Abstract: Selective finetuning of an autonomous driving model that employs a predictive model and a planning policy, where finetuning includes determining whether the predictive model or the planning policy predominantly contributes to performance degradation and based on that determination modifying the predictive model or the planning policy such as by retraining the model or adjusting the policy.

Abstract: A method of training an autonomous vehicle system including pretraining first and second components to form a pretrained model, employing the pretrained model in an operative environment to identify a predominant source of error, and updating either the first component or the second component based on which one is the predominant source of error. The first component being configured to predict future events from sensory inputs and a second component being configured to determine one or more actions based on the future events with a dataset.

Abstract: Provided is a fringe line detection and phase unwrapping method and system based on an FL-Net convolutional neural network. The method includes: constructing the FL-Net convolutional neural network; detecting fringe lines of an input image by using the FL-Net convolutional neural network to obtain an image with detected fringe lines; performing circulation integral on the detected fringe lines to repair the detected fringe lines to thereby obtain an image with repaired fringe lines; performing path integral on the repaired fringe lines to unwrap the repaired fringe lines to thereby obtain an image with unwrapped fringe lines; and identifying error points of the unwrapped fringe lines by using the FL-Net convolutional neural network; and processing the error points. Specifically, an HDC method is used to replace a down-sampling method to avoid the loss of resolution, and residual connection is used to prevent the network from being too deep.

Abstract: The present invention relates to a use of edaravone in the treatment of autism spectrum disorder.

Abstract: Biological research requires isolation and analysis of material, for example, RNA, DNA and protein, from tissue samples. The methods and compositions described herein allow for high resolution imaging of large and intact tissue samples, and subsequent isolation of material in a precise and location dependent-manner. The methods and compositions described herein may be used, for example, for biomarker discovery, identification of cell populations, pathology analysis, and generation of expression data in specific regions of interest.

Abstract: The present disclosure pertains to methods and systems for improving autonomous driving systems by utilizing extracted features from bird-eye-view (BEV) data. The technical challenge addressed involves the less-than-optimal condition of models trained solely on limited autonomous datasets, which results in inconsistencies in BEV feature generation. The described approach offers a solution by integrating foundation models to align textual descriptions with BEV features extracted from alternative-imaging sensor data (e.g., LiDAR, RADAR). This integration provides additional supervision signals, enhancing the accuracy of BEV features and improving the system's ability to interpret the environment. In an autonomous vehicle setting, the system enhances the performance of downstream tasks like object detection and trajectory prediction without additional runtime costs.

Abstract: The present invention relates to an antigen binding molecule specifically binding to EGFR and MUC1, a drug conjugate thereof, and a medical use thereof. Specifically, the present invention relates to an anti-MUC1 antibody or an antigen binding fragment thereof, an anti-EGFR antibody or an antigen binding fragment thereof, an antigen binding molecule specifically binding to EGFR and MUC1, a drug conjugate thereof, and a medical use thereof.

Abstract: In an example embodiment, a novel data structure called a “mind graph” is introduced, which defines all of the different possible combinations of tasks within a software project, as well as the potential flows among the tasks. This mind graph is then passed as input, along with a prompt generated by natural language input from a user, to an LLM. The LLM uses the mind graph and the prompt to generate an execution plan which defines which tasks from the mind graph will be generated and the path the flow takes though those tasks. This execution plan is then presented to the user in a graphical user interface that allows the user to edit the execution plan. The edited execution plan is then submitted to the LLM to generate the actual code for each of the tasks. This code is again presented to the user to accept, or modify, the code for each of these tasks.

Abstract: A pixel driving circuit is configured for driving a light-emitting unit to emit light. The pixel driving circuit includes a first driving transistor and a current compensation circuit. A first electrode of the first driving transistor is connected to a first power terminal, a second electrode thereof is connected to a first electrode of the light-emitting unit, and a gate electrode thereof is connected to a first node. The current compensation circuit and the first driving transistor are connected in parallel between the first power terminal and the first electrode of the light-emitting unit, and the current compensation circuit is configured to provide, in response to a control signal, a driving current to the light-emitting unit through the first power terminal.

Abstract: Biological research requires isolation and analysis of material, for example, RNA, DNA and protein, from tissue samples. The methods and compositions described herein allow for high resolution imaging of large and intact tissue samples, and subsequent isolation of material in a precise and location dependent-manner. The methods and compositions described herein may be used, for example, for biomarker discovery, identification of cell populations, pathology analysis, and generation of expression data in specific regions of interest.

Abstract: The present invention discloses a 3D stamping and forming device for a wave-wound sleeve, and the stamping and forming device includes a workbench, where a top plate is installed on the top of the workbench, two installation plates symmetrically arranged are installed on a working surface of the top plate, and a support assembly for winding the wave-wound sleeve is provided between the two installation plates; several upper end stamping assemblies for stamping and forming an upper end of the wave-wound sleeve are provided outside the support assembly; and lower end stamping assemblies for stamping and forming a lower end of the wave-wound sleeve are respectively provided on two sides of a top of the support assembly. The 3D stamping and forming device can stamp coiled 2D wave-wound wires, thereby stamping and forming upper ends, lower ends, and side ends of 3D preformed wave-wound wires.

