February 2024 arXiv papers — page 27

Darul Wiyono, Rinaldi Tanjung, Hedi Setiadi, Sri Marini

Orientation: The study explores the connections among servant leadership, burnout, and work ethic culture in organizations. It aims to provide a detailed understanding of how servant leadership influences work ethic culture, especially by considering the role of burnout. Research Purpose: This study aims to understand how servant leadership influences work e

Robert L. Bassett, Austin Van Dellen, Anthony P. Austin

We investigate the vulnerability of computer-vision-based signal classifiers to adversarial perturbations of their inputs, where the signals and perturbations are subject to physical constraints. We consider a scenario in which a source and interferer emit signals that propagate as waves to a detector, which attempts to classify the source by analyzing the s

Robin M. Cross, Steven T. Buccola

This paper incorporates information about the temporal order of regressors to estimate orthogonal and economically interpretable regression coefficients. We establish new finite sample properties for the Gram-Schmidt orthogonalization process. Coefficients are unbiased and stable with lower standard errors than those from Ordinary Least Squares. We provide c

Michael D. Pugh, SK Firoz Islam, Igor V. Bondarev

We present a theoretical study of the directionality effects in spontaneous emission and resonance fluorescence of a quantum two-level dipole emitter near an ultrathin closely packed periodically aligned single-wall carbon nanotube film. Such films present an example of highly anisotropic flexible metasurfaces that are now available experimentally. The nanot

Susan Epstein, Li Chen, Alessandro Vecchiato, Ankit Jain

Generative AI image models may inadvertently generate problematic representations of people. Past research has noted that millions of users engage daily across the world with these models and that the models, including through problematic representations of people, have the potential to compound and accelerate real-world discrimination and other harms (Bianc

Subhanker Howlader, Prasenjit Das

The equation of state for an ideal gas is simple, which is $P=nk_{\rm B}T$. In the case of imperfect gases where mutual interactions among the constituents are important, pressure $P$ can be expressed as the series expansion of density $n$ with appropriate coefficients, known as virial coefficients $B_m$. In this paper, we have obtained the first four virial

S. S. Ren, R. X. Zhou, Y. G. Zheng, S. J. Kang

Context.Unusually, there are still certain characteristics of the changing-look (CL) active galactic nuclei (AGNs) that remain undetected.Consequently,the trigger mechanism behind the CL phenomenon observed in partial AGNs remains unknown.Aims.We explore the light curve and spectral energy distribution (SED) of the CL blazar OQ 334 as obtained by Fermi-LAT.

Peng Gao, Shi-Min Li, Feng Gao, Fei Wang

Deep learning-based methods monopolize the latest research in the field of thermal infrared (TIR) object tracking. However, relying solely on deep learning models to obtain better tracking results requires carefully selecting feature information that is beneficial to representing the target object and designing a reasonable template update strategy, which un

Javier Ferrando, Elena Voita

Information flows by routes inside the network via mechanisms implemented in the model. These routes can be represented as graphs where nodes correspond to token representations and edges to operations inside the network. We automatically build these graphs in a top-down manner, for each prediction leaving only the most important nodes and edges. In contrast

Juyeon Kim, Jeongeun Lee, Yoonho Chang, Chanyeol Choi

Mitigating hallucination issues is a key challenge that must be overcome to reliably deploy large language models (LLMs) in real-world scenarios. Recently, various methods have been proposed to detect and revise factual errors in LLM-generated texts, in order to reduce hallucination. In this paper, we propose Re-Ex, a method for post-editing LLM-generated re

Fengyu Li, Huijiao Yu, Jun Yan, Xianyong Meng

The Monte Carlo algorithm is increasingly utilized, with its central step involving computer-based random sampling from stochastic models. While both Markov Chain Monte Carlo (MCMC) and Reject Monte Carlo serve as sampling methods, the latter finds fewer applications compared to the former. Hence, this paper initially provides a concise introduction to the t

Jong Sung Moon, Benjamin Whitefield, Lesley Spencer, Mehran Kianinia

Integrating quantum materials with fibre optics adds advanced functionalities to a variety of applications, and introduces fibre-based quantum devices such as remote sensors capable of probing multiple physical parameters. However, achieving optimal integration between quantum materials and fibres is challenging, particularly due to difficulties in fabricati

