Tootfinder

Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Yuhao Pan, Xiucheng Wang, Nan Cheng, Wenchao Xu
https://arxiv.org/abs/2510.09405

Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Radio frequency fingerprint identification (RFFI) is a critical technique for wireless network security, leveraging intrinsic hardware-level imperfections introduced during device manufacturing to enable precise transmitter identification. While deep neural networks have shown remarkable capability in extracting discriminative features, their real-world deployment is hindered by receiver-induced variability. In practice, RF fingerprint signals comprise transmitter-specific features as well as c…

Towards Multimodal Sentiment Analysis via Contrastive Cross-modal Retrieval Augmentation and Hierachical Prompts
Xianbing Zhao, Shengzun Yang, Buzhou Tang, Ronghuan Jiang
https://arxiv.org/abs/2508.07666

Towards Multimodal Sentiment Analysis via Contrastive Cross-modal Retrieval Augmentation and Hierachical Prompts
Multimodal sentiment analysis is a fundamental problem in the field of affective computing. Although significant progress has been made in cross-modal interaction, it remains a challenge due to the insufficient reference context in cross-modal interactions. Current cross-modal approaches primarily focus on leveraging modality-level reference context within a individual sample for cross-modal feature enhancement, neglecting the potential cross-sample relationships that can serve as sample-level …

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Zhiying Li, Guanggang Geng, Jian Weng
https://arxiv.org/abs/2508.05695

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Enterprises are facing increasing risks of insider threats, while existing detection methods are unable to effectively address these challenges due to reasons such as insufficient temporal dynamic feature modeling, computational efficiency and real-time bottlenecks and cross-modal information island problem. This paper proposes a new insider threat detection framework MambaITD based on the Mamba state space model and cross-modal adaptive fusion. First, the multi-source log preprocessing module …

MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
Dongki Jung, Jaehoon Choi, Yonghan Lee, Sungmin Eum, Heesung Kwon, Dinesh Manocha
https://arxiv.org/abs/2510.07119 …

MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a training-free Monocular Geometry Refinement method designed to improve cross-view consistency and achieve scale alignment. To induce inter-frame relationships, our method employs feature matching between frames to establish correspondences. Rather than applying simple least squares optimization on these matched points, we …

Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
Sankalp Tattwadarshi Swain, Anshika Krishnatray, Dhruv Kumar, Jagat Sesh Challa
https://arxiv.org/abs/2509.07389

Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
Existing evaluation studies on linguistic competence of large language models (LLM agents) have focused primarily on vocabulary learning, morphological rule induction, syntactic generalization, pragmatic inference, and cross-linguistic transfer. However, none assess whether LLM agents can acquire a language through pattern recognition and interactive feedback, a central feature of human language acquisition. We propose a novel experimental framework in which an LLM agent is evaluated on its abi…

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
M. Sajid, Deepanshu Gupta, Yash Modi, Sanskriti Jain, Harshith Jai Surya Ganji, A. Rahaman, Harshvardhan Choudhary, Nasir Saleem, Amir Hussain, M. Tanveer
https://arxiv.org/abs/2510.05295

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
In this paper, we propose AUREXA-SE (Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement), a progressive bimodal framework tailored for audio-visual speech enhancement (AVSE). AUREXA-SE jointly leverages raw audio waveforms and visual cues by employing a U-Net-based 1D convolutional encoder for audio and a Swin Transformer V2 for efficient and expressive visual feature extraction. Central to the architecture is a novel bidirect…

Fast Leave-One-Out Approximation from Fragment-Target Prevalence Vectors (molFTP) : From Dummy Masking to Key-LOO for Leakage-Free Feature Construction
Guillaume Godin
https://arxiv.org/abs/2510.06029 …

Fast Leave-One-Out Approximation from Fragment-Target Prevalence Vectors (molFTP) : From Dummy Masking to Key-LOO for Leakage-Free Feature Construction
We introduce molFTP (molecular fragment-target prevalence), a compact representation that delivers strong predictive performance. To prevent feature leakage across cross-validation folds, we implement a dummy-masking procedure that removes information about fragments present in the held-out molecules. We further show that key leave-one-out (key-loo) closely approximates true molecule-level leave-one-out (LOO), with deviation below 8% on our datasets. This enables near full data training while p…

