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Tracking and segmenting multiple objects in complex scenes has always been a challenge in the field of video object segmentation, especially in scenarios where objects are occluded and split into parts. In such cases, the definition of objects becomes very ambiguous. The motivation behind the MOSE dataset is how to clearly recognize and distinguish objects in complex scenes. In this challenge, we propose a semantic embedding video object segmentation model and use the salient features of objects as query representations. The semantic understanding helps the model to recognize parts of the objects and the salient feature captures the more discriminative features of the objects. Trained on a large-scale video object segmentation dataset, our model achieves first place (\textbf{84.45\%}) in the test set of PVUW Challenge 2024: Complex Video Object Segmentation Track.
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https://arxiv.org/abs/2406.04600v1
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Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes. Existing PSG methods utilize one-stage paradigm which simultaneously generates scene graphs and predicts semantic segmentation masks or two-stage paradigm that first adopt an off-the-shelf panoptic segmentor, then pairwise relationship prediction between these predicted objects. One-stage approach despite having a simplified training paradigm, its segmentation results are usually under-satisfactory, while two-stage approach lacks global context and leads to low performance on relation prediction. To bridge this gap, in this paper, we propose GRNet, a Global Relation Network in two-stage paradigm, where the pre-extracted local object features and their corresponding masks are fed into a transformer with class embeddings. To handle relation ambiguity and predicate classification bias caused by long-tailed distribution, we formulate relation prediction in the second stage as a multi-class classification task with soft label. We conduct comprehensive experiments on OpenPSG dataset and achieve the state-of-art performance on the leadboard. We also show the effectiveness of our soft label strategy for long-tailed classes in ablation studies. Our code has been released in https://github.com/wangqixun/mfpsg.
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https://arxiv.org/abs/2302.02651v1
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Video panoptic segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. We believe that the decoupling strategy proposed by DVIS enables more effective utilization of temporal information for both "thing" and "stuff" objects. In this report, we successfully validated the effectiveness of the decoupling strategy in video panoptic segmentation. Finally, our method achieved a VPQ score of 51.4 and 53.7 in the development and test phases, respectively, and ultimately ranked 1st in the VPS track of the 2nd PVUW Challenge. The code is available at https://github.com/zhang-tao-whu/DVIS
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https://arxiv.org/abs/2306.04091v2
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Video instance segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. In this report, we present further improvements to the SOTA VIS method, DVIS. First, we introduce a denoising training strategy for the trainable tracker, allowing it to achieve more stable and accurate object tracking in complex and long videos. Additionally, we explore the role of visual foundation models in video instance segmentation. By utilizing a frozen VIT-L model pre-trained by DINO v2, DVIS demonstrates remarkable performance improvements. With these enhancements, our method achieves 57.9 AP and 56.0 AP in the development and test phases, respectively, and ultimately ranked 1st in the VIS track of the 5th LSVOS Challenge. The code will be available at https://github.com/zhang-tao-whu/DVIS.
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https://arxiv.org/abs/2308.14392v1
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We describe our two-stage instance segmentation framework we use to compete in the challenge. The first stage of our framework consists of an object detector, which generates object proposals in the format of bounding boxes. Then, the images and the detected bounding boxes are fed to the second stage, where a segmentation network is applied to segment the objects in the bounding boxes. We train all our networks in a class-agnostic way. Our approach achieves the first place in the UVO 2021 Image-based Open-World Segmentation Challenge.
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https://arxiv.org/abs/2110.10239v1
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In this technical report, we introduce our winning solution "HorizonLiDAR3D" for the 3D detection track and the domain adaptation track in Waymo Open Dataset Challenge at CVPR 2020. Many existing 3D object detectors include prior-based anchor box design to account for different scales and aspect ratios and classes of objects, which limits its capability of generalization to a different dataset or domain and requires post-processing (e.g. Non-Maximum Suppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors. AFDet serves as a strong baseline in our winning solution and significant improvements are made over this baseline during the challenges. Specifically, we design stronger networks and enhance the point cloud data using densification and point painting. To leverage camera information, we append/paint additional attributes to each point by projecting them to camera space and gathering image-based perception information. The final detection performance also benefits from model ensemble and Test-Time Augmentation (TTA) in both the 3D detection track and the domain adaptation track. Our solution achieves the 1st place with 77.11% mAPH/L2 and 69.49% mAPH/L2 respectively on the 3D detection track and the domain adaptation track.
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https://arxiv.org/abs/2006.15505v1
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Video Instance Segmentation (VIS) is a multi-task problem performing detection, segmentation, and tracking simultaneously. Extended from image set applications, video data additionally induces the temporal information, which, if handled appropriately, is very useful to identify and predict object motions. In this work, we design a unified model to mutually learn these tasks. Specifically, we propose two modules, named Temporally Correlated Instance Segmentation (TCIS) and Bidirectional Tracking (BiTrack), to take the benefit of the temporal correlation between the object's instance masks across adjacent frames. On the other hand, video data is often redundant due to the frame's overlap. Our analysis shows that this problem is particularly severe for the YoutubeVOS-VIS2021 data. Therefore, we propose a Multi-Source Data (MSD) training mechanism to compensate for the data deficiency. By combining these techniques with a bag of tricks, the network performance is significantly boosted compared to the baseline, and outperforms other methods by a considerable margin on the YoutubeVOS-VIS 2019 and 2021 datasets.
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https://arxiv.org/abs/2106.06649v2
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The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer. Previous methods adopt multi-stage approach and design complex pipelines to obtain promising results. Recently, the end-to-end method based on Transformer has proved its superiority. In this work, we draw on the advantages of the above methods to provide a simple and effective pipeline for RVOS. Firstly, We improve the state-of-the-art one-stage method ReferFormer to obtain mask sequences that are strongly correlated with language descriptions. Secondly, based on a reliable and high-quality keyframe, we leverage the superior performance of video object segmentation model to further enhance the quality and temporal consistency of the mask results. Our single model reaches 70.3 J &F on the Referring Youtube-VOS validation set and 63.0 on the test set. After ensemble, we achieve 64.1 on the final leaderboard, ranking 1st place on CVPR2022 Referring Youtube-VOS challenge. Code will be available at https://github.com/Zhiweihhh/cvpr2022-rvos-challenge.git.
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https://arxiv.org/abs/2212.14679v1
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This paper presents the 1st place solution for the Google Universal Images Embedding Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to conduct training and fine-tuning; 2) The idea of a better ensemble in the pool of models that make embedding; 3) The potential trade-off between fine-tuning on high-resolution and overlapping patches; 4) The potential factors to work for the dynamic margin. Our solution reaches 0.728 in the private leader board, which achieve 1st place in Google Universal Images Embedding Competition.
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https://arxiv.org/abs/2210.08473v1
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This report introduce our work on Egocentric 3D Hand Pose Estimation workshop. Using AssemblyHands, this challenge focuses on egocentric 3D hand pose estimation from a single-view image. In the competition, we adopt ViT based backbones and a simple regressor for 3D keypoints prediction, which provides strong model baselines. We noticed that Hand-objects occlusions and self-occlusions lead to performance degradation, thus proposed a non-model method to merge multi-view results in the post-process stage. Moreover, We utilized test time augmentation and model ensemble to make further improvement. We also found that public dataset and rational preprocess are beneficial. Our method achieved 12.21mm MPJPE on test dataset, achieve the first place in Egocentric 3D Hand Pose Estimation challenge.
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https://arxiv.org/abs/2310.04769v2
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This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020. In this work, two characteristics of LVIS dataset are mainly considered: the long-tailed distribution and high quality instance segmentation mask. We adopt a two-stage training pipeline. In the first stage, we incorporate EQL and self-training to learn generalized representation. In the second stage, we utilize Balanced GroupSoftmax to promote the classifier, and propose a novel proposal assignment strategy and a new balanced mask loss for mask head to get more precise mask predictions. Finally, we achieve 41.5 and 41.2 AP on LVIS v1.0 val and test-dev splits respectively, outperforming the baseline based on X101-FPN-MaskRCNN by a large margin.
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https://arxiv.org/abs/2009.01559v1
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Multi-view egocentric hand tracking is a challenging task and plays a critical role in VR interaction. In this report, we present a method that uses multi-view input images and camera extrinsic parameters to estimate both hand shape and pose. To reduce overfitting to the camera layout, we apply crop jittering and extrinsic parameter noise augmentation. Additionally, we propose an offline neural smoothing post-processing method to further improve the accuracy of hand position and pose. Our method achieves 13.92mm MPJPE on the Umetrack dataset and 21.66mm MPJPE on the HOT3D dataset.
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https://arxiv.org/abs/2409.19362v2
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This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder and uses SegFormer as the segmentation framework. The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy. All the original labels are projected to a 256-class unified label space, and the model is trained using a cross-entropy loss. Without significant hyperparameter tuning or any specific loss weighting, our solution ranks the first place on all the testing semantic segmentation benchmarks from multiple domains (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, and WildDash 2). The proposed method can serve as a strong baseline for the multi-domain segmentation task and benefit future works. Code will be available at https://github.com/lambert-x/RVC_Segmentation.
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https://arxiv.org/abs/2210.12852v3
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The WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge focuses on effectively applying multimodal embedding features to improve click-through rate (CTR) prediction in recommender systems. This technical report presents our 1$^{st}$ place winning solution for Task 2, combining sequential modeling and feature interaction learning to effectively capture user-item interactions. For multimodal information integration, we simply append the frozen multimodal embeddings to each item embedding. Experiments on the challenge dataset demonstrate the effectiveness of our method, achieving superior performance with a 0.9839 AUC on the leaderboard, much higher than the baseline model. Code and configuration are available in our GitHub repository and the checkpoint of our model can be found in HuggingFace.
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https://arxiv.org/abs/2505.03543v1
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This article introduces the solutions of the two champion teams, `MMfruit' for the detection track and `MMfruitSeg' for the segmentation track, in OpenImage Challenge 2019. It is commonly known that for an object detector, the shared feature at the end of the backbone is not appropriate for both classification and regression, which greatly limits the performance of both single stage detector and Faster RCNN \cite{ren2015faster} based detector. In this competition, we observe that even with a shared feature, different locations in one object has completely inconsistent performances for the two tasks. \textit{E.g. the features of salient locations are usually good for classification, while those around the object edge are good for regression.} Inspired by this, we propose the Decoupling Head (DH) to disentangle the object classification and regression via the self-learned optimal feature extraction, which leads to a great improvement. Furthermore, we adjust the soft-NMS algorithm to adj-NMS to obtain stable performance improvement. Finally, a well-designed ensemble strategy via voting the bounding box location and confidence is proposed. We will also introduce several training/inferencing strategies and a bag of tricks that give minor improvement. Given those masses of details, we train and aggregate 28 global models with various backbones, heads and 3+2 expert models, and achieves the 1st place on the OpenImage 2019 Object Detection Challenge on the both public and private leadboards. Given such good instance bounding box, we further design a simple instance-level semantic segmentation pipeline and achieve the 1st place on the segmentation challenge.
