RNN
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BioUNER: A Benchmark Dataset for Clinical Urdu Named Entity Recognition
2026-04-03 09:24:23Wazir Ali, Adeeb Noor, Sanaullah Mahar, Alia, Muhammad Mazhar YounasAbstract
In this article, we present a gold-standard benchmark dataset for Biomedical Urdu Named Entity Recognition (BioUNER), developed by crawling health-related articles from online Urdu news portals, medical prescriptions, and hospital health blogs and websites. After preprocessing, three native annotators with familiarity in the medical domain participated in the annotation process using the Doccano text annotation tool and annotated 153K tokens. Following annotation, the proposed BioiUNER dataset was evaluated both intrinsically and extrinsically. An inter-annotator agreement score of 0.78 was achieved, thereby validating the dataset as gold-standard quality. To demonstrate the utility and benchmarking capability of the dataset, we evaluated several machine learning and deep learning models, including Support Vector Machines (SVM), Long Short-Term Memory networks (LSTM), Multilingual BERT (mBERT), and XLM-RoBERTa. The gold-standard BioUNER dataset serves as a reliable benchmark and a valuable addition to Urdu language processing resources.
Abstract (translated)
URL
https://arxiv.org/abs/2604.02904
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O-ConNet: Geometry-Aware End-to-End Inference of Over-Constrained Spatial Mechanisms
2026-04-02 13:43:53Haoyu Sun, Meng Zhao, Tianhao Wang, Jianxu WuAbstract
Deep learning has shown strong potential for scientific discovery, but its ability to model macroscopic rigid-body kinematic constraints remains underexplored. We study this problem on spatial over-constrained mechanisms and propose O-ConNet, an end-to-end framework that infers mechanism structural parameters from only three sparse reachable points while reconstructing the full motion trajectory, without explicitly solving constraint equations during inference. On a self-constructed Bennett 4R dataset of 42,860 valid samples, O-ConNet achieves Param-MAE 0.276 +/- 0.077 and Traj-MAE 0.145 +/- 0.018 (mean +/- std over 10 runs), outperforming the strongest sequence baseline (LSTM-Seq2Seq) by 65.1 percent and 88.2 percent, respectively. These results suggest that end-to-end learning can capture closed-loop geometric structure and provide a practical route for inverse design of spatial over-constrained mechanisms under extremely sparse observations.
Abstract (translated)
深度学习在科学发现中展现出巨大潜力，但其对宏观刚体运动学约束的建模能力仍未得到充分探索。本研究针对空间过约束机构开展研究，提出O-ConNet端到端框架，该框架仅通过三个稀疏可达点即可推断机构结构参数并重建完整运动轨迹，且在推理过程中无需显式求解约束方程。在自建的本特利4R机构数据集（42,860个有效样本）上，O-ConNet达到参数平均绝对误差0.276±0.077、轨迹平均绝对误差0.145±0.018（十次运行均值±标准差），分别超越最强序列基线模型（LSTM-Seq2Seq）达65.1%和88.2%。这些结果表明，端到端学习能够捕捉闭环几何结构，并为极端稀疏观测条件下的空间过约束机构逆设计提供了实用途径。
URL
https://arxiv.org/abs/2604.02038
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Multicentric thrombus segmentation using an attention-based recurrent network with gradual modality dropout
2026-04-01 12:25:00Sofia Vargas-Ibarra, Vincent Vigneron, Hichem Maaref, Sonia Garcia-SalicettiAbstract
Detecting and delineating tiny targets in 3D brain scans is a central yet under-addressed challenge in medical this http URL ischemic stroke, for instance, the culprit thrombus is small, low-contrast, and variably expressed across modalities(e.g., susceptibility-weighted T2 blooming, diffusion restriction on DWI/ADC), while real-world multi-center dataintroduce domain shifts, anisotropy, and frequent missing sequences. We introduce a methodology that couples an attention-based recurrent segmentation network (UpAttLLSTM), a training schedule that progressively increases the difficulty of hetero-modal learning, with gradual modality dropout, UpAttLLSTM aggregates context across slices via recurrent units (2.5D) and uses attention gates to fuse complementary cues across available sequences, making it robust to anisotropy and class imbalance. Gradual modality dropout systematically simulates site heterogeneity,noise, and missing modalities during training, acting as both augmentation and regularization to improve multi-center generalization. On a monocentric cohort, our approach detects thrombi in >90% of cases with a Dice score of 0.65. In a multi-center setting with missing modalities, it achieves-80% detection with a Dice score around 0.35. Beyond stroke, the proposed methodology directly transfers to other small-lesion tasks in 3D medical imaging where targets are scarce, subtle, and modality-dependent
Abstract (translated)
