February 2024 arXiv papers — page 156

Xiang-Ming Yang, Sarah A. Bird, Jiadong Li, Hai-Jun Tian

We present a comprehensive investigation on the mass function (MF) of a snake-like stellar structure in the solar neighbourhood, building on our previous discovery. To ensure the reliability of the data, we reselect the member stars of the Stellar ``Snake'' in the latest {\it Gaia} Data Release 3 using the same approach as the initial series of articles. We

Wei-Cheng Huang, Chun-Fu Chen, Hsiang Hsu

Recent works have shown that by using large pre-trained models along with learnable prompts, rehearsal-free methods for class-incremental learning (CIL) settings can achieve superior performance to prominent rehearsal-based ones. Rehearsal-free CIL methods struggle with distinguishing classes from different tasks, as those are not trained together. In this w

Stancho G. Stanchev, Nikolay V. Vitanov

We introduce a method to enhance the precision and accuracy of Quantum Process Tomography (QPT) by mitigating the errors caused by state preparation and measurement (SPAM), readout and shot noise. Instead of performing QPT solely on a single gate, we propose performing QPT on a sequence of multiple applications of the same gate. The method involves the measu

Eduardo C. Lima, Breno Marques, Marcelo Martinelli, Luciano S. Cruz

Quantum state production and characterization are fundamental elements for many quantum technological applications. In this work, we studied the generation of polarization quantum states by interacting light with a Kerr medium and the dependency of the outcome on orthogonal polarization seedlings. Starting from %Our experimental apparatus is based on coheren

Kajetan M. Fijalkowski, Nan Liu, Martin Klement, Steffen Schreyeck

The quantum anomalous Hall effect in magnetic topological insulators has been recognized as a promising platform for applications in quantum metrology. The primary reason for this is the electronic conductance quantization at zero external magnetic field, which allows to combine it with the quantum standard of voltage. Here we demonstrate a measurement schem

Kaize Wang, Yang Ge, Yashar Komijani

Magnetic oscillations in strongly correlated insulating systems have garnered interest due to oscillations seemingly originating from the bulk, despite an anticipated gapped spectrum. We use the large-$N$ mean-field theory to study the behavior of normal and topological Kondo insulators under a magnetic field. In both cases spinons acquire a charge and hybri

J. A. F. Torvisco, R. Benítez, M. R. Arias, J. Cabello Sánchez

A new package for nonlinear least squares fitting is introduced in this paper. This package implements a recently developed algorithm that, for certain types of nonlinear curve fitting, reduces the number of nonlinear parameters to be fitted. One notable feature of this method is the absence of initialization which is typically necessary for nonlinear fittin

Jinzheng Li, Pran Nath

A variety of possibilities exist for dark matter aside from WIMPS, such as hidden sector dark matter. We discuss synchronous thermal evolution of visible and hidden sectors and show that the density of thermal relics can change $O(100\%)$ and $\Delta N_{\rm eff}$ by a factor of up to $10^5$ depending of whether the hidden sector was hot or cold at the reheat

Joackim Bernier, Nicolas Camps

We consider nonlinear Schr\"odinger equations on flat tori satisfying a simple and explicit Diophantine non-degeneracy condition. Provided that the nonlinearity contains a cubic term, we prove the almost global existence and stability of most of the small solutions in high regularity Sobolev spaces. To this end, we develop a normal form approach designed to

Paweł Pasteczka

For a given $p$-variable mean $M \colon I^p \to I$ ($I$ is a subinterval of $\mathbb{R}$), following (Horwitz, 2002) and (Lawson and Lim, 2008), we can define (under certain assumption) its $(p+1)$-variable $\beta$-invariant extension as the unique solution $K \colon I^{p+1} \to I$ of the functional equation \begin{align*} K\big(M(x_2,\dots,x_{p+1})&,M(x_1,x

Wen-Bin Liu, Jiang Long, Xiao-Quan Ye

In this paper, we derive the Carrollian amplitude in the framework of bulk reduction. The Carrollian amplitude is shown to relate to the scattering amplitude by a Fourier transform in this method. We propose Feynman rules to calculate the Carrollian amplitude where the Fourier transforms emerge as the integral representation of the external lines in the Carr

