arXiv papers from December 2023

Y. Alhassid, M. Bonett-Matiz, C. N. Gilbreth, S. Vartak

Conventional diagonalization methods to calculate nuclear energy levels in the framework of the configuration-interaction (CI) shell model approach are prohibited in very large model spaces. The shell model Monte Carlo (SMMC) is a powerful technique for calculating thermal and ground-state observables of nuclei in very large model spaces, but it is challengi

Beata Randrianantoanina, Michał Wojciechowski, Pavel Zatitskii

We establish that the summability of the series $\sum\varepsilon_n$ is the necessary and sufficient criterion ensuring that every $(1+\varepsilon_n)$ Markushevich basis in a separable Hilbert space is a Riesz basis. Further we show that if $n\varepsilon_n\to \infty$, then in $\ell_2$ there exists a $(1+\varepsilon_n)$ Markushevich basis that under any permut

Tim Z. Xiao, Weiyang Liu, Robert Bamler

Bayesian neural networks (BNNs) are a principled approach to modeling predictive uncertainties in deep learning, which are important in safety-critical applications. Since exact Bayesian inference over the weights in a BNN is intractable, various approximate inference methods exist, among which sampling methods such as Hamiltonian Monte Carlo (HMC) are often

Saravanabalagi Ramachandran, Nathaniel Cibik, Ganesh Sistu, John McDonald

Motion segmentation is a complex yet indispensable task in autonomous driving. The challenges introduced by the ego-motion of the cameras, radial distortion in fisheye lenses, and the need for temporal consistency make the task more complicated, rendering traditional and standard Convolutional Neural Network (CNN) approaches less effective. The consequent la

Daniel T. Fokum, Zaria Chen Shui, Kerene Wright, Orr Paradise

This is a report on JamCoders, a four-week long computer-science camp for high school students in Jamaica. The camp teaches college-level coding and algorithms, and targets academically excellent students in grades 9--11 (ages 14--17). Qualitative assessment shows that the camp was, in general terms, a success. We reflect on the background and academic struc

Richard Sutcliffe

We present a review of personality in neural conversational agents (CAs), also called chatbots. First, we define Personality, Persona, and Profile. We explain all personality schemes which have been used in CAs, and list models under the scheme(s) which they use. Second we describe 21 datasets which have been developed in recent CA personality research. Thir

Vardaan Pahuja, Weidi Luo, Yu Gu, Cheng-Hao Tu

Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalization to new and unseen locations. Images are typically associated with diverse forms of context, which may exist in different modalities. In this work, we exploit the structured cont

Brett Kotschwar

We prove that a complete solution to the Ricci flow on $M\times [-T, 0)$ which has quadratic curvature decay on some end of $M$ and converges locally smoothly to the end of a cone on that neighborhood as $t\nearrow 0$ must be a gradient shrinking soliton.

Brett Kotschwar

We establish sufficient conditions which ensure that a locally-warped product structure propagates backward in time under the Ricci flow. As an application, we prove that if an asymptotically conical gradient shrinking soliton is asymptotic to a cone whose cross-section is a product of Einstein manifolds, the soliton must itself be a multiply-warped product

Fei Chen, Hoa Van Nguyen, Alex S. Leong, Sabita Panicker

We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field-of-views (FoVs), sensors with limited local computational resources and communication channel capacity. The r

Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang

Score distillation has emerged as one of the most prevalent approaches for text-to-3D asset synthesis. Essentially, score distillation updates 3D parameters by lifting and back-propagating scores averaged over different views. In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance. Through the lens of varianc

Vahidin Jeleskovic, Stephen Mackay

Cryptocurrencies have emerged as a novel financial asset garnering significant attention in recent years. A defining characteristic of these digital currencies is their pronounced short-term market volatility, primarily influenced by widespread sentiment polarization, particularly on social media platforms such as Twitter. Recent research has underscored the

Nancy Rodriguez, David White

The effect that different police protest management methods have on protesters' physical and mental trauma is still not well understood and is a matter of debate. In this paper, we take a two-pronged approach to gain insight into this issue. First, we perform statistical analysis on time series data of protests provided by ACLED and spanning the period of ti

