a host cluster of a virtualized computing system includes: commanding, at an initiator host in the host cluster, a programmable expansion bus device to reconfigure as a virtual accelerator based on specifications of a hardware accelerator in a target host of the host cluster; executing, in the programmable expansion bus device, software to emulate the virtual accelerator as connected to an expansion bus of the initiator host; receiving, at the programmable expansion bus device, compute tasks from an application executing in the initiator host; and sending, to the target host, the compute tasks for processing by the hardware accelerator.

Abstract: A method for preparing a microstructure on the surface of glass by titanium oxide nanoparticle-assisted infrared nanosecond laser, including the following steps: (1) dropwise applying a titanium oxide nanoparticle hydrogel onto the surface of a glass sample; (2) pressing another piece of glass on the surface of the hydrogel, so the hydrogel is evenly distributed between the two pieces of glass, and allowing the two pieces of glass to stand horizontally for a period of time to air-dry the hydrogel; (3) separating the two pieces of glass to obtain a glass with a uniform titanium oxide nanoparticle coating; (4) forming a microstructure using an infrared nanosecond laser with a wavelength of 1064 nm; and (5) performing after-treatment, including ultrasonically cleaning the sample with acetone, absolute ethanol and deionized water respectively for 10 min to remove titanium oxide nanoparticles attached to the surface, to obtain a glass sample with the microstructure.

Abstract: Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

Abstract: Techniques are provided for determining a cell count within a whole slide pathology image. The image is segmented using a global threshold value to define a tissue area. A plurality of patches comprising the tissue area are selected. Stain intensity vectors are determined within the plurality of patches to generate a stain intensity image. The stain intensity image is iteratively segmented to generate a cell mask using a local threshold value that is and gradually reduced after each iteration. A chamfer distance transform is applied to the cell mask to generate a distance map. Cell seeds are determined on the distance map. Cell segments are determined using a watershed transformation, and a whole cell count is calculated for the plurality of patches based on the cell segments. A client device may be configured for real-time cell counting based on the whole cell count.

Abstract: A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.

Abstract: An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.

Abstract: Provided is a virtual circuit-based data packet processing method, which includes that: identification information of a next-hop Provider Edge (PE) node of a routing packet and identification information of an Original PE (OPE) node of the routing packet are determined according to the routing packet corresponding to a Virtual Private Network (VPN) service instance; a context virtual circuit is determined, wherein nodes at both ends of the context virtual circuit are respectively the current PE node and the OPE node; a virtual circuit label of the context virtual circuit is determined; a final data packet to be forwarded is obtained by carrying a VPN label of the routing packet and the virtual circuit label with an initial data packet of the VPN service instance; and the final data packet to be forwarded is forwarded to the next-hop PE node.

Abstract: Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

Abstract: A honeycomb structure body, which includes a honeycomb body and a skin layer, the honeycomb body including axially extending channels defined by a porous wall, wherein a radial path of a radial section of the honeycomb body from a central axis to the skin layer consists of a porous wall inner section and a porous wall outer section in sequence, an average wall thickness of inner porous walls provided in the porous wall inner section is smaller than an average wall thickness of outer porous walls provided in the porous wall outer section, and a length of the porous wall inner section in the radial path accounts for 71%-95%. The specific structure of the honeycomb structure body not only increases the strength of the honeycomb structure body, but also ensures good thermal shock resistance and small back pressure.

Abstract: Apparatus, methods and systems of providing AR content are disclosed. Embodiments of the inventive subject matter can obtain an initial map of an area, derive views of interest, obtain AR content objects associated with the views of interest, establish experience clusters and generate a tile map tessellated based on the experience clusters. A user device could be configured to obtain and instantiate at least some of the AR content objects based on at least one of a location and a recognition.

Abstract: Techniques are provided for computing affinity for protein-protein interaction. 3D structure models of the first and second protein parts are generated using a trained first deep learning model. A 3D structure model of a protein-protein complex comprising the first and the second protein parts is generated using a trained second deep learning model. A low energy score state is determined for the 3D structure models of each of the first and second protein parts, and the protein-protein complex. A relax algorithm applied to amino acid side chain and backbone 3D structure models determines a low energy score state for the 3D structure models. Based on the low energy score states, an energy score is generated for the 3D structure models, and a score difference is determined between the energy scores, where the score difference defines a binding affinity score.

Abstract: Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: The present disclosure provides a packet processing method applied to a first edge device, including: sending a route advertisement message of a virtual private network to a second edge device, the route advertisement message indicating whether the first edge device supports a first network architecture and whether the first edge device supports a second network architecture, the first edge device supporting at least one of the first network architecture or the second network architecture; and receiving a second service packet sent by the second edge device, where the second service packet is obtained by the second edge device in response to a first service packet according to the network architecture supported by the first edge device. The present disclosure further provides a packet processing method applied to a second edge device, an edge device, and a computer-readable storage medium.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.