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https://arxiv.org/abs/2003.07557v1
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This report presents the methods of the winning entry of the RxR-Habitat Competition in CVPR 2022. The competition addresses the problem of Vision-and-Language Navigation in Continuous Environments (VLN-CE), which requires an agent to follow step-by-step natural language instructions to reach a target. We present a modular plan-and-control approach for the task. Our model consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller. In each decision loop, CWP first predicts a set of candidate waypoints based on depth observations from multiple views. It can reduce the complexity of the action space and facilitate planning. Then, a history-enhanced planner is adopted to select one of the candidate waypoints as the subgoal. The planner additionally encodes historical memory to track the navigation progress, which is especially effective for long-horizon navigation. Finally, we propose a non-parametric heuristic controller named tryout to execute low-level actions to reach the planned subgoal. It is based on the trial-and-error mechanism which can help the agent to avoid obstacles and escape from getting stuck. All three modules work hierarchically until the agent stops. We further take several recent advances of Vision-and-Language Navigation (VLN) to improve the performance such as pretraining based on large-scale synthetic in-domain dataset, environment-level data augmentation and snapshot model ensemble. Our model won the RxR-Habitat Competition 2022, with 48% and 90% relative improvements over existing methods on NDTW and SR metrics respectively.
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https://arxiv.org/abs/2206.11610v2
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This paper describes the approach we have taken in the challenge. We still adopted the two-stage scheme same as the last champion, that is, detection first and segmentation followed. We trained more powerful detector and segmentor separately. Besides, we also perform pseudo-label training on the test set, based on student-teacher framework and end-to-end transformer based object detection. The method ranks first on the 2nd Unidentified Video Objects (UVO) challenge, achieving AR@100 of 46.8, 64.7 and 32.2 in the limited data frame track, unlimited data frame track and video track respectively.
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https://arxiv.org/abs/2210.09629v1
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In this technical report, we briefly introduce the solution of our team "TAL-ai" for (Semi-) supervised Face detection in the low light condition in UG2+ Challenge in CVPR 2021. By conducting several experiments with popular image enhancement methods and image transfer methods, we pulled the low light image and the normal image to a more closer domain. And it is observed that using these data to training can achieve better performance. We also adapt several popular object detection frameworks, e.g., DetectoRS, Cascade-RCNN, and large backbone like Swin-transformer. Finally, we ensemble several models which achieved mAP 74.89 on the testing set, ranking 1st on the final leaderboard.
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https://arxiv.org/abs/2107.00818v1
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In this technical report, we briefly introduce the solution of our team ''summer'' for Atomospheric Turbulence Mitigation in UG$^2$+ Challenge in CVPR 2022. In this task, we propose a unified end-to-end framework to reconstruct a high quality image from distorted frames, which is mainly consists of a Restormer-based image reconstruction module and a NIMA-based image quality assessment module. Our framework is efficient and generic, which is adapted to both hot-air image and text pattern. Moreover, we elaborately synthesize more than 10 thousands of images to simulate atmospheric turbulence. And these images improve the robustness of the model. Finally, we achieve the average accuracy of 98.53\% on the reconstruction result of the text patterns, ranking 1st on the final leaderboard.
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https://arxiv.org/abs/2210.16847v1
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This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges. An efficient and pragmatic online tracking-by-detection framework named HorizonMOT is proposed for camera-based 2D tracking in the image space and LiDAR-based 3D tracking in the 3D world space. Within the tracking-by-detection paradigm, our trackers leverage our high-performing detectors used in the 2D/3D detection challenges and achieved 45.13% 2D MOTA/L2 and 63.45% 3D MOTA/L2 in the 2D/3D tracking challenges.
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https://arxiv.org/abs/2006.15506v1
|
In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track. We adopt FPN as our basic framework. Cascade RCNN, stacked PAFPN Neck and Double-Head are used for performance improvements. In order to handle the small object detection problem in WOD, we use very large image scales for both training and testing. Using our methods, our team RW-TSDet achieved the 1st place in the 2D Object Detection Track.
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https://arxiv.org/abs/2008.01365v1
|
Scene text recognition has attracted increasing interest in recent years due to its wide range of applications in multilingual translation, autonomous driving, etc. In this report, we describe our solution to the Out of Vocabulary Scene Text Understanding (OOV-ST) Challenge, which aims to extract out-of-vocabulary (OOV) words from natural scene images. Our oCLIP-based model achieves 28.59\% in h-mean which ranks 1st in end-to-end OOV word recognition track of OOV Challenge in ECCV2022 TiE Workshop.
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https://arxiv.org/abs/2209.00224v1
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We extend the classical tracking-by-detection paradigm to this tracking-any-object task. Solid detection results are first extracted from TAO dataset. Some state-of-the-art techniques like \textbf{BA}lanced-\textbf{G}roup \textbf{S}oftmax (\textbf{BAGS}\cite{li2020overcoming}) and DetectoRS\cite{qiao2020detectors} are integrated during detection. Then we learned appearance features to represent any object by training feature learning networks. We ensemble several models for improving detection and feature representation. Simple linking strategies with most similar appearance features and tracklet-level post association module are finally applied to generate final tracking results. Our method is submitted as \textbf{AOA} on the challenge website. Code is available at https://github.com/feiaxyt/Winner_ECCV20_TAO.
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https://arxiv.org/abs/2101.08040v2
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This paper presents the 1st place solution to the Google Landmark Retrieval 2020 Competition on Kaggle. The solution is based on metric learning to classify numerous landmark classes, and uses transfer learning with two train datasets, fine-tuning on bigger images, adjusting loss weight for cleaner samples, and esemble to enhance the model's performance further. Finally, it scored 0.38677 mAP@100 on the private leaderboard.
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https://arxiv.org/abs/2009.05132v1
|
This paper presents our proposed methods to ICDAR 2021 Robust Reading Challenge - Integrated Circuit Text Spotting and Aesthetic Assessment (ICDAR RRC-ICTEXT 2021). For the text spotting task, we detect the characters on integrated circuit and classify them based on yolov5 detection model. We balance the lowercase and non-lowercase by using SynthText, generated data and data sampler. We adopt semi-supervised algorithm and distillation to furtherly improve the model's accuracy. For the aesthetic assessment task, we add a classification branch of 3 classes to differentiate the aesthetic classes of each character. Finally, we make model deployment to accelerate inference speed and reduce memory consumption based on NVIDIA Tensorrt. Our methods achieve 59.1 mAP on task 3.1 with 31 FPS and 306M memory (rank 1), 78.7\% F2 score on task 3.2 with 30 FPS and 306M memory (rank 1).
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https://arxiv.org/abs/2104.03544v1
|
The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) aims to harness the substantial amount of remote sensing data gathered over extensive periods for the monitoring and analysis of Earth's ecosystems'health. The subtask, Multimodal SAR-to-EO Image Translation, involves the use of robust SAR data, even under adverse weather and lighting conditions, transforming it into high-quality, clear, and visually appealing EO data. In the context of the SAR2EO task, the presence of clouds or obstructions in EO data can potentially pose a challenge. To address this issue, we propose the Clean Collector Algorithm (CCA), designed to take full advantage of this cloudless SAR data and eliminate factors that may hinder the data learning process. Subsequently, we applied pix2pixHD for the SAR-to-EO translation and Restormer for image enhancement. In the final evaluation, the team 'CDRL' achieved an MAE of 0.07313, securing the top rank on the leaderboard.
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https://arxiv.org/abs/2306.12626v1
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The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsupervised domain adaptive model in semantic segmentation tasks for industrial waste sorting. In this paper, we introduce the SIA_Adapt method, which incorporates several methods for domain adaptive models. The core of our method in the transferable representation from large-scale pre-training. In this process, we choose a network architecture that differs from the state-of-the-art for domain adaptation. After that, self-training using pseudo-labels helps to make the initial adaptation model more adaptable to the target domain. Finally, the model soup scheme helped to improve the generalization performance in the target domain. Our method SIA_Adapt achieves 1st place in the VisDA2022 challenge. The code is available on https: //github.com/DaehanKim-Korea/VisDA2022_Winner_Solution.
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https://arxiv.org/abs/2211.14596v1
|
Speech emotion recognition is a challenging classification task with natural emotional speech, especially when the distribution of emotion types is imbalanced in the training and test data. In this case, it is more difficult for a model to learn to separate minority classes, resulting in those sometimes being ignored or frequently misclassified. Previous work has utilised class weighted loss for training, but problems remain as it sometimes causes over-fitting for minor classes or under-fitting for major classes. This paper presents the system developed by a multi-site team for the participation in the Odyssey 2024 Emotion Recognition Challenge Track-1. The challenge data has the aforementioned properties and therefore the presented systems aimed to tackle these issues, by introducing focal loss in optimisation when applying class weighted loss. Specifically, the focal loss is further weighted by prior-based class weights. Experimental results show that combining these two approaches brings better overall performance, by sacrificing performance on major classes. The system further employs a majority voting strategy to combine the outputs of an ensemble of 7 models. The models are trained independently, using different acoustic features and loss functions - with the aim to have different properties for different data. Hence these models show different performance preferences on major classes and minor classes. The ensemble system output obtained the best performance in the challenge, ranking top-1 among 68 submissions. It also outperformed all single models in our set. On the Odyssey 2024 Emotion Recognition Challenge Task-1 data the system obtained a Macro-F1 score of 35.69% and an accuracy of 37.32%.
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https://arxiv.org/abs/2405.20064v1
|
This paper presents the winning solution for the 1st SkatingVerse Challenge. We propose a method that involves several steps. To begin, we leverage the DINO framework to extract the Region of Interest (ROI) and perform precise cropping of the raw video footage. Subsequently, we employ three distinct models, namely Unmasked Teacher, UniformerV2, and InfoGCN, to capture different aspects of the data. By ensembling the prediction results based on logits, our solution attains an impressive leaderboard score of 95.73%.
