As Audio Large Language Models (ALLMs) emerge as powerful tools for speech processing, their safety implications demand urgent attention. While considerable research has explored textual and vision safety, audio’s distinct characteristics present significant challenges. This paper first investigates: Is ALLM vulnerable to backdoor attacks exploiting acoustic triggers? In response to this issue, we introduce Hidden in the Noise (HIN), a novel backdoor attack framework designed to exploit subtle, audio-specific features. HIN applies acoustic modifications to raw audio waveforms, such as alterations to temporal dynamics and strategic injection of spectrally tailored noise. These changes introduce consistent patterns that an ALLM’s acoustic feature encoder captures, embedding robust triggers within the audio stream. To evaluate ALLM robustness against audio-feature-based triggers, we develop the AudioSafe benchmark, assessing nine distinct risk types. Extensive experiments on AudioSafe and three established safety datasets reveal critical vulnerabilities in existing ALLMs: (I) audio features like environment noise and speech rate variations achieve over 90% average attack success rate, (II) ALLMs exhibit significant sensitivity differences across acoustic features, particularly showing minimal response to volume as a trigger, and (III) poisoned sample inclusion causes only marginal loss curve fluctuations, highlighting the attack’s stealth.

Understanding the spatiotemporal characteristics of criminal activities in a city, or urban crime dynamics for short, is essential for developing ways to control crime and improve urban safety. While much effort has been devoted to this field, most of the existing studies have led to overly generalized findings, obscuring the ways in which dynamic patterns of criminal activities vary by place, time, and situational context. To address this challenge, this article proposes a novel space-time-context-aware tensor decomposition framework, namelySTCTD-Crime, for an in-depth understanding of urban crime dynamics. Specifically,STCTD-Crimefirst constructs a third-order tensor to represent crime data, which provides an elegant way to model spatial, temporal, and contextual factors simultaneously. Then, it decouples the influence that the three factors exerts on criminal activities via the tensor decomposition, enabling the observation of the extent to which each factor affects crime incidents occurring at different regions, within different time slices, and under different situational contexts. Moreover,STCTD-Crimeexploits spatiotemporal correlations between criminal activities to facilitate the understanding of dynamics by seamlessly integrating a crime-number-guided correlation learning method into the framework. Finally, an alternating optimization based scheme is developed to solve the optimization problem, which results in an efficient urban crime dynamics discovery procedure. Extensive analyses on crime datasets drawn from real-world sources convincingly demonstrate the effectiveness ofSTCTD-Crime.

The modeling of complicated time-evolving physical dynamics from partial observations is a long-standing challenge. Particularly, observations can be sparsely distributed in a seemingly random or unstructured manner, making it difficult to capture highly nonlinear features in a variety of scientific and engineering problems. However, existing data-driven approaches are often constrained by fixed spatial and temporal discretization. While some researchers attempt to achieve spatio-temporal continuity by designing novel strategies, they either overly rely on traditional numerical methods or fail to truly overcome the limitations imposed by discretization. To address these, we propose CoPS, a purely data-driven methods, to effectively model continuous physics simulation from partial observations. Specifically, we employ multiplicative filter network to fuse and encode spatial information with the corresponding observations. Then we customize geometric grids and use message-passing mechanism to map features from original spatial domain to the customized grids. Subsequently, CoPS models continuous-time dynamics by designing multi-scale graph ODEs, while introducing a Markov-based neural auto-correction module to assist and constrain the continuous extrapolations. Comprehensive experiments demonstrate that CoPS advances the state-of-the-art methods in space-time continuous modeling across various scenarios. The source code is available at~\url{https://github.com/Sunxkissed/CoPS}.

Traffic flow forecasting task plays an essential role in intelligent transportation systems. Accurately capturing the intricate spatio-temporal dependencies in traffic network signals is the core of precise prediction. Recently, a paradigm that models spatio-temporal dependencies through graph neural networks and time series models has become one of the most promising methods to solve this problem. However, existing methods still have limitations due to ineffectively modeling dynamic spatial dependencies and high time and space complexity. To address these issues, we propose a simplifying and powerful general spatio-temporal traffic flow forecasting model called LightST. Specifically, LightST first embeds temporal covariates and spatial position information to enhance the spatio-temporal modeling capabilities. Then, stacked temporal linear layers are introduced to capture temporal dependencies efficiently. Finally,we propose a concise adaptive spatio-temporal embedding graph convolution method to extract implicit spatial dependencies over time via dynamic graph convolution with adaptive spatio-temporal embedding graph generation. Extensive experiment results on four public traffic flow datasets demonstrate the superiority of our LightST concerning computational efficiency and prediction performance.

Abstract With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is common, traffic accidents happen frequently, and traffic environments are deteriorating. To alleviate these issues and improve the efficiency of urban transportation, accurate traffic forecasting is crucial. In this study, we aim to provide a comprehensive overview of the overall architecture of traffic forecasting, covering aspects such as traffic data analysis, traffic data modeling, and traffic forecasting applications. We begin by introducing existing traffic forecasting surveys and preliminaries. Next, we delve into traffic data analysis from traffic data collection, traffic data formats, and traffic data characteristics. Additionally, we summarize traffic data modeling from spatial representation, temporal representation, and spatio-temporal representation. Furthermore, we discuss the application of traffic forecasting, including traffic flow forecasting, traffic speed forecasting, traffic demand forecasting, and other hybrid traffic forecasting. To support future research in this field, we also provide information on open datasets, source resources, challenges, and potential research directions. As far as we know, this paper represents the first comprehensive survey that focuses specifically on the overall architecture of traffic forecasting.

Accurate bus ridership forecasts can help city managers develop more effective transportation plans, such as bus schedules. With the continuous development of intelligent transportation systems, many methods have been proposed to predict bus ridership. However, there are two limitations to these existing efforts. First, the existing methods mainly consider temporal properties such as closeness, period, trends, holidays, and weekends, respectively. However, the temporal characteristics can be correlated and affect each other among different time scales. Besides, the spatial interactions are dynamic and complex. Thus, how to extract and represent the high-level temporal features and the dynamic spatial dependency among multiple time scales is significant and profound for bus ridership prediction. In this paper, we propose a novel dynamic spatio-temporal multi-scale representation method DSTMR to predict bus ridership. Specifically, DSTMR consists of several dynamic spatio-temporal representation blocks (DSTs). In DST, the graph generator is used to learn the adjacency of bus stations, the graph convolution module is employed to represent the dynamic spatial relationship, and the temporal convolution module is designed to extract the high-level temporal features. To demonstrate the effectiveness of our proposed method, we first construct a dataset (ASBus) based on real-world data, which is available for further study. Extensive experiments on two real-world datasets validate the effectiveness of our method.