OpenRaiser/Intern-Atlas huggingface dataset:
Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case
Venue: arXiv cs.CV 2025
Year: 2025

Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case. The major contributions of this paper include defining and modelling a SOTIF-related Use Case with 21 diverse weather conditions and generating a LiDAR point cloud dataset suitable for application of 3D object detection methods. The dataset consists of 547 frames, encompassing clear, cloudy, rainy weather conditions, corresponding to different times of the day, including noon, sunset, and night. Employing MMDetection3D and OpenPCDET toolkits, the performance of State-of-the-Art (SOTA) 3D object detection methods is evaluated and compared by testing the pre-trained Deep Learning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics.
High-Quality Virtual Single-Viewpoint Surgical Video: Geometric Autocalibration of Multiple Cameras in Surgical Lights
Venue: arXiv cs.CV 2025
Year: 2025

Occlusion-free video generation is challenging due to surgeons' obstructions in the camera field of view. Prior work has addressed this issue by installing multiple cameras on a surgical light, hoping some cameras will observe the surgical field with less occlusion. However, this special camera setup poses a new imaging challenge since camera configurations can change every time surgeons move the light, and manual image alignment is required. This paper proposes an algorithm to automate this alignment task. The proposed method detects frames where the lighting system moves, realigns them, and selects the camera with the least occlusion. This algorithm results in a stabilized video with less occlusion. Quantitative results show that our method outperforms conventional approaches. A user study involving medical doctors also confirmed the superiority of our method.
Optimal Beamforming for Multi-Target Multi-User ISAC Exploiting Prior Information: How Many Sensing Beams Are Needed?
Venue: arXiv cs.IT 2025
Year: 2025

This paper studies a multi-target multi-user integrated sensing and communication (ISAC) system where a multi-antenna base station (BS) communicates with multiple single-antenna users in the downlink and senses the unknown and random angle information of multiple targets based on their reflected echo signals at the BS receiver as well as their prior probability information. We focus on a general beamforming structure with both communication beams and dedicated sensing beams, whose design is highly non-trivial as more sensing beams provide more flexibility in sensing, but introduce extra interference to communication. To resolve this trade-off, we first characterize the periodic posterior Cramér-Rao bound (PCRB) as a lower bound of the mean-cyclic error (MCE) in multi-target sensing. Then, we optimize the beamforming to minimize the maximum periodic PCRB among all targets to ensure fairness, subject to individual communication rate constraints at multiple users. Despite the non-convexity of this problem, we propose a general construction method for the optimal solution by leveraging semi-definite relaxation (SDR), and derive a general bound on the number of sensing beams needed. Moreover, we unveil specific structures of the optimal solution in various cases, where tighter bounds on the number of sensing beams needed are derived (e.g., no or at most one sensing beam is needed under stringent rate constraints or with homogeneous targets). Next, we study the beamforming optimization to minimize the sum periodic PCRB under user rate constraints. By applying SDR, we propose a general construction method for the optimal solution and its specific structures which yield lower computational complexities. We derive a general bound and various tighter bounds on the number of sensing beams needed. Numerical results validate our analysis and effectiveness of our proposed beamforming designs.
Novel Complexity Results for Temporal Separators with Deadlines
Venue: arXiv cs.DS 2025
Year: 2025

We consider two variants, (s,z,l)-Temporal Separator and (s,z,l)-Temporal Cut, respectively, of the vertex separator and the edge cut problem in temporal graphs. The goal is to remove the minimum number of vertices (temporal edges, respectively) in order to delete all the temporal paths that have time travel at most l between a source vertex s and target vertex z. First, we solve an open problem in the literature showing that (s,z,l)-Temporal Separator is NP-complete even when the underlying graph has pathwidth bounded by four. We complement this result showing that (s,z,l)-Temporal Separator can be solved in polynomial time for graphs of pathwidth bounded by three. Then we consider the approximability of (s,z,l)-Temporal Separator and we show that it cannot be approximated within factor$2^{Ω(\log^{1-\varepsilon}|V|)}$ for any constant $\varepsilon> 0$, unless $NP \subseteq ZPP$ (V is the vertex set of the input temporal graph) and that the strict version is approximable within factor l - 1 (we show also that it is unliklely that this factor can be improved). Then we consider the (s,z,l)-Temporal Cut problem, we show that it is APX-hard and we present a $2 \log_2(2\ell)$ approximation algorithm.
Saturated Drawings of Geometric Thickness k
Venue: arXiv cs.CG 2025
Year: 2025

