Tootfinder

101 Dalmatians: a multimodal naturalistic fMRI dataset in typical development and congenital sensory loss #neuroscience

101 Dalmatians: a multimodal naturalistic fMRI dataset in typical development and congenital sensory loss - Scientific Data
In recent years, neuroscience has increasingly leveraged naturalistic stimuli, like movies and narratives, to investigate cognitive processes underlying real-world human behavior. Here, we present a functional Magnetic Resonance Imaging (fMRI) dataset featuring 50 participants, with and without congenital sensory loss (typical development, congenitally blind and deaf individuals), who were exposed to audiovisual, auditory, or visual versions of the live-action movie 101 Dalmatians. The dataset …

Molly interviews the founders of @…
Leah Greenberg and Ezra Levin,
about the origins and evolution of their grassroots political movement,
which emerged in response to Donald Trump's 2016 election.
As former congressional staffers,
they leveraged insider knowledge to empower everyday Americans
—from nurses …

Crypto markets crashed after Trump threatened an extra 100% tariff on China, triggering $19.1B in liquidations, 10x the liquidations during the FTX crash (Sam Reynolds/CoinDesk)
https://www.coindesk.com/markets/2025/10/1…

Crypto Price Drop Continues: $16B in Longs Liquidated as BTC, ETH Sell-Off Extends
Trump’s 100% tariff warning on China ignited a global sell-off that wiped out $16 billion in leveraged crypto longs and pushed Ethena’s USDe to a rare sub-$1 print.

Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
Matthew Adrian, Yunsie Chung, Kevin Boyd, Saee Paliwal, Srimukh Prasad Veccham, Alan C. Cheng
https://arxiv.org/abs/2510.12719

Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
Chemical pretrained models, sometimes referred to as foundation models, are receiving considerable interest for drug discovery applications. The general chemical knowledge extracted from self-supervised training has the potential to improve predictions for critical drug discovery endpoints, including on-target potency and ADMET properties. Multi-task learning has previously been successfully leveraged to improve predictive models. Here, we show that enabling multitasking in finetuning of chemic…

Quantum thermodynamics of Gross-Pitaevskii qubits
Sebastian Deffner
https://arxiv.org/abs/2510.12599 https://arxiv.org/pdf/2510.12599

Quantum thermodynamics of Gross-Pitaevskii qubits
What are the resources that can be leveraged for a thermodynamic device to exhibit genuine quantum advantage? Typically, the answer to this question is sought in quantum correlations. In the present work, we show that quantum Otto engines that operate with nonlinear qubits significantly outperform linear engines. To this end, we develop a comprehensive thermodynamic description of nonlinear qubits starting with identifying the proper thermodynamic equilibrium state. We then show that for ideal …

Viking, n.:
1. Daring Scandinavian seafarers, explorers, adventurers,
entrepreneurs world-famous for their aggressive, nautical import
business, highly leveraged takeovers and blue eyes.
2. Bloodthirsty sea pirates who ravaged northern Europe beginning
in the 9th century.
Hagar's note: The first definition is much preferred; the second is used
only by malcontents, the envious, and disgruntled owners of waterfront
property.

Anti-Regulatory AI: How "AI Safety" is Leveraged Against Regulatory Oversight
Rui-Jie Yew, Brian Judge
https://arxiv.org/abs/2509.22872 https://a…

Anti-Regulatory AI: How "AI Safety" is Leveraged Against Regulatory Oversight
AI companies increasingly develop and deploy privacy-enhancing technologies, bias-constraining measures, evaluation frameworks, and alignment techniques -- framing them as addressing concerns related to data privacy, algorithmic fairness, and AI safety. This paper examines the ulterior function of these technologies as mechanisms of legal influence. First, we examine how encryption, federated learning, and synthetic data -- presented as enhancing privacy and reducing bias -- can operate as mech…

A 3D Generation Framework from Cross Modality to Parameterized Primitive
Yiming Liang, Huan Yu, Zili Wang, Shuyou Zhang, Guodong Yi, Jin Wang, Jianrong Tan
https://arxiv.org/abs/2510.08656

A 3D Generation Framework from Cross Modality to Parameterized Primitive
Recent advancements in AI-driven 3D model generation have leveraged cross modality, yet generating models with smooth surfaces and minimizing storage overhead remain challenges. This paper introduces a novel multi-stage framework for generating 3D models composed of parameterized primitives, guided by textual and image inputs. In the framework, A model generation algorithm based on parameterized primitives, is proposed, which can identifies the shape features of the model constituent elements, …