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https://arxiv.org/abs/2404.14032v1
|
This report describes our 1st place solution to the 8th HANDS workshop challenge (ARCTIC track) in conjunction with ECCV 2024. In this challenge, we address the task of bimanual category-agnostic hand-object interaction reconstruction, which aims to generate 3D reconstructions of both hands and the object from a monocular video, without relying on predefined templates. This task is particularly challenging due to the significant occlusion and dynamic contact between the hands and the object during bimanual manipulation. We worked to resolve these issues by introducing a mask loss and a 3D contact loss, respectively. Moreover, we applied 3D Gaussian Splatting (3DGS) to this task. As a result, our method achieved a value of 38.69 in the main metric, CD$_h$, on the ARCTIC test set.
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https://arxiv.org/abs/2409.19215v2
|
In this report, we describe the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we develop the following two approaches. 1) Anticipation Time Knowledge Distillation using the soft labels learned by the teacher model as knowledge to guide the student network to learn the information of anticipation time; 2) Verb-Noun Relation Module for building the relationship between verbs and nouns. Our method achieves state-of-the-art results on the testing set of EPIC-Kitchens Action Anticipation Challenge 2022.
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https://arxiv.org/abs/2207.05730v1
|
This paper presents our proposed methods for domain adaptive pedestrian re-identification (Re-ID) task in Visual Domain Adaptation Challenge (VisDA-2020). Considering the large gap between the source domain and target domain, we focused on solving two biases that influenced the performance on domain adaptive pedestrian Re-ID and proposed a two-stage training procedure. At the first stage, a baseline model is trained with images transferred from source domain to target domain and from single camera to multiple camera styles. Then we introduced a domain adaptation framework to train the model on source data and target data simultaneously. Different pseudo label generation strategies are adopted to continuously improve the discriminative ability of the model. Finally, with multiple models ensembled and additional post processing approaches adopted, our methods achieve 76.56% mAP and 84.25% rank-1 on the test set. Codes are available at https://github.com/vimar-gu/Bias-Eliminate-DA-ReID
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https://arxiv.org/abs/2012.13498v1
|
The third Pixel-level Video Understanding in the Wild (PVUW CVPR 2024) challenge aims to advance the state of art in video understanding through benchmarking Video Panoptic Segmentation (VPS) and Video Semantic Segmentation (VSS) on challenging videos and scenes introduced in the large-scale Video Panoptic Segmentation in the Wild (VIPSeg) test set and the large-scale Video Scene Parsing in the Wild (VSPW) test set, respectively. This paper details our research work that achieved the 1st place winner in the PVUW'24 VPS challenge, establishing state of art results in all metrics, including the Video Panoptic Quality (VPQ) and Segmentation and Tracking Quality (STQ). With minor fine-tuning our approach also achieved the 3rd place in the PVUW'24 VSS challenge ranked by the mIoU (mean intersection over union) metric and the first place ranked by the VC16 (16-frame video consistency) metric. Our winning solution stands on the shoulders of giant foundational vision transformer model (DINOv2 ViT-g) and proven multi-stage Decoupled Video Instance Segmentation (DVIS) frameworks for video understanding.
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https://arxiv.org/abs/2406.05352v1
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Planar partial $3$-trees are subgraphs of those planar graphs obtained by
repeatedly inserting a vertex of degree $3$ into a face. In this paper, we show
that planar partial $3$-trees have $1$-string $B_1$-VPG representations, i.e.,
representations where every vertex is represented by an orthogonal curve with
at most one bend, every two curves intersect at most once, and intersections of
curves correspond to edges in the graph. We also that some subclasses of planar
partial 3-trees have L-representations, i.e., a $B_1$-VPG representation where
every curve has the shape of an L.
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http://arxiv.org/abs/1506.07246v1
|
In this technical report, we briefly introduce the solution of our team VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR 2023 UG$^2$+ Track 2.2. In this task, we propose an efficient multi-stage framework to restore a high quality image from distorted frames. Specifically, each distorted frame is initially aligned using image registration to suppress geometric distortion. We subsequently select the sharpest set of registered frames by employing a frame selection approach based on image sharpness, and average them to produce an image that is largely free of geometric distortion, albeit with blurriness. A learning-based deblurring method is then applied to remove the residual blur in the averaged image. Finally, post-processing techniques are utilized to further enhance the quality of the output image. Our framework is capable of handling different kinds of coded target dataset provided in the final testing phase, and ranked 1st on the final leaderboard. Our code will be available at https://github.com/xsqhust/Turbulence_Removal.
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https://arxiv.org/abs/2306.09379v1
|
In this technical report, we present the solution developed by our team VIELab-HUST for text recognition through atmospheric turbulence in Track 2.1 of the CVPR 2023 UG$^{2}$+ challenge. Our solution involves an efficient multi-stage framework that restores a high-quality image from distorted frames. Specifically, a frame selection algorithm based on sharpness is first utilized to select the sharpest set of distorted frames. Next, each frame in the selected frames is aligned to suppress geometric distortion through optical-flow-based image registration. Then, a region-based image fusion method with DT-CWT is utilized to mitigate the blur caused by the turbulence. Finally, a learning-based deartifacts method is applied to remove the artifacts in the fused image, generating a high-quality outuput. Our framework can handle both hot-air text dataset and turbulence text dataset provided in the final testing phase and achieved 1st place in text recognition accuracy. Our code will be available at https://github.com/xsqhust/Turbulence_Removal.
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https://arxiv.org/abs/2306.08963v1
|
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.
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https://arxiv.org/abs/2211.13508v2
|
The Hall ratio of a graph G is the maximum of |V(H)|/alpha(H) over all
subgraphs H of G. Clearly, the Hall ratio of a graph is a lower bound for the
fractional chromatic number. It has been asked whether conversely, the
fractional chromatic number is upper bounded by a function of the Hall ratio.
We answer this question in negative, by showing two results of independent
interest regarding 1-subdivisions (the 1-subdivision of a graph is obtained by
subdividing each edge exactly once).
* For every c > 0, every graph of sufficiently large average degree contains
as a subgraph the 1-subdivision of a graph of fractional chromatic number at
least c.
* For every d > 0, there exists a graph G of average degree at least d such
that every graph whose 1-subdivision appears as a subgraph of G has Hall ratio
at most 18.
We also discuss the consequences of these results in the context of graph
classes with bounded expansion.
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http://arxiv.org/abs/1812.07327v2
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We demonstrate 1 Tbit/s/$\lambda$ single-span transmission over a heterogeneous link consisting of graded-index 50 $\mu$m core multi-mode fiber and 6LP few-mode fiber using a Kramers-Kronig receiver structure. Furthermore, the link budget increase by transmitting only three modes while employing more than three receivers is investigated.
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https://arxiv.org/abs/2010.15498v1
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We present the first single-channel 1.001-Tb/s DP-36QAM-PCS recirculating transmission over 73 loops of 146.77-km ultra-low-loss & low-IMI DNANF-5 fiber, achieving a record transmission distance of 10,714.28 km.
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https://arxiv.org/abs/2503.24313v2
|
Discovery of intrinsic two-dimensional (2D) magnetic materials is crucial for understanding the fundamentals of 2D magnetism and realizing next-generation magnetoelectronic and magneto-optical devices. Although significant efforts have been devoted to identifying 2D magnetism by exfoliating bulk magnetic layered materials, seldom studies are performed to synthesize ultra-thin magnetic materials directly for non-layered magnetic materials. Here, we report the successful synthesis of a new type of theoretically proposed 2D metallic ferromagnet 1T FeS2, through the molten-salt-assisted chemical vapor deposition (CVD) method. The long-range 2D ferromagnetic order is confirmed by the observation of a large anomalous Hall effect (AHE) and a hysteretic magnetoresistance. The experimentally detected out-of-plane ferromagnetic ordering is theoretically suported with Stoner criterion. Our findings open up new possibilities to search novel 2D ferromagnets in non-layered compounds and render opportunities for realizing realistic ultra-thin spintronic devices.
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https://arxiv.org/abs/2202.00252v1
|
[1]Abstract: This is a contribution for the PANIC 2021 Proceedings based on the articles, Eur. Phys. J. A 57, 259 (2021) and the accompanied article $[$arXiv:2109.08636 $[$hep-ph$]]$ (Hadron 2021 contribution). We have estimated for the first time the mass shifts of the $\Upsilon$ and $\eta_b$ mesons in symmetric nuclear matter by an SU(5) flavor symmetric effective Lagrangian approach, as well as the in-medium mass of $B^*$ meson by the quark-meson coupling (QMC) model. The attractive potentials for the $\Upsilon$- and $\eta_b$-nuclear matter are obtained, and one can expect for these mesons to form nuclear bound states. We have indeed found such nuclear bound states with $^{12}$C nucleus, where the results for the $^{12}$C nucleus bound state energies are new, and we report here for the first time. [2]Abstract: We estimate for the first time the mass shifts (scalar potentials) in symmetric nuclear matter of the $\Upsilon$ and $\eta_b$ mesons using an effective Lagrangian approach, as well as the in-medium mass of the $B^*$ meson by the quark-meson coupling model. The attractive potentials of both $\Upsilon$ and $\eta_b$ are expected to be strong enough for these mesons to be bound to the $^4$He nucleus, and we have obtained such nuclear bound state energies.
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https://arxiv.org/abs/2109.08636v2
|
Binary similarity analysis is critical to many code-reuse-related issues and "1-to-1" mechanism is widely applied, where one function in a binary file is matched against one function in a source file or binary file. However, we discover that function mapping is a more complex problem of "1-to-n" or even "n-to-n" due to the existence of function inlining. In this paper, we investigate the effect of function inlining on binary similarity analysis. We first construct 4 inlining-oriented datasets for four similarity analysis tasks, including code search, OSS reuse detection, vulnerability detection, and patch presence test. Then, we further study the extent of function inlining, the performance of existing works under function inlining, and the effectiveness of existing inlining-simulation strategies. Results show that the proportion of function inlining can reach nearly 70%, while most existing works neglect it and use "1-to-1" mechanism. The mismatches cause a 30% loss in performance during code search and a 40% loss during vulnerability detection. Moreover, two existing inlining-simulation strategies can only recover 60% of the inlined functions. We discover that inlining is usually cumulative when optimization increases. Conditional inlining and incremental inlining are suggested to design low-cost and high-coverage inlining-simulation strategies.
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https://arxiv.org/abs/2112.12928v2
|
Using the Gemini Planet Imager (GPI) located at Gemini South, we measured the
near-infrared (1.0-2.4 micron) spectrum of the planetary companion to the
nearby, young star $\beta$ Pictoris. We compare the spectrum obtained with
currently published model grids and with known substellar objects and present
the best matching models as well as the best matching observed objects.
