Tootfinder

from my link log —
The Story of Mel, annotated.
https://users.cs.utah.edu/~elb/folklore/mel-annotated/mel-annotated.html
saved 2025-06-01

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

ELASTIC: Event-Tracking Data Synchronization in Soccer Without Annotated Event Locations
Hyunsung Kim, Hoyoung Choi, Sangwoo Seo, Tom Boomstra, Jinsung Yoon, Chanyoung Park
https://arxiv.org/abs/2508.09238

ELASTIC: Event-Tracking Data Synchronization in Soccer Without Annotated Event Locations
The integration of event and tracking data has become essential for advanced analysis in soccer. However, synchronizing these two modalities remains a significant challenge due to temporal and spatial inaccuracies in manually recorded event timestamps. Existing synchronizers typically rely on annotated event locations, which themselves are prone to spatial errors and thus can distort synchronization results. To address this issue, we propose ELASTIC (Event-Location-AgnoSTIC synchronizer), a syn…

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Tian-Yu Xiang, Rui-Ze Ma, Nu-Fang Xiao, Zeng-Guang Hou
https://arxiv.org/abs/2508.10794

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
Accurate vessel segmentation in X-ray angiograms is crucial for numerous clinical applications. However, the scarcity of annotated data presents a significant challenge, which has driven the adoption of self-supervised learning (SSL) methods such as masked image modeling (MIM) to leverage large-scale unlabeled data for learning transferable representations. Unfortunately, conventional MIM often fails to capture vascular anatomy because of the severe class imbalance between vessel and background…

Large Language Models for Summarizing Czech Historical Documents and Beyond
V\'aclav Tran, Jakub \v{S}m\'id, Ji\v{r}\'i Mart\'inek, Ladislav Lenc, Pavel Kr\'al
https://arxiv.org/abs/2508.10368

Large Language Models for Summarizing Czech Historical Documents and Beyond
Text summarization is the task of shortening a larger body of text into a concise version while retaining its essential meaning and key information. While summarization has been significantly explored in English and other high-resource languages, Czech text summarization, particularly for historical documents, remains underexplored due to linguistic complexities and a scarcity of annotated datasets. Large language models such as Mistral and mT5 have demonstrated excellent results on many natura…

Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
Andrea Gargano, Jasin Machkour, Mimma Nardelli, Enzo Pasquale Scilingo, Michael Muma
https://arxiv.org/abs/2508.10561

Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
In Affective Computing, a key challenge lies in reliably linking subjective emotional experiences with objective physiological markers. This preliminary study addresses the issue of reproducibility by identifying physiological features from cardiovascular and electrodermal signals that are associated with continuous self-reports of arousal levels. Using the Continuously Annotated Signal of Emotion dataset, we analyzed 164 features extracted from cardiac and electrodermal signals of 30 participa…

Criteria-Based LLM Relevance Judgments
Naghmeh Farzi, Laura Dietz
https://arxiv.org/abs/2507.09488 https://arxiv.org/pdf/2507.09488…

Criteria-Based LLM Relevance Judgments
Relevance judgments are crucial for evaluating information retrieval systems, but traditional human-annotated labels are time-consuming and expensive. As a result, many researchers turn to automatic alternatives to accelerate method development. Among these, Large Language Models (LLMs) provide a scalable solution by generating relevance labels directly through prompting. However, prompting an LLM for a relevance label without constraints often results in not only incorrect predictions but also…

T-CACE: A Time-Conditioned Autoregressive Contrast Enhancement Multi-Task Framework for Contrast-Free Liver MRI Synthesis, Segmentation, and Diagnosis
Xiaojiao Xiao, Jianfeng Zhao, Qinmin Vivian Hu, Guanghui Wang
https://arxiv.org/abs/2508.09919

T-CACE: A Time-Conditioned Autoregressive Contrast Enhancement Multi-Task Framework for Contrast-Free Liver MRI Synthesis, Segmentation, and Diagnosis
Magnetic resonance imaging (MRI) is a leading modality for the diagnosis of liver cancer, significantly improving the classification of the lesion and patient outcomes. However, traditional MRI faces challenges including risks from contrast agent (CA) administration, time-consuming manual assessment, and limited annotated datasets. To address these limitations, we propose a Time-Conditioned Autoregressive Contrast Enhancement (T-CACE) framework for synthesizing multi-phase contrast-enhanced MRI…