在3D脑部扫描中检测并勾画微小目标，是医学影像领域一个核心却尚未得到充分解决的技术挑战。以缺血性卒中为例，致病血栓体积小、对比度低，且在不同模态影像中表现各异（例如磁敏感加权T2序列的 blooming 效应、DWI/ADC序列上的弥散受限）。而真实世界中的多中心数据会引入域偏移、各向异性以及序列频繁缺失等问题。本研究提出一种方法论，将基于注意力的循环分割网络（UpAttLLSTM）与渐进式异模态学习训练策略相结合，后者通过逐步增加模态缺失的难度实现数据增强与正则化。UpAttLLSTM 通过循环单元（2.5D）跨切片聚合上下文信息，并利用注意力门控机制融合多序列间的互补特征，从而对数据各向异性及类别不平衡问题具有鲁棒性。渐进式模态缺失策略在训练阶段系统模拟中心异质性、噪声及模态缺失情况，作为数据增强与正则化手段提升多中心泛化能力。在单中心队列中，该方法对血栓的检测率超过90%，Dice系数达0.65；在多中心模态缺失场景下，仍可实现80%的检测率，Dice系数约0.35。除卒中领域外，该方法论可直接迁移至其他3D医学影像中的小病灶任务——这些目标普遍存在稀缺性、隐蔽性及模态依赖性特征。
URL
https://arxiv.org/abs/2604.00817
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A Benchmark of State-Space Models vs. Transformers and BiLSTM-based Models for Historical Newspaper OCR
2026-04-01 10:33:33Merveilles Agbeti-messan, Thierry Paquet, Cl\'ement Chatelain, Pierrick Tranouez, St\'ephane NicolasAbstract
End-to-end OCR for historical newspapers remains challenging, as models must handle long text sequences, degraded print quality, and complex layouts. While Transformer-based recognizers dominate current research, their quadratic complexity limits efficient paragraph-level transcription and large-scale deployment. We investigate linear-time State-Space Models (SSMs), specifically Mamba, as a scalable alternative to Transformer-based sequence modeling for OCR. We present to our knowledge, the first OCR architecture based on SSMs, combining a CNN visual encoder with bi-directional and autoregressive Mamba sequence modeling, and conduct a large-scale benchmark comparing SSMs with Transformer- and BiLSTM-based recognizers. Multiple decoding strategies (CTC, autoregressive, and non-autoregressive) are evaluated under identical training conditions alongside strong neural baselines (VAN, DAN, DANIEL) and widely used off-the-shelf OCR engines (PERO-OCR, Tesseract OCR, TrOCR, Gemini). Experiments on historical newspapers from the Bibliothèque nationale du Luxembourg, with newly released >99% verified gold-standard annotations, and cross-dataset tests on Fraktur and Antiqua lines, show that all neural models achieve low error rates (~2% CER), making computational efficiency the main differentiator. Mamba-based models maintain competitive accuracy while halving inference time and exhibiting superior memory scaling (1.26x vs 2.30x growth at 1000 chars), reaching 6.07% CER at the severely degraded paragraph level compared to 5.24% for DAN, while remaining 2.05x faster. We release code, trained models, and standardized evaluation protocols to enable reproducible research and guide practitioners in large-scale cultural heritage OCR.
Abstract (translated)
端到端历史报纸OCR仍具挑战性，因为模型需处理长文本序列、印刷质量退化及复杂版式。尽管基于Transformer的识别器主导当前研究，但其二次方复杂度限制了段落级转录的高效性与大规模部署。我们研究了线性时间复杂度的状态空间模型（SSMs），特别是Mamba，将其作为OCR中基于Transformer序列建模的可扩展替代方案。据我们所知，本文首次提出基于SSMs的OCR架构，该架构结合CNN视觉编码器与双向及自回归Mamba序列建模，并开展大规模基准测试，将SSMs与基于Transformer及BiLSTM的识别器进行对比。在相同训练条件下，评估了多种解码策略（CTC、自回归与非自回归），并与强神经基线模型（VAN、DAN、DANIEL）及广泛使用的现成OCR引擎（PERO-OCR、Tesseract OCR、TrOCR、Gemini）进行比较。基于卢森堡国家图书馆历史报纸的实验（采用新发布的>99%验证黄金标准标注）及跨数据集测试（Fraktur与Antiqua字体行），表明所有神经模型均达到低错误率（约2%字符错误率），使得计算效率成为主要区分因素。基于Mamba的模型在保持竞争力的准确率同时，将推理时间减半，并展现出更优的内存扩展性（1000字符时增长率为1.26倍 vs 2.30倍），在严重退化的段落级达到6.07%字符错误率，而DAN为5.24%，但速度快2.05倍。我们发布代码、训练模型及标准化评估协议，以支持可复现研究，并为文化遗产OCR领域从业者提供指导。
URL
https://arxiv.org/abs/2604.00725
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HarassGuard: Detecting Harassment Behaviors in Social Virtual Reality with Vision-Language Models
2026-04-01 07:59:01Junhee Lee, Minseok Kim, Hwanjo Heo, Seungwon Woo, Jinwoo KimAbstract
Social Virtual Reality (VR) platforms provide immersive social experiences but also expose users to serious risks of online harassment. Existing safety measures are largely reactive, while proactive solutions that detect harassment behavior during an incident often depend on sensitive biometric data, raising privacy concerns. In this paper, we present HarassGuard, a vision-language model (VLM) based system that detects physical harassment in social VR using only visual input. We construct an IRB-approved harassment vision dataset, apply prompt engineering, and fine-tune VLMs to detect harassment behavior by considering contextual information in social VR. Experimental results demonstrate that HarassGuard achieves competitive performance compared to state-of-the-art baselines (i.e., LSTM/CNN, Transformer), reaching an accuracy of up to 88.09% in binary classification and 68.85% in multi-class classification. Notably, HarassGuard matches these baselines while using significantly fewer fine-tuning samples (200 vs. 1,115), offering unique advantages in contextual reasoning and privacy-preserving detection.