Idris Assani, Jacob Folks, Ryo Moore

We will construct ``higher-dimensional" versions of the Wiener-Wintner dynamical system that was originally studied by I. Assani in 2003. We will show that on these systems we can provide very simple proofs of the a.e. convergence of the multiple recurrence averages, as well as the multiple recurrence return times averages. We will do so by obtaining a quant

Ruth M. Birch, T. Ben Britton

Zirconium alloys are widely used in nuclear reactors as fuel cladding materials. Fuel cladding is used to contain the nuclear fuel and cladding tubes are typically sealed using welds. Welding of zirconium alloys can result in changes in the local microstructure, with the potential to grow so called 'blocky {\alpha}' grains in the welded region during subsequ

Van Nguyen, Tingmin Wu, Xingliang Yuan, Marthie Grobler

Phishing attacks have become a serious and challenging issue for detection, explanation, and defense. Despite more than a decade of research on phishing, encompassing both technical and non-technical remedies, phishing continues to be a serious problem. Nowadays, AI-based phishing detection stands out as one of the most effective solutions for defending agai

Hanqiu Deng, Xingyu Li

Visual anomaly detection is a challenging open-set task aimed at identifying unknown anomalous patterns while modeling normal data. The knowledge distillation paradigm has shown remarkable performance in one-class anomaly detection by leveraging teacher-student network feature comparisons. However, extending this paradigm to multi-class anomaly detection int

M. Hild, I. Yahniuk, L. E. Golub, J. Amann

We report on the observation of the circular ratchet effect excited by terahertz laser radiation in a specially designed two-dimensional metamaterial consisting of a graphene monolayer deposited on a graphite gate patterned with an array of triangular antidots. We show that a periodically driven Dirac fermion system with spatial asymmetry converts the a.c. p

Artan Sheshmani, Yi-Zhuang You, Baturalp Buyukates, Amir Ziashahabi

Diffusion-based generative models represent a forefront direction in generative AI research today. Recent studies in physics have suggested that the renormalization group (RG) can be conceptualized as a diffusion process. This insight motivates us to develop a novel diffusion-based generative model by reversing the momentum-space RG flow. We establish a fram

Dmitry Yarotsky

We explore the theoretical possibility of learning $d$-dimensional targets with $W$-parameter models by gradient flow (GF) when $W<d$. Our main result shows that if the targets are described by a particular $d$-dimensional probability distribution, then there exist models with as few as two parameters that can learn the targets with arbitrarily high success

Zohar Levi

Incompressibility is a fundamental condition in most fluid models. Accumulation of simulation errors violates it and causes volume loss. Past work suggested correction methods to battle it. These methods, however, are imperfect and in some cases inadequate. We present a method for fluid simulation that strictly enforces incompressibility based on a grid-rela

Marco Zaffalon, Alessandro Antonucci

We characterise the likelihood function computed from a Bayesian network with latent variables as root nodes. We show that the marginal distribution over the remaining, manifest, variables also factorises as a Bayesian network, which we call empirical. A dataset of observations of the manifest variables allows us to quantify the parameters of the empirical B

Calvin Wooyoung Chin

We prove the Lindeberg--Feller central limit theorem without using characteristic functions or Taylor expansions, but instead by measuring how far a distribution is from the standard normal distribution according to the $2$-Wasserstein metric. This falls under the category of renormalization group methods. The facts we need about the metric are explained and

Mauricio Medina-Bárcenas, Martha Lizbeth Shaid Sandoval-Miranda, Ángel Zaldívar-Corichi

Torsion theories are a pinnacle in the theory of abelian categories. They are a generalization of torsion abelian groups and in this generalization one of the most studied is that whose torsionfree class consists of nonsingular modules. To introduce the concept of singular interval we use the symmetric idea of torsion theories, that is the torsion class dete

Rodrick Kuate Defo, Steven L. Richardson

The static electric dipole-dipole coupling between donor-acceptor pairs (DAPs) in wide-bandgap semiconductors has recently emerged as a means of realizing a quantum science platform through optically controllable, long-range interactions between defects in the solid state. In this work, we generalize DAPs to consider arbitrary dopant populations and demonstr