Pengfei Liu, Jun Tao, Zhixiang Ren

Deep learning has significantly advanced molecular modeling and design, enabling efficient understanding and discovery of novel molecules. In particular, large language models (LLMs) introduce a fresh research paradigm to tackle scientific problems from a natural language processing (NLP) perspective. LLMs significantly enhance our understanding and generati

Tommaso Cortopassi

We prove a novel stability estimate in $L^\infty _t (L^p _x)$ between the regular Lagrangian flow of a Sobolev vector field and a piecewise affine approximation of such flow. This approximation of the flow is obtained by a (sort of) explicit Euler method, and it is the crucial tool to prove approximation results for the solution of the continuity equation by

Beniamin Bogosel, Antoine Henrot, Marco Michetti

In this paper we study optimization problems for Neumann eigenvalues $\mu_k$ among convex domains with a constraint on the diameter or the perimeter. We work mainly in the plane, though some results are stated in higher dimension. We study the existence of an optimal domain in all considered cases. We also consider the case of the unit disk, giving values of

Itir Bakis Dogru-Yuksel, Allard P. Mosk, Sanli Faez

We present a facile desktop fabrication method for origami-based nano-gap indium tin oxide (ITO) electrokinetic particle traps, providing a simplified approach compared to traditional lithographic techniques and effectively trapping of nanoparticles. Our approach involves bending ITO thin films on optically transparent polyethylene terephthalate (PET), creat

Josu Amorebieta, Joao Pereira, Gaizka Durana, Carolina Franciscangelis

In this work, we report on a twin-core fiber sensor system that provides improved spectral efficiency, allows for multiplexing and gives low level of crosstalk. Pieces of the referred strongly coupled multicore fiber are used as sensors in a laser cavity incorporating a pulsed semiconductor optical amplifier (SOA). Each sensor has its unique cavity length an

Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin

In this paper, we analyze the sample and communication complexity of the federated linear stochastic approximation (FedLSA) algorithm. We explicitly quantify the effects of local training with agent heterogeneity. We show that the communication complexity of FedLSA scales polynomially with the inverse of the desired accuracy $\epsilon$. To overcome this, we

A. Suárez Mascareño, V. M. Passegger, J. I. González Hernández, D. J. Armstrong

The number of super-Earth and mini-Neptune planet discoveries has increased significantly in the last two decades thanks to transit and radial velocity surveys. When it is possible to apply both techniques, we can characterise the internal composition of exoplanets, which in turn provides unique insights on their architecture, formation and evolution. We per

G. Niccoli, V. Terras

We consider the open XYZ spin chain with boundary fields. We solve the model by the new Separation of Variables approach introduced in arXiv:1904.00852. In this framework, the transfer matrix eigenstates are obtained as a particular sub-class of the class of so-called separate states. We consider the problem of computing scalar products of such separate stat

Mohamed Akrout, Tiancheng Gao, Faouzi Bellili, Amine Mezghani

Approximate message passing (AMP) algorithms are devised under the Gaussianity assumption of the measurement noise vector. In this work, we relax this assumption within the vector AMP (VAMP) framework to arbitrary independent and identically distributed (i.i.d.) noise priors. We do so by rederiving the linear minimum mean square error (LMMSE) to accommodate

Erik Weber, Jérôme Rutinowski, Markus Pauly

ChatGPT is notorious for its intransparent behavior. This paper tries to shed light on this, providing an in-depth analysis of the dark personality traits and conspiracy beliefs of GPT-3.5 and GPT-4. Different psychological tests and questionnaires were employed, including the Dark Factor Test, the Mach-IV Scale, the Generic Conspiracy Belief Scale, and the

Richard Talman, John Talman

A predominantly electric E\&m storage ring, with weak superimposed magnetic bending, is shown to be capable of storing two different particle type bunches, such as helion (h) and deuteron (d), or h and electron ($e^-$), co-traveling with different velocities on the same central orbit. Rear-end collisions occurring periodically in a full acceptance particle d

Elena Doering, Merle C. Hönig, Tobias Deußer, Gerard N. Bischof

Alzheimer's disease (AD) is a progressive neurodegenerative disorder leading to cognitive decline. [$^{18}$F]-Fluorodeoxyglucose positron emission tomography ([$^{18}$F]-FDG PET) is used to monitor brain metabolism, aiding in the diagnosis and assessment of AD over time. However, the feasibility of multi-time point [$^{18}$F]-FDG PET scans for diagnosis is l

