February 2024 arXiv papers — page 106

Jan Wójcik

The behaviour of random quantum walks is known to be diffusive. Here we study discrete time quantum walks in weak stochastic gauge fields. In the case of position and spin dependent gauge field, we observe a transition from ballistic to diffusive motion, with the probability distribution becoming Gaussian. However, in contradiction to common belief, weak sto

Lukas Struppek, Minh Hieu Le, Dominik Hintersdorf, Kristian Kersting

The proliferation of large language models (LLMs) has sparked widespread and general interest due to their strong language generation capabilities, offering great potential for both industry and research. While previous research delved into the security and privacy issues of LLMs, the extent to which these models can exhibit adversarial behavior remains larg

Arsenii Sagdeev

We prove that among $n$ points in the plane in general position, the shortest distance occurs at most $43n/18$ times, improving upon the upper bound of $17n/7$ obtained by T\'oth in 1997.

Michał Malinowski

The enormous development of the Internet, both in the geographical scale and in the area of using its possibilities in everyday life, determines the creation and collection of huge amounts of data. Due to the scale, it is not possible to analyse them using traditional methods, therefore it makes a necessary to use modern methods and techniques. Such methods

Michael J. Curry, Zhou Fan, David C. Parkes

The role of a market maker is to simultaneously offer to buy and sell quantities of goods, often a financial asset such as a share, at specified prices. An automated market maker (AMM) is a mechanism that offers to trade according to some predetermined schedule; the best choice of this schedule depends on the market maker's goals. The literature on the desig

Ethan P. Shapera, Dejan-Krešimir Bučar, Rohit P. Prasankumar, Christoph Heil

We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to describe a crystal by more than an order of magnitude compared to the full crystal graph representation. We construct machin

Benny Avelin, Tuomo Kuusi, Patrik Nummi, Eero Saksman

We study unique solvability for one dimensional stochastic pressure equation with diffusion coefficient given by the Wick exponential of log-correlated Gaussian fields. We prove well-posedness for Dirichlet, Neumann and periodic boundary data, and the initial value problem, covering the cases of both the Wick renormalization of the diffusion and of point-wis

Nadav Schneider, Niranjan Hasabnis, Vy A. Vo, Tal Kadosh

The imperative need to scale computation across numerous nodes highlights the significance of efficient parallel computing, particularly in the realm of Message Passing Interface (MPI) integration. The challenging parallel programming task of generating MPI-based parallel programs has remained unexplored. This study first investigates the performance of stat

Richard S. J. Tol

A new version of the database for the meta-analysis of estimates of the social cost of carbon is presented. New records were added, and new fields on gender and stochasticity.

Lutz Oettershagen, Honglian Wang, Aristides Gionis

We consider a variant of the densest subgraph problem in networks with single or multiple edge attributes. For example, in a social network, the edge attributes may describe the type of relationship between users, such as friends, family, or acquaintances, or different types of communication. For conceptual simplicity, we view the attributes as edge colors.

Safak Alpay, Svetlana Gorokhova

We study (almost) limited operators in Banach lattices and their relations to L-weakly compact, semi-compact, and Dunford-Pettis operators. Several further related topics are investigated.

James Odgers, Ruby Sedgwick, Chrysoula Kappatou, Ruth Misener

This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Process Latent Variable Models (GPLVMs) for the case where each data point comprises the weighted sum of a known number of pure component signals, observed across several input locati

Tim Leys, Guillermo A. Perez

Compartmental models are used in epidemiology to capture the evolution of infectious diseases such as COVID-19 in a population by assigning members of it to compartments with labels such as susceptible, infected, and recovered. In a stochastic compartmental model the flow of individuals between compartments is determined probabilistically. We establish that

Henk Wymeersch, Sharief Saleh, Ahmad Nimr, Rreze Halili

As 6G emerges, cellular systems are envisioned to integrate sensing with communication capabilities, leading to multi-faceted communication and sensing (JCAS). This paper presents a comprehensive cross-layer overview of the Hexa-X-II project's endeavors in JCAS, aligning 6G use cases with service requirements and pinpointing distinct scenarios that bridge co

