OpenRaiser/Intern-Atlas huggingface dataset:
Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Accurate online map matching is fundamental to vehicle navigation and the activation of intelligent driving functions. Current online map matching methods are prone to errors in complex road networks, especially in multilevel road area. To address this challenge, we propose an online Standard Definition (SD) map matching method by constructing a Hidden Markov Model (HMM) with multiple probability factors. Our proposed method can achieve accurate map matching even in complex road networks by carefully leveraging lane markings and scenario recognition in the designing of the probability factors. First, the lane markings are generated by a multi-lane tracking method and associated with the SD map using HMM to build an enriched SD map. In areas covered by the enriched SD map, the vehicle can re-localize itself by performing Iterative Closest Point (ICP) registration for the lane markings. Then, the probability factor accounting for the lane marking detection can be obtained using the association probability between adjacent lanes and roads. Second, the driving scenario recognition model is applied to generate the emission probability factor of scenario recognition, which improves the performance of map matching on elevated roads and ordinary urban roads underneath them. We validate our method through extensive road tests in Europe and China, and the experimental results show that our proposed method effectively improves the online map matching accuracy as compared to other existing methods, especially in multilevel road area. Specifically, the experiments show that our proposed method achieves $F_1$ scores of 98.04% and 94.60% on the Zenseact Open Dataset and test data of multilevel road areas in Shanghai respectively, significantly outperforming benchmark methods. The implementation is available at https://github.com/TRV-Lab/LMSR-OMM.
xTrace: A Facial Expressive Behaviour Analysis Tool for Continuous Affect Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Recognising expressive behaviours in face videos is a long-standing challenge in Affective Computing. Despite significant advancements in recent years, it still remains a challenge to build a robust and reliable system for naturalistic and in-the-wild facial expressive behaviour analysis in real time. This paper addresses two key challenges in building such a system: (1). The paucity of large-scale labelled facial affect video datasets with extensive coverage of the 2D emotion space, and (2). The difficulty of extracting facial video features that are discriminative, interpretable, robust, and computationally efficient. Toward addressing these challenges, this work introduces xTrace, a robust tool for facial expressive behaviour analysis and predicting continuous values of dimensional emotions, namely valence and arousal, from in-the-wild face videos. To address challenge (1), the proposed affect recognition model is trained on the largest facial affect video data set, containing $\sim$450k videos that cover most emotion zones in the dimensional emotion space, making xTrace highly versatile in analysing a wide spectrum of naturalistic expressive behaviours. To address challenge (2), xTrace uses facial affect descriptors that are not only explainable, but can also achieve a high degree of accuracy and robustness with low computational complexity. The key components of xTrace are benchmarked against three existing tools: MediaPipe, OpenFace, and Augsburg Affect Toolbox. On an in-the-wild benchmarking set composed of $\sim$50k videos, xTrace achieves 0.86 mean Concordance Correlation Coefficient (CCC) and on the SEWA test set it achieves 0.75 mean CCC, outperforming existing SOTA by $\sim$7.1\%.
How to Infer Repeat Structures in MIDI Performances
Venue: arXiv cs.SD 2025
Year: 2025

MIDI performances are generally expedient in performance research and music information retrieval, and even more so if they can be connected to a score. This connection is usually established by means of alignment, linking either notes or time points between the score and the performance. The first obstacle when trying to establish such an alignment is that a performance realizes one (out of many) structural versions of the score that can plausibly result from instructions such as repeats, variations, and navigation markers like 'dal segno/da capo al coda'. A score needs to be unfolded, that is, its repeats and navigation markers need to be explicitly written out to create a single timeline without jumps matching the performance, before alignment algorithms can be applied. In the curation of large performance corpora this process is carried out manually, as no tools are available to infer the repeat structure of the performance. To ease this process, we develop a method to automatically infer the repeat structure of a MIDI performance, given a symbolically encoded score including repeat and navigation markers. The intuition guiding our design is: 1) local alignment of every contiguous section of the score with a section of a performance containing the same material should receive high alignment gain, whereas local alignment with any other performance section should accrue a low or zero gain. And 2) stitching local alignments together according to a valid structural version of the score should result in an approximate full alignment and correspondingly high global accumulated gain if the structural version corresponds to the performance, and low gain for all other, ill-fitting structural versions.
Enhancing Reinforcement Learning for the Floorplanning of Analog ICs with Beam Search
Venue: arXiv cs.AI 2025
Year: 2025