We investigate saturated geometric drawings of graphs with geometric thickness $k$, where no edge can be added without increasing $k$. We establish lower and upper bounds on the number of edges in such drawings if the vertices lie in convex position. We also study the more restricted version where edges are precolored, and for $k=2$ the case for vertices in non-convex position.
Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders
Venue: arXiv cs.CL 2025
Year: 2025

Artificial Text Detection (ATD) is becoming increasingly important with the rise of advanced Large Language Models (LLMs). Despite numerous efforts, no single algorithm performs consistently well across different types of unseen text or guarantees effective generalization to new LLMs. Interpretability plays a crucial role in achieving this goal. In this study, we enhance ATD interpretability by using Sparse Autoencoders (SAE) to extract features from Gemma-2-2b residual stream. We identify both interpretable and efficient features, analyzing their semantics and relevance through domain- and model-specific statistics, a steering approach, and manual or LLM-based interpretation. Our methods offer valuable insights into how texts from various models differ from human-written content. We show that modern LLMs have a distinct writing style, especially in information-dense domains, even though they can produce human-like outputs with personalized prompts.
Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs
Venue: arXiv cs.IR 2025
Year: 2025

Recommender ecosystems are an emerging subject of research. Such research examines how the characteristics of algorithms, recommendation consumers, and item providers influence system dynamics and long-term outcomes. One architectural possibility that has not yet been widely explored in this line of research is the consequences of a configuration in which recommendation algorithms are decoupled from the platforms they serve. This is sometimes called "the friendly neighborhood algorithm store" or "middleware" model. We are particularly interested in how such architectures might offer a range of different distributions of utility across consumers, providers, and recommendation platforms. In this paper, we create a model of a recommendation ecosystem that incorporates algorithm choice and examine the outcomes of such a design.
An Adaptive Underwater Image Enhancement Framework via Multi-Domain Fusion and Color Compensation
Venue: arXiv cs.CV 2025
Year: 2025

Underwater optical imaging is severely degraded by light absorption, scattering, and color distortion, hindering visibility and accurate image analysis. This paper presents an adaptive enhancement framework integrating illumination compensation, multi-domain filtering, and dynamic color correction. A hybrid illumination compensation strategy combining CLAHE, Gamma correction, and Retinex enhances visibility. A two-stage filtering process, including spatial-domain (Gaussian, Bilateral, Guided) and frequency-domain (Fourier, Wavelet) methods, effectively reduces noise while preserving details. To correct color distortion, an adaptive color compensation (ACC) model estimates spectral attenuation and water type to combine RCP, DCP, and MUDCP dynamically. Finally, a perceptually guided color balance mechanism ensures natural color restoration. Experimental results on benchmark datasets demonstrate superior performance over state-of-the-art methods in contrast enhancement, color correction, and structural preservation, making the framework robust for underwater imaging applications.
Token-Level Privacy in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

The use of language models as remote services requires transmitting private information to external providers, raising significant privacy concerns. This process not only risks exposing sensitive data to untrusted service providers but also leaves it vulnerable to interception by eavesdroppers. Existing privacy-preserving methods for natural language processing (NLP) interactions primarily rely on semantic similarity, overlooking the role of contextual information. In this work, we introduce dchi-stencil, a novel token-level privacy-preserving mechanism that integrates contextual and semantic information while ensuring strong privacy guarantees under the dchi differential privacy framework, achieving 2epsilon-dchi-privacy. By incorporating both semantic and contextual nuances, dchi-stencil achieves a robust balance between privacy and utility. We evaluate dchi-stencil using state-of-the-art language models and diverse datasets, achieving comparable and even better trade-off between utility and privacy compared to existing methods. This work highlights the potential of dchi-stencil to set a new standard for privacy-preserving NLP in modern, high-risk applications.
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Accurate and high-fidelity driving scene reconstruction demands the effective utilization of comprehensive scene information as conditional inputs. Existing methods predominantly rely on 3D bounding boxes and BEV road maps for foreground and background control, which fail to capture the full complexity of driving scenes and adequately integrate multimodal information. In this work, we present DualDiff, a dual-branch conditional diffusion model designed to enhance driving scene generation across multiple views and video sequences. Specifically, we introduce Occupancy Ray-shape Sampling (ORS) as a conditional input, offering rich foreground and background semantics alongside 3D spatial geometry to precisely control the generation of both elements. To improve the synthesis of fine-grained foreground objects, particularly complex and distant ones, we propose a Foreground-Aware Mask (FGM) denoising loss function. Additionally, we develop the Semantic Fusion Attention (SFA) mechanism to dynamically prioritize relevant information and suppress noise, enabling more effective multimodal fusion. Finally, to ensure high-quality image-to-video generation, we introduce the Reward-Guided Diffusion (RGD) framework, which maintains global consistency and semantic coherence in generated videos. Extensive experiments demonstrate that DualDiff achieves state-of-the-art (SOTA) performance across multiple datasets. On the NuScenes dataset, DualDiff reduces the FID score by 4.09% compared to the best baseline. In downstream tasks, such as BEV segmentation, our method improves vehicle mIoU by 4.50% and road mIoU by 1.70%, while in BEV 3D object detection, the foreground mAP increases by 1.46%. Code will be made available at https://github.com/yangzhaojason/DualDiff.
AEGIS: Towards Formalized and Practical Memory-Safe Execution of C programs via MSWASM
Venue: arXiv cs.PL 2025
Year: 2025

