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Low-light stereo image enhancement (LLSIE) is a task that improves the visual quality of stereo images captured in low-light environment. The core idea of LLSIE is to exploit the cross-view cues for enhancing the stereo image pairs. However, LLSIE is still a challenging task due to the perspective difference and insufficient information interaction between left and right views. In this paper, we propose a novel Multi-Scale Interaction Network (MSINet) for LLSIE. To be specific, we present a new multi-scale stereo Interaction module (MIM), which can promote the multi-scale cross-view information flow. Firstly, MIM extracts multi-scale features from both the left and right views. Then, the features from two views are fused in different scales, so that the information from stereo image pairs can be fully exchanged. Compared to the existing parallax attention module (PAM), our MIM explores the cross-view interaction in multiple scales with less computational cost more sufficiently. Besides, we also develop a new Spatial Gating Block (SGB) for refining intra-view features. By incorporating the long residual and spatial gating mechanism, SGB is capable of completing dynamic spatial modeling and obtaining strong feature representations. Extensive experiments on the Flickr1024, KITTI 2015 and Middlebury datasets show that the proposed MSINet reaches SOTA performance and obtains better visual results than other compared methods.

Despite significant advancements in Multi-Agent Deep Reinforcement Learning (MADRL) approaches for Traffic Light Control (TLC), effectively coordinating agents in diverse traffic environments remains a challenge. Studies in MADRL for TLC often focus on repeatedly constructing the same intersection models with sparse experience. However, real road networks comprise Multi-Type of Intersections (MTIs) rather than being limited to intersections with four directions. In the scenario with MTIs, each type of intersection exhibits a distinctive topology structure and phase set, leading to disparities in the spaces of state and action. This article introduces Adaptive Multi-agent Deep Mixed Reinforcement Learning (AMDMRL) for addressing tasks with multiple types of intersections in TLC. AMDMRL adopts a two-level hierarchy, where high-level proxies guide low-level agents in decision-making and updating. All proxies are updated by value decomposition to obtain the globally optimal policy. Moreover, the AMDMRL approach incorporates a mixed cooperative mechanism to enhance cooperation among agents, which adopts a mixed encoder to aggregate the information from correlated agents. We conduct comparative experiments involving four traditional and four DRL-based approaches, utilizing three training and four testing datasets. The results indicate that the AMDMRL approach achieves average reductions of 41% than traditional approaches, and 16% compared to DRL-based approaches in traveling time on three training datasets. During testing, the AMDMRL approach exhibits a 37% improvement in reward compared to the MADRL-based approaches.

With smartphones and geo-locating devices widely used around the world, ridesharing, as a main application field of spatial crowdsourcing, has been fast expanding its widespread adoption and potentially brings great benefits to human society and the environment. However, ridesharing has so far not been as popular as expected. A recent survey shows that notable obstacles come from concerns about social comfort and price fairness when riding with strangers. To defeat these obstacles, in this paper, we study a novel problem of utility-aware ride matching (URM) for dynamic ridesharing, where drivers and riders are matched in batches by considering their social comfort and price revenue. While the URM problem is of practical usefulness, we prove that this problem is Nondeterministic Polynomial Hard (NP-hard). To tackle the problem optimally and find exact answers, we present a novel bipartite matching algorithm by integrating an effective Driver-Rider Graph (DR-Graph) index. To balance the effectiveness and efficiency, we propose two efficient algorithms to solve the URM problem with only a small loss of utility. Leveraging a bounded Driver-Rider-group Graph (DRg-Graph) and several useful pruning bounds for matching utility and travel cost, we develop an $\epsilon$ε-refining algorithm to find an $\epsilon$ε-approximation matching utility of the optimal answer. Extensive experiments on real datasets showed that our proposed algorithms achieved effective matching results of social-pricing based utilities and the efficient performance of quick matching under various parameter settings.

In sound event localization and detection (SELD), traditional methods often treat localization and detection algorithms separately from data augmentation. During the model training process, the strategy for data augmentation is typically implemented in a non-learnable manner. Existing audio data augmentation strategies struggle to find optimal parameter solutions for data augmentation that can be effectively applied to SELD systems. To address this challenge, we introduce an innovative network-based strategy, termed the Automated Audio Data Augmentation (AADA) network. This strategy employs bi-level optimization to synergistically integrate audio data augmentation techniques with SELD tasks. In the AADA network, the lower-level SELD task serves as a constraint for the higher-level data augmentation process. The audio data augmentation parameters are adaptively optimized by utilizing the transfer of intermediate feature information from the SELD tasks, thus obtaining optimal parameters for these tasks. Evaluation of our approach on the Sony-TAU Realistic Spatial Soundscapes 2023 dataset achieves a SELD score of 0.4801, significantly surpassing the performance metrics of all traditional data augmentation strategies for SELD.

