February 2024 arXiv papers — page 113

Vitonofrio Crismale, Simone Del Vecchio, Stefano Rossi, Janusz Wysoczański

An abstract characterization of weakly monotone $C^*$-algebras, namely the concrete $C^*$-algebras generated by creators and annihilators acting on the so-called weakly monotone Fock spaces, is given in terms of (quotient of) suitable Exel-Laca algebras. The weakly monotone $C^*$-algebra indexed by $\mathbb{N}$ is shown to be a type-I $C^*$-algebra and its r

Alberto Larrauri, Stanislav Živný

We give a complete complexity classification for the problem of finding a solution to a given system of equations over a fixed finite monoid, given that a solution over a more restricted monoid exists. As a corollary, we obtain a complexity classification for the same problem over groups.

László Tóth

We use the convolution method for arithmetic functions of several variables to deduce an asymptotic formula for the number of $k$-tuples of positive integers with components which are pairwise non-coprime and $\le x$. More generally, we obtain asymptotic formulas on the number of $k$-tuples $(n_1,\ldots,n_k)\in {\Bbb N}^k$ such that at least $r$ pairs $(n_i,

Eniko Kevi, Nguyen Kim Thang

Matching problems have been widely studied in the research community, especially Ad-Auctions with many applications ranging from network design to advertising. Following the various advancements in machine learning, one natural question is whether classical algorithms can benefit from machine learning and obtain better-quality solutions. Even a small percent

Zihao Wang, Yifei Wang, Baoqi Shi, Chen Shen

Rotation symmetry of microresonators supports the generation of phase-locked counter-propagating (CP) solitons that can potentially miniaturize dual-comb systems. Realization of these dual-comb compatible solitons in photonic integrated circuits remains a challenge. Here, we synthesized such CP solitons in an integrated silicon nitride microresonator and obs

Vincenzo Corso, Leonardo Mariani, Daniela Micucci, Oliviero Riganelli

AI-based code assistants are promising tools that can facilitate and speed up code development. They exploit machine learning algorithms and natural language processing to interact with developers, suggesting code snippets (e.g., method implementations) that can be incorporated into projects. Recent studies empirically investigated the effectiveness of code

Ionut Daniel Fagadau, Leonardo Mariani, Daniela Micucci, Oliviero Riganelli

Generative AI is changing the way developers interact with software systems, providing services that can produce and deliver new content, crafted to satisfy the actual needs of developers. For instance, developers can ask for new code directly from within their IDEs by writing natural language prompts, and integrated services based on generative AI, such as

Hannah Schmitz, Lucas Dittmann, Klaas Padeken, Sebastian Neubert

A new downstream tracking system, known as the Mighty Tracker, is planned to be installed at LHCb during LS4 of the LHC. This will allow an increase in instantaneous luminosity from $2\cdot10^{33}~\mathrm{cm}^{-2}\mathrm{s}^{-1}$ to $1.5\cdot10^{34}~\mathrm{cm}^{-2}\mathrm{s}^{-1}$ and therefore an overall higher irradiation and up to six times higher occupa

Colin Decourt, Rufin VanRullen, Didier Salle, Thomas Oberlin

In recent years, driven by the need for safer and more autonomous transport systems, the automotive industry has shifted toward integrating a growing number of Advanced Driver Assistance Systems (ADAS). Among the array of sensors employed for object recognition tasks, radar sensors have emerged as a formidable contender due to their abilities in adverse weat

Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu

Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the importance of various interactions that reflect unique user/item characteristics and failed to utilize user and item high-order neighborhood information to model user preference, thu

Florian Beier, Hancheng Bi, Clément Sarrazin, Bernhard Schmitzer

In this paper, we are concerned with estimating the joint probability of random variables $X$ and $Y$, given $N$ independent observation blocks $(\boldsymbol{x}^i,\boldsymbol{y}^i)$, $i=1,\ldots,N$, each of $M$ samples $(\boldsymbol{x}^i,\boldsymbol{y}^i) = \bigl((x^i_j, y^i_{\sigma^i(j)}) \bigr)_{j=1}^M$, where $\sigma^i$ denotes an unknown permutation of i

Yigit Yildirim, Emre Ugur

Learning from Demonstration (LfD) is a widely used technique for skill acquisition in robotics. However, demonstrations of the same skill may exhibit significant variances, or learning systems may attempt to acquire different means of the same skill simultaneously, making it challenging to encode these motions into movement primitives. To address these chall