Yasmine Kotturi, Angel Anderson, Glenn Ford, Michael Skirpan

Generative AI platforms and features are permeating many aspects of work. Entrepreneurs from lean economies in particular are well positioned to outsource tasks to generative AI given limited resources. In this paper, we work to address a growing disparity in use of these technologies by building on a four-year partnership with a local entrepreneurial hub de

Keshav Rangan, Yiqiao Yin

This study presents an innovative enhancement to retrieval-augmented generation (RAG) systems by seamlessly integrating fine-tuned large language models (LLMs) with vector databases. This integration capitalizes on the combined strengths of structured data retrieval and the nuanced comprehension provided by advanced LLMs. Central to our approach are the LoRA

Seth K. Asante, Taylor Brysiewicz

Area variables are intrinsic to connection formulations of general relativity, in contrast to the fundamental length variables prevalent in metric formulations. Within 4D discrete gravity, particularly based on triangulations, the area-length system establishes a relationship between area variables associated with triangles and the edge length variables. Thi

Samuel Berweger, Alexandra B. Artusio-Glimpse, Nikunjkumar Prajapati, Andrew P. Rotunno

Rydberg atom-based electric field sensing can provide all-optical readout of radio frequency fields in a dielectric environment. However, because a single set of optical fields is typically used to prepare the Rydberg state and read out its response to RF fields, it is challenging to perform simultaneous and independent measurements of the RF field(s). Here

Gautam Singh, Yue Wang, Jiawei Yang, Boris Ivanovic

While modern best practices advocate for scalable architectures that support long-range interactions, object-centric models are yet to fully embrace these architectures. In particular, existing object-centric models for handling sequential inputs, due to their reliance on RNN-based implementation, show poor stability and capacity and are slow to train on lon

Soheil Hashtarkhani, David L Schwartz, Arash Shaban-Nejad

Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measur

Alessa Ibrahim Wiggins, Mia Sauda Bovill, Louis-Gregory Strolger, Massimo Stiavelli

Population III (Pop III) stars formed out of metal free gas in minihalos at $z>20$. While their ignition ended the Dark Ages and begin enrichment of the IGM, their mass distribution remains unconstrained. To date, no confirmed Pop III star has been observed and their direct detection is beyond the reach of the James Webb Space Telescope (JWST) without gravit

Beomjun Choi, Kyeongsu Choi, Soojung Kim

This paper shows the existence of convex translating surfaces under the flow by the $\alpha$-th power of Gauss curvature for the sub-affine-critical regime $ 0 < \alpha < 1/4$. The key aspect of our study is that our ansatz at infinity is the graph of homogeneous functions whose level sets are closed curves shrinking under the flow by the $\frac{\alpha}{1-\a

Siqi Wang, Zehui Zhu, Tao Ma, Jianwei Fan

Digital Image Correlation (DIC) is an optical technique that measures displacement and strain by tracking pattern movement in a sequence of captured images during testing. DIC has gained recognition in asphalt pavement engineering since the early 2000s. However, users often perceive the DIC technique as an out-of-box tool and lack a thorough understanding of

Christopher Archibald, Spencer Brosnahan

The game of Codenames has recently emerged as a domain of interest for intelligent agent design. The game is unique due to the way that language and coordination between teammates play important roles. Previous approaches to designing agents for this game have utilized a single internal language model to determine action choices. This often leads to good per

Abhishek Dalvi, Vasant Honavar

We introduce Hyperdimensional Graph Learner (HDGL), a novel method for node classification and link prediction in graphs. HDGL maps node features into a very high-dimensional space (\textit{hyperdimensional} or HD space for short) using the \emph{injectivity} property of node representations in a family of Graph Neural Networks (GNNs) and then uses HD operat

Ewa Bednarczuk, Dirk Lorenz, The Hung Tran

In this paper we analyze a class of nonconvex optimization problem from the viewpoint of abstract convexity. Using the respective generalizations of the subgradient we propose an abstract notion proximal operator and derive a number of algorithms, namely an abstract proximal point method, an abstract forward-backward method and an abstract projected subgradi

