OpenRaiser/Intern-Atlas huggingface dataset:
RectifiedHR: High-Resolution Diffusion via Energy Profiling and Adaptive Guidance Scheduling
Venue: arXiv cs.GR 2025
Year: 2025

High-resolution image synthesis with diffusion models often suffers from energy instabilities and guidance artifacts that degrade visual quality. We analyze the latent energy landscape during sampling and propose adaptive classifier-free guidance (CFG) schedules that maintain stable energy trajectories. Our approach introduces energy-aware scheduling strategies that modulate guidance strength over time, achieving superior stability scores (0.9998) and consistency metrics (0.9873) compared to fixed-guidance approaches. We demonstrate that DPM++ 2M with linear-decreasing CFG scheduling yields optimal performance, providing sharper, more faithful images while reducing artifacts. Our energy profiling framework serves as a powerful diagnostic tool for understanding and improving diffusion model behavior.
TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing
Venue: arXiv cs.DB 2025
Year: 2025

Efficiently re-identifying and tracking objects across a network of cameras is crucial for applications like traffic surveillance. Spatula is the state-of-the-art video database management system (VDBMS) for processing Re-ID queries. However, it suffers from two limitations. Its spatio-temporal filtering scheme has limited accuracy on large camera networks due to localized camera history. It is not suitable for critical video analytics applications that require high recall due to a lack of support for adaptive query processing. In this paper, we present Tracer, a novel VDBMS for efficiently processing Re-ID queries using an adaptive query processing framework. Tracer selects the optimal camera to process at each time step by training a recurrent network to model long-term historical correlations. To accelerate queries under a high recall constraint, Tracer incorporates a probabilistic adaptive search model that processes camera feeds in incremental search windows and dynamically updates the sampling probabilities using an exploration-exploitation strategy. To address the paucity of benchmarks for the Re-ID task due to privacy concerns, we present a novel synthetic benchmark for generating multi-camera Re-ID datasets based on real-world traffic distribution. Our evaluation shows that Tracer outperforms the state-of-the-art cross-camera analytics system by 3.9x on average across diverse datasets.
SmartphoneDemocracy: Privacy-Preserving E-Voting on Decentralized Infrastructure using Novel European Identity
Venue: arXiv cs.CR 2025
Year: 2025

The digitization of democratic processes promises greater accessibility but presents challenges in terms of security, privacy, and verifiability. Existing electronic voting systems often rely on centralized architectures, creating single points of failure and forcing too much trust in authorities, which contradicts democratic principles. This research addresses the challenge of creating a secure, private e-voting system with minimized trust dependencies designed for the most versatile personal device: the smartphone. We introduce SmartphoneDemocracy, a novel e-voting protocol that combines three key technologies: the emerging European Digital Identity (EUDI) Wallet for Sybil-resistant identity verification, Zero-Knowledge Proofs for privacy-preserving validation, and a peer-to-peer blockchain (TrustChain) for a resilient, serverless public bulletin board. Our protocol enables voters to register and cast ballots anonymously and verifiably directly from their smartphones. We provide a detailed protocol design, a security analysis against a defined threat model, and a performance evaluation demonstrating that the computational and network overhead is feasible for medium- to large-scale elections. By developing and prototyping this system, we demonstrate a viable path to empower citizens with a trustworthy, accessible, and user-controlled digital voting experience.
mmE-Loc: Facilitating Accurate Drone Landing with Ultra-High-Frequency Localization
Venue: arXiv cs.RO 2025
Year: 2025

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit the \textit{temporal consistency} and \textit{spatial complementarity} between these two modalities, we propose two innovative modules: \textit{(i)} the Consistency-instructed Collaborative Tracking module, which further leverages the drone's physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and \textit{(ii)} the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Real-world experiments conducted in landing scenarios with a drone delivery company demonstrate that mmE-Loc significantly outperforms state-of-the-art methods in both accuracy and latency.
Discrete Differential Principle for Continuous Smooth Function Representation
Venue: arXiv cs.LG 2025
Year: 2025

