Tootfinder

Mapping diverse hysteresis dynamics in scaled MoS$_2$ FETs using the universal method derived from TCAD modeling
Yezhu Lv, Haihui Cai, Yehao Wu, Yu. Yu. Illarionov
https://arxiv.org/abs/2507.13002

Mapping diverse hysteresis dynamics in scaled MoS$_2$ FETs using the universal method derived from TCAD modeling
Field-effect transistors (FETs) based on 2D materials have already reached the stage of trial FAB integration. However, reliability limitations caused by various defects present a serious obstacle for their smooth way forward, especially when scaling the device geometries. Still the ongoing research is mostly focused on pure technology aspects, while reliability is often recalled only when showing a randomly measured gate transfer curve to manifest that the hysteresis is "negligible".In fact th…

Automated Risk Management Mechanisms in DeFi Lending Protocols: A Crosschain Comparative Analysis of Aave and Compound
Erum Iftikhar, Wei Wei, John Cartlidge
https://arxiv.org/abs/2506.12855

Automated Risk Management Mechanisms in DeFi Lending Protocols: A Crosschain Comparative Analysis of Aave and Compound
Blockchain-based decentralised lending is a rapidly growing and evolving alternative to traditional lending, but it poses new risks. To mitigate these risks, lending protocols have integrated automated risk management tools into their smart contracts. However, the effectiveness of the latest risk management features introduced in the most recent versions of these lending protocols is understudied. To close this gap, we use a panel regression fixed effects model to empirically analyse the cross-…

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation
G\'abor Antal, D\'enes B\'an, Martin Isztin, Rudolf Ferenc, P\'eter Heged\H{u}s
https://arxiv.org/abs/2506.11559

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation
In the life-cycle of software development, testing plays a crucial role in quality assurance. Proper testing not only increases code coverage and prevents regressions but it can also ensure that any potential vulnerabilities in the software are identified and effectively fixed. However, creating such tests is a complex, resource-consuming manual process. To help developers and security experts, this paper explores the automatic unit test generation capability of one of the most widely used larg…

Suppressing excitations in the nonlinear Landau-Zener model
Sebastian Deffner, Steve Campbell
https://arxiv.org/abs/2506.11759 https://

Suppressing excitations in the nonlinear Landau-Zener model
Many complex quantum systems can be described by effectively nonlinear dynamics. While such dynamics have many appealing characteristics, they also make the analysis significantly more involved. This is due to the fact that only a few analytical treatments exist, and that the language of quantum mechanics is built for linear operators. For instance, the very formulation of the quantum adiabatic theorem requires the underlying dynamics to be linear. In this work we show that in a generalized Lan…

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Nonparametric Treatment Effect Identification in School Choice
Jiafeng Chen
https://arxiv.org/abs/2112.03872
- Potential weights and implicit causal designs in linear regression
Jiafeng Chen
https://arxiv.org/abs/2407.21119 https://mastoxiv.page/@arXiv_econEM_bot/112885535340833628
- Reinterpreting demand estimation
Jiafeng Chen
https://arxiv.org/abs/2503.23524 https://mastoxiv.page/@arXiv_econEM_bot/114262944644769755
- A step towards the integration of machine learning and classic model-based survey methods
Tomasz \.Z\k{a}d{\l}o, Adam Chwila
https://arxiv.org/abs/2402.07521 https://mastoxiv.page/@arXiv_statME_bot/111924442633277025
- Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forec...
Haojie Liu, Zihan Lin
https://arxiv.org/abs/2507.07469 https://mastoxiv.page/@arXiv_statML_bot/114833708254543213
toXiv_bot_toot

Towards Designing Social Interventions for Online Climate Change Denialism Discussions
Ruican zhong, Shruti Phadke, Beth Goldberg, Tanushree Mitra
https://arxiv.org/abs/2507.06561

Towards Designing Social Interventions for Online Climate Change Denialism Discussions
As conspiracy theories gain traction, it has become crucial to research effective intervention strategies that can foster evidence and science-based discussions in conspiracy theory communities online. This study presents a novel framework using insider language to contest conspiracy theory ideology in climate change denialism on Reddit. Focusing on discussions in two Reddit communities, our research investigates reactions to pro-social and evidence-based intervention messages for two cohorts o…

Testing Hypotheses of Covariate Effects on Topics of Discourse
Gabriel Phelan, David A. Campbell
https://arxiv.org/abs/2506.05570 https://

Testing Hypotheses of Covariate Effects on Topics of Discourse
We introduce an approach to topic modelling with document-level covariates that remains tractable in the face of large text corpora. This is achieved by de-emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are of interest. We propose combining a convex formulation of non-negative matrix factorization with standard regression techniques as a fast-to-compute and…

Identifying Optimal Regression Models For DEM Simulation Datasets
B. D. Jenkins, A. L. Nicusan, A. Neveu, G. Lumay, F. Francqui, J. P. K. Seville, C. R. K. Windows-Yule
https://arxiv.org/abs/2508.05308

Identifying Optimal Regression Models For DEM Simulation Datasets
Developing fast regression models (surrogate/metamodels) from DEM data is key for practical industrial application to allow real-time evaluations. However, benchmarking different models is often overlooked in particle technology for regression tasks, as model selection is frequently not the primary research focus. This can lead to the use of suboptimal models, resulting in subpar predictive accuracy, slow evaluations, or poor generalisation, hindering effective real-time decision-making and pro…

SSD Offloading for LLM Mixture-of-Experts Weights Considered Harmful in Energy Efficiency
Kwanhee Kyung, Sungmin Yun, Jung Ho Ahn
https://arxiv.org/abs/2508.06978 https://

SSD Offloading for LLM Mixture-of-Experts Weights Considered Harmful in Energy Efficiency
Large Language Models (LLMs) applying Mixture-of-Experts (MoE) scale to trillions of parameters but require vast memory, motivating a line of research to offload expert weights from fast-but-small DRAM (HBM) to denser Flash SSDs. While SSDs provide cost-effective capacity, their read energy per bit is substantially higher than that of DRAM. This paper quantitatively analyzes the energy implications of offloading MoE expert weights to SSDs during the critical decode stage of LLM inference. Our a…

Khintchine dichotomy and Schmidt estimates for self-similar measures on $\mathbb{R}^d$
Timoth\'ee B\'enard, Weikun He, Han Zhang
https://arxiv.org/abs/2508.09076 https:/…

Khintchine dichotomy and Schmidt estimates for self-similar measures on $\mathbb{R}^d$
We extend the classical theorems of Khintchine and Schmidt in metric Diophantine approximation to the context of self-similar measures on $\mathbb{R}^d$. For this, we establish effective equidistribution of associated random walks on $\text{SL}_{d+1}(\mathbb{R})/\text{SL}_{d+1}(\mathbb{Z})$. This generalizes our previous work which requires $d=1$ and restricts Schmidt-type counting estimates to approximation functions which decay fast enough. Novel techniques include a bootstrap scheme for …