Abstract (translated)
URL
https://arxiv.org/abs/2604.00592
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MambaVoiceCloning: Efficient and Expressive Text-to-Speech via State-Space Modeling and Diffusion Control
2026-03-31 22:35:58Sahil Kumar, Namrataben Patel, Honggang Wang, Youshan ZhangAbstract
MambaVoiceCloning (MVC) asks whether the conditioning path of diffusion-based TTS can be made fully SSM-only at inference, removing all attention and explicit RNN-style recurrence layers across text, rhythm, and prosody, while preserving or improving quality under controlled conditions. MVC combines a gated bidirectional Mamba text encoder, a Temporal Bi-Mamba supervised by a lightweight alignment teacher discarded after training, and an Expressive Mamba with AdaLN modulation, yielding linear-time O(T) conditioning with bounded activation memory and practical finite look-ahead streaming. Unlike prior Mamba-TTS systems that remain hybrid at inference, MVC removes attention-based duration and style modules under a fixed StyleTTS2 mel-diffusion-vocoder backbone. Trained on LJSpeech/LibriTTS and evaluated on VCTK, CSS10 (ES/DE/FR), and long-form Gutenberg passages, MVC achieves modest but statistically reliable gains over StyleTTS2, VITS, and Mamba-attention hybrids in MOS/CMOS, F0 RMSE, MCD, and WER, while reducing encoder parameters to 21M and improving throughput by 1.6x. Diffusion remains the dominant latency source, but SSM-only conditioning improves memory footprint, stability, and deployability.
Abstract (translated)
MambaVoiceCloning (MVC) 探讨能否在推理阶段将基于扩散的TTS的条件路径完全改造为仅使用SSM，从而在文本、节奏和韵律层面彻底移除所有注意力机制以及显式的RNN式循环层，同时在受控条件下保持或提升音质。MVC结合了门控双向Mamba文本编码器、由轻量级对齐教师网络（训练后丢弃）监督的时序双向Mamba，以及采用自适应层归一化调制的表现力Mamba，实现了线性时间复杂度O(T)的条件建模，并具备有界激活内存与实用的有限前瞻流式处理。与推理阶段仍为混合架构的先前Mamba-TTS系统不同，MVC在固定的StyleTTS2梅尔扩散声码器骨架上移除了基于注意力的时长与风格模块。该模型在LJSpeech/LibriTTS上训练，并在VCTK、CSS10（西班牙语/德语/法语）以及长篇古腾堡语料上进行评估。结果显示，MVC在MOS/CMOS听感评分、F0均方根误差、梅尔倒谱系数距离和词错误率上，相较于StyleTTS2、VITS及Mamba-注意力混合模型取得了适度但统计显著的提升，同时将编码器参数量压缩至2100万，并将吞吐量提高了1.6倍。扩散过程仍是主要延迟来源，但纯SSM条件路径在内存占用、稳定性与部署便利性方面具有改进优势。
URL
https://arxiv.org/abs/2604.00292
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PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO$_2$ and SO$_2$ Using Satellite-Ground Data Fusion
2026-03-31 15:39:26Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-PhelanAbstract
Accurate assessment of atmospheric nitrogen dioxide (NO$_2$) and sulfur dioxide (SO$_2$) is essential for understanding climate-air quality interactions, supporting environmental policy, and protecting public health. Traditional monitoring approaches face limitations: satellite observations provide broad spatial coverage but suffer from data gaps, while ground-based sensors offer high temporal resolution but limited spatial extent. To address these challenges, we propose PollutionNet, a Vision Transformer-based framework that integrates Sentinel-5P TROPOMI vertical column density (VCD) data with ground-level observations. By leveraging self-attention mechanisms, PollutionNet captures complex spatiotemporal dependencies that are often missed by conventional CNN and RNN models. Applied to Ireland (2020-2021), our case study demonstrates that PollutionNet achieves state-of-the-art performance (RMSE: 6.89 $\mu$g/m$^3$ for NO$_2$, 4.49 $\mu$g/m$^3$ for SO$_2$), reducing prediction errors by up to 14% compared to baseline models. Beyond accuracy gains, PollutionNet provides a scalable and data-efficient tool for applied climatology, enabling robust pollution assessments in regions with sparse monitoring networks. These results highlight the potential of advanced machine learning approaches to enhance climate-related air quality research, inform environmental management, and support sustainable policy decisions.