Taylor's formula holds significant importance in function representation, such as solving differential difference equations, ordinary differential equations, partial differential equations, and further promotes applications in visual perception, complex control, fluid mechanics, weather forecasting and thermodynamics. However, the Taylor's formula suffers from the curse of dimensionality and error propagation during derivative computation in discrete situations. In this paper, we propose a new discrete differential operator to estimate derivatives and to represent continuous smooth function locally using the Vandermonde coefficient matrix derived from truncated Taylor series. Our method simultaneously computes all derivatives of orders less than the number of sample points, inherently mitigating error propagation. Utilizing equidistant uniform sampling, it achieves high-order accuracy while alleviating the curse of dimensionality. We mathematically establish rigorous error bounds for both derivative estimation and function representation, demonstrating tighter bounds for lower-order derivatives. We extend our method to the two-dimensional case, enabling its use for multivariate derivative calculations. Experiments demonstrate the effectiveness and superiority of the proposed method compared to the finite forward difference method for derivative estimation and cubic spline and linear interpolation for function representation. Consequently, our technique offers broad applicability across domains such as vision representation, feature extraction, fluid mechanics, and cross-media imaging.
SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis. However, traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance. To address these challenges, we propose the Self-Adaptive Tensor- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks, ensuring optimal feature representation tailored to the complexity of the data. Building upon SDTN, we propose the Tensor-Regularized Network (TRN), which integrates the features extracted by SDTN into a lightweight network capable of capturing spectral-spatial features at multiple scales. This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments. Experiments on PaviaU datasets demonstrate significant improvements in accuracy and reduced model parameters compared to state-of-the-art methods.
Consistency Trajectory Planning: High-Quality and Efficient Trajectory Optimization for Offline Model-Based Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

This paper introduces Consistency Trajectory Planning (CTP), a novel offline model-based reinforcement learning method that leverages the recently proposed Consistency Trajectory Model (CTM) for efficient trajectory optimization. While prior work applying diffusion models to planning has demonstrated strong performance, it often suffers from high computational costs due to iterative sampling procedures. CTP supports fast, single-step trajectory generation without significant degradation in policy quality. We evaluate CTP on the D4RL benchmark and show that it consistently outperforms existing diffusion-based planning methods in long-horizon, goal-conditioned tasks. Notably, CTP achieves higher normalized returns while using significantly fewer denoising steps. In particular, CTP achieves comparable performance with over $120\times$ speedup in inference time, demonstrating its practicality and effectiveness for high-performance, low-latency offline planning.
On the Importance of Neural Membrane Potential Leakage for LIDAR-based Robot Obstacle Avoidance using Spiking Neural Networks
Venue: arXiv cs.RO 2025
Year: 2025

Using neuromorphic computing for robotics applications has gained much attention in recent year due to the remarkable ability of Spiking Neural Networks (SNNs) for high-precision yet low memory and compute complexity inference when implemented in neuromorphic hardware. This ability makes SNNs well-suited for autonomous robot applications (such as in drones and rovers) where battery resources and payload are typically limited. Within this context, this paper studies the use of SNNs for performing direct robot navigation and obstacle avoidance from LIDAR data. A custom robot platform equipped with a LIDAR is set up for collecting a labeled dataset of LIDAR sensing data together with the human-operated robot control commands used for obstacle avoidance. Crucially, this paper provides what is, to the best of our knowledge, a first focused study about the importance of neuron membrane leakage on the SNN precision when processing LIDAR data for obstacle avoidance. It is shown that by carefully tuning the membrane potential leakage constant of the spiking Leaky Integrate-and-Fire (LIF) neurons used within our SNN, it is possible to achieve on-par robot control precision compared to the use of a non-spiking Convolutional Neural Network (CNN). Finally, the LIDAR dataset collected during this work is released as open-source with the hope of benefiting future research.
Bounded Model Checking of RISC-V Machine Code with Context-Free-Language Ordered Binary Decision Diagrams
Venue: arXiv cs.PL 2025
Year: 2025

Symbolic execution is a powerful technique for analyzing the behavior of software yet scalability remains a challenge due to state explosion in control and data flow. Existing tools typically aim at managing control flow internally, often at the expense of completeness, while offloading reasoning over data flow to SMT solvers. Moreover, reasoning typically happens on source code or intermediate representation level to leverage structural information, making machine code generation part of the trust base. We are interested in changing the equation in two non-trivial ways: pushing reasoning down to machine code level, and then offloading reasoning entirely into SMT solvers and other, possibly more efficient solver technology. In more abstract terms, we are asking if bit-precise reasoning technology can be made scalable on software, and not just hardware. For this purpose, we developed two tools called rotor and bitme for model generation and bounded model checking, respectively. We chose RISC-V restricted to integer arithmetic as modeling target for rotor since RISC-V integer semantics is essentially equivalent to established SMT semantics over bitvectors and arrays of bitvectors. While state-of-the-art SMT solvers struggle in our experiments, we have evidence that there is potential for improvement. To show the potential, we have slightly generalized and then implemented in bitme two types of binary decision diagrams (BDDs): algebraic decision diagrams (ADDs) and context-free-language ordered binary decision diagrams (CFLOBDDs). Bitme uses BDDs to propagate program input through models, essentially generalizing constant propagation to domain propagation. SMT solvers only get involved when model input cannot be propagated, significanly speeding up SMT solving. We then study the impact on state explosion of CFLOBDDs, which are potentially more scalable than ADDs.
DRPCA-Net: Make Robust PCA Great Again for Infrared Small Target Detection
Venue: arXiv cs.CV 2025
Year: 2025

