Tootfinder

Granular focused jet
Kazuya U. Kobayashi, Pradipto, Yoshiyuki Tagawa
https://arxiv.org/abs/2508.11905 https://arxiv.org/pdf/2508.11905

Granular focused jet
We investigated the formation of granular focused jets defined as narrow upward ejections of grains triggered by impulsive forces and shaped by kinematic focusing on a concave free surface. The jets were generated from nonfluidized granular beds, in contrast to existing granular Worthington jets that originate from fluidized layers. To elucidate the jet dynamics, we performed experiments in which a test tube partially filled with dry glass beads was dropped onto a flat rigid floor, systematical…

from my link log —
Portable dynamically linked packages on Linux.
https://brioche.dev/blog/portable-dynamically-linked-packages-on-linux/
saved 2025-03-18

Assessing Dynamic Connectedness in Global Supply Chain Infrastructure Portfolios: The Impact of Risk Factors and Extreme Events
Haibo Wang
https://arxiv.org/abs/2508.04858 https…

Assessing Dynamic Connectedness in Global Supply Chain Infrastructure Portfolios: The Impact of Risk Factors and Extreme Events
This paper analyses the risk factors around investing in global supply chain infrastructure: the energy market, investor sentiment, and global shipping costs. It presents portfolio strategies associated with dynamic risks. A time-varying parameter vector autoregression (TVP-VAR) model is used to study the spillover and interconnectedness of the risk factors for global supply chain infrastructure portfolios from January 5th, 2010, to June 29th, 2023, which are associated with a set of environmen…

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Jialiang Shi, Yaguang Dou, Tian Qi
https://arxiv.org/abs/2508.09090 https:…

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Modeling multi-interests has arisen as a core problem in real-world RS. Current multi-interest retrieval methods pose three major challenges: 1) Interests, typically extracted from predefined external knowledge, are invariant. Failed to dynamically evolve with users' real-time consumption preferences. 2) Online inference typically employs an over-exploited strategy, mainly matching users' existing interests, lacking proactive exploration and discovery of novel and long-tail interests. To addres…

Local three-dimensional simulations of the convective overstability in protoplanetary discs
Robert J. Teed, Henrik N. Latter
https://arxiv.org/abs/2508.03557 https://

Local three-dimensional simulations of the convective overstability in protoplanetary discs
At certain radii protoplanetary discs may sustain a form of oscillatory convection (`convective overstability'; COS) due to localised adverse entropy gradients. The resulting hydrodynamical activity can produce coherent structures, such as zonal flows and vortices, that may concentrate solid material and aid their further coagulation. In this paper we extend previous axisymmetric runs by performing local three-dimensional simulations of the COS, using the code SNOOPY. As parameters are varied, …

Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
Aliaksandr Hubin
https://arxiv.org/abs/2507.06776 https://

Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
Sparse Identification of Nonlinear Dynamics (SINDy) has become a standard methodology for inferring governing equations of dynamical systems from observed data using statistical modeling. However, classical SINDy approaches rely on predefined libraries of candidate functions to model nonlinearities, which limits flexibility and excludes robust uncertainty quantification. This paper proposes Bayesian Generalized Nonlinear Models (BGNLMs) as a principled alternative for more flexible statistical …

Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions
Licai Sun, Xingxun Jiang, Haoyu Chen, Yante Li, Zheng Lian, Biu Liu, Yuan Zong, Wenming Zheng, Jukka M. Lepp\"anen, Guoying Zhao
https://arxiv.org/abs/2507.21015

Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions
Current facial emotion recognition systems are predominately trained to predict a fixed set of predefined categories or abstract dimensional values. This constrained form of supervision hinders generalization and applicability, as it reduces the rich and nuanced spectrum of emotions into oversimplified labels or scales. In contrast, natural language provides a more flexible, expressive, and interpretable way to represent emotions, offering a much broader source of supervision. Yet, leveraging s…

Understanding the Error Sensitivity of Privacy-Aware Computing
Mat\'ias Mazzanti (University of Buenos Aires), Esteban Mocskos (University of Buenos Aires), Augusto Vega (IBM T. J. Watson Research Center), Pradip Bose (IBM T. J. Watson Research Center)
https://arxiv.org/abs/2506.07957

Understanding the Error Sensitivity of Privacy-Aware Computing
Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit from HE by enabling third-party computation and services on sensitive data. In other words, HE constitutes the "Holy Grail" of cryptography: data remains encrypted all the time, being protected while in …

Freeze-in production of scalaron dark matter in $f(R)$ gravity
Basabendu Barman, Ashmita Das, Rakesh Kumar SivaKumar, Rudra Pratap Udgata
https://arxiv.org/abs/2506.06436

Freeze-in production of scalaron dark matter in $f(R)$ gravity
We demonstrate that the scalaron, a scalar degree of freedom, emerging from the $f(R)$ theory of gravity, can account for the observed dark matter (DM) abundance if its mass is around the MeV scale, to ensure its cosmological stability. Focusing on two well-known $f(R)$ gravity models, we systematically show that if scalaron production proceeds via the freeze-in mechanism, the right relic abundance is satisfied over a very narrow window of reheating temperature $10^{14}\lesssim T_{\rm rh}\lesss…

Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning
Hadi Keramati, Samaneh Jazayeri
https://arxiv.org/abs/2507.01972 https://…

Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning
We present a reinforcement learning (RL)-driven framework for optimizing block-preconditioner sizes in iterative solvers used in portfolio optimization and option pricing. The covariance matrix in portfolio optimization or the discretization of differential operators in option pricing models lead to large linear systems of the form $\mathbf{A}\textbf{x}=\textbf{b}$. Direct inversion of high-dimensional portfolio or fine-grid option pricing incurs a significant computational cost. Therefore, ite…