Peining Shen, Jianwu Fang, Hongkai Yu, Jianru Xue

In autonomous driving, predicting the behavior (turning left, stopping, etc.) of target vehicles is crucial for the self-driving vehicle to make safe decisions and avoid accidents. Existing deep learning-based methods have shown excellent and accurate performance, but the black-box nature makes it untrustworthy to apply them in practical use. In this work, w

Aryeh Kontorovich, Amichai Painsky

We present novel bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These are nearly optimal in various precise senses, including a kind of instance-optimality. Our data-dependent convergence guarantees for the maximum likelihood estimator significantly improve upon the currently known results. A variety of techniques are

Eslam Eldeeb, Houssem Sifaou, Osvaldo Simeone, Mohammad Shehab

Reinforcement learning (RL) has been widely adopted for controlling and optimizing complex engineering systems such as next-generation wireless networks. An important challenge in adopting RL is the need for direct access to the physical environment. This limitation is particularly severe in multi-agent systems, for which conventional multi-agent reinforceme

Soroush Shahi, Vimal Mollyn, Cori Tymoszek Park, Richard Kang

Hand gesture recognition is becoming a more prevalent mode of human-computer interaction, especially as cameras proliferate across everyday devices. Despite continued progress in this field, gesture customization is often underexplored. Customization is crucial since it enables users to define and demonstrate gestures that are more natural, memorable, and ac

Sergei M. Butorin

The employment of the parameter-free Armiento-K\"{u}mmel generalized gradient approximation (AK13-GGA) exchange functional was examined as means of the band gap prediction for hybrid metal halide perovskites (HaPs) or systems with strong spin-orbit coupling in the full-relativistic density-functional-theory (DFT) calculations. The new combination of AK13 wit

Jacob Fox, Zoe Himwich, Nitya Mani, Yunkun Zhou

We study variants of Sidorenko's conjecture in tournaments, where new phenomena arise that do not have clear analogues in the setting of undirected graphs. We first consider oriented graphs that are systematically under-represented in tournaments (called tournament anti-Sidorenko). We prove that such oriented graphs must be quite sparse; specifically, the ma

Lorenz Gärtner, Nikolai Hartmann, Lukas Heinrich, Malin Horstmann

Experimental High Energy Physics has entered an era of precision measurements. However, measurements of many of the accessible processes assume that the final states' underlying kinematic distribution is the same as the Standard Model prediction. This assumption introduces an implicit model-dependency into the measurement, rendering the reinterpretation of t

Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li

Large Language Models~(LLMs) have gained immense popularity and are being increasingly applied in various domains. Consequently, ensuring the security of these models is of paramount importance. Jailbreak attacks, which manipulate LLMs to generate malicious content, are recognized as a significant vulnerability. While existing research has predominantly focu

Igor Kuzmenko, Y. Avishai, Y. B. Band

We consider light propagation in a silicon dioxide substrate with implanted ${\mathrm{Er}}^{3+}$ ions and silver nanoparticles that are randomly and homogeneously distributed in the substrate. When their densities are large enough, the medium can have a negative refractive index over a certain range of frequencies, within which the following exotic property

Francesco Benini, Tomas Reis, Saman Soltani, Ziruo Zhang

We study a class of SYK models with $\mathcal{N}=2$ supersymmetry, described by $N$ fermions in chiral Fermi multiplets, as well as $\alpha N$ first-order bosons in chiral multiplets. The interactions are characterized by two integers $(p,q)$. We focus on the large $N$ and low energy limit of these models. Despite the presence of dynamical bosons, we find co

Soujanya Narayana, Ibrahim Radwan, Ramanathan Subramanian, Roland Goecke

Psychological studies observe that emotions are rarely expressed in isolation and are typically influenced by the surrounding context. While recent studies effectively harness uni- and multimodal cues for emotion inference, hardly any study has considered the effect of long-term affect, or \emph{mood}, on short-term \emph{emotion} inference. This study (a) p

Quanjun Lang, Xiong Wang, Fei Lu, Mauro Maggioni

Modeling multi-agent systems on networks is a fundamental challenge in a wide variety of disciplines. Given data consisting of multiple trajectories, we jointly infer the (weighted) network and the interaction kernel, which determine, respectively, which agents are interacting and the rules of such interactions. Our estimator is based on a non-convex optimiz

