Tootfinder

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…

That's neat. This is an annotated map of #UWM from the September 9, 1965 issue of The UWM Post.
#Milwaukee

Looking forward to reading this! “Making BERT Feel at Home. Modelling Domestic Space in 19th-Century British and Irish Fiction”, Journal of Computational Literary Studies4(1). doi: https://doi.org/10.48694/jcls.4164
By Guhr, S., Monaco, J., Sherman, A., Warner, M. & Algee-Hewitt, M

Making BERT Feel at Home. Modelling Domestic Space in 19th-Century British and Irish Fiction
We introduce a novel approach to detecting domestic space in literary texts beyond explicit spatial markers like “home” or “house.” Using a pre-trained English BERT model fine-tuned on manually annotated passages from a corpus of 19th-century British and Irish novels, we develop a method to operationalize and quantify domesticity in fiction. Our model captures the nuances of domestic space by analyzing contextual and relational cues rather than relying solely on toponymic and other expl…

DADO: A Depth-Attention framework for Object Discovery
Federico Gonzalez, Estefania Talavera, Petia Radeva
https://arxiv.org/abs/2510.07089 https://arxiv.o…

DADO: A Depth-Attention framework for Object Discovery
Unsupervised object discovery, the task of identifying and localizing objects in images without human-annotated labels, remains a significant challenge and a growing focus in computer vision. In this work, we introduce a novel model, DADO (Depth-Attention self-supervised technique for Discovering unseen Objects), which combines an attention mechanism and a depth model to identify potential objects in images. To address challenges such as noisy attention maps or complex scenes with varying depth…

Aligning Large Language Models via Fully Self-Synthetic Data
Shangjian Yin, Zhepei Wei, Xinyu Zhu, Wei-Lin Chen, Yu Meng
https://arxiv.org/abs/2510.06652 https://

Aligning Large Language Models via Fully Self-Synthetic Data
Traditional reinforcement learning from human feedback (RLHF) for large language models (LLMs) relies on expensive human-annotated datasets, while Reinforcement Learning from AI Feedback (RLAIF) also incurs significant costs, requiring the collection of diverse prompts and corresponding responses, often necessitating external reward models or proprietary models like GPT-4 to annotate preference pairs. In this work, we introduce Self-Alignment Optimization (SAO), a fully self-synthetic framework…

Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks
Jo\~ao Palmeiro, Diogo Duarte, Rita Costa, Pedro Bizarro
https://arxiv.org/abs/2510.06071

Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks
AI models are increasingly used for data analysis and visualization, yet benchmarks rarely address scatterplot-specific tasks, limiting insight into performance. To address this gap for one of the most common chart types, we introduce a synthetic, annotated dataset of over 18,000 scatterplots from six data generators and 17 chart designs, and a benchmark based on it. We evaluate proprietary models from OpenAI and Google using N-shot prompting on five distinct tasks derived from annotations of c…

Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning
Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Dyuman Aditya, Paulo Shakarian, Devendra Parkar, Lahari Pokala, Clark Dorman, Gerardo I. Simari
https://arxiv.org/abs/2509.02958

Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning
We introduce Lattice Annotated Temporal (LAT) Logic, an extension of Generalized Annotated Logic Programs (GAPs) that incorporates temporal reasoning and supports open-world semantics through the use of a lower lattice structure. This logic combines an efficient deduction process with temporal logic programming to support non-Markovian relationships and open-world reasoning capabilities. The open-world aspect, a by-product of the use of the lower-lattice annotation structure, allows for efficie…

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…

EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model
Zefu Lin, Rongxu Cui, Chen Hanning, Xiangyu Wang, Junjia Xu, Xiaojuan Jin, Chen Wenbo, Hui Zhou, Lue Fan, Wenling Li, Zhaoxiang Zhang
https://arxiv.org/abs/2510.06207

EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model
Recent advances in control robot methods, from end-to-end vision-language-action frameworks to modular systems with predefined primitives, have advanced robots' ability to follow natural language instructions. Nonetheless, many approaches still struggle to scale to diverse environments, as they often rely on large annotated datasets and offer limited interpretability.In this work, we introduce EmbodiedCoder, a training-free framework for open-world mobile robot manipulation that leverages codin…

nnSAM2: nnUNet-Enhanced One-Prompt SAM2 for Few-shot Multi-Modality Segmentation and Composition Analysis of Lumbar Paraspinal Muscles
Zhongyi Zhang, Julie A. Hides, Enrico De Martino, Abdul Joseph Fofanah, Gervase Tuxworth
https://arxiv.org/abs/2510.05555

nnSAM2: nnUNet-Enhanced One-Prompt SAM2 for Few-shot Multi-Modality Segmentation and Composition Analysis of Lumbar Paraspinal Muscles
Purpose: To develop and validate No-New SAM2 (nnsam2) for few-shot segmentation of lumbar paraspinal muscles using only a single annotated slice per dataset, and to assess its statistical comparability with expert measurements across multi-sequence MRI and multi-protocol CT. Methods: We retrospectively analyzed 1,219 scans (19,439 slices) from 762 participants across six datasets. Six slices (one per dataset) served as labeled examples, while the remaining 19,433 slices were used for testing.…