A Duality Theorem for Classical-Quantum States with Applications to Complete Relational Program Logics
Gilles Barthe, Minbo Gao, Jam Kabeer Ali Khan, Matthijs Muis, Ivan Renison, Keiya Sakabe, Michael Walter, Yingte Xu, Li Zhou
https://arxiv.org/abs/2510.07051

A Duality Theorem for Classical-Quantum States with Applications to Complete Relational Program Logics
Duality theorems play a fundamental role in convex optimization. Recently, it was shown how duality theorems for countable probability distributions and finite-dimensional quantum states can be leveraged for building relatively complete relational program logics for probabilistic and quantum programs, respectively. However, complete relational logics for classical-quantum programs, which combine classical and quantum computations and operate over classical as well as quantum variables, have rem…

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Disharee Bhowmick, Ranjith Ramanathan, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2510.09593

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Time series data often contain latent temporal structure, transitions between locally stationary regimes, repeated motifs, and bursts of variability, that are rarely leveraged in standard representation learning pipelines. Existing models typically operate on raw or fixed-window sequences, treating all time steps as equally informative, which leads to inefficiencies, poor robustness, and limited scalability in long or noisy sequences. We propose STaTS, a lightweight, unsupervised framework for …

Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging
Huifa Li, Feilong Tang, Haochen Xue, Yulong Li, Xinlin Zhuang, Bin Zhang, Eran Segal, Imran Razzak
https://arxiv.org/abs/2510.12384

Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging
Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological decline than chronological age. While previous studies have built aging clocks using single-omics data, they often fail to capture the full molecular complexity of human aging. In this work, we leveraged the Human Phenotype Project, a large-scale cohort of 12,000 adults aged 30--70 years, with extensive longitudinal prof…

Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Vishakha Lall, Yisi Liu
https://arxiv.org/abs/2510.08586 https://ar…

Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Detecting psychological stress from speech is critical in high-pressure settings. While prior work has leveraged acoustic features for stress detection, most treat stress as a static label. In this work, we model stress as a temporally evolving phenomenon influenced by historical emotional state. We propose a dynamic labelling strategy that derives fine-grained stress annotations from emotional labels and introduce cross-attention-based sequential models, a Unidirectional LSTM and a Transformer…

RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
Tommy Mordo, Sagie Dekel, Omer Madmon, Moshe Tennenholtz, Oren Kurland
https://arxiv.org/abs/2510.04096

RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
Competitive search is a setting where document publishers modify them to improve their ranking in response to a query. Recently, publishers have increasingly leveraged LLMs to generate and modify competitive content. We introduce Reinforcement Learning from Ranker Feedback (RLRF), a framework that trains LLMs using preference datasets derived from ranking competitions. The goal of a publisher (LLM-based) agent is to optimize content for improved ranking while accounting for the strategies of co…

Efficient Domain Generalization in Wireless Networks with Scarce Multi-Modal Data
Minsu Kim, Walid Saad, Dour Calin
https://arxiv.org/abs/2510.04359 https://

Efficient Domain Generalization in Wireless Networks with Scarce Multi-Modal Data
In 6G wireless networks, multi-modal ML models can be leveraged to enable situation-aware network decisions in dynamic environments. However, trained ML models often fail to generalize under domain shifts when training and test data distributions are different because they often focus on modality-specific spurious features. In practical wireless systems, domain shifts occur frequently due to dynamic channel statistics, moving obstacles, or hardware configuration. Thus, there is a need for learn…

Visual Jigsaw Post-Training Improves MLLMs
Penghao Wu, Yushan Zhang, Haiwen Diao, Bo Li, Lewei Lu, Ziwei Liu
https://arxiv.org/abs/2509.25190 https://arxiv…

Visual Jigsaw Post-Training Improves MLLMs
Reinforcement learning based post-training has recently emerged as a powerful paradigm for enhancing the alignment and reasoning capabilities of multimodal large language models (MLLMs). While vision-centric post-training is crucial for enhancing MLLMs' intrinsic understanding of visual signals, current post-training paradigms are predominantly text-centric, where dense visual inputs are only leveraged to extract sparse cues for text-based reasoning. There exist a few approaches in this directi…

That's how our hike at the #mummelsee started ... and how it was when we were up there. - But the walk was quite okay.
We checked the rain radar and decided to go back down rather quickly. We leveraged our rain gear and it did not disappoint us! 🙂
So we didn't see the area atop - the so called

AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation
Liyi Cai, Yijie Ren, Yitong Zhang, Jia Li
https://arxiv.org/abs/2510.00591 https://

AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation
Software automation has long been a central goal of software engineering, striving for software development that proceeds without human intervention. Recent efforts have leveraged Artificial Intelligence (AI) to advance software automation with notable progress. However, current AI functions primarily as assistants to human developers, leaving software development still dependent on explicit human intervention. This raises a fundamental question: Can AI move beyond its role as an assistant to b…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Vu Duc Anh Nguyen, Ziyue Li
https://arxiv.org/abs/2510.09350 https://…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while sim…

Sources: EA is nearing a deal to go private in a leveraged buyout valuing the company at ~$50B; investors include Silver Lake and Saudi Arabia's PIF (Wall Street Journal)
https://www.wsj.com/business/deals/ea-private-deal-buyout-video-game-maker-808aef…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
Anirban Chatterjee, Sayantan Choudhury, Rohan Hore
https://arxiv.org/abs/2509.25507 https://arxi…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference, and conditional distribution modeling in complex data settings. In such settings, compared with unconditional sampling, additional feature information can be leveraged to enable mo…

Interface Design to Support Legal Reading and Writing: Insights from Interviews with Legal Experts
Chelse Swoopes, Ziwei Gu, Elena L. Glassman
https://arxiv.org/abs/2509.24854 h…

Interface Design to Support Legal Reading and Writing: Insights from Interviews with Legal Experts
Legal professionals spend significant time reading, writing, and interpreting complex documents, yet research has not fully captured how they approach these tasks or what they expect from skimming and writing-support tools. To examine practices and views on emerging tools, we interviewed 22 legal professionals about workflows, challenges, and technology use. In each session, we leveraged prior HCI-based skimming and writing prototypes that surface emergent cross-document relationships and suppo…

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…

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Nishant Doshi
https://arxiv.org/abs/2509.21873 https://arxiv.org/pdf/2509.21873

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Conventional maneuver prediction methods use some sort of classification model on temporal trajectory data to predict behavior of agents over a set time horizon. Despite of having the best precision and recall, these models cannot predict a lane change accurately unless they incorporate information about the entire scene. Level-k game theory can leverage the human-like hierarchical reasoning to come up with the most rational decisions each agent can make in a group. This can be leveraged to mod…

Gravitational waves from axion inflation in the gradient expansion formalism II: Fermionic axion inflation
Richard von Eckardstein, Kai Schmitz, Oleksandr Sobol
https://arxiv.org/abs/2509.25013

Gravitational waves from axion inflation in the gradient expansion formalism II: Fermionic axion inflation
Axion inflation represents an intriguing source of gravitational waves (GWs) from the early Universe. In a companion paper arXiv:2508.00798, we previously leveraged the gradient expansion formalism (GEF) to investigate pure axion inflation (PAI), i.e., axion inflaton coupled to a pure gauge sector. In this paper, we extend our analysis to fermionic axion inflation (FAI), i.e., we allow for the presence of fermions in the gauge sector. PAI predicts a strongly blue-tilted GW spectrum; in our GEF …

How Trump leveraged Biden's TikTok ban law to help allies gain control of the app, amid fears it could be used to boost pro-Trump content and suppress criticism (Washington Post)

Trump turns Biden’s TikTok law into a big win
Biden officials said selling TikTok would safeguard it from political influence. Now, critics of a spinoff deal to Trump allies say that aim could be subverted.

Expressive Value Learning for Scalable Offline Reinforcement Learning
Nicolas Espinosa-Dice, Kiante Brantley, Wen Sun
https://arxiv.org/abs/2510.08218 https://

Expressive Value Learning for Scalable Offline Reinforcement Learning
Reinforcement learning (RL) is a powerful paradigm for learning to make sequences of decisions. However, RL has yet to be fully leveraged in robotics, principally due to its lack of scalability. Offline RL offers a promising avenue by training agents on large, diverse datasets, avoiding the costly real-world interactions of online RL. Scaling offline RL to increasingly complex datasets requires expressive generative models such as diffusion and flow matching. However, existing methods typically…

Trajectory Encryption Cooperative Salvo Guidance
Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav Sinha
https://arxiv.org/abs/2509.17341 https://arxiv.org…

Trajectory Encryption Cooperative Salvo Guidance
This paper introduces the concept of trajectory encryption in cooperative simultaneous target interception, wherein heterogeneity in guidance principles across a team of unmanned autonomous systems is leveraged as a strategic design feature. By employing a mix of heterogeneous time-to-go formulations leading to a cooperative guidance strategy, the swarm of vehicles is able to generate diverse trajectory families. This diversity expands the feasible solution space for simultaneous target interce…

Unveiling Many Faces of Surrogate Models for Configuration Tuning: A Fitness Landscape Analysis Perspective
Pengzhou Chen, Hongyuan Liang, Tao Chen
https://arxiv.org/abs/2509.21945

