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.

Current natural language processing (NLP) models such as BERT and RoBERTa have achieved high overall performance, but they often make systematic errors due to bias or certain difficult features to learn. Thus research on slice detection models (SDM) which automatically identifies underperforming groups of datapoints has gradually caught more attention, which aims at both understanding model behaviors and providing insights for future model training and designing. However, there is little systematic research on SDM and quantitative evaluation of its assessment for NLP models. Our paper fills this gap by proposing "Discover, Explanation, Improvement" framework that discovers coherent and underperforming groups of datapoints and unites datapoints of each slice under human-understandable concepts; it also provides comprehensive evaluation tasks and the corresponding quantitative metrics, which enable convenient comparison for future works. Results show that our framework can accurately select error-prone datapoints with informative semantic features that summarize error patterns, based on which it directly boosts model performance by an average of 2.85 points based on trained models without tuning any parameters across multiple datasets.

神经网络已广泛应用于垃圾邮件和网络钓鱼检测，入侵预防和恶意软件检测等安全应用程序。但是，这种黑盒方法通常在应用中具有不确定性和不良的解释性。此外，神经网络本身通常容易受到对抗攻击的影响。由于这些原因，人们对可信赖和严格的方法有很高的需求来验证神经网络模型的鲁棒性。对抗性的鲁棒性在处理恶意操纵输入时涉及神经网络的可靠性，是安全和机器学习中最热门的主题之一。在这项工作中，我们在神经网络的对抗性鲁棒性验证中调查了现有文献，并在机器学习，安全和软件工程领域收集了39项多元化研究工作。我们系统地分析了它们的方法，包括如何制定鲁棒性，使用哪种验证技术以及每种技术的优势和局限性。我们从正式验证的角度提供分类学，以全面理解该主题。我们根据财产规范，减少问题和推理策略对现有技术进行分类。我们还展示了使用样本模型在现有研究中应用的代表性技术。最后，我们讨论了未来研究的开放问题。

话语重写旨在恢复核心发挥，并从最新的多转话对话中省略信息。最近，在内部和室外重写设置中，标记而不是线性生成序列的方法已被证明更强。这是由于标记器的较小搜索空间，因为它只能从对话环境中复制令牌。但是，当必须将短语添加到源语言中时，单个上下文跨度不能涵盖这些方法时，这些方法可能会遭受较低的覆盖范围。这可能会以英语等语言发生，这些语言将令牌（例如介词）引入语法重写。我们提出了一个层次上下文标记器（HCT），该标记器通过预测插槽规则（例如，“ baster_”）来减轻此问题，其插槽后来充满了上下文跨度。 HCT（i）使用令牌级的编辑操作和开槽的规则标记源字符串，（ii）填充了对话环境中的跨度的结果规则插槽。此规则标记允许HCT一次添加外在代币和多个跨度。我们进一步集中了规则，以截断规则分布的长尾巴。几个基准测试的实验表明，HCT可以比2个BLEU点胜过最先进的重写系统。

Pre-trained language models have achieved promising success in code retrieval tasks, where a natural language documentation query is given to find the most relevant existing code snippet. However, existing models focus only on optimizing the documentation code pairs by embedding them into latent space, without the association of external knowledge. In this paper, we propose a generation-augmented query expansion framework. Inspired by the human retrieval process - sketching an answer before searching, in this work, we utilize the powerful code generation model to benefit the code retrieval task. Specifically, we demonstrate that rather than merely retrieving the target code snippet according to the documentation query, it would be helpful to augment the documentation query with its generation counterpart - generated code snippets from the code generation model. To the best of our knowledge, this is the first attempt that leverages the code generation model to enhance the code retrieval task. We achieve new state-of-the-art results on the CodeSearchNet benchmark and surpass the baselines significantly.

The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing to fully capture the interactions between the query and document. To alleviate this, recent work expects to get query-informed representations of documents. During training, it expands the document with a real query, while replacing the real query with a generated pseudo query at inference. This discrepancy between training and inference makes the dense retrieval model pay more attention to the query information but ignore the document when computing the document representation. As a result, it even performs worse than the vanilla dense retrieval model, since its performance depends heavily on the relevance between the generated queries and the real query. In this paper, we propose a curriculum sampling strategy, which also resorts to the pseudo query at training and gradually increases the relevance of the generated query to the real query. In this way, the retrieval model can learn to extend its attention from the document only to both the document and query, hence getting high-quality query-informed document representations. Experimental results on several passage retrieval datasets show that our approach outperforms the previous dense retrieval methods1.

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. To be specific, we regard the original input graph as the first view and generate the second view by graph augmentation with edge modifications. With the guidance of maximizing the similarity of the subgraph pairs, the proposed subgraph-subgraph contrast contributes to more robust subgraph embeddings despite of the structure variation. Moreover, the introduced subgraph-subgraph contrast cooperates well with the widely-adopted node-subgraph and node-node contrastive counterparts for mutual GAD performance promotions. Besides, we also conduct sufficient experiments to investigate the impact of different graph augmentation approaches on detection performance. The comprehensive experimental results well demonstrate the superiority of our method compared with the state-of-the-art approaches and the effectiveness of the multi-view subgraph pair contrastive strategy for the GAD task.

当系统中有某些未知术语和隐藏的物理机制时，基于第一原理的复杂物理系统的管理方程可能会非常具有挑战性。在这项工作中，我们采用深度学习体系结构来学习基于从完全动力学模型中获取的数据的等离子体系统的流体部分微分方程（PDE）。证明了学到的多臂流体PDE可以融合诸如Landau阻尼等动力学效应。基于学习的流体闭合，数据驱动的多音阶流体建模可以很好地再现从完全动力学模型中得出的所有物理量。Landau阻尼的计算阻尼率与完全动力学的模拟和线性理论一致。用于复杂物理系统的PDE的数据驱动的流体建模可以应用于改善流体闭合并降低全球系统多规模建模的计算成本。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

延时摄影是在电影和宣传电影中使用的，因为它可以在短时间内反映时间的流逝并增强视觉吸引力。但是，由于需要很长时间才需要稳定的射击，因此对摄影师来说是一个巨大的挑战。在本文中，我们提出了一个带有虚拟和真实机器人的延时摄影系统。为了帮助用户有效拍摄延时视频，我们首先参数化延时摄影并提出参数优化方法。对于不同的参数，使用不同的美学模型，包括图像和视频美学质量评估网络，用于生成最佳参数。然后，我们提出了一个延时摄影界面，以促进用户查看和调整参数，并使用虚拟机器人在三维场景中进行虚拟摄影。该系统还可以导出参数并将其提供给真实的机器人，以便可以在现实世界中拍摄延时视频。此外，我们提出了一种延时摄影美学评估方法，该方法可以自动评估及时视频的美学质量。实验结果表明，我们的方法可以有效地获得延时视频。我们还进行了用户研究。结果表明，我们的系统具有与专业摄影师相似的效果，并且更有效。