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Region selection is a fundamental task in interactive systems. In 2D user interfaces, users typically use a rectangle selection tool to formulate a region using a mouse or touchpad. Region selection in 3D spaces, especially in Augmented Reality (AR) Head-Mounted Displays (HMDs) is different and challenging because users need to select an intended region via freehand mid-air gestures or eye-based actions that are touchless interactions. In this work, we aim to fill in the gap in the design of region selection techniques in AR HMDs. We first analyzed and discretized the interaction procedure of region selection and explored design possibilities for each step. We then developed four techniques for region selection in AR HMDs, which leveraged users' hand and gaze for unimodal or multimodal interaction. The techniques were evaluated via a user study with a controlled region selection task. The findings led to three design recommendations and two proof-of-concept application examples.

Satellite imagery depicts the Earth’s surface remotely and provides comprehensive information for many applications, such as land use monitoring and urban planning. Existing studies on unsupervised representation learning for satellite images only take into account the images’ geographic information, ignoring human activity factors. To bridge this gap, we propose using the Point-of-Interest (POI) data to capture human factors and designing a contrastive learning-based framework to consolidate the representation of satellite imagery with POI information. Besides, we introduce a season-invariant representation learning model on satellite imagery, considering that human factors are mostly unchanging with respect to seasons. An attention model is designed at last to merge the representations from the geographic, seasonal, and POI perspectives adaptively. On the basis of real-world datasets collected from Beijing, 1 we evaluate our method for predicting socioeconomic indicators. The results show that the representation containing POI information outperforms the geographic representation in estimating commercial activity-related indicators. Our proposed attentional framework can estimate the socioeconomic indicators with R 2 of 0.874 and outperforms the baseline methods. Furthermore, we explore the differences in the representations of satellite images with varying socioeconomic statuses. Finally, we investigate the impact of geographic and POI perspective information in the representation learning process, as well as the effect of satellite imagery on various spatial resolutions.

Augmented Reality (AR) applications are becoming more mainstream, with successful examples in the mobile environment like Pokemon GO. Current malicious techniques can exploit these environments' immersive and mixed nature (physical-virtual) to trick users into providing more personal information, i.e., dark patterns. Dark patterns are deceiving techniques (e.g., interface tricks) designed to influence individuals' behavioural decisions. However, there are few studies regarding dark patterns' potential issues in AR environments. In this work, using scenario construction to build our prototypes, we investigate the potential future approaches that dark patterns can have. We use VR mockups in our user study to analyze the effects of dark patterns in AR. Our study indicates that dark patterns are effective in immersive scenarios, and the use of novel techniques such as `haptic grabbing' to drag participants' attention can influence their movements. Finally, we discuss the impact of such malicious techniques and what techniques can mitigate them.

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a Pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. The inter-diversity of app-category and individual app usage exhibits opposing trends: app-category usage assimilates while individual app usage diversifies. Nevertheless, the intra-diversity of both app-category and app usage declines over time. Also, we demonstrate the country barriers of app category usage. We further investigate how different demographics affect the evolutionary processes of app usage. Our study provides useful implications for app developers, market intermediaries, and service providers

The rapid proliferation of learning systems in an arbitrarily changing environment mandates the need for managing tensions between exploration and exploitation. This work proposes a quantum-inspired bandit learning approach for the learning-and-adapting-based offloading problem where a client observes and learns the costs of each task offloaded to the candidate resource providers, e.g., fog nodes. In this approach, a new action update strategy and novel probabilistic action selection are adopted, provoked by the amplitude amplification and collapse postulate in quantum computation theory, respectively. We devise a locally linear mapping between a quantum-mechanical phase in a quantum domain, e.g., Grover-type search algorithm, and a distilled probability-magnitude in a value-based decision-making domain, e.g., adversarial multi-armed bandit algorithm. The proposed algorithm is generalized, via the devised mapping, for better learning weight adjustments on favourable/unfavourable actions and its effectiveness is verified via simulation., Accepted to appear in IEEE TKDE

