There is no settled universal 3D representation for geometry with many alternatives such as point clouds, meshes, implicit functions, and voxels to name a few. In this work, we present a new, compelling alternative for representing shapes using a sequence of cross-sectional closed loops. The loops across all planes form an organizational hierarchy which we leverage for autoregressive shape synthesis and editing. Loops are a non-local description of the underlying shape, as simple loop manipulations (such as shifts) result in significant structural changes to the geometry. This is in contrast to manipulating local primitives such as points in a point cloud or a triangle in a triangle mesh. We further demonstrate that loops are intuitive and natural primitive for analyzing and editing shapes, both computationally and for users.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

Adversarial machine learning has been both a major concern and a hot topic recently, especially with the ubiquitous use of deep neural networks in the current landscape. Adversarial attacks and defenses are usually likened to a cat-and-mouse game in which defenders and attackers evolve over the time. On one hand, the goal is to develop strong and robust deep networks that are resistant to malicious actors. On the other hand, in order to achieve that, we need to devise even stronger adversarial attacks to challenge these defense models. Most of existing attacks employs a single $\ell_p$ distance (commonly, $p\in\{1,2,\infty\}$) to define the concept of closeness and performs steepest gradient ascent w.r.t. this $p$-norm to update all pixels in an adversarial example in the same way. These $\ell_p$ attacks each has its own pros and cons; and there is no single attack that can successfully break through defense models that are robust against multiple $\ell_p$ norms simultaneously. Motivated by these observations, we come up with a natural approach: combining various $\ell_p$ gradient projections on a pixel level to achieve a joint adversarial perturbation. Specifically, we learn how to perturb each pixel to maximize the attack performance, while maintaining the overall visual imperceptibility of adversarial examples. Finally, through various experiments with standardized benchmarks, we show that our method outperforms most current strong attacks across state-of-the-art defense mechanisms, while retaining its ability to remain clean visually.

The introduction of high-quality image generation models, particularly the StyleGAN family, provides a powerful tool to synthesize and manipulate images. However, existing models are built upon high-quality (HQ) data as desired outputs, making them unfit for in-the-wild low-quality (LQ) images, which are common inputs for manipulation. In this work, we bridge this gap by proposing a novel GAN structure that allows for generating images with controllable quality. The network can synthesize various image degradation and restore the sharp image via a quality control code. Our proposed QC-StyleGAN can directly edit LQ images without altering their quality by applying GAN inversion and manipulation techniques. It also provides for free an image restoration solution that can handle various degradations, including noise, blur, compression artifacts, and their mixtures. Finally, we demonstrate numerous other applications such as image degradation synthesis, transfer, and interpolation.

Robots have been brought to work close to humans in many scenarios. For coexistence and collaboration, robots should be safe and pleasant for humans to interact with. To this end, the robots could be both physically soft with multimodal sensing/perception, so that the robots could have better awareness of the surrounding environment, as well as to respond properly to humans' action/intention. This paper introduces a novel soft robotic link, named ProTac, that possesses multiple sensing modes: tactile and proximity sensing, based on computer vision and a functional material. These modalities come from a layered structure of a soft transparent silicon skin, a polymer dispersed liquid crystal (PDLC) film, and reflective markers. Here, the PDLC film can switch actively between the opaque and the transparent state, from which the tactile sensing and proximity sensing can be obtained by using cameras solely built inside the ProTac link. In this paper, inference algorithms for tactile proximity perception are introduced. Evaluation results of two sensing modalities demonstrated that, with a simple activation strategy, ProTac link could effectively perceive useful information from both approaching and in-contact obstacles. The proposed sensing device is expected to bring in ultimate solutions for design of robots with softness, whole-body and multimodal sensing, and safety control strategies.

