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Accurate mitosis is coordinated by the spindle assembly checkpoint (SAC) through the mitotic checkpoint complex (MCC), which inhibits the anaphase-promoting complex or cyclosome (APC/C). As an essential regulator, Cdc20 promotes mitotic exit through activating APC/C and monitors kinetochore-microtubule attachment through activating SAC. Cdc20 requires multiple interactions with APC/C and MCC subunits to elicit these functions. Functionally assessing these interactions within cells requires efficient depletion of endogenous Cdc20, which is highly difficult to achieve by RNA interference (RNAi). Here we generated Cdc20 RNAi-sensitive cell lines which display a penetrant metaphase arrest by a single RNAi treatment. In this null background, we accurately measured the contribution of each known motif of Cdc20 on APC/C and SAC activation. The CRY box, a previously identified degron, was found critical for SAC by promoting MCC formation and its interaction with APC/C. These data reveal additional regulation within the SAC and establish a novel method to interrogate Cdc20.

Accurate mitosis is coordinated by the spindle assembly checkpoint (SAC) through the mitotic checkpoint complex (MCC), which inhibits the anaphase-promoting complex or cyclosome (APC/C). As an essential regulator, Cdc20 promotes mitotic exit through activating APC/C and monitors kinetochore-microtubule attachment through activating SAC. Cdc20 requires multiple interactions with APC/C and MCC subunits to elicit these functions. Functionally assessing these interactions within cells requires efficient depletion of endogenous Cdc20, which is highly difficult to achieve by RNA interference (RNAi). Here we generated Cdc20 RNAi-sensitive cell lines which display a penetrant metaphase arrest by a single RNAi treatment. In this null background, we accurately measured the contribution of each known motif of Cdc20 on APC/C and SAC activation. The CRY box, a previously identified degron, was found critical for SAC by promoting MCC formation and its interaction with APC/C. These data reveal additional regulation within the SAC and establish a novel method to interrogate Cdc20.

LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However, the computational costs introduced by the dense feature maps grow quadratically as the perception range increases, making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue; nevertheless, the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work, we propose SAFDNet, a straightforward yet highly effective architecture, tailored for fully sparse 3D object detection. In SAFDNet, an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset, which features long-range detection, verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://github.com/zhanggang001/HEDNet., Comment: Accepted by CVPR 2024 (Oral)

In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes compared with traditional perception tasks, such as 3D object detection and bird's-eye view (BEV) semantic segmentation. Recent researchers have extensively explored various aspects of this task, including view transformation techniques, ground-truth label generation, and elaborate network design, aiming to achieve superior performance. However, the inference speed, crucial for running on an autonomous vehicle, is neglected. To this end, a new method, dubbed FastOcc, is proposed. By carefully analyzing the network effect and latency from four parts, including the input image resolution, image backbone, view transformation, and occupancy prediction head, it is found that the occupancy prediction head holds considerable potential for accelerating the model while keeping its accuracy. Targeted at improving this component, the time-consuming 3D convolution network is replaced with a novel residual-like architecture, where features are mainly digested by a lightweight 2D BEV convolution network and compensated by integrating the 3D voxel features interpolated from the original image features. Experiments on the Occ3D-nuScenes benchmark demonstrate that our FastOcc achieves state-of-the-art results with a fast inference speed., Comment: Accepted by ICRA 2024

In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model. In essence, VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images, including \textbf{point}, \textbf{box}, \textbf{scribble}, and \textbf{mask}. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths, thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM, the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM, we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably, VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore, VRP-SAM demonstrates strong generalization capabilities, allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at \url{https://github.com/syp2ysy/VRP-SAM}, Comment: Accepted by CVPR 2024; The camera-ready version

In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder, a projector, and a shallow DETR decoder. Our approach leverages recent advanced techniques, such as training-effective techniques, e.g., improved loss and pretraining, and interleaved window and global attentions for reducing the ViT encoder complexity. We improve the ViT encoder by aggregating multi-level feature maps, and the intermediate and final feature maps in the ViT encoder, forming richer feature maps, and introduce window-major feature map organization for improving the efficiency of interleaved attention computation. Experimental results demonstrate that the proposed approach is superior over existing real-time detectors, e.g., YOLO and its variants, on COCO and other benchmark datasets. Code and models are available at (https://github.com/Atten4Vis/LW-DETR).

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/., Comment: The report paper of NTIRE2024 Efficient Super-resolution, accepted by CVPRW2024

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

Gang Zhang,1 Xiaotong Wang,2 Mingyue Tong,1 Jian Chen,1 Qian Ji3 1Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, People’s Republic of China; 2Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221000, People’s Republic of China; 3Department of Emergency, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210003, People’s Republic of ChinaCorrespondence: Qian Ji, Department of Emergency, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, 210003, People’s Republic of China, Email jiqian19881212@126.comBackground: In the United States (U.S.) general population, the association between standardized serum 25-hydroxyvitamin D (25(OH)D) concentration and risk of low muscle mass (LMM) remains unclear. Our research aimed to determine whether or not there was a relationship between serum 25(OH)D concentration and risk of LMM.Methods: We analyzed the cross-sectional data of the US population that participated in the National Health and Nutrition Examination Survey between 2011 and 2014. The relationship between serum 25(OH)D concentration and LMM risk was evaluated using restricted cubic spline (RCS) with multivariate logistic regression model and subgroup analysis.Results: In all, we included 10,256 people in our analysis. The RCS plot demonstrated a U-shaped relationship between serum 25(OH)D concentration and risk of LMM (P for nonlinearity < 0.05). At a Vitamin D concentration of 38.5 nmol/L, LMM risk was at its lowest. Based on analyses stratified by age, sex, hypertension, and diabetes mellitus (DM), serum 25(OH)D concentration and risk of LMM were U-curve correlated for those age 40 or older, male, with hypertension, or without DM. However, LMM risk was positively related to serum 25(OH)D concentration in those younger than age 40 or in women.Conc