Anton Borg, Per Lingvall, Martin Svensson

EU directives stipulate a systematic follow-up of train delays. In Sweden, the Swedish Transport Administration registers and assigns an appropriate delay attribution code. However, this delay attribution code is assigned manually, which is a complex task. In this paper, a machine learning-based decision support for assigning delay attribution codes based on

J. S. Dowker

The contribution, E, of hyperbolic elements to the scalar Casimir energy on a compact quotient of the upper half hyperbolic plane is computed for a propagation operator conformal in three dimensions. Due to the proliferation of prime closed geodesics, the series form for the Casimir energy has an IR divergence. The expression for E is given as a sum of polyl

Mario Truss, Marc Schmitt

This paper addresses the complexities inherent in AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to non-experts. A Design Science Research (DSR) approach is presented which culminates in a conceptual framework aimed at improving the AI prototyping process.

Shen Zhao, Xin Huang, Roland Gillen, Zhijie Li

We report experimental and theoretical studies of MoTe$_2$-MoSe$_2$ heterobilayers with rigid moir\'e superlattices controlled by the twist angle. Using an effective continuum model that combines resonant interlayer electron tunneling with stacking-dependent moir\'e potentials, we identify the nature of moir\'e excitons and the dependence of their energies,

Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, Thomas L. Griffiths

Large language models (LLMs) can pass explicit social bias tests but still harbor implicit biases, similar to humans who endorse egalitarian beliefs yet exhibit subtle biases. Measuring such implicit biases can be a challenge: as LLMs become increasingly proprietary, it may not be possible to access their embeddings and apply existing bias measures; furtherm

Kwame Atta Gyamfi

We present and analyze a new finite volume scheme of Gudonov-type for a nonlinear scalar conservation law whose flux function has a discontinuous coefficient due to time-dependent changes in its sign along a Lipschitz continuous curve.

Jeen Mary John, Olamilekan Shobayo, Bayode Ogunleye

Recently, peoples awareness of online purchases has significantly risen. This has given rise to online retail platforms and the need for a better understanding of customer purchasing behaviour. Retail companies are pressed with the need to deal with a high volume of customer purchases, which requires sophisticated approaches to perform more accurate and effi

Tony Quertier, Grégoire Barrué

Existing research on malware detection focuses almost exclusively on the detection rate. However, in some cases, it is also important to understand the results of our algorithm, or to obtain more information, such as where to investigate in the file for an analyst. In this aim, we propose a new model to analyze Portable Executable files. Our method consists

Haotian Yang, Mingwu Zheng, Chongyang Ma, Yu-Kun Lai

In this paper, we introduce the Volumetric Relightable Morphable Model (VRMM), a novel volumetric and parametric facial prior for 3D face modeling. While recent volumetric prior models offer improvements over traditional methods like 3D Morphable Models (3DMMs), they face challenges in model learning and personalized reconstructions. Our VRMM overcomes these

Farshid Ashtiani

The significant advancements in integrated photonics have enabled high-speed and energy efficient systems for various applications from data communications and high-performance computing, to medical diagnosis, sensing and ranging. However, data storage in these systems has been dominated by electronic memories which necessitates signal conversion between opt

Pavel Kůs, Diana López Nacir, Federico R. Urban

Ultra-light dark matter perturbs the orbital motion of binary pulsars, in particular by causing peculiar time variations of a binary's orbital parameters, which then induce variations in the pulses' times-of-arrival. Binary pulsars have therefore been shown to be promising detectors of ultra-light dark matter. To date, the sensitivity of binary pulsars to ul

Etienne Russeil, Fabrício Olivetti de França, Konstantin Malanchev, Bogdan Burlacu

Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume a single dataset extracted from a single experiment. Nevertheless, frequently, the researcher is confronted with multiple sets of results obtained from experiments conducted with different s

Shijun Liang, Evan Bell, Qing Qu, Rongrong Wang

The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medical imaging including magnetic resonance imaging (MRI). However, conventional DIP suffers from severe overfitting and spectral bias effects. In this work, we first provide an analy