Kentaro Hoffman, Kai Zhang, Tyler McCormick, Jan Hannig

In this paper, we demonstrate that a new measure of evidence we developed called the Dempster-Shafer p-value which allow for insights and interpretations which retain most of the structure of the p-value while covering for some of the disadvantages that traditional p- values face. Moreover, we show through classical large-sample bounds and simulations that t

Ashutosh Tiwari, Nitheshnirmal Sadhashivam, Leonard O. Ohenhen, Jonathan Lucy

This study proposes a new convolutional long short-term memory (ConvLSTM) based architecture for selection of elite pixels (i.e., less noisy) in time series interferometric synthetic aperture radar (TS-InSAR). The model utilizes the spatial and temporal relation among neighboring pixels to identify both persistent and distributed scatterers. We trained the m

Reza Torabi

An exact solution is introduced for one dimensional space-fractional Edwards-Wilkinson equation. It is shown that the roughness obeys the Family-Viscek dynamic scaling form and the scaling exponents is derived. It is seen that the scaling exponents are different from those coming from ordinary Edwards-Wilkinson equation and depend on the fractional order. Sc

Ádám Timár

We prove that every (possibly infinite) graph of degree at most $d$ has a 4-dependent random proper $4^{d(d+1)/2}$-coloring, and one can construct it as a finitary factor of iid. For unimodular transitive (or unimodular random) graphs we construct an automorphism-invariant (respectively, unimodular) 2-dependent coloring by $3^{d(d+1)/2}$ colors. In particula

Jiaming Liang, Siddharth Mitra, Andre Wibisono

The mixing time of a Markov chain determines how fast the iterates of the Markov chain converge to the stationary distribution; however, it does not control the dependencies between samples along the Markov chain. In this paper, we study the question of how fast the samples become approximately independent along popular Markov chains for continuous-space sam

Per Östborn

Born's rule is the recipe for calculating probabilities from quantum mechanical amplitudes. There is no generally accepted derivation of Born's rule from first principles. In this paper, it is motivated from assumptions that link the ontological content of a proper physical model to the epistemic conditions of the experimental context. More precisely, it is

Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi, Mohamad H. Danesh

Despite extensive research on training generative adversarial networks (GANs) with limited training data, learning to generate images from long-tailed training distributions remains fairly unexplored. In the presence of imbalanced multi-class training data, GANs tend to favor classes with more samples, leading to the generation of low-quality and less divers

Y. Semerenko, E. Tabachnikovaa, T. Hryhorovaa, S. Shumilina

The mechanical properties and microstructural evolution of a medium-entropy alloy Co$_{17.5}$Cr$_{12.5}$Fe$_{55}$Ni$_{10}$Mo$_{5}$ (at%) in a low temperature range (including the record low temperatures region down to 0.5 K) were investigated. It has been established that low-temperature plastic deformation initiates martensitic phase transformations in this

Chellal Redha

In this paper, we introduce a novel identity for generalized Euler polynomials, leading to further generalizations for several relations involving classical Euler numbers, Euler polynomials, Genocchi polynomials, and Genocchi numbers.

Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis

Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus, existing methods often rely on intermediate 3D shape representations to increase performance. These representations are typically explicit, such

Bilal Mufti, Christian Perron, Dimitri N. Mavris

In the early stages of aerospace design, reduced order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-query scenarios requiring multiple evaluations. The intricacy of aerospace design demands the use of high-dimensional design spaces to capture detailed features and design variability

John Guckenheimaer

Umbilics are points of a surface embedded in three space where normal curvatures are independent of direction. The (in)famous Carath\'{e}odory Conjecture states that a compact simply connected embedded surface has at least two umbilic points. A counterexample to this conjecture would be a surface whose principal foliation has index two at a single umbilic. A

Marco Isopi, Benedetto Scoppola, Alessio Troiani

Quadratic Unconstrained Binary Optimization (QUBO or UBQP) is concerned with maximizing/minimizing the quadratic form $H(J, \eta) = W \sum_{i,j} J_{i,j} \eta_{i} \eta_{j}$ with $J$ a matrix of coefficients, $\eta \in \{0, 1\}^N$ and $W$ a normalizing constant. In the statistical mechanics literature, QUBO is a lattice gas counterpart to the (generalized) She