With the advent of the COVID-19 pandemic, the shortage in medical resources became increasingly more evident. Therefore, efficient strategies for medical resource allocation are urgently needed. However, conventional rule-based methods employed by public health experts have limited capability in dealing with the complex and dynamic pandemic-spreading situation. In addition, model-based optimization methods such as dynamic programming (DP) fail to work since we cannot obtain a precise model in real-world situations most of the time. Model-free reinforcement learning (RL) is a powerful tool for decision-making; however, three key challenges exist in solving this problem via RL: (1) complex situations and countless choices for decision-making in the real world; (2) imperfect information due to the latency of pandemic spreading; and (3) limitations on conducting experiments in the real world since we cannot set up pandemic outbreaks arbitrarily. In this article, we propose a hierarchical RL framework with several specially designed components. We design a decomposed action space with a corresponding training algorithm to deal with the countless choices, ensuring efficient and real-time strategies. We design a recurrent neural network–based framework to utilize the imperfect information obtained from the environment. We also design a multi-agent voting method, which modifies the decision-making process considering the randomness during model training and, thus, improves the performance. We build a pandemic-spreading simulator based on real-world data, serving as the experimental platform. We then conduct extensive experiments. The results show that our method outperforms all baselines, which reduces infections and deaths by 14.25% on average without the multi-agent voting method and up to 15.44% with it.

Community Question Answering Websites (CQAs) like Stack Overflow rely on continuous user contributions to keep their services active. Nevertheless, they often undergo a sharp decline in their user participation during the holiday season, undermining their performance. To address this issue, some CQAs have developed their own special promotional gamification schemes to incentivize users to maintain their contributions throughout the holiday season. These promotional gamification schemes are often time-limited, optional, and run alongside the default gamification schemes of their websites. However, the impact of such promotional gamification schemes on user behavior remains largely unexplored in the existing literature. This paper takes the first steps toward filling this knowledge gap by conducting a large-scale empirical study of a particular promotional gamification scheme called Winter Bash (WB) on the CQA of Stack Overflow. According to our findings, promotional gamification schemes may not be the panacea they are portrayed to be. For example, in the case of WB, we find that the scheme is not effective for improving the collective engagement of all users. Only some particular user types (i.e., experienced and reputable users) are often provoked under WB. Most novice users, who comprise the majority of Stack Overflow website's user base, seem to be indifferent to such a gamification scheme. Our research also shows the importance of studying the quantity and quality of user engagement in unison to better understand the effectiveness of a gamification scheme. Previous gamification studies in the literature have focused predominantly on studying the quantity of user engagement alone. Last but not least, we conclude our paper by presenting some practical considerations for improving the design of future promotional gamification schemes in CQAs and similar platforms.

In recent years, studies on the user experience have emerged as an indispensable part of any gamification research. The study of user experience enables gamification designers and practitioners to design or adapt their gamification schemes in a more knowledgeable and efficacious manner. However, one popular gamification scheme that has largely remained under-researched in terms of user experience is promotional gamification, which refers to an optional and time-limited gamification program that usually mounts an already gamified platform to increase user incentive and engagement for a short span of time (e.g., during the holiday season). The current study undertakes the first steps necessary to explore users' experiences of working with a promotional gamification scheme in a large-scale online community. To this end, we conduct an extensive qualitative case study of users' experiences with a promotional gamification scheme on the Community Question Answering Website (CQA) of Stack Exchange, called Winter Bash (WB). Notably, the purpose of WB is to operate as a makeshift solution that prevents the decline in user contributions during the holiday season. However, like many other gamification schemes, WB is not devoid of issues, and our research helps identify those issues without overlooking the WB's strengths. Our study denotes not only the first (empirical) typology of users' affective responses to promotional gamification schemes but also the first classification of (de)motivational factors involved in user engagement. At its core, this study comprises two salient parts: (1) a content analysis of user-generated data regarding WB (from the past eight years), and (2) a series of semi-structured interviews with 17 international users who are familiar with WB. We triangulate our findings from (1) and (2) by performing a similar content analysis for two other promotional gamification schemes, namely "Answerathon" (from Travel Meta) and "Discussion Tournament" (from Reddit). Based on the findings of this study, we present certain guidelines for gamification designers and practitioners, enabling them to deploy or adapt their promotional gamification schemes in a more knowledgeable and effective manner. Finally, our work is concluded by highlighting a few novel research opportunities for researchers invested in the fields of Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW).