Multi-scale features are essential for dense prediction tasks, such as object detection, instance segmentation, and semantic segmentation. The prevailing methods usually utilize a classification backbone to extract multi-scale features and then fuse these features using a lightweight module (e.g., the fusion module in FPN and BiFPN, two typical object detection methods). However, as these methods allocate most computational resources to the classification backbone, the multi-scale feature fusion in these methods is delayed, which may lead to inadequate feature fusion. While some methods perform feature fusion from early stages, they either fail to fully leverage high-level features to guide low-level feature learning or have complex structures, resulting in sub-optimal performance. We propose a streamlined cascade encoder-decoder network, dubbed CEDNet, tailored for dense \mbox{prediction} tasks. All stages in CEDNet share the same encoder-decoder structure and perform multi-scale feature fusion within the decoder. A hallmark of CEDNet is its ability to incorporate high-level features from early stages to guide low-level feature learning in subsequent stages, thereby enhancing the effectiveness of multi-scale feature fusion. We explored three well-known encoder-decoder structures: Hourglass, UNet, and FPN. When integrated into CEDNet, they performed much better than traditional methods that use a pre-designed classification backbone combined with a lightweight fusion module. Extensive experiments on object detection, instance segmentation, and semantic segmentation demonstrated the effectiveness of our method. The code is available at https://github.com/zhanggang001/CEDNet., Comment: Technical report; Code: https://github.com/zhanggang001/CEDNet

Knowledge distillation is an effective method for model compression. However, it is still a challenging topic to apply knowledge distillation to detection tasks. There are two key points resulting in poor distillation performance for detection tasks. One is the serious imbalance between foreground and background features, another one is that small object lacks enough feature representation. To solve the above issues, we propose a new distillation method named dual relation knowledge distillation (DRKD), including pixel-wise relation distillation and instance-wise relation distillation. The pixel-wise relation distillation embeds pixel-wise features in the graph space and applies graph convolution to capture the global pixel relation. By distilling the global pixel relation, the student detector can learn the relation between foreground and background features, and avoid the difficulty of distilling features directly for the feature imbalance issue. Besides, we find that instance-wise relation supplements valuable knowledge beyond independent features for small objects. Thus, the instance-wise relation distillation is designed, which calculates the similarity of different instances to obtain a relation matrix. More importantly, a relation filter module is designed to highlight valuable instance relations. The proposed dual relation knowledge distillation is general and can be easily applied for both one-stage and two-stage detectors. Our method achieves state-of-the-art performance, which improves Faster R-CNN based on ResNet50 from 38.4% to 41.6% mAP and improves RetinaNet based on ResNet50 from 37.4% to 40.3% mAP on COCO 2017., Comment: Accepted by IJCAI-2023

The Bub1 and BubR1 kinetochore proteins support proper chromosome segregation and mitotic checkpoint activity. Bub1 and BubR1 are paralogs with Bub1 being a kinase, while BubR1 localizes the PP2A-B56 protein phosphatase to kinetochores in humans. Whether this spatial separation of kinase and phosphatase activity is important is unclear as some organisms integrate both activities into one Bub protein. Here, we engineer human Bub1 and BubR1 proteins integrating kinase and phosphatase activities into one protein and show that these do not support normal mitotic progression. A Bub1-PP2A-B56 complex can support chromosome alignment but results in impairment of the checkpoint due to dephosphorylation of the Mad1 binding site in Bub1. Furthermore, a chimeric BubR1 protein containing the Bub1 kinase domain induces delocalized H2ApT120 phosphorylation, resulting in the reduction of centromeric hSgo2 and chromosome segregation errors. Collectively, these results argue that the spatial separation of kinase and phosphatase activities within the Bub complex is required for balancing its functions in the checkpoint and chromosome alignment.

The Bub1 and BubR1 kinetochore proteins support proper chromosome segregation and mitotic checkpoint activity. Bub1 and BubR1 are paralogs with Bub1 being a kinase, while BubR1 localizes the PP2A-B56 protein phosphatase to kinetochores in humans. Whether this spatial separation of kinase and phosphatase activity is important is unclear as some organisms integrate both activities into one Bub protein. Here, we engineer human Bub1 and BubR1 proteins integrating kinase and phosphatase activities into one protein and show that these do not support normal mitotic progression. A Bub1-PP2A-B56 complex can support chromosome alignment but results in impairment of the checkpoint due to dephosphorylation of the Mad1 binding site in Bub1. Furthermore, a chimeric BubR1 protein containing the Bub1 kinase domain induces delocalized H2ApT120 phosphorylation, resulting in the reduction of centromeric hSgo2 and chromosome segregation errors. Collectively, these results argue that the spatial separation of kinase and phosphatase activities within the Bub complex is required for balancing its functions in the checkpoint and chromosome alignment.

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence faces the following typical challenges: few shots, poor generalization, and a lack of multi-modal techniques. Foundation model technology can significantly alleviate the aforementioned issues. To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry. The challenge is divided into two tracks: all-in-one and cross-modal image retrieval. Furthermore, we provide a new baseline and benchmark for the two tracks, called Open-TransMind. According to our knowledge, Open-TransMind is the first open-source transportation foundation model with multi-task and multi-modal capabilities. Simultaneously, Open-TransMind can achieve state-of-the-art performance on detection, classification, and segmentation datasets of traffic scenarios. Our source code is available at https://github.com/Traffic-X/Open-TransMind.