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Jiaman He, Zikang Leng, Dana McKay, Damiano Spina, Johanne R. Trippas
https://arxiv.org/abs/2510.06658

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Many evaluations of large language models (LLMs) in text annotation focus primarily on the correctness of the output, typically comparing model-generated labels to human-annotated ``ground truth'' using standard performance metrics. In contrast, our study moves beyond effectiveness alone. We aim to explore how labeling decisions -- by both humans and LLMs -- can be statistically evaluated across individuals. Rather than treating LLMs purely as annotation systems, we approach LLMs as an alternat…

AnalysisGNN: Unified Music Analysis with Graph Neural Networks
Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth, Gerhard Widmer
https://arxiv.org/abs/2509.06654 https…

AnalysisGNN: Unified Music Analysis with Graph Neural Networks
Recent years have seen a boom in computational approaches to music analysis, yet each one is typically tailored to a specific analytical domain. In this work, we introduce AnalysisGNN, a novel graph neural network framework that leverages a data-shuffling strategy with a custom weighted multi-task loss and logit fusion between task-specific classifiers to integrate heterogeneously annotated symbolic datasets for comprehensive score analysis. We further integrate a Non-Chord-Tone prediction modu…

LabelImg: CNN-Based Surface Defect Detection
Mohsen Asghari Ilani, Yaser Mike Banad
https://arxiv.org/abs/2509.05813 https://arxiv.org/pdf/2509.05813

LabelImg: CNN-Based Surface Defect Detection
In the journey of computer vision system development, the acquisition and utilization of annotated images play a central role, providing information about object identity, spatial extent, and viewpoint in depicted scenes. However, thermal manufacturing processes like Laser Powder Bed Fusion (LPBF) often yield surfaces with defects such as Spatter, Crack, Pinhole, and Hole due to the Balling phenomenon. Preprocessing images from LPBF, riddled with defects, presents challenges in training machine…

SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin
https://arxiv.org/abs/2510.06596 https://

SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
The performance of machine learning models depends heavily on training data. The scarcity of large-scale, well-annotated datasets poses significant challenges in creating robust models. To address this, synthetic data generated through simulations and generative models has emerged as a promising solution, enhancing dataset diversity and improving the performance, reliability, and resilience of models. However, evaluating the quality of this generated data requires an effective metric. This pape…

Canonicalization of the E value from BLAST similarity search -- dissimilarity measure and distance function for a metric space of protein sequences
Boryeu Mao
https://arxiv.org/abs/2509.06849

Canonicalization of the E value from BLAST similarity search -- dissimilarity measure and distance function for a metric space of protein sequences
Sequence matching algorithms such as BLAST and FASTA have been widely used in searching for evolutionary origin and biological functions of newly discovered nucleic acid and protein sequences. As parts of these search tools, alignment scores and E values are useful indicators of the quality of search results from querying a database of annotated sequences, whereby a high alignment score (and inversely a low E value) reflects significant similarity between the query and the subject (target) sequ…

Do MLLMs Really Understand the Charts?
Xiao Zhang, Dongyuan Li, Liuyu Xiang, Yao Zhang, Cheng Zhong, Zhaofeng He
https://arxiv.org/abs/2509.04457 https://a…

Do MLLMs Really Understand the Charts?
Although Multimodal Large Language Models (MLLMs) have demonstrated increasingly impressive performance in chart understanding, most of them exhibit alarming hallucinations and significant performance degradation when handling non-annotated charts. Therefore, a question arises: Do MLLMs really understand the charts? Since a human is capable of understanding charts and estimating the values by visual reasoning, we first carefully establish a comprehensive Chart Reasoning Benchmark CRBench to rig…

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

Revisiting Modeling and Evaluation Approaches in Speech Emotion Recognition: Considering Subjectivity of Annotators and Ambiguity of Emotions
Huang-Cheng Chou, Chi-Chun Lee
https://arxiv.org/abs/2510.05934

Revisiting Modeling and Evaluation Approaches in Speech Emotion Recognition: Considering Subjectivity of Annotators and Ambiguity of Emotions
Over the past two decades, speech emotion recognition (SER) has received growing attention. To train SER systems, researchers collect emotional speech databases annotated by crowdsourced or in-house raters who select emotions from predefined categories. However, disagreements among raters are common. Conventional methods treat these disagreements as noise, aggregating labels into a single consensus target. While this simplifies SER as a single-label task, it ignores the inherent subjectivity of…

A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation
Mehdi Rabiee, Sergio Greco, Reza Shahbazian, Irina Trubitsyna
https://arxiv.org/abs/2510.06276

