All posts under category "Research"
This study investigates how target geometry and material influence pion and muon production from an 8 GeV proton beam, in support of target-system design for a muon collider demonstrator. A 2 m long, 0.7 m radius solenoid with a 5 T peak magnetic fie
Out-of-distribution (OOD) detection is essential for the safe deployment of neural networks, as it enables the identification of samples outside the training domain. We present FOODER, a real-time, privacy-preserving radar-based framework that integr
We describe four special families of ellipse-inscribed Poncelet triangles about the incircle which maintain certain triangle centers stationary and which also display interesting conservations.
This paper investigates the conditions under which the centralizer algebra S_n(c,R) of a matrix cin M_n(R) is a (separable) Frobenius extension of the base algebra R . For an algebra R over an integral domain mathbb{k} , we provide necessary
Multi-modal large language models (MLLMs) have shown tremendous promise in advancing healthcare. However, most existing models remain confined to single-image understanding, which greatly limits their applicability in real-world clinical workflows. I
Advanced Persistent Threats (APT) pose a major cybersecurity challenge due to their stealth, persistence, and adaptability. Traditional machine learning detectors struggle with class imbalance, high dimensional features, and scarce real world traces.
Energy consumption is becoming a serious bottleneck for integrating quantum technologies within the existing global information infrastructure. In photonic architectures, considerable energy overheads stem from using lasers, whose high coherence was
Let fcolon X to mathbb{A}^1_t be an affine flat morphism of finite type, and let V = f^{-1}(0) . Then, we obtain a morphism of log schemes fcolon (X V) to (mathbb{A}^1_t 0) . In this article, we develop algorithmic tools to study the log-geometri
We study the geometry and topology of compact submanifolds of Riemannian manifolds with arbitrary codimension that satisfy a pinching condition. The ambient space is not required to be a space form, and the pinching condition involves the length of t
We investigate the detailed dynamics of multidimensional Hamiltonian systems by studying the evolution of volume elements formed by unit deviation vectors about their orbits. The behavior of these volumes is strongly influenced by the regular or chao
This paper describes GeoPl@ntNet, an interactive web application designed to make Essential Biodiversity Variables accessible and understandable to everyone through dynamic maps and fact sheets. Its core purpose is to allow users to explore high-reso
Phenomenological rules play a central role in the design of chemical reactions and materials with targeted properties. Typically, these are formulated heuristically in terms of non-interacting orbitals and bands, yet show remarkable accuracy in predi
This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisi
Friction is the essential mediator of terrestrial locomotion, yet in robotic systems it is almost always treated as a passive property fixed by surface materials and conditions. Here, we introduce ultrasonic lubrication as a method to actively contro
In this paper, we study the Cauchy problem for a heat equation governed by a mixed local--nonlocal diffusion operator with spatially irregular coefficients. We first establish classical well-posedness in an energy framework for bounded, measurable co
Understanding why and how crystalline solids melt remains a central problem in condensed-matter physics. Dislocation loops are fundamental topological excitations that control the thermodynamic stability of crystals, yet their role in setting univers
This work introduces a novel two-stage distributed framework to globally estimate constant parameters in a networked system, separating shared information from local estimation. The first stage uses dynamic average consensus to aggregate agents' meas
The aim of this work is to apply a semi-implicit (SI) strategy within a Rosenbrock-type and IMEX linear multistep (LM) framework to a sequence of 1D time-dependent partial differential equations (PDEs) with high order spatial derivatives. This strate
We present high-precision spectroscopy of the ground-state hyperfine structure of HD ^+ at 4~T. We determine the bound-electron g factor, g_{e,mathrm{bound}} = -2.002,278,540,96(40) , to a relative uncertainty of 2times 10 ^{-10} , the most pr
This paper describes a recent mathematical method called conflation for consolidating data from independent experiments that are designed to measure the same quantity, such as Planck's constant or the mass of the top quark. Conflation is easy to calc
Autonomous laboratories typically rely on data-driven decision-making, occasionally with human-in-the-loop oversight to inject domain expertise. Fully leveraging AI agents, however, requires tightly coupled, collaborative workflows spanning hypothesi
Consider a measure-preserving transition kernel T on an arbitrary probability space (mathbb X,mathcal cA,π) . In this level of generality, we prove that a one-step hyper-contractivity estimate of the form T _{pto q}le 1 with p< q implies a on
We present a simple framework to compute hyperbolic Voronoi diagrams of finite point sets as affine diagrams. We prove that bisectors in Klein's non-conformal disk model are hyperplanes that can be interpreted as power bisectors of Euclidean balls. T
We give the basic definitions and some theoretical results about hyperdeterminants, introduced by A. Cayley in 1845. We prove integrability (understood as 4d-consistency) of a nonlinear difference equation defined by the 2x2x2-hyperdeterminant. This
Maintenance staff of Industrial Control Systems (ICS) is generally not aware about information technologies, and even less about cyber security problems. The scary impact of cyber attacks in the industrial world calls for tools to train defensive ski
