Tootfinder

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, Praveen Rao
https://arxiv.org/abs/2506.19268

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and privacy concerns. Creating HARPT required addressing multiple complexities, such as defining a nuanced label schema, isolating relevant content from large volumes of noisy data, and designing an annotati…

eu_procurements: EU procurement contract networks (2008-2016)
A bipartite network of public EU procurement contracts, from 2008 to 2016, between issuing buyers (public institutions such as a ministry or city hall) and supplying winners (a private firm). Contracts are aggregated into annual snapshots, edges are annotated with contract value information. Nodes are annotated with location information, including country of origin.
This network has 839824 nodes and 4098711 edges.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
Joris Wuts, Jakub Ceranka, Nicolas Michoux, Fr\'ed\'eric Lecouvet, Jef Vandemeulebroucke
https://arxiv.org/abs/2506.19590

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
The segmentation of metastatic bone disease (MBD) in whole-body MRI (WB-MRI) is a challenging problem. Due to varying appearances and anatomical locations of lesions, ambiguous boundaries, and severe class imbalance, obtaining reliable segmentations requires large, well-annotated datasets capturing lesion variability. Generating such datasets requires substantial time and expertise, and is prone to error. While self-supervised learning (SSL) can leverage large unlabeled datasets, learned generi…

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Zhou Chen, Sanjoy Kundu, Harsimran S. Baweja, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2506.17516

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Active event perception, the ability to dynamically detect, track, and summarize events in real time, is essential for embodied intelligence in tasks such as human-AI collaboration, assistive robotics, and autonomous navigation. However, existing approaches often depend on predefined action spaces, annotated datasets, and extrinsic rewards, limiting their adaptability and scalability in dynamic, real-world scenarios. Inspired by cognitive theories of event perception and predictive coding, we p…

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Ahmad Mustafa, Reza Rastegar, Ghassan AlRegib
https://arxiv.org/abs/2506.19687 …

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Prostate gland segmentation from T2-weighted MRI is a critical yet challenging task in clinical prostate cancer assessment. While deep learning-based methods have significantly advanced automated segmentation, most conventional approaches-particularly 2D convolutional neural networks (CNNs)-fail to leverage inter-slice anatomical continuity, limiting their accuracy and robustness. Fully 3D models offer improved spatial coherence but require large amounts of annotated data, which is often imprac…

Unfolding the Past: A Comprehensive Deep Learning Approach to Analyzing Incunabula Pages
Klaudia Ropel, Krzysztof Kutt, Luiz do Valle Miranda, Grzegorz J. Nalepa
https://arxiv.org/abs/2506.18069

Unfolding the Past: A Comprehensive Deep Learning Approach to Analyzing Incunabula Pages
We developed a proof-of-concept method for the automatic analysis of the structure and content of incunabula pages. A custom dataset comprising 500 annotated pages from five different incunabula was created using resources from the Jagiellonian Digital Library. Each page was manually labeled with five predefined classes: Text, Title, Picture, Table, and Handwriting. Additionally, the publicly available DocLayNet dataset was utilized as supplementary training data. To perform object detection, Y…

us_agencies: U.S. government agency websites (2018)
50 networks, one for each U.S. state, representing the web-based links between their associated government agencies websites. A node is an entire agency website and a directed edge (i,j) represents the existence of a hyperlink from any webpage in website i to some webpage in website j. Data was collected with a crawler. Nodes are annotated with the number of webpages per website, website name (related to its government function) and U…

SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification
Chengye Wang, Yifei Shen, Zexi Kuang, Arman Cohan, Yilun Zhao
https://arxiv.org/abs/2506.15569

SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification
We introduce SciVer, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context. SciVer consists of 3,000 expert-annotated examples over 1,113 scientific papers, covering four subsets, each representing a common reasoning type in multimodal scientific claim verification. To enable fine-grained evaluation, each example includes expert-annotated supporting evidence. We assess the performance of 21 state-of-the-art…

Revela: Dense Retriever Learning via Language Modeling
Fengyu Cai, Tong Chen, Xinran Zhao, Sihao Chen, Hongming Zhang, Sherry Tongshuang Wu, Iryna Gurevych, Heinz Koeppl
https://arxiv.org/abs/2506.16552

