Abstract: A high temperature-resistant fire extinguishing robot, comprising: a high temperature-resistant robot chassis system and a high temperature-resistant turret system. The high temperature-resistant robot chassis system carries the high temperature-resistant turret system to perform movements and obstacle avoidance, provides the high temperature-resistant turret system with energy, and further comprises: a high temperature-resistant tract drive system, a thermal protection system, and a control and sensor system, the high temperature-resistant track drive system being arranged outside of the thermal protection system, and the control and sensor system being arranged within the thermal protection system.

Abstract: Disclosed are a Nested Named Entity Recognition method based on part-of-speech awareness, system, device and storage medium therefor. The method uses a BiLSTM model to extract a feature of text word data in order to obtain a text word depth feature, and each text word of text to be recognized is initialized into a corresponding graph node, and a text heterogeneous graph of the text to be recognized is constructed according to a preset part-of-speech path, the text word data of the graph nodes is updated by an attention mechanism, and the features of all graph nodes of the text heterogeneous graph are extracted using the BiLSTM model, and a nested named entity recognition result is obtained after decoding and annotating. The present disclosure can recognize ordinary entities and nested entities accurately and effectively, and enhance the performance and advantages of the nested named entity recognition model.

Abstract: The present invention discloses a bacterium and an obtaining method and application thereof. The bacterium has a property of coproducing 1,3-propanediol and D-lactic acid. Further, the bacterium is Klebsiella oxytoca, including Klebsiella oxytoca PDL-5 CCTCC M 2016185. The obtaining method of the bacterium may be to obtain the bacterium by directly screening wild bacteria that satisfy conditions from the environment or performing gene engineering modification to wild bacteria. The present invention has the advantages that the bacteria can coproduce 1,3-propanediol and D-lactic acid through fermentation, the molar conversion rate and the concentration of the two products are very high, the types of byproducts are few, the concentration is low, the product extraction process is simplified, the high-efficiency biological production of 1,3-propanediol and D-lactic acid can be realized, and the industrial application prospect is very great.

Abstract: A method for preparing an ultra-fine and uniform acrylamide-based polymer hydrogel filament. The method comprises: formulating a reaction solution of acrylamide-based polymer under ice bath conditions, and drawing the reaction solution into a reaction tube by a syringe; parabolically bending the two ends of the reaction tube upward; hanging the reaction tube for 2 h-8 h, and performing a baking reaction to obtain an acrylamide-based polymer hydrogel; vacuum drying the reaction tube for 4 h-8 h to obtain a dry filament of acrylamide-based polymer; cutting the reaction tube to obtain a plurality of short polymerization tubes having a preset length; pushing the dry filament of acrylamide-based polymer out of the short tube by a stainless steel wire; purifying same in a soaking solution to obtain an expanded filament of acrylamide-based polymer; and drying same to obtain a acrylamide-based polymer hydrogel filament having a preset external diameter.

Abstract: A high temperature-resistant wheel, comprising: an elastic hub assembly (1), which has a thermal insulation layer integrated therewith so as to insulate the elastic hub assembly (1) from exterior high temperatures; and a rigid wheel barbed ring (2), which has the elastic hub assembly (1) fastened inside and rotates together with the elastic hub assembly (1). A robot, which comprises the high temperature-resistant wheel.

Abstract: A multi-modal pre-training model acquisition method, an electronic device and a storage medium, which relate to the fields of deep learning and natural language processing, are disclosed. The method may include: determining, for each image-text pair as training data, to-be-processed fine-grained semantic word in the text; masking the to-be-processed fine-grained semantic words; and training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.

Abstract: A sliced graphics processing unit (GPU) architecture in processor-based devices is disclosed. In some aspects, a GPU based on a sliced GPU architecture includes multiple hardware slices. The GPU further includes a command processor (CP) circuit and an unslice primitive controller (PC_US). Upon receiving a graphics instruction from a central processing unit (CPU), the CP circuit determines a graphics workload, and transmits the graphics workload to the PC_US. The PC_US then partitions the graphics workload into multiple subbatches and distributes each subbatch to a PC_S of a hardware slice for processing.

Abstract: A sliced graphics processing unit (GPU) architecture in processor-based devices is disclosed. In some aspects, a GPU based on a sliced GPU architecture includes multiple hardware slices. The GPU further includes a command processor (CP) circuit and an unslice primitive controller (PC_US). Upon receiving a graphics instruction from a central processing unit (CPU), the CP circuit determines a graphics workload, and transmits the graphics workload to the PC_US. The PC_US then partitions the graphics workload into multiple subbatches and distributes each subbatch to a PC_S of a hardware slice for processing.

Abstract: Disclosed is a system for classifying encrypted traffic based on a data packet. The system includes a traffic capture module, a traffic analysis module, and a traffic classification module. The system collects data packets from a network flow to construct a machine learning model, so as to classify encrypted traffic and identify normal traffic and malicious traffic. In a process of constructing a feature matrix, basic spatial-temporal features, header features, load features, and statistical features are obtained. In addition, behavioral features of the data packets are obtained and used to demonstrate differences between the normal traffic and the malicious traffic. Moreover, the present disclosure focuses on a difference between different versions of an encryption protocol, especially a transport layer security (TLS) protocol, and introduces the difference into a model for analysis, so that the system classifies encrypted traffic in a more efficient manner.

