OpenRaiser/Intern-Atlas huggingface dataset:
PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images
Venue: arXiv cs.CV 2025
Year: 2025

This paper introduces the physics-inspired synthesized underwater image dataset (PHISWID), a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis. For underwater image enhancement, data-driven approaches (e.g., deep neural networks) typically demand extensive datasets, yet acquiring paired clean atmospheric images and degraded underwater images poses significant challenges. Existing datasets have limited contributions to image enhancement due to lack of physics models, publicity, and ground-truth atmospheric images. PHISWID addresses these issues by offering a set of paired atmospheric and underwater images. Specifically, underwater images are synthetically degraded by color degradation and marine snow artifacts from atmospheric RGB-D images. It is enabled based on a physics-based underwater image observation model. Our synthetic approach generates a large quantity of the pairs, enabling effective training of deep neural networks and objective image quality assessment. Through benchmark experiments with some datasets and image enhancement methods, we validate that our dataset can improve the image enhancement performance. Our dataset, which is publicly available, contributes to the development in underwater image processing.
Simplifying explicit subtyping coercions in a polymorphic calculus with effects
Venue: arXiv cs.PL 2025
Year: 2025

Algebraic effect handlers are becoming an increasingly popular way of structuring effectful computations, and their performance is often a concern. One of the proposed approaches towards efficient compilation is tracking effect information through explicit subtyping coercions. However, in the presence of polymorphism, these coercions are compiled into additional arguments of compiled functions, incurring significant overhead. In this paper, we present a polymorphic effectful calculus, identify simplification phases needed to reduce the number of unnecessary constraints, and prove that they preserve semantics. In addition, we implement the simplification algorithm in the Eff language and evaluate its performance on a number of benchmarks. Though we do not prove the optimality of the presented simplifications, the results show that the algorithm eliminates all coercions, resulting in code as efficient as manually monomorphised one.
ARS548_ros. An ARS 548 RDI radar driver for ROS
Venue: arXiv cs.RO 2025
Year: 2025

The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long range radar sensors with new RF antenna arrays, which offer digital beam forming. This radar measures independently the distance, speed and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to the best of our knowledge, there are no open source drivers available for Linux systems to enable users to analyze the data acquired by the sensor. In this paper, we present a driver that can interpret the data from the ARS 548 RDI sensor and make it available over the Robot Operating System versions 1 and 2 (ROS and ROS2). Thus, these data can be stored, represented, and analyzed using the powerful tools offered by ROS. Besides, our driver offers advanced object features provided by the sensor, such as relative estimated velocity and acceleration of each object, its orientation and angular velocity. We focus on the configuration of the sensor and the use of our driver including its filtering and representation tools. Besides, we offer a video tutorial to help in its configuration process. Finally, a dataset acquired with this sensor and an Ouster OS1-32 LiDAR sensor, to have baseline measurements, is available, so that the user can check the correctness of our driver.
Msmsfnet: a multi-stream and multi-scale fusion net for edge detection
Venue: arXiv cs.CV 2025
Year: 2025

Edge detection is a long-standing problem in computer vision. Despite the efficiency of existing algorithms, their performance, however, rely heavily on the pre-trained weights of the backbone network on the ImageNet dataset. The use of pre-trained weights in previous methods significantly increases the difficulty to design new models for edge detection without relying on existing well-trained ImageNet models, as pre-training the model on the ImageNet dataset is expensive and becomes compulsory to ensure the fairness of comparison. Besides, the pre-training and fine-tuning strategy is not always useful and sometimes even inaccessible. For instance, the pre-trained weights on the ImageNet dataset are unlikely to be helpful for edge detection in Synthetic Aperture Radar (SAR) images due to strong differences in the statistics between optical images and SAR images. Moreover, no dataset has comparable size to the ImageNet dataset for SAR image processing. In this work, we study the performance achievable by state-of-the-art deep learning based edge detectors in publicly available datasets when they are trained from scratch, and devise a new network architecture, the multi-stream and multi-scale fusion net (msmsfnet), for edge detection. We show in our experiments that by training all models from scratch, our model outperforms state-of-the-art edge detectors in three publicly available datasets. We also demonstrate the efficiency of our model for edge detection in SAR images, where no useful pre-trained weight is available. Finally, We show that our model is able to achieve competitive performance on the BSDS500 dataset when the pre-trained weights are used.
Performance Analysis of Near-Field Sensing in Wideband MIMO Systems
Venue: arXiv cs.IT 2025
Year: 2025

