arXiv preprints from January 1, 2026 through September 23, 2026 — 03:54:20 EST
We study difference-in-differences (DiD) designs in which a binary treatment changes an endogenous time-varying (continuous, discrete, or mixed) mediator that in turn affects an outcome. Under our model assumptions, we show that the usual DiD estimand mixes the average direct effect on the treated, the average indirect effect, and a...
We examine how agents learn when information from original sources only reaches them after noisy relay. A receiver learns if and only if they have access to sufficiently many chains of noisy relay and they perfectly understand the noise process. However, even slight uncertainty over message mutation rates makes learning from long...
We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language model a ``type vector'' and then prompt it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) becomes ``You are a player characterized...
We classify maximally enhanced, rank-preserving non-supersymmetric heterotic strings in eight dimensions by studying orbifolds of supersymmetric heterotic strings on $T^2$. We first review the construction in nine dimensions, where it reproduces the $95$ maximally semisimple enhancement points obtained from previous extended-Dynkin...
Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precipitation itself. Here, we employ a deep learning model that translates dynamical circulation predictions into precipitation estimates. Predictions initialized from March to May...
One of the most basic conjectures in quantum complexity theory states that every $t$-query quantum algorithm can be simulated on most inputs by a $\mathrm{poly}(t)$-query classical algorithm. If true, this would provide broad justification for the need for structure in quantum speedups. We settle this conjecture for parallel quantum...
Silicon Photomultipliers (SiPMs) based on NUV-MT technology offer improved photo-detection efficiency and reduced correlated noise compared to earlier designs, making them increasingly attractive for particle and astroparticle physics applications. We present a systematic characterisation of commercially available Broadcom AFBR-S4N...
The formation and evolution of galaxies are interconnected with that of their host halos. Given this closely related evolutionary history, it should follow that galaxy properties are correlated with their host halo mass. Previous works have shown that star formation rates of galaxies in cluster halos differ systematically from those...
In this work, we present a unified entropic description of the quantum bounce that is present in Loop Quantum Cosmology (LQG) and dark energy that drives the late time acceleration of the Universe. By considering a minimal area $A_0$ for the Universe and a modified entropy area constructed as the sum of $S_Q$, $S_{BH}$ and $S_{DE}$,...
Horizontal earthquake ground motions exhibit substantial changes in amplitude with changes in orientation. While the effects of style of faulting on central tendency measures of ground motion intensity such as RotD50 are well established in ground motion models, less attention has been given to how faulting mechanism influences the...
In this work, we study the limitations and advantages of using entanglement battery and entanglement catalyst in local state discrimination problems. We consider both cases of such tools, i.e., exact and approximate cases. We show that to distinguish any set of orthogonal pure bipartite entangled states perfectly under local...
The surface $r=\infty$ of the Kerr--Bertotti--Robinson (KBR) spacetime is not the collection of the endpoints of infinitely extended light rays, and the Coulomb type component of gravitational field strength represented by $Ψ_2$ remains nonvanishing there, indicating that this surface is not a real boundary. We construct a natural...
These notes develop the diagnostics of chaos from their classical definitions through to holography, in six lectures given at the ST4 Workshop, Chennai Mathematical Institute, in 2026. No prior exposure to chaos theory is assumed. Lectures 1 and 2 define classical chaos geometrically and build the instruments that measure it, every...
Adding links generally improves classical transport by increasing the number of available paths. We show that coherent quantum transport can display the opposite behavior. Using continuous-time quantum walks on a percolated hierarchical small-world network, we identify a coherent overconnectivity penalty: root-to-boundary transport is...
Complex concentrated alloys (CCAs), including high- and medium-entropy alloys, deform in chemically heterogeneous energy landscapes where dislocation glide, solute aging, twinning, phase transformation and microstructural barriers may all contribute. This review discusses avalanche-like and serrated plasticity in CCAs across scales....
The search for atmospheres on temperate terrestrial planets is important for understanding how these objects form and evolve, and their potential habitability. Recent transit observations of LHS 1140 b, a temperate ($T_{\rm eq}=226$ K) planet straddling the radius valley ($R_{\rm p}\approx1.7 R_\oplus$), with the WINERED...
Quantitative microstructural characterization of Li-ion battery electrode materials using electron backscatter diffraction (EBSD) has been proven as a critical method for optimizing cell performance. However, the inherently slow nature of EBSD can hinder the throughput of analyses needed for statistical representation of a material...
Many problems in disordered materials require sampling beyond fixed composition and volume, where coupled changes in atomic identities and structure create a prohibitively expensive discrete-continuous sampling problem. Here we introduce JANUS, a multimodal neural sampler that couples continuous and masked discrete diffusion through...
Cosmic Explorer (CE) is a proposed next-generation gravitational-wave observatory that aims to extend our gravitational-wave vision to the edge of the observable universe. With a foundation of technology proven by the National Science Foundation's Laser Interferometer Gravitational-Wave Observatory (LIGO), CE will observe black holes...
The lecture discusses the properties of low-energy electrons compared to the electrons of conventional energies employed in commercial transmission electron microscopes. Low-energy electron diffraction and low-energy electron holography principles and experimental schemes are presented and discussed.
Resolving and controlling the local dynamical properties of covalent organic frameworks (COFs) remains a central challenge, particularly when assembled from large, flexible building units. Here, we combine synchrotron X-ray pair distribution function (PDF) analyses with machine learning-accelerated molecular dynamics (MD) simulations...
The practical implementation of quantum algorithms on noisy intermediate-scale quantum devices encounters operational limitations due to decoherence and other sources of noise inherent in real hardware. To mitigate these errors while preserving the original functionality of the algorithm, shorter quantum circuits are therefore...
We present a new data-driven learning of a Random Geometric Graph (RGG) of a multivariate dataset, where the graph is drawn in a probabilistic metric space. This graph learning works for generic datasets, irrespective of the type of the observables; their probability distributions; or size of the data. We identify a metric of the...
An agent still learning its environment should be cautious while ignorant and bold once confident. The entropic value-at-risk captures this through a robust-optimization identity---a confidence level fixes the radius of a relative-entropy ball of alternative models---but that ball cannot reach catastrophes the nominal deems...
This paper investigates the estimation of the regression operator in function-on-function regression models. While traditional research has predominantly focused on linear models or their immediate nonlinear extensions, we propose a neural operator approach to accommodate general regression operators under mild smoothness assumptions....