Matus Benko, R. Tyrrell Rockafellar

Much is known about when a locally optimal solution depends in a single-valued Lipschitz continuous way on the problem's parameters, including tilt perturbations. Much less is known, however, about when that solution and a uniquely determined multiplier vector associated with it exhibit that dependence as a primal-dual pair. In classical nonlinear programmin

Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang

Despite the remarkable performance of score distillation in text-to-3D generation, such techniques notoriously suffer from view inconsistency issues, also known as "Janus" artifact, where the generated objects fake each view with multiple front faces. Although empirically effective methods have approached this problem via score debiasing or prompt engineerin

Dongsheng Wang, Natraj Raman, Mathieu Sibue, Zhiqiang Ma

Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities. The visual cues offered by their complex layouts play a crucial role in comprehending these documents effectively. In this paper, we present DocLLM, a lightweight extension to

Julian B. B. Beckmann, Mick D. Mantle, Andrew J. Sederman, Lynn F. Gladden

Sub-sampling is applied to simulated $T_1$-$D$ NMR signals and its influence on inversion performance is evaluated. For this different levels of sub-sampling were employed ranging from the fully sampled signal down to only less than two percent of the original data points. This was combined with multiple sample schemes including fully random sampling, trunca

David R. Pitts

Let $\pi: Y\rightarrow X$ be a continuous surjection between compact Hausdorff spaces $Y$ and $X$ which is irreducible in the sense that if $F\subsetneq Y$ is closed, then $\pi(F)\neq X$. We exhibit isomorphisms between various Boolean algebras associated to this data: the regular open sets of $X$, the regular open sets of $Y$, the regular ideals of $C(X)$ a

Vahidin Jeleskovic, Steffen Loeber

In this paper, we employ spatial econometric methods to analyze panel data from German NUTS 3 regions. Our goal is to gain a deeper understanding of the significance and interdependence of industry clusters in shaping the dynamics of GDP. To achieve a more nuanced spatial differentiation, we introduce indicator matrices for each industry sector which allows

Justin Chen, Marc Härkönen, Anton Leykin

Generalizing the concept of the Macaulay inverse system, we introduce a way to describe localizations of an ideal in a polynomial ring. This leads to an approach to the differential primary decomposition as a description of the affine scheme defined by the ideal.

Lipei Du

We introduce a method to reconstruct full rapidity distributions of charged particle multiplicity and net proton yields, crucial for constraining the longitudinal dynamics of nuclear matter created in the beam energy scan program. Employing rapidity distributions within a multistage hydrodynamic model calibrated for Au+Au collisions at $\sqrt{s_\mathrm{NN}}=

Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror

Recent advances in large language models (LLMs) have led to the development of various evaluation benchmarks. These benchmarks typically rely on a single instruction template for evaluating all LLMs on a specific task. In this paper, we comprehensively analyze the brittleness of results obtained via single-prompt evaluations across 6.5M instances, involving

Mohammad Ebrahimi, Min Dong, Mitra Hekmat

This paper considers downlink multi-group multicasting via beamforming facilitated by a reconfigurable intelligent surface (RIS). We develop a fast and scalable algorithm for the joint base station (BS) and RIS beamforming optimization to minimize the transmit power while meeting user quality-of-service (QoS) targets. By analyzing the structure of the QoS co

Boumediene Hamzi, Kamaludin Dingle

Simplicity bias is an intriguing phenomenon prevalent in various input-output maps, characterized by a preference for simpler, more regular, or symmetric outputs. Notably, these maps typically feature high-probability outputs with simple patterns, whereas complex patterns are exponentially less probable. This bias has been extensively examined and attributed

Pavel Chebotarev, Vadim Afonkin

Within the ViSE (Voting in Stochastic Environment) model, we study the effectiveness of majority voting in various environments. As shown by the pit-of-losses paradox identified in previous work, majority decisions in apparently hostile environments tend to reduce the capital of society. In such cases, the simple social decision rule of ``rejecting all propo

M Sabbir Salek, Abdullah Al Mamun, Mashrur Chowdhury

This study developed a generative adversarial network (GAN)-based defense method for traffic sign classification in an autonomous vehicle (AV), referred to as the attack-resilient GAN (AR-GAN). The novelty of the AR-GAN lies in (i) assuming zero knowledge of adversarial attack models and samples and (ii) providing consistently high traffic sign classificatio