The performance of near-field sensing (NISE) in a legacy wideband multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system is analyzed. The maximum likelihood estimates (MLE) for the target's distance and angle relative to the antenna array are derived. To evaluate the estimation error, closed-form analytical expressions of Cramer-Rao bounds (CRBs) are derived for both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). The asymptotic CRBs are then analyzed to reveal the scaling laws of CRBs with respect to key system parameters, including array size, bandwidth, and target distance. Our results reveal that 1) the mean-squared error achieved by MLEs approaches CRBs in the high signal-to-noise ratio regime; 2) a larger array aperture does not necessarily improve NISE performance, especially with ultra-large bandwidth; 3) large bandwidth sets an estimation error ceiling for NISE as target distance increases; 4) array aperture and bandwidth, rather than the number of antennas and subcarriers, are the key factors affecting wideband NISE performance; and 5) UCAs offer superior, angle-independent wideband NISE performance compared to ULAs with the same aperture.
What is Intelligence? A Cycle Closure Perspective
Venue: arXiv cs.LG 2025
Year: 2025

What is intelligence? We argue for a structural-dynamical account rooted in a topological closure law: \emph{the boundary of a boundary vanishes} ($\partial^2=0$). This principle forces transient fragments to cancel while closed cycles persist as invariants, yielding the cascade $\partial^2\!=\!0 \Rightarrow \text{cycles (invariants)} \Rightarrow \text{memory} \Rightarrow \text{prediction (intelligence)}$. Prediction requires invariance: only order-invariant cycles can stabilize the predictive substrate. This motivates the \textbf{Structure-before-Specificity (SbS)} principle, where persistent structures ($Φ$) must stabilize before contextual specificities ($Ψ$) can be meaningfully interpreted, and is formalized by the \textbf{Context-Content Uncertainty Principle (CCUP)}, which casts cognition as dynamic alignment that minimizes the joint uncertainty $H(Φ,Ψ)$. We show that \textbf{Memory-Amortized Inference (MAI)} is the computational mechanism that implements SbS\,$\rightarrow$\,CCUP through dual bootstrapping: \emph{temporal} bootstrapping consolidates episodic specifics into reusable latent trajectories, while \emph{spatial} bootstrapping reuses these invariants across latent manifolds. This framework explains why \emph{semantics precedes syntax}: stable cycles anchor meaning, and symbolic syntax emerges only after semantic invariants are in place. In an evolutionary perspective, the same closure law unifies the trajectory of natural intelligence: from primitive memory traces in microbes, to cyclic sensorimotor patterns in bilaterians, to semantic generalization in mammals, culminating in human symbolic abstraction by natural language. In sum, intelligence arises from the progressive collapse of specificity into structure, grounded in the closure-induced emergence of invariants.
On Optimal MMSE Channel Estimation for One-Bit Quantized MIMO Systems
Venue: arXiv cs.IT 2025
Year: 2025

This paper focuses on the minimum mean squared error (MMSE) channel estimator for multiple-input multiple-output (MIMO) systems with one-bit quantization at the receiver side. Despite its optimality and significance in estimation theory, the MMSE channel estimator has not been fully investigated in this context due to its general non-linearity and computational complexity. Instead, the typically suboptimal Bussgang linear MMSE (BLMMSE) estimator has been widely adopted. In this work, we develop a new framework to compute the MMSE channel estimator that hinges on computation of the orthant probability of the multivariate normal distribution. Based on this framework, we determine a necessary and sufficient condition for the BLMMSE channel estimator to be optimal and equivalent to the MMSE estimator. Under the assumption of specific channel correlation or pilot symbols, we further utilize the framework to derive analytical expressions for the MMSE channel estimator that are particularly convenient for computation when certain system dimensions become large, thereby enabling a comparison between the BLMMSE and MMSE channel estimators in these cases.
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain
Venue: arXiv cs.CL 2025
Year: 2025

Due to privacy restrictions, there's a shortage of publicly available speech recognition datasets in the medical domain. In this work, we present VietMed - a Vietnamese speech recognition dataset in the medical domain comprising 16h of labeled medical speech, 1000h of unlabeled medical speech and 1200h of unlabeled general-domain speech. To our best knowledge, VietMed is by far the world's largest public medical speech recognition dataset in 7 aspects: total duration, number of speakers, diseases, recording conditions, speaker roles, unique medical terms and accents. VietMed is also by far the largest public Vietnamese speech dataset in terms of total duration. Additionally, we are the first to present a medical ASR dataset covering all ICD-10 disease groups and all accents within a country. Moreover, we release the first public large-scale pre-trained models for Vietnamese ASR, w2v2-Viet and XLSR-53-Viet, along with the first public large-scale fine-tuned models for medical ASR. Even without any medical data in unsupervised pre-training, our best pre-trained model XLSR-53-Viet generalizes very well to the medical domain by outperforming state-of-the-art XLSR-53, from 51.8% to 29.6% WER on test set (a relative reduction of more than 40%). All code, data and models are made publicly available: https://github.com/leduckhai/MultiMed/tree/master/VietMed.
Box Filtration
Venue: arXiv cs.CG 2025
Year: 2025

