March 2024 arXiv papers — page 125

Jiace Sun, Lixue Cheng, Shi-Xin Zhang

Stabilizer states, which are also known as the Clifford states, have been commonly utilized in quantum information, quantum error correction, and quantum circuit simulation due to their simple mathematical structure. In this work, we apply stabilizer states to tackle quantum many-body ground state problems and introduce the concept of stabilizer ground state

Chun Liu, Suliang Si, Guanghui Hu, Bo Zhang

This paper is concerned with inverse source problems for the acoustic wave equation in the full space R^3, where the source term is compactly supported in both time and spatial variables. The main goal is to investigate increasing stability for the wave equation in terms of the interval length of given parameters (e.g., bandwith of the temporal component of

Ching-En Chiu, Arieh Levy Pinto, Rasheda A Chowdhury, Kim Christensen

The ability to accurately infer cardiac electrophysiological (EP) properties is key to improving arrhythmia diagnosis and treatment. In this work, we developed a physics-informed neural networks (PINNs) framework to predict how different myocardial EP parameters are modulated by anti-arrhythmic drugs. Using $\textit{in vitro}$ optical mapping images and the

Tobias Hille, Maximilian Stubbemann, Tom Hanika

Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible

Souvik Sadhukhan, Manoj Kumar Nandi, Satyam Pandey, Matteo Paoluzzi

As wounds heal, embryos develop, cancer spreads, or asthma progresses, the cellular monolayer undergoes glass transition between solid-like jammed and fluid-like flowing states. During some of these processes, the cells undergo an epithelial-to-mesenchymal transition (EMT): they acquire in-plane polarity and become motile. Thus, how motility drives the glass

Tuomas Varanka, Tapani Toivonen, Soumya Tripathy, Guoying Zhao

Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with respect to the identity of the person as the models lack necessary context. In this paper, we explore the potential of personalized face restoration with diffusion models. In our

Jiangtao Shi, Fanjie Xu, Mengjiao Shan

Iwasawa's theorem indicates that a finite group $G$ is supersolvable if and only if all maximal chains of the identity in $G$ have the same length. As generalizations of Iwasawa's theorem, we provide some characterizations of the structure of a finite group $G$ in which all maximal chains of every minimal subgroup have the same length. Moreover, let $\delta(

Antonino Ficarra, Emanuele Sgroi

The $\text{v}$-function of a graded filtration $\mathcal{I}=\{I_{[k]}\}_{k\ge0}$ is introduced. Under the assumption that $\mathcal{I}$ is Noetherian, we prove that the $\text{v}$-function $\text{v}(I_{[k]})$ is an eventually quasi-linear function. This result applies to several situations, including ordinary powers, and integral closures of ordinary powers,

Chenghao Yu, Dengyu Zhang, Qingrui Zhang

It is promising but challenging to design flocking control for a robot swarm to autonomously follow changing patterns or shapes in a optimal distributed manner. The optimal flocking control with dynamic pattern formation is, therefore, investigated in this paper. A predictive flocking control algorithm is proposed based on a Gibbs random field (GRF), where b

Yuxin Tian, Mouxing Yang, Yunfan Li, Dayiheng Liu

Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important factors for various PEFTs, namely, the accessible data size and fine-tunable parameter size. A natural expectation for PEFTs is that the performance of various PEFTs is positively rel

M. Magri, D. Riccobelli

This study addresses the modelling of elastic bodies, particularly when the relaxed configuration is unknown or non-existent. We adopt the theory of initially stressed materials, incorporating the deformation gradient and stress state of the reference configuration (initial stress tensor) into the response function. We show that for the theory to be applicab

Roberta Tripodi, Francesco D'Eugenio, Roberto Maiolino, Mirko Curti

We present the first statistical investigation of spatially resolved emission-line properties in a sample of 63 low-mass galaxies at $4\leq z<10$, using JWST/NIRSpec MSA data from the JWST Advanced Deep Extragalactic (JADES) survey focusing on deep, spatially resolved spectroscopy in the GOODS-S extragalactic field. By performing a stacking of the 2D spectra

Vali Tawosi, Salwa Alamir, Xiaomo Liu

One of the ways Large Language Models (LLMs) are used to perform machine learning tasks is to provide them with a few examples before asking them to produce a prediction. This is a meta-learning process known as few-shot learning. In this paper, we use available Search-Based methods to optimise the number and combination of examples that can improve an LLM's