Mario Fuest, Johannes Lankeit

In bounded, spatially two-dimensional domains, the system \begin{equation*} \left\lbrace\begin{alignedat}{3} u_t &= d_1 \Delta u && &&+ u(\lambda_1 - \mu_1 u - a_1 v - a_2 w), \\ v_t &= d_2 \Delta v &&- \xi \nabla \cdot (v \nabla u) &&+ v(\lambda_2 - \mu_2 v + b_1 u - a_3 w),\\ w_t &= d_3 \Delta w &&- \chi \nabla \cdot (w \nabla (uv)) &&+ w(\lambda_3 - \mu_3

Yajun Zhao

In recent years, RIS has made significant progress in engineering application research and industrialization and academic research. However, the engineering application research field of RIS still faces several challenges. This article analyzes and discusses the two deployment modes of RIs-assisted wireless networks: Network Controlled Mode and Standalone mo

Attila Losonczi

We are going to widen the scope of the previously defined Hausdorff-integral in two ways. First, in the sense, that we develop the theory of the integral on some naturally generalized measure spaces. Second, we extend it to functions taking values in $[0,+\infty)\times[0,+\infty)$. In all our intentions, we follow the same attitude that we had in our previou

Pau Colomer, Christian Deppe, Holger Boche, Andreas Winter

Following initial work by JaJa, Ahlswede and Cai, and inspired by a recent renewed surge in interest in deterministic identification (DI) via noisy channels, we consider the problem in its generality for memoryless channels with finite output, but arbitrary input alphabets. Such a channel is essentially given by its output distributions as a subset in the pr

Pau Colomer, Christian Deppe, Holger Boche, Andreas Winter

Motivated by deterministic identification via classical channels, where the encoder is not allowed to use randomization, we revisit the problem of identification via quantum channels but now with the additional restriction that the message encoding must use pure quantum states, rather than general mixed states. Together with the previously considered distinc

Chen Griner, Chen Avin

The state-of-the-art topologies of datacenter networks are fixed, based on electrical switching technology, and by now, we understand their throughput and cost well. For the past years, researchers have been developing novel optical switching technologies that enable the emergence of reconfigurable datacenter networks (RDCNs) that support dynamic psychical t

Maksim Stebliy, Alex S. Jenkins, Luana Benetti, Elvira Paz

Magnetic tunnel junctions are nanoscale devices which have recently attracted interested in the context of frequency multiplexed spintronic neural networks, due to their interesting dynamical properties, which are defined during the fabrication process, and depend on the material parameters and geometry. This paper proposes an approach to extending the funct

Reabetswe M. Nkhumise, Debabrota Basu, Tony J. Prescott, Aditya Gilra

The rising successes of RL are propelled by combining smart algorithmic strategies and deep architectures to optimize the distribution of returns and visitations over the state-action space. A quantitative framework to compare the learning processes of these eclectic RL algorithms is currently absent but desired in practice. We address this gap by representi

Jakub Rybak, Heather Battey, Karthik Bharath

That parametrization and sparsity are inherently linked raises the possibility that relevant models, not obviously sparse in their natural formulation, exhibit a population-level sparsity after reparametrization. In covariance models, positive-definiteness enforces additional constraints on how sparsity can legitimately manifest. It is therefore natural to c

Jiajie Mei, Yuyu Mo

We propose an algorithm to recursively bootstrap $n$-point gluon and graviton Mellin-Momentum amplitudes in (A)dS spacetime using only three-point amplitude. We discover that gluon amplitudes are simply determined by factorization for $n\geq 5$. The same principle applies to $n$-point graviton amplitudes, but additional constraints such as flat space and sof

Andrey Kudlis, Ivan A. Aleksandrov, Mikhail M. Glazov, Ivan A. Shelykh

We theoretically investigate a nonlinear optical response of a planar microcavity with an embedded transition metal dicalcogenide monolayer when the energy of a biexcitonic transition is brought in resonance with the energy of a cavity mode. We demonstrate that the emission spectrum of this system strongly depends on an external pump. For small and moderate

Zihang Song, Prabodh Katti, Osvaldo Simeone, Bipin Rajendran

Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency. Yet, the implementation of the attention mechanism using spiking signals on general-purpose computing platforms remains inefficient. In this paper, we propose a novel framework lev