Programs written in unsafe languages such as C are prone to memory safety errors, which can lead to program compromises and serious real-world security consequences. Recently, Memory-Safe WebAssembly (MSWASM) is introduced as a general-purpose intermediate bytecode with built-in memory safety semantics. Programs written in C can be compiled into MSWASM to get complete memory safety protection. In this paper, we present our extensions on MSWASM, which improve its semantics and practicality. First, we formalize MSWASM semantics in Coq/Iris, extending it with inter-module interaction, showing that MSWASM provides fine-grained isolation guarantees analogous to WASM's coarse-grained isolation via linear memory. Second, we present Aegis, a system to adopt the memory safety of MSWASM for C programs in an interoperable way. Aegis pipeline generates Checked C source code from MSWASM modules to enforce spatial memory safety. Checked C is a recent binary-compatible extension of C which can provide guaranteed spatial safety. Our design allows Aegis to protect C programs that depend on legacy C libraries with no extra dependency and with low overhead. Aegis pipeline incurs 67% runtime overhead and near-zero memory overhead on PolyBenchC programs compared to native.
When Radiation Meets Linux: Analyzing Soft Errors in Linux on COTS SoCs under Proton Irradiation
Venue: arXiv cs.OS 2025
Year: 2025

The increasing use of Linux on commercial off-the-shelf (COTS) system-on-chip (SoC) in spaceborne computing inherits COTS susceptibility to radiation-induced failures like soft errors. Modern SoCs exacerbate this issue as aggressive transistor scaling reduces critical charge thresholds to induce soft errors and increases radiation effects within densely packed transistors, degrading overall reliability. Linux's monolithic architecture amplifies these risks, as tightly coupled kernel subsystems propagate errors to critical components (e.g., memory management), while limited error-correcting code (ECC) offers minimal mitigation. Furthermore, the lack of public soft error data from irradiation tests on COTS SoCs running Linux hinders reliability improvements. This study evaluates proton irradiation effects (20-50 MeV) on Linux across three COTS SoC architectures: Raspberry Pi Zero 2 W (40 nm CMOS, Cortex-A53), NXP i MX 8M Plus (14 nm FinFET, Cortex-A53), and OrangeCrab (40 nm FPGA, RISC-V). Irradiation results show the 14 nm FinFET NXP SoC achieved 2-3x longer Linux uptime without ECC memory versus both 40 nm CMOS counterparts, partially due to FinFET's reduced charge collection. Additionally, this work presents the first cross-architecture analysis of soft error-prone Linux kernel components in modern SoCs to develop targeted mitigations. The findings establish foundational data on Linux's soft error sensitivity in COTS SoCs, guiding mission readiness for space applications.
Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames
Venue: arXiv cs.CV 2025
Year: 2025

Estimating the 6D pose of textureless objects from RGB images is an important problem in robotics. Due to appearance ambiguities, rotational symmetries, and severe occlusions, single-view based 6D pose estimators are still unable to handle a wide range of objects, motivating research towards multi-view pose estimation and next-best-view prediction that addresses these limitations. In this work, we propose a comprehensive active perception framework for estimating the 6D poses of textureless objects using only RGB images. Our approach is built upon a key idea: decoupling the 6D pose estimation into a two-step sequential process can greatly improve both accuracy and efficiency. First, we estimate the 3D translation of each object, resolving scale and depth ambiguities inherent to RGB images. These estimates are then used to simplify the subsequent task of determining the 3D orientation, which we achieve through canonical scale template matching. Building on this formulation, we then introduce an active perception strategy that predicts the next best camera viewpoint to capture an RGB image, effectively reducing object pose uncertainty and enhancing pose accuracy. We evaluate our method on the public ROBI and TOD datasets, as well as on our reconstructed transparent object dataset, T-ROBI. Under the same camera viewpoints, our multi-view pose estimation significantly outperforms state-of-the-art approaches. Furthermore, by leveraging our next-best-view strategy, our approach achieves high pose accuracy with fewer viewpoints than heuristic-based policies across all evaluated datasets. The accompanying video and T-ROBI dataset will be released on our project page: https://trailab.github.io/ActiveODPE.
Generative Diffusion Model-based Compression of MIMO CSI
Venue: arXiv cs.IT 2025
Year: 2025