Y. Alhassid, P. Fanto, A. Mercenne

A low-energy enhancement (LEE) has been observed in the deexcitation $\gamma$-ray strength function ($\gamma$SF) of compound nuclei. The LEE has been a subject of intense experimental and theoretical interest since its discovery, and, if the LEE persists in heavy neutron-rich nuclei, it would have significant effects on calculations of r-process nucleosynthe

Bongjun Choi, Kiyoung Jo, Mahfujur Rahaman, Adam Alfieri

Optical anisotropy is a fundamental attribute of some crystalline materials and is quantified via birefringence. A birefringent crystal not only gives rise to asymmetrical light propagation but also attenuation along two distinct polarizations, a phenomenon called linear dichroism (LD). Two-dimensional (2D) layered materials with high in- and out-of-plane an

Qianxi Li, Yingyue Cao, Jikun Kang, Tianpei Yang

Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance. Supervised Fine-Tuning (SFT) is a common approach, where an LLM is trained to produce desired answers. However, LLMs trained with SFT sometimes make simple mistakes and result in hallucinations on reasoning tasks su

Laurence Sebastian Bowes, Vincent Drach, Patrick Fritzsch, Antonio Rago

Composite Higgs models are a class of models proposed to address the hierarchy and naturalness problems associated with the Standard Model fundamental scalar Higgs. $SU(2)$ with two fundamental flavours is a minimal model for the composite Higgs sector which is not yet ruled out by experimental data. We present lattice results for $SU(2)$ with two fundamenta

Ying Sheng, Shiyi Cao, Dacheng Li, Banghua Zhu

High-demand LLM inference services (e.g., ChatGPT and BARD) support a wide range of requests from short chat conversations to long document reading. To ensure that all client requests are processed fairly, most major LLM inference services have request rate limits, to ensure that no client can dominate the request queue. However, this rudimentary notion of f

Zachary Schwehr, Sriman Achanta

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%. A quick and accurate diagnosis is crucial to increase the chance of survival. However, in medical analysis, the manual annotation and segmentation of a brain tumor can be a complicated task. Multiple MRI modalities are typically analyzed as they provide unique informatio

Thomas L. Curtright

Non-relativistic quantum mechanical scattering from an inverse square potential in two spatial dimensions leads to a novel representation of the Bernoulli numbers.

Andrzej Derdzinski

We provide a step towards classifying Riemannian four-manifolds in which the curvature tensor has zero divergence, or -- equivalently -- the Ricci tensor Ric satisfies the Codazzi equation. Every known compact manifold of this type belongs to one of five otherwise-familiar classes of examples. The main result consists in showing that, if such a manifold (not

Seppo Hassi, Henk de Snoo

For a semibounded sesquilinear form ${\mathfrak t}$ in a Hilbert space ${\mathfrak H}$ there exists a representing map $Q$ from ${\mathfrak H}$ to another Hilbert space ${\mathfrak K}$, such that ${\mathfrak t}[\varphi, \psi]-c(\varphi, \psi)=(Q\varphi,Q\psi)$, $\varphi,\psi \in {\rm dom\,}{\mathfrak t}$, with $c \in {\mathbb R}$ a lower bound of ${\mathfrak

Rachel Redberg, Antti Koskela, Yu-Xiang Wang

In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity and interest. Though unrivaled in versatility, DP-SGD requires a non-trivial privacy overhead (for privately tuning the model's hyperparameters) and a computational complexity whic

Yash Bingi, Yiqiao Yin

Large Lanugage Models (LLMs) are gaining increasing popularity in a variety of use cases, from language understanding and writing to assistance in application development. One of the most important aspects for optimal funcionality of LLMs is embedding layers. Word embeddings are distributed representations of words in a continuous vector space. In the contex

S. Lotfimarangloo, V. A. Chirayath, P. A. Sterne, H. Mahdy

We present measurements and theoretical modeling demonstrating the capability of Doppler Broadened annihilation gamma Spectroscopy (DBS) to provide element-specific information from the topmost atomic layer of surfaces that are either clean or covered with adsorbates or thin films. Our measurements show that the energy spectra of Doppler-shifted annihilation

Yunkun Zhao, Aditya A Bhosale, Xiaoliang Zhang

Leveraging the potential of low-field Magnetic Resonance Imaging (MRI), our study introduces the multimodal surface RF coil, a design tailored to overcome the limitations of conventional coils in this context. The inherent challenges of low-field MRI, notably suboptimal signal-to-noise ratio (SNR) and the need for specialized RF coils, are effectively addres

