Know Unreported Roadway Incidents in Real-time: A Deep Learning Framework for Early Traffic Anomaly Detection

Conventional automatic incident detection (AID) has relied heavily on all incident reports exclusively for training and evaluation. However, these reports suffer from a number of issues, such as delayed reports, inaccurate descriptions, false alarms, missing reports, and incidents that do not necessarily influence traffic. Relying on these reports to train or calibrate AID models hinders their ability to detect traffic anomalies effectively and timely, even leading to convergence issues in the model training process. Moreover, conventional AID models are not inherently designed to capture the early indicators of any generic incidents. It remains unclear how far ahead an AID model can report incidents. The AID applications in the literature are also spatially limited because the data used by most models is often limited to specific test road segments. To solve these problems, we propose a deep learning framework utilizing prior domain knowledge and model-designing strategies. This allows the model to detect a broader range of anomalies, not only incidents that significantly influence traffic flow but also early characteristics of incidents along with historically unreported anomalies. We specially design the model to target the early-stage detection/prediction of an incident. Additionally, unlike most conventional AID studies, we use widely available data, enhancing our method's scalability. The experimental results across numerous road segments on different maps demonstrate that our model leads to more effective and early anomaly detection. Our framework does not focus on stacking or tweaking various deep learning models; instead, it focuses on model design and training strategies to improve early detection performance.

Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation

We study the problem of privately releasing an approximate minimum spanning tree (MST). Given a graph $G = (V, E, \vec{W})$ where $V$ is a set of $n$ vertices, $E$ is a set of $m$ undirected edges, and $ \vec{W} \in \mathbb{R}^{|E|} $ is an edge-weight vector, our goal is to publish an approximate MST under edge-weight differential privacy, as introduced by Sealfon in PODS 2016, where $V$ and $E$ are considered public and the weight vector is private. Our neighboring relation is $\ell_\infty$-distance on weights: for a sensitivity parameter $\Delta_\infty$, graphs $ G = (V, E, \vec{W}) $ and $ G' = (V, E, \vec{W}') $ are neighboring if $\|\vec{W}-\vec{W}'\|_\infty \leq \Delta_\infty$. Existing private MST algorithms face a trade-off, sacrificing either computational efficiency or accuracy. We show that it is possible to get the best of both worlds: With a suitable random perturbation of the input that does not suffice to make the weight vector private, the result of any non-private MST algorithm will be private and achieves a state-of-the-art error guarantee. Furthermore, by establishing a connection to Private Top-k Selection [Steinke and Ullman, FOCS '17], we give the first privacy-utility trade-off lower bound for MST under approximate differential privacy, demonstrating that the error magnitude, $\tilde{O}(n^{3/2})$, is optimal up to logarithmic factors. That is, our approach matches the time complexity of any non-private MST algorithm and at the same time achieves optimal error. We complement our theoretical treatment with experiments that confirm the practicality of our approach.

DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding

Image captioning models often suffer from performance degradation when applied to novel datasets, as they are typically trained on domain-specific data. To enhance generalization in out-of-domain scenarios, retrieval-augmented approaches have garnered increasing attention. However, current methods face two key challenges: (1) image features used for retrieval are often optimized based on ground-truth (GT) captions, which represent the image from a specific perspective and are influenced by annotator biases, and (2) they underutilize the full potential of retrieved text, typically relying on raw captions or parsed objects, which fail to capture the full semantic richness of the data. In this paper, we propose Dive Into Retrieval (DIR), a method designed to enhance both the image-to-text retrieval process and the utilization of retrieved text to achieve a more comprehensive understanding of the visual content. Our approach introduces two key innovations: (1) diffusion-guided retrieval enhancement, where a pretrained diffusion model guides image feature learning by reconstructing noisy images, allowing the model to capture more comprehensive and fine-grained visual information beyond standard annotated captions; and (2) a high-quality retrieval database, which provides comprehensive semantic information to enhance caption generation, especially in out-of-domain scenarios. Extensive experiments demonstrate that DIR not only maintains competitive in-domain performance but also significantly improves out-of-domain generalization, all without increasing inference costs.