Yifan Zhou, Xinlin Zhou, Zi Yan Li, Yew Kee Wong

With the advent of Web 3.0, the swift advancement of technology confronts an imminent threat from quantum computing. Security protocols safeguarding the integrity of Web 2.0 and Web 3.0 are growing more susceptible to both quantum attacks and sophisticated classical threats. The article introduces our novel long-distance free-space quantum secure direct comm

Fatemeh Ghorbani Lohesara, Davi Rabbouni Freitas, Christine Guillemot, Karen Eguiazarian

The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this

Jorge Vicente Martinez, Edgar Ramirez-Laboreo, Pablo Calderon Gil

Microfluidics, the study of fluids in microscopic channels, has led to important advances in fields as diverse as microelectronics, biotechnology and chemistry. Microfluidic research is primarily based on the use of microfluidic chips, low-cost devices that can be used to perform laboratory experiments using small amounts of fluid. These systems, however, re

Kazuhiro Hikami

We propose a generalization of the double affine Hecke algebra of type-C C1 at specific parameters by introducing a ``Heegaard dual'' of the Hecke operators. Shown is a relationship with the skein algebra on double torus. We give automorphisms of the algebra associated with the Dehn twists on the double torus.

Xiaoyuan Zhang, Xi Lin, Qingfu Zhang

It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives and multi-objective reinforcement learning, to find a Pareto solution that can match a given preference of a decision maker. These problems are often large-scale with available gradient information but cannot be handled very well by

Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar Popovski

Mega-constellations of small satellites have evolved into a source of massive amount of valuable data. To manage this data efficiently, on-board federated learning (FL) enables satellites to train a machine learning (ML) model collaboratively without having to share the raw data. This paper introduces a scheme for scheduling on-board FL for constellations co

Gonzalo Usaj

Arrays of microcavity polaritons are very versatile systems that allow for broad possibilities for the engineering of multi-orbital lattice geometries using different state preparation schemes. One of these schemes, spatially modulated resonant driving, can be used to selectively localize the polariton field on a particular region of the lattice. Both the fr

Zhilei Zhang, Linan Zhong

Let $PU_n$ denote the projective unitary group of rank $n$, and let $BPU_n$ be its classifying space. We extend our previous results to a description of $H^s(BPU_n;\mathbb{Z})_{(p)}$ for $s<2p+9$ by showing that $p$-primary subgroups of $H^s(BPU_n;\mathbb{Z})$ is $\mathbb{Z}/p$ for $s=2p+5$ and are trivial for $s = 2p+7$ and $s = 2p+8$, where $p$ is an odd p

Derendarz Dominik, Rafal Staszewski, Maciej Trzebinski, Patrycja Potępa

IFJ PAN PPSS Alumni Conference is organized by the Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN). It is addressed to: participants of previous editions of Particle Physics Summer Student Programme, attendees of current PPSS edition and students interested in cooperation with IFJ PAN. Second IFJ PAN Particle Physics Summer Student Alumni C

Shiqi Yang, Hanlin Qin, Shuai Yuan, Xiang Yan

CycleGAN has been proven to be an advanced approach for unsupervised image restoration. This framework consists of two generators: a denoising one for inference and an auxiliary one for modeling noise to fulfill cycle-consistency constraints. However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to

Fatemeh Ghorbani Lohesara, Karen Egiazarian, Sebastian Knorr

Facial video inpainting plays a crucial role in a wide range of applications, including but not limited to the removal of obstructions in video conferencing and telemedicine, enhancement of facial expression analysis, privacy protection, integration of graphical overlays, and virtual makeup. This domain presents serious challenges due to the intricate nature

Xiongye Xiao, Heng Ping, Chenyu Zhou, Defu Cao

In recent years, there has been increasing attention on the capabilities of large models, particularly in handling complex tasks that small-scale models are unable to perform. Notably, large language models (LLMs) have demonstrated ``intelligent'' abilities such as complex reasoning and abstract language comprehension, reflecting cognitive-like behaviors. Ho