S. N. Artekha, A. V. Belyan

The article is devoted to electromagnetic phenomena in the atmosphere. The set of experimental data on the thunderstorm activity is analyzed. It helps to identify a possible physical mechanism of lightning flashes. This mechanism can involve the formation of metallic bonds in thunderclouds. The analysis of the problem is performed at a microphysical level wi

Gracie Chaney, Andrey Golov, Ambroise van Roekeghem, Javier Carrasco

The structure and growth of the Solid Electrolyte Interphase (SEI) region between an electrolyte and an electrode is one of the most fundamental, yet less-well understood phenomena in solid-state batteries. We present a parameter-free atomistic simulation of the SEI growth for one of the currently promising solid electrolytes (Li$_6$PS$_5$Cl), based on \text

Yuanling Niu, Jiaxin Wei, Zhi Yin, Dan Zeng

We introduce free probability analogues of the stochastic theta methods for free stochastic differential equations in this work. Assume that the drift coefficient of the free stochastic differential equations is operator Lipschitz and the diffusion coefficients are locally operator Lipschitz, we prove the strong convergence of the numerical methods. Moreover

Yingkai Ouyang

Quantum measurements are ubiquitous in quantum information processing tasks, but errors can render their outputs unreliable. Here, we present a scheme that implements a robust projective measurement through measuring code-inspired observables. Namely, given a projective POVM, a classical code and a constraint on the number of measurement outcomes each observ

D. R. Michiel Renger

Recent developments in Macroscopic Fluctuation Theory show that many interacting particle systems behave macroscopically as a combination of a gradient flow with Hamiltonian dynamics. This observation leads to the natural question how these structures compare to the GENERIC framework. This paper serves as a brief survey of both fields and a comparison betwee

Alberto Corpas, Luis Costero, Guillermo Botella, Francisco D. Igual

This paper proposes a mechanism to accelerate and optimize the energy consumption of a face detection software based on Haar-like cascading classifiers, taking advantage of the features of low-cost Asymmetric Multicore Processors (AMPs) with limited power budget. A modelling and task scheduling/allocation is proposed in order to efficiently make use of the e

Rui Sun

To naturally allow for string compactification with duality manifested, here we investigate in the self-mirror large volume scenarios from Schoen Calabi-Yau manifold. We explicitly study the geometry of Schoen Calabi-Yau threefold and complete its triple intersection from both ambient and non-ambient spaces. Based on these, we study the large volume scenario

Bayode Ogunleye, Babitha Dharmaraj

The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in todays cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social m

Zhengbo Wang, Jian Liang, Lijun Sheng, Ran He

Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods, such as prompt learning and adapter, to enhance CLIP's performance in downstream tasks. However, these methods still require additional training time and computational resources, w

S. V. Mousavi

Taking a system of two coupled qubits described by a X-shaped state and interacting through an anisotropic Heisenberg XY interaction, we examine the evolution of quantum entanglement and a few quantum correlations beyond entanglement, local quantum uncertainty and measurement-induced nonlocality, under the environmental decoherence both for zero and finite t

Jongmin Yu, Jiaqi Jiang, Sebastiano Fichera, Paolo Paoletti

Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the in

Kwara Nantomah

In this recreative piece of work, we present Gauss' calendar formula with some examples to demonstrate how it is applied. Then, based on it, we give a formula for determining dates of particular week days of a given month, and some examples are also given to demonstrate how it is applied. The key mathematical concept behind the formulas is modular arithmetic

Nicos Isaak

The Winograd Schema Challenge (WSC), a seemingly well-thought-out test for machine intelligence, has been proposed to shed light on developing systems that exhibit human behavior. Since its introduction, it aimed to pivot the focus of the AI community from the technology to the science of AI. While common and trivial for humans, studies show that it is still

Sitan Chen, Yuanzhi Li

The multi-head attention layer is one of the key components of the transformer architecture that sets it apart from traditional feed-forward models. Given a sequence length $k$, attention matrices $\mathbf{\Theta}_1,\ldots,\mathbf{\Theta}_m\in\mathbb{R}^{d\times d}$, and projection matrices $\mathbf{W}_1,\ldots,\mathbf{W}_m\in\mathbb{R}^{d\times d}$, the cor