A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation
Focal Cortical Dysplasia (FCD) is a primary cause of drug-resistant epilepsy and is difficult to detect in brain {magnetic resonance imaging} (MRI) due to the subtle and small-scale nature of its lesions. Accurate segmentation of FCD regions in 3D multimodal brain MRI images is essential for effective surgical planning and treatment. However, this task remains highly challenging due to the limited availability of annotated FCD datasets, the extremely small size and weak contrast of FCD lesions,…

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Ariel Basso Madjoukeng, J\'er\^ome Fink, Pierre Poitier, Edith Belise Kenmogne, Benoit Frenay
https://arxiv.org/abs/2509.05188

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Sign language recognition (SLR) is a machine learning task aiming to identify signs in videos. Due to the scarcity of annotated data, unsupervised methods like contrastive learning have become promising in this field. They learn meaningful representations by pulling positive pairs (two augmented versions of the same instance) closer and pushing negative pairs (different from the positive pairs) apart. In SLR, in a sign video, only certain parts provide information that is truly useful for its r…

The ARRL has a long online text band plan which they have condensed for HF into The Considerate Operator's Frequency Guide which is not really in a convenient form to print and carry with you into the field when operating portable

IntrEx: A Dataset for Modeling Engagement in Educational Conversations
Xingwei Tan, Mahathi Parvatham, Chiara Gambi, Gabriele Pergola
https://arxiv.org/abs/2509.06652 https://…

IntrEx: A Dataset for Modeling Engagement in Educational Conversations
Engagement and motivation are crucial for second-language acquisition, yet maintaining learner interest in educational conversations remains a challenge. While prior research has explored what makes educational texts interesting, still little is known about the linguistic features that drive engagement in conversations. To address this gap, we introduce IntrEx, the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. Built upon the Teac…

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

15GB of raw footage, 45 minutes of edited video so far.
Still haven't even done a bare-die (vs partial) decap yet, or any delayering, or any extensive circuit analysis.
This is going to be LONG.

UrbanTwin: High-Fidelity Synthetic Replicas of Roadside Lidar Datasets
Muhammad Shahbaz, Shaurya Agarwal
https://arxiv.org/abs/2509.06781 https://arxiv.org…

UrbanTwin: High-Fidelity Synthetic Replicas of Roadside Lidar Datasets
This article presents UrbanTwin datasets - high-fidelity, realistic replicas of three public roadside lidar datasets: LUMPI, V2X-Real-IC, and TUMTraf-I. Each UrbanTwin dataset contains 10K annotated frames corresponding to one of the public datasets. Annotations include 3D bounding boxes, instance segmentation labels, and tracking IDs for six object classes, along with semantic segmentation labels for nine classes. These datasets are synthesized using emulated lidar sensors within realistic dig…

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Ruicheng Zhang, Jun Zhou, Zunnan Xu, Zihao Liu, Jiehui Huang, Mingyang Zhang, Yu Sun, Xiu Li
https://arxiv.org/abs/2509.06723

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Trajectory-Guided image-to-video (I2V) generation aims to synthesize videos that adhere to user-specified motion instructions. Existing methods typically rely on computationally expensive fine-tuning on scarce annotated datasets. Although some zero-shot methods attempt to trajectory control in the latent space, they may yield unrealistic motion by neglecting 3D perspective and creating a misalignment between the manipulated latents and the network's noise predictions. To address these challenge…

Crosslisted article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning
Mukherji, Patil, Aditya, Shakarian, Parkar, Pokala, Dorman, Simari

ToonOut: Fine-tuned Background-Removal for Anime Characters
Matteo Muratori, Jo\"el Seytre
https://arxiv.org/abs/2509.06839 https://arxiv.org/pdf/2509…

ToonOut: Fine-tuned Background-Removal for Anime Characters
While state-of-the-art background removal models excel at realistic imagery, they frequently underperform in specialized domains such as anime-style content, where complex features like hair and transparency present unique challenges. To address this limitation, we collected and annotated a custom dataset of 1,228 high-quality anime images of characters and objects, and fine-tuned the open-sourced BiRefNet model on this dataset. This resulted in marked improvements in background removal accurac…

Gamma Acyclicity, Annotated Relations, and Consistency Witness Functions
Albert Atserias, Phokion G. Kolaitis
https://arxiv.org/abs/2509.19621 https://arxi…

Gamma Acyclicity, Annotated Relations, and Consistency Witness Functions
During the early days of relational database theory it was realized that "acyclic" database schemas possess a number of desirable semantic properties. In fact, three different notions of "acyclicity" were identified and extensively investigated during the 1980s, namely, alpha-acyclicity, beta-acyclicity, and gamma-acyclicity. Much more recently, the study of alpha-acyclicity was extended to annotated relations, where the annotations are values from some positive commutative monoid. The recent r…

A Graded Modal Type Theory for Pulse Schedules
Robin Adams
https://arxiv.org/abs/2510.03130 https://arxiv.org/pdf/2510.03130…