Using human intelligence to identify news articles' political stances is common in research and practical applications. But human judgement can be biased and prone to errors stemming from the comprehension of tasks and political alignment. This paper proposes a relative rating method based on news articles' stances relative to raters' own stances to avoid comprehension inconsistency and to control for human bias in crowdsourced stance detection of news articles. We also show how to use the relative ratings to construct a measure for raters' stances on a political topic and to identify raters whose ratings are of higher quality than others. We implement our proposed methods in an online experiment that recruits Amazon Mechanical Turk users as raters for news articles on Gun Control. Using the data from the experiment, we find evidence that raters' own stances on Gun Control significantly impact ratings of related news articles, both at the individual levels and at the aggregate levels. We also present evidence that our relative-rating-based stance measure captures more information about raters' actual stances than their self-reported stance does.

Wearable computers engage in percutaneous interactions with human users and revolutionize the way of learning human activities. Due to rising privacy concerns, federated learning has been recently proposed to train wearable data with privacy preservation collaboratively. However, under the state-of-the-art (SOTA) schemes, user profiles on wearable devices of inferior networking conditions are regarded as 'left out'. Such schemes suffer from three fundamental limitations: (1) the widely adopted network-capacity-based client selection leads to biased training; (2) the aggregation has low communication efficiency; (3) users lack convenient channels for providing feedback on wearable devices. Therefore, this paper proposes a Fair and Communication-efficient Federated Learning scheme, namely FCFL. FCFL is a full-stack learning system specifically designed for wearable computers, improving the SOTA performance in terms of communication efficiency, fairness, personalization, and user experience. To this end, we design a technique named ThrowRightAway (TRA) to loose the network capacity constraints. Clients with poor networks are allowed to be selected as participators to improve the representation and guarantee the model's fairness. Remarkably, we propose Movement Aware Federated Learning (MAFL) to aggregate only the model updates with top contributions to the global model for the sake of communication efficiency. Accordingly, we implemented an FCFL-supported prototype as a sports application on smartwatches. Our comprehensive evaluation demonstrated that FCFL is a communication efficient scheme significantly reducing uploaded data by up to 29.77%, with a prominent feature of guaranteeing enhanced fairness up to 65.07%. Also, FCFL achieves robust personalization performance (i.e., 20% improvements of global model accuracy) in the face of packet loss below a certain fraction (10%-30%). A follow-up user survey shows that our FCFL-supported prototypical system on wearable devices significantly reduces users' workload.

Littering is a significant challenge for environmental sustainability and a major burden for cities and densely populated areas. Current solutions for litter monitoring, such as litter watch campaigns and city-operated litter collection, are costly and challenging to conduct at a large scale. This article presents a vision for using autonomous ground vehicles (AGVs) for litter monitoring and removal and introduces a mechanism for AGVs that uses thermal dissipation resulting from sunlight to identify and remove litter objects. We identify and highlight key challenges for deploying the envisioned solution on a city scale, and demonstrate the feasibility of the solution through extensive experiments.

Mobile apps have become an indispensable part of people’s daily lives. Users determine what apps to use and when and where to use them based on their tastes, interests, and personal demands, depending on their personality traits. This paper aims to infer user profiles from their spatiotemporal mobile app usage behavior. Specifically, we first transform mobile app usage records into a heterogeneous graph. On the graph, nodes represent users, apps, locations, and time slots. Edges describe the co-occurrence of entities in usage records. We then develop a multi-relational heterogeneous graph attention network (MRel-HGAN), an end-to-end system for user profiling. MRel-HGAN first adopts a neighbor sampling strategy based on bootstrapping to sample heavily connected neighbors of a fixed size for each node. Next, we design a relational graph convolutional operation and a multi-relational attention operation. Through such modules, MRel-HGAN can generate node embedding by sufficiently leveraging the rich semantic information of the multi-relational structure in the mobile app usage graph. Experimental results on real-world mobile app usage datasets show the effectiveness and superiority of our MRel-HGAN in the user profiling task for attributes of gender and age.