Luis A. Guardiola, Ana Meca, Judith Timmer

We study the coordination of actions and the allocation of profit in supply chains under decentralized control in which a single supplier supplies several retailers with goods for replenishment of stocks. The goal of the supplier and the retailers is to maximize their individual profits. Since the outcome under decentralized control is inefficient, cooperati

Hemlata Sharma, Hitesh Harsora, Bayode Ogunleye

An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing

Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang

Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural fields (INRs, NeRFs), as well as making inferences about other types of neural networks. Unfortunately, weight space models tend to suffer from substantial overfitting. We empirical

Ruoqi Zhang, Ziwei Luo, Jens Sjölund, Thomas B. Schön

This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL). At the core is a mean-reverting stochastic differential equation (SDE) that transfers a complex action distribution into a standard Gaussian and then samples actions conditioned on the environment state with a corresponding reverse-time SDE, like a

Angel-Grover Perez-Munoz, Jose-Carlos Gamazo-Real, David Gonzalez-Barcena, Juan Zamorano

Stratospheric balloons have emerged as an affordable and flexible alternative to traditional spacecrafts as they are implemented using commercial off-the-shelf (COTS) equipment without following strict methodologies. HERCCULES is a stratospheric balloon mission that aims to characterize the convective heat and radiative environment in the stratosphere. The p

Niklas Euler, Adrian Braemer, Luca Benn, Martin Gärttner

We investigate a disorder-free quantum Ising chain subject to a time-periodic drive that rotates each spin by an angle $\pi(1-\epsilon_i)$. In case all spins experience the same deviation $\epsilon$ and the system is initialized in a fully polarized state, the dynamics is known to be time-crystalline: the magnetization of the system exhibits period-doubled o

Yusef Farah, Gabe Murray, Jeff Field, Maxine Xiu

Third harmonic generation (THG) provides a valuable, label-free approach to imaging biological systems. To date, THG microscopy has been performed using point scanning methods that rely on intensity measurements lacking phase information of the complex field. We report the first demonstration of THG holographic microscopy and the reconstruction of the comple

Reza Khanmohammadi, Ahmed I Ghanem, Kyle Verdecchia, Ryan Hall

This study introduces a novel teacher-student architecture utilizing Large Language Models (LLMs) to improve prostate cancer radiotherapy symptom extraction from clinical notes. Mixtral, the student model, initially extracts symptoms, followed by GPT-4, the teacher model, which refines prompts based on Mixtral's performance. This iterative process involved 2

Junhui Li, Jieying Lu, Weizhou Su

In this paper, the feedback stabilization of a linear time-invariant (LTI) multiple-input multiple-output (MIMO) system cascaded by a linear stochastic system is studied in the mean-square sense. Here, the linear stochastic system can model a class of correlated stochastic uncertainties such as channel uncertainties induced by packet loss and random transmis

Natsumi Ikeno, Jorgivan M. Dias, Wei-Hong Liang, Eulogio Oset

We study the $D^+ \to \bar K^0 \pi^+ \eta$ reaction where the $a_0(980)$ excitation plays a dominant role. We consider mechanisms of external and internal emission at the quark level, hadronize the $q \bar q$ components into two mesons and allow these mesons to undergo final state interaction where the $a_0(980)$ state is generated. While the $a_0(980)$ prod

H. Dawczak-Dębicki, Kristin Kliemt, M. Victoria Ale Crivillero, R. Küchler

Single crystals of DyRh$_2$Si$_2$ and HoRh$_2$Si$_2$ were investigated by thermal expansion and magnetostriction. The different types of magnetic order can clearly be seen in these measurements, particularly the canting of the moments away from the crystallographic $c$ direction below about 12~K and the spin-flip for magnetic field applied along the $c$ dire

Mohammed Osman

We consider real, Gauss-divisible matrices $A_{t}=A+\sqrt{t}B$, where $B$ is from the real Ginibre ensemble. We prove that the bulk correlation functions converge to a universal limit for $t=O(N^{-1/3+\epsilon})$ if $A$ satisfies certain local laws. If $A=\frac{1}{\sqrt{N}}(\xi_{jk})_{j,k=1}^{N}$ with $\xi_{jk}$ independent and identically distributed real r