Sergio Rico

Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context descrip

Frédéric Bihan, Alicia Dickenstein, Jens Forsgård

Consider a sparse system of n Laurent polynomials in n variables with complex coefficients and support in a finite lattice set A. The maximal number of isolated roots of the system in the complex n-torus is known to be the normalized volume of the convex hull of A (the BKK bound). We explore the following question: if the cardinality of A equals n+m+1, what

Simon F. Martina-Perez, Isaac B. Breinyn, Daniel J. Cohen, Ruth E. Baker

Epithelial monolayers are some of the best-studied models for collective cell migration due to their abundance in multicellular systems and their tractability. Experimentally, the collective migration of epithelial monolayers can be robustly steered e.g. using electric fields, via a process termed electrotaxis. Theoretically, however, the question of how to

Kwanseok Oh, Jieun Lee, Da-Woon Heo, Dinggang Shen

Ultrahigh-field (UHF) magnetic resonance imaging (MRI), i.e., 7T MRI, provides superior anatomical details of internal brain structures owing to its enhanced signal-to-noise ratio and susceptibility-induced contrast. However, the widespread use of 7T MRI is limited by its high cost and lower accessibility compared to low-field (LF) MRI. This study proposes a

Ruby J. Wright, Rachel S. Somerville, Claudia del P. Lagos, Matthieu Schaller

In recent years, cosmological hydrodynamical simulations have proven their utility as key interpretative tools in the study of galaxy formation and evolution. In this work, we present a like-for-like comparison between the baryon cycle in three publicly available, leading cosmological simulation suites: EAGLE, IllustrisTNG, and SIMBA. While these simulations

Saar Tarnopolsky, Alejandro Cohen

We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) in the finite blocklength regime that provides Post-Quantum (PQ) security at high communication rates. Recently, hybrid cryptosystems offered PQ security by premixing the data using secure coding schemes and encrypting only a small portion of it, assuming th

Jose Pablo Folch, Calvin Tsay, Robert M Lee, Behrang Shafei

Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many real-life problems do not offer this flexibility; in particular, the search space of the next query may depend on previous ones. Example challenges arise in the physical sciences in t

Aditya Challa, Sravan Danda, Laurent Najman

In this paper, we propose a class of non-parametric classifiers, that learn arbitrary boundaries and generalize well. Our approach is based on a novel way to regularize 1NN classifiers using a greedy approach. We refer to this class of classifiers as Watershed Classifiers. 1NN classifiers are known to trivially over-fit but have very large VC dimension, henc

Roni Koitermaa, Andreas Kyritsakis, Tauno Tiirats, Veronika Zadin

Vacuum arcing poses significant challenges for high-field vacuum devices, underscoring the importance of understanding it for their efficient design. A detailed description of the physical mechanisms involved in vacuum arcing is yet to be achieved, despite extensive research. In this work, we further develop the modelling of the physical processes involved i

Peter Wallis

Theory based AI research has had a hard time recently and the aim here is to propose a model of what LLMs are actually doing when they impress us with their language skills. The model integrates three established theories of human decision-making from philosophy, sociology, and computer science. The paper starts with the collective understanding of reasoning

Hong-Xuan Jiang, Indu K. Dihingia, Cheng Liu, Yosuke Mizuno

The Blandford-Znajek (BZ) process powers energetic jets by extracting the rotating energy of a Kerr black hole. It is important to understand this process in non-Kerr black hole spacetimes. In this study, we conduct two-dimensional and three-dimensional two-temperature General Relativistic Magnetohydrodynamic (GRMHD) simulations of magnetized accretion flows

Batool Lakzaei, Mostafa Haghir Chehreghani, Alireza Bagheri

In the era of widespread social networks, the rapid dissemination of fake news has emerged as a significant threat, inflicting detrimental consequences across various dimensions of people's lives. Machine learning and deep learning approaches have been extensively employed for identifying fake news. However, a significant challenge in identifying fake news i

Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz

Certification for machine learning is proving that no adversarial sample can evade a model within a range under certain conditions, a necessity for safety-critical domains. Common certification methods for segmentation use a flat set of fine-grained classes, leading to high abstain rates due to model uncertainty across many classes. We propose a novel, more

Junyoung Choi, Sagnik Bhattacharya

As commercial interest in proximity services increased, the development of various wireless localization techniques was promoted. In line with this trend, Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services thanks to centimeter-level localization accuracy. In addition, since the actual location of the mobile device (M