Xinyang Li, Vlad C. Andrei, Aladin Djuhera, Ullrich J. Mönich

This manuscript investigates the information-theoretic limits of integrated sensing and communications (ISAC), aiming for simultaneous reliable communication and precise channel state estimation. We model such a system with a state-dependent discrete memoryless channel (SD-DMC) with present or absent channel feedback and generalized side information at the t

James Maldaner, Mitja Fridman, Saurya Das, Gil Porat

We present an analysis of the sensitivity limits of a proposed experimental search for quantum gravity, using a novel approach based on optical magnetometry in the noble gas isotope $^{129}$Xe. The analysis relies on a general uncertainty principle model that is consistent with most formulations of quantum gravity theory, where the canonical uncertainty rela

Hisham Mahmood, Samrat Acharya, Francis Tuffner, Priya Mana

The power system is expected to evolve rapidly with the increasing deployment of power electronic interface and conditioning systems, microgrids, and hybrid AC/DC grids. Among power electronic systems, back-to-back (BTB) converters can be a powerful interface to integrate microgrids and networked microgrids. To study the integration of such devices into larg

Olivia Reade

This paper proves the existence of a chiral map with alternating automorphism group for every hyperbolic type. We present a set of constructions using permutations for when at least one parameter is even, and call on previously known results for when both the valency and the face-length are odd.

Ma. Louise Antonette De Las Peñas, Agnes Garciano, Debbie Marie Verzosa, Mark Tomenes

This paper discusses the symmetry structures of the decorative weave patterns in baskets and other household items of the Batak, an indigenous community in the Philippines. The study confirms the realization of 15 layer groups that occur as symmetry groups of the Batak weaves. Layers groups are crystallographic space groups that have translational symmetries

J. Miguel Calderón

In this article, we describe the lattice of ideals of some Green biset functors. We consider Green biset functors which satisfy that each evaluation is a finite dimensional split semisimple commutative algebra and use the idempotents in these evaluations to characterize any ideal of these Green biset functors. For this we will give the definition of M C-grou

Jan Malamant, Natali Gusakova, Heidi Sandaker, Irina T. Sorokina

We propose to extend coherent laser cooling from narrow-band to broad-band transitions by using trains of ultrashort broadband pulses. We study analytically two possible methods to reduce the momentum spread of a distribution by several units of photon momentum in a single spontaneous emission lifetime. We report on numerical simulations of one-dimensional l

Reza Torabi

The propagation of electromagnetic waves in unmagnetized periodic plasma media is studied using the semiclassical wave packet approximation. The formalism gives rise to Berry effect terms in the equation of motion. The Berry effect manifests itself as Rytov polarization rotation law and the polarization-dependent Hall effect. The formalism is also applied to

Kathy Jang, Nathan Lichtlé, Eugene Vinitsky, Adit Shah

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental con

Yuntian Li, Mark P. Zic, Linda Ye, W. Joe Meese

We report an investigation of the effect of substitution of Y for Tm in $Tm_{1-x}Y_xVO4$ via low-temperature heat capacity measurements, with the yttrium content $x$ varying from $0$ to $0.997$. Because the Tm ions support a local quadrupolar (nematic) moment, they act as reporters of the local strain state in the material, with the splitting of the ion's no

Sonia Acinas, Sergio Favier, Rosa Lorenzo

In this paper, we consider the best multivalued polynomial approximation operator for functions in an Orlicz Space $L^{\varphi}(\Omega)$. We obtain its characterization involving $\psi^-$ and $\psi^+$, which are the left and right derivatives functions of $\varphi$. And then, we extend the operator to $L^{\psi^+}(\Omega)$. We also get pointwise convergence o

Sidhanth Raman

This article grew out of an effort to understand the smooth mapping class groups of certain 4-manifolds in a geometric manner. We prove a smooth analog of the Birman-Hilden theorem for manifolds that admit a hyperk\"ahler structure. This allows us to probe the smooth mapping class groups associated to certain manifolds with nontrivial fundamental groups. Alo

Sergey Bezuglyi, Olena Karpel, Jan Kwiatkowski

In 2010, Bezuglyi, Kwiatkowski, Medynets and Solomyak [Ergodic Theory Dynam. Systems 30 (2010), no.4, 973-1007] found a complete description of the set of probability ergodic tail invariant measures on the path space of a standard (classical) stationary reducible Bratteli diagram. It was shown that every distinguished eigenvalue for the incidence matrix dete

Tosin Ige, Christopher Kiekintveld, Aritran Piplai

In this research, we analyzed the suitability of each of the current state-of-the-art machine learning models for various cyberattack detection from the past 5 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack.

