arXiv preprints from January 1, 2026 through September 11, 2026 — 06:26:55 EST
Using a mean-field game framework, we study a dynamic model of bank runs in which more withdrawals raise the risk of bank failure. Even though depositors receive gradual and idiosyncratic shocks, withdrawals occur in clusters. The main mechanism is latent fragility: run-prone depositors accumulate gradually over time and may prefer to...
In multi-objective and multi-criteria decision-making under risk, especially in settings involving individual behavior, risk-aware analysis based on subjective evaluation has become increasingly important. Moving beyond risk-neutral modeling and the constraints of Expected Utility Theory (EUT), Cumulative Prospect Theory (CPT)...
The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices mainly driven by investor sentiment, becoming a potential source of financial bubbles and...
This paper investigates the optimal surplus management problem of an insurance company operating in a financial market with stochastic interest rates and jump-driven liabilities. The insurer dynamically allocates its surplus between a risky stock and a risk-free zero-coupon bond while facing insurance claims modeled by a compound...
Understanding similarity among financial assets is essential for effective portfolio diversification. This paper proposes a novel sentiment-adjusted portfolio optimization framework that integrates Topological Data Analysis (TDA) with technical indicators and FinBERT-based sentiment scores extracted from financial news. A TDA-based...
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO)...
By Godunov's theorem, linear second-order finite-difference schemes for the Fokker-Planck equation cannot preserve positivity. The Diagonal Frog (DF) framework previously bypassed this barrier using eventual positivity, but required a strict minimum time step. This paper resolves the small-step limitation using a nonlinear extension...
We propose a signature-induced, optimal transport framework for path-space model risk, in which ambiguity between stochastic path laws is factorized through optimal transport costs on signature coordinates under a common coupling. Via an ambient feature-space relaxation, we derive for affine signature scores and affine half-space...
Do quantum kernels improve cross-sectional stock return prediction? We run a controlled horse race on the Chinese A-share market in which a quantum fidelity kernel, a projected quantum kernel, and a classical RBF control share identical training subsamples, solver, and tuning budgets, so that only the kernel is exchanged. On the main...
Four centuries ago Francis Bacon warned against the anticipations of nature, hasty generalization that wins assent on a few facts, and set against it the table of absence: checking that a property fails to appear where it should not. The demand was that looking convincing should not, on its own, count as evidence. Science has...
Despite the variability in gene regulation and environmental conditions, development of an organism occurs through a sequence of highly coordinated patterning processes. Cells integrate different signals to accurately infer their position in order to adopt an appropriate identity. Using a model of epigenetic landscape originally...
The Information Dynamics of Music model (IDyOM) has played a central role in computational accounts of musical expectation by providing event-by-event estimates of uncertainty and surprise from symbolic musical sequences. However, its reference implementation is difficult to integrate with contemporary Python workflows, and its...
Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts. Here, we present a contrastive representation learning framework that models multivariate disease trajectories as temporal graphs and learns representations using contrastive graph...
We study the long time dynamics of a selection-mutation integro-differential Lotka-Volterra system of $N$ populations. In our model, fitness depends on a continuous phenotypic trait, but the effect of one population on another is independent of this trait. We establish that, under some usual assumptions on the interactions between...
Computational AMP discovery is often evaluated through AMP/non-AMP recognition, yet follow-up decisions depend on assay-derived evidence such as target-species potency, hemolysis, toxicity, and selectivity. Existing AMP and peptide benchmarks cover binary recognition, multilabel annotation, assay regression, or broader peptide-model...
Epidemics on complex networks have been shown to exhibit dynamics that are strongly influenced by the topology of the network. However, it remains unclear how the topology of the network is influenced by epidemic properties, such as waning immunity and disease fatality, coupled with demographic changes. To explore the interplay...
Dynamics of reaction networks is often modelled by parameterised polynomials and describing the set of parameters for which the system attains multiple positive equilibrium states is a challenging problem. In the full parameter space, determined by the reaction rate constants and the total concentrations, the existing methods can give...
Biological systems often synchronize their dynamics with periodic environmental and intracellular signals. Whether such periodic coordination can also improve performance, however, remains unclear. Here, we study this question in the ribosome flow model, a nonlinear dynamical model of ribosome movement along an mRNA transcript during...
Single-cell ribonucleic acid sequencing (scRNA-seq) is a foundational technology for precision-medicine workflows that contribute to United Nations Sustainable Development Goal 3 on Good Health and Well-being, and unsupervised clustering is the analytical step that turns raw expression matrices into interpretable cell populations....
Genome-scale modelling methods primarily predict reaction fluxes, whereas established high throughput experimental technologies primarily measure molecular species concentrations.This apparently paradoxical situation has arisen because implementing the non-linear constraints that represent reaction kinetic rate equations is...
Empirical user studies are essential for evaluating visual encodings and can reveal perceptual and cognitive mechanisms, but they do not by themselves provide causal, predictive accounts of interpretation errors. Evaluations are therefore often post hoc: they measure performance after a design has been specified rather than predicting...
Predicting how tightly two biomolecules bind remains a major challenge, in part because different interaction classes present dissimilar interfaces, from compact metal-coordinated pockets to broad, featureless protein surfaces. We introduce persistent manifold learning (PML), a novel computational framework that describes a binding...
Electroencephalography (EEG) analysis in cognitive studies requires specialized expertise and involves many defensible choices over contrasts, channels, time windows, and statistical tests. LLM agents can translate varied natural-language questions into analysis choices, offering a flexible interface for automation. Yet fluent reports...
mRNA translation in the cell requires efficient allocation of shared and limited resources including free ribosomes, tRNA molecules, and initiation factors across multiple transcripts. Using a network of dynamic mathematical models for ribosome flow along the mRNA, we pose the problem of maximizing the total steady-state protein...
Muscle redundancy can be formulated as a constrained selection on a time-varying convex set of feasible activations. We introduce Torque Fiber Proximal Dynamics (TFPD), where activation evolves as the Euclidean projection of the previous state onto a convex polytope defined by torque equality and physiological bounds. TFPD is...