A Graded Modal Type Theory for Pulse Schedules
We propose a language for representing the pulse schedules that a superconducting quantum computer accepts as input. The language is a graded modal type theory named PSTT (Pulse Schedule Type Theory). Graded modals type theories are type systems where each variable is annotated with a parameter or grade. These can be used to represent, for example, resource usage, where the grade denotes how many times a given resource may be used; or privacy levels, whether a resource is private or public. In …

MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane
Ruichen Yao, Aslanbek Murzakhmetov, Raaghav Pillai, Aliya Maussymbayeva, Zelin Li, Yifan Liu, Yaokun Liu, Lanyu Shang, Yang Zhang, Na Wei, Ximing Cai, Dong Wang
https://arxiv.org/abs/2509.23627

MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane
Natural disasters cause multidimensional threats to human societies, with hurricanes exemplifying one of the most disruptive events that not only caused severe physical damage but also sparked widespread discussion on social media platforms. Existing datasets for studying societal impacts of hurricanes often focus on outdated hurricanes and are limited to a single social media platform, failing to capture the broader societal impact in today's diverse social media environment. Moreover, existin…

AuditAgent: Expert-Guided Multi-Agent Reasoning for Cross-Document Fraudulent Evidence Discovery
Songran Bai, Bingzhe Wu, Yiwei Zhang, Chengke Wu, Xiaolong Zheng, Yaze Yuan, Ke Wu, Jianqiang Li
https://arxiv.org/abs/2510.00156

AuditAgent: Expert-Guided Multi-Agent Reasoning for Cross-Document Fraudulent Evidence Discovery
Financial fraud detection in real-world scenarios presents significant challenges due to the subtlety and dispersion of evidence across complex, multi-year financial disclosures. In this work, we introduce a novel multi-agent reasoning framework AuditAgent, enhanced with auditing domain expertise, for fine-grained evidence chain localization in financial fraud cases. Leveraging an expert-annotated dataset constructed from enforcement documents and financial reports released by the China Securit…

A large-scale complexity-graded dataset of neuronal images and annotations
Wu Chen, Mingwei Liao, Xueyan Jia, Xiaowei Chen, Chi Xiao, Qingming Luo, Hui Gong, Anan Li
https://arxiv.org/abs/2508.02059

A large-scale complexity-graded dataset of neuronal images and annotations
Accurate reconstruction of neuronal morphology is essential for classifying cell types and understanding brain connectivity. Recent advances in imaging and reconstruction techniques have greatly expanded the scale and quality of neuronal data. However, large-scale, standardized annotated datasets remain limited. Here, we present an open, multi-level neuronal dataset covering the whole mouse brain. Using a hierarchical strategy, we divided imaging data from 237 mouse brains into about 13,570,000…

D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
Sai Kartheek Reddy Kasu, Mohammad Zia Ur Rehman, Shahid Shafi Dar, Rishi Bharat Junghare, Dhanvin Sanjay Namboodiri, Nagendra Kumar
https://arxiv.org/abs/2509.06771

D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
Dark humor in online memes poses unique challenges due to its reliance on implicit, sensitive, and culturally contextual cues. To address the lack of resources and methods for detecting dark humor in multimodal content, we introduce a novel dataset of 4,379 Reddit memes annotated for dark humor, target category (gender, mental health, violence, race, disability, and other), and a three-level intensity rating (mild, moderate, severe). Building on this resource, we propose a reasoning-augmented f…

Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection
Franco Javier Arellano, Jos\'e Ignacio Orlando
https://arxiv.org/abs/2510.07277 https://

Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection
Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promising results for automatically detecting this condition from fundus images, its application remains challenging due the limited availability of annotated data. Foundation Models (FM) have emerged as an alternative solution. However, it is unclear if they can cope with DME detection in particular. In this paper, we systematically compare different FM an…

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…

ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
Adrian Catalin Lutu, Ioana Pintilie, Elena Burceanu, Andrei Manolache
https://arxiv.org/abs/2509.04449 htt…

ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
We present ChronoGraph, a graph-structured multivariate time series forecasting dataset built from real-world production microservices. Each node is a service that emits a multivariate stream of system-level performance metrics, capturing CPU, memory, and network usage patterns, while directed edges encode dependencies between services. The primary task is forecasting future values of these signals at the service level. In addition, ChronoGraph provides expert-annotated incident windows as anom…

When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA
Elisei Rykov, Kseniia Petrushina, Maksim Savkin, Valerii Olisov, Artem Vazhentsev, Kseniia Titova, Alexander Panchenko, Vasily Konovalov, Julia Belikova
https://arxiv.org/abs/2510.04849

When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA
Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy. Existing hallucination benchmarks often operate at the sequence level and are limited to English, lacking the fine-grained, multilingual supervision needed for a comprehensive evaluation. In this work, we introduce PsiloQA, a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languag…

