Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

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.

In a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be utilized in the paper summarization task, for exploring beyond the intra-document information. In this work, we focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task. MUS finetunes a pre-trained encoder model on the citation graph by link prediction tasks. Then, the abstract sentences are extracted from the corresponding paper considering multi-granularity information. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we next propose a Graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available. Apart from employing the link prediction as an auxiliary task, GSS introduces a gated sentence encoder and a graph information fusion module to take advantage of the graph information to polish the sentence representation. Experiments on a public benchmark dataset show that MUS and GSS bring substantial improvements over the prior state-of-the-art model.

Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios. Different from previous works that make use of mutually similar but redundant translation memories~(TMs), we propose a new retrieval-augmented NMT to model contrastively retrieved translation memories that are holistically similar to the source sentence while individually contrastive to each other providing maximal information gains in three phases. First, in TM retrieval phase, we adopt a contrastive retrieval algorithm to avoid redundancy and uninformativeness of similar translation pieces. Second, in memory encoding stage, given a set of TMs we propose a novel Hierarchical Group Attention module to gather both local context of each TM and global context of the whole TM set. Finally, in training phase, a Multi-TM contrastive learning objective is introduced to learn salient feature of each TM with respect to target sentence. Experimental results show that our framework obtains improvements over strong baselines on the benchmark datasets.

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

非视线（NLOS）成像是一种用于检测障碍物或角落周围物体的物体的新兴技术。关于被动NLOS的最新研究主要集中在稳态测量和重建方法上，这些方法显示出识别移动目标的局限性。据我们所知，我们提出了一种新颖的基于事件的无源NLOS成像方法。我们获得了基于事件的异步数据，其中包含NLOS目标的详细动态信息，并有效缓解由运动引起的斑点降解。此外，我们创建了第一个基于事件的NLOS成像数据集NLOS-ES，并且由时间表面表示提取基于事件的功能。我们通过基于事件的数据与基于框架的数据比较重建。基于事件的方法在PSNR和LPIP上表现良好，该方法比基于框架的方法好20％和10％，而数据量仅占传统方法的2％。

大规模图在现实情况下无处不在，可以通过图神经网络（GNN）训练以生成下游任务的表示形式。鉴于大规模图的丰富信息和复杂的拓扑结构，我们认为在这样的图中存在冗余，并将降低训练效率。不幸的是，模型可伸缩性严重限制了通过香草GNNS训练大规模图的效率。尽管在基于抽样的培训方法方面取得了最新进展，但基于抽样的GNN通常忽略了冗余问题。在大规模图上训练这些型号仍然需要无法容忍的时间。因此，我们建议通过重新思考图中的固有特征来降低冗余并提高使用GNN的大规模训练效率。在本文中，我们开拓者提出了一种称为dropreef的曾经使用的方法，以在大规模图中删除冗余。具体而言，我们首先进行初步实验，以探索大规模图中的潜在冗余。接下来，我们提出一个度量标准，以量化图中所有节点的异质性。基于实验和理论分析，我们揭示了大规模图中的冗余，即具有高邻居异质的节点和大量邻居。然后，我们建议Dropreef一劳永逸地检测并删除大规模图中的冗余，以帮助减少训练时间，同时确保模型准确性没有牺牲。为了证明DropReef的有效性，我们将其应用于最新的基于最新的采样GNN，用于训练大规模图，这是由于此类模型的高精度。使用Dropreef杠杆，可以大力提高模型的训练效率。 Dropreef高度兼容，并且在离线上执行，从而在很大程度上使目前和未来的最新采样GNN受益。

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。

作为非遗迹渲染（NPR）的主要分支，图像样式主要使用计算机算法将照片渲染为艺术绘画。最近的工作表明，样式信息的提取，例如笔触纹理和目标样式图像的颜色是图像风格的关键。鉴于其中风质地和颜色特征，提出了一种新的中风渲染方法，该方法完全考虑了音调特征和原始油画的代表性，以便将原始油画图像的音调适应风格化的图像并制作它接近艺术家的创造性效果。实验验证了所提出模型的功效。这种方法更适合具有相对均匀的方向意识的点尔主义画家的作品，尤其是对于自然场景。当原始绘画笔触具有更清晰的方向感时，使用此方法模拟刷子纹理特征可能会不那么令人满意。

我们提出了Pangu-Coder，这是一种仅预读的解码器语言模型，该模型采用pangu-alpha架构进行文本到代码生成，即给定自然语言问题描述的编程语言解决方案的合成。我们使用两阶段策略训练Pangu-Coder：第一阶段采用因果语言建模（CLM）来预先培训原始编程语言数据，而第二阶段则使用因果语言建模和掩盖语言建模（MLM）的组合培训目标，专注于文本到代码生成的下游任务，并培训松散的自然语言程序定义和代码功能。最后，我们讨论了pangu-coder-ft，该pander the是通过竞争性编程问题和代码与持续集成测试的结合进行了微调的。我们评估了pangu-coder，重点是它是否生成功能上正确的程序，并证明它在参加较小的上下文窗口和较少的数据培训的同时，它比诸如Codex之类的类似大小的模型（例如Codex）实现等效性或更好的性能。