Waverly Gorman, Kristopher G. Klein

Characterizing the thermodynamics of turbulent plasmas is key to decoding observable signatures from astrophysical systems. In magnetohydrodynamic (MHD) turbulence, nonlinear interactions between counter-propagating Alfv\'en waves cascade energy to smaller spatial scales where dissipation heats the protons and electrons. When the thermal pressure far exceeds

Jonathan W. Lee, Han Wang, Kathy Jang, Amaury Hayat

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be dep

Xin Wang, Yineng Chen, Shu Hu, Heng Fan

Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical applications. This paper presents a comprehensive examination of appli

Heming Huang, Charles S. Peskin

In the physiology of oxygen-hemoglobin binding, an important role is played by the influence of $\text{H}^+$ and $\text{CO}_2$ on the affinity of hemoglobin for $\text{O}_2$. Here we extend the allosteric model of hemoglobin to include these effects. We assume purely allosteric modulation, i.e., that the modulatory effects of $\text{H}^+$ and $\text{CO}_2$ o

Melody Y Huang, Harsh Parikh

Randomized Controlled Trials (RCTs) are pivotal in generating internally valid estimates with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing causal inference methods. However, extending these findings beyond the experimental cohort to achieve externally valid estimates is crucial for broader scientific inquiry. This pape

Clifford J. Allington, Carina A. Belvin, Urban F. P. Seifert, Mengxing Ye

The control of antiferromagnets with ultrashort optical pulses has emerged as a prominent field of research. Tailored laser excitation can launch coherent spin waves at terahertz frequencies, yet a comprehensive description of their generation mechanisms is still lacking despite extensive efforts. Using terahertz emission spectroscopy, we investigate the gen

Noam Goldberg, Mark P. Langer, Shimrit Shtern

Radiotherapy treatment planning is a challenging large-scale optimization problem plagued by uncertainty. Following the robust optimization methodology, we propose a novel, spatially based uncertainty set for robust modeling of radiotherapy planning, producing solutions that are immune to unexpected changes in biological conditions. Our proposed uncertainty

Inès Harang, Fabian Heymann, Laurens P. Stoop

The demand-supply balance of electricity systems is fundamentally linked to climate conditions. In light of this, the present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability. A resource adequate power system -- a system where electricity supply covers demand -- is sensitive to gen

Ishak Cheniouni, Soulaimane Berkane, Abdelhamid Tayebi

We propose a hybrid feedback control strategy that safely steers a point-mass robot to a target location optimally from all initial conditions in the n-dimensional Euclidean space with a single spherical obstacle. The robot moves straight to the target when it has a clear line-of-sight to the target location. Otherwise, it engages in an optimal obstacle avoi

Mohammad Farajzadeh-Tehrani, Charles Frohman, Joanna Kania-Bartoszynska

In this paper, we study the Kauffman bracket skein module of closed oriented three-manifolds at a non-multiple-of-four roots of unity. Our main result establishes that the localization of this module at a maximal ideal, which corresponds to an irreducible representation of the fundamental group of the manifold, forms a one-dimensional free module over the lo

Rafael Anderka, Marc Peter Deisenroth, So Takao

Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data. Popular techniques such as ensemble methods that handle high-dimensional, nonlinear PDE priors focus mostly on state estimation, however can have difficulty learning the parameters accurately. On the other hand, machine learning based a

Regis Barille

An aircraft cabin is used as a laboratory for studying the atmospheric pressure during a flight. All the different steps of the flight: take-off, cruise altitude climbing and landing are monitored with the use of the pressure sensor of a smartphone. Specific details of the atmospheric pressure during take-off and landing are given. Calculations are done for