Unveiling Many Faces of Surrogate Models for Configuration Tuning: A Fitness Landscape Analysis Perspective
To efficiently tune configuration for better system performance (e.g., latency), many tuners have leveraged a surrogate model to expedite the process instead of solely relying on the profoundly expensive system measurement. As such, it is naturally believed that we need more accurate models. However, the fact of accuracy can lie-a somewhat surprising finding from prior work-has left us many unanswered questions regarding what role the surrogate model plays in configuration tuning. This paper pr…

Factorizing Diffusion Policies for Observation Modality Prioritization
Omkar Patil, Prabin Rath, Kartikay Pangaonkar, Eric Rosen, Nakul Gopalan
https://arxiv.org/abs/2509.16830 …

Factorizing Diffusion Policies for Observation Modality Prioritization
Diffusion models have been extensively leveraged for learning robot skills from demonstrations. These policies are conditioned on several observational modalities such as proprioception, vision and tactile. However, observational modalities have varying levels of influence for different tasks that diffusion polices fail to capture. In this work, we propose 'Factorized Diffusion Policies' abbreviated as FDP, a novel policy formulation that enables observational modalities to have differing influ…

Transabdominal Fetal Oximetry via Diffuse Optics: Principled Analysis and Demonstration in Pregnant Ovine Models
Weitai Qian, Rishad Raiyan Joarder, Randall Fowler, Begum Kasap, Mahya Saffarpour, Kourosh Vali, Tailai Lihe, Aijun Wang, Diana Farmer, Soheil Ghiasi
https://arxiv.org/abs/2509.21594

Transabdominal Fetal Oximetry via Diffuse Optics: Principled Analysis and Demonstration in Pregnant Ovine Models
Diffuse optics has the potential to offer a substantial advancement in fetal health monitoring via enabling continuous measurement of fetal blood oxygen saturation (fSpO$_2$). Aiming to enhance the sensing accuracy and to elucidate the foundational limits of Transabdominal Fetal Oximetry (TFO) via diffuse optics, we introduce a theoretical derivation, and a comprehensive pipeline for fSpO$_2$ estimation from non-invasively sensed diffuse light intensity values, which are leveraged to analyze da…

Leveraging Familiarity with Television to Enrich Older Adults' Engagement and Wellbeing: A Feasibility Study Using Video Probes
Shyama Sastha Krishnamoorthy Srinivasan, Mohan Kumar, Pushpendra Singh
https://arxiv.org/abs/2509.15618

Leveraging Familiarity with Television to Enrich Older Adults' Engagement and Wellbeing: A Feasibility Study Using Video Probes
The shift away from multigenerational families to nuclear families in India has created a growing need to support older adults living independently. While technology can help address this gap, older adults' limited exposure to newer technology restricts the adoption of such solutions. However, they remain comfortable with long-standing technologies like television (TV). This study explores their daily technology usage and challenges, aiming to determine whether TV can be leveraged to improve th…

Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Ethan Baron, Boris Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova
https://arxiv.org/abs/2510.02224

Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step lookahead predictions to improve the quality and efficiency of multi-step forecasts. However, these models only predict independent marginal distributions for each time step, rather than a full joint predictive distribution. To generate forecast sample paths with realistic correlation structures, one typically resorts to …

Signatures of $K^-$ condensation on neutron star structure and $f-$mode frequencies
Debanjan Guha Roy, Sarmistha Banik
https://arxiv.org/abs/2509.15376 https://

Signatures of $K^-$ condensation on neutron star structure and $f-$mode frequencies
Antikaon ($K^-$) condensation within neutron star matter (NS) depends on the antikaon-nucleon interaction potential ($U_K$). Appearance of $K^-$ generally softens the equation of state (EOS). The impact of this softening on the structure of the NS can be leveraged to find a telltale sign of the phase transition from nucleonic matter to $K^-$ condensation. To investigate the impact of $K^-$ condensation on NS properties using a Bayesian inference framework, we choose two sets of RMF model parame…

ArchGPT: Understanding the World's Architectures with Large Multimodal Models
Yuze Wang, Luo Yang, Junyi Wang, Yue Qi
https://arxiv.org/abs/2509.20858 https://

ArchGPT: Understanding the World's Architectures with Large Multimodal Models
Architecture embodies aesthetic, cultural, and historical values, standing as a tangible testament to human civilization. Researchers have long leveraged virtual reality (VR), mixed reality (MR), and augmented reality (AR) to enable immersive exploration and interpretation of architecture, enhancing accessibility, public understanding, and creative workflows around architecture in education, heritage preservation, and professional design practice. However, existing VR/MR/AR systems are often de…