Jong Chan Lee, Begüm Rukiye Özer, In Heo, Thomas Schultz

Fragment signals in ordinary mass spectra carry no label to identify their parent molecule. By correlating mass signals with rotational Raman spectra, we created a method to label each ion signal with the spectroscopic fingerprint of its neutral parent molecule. In data for a carbon disulfide molecular cluster beam, we assigned 28 distinct ionization and fra

Yanchen Zhao, Wenxuan He, Chuanmin Jia, Qizhe Wang

In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) stan

Juan D. Moreno-Ternero, Tim Pawlowski, Shlomo Weber

This paper examines the interdependence of international success and competitive balance of domestic sports competitions. More specifically, we apply the notion of the Herfindahl-Hirschman index to examine the effect of international rewards on distortion of competitive balance in domestic competitions and derive conditions under which the level of domestic

James Woodfield, Hilary Weller, Colin J Cotter

Accurate transport algorithms are crucial for computational fluid dynamics and more accurate and efficient schemes are always in development. One dimensional limiting is commonly employed to suppress nonphysical oscillations. However, the application of such limiters can reduce accuracy. It is important to identify the weakest set of sufficient conditions re

Xueqian Sun, Ermin Malic, Yuerui Lu

Highly customizable interfaces created by van der Waals stacked 2D materials provide an extremely flexible opportunity for engineering and effectively controlling material properties. The atomic-thin nature and strong scalability of transition metal dichalcogenides (TMDs), the star family of two-dimensional semiconducting materials, allow for the modulation

Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp

Normal-form games (NFGs) are the fundamental model of strategic interaction. We study their representation using neural networks. We describe the inherent equivariance of NFGs -- any permutation of strategies describes an equivalent game -- as well as the challenges this poses for representation learning. We then propose the NfgTransformer architecture that

Chris Madge, Massimo Poesio

In this work we examine the use of Large Language Models (LLMs) in the challenging setting of acting as a Minecraft agent. We apply and evaluate LLMs in the builder and architect settings, introduce clarification questions and examining the challenges and opportunities for improvement. In addition, we present a platform for online interaction with the agents

Zihua Guo

We consider the oscillatory integrals with parameter-dependent phases. We decompose the integrals into a leading term and a remainder term. Instead of the pointwise estimate, we use some $L^p$-estimate for the remainder term and get various uniform estimates when the phase functions satisfy certain conditions. This enables us to reduce the requirement of the

Sangwoo Kim, Sascha Hilgenfeldt

Understanding the way disordered particle packings transition between jammed (rigid) and unjammed (fluid) states is of both great practical importance and strong fundamental interest. The values of critical packing fraction (and other state variables) at the jamming transition are protocol dependent. Here, we demonstrate that this variability can be systemat

M. Charkaoui, R. Sammani, E. H Saidi, R. Ahl Laamara

The Asymptotic WGC has been proposed as a special case of the tower WGC that probes infinite distances in the moduli space corresponding to weakly coupled gauge regimes. The conjecture has been studied in M-theory on Calabi-Yau threefold (CY3) with finite volume inducing a 5D effective QFT. In this paper, we extend the scope of the previous study to encompas

Sara van de Geer

We derive entropy bounds for the absolute convex hull of vectors $X= (x_1 , \ldots , x_p)\in \mathbb{R}^{n \times p} $ in $\mathbb{R}^n$ and apply this to the case where $X$ is the $d$-fold tensor matrix $$X = \underbrace{\Psi \otimes \cdots \otimes \Psi}_{d \ {\rm times} }\in \mathbb{R}^{m^d \times r^d },$$ with a given $\Psi = ( \psi_1 , \ldots , \psi_r )

Martin Herdegen, David Hobson, Alex S. L. Tse

The Merton investment-consumption problem is fundamental, both in the field of finance, and in stochastic control. An important extension of the problem adds transaction costs, which is highly relevant from a financial perspective but also challenging from a control perspective because the solution now involves singular control. A further significant extensi

Clément Staelen, Jean-Marc Huré

We show that the two-dimensional structure of a rigidly rotating self-gravitating body is accessible with relatively good precision by assuming a purely spheroidal stratification. With this hypothesis, the two-dimensional problem becomes one-dimensional, and consists in solving two coupled fixed-point equations in terms of equatorial mass density and eccentr

O. K. Sheinman, Bin Wang

A survey of some recent advances in parabolic Hitchin systems (parabolic Bouville--Narasimhan--Ramanan correspondence, mirror symmetry for parabolic Hitchin systems), and in exact methods of solving the non-parabolic Hitchin systems.