BS-1-to-N: Diffusion-Based Environment-Aware Cross-BS Channel Knowledge Map Generation for Cell-Free Networks
Zhuoyin Dai, Di Wu, Yong Zeng, Xiaoli Xu, Xinyi Wang, Zesong Fei
https://arxiv.org/abs/2507.23236

BS-1-to-N: Diffusion-Based Environment-Aware Cross-BS Channel Knowledge Map Generation for Cell-Free Networks
Channel knowledge map (CKM) inference across base stations (BSs) is the key to achieving efficient environmentaware communications. This paper proposes an environmentaware cross-BS CKM inference method called BS-1-to-N based on the generative diffusion model. To this end, we first design the BS location embedding (BSLE) method tailored for cross-BS CKM inference to embed BS location information in the feature vector of CKM. Further, we utilize the cross- and self-attention mechanism for the pro…

DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
Ruohong Yang, Peng Hu, Yunfan Li, Xi Peng
https://arxiv.org/abs/2509.04193 https://arxiv.or…

DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images of the same category across diverse domains without relying on annotations. Existing UCIR methods, which align cross-domain features for the entire image, often struggle with the domain gap, as the object features critical for retrieval are frequently entangled with domain-specific styles. To address this challenge, we propose DUDE, a novel UCIR method building upon feature disentanglement. In brief, DUDE leverages a text-…

Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision
Manish Kansana, Sindhuja Penchala, Shahram Rahimi, Noorbakhsh Amiri Golilarz
https://arxiv.org/abs/2509.04658

Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision
Multimodal surface material classification plays a critical role in advancing tactile perception for robotic manipulation and interaction. In this paper, we present Surformer v2, an enhanced multi-modal classification architecture designed to integrate visual and tactile sensory streams through a late(decision level) fusion mechanism. Building on our earlier Surformer v1 framework [1], which employed handcrafted feature extraction followed by mid-level fusion architecture with multi-head cross-…

On-chip room-temperature CW lasing from a III-V nanowire integrated with a Si photonic crystal platform
Masato Takiguchi, Takuro Fujii, Hisashi Sumikura, Akihiko Shinya, Shinji Matsuo, Masaya Notomi
https://arxiv.org/abs/2510.05477

On-chip room-temperature CW lasing from a III-V nanowire integrated with a Si photonic crystal platform
We report the demonstration of continuous-wave (CW) lasing at room temperature from a III-V semiconductor nanowire integrated into a Si photonic crystal (PhC) cavity. Conventional hybrid nanowire lasers [M. Takiguchi. et.al., APL Photonics, 2, 046106 (2017)], which typically feature circular nanowire-cross-sections, suffer from a weak optical confinement, preventing CW lasing under ambient conditions. To overcome this limitation, we fabricated nanowires with rectangular cross-sections via dry e…

Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss
Javier Sequeiro Gonz\'alez, Arthur Longuefosse, Miguel D\'iaz Benito, \'Alvaro Garc\'ia Mart\'in, Fabien Baldacci
https://arxiv.org/abs/2509.22394

Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss
We present a patch-based 3D nnUNet adaptation for MR to CT and CBCT to CT image translation using the multicenter SynthRAD2025 dataset, covering head and neck (HN), thorax (TH), and abdomen (AB) regions. Our approach leverages two main network configurations: a standard UNet and a residual UNet, both adapted from nnUNet for image synthesis. The Anatomical Feature-Prioritized (AFP) loss was introduced, which compares multilayer features extracted from a compact segmentation network trained on To…

EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Kwanhyung Lee, Sungsoo Hong, Joonhyung Park, Jeonghyeop Lim, Juhwan Choi, Donghwee Yoon, Eunho Yang
https://arxiv.org/abs/2510.00549

EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Machine learning models for clinical prediction rely on structured data extracted from Electronic Medical Records (EMRs), yet this process remains dominated by hardcoded, database-specific pipelines for cohort definition, feature selection, and code mapping. These manual efforts limit scalability, reproducibility, and cross-institutional generalization. To address this, we introduce EMR-AGENT (Automated Generalized Extraction and Navigation Tool), an agent-based framework that replaces manual r…