W. Sengupta, E. Rodriguez, R. Jorge, M. Landreman A. Bhattacharjee

A systematic theory of the asymptotic expansion of the magnetohydrodynamic (MHD) equilibrium in the distance from the magnetic axis is developed to include arbitrary smooth currents near the magnetic axis. Compared to the vacuum and the force-free system, an additional magnetic differential equation must be solved to obtain the pressure-driven currents. It i

Cesar L. Pastrana, Luyi Qiu, John W. Hutchinson, Ariel Amir

Take a drinking straw and bend it from its ends. After sufficient bending, the tube buckles forming a kink, where the curvature is localized in a very small area. This instability, known generally as the Brazier effect, is inherent to thin-walled cylindrical shells, which are particularly ubiquitous in living systems, such as rod-shaped bacteria. However, tu

Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger Grosse

Human mathematicians are often good at recognizing modular and reusable theorems that make complex mathematical results within reach. In this paper, we propose a novel method called theoREm-from-prooF extrACTOR (REFACTOR) for training neural networks to mimic this ability in formal mathematical theorem proving. We show on a set of unseen proofs, REFACTOR is

Andrea Davini

We prove homogenization for possibly degenerate viscous Hamilton-Jacobi equations with a Hamiltonian of the form $G(p)+V(x,\omega)$, where $G$ is a quasiconvex, locally Lipschitz function with superlinear growth, the potential $V(x,\omega)$ is bounded and Lipschitz continuous, and the diffusion coefficient $a(x,\omega)$ is allowed to vanish on some regions o

Andrea Davini

We prove homogenization for degenerate viscous Hamilton-Jacobi equations in dimension one in stationary ergodic environments with a quasiconvex and superlinear Hamiltonian of fairly general type. We furthermore show that the effective Hamiltonian is quasiconvex. This latter result is new even in the periodic setting, despite homogenization has been known for

Jan Scheffel

Stiff and chaotic differential equations are challenging for time-stepping numerical methods. For explicit methods, the required time step resolution significantly exceeds the resolution associated with the smoothness of the exact solution for specified accuracy. In order to improve efficiency, the question arises whether transformation to asymptotically sta

Vincent Christlein, David Bernecker, Andreas Maier, Elli Angelopoulou

Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the

H M Fairooz Adnan, Md Khalekuzzaman, Md. Atik Fayshal, Md. Mehedi Hasan

In this study faecal sludge is used as raw biomass due to its abundance, low cost, and easy availability. After HTL operation, product separation is getting challenging. Current developed studies observed the separation of aqueous and biocrude oil products occurs during the HTL process more popularly with the use of an organic solvent which is quite expensiv

Ibrahim Saleh

For a fixed seed $(X, Q)$, a \emph{rooted mutation loop} is a sequence of mutations that preserves $(X, Q)$. The group generated by all rooted mutation loops is called \emph{rooted mutation group} and will be denoted by $\mathcal{M}(Q)$. The \emph{global mutation group} of $(X, Q)$, denoted $\mathcal{M}$, is the group of all mutation sequences subject to the

Panagiotis Mavrogiannis

Interesting phenomena and problems arising from the coupling of large-scale electromagnetic fields and spacetime curvature, are introduced and studied within this thesis. From electromagnetic wave propagation in curved spacetime to envisaging a gravito-electromagnetic equivalence on large scales; from magnetic fields' cosmic evolution, and magnetised gravita

N. Abgrall, H. Adhikary, P. Adrich, K. K. Allison

Measurements of $K_S^0$ meson production via its $\pi^{+} \pi^{-}$ decay mode in inelastic $\textit{p+p}$ interactions at incident projectile momenta of 31, 40 and 80 GeV/$c$ ($\sqrt{s_{NN}}=7.7, 8.8$ and $12.3$ GeV, respectively) are presented. The data were recorded by the NA61/SHINE spectrometer at the CERN Super Proton Synchrotron. Double-differential di

Zachary Weller-Davies

In hybrid classical-quantum theories, the dynamics of the classical system induce the classicality of the quantum system, meaning that such models do not necessarily require a measurement postulate to describe probabilistic measurement outcomes. It has recently been shown that covariant classical-quantum dynamics can be constructed using path integral method