The "pre-training $\rightarrow$ downstream adaptation" presents both new opportunities and challenges for Continual Learning (CL). Although the recent state-of-the-art in CL is achieved through Parameter-Efficient-Tuning (PET) adaptation paradigm, only prompt has been explored, limiting its application to Transformers only. In this paper, we position prompting as one instantiation of PET, and propose a unified CL framework with general PET, dubbed as Learning-Accumulation-Ensemble (LAE). PET, e.g., using Adapter, LoRA, or Prefix, can adapt a pre-trained model to downstream tasks with fewer parameters and resources. Given a PET method, our LAE framework incorporates it for CL with three novel designs. 1) Learning: the pre-trained model adapts to the new task by tuning an online PET module, along with our adaptation speed calibration to align different PET modules, 2) Accumulation: the task-specific knowledge learned by the online PET module is accumulated into an offline PET module through momentum update, 3) Ensemble: During inference, we respectively construct two experts with online/offline PET modules (which are favored by the novel/historical tasks) for prediction ensemble. We show that LAE is compatible with a battery of PET methods and gains strong CL capability. For example, LAE with Adaptor PET surpasses the prior state-of-the-art by 1.3% and 3.6% in last-incremental accuracy on CIFAR100 and ImageNet-R datasets, respectively. Code is available at \url{https://github.com/gqk/LAE}., Comment: Accepted to ICCV 2023

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We present two key innovations: Vision Guidance and the Layered Rendering Diffusion (LRDiff) framework. Vision Guidance, a spatial layout condition, acts as a clue in the perturbed distribution, greatly narrowing down the search space, to focus on the image sampling process adhering to the spatial layout condition. The LRDiff framework constructs an image-rendering process with multiple layers, each of which applies the vision guidance to instructively estimate the denoising direction for a single object. Such a layered rendering strategy effectively prevents issues like unintended conceptual blending or mismatches, while allowing for more coherent and contextually accurate image synthesis. The proposed method provides a more efficient and accurate means of synthesising images that align with specific spatial and contextual requirements. We demonstrate through our experiments that our method provides better results than existing techniques both quantitatively and qualitatively. We apply our method to three practical applications: bounding box-to-image, semantic mask-to-image and image editing.

LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent proposal-free methods accelerate the algorithm, but their effectiveness and efficiency are still limited owing to the difficulty of modeling non-existent instance centers and the costly center-based clustering modules. To achieve accurate and real-time LiDAR panoptic segmentation, a novel center focusing network (CFNet) is introduced. Specifically, the center focusing feature encoding (CFFE) is proposed to explicitly understand the relationships between the original LiDAR points and virtual instance centers by shifting the LiDAR points and filling in the center points. Moreover, to leverage the redundantly detected centers, a fast center deduplication module (CDM) is proposed to select only one center for each instance. Experiments on the SemanticKITTI and nuScenes panoptic segmentation benchmarks demonstrate that our CFNet outperforms all existing methods by a large margin and is 1.6 times faster than the most efficient method. The code is available at https://github.com/GangZhang842/CFNet., Comment: Published in the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels to extract features. To reduce computational costs, these methods resort to submanifold sparse convolutions, which prevent the information exchange among spatially disconnected features. Some recent approaches have attempted to address this problem by introducing large-kernel convolutions or self-attention mechanisms, but they either achieve limited accuracy improvements or incur excessive computational costs. We propose HEDNet, a hierarchical encoder-decoder network for 3D object detection, which leverages encoder-decoder blocks to capture long-range dependencies among features in the spatial space, particularly for large and distant objects. We conducted extensive experiments on the Waymo Open and nuScenes datasets. HEDNet achieved superior detection accuracy on both datasets than previous state-of-the-art methods with competitive efficiency. The code is available at https://github.com/zhanggang001/HEDNet., Comment: Accepted by NeurIPS 2023

LiDAR and camera are two critical sensors for multi-modal 3D semantic segmentation and are supposed to be fused efficiently and robustly to promise safety in various real-world scenarios. However, existing multi-modal methods face two key challenges: 1) difficulty with efficient deployment and real-time execution; and 2) drastic performance degradation under weak calibration between LiDAR and cameras. To address these challenges, we propose CPGNet-LCF, a new multi-modal fusion framework extending the LiDAR-only CPGNet. CPGNet-LCF solves the first challenge by inheriting the easy deployment and real-time capabilities of CPGNet. For the second challenge, we introduce a novel weak calibration knowledge distillation strategy during training to improve the robustness against the weak calibration. CPGNet-LCF achieves state-of-the-art performance on the nuScenes and SemanticKITTI benchmarks. Remarkably, it can be easily deployed to run in 20ms per frame on a single Tesla V100 GPU using TensorRT TF16 mode. Furthermore, we benchmark performance over four weak calibration levels, demonstrating the robustness of our proposed approach., Comment: 7 pages, 3 figures

Recently, Vision Transformers (ViTs) have attracted a lot of attention in the field of computer vision. Generally, the powerful representative capacity of ViTs mainly benefits from the self-attention mechanism, which has a high computation complexity. To accelerate ViTs, we propose an integrated compression pipeline based on observed heterogeneous attention patterns across layers. On one hand, different images share more similar attention patterns in early layers than later layers, indicating that the dynamic query-by-key self-attention matrix may be replaced with a static self-attention matrix in early layers. Then, we propose a dynamic-guided static self-attention (DGSSA) method where the matrix inherits self-attention information from the replaced dynamic self-attention to effectively improve the feature representation ability of ViTs. On the other hand, the attention maps have more low-rank patterns, which reflect token redundancy, in later layers than early layers. In a view of linear dimension reduction, we further propose a method of global aggregation pyramid (GLAD) to reduce the number of tokens in later layers of ViTs, such as Deit. Experimentally, the integrated compression pipeline of DGSSA and GLAD can accelerate up to 121% run-time throughput compared with DeiT, which surpasses all SOTA approaches.