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Yavuz Bakman, Sungmin Kang, Zhiqi Huang, Duygu Nur Yaldiz, Catarina G. Bel\'em, Chenyang Zhu, Anoop Kumar, Alfy Samuel, Salman Avestimehr, Daben Liu, Sai Praneeth Karimireddy
https://arxiv.org/abs/2510.02671

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Uncertainty Quantification (UQ) research has primarily focused on closed-book factual question answering (QA), while contextual QA remains unexplored, despite its importance in real-world applications. In this work, we focus on UQ for the contextual QA task and propose a theoretically grounded approach to quantify epistemic uncertainty. We begin by introducing a task-agnostic, token-level uncertainty measure defined as the cross-entropy between the predictive distribution of the given model and…

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Xinzhu Li, Juepeng Zheng, Yikun Chen, Xudong Mao, Guanghui Yue, Wei Zhou, Chenlei Lv, Ruomei Wang, Fan Zhou, Baoquan Zhao
https://arxiv.org/abs/2508.03397

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait r…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[8/15]:
- Reinforcement Learning Driven Generalizable Feature Representation for Cross-User Activity Recogn...
Xiaozhou Ye, Kevin I-Kai Wang

PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks
Yiting Dong, Jianhao Ding, Zijie Xu, Tong Bu, Zhaofei Yu, Tiejun Huang
https://arxiv.org/abs/2509.24844

PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks
Spiking Neural Networks (SNNs), with their temporal processing capabilities and biologically plausible dynamics, offer a natural platform for unsupervised representation learning. However, current unsupervised SNNs predominantly employ shallow architectures or localized plasticity rules, limiting their ability to model long-range temporal dependencies and maintain temporal feature consistency. This results in semantically unstable representations, thereby impeding the development of deep unsupe…

Looking Alike From Far to Near: Enhancing Cross-Resolution Re-Identification via Feature Vector Panning
Zanwu Liu, Chao Yuan, Bo Li, Xiaowei Zhang, Guanglin Niu
https://arxiv.org/abs/2510.00936

Looking Alike From Far to Near: Enhancing Cross-Resolution Re-Identification via Feature Vector Panning
In surveillance scenarios, varying camera distances cause significant differences among pedestrian image resolutions, making it hard to match low-resolution (LR) images with high-resolution (HR) counterparts, limiting the performance of Re-Identification (ReID) tasks. Most existing Cross-Resolution ReID (CR-ReID) methods rely on super-resolution (SR) or joint learning for feature compensation, which increases training and inference complexity and has reached a performance bottleneck in recent s…

Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations
Patrick Amadeus Irawan, Ryandito Diandaru, Belati Jagad Bintang Syuhada, Randy Zakya Suchrady, Alham Fikri Aji, Genta Indra Winata, Fajri Koto, Samuel Cahyawijaya
https://arxiv.org/abs/2509.05060

Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations
We introduce Entropy2Vec, a novel framework for deriving cross-lingual language representations by leveraging the entropy of monolingual language models. Unlike traditional typological inventories that suffer from feature sparsity and static snapshots, Entropy2Vec uses the inherent uncertainty in language models to capture typological relationships between languages. By training a language model on a single language, we hypothesize that the entropy of its predictions reflects its structural sim…

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Afra Kilic, Kim Batselier
https://arxiv.org/abs/2507.11136 …

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model complexity hyperparameters like tensor rank and feature dimensions, often through trial-and-error or computationally costly methods like cross-validation. We propose Bayesian Tensor Network Kernel Machines, a fully probabil…

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Debojyoti Ghosh, Soumya K Ghosh, Adrijit Goswami
https://arxiv.org/abs/2510.05071

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Efficient and accurate classification of waste and industrial surface defects is essential for ensuring sustainable waste management and maintaining high standards in quality control. This paper introduces the Neuroplastic Modular Classifier, a novel hybrid architecture designed for robust and adaptive image classification in dynamic environments. The model combines a ResNet-50 backbone for localized feature extraction with a Vision Transformer (ViT) to capture global semantic context. Addition…

Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking
Zhuohao Li, Wenqing Chen, Jianxing Yu, Zhichao Lu
https://arxiv.org/abs/2508.19558

Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking
Embedding models have demonstrated strong performance in tasks like clustering, retrieval, and feature extraction while offering computational advantages over generative models and cross-encoders. Benchmarks such as MTEB have shown that text embeddings from large language models (LLMs) capture rich semantic information, but their ability to reflect code-level functional semantics remains unclear. Existing studies largely focus on code clone detection, which emphasizes syntactic similarity and o…

This is the #Garbce stop on railway line 271 (Poznań — Wrocław), renovated a few years ago.
The platforms end a dozen metres from the railway crossing. However, there is no walkway from the platforms to the crossing; probably because it would need to cross a tiny stream. Instead, a 100 m long walkways have been added to the other end of the platforms, with a tunnel joining the platforms and access to a side road in the town. To reach the railway crossing, you have to walk 700 m.
Ironically, even in the general drought the tunnel manages to feature puddles of ground water. Also including the photo of a platform roofing with weird orange marks.
#rail

Cross-Learning Fine-Tuning Strategy for Dysarthric Speech Recognition Via CDSD database
Qing Xiao, Yingshan Peng, PeiPei Zhang
https://arxiv.org/abs/2508.18732 https://

Cross-Learning Fine-Tuning Strategy for Dysarthric Speech Recognition Via CDSD database
Dysarthric speech recognition faces challenges from severity variations and disparities relative to normal speech. Conventional approaches individually fine-tune ASR models pre-trained on normal speech per patient to prevent feature conflicts. Counter-intuitively, experiments reveal that multi-speaker fine-tuning (simultaneously on multiple dysarthric speakers) improves recognition of individual speech patterns. This strategy enhances generalization via broader pathological feature learning, mi…

Modality-Aware Feature Matching: A Comprehensive Review of Single- and Cross-Modality Techniques
Weide Liu, Wei Zhou, Jun Liu, Ping Hu, Jun Cheng, Jungong Han, Weisi Lin
https://arxiv.org/abs/2507.22791

Modality-Aware Feature Matching: A Comprehensive Review of Single- and Cross-Modality Techniques
Feature matching is a cornerstone task in computer vision, essential for applications such as image retrieval, stereo matching, 3D reconstruction, and SLAM. This survey comprehensively reviews modality-based feature matching, exploring traditional handcrafted methods and emphasizing contemporary deep learning approaches across various modalities, including RGB images, depth images, 3D point clouds, LiDAR scans, medical images, and vision-language interactions. Traditional methods, leveraging de…

Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
Sarthak Khanna, Armin Berger, David Berghaus, Tobias Deusser, Lorenz Sparrenberg, Rafet Sifa
https://arxiv.org/abs/2508.13327

Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
We propose STONK (Stock Optimization using News Knowledge), a multimodal framework integrating numerical market indicators with sentiment-enriched news embeddings to improve daily stock-movement prediction. By combining numerical & textual embeddings via feature concatenation and cross-modal attention, our unified pipeline addresses limitations of isolated analyses. Backtesting shows STONK outperforms numeric-only baselines. A comprehensive evaluation of fusion strategies and model configuratio…

Observing Double Higgs Production at the LHC via Neutrino Feature Engineering in $hh\to b\bar{b}\ell\ell\nu\nu$
Alexandre Alves, Eduardo da Silva Almeida, Diego S. V. Gon\c{c}alves
https://arxiv.org/abs/2509.12450

Observing Double Higgs Production at the LHC via Neutrino Feature Engineering in $hh\to b\bar{b}\ell\ellνν$
Double Higgs production is challenging even at the High Luminosity LHC. The Standard Model (SM) $hh\to b\bar{b}WW(ZZ)\to b\bar{b}\ell\ellνν$ has a moderate cross section compared to other decay modes, but its backgrounds, mainly top quark pairs and Drell-Yan production, are overwhelming. In this work, we propose new kinematic features designed to improve the discrimination of double Higgs pairs, in the $b\bar{b}\ell\ellνν$ channel, with cut-based and multivariate analyses. The new features …

Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems
Mingyu Cui, Mengzhe Geng, Jiajun Deng, Chengxi Deng, Jiawen Kang, Shujie Hu, Guinan Li, Tianzi Wang, Zhaoqing Li, Xie Chen, Xunying Liu
https://arxiv.org/abs/2508.10456

Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems
This paper investigates four types of cross-utterance speech contexts modeling approaches for streaming and non-streaming Conformer-Transformer (C-T) ASR systems: i) input audio feature concatenation; ii) cross-utterance Encoder embedding concatenation; iii) cross-utterance Encoder embedding pooling projection; or iv) a novel chunk-based approach applied to C-T models for the first time. An efficient batch-training scheme is proposed for contextual C-Ts that uses spliced speech utterances withi…

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, Chih-Chung Hsu
https://arxiv.org/abs/2507.19858

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
Multi-source CT-scan classification suffers from domain shifts that impair cross-source generalization. While preprocessing pipelines combining Spatial-Slice Feature Learning (SSFL++) and Kernel-Density-based Slice Sampling (KDS) have shown empirical success, the mechanisms underlying their domain robustness remain underexplored. This study analyzes how this input-space standardization manages the trade-off between local discriminability and cross-source generalization. The SSFL++ and KDS pipel…

Less is More: The Effectiveness of Compact Typological Language Representations
York Hay Ng, Phuong Hanh Hoang, En-Shiun Annie Lee
https://arxiv.org/abs/2509.20129 https://

Less is More: The Effectiveness of Compact Typological Language Representations
Linguistic feature datasets such as URIEL+ are valuable for modelling cross-lingual relationships, but their high dimensionality and sparsity, especially for low-resource languages, limit the effectiveness of distance metrics. We propose a pipeline to optimize the URIEL+ typological feature space by combining feature selection and imputation, producing compact yet interpretable typological representations. We evaluate these feature subsets on linguistic distance alignment and downstream tasks, …

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting
Yuxi Hu, Jun Zhang, Kuangyi Chen, Zhe Zhang, Friedrich Fraundorfer
https://arxiv.org/abs/2508.20754

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting
Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization. In particular, recent advancements utilize feed-forward networks to predict per-pixel Gaussian parameters, enabling high-quality synthesis from sparse input views. However, existing approaches fall short in encoding discriminative, multi-view consistent features for Gaussian predictions, which struggle to construct accurate geometry with sparse views. To address this, we propose $\m…

CapRecover: A Cross-Modality Feature Inversion Attack Framework on Vision Language Models
Kedong Xiu, Saiqian Zhang
https://arxiv.org/abs/2507.22828 https://

CapRecover: A Cross-Modality Feature Inversion Attack Framework on Vision Language Models
As Vision-Language Models (VLMs) are increasingly deployed in split-DNN configurations--with visual encoders (e.g., ResNet, ViT) operating on user devices and sending intermediate features to the cloud--there is a growing privacy risk from semantic information leakage. Existing approaches to reconstructing images from these intermediate features often result in blurry, semantically ambiguous images. To directly address semantic leakage, we propose CapRecover, a cross-modality inversion framewor…

Beam Cross Sections Create Mixtures: Improving Feature Localization in Secondary Electron Imaging
Vaibhav Choudhary, Akshay Agarwal, Vivek K Goyal
https://arxiv.org/abs/2508.09942

Beam Cross Sections Create Mixtures: Improving Feature Localization in Secondary Electron Imaging
Secondary electron (SE) imaging techniques, such as scanning electron microscopy and helium ion microscopy (HIM), use electrons emitted by a sample in response to a focused beam of charged particles incident at a grid of raster scan positions. Spot size -- the diameter of the incident beam's spatial profile -- is one of the limiting factors for resolution, along with various sources of noise in the SE signal. The effect of the beam spatial profile is commonly understood as convolutional. We sho…

Microscopic model of the operation of the Single-chalcogenide X-point Memory
P. Fantini, A. Ghetti, E. Varesi, A. Pirovano, F. Pellizzer, D. Baratella, C. Ribaldone, S. Caravati, D. Campi, M. Bernasconi, R. Bez
https://arxiv.org/abs/2508.12118