We define a new framework that unifies the filtration and mapper approaches from TDA, and present efficient algorithms to compute it. Termed the box filtration of a PCD, we grow boxes (hyperrectangles) that are not necessarily centered at each point (in place of balls centered at points). We grow the boxes non-uniformly and asymmetrically in different dimensions based on the distribution of points. We present two approaches to handle the boxes: a point cover where each point is assigned its own box at start, and a pixel cover that works with a pixelization of the space of the PCD. Any box cover in either setting automatically gives a mapper of the PCD. We show that the persistence diagrams generated by the box filtration using both point and pixel covers satisfy the classical stability based on the Gromov-Hausdorff distance. Using boxes also implies that the box filtration is identical for pairwise or higher order intersections whereas the VR and Cech filtration are not the same. Growth in each dimension is computed by solving a linear program (LP) that optimizes a cost functional balancing the cost of expansion and benefit of including more points in the box. The box filtration algorithm runs in $O(m|U(0)|\log(mnπ)L(q))$ time, where $m$ is number of steps of increments considered for box growth, $|U(0)|$ is the number of boxes in the initial cover ($\leq$ number of points), $π$ is the step length for increasing each box dimension, each LP is solved in $O(L(q))$ time, $n$ is the PCD dimension, and $q = n \times |X|$. We demonstrate through multiple examples that the box filtration can produce more accurate results to summarize the topology of the PCD than VR and distance-to-measure (DTM) filtrations. Software for our implementation is available at https://github.com/pragup/Box-Filteration.
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Venue: arXiv cs.LG 2025
Year: 2025

Single-cell RNA sequencing (scRNA-seq) is essential for unraveling cellular heterogeneity and diversity, offering invaluable insights for bioinformatics advancements. Despite its potential, traditional clustering methods in scRNA-seq data analysis often neglect the structural information embedded in gene expression profiles, crucial for understanding cellular correlations and dependencies. Existing strategies, including graph neural networks, face challenges in handling the inefficiency due to scRNA-seq data's intrinsic high-dimension and high-sparsity. Addressing these limitations, we introduce scCDCG (single-cell RNA-seq Clustering via Deep Cut-informed Graph), a novel framework designed for efficient and accurate clustering of scRNA-seq data that simultaneously utilizes intercellular high-order structural information. scCDCG comprises three main components: (i) A graph embedding module utilizing deep cut-informed techniques, which effectively captures intercellular high-order structural information, overcoming the over-smoothing and inefficiency issues prevalent in prior graph neural network methods. (ii) A self-supervised learning module guided by optimal transport, tailored to accommodate the unique complexities of scRNA-seq data, specifically its high-dimension and high-sparsity. (iii) An autoencoder-based feature learning module that simplifies model complexity through effective dimension reduction and feature extraction. Our extensive experiments on 6 datasets demonstrate scCDCG's superior performance and efficiency compared to 7 established models, underscoring scCDCG's potential as a transformative tool in scRNA-seq data analysis. Our code is available at: https://github.com/XPgogogo/scCDCG.
$Prooφ$: A ZKP Market Mechanism
Venue: arXiv cs.GT 2025
Year: 2025

Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market model that captures the interactions between users submitting ZKP tasks and provers competing to generate proofs. Building on this model, we introduce $Prooφ$, an auction-based ZKP market mechanism. We prove that $Prooφ$ is incentive compatible for users and provers, and budget balanced. We augment $Prooφ$ with system-level designs to address the practical challenges of our setting, such as Sybil attacks, misreporting of prover capacity, and collusion. We analyze our system-level designs and show how they can mitigate the various security concerns.
Biomedical Open Source Software: Crucial Packages and Hidden Heroes
Venue: arXiv cs.SE 2025
Year: 2025

Despite the importance of scientific software for research, it is often not formally recognized and rewarded. This is especially true for foundational libraries, which are hidden below packages visible to the users (and thus doubly hidden, since even the packages directly used in research are frequently not visible in the paper). Research stakeholders like funders, infrastructure providers, and other organizations need to understand the complex network of computer programs that contemporary research relies upon. In this work, we use the CZ Software Mentions Dataset to map the upstream dependencies of software used in biomedical papers and find the packages critical to scientific software ecosystems. We propose centrality metrics for the network of software dependencies, analyze three ecosystems (PyPi, CRAN, Bioconductor), and determine the packages with the highest centrality.
MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education
Venue: arXiv cs.CL 2025
Year: 2025