Omid Rohanian, Mohammadmahdi Nouriborji, David A. Clifton

Large Language Models (LLMs), particularly those similar to ChatGPT, have significantly influenced the field of Natural Language Processing (NLP). While these models excel in general language tasks, their performance in domain-specific downstream tasks such as biomedical and clinical Named Entity Recognition (NER), Relation Extraction (RE), and Medical Natur

Agostino Capponi, Mihailo Stojnic

We study the completion of approximately low rank matrices with entries missing not at random (MNAR). In the context of typical large-dimensional statistical settings, we establish a framework for the performance analysis of the nuclear norm minimization ($\ell_1^*$) algorithm. Our framework produces \emph{exact} estimates of the worst-case residual root mea

Julien Frison

Bayesian inference provides a rigorous framework to encapsulate our knowledge and uncertainty regarding various physical quantities in a well-defined and self-contained manner. Utilising modern tools, such Bayesian models can be constructed with a remarkable flexibility, leaving us totally free to carefully choose which assumption should be strictly enforced

Viktoriya Morozova, James G. Coder, Kevin Holst

A wide range of implicit time integration methods, including multi-step, implicit Runge-Kutta, and Galerkin finite-time element schemes, is evaluated in the context of chaotic dynamical systems. The schemes are applied to solve the Lorenz equations, the equation of motion of a Duffing oscillator, and the Kuramoto-Sivashinsky system, with the goal of finding

Payam Karisani

We propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. One of the weaknesses of self-training is the semantic drift problem, where noisy pseudo-labels accumulate over iterations and consequently the error rate soars. In order to tackle this challenge, we reshape the role of pseudo-

Syo Kamata

We consider exact WKB analysis to a ${\cal PT}$ symmetric quantum mechanics defined by the potential, $V(x) = \omega^2 x^2 + g x^2(i x)^{\varepsilon=2}$ with $\omega \in {\mathbb R}_{\ge 0}$, $g \in {\mathbb R} _{> 0}$. We in particular aim to verify a conjecture proposed by Ai-Bender-Sarkar (ABS), that pertains to a relation between $D$-dimensional ${\cal P

Md Fahim F Chowdhury, Mohamad Niknam, Md Mahadi Rajib, Louis S. Bouchard

Selective control of individual spin qubits is needed for scalable quantum computing based on spin states. Achieving high-fidelity in both single and two-qubit gates, essential components of universal quantum computers, necessitates highly localized control fields. These fields must be capable of addressing specific spin qubits while minimizing gate errors a

Dengxin Huang

This document presents a stock market analysis conducted on a dataset consisting of 750 instances and 16 attributes donated in 2014-10-23. The analysis includes an exploratory data analysis (EDA) section, feature engineering, data preparation, model selection, and insights from the analysis. The Fama French 3-factor model is also utilized in the analysis. Th

Itamar Giron, Yair Hayut

In this paper we investigate the problem of the distributivity of Kurepa trees. We show that it is consistent that there are Kurepa trees and for every Kurepa tree there is a small forcing notion which adds a branch to it without collapsing cardinals. On the other hand, we derive a proper forcing notion for making an arbitrary Kurepa tree into a non-distribu

Yuhan Lim

We define an invariant ${\varphi}$ for knots in the 3-sphere by means of Donaldson invariants and Floer's instanton homology. Some basic properties of this invariant are established and it is shown that ${\varphi}$ coincides with a special case of an invariant defined by Froyshov

Maarten Mol

In this paper we study a natural generalization of symplectic toric manifolds in the context of regular Poisson manifolds of compact types. To be more precise, we consider a class of multiplicity-free Hamiltonian actions by regular proper symplectic groupoids that we call faithful. Given such a groupoid, we classify its faithful multiplicity-free Hamiltonian

Renyuan Xu, Thaleia Zariphopoulou, Luhao Zhang

Decision making in modern stochastic systems, including e-commerce platforms, financial markets and healthcare systems, has evolved into a multifaceted process that combines information acquisition and adaptive information sources. This paper initiates a study on such integrated settings, where these elements are not only fundamental but, also, interact in a