Revela: Dense Retriever Learning via Language Modeling
Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-document pairs, which are costly and hard to obtain in specialized domains such as code-motivating growing interest in self-supervised retriever learning. Since LMs are trained to capture token-level dependencies through a self-supervised learning objective (i.e., next-token prediction), we can analogously cast retrieva…

An Annotated Translation of D. Bernoulli's "A New Theory on the Motion of Waters Through Channels of Any Kind"
Sylvio R. Bistafa
https://arxiv.org/abs/2506.14905

An Annotated Translation of D. Bernoulli's "A New Theory on the Motion of Waters Through Channels of Any Kind"
This paper presents an annotated English translation of Daniel Bernoulli's 1727 work A New Theory on the Motion of Waters through Channels of Any Kind, originally published in the Commentarii Academiae Scientiarum Imperialis Petropolitanae. Written over a decade before his renowned Hydrodynamica (1738), this early treatise reveals D. Bernoulli's foundational approach to fluid motion based on the conservation of vis viva-the kinetic energy of moving bodies-at a time when the principle was still …

H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing
Akanksha Singh, Yi-Ping Phoebe Chen, Vipul Arora
https://arxiv.org/abs/2506.16751

H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing
Query-by-example spoken term detection (QbE-STD) searches for matching words or phrases in an audio dataset using a sample spoken query. When annotated data is limited or unavailable, QbE-STD is often done using template matching methods like dynamic time warping (DTW), which are computationally expensive and do not scale well. To address this, we propose H-QuEST (Hierarchical Query-by-Example Spoken Term Detection), a novel framework that accelerates spoken term retrieval by utilizing Term Fre…

Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun
https://arxiv.org/abs/2506.14146 https://arxiv.org/pdf/2506.…

Collaborative Editable Model
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues c…

VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based Mosquito Breeding Site Detection and Reasoning
Md. Adnanul Islam, Md. Faiyaz Abdullah Sayeedi, Md. Asaduzzaman Shuvo, Muhammad Ziaur Rahman, Shahanur Rahman Bappy, Raiyan Rahman, Swakkhar Shatabda
https://arxiv.org/abs/2506.14629

VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based Mosquito Breeding Site Detection and Reasoning
Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreaks. In this paper, we present VisText-Mosquito, a multimodal dataset that integrates visual and textual data to support automated detection, segmentation, and reasoning for mosquito breeding site analysis. The dataset includes 1,828 annotated images for object detection, 142 images for water surface segmentation, and natural language reasoning texts linked…

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Amirreza Fateh, Yasin Rezvani, Sara Moayedi, Sadjad Rezvani, Fatemeh Fateh, Mansoor Fateh
https://arxiv.org/abs/2506.14318

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation and classification tasks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by certified radiologists and physicians, spanning three major tumor types-g…

Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
Linlin Wang, Wei Wang, Dezhao Wang, Shanwen Wang
https://arxiv.org/abs/2506.15125 http…

Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
With the applicability of optical fiber-based distributed acoustic sensing (DAS) systems, effective signal processing and analysis approaches are needed to promote its popularization in the field of intelligent transportation systems (ITS). This paper presents a signal denoising algorithm using a hybrid deep-learning network (HDLNet). Without annotated data and time-consuming labeling, this self-supervised network runs in parallel, combining an autoencoder for denoising (DAE) and a long short-t…

nematode_mammal: Global nematode–mammal interactions (2018)
A global interaction web of interactions between nematodes and their host mammal species, extracted from the helminthR package and dataset. Nodes are annotated with species-level information.
This network has 30516 nodes and 146683 edges.
Tags: Biological, Food web, Unweighted, Metadata

BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization
Alejandro Golfe, Natalia P. Garc\'ia-de-la-puente, Adri\'an Colomer, Valery Naranjo
https://arxiv.org/abs/2506.13843

BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization
Accurately identifying oocytes that progress to the blastocyst stage is crucial in reproductive medicine, but the limited availability of annotated high-quality embryo images presents challenges for developing automated diagnostic tools. To address this, we propose BlastDiffusion, a generative model based on Latent Diffusion Models (LDMs) that synthesizes realistic oocyte images conditioned on developmental outcomes. Our approach utilizes a pretrained Variational Autoencoder (VAE) for latent sp…

