Tootfinder

Towards Generalized Parameter Tuning in Coherent Ising Machines: A Portfolio-Based Approach
Tatsuro Hanyu, Takahiro Katagiri, Daichi Mukunoki, Tetsuya Hoshino
https://arxiv.org/abs/2507.20295

Towards Generalized Parameter Tuning in Coherent Ising Machines: A Portfolio-Based Approach
Coherent Ising Machines (CIMs) have recently gained attention as a promising computing model for solving combinatorial optimization problems. In particular, the Chaotic Amplitude Control (CAC) algorithm has demonstrated high solution quality, but its performance is highly sensitive to a large number of hyperparameters, making efficient tuning essential. In this study, we present an algorithm portfolio approach for hyperparameter tuning in CIMs employing Chaotic Amplitude Control with momentum (…

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks
Joshua H. Davis, Daniel Nichols, Ishan Khillan, Abhinav Bhatele
https://arxiv.org/abs/2506.20938

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks
GPGPU architectures have become significantly diverse in recent years, which has led to an emergence of a variety of specialized programming models and software stacks to support them. While portable execution models exist, they still require significant developer effort to port to and optimize for different hardware architectures. Recent advances in large language models (LLMs) can help us reduce some of this programmer burden. In this paper, we present a novel benchmark and testing framework,…

ROBBO: An Efficient Method for Pareto Front Estimation with Guaranteed Accuracy
Roberto Boffadossi, Marco Leonesio, Lorenzo Fagiano
https://arxiv.org/abs/2506.18004

ROBBO: An Efficient Method for Pareto Front Estimation with Guaranteed Accuracy
A new method to estimate the Pareto Front (PF) in bi-objective optimization problems is presented. Assuming a continuous PF, the approach, named ROBBO (RObust and Balanced Bi-objective Optimization), needs to sample at most a finite, pre-computed number of PF points. Upon termination, it guarantees that the worst-case approximation error lies within a desired tolerance range, predefined by the decision maker, for each of the two objective functions. Theoretical results are derived, about the wo…

Can Limited Liability Increase Stability for Banks: A Dynamic Portfolio Approach
Deb Narayan Barik, Siddhartha P. Chakrabarty
https://arxiv.org/abs/2507.16494 https://

Can Limited Liability Increase Stability for Banks: A Dynamic Portfolio Approach
We present a novel approach for the bank's decision problem, incorporating Limited Liability in the objective function. Accordingly, we consider continuous time models, with and without Limited Liability. We compare the solutions of these two models to demonstrate the effect of inclusion of Limited Liability. To solve the problem with the objective function incorporating Limited Liability, we approximate the payoff function to another set of functions for which we have closed-form solutions. Th…

FairVizARD: A Visualization System for Assessing Multi-Party Fairness of Ride-Sharing Matching Algorithms
Ashwin Kumar, Sanket Shah, Meghna Lowalekar, Pradeep Varakantham, Alvitta Ottley, William Yeoh
https://arxiv.org/abs/2508.11770

FairVizARD: A Visualization System for Assessing Multi-Party Fairness of Ride-Sharing Matching Algorithms
There is growing interest in algorithms that match passengers with drivers in ride-sharing problems and their fairness for the different parties involved (passengers, drivers, and ride-sharing companies). Researchers have proposed various fairness metrics for matching algorithms, but it is often unclear how one should balance the various parties' fairness, given that they are often in conflict. We present FairVizARD, a visualization-based system that aids users in evaluating the fairness of rid…

The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): X. Dust Substructures, Disk Geometries, and Dust-disk Radii
Miguel Vioque, Nicol\'as T. Kurtovic, Leon Trapman, Anibal Sierra, Laura M. P\'erez, Ke Zhang, Pietro Curone, Giovanni P. Rosotti, John Carpenter, Beno\^it Tabone, Paola Pinilla, Dingshan Deng, Ilaria Pascucci, James Miley, Carolina Agurto-Gangas, Lucas A. Cieza, Rossella Anania, Dary A. Ruiz-Rodriguez, Camilo Gonz\'alez-Ruilova, Estephani E. Torre…

The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): X. Dust Substructures, Disk Geometries, and Dust-disk Radii
We perform visibility fitting to the dust continuum Band 6 1.3 mm data of the 30 protoplanetary disks in the AGE-PRO ALMA Large Program. We obtain disk geometries, dust-disk radii, and azimuthally symmetric radial profiles of the intensity of the dust continuum emission. We examine the presence of continuum substructures in the AGE-PRO sample by using these radial profiles and their residuals. We detect substructures in 15 out of 30 disks. We report five disks with large ($>$15 au) inner dust c…

PyVision: Agentic Vision with Dynamic Tooling
Shitian Zhao, Haoquan Zhang, Shaoheng Lin, Ming Li, Qilong Wu, Kaipeng Zhang, Chen Wei
https://arxiv.org/abs/2507.07998

PyVision: Agentic Vision with Dynamic Tooling
LLMs are increasingly deployed as agents, systems capable of planning, reasoning, and dynamically calling external tools. However, in visual reasoning, prior approaches largely remain limited by predefined workflows and static toolsets. In this report, we present PyVision, an interactive, multi-turn framework that enables MLLMs to autonomously generate, execute, and refine Python-based tools tailored to the task at hand, unlocking flexible and interpretable problem-solving. We develop a taxonom…

Distribution-free inference for LightGBM and GLM with Tweedie loss
Alokesh Manna, Aditya Vikram Sett, Dipak K. Dey, Yuwen Gu, Elizabeth D. Schifano, Jichao He
https://arxiv.org/abs/2507.06921

Distribution-free inference for LightGBM and GLM with Tweedie loss
Prediction uncertainty quantification is a key research topic in recent years scientific and business problems. In insurance industries (\cite{parodi2023pricing}), assessing the range of possible claim costs for individual drivers improves premium pricing accuracy. It also enables insurers to manage risk more effectively by accounting for uncertainty in accident likelihood and severity. In the presence of covariates, a variety of regression-type models are often used for modeling insurance clai…

Portfolio optimization in incomplete markets and price constraints determined by maximum entropy in the mean
Argimiro Arratia, Henryk Gzyl
https://arxiv.org/abs/2507.07053

Portfolio optimization in incomplete markets and price constraints determined by maximum entropy in the mean
A solution to a portfolio optimization problem is always conditioned by constraints on the initial capital and the price of the available market assets. If a risk neutral measure is known, then the price of each asset is the discounted expected value of the asset's price under this measure. But if the market is incomplete, the risk neutral measure is not unique, and there is a range of possible prices for each asset, which can be identified with bid-ask ranges. We present in this paper an effec…

A Fuzzy Set-based Approach for Matching Hand-Drawing Shapes of Touch-based Gestures for Graphical Passwords
Adel Sabour, Ahmed Gadallah, Hesham Hefny
https://arxiv.org/abs/2507.22382

A Fuzzy Set-based Approach for Matching Hand-Drawing Shapes of Touch-based Gestures for Graphical Passwords
This paper presents a two-dimension fuzzy set based approach for matching touch-based gestures using fuzzy cued click point technique. The pro posed approach aims mainly to improve the acceptance of the most closed inac curate hand drawn gestures generated by the user compared with a predefined referenced gesture value that is stored in the user profile. Commonly, gestures are used in order to facilitate the interactive capabilities between humans and computerized systems. Unfortunately, most o…