利用基于文本的节点属性的节点分类具有许多真实的应用程序，从学术引用图中的纸张主题到社交媒体网络中用户特征的分类范围。最新的节点分类框架（例如Giant）使用两阶段管道：首先嵌入图节点的文本属性，然后将所得嵌入的嵌入到节点分类模型中。在本文中，我们消除了这两个阶段，而是开发了建立在巨人基于端到端巨型（E2EG）的端到端节点分类模型。在我们的方法中，主体和辅助分类目标的串联利用导致了更强大的模型，从而使BERT主链可以切换为蒸馏编码器，其参数数量减少了25％-40％。此外，模型的端到端性质提高了易用性，因为它避免了链接多个模型进行节点分类的需求。与OGBN-ARXIV和OGBN产品数据集的巨型+MLP基线相比，我们的模型能够在换电环境（+0.5％）中获得稍好的精度，同时将模型培训时间最多减少40％。我们的模型也适用于电感设置，优于巨型 +MLP高达 +2.23％。

客户的评论在在线购物中起着至关重要的作用。人们经常参考以前客户的评论或评论，以决定是否购买新产品。赶上这种行为，有些人会为骗子的客户创建不真实的评论，以了解产品的假质量。这些评论称为垃圾邮件评论，它使消费者在在线购物平台上混淆，并对在线购物行为产生负面影响。我们提出了称为Vispamreviews的数据集，该数据集具有严格的注释程序，用于检测电子商务平台上的垃圾邮件评论。我们的数据集由两个任务组成：用于检测评论是否为垃圾邮件的二进制分类任务以及用于识别垃圾邮件类型的多类分类任务。Phobert在这两个任务上均以宏平均F1分别获得了最高的结果，分别为88.93％和72.17％。

基于硬件的加速度是促进许多计算密集型数学操作的广泛尝试。本文提出了一个基于FPGA的体系结构来加速卷积操作 - 在许多卷积神经网络模型中出现的复杂且昂贵的计算步骤。我们将设计定为标准卷积操作，打算以边缘-AI解决方案启动产品。该项目的目的是产生一个可以一次处理卷积层的FPGA IP核心。系统开发人员可以使用Verilog HDL作为体系结构的主要设计语言来部署IP核心。实验结果表明，我们在简单的边缘计算FPGA板上合成的单个计算核心可以提供0.224 GOPS。当董事会充分利用时，可以实现4.48 GOP。

由于GaN潜在空间的勘探和利用，近年来，现实世界的图像操纵实现了奇妙的进展。 GaN反演是该管道的第一步，旨在忠实地将真实图像映射到潜在代码。不幸的是，大多数现有的GaN反演方法都无法满足下面列出的三个要求中的至少一个：重建质量，可编辑性和快速推断。我们在本研究中提出了一种新的两阶段策略，同时适合所有要求。在第一阶段，我们训练编码器将输入图像映射到StyleGan2 $ \ Mathcal {W} $ - 空间，这被证明具有出色的可编辑性，但重建质量较低。在第二阶段，我们通过利用一系列HyperNetWorks来补充初始阶段的重建能力以在反转期间恢复缺失的信息。这两个步骤互相补充，由于Hypernetwork分支和由于$ \ Mathcal {W} $ - 空间中的反转，因此由于HyperNetwork分支和优异的可编辑性而相互作用。我们的方法完全是基于编码器的，导致极快的推断。关于两个具有挑战性的数据集的广泛实验证明了我们方法的优越性。

域适应（DA）从严格的理论作品中获益，研究其富有识别特征和各个方面，例如学习领域 - 不变的表示及其权衡。然而，由于多个源域的参与和训练期间目标域的潜在不可用的域，因此似乎不是这种源DA和域泛化（DG）设置的情况非常复杂和复杂。在本文中，我们为目标一般损失开发了新的上限，吸引我们来定义两种域名不变的表示。我们进一步研究了利弊以及执行学习每个领域不变的表示的权衡。最后，我们进行实验检查这些陈述的权衡，以便在实践中提供有关如何使用它们的实践提示，并探索我们发达理论的其他有趣性质。