Tootfinder

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…

Non-Stationary Online Structured Prediction with Surrogate Losses
Shinsaku Sakaue, Han Bao, Yuzhou Cao
https://arxiv.org/abs/2510.07086 https://arxiv.org/p…

Non-Stationary Online Structured Prediction with Surrogate Losses
Online structured prediction, including online classification as a special case, is the task of sequentially predicting labels from input features. Therein the surrogate regret -- the cumulative excess of the target loss (e.g., 0-1 loss) over the surrogate loss (e.g., logistic loss) of the fixed best estimator -- has gained attention, particularly because it often admits a finite bound independent of the time horizon $T$. However, such guarantees break down in non-stationary environments, where…

Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
Zhen Tian, Fujiang Yuan, Yangfan He, Qinghao Li, Changlin Chen, Huilin Chen, Tianxiang Xu, Jianyu Duan, Yanhong Peng, Zhihao Lin
https://arxiv.org/abs/2509.07412

Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
Autonomous driving has attracted great interest due to its potential capability in full-unsupervised driving. Model-based and learning-based methods are widely used in autonomous driving. Model-based methods rely on pre-defined models of the environment and may struggle with unforeseen events. Proximal policy optimization (PPO), an advanced learning-based method, can adapt to the above limits by learning from interactions with the environment. However, existing PPO faces challenges with poor tr…

DNN-based Digital Twin Framework of a DC-DC Buck Converter using Spider Monkey Optimization Algorithm
Tahmin Mahmud, Euzeli Cipriano Dos Santos Jr
https://arxiv.org/abs/2509.06279

DNN-based Digital Twin Framework of a DC-DC Buck Converter using Spider Monkey Optimization Algorithm
Component ageing is a critical concern in power electronic converter systems (PECSs). It directly impacts the reliability, performance, and operational lifespan of converters used across diverse applications, including electric vehicles (EVs), renewable energy systems (RESs) and industrial automation. Therefore, understanding and monitoring component ageing is crucial for developing robust converters and achieving long-term system reliability. This paper proposes a data-driven digital twin (DT)…

Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD
Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal, Michelle Marie Martel
https://arxiv.org/abs/2510.07409

Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD
Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level…

Wrangling Entropy: Next-Generation Multi-Factor Key Derivation, Credential Hashing, and Credential Generation Functions
Colin Roberts, Vivek Nair, Dawn Song
https://arxiv.org/abs/2509.05893

Wrangling Entropy: Next-Generation Multi-Factor Key Derivation, Credential Hashing, and Credential Generation Functions
The Multi-Factor Key Derivation Function (MFKDF) offered a novel solution to the classic problem of usable client-side key management by incorporating multiple popular authentication factors into a key derivation process, but was later shown to be vulnerable to cryptanalysis that degraded its security over multiple invocations. In this paper, we present the Entropy State Transition Modeling Framework (ESTMF), a novel cryptanalytic technique designed to reveal pernicious leaks of entropy across …

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
Wenxuan Wang, Kai Wu, Yujian Betterest Li, Dan Wang, Xiaoyu Zhang
https://arxiv.org/abs/2510.08445 http…

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their development. Inspired by complex dynamic system theories, we design a series-symbol data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic expressions. To leverage series-symbol data pairs with strong correlations, we develop \texttt{SymTime}, a p…

MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Siyoon Jin, Seongchan Kim, Dahyun Chung, Jaeho Lee, Hyunwook Choi, Jisu Nam, Jiyoung Kim, Seungryong Kim
https://arxiv.org/abs/2510.07310

MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a systematic analysis that formalizes two perspectives of video DiTs: semantic grounding, via video-to-text attention, which evaluates whether n…

Robotic Manipulation Framework Based on Semantic Keypoints for Packing Shoes of Different Sizes, Shapes, and Softness
Yi Dong, Yangjun Liu, Jinjun Duan, Yang Li, Zhendong Dai
https://arxiv.org/abs/2509.06048

Robotic Manipulation Framework Based on Semantic Keypoints for Packing Shoes of Different Sizes, Shapes, and Softness
With the rapid development of the warehousing and logistics industries, the packing of goods has gradually attracted the attention of academia and industry. The packing of footwear products is a typical representative paired-item packing task involving irregular shapes and deformable objects. Although studies on shoe packing have been conducted, different initial states due to the irregular shapes of shoes and standard packing placement poses have not been considered. This study proposes a robo…

Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Harrison Field, Max Yang, Yijiong Lin, Efi Psomopoulou, David Barton, Nathan F. Lepora
https://arxiv.org/abs/2509.07445

Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Large language models (LLMs) are beginning to automate reward design for dexterous manipulation. However, no prior work has considered tactile sensing, which is known to be critical for human-like dexterity. We present Text2Touch, bringing LLM-crafted rewards to the challenging task of multi-axis in-hand object rotation with real-world vision based tactile sensing in palm-up and palm-down configurations. Our prompt engineering strategy scales to over 70 environment variables, and sim-to-real di…