Tootfinder

The fact that I instinctively searched for Pano Scrobbler without even thinking proves my brain is hardwired to scrobble everything I play.
They’ve got builds for Android.apk, Linux.AppImage, and Windows.exe.
https://github.com/kawaiiDango/pano-scrobbler

SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning
Chih-Chuan Cheng, Yi-Ju Tseng
https://arxiv.org/abs/2510.12659

SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning
We propose SG-XDEAT (Sparsity-Guided Cross Dimensional and Cross-Encoding Attention with Target Aware Conditioning), a novel framework designed for supervised learning on tabular data. At its core, SG-XDEAT employs a dual-stream encoder that decomposes each input feature into two parallel representations: a raw value stream and a target-conditioned (label-aware) stream. These dual representations are then propagated through a hierarchical stack of attention-based modules. SG-XDEAT integrates th…

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 …

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…

M3ST-DTI: A multi-task learning model for drug-target interactions based on multi-modal features and multi-stage alignment
Xiangyu Li, Ran Su, Liangliang Liu
https://arxiv.org/abs/2510.12445

M3ST-DTI: A multi-task learning model for drug-target interactions based on multi-modal features and multi-stage alignment
Accurate prediction of drug-target interactions (DTI) is pivotal in drug discovery. However, existing approaches often fail to capture deep intra-modal feature interactions or achieve effective cross-modal alignment, limiting predictive performance and generalization. To address these challenges, we propose M3ST-DTI, a multi-task learning model that enables multi-stage integration and alignment of multi modal features for DTI prediction. M3ST-DTI incorporates three types of features-textual, st…

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 …

HoMer: Addressing Heterogeneities by Modeling Sequential and Set-wise Contexts for CTR Prediction
Shuwei Chen, Jiajun Cui, Zhengqi Xu, Fan Zhang, Jiangke Fan, Teng Zhang, Xingxing Wang
https://arxiv.org/abs/2510.11100

HoMer: Addressing Heterogeneities by Modeling Sequential and Set-wise Contexts for CTR Prediction
Click-through rate (CTR) prediction, which models behavior sequence and non-sequential features (e.g., user/item profiles or cross features) to infer user interest, underpins industrial recommender systems. However, most methods face three forms of heterogeneity that degrade predictive performance: (i) Feature Heterogeneity persists when limited sequence side features provide less granular interest representation compared to extensive non-sequential features, thereby impairing sequence modeling…

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Vasudha Yanuganti, Ishaan Puri, Swapnil Chhatre, Mantinder Singh, Ashok Jallepalli, Hritvik Shrivastava, Pradeep Kumar Sharma
https://arxiv.org/abs/2510.08850

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Modern codebases make it hard for developers and AI coding assistants to find the right source files when answering questions like "How does this feature work?" or "Where was the bug introduced?" Traditional code search (keyword or IR based) often misses semantic context and cross file links, while large language models (LLMs) understand natural language but lack repository specific detail. We present a method for file path retrieval that fine tunes a strong LLM (Qwen3-8B) with QLoRA and Unslot…

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…

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…

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…

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…

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…

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…

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…

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…

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, …

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…

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…

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

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