Infrared small target detection plays a vital role in remote sensing, industrial monitoring, and various civilian applications. Despite recent progress powered by deep learning, many end-to-end convolutional models tend to pursue performance by stacking increasingly complex architectures, often at the expense of interpretability, parameter efficiency, and generalization. These models typically overlook the intrinsic sparsity prior of infrared small targets--an essential cue that can be explicitly modeled for both performance and efficiency gains. To address this, we revisit the model-based paradigm of Robust Principal Component Analysis (RPCA) and propose Dynamic RPCA Network (DRPCA-Net), a novel deep unfolding network that integrates the sparsity-aware prior into a learnable architecture. Unlike conventional deep unfolding methods that rely on static, globally learned parameters, DRPCA-Net introduces a dynamic unfolding mechanism via a lightweight hypernetwork. This design enables the model to adaptively generate iteration-wise parameters conditioned on the input scene, thereby enhancing its robustness and generalization across diverse backgrounds. Furthermore, we design a Dynamic Residual Group (DRG) module to better capture contextual variations within the background, leading to more accurate low-rank estimation and improved separation of small targets. Extensive experiments on multiple public infrared datasets demonstrate that DRPCA-Net significantly outperforms existing state-of-the-art methods in detection accuracy. Code is available at https://github.com/GrokCV/DRPCA-Net.
Assessing reliability of explanations in unbalanced datasets: a use-case on the occurrence of frost events
Venue: arXiv cs.LG 2025
Year: 2025

The usage of eXplainable Artificial Intelligence (XAI) methods has become essential in practical applications, given the increasing deployment of Artificial Intelligence (AI) models and the legislative requirements put forward in the latest years. A fundamental but often underestimated aspect of the explanations is their robustness, a key property that should be satisfied in order to trust the explanations. In this study, we provide some preliminary insights on evaluating the reliability of explanations in the specific case of unbalanced datasets, which are very frequent in high-risk use-cases, but at the same time considerably challenging for both AI models and XAI methods. We propose a simple evaluation focused on the minority class (i.e. the less frequent one) that leverages on-manifold generation of neighbours, explanation aggregation and a metric to test explanation consistency. We present a use-case based on a tabular dataset with numerical features focusing on the occurrence of frost events.
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
Venue: arXiv cs.CV 2025
Year: 2025

The Segment Anything Model (SAM) has revolutionized image segmentation through its innovative prompt-based approach, yet the critical role of prompt engineering in its success remains underexplored. This paper presents the first comprehensive survey focusing specifically on prompt engineering techniques for SAM and its variants. We systematically organize and analyze the rapidly growing body of work in this emerging field, covering fundamental methodologies, practical applications, and key challenges. Our review reveals how prompt engineering has evolved from simple geometric inputs to sophisticated multimodal approaches, enabling SAM's adaptation across diverse domains including medical imaging and remote sensing. We identify unique challenges in prompt optimization and discuss promising research directions. This survey fills an important gap in the literature by providing a structured framework for understanding and advancing prompt engineering in foundation models for segmentation.
A Login Page Transparency and Visual Similarity Based Zero Day Phishing Defense Protocol
Venue: arXiv cs.CR 2025
Year: 2025