Philip Cooney, Arthur White

Introduction: Modelling of relative treatment effects is an important aspect to consider when extrapolating the long-term survival outcomes of treatments. Flexible parametric models offer the ability to accurately model the observed data, however, the extrapolated relative treatment effects and subsequent survival function may lack face validity. Methods: We

el Houcein el Abdalaoui, Michael Lin

Let $T$ be the Koopman operator of a measure preserving transformation $\theta$ of a probability space $(X,\Sigma,\mu)$. We study the convergence properties of the averages $M_nf:=\frac1n\sum_{k=0}^{n-1}T^kf$ when $f \in L^r(\mu)$, $0<r<1$. We prove that if $\int |M_nf|^r d\mu \to 0$, then $f \in \overline{(I-T)L^r}$, and show that the converse fails wheneve

Žikica Lukić, Bojana Milošević

In this study, we introduce the first-of-its-kind class of tests for detecting change points in the distribution of a sequence of independent matrix-valued random variables. The tests are constructed using the weighted square integral difference of the empirical orthogonal Hankel transforms. The test statistics have a convenient closed-form expression, makin

Shanquan Gui, Kun Xu, Y. P. Jing, Donghai Zhao

The Photometric objects Around Cosmic webs (PAC) approach developed in Xu et al. (2022b) has the advantage of making full use of spectroscopic and deeper photometric surveys. With the merits of PAC, the excess surface density $\bar{n}_2w_{{\rm{p}}}$ of neighboring galaxies can be measured down to stellar mass $10^{10.80}\,M_{\odot}$ around quasars at redshif

Eoin Dowd, Gaston Giribet

We study a class of time-dependent backgrounds in string theory which consist of marginal deformations of minimal strings on AdS$_3$. For such backgrounds, we compute the three-point amplitudes and analyze their properties.

Chenyuan Yang, Zijie Zhao, Lingming Zhang

Bugs in operating system kernels can affect billions of devices and users all over the world. As a result, a large body of research has been focused on kernel fuzzing, i.e., automatically generating syscall (system call) sequences to detect potential kernel bugs or vulnerabilities. Kernel fuzzing aims to generate valid syscall sequences guided by syscall spe

Tim Sziburis, Susanne Blex, Tobias Glasmachers, Ioannis Iossifidis

Neurological conditions are a major source of movement disorders. Motion modelling and variability analysis have the potential to identify pathology but require profound data. We introduce a systematic dataset of 3D center-out task-space trajectories of human hand transport movements in a natural setting. The transport tasks of this study consist of grasping

Claudio Afeltra

We prove the compactness of the set of solutions to the CR Yamabe problem on a compact strictly pseudoconvex CR manifold of dimension three whose blow-up manifolds at every point have positive p-mass. As a corollary we deduce that compactness holds for CR-embeddable manifolds which are not CR-equivalent to $S^3$. The theorem is proved by blow-up analysis.

Roy H. Goodman, Grace Conte, Jeremy L. Marzuola

We describe QGLAB, a new MATLAB package for analyzing partial differential equations on quantum graphs. The software is built on the existing, object-oriented MATLAB directed-graph class, inheriting its structure and adding additional easy-to-use features. The package allows one to construct a quantum graph and accurately compute the spectrum of elliptic ope

Daniele Perri, Kyrilo Bondarenko, Michele Doro, Takeshi Kobayashi

We provide a comprehensive analysis of the acceleration of magnetic monopoles in intergalactic magnetic fields. We demonstrate that monopoles with intermediate to low masses can be accelerated to relativistic velocities. This can significantly affect direct and indirect searches for magnetic monopoles. As an example, we show that the Parker bound is relaxed

Michael Molloy, Pawel Pralat, Gregory B. Sorkin

We study the 2-offer semirandom 3-uniform hypergraph model on $n$ vertices. At each step, we are presented with 2 uniformly random vertices. We choose any other vertex, thus creating a hyperedge of size 3. We show a strategy that constructs a perfect matching, and another that constructs a loose Hamilton cycle, both succeeding asymptotically almost surely wi

Yuri Luchko

In this paper, we introduce a new class of the kernels of the integral transforms of the Laplace convolution type that we call symmetrical Sonin kernels. For a symmetrical Sonin kernel given in terms of some elementary or special functions, its associated kernel has the same form with possibly different parameter values. Several known and new kernels of this

Ky T. Bataka, Murphy E. Egwe, Yaogan Mensah

This paper introduces Sobolev spaces over Gelfand pairs in the framework of hypergroups. The Sobolev spaces in question are constructed from the Fourier transform on hypergroup Gelfand pairs. Mainly, the paper focuses on the investigation of Sobolev embedding results.