Xuzhi Yang, Tengyao Wang

Composite quantile regression has been used to obtain robust estimators of regression coefficients in linear models with good statistical efficiency. By revealing an intrinsic link between the composite quantile regression loss function and the Wasserstein distance from the residuals to the set of quantiles, we establish a generalization of the composite qua

Mohamed AbdElSalam, Loai Ali, Saddek Bensalem, Weicheng He

In this paper, we present a novel digital twin prototype for a learning-enabled self-driving vehicle. The primary objective of this digital twin is to perform traffic sign recognition and lane keeping. The digital twin architecture relies on co-simulation and uses the Functional Mock-up Interface and SystemC Transaction Level Modeling standards. The digital

Alon Herman, Gideon Segev

In recent years there has been significant progress in the development of artificial ion pumping membranes. Ion pumps based on asymmetric nano-pores have been shown to operate as ionic current rectifiers, thus pumping a net ion flux against a concentration gradient even when driven with unbiased ac signals. However, since ion transport relies on charged nano

Yousef Alsenani, Rahul Mishra, Khaled R. Ahmed, Atta Ur Rahman

In recent years, federated learning (FL) has emerged as a promising technique for training machine learning models in a decentralized manner while also preserving data privacy. The non-independent and identically distributed (non-i.i.d.) nature of client data, coupled with constraints on client or edge devices, presents significant challenges in FL. Furtherm

Zexu Wang, Jiachi Chen, Zibin Zheng, Peilin Zheng

Reentrancy is one of the most notorious vulnerabilities in smart contracts, resulting in significant digital asset losses. However, many previous works indicate that current Reentrancy detection tools suffer from high false positive rates. Even worse, recent years have witnessed the emergence of new Reentrancy attack patterns fueled by intricate and diverse

K. -H. Müller, E. E. Mitchell

In this study, we investigated the performance of two 2D superconducting quantum interference filter (SQIF) arrays fabricated from YBCO thin films at a temperature of 77 K. Each array consisted of 6 Josephson junctions (JJs) in parallel and 167 in series. We conducted both experimental and theoretical analyses, measuring the arrays' voltage responses to an a

Vladimír Kunc, Jiří Kléma

Neural networks have proven to be a highly effective tool for solving complex problems in many areas of life. Recently, their importance and practical usability have further been reinforced with the advent of deep learning. One of the important conditions for the success of neural networks is the choice of an appropriate activation function introducing non-l

Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang

With the development of LLMs, the security threats of LLMs are getting more and more attention. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs. Current jailbreak attacks primarily utilize scenario camouflage techniques. However their explicitly mention of malicious intent will be easily recognized and defended by LLMs. I

Fernando E. Rosas, Bernhard C. Geiger, Andrea I Luppi, Anil K. Seth

Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels; however, while these approaches are successful in identifying when emergence takes place, they are limited

Marie-Luise Steinmeyer, Anders Johansen

The gaseous envelope of an accreting rocky planet becomes hot enough to sublimate silicates and other refractory minerals. For this work, we studied the effect of the resulting envelope enrichment with a heavy vapor species on the composition and temperature of the envelope. For simplification, we used the gas-phase molecule SiO to represent the sublimation

KM3NeT Collaboration

KM3NeT/ARCA is a Cherenkov neutrino telescope under construction in the Mediterranean sea, optimised for the detection of astrophysical neutrinos with energies above $\sim$1~TeV. In this work, using Monte Carlo simulations including all-flavour neutrinos, the integrated and differential sensitivities for KM3NeT/ARCA are presented considering the case of a di

Florian Freitag, Linus Halder, Simon Himmelbauer, Christoph Hochrainer

The Vienna Architecture Description Language (VADL) is a powerful processor description language (PDL) that enables the concise formal specification of processor architectures. By utilizing a single VADL processor specification, the VADL system exhibits the capability to automatically generate a range of artifacts necessary for rapid design space exploration

Guilherme W. F. Barros, Jenny Häggström

In medical and epidemiological studies, one of the most common settings is studying the effect of a treatment on a time-to-event outcome, where the time-to-event might be censored before end of study. A common parameter of interest in such a setting is the marginal hazard ratio (MHR). When a study is based on observational data, propensity score (PS) based m