Rasmus Ingemann Tuffveson Jensen, Vali Tawosi, Salwa Alamir

While code review is central to the software development process, it can be tedious and expensive to carry out. In this paper, we investigate whether and how Large Language Models (LLMs) can aid with code reviews. Our investigation focuses on two tasks that we argue are fundamental to good reviews: (i) flagging code with security vulnerabilities and (ii) per

Florian Eilers, Xiaoyi Jiang

Deep Neural Networks are widely used in academy as well as corporate and public applications, including safety critical applications such as health care and autonomous driving. The ability to explain their output is critical for safety reasons as well as acceptance among applicants. A multitude of methods have been proposed to explain real-valued neural netw

B. A. Nizamov, M. S. Pshirkov

The origin of the diffuse gamma-ray background in the range from hundreds keV to several MeV is not known conclusively. From current models and observations it is believed that, at least partially, this background is formed by blazars and remnants of supernovae (SN) of type Ia in distant galaxies. However, these contributions are not sufficient to reproduce

Zicheng Zhang, Tong Zhang, Yi Zhu, Jianzhuang Liu

The pre-trained vision-language model, exemplified by CLIP, advances zero-shot semantic segmentation by aligning visual features with class embeddings through a transformer decoder to generate semantic masks. Despite its effectiveness, prevailing methods within this paradigm encounter challenges, including overfitting on seen classes and small fragmentation

Benjamin Roth, Pedro Henrique Luz de Araujo, Yuxi Xia, Saskia Kaltenbrunner

Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Consequently, regulatory bodies struggle with containing this technology's potential negative side effects. High-level requirements such as fairness and robustness need to be formalize

Zeguan Xiao, Yan Yang, Guanhua Chen, Yun Chen

Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values. However, even meticulously aligned LLMs remain vulnerable to malicious manipulations such as jailbreaking, leading to unintended behaviors. In this work, we propose a novel black-box jailbreak framework for automated red team

Zein-Eddine Meziani

The gravitational form factors (GFFs) are a fundamental and elegant way to describe the structure of nucleons and nuclei. Their Fourier transform allows a description of the spatial distribution of the mass, angular momentum, pressure, and shear force densities for both quarks and gluons in the nucleon. While previous investigations predominantly focused on

Dominic Shea, Alessandro Romito

We study the effect of local unitary noise on the entanglement evolution of a two-qubit system subject to local monitoring and inter-qubit coupling. We construct a stochastic Hamiltonian by incorporating the noise into the Chantasri-Dressel-Jordan path integral and use it to identify the optimal entanglement dynamics and to develop a diagrammatic method for

Juan Carlos Gonçalves-Dosantos, Ricardo Martínez, Joaquín Sánchez-Soriano

Digital streaming platforms, including Twitch, Spotify, Netflix, Disney, and Kindle, have emerged as one of the main sources of entertainment with significant growth potential. Many of these platforms distribute royalties among streamers, artists, producers, or writers based on their impact. In this paper, we measure the relevance of each of these contributo

Zhicheng Wang, Wensheng Liang, Ruiyan Zhuang, Shuai Li

Action recognition (AR) in industrial environments -- particularly for identifying actions and operational gestures -- faces persistent challenges due to high deployment costs, poor cross-scenario generalization, and limited real-time performance. To address these issues, we propose a low-cost real-time framework for industrial action recognition using found

EFthymios N. Karatzas

We consider fully discrete finite element approximations for a semilinear optimal control system of partial differential equations in two cases: for distributed and Robin boundary control. The ecological predator-prey optimal control model is approximated by conforming finite element methods mimicking the spatial part, while a discontinuous Galerkin method i

Dimple Saini, Harsh Trivedi, Shankar Veerabathiran

Isometric covariant representations play an important role in the study of Cuntz-Pimsner algebras. In this article, we study partial isometric covariant representations and explore under what conditions powers and roots of partial isometric covariant representations are also partial isometric covariant representations.

Lao-Tzu Allan-Blitz, Sithira Ambepitiya, Raghavendra Tirupathi, Jeffrey D. Klausner

Machine-learning algorithms can facilitate low-cost, user-guided visual diagnostic platforms for addressing disparities in access to sexual health services. We developed a clinical image dataset using original and augmented images for five penile diseases: herpes eruption, syphilitic chancres, penile candidiasis, penile cancer, and genital warts. We used a U

Daniel Pérez-Cruz, Grigori E. Astrakharchik, Pietro Massignan

The superfluid fraction $f$ of a quantum fluid is defined in terms of the response of the system to a weak and constant drag. Notably, Leggett long ago derived two simple expressions providing a rigorous upper bound and a heuristic lower bound for $f$. Here we study the superfluid fraction of bosonic gases in various two-dimensional potentials, such as regul

