Tootfinder

DMER-Ranker: Learning to Rank Emotion Descriptions in the Absence of Ground Truth
Zheng Lian, Licai Sun, Haoyu Chen, Zebang Cheng, Fan Zhang, Ziyu Jia, Ziyang Ma, Fei Ma, Xiaojiang Peng, Jianhua Tao
https://arxiv.org/abs/2507.04278

DMER-Ranker: Learning to Rank Emotion Descriptions in the Absence of Ground Truth
Descriptive Multimodal Emotion Recognition (DMER) is a newly proposed task that aims to describe a person's emotional state using free-form natural language. Unlike traditional discriminative methods that rely on predefined emotion taxonomies, DMER provides greater flexibility in emotional expression, enabling fine-grained and interpretable emotion representations. However, this free-form prediction paradigm introduces significant challenges in evaluation. Existing methods either depend on grou…

Assessing Dynamic Connectedness in Global Supply Chain Infrastructure Portfolios: The Impact of Risk Factors and Extreme Events
Haibo Wang
https://arxiv.org/abs/2508.04858 https…

Assessing Dynamic Connectedness in Global Supply Chain Infrastructure Portfolios: The Impact of Risk Factors and Extreme Events
This paper analyses the risk factors around investing in global supply chain infrastructure: the energy market, investor sentiment, and global shipping costs. It presents portfolio strategies associated with dynamic risks. A time-varying parameter vector autoregression (TVP-VAR) model is used to study the spillover and interconnectedness of the risk factors for global supply chain infrastructure portfolios from January 5th, 2010, to June 29th, 2023, which are associated with a set of environmen…

$NavA^3$: Understanding Any Instruction, Navigating Anywhere, Finding Anything
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang, Wenbo Ding, Shanghang Zhang
https://arxiv.org/abs/2508.04598

$NavA^3$: Understanding Any Instruction, Navigating Anywhere, Finding Anything
Embodied navigation is a fundamental capability of embodied intelligence, enabling robots to move and interact within physical environments. However, existing navigation tasks primarily focus on predefined object navigation or instruction following, which significantly differs from human needs in real-world scenarios involving complex, open-ended scenes. To bridge this gap, we introduce a challenging long-horizon navigation task that requires understanding high-level human instructions and perf…

Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails
Ayush Jha, Abootaleb Shirvani, Ali M. Jaffri, Svetlozar T. Rachev, Frank J. Fabozzi
https://arxiv.org/abs/2507.04208

Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails
This paper develops a unified framework that integrates behavioral distortions into rational portfolio optimization by extracting implied probability weighting functions (PWFs) from optimal portfolios modeled under Gaussian and Normal-Inverse-Gaussian (NIG) return distributions. Using DJIA constituents, we construct mean-CVaR99 frontiers, alongwith Sharpe- and CVaR-maximizing portfolios, and estimate PWFs that capture nonlinear beliefs consistent with fear and greed. We show that increasing tai…

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Tianyi Ding, Hongli Chen, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Hongfu Sun
https://arxiv.org/abs/2507.03369

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by matching signal evolutions to a predefined dictionary. However, conventional dictionary matching suffers from exponential growth in computational cost and memory usage as the number of parameters increases, limiting its scalability to multi-parametric mapping. To address this, recent work has explored deep learning-based approaches as alternatives to DM. We propose GAST-Mamba, an end-to-end framework that combines a du…

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Yen Jui Chang, Wei-Ting Wang, Yun-Yuan Wang, Chen-Yu Liu, Kuan-Cheng Chen, Ching-Ray Chang
https://arxiv.org/abs/2507.03963

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk's stationary distribution. Three empirical studies support the approach. (i) For the top 100 S\&P 500 constituents over 2016-2024, six scenario portfolio…

STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements
Moshe Unger, Alexander Tuzhilin, Michel Wedel
https://arxiv.org/abs/2508.04148 https://

STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements
The present work proposes a Deep Learning architecture for the prediction of various consumer choice behaviors from time series of raw gaze or eye fixations on images of the decision environment, for which currently no foundational models are available. The architecture, called STARE (Spatio-Temporal Attention Representation for Eye Tracking), uses a new tokenization strategy, which involves mapping the x- and y- pixel coordinates of eye-movement time series on predefined, contiguous Regions of…

Causal identification with $Y_0$
Charles Tapley Hoyt, Craig Bakker, Richard J. Callahan, Joseph Cottam, August George, Benjamin M. Gyori, Haley M. Hummel, Nathaniel Merrill, Sara Mohammad Taheri, Pruthvi Prakash Navada, Marc-Antoine Parent, Adam Rupe, Olga Vitek, Jeremy Zucker
https://arxiv.org/abs/2508.03167…

Causal identification with $Y_0$
We present the $Y_0$ Python package, which implements causal identification algorithms that apply interventional, counterfactual, and transportability queries to data from (randomized) controlled trials, observational studies, or mixtures thereof. $Y_0$ focuses on the qualitative investigation of causation, helping researchers determine whether a causal relationship can be estimated from available data before attempting to estimate how strong that relationship is. Furthermore, $Y_0$ provides gu…

Directional Flow of Confined Polaritons in CrSBr
Pratap Chandra Adak, Sichao Yu, Jaime Abad-Arredondo, Biswajit Datta, Andy Cruz, Sorah Fischer, Kseniia Mosina, Zden\v{e}k Sofer, Antonio I. Fern\'andez-Dom\'inguez, Francisco J. Garc\'ia-Vidal, Vinod M. Menon
https://arxiv.org/abs/2507.04367

Directional Flow of Confined Polaritons in CrSBr
Nanoscale control of energy transport is a central challenge in modern photonics. Utilization of exciton-polaritons hybrid light-matter quasiparticles is one viable approach, but it typically demands complex device engineering to enable directional transport. Here, we demonstrate that the van der Waals magnet CrSBr offers an inherent avenue for steering polariton transport leveraging a unique combination of intrinsic optical anisotropy, high refractive index, and excitons dressed by photons. Th…

Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
He Wang, Liang Zeng
https://arxiv.org/abs/2508.03661

Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
Computational scientific discovery increasingly relies on algorithms to process complex data and identify meaningful patterns - yet faces persistent challenges in gravitational-wave signal identification. While existing algorithmic approaches like matched filtering (MF) and deep neural networks (DNNs) have achieved partial success, their limitations directly stem from fundamental limitations: MF's excessive computational demands arise from its reliance on predefined theoretical waveform templat…