Oliver Broadrick, Honghua Zhang, Guy Van den Broeck

Probabilistic circuits compute multilinear polynomials that represent multivariate probability distributions. They are tractable models that support efficient marginal inference. However, various polynomial semantics have been considered in the literature (e.g., network polynomials, likelihood polynomials, generating functions, and Fourier transforms). The r

Namkyeong Cho, Junseung Ryu, Hyung Ju Hwang

This study investigates the impact of Sobolev Training on operator learning frameworks for improving model performance. Our research reveals that integrating derivative information into the loss function enhances the training process, and we propose a novel framework to approximate derivatives on irregular meshes in operator learning. Our findings are suppor

Jean-François Burnol

We consider the series of reciprocals of those positive integers with exactly $k$ occurrences of a given $b$-ary digit $d$ (Irwin series), and obtain geometrically convergent representations for their sums. They are expressed in terms of the moments and Stieltjes transforms of some measures on the unit interval. The moments obey linear recurrences allowing s

Ana Lavalle, Alejandro Maté, Juan Trujillo, Stefano Rizzi

Information visualization plays a key role in business intelligence analytics. With ever larger amounts of data that need to be interpreted, using the right visualizations is crucial in order to understand the underlying patterns and results obtained by analysis algorithms. Despite its importance, defining the right visualization is still a challenging task.

Dan Garber, Atara Kaplan

We consider several classes of highly important semidefinite optimization problems that involve both a convex objective function (smooth or nonsmooth) and additional linear or nonlinear smooth and convex constraints, which are ubiquitous in statistics, machine learning, combinatorial optimization, and other domains. We focus on high-dimensional and plausible

Marzena A. Kubiak, Maciej Bzowski, Eberhard Moebius, Nathan A. Schwadron

Direct-sampling observations of interstellar neutral gas, including hydrogen and deuterium, have been performed for more than one cycle of solar activity by IBEX. IBEX viewing is restricted to directions perpendicular to the spacecraft--Sun line, which limits the observations to several months each year. This restriction is removed in a forthcoming mission I

Wei Dai, Yafei Li, Zhao Liu

In this paper, we mainly consider nonnegative weak solution to the $D^{1,p}(\R^{N})$-critical quasi-linear static Schr\"{o}dinger-Hartree equation with $p$-Laplacian $-\Delta_{p}$ and nonlocal nonlinearity: \begin{align*} -\Delta_p u =\left(|x|^{-2p}\ast |u|^{p}\right)|u|^{p-2}u \qquad &\mbox{in} \,\, \mathbb{R}^N, \end{align*} where $1<p<\frac{N}{2}$, $N\ge

Niccolò Turcato, Alberto Sinigaglia, Alberto Dalla Libera, Ruggero Carli

Continuous control Deep Reinforcement Learning (RL) approaches are known to suffer from estimation biases, leading to suboptimal policies. This paper introduces innovative methods in RL, focusing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning. We design a Bias Exploiting (BE)

Huizhi Zhu, Wenxia Xu, Jian Huang, Jiaxin Li

In this paper, we propose a graph neural network, DisGNet, for learning the graph distance matrix to address the forward kinematics problem of the Gough-Stewart platform. DisGNet employs the k-FWL algorithm for message-passing, providing high expressiveness with a small parameter count, making it suitable for practical deployment. Additionally, we introduce

Marco Mancastroppa, Alessandro Vezzani, Vittoria Colizza, Raffaella Burioni

Human behaviour strongly influences the spread of infectious diseases: understanding the interplay between epidemic dynamics and adaptive behaviours is essential to improve response strategies to epidemics, with the goal of containing the epidemic while preserving a sufficient level of operativeness in the population. Through activity-driven temporal network

Liyao Wang, Zishun Zheng, Yuan Lin

The selection of a reward function in Reinforcement Learning (RL) has garnered significant attention because of its impact on system performance. Issues of significant steady-state errors often manifest when quadratic reward functions are employed. Although absolute-value-type reward functions alleviate this problem, they tend to induce substantial fluctuati

Daigo Oue, J. B. Pendry, Mário G. Silveirinha

We investigate the frictional force arising from quantum fluctuations when two dissipative metallic plates are set in a shear motion. While early studies showed that the electromagnetic fields in the quantum friction setup reach nonequilibrium steady states, yielding a time-independent force, other works have demonstrated the failure to attain steady states,