Abstract: The present invention discloses a 2D wave-wound wire forming device. The forming device includes a workbench, and further includes a moving mechanism installed on the top of the workbench, a forming mechanism and a movable end forming and folding mechanism that are installed on the top of the moving mechanism and driven by the moving mechanism, and a fixed end forming and folding mechanism installed on the top of the workbench and used for cooperating with the forming mechanism during operation. The present invention reduces waste of labor and waiting time, and can automatically complete one-step forming of 2D wave-wound wires from copper wires in a straight state. The moving mechanism, the forming mechanism, the movable end forming and folding mechanism, and the fixed end forming and folding mechanism cooperate with each other to achieve automatic reset after single forming, facilitating preparation for subsequent forming of the copper wires.

Abstract: The present invention discloses a 3D wave-wound wire preforming device, and the preforming device includes a workbench, where a top plate is installed on the top of the workbench, an adjustment and lifting mechanism and a pre-winding mechanism are installed on a working surface of the top plate, and the pre-winding mechanism is positioned on the top of an output end of the adjustment and lifting mechanism. Through the 3D preforming device, a single wave-wound wire that has already undergone 2D forming is wound, and the wound wave-wound wire is subjected to 3D stamping. After the 2D wave-wound wire is formed into a coil, the coil forms a closed integral structure. During stamping, the performing device can more accurately maintain a winding position, thereby reducing a risk of deformation and misalignment, and improving quality of a finished product.

Abstract: The present invention discloses a stator wave-wound wire forming process and device, and the forming process includes sequential steps of 2D forming, 3D preforming, and 3D stamping and forming. A device used in the process includes a 2D forming device, a 3D preforming device, and a 3D stamping and forming device. After a 2D wave-wound wire is processed into a ring or coil shape, an overall structure of a winding becomes more stable, which facilitates a subsequent stamping operation and prevents winding scattering or misalignment. The 2D wave-wound wire is processed into the coil shape, such that wires can be distributed more evenly and packing density thereof can be adjusted, helping optimize utilization of stator slots, thereby enhancing the power density and efficiency of a motor. Once the 2D wave-wound wire is formed into the coil shape, a coil forms a closed integral structure, which can more precisely maintain a winding position during stamping and reduce a risk of deformation and misalignment.

Abstract: Methods and systems for inspecting a photomask are provided. One system includes an illumination subsystem configured to direct light to the photomask. The system also includes a pupil filter positioned in a path of only the light from the photomask and configured for controlling a polarization of the light in the path by mixing four elements in a Jones matrix for the photomask in a coherent manner. In addition, the system includes a detector configured for detecting the light from the pupil filter and generating output responsive to the detected light. The system further includes a computer subsystem configured for detecting defects on the photomask based on the output.

Abstract: A system for training a drive system for an autonomous vehicle (AV) includes one or more computing devices configured to output a first drive plan and a second drive plan using an initial autonomous drive system (ADS) and an initial behavior foundation system (BFS). The computing devices is configured to generate a system-to-system (SS) loss between the initial ADS and the initial BFS using data from the respective system, and generate a module task loss for each system using respective drive plan and respective ground truth data. The computing devices is configured to adjust tunable parameters of the initial ADS and/or tunable parameters of the initial BFS to reduce a total loss provided by the SS loss and the module task loss. The initial ADS and/or the initial BFS is outputted as a trained drive system to be employed for the AV in response to the total loss being reduced.

Abstract: A Bird's Eye View (BEV)-based object detection framework for autonomous vehicles. The disclosed embodiments pretrain a diffusion model system on BEV representations generated from sensor data, such as cameras, LiDARs, and radars. The pretrained diffusion model system may be integrated into the BEV generation network to denoise BEV features through a supervision loss mechanism during training. This approach enhances the quality of BEV representations used in downstream tasks, such as object detection and trajectory prediction, without introducing any incremental computational cost during run-time.

Abstract: Methods and systems for training an end-to-end autonomous driving system using a vision-language planning (VLP) machine learning model in a closed-loop environment. Images associated with an environment about a vehicle are generated, and a BEV model is executed to generate a BEV view based on the images. A planning model predicts navigation trajectories based on the BEV. The VLP model enhances the system by extracting vision-based planning features, generating text prompts, and employing a language encoder to create text-based expectation features. A contrastive learning model identifies similarities between vision and text features, boosting the performance of the BEV and planning models. The system undergoes closed-loop evaluation in a simulated environment, capturing metrics to refine the autonomous driving system.

Abstract: Provided are an antigen-binding molecule specifically binding to GUCY2C and CD3 and pharmaceutical use thereof. The antigen-binding molecule can be used for treating tumor.

Abstract: A recipe generation system may include a controller with one or more processors configured to execute program instructions. The instructions may cause the processors to generate a performance matrix for one or more defect types. The performance matrix may include inspection data generated with multiple polarization states of illumination light associated with rotational positions of at least two waveplates in a polarization controlling optics of an inspection system. The polarization controlling optics may include a polarizing beam splitter and the waveplates. The system may generate inspection data by propagating illumination light through the optics to the sample and capturing light from the sample by a detector through the optics. The processors may identify a run-time polarization state satisfying an inspection tolerance and generate an inspection recipe using the run-time polarization states.