Comparing the empirical measurement of the bolometric luminosity to
evolutionary models, we find a mass of $12.9\pm0.2$ $\mathcal{M}_\mathrm{Jup}$,
an effective temperature of $1724\pm15$ K, a radius of $1.46\pm0.01$
$\mathcal{R}_\mathrm{Jup}$, and a surface gravity of $\log g = 4.18\pm0.01$
[dex] (cgs). The stated uncertainties are statistical errors only, and do not
incorporate any uncertainty on the evolutionary models. Using atmospheric
models, we find an effective temperature of $1700-1800$ K and a surface gravity
of $\log g = 3.5$-$4.0$ [dex] depending upon model. These values agree well
with other publications and with "hot-start" predictions from planetary
evolution models. Further, we find that the spectrum of $\beta$ Pic b best
matches a low-surface gravity L2$\pm$1 brown dwarf. Finally comparing the
spectrum to field brown dwarfs we find the the spectrum best matches 2MASS
J04062677-381210 and 2MASS J03552337+1133437.
|
http://arxiv.org/abs/1703.00011v1
|
In economics and psychology, delay discounting is often used to characterize
how individuals choose between a smaller immediate reward and a larger delayed
reward. People with higher delay discounting rate (DDR) often choose smaller
but more immediate rewards (a "today person"). In contrast, people with a lower
discounting rate often choose a larger future rewards (a "tomorrow person").
Since the ability to modulate the desire of immediate gratification for long
term rewards plays an important role in our decision-making, the lower
discounting rate often predicts better social, academic and health outcomes. In
contrast, the higher discounting rate is often associated with problematic
behaviors such as alcohol/drug abuse, pathological gambling and credit card
default. Thus, research on understanding and moderating delay discounting has
the potential to produce substantial societal benefits.
|
http://arxiv.org/abs/1703.07726v3
|
In this paper, we propose the 1 Trillion Token Platform (1TT Platform), a novel framework designed to facilitate efficient data sharing with a transparent and equitable profit-sharing mechanism. The platform fosters collaboration between data contributors, who provide otherwise non-disclosed datasets, and a data consumer, who utilizes these datasets to enhance their own services. Data contributors are compensated in monetary terms, receiving a share of the revenue generated by the services of the data consumer. The data consumer is committed to sharing a portion of the revenue with contributors, according to predefined profit-sharing arrangements. By incorporating a transparent profit-sharing paradigm to incentivize large-scale data sharing, the 1TT Platform creates a collaborative environment to drive the advancement of NLP and LLM technologies.
|
https://arxiv.org/abs/2409.20149v1
|
We investigate the following question: Do there exist Riemannian polyhedra $X$ such that the 1-Uryson width of their universal covers $\mathrm{UW}_1(\widetilde{X})$ is bounded but $\mathrm{UW}_1(X)$ is arbitrarily large? We rule out two specific cases: when $\pi_1(X)$ is virtually cyclic and when $X$ is a Riemannian surface. More specifically, we show that if $X$ is a compact polyhedron with a virtually cyclic fundamental group, then its 1-Uryson width is bounded by the 1-Uryson width of its universal cover $\widetilde{X}$. Precisely: $$\mathrm{UW}_1(X) \leq 6 \cdot \mathrm{UW}_1(\widetilde{X}).$$ We show that if $X$ is a Riemannian surface with boundary then $$\mathrm{UW}_1(X) \leq \mathrm{UW}_1(\widetilde{X}).$$ Furthermore, we show that if there exist spaces $X$ for which $\mathrm{UW}_1(\widetilde{X})$ is bounded while $\mathrm{UW}_1(X)$ is arbitrarily large, then such examples must already appear in low dimensions. In particular, such $X$ can be found among Riemannian $2$-complexes.
|
https://arxiv.org/abs/2505.21126v1
|
We consider the $(1+\varepsilon)$-Approximate Nearest Neighbour (ANN) Problem for polygonal curves in $d$-dimensional space under the Fr\'echet distance and ask to what extent known data structures for doubling spaces can be applied to this problem. Initially, this approach does not seem viable, since the doubling dimension of the target space is known to be unbounded -- even for well-behaved polygonal curves of constant complexity in one dimension. In order to overcome this, we identify a subspace of curves which has bounded doubling dimension and small Gromov-Hausdorff distance to the target space. We then apply state-of-the-art techniques for doubling spaces and show how to obtain a data structure for the $(1+\varepsilon)$-ANN problem for any set of parametrized polygonal curves. The expected preprocessing time needed to construct the data-structure is $F(d,k,S,\varepsilon)n\log n$ and the space used is $F(d,k,S,\varepsilon)n$, with a query time of $F(d,k,S,\varepsilon)\log n + F(d,k,S,\varepsilon)^{-\log(\varepsilon)}$, where $F(d,k,S,\varepsilon)=O\left(2^{O(d)}k\Phi(S)\varepsilon^{-1}\right)^k$ and $\Phi(S)$ denotes the spread of the set of vertices and edges of the curves in $S$. We extend these results to the realistic class of $c$-packed curves and show improved bounds for small values of $c$.
|
https://arxiv.org/abs/2307.08521v1
|
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.
|
https://arxiv.org/abs/1906.06096v1
|
With the original Bak-Tang-Wisenefeld (BTW) sandpile we uncover the $1/\varphi$ noise in the mechanism maintaining self-organized criticality (SOC) - the question raised together with the concept of SOC. We posit that the dynamics of stress in the BTW sandpile follows quasi-cycles of graduate stress accumulation that end up with an abrupt stress-release and the drop of the system to subcritical state. In thermodynamic limit, the intra-cycle dynamics exhibits the $1/\varphi$ spectrum that extends infinitely and corresponds to the stress-release within the critical state.
|
https://arxiv.org/abs/2212.14726v3
|
Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In this work, we propose LoRand, a method for fine-tuning large-scale vision models with a better trade-off between task performance and the number of trainable parameters. LoRand generates tiny adapter structures with low-rank synthesis while keeping the original backbone parameters fixed, resulting in high parameter sharing. To demonstrate LoRand's effectiveness, we implement extensive experiments on object detection, semantic segmentation, and instance segmentation tasks. By only training a small percentage (1% to 3%) of the pre-trained backbone parameters, LoRand achieves comparable performance to standard fine-tuning on COCO and ADE20K and outperforms fine-tuning in low-resource PASCAL VOC dataset.
|
http://openaccess.thecvf.com//content/CVPR2023/html/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.html
|
Wasserstein distances provide a metric on a space of probability measures. We
consider the space $\Omega$ of all probability measures on the finite set $\chi
= \{1, \dots ,n\}$ where $n$ is a positive integer. 1-Wasserstein distance,
$W_1(\mu,\nu)$ is a function from $\Omega \times \Omega$ to $[0,\infty)$. This
paper derives closed form expressions for the First and Second moment of $W_1$
on $\Omega \times \Omega$ assuming a uniform distribution on $\Omega \times
\Omega$.
|
http://arxiv.org/abs/1912.04945v1
|
A graph is well-covered if all its maximal independent sets are of the same
size (M. D. Plummer, 1970). A well-covered graph is 1-well-covered if the
deletion of every vertex leaves a graph which is well-covered as well (J. W.
Staples, 1975). A graph G belongs to class W_{n} if every n pairwise disjoint
independent sets in G are included in $n$ pairwise disjoint maximum independent
sets (J. W. Staples, 1975). Clearly, W_{1} is the family of all well-covered
graphs. It turns out that G belongs to W_{2} if and only if it is a
1-well-covered graph without isolated vertices. We show that deleting a
shedding vertex does not change the maximum size of a maximal independent set
including a given independent set A in a graph G. Specifically, for
well-covered graphs, it means that the vertex v is shedding if and only if G-v
is well-covered. In addition, we provide new characterizations of
1-well-covered graphs, which we further use in building 1-well-covered graphs
by corona, join, and concatenation operations.
|
http://arxiv.org/abs/1610.03972v3
|
Blazars are the dominant type of extragalactic sources at microwave and at
$\gamma$-ray energies. In the most energetic part of the electromagnetic
spectrum (E>100GeV) a large fraction of high Galactic latitude sources are
blazars of the High Synchrotron Peaked (HSP) type, that is BL Lac objects with
synchrotron power peaking in the UV or in the X-ray band. HSP blazars are
remarkably rare, with only a few hundreds of them expected to be above the
sensitivity limits of currently available surveys. To find these very uncommon
objects, we have devised a method that combines ALLWISE survey data with
multi-frequency selection criteria. The sample was defined starting from a
primary list of infrared colour-colour selected sources from the ALLWISE all
sky survey database, and applying further restrictions on IR-radio and IR-X-ray
flux ratios. Using a polynomial fit to the multi-frequency data (radio to
X-ray) we estimated synchrotron peak frequencies and fluxes of each object. We
assembled a sample including 992 sources, which is currently the largest
existing list of confirmed and candidates HSP blazars. All objects are expected
to radiate up to the highest $\gamma$-ray photon energies. In fact, 299 of
these are confirmed emitters of GeV $\gamma$-ray photons (based on Fermi-LAT
catalogues), and 36 have already been detected in the TeV band. The majority of
sources in the sample are within reach of the upcoming Cherenkov Telescope
Array (CTA), and many may be detectable even by the current generation of
Cherenkov telescopes during flaring episodes. The sample includes 425
previously known blazars, 151 new identifications, and 416 HSP candidates
(mostly faint sources) for which no optical spectra is available yet. The full
1WHSP catalogue is on-line at http://www.asdc.asi.it/1whsp/ providing a direct
link to the SED building tool where multifrequency data can be easily
visualised.
|
http://arxiv.org/abs/1504.02801v1
|
It has been shown that a message passing neural networks (MPNNs), a popular family of neural networks for graph-structured data, are at most as expressive as the first-order Weisfeiler-Leman (1-WL) graph isomorphism test, which has motivated the development of more expressive architectures. In this work, we analyze if the limited expressiveness is actually a limiting factor for MPNNs and other WL-based models in standard graph datasets. Interestingly, we find that the expressiveness of WL is sufficient to identify almost all graphs in most datasets. Moreover, we find that the classification accuracy upper bounds are often close to 100\%. Furthermore, we find that simple WL-based neural networks and several MPNNs can be fitted to several datasets. In sum, we conclude that the performance of WL/MPNNs is not limited by their expressiveness in practice.