Susumu Ito, Nariya Uchida

Collective motion provides a spectacular example of self-organization in Nature. Visual information plays a crucial role among various types of information in determining interactions. Recently, experiments have revealed that organisms such as fish and insects selectively utilize a portion, rather than the entirety, of visual information. Here, focusing on f

Mohammed Bouallala, Franck Dufrenois, khalide jbilou, Ahmed Ratnani

In this paper, we propose an extension of trace ratio based Manifold learning methods to deal with multidimensional data sets. Based on recent progress on the tensor-tensor product, we present a generalization of the trace ratio criterion by using the properties of the t-product. This will conduct us to introduce some new concepts such as Laplacian tensor an

David Torpey, Richard Klein

The standard approach to modern self-supervised learning is to generate random views through data augmentations and minimise a loss computed from the representations of these views. This inherently encourages invariance to the transformations that comprise the data augmentation function. In this work, we show that adding a module to constrain the representat

Juheon Lee, Byung Gyu Chae, Hyuneui Kim, MinSung Yoon

Dielectric tensor tomography is an imaging technique for mapping three-dimensional distributions of dielectric properties in transparent materials. This work introduces an enhanced illumination strategy employing a micro-electromechanical system mirror to achieve high precision and reduced noise in imaging. This illumination approach allows for precise manip

Anders Irbäck, Lucas Knuthson, Sandipan Mohanty, Carsten Peterson

Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups: hydrophobic (H) and polar (P). Using a set o

J. C. Rivera Hernández, Fabio Lingua, Shan W. Jolin, David B. Haviland

Control over the coupling between multiple modes of a frequency comb is an important step toward measurement-based quantum computation with a continuous-variable system. We demonstrate the creation of square-ladder correlation graphs in a microwave comb with 95 modes. The graphs are engineered through precise control of the relative phase of three pumps appl

R. Gratton, M. Bonavita, D. Mesa, A. Zurlo

Dynamical masses of young planets aged between 10 and 200 Myr detected in imaging play a crucial role in shaping models of giant planet formation. Regrettably, only a few such objects possess these characteristics. Furthermore, the evolutionary pattern of young sub-stellar companions in near-infrared colour-magnitude diagrams might diverge from free-floating

Emiel Dieussaert, Roel Baets, Hilde Jans, Xavier Rottenberg

Photoacoustic imaging has emerged as a powerful, non-invasive modality for various biomedical applications. Conventional photoacoustic systems require contact-based ultrasound detection and expensive and bulky high-power lasers for the excitation. The use of contact-based detectors involves the risk of contamination, which is undesirable for most biomedical

Piero Fraternali, Luca Morandini, Sergio Luis Herrera González

The detection and characterization of illegal solid waste disposal sites are essential for environmental protection, particularly for mitigating pollution and health hazards. Improperly managed landfills contaminate soil and groundwater via rainwater infiltration, posing threats to both animals and humans. Traditional landfill identification approaches, such

A. Basu, P. Weltevrede, M. J. Keith, S. Johnston

A number of pulsars are known to have profile evolution on timescales of months, often correlated with spin-down rate changes. Here, we present the first result from 3 years of monitoring observations from MeerKAT as part of the Thousand Pulsar Array programme. This programme obtains high-fidelity pulse profiles for $\sim$ 500 pulsars, which enabled the dete

Hibiki Gima, Toshiki Matsusaka, Taichi Miyazaki, Shunta Yara

Recently, Matsuhira, Matsusaka, and Tsuchida revisited old studies on the integrality of $k$-G\"{o}bel sequences and showed that the first 19 terms are always integers for any integer $k\ge 2$. In this article, we further explore two topics: Ibstedt's $(k,l)$-G\"{o}bel sequences and Zagier's asymptotic formula for the $2$-G\"{o}bel sequence, and extend their

Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel

Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be directly transferred to closed-source models. However, this approach neglects the steady progression of open-source models. As open-source models advance in capability, ensuring their safety also becomes increasingly imperati

