arXiv preprints from January 1, 2026 through September 23, 2026 — 06:44:40 EST
Regional wet-bulb temperature means omit the spatial distribution of humid heat. We compare upper-quartile and middle-half days of the monthly regional mean at 121 sites in a specified eastern-China domain. A prespecified multiscale architecture combines Gaussian-weighted semivariances at five bandwidths with equal-month, equal-scale...
Occam's inversion is a robust algorithm to perform nonlinear geophysical inversion. It provides the smoothest model within observation noise, thereby discouraging geological overinterpretation. While Occam originally penalised l2 model roughness, l1 can be used to provide models that are visually sharp. However, l1 regularised...
Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parametric scorers (COPOD, ECOD, Deep SVDD), all of which approximate the inlier distribution only indirectly; explicit energy-based models are largely absent....
Spillover of volatility shocks across borders during turbulent periods makes accurate equity market volatility forecasts especially critical for risk management, derivatives pricing, and regulatory capital. In this paper, we examine whether volatility forecasts improve when models incorporate information on how markets are connected,...
Network data obtained through link-tracing designs, such as snowball sampling, are collected through a mechanism that depends on the very structure the analysis seeks to estimate. Ignoring this dependence and treating the observed sample as though it were itself a complete network can lead to substantially biased inference. While the...
Joint models provide a flexible framework for characterizing the association between longitudinal and time-to-event processes and have been widely applied in biomedical research. However, fitting joint models can be computationally challenging for large-scale and complex biomedical data. This paper introduces the \proglang{R} package...
When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation. We call this forecast collapse. Surprisingly, the phenomenon largely disappears when forecasting trading volume under the same setting. We...
We propose a novel methodology for fitting decision trees to latent classes. The latent class analysis methodological literature has previously been focusing on logistic models of class membership given covariates, which has important drawbacks in the presence of complex interactions between covariates: logistic models are easily...
Accurate small-area estimation of voter turnout for demographic subgroups is crucial for political analysis but methodologically challenging. Survey data suffer from over-reporting, non-representativeness, and non-ignorable non-response, while ecological inference (EI) from aggregate data is vulnerable to the ecological fallacy. We...
Distributional mismatch between the data used to construct a statistical procedure and the population to which it is ultimately applied is pervasive in modern data analysis. We study covariate shift, a fundamental instance of this problem, and develop an adaptive importance-weighted model averaging method for prediction when labeled...
Adding data known to be correct ought to be safe. Not always. Larsen, Pabbaraju and Shetty model the failure with a monotone adversary, which reads an i.i.d. training sample and may append as many further examples as it likes, provided the target hypothesis labels them all. Mehrotra has since settled the cost, showing that for classes...
Bayesian statistics rests on a few familiar distinctions: frequentist vs. Bayesian, objective versus subjective probability, a model versus the data it is fitted to, a prior versus a posterior. Here, I use Donald Davidson's "third dogma of empiricism" to critique such distinctions in terms of scheme/content dualisms. With a single...
The Weibull distribution is one of the most flexible continuous probability distributions used to model various failure rates and skewed data in reliability engineering, industry, weather studies and cancer studies. It is a common scenario in statistical inference that several Weibull populations share the same shape parameter, which...
As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model quality. However, QAT computes the loss and surrogate gradients using a lossy reconstruction of latent full-precision weights, while...
Finding the optimal individualized treatment rule that maps individual characteristics or contextual information to treatment assignments has been extensively investigated in existing literature, with widespread practical applications. This paper considers the estimation of optimal treatment regimes within a semi-supervised data...
Cellular activity drives epithelial fluidization --- a widespread phenomenon observed during tissue development, remodeling, and repair both in vivo and in vitro. Yet the physical origins and spatial organization of active forces vary widely across biological systems and are often represented by a single generic mechanism in...
Extinction is inevitable; every species eventually dies out, impacting the ecosystem it is part of. Over the past few decades, extensive research stemming from the stability-diversity debate has addressed how species diversity contributes to the stability of large ecosystems. However, conventional stability criteria often rely on...
Bioinformatics workflows rely heavily on visual representations. Quality-control plots, cell embeddings, heatmaps, genome-browser tracks, and interactive dashboards are not merely illustrations, but instruments for making analytical decisions. For blind and low-vision researchers who use screen readers, braille displays, or...
What decides whether people do things together or separately? Many activities cannot be carried out alone, and an individual must rank them against the private tasks competing for the same time. We address this within the priority-queue description of human activity by letting each agent choose the priority of a shared task rather...
Multimodal Large Language Models (MLLMs) can generate pathological descriptions from histological images, but gigapixel Whole Slide Images (WSIs) exceed their visual context limits. The standard tiling workaround makes WSIs tractable yet severs the tissue neighborhoods that define tumor-stroma interfaces and morphology. We introduce...
In social insects, mortality risk comprises distinct components that may not share the same predictors: lifespan duration, senescence trajectory, and thermal vulnerability. We tested these three axes in 18 Australian ant species using paired field-laboratory survival assays (2,363 cohort-day observations; 1,148 workers). Body size...
Multi-strain epidemiological systems frequently exhibit self-sustained oscillations, yet severe algebraic complexity has long obstructed a complete analytical characterization of these dynamics. Seeking to bypass these barriers, we have identified a broad, analytically tractable class of two-strain models featuring asymmetric...
Self-organized spatial patterns are central to nonequilibrium physics and cell biology, yet locating instabilities in multi-component, reaction-diffusion networks remains challenging because standard eigenvalue analyses scale with the number of biochemical states and rely on reaction kinetics often poorly constrained by experiments....
Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible, neuronal loss has already occurred, creating an urgent need for quantitative tools that can detect subtle, early, and individual-specific brain changes from neuroimaging data. This...
Biochemical reaction networks have become a central theoretical framework for implementing molecular computation. A key challenge is finite time computational accuracy, as computation outputs are encoded in limiting steady states (LSSs) of species concentrations while practical implementations operate for only finite time. This work...