Tootfinder

Reposted with alt
https://mastodon.social/@VeroniqueB99/114604050608739836

Vee (@VeroniqueB99@mastodon.social)
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I just updated the prints on my office door to reflect the kind of photos (particularly event photos) that I am taking now -- as well the the new system which supersedes "three sided cards" that attains better branding in every respect except that it doesn't have a name (yet) #photo #photography

This https://arxiv.org/abs/2112.14249 has been replaced.
link: https://scholar.google.com/scholar?q=a

Nested Nonparametric Instrumental Variable Regression
Several causal parameters in short panel data models are functionals of a nested nonparametric instrumental variable regression (nested NPIV). Recent examples include mediated, time varying, and long term treatment effects identified using proxy variables. In econometrics, examples arise in triangular simultaneous equations and hedonic price systems. However, it appears that explicit mean square convergence rates for nested NPIV are unknown, preventing inference on some of these parameters with…

This https://arxiv.org/abs/2212.11833 has been replaced.
link: https://scholar.google.com/scholar?q=a

Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models
This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on the permitted sampling information, the RV estimator is most efficient under either hitting time sampling that samples whenever the price changes by a pre-determined threshold, or under the new concept of realized business time that samples according to a combination of observed trades and estimated tick variance. The…

Controller Design for Bilinear Neural Feedback Loops
Dhruv Shah, Jorge Cort\'es
https://arxiv.org/abs/2505.24106 https://arxiv.or…

Controller Design for Bilinear Neural Feedback Loops
This paper considers a class of bilinear systems with a neural network in the loop. These arise naturally when employing machine learning techniques to approximate general, non-affine in the input, control systems. We propose a controller design framework that combines linear fractional representations and tools from linear parameter varying control to guarantee local exponential stability of a desired equilibrium. The controller is obtained from the solution of linear matrix inequalities, whic…

Adsorbate phase transitions on nanoclusters from nested sampling
Thanawitch Chatbipho, Ray Yang, Robert B. Wexler, Livia B. P\'artay
https://arxiv.org/abs/2506.01295

Adsorbate phase transitions on nanoclusters from nested sampling
Nested sampling was employed to investigate adsorption equilibria on the truncated-octahedral Lennard-Jones nanocluster LJ$_{38}$ while systematically varying adsorbate-surface well depth and Lennard-Jones size parameters. Evaluation of the canonical partition function over a wide temperature range identifies two successive phase transitions: (i) condensation of the gas phase onto the cluster surface at higher temperatures, and (ii) lateral rearrangement of the adsorbed layer at lower temperatu…

Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
Mohammad Shah Alam, William Ott, Ilya Timofeyev
https://arxiv.org/abs/2505.24099

Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
In this paper, we explore the predictive capabilities of echo state networks (ESNs) for the generalized Kuramoto-Sivashinsky (gKS) equation, an archetypal nonlinear PDE that exhibits spatiotemporal chaos. We introduce a novel methodology that integrates ESNs with transfer learning, aiming to enhance predictive performance across various parameter regimes of the gKS model. Our research focuses on predicting changes in long-term statistical patterns of the gKS model that result from varying the d…

Optimization and variability can coexist
Marianne Bauer, William Bialek, Chase Goddard, Caroline M. Holmes, Kamesh Krishnamurthy, Stephanie E. Palmer, Rich Pang, David J. Schwab, Lee Susman
https://arxiv.org/abs/2505.23398

Optimization and variability can coexist
Many biological systems perform close to their physical limits, but promoting this optimality to a general principle seems to require implausibly fine tuning of parameters. Using examples from a wide range of systems, we show that this intuition is wrong. Near an optimum, functional performance depends on parameters in a "sloppy'' way, with some combinations of parameters being only weakly constrained. Absent any other constraints, this predicts that we should observe widely varying parameters,…

This https://arxiv.org/abs/2505.14456 has been replaced.
initial toot: https://mastoxiv.page/@…

Influence of microscopic parameters on phase behavior of a cell model with Curie-Weiss interaction
We have investigated the effect of changing two microscopic parameters in a cell model with Curie-Weiss-type interaction on its phase behavior, namely the cell volume and ratio between repulsion and attraction intensities. The results are based on an exact solution previously derived for this model in the grand canonical ensemble. At sufficiently low temperatures, the cell model exhibits multiple first-order phase transitions. Varying the cell volume and the repulsion-to-attraction ratio, we re…

Reduced order modelling of air puff test for corneal material characterisation
Osama M. Maklad, Muting Hao
https://arxiv.org/abs/2505.22495 https://…

Reduced order modelling of air puff test for corneal material characterisation
Models of the fluid-structure interaction (FSI) model for the air puff test were analysed. Using Abaqus, the air puff test is applied to eyes with varying biomechanical parameters, such as material properties, corneal thickness, and radius. A reduced order model of the air puff (a turbulent impinging jet) has been acquired to decrease simulation time from 48 hours for the FSI model to approximately 12 minutes for the finite element analysis (FEA) model alone. To further accelerate simulations a…