Ivan Gonzalez, John Lopez Santander, Victor H. Moll

The method of brackets is an procedure to evaluate definite integrals. It is based on a small number of operational rules. The flexibility of this method is illustrated with the evaluation of an integral involving the Bessel K0 function and the exponential integral. Several proofs are presented.

Xin-Zhe Wang, Can-Min Deng

Recently, the pulsar timing array (PTA) collaborations, including CPTA, EPTA, NANOGrav, and PPTA, announced that they detected a stochastic gravitational wave background spectrum in the nHz band. This may be relevant to the cosmological phase transition suggested by some models. Magnetic monopoles and primordial black holes (PBHs), two unsolved mysteries in

Hongjie Dong, Yan Guo, Timur Yastrzhembskiy

The control of plasma-wall interactions is crucial to fusion devices from both physical and mathematical perspectives. It is well known that a magnetic field satisfying the classical perfect conducting conditions at the wall, $$ \mathbf{E} \times n_x = 0, \quad \mathbf{B} \cdot n_x = 0, $$ plays an important role in fusion plasma dynamics studies. Since the

R. García-Delgado

In this work we state a result that relates the cohomology groups of a Lie algebra $\mathfrak{g}$ and a current Lie algebra $\mathfrak{g} \otimes \mathcal{S}$, by means of a short exact sequence -- similar to the universal coefficients theorem for modules -- where $\mathcal{S}$ is a finite dimensional, commutative and associative algebra with unit over a fie

Calvin Alvares, Soumitro Banerjee

Reasonably large perturbations may push a power grid from its stable synchronous state into an undesirable state. Identifying vulnerabilities in power grids by studying power grid stability against such perturbations can aid in preventing future blackouts. Probabilistic stability quantifiers such as basin stability, which measures the asymptotic stability of

Yue Han, Jiangning Zhang, Junwei Zhu, Xiangtai Li

This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously. To achieve this goal, we initially formulate a unified facial representation for a broad spectrum of facial editing tasks, which macroscopically decomposes a face into fundamental identity, intra-personal variation, and environmental f

Miloš S. Kurilić

The poset of copies of a relational structure ${\mathbb X}$ is the partial order $\langle {\mathbb P} ({\mathbb X}) ,\subset \rangle$, where ${\mathbb P} ({\mathbb X})=\{ Y\subset X: {\mathbb Y} \cong {\mathbb X}\}$. Investigating the classification of structures related to isomorphism of the Boolean completions ${\mathbb B}_{\mathbb X} ={\mathop{\rm ro}\nol

Jose Juan Fernandez-Melgarejo, Giacomo Giorgi, Carmen Gomez-Fayren, Tomas Ortin

The dualization of the scalar fields of a theory into (d-2)-form potentials preserving all the global symmetries is one of the main problems in the construction of democratic pseudoactions containing simultaneously all the original fields and their duals. We study this problem starting with the simplest cases and we show how it can be solved for scalars para

Soumitra Ghara, Rajeev Gupta, Md. Ramiz Reza

For a positive integer $m$ and a finite non-negative Borel measure $\mu$ on the unit circle, we study the Hadamard multipliers of higher order weighted Dirichlet-type spaces $\mathcal H_{\mu, m}$. We show that if $\alpha>\frac{1}{2},$ then for any $f$ in $\mathcal H_{\mu, m},$ the sequence of generalized Ces{\`a}ro sums $\{\sigma_n^{\alpha}[f]\}$ converges t

A Ch Madhusudanarao, Rahul Singh

We study chance constrained optimization problems $\min_x f(x)$ s.t. $P(\left\{ \theta: g(x,\theta)\le 0 \right\})\ge 1-\epsilon$ where $\epsilon\in (0,1)$ is the violation probability, when the distribution $P$ is not known to the decision maker (DM). When the DM has access to a set of distributions $\mathcal{U}$ such that $P$ is contained in $\mathcal{U}$,