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…

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…

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

Evaluating Multiple Instance Learning Strategies for Automated Sebocyte Droplet Counting
Maryam Adelipour, Gustavo Carneiro, Jeongkwon Kim
https://arxiv.org/abs/2509.04895 https…

Evaluating Multiple Instance Learning Strategies for Automated Sebocyte Droplet Counting
Sebocytes are lipid-secreting cells whose differentiation is marked by the accumulation of intracellular lipid droplets, making their quantification a key readout in sebocyte biology. Manual counting is labor-intensive and subjective, motivating automated solutions. Here, we introduce a simple attention-based multiple instance learning (MIL) framework for sebocyte image analysis. Nile Red-stained sebocyte images were annotated into 14 classes according to droplet counts, expanded via data augme…

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…

Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric
Harald Vilhelm Skat-R{\o}rdam, Sneha Das, Kathrine Sofie Rasmussen, Nicole Nadine L{\o}nfeldt, Line Clemmensen
https://arxiv.org/abs/2509.03240

Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric
Accurate evaluation of event detection in time series is essential for applications such as stress monitoring with wearable devices, where ground truth is typically annotated as single-point events, even though the underlying phenomena are gradual and temporally diffused. Standard metrics like F1 and point-adjusted F1 (F1$_{pa}$) often misrepresent model performance in such real-world, imbalanced datasets. We introduce a window-based F1 metric (F1$_w$) that incorporates temporal tolerance, enab…

Physics-Informed Machine Learning with Adaptive Grids for Optical Microrobot Depth Estimation
Lan Wei, Lou Genoud, Dandan Zhang
https://arxiv.org/abs/2509.02343 https://

Physics-Informed Machine Learning with Adaptive Grids for Optical Microrobot Depth Estimation
Optical microrobots actuated by optical tweezers (OT) offer great potential for biomedical applications such as cell manipulation and microscale assembly. These tasks demand accurate three-dimensional perception to ensure precise control in complex and dynamic biological environments. However, the transparent nature of microrobots and low-contrast microscopic imaging challenge conventional deep learning methods, which also require large annotated datasets that are costly to obtain. To address t…

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

WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation
Longhao Li, Zhao Guo, Hongjie Chen, Yuhang Dai, Ziyu Zhang, Hongfei Xue, Tianlun Zuo, Chengyou Wang, Shuiyuan Wang, Jie Li, Xin Xu, Hui Bu, Binbin Zhang, Ruibin Yuan, Ziya Zhou, Wei Xue, Lei Xie
https://arxiv.org/abs/2509.03959

WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation
The development of speech understanding and generation has been significantly accelerated by the availability of large-scale, high-quality speech datasets. Among these, ASR and TTS are regarded as the most established and fundamental tasks. However, for Cantonese (Yue Chinese), spoken by approximately 84.9 million native speakers worldwide, limited annotated resources have hindered progress and resulted in suboptimal ASR and TTS performance. To address this challenge, we propose WenetSpeech-Pip…

Mapping Toxic Comments Across Demographics: A Dataset from German Public Broadcasting
Jan Fillies, Michael Peter Hoffmann, Rebecca Reichel, Roman Salzwedel, Sven Bodemer, Adrian Paschke
https://arxiv.org/abs/2508.21084

Mapping Toxic Comments Across Demographics: A Dataset from German Public Broadcasting
A lack of demographic context in existing toxic speech datasets limits our understanding of how different age groups communicate online. In collaboration with funk, a German public service content network, this research introduces the first large-scale German dataset annotated for toxicity and enriched with platform-provided age estimates. The dataset includes 3,024 human-annotated and 30,024 LLM-annotated anonymized comments from Instagram, TikTok, and YouTube. To ensure relevance, comments we…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning
Mukherji, Patil, Aditya, Shakarian, Parkar, Pokala, Dorman, Simari

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.

Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang
https://arxiv.org/abs/2510.04939 https://arxi…

Unsupervised Active Learning via Natural Feature Progressive Framework
The effectiveness of modern deep learning models is predicated on the availability of large-scale, human-annotated datasets, a process that is notoriously expensive and time-consuming. While Active Learning (AL) offers a strategic solution by labeling only the most informative and representative data, its iterative nature still necessitates significant human involvement. Unsupervised Active Learning (UAL) presents an alternative by shifting the annotation burden to a single, post-selection step…

BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping
Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles, Raimon Ramos, Rafael Garcia, Nuno Gracias
https://arxiv.org/abs/2510.04876

BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping
Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management. Yet, the scarcity of large, annotated datasets limits the development and benchmarking of machine learning models in this domain. This paper introduces a thorough multi-modal dataset, comprising about a million side-scan sonar (SSS) tiles collected along the coast of Catalonia (Spain), complemented by bathymetric maps and a set of co-registere…

Human-Annotated NER Dataset for the Kyrgyz Language
Timur Turatali, Anton Alekseev, Gulira Jumalieva, Gulnara Kabaeva, Sergey Nikolenko
https://arxiv.org/abs/2509.19109 https://…

Sequential Hard Mining: a data-centric approach for Mitosis Detection
Maxime W. Lafarge, Viktor H. Koelzer
https://arxiv.org/abs/2509.02588 https://arxiv.o…

Sequential Hard Mining: a data-centric approach for Mitosis Detection
With a continuously growing availability of annotated datasets of mitotic figures in histology images, finding the best way to optimally use with this unprecedented amount of data to optimally train deep learning models has become a new challenge. Here, we build upon previously proposed approaches with a focus on efficient sampling of training data inspired by boosting techniques and present our candidate solutions for the two tracks of the MIDOG 2025 challenge.

