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.

随着在充满挑战的环境中越来越需要多机器人探索未知区域的需求，需要有效的协作探索策略来实现此类壮举。可以部署基于边界的快速探索随机树（RRT）探索来探索未知的环境。然而，它的贪婪行为导致多个机器人探索收入最高的地区，从而导致勘探过程中大规模重叠。为了解决这个问题，我们提出了基于时间内存的RRT（TM-RRT）探索策略，用于多机器人在未知环境中执行强大的探索。它根据每个机器人的相对位置计算分配的每个边界的自适应持续时间，并计算边界的收入。此外，每个机器人都配备了由分配的边界和舰队共享的内存，以防止重复对同一边界的分配。通过模拟和实际部署，我们通过在25.0m x 540m（1350.0m2）区域完成勘探，展示了TM-RRT勘探策略的鲁棒性，而常规的RRT勘探策略则不足。

为了在多个机器人系统中有效完成任务，必须解决的问题是同时定位和映射（SLAM）。激光雷达（光检测和范围）由于其出色的精度而用于许多SLAM解决方案，但其性能在无特征环境（如隧道或长走廊）中降低。集中式大满贯解决了云服务器的问题，云服务器需要大量的计算资源，并且缺乏针对中央节点故障的鲁棒性。为了解决这些问题，我们提出了一个分布式的SLAM解决方案，以使用超宽带（UWB）范围和探测测量值估算一组机器人的轨迹。所提出的方法在机器人团队之间分配了处理，并显着减轻了从集中式大满贯出现的计算问题。我们的解决方案通过最大程度地减少在机器人处于近距离接近时在不同位置进行的UWB范围测量方法来确定两个机器人之间的相对姿势（也称为环闭合）。 UWB在视线条件下提供了良好的距离度量，但是由于机器人的噪声和不可预测的路径，检索精确的姿势估计仍然是一个挑战。为了处理可疑的循环封闭，我们使用成对的一致性最大化（PCM）来检查循环封闭质量并执行异常拒绝。然后，在分布式姿势图优化（DPGO）模块中将过滤的环闭合与探光仪融合，以恢复机器人团队的完整轨迹。进行了广泛的实验以验证所提出的方法的有效性。

在室内运行的自主机器人和GPS拒绝的环境可以使用LIDAR进行大满贯。但是，由于循环闭合检测和计算负载以执行扫描匹配的挑战，在几何衰减的环境中，LIDAR的表现不佳。现有的WiFi基础架构可以用低硬件和计算成本来进行本地化和映射。然而，使用WiFi进行准确的姿势估计是具有挑战性的，因为由于信号传播的不可预测性，可以在同一位置测量不同的信号值。因此，我们介绍了WiFi指纹序列的使用量估计（即循环闭合）。这种方法利用移动机器人移动时获得的位置指纹的空间连贯性。这具有更好的校正探针流漂移的能力。该方法还结合了激光扫描，从而提高了大型和几何衰减环境的计算效率，同时保持LIDAR SLAM的准确性。我们在室内环境中进行了实验，以说明该方法的有效性。基于根平方误差（RMSE）评估结果，并在测试环境中达到了88m的精度。

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.

Predictive coding is a message-passing framework initially developed to model information processing in the brain, and now also topic of research in machine learning due to some interesting properties. One of such properties is the natural ability of generative models to learn robust representations thanks to their peculiar credit assignment rule, that allows neural activities to converge to a solution before updating the synaptic weights. Graph neural networks are also message-passing models, which have recently shown outstanding results in diverse types of tasks in machine learning, providing interdisciplinary state-of-the-art performance on structured data. However, they are vulnerable to imperceptible adversarial attacks, and unfit for out-of-distribution generalization. In this work, we address this by building models that have the same structure of popular graph neural network architectures, but rely on the message-passing rule of predictive coding. Through an extensive set of experiments, we show that the proposed models are (i) comparable to standard ones in terms of performance in both inductive and transductive tasks, (ii) better calibrated, and (iii) robust against multiple kinds of adversarial attacks.

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.

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.