A compact, stable, sustainable, and high-energy density power supply system is crucial for the engineering deployment of mobile electromechanical devices/systems either at the small- or large-scale. This work proposes a spiral-based mechanical energy storage scheme utilizing the newly synthesized 2D diamane. Atomistic simulations show that diamane spiral can achieve a high theoretical gravimetric energy density of about 564 Wh kg−1, about 14 500 times the steel spring. The interlayer friction between diamane is found to cause a strong stick–slip effect that results in local stress/strain concentration. As such, the energy storage capacity of the diamane spiral can be tuned by suppressing the influence from the interlayer friction. Simulations affirm that higher gravimetric energy density can be achieved by reducing the turn number or adopting a low friction contact pair. The fundamental principles that dominate the energy storage capacity of the spiral spring are theoretically analyzed, respectively. The obtained insights suggest that the 2D vdW solids can be promising candidates to construct spiral structures with a high gravimetric energy density. This work should be beneficial for the design of reliable, stable, and sustainable nanoscale mechanical energy storage schemes that can be used as an alternative low-carbon footage energy supplier for novel micro-/nanoscale devices or systems.

Multi-principal-element alloys (MPEAs) are attracting increasing attentions because of their high strength and ductility, high fracture toughness, excellent corrosion resistance, outstanding thermal-softening resistance and high oxidation resistance. Moreover, gradient structures (GSs) have been shown to be effective in alleviating the strength-ductility trade-off although strength and ductility are mutually exclusive properties for metals, which provides an opportunity for developing high-performance MPEAs. Here, we summarized four processing methods for creating GSs in MPEAs, including rotationally accelerated shot peening (RASP), ultra-precision machining technology (UPMT), cyclic dynamic torsion (CDT), and ultrasonic surface rolling processing (USRP). Principles, advantages, disadvantages, and typical applications of these methods are discussed in this work. Graphical abstract: [Figure not available: see fulltext.].

DETR is a novel end-to-end transformer architecture object detector, which significantly outperforms classic detectors when scaling up the model size. In this paper, we focus on the compression of DETR with knowledge distillation. While knowledge distillation has been well-studied in classic detectors, there is a lack of researches on how to make it work effectively on DETR. We first provide experimental and theoretical analysis to point out that the main challenge in DETR distillation is the lack of consistent distillation points. Distillation points refer to the corresponding inputs of the predictions for student to mimic, and reliable distillation requires sufficient distillation points which are consistent between teacher and student. Based on this observation, we propose a general knowledge distillation paradigm for DETR(KD-DETR) with consistent distillation points sampling. Specifically, we decouple detection and distillation tasks by introducing a set of specialized object queries to construct distillation points. In this paradigm, we further propose a general-to-specific distillation points sampling strategy to explore the extensibility of KD-DETR. Extensive experiments on different DETR architectures with various scales of backbones and transformer layers validate the effectiveness and generalization of KD-DETR. KD-DETR boosts the performance of DAB-DETR with ResNet-18 and ResNet-50 backbone to 41.4$\%$, 45.7$\%$ mAP, respectively, which are 5.2$\%$, 3.5$\%$ higher than the baseline, and ResNet-50 even surpasses the teacher model by $2.2\%$.

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO. Group DETR v2 achieves $\textbf{64.5}$ mAP on COCO test-dev, and establishes a new SoTA on the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-coco, Comment: Tech report, 3 pages. We establishes a new SoTA (64.5 mAP) on the COCO test-dev

The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting, named active pointly-supervised instance segmentation (APIS), which starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object. The key of APIS is to find the most desirable points to maximize the segmentation accuracy with limited annotation budgets. We formulate this setting and propose several uncertainty-based sampling strategies. The model developed with these strategies yields consistent performance gain on the challenging MS-COCO dataset, compared against other learning strategies. The results suggest that APIS, integrating the advantages of active learning and point-based supervision, is an effective learning paradigm for label-efficient instance segmentation., Comment: ECCV 2022

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collection of multiple AI functions. UFO aims to benefit each single task with a large-scale pretraining on all tasks. Compared with the well known foundation model, UFO has two different points of emphasis, i.e., relatively smaller model size and NO adaptation cost: 1) UFO squeezes a wide range of tasks into a moderate-sized unified model in a multi-task learning manner and further trims the model size when transferred to down-stream tasks. 2) UFO does not emphasize transfer to novel tasks. Instead, it aims to make the trimmed model dedicated for one or more already-seen task. With these two characteristics, UFO provides great convenience for flexible deployment, while maintaining the benefits of large-scale pretraining. A key merit of UFO is that the trimming process not only reduces the model size and inference consumption, but also even improves the accuracy on certain tasks. Specifically, UFO considers the multi-task training and brings two-fold impact on the unified model: some closely related tasks have mutual benefits, while some tasks have conflicts against each other. UFO manages to reduce the conflicts and to preserve the mutual benefits through a novel Network Architecture Search (NAS) method. Experiments on a wide range of deep representation learning tasks (i.e., face recognition, person re-identification, vehicle re-identification and product retrieval) show that the model trimmed from UFO achieves higher accuracy than its single-task-trained counterpart and yet has smaller model size, validating the concept of UFO. Besides, UFO also supported the release of 17 billion parameters computer vision (CV) foundation model which is the largest CV model in the industry., Comment: Accepted in ECCV 2022

LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms. Previous point-based or sparse voxel-based methods are far away from real-time applications since time-consuming neighbor searching or sparse 3D convolution are employed. Recent 2D projection-based methods, including range view and multi-view fusion, can run in real time, but suffer from lower accuracy due to information loss during the 2D projection. Besides, to improve the performance, previous methods usually adopt test time augmentation (TTA), which further slows down the inference process. To achieve a better speed-accuracy trade-off, we propose Cascade Point-Grid Fusion Network (CPGNet), which ensures both effectiveness and efficiency mainly by the following two techniques: 1) the novel Point-Grid (PG) fusion block extracts semantic features mainly on the 2D projected grid for efficiency, while summarizes both 2D and 3D features on 3D point for minimal information loss; 2) the proposed transformation consistency loss narrows the gap between the single-time model inference and TTA. The experiments on the SemanticKITTI and nuScenes benchmarks demonstrate that the CPGNet without ensemble models or TTA is comparable with the state-of-the-art RPVNet, while it runs 4.7 times faster., Comment: Accepted in the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022)

FASER, the ForwArd Search ExpeRiment, is an experiment dedicated to searching for light, extremely weakly-interacting particles at CERN's Large Hadron Collider (LHC). Such particles may be produced in the very forward direction of the LHC's high-energy collisions and then decay to visible particles inside the FASER detector, which is placed 480 m downstream of the ATLAS interaction point, aligned with the beam collisions axis. FASER also includes a sub-detector, FASER$\nu$, designed to detect neutrinos produced in the LHC collisions and to study their properties. In this paper, each component of the FASER detector is described in detail, as well as the installation of the experiment system and its commissioning using cosmic-rays collected in September 2021 and during the LHC pilot beam test carried out in October 2021. FASER will start taking LHC collision data in 2022, and will run throughout LHC Run 3., Comment: 92 pages, 72 Figures

Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This report introduces our solution to the iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing image. The challenge requires segmenting cultivated land objects in very high-resolution multispectral remote sensing images. We established a highly effective and efficient pipeline to solve this problem. We first divided the original images into small tiles and separately performed instance segmentation on each tile. We explored several instance segmentation algorithms that work well on natural images and developed a set of effective methods that are applicable to remote sensing images. Then we merged the prediction results of all small tiles into seamless, continuous segmentation results through our proposed overlap-tile fusion strategy. We achieved the first place among 486 teams in the challenge.

Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors are more prevalent in the industry because they have a simple and fast pipeline that is easy to deploy. However, in the long-tailed scenario, this line of work has not been explored so far. In this paper, we investigate whether one-stage detectors can perform well in this case. We discover the primary obstacle that prevents one-stage detectors from achieving excellent performance is: categories suffer from different degrees of positive-negative imbalance problems under the long-tailed data distribution. The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem. To address this issue, we propose the Equalized Focal Loss (EFL) that rebalances the loss contribution of positive and negative samples of different categories independently according to their imbalance degrees. Specifically, EFL adopts a category-relevant modulating factor which can be adjusted dynamically by the training status of different categories. Extensive experiments conducted on the challenging LVIS v1 benchmark demonstrate the effectiveness of our proposed method. With an end-to-end training pipeline, EFL achieves 29.2% in terms of overall AP and obtains significant performance improvements on rare categories, surpassing all existing state-of-the-art methods. The code is available at https://github.com/ModelTC/EOD., Comment: Accepted by the IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022

Jia L, Tian Y, Chen Y, Zhang G. Onco Targets Ther. 2018;11:313–321. The Editor and Publisher of OncoTargets and Therapy wish to retract the published article. Concerns were raised regarding the alleged duplication of flow cytometry images in Figure 1 with images from other articles. Specifically, Figure 1E, panel U-251TMZ TMZ+H19 scramble, appears to have been duplicated with Figure 2A, panel SW480 AS-IV (10 µg/ml) from Sun et al, 2019 (https://doi.org/10.2741/4738). Figure 1G, panel M059JTMZ TMZ+H19 scramble, appears to have been duplicated with Figure 2B, panel HeLa Ctrl, from Wang et al (https://doi.org/10.3727/096504016X14685034103879). The authors did not respond to our queries and were unable to provide any original data for their study. Our decision-making was informed by our policy on publishing ethics and integrity and the COPE guidelines on retraction. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”. This retraction relates to this paper

Min Xi,1 Gang Zhang,1 Liang Wang,2 Hu Chen,2 Li Gao,2 Luyi Zhang,1 Zhangkai Yang,1 Hangyu Shi1 1Department of Neurosurgery, Xi’an Children’s Hospital, Xi’an, 710043, People’s Republic of China; 2Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi’an, 710038, People’s Republic of ChinaCorrespondence: Hangyu Shi, Department of Neurosurgery, Xi’an Children’s Hospital, #69, Xijuyuan Lane, Lianhu District, Xi’an, 710043, Shaanxi, People’s Republic of China, Tel/Fax +86-15202910508, Email shihangyu723@aliyun.comBackground: This study aimed to evaluate the relationship between CARMN polymorphisms and glioma risk and prognosis in a Chinese Han population.Methods: Seven single nucleotide polymorphisms (SNPs) in CARMN were genotyped among 592 glioma patients and 502 healthy controls. Log-additive models were used for risk assessment by the odds ratios (ORs) and 95% confidence intervals (CIs). Univariate and multivariate Cox regression analysis was applied to calculate Hazard ratios (HRs) and 95% CIs for prognosis assessment.Results: CARMN rs13177623 was a protective factor for glioma susceptibility (OR = 0.78, p = 0.043). In addition, rs13177623, rs11168100, rs12654195 and rs17796757 were associated with the risk of glioma among the subgroup stratified by age or gender. We also found that G rs13177623G rs12654195 haplotype was related to the decreased risk of glioma (OR = 0.61, p = 0.005). Importantly, rs13177623 [overall survival (OS): HR = 0.83, p = 0.047, and progression free survival (PFS): HR = 0.82, p = 0.031], rs12654195 (OS: HR = 0.64, p = 0.005 and PFS: HR = 0.65, p = 0.007) and rs11168100 (OS: HR = 0.71, p = 0.035) were associated with a better prognosis for glioma, especially in grade I-II glioma. In patients with grade III-IV glioma, rs17796757 polymorphism presented an improved OS.Conclusion: Our results firstly reported the contribution of CARMN variants (rs11168100, rs12654195, rs13177623, and rs17796757) to the sus