Phishing is a prevalent cyberattack that uses look-alike websites to deceive users into revealing sensitive information. Numerous efforts have been made by the Internet community and security organizations to detect, prevent, or train users to avoid falling victim to phishing attacks. Most of this research over the years has been highly diverse and application-oriented, often serving as standalone solutions for HTTP clients, servers, or third parties. However, limited work has been done to develop a comprehensive or proactive protocol-oriented solution to effectively counter phishing attacks. Inspired by the concept of certificate transparency, which allows certificates issued by Certificate Authorities (CAs) to be publicly verified by clients, thereby enhancing transparency, we propose a concept called Page Transparency (PT) for the web. The proposed PT requires login pages that capture users' sensitive information to be publicly logged via PLS and made available to web clients for verification. The pages are verified to be logged using cryptographic proofs. Since all pages are logged on a PLS and visually compared with existing pages through a comprehensive visual page-matching algorithm, it becomes impossible for an attacker to register a deceptive look-alike page on the PLS and receive the cryptographic proof required for client verification. All implementations occur on the client side, facilitated by the introduction of a new HTTP PT header, eliminating the need for platform-specific changes or the installation of third-party solutions for phishing prevention.
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning enables collaborative machine learning while preserving data privacy. However, the rise of federated unlearning, designed to allow clients to erase their data from the global model, introduces new privacy concerns. Specifically, the gradient exchanges during the unlearning process can leak sensitive information about deleted data. In this paper, we introduce DRAGD, a novel attack that exploits gradient discrepancies before and after unlearning to reconstruct forgotten data. We also present DRAGDP, an enhanced version of DRAGD that leverages publicly available prior data to improve reconstruction accuracy, particularly for complex datasets like facial images. Extensive experiments across multiple datasets demonstrate that DRAGD and DRAGDP significantly outperform existing methods in data reconstruction.Our work highlights a critical privacy vulnerability in federated unlearning and offers a practical solution, advancing the security of federated unlearning systems in real-world applications.
Inter2Former: Dynamic Hybrid Attention for Efficient High-Precision Interactive
Venue: arXiv cs.CV 2025
Year: 2025

Interactive segmentation (IS) improves annotation efficiency by segmenting target regions from user prompts, with widespread applications in real-world scenarios. Current approaches face a critical trade-off: dense-token methods achieve superior accuracy and detail preservation but suffer from prohibitively slow processing on CPU devices, while the Segment Anything Model (SAM) advances the field with sparse prompt tokens for fast inference but compromises segmentation quality. In this paper, we propose Inter2Former to address this challenge by optimizing computation allocation in dense-token processing, which introduces four key enhancements. First, we propose Dynamic Prompt Embedding (DPE) that adaptively processes only regions of interest while avoiding additional overhead from background tokens. Second, we introduce Dynamic Hybrid Attention (DHA), which leverages previous segmentation masks to route tokens through either full attention (O(N2)) for boundary regions or our proposed efficient BSQ attention (O(N)) for non-boundary regions. Third, we develop Hybrid Mixture of Experts (HMoE), which applies similar adaptive computation strategies in FFN modules with CPU-optimized parallel processing. Finally, we present Dynamic Local Upsampling (DLU), a reverse operation of DPE, which localizes objects with a lightweight MLP and performs fine-grained upsampling only in detected regions. Experimental results on high-precision IS benchmarks demonstrate that Inter2Former achieves SOTA performance with high efficiency on CPU devices.
Bridging Bots: from Perception to Action via Multimodal-LMs and Knowledge Graphs
Venue: arXiv cs.AI 2025
Year: 2025

Personal service robots are deployed to support daily living in domestic environments, particularly for elderly and individuals requiring assistance. These robots must perceive complex and dynamic surroundings, understand tasks, and execute context-appropriate actions. However, current systems rely on proprietary, hard-coded solutions tied to specific hardware and software, resulting in siloed implementations that are difficult to adapt and scale across platforms. Ontologies and Knowledge Graphs (KGs) offer a solution to enable interoperability across systems, through structured and standardized representations of knowledge and reasoning. However, symbolic systems such as KGs and ontologies struggle with raw and noisy sensory input. In contrast, multimodal language models are well suited for interpreting input such as images and natural language, but often lack transparency, consistency, and knowledge grounding. In this work, we propose a neurosymbolic framework that combines the perceptual strengths of multimodal language models with the structured representations provided by KGs and ontologies, with the aim of supporting interoperability in robotic applications. Our approach generates ontology-compliant KGs that can inform robot behavior in a platform-independent manner. We evaluated this framework by integrating robot perception data, ontologies, and five multimodal models (three LLaMA and two GPT models), using different modes of neural-symbolic interaction. We assess the consistency and effectiveness of the generated KGs across multiple runs and configurations, and perform statistical analyzes to evaluate performance. Results show that GPT-o1 and LLaMA 4 Maverick consistently outperform other models. However, our findings also indicate that newer models do not guarantee better results, highlighting the critical role of the integration strategy in generating ontology-compliant KGs.