Zongbo Han, Yifeng Yang, Changqing Zhang, Linjun Zhang

Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. This problem usually arises due to the overfitting problem, which is characterized by learning everything presented in the training set, resulting in overconfident predictions during testing. Existing methods typically address overfitting and mi

Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon

Deep learning-based surrogate models have demonstrated remarkable advantages over classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over traditional partial differential equation (PDE) solvers. However, a significant challenge hindering their widespread adoption in both scientific and industrial domains is the lack of understa

Junghyun Min, Minho Lee, Woochul Lee, Yeonsoo Lee

Unsupervised learning objectives like autoregressive and masked language modeling constitute a significant part in producing pre-trained representations that perform various downstream applications from natural language understanding to conversational tasks. However, despite impressive generative capabilities of recent large language models, their abilities

Hang Yu, Christophe De Wagter, Guido C. H. E de Croon

Many existing obstacle avoidance algorithms overlook the crucial balance between safety and agility, especially in environments of varying complexity. In our study, we introduce an obstacle avoidance pipeline based on reinforcement learning. This pipeline enables drones to adapt their flying speed according to the environmental complexity. Moreover, to impro

Melissa Pesce-Rollins, Karl-Ludwig Klein, Säm Krucker, Alexander Warmuth

We report on the detection of the gamma-ray emission above 100 MeV from the solar flare of September 29, 2022, by Fermi LAT with simultaneous coverage in HXR by Solar Orbiter STIX. The Solar Orbiter-Earth separation was 178$^{\circ}$ at the time of the flare as seen from Earth, with Solar Orbiter observing the east limb. Based on STIX imaging, the flare was

Charles Wan, Rodrigo Belo, Leid Zejnilović, Susana Lavado

An algorithm effects a causal representation of relations between features and labels in the human's perception. Such a representation might conflict with the human's prior belief. Explanations can direct the human's attention to the conflicting feature and away from other relevant features. This leads to causal overattribution and may adversely affect the h

Christian Berg

Let $f_r(x)=\log(1+rx)/\log(1+x)$ for $x>0$. We prove that $f_r$ is a complete Bernstein function for $0\le r\le 1$ and a Stieltjes function for $1\le r$. This answers a conjecture of David Bradley that $f_r$ is a Bernstein function when $0\le r\le 1$.

Magnar Bjørgve, Christian Tantardini, Stig Rune Jensen, Gabriel A. Gerez S.

Wavelets and Multiwavelets have lately been adopted in Quantum Chemistry to overcome challenges presented by the two main families of basis sets: Gaussian atomic orbitals and plane waves. In addition to their numerical advantages (high precision, locality, fast algorithms for operator application, linear scaling with respect to system size, to mention a few)

Lucia Guastadisegni, Silvia Cagnone, Irini Moustaki, Vassilis Vasdekis

This paper introduces the generalized Hausman test as a novel method for detecting non-normality of the latent variable distribution of unidimensional Item Response Theory (IRT) models for binary data. The test utilizes the pairwise maximum likelihood estimator obtained for the parameters of the classical two-parameter IRT model, which assumes normality of t

Luis J. Garay, Mario López González, Mercedes Martín-Benito, Rita B. Neves

We study the scalar modes that, being observable today, were trans-Planckian before inflation, within the context of hybrid Loop Quantum Cosmology (LQC). We analyse the dynamics of these highly ultraviolet modes by introducing modified dispersion relations to their equations of motion and discuss the impact that these relations would introduce in the power s

George T. Fortune, Eric Lauga, Raymond E. Goldstein

The humble Petri dish is perhaps the simplest setting in which to examine the locomotion of swimming organisms, particularly those whose body size is tens of microns to millimetres. The fluid layer in such a container has a bottom no-slip surface and a stress-free upper boundary. It is of fundamental interest to understand the flow fields produced by the ele

Riku Green, Grant Stevens, Telmo de Menezes e Silva Filho, Zahraa Abdallah

Multi-step forecasting (MSF) in time-series, the ability to make predictions multiple time steps into the future, is fundamental to almost all temporal domains. To make such forecasts, one must assume the recursive complexity of the temporal dynamics. Such assumptions are referred to as the forecasting strategy used to train a predictive model. Previous work

Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin

To evaluate code large language models (LLMs), research has relied on a few small manually curated benchmarks, such as HumanEval and MBPP, which represent a narrow part of the real-world software domains. In this work, we introduce round-trip correctness (RTC) as an alternative evaluation method. RTC allows Code LLM evaluation on a broader spectrum of real-w

Arijit Maitra, M. P. Gururajan

Nanomechanical responses (force-time profiles) of crystal lattices under deformation exhibit random critical jumps, reflecting the underlying structural transition processes. Despite extensive data collection, interpreting dynamic critical responses and their underlying mechanisms remains a significant challenge. This study explores a microscopic theoretical

A. Esteban, A. Zafra, C. Romero

The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help students to choose courses that suit to their personal interests and their academic performance. This paper presents a hybrid RS that combin

Fei Wang, Oleg Tschukin, Thomas Leisner, Haodong Zhang

Breakup of a liquid jet into a chain of droplets is common in nature and industry. Previous researchers developed profound mathematic and fluid dynamic models to address this breakup phenomenon starting from tiny perturbations. However, the morphological evolution of the jets at large amplitude perturbations is still an open question. Here, we report a conci

Sangwoo Shin, Daehee Lee, Minjong Yoo, Woo Kyung Kim

One-shot imitation is to learn a new task from a single demonstration, yet it is a challenging problem to adopt it for complex tasks with the high domain diversity inherent in a non-stationary environment. To tackle the problem, we explore the compositionality of complex tasks, and present a novel skill-based imitation learning framework enabling one-shot im

Delio Mugnolo, Diego Noja, Christian Seifert

We study the Korteweg--de Vries equation on a metric star graph and investigate existence of solitary waves on the metric graph in terms of the coefficients of the equation on each edge, the coupling condition at the central vertex of the star and the speeds of the travelling wave. We show that, with a continuity condition at the vertex, solitary waves can o

Chengxi Zeng, Tilo Burghardt, Alberto M Gambaruto

While many recent Physics-Informed Neural Networks (PINNs) variants have had considerable success in solving Partial Differential Equations, the empirical benefits of feature mapping drawn from the broader Neural Representations research have been largely overlooked. We highlight the limitations of widely used Fourier-based feature mapping in certain situati

Kishore Gopalakrishnan, Nishant K Singh

The small-scale dynamo is typically studied by assuming that the correlation time of the velocity field is zero. Some authors have used a smooth renovating flow model to study how the properties of the dynamo are affected by the correlation time being nonzero. Here, we assume the velocity is an incompressible Gaussian random field (which need not be smooth),

Mohamed Ghanem, Frederik Schmitt, Julian Siber, Bernd Finkbeiner

Training neural networks on NP-complete problems typically demands very large amounts of training data and often needs to be coupled with computationally expensive symbolic verifiers to ensure output correctness. In this paper, we present NeuRes, a neuro-symbolic approach to address both challenges for propositional satisfiability, being the quintessential N

Davide Abriola, Daniele Della Pergola, Marco Lombardi, Pietro Bergamini

We present a new weak lensing analysis of the Hubble Frontier Fields galaxy cluster Abell 2744 ($z$ = 0.308) using new Magellan/MegaCam multi-band $gri$ imaging data. We carry out our study by applying brand-new PSF and shape measurement softwares that allow for the use of multi-band data simultaneously, which we first test on Subaru/Suprime-Cam $BR_cz'$ ima

S. Aiello, A. Albert, M. Alshamsi, S. Alves Garre

The KM3NeT/ARCA neutrino detector is currently under construction at 3500 m depth offshore Capo Passero, Sicily, in the Mediterranean Sea. The main science objectives are the detection of high-energy cosmic neutrinos and the discovery of their sources. Simulations were conducted for the full KM3NeT/ARCA detector, instrumenting a volume of 1 km$^3$, to estima

Huan Liu, Jing Shen, Xianguo Geng

In this study, we explore multiple higher-order pole solutions in spinor Bose--Einstein condensates. These solutions are associated with different pairs of higher-order poles of the transmission coefficient in the inverse scattering transform, and they represent solutions of the spin-1 Gross--Pitaevskii equation. We introduce a direct scattering map that map

