The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly focus on model-based adaptation in a self-training manner, such as predicting pseudo labels for new domain datasets. Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions. Motivated by the prompt learning in NLP, in this paper, we propose to learn an image-level visual domain prompt for target domains while having the source model parameters frozen. During testing, the changing target datasets can be adapted to the source model by reformulating the input data with the learned visual prompts. Specifically, we devise two types of prompts, i.e., domains-specific prompts and domains-agnostic prompts, to extract current domain knowledge and maintain the domain-shared knowledge in the continual adaptation. Furthermore, we design a homeostasis-based prompt adaptation strategy to suppress domain-sensitive parameters in domain-invariant prompts to learn domain-shared knowledge more effectively. This transition from the model-dependent paradigm to the model-free one enables us to bypass the catastrophic forgetting and error accumulation problems. Experiments show that our proposed method achieves significant performance gains over state-of-the-art methods on four widely-used benchmarks, including CIFAR-10C, CIFAR-100C, ImageNet-C, and VLCS datasets.

This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. However, the workflows, computational patterns, communication patterns, and optimization techniques of distributed GNN training remain preliminarily understood. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.

User-generated-content (UGC) videos have dominated the Internet during recent years. While many methods attempt to objectively assess the quality of these UGC videos, the mechanisms of human quality perception in the UGC-VQA problem is still yet to be explored. To better explain the quality perception mechanisms and learn more robust representations, we aim to disentangle the effects of aesthetic quality issues and technical quality issues risen by the complicated video generation processes in the UGC-VQA problem. To overcome the absence of respective supervisions during disentanglement, we propose the Limited View Biased Supervisions (LVBS) scheme where two separate evaluators are trained with decomposed views specifically designed for each issue. Composed of an Aesthetic Quality Evaluator (AQE) and a Technical Quality Evaluator (TQE) under the LVBS scheme, the proposed Disentangled Objective Video Quality Evaluator (DOVER) reach excellent performance (0.91 SRCC for KoNViD-1k, 0.89 SRCC for LSVQ, 0.88 SRCC for YouTube-UGC) in the UGC-VQA problem. More importantly, our blind subjective studies prove that the separate evaluators in DOVER can effectively match human perception on respective disentangled quality issues. Codes and demos are released in https://github.com/teowu/dover.

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.

Time series motif discovery has been a fundamental task to identify meaningful repeated patterns in time series. Recently, time series chains were introduced as an expansion of time series motifs to identify the continuous evolving patterns in time series data. Informally, a time series chain (TSC) is a temporally ordered set of time series subsequences, in which every subsequence is similar to the one that precedes it, but the last and the first can be arbitrarily dissimilar. TSCs are shown to be able to reveal latent continuous evolving trends in the time series, and identify precursors of unusual events in complex systems. Despite its promising interpretability, unfortunately, we have observed that existing TSC definitions lack the ability to accurately cover the evolving part of a time series: the discovered chains can be easily cut by noise and can include non-evolving patterns, making them impractical in real-world applications. Inspired by a recent work that tracks how the nearest neighbor of a time series subsequence changes over time, we introduce a new TSC definition which is much more robust to noise in the data, in the sense that they can better locate the evolving patterns while excluding the non-evolving ones. We further propose two new quality metrics to rank the discovered chains. With extensive empirical evaluations, we demonstrate that the proposed TSC definition is significantly more robust to noise than the state of the art, and the top ranked chains discovered can reveal meaningful regularities in a variety of real world datasets.

增加片上光子神经网络（PNN）的层数对于改善其模型性能至关重要。但是，网络隐藏层的连续级联导致更大的集成光子芯片区域。为了解决此问题，我们提出了光学神经常规微分方程（ON-ON-ON-OD-ON-OD-ON-OD-ON-OD-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ODINE），该架构用光ODE求解器参数化了隐藏层的连续动力学。 On-Ode包括PNN，然后是光子积分器和光反馈回路，可以配置为代表残留的神经网络（RESNET）和复发性神经网络，并有效地降低了芯片面积占用率。对于基于干扰的光电非线性隐藏层，数值实验表明，单个隐藏层ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ONE表示与图像分类任务中的两层光学重新系统大致相同。此外，Onode提高了基于衍射的全光线性隐藏层的模型分类精度。 On-Eod的时间依赖性动力学属性进一步应用于高精度的轨迹预测。

我们考虑在编码晶体材料的周期图上的表示形式学习。与常规图不同，周期图由最小单位单元组成，该单元在3D空间中的常规晶格上重复出现。如何有效编码这些周期结构会带来常规图表学习中不存在的独特挑战。除了E（3）不变外，周期性的图表表示还需要定期不变。也就是说，学到的表示形式应该不变，因为它们是人为强加的。此外，需要明确捕获周期性重复模式，因为不同尺寸和方向的晶格可能对应于不同的材料。在这项工作中，我们提出了一个变压器体系结构，称为Matformer，以进行周期性图表学习。我们的拟合器设计为周期性不变，可以明确捕获重复模式。特别是，Matformer通过有效使用相邻细胞中相同原子之间的几何距离来编码周期模式。多个通用基准数据集的实验结果表明，我们的配合器的表现始终超过基线方法。此外，我们的结果证明了定期不变性和对晶体表示学习的明确重复模式编码的重要性。

对具有代理商初始位置未知的有限3D环境的多代理探索是一个具有挑战性的问题。它需要快速探索环境，并坚定合并代理商构建的子图。我们认为现有方法是侵略性或保守的：在检测到重叠时，积极的策略合并了两种由不同代理构建的子图，这可能导致由于对重叠的错误阳性检测而导致不正确的合并，因此是如此。不健全。保守策略指导一个代理人在合并之前重新审视另一个代理商的过量验证历史轨迹，这可以降低由于对同一空间的反复探索而引起的勘探效率。为了巧妙地平衡子图合并和勘探效率的鲁棒性，我们为基于激光雷达的多代理探索开发了一种新方法，该方法可以指导一个代理商以\ emph {自适应}方式重复另一个代理商的轨迹子图合并过程的指标。此外，我们的方法通过计划合并子图的代理人共同计划，以进一步提高勘探效率，以\ emph {Cooperative}方式将最近的单格分层勘探策略扩展到多个代理。我们的实验表明，我们的方法平均比基线高出50 \％，同时稳固地合并子映射。