Microscopic model of the operation of the Single-chalcogenide X-point Memory
Ovonic threshold switching is the key process for several applications of chalcogenide alloys including phase change memories and selector elements in cross-points arrays. Very recently, it has been shown that the threshold switching voltage VT depends on the polarity of the applied field. This feature has been already exploited in the realization of the Single Chalcogenide X-point Memory (SXM) in which a single film of a chalcogenide alloy can serve as both a memory and selector unit. In this …

TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution
Yifei Jia, Shiyu Cheng, Yu Dong, Guan Li, Dong Tian, Ruixiao Peng, Xuyi Lu, Yu Wang, Wei Yao, Guihua Shan
https://arxiv.org/abs/2509.04834

TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution
Understanding the complex combustion dynamics within scramjet engines is critical for advancing high-speed propulsion technologies. However, the large scale and high dimensionality of simulation-generated temporal flow field data present significant challenges for visual interpretation, feature differentiation, and cross-case comparison. In this paper, we present TemporalFlowViz, a parameter-aware visual analytics workflow and system designed to support expert-driven clustering, visualization, …

Cross-Modal Knowledge Distillation with Multi-Level Data Augmentation for Low-Resource Audio-Visual Sound Event Localization and Detection
Qing Wang, Ya Jiang, Hang Chen, Sabato Marco Siniscalchi, Jun Du, Jianqing Gao
https://arxiv.org/abs/2508.12334

Cross-Modal Knowledge Distillation with Multi-Level Data Augmentation for Low-Resource Audio-Visual Sound Event Localization and Detection
This work presents a cross-modal knowledge distillation (CMKD) framework combined with multi-level data augmentation for low-resource audio-visual (AV) sound event localization and detection (SELD). An audio-only SELD model acts as the teacher, transferring knowledge to an AV student model through both output responses and intermediate feature representations. To enhance learning, data augmentation is applied by mixing features randomly selected from multiple network layers and associated loss …

Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network
Rasil Baidar, Sasa Maric, Robert Abbas
https://arxiv.org/abs/2509.15555

Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network
The exponential expansion of IoT and 5G-Advanced applications has enlarged the attack surface for DDoS, malware, and zero-day intrusions. We propose an intrusion detection system that fuses a convolutional neural network (CNN), a bidirectional LSTM (BiLSTM), and an autoencoder (AE) bottleneck within a privacy-preserving federated learning (FL) framework. The CNN-BiLSTM branch captures local and gated cross-feature interactions, while the AE emphasizes reconstruction-based anomaly sensitivity. T…

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
Ziyi Huang, Xia Cui
https://arxiv.org/abs/2507.08499

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
This paper presents our system for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Track A), which focuses on multi-label emotion detection in short texts. We propose a feature-centric framework that dynamically adapts document representations and learning algorithms to optimize language-specific performance. Our study evaluates three key components: document representation, dimensionality reduction, and model training in 28 languages, highlighting five for detailed anal…

A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
Jingyang Shan, Qishuai Yu, Jiacen Liu, Shaolin Zhang, Wen Shen, Yanxiao Zhao, Tianyi Wang, Xiaolin Qin, Yiheng Yin
https://arxiv.org/abs/2509.02256

A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
Neck pain is the primary symptom of cervical spondylosis, yet its underlying mechanisms remain unclear, leading to uncertain treatment outcomes. To address the challenges of multimodal feature fusion caused by imaging differences and spatial mismatches, this paper proposes an Adaptive Bidirectional Pyramid Difference Convolution (ABPDC) module that facilitates multimodal integration by exploiting the advantages of difference convolution in texture extraction and grayscale invariance, and a Feat…

A Multimodal Neural Network for Recognizing Subjective Self-Disclosure Towards Social Robots
Henry Powell, Guy Laban, Emily S. Cross
https://arxiv.org/abs/2508.10828 https://