The layout of analog ICs requires making complex trade-offs, while addressing device physics and variability of the circuits. This makes full automation with learning-based solutions hard to achieve. However, reinforcement learning (RL) has recently reached significant results, particularly in solving the floorplanning problem. This paper presents a hybrid method that combines RL with a beam (BS) strategy. The BS algorithm enhances the agent's inference process, allowing for the generation of flexible floorplans by accomodating various objective weightings, and addressing congestion without without the need for policy retraining or fine-tuning. Moreover, the RL agent's generalization ability stays intact, along with its efficient handling of circuit features and constraints. Experimental results show approx. 5-85% improvement in area, dead space and half-perimeter wire length compared to a standard RL application, along with higher rewards for the agent. Moreover, performance and efficiency align closely with those of existing state-of-the-art techniques.
ULFine: Unbiased Lightweight Fine-tuning for Foundation-Model-Assisted Long-Tailed Semi-Supervised Learning
Venue: arXiv cs.CV 2025
Year: 2025

Based on the success of large-scale visual foundation models like CLIP in various downstream tasks, this paper initially attempts to explore their impact on Long-Tailed Semi-Supervised Learning (LTSSL) by employing the foundation model with three strategies: Linear Probing (LP), Lightweight Fine-Tuning (LFT), and Full Fine-Tuning (FFT). Our analysis presents the following insights: i) Compared to LTSSL algorithms trained from scratch, FFT results in a decline in model performance, whereas LP and LFT, although boosting overall model performance, exhibit negligible benefits to tail classes. ii) LP produces numerous false pseudo-labels due to \textit{underlearned} training data, while LFT can reduce the number of these false labels but becomes overconfident about them owing to \textit{biased fitting} training data. This exacerbates the pseudo-labeled and classifier biases inherent in LTSSL, limiting performance improvement in the tail classes. With these insights, we propose a Unbiased Lightweight Fine-tuning strategy, \textbf{ULFine}, which mitigates the overconfidence via confidence-aware adaptive fitting of textual prototypes and counteracts the pseudo-labeled and classifier biases via complementary fusion of dual logits. Extensive experiments demonstrate that ULFine markedly decreases training costs by over ten times and substantially increases prediction accuracies compared to state-of-the-art methods.
CodeMixBench: Evaluating Large Language Models on Code Generation with Code-Mixed Prompts
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) have achieved remarkable success in code generation tasks, powering various applications like code completion, debugging, and programming assistance. However, existing benchmarks such as HumanEval, MBPP, and BigCodeBench primarily evaluate LLMs on English-only prompts, overlooking the real-world scenario where multilingual developers often use code-mixed language while interacting with LLMs. To address this gap, we introduce CodeMixBench, a novel benchmark designed to evaluate the robustness of LLMs on code generation from code-mixed prompts. Built upon BigCodeBench, CodeMixBench introduces controlled code-mixing (CMD) into the natural language parts of prompts across three language pairs: Hinglish (Hindi-English), Spanish-English, and Chinese Pinyin-English. We comprehensively evaluate a diverse set of open-source code generation models ranging from 1.5B to 15B parameters. Our results show that code-mixed prompts consistently degrade Pass@1 performance compared to their English-only counterparts, with performance drops increasing under higher CMD levels for smaller models. CodeMixBench provides a realistic evaluation framework for studying multilingual code generation and highlights new challenges and directions for building robust code generation models that generalize well across diverse linguistic settings.
ReverbMiipher: Generative Speech Restoration meets Reverberation Characteristics Controllability
Venue: arXiv cs.SD 2025
Year: 2025

Reverberation encodes spatial information regarding the acoustic source environment, yet traditional Speech Restoration (SR) usually completely removes reverberation. We propose ReverbMiipher, an SR model extending parametric resynthesis framework, designed to denoise speech while preserving and enabling control over reverberation. ReverbMiipher incorporates a dedicated ReverbEncoder to extract a reverb feature vector from noisy input. This feature conditions a vocoder to reconstruct the speech signal, removing noise while retaining the original reverberation characteristics. A stochastic zero-vector replacement strategy during training ensures the feature specifically encodes reverberation, disentangling it from other speech attributes. This learned representation facilitates reverberation control via techniques such as interpolation between features, replacement with features from other utterances, or sampling from a latent space. Objective and subjective evaluations confirm ReverbMiipher effectively preserves reverberation, removes other artifacts, and outperforms the conventional two-stage SR and convolving simulated room impulse response approach. We further demonstrate its ability to generate novel reverberation effects through feature manipulation.
DispBench: Benchmarking Disparity Estimation to Synthetic Corruptions
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning (DL) has surpassed human performance on standard benchmarks, driving its widespread adoption in computer vision tasks. One such task is disparity estimation, estimating the disparity between matching pixels in stereo image pairs, which is crucial for safety-critical applications like medical surgeries and autonomous navigation. However, DL-based disparity estimation methods are highly susceptible to distribution shifts and adversarial attacks, raising concerns about their reliability and generalization. Despite these concerns, a standardized benchmark for evaluating the robustness of disparity estimation methods remains absent, hindering progress in the field. To address this gap, we introduce DispBench, a comprehensive benchmarking tool for systematically assessing the reliability of disparity estimation methods. DispBench evaluates robustness against synthetic image corruptions such as adversarial attacks and out-of-distribution shifts caused by 2D Common Corruptions across multiple datasets and diverse corruption scenarios. We conduct the most extensive performance and robustness analysis of disparity estimation methods to date, uncovering key correlations between accuracy, reliability, and generalization. Open-source code for DispBench: https://github.com/shashankskagnihotri/benchmarking_robustness/tree/disparity_estimation/final/disparity_estimation
An Active Contour Model for Silhouette Vectorization using Bézier Curves
Venue: arXiv cs.GR 2025
Year: 2025