HingeNet: A Harmonic-Aware Fine-Tuning Approach for Beat Tracking
Ganghui Ru, Jieying Wang, Jiahao Zhao, Yulun Wu, Yi Yu, Nannan Jiang, Wei Wang, Wei Li
https://arxiv.org/abs/2508.09788

HingeNet: A Harmonic-Aware Fine-Tuning Approach for Beat Tracking
Fine-tuning pre-trained foundation models has made significant progress in music information retrieval. However, applying these models to beat tracking tasks remains unexplored as the limited annotated data renders conventional fine-tuning methods ineffective. To address this challenge, we propose HingeNet, a novel and general parameter-efficient fine-tuning method specifically designed for beat tracking tasks. HingeNet is a lightweight and separable network, visually resembling a hinge, design…

SimAQ: Mitigating Experimental Artifacts in Soft X-Ray Tomography using Simulated Acquisitions
Jacob Egebjerg, Daniel W\"ustner
https://arxiv.org/abs/2508.10821 https://

SimAQ: Mitigating Experimental Artifacts in Soft X-Ray Tomography using Simulated Acquisitions
Soft X-ray tomography (SXT) provides detailed structural insight into whole cells but is hindered by experimental artifacts such as the missing wedge and by limited availability of annotated datasets. We present \method, a simulation pipeline that generates realistic cellular phantoms and applies synthetic artifacts to produce paired noisy volumes, sinograms, and reconstructions. We validate our approach by training a neural network primarily on synthetic data and demonstrate effective few-shot…

Algorithmic Delegated Choice: An Annotated Reading List
Mohammad T. Hajiaghayi, Suho Shin
https://arxiv.org/abs/2508.06562 https://arxiv.org/pdf/2508.06562…

Algorithmic Delegated Choice: An Annotated Reading List
The problem of delegated choice has been of long interest in economics and recently on computer science. We overview a list of papers on delegated choice problem, from classic works to recent papers with algorithmic perspectives.

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

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…

Description and Comparative Analysis of QuRE: A New Industrial Requirements Quality Dataset
Henning Femmer, Frank Houdek, Max Unterbusch, Andreas Vogelsang
https://arxiv.org/abs/2508.08868

Description and Comparative Analysis of QuRE: A New Industrial Requirements Quality Dataset
Requirements quality is central to successful software and systems engineering. Empirical research on quality defects in natural language requirements relies heavily on datasets, ideally as realistic and representative as possible. However, such datasets are often inaccessible, small, or lack sufficient detail. This paper introduces QuRE (Quality in Requirements), a new dataset comprising 2,111 industrial requirements that have been annotated through a real-world review process. Previously used…

SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition
Song Cheng, Qiannan Cheng, Linbo Jin, Lei Yi, Guannan Zhang
https://arxiv.org/abs/2507.10629

SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition
Transforming natural language into SQL queries (NL2SQL) is crucial for data-driven business applications. Existing frameworks, trained on open-source datasets, struggle with complex business logic and lack domain-specific data for fine-tuning. Additionally, evaluation methods often require annotated data and executable database environments, which are scarce in real-world scenarios. To address these challenges, we propose SQLord, an enterprise-level NL2SQL framework. First, SQLord introduces a …

How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?
Niranjana Arun Menon, Iqra Farooq, Yulong Li, Sara Ahmed, Yutong Xie, Muhammad Awais, Imran Razzak
https://arxiv.org/abs/2508.07127

How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?
Cardiovascular disease (CVD) prediction remains a tremendous challenge due to its multifactorial etiology and global burden of morbidity and mortality. Despite the growing availability of genomic and electrophysiological data, extracting biologically meaningful insights from such high-dimensional, noisy, and sparsely annotated datasets remains a non-trivial task. Recently, LLMs has been applied effectively to predict structural variations in biological sequences. In this work, we explore the po…

VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding
Zhaowei Liu, Xin Guo, Haotian Xia, Lingfeng Zeng, Fangqi Lou, Jinyi Niu, Mengping Li, Qi Qi, Jiahuan Li, Wei Zhang, Yinglong Wang, Weige Cai, Weining Shen, Liwen Zhang
https://arxiv.org/abs/2508.09641

VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding
Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question-answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical sc…

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.

PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion
Yiran Rex Ma
https://arxiv.org/abs/2508.06110 https://arxiv.org/pdf/…

PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion
Table reasoning, including tabular QA and fact verification, often depends on annotated data or complex data augmentation, limiting flexibility and generalization. LLMs, despite their versatility, often underperform compared to simple supervised models. To approach these issues, we introduce PanelTR, a framework utilizing LLM agent scientists for robust table reasoning through a structured scientific approach. PanelTR's workflow involves agent scientists conducting individual investigations, en…

SurgPub-Video: A Comprehensive Surgical Video Dataset for Enhanced Surgical Intelligence in Vision-Language Model
Yaoqian Li, Xikai Yang, Dunyuan Xu, Yang Yu, Litao Zhao, Xiaowei Hu, Jinpeng Li, Pheng-Ann Heng
https://arxiv.org/abs/2508.10054

SurgPub-Video: A Comprehensive Surgical Video Dataset for Enhanced Surgical Intelligence in Vision-Language Model
Vision-Language Models (VLMs) have shown significant potential in surgical scene analysis, yet existing models are limited by frame-level datasets and lack high-quality video data with procedural surgical knowledge. To address these challenges, we make the following contributions: (i) SurgPub-Video, a comprehensive dataset of over 3,000 surgical videos and 25 million annotated frames across 11 specialties, sourced from peer-reviewed clinical journals, (ii) SurgLLaVA-Video, a specialized VLM for…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Zhiyuan Yang, Kai Li, Sophia Ghamoshi Ramandi, Patricia Brassard, Hakim Khellaf, Vincent Quoc-Huy Trinh, Jennifer Zhang, Lina Chen, Corwyn Rowsell, Sonal Varma, Kostas Plataniotis, Mahdi S. Hosseini
https://ar…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CoPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to mul…

Overview of the TREC 2021 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin
https://arxiv.org/abs/2507.08191 https:/…

Overview of the TREC 2021 deep learning track
This is the third year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels available for both passage and document ranking tasks. In addition, this year we refreshed both the document and the passage collections which also led to a nearly four times increase in the document collection size and nearly $16$ times increase in the size of the passage collection. Deep neural ranking models that em…

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
Ziyan Liu, Chunxiao Fan, Haoran Lou, Yuexin Wu, Kaiwei Deng
https://arxiv.org/abs/2507.06908

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. MIND implements three key strategies: 1) We retrieve similar memes from an unannotated…

Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex
Xavier Vasques, Laura Cif
https://arxiv.org/abs/2508.06514

Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex
The accurate classification of neuronal cell types is central to decoding brain function, yet remains hindered by data scarcity and cellular heterogeneity. Here, we benchmarked classical and deep generative synthetic data augmentation strategies -- including SMOTE, GANs, VAEs, Normalizing Flows, and DDPMs -- for supervised classification of both electrophysiological (e-type) and morpho-electrophysiological (mee-type) neuron types from the mouse visual cortex. Using a curated dataset annotated w…

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

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Haoran Xi, Chen Liu, Xiaolin Li
https://arxiv.org/abs/2508.06517

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequen…

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…

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

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 …

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…

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

SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis
Vojt\v{e}ch Stan\v{e}k, Karel Srna, Anton Firc, Kamil Malinka
https://arxiv.org/abs/2508.07944 https://…

SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis
Despite growing attention to deepfake speech detection, the aspects of bias and fairness remain underexplored in the speech domain. To address this gap, we introduce the Speaker Characteristics Deepfake (SCDF) dataset: a novel, richly annotated resource enabling systematic evaluation of demographic biases in deepfake speech detection. SCDF contains over 237,000 utterances in a balanced representation of both male and female speakers spanning five languages and a wide age range. We evaluate seve…

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…

Challenges for AI in Multimodal STEM Assessments: a Human-AI Comparison
Aymeric de Chillaz, Anna Sotnikova, Patrick Jermann, Antoine Bosselut
https://arxiv.org/abs/2507.03013

Challenges for AI in Multimodal STEM Assessments: a Human-AI Comparison
Generative AI systems have rapidly advanced, with multimodal input capabilities enabling reasoning beyond text-based tasks. In education, these advancements could influence assessment design and question answering, presenting both opportunities and challenges. To investigate these effects, we introduce a high-quality dataset of 201 university-level STEM questions, manually annotated with features such as image type, role, problem complexity, and question format. Our study analyzes how these fea…

A Cross-Perspective Annotated Dataset for Dynamic Object-Level Attention Modeling in Cloud Gaming
Hongqin Lei, Haowei Tang, Zhe Zhang
https://arxiv.org/abs/2508.06077 https://…