Homologous nodes in annotated complex networks
Sung Soo Moon, Sebastian E. Ahnert
https://arxiv.org/abs/2505.23668 https://arxiv.org/…

Homologous nodes in annotated complex networks
Many real-world networks have associated metadata that assigns categorical labels to nodes. Analysis of these annotations can complement the topological analysis of complex networks. Annotated networks have typically been used to evaluate community detection approaches. Here, we introduce an approach that combines the quantitative analysis of annotations and network structure, which groups nodes according to similar distributions of node annotations in their neighbourhoods. Importantly the node…

This https://arxiv.org/abs/2506.05673 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery
The development of modern Artificial Intelligence (AI) models, particularly diffusion-based models employed in computer vision and image generation tasks, is undergoing a paradigmatic shift in development methodologies. Traditionally dominated by a "Model Centric" approach, in which performance gains were primarily pursued through increasingly complex model architectures and hyperparameter optimization, the field is now recognizing a more nuanced "Data-Centric" approach. This emergent framework…

When @… goes on a dunk-fest like this,
somebody should provide Talmudic annotated marginalia explaining each joke (I only understand about half of them.)
This commentary would provide a complete reference guide to the socio=techno-political history of the Fediverse

nematode_mammal: Global nematode–mammal interactions (2018)
A global interaction web of interactions between nematodes and their host mammal species, extracted from the helminthR package and dataset. Nodes are annotated with species-level information.
This network has 30516 nodes and 146683 edges.
Tags: Biological, Food web, Unweighted, Metadata

The MAHA Report's AI fingerprints, annotated (Washington Post)
https://www.washingtonpost.com/health/2025/05/30/maha-report-ai-white-house/
http://www.memeorandum.com/250531/p32#a250531p32

ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour
Jack Contro, Simrat Deol, Yulan He, Martim Brand\~ao
https://arxiv.org/abs/2506.12090

ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour
This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful. We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation. Each conversation is annotated b…

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata

Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new
[1/1]:
Chronicling Germany: An Annotated Historical Newspaper Dataset
https://

OxO2 -- A SSSOM mapping browser for logically sound crosswalks
Henriette Harmse, Haider Iqbal, Helen Parkinson, James McLaughlin
https://arxiv.org/abs/2506.04286

OxO2 -- A SSSOM mapping browser for logically sound crosswalks
EMBL-EBI created OxO to enable users to map between datasets that are annotated with different ontologies. Mappings identified by the first version of OxO were not necessarily logically sound, missed important provenance information such as author and reviewer, and could timeout or crash for certain requests. In this paper we introduce OxO2 to address these concerns. Provenance is addressed by implementing SSSOM, a mapping standard that defines provenance for mappings. SSSOM defines the conditi…

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata

Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Emiliano Acevedo, Mart\'in Rocamora, Magdalena Fuentes
https://arxiv.org/abs/2506.04376

Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Audio-text models are widely used in zero-shot environmental sound classification as they alleviate the need for annotated data. However, we show that their performance severely drops in the presence of background sound sources. Our analysis reveals that this degradation is primarily driven by SNR levels of background soundscapes, and independent of background type. To address this, we propose a novel method that quantifies and integrates the contribution of background sources into the classifi…

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
Abdul Haque, Umm e Hani, Ahmad Din, Muhammad Babar, Ali Abbas, Insaf Ullah
https://arxiv.org/abs/2506.11600

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
GraphRAG-Causal introduces an innovative framework that combines graph-based retrieval with large language models to enhance causal reasoning in news analysis. Traditional NLP approaches often struggle with identifying complex, implicit causal links, especially in low-data scenarios. Our approach addresses these challenges by transforming annotated news headlines into structured causal knowledge graphs. It then employs a hybrid retrieval system that merges semantic embeddings with graph-based s…

Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises
James C. Kosmopoulos, Karthik Anantharaman
https://arxiv.org/abs/2506.11942

Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises
Viruses are the most abundant biological entities on Earth and play central roles in shaping microbiomes and influencing ecosystem functions. Yet, most viral genes remain uncharacterized, comprising what is commonly referred to as "viral dark matter." Metagenomic studies across diverse environments consistently show that 40-90% of viral genes lack known homologs or annotated functions. This persistent knowledge gap limits our ability to interpret viral sequence data, understand virus-host inter…

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
Fuaing Niu, Zini Chen, Zhiyu Xie, Genan Dai, Bowen Zhang
https://arxiv.org/abs/2506.13343

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduce TwiUSD, the first large-scale, manually annotated UserSD benchmark with explicit followee relationships, containing 16,211 users and 47,757 tweets. TwiUSD enables rigorous evaluation of stance models by integrating tweet content and social links, with superior scale and annotation quality. Building on this resource,…

This https://arxiv.org/abs/2506.05008 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Structure-Aware Radar-Camera Depth Estimation
Monocular depth estimation aims to determine the depth of each pixel from an RGB image captured by a monocular camera. The development of deep learning has significantly advanced this field by facilitating the learning of depth features from some well-annotated datasets \cite{Geiger_Lenz_Stiller_Urtasun_2013,silberman2012indoor}. Eigen \textit{et al.} \cite{eigen2014depth} first introduce a multi-scale fusion network for depth regression. Following this, subsequent improvements have come from r…

This https://arxiv.org/abs/2504.12684 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials
We present SOPHY, a generative model for 3D physics-aware shape synthesis. Unlike existing 3D generative models that focus solely on static geometry or 4D models that produce physics-agnostic animations, our method jointly synthesizes shape, texture, and material properties related to physics-grounded dynamics, making the generated objects ready for simulations and interactive, dynamic environments. To train our model, we introduce a dataset of 3D objects annotated with detailed physical materi…

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Zixuan Jiang, Renjing Xu
https://arxiv.org/abs/2506.07035 https://

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Deciphering protein function remains a fundamental challenge in protein representation learning. The task presents significant difficulties for protein language models (PLMs) due to the sheer volume of functional annotation categories and the highly imbalanced distribution of annotated instances across biological ontologies. Inspired by the remarkable success of reinforcement learning from human feedback (RLHF) in large language model (LLM) alignment, we propose AnnoDPO, a novel multi-modal fra…

AIMSCheck: Leveraging LLMs for AI-Assisted Review of Modern Slavery Statements Across Jurisdictions
Adriana Eufrosina Bora, Akshatha Arodi, Duoyi Zhang, Jordan Bannister, Mirko Bronzi, Arsene Fansi Tchango, Md Abul Bashar, Richi Nayak, Kerrie Mengersen
https://arxiv.org/abs/2506.01671

AIMSCheck: Leveraging LLMs for AI-Assisted Review of Modern Slavery Statements Across Jurisdictions
Modern Slavery Acts mandate that corporations disclose their efforts to combat modern slavery, aiming to enhance transparency and strengthen practices for its eradication. However, verifying these statements remains challenging due to their complex, diversified language and the sheer number of statements that must be reviewed. The development of NLP tools to assist in this task is also difficult due to a scarcity of annotated data. Furthermore, as modern slavery transparency legislation has bee…

EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
Wang Xinjie, Liu Liu, Cao Yu, Wu Ruiqi, Qin Wenkang, Wang Dehui, Sui Wei, Su Zhizhong
https://arxiv.org/abs/2506.10600

EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
Constructing a physically realistic and accurately scaled simulated 3D world is crucial for the training and evaluation of embodied intelligence tasks. The diversity, realism, low cost accessibility and affordability of 3D data assets are critical for achieving generalization and scalability in embodied AI. However, most current embodied intelligence tasks still rely heavily on traditional 3D computer graphics assets manually created and annotated, which suffer from high production costs and li…

Model-Driven Graph Contrastive Learning
Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
https://arxiv.org/abs/2506.06212 https://…