Collaborative problem solving (CPS) is essential in mathematics education, fostering deeper learning through the exchange of ideas. Yet, classrooms often lack the resources, time, and peer dynamics needed to sustain productive CPS. Recent advancements in Large Language Models (LLMs) offer a promising avenue to enhance CPS in mathematical education. We designed and developed MathVC, a multi-persona LLM simulated virtual classroom platform to facilitate CPS in mathematics. MathVC combines a meta planning controller that monitors CPS stages-sense-making, team organization, planning, execution, validation, and predicts the next speaker, with a persona simulation stack that encodes mathematical thinking via a task schema and error-injected persona schemas seeded from teacher-specified misconceptions. We evaluated MathVC with 14 U.S. middle schoolers. Students reported constructive interaction and reaching shared solutions, describing gains in engagement, motivation, and confidence through diverse perspectives, immediate scaffolding, and human-like fallibility. Our findings also provide insights into simulating peers via LLM-based technologies for collaboration to support learning.
Private Wasserstein Distance
Venue: arXiv cs.LG 2025
Year: 2025

Wasserstein distance is a key metric for quantifying data divergence from a distributional perspective. However, its application in privacy-sensitive environments, where direct sharing of raw data is prohibited, presents significant challenges. Existing approaches, such as Differential Privacy and Federated Optimization, have been employed to estimate the Wasserstein distance under such constraints. However, these methods often fall short when both accuracy and security are required. In this study, we explore the inherent triangular properties within the Wasserstein space, leading to a novel solution named TriangleWad. This approach facilitates the fast computation of the Wasserstein distance between datasets stored across different entities, ensuring that raw data remain completely hidden. TriangleWad not only strengthens resistance to potential attacks but also preserves high estimation accuracy. Through extensive experiments across various tasks involving both image and text data, we demonstrate its superior performance and significant potential for real-world applications.
RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

We introduce RealmDreamer, a technique for generating forward-facing 3D scenes from text descriptions. Our method optimizes a 3D Gaussian Splatting representation to match complex text prompts using pretrained diffusion models. Our key insight is to leverage 2D inpainting diffusion models conditioned on an initial scene estimate to provide low variance supervision for unknown regions during 3D distillation. In conjunction, we imbue high-fidelity geometry with geometric distillation from a depth diffusion model, conditioned on samples from the inpainting model. We find that the initialization of the optimization is crucial, and provide a principled methodology for doing so. Notably, our technique doesn't require video or multi-view data and can synthesize various high-quality 3D scenes in different styles with complex layouts. Further, the generality of our method allows 3D synthesis from a single image. As measured by a comprehensive user study, our method outperforms all existing approaches, preferred by 88-95%. Project Page: https://realmdreamer.github.io/
PillarTrack:Boosting Pillar Representation for Transformer-based 3D Single Object Tracking on Point Clouds
Venue: arXiv cs.CV 2025
Year: 2025

LiDAR-based 3D single object tracking (3D SOT) is a critical issue in robotics and autonomous driving. Existing 3D SOT methods typically adhere to a point-based processing pipeline, wherein the re-sampling operation invariably leads to either redundant or missing information, thereby impacting performance. To address these issues, we propose PillarTrack, a novel pillar-based 3D SOT framework. First, we transform sparse point clouds into dense pillars to preserve the local and global geometrics. Second, we propose a Pyramid-Encoded Pillar Feature Encoder (PE-PFE) design to enhance the robustness of pillar feature for translation/rotation/scale. Third, we present an efficient Transformer-based backbone from the perspective of modality differences. Finally, we construct our PillarTrack based on above designs. Extensive experiments show that our method achieves comparable performance on the KITTI and NuScenes datasets, significantly enhancing the performance of the baseline.
Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes
Venue: arXiv cs.CV 2025
Year: 2025

Detecting and localising unknown or out-of-distribution (OOD) objects in any scene can be a challenging task in vision, particularly in safety-critical cases involving autonomous systems like automated vehicles or trains. Supervised anomaly segmentation or open-world object detection models depend on training on exhaustively annotated datasets for every domain and still struggle in distinguishing between background and OOD objects. In this work, we present a plug-and-play framework - PRototype-based OOD detection Without Labels (PROWL). It is an inference-based method that does not require training on the domain dataset and relies on extracting relevant features from self-supervised pre-trained models. PROWL can be easily adapted to detect in-domain objects in any operational design domain (ODD) in a zero-shot manner by specifying a list of known classes from this domain. PROWL, as a first zero-shot unsupervised method, achieves state-of-the-art results on the RoadAnomaly and RoadObstacle datasets provided in road driving benchmarks - SegmentMeIfYouCan (SMIYC) and Fishyscapes, as well as comparable performance against existing supervised methods trained without auxiliary OOD data. We also demonstrate its generalisability to other domains such as rail and maritime.