HIV-1 is a major global health challenge. The development of an effective vaccine and a therapeutic cure are top priorities. The creation of vaccines that focus an antibody response toward a particular epitope of a protein has shown promise, but the genetic diversity of HIV-1 stymies this progress. Therapeutic strategies that provide effective and broad-spectrum neutralization against HIV-1 infection are highly desirable. Methods: We investigated the potential of nanoengineered CD4+ T cell membrane-coated nanoparticles (TNP) encapsulating the DIABLO/SMAC mimetics LCL-161 or AT-406 (also known as SM-406 or Debio 1143) to both neutralize HIV-1 and selectively kill HIV-1-infected resting CD4+ T cells and macrophages. Results: DIABLO/SMAC mimetic-loaded TNP displayed outstanding neutralizing breadth and potency, and selectively kill HIV-1-infected cells via autophagy-dependent apoptosis while having no drug-induced off-target or cytotoxic effects on bystander cells. Genetic inhibition of early stages of autophagy abolishes this effect. Conclusion: DIABLO/SMAC mimetic loaded TNP have the potential to be used as therapeutic agents to neutralize cell-free HIV-1 and to kill specifically HIV-1-infected cells as part of an HIV-1 cure strategy.

In the version of this Article originally published, in the sentence beginning “We also found that the PL intensity...”, the values of the laser fluence (in W μm–2) were incorrect: ‘8 × 104’ and ‘5 × 105’ should have been ‘8 × 10–4’ and ‘5 × 10–5’, respectively. Furthermore, in the units of the x-axis labels of Extended Data Fig. 4 and Supplementary Fig. 6, ‘105’ should have been ‘10–5’. The online versions of the Article have been amended and the Supplementary Information file has been replaced.

The ageing of human bone leads to bone diseases and the functional failure of joints. Joint replacement is effective in relieving paints and recovering functions. The mechanical properties of materials determine the performance of artificial replacements. The newly developed Ti2448 (Ti-24Nb-4Zr-8Sn-0.130, wt.%) possesses excellent mechanical properties, especially its low modulus. Usually, metals show linear elasticity, complying with the Hooke's law. However, Ti2448 exhibits a special non-linear elastic deformation behavior, which is in contrast to most metals. While, deformation mechanisms underpinning this special non-linear elasticity is still unclear although extensive effort has been made in recent years. In this study, direct experimental evidences of stress-induced beta ->alpha " martensitic transformation are obtained by employing in situ XRD and high-resolution transmission electron microscopy. It is found that martensitic transformation is the physical origin of the nonlinear elasticity of Ti2448. The findings of this study shed light on understanding and further optimizing mechanical properties of Ti2448 and other multifunctional shape memory alloys.

FASER$\nu$ at the CERN Large Hadron Collider (LHC) is designed to directly detect collider neutrinos for the first time and study their cross sections at TeV energies, where no such measurements currently exist. In 2018, a pilot detector employing emulsion films was installed in the far-forward region of ATLAS, 480 m from the interaction point, and collected 12.2 fb$^{-1}$ of proton-proton collision data at a center-of-mass energy of 13 TeV. We describe the analysis of this pilot run data and the observation of the first neutrino interaction candidates at the LHC. This milestone paves the way for high-energy neutrino measurements at current and future colliders.

The ageing of human bone leads to bone diseases and the functional failure of joints. Joint replacement is effective in relieving paints and recovering functions. The mechanical properties of materials determine the performance of artificial replacements. The newly developed Ti2448 (Ti-24Nb-4Zr-8Sn-0.130, wt.%) possesses excellent mechanical properties, especially its low modulus. Usually, metals show linear elasticity, complying with the Hooke's law. However, Ti2448 exhibits a special non-linear elastic deformation behavior, which is in contrast to most metals. While, deformation mechanisms underpinning this special non-linear elasticity is still unclear although extensive effort has been made in recent years. In this study, direct experimental evidences of stress-induced beta ->alpha " martensitic transformation are obtained by employing in situ XRD and high-resolution transmission electron microscopy. It is found that martensitic transformation is the physical origin of the nonlinear elasticity of Ti2448. The findings of this study shed light on understanding and further optimizing mechanical properties of Ti2448 and other multifunctional shape memory alloys.

Polymer nanocomposites with high thermal conductivity have been increasingly sought after in the electronic industry. Based on molecular dynamics simulations, this work assesses the thermal transport in polyethylene (PE) nanocomposites with the presence of a new one-dimensional nanofiller-a carbon nanothread (NTH). It is found that the axial thermal conductivity of PE nanocomposites increases linearly with the content of regularly aligned NTH fillers, while the aggregated pattern suppresses the enhancement effect. This phenomenon is explained by a stronger filler-filler interaction that reduces the intrinsic thermal conductivity of the NTH. Results show that the randomly dispersed NTHs can hardly promote heat transfer because effective heat transfer channels are lacking. Strikingly, surface functionalization has an adverse effect on the thermal conductivity due to the presence of additional voids. The presence of voids answers a long-standing open question that functionalization of the heat conductive filler only slightly improves the thermal conductivity of the polymer composite. Additionally, the transverse thermal conductivity degrades in the presence of the NTH and exhibits no clear correlation with the filler content or the distribution pattern. Overall, this study provides an in-depth understanding of the heat transfer within the polymer nanocomposites, which opens up possibilities for the preparation of highly conductive polymers.