Run Shao, Cheng Yang, Qiujun Li, Qing Zhu

Leveraging multimodal data is an inherent requirement for comprehending geographic objects. However, due to the high heterogeneity in structure and semantics among various spatio-temporal modalities, the joint interpretation of multimodal spatio-temporal data has long been an extremely challenging problem. The primary challenge resides in striking a trade-of

G. Contursi, P. de Laverny, A. Recio-Blanco, P. A. Palicio

The recent parameterisation by the GSP-spec module of Gaia/RVS spectra has produced an homogeneous catalogue of about 174,000 AGB stars. Among the 13 chemical elements presented in this catalogue, the abundance of 2 of them (Ce and Nd) have been estimated in most of these AGBs. These 2 species formed by slow n-captures in the interior of low- and intermediat

Vansh Sharma, Venkat Raman

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, si

Christos Pelekis

Fix a positive integer $n$, a real number $p\in (0,1]$, and a (perhaps random) hypergraph $\mathcal{H}$ on $[n]$. We introduce and investigate the following random multigraph model, which we denote $\mathbb{G}(n,p\, ; \,\mathcal{H})$: begin with an empty graph on $n$ vertices, which are labelled by the set $[n]$. For every $H\in \mathcal{H}$ choose, independ

Giuseppe Savaré, Giacomo Enrico Sodini

We present a general convex relaxation approach to study a wide class of Unbalanced Optimal Transport problems for finite non-negative measures with possibly different masses. These are obtained as the lower semicontinuous and convex envelope of a cost for non-negative Dirac masses. New general primal-dual formulations, optimality conditions, and metric-topo

Joseph Y. Halpern

A definition of what counts as an explanation of mathematical statement, and when one explanation is better than another, is given. Since all mathematical facts must be true in all causal models, and hence known by an agent, mathematical facts cannot be part of an explanation (under the standard notion of explanation). This problem is solved using impossible

David Eisenbud, Antonino Ficarra, Jürgen Herzog, Somayeh Moradi

For an ideal $I$ in a Noetherian ring $R$, the Fitting ideals $\textrm{Fitt}_j(I)$ are studied. We discuss the question of when $\textrm{Fitt}_j(I)=I$ or $\sqrt{\textrm{Fitt}_j(I)}=\sqrt{I}$ for some $j$. A classical case is the Hilbert-Burch theorem when $j=1$ and $I$ is a perfect ideal of grade $2$ in a local ring.

Hong Zhang, Jie Pu, Shibing Deng, Satrajit Roychoudhury

In the era of precision medicine, more and more clinical trials are now driven or guided by biomarkers, which are patient characteristics objectively measured and evaluated as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic interventions. With the overarching objective to optimize and personalize dis

Elisa Alòs, Eulalia Nualart, Makar Pravosud

In this paper we study short-time behavior of the at-the-money implied volatility for Inverse European options with fixed strike price. The asset price is assumed to follow a general stochastic volatility process. Using techniques of the Malliavin calculus such as the anticipating It^o's formula we first compute the level of the implied volatility of the opt

Wolfgang Korsch, Mark Broering, Ashok Timsina, Kent K. H. Leung

This paper presents a new technique to study the adsorption and desorption of ions and electrons on insulating surfaces in the presence of strong electric fields in cryoliquids. The experimental design consists of a compact cryostat coupled with a sensitive electro-optical Kerr device to monitor the stability of the electric fields. The behavior of nitrogen

Guang Hu

We prove that the set of anisotropic quadratic forms over global fields of characteristic different from 2 is a diophantine set. Our proof builds upon and extends the method of Koenigsmann, using tools from class field theory, the local-global principle, and advances on the diophantine definability of non-norm sets over global fields.

Guanhong Tao, Siyuan Cheng, Zhuo Zhang, Junmin Zhu

The emergence of large language models (LLMs) has significantly accelerated the development of a wide range of applications across various fields. There is a growing trend in the construction of specialized platforms based on LLMs, such as the newly introduced custom GPTs by OpenAI. While custom GPTs provide various functionalities like web browsing and code

Alex-Răzvan Ispas, Théo Deschamps-Berger, Laurence Devillers

Speech emotion recognition (SER) has received a great deal of attention in recent years in the context of spontaneous conversations. While there have been notable results on datasets like the well known corpus of naturalistic dyadic conversations, IEMOCAP, for both the case of categorical and dimensional emotions, there are few papers which try to predict bo