Model-Driven Graph Contrastive Learning
We propose $\textbf{MGCL}$, a model-driven graph contrastive learning (GCL) framework that leverages graphons (probabilistic generative models for graphs) to guide contrastive learning by accounting for the data's underlying generative process. GCL has emerged as a powerful self-supervised framework for learning expressive node or graph representations without relying on annotated labels, which are often scarce in real-world data. By contrasting augmented views of graph data, GCL has demonstrat…

nematode_mammal: Global nematode–mammal interactions (2018)
A global interaction web of interactions between nematodes and their host mammal species, extracted from the helminthR package and dataset. Nodes are annotated with species-level information.
This network has 30516 nodes and 146683 edges.
Tags: Biological, Food web, Unweighted, Metadata

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Shanawaj S Madarkar, Mahajabeen Madarkar, Madhumitha V, Teli Prakash, Konda Reddy Mopuri, Vinaykumar MV, KVL Sathwika, Adarsh Kasturi, Gandla Dilip Raj, PVN Supranitha, Harsh Udai
https://arxiv.org/abs/2506.06099…

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively cu…

eu_procurements: EU procurement contract networks (2008-2016)
A bipartite network of public EU procurement contracts, from 2008 to 2016, between issuing buyers (public institutions such as a ministry or city hall) and supplying winners (a private firm). Contracts are aggregated into annual snapshots, edges are annotated with contract value information. Nodes are annotated with location information, including country of origin.
This network has 839824 nodes and 4098711 edges.

This https://arxiv.org/abs/2506.05675 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models
Event Causality Identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely on supervised methodologies, suffering from dependence on large-scale annotated data. Although Large Language Models (LLMs) enable zero-shot ECI, they are prone to causal hallucination-erroneously establishing spurious causal links. To address these challenges, we propose MEFA, a novel zero-shot framework based on Multi-source Evidence Fuzzy Aggrega…

Improving Prostate Gland Segmenting Using Transformer based Architectures
Shatha Abudalou
https://arxiv.org/abs/2506.14844 https://ar…

Improving Prostate Gland Segmenting Using Transformer based Architectures
Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transformer models can retain precision amid such heterogeneity. We compare the performance of UNETR and SwinUNETR in prostate gland segmentation against our previous 3D UNet model [1], based on 546 MRI (T2weighted) volumes annotated by two independent experts. Three training strategies were analyzed: single cohort dataset, 5…

GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
Sahiti Yerramilli, Nilay Pande, Rynaa Grover, Jayant Sravan Tamarapalli
https://arxiv.org/abs/2506.00785

GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
This paper introduces GeoChain, a large-scale benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs). Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs). These sequences guide models from coarse attributes to fine-grained localization across four reasoning categories - visual, spatial, cultural, and precise geolocation - annotated by d…

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
Fatemeh Mohammadi Amin, Darwin G. Caldwell, Hans Wernher van de Venn
https://arxiv.org/abs/2506.09552

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
The robust interpretation of 3D environments is crucial for human-robot collaboration (HRC) applications, where safety and operational efficiency are paramount. Semantic segmentation plays a key role in this context by enabling a precise and detailed understanding of the environment. Considering the intense data hunger for real-world industrial annotated data essential for effective semantic segmentation, this paper introduces a pioneering approach in the Sim2Real domain adaptation for semantic…

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Niran Nataraj, Maina Sogabe, Kenji Kawashima
https://arxiv.org/abs/2506.14303

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotations. Bleeding detection and localization during surgery is especially challenging due to the scarcity of high-quality datasets that reflect real surgical scenarios. We propose orGAN, a GAN-based system for generating high-fidelity, annotated surgical images of bleeding. By leveraging small "mimicking organ" datasets, synthetic models that replicate t…

Fine-Tuning ASR for Stuttered Speech: Personalized vs. Generalized Approaches
Dena Mujtaba, Nihar Mahapatra
https://arxiv.org/abs/2506.00853 https://

Fine-Tuning ASR for Stuttered Speech: Personalized vs. Generalized Approaches
Stuttering -- characterized by involuntary disfluencies such as blocks, prolongations, and repetitions -- is often misinterpreted by automatic speech recognition (ASR) systems, resulting in elevated word error rates and making voice-driven technologies inaccessible to people who stutter. The variability of disfluencies across speakers and contexts further complicates ASR training, compounded by limited annotated stuttered speech data. In this paper, we investigate fine-tuning ASRs for stuttered…