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

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…

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

CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking
Ruiling Guo, Xinwei Yang, Chen Huang, Tong Zhang, Yong Hu
https://arxiv.org/abs/2509.03957

CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking
The effectiveness of large language models (LLMs) to fact-check misinformation remains uncertain, despite their growing use. To this end, we present CANDY, a benchmark designed to systematically evaluate the capabilities and limitations of LLMs in fact-checking Chinese misinformation. Specifically, we curate a carefully annotated dataset of ~20k instances. Our analysis shows that current LLMs exhibit limitations in generating accurate fact-checking conclusions, even when enhanced with chain-of-…

Decoding the Poetic Language of Emotion in Korean Modern Poetry: Insights from a Human-Labeled Dataset and AI Modeling
Iro Lim, Haein Ji, Byungjun Kim
https://arxiv.org/abs/2509.03932

Decoding the Poetic Language of Emotion in Korean Modern Poetry: Insights from a Human-Labeled Dataset and AI Modeling
This study introduces KPoEM (Korean Poetry Emotion Mapping) , a novel dataset for computational emotion analysis in modern Korean poetry. Despite remarkable progress in text-based emotion classification using large language models, poetry-particularly Korean poetry-remains underexplored due to its figurative language and cultural specificity. We built a multi-label emotion dataset of 7,662 entries, including 7,007 line-level entries from 483 poems and 615 work-level entries, annotated with 44 f…

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

MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis
Jinwei Zhang, Lianrui Zuo, Yihao Liu, Hang Zhang, Samuel W. Remedios, Bennett A. Landman, Peter A. Calabresi, Shiv Saidha, Scott D. Newsome, Dzung L. Pham, Jerry L. Prince, Ellen M. Mowry, Aaron Carass
https://

MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis
In multiple sclerosis, lesions interfere with automated magnetic resonance imaging analyses such as brain parcellation and deformable registration, while lesion segmentation models are hindered by the limited availability of annotated training data. To address both issues, we propose MSRepaint, a unified diffusion-based generative model for bidirectional lesion filling and synthesis that restores anatomical continuity for downstream analyses and augments segmentation through realistic data gene…

Learning in an Echo Chamber: Online Learning with Replay Adversary
Daniil Dmitriev, Harald Eskelund Franck, Carolin Heinzler, Amartya Sanyal
https://arxiv.org/abs/2509.25135 htt…

Learning in an Echo Chamber: Online Learning with Replay Adversary
As machine learning systems increasingly train on self-annotated data, they risk reinforcing errors and becoming echo chambers of their own beliefs. We model this phenomenon by introducing a learning-theoretic framework: Online Learning in the Replay Setting. In round $t$, the learner outputs a hypothesis $\hat{h}_t$; the adversary then reveals either the true label $f^\ast(x_t)$ or a replayed label $\hat{h}_i(x_t)$ from an earlier round $i < t$. A mistake is counted only when the true label is…

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.

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…

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision
Safouane El Ghazouali, Umberto Michelucci
https://arxiv.org/abs/2509.04180 https://

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision
AI models rely on annotated data to learn pattern and perform prediction. Annotation is usually a labor-intensive step that require associating labels ranging from a simple classification label to more complex tasks such as object detection, oriented bounding box estimation, and instance segmentation. Traditional tools often require extensive manual input, limiting scalability for large datasets. To address this, we introduce VisioFirm, an open-source web application designed to streamline imag…

AccidentBench: Benchmarking Multimodal Understanding and Reasoning in Vehicle Accidents and Beyond
Shangding Gu, Xiaohan Wang, Donghao Ying, Haoyu Zhao, Runing Yang, Ming Jin, Boyi Li, Marco Pavone, Serena Yeung-Levy, Jun Wang, Dawn Song, Costas Spanos
https://arxiv.org/abs/2509.26636

AccidentBench: Benchmarking Multimodal Understanding and Reasoning in Vehicle Accidents and Beyond
Rapid advances in multimodal models demand benchmarks that rigorously evaluate understanding and reasoning in safety-critical, dynamic real-world settings. We present AccidentBench, a large-scale benchmark that combines vehicle accident scenarios with Beyond domains, safety-critical settings in air and water that emphasize spatial and temporal reasoning (e.g., navigation, orientation, multi-vehicle motion). The benchmark contains approximately 2000 videos and over 19000 human-annotated question…