Hannes Mareen, Lucas Antchougov, Glenn Van Wallendael, Peter Lambert

Digital watermarking enables protection against copyright infringement of images. Although existing methods embed watermarks imperceptibly and demonstrate robustness against attacks, they typically lack resilience against geometric transformations. Therefore, this paper proposes a new watermarking method that is robust against geometric attacks. The proposed

Valentina G. Klochkova, Vladimir E. Panchuk, Nonna S. Tavolzhanskaya

High resolution optical spectra (R = 60 000) of the LBV star P Cyg beyond outburst were obtained on the 6-meter BTA telescope in the wavelength range 477-780 nm. We perform a detailed identification of different types lines (photospheric absorptions, permitted and forbidden emissions, components of lines with P Cyg type profiles), and studied the variability

Anna Ivleva, Rhea-Silvia Remus, Lucas M. Valenzuela, Klaus Dolag

Recent observations of galaxy mergers inside galaxy cluster environments report high star formation rates in the ejected tidal tails, which point towards currently developing tidal dwarf galaxies. We test whether these dwarf objects could get stripped from the galaxy potential by the galaxy cluster and thus populate it with dwarf galaxies. To this end, we pe

Prajwal Panzade, Daniel Takabi, Zhipeng Cai

In today's machine learning landscape, fine-tuning pretrained transformer models has emerged as an essential technique, particularly in scenarios where access to task-aligned training data is limited. However, challenges surface when data sharing encounters obstacles due to stringent privacy regulations or user apprehension regarding personal information dis

Shilpa Bijavara Seshashayana, Henrik Jönsson, Valentina D'Orazi, Govind Nandakumar

The age, evolution, and chemical properties of the Galactic disk can be effectively ascertained using open clusters. Within the large program Stellar Populations Astrophysics at the Telescopio Nazionale Galileo, we specifically focused on stars in open clusters, to investigate various astrophysical topics, from the chemical content of very young systems to t

Brett C. Hannigan, Tyler J. Cuthbert, Chakaveh Ahmadizadeh, Carlo Menon

Textile sensors transform our everyday clothing into a means to track movement and bio-signals in a completely unobtrusive way. One major hindrance to the adoption of "smart" clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics:

Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier

Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both aleatoric and epistemic uncertainty, but alternative approaches, such as evidential deep learning methods, have become popular in recent years. The latter group of methods in essence

Victor L. Chernyak

Considered is the standard 4-dimensional ${\cal N}=1\,\,SU(N_c)$ SQCD with $1\leq N_F\leq N_c$ quark flavors with masses $m_{Q,i}$. {\bf The gauge invariant order parameter $\rho$ is introduced in \cite{ch1} distinguishing confinement (with $\rho=0$) and higgs (with $\rho\neq 0$) phases}. Using a number of independent arguments for different variants of tran

Yang Liu, Tongfei Shen, Dong Zhang, Qingying Sun

The growing importance of multi-modal humor detection within affective computing correlates with the expanding influence of short-form video sharing on social media platforms. In this paper, we propose a novel two-branch hierarchical model for short-form video humor detection (SVHD), named Comment-aided Video-Language Alignment (CVLA) via data-augmented mult

Duván Cardona, Michael Ruzhansky

Let $T$ be a Fourier integral operator of order $-(n-1)/2$ associated with a canonical relation locally parametrised by a real-phase function. A fundamental result due to Seeger, Sogge, and Stein proved in the 90's, gives the boundedness of $T$ from the Hardy space $H^1$ into $L^1.$ Additionally, it was shown by T. Tao the weak (1,1) type of $T$. In this wor

Sayan Goswami

The notions of CR set is intimately related with the generalized van der Waerden's theorem. In this article, we prove the product of two CR sets is again a CR set. This answers [Question 4.2., N. Hindman, H. Hosseini, D. Strauss, and M. Tootkaboni: Combinatorially rich sets in arbitrary semigroups, Semigroup Forum, 107 (2023), 127-143.] . We use combinatoria

Yutaro Yamada, Khyathi Chandu, Yuchen Lin, Jack Hessel

Diffusion-based image generation models such as DALL-E 3 and Stable Diffusion-XL demonstrate remarkable capabilities in generating images with realistic and unique compositions. Yet, these models are not robust in precisely reasoning about physical and spatial configurations of objects, especially when instructed with unconventional, thereby out-of-distribut

