Abstract: A method, apparatus, and system for timing synchronization between multiple computing nodes in an autonomous vehicle host system is disclosed. Timing of a first computing node of an autonomous vehicle host system is calibrated based on an external time source. A first timing message is transmitted from the first computing node to a second computing node of the autonomous vehicle host system via a first communication channel between the first computing node and the second computing node. Timing of the second computing node is calibrated based on the first timing message, wherein immediately subsequent to the calibration of timing of the second computing node, timing of the first computing node and of the second computing node is synchronized.

Abstract: In one embodiment, a computer-implemented method of performing a secure boot operation in an autonomous driving vehicle includes reading a first marker from a storage device in which the storage device includes a plurality of partitions and at least the first marker. The plurality of partitions includes a first partition including stored software, the first marker associated with the first partition, and wherein the first marker includes a unique identifier and an authentication code. The method further includes determining if the read first marker associated with the first partition is valid during a boot-up operation and executing the stored software in the first partition if the read first marker is determined valid.

Abstract: A water hardness detection probe, sensor, detection method and water softener are provided. The sensor includes a control unit which includes a processing module and a potential detection module. The detection probe includes a first probe and a second probe. When the first and second probes are both exposed in raw water, a potential between the first and second probes is regarded as first potential. When the first probe is in the raw water and the second probe is in softened water, the potential between the first and second probes is regarded as the second potential. The potential detection module measures the potential between the first and second probes. The processing module determines the water hardness of the softened water according to a difference between the first potential and the second potential. The sensor can detect the water hardness of the water softener in real time and eliminate detection deviations.

Abstract: A device and a method for continuous temperature gradient heat treatment of a rod-shaped material are disclosed. The furnace body of the device includes an upper heating zone and a lower heating zone inside, which are independently controlled in temperature by means of an upper heating power supply and a lower heating power supply. Moreover, both the upper heating zone and the lower heating zone are closed heating zones. The closed heat insulation plates could prevent heat loss and ensure precise temperature control of the upper heating zone and the lower heating zone. In the device, a vacuum pumping equipment is included; an annular radiation screen is configured between the upper heating zone and the lower heating zone, and the rod-shaped material is not in contact with the annular radiation screen. The rod-shaped material conducts one-dimensional heat transfer along the axial direction.

Abstract: A sensor unit includes a sensor interface, host interface, and pre-processing hardware. The sensor interface is coupled to a plurality of cameras configured to capture images around an autonomous driving vehicle (ADV). The host interface is coupled to a perception and planning system. The pre-processing hardware is coupled to the sensor interface to receive images from the plurality of cameras and to perform one or more pre-processing functions on the images and to transmit pre-processed images to the perception and planning system via the host interface. The perception and planning system is configured to perceive a driving environment surrounding the ADV based on the pre-processed images and to plan a path to control the ADV to navigate through the driving environment. The pre-processing functions can adjust for different calibrations and formats across the plurality of cameras.

Abstract: In the method for preparing single crystal superalloy test bars by using a Ni—W heterogeneous seed crystal, on the premise of ensuring that the single crystal superalloy has the required orientation, by reusing the seed crystal, it is achieved that the trouble caused by the need of preparing a new seed crystal when a single crystal superalloy is produced by the seed crystal method every time is avoided, and the production cost is significantly reduced. In the present disclosure, the formation of the stray grains in mushy zone could be avoided by using a Ni—W heterogeneous seed crystal without mushy zone and a built-in corundum tube.

Abstract: Various embodiments for systems and methods for improved axial resolution in a microscopy using photoswitching and standing-wave illumination techniques are described.

Abstract: Embodiments relate to systems and methods to optimize quantization of tensors of an AI model. According to one embodiment, a system receives an AI model having one or more layers. The system receives a number of input data for offline inferencing and applies offline inferencing to the AI model based on the input data to generate offline data distributions for the AI model. The system quantizes one or more tensors of the AI model based on the offline data distributions to generate a low-bit representation AI model, where each layer of the AI model includes the one or more tensors, where the one or more tensors include the one or more tensors. In one embodiment, the system applies online inferencing using the low-bit representation AI model to generate online data distributions for a feature map, and quantizes a feature map tensor based on the online data distributions.

Abstract: Provided is the use of an aromatic ring drug in inhibiting a key transcription factor of malignant melanoma. In particular, provided is the use of a compound as represented by formula A, or an optical isomer thereof or a racemate thereof, or a solvate thereof, or a pharmaceutically acceptable salt thereof in the preparation of a pharmaceutical composition or preparation. The pharmaceutical composition or preparation is used for: (a) inhibiting a key transcriptional regulatory factor of malignant melanoma, namely MITF (Microphthalmia-associated Transcription Factor); (b) treating MITF-related diseases such as melanoma, pancreatic cancer, skin hypersensitivity and asthma; and (c) regulating physiological activities in which MITF is involved, such as skin whitening. In the formula, each group is as defined in the description.

Abstract: Provided are an ADH protein mutant and the use thereof. Compared with a wild-type ADH protein, the mutant is capable of (i) enhancing the expression purity, efficiency and yield of exogenous proteins in an in-vitro cell-free synthesis system; and/or (ii) reducing the binding ability of the mutant protein to Ni medium.