A Cross-Perspective Annotated Dataset for Dynamic Object-Level Attention Modeling in Cloud Gaming
Cloud gaming has gained popularity as it provides high-quality gaming experiences on thin hardware, such as phones and tablets. Transmitting gameplay frames at high resolutions and ultra-low latency is the key to guaranteeing players' quality of experience (QoE). Numerous studies have explored deep learning (DL) techniques to address this challenge. The efficiency of these DL-based approaches is highly affected by the dataset. However, existing datasets usually focus on the positions of objects…

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

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…

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…

Wrist bone segmentation in X-ray images using CT-based simulations
Youssef ElTantawy, Alexia Karantana, Xin Chen
https://arxiv.org/abs/2507.07131 https://

Wrist bone segmentation in X-ray images using CT-based simulations
Plain X-ray is one of the most common image modalities for clinical diagnosis (e.g. bone fracture, pneumonia, cancer screening, etc.). X-ray image segmentation is an essential step for many computer-aided diagnostic systems, yet it remains challenging. Deep-learning-based methods have achieved superior performance in medical image segmentation tasks but often require a large amount of high-quality annotated data for model training. Providing such an annotated dataset is not only time-consuming …

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

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…

That @… now links out to the #panoramax images if they are annotated on objects (using the `panoramax=` tag), it's a good reason to go through existing sequences and link images to things!
#panoramax #openstreetmap

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Achraf Ait Laydi, Louis Cueff, Mewen Crespo, Yousef El Mourabit, H\'el\`ene Bouvrais
https://arxiv.org/abs/2507.07800

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Segmenting curvilinear structures in fluorescence microscopy remains a challenging task, particularly under noisy conditions and in dense filament networks commonly seen in vivo. To address this, we created two original datasets consisting of hundreds of synthetic images of fluorescently labelled microtubules within cells. These datasets are precisely annotated and closely mimic real microscopy images, including realistic noise. The second dataset presents an additional challenge, by simulating…

Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Luisa Gall\'ee, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael G\"otz
https://arxiv.org/abs/2508.00639

Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and prediction of pathology-related visual attributes used by radiologists alongside the diagnosis, aligning AI decision-making with clinical reasoning. Radiologists use attributes like shape and texture as established diagnostic criteria and mirroring these in AI decision-making both enhances transparency and enables expli…

Computation by infinite descent made explicit
Sebastian Enqvist
https://arxiv.org/abs/2506.22206 https://arxiv.org/pdf/2506.22206

Computation by infinite descent made explicit
We introduce a non-wellfounded proof system for intuitionistic logic extended with ordinary inductive and co-inductive definitions, based on a syntax in which fixpoint formulas are annotated with explicit variables for ordinals. We explore the computational content of this system, in particular we introduce a notion of computability and show that every valid proof is computable. As a consequence, we obtain a normalization result for proofs of what we call finitary formulas. A special case of th…

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/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…

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…

LISTEN: Lightweight Industrial Sound-representable Transformer for Edge Notification
Changheon Han, Yun Seok Kang, Yuseop Sim, Martin Byung-Guk Jun, Hyung Wook Park
https://arxiv.org/abs/2507.07879

LISTEN: Lightweight Industrial Sound-representable Transformer for Edge Notification
Deep learning-based machine listening is broadening the scope of industrial acoustic analysis for applications like anomaly detection and predictive maintenance, thereby improving manufacturing efficiency and reliability. Nevertheless, its reliance on large, task-specific annotated datasets for every new task limits widespread implementation on shop floors. While emerging sound foundation models aim to alleviate data dependency, they are too large and computationally expensive, requiring cloud …

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,…

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…

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…

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

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…

Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Pinar Bisgin, Tom Strube, Niklas Tschorn, Michael Pantf\"order, Maximilian Fecke, Ingrid Ljungvall, Jens H\"aggstr\"om, Gerhard Wess, Christoph Schummer, Sven Meister, Falk M. Howar
https://arxiv.org/abs/2507.05950…

Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Noisy labels pose significant challenges for AI model training in veterinary medicine. This study examines expert assessment ambiguity in canine auscultation data, highlights the negative impact of label noise on classification performance, and introduces methods for label noise reduction. To evaluate whether label noise can be minimized by incorporating multiple expert opinions, a dataset of 140 heart sound recordings (HSR) was annotated regarding the intensity of holosystolic heart murmurs ca…

Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques
Yassin Hussein Rassul, Aram M. Ahmed, Polla Fattah, Bryar A. Hassan, Arwaa W. Abdulkareem, Tarik A. Rashid, Joan Lu
https://arxiv.org/abs/2507.06275

Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques
Offline Handwritten Text Recognition (HTR) systems play a crucial role in applications such as historical document digitization, automatic form processing, and biometric authentication. However, their performance is often hindered by the limited availability of annotated training data, particularly for low-resource languages and complex scripts. This paper presents a comprehensive survey of offline handwritten data augmentation and generation techniques designed to improve the accuracy and robu…

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.