Abstract (translated)
准确评估大气中二氧化氮（NO₂）和二氧化硫（SO₂）的浓度，对于理解气候与空气质量的相互作用、支持环境政策制定以及保护公众健康至关重要。传统监测方法存在局限：卫星观测虽能提供广阔的空间覆盖，但存在数据缺失问题；而地面传感器虽具备高时间分辨率，却受限于空间覆盖范围。为应对这些挑战，本研究提出一种基于视觉Transformer的框架——PollutionNet，该框架整合了Sentinel-5P卫星TROPOMI传感器的垂直柱浓度（VCD）数据与地面观测数据。通过利用自注意力机制，PollutionNet能够捕捉到传统卷积神经网络（CNN）和循环神经网络（RNN）模型常忽略的复杂时空依赖关系。在爱尔兰（2020-2021年）的案例研究中，PollutionNet展现出卓越性能（NO₂预测均方根误差：6.89微克/立方米，SO₂：4.49微克/立方米），相较于基线模型预测误差最高降低14%。除精度提升外，PollutionNet还为应用气候学提供了可扩展且数据高效的解决方案，能够在监测网络稀疏的地区实现稳健的污染评估。这些结果凸显了先进机器学习方法在提升气候相关空气质量研究、指导环境管理及支持可持续政策决策方面的巨大潜力。
URL
https://arxiv.org/abs/2604.03311
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Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
2026-03-31 08:12:13Fu Wang, Qifeng Lu, Xinyu Long, Meng Zhang, Xiaofei Yang, Weijia Cao, Xiaowen ChuAbstract
Accurate forecasting of three-dimensional (3D) cloud fields is important for atmospheric analysis and short-range numerical weather prediction, yet it remains challenging because cloud evolution involves cross-layer interactions, nonlocal dependencies, and multiscale spatiotemporal dynamics. Existing spatiotemporal prediction models based on convolutions, recurrence, or attention often rely on locality-biased representations and therefore struggle to preserve fine cloud structures in volumetric forecasting tasks. To address this issue, we propose QENO, a hybrid quantum-inspired spatiotemporal forecasting framework for 3D cloud fields. The proposed architecture consists of four components: a classical spatiotemporal encoder for compact latent representation, a topology-aware quantum enhancement block for modeling nonlocal couplings in latent space, a dynamic fusion temporal unit for integrating measurement-derived quantum features with recurrent memory, and a decoder for reconstructing future cloud volumes. Experiments on CMA-MESO 3D cloud fields show that QENO consistently outperforms representative baselines, including ConvLSTM, PredRNN++, Earthformer, TAU, and SimVP variants, in terms of MSE, MAE, RMSE, SSIM, and threshold-based detection metrics. In particular, QENO achieves an MSE of 0.2038, an RMSE of 0.4514, and an SSIM of 0.6291, while also maintaining a compact parameter budget. These results indicate that topology-aware hybrid quantum-classical feature modeling is a promising direction for 3D cloud structure forecasting and atmospheric Earth observation data analysis.
Abstract (translated)
三维（3D）云场的精确预报对大气分析和短临数值天气预报至关重要，然而由于云演变涉及跨层相互作用、非局部依赖关系以及多尺度时空动力学，该任务仍具有挑战性。现有基于卷积、循环或注意力机制的时空预测模型通常依赖局部偏置表示，因此在体量预测任务中难以保持精细的云结构。针对此问题，我们提出QENO——一种混合量子启发的3D云场时空预测框架。该架构包含四个组件：用于紧凑潜在表示的经典时空编码器、用于建模潜在空间中非局部耦合的拓扑感知量子增强块、用于融合测量衍生量子特征与循环记忆的动态融合时序单元，以及用于重建未来云体体积的解码器。在CMA-MESO 3D云场数据集上的实验表明，QENO在MSE、MAE、RMSE、SSIM及基于阈值的检测指标上持续优于代表性基线模型，包括ConvLSTM、PredRNN++、Earthformer、TAU及SimVP变体。尤其值得注意的是，QENO实现了0.2038的MSE、0.4514的RMSE和0.6291的SSIM，同时保持紧凑的参数规模。这些结果表明，拓扑感知的混合量子-经典特征建模是3D云结构预报及大气地球观测数据分析的一个有前景的方向。
URL
https://arxiv.org/abs/2603.29407
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TinyGaze: Lightweight Gaze-Gesture Recognition on Commodity Mobile Devices
2026-03-30 14:30:37Yaxiong Lei, Hyochan Cho, Fergus Buchanan, Shijing He, Xinya Gong, Yuheng Wang, Juan YeAbstract
Gaze gestures can provide hands free input on mobile devices, but practical use requires (i) gestures users can learn and recall and (ii) recognition models that are efficient enough for on-device deployment. We present an end-to-end pipeline using commodity ARKit head/eye transforms and a scaffolded guidance-to-recall protocol grounded in learning theory. In a pilot feasibility study (N=4 participants; 240 trials; controlled single-session setting), we benchmark a compact time-series model (TinyHAR) against deeper baselines (DeepConvLSTM, SA-HAR) on 5-way gesture recognition and 4-way user identification. TinyHAR achieves strong performance in this pilot benchmark (Macro F1 = 0.960 for gesture recognition; Macro F1 = 0.997 for user identification) while using only 46k parameters. A modality analysis further indicates that head pose dynamics are highly informative for mobile gaze gestures, highlighting embodied head--eye coordination as a key design consideration. Although the small sample size and controlled setting limit generalizability, these results indicate a potential direction for further investigation into on-device gaze gesture recognition.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09658
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Dynamic Forecasting and Temporal Feature Evolution of Stock Repurchases in Listed Companies Using Attention-Based Deep Temporal Networks
2026-03-29 14:03:26Xiang Ao, Jingxuan Zhang, Xinyu ZhaoAbstract
Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the complex temporal dependencies of corporate financial conditions. This paper proposes a dynamic early warning system integrating economic theory with deep temporal networks. Using Chinese A-share panel data (2014-2024), we employ a hybrid Temporal Convolutional Network (TCN) and Attention-based LSTM to capture long- and short-term financial evolutionary patterns. Rolling-window cross-validation demonstrates our model significantly outperforms static baselines like Logistic Regression and XGBoost. Furthermore, utilizing Explainable AI (XAI), we reveal the temporal dynamics of repurchase decisions: prolonged "undervaluation" serves as the long-term underlying motive, while a sharp increase in "cash flow" acts as the decisive short-term trigger. This study provides a robust deep learning paradigm for financial forecasting and offers dynamic empirical support for classic corporate finance hypotheses.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09650
