Tootfinder

Profiling and optimization of multi-card GPU machine learning jobs
Marcin Lawenda, Kyrylo Khloponin, Krzesimir Samborski, {\L}ukasz Szustak
https://arxiv.org/abs/2505.22905

Profiling and optimization of multi-card GPU machine learning jobs
The effectiveness and efficiency of machine learning methodologies are crucial, especially with respect to the quality of results and computational cost. This paper discusses different model optimization techniques, providing a comprehensive analysis of key performance indicators. Several parallelization strategies for image recognition, adapted to different hardware and software configurations, including distributed data parallelism and distributed hardware processing, are analyzed. Selected o…

Fast Derivative Valuation from Volatility Surfaces using Machine Learning
Lijie Ding, Egang Lu, Kin Cheung
https://arxiv.org/abs/2505.22957 https://…

Fast Derivative Valuation from Volatility Surfaces using Machine Learning
We introduce a fast and flexible Machine Learning (ML) framework for pricing derivative products whose valuation depends on volatility surfaces. By parameterizing volatility surfaces with the 5-parameter stochastic volatility inspired (SVI) model augmented by a one-factor term structure adjustment, we first generate numerous volatility surfaces over realistic ranges for these parameters. From these synthetic market scenarios, we then compute high-accuracy valuations using conventional methodolo…

An Iterative Methodology for Unitary Quantum Channel Search
Matthew M. Lin, Hao-Wei Huang, Bing-Ze Lu
https://arxiv.org/abs/2506.21455 https://

An Iterative Methodology for Unitary Quantum Channel Search
In this paper, we propose an iterative algorithm using polar decomposition to approximate a channel characterized by a single unitary matrix based on input-output quantum state pairs. In limited data, we state and prove that the optimal solution obtained from our method using one pair with a specific structure will generate an equivalent class, significantly reducing the dimension of the searching space. Furthermore, we prove that the unitary matrices describing the same channel differ by a com…

PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models
Lintao Wang, Encheng Su, Jiaqi Liu, Pengze Li, Peng Xia, Jiabei Xiao, Wenlong Zhang, Xinnan Dai, Xi Chen, Yuan Meng, Mingyu Ding, Lei Bai, Wanli Ouyang, Shixiang Tang, Aoran Wang, Xinzhu Ma
https://arxiv.org/abs/2506.17667

PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models
Physics problem-solving is a challenging domain for large AI models, requiring integration of conceptual understanding, mathematical reasoning, and interpretation of physical diagrams. Current evaluation methodologies show notable limitations in capturing the breadth and complexity of undergraduate-level physics, underscoring the need for more rigorous assessments. To this end, we present PhysUniBench, a large-scale multimodal benchmark designed to evaluate and improve the reasoning capabilitie…

A Proof of the Hodge Conjecture for a Special Class of Calabi-Yau 5-Folds via Exceptional Group Constraints
Dongzhe Zheng
https://arxiv.org/abs/2506.17754 …

A Proof of the Hodge Conjecture for a Special Class of Calabi-Yau 5-Folds via Exceptional Group Constraints
This paper provides a complete proof of the (1,1)-type Hodge conjecture for a special class of 5-dimensional Calabi-Yau manifolds ($h^{1,1}=56$), while establishing and implementing a novel methodology based on exceptional group constraints. The core idea of this methodology is to replace the "geometric construction" that traditional methods rely on -- which becomes infeasible in high-dimensional cases -- with the "algebraic rigidity" of Lie algebra representation theory. We prove that by intro…

Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
Jing Luo, Yejun Gu, Yanfei Wang, Xiaolong Ma, Jaafar. A El-Awady
https://arxiv.org/abs/2506.20839

Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
Machine learning has significantly advanced the understanding and application of structural materials, with an increasing emphasis on integrating existing data and quantifying uncertainties in predictive modeling. This study presents a comprehensive methodology utilizing a mixed density network (MDN) model, trained on extensive experimental data from literature. This approach uniquely predicts the distribution of dislocation density, inferred as a latent variable, and the resulting stress distr…

Skeletal Reaction Models for Gasoline Surrogate Combustion
Yinmin Liu, Hessam Babaee, Peyman Givi, Daniel Livescu, Arash Nouri
https://arxiv.org/abs/2506.18853

Skeletal Reaction Models for Gasoline Surrogate Combustion
Skeletal reaction models are derived for a four-component gasoline surrogate model via an instantaneous local sensitivity analysis technique. The sensitivities of the species mass fractions and the temperature with respect to the reaction rates are estimated by a reduced-order modeling (ROM) methodology. Termed "implicit time-dependent basis CUR (implicit TDB-CUR)," this methodology is based on the CUR matrix decomposition and incorporates implicit time integration for evolving the bases. The e…

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
Chenyang Peng, Haijun Wang, Yin Wu, Hao Wu, Ming Fan, Yitao Zhao, Ting Liu
https://arxiv.org/abs/2506.15790

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel…

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo, Taehyun Yang, Daniel Archambault, Sungahn Ko, Takanori Fujiwara, Kwan-Liu Ma, Jinwook Seo
https://arxiv.org/abs/2506.14820

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual ana…

Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources?
Houyi Li, Ka Man Lo, Ziqi Wang, Zili Wang, Wenzhen Zheng, Shuigeng Zhou, Xiangyu Zhang, Daxin Jiang
https://arxiv.org/abs/2506.12119

Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources?
Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token compute. However, can MoEs surpass dense architectures under strictly equal resource constraints - that is, when the total parameter count, training compute, and data budget are identical? This question remains under-explored despite its significant practical value and potential. In this paper, we propose a novel perspective and methodological framework to …