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…

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…

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Zhetao Chen, Wenhao Xu, Fei Wu, Zhou Zhao
https://arxiv.org/abs/2507.06670

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Recent breakthroughs in singing voice synthesis (SVS) have heightened the demand for high-quality annotated datasets, yet manual annotation remains prohibitively labor-intensive and resource-intensive. Existing automatic singing annotation (ASA) methods, however, primarily tackle isolated aspects of the annotation pipeline. To address this fundamental challenge, we present STARS, which is, to our knowledge, the first unified framework that simultaneously addresses singing transcription, alignme…

Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection
Soumyadeep Jana, Abhrajyoti Kundu, Sanasam Ranbir Singh
https://arxiv.org/abs/2507.04468

Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection
The widespread use of multimodal content on social media has heightened the need for effective sarcasm detection to improve opinion mining. However, existing models rely heavily on large annotated datasets, making them less suitable for real-world scenarios where labeled data is scarce. This motivates the need to explore the problem in a few-shot setting. To this end, we introduce DMDP (Deep Modality-Disentangled Prompt Tuning), a novel framework for few-shot multimodal sarcasm detection. Unlik…

When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking
Weiran Li, Yeqiang Liu, Qiannan Guo, Yijie Wei, Hwa Liang Leo, Zhenbo Li
https://arxiv.org/abs/2507.06400

When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking
Multiple object tracking (MOT) technology has made significant progress in terrestrial applications, but underwater tracking scenarios remain underexplored despite their importance to marine ecology and aquaculture. We present Multiple Fish Tracking Dataset 2025 (MFT25), the first comprehensive dataset specifically designed for underwater multiple fish tracking, featuring 15 diverse video sequences with 408,578 meticulously annotated bounding boxes across 48,066 frames. Our dataset captures var…

RSD-15K: A Large-Scale User-Level Annotated Dataset for Suicide Risk Detection on Social Media
Shouwen Zheng, Yingzhi Tao, Taiqi Zhou
https://arxiv.org/abs/2507.11559

RSD-15K: A Large-Scale User-Level Annotated Dataset for Suicide Risk Detection on Social Media
In recent years, cognitive and mental health (CMH) disorders have increasingly become an important challenge for global public health, especially the suicide problem caused by multiple factors such as social competition, economic pressure and interpersonal relationships among young and middle-aged people. Social media, as an important platform for individuals to express emotions and seek help, provides the possibility for early detection and intervention of suicide risk. This paper introduces a…

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

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…

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

A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer
Joey Spronck, Leander van Eekelen, Dominique van Midden, Joep Bogaerts, Leslie Tessier, Valerie Dechering, Muradije Demirel-Andishmand, Gabriel Silva de Souza, Roland Nemeth, Enrico Munari, Giuseppe Bogina, Ilaria Girolami, Albino Eccher, Balazs Acs, Ceren Boyaci, Natalie Klubickova, Monika Looijen-Salamon, Shoko Vos, Francesco Ciompi

A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer
The tumor immune microenvironment (TIME) in non-small cell lung cancer (NSCLC) histopathology contains morphological and molecular characteristics predictive of immunotherapy response. Computational quantification of TIME characteristics, such as cell detection and tissue segmentation, can support biomarker development. However, currently available digital pathology datasets of NSCLC for the development of cell detection or tissue segmentation algorithms are limited in scope, lack annotations o…

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…

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…

Towards Human-in-the-Loop Onset Detection: A Transfer Learning Approach for Maracatu
Ant\'onio S\'a Pinto
https://arxiv.org/abs/2507.04858 https://…

Towards Human-in-the-Loop Onset Detection: A Transfer Learning Approach for Maracatu
We explore transfer learning strategies for musical onset detection in the Afro-Brazilian Maracatu tradition, which features complex rhythmic patterns that challenge conventional models. We adapt two Temporal Convolutional Network architectures: one pre-trained for onset detection (intra-task) and another for beat tracking (inter-task). Using only 5-second annotated snippets per instrument, we fine-tune these models through layer-wise retraining strategies for five traditional percussion instru…