Simin Bao

In 2005, Liu et al. calculated the dimensionality of the intersection of Sierpinski carpet and a straight line with rational slope in the sense of Lebesgue measure.Sierpinski carpet is a self-similar set in two-dimensional planes obtained by an iterative function system, so each layer has the same structure. While the Sierpinski carpet set with Moran structu

Shan Zhao, Ioannis Prapas, Ilektra Karasante, Zhitong Xiong

Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from data. However, to inform critical decision making, we argue that we need models that are right for the right r

Joanna E. Sobczyk

We present a coupled-cluster calculation for the electron-$^4$He scattering in the region of the quasi-elastic peak. We show the longitudinal and transverse responses separately, and discuss results within two distinct theoretical methods: the Lorentz integral transform and spectral functions. The comparison between them allows to investigate the role of fin

Wolfgang Körner, Daniel F. Urban, Christian Elsässer

We present a theoretical study on the magnetic signals of structural surface defects like cracks or indents combined with rough surfaces or subsurface inclusions of soft ferromagnetic metals like body-centered cubic Fe or amorphous CoFeB. We discuss limits of early detection of small surface defects on the basis of calculated magnetic stray fields few tens o

Eyyup Tasci, Ezgi Ozyilkan, Oguzhan Kubilay Ulger, Elza Erkip

We consider lossy compression of an information source when decoder-only side information may be absent. This setup, also referred to as the Heegard-Berger or Kaspi problem, is a special case of robust distributed source coding. Building upon previous works on neural network-based distributed compressors developed for the decoder-only side information (Wyner

C. Uma Maheswari, N. Muthuchamy, V. K. Chandrasekar, R. Sahadevan

We consider a modified damped version of H\'enon-Heiles system and investigate its integrability. By extending the Painlev\'e analysis of ordinary differential equations we find that the modified H\'enon-Heiles system possesses the Painlev\'e property for three distinct parametric restrictions. For each of the identified cases, we construct two independent i

Sergey Belan

Poisson restart assumes that a stochastic process is interrupted and starts again at random time moments. A number of studies have demonstrated that this strategy may minimize the expected completion time in some classes of random search tasks. What is more, it turned out that under optimally tuned restart rate, any stochastic process, regardless of its natu

Mohammad Lashkari, Amin Gheibi

The generalization performance of deep neural networks in classification tasks is a major concern in machine learning research. Despite widespread techniques used to diminish the over-fitting issue such as data augmentation, pseudo-labeling, regularization, and ensemble learning, this performance still needs to be enhanced with other approaches. In recent ye

Shuhan Li, Yi Lin, Hao Chen, Kwang-Ting Cheng

Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and inherent imbalanced distributions, due to difficulties in data collection and variations in disease prevalence across different types. In this paper, we introduce an Iterative Onli

Masaya Nakagawa, Masahito Ueda

A general framework for analyzing the topology of quantum channels of single-particle systems is developed to find a class of genuinely dynamical topological phases that can be realized by means of discrete quantum feedback control. We provide a symmetry classification of quantum channels by identifying ten symmetry classes of discrete quantum feedback contr

C. Pinçon, L. Petitdemange, R. Raynaud, L. J. Garcia

Modeling the surface brightness distribution of stars is of prime importance to interpret observations. Nevertheless, this remains quite challenging for cool stars as it requires one to model the MHD turbulence that develops in their convective envelope. In Paper I, the effect of the Coriolis acceleration on the surface heat flux has been studied by means of

Józef Kuśba

Previously obtained expressions describing the intensity of stationary fluorescence emitted by a multicomponent solution were significantly improved by using matrix calculus. Then, using a similar technique, new expressions describing the decay of the fluorescence intensity of the multicomponent system after pulsed excitation were found. In both of these cas

Lukas Siedentop, Gianluc Lui, Georg Maret, Paul M. Chaikin

In photonic crystals the propagation of light is governed by their photonic band structure, an ensemble of propagating states grouped into bands, separated by photonic band gaps. Due to discrete symmetries in spatially strictly periodic dielectric structures their photonic band structure is intrinsically anisotropic. However, for many applications, such as m

Mohammad Rahman, Manzur Murshed, Shyh Wei Teng, Manoranjan Paul

Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational complexities with high dimensional data. To address this challenge, we introduce a Deep Learning (DL)-based feature selec

Marius Landry Foka, Romain Pefoukeu Nimpa, Salomon Joseph Mbatakou, Michel Bertrand Ngaha Djiadeu

In this paper, using the Milnor-type theorem technique, we provide on each nilpotent five dimensional Lie group, some global existence result of a pair (g, c) consisting of a left-invariant Riemannian metric g and a positive constant c such that Ric(g) =cT, where Ric(g) is the Ricci curvature of g and T a given left-invariant symmetric (0, 2)-tensor field.