FASER$\nu$ at the CERN Large Hadron Collider (LHC) is designed to directly detect collider neutrinos for the first time and study their cross sections at TeV energies, where no such measurements currently exist. In 2018, a pilot detector employing emulsion films was installed in the far-forward region of ATLAS, 480 m from the interaction point, and collected 12.2 fb$^{-1}$ of proton-proton collision data at a center-of-mass energy of 13 TeV. We describe the analysis of this pilot run data and the observation of the first neutrino interaction candidates at the LHC. This milestone paves the way for high-energy neutrino measurements at current and future colliders.

HIV-1 is a major global health challenge. The development of an effective vaccine and a therapeutic cure are top priorities. The creation of vaccines that focus an antibody response toward a particular epitope of a protein has shown promise, but the genetic diversity of HIV-1 stymies this progress. Therapeutic strategies that provide effective and broad-spectrum neutralization against HIV-1 infection are highly desirable. Methods: We investigated the potential of nanoengineered CD4+ T cell membrane-coated nanoparticles (TNP) encapsulating the DIABLO/SMAC mimetics LCL-161 or AT-406 (also known as SM-406 or Debio 1143) to both neutralize HIV-1 and selectively kill HIV-1-infected resting CD4+ T cells and macrophages. Results: DIABLO/SMAC mimetic-loaded TNP displayed outstanding neutralizing breadth and potency, and selectively kill HIV-1-infected cells via autophagy-dependent apoptosis while having no drug-induced off-target or cytotoxic effects on bystander cells. Genetic inhibition of early stages of autophagy abolishes this effect. Conclusion: DIABLO/SMAC mimetic loaded TNP have the potential to be used as therapeutic agents to neutralize cell-free HIV-1 and to kill specifically HIV-1-infected cells as part of an HIV-1 cure strategy.

FASER$\nu$ at the CERN Large Hadron Collider (LHC) is designed to directly detect collider neutrinos for the first time and study their cross sections at TeV energies, where no such measurements currently exist. In 2018, a pilot detector employing emulsion films was installed in the far-forward region of ATLAS, 480 m from the interaction point, and collected 12.2 fb$^{-1}$ of proton-proton collision data at a center-of-mass energy of 13 TeV. We describe the analysis of this pilot run data and the observation of the first neutrino interaction candidates at the LHC. This milestone paves the way for high-energy neutrino measurements at current and future colliders., Comment: Auxiliary materials are available at https://faser.web.cern.ch/fasernu-first-neutrino-interaction-candidates

The ageing of human bone leads to bone diseases and the functional failure of joints. Joint replacement is effective in relieving paints and recovering functions. The mechanical properties of materials determine the performance of artificial replacements. The newly developed Ti2448 (Ti-24Nb-4Zr-8Sn-0.130, wt.%) possesses excellent mechanical properties, especially its low modulus. Usually, metals show linear elasticity, complying with the Hooke's law. However, Ti2448 exhibits a special non-linear elastic deformation behavior, which is in contrast to most metals. While, deformation mechanisms underpinning this special non-linear elasticity is still unclear although extensive effort has been made in recent years. In this study, direct experimental evidences of stress-induced beta ->alpha " martensitic transformation are obtained by employing in situ XRD and high-resolution transmission electron microscopy. It is found that martensitic transformation is the physical origin of the nonlinear elasticity of Ti2448. The findings of this study shed light on understanding and further optimizing mechanical properties of Ti2448 and other multifunctional shape memory alloys.

Despite superior performance on many computer vision tasks, deep convolution neural networks are well known to be compressed on devices that have resource constraints. Most existing network pruning methods require laborious human efforts and prohibitive computation resources, especially when the constraints are changed. This practically limits the application of model compression when the model needs to be deployed on a wide range of devices. Besides, existing methods are still challenged by the missing theoretical guidance. In this paper we propose an information theory-inspired strategy for automatic model compression. The principle behind our method is the information bottleneck theory, i.e., the hidden representation should compress information with each other. We thus introduce the normalized Hilbert-Schmidt Independence Criterion (nHSIC) on network activations as a stable and generalized indicator of layer importance. When a certain resource constraint is given, we integrate the HSIC indicator with the constraint to transform the architecture search problem into a linear programming problem with quadratic constraints. Such a problem is easily solved by a convex optimization method with a few seconds. We also provide a rigorous proof to reveal that optimizing the normalized HSIC simultaneously minimizes the mutual information between different layers. Without any search process, our method achieves better compression tradeoffs comparing to the state-of-the-art compression algorithms. For instance, with ResNet-50, we achieve a 45.3%-FLOPs reduction, with a 75.75 top-1 accuracy on ImageNet. Codes are avaliable at https://github.com/MAC-AutoML/ITPruner/tree/master.

Microservice architecture advocates a number of technologies and practices such as lightweight container, container orchestration, and DevOps, with the promised benefits of faster delivery, improved scalability, and greater autonomy. However, microservice systems implemented in industry vary a lot in terms of adopted practices and achieved benefits, drastically different from what is advocated in the literature. In this article, we conduct an empirical study, including an online survey with 51 responses and 14 interviews for experienced microservice experts to advance our understanding regarding to microservice practices in industry. As a part of our findings, the empirical study clearly revealed three levels of maturity of microservice systems (from basic to advanced): independent development and deployment, high scalability and availability, and service ecosystem, categorized by the fulfilled benefits of microservices. We also identify 11 practical issues that constrain the microservice capabilities of organizations. For each issue, we summarize the practices that have been explored and adopted in industry, along with the remaining challenges. Our study can help practitioners better position their microservice systems and determine what infrastructures and capabilities are worth investing. Our study can also help researchers better understand industrial microservice practices and identify useful research problems.

Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many aspects and this work focus on computing resource constraint and long-tailed class distribution. Recently, classification-based representation learning with deep neural networks and well-designed losses have demonstrated good recognition performance. However, the computing and memory cost linearly scales up to the number of identities (classes) in the training set, and the learning process suffers from unbalanced classes. In this work, we propose a dynamic class queue (DCQ) to tackle these two problems. Specifically, for each iteration during training, a subset of classes for recognition are dynamically selected and their class weights are dynamically generated on-the-fly which are stored in a queue. Since only a subset of classes is selected for each iteration, the computing requirement is reduced. By using a single server without model parallel, we empirically verify in large-scale datasets that 10% of classes are sufficient to achieve similar performance as using all classes. Moreover, the class weights are dynamically generated in a few-shot manner and therefore suitable for tail classes with only a few instances. We show clear improvement over a strong baseline in the largest public dataset Megaface Challenge2 (MF2) which has 672K identities and over 88% of them have less than 10 instances. Code is available at https://github.com/bilylee/DCQ, Comment: Accepted in CVPR 2021

The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsampling operations in both the feature pyramid and the instance-wise pooling process, especially for large objects. In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner. Through fusing more detailed information stage by stage, RefineMask is able to refine high-quality masks consistently. RefineMask succeeds in segmenting hard cases such as bent parts of objects that are over-smoothed by most previous methods and outputs accurate boundaries. Without bells and whistles, RefineMask yields significant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and Cityscapes benchmarks respectively at a small amount of additional computational cost. Furthermore, our single-model result outperforms the winner of the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and establishes a new state-of-the-art. Code will be available at https://github.com/zhanggang001/RefineMask., Comment: Accepted by CVPR 2021. Code is available at https://github.com/zhanggang001/RefineMask

FASER$\nu$ at the CERN Large Hadron Collider (LHC) is designed to directly detect collider neutrinos for the first time and study their cross sections at TeV energies, where no such measurements currently exist. In 2018, a pilot detector employing emulsion films was installed in the far-forward region of ATLAS, 480 m from the interaction point, and collected 12.2 fb$^{-1}$ of proton-proton collision data at a center-of-mass energy of 13 TeV. We describe the analysis of this pilot run data and the observation of the first neutrino interaction candidates at the LHC. This milestone paves the way for high-energy neutrino measurements at current and future colliders., Comment: Auxiliary materials are available at https://faser.web.cern.ch/fasernu-first-neutrino-interaction-candidates

FASERnu is a proposed small and inexpensive emulsion detector designed to detect collider neutrinos for the first time and study their properties. FASERnu will be located directly in front of FASER, 480 m from the ATLAS interaction point along the beam collision axis in the unused service tunnel TI12. From 2021-23 during Run 3 of the 14 TeV LHC, roughly 1,300 electron neutrinos, 20,000 muon neutrinos, and 20 tau neutrinos will interact in FASERnu with TeV-scale energies. With the ability to observe these interactions, reconstruct their energies, and distinguish flavors, FASERnu will probe the production, propagation, and interactions of neutrinos at the highest human-made energies ever recorded. The FASERnu detector will be composed of 1000 emulsion layers interleaved with tungsten plates. The total volume of the emulsion and tungsten is 25cm x 25cm x 1.35m, and the tungsten target mass is 1.2 tonnes. From 2021-23, 7 sets of emulsion layers will be installed, with replacement roughly every 20-50 1/fb in planned Technical Stops. In this document, we summarize FASERnu's physics goals and discuss the estimates of neutrino flux and interaction rates. We then describe the FASERnu detector in detail, including plans for assembly, transport, installation, and emulsion replacement, and procedures for emulsion readout and analyzing the data. We close with cost estimates for the detector components and infrastructure work and a timeline for the experiment.

52 groundwater samples from the main aquifers in the Xieqiao coal mine (Anhui Province, China) were analysed using hydrochemical, multivariate statistical methods, and stable isotope analyses. In the Permian aquifer, the main hydrogeochemical processes are halite and silicate dissolution and sulphate reduction, followed by cation exchange, while in the Carboniferous and Ordovician aquifers, the main hydrogeochemical processes are carbonate, gypsum, and halite dissolution, followed by cation exchange. This causes the hydrochemical characteristics of these two aquifers to be similar. The Permian aquifer contains less SO42-, more HCO3- concentration, and a concentration ratio of SO42- to HCO3- less than 0.25 due to sulphate reduction, which allows the Permian aquifer to be distinguished easily from the other two. Both hierarchical cluster and stable isotope analysis show that water samples from both aquifers appear to have been mixed via the karst collapse columns, which may serve as a potential channel for groundwater inrush into the coal mine. These results may assist in accurately predicting potential water inrush sources in this coal mine as well as in other mines. (Authors.), 52 groundwater samples from the main aquifers in the Xieqiao coal mine (Anhui Province, China) were analysed using hydrochemical, multivariate statistical methods, and stable isotope analyses. In the Permian aquifer, the main hydrogeochemical processes are halite and silicate dissolution and sulphate reduction, followed by cation exchange, while in the Carboniferous and Ordovician aquifers, the main hydrogeochemical processes are carbonate, gypsum, and halite dissolution, followed by cation exchange. This causes the hydrochemical characteristics of these two aquifers to be similar. The Permian aquifer contains less SO42-, more HCO3- concentration, and a concentration ratio of SO42- to HCO3- less than 0.25 due to sulphate reduction, which allows the Permian aquifer to be distinguished easily from the other two. Both hierarchical cluster and stable isotope analysis show that water samples from both aquifers appear to have been mixed via the karst collapse columns, which may serve as a potential channel for groundwater inrush into the coal mine. These results may assist in accurately predicting potential water inrush sources in this coal mine as well as in other mines. (Authors.)