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The Degree of Language Diacriticity and Its Effect on Tasks
Abstract
Diacritics are orthographic marks that clarify pronunciation, distinguish similar words, or alter meaning. They play a central role in many writing systems, yet their impact on language technology has not been systematically quantified across scripts. While prior work has examined diacritics in individual languages, there's no cross-linguistic, data-driven framework for measuring the degree to which writing systems rely on them and how this affects downstream tasks. We propose a data-driven framework for quantifying diacritic complexity using corpus-level, information-theoretic metrics that capture the frequency, ambiguity, and structural diversity of character-diacritic combinations. We compute these metrics over 24 corpora in 15 languages, spanning both single- and multi-diacritic scripts. We then examine how diacritic complexity correlates with performance on the task of diacritics restoration, evaluating BERT- and RNN-based models. We find that across languages, higher diacritic complexity is strongly associated with lower restoration accuracy. In single-diacritic scripts, where character-diacritic combinations are more predictable, frequency-based and structural measures largely align. In multi-diacritic scripts, however, structural complexity exhibits the strongest association with performance, surpassing frequency-based measures. These findings show that measurable properties of diacritic usage influence the performance of diacritic restoration models, demonstrating that orthographic complexity is not only descriptive but functionally relevant for modeling.
Abstract (translated)
URL
https://arxiv.org/abs/2603.27653
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Copilot-Assisted Second-Thought Framework for Brain-to-Robot Hand Motion Decoding
2026-03-29 03:12:08Yizhe Li (University of Birmingham, Birmingham, United Kingdom), Shixiao Wang (University of Birmingham, Birmingham, United Kingdom), Jian K. Liu (University of Birmingham, Birmingham, United Kingdom)Abstract
Motor kinematics prediction (MKP) from electroencephalography (EEG) is an important research area for developing movement-related brain-computer interfaces (BCIs). While traditional methods often rely on convolutional neural networks (CNNs) or recurrent neural networks (RNNs), Transformer-based models have shown strong ability in modeling long sequential EEG data. In this study, we propose a CNN-attention hybrid model for decoding hand kinematics from EEG during grasp-and-lift tasks, achieving strong performance in within-subject experiments. We further extend this approach to EEG-EMG multimodal decoding, which yields substantially improved results. Within-subject tests achieve PCC values of 0.9854, 0.9946, and 0.9065 for the X, Y, and Z axes, respectively, computed on the midpoint trajectory between the thumb and index finger, while cross-subject tests result in 0.9643, 0.9795, and 0.5852. The decoded trajectories from both modalities are then used to control a Franka Panda robotic arm in a MuJoCo simulation. To enhance trajectory fidelity, we introduce a copilot framework that filters low-confidence decoded points using a motion-state-aware critic within a finite-state machine. This post-processing step improves the overall within-subject PCC of EEG-only decoding to 0.93 while excluding fewer than 20% of the data points.
Abstract (translated)
URL
https://arxiv.org/abs/2603.27492
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Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction
2026-03-28 04:18:20Alireza Nezhadettehad, Arkady Zaslavsky, Abdur Rakib, Seng W. LokeAbstract
Accurate parking availability prediction is critical for intelligent transportation systems, but real-world deployments often face data sparsity, noise, and unpredictable changes. Addressing these challenges requires models that are not only accurate but also uncertainty-aware. In this work, we propose a loosely coupled neuro-symbolic framework that integrates Bayesian Neural Networks (BNNs) with symbolic reasoning to enhance robustness in uncertain environments. BNNs quantify predictive uncertainty, while symbolic knowledge extracted via decision trees and encoded using probabilistic logic programming is leveraged in two hybrid strategies: (1) using symbolic reasoning as a fallback when BNN confidence is low, and (2) refining output classes based on symbolic constraints before reapplying the BNN. We evaluate both strategies on real-world parking data under full, sparse, and noisy conditions. Results demonstrate that both hybrid methods outperform symbolic reasoning alone, and the context-refinement strategy consistently exceeds the performance of Long Short-Term Memory (LSTM) networks and BNN baselines across all prediction windows. Our findings highlight the potential of modular neuro-symbolic integration in real-world, uncertainty-prone prediction tasks.