In this paper, we propose an active contour model for silhouette vectorization using cubic Bézier curves. Among the end points of the Bézier curves, we distinguish between corner and regular points where the orientation of the tangent vector is prescribed. By minimizing the distance of the Bézier curves to the silhouette boundary, the active contour model optimizes the location of the Bézier curves end points, the orientation of the tangent vectors in the regular points, and the estimation of the Bézier curve parameters. This active contour model can use the silhouette vectorization obtained by any method as an initial guess. The proposed method significantly reduces the average distance between the silhouette boundary and its vectorization obtained by the world-class graphic software Inkscape, Adobe Illustrator, and a curvature-based vectorization method, which we introduce for comparison. Our method also allows us to impose additional regularity on the Bézier curves by reducing their lengths.
Automated vision-based assistance tools in bronchoscopy: stenosis severity estimation
Venue: arXiv cs.CV 2025
Year: 2025

Purpose: Subglottic stenosis refers to the narrowing of the subglottis, the airway between the vocal cords and the trachea. Its severity is typically evaluated by estimating the percentage of obstructed airway. This estimation can be obtained from CT data or through visual inspection by experts exploring the region. However, visual inspections are inherently subjective, leading to less consistent and robust diagnoses. No public methods or datasets are currently available for automated evaluation of this condition from bronchoscopy video. Methods: We propose a pipeline for automated subglottic stenosis severity estimation during the bronchoscopy exploration, without requiring the physician to traverse the stenosed region. Our approach exploits the physical effect of illumination decline in endoscopy to segment and track the lumen and obtain a 3D model of the airway. This 3D model is obtained from a single frame and is used to measure the airway narrowing. Results: Our pipeline is the first to enable automated and robust subglottic stenosis severity measurement using bronchoscopy images. The results show consistency with ground-truth estimations from CT scans and expert estimations, and reliable repeatability across multiple estimations on the same patient. Our evaluation is performed on our new Subglottic Stenosis Dataset of real bronchoscopy procedures data. Conclusion: We demonstrate how to automate evaluation of subglottic stenosis severity using only bronchoscopy. Our approach can assist with and shorten diagnosis and monitoring procedures, with automated and repeatable estimations and less exploration time, and save radiation exposure to patients as no CT is required. Additionally, we release the first public benchmark for subglottic stenosis severity assessment.
Guiding Evolutionary AutoEncoder Training with Activation-Based Pruning Operators
Venue: arXiv cs.NE 2025
Year: 2025

This study explores a novel approach to neural network pruning using evolutionary computation, focusing on simultaneously pruning the encoder and decoder of an autoencoder. We introduce two new mutation operators that use layer activations to guide weight pruning. Our findings reveal that one of these activation-informed operators outperforms random pruning, resulting in more efficient autoencoders with comparable performance to canonically trained models. Prior work has established that autoencoder training is effective and scalable with a spatial coevolutionary algorithm that cooperatively coevolves a population of encoders with a population of decoders, rather than one autoencoder. We evaluate how the same activity-guided mutation operators transfer to this context. We find that random pruning is better than guided pruning, in the coevolutionary setting. This suggests activation-based guidance proves more effective in low-dimensional pruning environments, where constrained sample spaces can lead to deviations from true uniformity in randomization. Conversely, population-driven strategies enhance robustness by expanding the total pruning dimensionality, achieving statistically uniform randomness that better preserves system dynamics. We experiment with pruning according to different schedules and present best combinations of operator and schedule for the canonical and coevolving populations cases.
Societal and technological progress as sewing an ever-growing, ever-changing, patchy, and polychrome quilt
Venue: arXiv cs.AI 2025
Year: 2025