|
https://arxiv.org/abs/2202.10156v1
|
Consider $n^2-1$ unit-square blocks in an $n \times n$ square board, where each block is labeled as movable horizontally (only), movable vertically (only), or immovable -- a variation of Rush Hour with only $1 \times 1$ cars and fixed blocks. We prove that it is PSPACE-complete to decide whether a given block can reach the left edge of the board, by reduction from Nondeterministic Constraint Logic via 2-color oriented Subway Shuffle. By contrast, polynomial-time algorithms are known for deciding whether a given block can be moved by one space, or when each block either is immovable or can move both horizontally and vertically. Our result answers a 15-year-old open problem by Tromp and Cilibrasi, and strengthens previous PSPACE-completeness results for Rush Hour with vertical $1 \times 2$ and horizontal $2 \times 1$ movable blocks and 4-color Subway Shuffle.
|
https://arxiv.org/abs/2003.09914v2
|
Dense Wavelength Division Multiplexing (DWDM) is a key technology for realizing high-capacity and flexible quantum communication networks. In addition, to realize the emerging quantum internet, quantum frequency conversion is also essential for bridging different quantum systems over optical fiber networks. In this work, we demonstrate a channel-selective quantum frequency conversion (CS-QFC), which allows active selection of the frequency of the converted photon from multiple DWDM channels. The 2.5 THz bandwidth of our CS-QFC system shows the ability to establish a 100-ch DWDM dynamic link from a single quantum system. It promises to increase the diversity of the quantum network.
|
https://arxiv.org/abs/2409.08025v1
|
Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as well as achieve significant speedups on general CPUs. In this paper, we propose a novel 1xN pruning pattern to break this limitation. In particular, consecutive N output kernels with the same input channel index are grouped into one block, which serves as a basic pruning granularity of our pruning pattern. Our 1xN pattern prunes these blocks considered unimportant. We also provide a workflow of filter rearrangement that first rearranges the weight matrix in the output channel dimension to derive more influential blocks for accuracy improvements and then applies similar rearrangement to the next-layer weights in the input channel dimension to ensure correct convolutional operations. Moreover, the output computation after our 1xN pruning can be realized via a parallelized block-wise vectorized operation, leading to significant speedups on general CPUs. The efficacy of our pruning pattern is proved with experiments on ILSVRC-2012. For example, given the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Our project is made available at https://github.com/lmbxmu/1xN.
|
https://arxiv.org/abs/2105.14713v6
|
The mechanism of self-organized criticality is based on a steady slow loading and a quick huge stress-release. We add the clustering of the events in space and time to the Bak-Tang-Wiesenfeld cellular automaton and obtain the truncated $1/x$ probability distribution of the events over their sizes.
|
https://arxiv.org/abs/2105.04375v1
|
We present improved estimates of the couplings, masses and mass ratios of the $X_Q, Z_Q$ and $T_{QQ\bar q\bar q'}$ states using (inverse) QCD Laplace sum rules (LSR), their ratios ${\cal R}$ and double ratios (DRSR), within stability criteria. We conclude that the observed $X_c(3872)$ and $Z_c(3900)$ are tetramoles states (superposition of quasi-degenerated molecule and tetraquark states having similar couplings to the currents) with the predicted masses: $M_{{\cal T}_{X_c}}=3876(44)$ MeV and $M_{{\cal T}_{Z_c}}=3900(42)$ MeV. We also do an extensive analysis of the four-quark nature of different $T_{QQ\bar q\bar q'}$ axial-vector states. Then, combining ${\cal R}$ and DRSR, we reanalyze the observed state $X_c(3872)$ and we obtain a precise prediction of $M_{T_{cc}^{1^+}}$=3886(6) MeV. Extending to the beauty sector, we find the results: $M_{{\cal T}_{Z_b}}=10579(99)$ MeV and $M_{X_b}=10545(131)$ MeV. Finally, we confront our combined LSR $\oplus$ DRSR results with the ones from some other approaches (lattices and quark models).
|
https://arxiv.org/abs/2212.12136v1
|
We consider the problem of maintaining a $(1-\epsilon)$-approximation to the densest subgraph (DSG) in an undirected multigraph as it undergoes edge insertions and deletions (the fully dynamic setting). Sawlani and Wang [SW20] developed a data structure that, for any given $\epsilon > 0$, maintains a $(1-\epsilon)$-approximation with $O(\log^4 n/\epsilon^6)$ worst-case update time for edge operations, and $O(1)$ query time for reporting the density value. Their data structure was the first to achieve near-optimal approximation, and improved previous work that maintained a $(1/4 - \epsilon)$ approximation in amortized polylogarithmic update time [BHNT15]. In this paper we develop a data structure for $(1-\epsilon)$-approximate DSG that improves the one from [SW20] in two aspects. First, the data structure uses linear space improving the space bound in [SW20] by a logarithmic factor. Second, the data structure maintains a $(1-\epsilon)$-approximation in amortized $O(\log^2 n/\epsilon^4)$ time per update while simultaneously guaranteeing that the worst case update time is $O(\log^3 n \log \log n/\epsilon^6)$. We believe that the space and update time improvements are valuable for current large scale graph data sets. The data structure extends in a natural fashion to hypergraphs and yields improvements in space and update times over recent work [BBCG22] that builds upon [SW20].
|
https://arxiv.org/abs/2210.02611v1
|
The maximum weighted matching (MWM) problem is one of the most well-studied combinatorial optimization problems in distributed graph algorithms. Despite a long development on the problem, and the recent progress of Fischer, Mitrovic, and Uitto [FMU22] who gave a $\text{poly}(1/\epsilon, \log n)$-round algorithm for obtaining a $(1-\epsilon)$-approximate solution for unweighted maximum matching, it had been an open problem whether a $(1-\epsilon)$-approximate MWM can be obtained in $\text{poly}(1/\epsilon, \log n)$ rounds in the CONGEST model. Algorithms with such running times were only known for special graph classes such as bipartite graphs [AKO18] and minor-free graphs [CS22]. For general graphs, the previously known algorithms require exponential in $(1/\epsilon)$ rounds for obtaining a $(1-\epsilon)$-approximate solution [FFK21] or achieve an approximation factor of at most 2/3 [AKO18]. In this work, we settle this open problem by giving a deterministic $\text{poly}(1/\epsilon, \log n)$-round algorithm for computing a $(1-\epsilon)$-approximate MWM for general graphs in the CONGEST model. Our proposed solution extends the algorithm of Fischer, Mitrovic, and Uitto [FMU22], blends in the sequential algorithm from Duan and Pettie [DP14] and the work of Faour, Fuchs, and Kuhn [FFK21]. Interestingly, this solution also implies a CREW PRAM algorithm with $\text{poly}(1/\epsilon, \log n)$ span using only $O(m)$ processors. In addition, with the reduction from Gupta and Peng [GP13], we further obtain a semi-streaming algorithm with $\text{poly}(1/\epsilon)$ passes. When $\epsilon$ is smaller than a constant $o(1)$ but at least $1/\log^{o(1)} n$, our algorithm is more efficient than both Ahn and Guha's $\text{poly}(1/\epsilon, \log n)$-passes algorithm [AG13] and Gamlath, Kale, Mitrovic, and Svensson's $(1/\epsilon)^{O(1/\epsilon^2)}$-passes algorithm [GKMS19].
|
https://arxiv.org/abs/2212.14425v2
|
Computing approximate shortest paths in the dynamic streaming setting is a fundamental challenge that has been intensively studied during the last decade. Currently existing solutions for this problem either build a sparse multiplicative spanner of the input graph and compute shortest paths in the spanner offline, or compute an exact single source BFS tree. Solutions of the first type are doomed to incur a stretch-space tradeoff of $2\kappa-1$ versus $n^{1+1/\kappa}$, for an integer parameter $\kappa$. (In fact, existing solutions also incur an extra factor of $1+\epsilon$ in the stretch for weighted graphs, and an additional factor of $\log^{O(1)}n$ in the space.) The only existing solution of the second type uses $n^{1/2 - O(1/\kappa)}$ passes over the stream (for space $O(n^{1+1/\kappa})$), and applies only to unweighted graphs. In this paper we show that $(1+\epsilon)$-approximate single-source shortest paths can be computed in this setting with $\tilde{O}(n^{1+1/\kappa})$ space using just \emph{constantly} many passes in unweighted graphs, and polylogarithmically many passes in weighted graphs (assuming $\epsilon$ and $\kappa$ are constant). Moreover, in fact, the same result applies for multi-source shortest paths, as long as the number of sources is $O(n^{1/\kappa})$. We achieve these results by devising efficient dynamic streaming constructions of $(1 + \epsilon, \beta)$-spanners and hopsets. We believe that these constructions are of independent interest.
|
https://arxiv.org/abs/2107.13309v2
|
Knapsack is one of the most fundamental problems in theoretical computer science. In the $(1 - \epsilon)$-approximation setting, although there is a fine-grained lower bound of $(n + 1 / \epsilon) ^ {2 - o(1)}$ based on the $(\min, +)$-convolution hypothesis ([K{\"u}nnemann, Paturi and Stefan Schneider, ICALP 2017] and [Cygan, Mucha, Wegrzycki and Wlodarczyk, 2017]), the best algorithm is randomized and runs in $\tilde O\left(n + (\frac{1}{\epsilon})^{11/5}/2^{\Omega(\sqrt{\log(1/\epsilon)})}\right)$ time [Deng, Jin and Mao, SODA 2023], and it remains an important open problem whether an algorithm with a running time that matches the lower bound (up to a sub-polynomial factor) exists. We answer the question positively by showing a deterministic $(1 - \epsilon)$-approximation scheme for knapsack that runs in $\tilde O(n + (1 / \epsilon) ^ {2})$ time. We first extend a known lemma in a recursive way to reduce the problem to $n \epsilon$-additive approximation for $n$ items with profits in $[1, 2)$. Then we give a simple efficient geometry-based algorithm for the reduced problem.
|
https://arxiv.org/abs/2308.07004v3
|
Very metal-poor stars ($\rm[Fe/H] < -2$) in the Milky Way are fossil records of early chemical evolution and the assembly and structure of the Galaxy. However, they are rare and hard to find. Gaia DR3 has provided over 200 million low-resolution ($R \approx 50$) XP spectra, which provides an opportunity to greatly increase the number of candidate metal-poor stars. In this work, we utilise the \texttt{XGBoost} classification algorithm to identify $\sim$200,000 very metal-poor star candidates. Compared to past work, we increase the candidate metal-poor sample by about an order of magnitude, with comparable or better purity than past studies. Firstly, we develop three classifiers for bright stars ($BP$ $<$ 16). They are Classifier-T (for Turn-off stars), Classifier-GC (for Giant stars with high completeness), and Classifier-GP (for Giant stars with high purity) with expected purity of 52\%/45\%/76\% and completeness of 32\%/93\%/66\% respectively. These three classifiers obtained a total of 11,000/111,000/44,000 bright metal-poor candidates. We apply model-T and model-GP on faint stars ($BP$ $>$ 16) and obtain 38,000/41,000 additional metal-poor candidates with purity 29\%/52\%, respectively. We make our metal-poor star catalogs publicly available, for further exploration of the metal-poor Milky Way.
|
https://arxiv.org/abs/2303.17676v2
|
We demonstrate single-atom resolved imaging with a survival probability of
$0.99932(8)$ and a fidelity of $0.99991(1)$, enabling us to perform repeated
high-fidelity imaging of single atoms in tweezers for thousands of times. We
further observe lifetimes under laser cooling of more than seven minutes, an
order of magnitude longer than in previous tweezer studies. Experiments are
performed with strontium atoms in $813.4~\text{nm}$ tweezer arrays, which is at
a magic wavelength for the clock transition. Tuning to this wavelength is
enabled by off-magic Sisyphus cooling on the intercombination line, which lets
us choose the tweezer wavelength almost arbitrarily. We find that a single not
retro-reflected cooling beam in the radial direction is sufficient for
mitigating recoil heating during imaging. Moreover, this cooling technique
yields temperatures below $5~\mu$K, as measured by release and recapture.