Gunter Malle, Alexander Moretó, Noelia Rizo, A. A. Schaeffer Fry

Recently, Malle and Navarro obtained a Galois strengthening of Brauer's height zero conjecture for principal $p$-blocks when $p=2$, considering a particular Galois automorphism of order~$2$. In this paper, for any prime $p$ we consider a certain elementary abelian $p$-subgroup of the absolute Galois group and propose a Galois version of Brauer's height zero

Jusung Lee, Sungguk Cha, Younghyun Lee, Cheoljong Yang

Having revolutionized natural language processing (NLP) applications, large language models (LLMs) are expanding into the realm of multimodal inputs. Owing to their ability to interpret images, multimodal LLMs (MLLMs) have been primarily used for vision-language tasks. Currently, MLLMs have not yet been extended for domain-specific visual tasks, which requir

Khang Truong Giang, Soohwan Song, Sungho Jo

This study addresses the challenge of performing visual localization in demanding conditions such as night-time scenarios, adverse weather, and seasonal changes. While many prior studies have focused on improving image-matching performance to facilitate reliable dense keypoint matching between images, existing methods often heavily rely on predefined feature

Occorsio Donatella, Woula Themistoclakis

On the half line we introduce a new sequence of near--best uniform approximation polynomials, easily computable by the values of the approximated function at a truncated number of Laguerre zeros. Such approximation polynomials come from a discretization of filtered Fourier--Laguerre partial sums, which are filtered by using a de la Vall\'ee Poussin (VP) filt

Nick Gill, Pierre Guillot, Martin W. Liebeck

Let $\mathcal{C}$ be a conjugacy class of involutions in a group $G$. We study the graph $\Gamma(\mathcal{C})$ whose vertices are elements of $\mathcal{C}$ with $g,h\in\mathcal{C}$ connected by an edge if and only if $gh\in\mathcal{C}$. For $t\in \mathcal{C}$, we define the component group of $t$ to be the subgroup of $G$ generated by all vertices in $\Gamma

Victor P. Debattista, Tigran Khachaturyants, Joao A. S. Amarante, Christopher Carr

We study azimuthal variations in the mean stellar metallicity, <[Fe/H]>, in a self-consistent, isolated simulation in which all stars form out of gas. We find <[Fe/H]> variations comparable to those observed in the Milky Way and which are coincident with the spiral density waves. The azimuthal variations are present in young and old stars and therefore are n

E. A. Eronen, A. Vladyka, Ch. J. Sahle, J. Niskanen

Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose. We benchmark the performance of six different descriptor families using a computational data set of 24200 sulfur K$\beta$

Abinash Adhikari, Pawel Strak, Piotr Dluzewski, Agata Kaminska

Semiconductor superlattices have found widespread applications in electronic industries. In this work, short-period superlattice structure (SLs) composed of CdO and MgO layers was grown using the plasma-assisted molecular beam epitaxy (PA-MBE) technique. The optical property of the SLs was investigated by absorption measurement at room temperature. The ambie

Claudia Strauch, Anton Tiepner

We investigate pointwise estimation of the function-valued velocity field of a second-order linear SPDE. Based on multiple spatially localised measurements, we construct a weighted augmented MLE and study its convergence properties as the spatial resolution of the observations tends to zero and the number of measurements increases. By imposing H\"older smoot

Huan Liu, Jing Shen, Xianguo Geng

We focused on the Ablowitz--Ladik equation on a zero background, specifically considering the scenario of $N$ pairs of multiple poles. Our first goal was to establish a mapping between the initial data and the scattering data. This allowed us to introduce a direct problem by analyzing the discrete spectrum associated with $N$ pairs of higher-order zeros. Nex

Nurettin Turan, Benedikt Böck, Kai Jie Chan, Benedikt Fesl

In this work, we utilize a Gaussian mixture model (GMM) to capture the underlying probability density function (PDF) of the channel trajectories of moving mobile terminals (MTs) within the coverage area of a base station (BS) in an offline phase. We propose to leverage the same GMM for channel prediction in the online phase. Our proposed approach does not re

Antoine Médoc

We provide a refinement of Horn's conjecture by considering spectra with repetitions. To do this we adapt P. Belkale's techniques to our context, in the form proposed by N. Berline, M. Vergne and M. Walter.