Luca Morando, Giuseppe Loianno

Aerial robots have the potential to play a crucial role in assisting humans with complex and dangerous tasks. Nevertheless, the future industry demands innovative solutions to streamline the interaction process between humans and drones to enable seamless collaboration and efficient co-working. In this paper, we present a novel tele-immersive framework that

Stasis Chuchurka, Vladislav Sukharnikov, Nina Rohringer

A recently introduced theoretical framework for modeling the dynamics of X-ray amplified spontaneous emission is based on stochastic sampling of the density matrix of quantum emitters and the radiation field, similarly to other phase-space sampling techniques. While based on first principles and providing valuable theoretical insights, the original stochasti

Ravi Patel, Angus Brayne, Rogier Hintzen, Daniel Jaroslawicz

Language models hold incredible promise for enabling scientific discovery by synthesizing massive research corpora. Many complex scientific research questions have multiple plausible answers, each supported by evidence of varying strength. However, existing language models lack the capability to quantitatively and faithfully compare answer plausibility in te

Hao Li, Xiaogang Liu

In [Discrete Mathematics 306 (2005) 153-158], So proposed a conjecture saying that integral circulant graphs with different connection sets have different spectra. This conjecture is still open. We prove that this conjecture holds for integral circulant graphs whose orders have prime factorization of $4$ types.

Kamirul Kamirul, Odysseas Pappas, Igor G. Rizaev, Alin Achim

We present a novel ship wake simulation system for generating S-band Synthetic Aperture Radar (SAR) images, and demonstrate the use of such imagery for the classification of ships based on their wake signatures via a deep learning approach. Ship wakes are modeled through the linear superposition of wind-induced sea elevation and the Kelvin wakes model of a m

Farmer Schlutzenberg

Suppose there is a Reinhardt cardinal. Then (1) $M_n(X)$ exists and is fully iterable (above $X$) for every transitive set $X$ and every $n<\omega$ (here $M_n(X)$ denotes the canonical minimal proper class inner model containing $X$ and having $n$ Woodin cardinals above the rank of $X$); and (2) Projective Determinacy holds in every set generic extension.

Jongmin Yu, Chen Bene Chi, Sebastiano Fichera, Paolo Paoletti

Road pavement detection and segmentation are critical for developing autonomous road repair systems. However, developing an instance segmentation method that simultaneously performs multi-class defect detection and segmentation is challenging due to the textural simplicity of road pavement image, the diversity of defect geometries, and the morphological ambi

A. De Vita, R. Sant, V. Polewczyk, G. van der Laan

Van der Waals materials provide a versatile toolbox for the emergence of new quantum phenomena and the fabrication of functional heterostructures. Among them, the trihalide VI3 stands out for its unique magnetic and structural landscape. Here we investigate the spin and orbital magnetic degrees of freedom in the layered ferromagnet VI3 by means of temperatur

Xingyue Huang, Miguel Romero Orth, Pablo Barceló, Michael M. Bronstein

Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications. Nonetheless, it remains challenging to transfer the success of these architectures to relational hypergraphs, where the task of link prediction is over $k$-ary relations,

Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy, Jade Freeman

Autonomous robots exploring unknown environments face a significant challenge: navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration techniques often fail. In this paper, we present TopoNav, a novel topological navigation framework that integrates

Evan Jellison, Matthew Johnson, Andrea Banzatti, Simon Bruderer

We present iSLAT (the Interactive Spectral-Line Analysis Tool), a python-based graphical tool that allows users to interactively explore and manually fit line emission observed in molecular spectra. iSLAT adopts a simple slab model that simulates emission spectra with a small set of parameters (temperature, emitting area, column density, and line broadening)

Jun Wang, Wenjie Du, Yiyuan Yang, Linglong Qian

Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications. In recent years, deep learning-based methods have successfully handled missing data by leveraging complex temporal dependencies and learned data distributions. In this survey, we provide a comprehensive summar

Veyis Gunes

We propose a synthesis method for the design of digital twins applicable to various systems (pneumatic, hydraulic, electrical/electronic circuits). The methodology allows representing the operation of these systems through an active digital twin, thereby enabling a more suitable and easier computer-aided design, simulation, control, and monitoring. Furthermo