Abstract: Methods are disclosed for accelerating inference operations of neural network (NN) models that take advantage of sparse channels. An offline method consolidates sparse channels for a layer into one consolidated sparse channel by concatenating the channel kernels associated with the sparse channels into one channel kernel. The method retrains the channel kernel of the consolidated sparse channel while keeping fixed the channel kernels for the dense channels to modify the channel model. The sparsity level of the retrained channel kernel of the consolidated sparse channel may be determined and stored in metadata. When the modified channel model is online, a method may compare the sparsity level of the retrained channel kernel of the consolidated sparse channel against a sparsity inference threshold to determine whether to inference the consolidated sparse channel. The sparsity inference threshold may be dynamically adjusted to strike a balance between speed and accuracy of the NN model.

Abstract: Provided are a fusion protein and a preparation method therefor. The fusion protein can improve in-vitro translation efficiency. A constitutive or inducible promoter (for example, pK1PGK1) is inserted in front of eIF4G in the fusion protein for increasing in-vitro protein synthesis ability.

Abstract: A data format converter rearranges data of an input image for input to a systolic array of multiply and accumulate processing elements. The image has a pixel height and a pixel width in a number of channels equal to a number of colors per pixel. The data format converter rearranges the data to a second, greater number of channels and inputs the second number of channels to one side of the systolic array. The second number of channels is less than or equal to the number of MAC PEs on the one side of the systolic array, and results in greater MAC PE utilization in the systolic array.

Abstract: A method and system is disclosed for protecting neural network models by segmenting partitions of the models into segments of pre-configured memory size, hashing the segmented models, and concatenating the hash segments. The concatenated hash segment may be further hashed, encrypted, and stored with the neural network models as an executable loadable file (ELF) in memories external to the neural network prior to the use of the models by the neural network. The models may include model weights of the inference layers and metadata. The model weights and the metadata may be hashed as separate hash segments and concatenated. Segmenting the models into segments of pre-configured memory size and hashing the segmented models offline prior to the operation of the neural network enables rapid validation of the models when the models are used in the inference layers during online operation of the neural network.

Abstract: Various embodiments related to systems and methods for instant structured microscopy where total internal reflection fluorescence techniques are used to improve optical sectioning and signal-to-noise ratio of structured illumination microscopy are herein disclosed.

Abstract: In one embodiment, an exemplary computer-implemented method of storing point cloud data in an autonomous driving vehicle can include the operations of receiving raw point cloud data from a LiDAR sensor mounted on the autonomous driving vehicle, the raw point cloud data representing cloud data points acquired in response to laser beams emitted at a given angle; retrieving configuration information of the LiDAR sensor, the configuration information including at least a number of laser lines of the LiDAR sensor. The method further includes the operations of constructing, based on the configuration information, a data structure that includes a data entry for each of the cloud data points, the data entry including multiple fields for storing attributes of the cloud data point, each field having a bit width determined based on the configuration information using a predetermined algorithm; and writing the cloud data points to a storage medium using the data structure.

Abstract: Batch normalization (BN) layer fusion and quantization method for model inference in artificial intelligence (AI) network engine are disclosed. A method for a neural network (NN) includes merging batch normalization (BN) layer parameters with NN layer parameters and computing merged BN layer and NN layer functions using the merged BN and NN layer parameters. A rectified linear unit (RELU) function can be merged with the BN and NN layer functions.

Abstract: The disclosure describes various embodiments for quantizing a trained neural network model. In one embodiment, a two-stage quantization method is described. In the offline stage, statically generated metadata (e.g., weights and bias) of the neural network model is quantized from floating-point numbers to integers of a lower bit width on a per-channel basis for each layer. Dynamically generated metadata (e.g., an input feature map) is not quantized in the offline stage. Instead, a quantization model is generated for the dynamically generated metadata on a per-channel basis for each layer. The quantization models and the quantized metadata can be stored in a quantization meta file, which can be deployed as part of the neural network model to an AI engine for execution. One or more specially programmed hardware components can quantize each layer of the neural network model based on information in the quantization meta file.

Abstract: Flexible hardware designs for camera calibration and image pre-processing are disclosed for vehicles including autonomous driving (AD) vehicles. For one example, a sensor unit includes a sensor interface, host interface, and pre-processing hardware. The sensor interface is coupled to a plurality of cameras configured to capture images around an autonomous driving vehicle (ADV). The host interface is coupled to a perception and planning system. The pre-processing hardware is coupled to the sensor interface to receive images from the plurality of cameras and to perform one or more pre-processing functions on the images and to transmit pre-processed images to the perception and planning system via the host interface. The perception and planning system is configured to perceive a driving environment surrounding the ADV based on the pre-processed images and to plan a path to control the ADV to navigate through the driving environment.

Abstract: Memory layout and conversion are disclosed to improve neural network (NN) inference performance. For one example, a NN selects a memory layout for a neural network (NN) among a plurality of different memory layouts based on thresholds derived from performance simulations of the NN. The NN stores multi-dimensional NN kernel computation data using the selected memory layout during NN inference. The memory layouts to be selected can be a channel, height, width, and batches (CHWN) layout, a batches, height, width and channel (NHWC) layout, and a batches, channel, height and width (NCHW) layout. If the multi-dimensional NN kernel computation data is not in the selected memory layout, the NN transforms the multi-dimensional NN kernel computation data for the selected memory layout.