This https://arxiv.org/abs/2505.21652 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation
Fine-grained robot manipulation, such as lifting and rotating a bottle to display the label on the cap, requires robust reasoning about object parts and their relationships with intended tasks. Despite recent advances in training general-purpose robot manipulation policies guided by language instructions, there is a notable lack of large-scale datasets for fine-grained manipulation tasks with part-level instructions and diverse 3D object instances annotated with part-level labels. In this work,…

This https://arxiv.org/abs/2410.00527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Wanna hear your voice? A sample is all we need!
Research on audio clue-based target speaker extraction (TSE) has focused on modeling mixtures and reference speech, achieving strong results in English due to abundant datasets. However, cross-lingual properties remain underexplored, as low-resource languages face challenges from limited annotated data and linguistic resources. To bridge this gap, we propose WHYV (Wanna Hear Your Voice), a cross-lingual TSE framework enabling zero-shot adaptation without fine-tuning. WHYV employs a frequency-mo…

fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata

PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models
Pawan Neupane, Jian Liu, Jianlin Cheng
https://arxiv.org/abs/2505.22674

PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models
Predicting protein complex structures is essential for protein function analysis, protein design, and drug discovery. While AI methods like AlphaFold can predict accurate structural models for many protein complexes, reliably estimating the quality of these predicted models (estimation of model accuracy, or EMA) for model ranking and selection remains a major challenge. A key barrier to developing effective machine learning-based EMA methods is the lack of large, diverse, and well-annotated dat…

fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata

This https://arxiv.org/abs/2505.20956 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection
Bioacoustic sound event detection (BioSED) is crucial for biodiversity conservation but faces practical challenges during model development and training: limited amounts of annotated data, sparse events, species diversity, and class imbalance. To address these challenges efficiently with a limited labeling budget, we apply the mismatch-first farthest-traversal (MFFT), an active learning method integrating committee voting disagreement and diversity analysis. We also refine an existing BioSED da…

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Cheng Wang, Siqi Chen, Donghua Mi, Yang Chen, Yudong Zhang, Yinsheng Li
https://arxiv.org/abs/2506.10325

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Recent advances in medical imaging have established deep learning-based segmentation as the predominant approach, though it typically requires large amounts of manually annotated data. However, obtaining annotations for intracranial hemorrhage (ICH) remains particularly challenging due to the tedious and costly labeling process. Semi-supervised learning (SSL) has emerged as a promising solution to address the scarcity of labeled data, especially in volumetric medical image segmentation. Unlike …

This https://arxiv.org/abs/2406.02149 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Von Neumann's 1927 Trilogy on the Foundations of Quantum Mechanics. Annotated Translations
English translation of John von Neumann's 1927 trilogy on the foundations of quantum mechanics with an introduction and detailed commentary.

blumenau_drug: Blumenau drug interactions (2019)
A network of drug-drug interactions, extracted from 18 months of electronic health records (EHRs) from the city of Blumenau in Southern Brazil. Nodes are annotated with drug information (separate file), and edges are weighted by the severity of the interaction, along with other information.
This network has 75 nodes and 181 edges.
Tags: Biological, Drug interactions, Weighted, Metadata

Foundation Models in Medical Imaging -- A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu, Melis Erdal Cesur, Kevin Groot Lipman, Edwin D. de Jong, Hugo Horlings, Cl\'arisa Sanchez, Cees Snoek, Ritse Mann, Eric Marcus, Jonas Teuwen
https://arxiv.org/abs/2506.09095…

Foundation Models in Medical Imaging -- A Review and Outlook
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the co…

blumenau_drug: Blumenau drug interactions (2019)
A network of drug-drug interactions, extracted from 18 months of electronic health records (EHRs) from the city of Blumenau in Southern Brazil. Nodes are annotated with drug information (separate file), and edges are weighted by the severity of the interaction, along with other information.
This network has 75 nodes and 181 edges.
Tags: Biological, Drug interactions, Weighted, Metadata

Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Xiaofeng Pan, Jing Chen, Haitong Zhang, Menglin Xing, Jiayi Wei, Xuefeng Mu, Zhongqian Xie
https://arxiv.org/abs/2505.23298

Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Recent works of music representation learning mainly focus on learning acoustic music representations with unlabeled audios or further attempt to acquire multi-modal music representations with scarce annotated audio-text pairs. They either ignore the language semantics or rely on labeled audio datasets that are difficult and expensive to create. Moreover, merely modeling semantic space usually fails to achieve satisfactory performance on music recommendation tasks since the user preference spac…

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
Tyler J. Richards, Adam E. Flanders, Errol Colak, Luciano M. Prevedello, Robyn L. Ball, Felipe Kitamura, John Mongan, Maryam Vazirabad, Hui-Ming Lin, Anne Kendell, Thanat Kanthawang, Salita Angkurawaranon, Emre Altinmakas, Hakan Dogan, Paulo Eduardo de Aguiar Kuriki, Arjuna Somasundaram, Christopher Ruston, Deniz Bulja, Naida Spahovic, Jennifer Sommer, Sirui Jiang, Eduardo Moreno Judice de Mattos Farina, Edu…

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar spine examinations annotated for degenerative changes. The dataset includes 2,697 patients with a total of 8,593 image series from 8 institutions across 6 countries and 5 continents. The dataset is available for free for non-commercial use via Kaggle and RSNA Medical Imaging Resource of AI (MIRA). The dataset was cre…

us_agencies: U.S. government agency websites (2018)
50 networks, one for each U.S. state, representing the web-based links between their associated government agencies websites. A node is an entire agency website and a directed edge (i,j) represents the existence of a hyperlink from any webpage in website i to some webpage in website j. Data was collected with a crawler. Nodes are annotated with the number of webpages per website, website name (related to its government function) and U…

Towards Temporally Explainable Dysarthric Speech Clarity Assessment
Seohyun Park, Chitralekha Gupta, Michelle Kah Yian Kwan, Xinhui Fung, Alexander Wenjun Yip, Suranga Nanayakkara
https://arxiv.org/abs/2506.00454

Towards Temporally Explainable Dysarthric Speech Clarity Assessment
Dysarthria, a motor speech disorder, affects intelligibility and requires targeted interventions for effective communication. In this work, we investigate automated mispronunciation feedback by collecting a dysarthric speech dataset from six speakers reading two passages, annotated by a speech therapist with temporal markers and mispronunciation descriptions. We design a three-stage framework for explainable mispronunciation evaluation: (1) overall clarity scoring, (2) mispronunciation localiza…

us_agencies: U.S. government agency websites (2018)
50 networks, one for each U.S. state, representing the web-based links between their associated government agencies websites. A node is an entire agency website and a directed edge (i,j) represents the existence of a hyperlink from any webpage in website i to some webpage in website j. Data was collected with a crawler. Nodes are annotated with the number of webpages per website, website name (related to its government function) and U…

Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
Lianhao Yin, Ozanan Meireles, Guy Rosman, Daniela Rus
https://arxiv.org/abs/2506.01980

Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
Real-time video understanding is critical to guide procedures in minimally invasive surgery (MIS). However, supervised learning approaches require large, annotated datasets that are scarce due to annotation efforts that are prohibitive, e.g., in medical fields. Although self-supervision methods can address such limitations, current self-supervised methods often fail to capture structural and physical information in a form that generalizes across tasks. We propose Compress-to-Explore (C2E), a no…

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Shengyuan Liu, Wenting Chen, Boyun Zheng, Wentao Pan, Xiang Li, Yixuan Yuan
https://arxiv.org/abs/2505.24687

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diversity by using predefined masks or employ computationally expensive two-stage processes with multiple denoising steps, causing computational inefficiency. Additionally, these methods typically rely on binary masks that fail to capture the gradual transitions characteristic of tumor boundaries. We present TumorGen, a novel Boundary-Aware Tumor-Mask Sy…

This https://arxiv.org/abs/2503.00741 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

LesionDiffusion: Towards Text-controlled General Lesion Synthesis
Fully-supervised lesion recognition methods in medical imaging face challenges due to the reliance on large annotated datasets, which are expensive and difficult to collect. To address this, synthetic lesion generation has become a promising approach. However, existing models struggle with scalability, fine-grained control over lesion attributes, and the generation of complex structures. We propose LesionDiffusion, a text-controllable lesion synthesis framework for 3D CT imaging that generates …