RESTRAIN: From Spurious Votes to Signals -- Self-Driven RL with Self-Penalization
Zhaoning Yu, Will Su, Leitian Tao, Haozhu Wang, Aashu Singh, Hanchao Yu, Jianyu Wang, Hongyang Gao, Weizhe Yuan, Jason Weston, Ping Yu, Jing Xu
https://arxiv.org/abs/2510.02172

RESTRAIN: From Spurious Votes to Signals -- Self-Driven RL with Self-Penalization
Reinforcement learning with human-annotated data has boosted chain-of-thought reasoning in large reasoning models, but these gains come at high costs in labeled data while faltering on harder tasks. A natural next step is experience-driven learning, where models improve without curated labels by adapting to unlabeled data. We introduce RESTRAIN (REinforcement learning with Self-restraint), a self-penalizing RL framework that converts the absence of gold labels into a useful learning signal. Ins…

Adapting Large Language Models to Mitigate Skin Tone Biases in Clinical Dermatology Tasks: A Mixed-Methods Study
Kiran Nijjer, Ryan Bui, Derek Jiu, Adnan Ahmed, Peter Wang, Benjamin Liu, Kevin Zhu, Lilly Zhu
https://arxiv.org/abs/2510.00055

Adapting Large Language Models to Mitigate Skin Tone Biases in Clinical Dermatology Tasks: A Mixed-Methods Study
SkinGPT-4, a large vision-language model, leverages annotated skin disease images to augment clinical workflows in underserved communities. However, its training dataset predominantly represents lighter skin tones, limiting diagnostic accuracy for darker tones. Here, we evaluated performance biases in SkinGPT-4 across skin tones on common skin diseases, including eczema, allergic-contact dermatitis, and psoriasis using the open-sourced SCIN dataset. We leveraged the SkinGPT-4 backbone to develo…

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.

EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
Lingzhi Shen, Xiaohao Cai, Yunfei Long, Imran Razzak, Guanming Chen, Shoaib Jameel
https://arxiv.org/abs/2509.02450

EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
Personality detection from text is commonly performed by analysing users' social media posts. However, existing methods heavily rely on large-scale annotated datasets, making it challenging to obtain high-quality personality labels. Moreover, most studies treat emotion and personality as independent variables, overlooking their interactions. In this paper, we propose a novel self-supervised framework, EmoPerso, which improves personality detection through emotion-aware modelling. EmoPerso first…

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

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…

L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models
Aishwarya Mirashi, Ananya Joshi, Raviraj Joshi
https://arxiv.org/abs/2508.21569 https://ar…

L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models
We present MahaSTS, a human-annotated Sentence Textual Similarity (STS) dataset for Marathi, along with MahaSBERT-STS-v2, a fine-tuned Sentence-BERT model optimized for regression-based similarity scoring. The MahaSTS dataset consists of 16,860 Marathi sentence pairs labeled with continuous similarity scores in the range of 0-5. To ensure balanced supervision, the dataset is uniformly distributed across six score-based buckets spanning the full 0-5 range, thus reducing label bias and enhancing …

Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Pablo Ayuso-Albizu, Juan C. SanMiguel, Pablo Carballeira
https://arxiv.org/abs/2509.02161

Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Pedestrian Attribute Recognition (PAR) involves identifying various human attributes from images with applications in intelligent monitoring systems. The scarcity of large-scale annotated datasets hinders the generalization of PAR models, specially in complex scenarios involving occlusions, varying poses, and diverse environments. Recent advances in diffusion models have shown promise for generating diverse and realistic synthetic images, allowing to expand the size and variability of training …

Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation
Yubo Xie, Chenkai Wang, Zongyang Ma, Fahui Miao
https://arxiv.org/abs/2510.00567

Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation
Large language models (LLMs) are trained on vast amounts of text from the Internet, but do they truly understand the viral content that rapidly spreads online -- commonly known as memes? In this paper, we introduce CHIME, a dataset for CHinese Internet Meme Explanation. The dataset comprises popular phrase-based memes from the Chinese Internet, annotated with detailed information on their meaning, origin, example sentences, types, etc. To evaluate whether LLMs understand these memes, we designe…

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.

GeoPurify: A Data-Efficient Geometric Distillation Framework for Open-Vocabulary 3D Segmentation
Weijia Dou, Xu Zhang, Yi Bin, Jian Liu, Bo Peng, Guoqing Wang, Yang Yang, Heng Tao Shen
https://arxiv.org/abs/2510.02186

GeoPurify: A Data-Efficient Geometric Distillation Framework for Open-Vocabulary 3D Segmentation
Recent attempts to transfer features from 2D Vision-Language Models (VLMs) to 3D semantic segmentation expose a persistent trade-off. Directly projecting 2D features into 3D yields noisy and fragmented predictions, whereas enforcing geometric coherence necessitates costly training pipelines and large-scale annotated 3D data. We argue that this limitation stems from the dominant segmentation-and-matching paradigm, which fails to reconcile 2D semantics with 3D geometric structure. The geometric c…