Finally, we demonstrate clock-state resolved detection with average survival
probability of $0.996(1)$ and average state detection fidelity of $0.981(1)$.
Our work paves the way for atom-by-atom assembly of large defect-free arrays of
alkaline-earth atoms, in which repeated interrogation of the clock transition
is an imminent possibility.
|
http://arxiv.org/abs/1811.06014v3
|
The first proposed Brazilian mission to deep space, the ASTER mission, has the triple asteroid system (153591) 2001 SN263 as a target. One of the mission's main goals is to analyze the physical and dynamical structures of the system to understand its origin and evolution. The present work aims to analyze how the asteroid's irregular shape interferes with the stability around the system. The results show that the irregular shape of the bodies plays an important role in the dynamics nearby the system. For instance, the perturbation due to the (153591) 2001 SN263 Alpha's shape affects the stability in the (153591) 2001 SN263 Gamma's vicinity. Similarly, the (153591) 2001 SN263 Beta's irregularity causes a significant instability in its nearby environment. As expected, the prograde case is the most unstable, while the retrograde scenario presents more stability. Additionally, we investigate how the solar radiation pressure perturbs particles of different sizes orbiting the triple system. We found that particles with a 10-50 cm radius could survive the radiation pressure for the retrograde case. Meanwhile, to resist solar radiation, the particles in prograde orbit must be larger than the particles in retrograde orbits, at least one order of magnitude.
|
https://arxiv.org/abs/2207.01726v1
|
We have analyzed 16 years of observations dedicated to the Crab (pulsar +
nebula) with the INTEGRAL SPI instrument to investigate its polarization
properties. We find that the source presents a substantially polarized emission
(PF = 24%) in the hard X-ray domain, with the electric vector aligned with the
pulsar spin axis, in agreement with other results at various wavelengths. The
stability of the polarization characteristics with energy and over the 16 years
covered by the data is remarkable, completing the standard candle status of the
source in the spectral domain. The polarization measurements imply that the
synchrotron emission is the dominant mechanism of photon production from radio
to hard X-rays. The high level of polarized emission points out the steadiness
of the source, in particular of the magnetic field configuration and geometry.
|
http://arxiv.org/abs/1907.09341v1
|
The Crab Nebula is used by many instruments as a calibration source, in
particular at high energy, where it is one of the brightest celestial object.
The spectrometer INTEGRAL SPI (20 keV - 8 MeV), in operation since October
2002, offers a large dataset dedicated to this source, with regular campaigns
planned twice per year. We have analyzed the available data to quantify the
source behavior on a long term scale and examine the stability level on
timescales from hour to years. As a result, the source flux variability appears
to be contained within less than +/- 5% around a ~ 20 yr mean value, for broad
bands covering the 20 keV - 400 keV energy domain, above which statistic limits
any firm conclusion. In term of spectral shape, the Band model provides a good
description of the observed emission between 20 keV and 2.2 MeV. The averaged
spectrum best fit parameters correspond to a low energy slope of 1.99 +/- 0.01,
a high energy slope of -2.32 +/- 0.02 and a characteristic energy E c of 531
+/- 50 keV to describe the curvature joining both power laws. The spectral
parameters have then been determined on the revolution timescale (~ 1 to 2
days) and their steadiness confirms the source emission stability.
As a complementary result, this study demonstrates that the SPI instrument
efficiency remains within 5% of its initial value, after 17 years of operation.
|
http://arxiv.org/abs/2007.11519v1
|
Models of the Solar System's dynamical evolution predict the dispersal of
primitive planetesimals from their formative regions amongst the gas-giant
planets due to the early phases of planetary migration. Consequently,
carbonaceous objects were scattered both into the outer asteroid belt and out
to the Kuiper Belt. These models predict that the Kuiper Belt should contain a
small fraction of objects with carbonaceous surfaces, though to date, all
reported visible reflectance spectra of small Kuiper Belt Objects (KBOs) are
linear and featureless. We report the unusual reflectance spectrum of a small
KBO, (120216) 2004 EW95, exhibiting a large drop in its near-UV reflectance and
a broad shallow optical absorption feature centered at ~700 nm which is
detected at greater than 4-sigma significance. These features, confirmed
through multiple epochs of spectral photometry and spectroscopy, have
respectively been associated with ferric oxides and phyllosilicates. The
spectrum bears striking resemblance to those of some C-type asteroids,
suggesting that 2004 EW95 may share a common origin with those objects. 2004
EW95 orbits the Sun in a stable mean motion resonance with Neptune, at
relatively high eccentricity and inclination, suggesting it may have been
emplaced there by some past dynamical instability. These results appear
consistent with the aforementioned model predictions and are the first to show
a reliably confirmed detection of silicate material on a small KBO.
|
http://arxiv.org/abs/1801.10163v3
|
We report photometric observations of the trans-Neptunian object
2004~TT$_{357}$ obtained in 2015 and 2017 using the 4.3~m Lowell's Discovery
Channel Telescope. We derive a rotational period of 7.79$\pm$0.01~h and a
peak-to-peak lightcurve amplitude of 0.76$\pm$0.03~mag. 2004 TT$_{357}$
displays a large variability that can be explained by a very elongated single
object or can be due to a contact/close binary. The most likely scenario is
that 2004 TT$_{357}$ is a contact binary. If it is in hydrostatic equilibrium,
we find that the lightcurve can be explained by a system with a mass ratio
q$_{min}$=0.45$\pm$0.05, and a density of $\rho_{min}$=2g cm$^{-3}$, or less
likely a system with q$_{max}$=0.8$\pm$0.05, and $\rho_{max}$=5g cm$^{-3}$.
Considering a single triaxial ellipsoid in hydrostatic equilibrium, we derive a
lower limit to the density of 0.78g cm$^{-3}$, and an elongation (a/b) of 2.01
assuming an equatorial view. From Hubble Space Telescope data, we report no
resolved companion orbiting 2004 TT$_{357}$. Despite an expected high fraction
of contact binaries in the trans-Neptunian belt, 2001 QG$_{298}$ is the unique
confirmed contact binary in the trans-Neptunian belt, and 2004 TT$_{357}$ is
only the second candidate to this class of systems, with 2003 SQ$_{317}$.
|
http://arxiv.org/abs/1707.09927v1
|
The solar system object 2005 VL1 passed close to Earth in late 1965. It has been suggested that it is actually the space probe Venera-2. However, a comparison of the orbits presented in this note demonstrates that the proposed association is incorrect.
|
https://arxiv.org/abs/2503.07972v2
|
From two observing runs during the 2014 summer at Calar Alto Observatory in
Almer\'ia (Spain) and at Sierra Nevada Observatory in Granada (Spain), we were
able to derive CCD photometry of the Trans-Neptunian Object 2008 OG$_{19}$. We
analyzed the time series and obtained a double-peaked light curve with a peak
to valley amplitude of (0.437 $\pm$ 0.011) mag and a rotational period of
(8.727$\pm$ 0.003) h. This implies that this object is very elongated, closely
resembling Varuna's case. The photometry also allowed us to obtain an absolute
magnitude in R-band of (4.39 $\pm$ 0.07) mag. From this result we estimated an
equivalent diameter of 2008 OG$_{19}$ which is 619$^{+56}_{-113}$ km using an
average albedo for Scattered Disk Objects. Finally we interpreted the results
under the assumption of hydrostatic equilibrium and found a lower limit for the
density of 544$^{+42}_{-4}$ kg$\,$m$^{-3}$. However, a more likely density is
(609 $\pm$ 4) kg$\,$m$^{-3}$ using an aspect angle of 60$^\circ$, which
corresponds to the most likely configuration for the spin axis with respect to
the observer assuming random orientations.
|
http://arxiv.org/abs/1511.06584v1
|
We show a beyond 200Gb/s VCSEL transmission experiment. Results are based on 35GHz VCSEL and advanced DSP. We show an AIR of 245Gb/s PAM-6 back-to-back, and 200Gb/s PAM-4 over 60m OM4 fiber assuming KP4-FEC.
|
https://arxiv.org/abs/2403.17275v1
|
In this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods with neural network architectures. The experiments show that some of the manual tasks are suitable for automatization. In particular, the best methods achieve a 90{\%} top-5 accuracy in a multi-class classification of arguments, and 65{\%} macro-averaged F1-score for tagging arguments according to a three-part argumentation model.
|
https://aclanthology.org/W17-5101
|
Fourier ptychography (FP) imaging, drawing on the idea of synthetic aperture, has been demonstrated as a potential approach for remote sub-diffraction-limited imaging. Nevertheless, the farthest imaging distance is still limited around 10 m even though there has been a significant improvement in macroscopic FP. The most severely issue in increasing the imaging distance is field of view (FoV) limitation caused by far-field condition for diffraction. Here, we propose to modify the Fourier far-field condition for rough reflective objects, aiming to overcome the small FoV limitation by using a divergent beam to illuminate objects. A joint optimization of pupil function and target image is utilized to attain the aberration-free image while estimating the pupil function simultaneously. Benefiting from the optimized reconstruction algorithm which effectively expands the camera's effective aperture, we experimentally implement several FP systems suited for imaging distance of 12 m, 65 m and 120m with the maximum synthetic aperture of 200 mm. The maximum synthetic aperture is thus improved by more than one order of magnitude of the state-of-the-art works from the furthest distance, with an over fourfold improvement in the resolution compare to single aperture. Our findings demonstrate significant potential for advancing the field of macroscopic FP, propelling it into a new stage of development.
