Cancer development is driven by somatic evolution and clonal selection. However, traditional selective pressure analysis methods have treated all sites within a gene equally, such a gene-level model oversimplifies the complexity of cancer evolution. In this study, we introduced CN/CS-calculator, a novel site-specific method that can capture selective pressures acting across different gene sites. By deciphering the interplay between the selection pattern and the function of a gene in oncogenesis, CN/CS-calculator uncovers a unique class of mini-driver genes, which exhibit weak positive selection, with certain critical sites providing context-dependent promoter effects on the fitness of cancer subclones while others are constrained by evolutionary conservation. Our method emphasizes the importance of site-specific analysis in uncovering how subtle evolutionary forces shape cancer biology. The refined understanding offers new insights into the mechanisms of cancer heterogeneity and molecular evolution, with potential implications for advancing therapeutic strategies and prognostic assessments.

Major Depressive Disorder (MDD) is a prevalent and severe mental disease. Functional Magnetic Resonance Imaging (fMRI)-based diagnostic methods, which analyze Functional Connectivity (FC) to identify abnormal functional connections, have shown promise as biomarker-based approaches for diagnosing depression. However, the high costs of fMRI data result in small sample sizes, hindering the effective identification of abnormal FC patterns. Moreover, existing methods often overlook the potential benefits of incorporating domain knowledge into their models. In this paper, we propose KnowMDD, a novel knowledge-guided cross contrastive learning framework for MDD diagnosis. By incorporating domain knowledge and employing data augmentation, KnowMDD addresses data sparsity while improving robustness and interpretability. Specifically, multiple atlases are used to construct complementary brain graph representations. The default mode network, closely associated with depression, is introduced into the contrastive learning paradigm for diverse subgraph augmentations, while an attention mechanism captures global semantic relationships between brain regions. Based on them, a cross contrastive learning is designed to learn robust representations for accurate diagnosis. Extensive experiments demonstrate the effectiveness, robustness, and interpretability of KnowMDD, which outperforms state-of-the-art methods. We also develop a demonstration system to show its practical application.

In recent years, the burgeoning urban population, coupled with expanding urban dimensions and various sociodemographic factors, has led to an alarming escalation in criminal activities within urban centers. This escalation has further exacerbated the existing strain on police resources, rendering them increasingly inadequate for effective law enforcement. Concurrently, police patrol operations have emerged as a pivotal instrument in the ongoing battle against violent criminal activities. The judicious planning of patrol routes has the potential to markedly enhance the efficiency of police patrolling endeavors, thereby bolstering the overall security infrastructure within the jurisdiction while simultaneously conserving invaluable police resources. The formulation of an efficient patrol strategy within the intricate and ever-evolving landscape of urban regions replete with crime hotspots represents an intellectually taxing challenge. To address this exigent problem, this paper proffers a novel real-time patrol route planning algorithm tailored to dynamic environments, employing the principles of deep reinforcement learning, specifically the Integrated Double Q-Network (IDQN) method. Subsequently, the efficacy of the proposed method is empirically substantiated through experimentation, attesting to its practical viability and utility in the field of computer science and urban security management.

Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it's often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance. These approaches may not take full advantage of established chemical knowledge and could inadvertently introduce noise into the respective model. In this study, we introduce a more elegant transformer-based framework with focused attention for molecular representation (TransFoxMol) to improve the understanding of artificial intelligence (AI) of molecular structure property relationships. TransFoxMol incorporates a multi-scale 2D molecular environment into a graph neural network + Transformer module and uses prior chemical maps to obtain a more focused attention landscape compared to that obtained using existing approaches. Experimental results show that TransFoxMol achieves state-of-the-art performance on MoleculeNet benchmarks and surpasses the performance of baselines that use self-supervised learning or geometry-enhanced strategies on small-scale datasets. Subsequent analyses indicate that TransFoxMol's predictions are highly interpretable and the clever use of chemical knowledge enables AI to perceive molecules in a simple but rational way, enhancing performance.

Crime risk prediction is crucial for city safety and residents’ life quality. However, without labeled data, it is challenging to predict crime risk in cities. Due to municipal regulations and maintenance costs, it is not trivial for many cities to collect high-quality labeled crime data. In particular, some cities have lots of labeled data while others may have few. It has been possible to develop a crime prediction model for a city without labeled crime data by learning knowledge from a city with abundant data. Nevertheless, the inconsistency of relevant context data between cities exacerbates the difficulty of this prediction task. To this end, this article proposes an effective unsupervised domain adaptation model (UDAC) for crime risk prediction across cities while addressing the contexts’ inconsistency issue. More specifically, we first identify several similar source city grids for each target city grid. Based on these source city grids, we then construct auxiliary contexts for the target city, to make contexts consistent between the two cities. A dense convolutional network with unsupervised domain adaptation is designed to learn high-level representations for accurate crime risk prediction and simultaneously learn domain-invariant features for domain adaptation. The effectiveness of our model is verified through extensive experiments using three real-world datasets.

Recent years have witnessed rapid advances in autonomous driving technologies. Autonomous vehicles are more likely to be accepted if they drive comfortably to avoid potholes, bumps, and other road damage conditions, especially when there are elderly and disabled passengers on board. Traditionally, sensing road damage conditions is either labor-intensive by field investigation and reporting, or inaccurate by surveillance cameras and driving recorders due to limited perspective. In this paper, we propose GoComfort, a crowdsensing-based framework to provide low-cost and fine-grained comfortable navigation for autonomous vehicles with road damage identification leveraging high-precision road sensing data. First, we propose to exploit city-wide autonomous vehicle fleets as crowdsensing participants, and employ an edge-cloud-hybrid computing paradigm to efficiently collect high-precision road damage-related data, including 3D LiDAR point clouds and street view images. Second, we design an accurate road damage identification model fusing spatial structures of point clouds and texture features of street view images, and use an active learning-based method to address the sparse labels issue. Finally, we devise two comfortable navigation scenarios, i.e., fine-grained road damage avoidance and coarse-grained city-wide navigation, and propose a hierarchical road damage assessment diagram for comfortable route planning. Experiments using real-world road sensing data in Xiamen, China show that our approach identifies road damage conditions with an accuracy of 87.5%, and achieves a user acceptance rate of 93.8% in riding comfort evaluation, outperforming the state-of-the-art baselines.

