Tootfinder

Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory
Yixuan Sun, Srinivas Eswar, Yin Lin, William Detmold, Phiala Shanahan, Xiaoye Li, Yang Liu, Prasanna Balaprakash
https://arxiv.org/abs/2509.10378

Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory
Linear systems arise in generating samples and in calculating observables in lattice quantum chromodynamics~(QCD). Solving the Hermitian positive definite systems, which are sparse but ill-conditioned, involves using iterative methods, such as Conjugate Gradient (CG), which are time-consuming and computationally expensive. Preconditioners can effectively accelerate this process, with the state-of-the-art being multigrid preconditioners. However, constructing useful preconditioners can be challe…

Is Dark Energy Changing? Probing the Universe's Expansion with present and future astronomical probes
Mehdi Rezaei, Supriya Pan, Weiqiang Yang, David F. Mota
https://arxiv.org/abs/2510.09766

Is Dark Energy Changing? Probing the Universe's Expansion with present and future astronomical probes
This study explores the possibility of a time-varying dark energy (DE) equation of state (EoS) deviating from -1. We employ a comprehensive dataset of usual astronomical probes (Type Ia supernovae, baryon acoustic oscillations, Big Bang nucleosynthesis, Hubble data, and Planck 2018 CMB) alongside future mock gravitational wave (GW) distance measurements from the Einstein Telescope. We utilize the Pad'e approximation, a versatile framework encompassing well-known DE models like constant EoS, Che…

Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Maggie Wang, Stephen Tian, Aiden Swann, Ola Shorinwa, Jiajun Wu, Mac Schwager
https://arxiv.org/abs/2510.11689

Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Learning robotic manipulation policies directly in the real world can be expensive and time-consuming. While reinforcement learning (RL) policies trained in simulation present a scalable alternative, effective sim-to-real transfer remains challenging, particularly for tasks that require precise dynamics. To address this, we propose Phys2Real, a real-to-sim-to-real RL pipeline that combines vision-language model (VLM)-inferred physical parameter estimates with interactive adaptation through unce…

Source: Blue Owl's Digital Infrastructure funds are investing ~$3B in a New Mexico-based Stargate data center, set to consume 4.5GW, as Blue Owl expands into AI (Miles Kruppa/The Information)
https://www.theinformation.com/articles/open…

OpenAI’s Stargate Project Gets $3 Billion Blue Owl Investment
Blue Owl Capital, one of Wall Street’s biggest private lenders, is ramping up its big bet on artificial intelligence with a roughly $3 billion investment in a New Mexico data center that’s part of OpenAI’s Stargate project, said a person with knowledge of the deal. Blue Owl, which has nearly ...

FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash
Sumukh Pinge, Ashkan Moradifirouzabadi, Keming Fan, Prasanna Venkatesan Ravindran, Tanvir H. Pantha, Po-Kai Hsu, Zheyu Li, Weihong Xu, Zihan Xia, Flavio Ponzina, Winston Chern, Taeyoung Song, Priyankka Ravikumar, Mengkun Tian, Lance Fernandes, Huy Tran, Hari Jayasankar, Hang Chen, Chinsung Park, Amrit Garlapati, Kijoon Kim, Jongho Woo, Suhwan Lim, Kwangsoo Kim, Wa…

FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash
The rapid expansion of mass spectrometry (MS) data, now exceeding hundreds of terabytes, poses significant challenges for efficient, large-scale library search - a critical component for drug discovery. Traditional processors struggle to handle this data volume efficiently, making in-storage computing (ISP) a promising alternative. This work introduces an ISP architecture leveraging a 3D Ferroelectric NAND (FeNAND) structure, providing significantly higher density, faster speeds, and lower volt…

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space
Matou\v{s} Sold\'at, Ji\v{r}\'i Kl\'ema
https://arxiv.org/abs/2509.04998 https://

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space
Directed evolution is an iterative laboratory process of designing proteins with improved function by iteratively synthesizing new protein variants and evaluating their desired property with expensive and time-consuming biochemical screening. Machine learning methods can help select informative or promising variants for screening to increase their quality and reduce the amount of necessary screening. In this paper, we present a novel method for machine-learning-assisted directed evolution of pr…

Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang
https://arxiv.org/abs/2510.04939 https://arxi…

Unsupervised Active Learning via Natural Feature Progressive Framework
The effectiveness of modern deep learning models is predicated on the availability of large-scale, human-annotated datasets, a process that is notoriously expensive and time-consuming. While Active Learning (AL) offers a strategic solution by labeling only the most informative and representative data, its iterative nature still necessitates significant human involvement. Unsupervised Active Learning (UAL) presents an alternative by shifting the annotation burden to a single, post-selection step…

Multivariate Zero-Inflated Causal Model for Regional Mobility Restriction Effects on Consumer Spending
Taekwon Hong, Wenbin Lu, Shu Yang, Pulak Ghosh
https://arxiv.org/abs/2510.03422

Multivariate Zero-Inflated Causal Model for Regional Mobility Restriction Effects on Consumer Spending
The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, reacts to changes in mobility. This investigation is crucial for developing policies that balance public health and the economic and social impacts. We utilize extensive scanner data from a major retail chain in India and Google mobility data to address this important question. However, there are a few challenges, incl…

Two alternative proofs of weak Harnack inequality for mixed local and nonlocal $p$-Laplace equations with a nonhomogeneity
Prashanta Garain
https://arxiv.org/abs/2510.04065 http…

Two alternative proofs of weak Harnack inequality for mixed local and nonlocal $p$-Laplace equations with a nonhomogeneity
We study a class of mixed local and nonlocal $p$-Laplace equations with prototype \[ -Δ_p u + (-Δ_p)^s u = f \quad \text{in } Ω, \] where $Ω\subset \mathbb{R}^n$ is bounded and open. We provide sufficient condition on $f$ to ensure weak Harnack inequality with a tail term for sign-changing supersolutions. Two different proofs are presented, avoiding the Krylov--Safonov covering lemma and expansion of positivity: one via the John--Nirenberg lemma, the other via the Bombieri--Giusti lemma. To…

Source: President Trump has picked Michael Selig, the chief counsel for the SEC's crypto task force, to chair the CFTC, amid a crypto push (Bloomberg Law)
https://news.bloomberglaw.com/banking-law/trump-to-pick-michael-selig-for-…

Trump to Pick Michael Selig for CFTC Chair Amid Crypto Expansion
President Donald Trump has picked Michael Selig to chair the Commodity Futures Trading Commission, according to an administration official, who asked not to be identified discussing personnel matters.