Zhenjie Li

The decomposition of the four-point ABJM amplituhedron into negative geometries produces compact integrands of logarithmic of amplitudes such that the infrared divergence only comes from the last loop integration, from which we can compute the cusp anomalous dimension of the ABJM theory. In this note, we integrate $L-1$ loop momenta of the $L$-loop negative

B. Bermond, R. Wawrzynczak, S. Zherlitsyn, T. Kotte

Oscillations of conductance observed in strong magnetic fields are a striking manifestation of the quantum dynamics of charge carriers in solids. The large charge carrier density in typical metals sets the scale of oscillations in both electrical and thermal conductivity, which characterize the Fermi surface. In semimetals, thermal transport at low-charge ca

Zoe Himwich

We demonstrate that Liggett's condition can be relaxed without disrupting the convergence of open ASEP stationary measures to the open KPZ stationary measure. This is equivalent to demonstrating that, under weak asymmetry scaling and appropriate scaling of time and space, the four-parameter Askey-Wilson process converges to a two-parameter continuous dual Ha

Richard Kimanzi, Peter Kimanga, Dedan Cherori, Patrick K. Gikunda

The increase in network attacks has necessitated the development of robust and efficient intrusion detection systems (IDS) capable of identifying malicious activities in real-time. In the last five years, deep learning algorithms have emerged as powerful tools in this domain, offering enhanced detection capabilities compared to traditional methods. This revi

Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler

Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large language models (LLMs) in legal education to help non-exper

Leonid Boytsov, Ameya Joshi, Filipe Condessa

We experimented with front-end enhanced neural models where a differentiable and fully convolutional model with a skip connection is added before a frozen backbone classifier. By training such composite models using a small learning rate for about one epoch, we obtained models that retained the accuracy of the backbone classifier while being unusually resist

Yanbin Li, Shuangyue Yu, Haitao Qing, Yaoye Hong

Previous mechanical meta-structures used for mechanical memory storage, computing and information processing are severely constrained by low information density and/or non-robust structural stiffness to stably protect the maintained information. To address these challenges, we proposed a novel reprogrammable multifunctional mechanical metastructure made by a

Isabelle Mohr, Markus Krimmel, Saba Sturua, Mohammad Kalim Akram

We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of processing lengthy text inputs with up to 8192 tokens, making them highly versatile for a range of natural language processing tasks such as text retrieval, clustering, and semantic textu

Mark Chimes, Radu Iosif, Florian Zuleger

Hyperedge-Replacement grammars (HR) have been introduced by Courcelle in order to extend the notion of context-free sets from words and trees to graphs of bounded tree-width. While for words and trees the syntactic restrictions that guarantee that the associated languages of words resp. trees are regular - and hence, MSO-definable - are known, the situation

Nikhil Narayan, Mrutyunjay Biswal

In the digital realm, rich data serves as a crucial source of insights into the complexities of social, political, and economic landscapes. Addressing the growing need for high-quality information on events and the imperative to combat hate speech, this research led to the establishment of the Shared Task on Climate Activism Stance and Hate Event Detection a

Santosh T. Y. S. S, Nina Baumgartner, Matthias Stürmer, Matthias Grabmair

The assessment of explainability in Legal Judgement Prediction (LJP) systems is of paramount importance in building trustworthy and transparent systems, particularly considering the reliance of these systems on factors that may lack legal relevance or involve sensitive attributes. This study delves into the realm of explainability and fairness in LJP models,

Jeffrey G. Wang, Jason Wang, Marvin Li, Seth Neel

In this paper we develop state-of-the-art privacy attacks against Large Language Models (LLMs), where an adversary with some access to the model tries to learn something about the underlying training data. Our headline results are new membership inference attacks (MIAs) against pretrained LLMs that perform hundreds of times better than baseline attacks, and

Silin Gao, Mete Ismayilzada, Mengjie Zhao, Hiromi Wakaki

Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, DiffuCOMET, that leverage diffusion to learn to reconstruct the implicit semantic connections between narrative contexts and relevant commonsense knowledge. Across multiple diffusion