N-methyl-D-aspartate receptors (NMDARs) play a critical role in excitotoxicity caused by ischemic stroke, but NMDAR antagonists have failed to be translated into clinical practice for treating stroke patients. Recent studies suggest that targeting the specific protein–protein interactions that regulate NMDARs may be an effective strategy to reduce excitotoxicity associated with brain ischemia. α2δ-1 (encoded by the Cacna2d1 gene), previously known as a subunit of voltage-gated calcium channels, is a binding protein of gabapentinoids used clinically for treating chronic neuropathic pain and epilepsy. Recent studies indicate that α2δ-1 is an interacting protein of NMDARs and can promote synaptic trafficking and hyperactivity of NMDARs in neuropathic pain conditions. In this review, we highlight the newly identified roles of α2δ-1-mediated NMDAR activity in the gabapentinoid effects and NMDAR excitotoxicity during brain ischemia as well as targeting α2δ-1-bound NMDARs as a potential treatment for ischemic stroke.

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed., Comment: Accepted by IEEE Transactions on Big Data

In this paper, we present the design, implementation, and evaluation of SEAR, a collaborative framework for Scaling Experiences in multi-user Augmented Reality (AR). Most AR systems benefit from computer vision (CV) algorithms to detect, classify, or recognize physical objects for augmentation. A widely used acceleration method for mobile AR is to offload the compute-intensive tasks (e.g., CV algorithms) to the network edge. However, we show that the end-to-end latency, an important metric of mobile AR, may dramatically increase when offloading AR tasks from a large number of concurrent users to the edge. SEAR tackles this scalability issue through the innovation of a lightweight collaborative local caching scheme. Our key observation is that nearby AR users may share some common interests, and may even have overlapped views to augment (e.g., when playing a multi-user AR game). Thus, SEAR opportunistically exchanges the results of offloaded AR tasks among users when feasible and leverages compute resources on mobile devices to relieve, if necessary, the edge workload by intelligently reusing these results. We build a prototype of SEAR to demonstrate its efficacy in scaling AR experiences. We conduct extensive evaluations through both real-world experiments and trace-driven simulations. We observe that SEAR not only reduces the end-to-end latency, by up to 130×, compared to the state-of-the-art adaptive edge offloading scheme, but also achieves high object-recognition accuracy for mobile AR.

The computing power of various Internet of Things (IoT) devices is quite different. To enable IoT devices with lower computing power to perform machine learning, all nodes can only train smaller models, which results in the waste of computing power for high-performance devices. In this paper, a heterogeneous model fusion federated learning mechanism is proposed. Each node trains learning models of different scales according to its own computing capabilities. After receiving the gradient trained by each node, the parameter server (PS) corrects the received gradient with the repeat matrix, and then update the corresponding region of the global model according to the mapping matrix. After all update operations are over, the PS assigns the compressed model to the corresponding node. The paper uses a variety of experimental schemes to evaluate the proposed method, including three datasets, two model structures, and three computational complexity levels. The proposed method have been proved it not only maximizes the use of the unbalanced computing power of edge nodes, but also enables different structural models to compensate for the shortcomings of others, improving the overall performance.