GelSight FlexiRay: Breaking Planar Limits by Harnessing Large Deformations for Flexible,Full-Coverage Multimodal Sensing

The integration of tactile sensing into compliant soft robotic grippers offers a compelling pathway toward advanced robotic grasping and safer human-robot interactions. Visual-tactile sensors realize high-resolution, large-area tactile perception with affordable cameras. However, conventional visual-tactile sensors rely heavily on rigid forms, sacrificing finger compliance and sensing regions to achieve localized tactile feedback. Enabling seamless, large-area tactile sensing in soft grippers remains challenging, as deformations inherent to soft structures can obstruct the optical path and restrict the camera's field of view. To address these, we present Gelsight FlexiRay, a multimodal visual-tactile sensor designed for safe and compliant interactions with substantial structural deformation through integration with Finray Effect grippers. First, we adopt a multi-mirror configuration, which is systematically modeled and optimized based on the physical force-deformation characteristics of FRE grippers. Second, we enhanced Gelsight FlexiRay with human-like multimodal perception, including contact force and location, proprioception, temperature, texture, and slippage. Experiments demonstrate Gelsight FlexiRay's robust tactile performance across diverse deformation states, achieving a force measurement accuracy of 0.14 N and proprioceptive positioning accuracy of 0.19 mm. Compared with state of art compliant VTS, the FlexiRay demonstrates 5 times larger structural deformation under the same loads. Its expanded sensing area and ability to distinguish contact information and execute grasping and classification tasks highlights its potential for versatile, large-area multimodal tactile sensing integration within soft robotic systems. This work establishes a foundation for flexible, high-resolution tactile sensing in compliant robotic applications.

Fast High-Quality Enhanced Imaging Algorithm for Layered Dielectric Targets Based on MMW MIMO-SAR System

Millimeter-wave (MMW) multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system is a technology that can achieve high resolution, high frame rate, and all-weather imaging and has received extensive attention in the non-destructive testing and internal imaging applications of layered dielectric targets. However, the non-ideal scattering effect caused by dielectric materials can significantly deteriorate the imaging quality when using the existing MIMO-SAR fast algorithms. This paper proposes a rapid, high-quality dielectric target-enhanced imaging algorithm for a new universal non-uniform MIMO-SAR system. The algorithm builds on the existing non-uniform MIMO-SAR dielectric target frequency-domain algorithm (DT-FDA) by constructing a forward sensing operator and incorporating it into the alternating direction method of multipliers (ADMM) framework. This approach avoids large matrix operations while maintaining computational efficiency. By integrating an optimal regularization parameter search, the algorithm enhances the image reconstruction quality of dielectric internal structures or defects. Experimental results show the proposed algorithm outperforms IBP and DT-FDA, achieving better focusing, sidelobe suppression, and 3D imaging accuracy. It yields the lowest image entropy (8.864) and significantly improves efficiency (imaging time: 15.29 s vs. 23295.3 s for IBP).

DeepContext: A Context-aware, Cross-platform, and Cross-framework Tool for Performance Profiling and Analysis of Deep Learning Workloads

Effective performance profiling and analysis are essential for optimizing training and inference of deep learning models, especially given the growing complexity of heterogeneous computing environments. However, existing tools often lack the capability to provide comprehensive program context information and performance optimization insights for sophisticated interactions between CPUs and GPUs. This paper introduces DeepContext, a novel profiler that links program contexts across high-level Python code, deep learning frameworks, underlying libraries written in C/C++, as well as device code executed on GPUs. DeepContext incorporates measurements of both coarse- and fine-grained performance metrics for major deep learning frameworks, such as PyTorch and JAX, and is compatible with GPUs from both Nvidia and AMD, as well as various CPU architectures, including x86 and ARM. In addition, DeepContext integrates a novel GUI that allows users to quickly identify hotpots and an innovative automated performance analyzer that suggests users with potential optimizations based on performance metrics and program context. Through detailed use cases, we demonstrate how DeepContext can help users identify and analyze performance issues to enable quick and effective optimization of deep learning workloads. We believe Deep Context is a valuable tool for users seeking to optimize complex deep learning workflows across multiple compute environments.