Coupling the classification and localization head compromises the defect detection performance due to feature conflict and spatial misalignment between these two subtasks. In this article, we propose a task-aware attention network (TAANet) for weak surface defect detection, referred to as TAANet. Specifically, we devise a feature-aware decoupled head (FADH) to learn task-specific features for defect classification and box regression, respectively. FADH is composed of two parallel branches, where one aims to encode a strong semantic context preferred for classification, and the other is to learn more structural information expected for regression, thus disentangling the two tasks at the feature level. Moreover, an efficient attention-guided feature pyramid network (AG-FPN) is developed by introducing attention guidance maps in the unidirectional FPN to enhance the feature representations of weak defects, replacing continuously stacked top-down and bottom-up connections. AG-FPN shortens the information transmission pathway in an efficient fashion and extracts more salient and discriminative features for the prediction layers in FADH. The proposed TAANet yields state-of-the-art 86.3% and 77.6% mAP@IoU = 0.5 at 58.7 FPS on the publicly available datasets, NEU-DET and GC10-DET, which effectively improves the defect detection performance over comparable detectors.

Surface defect detection is an important step in ensuring product quality in various manufacturing industries. Existing methods have achieved significant results, but there are still challenges, such as the lack of real-time detection speed, low contrast between defects and background, and insufficient handling of defect details. To address these issues, we introduce a lightweight and efficient method called context-aware adaptive weighted attention network (CAWANet) for real-time surface defect segmentation. To handle the computational resource constraints when dealing with deep features, we introduce context-aware adaptive weighted convolution (CAWAConv), which aims to extract deep features while suppressing noise interference. This allows the model to remain lightweight without compromising its ability to recognize subtle defect characteristics. In addition, during the feature fusion stage, we propose the feature detail improvement (FDI) module to capture more complex defect details. The FDI module enhances the representation of defect-related information, further optimizing the segmentation results. We conducted experimental evaluations of CAWANet on three surface defect detection datasets: the magnetic tile, NEU-Seg, and MSD. The experimental results indicate that our proposed CAWANet strikes a favorable balance between accuracy and inference speed compared to other state-of-the-art methods. Our code will be available at https://github.com/ZGWzzu/CAWANet.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.

Graph Convolutional Networks (GCNs) derive inspiration from recent advances in computer vision, by stacking layers of first-order filters followed by a nonlinear activation function to learn entity or graph embeddings. Although GCNs have been shown to boost the performance of many network analysis tasks, they still face tremendous challenges in learning from Heterogeneous Information Networks (HINs), where relations play a decisive role in knowledge reasoning. What’s more, there are multiaspect representations of entities in HINs, and a filter learned in one aspect do not necessarily apply to another. We address these challenges by proposing the Aspect-Aware Graph Attention Network (AGAT), a model that extends GCNs with alternative learnable filters to incorporate entity and relational information. Instead of focusing on learning the general entity embeddings, AGAT learns the adaptive entity embeddings based on prediction scenario. Experiments of link prediction and semi-supervised classification verify the effectiveness of our algorithm.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

The adoption of unmanned vehicles in urban logistics has gradually become a trend. It can effectively lower carbon emissions, reduce labor costs, and improve logistics efficiency. In this paper, we investigate a novel problem of unmanned vehicle scheduling (UVS) for large-scale urban logistics, where the logistics platform assigns unmanned vehicles to deliver parcels among stations under the constraints of time, capacity, and electricity to maximize the overall revenue of the logistics platform. Although the UVS problem is of practical usefulness, solving it requires non-trivial efforts, because we have proved that the UVS problem is NP-hard. To solve the UVS problem efficiently, we propose an efficient two-stage processing framework, including task assignment and vehicle reposition. Specifically, in the first stage, we propose an effective preference-aware matching (PAM) algorithm to deal with task assignments between unmanned vehicles and delivery tasks, which considers not only the electricity consumption of unmanned vehicles but also the supply-demand balance between delivery tasks and unmanned vehicles. In the second stage, we propose two vehicle repositioning algorithms based on deep reinforcement learning, termed restricted DQN repositioning algorithm (RDR) and restricted A2C repositioning algorithm (RAR), which can effectively refine the vehicle’s reposition stations based on vehicle supply and demand, electricity supply and demand, charging pile availability and collision avoidance restriction rules at current and neighbor stations, so that the vehicles can be efficiently relocated to stations with over-delivery tasks. Finally, extensive experiments have demonstrated that our proposed algorithms can achieve desirable efficiency and effectiveness.

In soccer, player action evaluation provides a fine-grained method to analyze player performance and plays an important role in improving winning chances in future matches. However, previous studies on action evaluation only provide a score for each action, and hardly support inspecting and comparing player actions integrated with complex match context information such as team tactics and player locations. In this work, we collaborate with soccer analysts and coaches to characterize the domain problems of evaluating player performance based on action scores. We design a tailored visualization of soccer player actions that places the action choice together with the tactic it belongs to as well as the player locations in the same view. Based on the design, we introduce a visual analytics system, Action-Evaluator, to facilitate a comprehensive player action evaluation through player navigation, action investigation, and action explanation. With the system, analysts can find players to be analyzed efficiently, learn how they performed under various match situations, and obtain valuable insights to improve their action choices. The usefulness and effectiveness of this work are demonstrated by two case studies on a real-world dataset and an expert interview.

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.