Jia Zou, Xiaokai Zhang, Yiming He, Na Zhu

The human-like automatic deductive reasoning has always been one of the most challenging open problems in the interdiscipline of mathematics and artificial intelligence. This paper is the third in a series of our works. We built a neural-symbolic system, called FGeoDRL, to automatically perform human-like geometric deductive reasoning. The neural part is an

Keitaro Sakamoto, Issei Sato

End-to-end (E2E) training, optimizing the entire model through error backpropagation, fundamentally supports the advancements of deep learning. Despite its high performance, E2E training faces the problems of memory consumption, parallel computing, and discrepancy with the functionalities of the actual brain. Various alternative methods have been proposed to

Jia-Xing Zhang, Wei Chen

The phonon induced anomalous Hall or thermal Hall effects have been observed in various systems in recent experiments. However, the theoretical studies on this subject are very scarce and incomplete. In this work, we present a systematic quantum field theory study on the phonon induced anomalous Hall effect, including both the side jump and skew scattering c

Kai Wu, Jacopo Pegoraro, Francesca Meneghello, J. Andrew Zhang

Integrated Sensing and Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchron

Yiming He, Jia Zou, Xiaokai Zhang, Na Zhu

The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proof has always been a grand challenge to the interdiscipline field of mathematics and artificial Intelligence. This is the fourth article in a series of our works, in our previous work, we established of a geometric formalized system kno

Hiroyuki Kido

Inspired by Bayesian approaches to brain function in neuroscience, we give a simple theory of probabilistic inference for a unified account of reasoning and learning. We simply model how data cause symbolic knowledge in terms of its satisfiability in formal logic. The underlying idea is that reasoning is a process of deriving symbolic knowledge from data via

Marko Milivojević, Martin Gmitra, Marcin Kurpas, Ivan Štich

We analyze the spin-orbit coupling effects in a three-degree twisted bilayer heterostructure made of graphene and an in-plane ferroelectric SnTe, with the goal of transferring the spin-orbit coupling from SnTe to graphene, via the proximity effect. Our results indicate that the point-symmetry breaking due to the incompatible mutual symmetry of the twisted mo

Soham Acharya, Sudipta Sarkar

The black hole no-short hair theorem establishes a universal lower bound on the extension of hairs outside any 4-dimensional spherically symmetric black hole solutions. We generalise this theorem beyond spherical symmetry, specifically for static, axisymmetric hairy black hole solutions and prove that the ``hairosphere'' must extend beyond the radial extent

Augustin Godinot, Gilles Tredan, Erwan Le Merrer, Camilla Penzo

Auditors need robust methods to assess the compliance of web platforms with the law. However, since they hardly ever have access to the algorithm, implementation, or training data used by a platform, the problem is harder than a simple metric estimation. Within the recent framework of manipulation-proof auditing, we study in this paper the feasibility of rob

Carmen Delgado, María Canales, Jorge Ortín, José Ramón Gállego

We focus on the problem of managing a shared physical wireless sensor network where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of Application Admission Control (AAC), that is, whether to admit the

Dimitrios G. Konstantinides, Charalampos D. Passalidis

This paper is organized in three parts closely related to closure properties of heavy-tailed distributions and heavy-tailed random vectors. In the first part we consider two random variables X and Y with distributions F and G respectively. We assume that these random variables satisfy one type of a weak dependence structure. Under some mild conditions, we ex

Dimitrios G. Konstantinides, Charalampos D. Passalidis

We consider a new approach in the definition of two-dimensional heavy-tailed distributions. Namely, we introduce the classes of two-dimensional long-tailed, of twodimensional dominatedly varying and of two-dimensional consistently varying distributions. Next, we define the closure property with respect to two-dimensional convolution and to joint max-sum equi

Mario Gauvrit, Paul Laurain

We prove stability results of the Morse index plus nullity of Yang-Mills connections in dimension 4 under weak convergence. Precisely we establish that the sum of the Morse indices and the nullity of a bounded sequence of Yang-Mills connections is asymptotically bounded above by the sum of the Morse index and the nullity of the weak limit and the bubbles whi