|
https://arxiv.org/abs/2310.14515v2
|
The Linac Coherent Light Source (LCLS) is an X-ray science facility at SLAC National Accelerator Laboratory. The LCLS-II project (an upgrade to LCLS) is in the commissioning phase; the LCLS-II-HE (High Energy) project is another upgrade to the facility, enabling higher energy operation. An electron beam is accelerated using superconducting radio frequency (SRF) cavities built into cryomodules. It is planned to build 24 1.3 GHz standard cryomodules and one 1.3 GHz single-cavity Buncher Capture Cavity (BCC) cryomodule for the LCLS-II-HE project. Fourteen of these standard cryomodules and the BCC are planned to be assembled and tested at Fermilab. Procurements for standard cryomodule components are nearing completion. The first LCLS-II-HE cryomodule, referred to as the verification cryomodule (vCM) was assembled and tested at Fermilab. Fermilab has completed the assembly of the second cryomodule. This paper presents LCLS-II-HE cryomodule production status at Fermilab, emphasizing the changes done based on the successes, challenges, mitigations, and lessons learned from LCLS-II; validation of the changes with the excellent vCM results.
|
https://arxiv.org/abs/2208.12780v1
|
Positron emission tomography (PET) is widely used in various clinical
applications, including cancer diagnosis, heart disease and neuro disorders.
The use of radioactive tracer in PET imaging raises concerns due to the risk of
radiation exposure. To minimize this potential risk in PET imaging, efforts
have been made to reduce the amount of radio-tracer usage. However, lowing dose
results in low Signal-to-Noise-Ratio (SNR) and loss of information, both of
which will heavily affect clinical diagnosis. Besides, the ill-conditioning of
low-dose PET image reconstruction makes it a difficult problem for iterative
reconstruction algorithms. Previous methods proposed are typically complicated
and slow, yet still cannot yield satisfactory results at significantly low
dose. Here, we propose a deep learning method to resolve this issue with an
encoder-decoder residual deep network with concatenate skip connections.
Experiments shows the proposed method can reconstruct low-dose PET image to a
standard-dose quality with only two-hundredth dose. Different cost functions
for training model are explored. Multi-slice input strategy is introduced to
provide the network with more structural information and make it more robust to
noise. Evaluation on ultra-low-dose clinical data shows that the proposed
method can achieve better result than the state-of-the-art methods and
reconstruct images with comparable quality using only 0.5% of the original
regular dose.
|
http://arxiv.org/abs/1712.04119v1
|
The year 2022 marked the 200th anniversary of the first appearance of the Navier-Stokes equation, a landmark in Fluid Dynamics introduced by Claude-Louis Navier in 1822. This equation revolutionized the understanding of fluid motion by incorporating viscosity and friction into the equations, expanding their applicability beyond idealized fluids. In this manuscript, we explore the historical development of the Navier-Stokes equation and its profound impact on Fluid Dynamics over the past two centuries. From Navier's initial insights to George Stokes' experimental validations and subsequent contributions by other scientists, we trace the evolution of this equation. We also delve into its practical applications, including its role in the development of Computational Fluid Dynamics. The Navier-Stokes equation has played a pivotal role in advancing our understanding of fluid behavior, making it a cornerstone of modern science and engineering.
|
https://arxiv.org/abs/2401.13669v1
|
Using a potential field source surface (PFSS) model, we recently analyzed the
global topology of the background coronal magnetic field for a sequence of
coronal mass ejections (CMEs) that occurred on 2010 August 1-2. Here we repeat
this analysis for the background field reproduced by a magnetohydrodynamic
(MHD) model that incorporates plasma thermodynamics. As for the PFSS model, we
find that all three CME source regions contain a coronal hole that is separated
from neighboring coronal holes by topologically very similar pseudo-streamer
structures. However, the two models yield very different results for the size,
shape, and flux of the coronal holes. We find that the helmet-streamer cusp
line, which corresponds to a source-surface null line in the PFSS model, is
structurally unstable and does not form in the MHD model. Our analysis
indicates that generally, in MHD configurations, this line rather consists of a
multiple-null separator passing along the edge of disconnected flux regions.
Some of these regions are transient and may be the origin of so-called streamer
blobs. We show that the core topological structure of such blobs is a
three-dimensional "plasmoid", consisting of two conjoined flux ropes of
opposite handedness, which connect at a spiral null point of the magnetic
field. Our analysis reveals that such plasmoids appear also in pseudo-streamers
on much smaller scales. These new insights into the coronal magnetic topology
provide some intriguing implications for solar energetic particle events and
for the properties of the slow solar wind.
|
http://arxiv.org/abs/1707.07773v1
|
The last few years, 2013-2016, the high energy neutrino events in ICECUBE and
the last rich UHECR maps by AUGER and TA were hopefully opening a new High
Energy astronomy age. Unfortunately the foreseen correlation between neutrino
with best gamma X sources has not (yet) been found. The most celebrated GRB
gamma sources do not correlate to any neutrino events. The expected Local Group
anisotropy in UHECR within the nuleon GZK cut off, has just fade away. UHECR
events from Virgo are almost absent. Above two hundred TeV energy tau neutrino
might shine by double bang in detectable way in ICECUBE. Within a dozen of
events no tau neutrino arised (yet) in ICECUBE. Finally GRBs Fireball models
calling since decades for HE neutrinos correlated imprint at TeVs energy are
not (yet) found. So many absences are making a huge question mark: is there a
new reading key?
|
http://arxiv.org/abs/1702.00021v1
|
The 2014 update of the discovery of nuclide project is presented. Only six
new nuclides were observed for the first time in 2014 while the assignments of
seventeen other nuclides were revised. In addition, for another fourteen
nuclides the laboratories where they were discovered were reassigned.
|
http://arxiv.org/abs/1501.06761v1
|
On the night of Oct 31, 2015 two bright Southern Taurid fireballs occurred
over Poland, being one of the most spectacular bolides of this shower in recent
years. The first fireball - PF311015a Okonek - was detected by six video
stations of Polish Fireball Network (PFN) and photographed by several
bystanders, allowing for precise determination of the trajectory and orbit of
the event. The PF311015a Okonek entered Earth's atmosphere with the velocity of
33.2 +\- 0.1 km/s and started to shine at height of 117.88 +\- 0.05 km. The
maximum brightness of -16.0 +\- 0.4 mag was reached at height of 82.5 +\- 0.1
km. The trajectory of the fireball ended at height of 60.2 +\- 0.2 km with
terminal velocity of 30.2 +\- 1.0 km/s.
The second fireball - PF311015b Ostrowite - was detected by six video
stations of PFN. It started with velocity of 33.2 +\- 0.1 km/s at height of
108.05 +\- 0.02 km. The peak brightness of -14.8 +\- 0.5 mag was recorded at
height of 82.2 +\- 0.1 km. The terminal velocity was 31.8 +\- 0.5 km/s and was
observed at height of 57.86 +\- 0.03 km.
The orbits of both fireballs are similar not only to orbits of Southern
Taurids and comet 2P/Encke, but even closer resemblance was noticed for orbits
of 2005 UR and 2005 TF50 asteroids. Especially the former object is interesting
because of its close flyby during spectacular Taurid maximum in 2005. We
carried out a further search to investigate the possible genetic relationship
of Okonek and Ostrowite fireballs with both asteroids, that are considered to
be associated with Taurid complex. Although, we could not have confirmed
unequivocally the relation between fireballs and these objects, we showed that
both asteroids could be associated, having the same origin in a disruption
process that separates them.
|
http://arxiv.org/abs/1605.06283v1
|
The 2016 edition of Google Scholar Metrics was released on July 15th 2016.
There haven't been any structural changes respect to previous versions, which
means that most of its limitations still persist. The biggest changes are the
addition of five new language rankings (Russian, Korean, Polish, Ukrainian, and
Indonesian) and elimination of two other language rankings (Italian and Dutch).
In addition, for reasons still unknown, this new edition doesn't include as
many working paper and discussion paper series as previous editions.
|
http://arxiv.org/abs/1607.06260v1
|
We report the detection of type C QPO along with the upper harmonic in the
commensurate ratio of 1:2 in the two observations of the low-mass black hole
transient H~1743--322 jointly observed by \textit{XMM-Newton} and
\textit{NuSTAR} during the 2016 outburst. We find that the QPO and the upper
harmonic exhibit shifts in their centroid frequencies in the second observation
with respect to the first one. The hardness intensity diagram implies that in
contrast to 2008 and 2014 failed outbursts, 2016 outburst was a successful one.
We also detect the presence of a broad iron K$\alpha$ line at $\sim$6.5 keV and
reflection hump in the energy range of 15--30 keV in both the observations.
Along with the shape of the power density spectra, the nature of the
characteristic frequencies and the fractional rms amplitude of the timing
features imply that the source stayed in the low/hard state during these
observations. Moreover, photon index and other spectral parameters also
indicate the low/hard state behavior of the source. Unlike the soft lag
detected in this source during the 2008 and 2014 failed outbursts, we observe
hard time-lag of $0.40\pm0.15$ and $0.32\pm0.07$ s in the 0.07--0.4 Hz
frequency range in the two observations during the 2016 outburst. The
correlation between the photon index and the centroid frequency of the QPO is
consistent with the previous results. Furthermore, the high value of the
Comptonized fraction and the weak thermal component indicate that the QPO is
being modulated by the Comptonization process.
|
http://arxiv.org/abs/2003.09245v1
|
The 2016 update of the discovery of nuclide project is presented. Only twelve
new nuclides were observed for the first time in 2016. A large number of
isotopes is still only published in conference proceedings or internal reports.
