Abstract: The present disclosure provides a bainite geological drilling pipe, comprising the following chemical elements in percentage by mass: 0.14-0.22% of C, 0.2-0.55% of Si, 2.1-2.9% of Mn, 0.01-0.04% of Nb, 0.015-0.04% of Al, 0.001-0.005% of B, 0<N?0.007%, with the balance being Fe and inevitable impurities, wherein a content ratio of Al to N is Al/N?3. In addition, the present disclosure further provides a manufacturing method for the bainite geological drilling pipe, comprising the following steps: (1) performing smelting and casting on molten steel to obtain a pipe blank; (2) performing heating, piercing, continuous rolling and sizing on the pipe blank to obtain a pipe body; and (3) performing two-stage air cooling on the pipe body; in first-stage air cooling, performing air circular blowing cooling on the outer surface of the pipe body, the temperature before cooling is greater than or equal to Ar/3+50° C., the cooling rate is 5-15° C./s, and cooling to a temperature range from Bs-100° C. to Bs-50° C.

Abstract: The disclosed technology provides solutions for evaluating sensor calibration algorithms and in particular, for evaluating calibration mechanisms used in autonomous vehicle (AV) deployments. The disclosed technology encompasses a process that includes steps for determining calibration parameters for a sensor mounted to an autonomous vehicle (AV), determining a fault injection offset for at least one of the one or more calibration parameters, and modifying at least one of the one or more calibration parameters based on the fault injection offset. The process may additionally include steps for collecting sensor data from the sensor and evaluating a calibration algorithm associated with the sensor based on the fault injection offset and sensor data. Systems and machine-readable media are also provided.

Abstract: The subject technology is related to autonomous vehicles (AV) and, in particular, to calibrating multiple sensors of an AV in an undefined training area. An example method includes instructing the AV to pilot itself in an undefined training area subject to at least one constraint, wherein the AV is instructed to pilot itself along a path until the AV has overlapped at least a portion of the path, and at a location at which the AV has overlapped at least the portion of the path, determining that first returns from a previous LIDAR scan overlaps with second returns from a subsequent LIDAR scan taken when the AV has overlapped at least the portion of the path. Initially, the AV does not include a location reference to identify locations of objects in the undefined training area and a plurality of sensors of the AV are uncalibrated with respect to each other.

Abstract: The subject technology is related to autonomous vehicles (AV) and, in particular, to calibration of an sensors of an AV on open roads after calibrating the sensors in an undefined training area. An example method includes calibrating a plurality of sensors of the AV based on performing a plurality of training steps of increasing complexity within the undefined environment, wherein the plurality of sensors of the AV are uncalibrated with respect to each other, determining that the AV satisfies an open road requirement to navigate an open road based on completing the plurality of training steps in the undefined environment, and operating the AV on open roads subject to at least one constraint. The method of claim 1, wherein the at least one constraint comprises a list of prohibited maneuvers that limit operation of the AV on open roads. The constraints are configured to dynamically change.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: Provided are a liposome containing Eribulin or a pharmaceutically acceptable salt thereof and a preparation method therefor. An inner aqueous phase of the liposome contains Eribulin or a salt thereof, and one or more selected from ethylenediamine tetraacetic acid, phosphoric acid, pentetic acid, or salts thereof. Further provided are a pharmaceutical composition containing the aforementioned liposome and a preparation method therefor.

Abstract: Sensors coupled to a vehicle are calibrated, optionally using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. One vehicle sensor captures a representation of one feature of a sensor target, while another vehicle sensor captures a representation of a different feature of the sensor target, the two features of the sensor target having known relative positioning on the target. The vehicle generates a transformation that maps the captured representations of the two features to positions around the vehicle based on the known relative positioning of the two features on the target.

Abstract: Sensors coupled to a vehicle are calibrated, optionally using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. One vehicle sensor captures a representation of one feature of a sensor target, while another vehicle sensor captures a representation of a different feature of the sensor target, the two features of the sensor target having known relative positioning on the target. The vehicle generates a transformation that maps the captured representations of the two features to positions around the vehicle based on the known relative positioning of the two features on the target.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: Sensors coupled to a vehicle are calibrated, optionally using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. One vehicle sensor captures a representation of one feature of a sensor target, while another vehicle sensor captures a representation of a different feature of the sensor target, the two features of the sensor target having known relative positioning on the target. The vehicle generates a transformation that maps the captured representations of the two features to positions around the vehicle based on the known relative positioning of the two features on the target.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: In one embodiment of the present invention, an encode validator identifies and classifies errors introduced during the parallel chunk-based translation of a source to a corresponding aggregate encode. In operation, upon receiving a source for encoding, a frame difference generator creates a frame difference file for the source. A parallel encoder then distributes per-chunk encoding operations across machines and creates an aggregate encode. The encode validator decodes the aggregate encode and creates a corresponding frame difference file. Subsequently, the encode validator performs phase correlation operations between the two frame difference files to detect errors generated by encoding process faults (i.e., dropping a frame, etc.) while suppressing discrepancies inherent in encoding, such as those attributable to low bit-rate encoding.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: In one embodiment of the present invention, an encode validator identifies and classifies errors introduced during the parallel chunk-based translation of a source to a corresponding aggregate encode. In operation, upon receiving a source for encoding, a frame difference generator creates a frame difference file for the source. A parallel encoder then distributes per-chunk encoding operations across machines and creates an aggregate encode. The encode validator decodes the aggregate encode and creates a corresponding frame difference file. Subsequently, the encode validator performs phase correlation operations between the two frame difference files to detect errors generated by encoding process faults (i.e., dropping a frame, etc.) while suppressing discrepancies inherent in encoding, such as those attributable to low bit-rate encoding.

Abstract: Disclosed is a method of image coding for joint decoding of images from different viewpoints using distributed coding techniques. The method receives a first set of features (205) and error correction bits (203) corresponding to a first image (201) obtained at a first viewpoint (122) and a second set of features (425) from a second image (254, 415) corresponding to a second viewpoint (124). An approximation (437) of said first image (201) at said first viewpoint (122) is determined (432, 434, 436) an based on the first and second sets of features (205, 425) and the second image at the second viewpoint. A reliability measure (445) of the approximation of the first image is then determined (450) by joint decoding (438) the approximation (437) using the error correction bits (203). The approximation of the first image is then refined iteratively (460, 438) based on the reliability measure (445) and image information (448) derived from the joint decoding.

Abstract: Methods, apparatuses (100, 400, 1000), and computer program products for generating an enhanced digital image (490, 495, 1022) comprising a plurality of pixels are disclosed. Using a first digital image (420, 1020) captured from a first camera (124) and parity bits (410, 415, 1010) generated from a second digital image captured by a second camera (122, 126), a third digital image (445, 447, 1045) is constructed. The second camera (122, 126) captures the second image at a resolution different to the resolution of the first camera (124) capturing the first image (420, 1020). A disparity map (455, 457, 1055) between the first image (420, 1020) and the third image (445, 447, 1045) is determined (450, 452, 1050). One of the first image (420, 1020) and the third image (445, 447, 1045) is enhanced (470, 472, 1070) dependent upon the determined disparity map (455, 457, 1055) to generate the enhanced digital image (490, 495, 1022).