Richard Murdoch Montgomery

This study presents a computational simulation exploring the complex interactions between population density and economic factors over a 100-year period. Inspired by the Keller-Segel model, traditionally applied in biological contexts, my model adapts this framework to analyze urban and economic dynamics. The simulation employs two coupled partial differenti

Theodoros Chatzivasileiadis, Ignasi Cortes Arbues, Jochen Hinkel, Daniel Lincke

This study investigates the long-term economic impact of sea-level rise (SLR) on coastal regions in Europe, focusing on Gross Domestic Product (GDP). Using a novel dataset covering regional SLR and economic growth from 1900 to 2020, we quantify the relationships between SLR and regional GDP per capita across 79 coastal EU & UK regions. Our results reveal tha

Shun Liu, Kexin Wu, Chufeng Jiang, Bin Huang

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short t

Erna Begovic, Vjeran Hari

The paper considers the convergence of the complex block Jacobi diagonalization methods under the large set of the generalized serial pivot strategies. The global convergence of the block methods for Hermitian, normal and $J$-Hermitian matrices is proven. In order to obtain the convergence results for the block methods that solve other eigenvalue problems, s

Lie-Juan Li, Xiao-Wei Sun, Melike Mohamedsedik, Li Wang

For different alternating-sign multi-pulse trains electric fields with oscillation, the effects of the electric field pulse number and the relative phase of the combined electric field on pair production are investigated by solving quantum Vlasov equation. It is found that the number density of created particles in the combined electric fields is increased b

Aristeidis Zachos, Christos-Nikolaos Anagnostopoulos

Preserving and safeguarding the Cultural Heritage (CH) of our world from unforeseen hazards should be viewed as a collective responsibility for humanity. Consequently, there is a growing imperative for targeted measures aimed at conserving, rejuvenating, and safeguarding historical assets that carry cultural significance. In recent times, Terrestrial Laser S

Massinissa Hamidi, Aomar Osmani

In this paper we will discuss metalearning and how we can go beyond the current classical learning paradigm. We will first address the importance of inductive biases in the learning process and what is at stake: the quantities of data necessary to learn. We will subsequently see the importance of choosing suitable parameterizations to end up with well-define

Omar Dennaoui, Jonathon Villareal

Recently, Steinberg used discrete Morse theory to give a new proof of a theorem of Symonds that the orbit space of the poset of nontrivial $p$-subgroups of a finite group is contractible. We extend Steinberg's argument in two ways, covering more general versions of the theorem that were already known. In particular, following a strategy of Libman, we give a

Jingcheng Liang, Chen Fang, Jiangping Hu

We demonstrate that non-Hermitian perturbations can probe topological phase transitions and unambiguously detect non-Abelian zero modes. We show that under carefully designed non-Hermitian perturbations, the Loschmidt echo(LE) decays into 1/N where N is the ground state degeneracy in the topological non-trivial phase, while it approaches 1 in the trivial pha

Qifang Zhao, Weidong Ren, Tianyu Li, Hong Liu

We introduceGraphGPT, a novel self-supervised generative pre-trained model for graph learning based on the Graph Eulerian Transformer (GET). First, we propose GET, which combines a standard transformer encoder or decoder architecture with an innovative graph-to-sequence transformation method. This method converts graphs or sampled subgraphs into sequences of

Oliver Goertsches, Panagiotis Konstantis, Leopold Zoller

We show that under standard assumptions on the isotropy groups of an integer GKM manifold, the equivariant Stiefel-Whitney classes of the action are determined by the GKM graph. This is achieved via a GKM-style description of the equivariant cohomology with coefficients in a finite field $\mathbb Z_{p}$ even though in this setting the restriction map to the

Alexander I. Bufetov

The aim of this note is to estimate the tail of the distribution of the number of particles in an interval under determinantal and Pfaffian point processes. The main result of the note is that the square of the number of particles under the determinantal point process whose correlation kernel is an entire function of finite order has sub-Poissonian tails. Th

Bhilahari Jeevanesan

Given the recent advances in quantum technology, the complexity of quantum states is an important notion. The idea of the Krylov spread complexity has come into focus recently with the goal of capturing this in a quantitative way. The present paper sheds new light on the Krylov complexity measure by exploring it in the context of continuous-time quantum-walk