I. Garate-Lopez, Sébastien Lebonnois

Global Climate Models (GCM) are very useful tools to study theoretically the general dynamics and specific phenomena in planetary atmospheres. In the case of Venus, several GCMs succeeded in reproducing the atmosphere's superrotation and the global temperature field. However, the highly variable polar temperature and the permanent cold collar have not been r

Yang Cao, Shao-Yu Lien, Ying-Chang Liang, Dusit Niyato

Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services. Due to the rapid mobility of LEO satellite, inter-beam/satellite handovers happen frequently for a specific user equipment (UE). To tackle this issue, earth-fixed cell scenarios have been under studied

Guillaume Bossard, Martin Cederwall, Jakob Palmkvist

We construct the teleparallel dynamics for extended geometry where the structure algebra is (an extension of) an untwisted affine Kac-Moody algebra. This provides a geometrisation of the Geroch symmetry appearing on dimensional reduction of a gravitational theory to two dimensions. The formalism is adapted to the underlying tensor hierarchy algebra, and will

Hossein Zakerinia, Amin Behjati, Christoph H. Lampert

We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory. Its main advantage over previous work is that it allows for more flexibility in how the transfer of knowledge between tasks is realized. For previous approaches, this could only happen indirectly, by means of learning prior distributions over models. In contrast, the n

Victor Abrashkin

Let $\mathcal K$ be a field of formal Laurent series with coefficients in a finite field of characteristic $p$. For $M\ge 1$, let $\mathcal G_{<p,M}$ be the maximal quotient of the Galois group of $\mathcal K$ of period $p^M$ and nilpotent class $<p$ and $\{\mathcal G_{<p,M}^{(v)}\}_{v\geqslant 0}$ -- the ramification subgroups in upper numbering. Let $\math

C. Simmonds, A. Verhamme, A. K. Inoue, H. Katz

Several Lyman Continuum (LyC) emitters have been detected so far, but their observed ionising spectra sometimes differ from attenuated stellar spectra predicted by stellar population synthesis modelling. This discrepancy may be due to a significant contribution of LyC nebular emission. We aim to quantify the importance this emission in LyC leakers: its contr

Akira Ito, Masanori Yamada, Atsutoshi Kumagai

Recently, Ainsworth et al. showed that using weight matching (WM) to minimize the $L^2$ distance in a permutation search of model parameters effectively identifies permutations that satisfy linear mode connectivity (LMC), where the loss along a linear path between two independently trained models with different seeds remains nearly constant. This paper analy

Zhengbo Wang, Jian Liang, Ran He, Zilei Wang

With the emergence of pretrained vision-language models (VLMs), considerable efforts have been devoted to fine-tuning them for downstream tasks. Despite the progress made in designing efficient fine-tuning methods, such methods require access to the model's parameters, which can be challenging as model owners often opt to provide their models as a black box

Amir Taubenfeld, Yaniv Dover, Roi Reichart, Ariel Goldstein

The emergence of Large Language Models (LLMs), has opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these AI agents as substitutes for human participants in be

Clarissa Astuto, Daniele Boffi, Giovanni Russo, Umberto Zerbinati

This paper focuses on the numerical solution of elliptic partial differential equations (PDEs) with Dirichlet and mixed boundary conditions, specifically addressing the challenges arising from irregular domains. Both finite element method (FEM) and finite difference method (FDM), face difficulties in dealing with arbitrary domains. The paper introduces a nov

Tony Rothman

It is universally believed that with his 1905 paper "Does the inertia of a body depend on its energy content?" Einstein first demonstrated the equivalence of mass and energy by making use of his special theory of relativity. In the final step of that paper, however, Einstein equates the kinetic energy of a body to its Newtonian value, indicating that his res

Nimrod Berman, Eitan Kosman, Dotan Di Castro, Omri Azencot

Graph generation is integral to various engineering and scientific disciplines. Nevertheless, existing methodologies tend to overlook the generation of edge attributes. However, we identify critical applications where edge attributes are essential, making prior methods potentially unsuitable in such contexts. Moreover, while trivial adaptations are available

Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan

In recent years, large language models (LLMs) have become increasingly capable and can now interact with tools (i.e., call functions), read documents, and recursively call themselves. As a result, these LLMs can now function autonomously as agents. With the rise in capabilities of these agents, recent work has speculated on how LLM agents would affect cybers

Martin Aubard, Sergio Quijano, Olaya Álvarez-Tuñón, László Antal

The recent advance in autonomous underwater robotics facilitates autonomous inspection tasks of offshore infrastructure. However, current inspection missions rely on predefined plans created offline, hampering the flexibility and autonomy of the inspection vehicle and the mission's success in case of unexpected events. In this work, we address these challeng

Timothy A. Sipkens, Ruth Perez Calderon, Richard G. Green, Andrew Oldershaw

This work presents the results of two interlaboratory comparisons of particle filtration efficiency measurements performed by a network of laboratories across Canada and Australia. Testing across multiple layers of a common verification material demonstrates a constant size-resolved quality factor when layering uncharged materials. Size-resolved filtration c

Jun Liu, Tobias Tolle, Tomislav Maric

Non-orthogonality errors in unstructured Finite Volume methods for simulating incompressible two-phase flows may break the force-balanced discretization. We show that applying the same explicit non-orthogonality correction for all gradient terms in the context of segregated solution algorithms is not sufficient to achieve force balance. To ensure force balan

Xue-Jia Yu, Sheng Yang, Hai-Qing Lin, Shao-Kai Jian

Quantum entanglement marks a definitive feature of topological states. However, the entanglement spectrum remains insufficiently explored for topological states without a bulk energy gap. Using a combination of field theory and numerical techniques, we accurately calculate and analyze the entanglement spectrum of gapless symmetry protected topological states

Rafael Bravo de La Parra, Luis Sanz

In this work we develop a discrete model of competing species affected by a common parasite. We analyze the influence of the fast development of the shared disease on the community dynamics. The model is presented under the form of a two time scales discrete system with four variables. Thus, it becomes analytically tractable with the help of the appropriate

Yifan Feng, Yihe Luo, Shihui Ying, Yue Gao

Hypergraph Neural Networks (HGNNs) have recently attracted much attention and exhibited satisfactory performance due to their superiority in high-order correlation modeling. However, it is noticed that the high-order modeling capability of hypergraph also brings increased computation complexity, which hinders its practical industrial deployment. In practice,

Arghya Choudhury, Arpita Mondal, Subhadeep Mondal

In this article, we discuss various phenomenological implications of possible R-parity violating (RPV) supersymmetric scenarios. In this context, the implications of both bilinear and trilinear RPV terms are reviewed from the viewpoint of neutrino physics, anomalous muon magnetic moment, different flavor observables, and collider physics. Apart from discussi

Tan Sun, Junhong Lin

Graph neural networks (GNNs) have gained popularity for various graph-related tasks. However, similar to deep neural networks, GNNs are also vulnerable to adversarial attacks. Empirical studies have shown that adversarially robust generalization has a pivotal role in establishing effective defense algorithms against adversarial attacks. In this paper, we con

Sucharita Biswas

In this paper, we study the automorphism group and geodesic transitivity of a family of vertex-transitive graphs $H(n,k)$, introduced by Fu-Tao Hu \textit{et.al.} in 2010. In the process, we address some naturally arising, unanswered questions from that paper.

Samuel S. M. Santos, Lucy V. C. Assali, João F. Justo

The electrical conductivity of magnesium silicate MgSiO3 has been studied, using the framework of the first-principles density functional theory and the Boltzmann transport theory, under the thermodynamic conditions of the Earth's lower mantle. We find that the conductivity of pristine MgSiO3 depends strongly on the structural phase of the material, as well

Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo

Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of $n$ agents located in an underlying metric space, our goal is to partition them into $k$ clusters, optimizing some social cost objective. The metric space is defined by a distance function $d$ between the agent locations.

Anang Dadhich, Philippe Fazilleau, Enric Pardo

High-field superconducting REBCO magnets contain several coils with many turns. For these magnets, electro-thermal quench is an issue that magnet designers need to take into account. Thus, there is a need for a fast and accurate software to numerically model the overall performance of full-scale magnets. High temperature superconductors can be modeled using