Tootfinder

Cost-Effective Strategies for Infectious Diseases: A Multi-Objective Framework with an Interactive Dashboard
Jongmin Lee, Renier Mendoza, Victoria May P. Mendoza, Eunok Jung
https://arxiv.org/abs/2509.09087

Cost-Effective Strategies for Infectious Diseases: A Multi-Objective Framework with an Interactive Dashboard
During an infectious disease outbreak, policymakers must balance medical costs with social and economic burdens and seek interventions that minimize both. To support this decision-making process, we developed a framework that integrates multi-objective optimization, cost-benefit analysis, and an interactive dashboard. This platform enables users to input cost parameters and immediately obtain a cost-optimal intervention strategy. We applied this framework to the early outbreak of COVID-19 in So…

Can Understanding and Generation Truly Benefit Together -- or Just Coexist?
Zhiyuan Yan, Kaiqing Lin, Zongjian Li, Junyan Ye, Hui Han, Zhendong Wang, Hao Liu, Bin Lin, Hao Li, Xue Xu, Xinyan Xiao, Jingdong Wang, Haifeng Wang, Li Yuan
https://arxiv.org/abs/2509.09666

Can Understanding and Generation Truly Benefit Together -- or Just Coexist?
In this paper, we introduce an insightful paradigm through the Auto-Encoder lens-understanding as the encoder (I2T) that compresses images into text, and generation as the decoder (T2I) that reconstructs images from that text. Using reconstruction fidelity as the unified training objective, we enforce the coherent bidirectional information flow between the understanding and generation processes, bringing mutual gains. To implement this, we propose UAE, a novel framework for unified multimodal l…

Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Ning Li, Kounianhua Du, Han Zhang, Quan Gan, Minjie Wang, David Wipf, Weinan Zhang
https://arxiv.org/abs/2508.08327

Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Relational databases (RDBs) have become the industry standard for storing massive and heterogeneous data. However, despite the widespread use of RDBs across various fields, the inherent structure of relational databases hinders their ability to benefit from flourishing deep learning methods. Previous research has primarily focused on exploiting the unary dependency among multiple tables in a relational database using the primary key - foreign key relationships, either joining multiple tables in…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
Tasfia Mashiat, Patrick J. Fowler, Sanmay Das
https://arxiv.org/abs/2508.08573 htt…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial d…

Not in My Backyard! Temporal Voting Over Public Chores
Edith Elkind, Tzeh Yuan Neoh, Nicholas Teh
https://arxiv.org/abs/2508.08810 https://arxiv.org/pdf/25…

Not in My Backyard! Temporal Voting Over Public Chores
We study a temporal voting model where voters have dynamic preferences over a set of public chores -- projects that benefit society, but impose individual costs on those affected by their implementation. We investigate the computational complexity of optimizing utilitarian and egalitarian welfare. Our results show that while optimizing the former is computationally straightforward, minimizing the latter is computationally intractable, even in very restricted cases. Nevertheless, we identify sev…

Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields
Zhen Tian, Fujiang Yuan, Chunhong Yuan, Yanhong Peng
https://arxiv.org/abs/2509.08147

Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields
Interactive trajectory planning in autonomous driving must balance safety, efficiency, and scalability under heterogeneous driving behaviors. Existing methods often face high computational cost or rely on external safety critics. To address this, we propose an Interaction-Enriched Unified Potential Field (IUPF) framework that fuses style-dependent benefit and risk fields through a physics-inspired variational model, grounded in mean field game theory. The approach captures conservative, aggress…

OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools
Jiaxuan Liang, Shide Zhou, Kailong Wang
https://arxiv.org/abs/2508.05650 https://

OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools
While Retrieval Augmented Generation (RAG) is now widely adopted to enhance LLMs, evaluating its true performance benefits in a reproducible and interpretable way remains a major hurdle. Existing methods often fall short: they lack domain coverage, employ coarse metrics that miss sub document precision, and fail to capture computational trade offs. Most critically, they provide no standardized framework for comparing RAG effectiveness across different models and domains. We introduce OmniBenc…

RIS-Assisted Near-Field ISAC for Multi-Target Indication in NLoS Scenarios
Hang Ruan (Weizmann Institute of Science), Homa Nikbakht (Weizmann Institute of Science, Princeton University), Ruizhi Zhang (Weizmann Institute of Science, University of Electronic Science and Technology of China), Honglei Chen (MathWorks, Inc), Yonina C. Eldar (Weizmann Institute of Science)

RIS-Assisted Near-Field ISAC for Multi-Target Indication in NLoS Scenarios
Enabling multi-target sensing in near-field integrated sensing and communication (ISAC) systems is a key challenge, particularly when line-of-sight paths are blocked. This paper proposes a beamforming framework that leverages a reconfigurable intelligent surface (RIS) to achieve multi-target indication. Our contribution is the extension of classic beampattern gain and inter-target cross-correlation metrics to the near-field, leveraging both angle and distance information to discriminate between…

When Secure Isn't: Assessing the Security of Machine Learning Model Sharing
Gabriele Digregorio, Marco Di Gennaro, Stefano Zanero, Stefano Longari, Michele Carminati
https://arxiv.org/abs/2509.06703

When Secure Isn't: Assessing the Security of Machine Learning Model Sharing
The rise of model-sharing through frameworks and dedicated hubs makes Machine Learning significantly more accessible. Despite their benefits, these tools expose users to underexplored security risks, while security awareness remains limited among both practitioners and developers. To enable a more security-conscious culture in Machine Learning model sharing, in this paper we evaluate the security posture of frameworks and hubs, assess whether security-oriented mechanisms offer real protection, …

A RIS-Enabled Computational Radar Coincidence Imaging
Kavian Zirak, Mohammadreza F. Imani
https://arxiv.org/abs/2507.07285 https://ar…

A RIS-Enabled Computational Radar Coincidence Imaging
This paper introduces an innovative imaging method using reconfigurable intelligent surfaces (RISs) by combining radar coincidence imaging (RCI) and computational imaging techniques. In the proposed framework, RISs simultaneously redirect beams toward a desired region of interest (ROI). The interference of these beams forms spatially diverse speckle patterns that carry information about the entire ROI. As a result, this method can take advantage of the benefits of both random patterns and spotl…