Palmistry-Informed Feature Extraction and Analysis using Machine Learning
Shweta Patil
https://arxiv.org/abs/2509.02248 https://arxiv.org/pdf/2509.02248

Palmistry-Informed Feature Extraction and Analysis using Machine Learning
This paper explores the automated analysis of palmar features using machine learning techniques. We present a computer vision pipeline that extracts key characteristics from palm images, such as principal line structures, texture, and shape metrics. These features are used to train predictive models on a novel dataset curated from annotated palm images. Our approach moves beyond traditional subjective interpretation by providing a data-driven, quantitative framework for studying the correlation…

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

MENLO: From Preferences to Proficiency - Evaluating and Modeling Native-like Quality Across 47 Languages
Chenxi Whitehouse, Sebastian Ruder, Tony Lin, Oksana Kurylo, Haruka Takagi, Janice Lam, Nicol\`o Busetto, Denise Diaz
https://arxiv.org/abs/2509.26601

MENLO: From Preferences to Proficiency - Evaluating and Modeling Native-like Quality Across 47 Languages
Ensuring native-like quality of large language model (LLM) responses across many languages is challenging. To address this, we introduce MENLO, a framework that operationalizes the evaluation of native-like response quality based on audience design-inspired mechanisms. Using MENLO, we create a dataset of 6,423 human-annotated prompt-response preference pairs covering four quality dimensions with high inter-annotator agreement in 47 language varieties. Our evaluation reveals that zero-shot LLM j…

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

Evaluating Spatiotemporal Consistency in Automatically Generated Sewing Instructions
Luisa Geiger, Mareike Hartmann, Michael Sullivan, Alexander Koller
https://arxiv.org/abs/2509.24792

Evaluating Spatiotemporal Consistency in Automatically Generated Sewing Instructions
In this paper, we propose a novel, automatic tree-based evaluation metric for LLM-generated step-by-step assembly instructions, that more accurately reflects spatiotemporal aspects of construction than traditional metrics such as BLEU and BERT similarity scores. We apply our proposed metric to the domain of sewing instructions, and show that our metric better correlates with manually-annotated error counts as well as human quality ratings, demonstrating our metric's superiority for evaluating t…

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

A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI
Omer Faruk Durugol, Maximilian Rokuss, Yannick Kirchhoff, Klaus H. Maier-Hein
https://arxiv.org/abs/2508.21775

A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI
Automated segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) from MRI is critical for clinical workflows but is hindered by poor tumor-tissue contrast and a scarcity of annotated data. This paper details our submission to the PANTHER challenge, addressing both diagnostic T1-weighted (Task 1) and therapeutic T2-weighted (Task 2) segmentation. Our approach is built upon the nnU-Net framework and leverages a deep, multi-stage cascaded pre-training strategy, starting from a general anatomical …

Personalized Vision via Visual In-Context Learning
Yuxin Jiang, Yuchao Gu, Yiren Song, Ivor Tsang, Mike Zheng Shou
https://arxiv.org/abs/2509.25172 https://

Personalized Vision via Visual In-Context Learning
Modern vision models, trained on large-scale annotated datasets, excel at predefined tasks but struggle with personalized vision -- tasks defined at test time by users with customized objects or novel objectives. Existing personalization approaches rely on costly fine-tuning or synthetic data pipelines, which are inflexible and restricted to fixed task formats. Visual in-context learning (ICL) offers a promising alternative, yet prior methods confine to narrow, in-domain tasks and fail to gener…

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.

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.

PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection
Tuan Nguyen, Naseem Khan, Khang Tran, NhatHai Phan, Issa Khalil
https://arxiv.org/abs/2509.26272

PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection
The rapid rise of synthetic media has made deepfake detection a critical challenge for online safety and trust. Progress remains constrained by the scarcity of large, high-quality datasets. Although multimodal large language models (LLMs) exhibit strong reasoning capabilities, their performance on deepfake detection is poor, often producing explanations that are misaligned with visual evidence or hallucinatory. To address this limitation, we introduce a reasoning-annotated dataset for deepfake …

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

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

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…

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

B\'ezier Meets Diffusion: Robust Generation Across Domains for Medical Image Segmentation
Chen Li, Meilong Xu, Xiaoling Hu, Weimin Lyu, Chao Chen
https://arxiv.org/abs/2509.22476

Bézier Meets Diffusion: Robust Generation Across Domains for Medical Image Segmentation
Training robust learning algorithms across different medical imaging modalities is challenging due to the large domain gap. Unsupervised domain adaptation (UDA) mitigates this problem by using annotated images from the source domain and unlabeled images from the target domain to train the deep models. Existing approaches often rely on GAN-based style transfer, but these methods struggle to capture cross-domain mappings in regions with high variability. In this paper, we propose a unified framew…

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.

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…

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…