Abstract (translated)
精准的停车位可用性预测对智能交通系统至关重要，但实际部署常面临数据稀疏、噪声及不可预测变化等挑战。应对这些挑战需要模型不仅具备高精度，还需具备不确定性感知能力。本研究提出一种松耦合神经符号框架，通过整合贝叶斯神经网络（BNNs）与符号推理，增强不确定环境下的鲁棒性。BNNs量化预测不确定性，而通过决策树提取并利用概率逻辑编程编码的符号知识，在两种混合策略中得到应用：（1）当BNN置信度较低时，采用符号推理作为回退机制；（2）在重新应用BNN前，基于符号约束对输出类别进行细化修正。我们在完整、稀疏及含噪的真实停车数据上评估了两种策略。结果表明，两种混合方法均优于单独使用符号推理，且上下文细化策略在所有预测时间窗口内持续超越长短期记忆网络（LSTM）与BNN基线模型。我们的发现凸显了模块化神经符号集成在现实世界易受不确定性影响的预测任务中的潜力。
URL
https://arxiv.org/abs/2603.27119
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Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort
2026-03-27 01:53:59Ishaan CherukuriAbstract
Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration. This uncertainty limits enrollment in clinical trials and delays urgent treatment. The Boundary Sharpness Coefficient (BSC) measures how well-defined the gray-white matter boundary looks on structural MRI. This study measures how BSC changes over time, namely, how fast the boundary degrades each year works much better than looking at a single baseline scan for predicting MCI-to-AD conversion. This study analyzed 1,824 T1-weighted MRI scans from 450 ADNI subjects (95 converters, 355 stable; mean follow-up: 4.84 years). BSC voxel-wise maps were computed using tissue segmentation at the gray-white matter cortical ribbon. Previous studies have used CNN and RNN models that reached 96.0% accuracy for AD classification and 84.2% for MCI conversion, but those approaches disregard specific regions within the brain. This study focused specifically on the gray-white matter interface. The approach uses temporal slope features capturing boundary degradation rates, feeding them into Random Survival Forest, a non-parametric ensemble method for right-censored survival data. The Random Survival Forest trained on BSC slopes achieved a test C-index of 0.63, a 163% improvement over baseline parametric models (test C-index: 0.24). Structural MRI costs a fraction of PET imaging ($800--$1,500 vs. $5,000--$7,000) and does not require CSF collection. These temporal biomarkers could help with patient-centered safety screening as well as risk assessment.
Abstract (translated)
预测轻度认知障碍（MCI）患者是否会进展为阿尔茨海默病（AD），在神经退行性病变早期至关重要。这种不确定性限制了临床试验的入组，并延误了急需的治疗。边界锐度系数（BSC）用于测量结构磁共振成像中灰质-白质边界清晰度。本研究通过测量BSC随时间的变化，即每年边界退化的速度，来预测MCI向AD的转化，这比单一基线扫描效果更好。本研究分析了来自450名ADNI受试者（95名转化者，355名稳定者；平均随访时间：4.84年）的1,824个T1加权磁共振扫描。基于灰质-白质皮层带组织分割计算了体素级BSC图谱。先前研究使用CNN和RNN模型在AD分类上达到96.0%的准确率，在MCI转化预测上达到84.2%，但这些方法忽略了大脑特定区域。本研究专门聚焦于灰质-白质界面。该方法采用捕捉边界退化速率的时间斜率特征，将其输入随机生存森林——一种用于右删失生存数据的非参数集成方法。基于BSC斜率训练的随机生存森林在测试中达到0.63的C指数，比基线参数模型（测试C指数：0.24）提升了163%。结构磁共振成像成本远低于PET成像（800-1,500美元 vs. 5,000-7,000美元），且无需采集脑脊液。这些时间生物标志物或可辅助以患者为中心的安全筛查及风险评估。
URL
https://arxiv.org/abs/2603.26007
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Development of ML model for triboelectric nanogenerator based sign language detection system
2026-03-26 08:35:02Meshv Patel, Bikash Baro, Sayan Bayan, Mohendra RoyAbstract
Sign language recognition (SLR) is vital for bridging communication gaps between deaf and hearing communities. Vision-based approaches suffer from occlusion, computational costs, and physical constraints. This work presents a comparison of machine learning (ML) and deep learning models for a custom triboelectric nanogenerator (TENG)-based sensor glove. Utilizing multivariate time-series data from five flex sensors, the study benchmarks traditional ML algorithms, feedforward neural networks, LSTM-based temporal models, and a multi-sensor MFCC CNN-LSTM architecture across 11 sign classes (digits 1-5, letters A-F). The proposed MFCC CNN-LSTM architecture processes frequency-domain features from each sensor through independent convolutional branches before fusion. It achieves 93.33% accuracy and 95.56% precision, a 23-point improvement over the best ML algorithm (Random Forest: 70.38%). Ablation studies reveal 50-timestep windows offer a tradeoff between temporal context and training data volume, yielding 84.13% accuracy compared to 58.06% with 100-timestep windows. MFCC feature extraction maps temporal variations to execution-speed-invariant spectral representations, and data augmentation methods (time warping, noise injection) are essential for generalization. Results demonstrate that frequency-domain feature representations combined with parallel multi-sensor processing architectures offer enhancement over classical algorithms and time-domain deep learning for wearable sensor-based gesture recognition. This aids assistive technology development.