Reliable and Compact Graph Fine-tuning via GraphSparse Prompting

Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g., nodes, edges, node attributes, etc.), which is suboptimal and obviously redundant. To address this issue, we propose exploiting sparse representation theory for graph prompting and present Graph Sparse Prompting (GSP). GSP aims to adaptively and sparsely select the optimal elements (e.g., certain node attributes) to achieve compact prompting for downstream tasks. Specifically, we propose two kinds of GSP models, termed Graph Sparse Feature Prompting (GSFP) and Graph Sparse multi-Feature Prompting (GSmFP). Both GSFP and GSmFP provide a general scheme for tuning any specific pre-trained GNNs that can achieve attribute selection and compact prompt learning simultaneously. A simple yet effective algorithm has been designed for solving GSFP and GSmFP models. Experiments on 16 widely-used benchmark datasets validate the effectiveness and advantages of the proposed GSFPs.

Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need

Design space exploration (DSE) enables architects to systematically evaluate various design options, guiding decisions on the most suitable configurations to meet specific objectives such as optimizing performance, power, and area. However, the growing complexity of modern CPUs has dramatically increased the number of micro-architectural parameters and expanded the overall design space, making DSE more challenging and time-consuming. Existing DSE frameworks struggle in large-scale design spaces due to inaccurate models and limited insights into parameter impact, hindering efficient identification of optimal micro-architectures within tight timeframes. In this work, we introduce AttentionDSE. Its key idea is to use the attention mechanism to establish a direct mapping of micro-architectural parameters to their contributions to predicted performance. This approach enhances both the prediction accuracy and interpretability of the performance model. Furthermore, the weights are dynamically adjusted, enabling the model to respond to design changes and effectively pinpoint the key micro-architectural parameters/components responsible for performance bottlenecks. Thus, AttentionDSE accurately, purposefully, and rapidly discovers optimal designs. Experiments on SPEC 2017 demonstrate that AttentionDSE significantly reduces exploration time by over 80\% and achieves 3.9\% improvement in Pareto Hypervolume compared to state-of-the-art DSE frameworks while maintaining superior prediction accuracy and efficiency with an increasing number of parameters.

Making Every Frame Matter: Continuous Video Understanding for Large Models via Adaptive State Modeling

Video understanding has become increasingly important with the rise of multi-modality applications. Understanding continuous video poses considerable challenges due to the fast expansion of streaming video, which contains multi-scale and untrimmed events. We introduce a novel system, C-VUE, to overcome these issues through adaptive state modeling. C-VUE has three key designs. The first is a long-range history modeling technique that uses a video-aware approach to retain historical video information. The second is a spatial redundancy reduction technique, which enhances the efficiency of history modeling based on temporal relations. The third is a parallel training structure that incorporates the frame-weighted loss to understand multi-scale events in long videos. Our C-VUE offers high accuracy and efficiency. It runs at speeds >30 FPS on typical edge devices and outperforms all baselines in accuracy. Moreover, applying C-VUE to a video foundation model as a video encoder in our case study resulted in a 0.46-point enhancement (on a 5-point scale) on the in-distribution dataset, and an improvement ranging from 1.19\% to 4\% on zero-shot datasets.

Learning Graph Quantized Tokenizers for Transformers

Transformers serve as the backbone architectures of Foundational Models, where a domain-specific tokenizer helps them adapt to various domains. Graph Transformers (GTs) have recently emerged as a leading model in geometric deep learning, outperforming Graph Neural Networks (GNNs) in various graph learning tasks. However, the development of tokenizers for graphs has lagged behind other modalities, with existing approaches relying on heuristics or GNNs co-trained with Transformers. To address this, we introduce GQT (\textbf{G}raph \textbf{Q}uantized \textbf{T}okenizer), which decouples tokenizer training from Transformer training by leveraging multi-task graph self-supervised learning, yielding robust and generalizable graph tokens. Furthermore, the GQT utilizes Residual Vector Quantization (RVQ) to learn hierarchical discrete tokens, resulting in significantly reduced memory requirements and improved generalization capabilities. By combining the GQT with token modulation, a Transformer encoder achieves state-of-the-art performance on 16 out of 18 benchmarks, including large-scale homophilic and heterophilic datasets. The code is available at: https://github.com/limei0307/graph-tokenizer