With the prevalence of smartphones, people have left abundant behavior records in cyberspace. Discovering and understanding individuals' cyber activities can provide useful implications for policymakers, service providers, and app developers. In this paper, we propose a framework to discover daily cyber activity patterns across people's mobile app usage. We first segment app usage traces into small time windows and then design a probabilistic topic model to infer users' cyber activities of each window. By exploring the coherence of users' activity sequences, the daily patterns of individuals are identified. Next, we recognize the common patterns across diverse groups of individuals using a hierarchical clustering algorithm. We then apply our framework on a large-scale and real-world dataset, consisting of 653,092 users with 971,818,946 usage records of 2,000 popular mobile apps. Our analysis shows that people usually obey yesterday's activity patterns, but the patterns tend to deviate as the time-lapse increases. We also discover five common daily cyber activity patterns, including afternoon reading, nightly entertainment, pervasive socializing, commuting, and nightly socializing. Our findings have profound implications on identifying the demographics of users and their lifestyles, habits, service requirements, and further detecting other disrupting trends such as working overtime and addiction to the game and social media.

Supplemental Material for Pretreatment Serum Lactate Dehydrogenase and Metastases Numbers as Potential Determinants of Anti-PD-1 Therapy Outcome in Nasopharyngeal Carcinoma by Wael Abdullah Sultan Ali, Xinxin Huang, Yuehan Wu, Yuxiang Ma, Hui Pan, Jun Liao, Zhang Yang, Shaodong Hong, Yunpeng Yang, Yan Huang, Yuanyuan Zhao, Wenfeng Fang, Hongyun Zhao, and Li Zhang in Cancer Control.

Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising prediction results. However, some challenges remain, especially regarding graph construction and the time complexity of models. In this paper, we propose a multi-stream feature fusion approach to extract and integrate rich features from traffic data and leverage a data-driven adjacent matrix instead of the distance-based matrix to construct graphs. We calculate the Spearman rank correlation coefficient between monitor stations to obtain the initial adjacent matrix and fine-tune it while training. As to the model, we construct a multi-stream feature fusion block (MFFB) module, which includes a three-channel network and the soft-attention mechanism. The three-channel networks are graph convolutional neural network (GCN), gated recurrent unit (GRU) and fully connected neural network (FNN), which are used to extract spatial, temporal and other features, respectively. The soft-attention mechanism is utilized to integrate the obtained features. The MFFB modules are stacked, and a fully connected layer and a convolutional layer are used to make predictions. We conduct experiments on two real-world traffic prediction tasks and verify that our proposed approach outperforms the state-of-the-art methods within an acceptable time complexity.

Interaction design for Augmented Reality (AR) is gaining attention from both academia and industry. This survey discusses 260 articles (68.8% of articles published between 2015–2019) to review the field of human interaction in connected cities with emphasis on augmented reality-driven interaction. We provide an overview of Human-City Interaction and related technological approaches, followed by reviewing the latest trends of information visualization, constrained interfaces, and embodied interaction for AR headsets. We highlight under-explored issues in interface design and input techniques that warrant further research and conjecture that AR with complementary Conversational User Interfaces (CUIs) is a crucial enabler for ubiquitous interaction with immersive systems in smart cities. Our work helps researchers understand the current potential and future needs of AR in Human-City Interaction.

Peer reviewed: True, Almost all recent deep reinforcement learning algorithms use four consecutive frames as the state space to retain the dynamic information. If the training state data constitute an image, the state space is used as the input of the neural network for training. As an AI-assisted decision-making technology, a dynamic influence map can describe dynamic information. In this paper, we propose the use of a frame image superimposed with an influence map as the state space to express dynamic information. Herein, we optimize Ape-x as a distributed reinforcement learning algorithm. Sparse reward is an issue that must be solved in refined intelligent decision making. The use of an influence map is proposed to generate the intrinsic reward when there is no external reward. The experiments conducted in this study prove that the combination of a dynamic influence map and deep reinforcement learning is effective. Compared with the traditional method that uses four consecutive frames to represent dynamic information, the score of the proposed method is increased by 11–13%, the training speed is increased by 59%, the video memory consumption is reduced by 30%, and the memory consumption is reduced by 50%. The proposed method is compared with the Ape-x algorithm without an influence map, DQN, N-Step DQN, QR-DQN, Dueling DQN, and C51. The experimental results show that the final score of the proposed method is higher than that of the compared baseline methods. In addition, the influence map is used to generate an intrinsic reward to effectively resolve the sparse reward problem.

