arXiv preprints from January 1, 2026 through September 9, 2026 — 13:29:08 EST
We present a density functional-based equation of state for warm, dense nuclear matter with a transition to deconfined quark matter for applications to simulations of supernova explosions and neutron star mergers, but also for the cosmological evolution of Q-balls. For the quark matter equation of state, we employ a recently developed...
Based on archival spectra, we derive the quiescent instantaneous mass transfer rates in novae using synthetic disk spectra generated with tlusty, Gaia parallax-derived distances, and updated color excess values. Our results for nine novae, based on ultraviolet spectra and on a number of optical spectra, yield mass accretion rates that...
Variations in dynamical states of galaxy clusters can introduce biases and scatter in observable-mass relations. The dynamical state of a cluster is an emergent feature of its mass accretion history (MAH), it is therefore useful to constrain the MAH of the cluster. In this work, we characterize 305 massive clusters from The300 project...
The familiar Bethe-Heitler process on the proton target $e+p\to e+p+γ$ is transformed into a laboratory for studying multiparticle entanglement. We discuss how bipartite and genuine tripartite entanglement between the final state electron, proton and photon are built up by successive $1\to 2$ and $2\to 2$ elementary interactions. We...
High-energy $γγ$ collisions, realizable as an operational mode of future lepton linear colliders such as the ILC and CLIC, provide a promising environment to probe extended Higgs sectors. We investigate the sensitivity of such colliders to doubly charged Higgs bosons within the 2-Higgs Doublet Model with type-II seesaw (2HDMcT)....
The Light-Heavy (LH) model involves two species of particles (light and heavy) coupled with a fluctuating surface (described by tilts). The dynamics include the inherent diffusion of the particles (or tilts) as well as the drive provided by the tilts (or particles). When the two are of similar magnitude, the system lies in the...
Two-dimensional transition-metal dichalcogenide alloys are potential candidates for advanced optoelectronic and neuromorphic applications due to their strong light-matter interactions and controllable defect properties. However, large-area growth of such alloys remains challenging, while the correlation between their physical and...
Gisin has provided an argument for the conclusion that no covariant nonlocal model can reproduce the operational statistics observed in Bell experiments. Gisin's argument applies only to deterministic models and we argue that proposed generalizations of the argument beyond determinism based solely on statistical notions are...
Surface lattice resonances (SLRs) are collective polaritonic excitations in nanoparticle arrays, with band-edge states enabling symmetry-based control of radiation including scattering, photoluminescence, conventional and polariton lasing, and condensation. Here, we show that the integer pseudoangular momentum (PAM) of...
Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-based models (EBMs) are a natural fit since energies compose additively, which enables augmenting physics information during inference. However, EBMs have been difficult to train and...
We develop parametric Entropic Value-at-Risk (EVaR) portfolio optimization for tempered stable Lévy returns. We derive portfolio cumulant-generating functions and weight-dependent admissible moment-generating-function domains under two multivariate constructions: a multivariate normal tempered stable approach and an independent...
We develop simulation-based policy iteration for continuous-time portfolio choice with predictable returns and convex constraints. Each outer step re-evaluates a fixed-latent OL-BPTT adjoint after deployment and solves the constrained update. Shifted-adjoint cancellation controls the adjoint--HJB Hamiltonian-gradient discrepancy by...
Credit decisioning is a high-stakes task in which model outputs must be accurate and explainable to support compliant decisions. Although modern credit risk models such as eXtreme Gradient Boosting (XGBoost) and Graph Neural Networks (GNNs) improve predictive performance, their explanations are often too technical for stakeholders...
This paper considers European multi-asset option pricing under Lévy and affine characteristic-function models. The main obstruction is the curse of dimensionality: direct multidimensional COS pricing forms tensor-product coefficient arrays whose size grows exponentially with the number of assets. We study and extend COS-TT-CHF, a...
We present in this article a non-parametric value-at-risk (VaR+CVaR) algorithm that remains accurate for an arbitrarily large number of underlying positions. The algorithm solves the two inherent problems of VaR estimation. First, past history is not directly applicable to the future, but all predictions of the future are based on the...
AI agents increasingly perform knowledge work (i.e., produce and modify persistent digital artifacts such as code repositories, documents, spreadsheets, slides, reports), yet the parsed views they search, the native files they edit, the changes they review, and the artifacts they submit can refer to different versions of the same work...
We formulate an approximate Cauchy-Schwarz inequality and show that it is satisfied by solutions to the Sherali-Adams linear programming hierarchy (interpreted as ``pseudo-distributions''). As a consequence, we resolve a question left open by the work of O'Donnell and Schramm [OS19] that they had explicitly attributed to the lack of...
Language has two parameters. Count how often words occur together and you estimate amplitude, the strength of association. Word embeddings and attention weights refine that count, which sums every writer in the corpus together. This paper claims a second parameter, phase, which signed weights learned from a corpus do not supply. Phase...
Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on efficient solvers and samplers, comparatively little attention has been paid to selecting the sampling timesteps themselves. A recent line of work optimizes...
MongoDB maintains client libraries in a dozen programming languages, used by tens of thousands of organizations and millions of developers. Most are implemented natively rather than as wrappers around a shared core. Ensuring consistent behavior across these libraries, comprising millions of lines of code, is hard but essential. Over...
Traffic elements such as traffic lights and road signs play a fundamental role in human driving decisions and should naturally influence end-to-end driving performance. However, existing end-to-end driving research predominantly focuses on dynamic road participants (e.g., vehicles and pedestrians), while the role of traffic elements...
Academic surveys play a central role in organizing rapidly expanding scholarly literature, yet their construction requires extensive paper analysis, coherent knowledge organization, fine-grained citation support, and reliable manuscript assembly. Existing Deep Research and automated survey generation systems address parts of this...
Categorising invoices into the correct General Ledger (GL) code underpins financial reporting and tax compliance. This is a skilled accounting judgement rather than a routine task: the correct category depends subtly on the nature of the purchasing business, the vendor and the invoice text. Whilst AI is increasingly being adopted...
A popular class of Byzantine fault-tolerant atomic broadcast protocols rely on directed acyclic graphs (DAGs) that represent causal relations between broadcast messages. Each process applies a protocol-specific \emph{commit rule} on its local DAG to determine which vertices can be delivered in a total order. Intuitively, commit rules...
Initialization-free bundle adjustment (InitFree BA) aims to recover camera poses and scene structure directly from image observations, avoiding the geometric initialization stages of conventional structure-from-motion pipelines. Recent methods based on Object-Space Error (OSE) formulations and Variable Projection (VarPro) show...