A Multimodal Neural Network for Recognizing Subjective Self-Disclosure Towards Social Robots
Subjective self-disclosure is an important feature of human social interaction. While much has been done in the social and behavioural literature to characterise the features and consequences of subjective self-disclosure, little work has been done thus far to develop computational systems that are able to accurately model it. Even less work has been done that attempts to model specifically how human interactants self-disclose with robotic partners. It is becoming more pressing as we require so…

C3RL: Rethinking the Combination of Channel-independence and Channel-mixing from Representation Learning
Shusen Ma, Yun-Bo Zhao, Yu Kang
https://arxiv.org/abs/2507.17454

C3RL: Rethinking the Combination of Channel-independence and Channel-mixing from Representation Learning
Multivariate time series forecasting has drawn increasing attention due to its practical importance. Existing approaches typically adopt either channel-mixing (CM) or channel-independence (CI) strategies. CM strategy can capture inter-variable dependencies but fails to discern variable-specific temporal patterns. CI strategy improves this aspect but fails to fully exploit cross-variable dependencies like CM. Hybrid strategies based on feature fusion offer limited generalization and interpretabi…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/3]:
- VIFO: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion
Wang, Yu, Xu, Ma, Zhang, Feng, Zhang, Huang, Sun, Zhang

Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle
Mark Cote, Susana Aires
https://arxiv.org/abs/2508.15680 https://arxi…

Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle
This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded…

Inert dark matter in three Higgs doublet model: a blind spot narrative
Amit Dutta Banik, Tapoja Jha, Eija Tanskanen
https://arxiv.org/abs/2508.13583 https://

Inert dark matter in three Higgs doublet model: a blind spot narrative
We explore the phenomenology of three Higgs doublet scenario, where the scalar potential is augmented by $\mathbb{Z}_{3} \times \mathbb{Z}_{2}$ symmetry making one doublet inert. Thus in effect, our model of interest is two Higgs plus inert Higgs doublet model charged under $\mathbb{Z}_3$ ((2+I)HDM-$\mathbb{Z}_3$) symmetry. We observe a blind spot feature for dark matter direct detection, as the tree-level dark matter-nucleon scattering cross-section vanishes depending on the mass splitting of …

Crosslisted article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- Improving Noise Robust Audio-Visual Speech Recognition via Router-Gated Cross-Modal Feature Fusion
DongHoon Lim, YoungChae Kim, Dong-Hyun Kim, Da-Hee Yang, Joon-Hyuk Chang

Slot Attention with Re-Initialization and Self-Distillation
Rongzhen Zhao, Yi Zhao, Juho Kannala, Joni Pajarinen
https://arxiv.org/abs/2507.23755 https://a…

Slot Attention with Re-Initialization and Self-Distillation
Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are highly versatile for tasks involving visual modalities. OCL typically aggregates object superpixels into slots by iteratively applying competitive cross attention, known as Slot Attention, with the slots as the query. However, once initialized, these slots are reused naively, causing redundant slots to compete with inf…

FractMorph: A Fractional Fourier-Based Multi-Domain Transformer for Deformable Image Registration
Shayan Kebriti, Shahabedin Nabavi, Ali Gooya
https://arxiv.org/abs/2508.12445 h…

FractMorph: A Fractional Fourier-Based Multi-Domain Transformer for Deformable Image Registration
Deformable image registration (DIR) is a crucial and challenging technique for aligning anatomical structures in medical images and is widely applied in diverse clinical applications. However, existing approaches often struggle to capture fine-grained local deformations and large-scale global deformations simultaneously within a unified framework. We present FractMorph, a novel 3D dual-parallel transformer-based architecture that enhances cross-image feature matching through multi-domain fracti…

ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
Jianwen Tan, Huiyao Zhang, Rui Xiong, Han Zhou, Hongfei Wang, Ye Li
https://arxiv.org/abs/2508.18050

ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
Camouflaged Object Segmentation (COS) poses a significant challenge due to the intrinsic high similarity between targets and backgrounds, demanding models capable of profound holistic understanding beyond superficial cues. Prevailing methods, often limited by shallow feature representation, inadequate reasoning mechanisms, and weak cross-modal integration, struggle to achieve this depth of cognition, resulting in prevalent issues like incomplete target separation and imprecise segmentation. Ins…

