Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN.

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.

The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material design and synthesis. To this end, we used natural language processing (NLP) and computer vision (CV) techniques based on convolutional neural networks (CNN) to discover valuable experimental-based information about nanomaterials and synthesis methods in energy-material-related publications. Our first system, TextMaster, extracts opinions from texts and classifies them into challenges and opportunities, achieving 94% and 92% accuracy, respectively. Our second system, GraphMaster, realizes data extraction of tables and figures from publications with 98.3\% classification accuracy and 4.3% data extraction mean square error. Our results show that these systems could assess the suitability of materials for a certain application by evaluation of synthesis insights and case analysis with detailed references. This work offers a fresh perspective on mining knowledge from scientific literature, providing a wide swatch to accelerate nanomaterial research through CNN.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

从单眼RGB图像中重建3D手网络，由于其在AR/VR领域的巨大潜在应用，引起了人们的注意力越来越多。大多数最先进的方法试图以匿名方式解决此任务。具体而言，即使在连续录制会话中用户没有变化的实际应用程序中实际上可用，因此忽略了该主题的身份。在本文中，我们提出了一个身份感知的手网格估计模型，该模型可以结合由受试者的内在形状参数表示的身份信息。我们通过将提出的身份感知模型与匿名对待主题的基线进行比较来证明身份信息的重要性。此外，为了处理未见测试对象的用例，我们提出了一条新型的个性化管道来校准固有的形状参数，仅使用该受试者的少数未标记的RGB图像。在两个大型公共数据集上进行的实验验证了我们提出的方法的最先进性能。

我们为致密氢的方程式提供了基于深层生成模型的变化自由能方法。我们采用归一化流网络来对质子玻尔兹曼分布和费米子神经网络进行建模，以在给定的质子位置对电子波函数进行建模。通过共同优化两个神经网络，我们达到了与先前的电子蒙特卡洛计算相当的变异自由能。我们的结果表明，与先前的蒙特卡洛和从头算分子动力学数据相比，行星条件下的氢甚至更浓密，这远离经验化学模型的预测。获得可靠的密集氢状态方程，尤其是直接进入熵和自由能，为行星建模和高压物理学研究开辟了新的机会。

在多代理系统中，植入是一个非常具有挑战性的问题。传统的羊群方法还需要完全了解环境和控制模型。在本文中，我们建议在羊群任务中进化多代理增强学习（EMARL），这是一种混合算法，将合作和竞争与很少的先验知识相结合。至于合作，我们根据BOIDS模型设计了代理商对羊群任务的奖励。在竞争中，具有高健身的代理商被设计为高级代理商，并且那些健身较低的代理商被设计为初中，让初级代理商随机继承了高级代理人的参数。为了加强竞争，我们还设计了一种进化选择机制，该机制在羊群任务中显示出对信用分配的有效性。一系列具有挑战性和自我对比的基准测试的实验结果表明，EMARL显着超过了完整的竞争或合作方法。

近年来，人们见证了应用上下文框架以提高对象检测作为视频对象检测的性能的趋势。现有方法通常一次汇总功能以增强功能。但是，这些方法通常缺少来自相邻帧的空间信息，并且缺乏功能聚合不足。为了解决这些问题，我们执行一种渐进式方式来引入时间信息和空间信息以进行集成增强。时间信息由时间特征聚合模型（TFAM）引入，通过在上下文框架和目标框架之间进行注意机制（即要检测到的框架）。同时，我们采用空间过渡意识模型（StAM）来传达每个上下文框架和目标框架之间的位置过渡信息。我们的PTSeformer建立在基于变压器的检测器DETR上，还遵循端到端的方式，以避免重大的后处理程序，同时在Imagenet VID数据集上获得88.1％的地图。代码可在https://github.com/hon-wong/ptseformer上找到。

作为人类识别的重要生物标志物，可以通过被动传感器在没有主题合作的情况下以远距离收集人步态，这在预防犯罪，安全检测和其他人类识别应用中起着至关重要的作用。目前，大多数研究工作都是基于相机和计算机视觉技术来执行步态识别的。但是，在面对不良的照明时，基于视觉的方法并不可靠，导致性能降解。在本文中，我们提出了一种新型的多模式步态识别方法，即gaitfi，该方法利用WiFi信号和视频进行人类识别。在GAITFI中，收集了反映WiFi多路径传播的通道状态信息（CSI），以捕获人体步态，而视频则由相机捕获。为了了解强大的步态信息，我们建议使用轻量级残留卷积网络（LRCN）作为骨干网络，并通过集成WiFi和Vision功能来进一步提出两流性gaitfi，以进行步态检索任务。通过在不同级别的特征上的三胞胎损失和分类损失进行训练。广泛的实验是在现实世界中进行的，该实验表明，基于单个WiFi或摄像机的GAITFI优于最先进的步态识别方法，对于12个受试者的人类识别任务而达到94.2％。

阿凡达（Avatar）是指虚拟世界中物理用户的代表，该代表可以从事不同的活动并与Metaverse中的其他对象进行交互。模拟化身需要准确的人类姿势估计。尽管基于摄像头的解决方案产生了出色的性能，但它们遇到了隐私问题，并因不同的照明而引起的性能退化，尤其是在智能家居中。在本文中，我们提出了一种基于WiFi的IOT基于Metavers Avatar模拟的人类姿势估计方案，即Metafi。具体而言，深度神经网络设计具有定制的卷积层和残留块，以将渠道状态信息映射到人体姿势地标。它被强制从准确的计算机视觉模型中学习注释，从而实现跨模式监督。 WiFi无处不在且强大的照明，使其成为智能家居中的头像应用的可行解决方案。实验是在现实世界中进行的，结果表明，METAFI以95.23％的50@PCK实现了很高的性能。