No changes to earlier assignments were made.
|
http://arxiv.org/abs/1704.07169v1
|
The latest results from the search for a Standard Model Higgs boson produced
in association with a top quark pair ($\mathrm{t\bar{t}H}$) at $\sqrt{s} = 13$
TeV decaying to final states with multiple leptons is presented using the 2016
dataset from the CMS experiment. The Higgs decays into either WW*, ZZ*, or
$\tau\tau$, and the top quark pair decays considered are either fully leptonic,
or semi-leptonic. The leptons defining the final states are muons and/or
electrons. The overall analysis strategy, as well as new techniques with
respect to the 2015 results are outlined.
|
http://arxiv.org/abs/1612.07812v1
|
We perform a comprehensive timing and broadband spectral analysis using an AstroSat observation of the low-mass black hole X-ray binary H~1743--322 during 2017 outburst. Additionally, we use two Swift/XRT observations, one of which is simultaneous with AstroSat and the other taken three days earlier, for timing analysis. The hardness-intensity diagram indicates that the 2017 outburst was a failed one unlike the previous successful outburst in 2016. We detect type C quasi-periodic oscillation (QPO) in the simultaneous AstroSat and Swift/XRT observations at $\sim0.4$ Hz, whereas an upper harmonic is noticed at $\sim0.9$ Hz in the AstroSat data only. Although these features are found to be energy independent, we notice a shift of $\sim0.08$ Hz in the QPO frequency over the interval of three days. We also investigate the nature of variability in the two consecutive failed outbursts in 2017 and 2018. We detect soft time lags of $23.2\pm12.2$ ms and $140\pm80$ ms at the type C QPO frequencies in 2017 Astrosat and 2018 XMM-Newton data, respectively. The lag-energy spectra from both the outbursts suggest that the soft lags may be associated with the reflection features. The broadband spectral analysis indicates that the source was in the low/hard state during our AstroSat observation. Modeling of the disk and reflection continuum suggests the presence of a significantly truncated accretion disk by at least $27.4~r_{\rm{g}}$ from the ISCO when the source luminosity is $\sim1.6\%$ of the Eddington luminosity.
|
https://arxiv.org/abs/2409.10253v1
|
In mainstream computer vision and machine learning, public datasets such as
ImageNet, COCO and KITTI have helped drive enormous improvements by enabling
researchers to understand the strengths and limitations of different algorithms
via performance comparison. However, this type of approach has had limited
translation to problems in robotic assisted surgery as this field has never
established the same level of common datasets and benchmarking methods. In 2015
a sub-challenge was introduced at the EndoVis workshop where a set of robotic
images were provided with automatically generated annotations from robot
forward kinematics. However, there were issues with this dataset due to the
limited background variation, lack of complex motion and inaccuracies in the
annotation. In this work we present the results of the 2017 challenge on
robotic instrument segmentation which involved 10 teams participating in
binary, parts and type based segmentation of articulated da Vinci robotic
instruments.
|
http://arxiv.org/abs/1902.06426v2
|
The 2017 update of the discovery of nuclide project is presented. 34 new
nuclides were observed for the first time in 2017. However, the assignment of
six previously identified nuclides had to be retracted.
|
http://arxiv.org/abs/1802.03612v1
|
To date, 204 individual molecular species, comprised of 16 different
elements, have been detected in the interstellar and circumstellar medium by
astronomical observations. These molecules range in size from two atoms to
seventy, and have been detected across the electromagnetic spectrum from
cm-wavelengths to the ultraviolet. This census presents a summary of the first
detection of each molecular species, including the observational facility,
wavelength range, transitions, and enabling laboratory spectroscopic work, as
well as listing tentative and disputed detections. Tables of molecules detected
in interstellar ices, external galaxies, protoplanetary disks, and exoplanetary
atmospheres are provided. A number of visual representations of this aggregate
data are presented and briefly discussed in context.
|
http://arxiv.org/abs/1809.09132v1
|
The Low-Power Image Recognition Challenge (LPIRC,
https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in
2015. The competition identifies the best technologies that can classify and
detect objects in images efficiently (short execution time and low energy
consumption) and accurately (high precision). Over the four years, the winners'
scores have improved more than 24 times. As computer vision is widely used in
many battery-powered systems (such as drones and mobile phones), the need for
low-power computer vision will become increasingly important. This paper
summarizes LPIRC 2018 by describing the three different tracks and the winners'
solutions.
|
http://arxiv.org/abs/1810.01732v1
|
This report provides detailed findings on the critical laboratory
astrophysics data needs that are required to maximize the scientific return for
NASA's current and near-term planned astrophysics missions. It also provides
prioritized rankings on said laboratory astrophysics data, generally by
waveband. The Report is based on community input gathered at the 2018 NASA
Laboratory Astrophysics Workshop (LAW) from presentations, from discussions
during workshop breakout sessions, and from other solicited input deemed
appropriate by the Scientific Organizing Committee (SOC) obtained prior to and
after the meeting. Hence, the Report is a direct reflection of the spirit and
participant make-up of LAW 2018. The Report also outlines specific
opportunities and threats facing NASA's Laboratory Astrophysics Program, and
articulates concrete actions by which the Agency can capitalize on the
opportunities and mitigate the challenges. The Report was prepared by the SOC,
with help from some invited speakers, and input and review from community
members.
|
http://arxiv.org/abs/2001.11361v1
|
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modifications on U-Nets and other popular CNN architectures. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs.
|
https://arxiv.org/abs/2001.11190v3
|
We present updated results for $\varepsilon_K$ determined directly from the
standard model (SM) with lattice QCD inputs such as $\hat{B}_K$, $|V_{cb}|$,
$|V_{us}|$, $\xi_0$, $\xi_2$, $\xi_\text{LD}$, $F_K$, and $m_c$. We find that
the standard model with exclusive $|V_{cb}|$ and other lattice QCD inputs
describes only 70% of the experimental value of $|\varepsilon_K|$ and does not
explain its remaining 30%, which leads to a strong tension in $|\varepsilon_K|$
at the $4\sigma$ level between the SM theory and experiment. We also find that
this tension disappears when we use the inclusive value of $|V_{cb}|$ obtained
using the heavy quark expansion based on QCD sum rules.
|
http://arxiv.org/abs/1810.09761v1
|
Our 2003 "Cicerone" had discussed charm dynamics with different directions
and levels \cite{CICERONE}. Here we focus on two items, where the `landscape'
has changed sizably. (A) The lifetimes \& semi-leptonic decays of charm hadrons
show the impact of non-perturbative QCD and to which degree one can apply Heavy
Quark Expansion (HQE) for charm hadrons. It is more complex as we have learnt
from 2019/20 data. (B) {\bf CP} asymmetry has been established in 2019
\cite{LHCbGuy}: $\Delta A_{\bf CP} \equiv A_{\bf CP}(D^0 \to K^+K^-) - A_{\bf
CP}(D^0 \to \pi^+\pi^-) $=$\, -\, (1.54 \pm 0.29 ) \cdot 10^{-3}$ is quite an
achievement by the LHCb collaboration! Our community is at the beginning of a
long travel for fundamental dynamics. Can the SM account for these? We discuss
the assumptions that were made up to 2018 data and new conclusions from 2019/20
ones. We need more data; however, one has to discuss correlations between
different transitions. We give an {\bf Appendix} what we have learnt for large
{\bf CP} asymmetry in $K_L \to \pi^+\pi^-e^+e^-$.
|
http://arxiv.org/abs/2001.06908v3
|
Evolutionary algorithm research and applications began over 50 years ago. Like other artificial intelligence techniques, evolutionary algorithms will likely see increased use and development due to the increased availability of computation, more robust and available open source software libraries, and the increasing demand for artificial intelligence techniques. As these techniques become more adopted and capable, it is the right time to take a perspective of their ability to integrate into society and the human processes they intend to augment. In this review, we explore a new taxonomy of evolutionary algorithms and resulting classifications that look at five main areas: the ability to manage the control of the environment with limiters, the ability to explain and repeat the search process, the ability to understand input and output causality within a solution, the ability to manage algorithm bias due to data or user design, and lastly, the ability to add corrective measures. These areas are motivated by today's pressures on industry to conform to both societies concerns and new government regulatory rules. As many reviews of evolutionary algorithms exist, after motivating this new taxonomy, we briefly classify a broad range of algorithms and identify areas of future research.
|
https://arxiv.org/abs/1906.08870v1
|
Saturn has long been the only giant planet in our solar system without any known Trojan members. In this paper, with serendipitous archival observations and refined orbit determination, we report that 2019 UO$_{14}$ is a Trojan of the gas giant. However, the object is only a transient Trojan currently librating around the leading Lagrange point $L_4$ of the Sun-Saturn system in a period of $\sim\!0.7$ kyr. Our N-body numerical simulation shows that 2019 UO$_{14}$ was likely captured as a Centaur and became trapped around $L_4$ $\sim\!2$ kyr ago from a horseshoe coorbital. The current Trojan state will be maintained for another millennium or thereabouts before transitioning back to a horseshoe state. Additionally, we characterize the physical properties of 2019 UO$_{14}$. Assuming a linear phase slope of $0.06 \pm 0.01$ mag/deg, the mean $r$-band absolute magnitude of the object was determined to be $H_r = 13.11 \pm 0.07$, with its color measured to be consistent with those of Jupiter and Neptune Trojans and not statistically different from Centaurs. Although the short-lived Saturn Trojan exhibited no compelling evidence of activity in the observations, we favour the possibility that it could be an active Trojan. If confirmed, 2019 UO$_{14}$ would be marked as the first active Trojan in our solar system. We conservatively determine the optical depth of dust within our photometric aperture to be $\lesssim\!10^{-7}$, corresponding to a dust mass-loss rate to be $\lesssim\!1$ kg s$^{-1}$, provided that the physical properties of dust grains resemble Centaur 29P/Schwassmann-Wachmann 1.
|
https://arxiv.org/abs/2409.19725v1
|
We present updated results for $\varepsilon_K$ determined directly from the
standard model (SM) with lattice QCD inputs such as $\hat{B}_K$, $|V_{cb}|$,
$|V_{us}|$, $\xi_0$, $\xi_2$, $\xi_\text{LD}$, $f_K$, and $m_c$. We find that
the standard model with exclusive $|V_{cb}|$ and other lattice QCD inputs
describes only 65\% of the experimental value of $|\varepsilon_K|$ and does not
explain its remaining 35\%, which leads to a strong tension in
$|\varepsilon_K|$ at the $4.6\sigma \sim 4.2\sigma$ level between the SM theory
and experiment. We also find that this tension disappears when we use the
inclusive value of $|V_{cb}|$ obtained using the heavy quark expansion based on
QCD sum rules.
|
http://arxiv.org/abs/1912.03024v2
|
Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmentation Challenge, which was a sub-challenge of the 2020 MICCAI Endoscopic Vision (EndoVis) Challenge, presented three sub-tasks to assess participating solutions on anatomical structure and instrument segmentation. Their performance was assessed on a hidden test set of 531 images from 10 videos of the CATARACTS test set.
|
https://arxiv.org/abs/2110.10965v2
|
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