Autonomous drones are reaching a level of maturity when they can be deployed in cities to support tasks ranging from medicine or food delivery to environmental monitoring. These operations rely on powerful AI models integrated into the drones. Ensuring these models are robust is essential for operating in cities as any errors in the decisions of the autonomous drones can cause damage to the citizens or the urban infrastructure. We contribute a research vision for trustworthy city-scale deployments of autonomous drones. We highlight current key requirements and challenges that have to be fulfilled for achieving city-scale autonomous drone deployments. In addition, we also analyze the complexity of using XAI methods to monitor drone behavior. We demonstrate this by inducing changes in AI model behavior using data poisoning attacks. Our results demonstrate that XAI methods are sensitive enough to detect the possibility of a data attack, but a combination of multiple XAI methods is better to improve the robustness of the estimation. Our results also suggest that currently, the reaction time to counter an attack in city-scale deployment is large due to the complexity of the XAI analysis.

User Interaction for NFTs (Non-fungible Tokens) is gaining increasing attention. Although NFTs have been traditionally single-use and monolithic, recent applications aim to connect multimodal interaction with human behaviour. This paper reviews the related technological approaches and business practices in NFT art. We highlight that multimodal interaction is a currently under-studied issue in mainstream NFT art, and conjecture that multimodal interaction is a crucial enabler for decentralization in the NFT community. We propose a framework that combines a bottom-up approach with AI multimodal process. Through this framework, we put forward integrating human behaviour data into generative NFT units, as "multimodal interactive NFT". Our work displays the possibilities of NFTs in the art world, beyond the traditional 2D and 3D static content., 7 pages, 5 figures

BackgroundThe association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy.MethodsA retrospective cohort study was conducted using information from the National Free Preconception Health Examination Project between 2010 and 2012. A development set (n = 606) and a validation set (n = 151) of the dataset were split at random. Traditional logistic regression (LR) method and six machine learning classifiers were used to develop prediction models for SGA neonates. The Shapley Additive Explanation (SHAP) model was applied to determine the most influential variables that contributed to the outcome of the prediction.Results757 neonates in total were analyzed. SGA occurred in 12.9% (n = 98) of cases overall. With an area under the receiver-operating-characteristic curve (AUC) of 0.855 [95% confidence interval (CI): 0.752–0.959], the model based on category boosting (CatBoost) algorithm obtained the best performance in the validation set. With the exception of the LR model (AUC: 0.691, 95% CI: 0.554–0.828), all models had good AUCs. Using recursive feature elimination (RFE) approach to perform the feature selection, we included 15 variables in the final model based on CatBoost classifier, achieving the AUC of 0.811 (95% CI: 0.675–0.947).ConclusionsMachine learning algorithms can develop satisfactory tools for SGA prediction in mothers exposed to pesticides prior to pregnancy, which might become a tool to predict SGA neonates in the high-risk population.

After spinal cord injury (SCI), a fibroblast- and microglia-mediated fibrotic scar is formed in the lesion core, and a glial scar is formed around the fibrotic scar as a result of the activation and proliferation of astrocytes. Simultaneously, a large number of neurons are lost in the injured area. Regulating the dense glial scar and replenishing neurons in the injured area are essential for SCI repair. Polypyrimidine tract binding protein (PTB), known as an RNA-binding protein, plays a key role in neurogenesis. Here, we utilized short hairpin RNAs (shRNAs) and antisense oligonucleotides (ASOs) to knock down PTB expression. We found that reactive spinal astrocytes from mice were directly reprogrammed into motoneuron-like cells by PTB downregulation in vitro. In a mouse model of compression-induced SCI, adeno-associated viral shRNA-mediated PTB knockdown replenished motoneuron-like cells around the injured area. Basso Mouse Scale scores and forced swim, inclined plate, cold allodynia, and hot plate tests showed that PTB knockdown promoted motor function recovery in mice but did not improve sensory perception after SCI. Furthermore, ASO-mediated PTB knockdown improved motor function restoration by not only replenishing motoneuron-like cells around the injured area but also by modestly reducing the density of the glial scar without disrupting its overall structure. Together, these findings suggest that PTB knockdown may be a promising therapeutic strategy to promote motor function recovery during spinal cord repair.

Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3- D imensional G raph C onvolution N etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.

A rapid-spreading epidemic of COVID-19 hit China at the end of 2019, resulting in unignorable social and economic damage in the epicenter, Wuhan. POIs capture the microscopic behavior of citizens, providing valuable information to understand city reactions toward the epidemic. Leveraging large-scale check-in records, we analyze the POI visit trends over the epidemic period and normal times. We demonstrate that COVID-19 greatly influences the society, where most POIs demonstrate more than 60% of visit drops during the city lockdown period. Among them, Tourist Attractions received greatest impact with a 78.8% drop. Entertainment, Food, Medical and Shopping are sensible to the disease before lockdown, and we identify these "early birds" to investigate the public reaction in the early stage of the epidemic. We further analyze the revival trends, generating four different revival patterns that correlated with the necessity of POI functions. Finally, we analyze the perseverance during the COVID-19, finding no large-scale closures compared with the tremendous visit drop. The strong resilience in Wuhan supports the rapid recovery of society. These findings are important for researchers, industries, and governments to understand the city respondence under severe epidemic, proposing better regulations to respond, control, and prevent public emergencies.

Publisher Copyright: © 2021 ACM. Copy-and-paste operations are the most popular features on computing devices such as desktop computers, smartphones and tablets. However, the copy-and-paste operations are not sufficiently addressed on the Augmented Reality (AR) smartglasses designated for real-time interaction with texts in physical environments. This paper proposes two system solutions, namely Granularity Scrolling (GS) and Two Ends (TE), for the copy-and-paste operations on AR smartglasses. By leveraging a thumb-size button on a touch-sensitive and pressure-sensitive surface, both the multi-step solutions can capture the target texts through indirect manipulation and subsequently enables the copy-and-paste operations. Based on the system solutions, we implemented an experimental prototype named Press-n-Paste (PnP). After the eight-session evaluation capturing 1,296 copy-and-paste operations, 18 participants with GS and TE achieve the peak performance of 17,574 ms and 13,951 ms per copy-and-paste operation, with 93.21% and 98.15% accuracy rates respectively, which are as good as the commercial solutions using direct manipulation on touchscreen devices. The user footprints also show that PnP has a distinctive feature of miniaturized interaction area within 12.65 mm∗14.48 mm. PnP not only proves the feasibility of copy-and-paste operations with the flexibility of various granularities on AR smartglasses, but also gives significant implications to the design space of pressure widgets as well as the input design on smart wearables.

Problem-Based Learning (PBL) is a popular approach to instruction that supports students to get hands-on training by solving problems. Question Pool websites (QPs) such as LeetCode, Code Chef, and Math Playground help PBL by supplying authentic, diverse, and contextualized questions to students. Nonetheless, empirical findings suggest that 40% to 80% of students registered in QPs drop out in less than two months. This research is the first attempt to understand and predict student dropouts from QPs via exploiting students' engagement moods. Adopting a data-driven approach, we identify five different engagement moods for QP students, which are namely challenge-seeker, subject-seeker, interest-seeker, joy-seeker, and non-seeker. We find that students have collective preferences for answering questions in each engagement mood, and deviation from those preferences increases their probability of dropping out significantly. Last but not least, this paper contributes by introducing a new hybrid machine learning model (we call Dropout-Plus) for predicting student dropouts in QPs. The test results on a popular QP in China, with nearly 10K students, show that Dropout-Plus can exceed the rival algorithms' dropout prediction performance in terms of accuracy, F1-measure, and AUC. We wrap up our work by giving some design suggestions to QP managers and online learning professionals to reduce their student dropouts., Accepted in the 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2021)