J. Alvarez Ruiz, A. Rivera, K. Mima, D. Garoz

Dry-wall laser inertial fusion (LIF) chambers will have to withstand strong bursts of fast charged particles which will deposit tens of kJ m$^{-2}$ and implant more than 10$^{18}$ particles m$^{-2}$ in a few microseconds at a repetition rate of some Hz. Large chamber dimensions and resistant plasma-facing materials must be combined to guarantee the chamber p

Jonathan Fürst, Catherine Kosten, Farhad Nooralahzadeh, Yi Zhang

Text-to-SQL systems (also known as NL-to-SQL systems) have become an increasingly popular solution for bridging the gap between user capabilities and SQL-based data access. These systems translate user requests in natural language to valid SQL statements for a specific database. Recent Text-to-SQL systems have benefited from the rapid improvement of transfor

Sungguk Cha, Jusung Lee, Younghyun Lee, Cheoljong Yang

In recent years, synthetic visual instructions by generative language model have demonstrated plausible text generation performance on the visual question-answering tasks. However, challenges persist in the hallucination of generative language models, i.e., the generated image-text data contains unintended contents. This paper presents a novel and scalable m

ATLAS Collaboration

Statistical combinations of searches for charginos and neutralinos using various decay channels are performed using $139\,$fb$^{-1}$ of $pp$ collision data at $\sqrt{s}=13\,$TeV with the ATLAS detector at the Large Hadron Collider. Searches targeting pure-wino chargino pair production, pure-wino chargino-neutralino production, or higgsino production decaying

Dipayan Chakraborty, Florent Foucaud, Diptapriyo Majumdar, Prafullkumar Tale

We investigate fine-grained algorithmic aspects of identification problems in graphs and set systems, with a focus on Locating-Dominating Set and Test Cover. We prove the (tight) conditional lower bounds for these problems when parameterized by treewidth and solution as. Formally, \textsc{Locating-Dominating Set} (respectively, \textsc{Test Cover}) parameter

Ufuk Can Bicici, Tuna Han Salih Meral, Lale Akarun

Conditional computing processes an input using only part of the neural network's computational units. Learning to execute parts of a deep convolutional network by routing individual samples has several advantages: Reducing the computational burden is an obvious advantage. Furthermore, if similar classes are routed to the same path, that part of the network l

Tom Sander, Maxime Sylvestre, Alain Durmus

Training Deep Neural Networks (DNNs) with small batches using Stochastic Gradient Descent (SGD) yields superior test performance compared to larger batches. The specific noise structure inherent to SGD is known to be responsible for this implicit bias. DP-SGD, used to ensure differential privacy (DP) in DNNs' training, adds Gaussian noise to the clipped grad

Elie Saad, Mariem Besbes, Marc Zolghadri, Victor Czmil

Electrical component obsolescence poses a major issue especially within systems with large life cycles. Thus, finding the optimal management solution for each obsolescence case is as crucial as knowing what to consider when faced with an obsolescence case. In this paper, a novel hybrid approach for identifying features affecting electrical component obsolesc

Daniel Bath

We consider the Bernstein--Sato polynomial of a locally quasi-homogeneous polynomial $f \in R = \mathbb{C}[x_{1}, x_{2}, x_{3}]$. We construct, in the analytic category, a complex of $\mathscr{D}_{X}[s]$-modules that can be used to compute the $\mathscr{D}_{X}[s]$-dual of $\mathscr{D}_{X}[s] f^{s-1}$ as the middle term of a short exact sequence where the out

Airlie Hilliard, Cristian Munoz, Zekun Wu, Adriano Soares Koshiyama

Large Language Models (LLMs) are increasingly being utilized by both candidates and employers in the recruitment context. However, with this comes numerous ethical concerns, particularly related to the lack of transparency in these "black-box" models. Although previous studies have sought to increase the transparency of these models by investigating the pers

Kathrin Bringmann, William Craig, Jan-Willem van Ittersum, Badri Vishal Pandey

Recently, MacMahon's generalized sum-of-divisor functions were shown to link partitions, quasimodular forms, and q-multiple zeta values. In this paper, we explore many further properties and extensions of these. Firstly, we address a question of Ono by producing infinite families of MacMahon-like functions that approximate the colored partition functions (an

Ralf Stephan

We formally define the literature (reference) snowballing method and present a refined version of it. We show that the improved algorithm can substantially reduce curator work, even before application of text classification, by reducing the number of candidates to classify. We also present a desktop application named LitBall that implements this and other li