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URL
https://arxiv.org/abs/2604.06220
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Enhancing Efficiency and Performance in Deepfake Audio Detection through Neuron-level dropin & Neuroplasticity Mechanisms
2026-03-25 14:22:32Yupei Li, Shuaijie Shao, Manuel Milling, Bj\"orn SchullerAbstract
Current audio deepfake detection has achieved remarkable performance using diverse deep learning architectures such as ResNet, and has seen further improvements with the introduction of large models (LMs) like Wav2Vec. The success of large language models (LLMs) further demonstrates the benefits of scaling model parameters, but also highlights one bottleneck where performance gains are constrained by parameter counts. Simply stacking additional layers, as done in current LLMs, is computationally expensive and requires full retraining. Furthermore, existing low-rank adaptation methods are primarily applied to attention-based architectures, which limits their scope. Inspired by the neuronal plasticity observed in mammalian brains, we propose novel algorithms, dropin and further plasticity, that dynamically adjust the number of neurons in certain layers to flexibly modulate model parameters. We evaluate these algorithms on multiple architectures, including ResNet, Gated Recurrent Neural Networks, and Wav2Vec. Experimental results using the widely recognised ASVSpoof2019 LA, PA, and FakeorReal dataset demonstrate consistent improvements in computational efficiency with the dropin approach and a maximum of around 39% and 66% relative reduction in Equal Error Rate with the dropin and plasticity approach among these dataset, respectively. The code and supplementary material are available at Github link.
Abstract (translated)
URL
https://arxiv.org/abs/2603.24343
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AI Generalisation Gap In Comorbid Sleep Disorder Staging
2026-03-24 15:02:35Saswata Bose, Suvadeep Maiti, Shivam Kumar Sharma, Mythirayee S, Tapabrata Chakraborti, Srijitesh Rajendran, Raju S. BapiAbstract
Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and manually scored. While deep learning enables automated EEG-based sleep staging in healthy subjects, our analysis shows poor generalization to clinical populations with disrupted sleep. Using Grad-CAM interpretations, we systematically demonstrate this limitation. We introduce iSLEEPS, a newly clinically annotated ischemic stroke dataset (to be publicly released), and evaluate a SE-ResNet plus bidirectional LSTM model for single-channel EEG sleep staging. As expected, cross-domain performance between healthy and diseased subjects is poor. Attention visualizations, supported by clinical expert feedback, show the model focuses on physiologically uninformative EEG regions in patient data. Statistical and computational analyses further confirm significant sleep architecture differences between healthy and ischemic stroke cohorts, highlighting the need for subject-aware or disease-specific models with clinical validation before deployment. A summary of the paper and the code is available at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2603.23582
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A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity
Abstract
Accurate short-term forecasting of air temperature and relative humidity is critical for urban management, especially in topographically complex cities such as Chongqing, China. This study compares seven machine learning models: eXtreme Gradient Boosting (XGBoost), Random Forest, Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Decision Tree, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Network (CNN)-LSTM (CNN-LSTM), for hourly prediction using real-world open data. Based on a unified framework of data preprocessing, lag-feature construction, rolling statistical features, and time-series validation, the models are systematically evaluated in terms of predictive accuracy and robustness. The results show that XGBoost achieves the best overall performance, with a test mean absolute error (MAE) of 0.302 °C for air temperature and 1.271% for relative humidity, together with an average R2 of 0.989 across the two forecasting tasks. These findings demonstrate the strong effectiveness of tree-based ensemble learning for structured meteorological time-series forecasting and provide practical guidance for intelligent meteorological forecasting in mountainous cities.