While neural lossy compression techniques have markedly advanced the efficiency of Channel State Information (CSI) compression and reconstruction for feedback in MIMO communications, efficient algorithms for more challenging and practical tasks-such as CSI compression for future channel prediction and reconstruction with relevant side information-remain underexplored, often resulting in suboptimal performance when existing methods are extended to these scenarios. To that end, we propose a novel framework for compression with side information, featuring an encoding process with fixed-rate compression using a trainable codebook for codeword quantization, and a decoding procedure modeled as a backward diffusion process conditioned on both the codeword and the side information. Experimental results show that our method significantly outperforms existing CSI compression algorithms, often yielding over twofold performance improvement by achieving comparable distortion at less than half the data rate of competing methods in certain scenarios. These findings underscore the potential of diffusion-based compression for practical deployment in communication systems.
Optimal Indoor AP Placement: A Case Study
Venue: arXiv cs.NI 2025
Year: 2025

Wireless networks in a room are strongly affected by interferences. To alleviate these effects and enhance the performance of the wireless networks, some optimization was carried out. In this work, an analytical study was introduced to determine the optimal number of access points with their positions on the ground floor at the Architecture Engineering department building - University of Mosul. The implementation has been done using a web-based Wi-Fi and IoT design tool called Hamina Network Planner, and a Wi-Fi Network Planning and Site Survey Software called NetSpot. The experimental results show that the simulation values of the available APs are approximately matched with the real time manual values, achieving the best rates of -22dBm and -31dBm respectively. However, the number of currently available Access Points is not sufficient to cover the building area, so that two scenarios were suggested to overcome this issue. In the first scenario, two access points have been added at different positions in the building depending on the Hamina Network Planner, and in the second one, the transmitted power has been increased. The simulation results demonstrate that the overall coverage rate enhanced and can include most of the building area.
Routing Dynamics in Distributed Quantum Networks
Venue: arXiv cs.NI 2025
Year: 2025

Distributed quantum networks are not merely information conduits but intricate systems that embody the principles of quantum mechanics. In our study, we examine the underlying mechanisms of quantum connectivity within a distributed framework by exploring phenomena such as superposition and entanglement and their influence on information propagation. We investigate how these fundamental quantum effects interact with routing strategies that, while inspired by classical methods, must contend with quantum decoherence and measurement uncertainties. By simulating distributed networks of 10, 20, 50 and 100 nodes, we assess the performance of routing mechanisms through metrics that reflect both quantum fidelity and operational efficiency. Our findings reveal that the quantum coherence inherent in entangled states can enhance routing fidelity under specific conditions, yet also introduce challenges such as increased computational overhead and sensitivity to network scale. This work bridges the gap between the underlying principles of quantum systems and practical routing implementations, offering new insights into the design of robust distributed quantum networks.
Stable recovery guarantees for blind deconvolution under random mask assumption
Venue: arXiv cs.IT 2025
Year: 2025

This study addresses the blind deconvolution problem with modulated inputs, focusing on a measurement model where an unknown blurring kernel $\boldsymbol{h}$ is convolved with multiple random modulations $\{\boldsymbol{d}_l\}_{l=1}^{L}$(coded masks) of a signal $\boldsymbol{x}$, subject to $\ell_2$-bounded noise. We introduce a more generalized framework for coded masks, enhancing the versatility of our approach. Our work begins within a constrained least squares framework, where we establish a robust recovery bound for both $\boldsymbol{h}$ and $\boldsymbol{x}$, demonstrating its near-optimality up to a logarithmic factor. Additionally, we present a new recovery scheme that leverages sparsity constraints on $\boldsymbol{x}$. This approach significantly reduces the sampling complexity to the order of $L=O(\log n)$ when the non-zero elements of $\boldsymbol{x}$ are sufficiently separated. Furthermore, we demonstrate that incorporating sparsity constraints yields a refined error bound compared to the traditional constrained least squares model. The proposed method results in more robust and precise signal recovery, as evidenced by both theoretical analysis and numerical simulations. These findings contribute to advancing the field of blind deconvolution and offer potential improvements in various applications requiring signal reconstruction from modulated inputs.