我们提出了一种便携式多型摄像头系统，该系统具有专用模型，用于动态场景中的新型视图和时间综合。我们的目标是使用我们的便携式多座相机从任何角度从任何角度出发为动态场景提供高质量的图像。为了实现这种新颖的观点和时间综合，我们开发了一个配备了五个相机的物理多型摄像头，以在时间和空间域中训练神经辐射场（NERF），以进行动态场景。我们的模型将6D坐标（3D空间位置，1D时间坐标和2D观看方向）映射到观看依赖性且随时间变化的发射辐射和体积密度。量渲染用于在指定的相机姿势和时间上渲染光真实的图像。为了提高物理相机的鲁棒性，我们提出了一个摄像机参数优化模块和一个时间框架插值模块，以促进跨时间的信息传播。我们对现实世界和合成数据集进行了实验以评估我们的系统，结果表明，我们的方法在定性和定量上优于替代解决方案。我们的代码和数据集可从https://yuenfuilau.github.io获得。

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.

Binarized Neural Networks (BNNs) are receiving increasing attention due to their lightweight architecture and ability to run on low-power devices. The state-of-the-art for training classification BNNs restricted to few-shot learning is based on a Mixed Integer Programming (MIP) approach. This paper proposes the BeMi ensemble, a structured architecture of BNNs based on training a single BNN for each possible pair of classes and applying a majority voting scheme to predict the final output. The training of a single BNN discriminating between two classes is achieved by a MIP model that optimizes a lexicographic multi-objective function according to robustness and simplicity principles. This approach results in training networks whose output is not affected by small perturbations on the input and whose number of active weights is as small as possible, while good accuracy is preserved. We computationally validate our model using the MNIST and Fashion-MNIST datasets using up to 40 training images per class. Our structured ensemble outperforms both BNNs trained by stochastic gradient descent and state-of-the-art MIP-based approaches. While the previous approaches achieve an average accuracy of 51.1% on the MNIST dataset, the BeMi ensemble achieves an average accuracy of 61.7% when trained with 10 images per class and 76.4% when trained with 40 images per class.

The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, $k$-means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users.

We present HetNet (Multi-level \textbf{Het}erogeneous \textbf{Net}work), a highly efficient mirror detection network. Current mirror detection methods focus more on performance than efficiency, limiting the real-time applications (such as drones). Their lack of efficiency is aroused by the common design of adopting homogeneous modules at different levels, which ignores the difference between different levels of features. In contrast, HetNet detects potential mirror regions initially through low-level understandings (\textit{e.g.}, intensity contrasts) and then combines with high-level understandings (contextual discontinuity for instance) to finalize the predictions. To perform accurate yet efficient mirror detection, HetNet follows an effective architecture that obtains specific information at different stages to detect mirrors. We further propose a multi-orientation intensity-based contrasted module (MIC) and a reflection semantic logical module (RSL), equipped on HetNet, to predict potential mirror regions by low-level understandings and analyze semantic logic in scenarios by high-level understandings, respectively. Compared to the state-of-the-art method, HetNet runs 664$\%$ faster and draws an average performance gain of 8.9$\%$ on MAE, 3.1$\%$ on IoU, and 2.0$\%$ on F-measure on two mirror detection benchmarks.

Target-specific stance detection on social media, which aims at classifying a textual data instance such as a post or a comment into a stance class of a target issue, has become an emerging opinion mining paradigm of importance. An example application would be to overcome vaccine hesitancy in combating the coronavirus pandemic. However, existing stance detection strategies rely merely on the individual instances which cannot always capture the expressed stance of a given target. In response, we address a new task called conversational stance detection which is to infer the stance towards a given target (e.g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread. To tackle the task, we first propose a benchmarking conversational stance detection (CSD) dataset with annotations of stances and the structures of conversation threads among the instances based on six major social media platforms in Hong Kong. To infer the desired stances from both data instances and conversation threads, we propose a model called Branch-BERT that incorporates contextual information in conversation threads. Extensive experiments on our CSD dataset show that our proposed model outperforms all the baseline models that do not make use of contextual information. Specifically, it improves the F1 score by 10.3% compared with the state-of-the-art method in the SemEval-2016 Task 6 competition. This shows the potential of incorporating rich contextual information on detecting target-specific stances on social media platforms and implies a more practical way to construct future stance detection tasks.

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://github.com/HKUST-KnowComp/BEKG.

在本文中，我们报告了我们提交给文献综述的多文章摘要（MSLR）共享任务。具体而言，我们通过以多种方式将全球关注对重要的生物医学实体放置在生物医学领域中。我们分析了23个结果模型的输出，并报告了与存在额外的全球注意力，训练步骤的数量和输入配置有关的结果中的模式。

我们提出了一种使用预训练的语言模型的新的无监督方法，用于词汇替换。与以前使用语言模型的生成能力预测替代品的方法相比，我们的方法基于上下文化和脱皮的单词嵌入的相似性检索替代品，即单词在多个上下文中的平均上下文表示。我们以英语和意大利语进行实验，并表明我们的方法基本上要优于强大的基准，并在没有任何明确的监督或微调的情况下建立了新的最新技术。我们进一步表明，我们的方法在预测低频替代品方面的表现特别出色，还产生了多种替代候选者列表，从而减少了根据文章 - 名称协议引起的形态寄电或形态句法偏见。

玻璃在我们的日常生活中非常普遍。现有的计算机视觉系统忽略了它，因此可能会产生严重的后果，例如，机器人可能会坠入玻璃墙。但是，感知玻璃的存在并不简单。关键的挑战是，任意物体/场景可以出现在玻璃后面。在本文中，我们提出了一个重要的问题，即从单个RGB图像中检测玻璃表面。为了解决这个问题，我们构建了第一个大规模玻璃检测数据集（GDD），并提出了一个名为GDNet-B的新颖玻璃检测网络，该网络通过新颖的大型场探索大型视野中的丰富上下文提示上下文特征集成（LCFI）模块并将高级和低级边界特征与边界特征增强（BFE）模块集成在一起。广泛的实验表明，我们的GDNET-B可以在GDD测试集内外的图像上达到满足玻璃检测结果。我们通过将其应用于其他视觉任务（包括镜像分割和显着对象检测）来进一步验证我们提出的GDNET-B的有效性和概括能力。最后，我们显示了玻璃检测的潜在应用，并讨论了可能的未来研究方向。