Abstract (translated)
空气温度与相对湿度的精准短期预报对城市管理至关重要，尤其在地形复杂的城市（如中国重庆）更是如此。本研究采用真实开放数据，对比分析了七种机器学习模型（极限梯度提升XGBoost、随机森林、支持向量回归SVR、多层感知机MLP、决策树、长短期记忆网络LSTM及卷积神经网络-长短期记忆网络CNN-LSTM）的小时级预测效果。基于统一的数据预处理、滞后特征构建、滚动统计特征及时序验证框架，研究从预测精度与稳健性两个维度对模型进行了系统评估。结果表明：XGBoost综合表现最优，在气温预测中测试集平均绝对误差（MAE）达0.302℃，相对湿度预测MAE为1.271%，两项任务平均R²值高达0.989。这些发现验证了基于树的集成学习方法对结构化气象时间序列预测的显著有效性，并为山地城市智能气象预报提供了实践指导。
URL
https://arxiv.org/abs/2603.23282
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Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts
2026-03-24 10:29:54Maria Conchita Agana Navarro, Geng Li, Theo Wolf, Maria Perez-OrtizAbstract
The accelerating pace of climate change introduces profound non-stationarities that challenge the ability of Machine Learning based climate emulators to generalize beyond their training distributions. While these emulators offer computationally efficient alternatives to traditional Earth System Models, their reliability remains a potential bottleneck under "no-analog" future climate states, which we define here as regimes where external forcing drives the system into conditions outside the empirical range of the historical training data. A fundamental challenge in evaluating this reliability is data contamination; because many models are trained on simulations that already encompass future scenarios, true out-of-distribution (OOD) performance is often masked. To address this, we benchmark the OOD robustness of three state-of-the-art architectures: U-Net, ConvLSTM, and the ClimaX foundation model specifically restricted to a historical-only training regime (1850-2014). We evaluate these models using two complementary strategies: (i) temporal extrapolation to the recent climate (2015-2023) and (ii) cross-scenario forcing shifts across divergent emission pathways. Our analysis within this experimental setup reveals an accuracy vs. stability trade-off: while the ClimaX foundation model achieves the lowest absolute error, it exhibits higher relative performance changes under distribution shifts, with precipitation errors increasing by up to 8.44% under extreme forcing scenarios. These findings suggest that when restricted to historical training dynamics, even high-capacity foundation models are sensitive to external forcing trajectories. Our results underscore the necessity of scenario-aware training and rigorous OOD evaluation protocols to ensure the robustness of climate emulators under a changing climate.
Abstract (translated)
气候变化加速的步伐带来了深刻的非平稳性，这挑战了基于机器学习的气候模拟器在训练分布之外进行泛化的能力。尽管这些模拟器为传统地球系统模型提供了计算高效的替代方案，但它们在“无先例”的未来气候状态下的可靠性仍是一个潜在瓶颈——本文中我们将“无先例”定义为外部强迫将系统推至历史训练数据经验范围之外的状态。评估这种可靠性的一个根本挑战在于数据污染；由于许多模型在已包含未来情景的模拟上进行训练，真实的分布外性能往往被掩盖。为解决此问题，我们评估了三种最先进架构的分布外鲁棒性：U-Net、ConvLSTM以及严格限制在仅历史训练 regime（1850-2014）下的ClimaX基础模型。我们通过两种互补策略评估这些模型：(i) 向近期气候（2015-2023）的时间外推，以及(ii) 跨 divergent 排放路径的强迫情景转换。在此实验设置内的分析揭示了精度与稳定性之间的权衡：虽然ClimaX基础模型实现了最低绝对误差，但在分布转移下表现出更高的相对性能变化，在极端强迫情景下降水误差增幅高达8.44%。这些发现表明，当限制在历史训练动态下时，即使是高容量基础模型也对外部强迫轨迹敏感。我们的结果强调了情景感知训练和严格分布外评估协议的必要性，以确保气候模拟器在变化气候下的鲁棒性。
URL
https://arxiv.org/abs/2603.23043
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L-UNet: An LSTM Network for Remote Sensing Image Change Detection
2026-03-24 06:30:32Shuting Sun, Lin Mu, Lizhe Wang, Peng LiuAbstract
Change detection of high-resolution remote sensing images is an important task in earth observation and was extensively investigated. Recently, deep learning has shown to be very successful in plenty of remote sensing tasks. The current deep learning-based change detection method is mainly based on conventional long short-term memory (Conv-LSTM), which does not have spatial characteristics. Since change detection is a process with both spatiality and temporality, it is necessary to propose an end-to-end spatiotemporal network. To achieve this, Conv-LSTM, an extension of the Conv-LSTM structure, is introduced. Since it shares similar spatial characteristics with the convolutional layer, L-UNet, which substitutes partial convolution layers of UNet-to-Conv-LSTM and Atrous L-UNet (AL-UNet), which further using Atrous structure to multiscale spatial information is proposed. Experiments on two data sets are conducted and the proposed methods show the advantages both in quantity and quality when compared with some other methods.
Abstract (translated)
高分辨率遥感图像变化检测是地球观测中的重要任务，已被广泛研究。近年来，深度学习在众多遥感任务中展现出显著成效。当前基于深度学习的变化检测方法主要依赖于传统的长短期记忆网络（Conv-LSTM），该方法缺乏空间特征。由于变化检测是一个兼具空间性与时域性的过程，因此有必要提出一种端到端的时空网络。为实现这一目标，引入了Conv-LSTM结构扩展。鉴于其与卷积层具有相似的空间特性，提出了L-UNet（将UNet中的部分卷积层替换为Conv-LSTM）以及进一步采用空洞结构以获取多尺度空间信息的AL-UNet（Atrous L-UNet）。在两个数据集上进行的实验表明，所提方法在定量与定性评估中均优于其他部分方法。
URL
https://arxiv.org/abs/2603.22842
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