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

The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
Katharina Beckh, Elisa Studeny, Sujan Sai Gannamaneni, Dario Antweiler, Stefan R\"uping
https://arxiv.org/abs/2507.01802

The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
Automatic medical coding has the potential to ease documentation and billing processes. For this task, transparency plays an important role for medical coders and regulatory bodies, which can be achieved using explainability methods. However, the evaluation of these approaches has been mostly limited to short text and binary settings due to a scarcity of annotated data. Recent efforts by Cheng et al. (2023) have introduced the MDACE dataset, which provides a valuable resource containing code ev…

No time to train! Training-Free Reference-Based Instance Segmentation
Miguel Espinosa, Chenhongyi Yang, Linus Ericsson, Steven McDonagh, Elliot J. Crowley
https://arxiv.org/abs/2507.02798

No time to train! Training-Free Reference-Based Instance Segmentation
The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviates this original problem through a promptable, semantics-agnostic, segmentation paradigm and yet still requires manual visual-prompts or complex domain-dependent prompt-generation rules to process a new image. Towards reducing this new burden, our work investigates the task of object segmentation when provided with, al…

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…

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…

Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis
Ethan Dack, Lorenzo Brigato, Vasilis Dedousis, Janine Gote-Schniering, Cheryl, Hanno Hoppe, Aristomenis Exadaktylos, Manuela Funke-Chambour, Thomas Geiser, Andreas Christe, Lukas Ebner, Stavroula Mougiakakou
https://arxiv.org/abs/2508.04429

Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis
Masked autoencoders (MAEs) have emerged as a powerful approach for pre-training on unlabelled data, capable of learning robust and informative feature representations. This is particularly advantageous in diffused lung disease research, where annotated imaging datasets are scarce. To leverage this, we train an MAE on a curated collection of over 5,000 chest computed tomography (CT) scans, combining in-house data with publicly available scans from related conditions that exhibit similar radiolog…

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…

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.

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…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/4]:
- ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting
Wang, Zubkov, Fan, Harrell, Sun, Chen, Plesner, Wattenhofer

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

MOVE: Motion-Guided Few-Shot Video Object Segmentation
Kaining Ying, Hengrui Hu, Henghui Ding
https://arxiv.org/abs/2507.22061 https://arxiv.org/pdf/2507.2…

MOVE: Motion-Guided Few-Shot Video Object Segmentation
This work addresses motion-guided few-shot video object segmentation (FSVOS), which aims to segment dynamic objects in videos based on a few annotated examples with the same motion patterns. Existing FSVOS datasets and methods typically focus on object categories, which are static attributes that ignore the rich temporal dynamics in videos, limiting their application in scenarios requiring motion understanding. To fill this gap, we introduce MOVE, a large-scale dataset specifically designed for…

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.

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…

TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis
Xuebo Jin, Longfei Gao, Anshuo Tong, Zhengyang Chen, Jianlei Kong, Ning Sun, Huijun Ma, Qiang Wang, Yuting Bai, Tingli Su
https://arxiv.org/abs/2507.18288

TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis
Traditional Chinese medicine (TCM) tongue diagnosis, while clinically valuable, faces standardization challenges due to subjective interpretation and inconsistent imaging protocols, compounded by the lack of large-scale, annotated datasets for AI development. To address this gap, we present the first specialized dataset for AI-driven TCM tongue diagnosis, comprising 6,719 high-quality images captured under standardized conditions and annotated with 20 pathological symptom categories (averaging …

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

new_zealand_collab: New Zealand scientific collaborations (2015)
A network of scientific collaborations among institutions in New Zealand. Nodes are institutions (universities, organizations, etc.), and two nodes i,j are connected if Scopus lists at least one publication with authors at institutions i and j, in the period 2010-2015. Edges are weighted by the number of such collaborations. Nodes are annotated with the categorical type of institution.
This network has 1511 nodes an…

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

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

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…

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

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

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…

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…

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

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

new_zealand_collab: New Zealand scientific collaborations (2015)
A network of scientific collaborations among institutions in New Zealand. Nodes are institutions (universities, organizations, etc.), and two nodes i,j are connected if Scopus lists at least one publication with authors at institutions i and j, in the period 2010-2015. Edges are weighted by the number of such collaborations. Nodes are annotated with the categorical type of institution.
This network has 1511 nodes an…

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

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