Traffic violations have become one of the major threats to urban transportation systems, undermining human safety and causing economic losses. To alleviate this problem, crowd-based patrol forces including traffic police and voluntary participants have been employed in many cities. To adaptively optimize patrol routes with limited manpower, it is essential to be aware of traffic violation hotspots. Traditionally, traffic violation hotspots are directly inferred from experiences, and existing patrol routes are usually fixed. In this paper, we propose a mobile crowdsensing-based framework to dynamically infer traffic violation hotspots and adaptively schedule crowd patrol routes. Specifically, we first extract traffic violation-prone locations from heterogeneous crowd-sensed data and propose a spatiotemporal context-aware self-adaptive learning model (CSTA) to infer traffic violation hotspots. Then, we propose a tensor-based integer linear problem modeling method (TILP) to adaptively find optimal patrol routes under human labor constraints. Experiments on real-world data from two Chinese cities (Xiamen and Chengdu) show that our approach accurately infers traffic violation hotspots with F1-scores above 90% in both cities, and generates patrol routes with relative coverage ratios above 85%, significantly outperforming baseline methods.

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a novel policy optimization method based on Constrained Update Projection framework that enjoys rigorous safety guarantee. Central to our CUP development is the newly proposed surrogate functions along with the performance bound. Compared to previous safe reinforcement learning meth- ods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance. 2) CUP unifies performance bounds, providing a better understanding and in- terpretability for some existing algorithms; 3) CUP provides a non-convex im- plementation via only first-order optimizers, which does not require any strong approximation on the convexity of the objectives. To validate our CUP method, we compared CUP against a comprehensive list of safe RL baselines on a wide range of tasks. Experiments show the effectiveness of CUP both in terms of reward and safety constraint satisfaction. We have opened the source code of CUP at https://github.com/zmsn-2077/CUP-safe-rl.

Traffic violations have become one of the major threats to urban transportation systems, undermining road safety and causing economic losses. Although various methods have been proposed by road authorities and researchers to find out the possible causes of traffic violations, existing methods often fail to diagnose traffic violations from drivers’ perspectives and contexts or consider their visual and comprehension loads while driving. In this work, we propose a driver-centered simulation platform to inspect drivers’ loads in traffic violation hotspots. Specifically, we first build a driving simulator based on the 3D point clouds of real-world traffic violation hotspots. We then recruit drivers to simulate driving in designated traffic scenes. Indicators for drivers’ visual and comprehension loads are derived based on drivers’ feedback. Upon this basis, we build an explainable model to automatically indicate drivers’ visual and comprehension loads under various crowd-sensed traffic scenes. Experiments using real-world data from a Chinese City (Xiamen) and case studies show that our approach successfully derives a set of prominent indicators to effectively diagnose drivers’ visual and comprehension loads in real-world traffic violation hotspots.

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term prediction using historical data. Recently, cross-domain open data provides possibilities to conduct fine-grained price forecasting, since they can be leveraged to extract various direct and indirect factors of the price. In this article, we predict the price trend over upcoming days, by leveraging cross-domain open data fusion. More specifically, we formulate the price trend into three classes (rise, slight-change, and fall), and then we predict the specific class in which the price trend of the future day lies. We take three factors into consideration: (1) supply factor considering sources providing bulk commodities,<?brk?> (2) demand factor focusing on vessel transportation with reflection of short time needs, and (3) expectation factor encompassing indirect features (e.g., air quality) with latent influences. A hybrid classification framework is proposed for the price trend forecasting. Evaluation conducted on nine real-world cross-domain open datasets shows that our framework can forecast the price trend accurately, outperforming multiple state-of-the-art baselines.

We investigate the problem of adaptive traffic light control of multiple intersections using real-time traffic data collected by a wireless sensor network (WSN). Previous studies mainly focused on optimizing the intervals of green lights in fixed sequences of traffic lights and ignored the traffic flow's characteristics and special traffic circumstances. In this paper, we propose an adaptive traffic light control scheme that adjusts the sequences of green lights in multiple intersections based on the real- time traffic data, including traffic volume, waiting time, number of stops, and vehicle density. Subsequently, the optimal green light length can be calculated from the local traffic data and traffic condition of neighbor intersections. Simulation results demonstrate that our scheme produces much higher throughput, lower average waiting time and fewer number of stops, compared with three control approaches: the optimal fixed-time control, an actuated control and an adaptive control.

We investigate the problem of adaptive traffic light control using real-time traffic information collected by a wireless sensor network (WSN). Existing studies mainly focused on determining the green light length in a fixed sequence of traffic lights. In this paper, we propose an adaptive traffic light control algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. Our algorithm considers a number of traffic factors such as traffic volume, waiting time, vehicle density, etc., to determine green light sequence and the optimal green light length. Simulation results demonstrate that our algorithm produces much higher throughput and lower vehicle's average waiting time, compared with a fixed-time control algorithm and an actuated control algorithm. We also implement proposed algorithm on our transportation testbed, iSensNet, and the result shows that our algorithm is effective and practical.