Sommerfeld Enhancement from Quantum Forces for Dark Matter
Steven Ferrante, Maxim Perelstein, Bingrong Yu
https://arxiv.org/abs/2507.12522 https://

Sommerfeld Enhancement from Quantum Forces for Dark Matter
Quantum forces are long-range interactions that arise only at the loop level. In this work, we study the Sommerfeld enhancement of dark matter (DM) annihilation cross sections caused by quantum forces. One notable feature of quantum forces is that they are subject to coherent enhancement in the presence of a background of mediator particles, which occurs in many situations in cosmology. We show that this effect has important implications for the Sommerfeld enhancement and DM physics. For the fi…

HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters
Ruru Xu, Ilkay Oksuz
https://arxiv.org/abs/2508.13026 https://arxiv…

HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters
Deep learning-based cardiac MRI reconstruction faces significant domain shift challenges when deployed across multiple clinical centers with heterogeneous scanner configurations and imaging protocols. We propose HierAdaptMR, a hierarchical feature adaptation framework that addresses multi-level domain variations through parameter-efficient adapters. Our method employs Protocol-Level Adapters for sequence-specific characteristics and Center-Level Adapters for scanner-dependent variations, built …

Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries
Pengfei Cai, Yan Song, Qing Gu, Nan Jiang, Haoyu Song, Ian McLoughlin
https://arxiv.org/abs/2507.16343

Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries
Most existing sound event detection~(SED) algorithms operate under a closed-set assumption, restricting their detection capabilities to predefined classes. While recent efforts have explored language-driven zero-shot SED by exploiting audio-language models, their performance is still far from satisfactory due to the lack of fine-grained alignment and cross-modal feature fusion. In this work, we propose the Detect Any Sound Model (DASM), a query-based framework for open-vocabulary SED guided by …

Mix, Align, Distil: Reliable Cross-Domain Atypical Mitosis Classification
Kaustubh Atey, Sameer Anand Jha, Gouranga Bala, Amit Sethi
https://arxiv.org/abs/2508.20745 https://

Mix, Align, Distil: Reliable Cross-Domain Atypical Mitosis Classification
Atypical mitotic figures (AMFs) are important histopathological markers yet remain challenging to identify consistently, particularly under domain shift stemming from scanner, stain, and acquisition differences. We present a simple training-time recipe for domain-robust AMF classification in MIDOG 2025 Task 2. The approach (i) increases feature diversity via style perturbations inserted at early and mid backbone stages, (ii) aligns attention-refined features across sites using weak domain label…

CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Prediction
Zhiwei Ning, Zhaojiang Liu, Xuanang Gao, Yifan Zuo, Jie Yang, Yuming Fang, Wei Liu
https://arxiv.org/abs/2508.12917

CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Prediction
Multi-modal methods based on camera and LiDAR sensors have garnered significant attention in the field of 3D detection. However, many prevalent works focus on single or partial stage fusion, leading to insufficient feature extraction and suboptimal performance. In this paper, we introduce a multi-stage cross-modal fusion 3D detection framework, termed CMF-IOU, to effectively address the challenge of aligning 3D spatial and 2D semantic information. Specifically, we first project the pixel inform…

JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation
Siheng Wan, Zhengtao Yao, Zhengdao Li, Junhao Dong, Yanshu Li, Yikai Li, Linshan Li, Haoyan Xu, Yijiang Li, Zhikang Dong, Huacan Wang, Jifeng Shen
https://arxiv.org/abs/2510.00974

JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation
Modern Text-to-Image (T2I) generation increasingly relies on token-centric architectures that are trained with self-supervision, yet effectively fusing text with visual tokens remains a challenge. We propose \textbf{JEPA-T}, a unified multimodal framework that encodes images and captions into discrete visual and textual tokens, processed by a joint-embedding predictive Transformer. To enhance fusion, we incorporate cross-attention after the feature predictor for conditional denoising while main…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature P...
Gonz\'alez, Longuefosse, Benito, Mart\'in, Baldacci

CATVis: Context-Aware Thought Visualization
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
https://arxiv.org/abs/2507.11522 https://

CATVis: Context-Aware Thought Visualization
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) captio…