Hybrid received signal strength (RSS)-angle of arrival (AoA)-based positioning offers low-cost distance estimation and high-resolution angular measurements. Still, it comes at a cost of inherent nonlinearities, geometry-dependent noise, and suboptima
Doppler holography is an emerging retinal imaging technique that captures the dynamic behavior of blood flow with high temporal resolution, enabling quantitative assessment of retinal hemodynamics. This requires accurate segmentation of retinal arter
Achieving a balance between lightweight design and high performance remains a significant challenge for speech enhancement (SE) tasks on resource-constrained devices. Existing state-of-the-art methods, such as MUSE, have established a strong baseline
This paper addresses graph learning in Gaussian Graphical Models (GGMs). In this context, data matrices often come with auxiliary metadata (e.g., textual descriptions associated with each node) that is usually ignored in traditional graph estimation
Labor productivity was studied at the microscopic level in terms of distributions based on individual firm financial data from Japan and the US. A power-law distribution in terms of firms and sector productivity was found in both countries' data. The
Photochromic materials enable dynamic optical modulation through reversible transitions between distinct absorption states, with broad potential for smart windows, adaptive optics, and reconfigurable photonic devices. Micron-scale photochromic hybrid
Diffraction tomography aims to recover an object's scattering potential from measured wave fields. In the classical setting, the object is illuminated by plane waves from many directions, and the Fourier diffraction theorem gives a direct relation be
Protein-protein interactions (PPIs) are governed by surface complementarity and hydrophobic interactions at protein interfaces. However, designing diverse and physically realistic protein structure and surfaces that precisely complement target recept
We explore the multimetric theory of gravitation, also known as multigravity. We derive additional new exact solutions for the theory in proportional Kerr-Schild and double Kerr-Schild forms. We extend several solutions from the theory of General Rel
We study global minimizers of the Landau-de Gennes energy for the nematic liquid crystals in simply connected, bounded, smooth domains of dimension 3, subject to a weak planar anchoring. The boundary condition is degenerate. In the regime where the e
A novel ultra-bright high-intensity source of X-ray and gamma radiation is suggested. It is based on the double Doppler effect, where a relativistic flying mirror reflects a counter-propagating electromagnetic radiation causing its frequency multipli
We find an interpretation of the complex of variational calculus in terms of the Lie conformal algebra cohomology theory. This leads to a better understanding of both theories. In particular, we give an explicit construction of the Lie conformal alge
We consider a strongly interacting {}^{6} Li- {}^{40} K mixture, which is imbalanced both in the masses and the densities of the two fermionic species. At present, it is the experimentalist's favorite for reaching the superfluid regime. We construct
The so-called light logics have been introduced as logical systems enjoying quite remarkable normalization properties. Designing a type assignment system for pure lambda calculus from these logics, however, is problematic. In this paper we show that
Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or final outputs while overlooking the rich internal representations and inte
Recently, machine learning Hamiltonian (MLH) models have gained traction as fast approximations of electronic structures such as orbitals and electron densities, while also enabling direct evaluation of energies and forces from their predictions. How
Cloud resource allocation has emerged as a major challenge in modern computing environments, with organizations struggling to manage complex, dynamic workloads while optimizing performance and cost efficiency. Traditional heuristic approaches prove i
In thin ( Delta< few lambda_i ) collisionless current sheets in a space plasma like the magnetospheric tail or magnetopause current layer, magnetic fields can grow {from thermal fluctuation level by the action of the non-magnetic Weibel instability
Large Language Models (LLMs) are powerful linguistic engines but remain susceptible to hallucinations-plausible-sounding outputs that are factually incorrect or unsupported. In this work, we present a coherent, mathematically grounded framework to un
Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification from moment constraints but require sampling via MCMC or Langevin dynamics
Micro-sized cold atmospheric plasma (uCAP) has been developed to expand the applications of CAP in cancer therapy. In this paper, uCAP devices with different nozzle lengths were applied to investigate effects on both brain (glioblastoma U87) and brea
Laser cooling and trapping of atomic matter waves in optical potentials has enabled rapid progress in quantum science, particularly when combined with Rydberg excitation of the atoms to induce long-range interactions. Here, we propose the local manip
An encoder, subject to a rate constraint, wishes to describe a Gaussian source under squared error distortion. The decoder, besides receiving the encoder's description, also observes side information consisting of uncompressed source symbol subject t
Large Language Models (LLMs) are predominantly deployed as dense transformers, where every parameter in every feed-forward block is activated for every token. While architecturally simple, this is computationally inefficient, since inference costs sc
Imperfect channel state information degrades the performance of multiple-input multiple-output (MIMO) communications; its effect on single-user (SU) and multi-user (MU) MIMO transmissions are quite different. In particular, MU-MIMO suffers from resid
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