Abstract: The present disclosure provides a synthesis method of a g-C3N4/C composite material based on a hollyhock stalk, including the following steps: (1) pretreatment of hollyhock stalks; and (2) fabrication of the g-C3N4/C composite material. In this method, with the hollyhock stalk as a carbon skeleton, g-C3N4 is spread on a template surface to form a laminated layer, and a composite system with a special structure is constructed. Compared with pure phase g-C3N4, the composite material substantially increases specific surface area and has a clear interface; the carbon skeleton not only functions as a rigid support, but also increases the electron transfer efficiency of the composite material, thereby improving the separation efficiency of photogenerated carriers and the utilization rate of visible light.

Abstract: A method is provided for automatically creating road and lane geometry from images representing probe data within a geographical area using graph estimation. Methods may include: receiving a rasterized image representative of map geometry within a geographic area, where each pixel of the rasterized image includes a property representing at least one feature of the map geometry; identifying pixels within the rasterized image corresponding to nodes of one or more map elements as node pixels; determining, for a respective node pixel, presence of a next node based on the at least one property of the respective node pixel; determining for the respective node pixel, a location corresponding to the next node based on the at least one property of the respective node pixel; generating, from a sequence of node pixels, at least one map element; and updating a map in a map database with the at least one map element.

Abstract: A semiconductor structure includes a substrate. The substrate includes a shielding region and a device region. The shielding region includes a first active region parallel to a first direction. The device region includes a second active region parallel to the first direction. The semiconductor structure also includes first isolation structures located on the shielding region. The first isolation structures run through the first active region along the second direction. The semiconductor structure also includes first gate structures located on the device region. The first gate structures cross the second active region along the second direction. The semiconductor structure also includes a second isolation structure located on the device region. The second isolation structure runs through the second active region along the second direction. The semiconductor structure also includes a dielectric layer on the substrate and an inductance coil on the dielectric layer.

Abstract: A method is provided to using a generative adversarial network to generate map geometry from images representing probe data. Methods may include: receiving a rasterized image representative of probe data within a geographic area, where each pixel of the rasterized image includes a property representing at least one component of the probe data; generating a prediction image of road features within the geographic area using a generative adversarial network based on trained model parameters and the rasterized image; determining one or more map elements based, at least in part, on the prediction image and georeferenced locations of the road features within the prediction image; and updating a map of the geographic area with one or more map elements.

Abstract: A method is provided automatically creating map geometry from data from various sources gathered within a geographical area using data aggregation and conflation with statistical analysis. Methods may include: receiving observation data associated with a geographic area; rasterizing objects within the observation data onto corresponding channels in a raster image corresponding to the geographic area having a given resolution; determining a distribution of locations of the objects parameterized by values in different channels in the raster image from the observation data; extracting analytic geometries from the raster image; generating map geometry based on the extracted analytic geometries; and updating a map database with the generated map geometry.

Abstract: A method is provided for automatically creating road and lane geometry from images representing probe data within a geographical area using data aggregation and conflation. Methods may include: receiving first observation data associated with a geographic area; processing the first observation data through a first encoder to produce first embeddings; receiving second observation data associated with the geographic area; processing the second observation data through a second encoder to produce second embeddings; establishing contrastive loss between the first embeddings and the second embeddings; producing consistent embeddings from the first embeddings and the second embeddings using the contrastive loss; generating, from the consistent embeddings, map data reflecting the first observation data and the second observation data; and updating a map database with the map data.

Abstract: A method is provided for generation of map geometry through rasterization of probe data over time, where the generated map geometry can be used for map element generation. Methods may include: receiving probe data from a plurality of probe apparatuses within a geographic area where the probe data includes location information and time information; rasterizing the received probe data to generate an image corresponding to the probe data associated with the geographic area, where each pixel of the image includes a property representing at least one component of the probe data; processing the image to obtain one or more map elements; and updating a map database with the one or more map elements.

Abstract: Disclosed is a photoplethysmography-based non-invasive diabetes prediction system, comprising an optical signal transmitter, a driving circuit, a receiving circuit and a processor module. The optical signal transmitter, when driven by the driving circuit, obtains and emits an optical signal of a fixed wavelength and intensity. The receiving circuit converts a received optical signal of photoplethysmography into an electrical signal of photoplethysmography, performs amplification, filtering, digital-analog conversion, and sends the digital photoplethysmography signal to the processor module. The processor module performs signal processing, feature extraction, modeling and prediction on the digital photoplethysmographic signal, and visually displays same. The photoplethysmography-based non-invasive diabetes prediction system and method can achieve non-invasive, real-time and convenient diabetes screening and disease risk prediction.

Abstract: A method is provided to automatically create road and lane geometry from images representing map geometry within a geographical area using object detection. Methods may include: receiving a rasterized image representative of map geometry within a geographic area, where each pixel of the rasterized image includes a property representing at least one component of the map geometry; applying an object detection model to the rasterized image; generating a list of the bounding boxes together with classes of objects within the bounding boxes based on the object detection model; generating map data from the list of bounding boxes and the classes of objects within the bounding boxes; and updating a map in a map database with the map data.