Artificial Intelligence (AI) systems are increasingly placed in positions where their decisions have real consequences, e.g., moderating online spaces, conducting research, and advising on policy. Ensuring they operate in a safe and ethically acceptable fashion is thus critical. However, most solutions have been a form of one-size-fits-all "alignment". We are worried that such systems, which overlook enduring moral diversity, will spark resistance, erode trust, and destabilize our institutions. This paper traces the underlying problem to an often-unstated Axiom of Rational Convergence: the idea that under ideal conditions, rational agents will converge in the limit of conversation on a single ethics. Treating that premise as both optional and doubtful, we propose what we call the appropriateness framework: an alternative approach grounded in conflict theory, cultural evolution, multi-agent systems, and institutional economics. The appropriateness framework treats persistent disagreement as the normal case and designs for it by applying four principles: (1) contextual grounding, (2) community customization, (3) continual adaptation, and (4) polycentric governance. We argue here that adopting these design principles is a good way to shift the main alignment metaphor from moral unification to a more productive metaphor of conflict management, and that taking this step is both desirable and urgent.
GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we consider the radio resource allocation problem in a wireless system with various integrated functionalities, such as communication, sensing and computing. We design suitable resource management techniques that can simultaneously cater to those heterogeneous requirements, and scale appropriately with the high-dimensional and discrete nature of the problem. We propose a novel active learning framework where resource allocation patterns are drawn sequentially, evaluated in the environment, and then used to iteratively update a surrogate model of the environment. Our method leverages a generative flow network (GFlowNet) to sample favorable solutions, as such models are trained to generate compositional objects proportionally to their training reward, hence providing an appropriate coverage of its modes. As such, GFlowNet generates diverse and high return resource management designs that update the surrogate model and swiftly discover suitable solutions. We provide simulation results showing that our method can allocate radio resources achieving 20% performance gains against benchmarks, while requiring less than half of the number of acquisition rounds.
PUDTune: Multi-Level Charging for High-Precision Calibration in Processing-Using-DRAM
Venue: arXiv cs.AR 2025
Year: 2025

Recently, practical analog in-memory computing has been realized using unmodified commercial DRAM modules. The underlying Processing-Using-DRAM (PUD) techniques enable high-throughput bitwise operations directly within DRAM arrays. However, the presence of inherent error-prone columns hinders PUD's practical adoption. While selectively using only error-free columns would ensure reliability, this approach significantly reduces PUD's computational throughput. This paper presents PUDTune, a novel high-precision calibration technique for increasing the number of error-free columns in PUD. PUDTune compensates for errors by applying pre-identified column-specific offsets to PUD operations. By leveraging multi-level charge states of DRAM cells, PUDTune generates fine-grained and wide-range offset variations despite the limited available rows. Our experiments with DDR4 DRAM demonstrate that PUDTune increases the number of error-free columns by 1.81$\times$ compared to conventional implementations, improving addition and multiplication throughput by 1.88$\times$ and 1.89$\times$ respectively.
The calculus of neo-Peircean relations
Venue: arXiv cs.LO 2025
Year: 2025

The calculus of relations was introduced by De Morgan and Peirce during the second half of the 19th century, as an extension of Boole's algebra of classes. Later developments on quantification theory by Frege and Peirce himself, paved the way to what is known today as first-order logic, causing the calculus of relations to be long forgotten. This was until 1941, when Tarski raised the question on the existence of a complete axiomatisation for it. This question found only negative answers: there is no finite axiomatisation for the calculus of relations and many of its fragments, as shown later by several no-go theorems. In this paper we show that -- by moving from traditional syntax (cartesian) to a diagrammatic one (monoidal) -- it is possible to have complete axiomatisations for the full calculus. The no-go theorems are circumvented by the fact that our calculus, named the calculus of neo-Peircean relations, is more expressive than the calculus of relations and, actually, as expressive as first-order logic. The axioms are obtained by combining two well known categorical structures: cartesian and linear bicategories.
Advanced Stock Market Prediction Using Long Short-Term Memory Networks: A Comprehensive Deep Learning Framework
Venue: arXiv cs.CE 2025
Year: 2025

Predicting stock market movements remains a persistent challenge due to the inherently volatile, non-linear, and stochastic nature of financial time series data. This paper introduces a deep learning-based framework employing Long Short-Term Memory (LSTM) networks to forecast the closing stock prices of major technology firms: Apple, Google, Microsoft, and Amazon, listed on NASDAQ. Historical data was sourced from Yahoo Finance and processed using normalization and feature engineering techniques. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 2.72 on unseen test data, significantly outperforming traditional models like ARIMA. To further enhance predictive accuracy, sentiment scores were integrated using real-time news articles and social media data, analyzed through the VADER sentiment analysis tool. A web application was also developed to provide real-time visualizations of stock price forecasts, offering practical utility for both individual and institutional investors. This research demonstrates the strength of LSTM networks in modeling complex financial sequences and presents a novel hybrid approach combining time series modeling with sentiment analysis.