Karla Z. Arellano-Córdova, Danielle A. Berg, Matilde Mingozzi, Bethan L. James

To study the chemical evolution across cosmic epochs, we investigate Ne, S, Cl, and Ar abundance patterns in the COS Legacy Archive Spectroscopic SurveY (CLASSY). CLASSY comprises local star-forming galaxies (0.02 < z < 0.18) with enhanced star-formation rates, making them strong analogues to high-z star-forming galaxies. With direct measurements of electron

Alexander Kamenshchik, Polina Petriakova

We apply a very simple procedure to construct non-singular cosmological models for flat Friedmann universes filled with minimally coupled scalar fields or by tachyon Born-Infeld-type fields. Remarkably, for the minimally coupled scalar field and the tachyon field, the regularity of the cosmological evolution, or in other words, the existence of bounce, impli

Abdul Malik Sami, Zeeshan Rasheed, Kai-Kristian Kemell, Muhammad Waseem

Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduc

inghao Cao, Subhan Khan, Wanchun Liu, Yonghui Li

In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise. A discussion of the quasi-static Rayleigh fading channel is presented herein t

Rania Abdelghani, Edith Law, Chloé Desvaux, Pierre-Yves Oudeyer

Curiosity-driven learning has shown significant positive effects on students' learning experiences and outcomes. But despite this importance, reports show that children lack this skill, especially in formal educational settings. To address this challenge, we propose an 8-session workshop that aims to enhance children's curiosity through training a set of spe

Bruno Pereira Cipriano, Pedro Alves, Paul Denny

Much research has highlighted the impressive capabilities of large language models (LLMs), like GPT and Bard, for solving introductory programming exercises. Recent work has shown that LLMs can effectively solve a range of more complex object-oriented programming (OOP) exercises with text-based specifications. This raises concerns about academic integrity, a

Danbi Kim, Jiho Park, Changhoon Baek, Sun Kyung Lee and

We report a systematic approach for establishing a complementary relationship between the interference visibility, concurrence, and predictability in the crossing of induced and stimulated coherences of two-mode squeezed coherent states. This is achieved using a double-path interferometer involving two independent four-wave mixing (FWM) atomic samples genera

Amir Abboud, Nathan Wallheimer

A recent paper by Abboud and Wallheimer [ITCS 2023] presents self-reductions for various fundamental graph problems, which transform worst-case instances to expanders, thus proving that the complexity remains unchanged if the input is assumed to be an expander. An interesting corollary of their self-reductions is that if some problem admits such reduction, t

Riccardo Aragona, Giuseppe Nozzi

Let $\mathbb{F}_{p^k}$ be a finite field of odd characteristic $p$. In this paper we give a classification, up to isomorphism, of the associative commutative $\mathbb{F}_{p^k}$-algebras, starting from the connection with their bi-brace structure. Such classification is the generalization in odd characteristic of the result proved by Civino at al. in characte

Michael Phillips, Giuseppe Tronci, Christopher M. Pask, Stephen J. Russell

Implantable hydrogels should ideally possess mechanical properties matched to the surrounding tissues to enable adequate mechanical function while regeneration occurs. This can be challenging, especially when degradable systems with high water content and hydrolysable chemical bonds are required in anatomical sites under constant mechanical stimulation, e.g.

JooYoung Lee, Emily Booth, Hany Farid, Marian-Andrei Rizoiu

What if misinformation is not an information problem at all? To understand the role of news publishers in potentially unintentionally propagating misinformation, we examine how far-right and fringe online groups share and leverage established legacy news media articles to advance their narratives. Our findings suggest that online fringe ideologies spread thr

Pavel V. Kolesnichenko, Lukas Wittenbecher, Qianhui Zhang, Run Yan Teh

Two-dimensional semiconducting transition metal dichalcogenides (TMDs) are promising for optoelectronic applications due to their strongly bound excitons. While bright excitons have been thoroughly scrutinized, dark excitons are much less investigated as they are not observable with far-field spectroscopy. However, with their non-zero momenta, dark excitons

Garry Goldstein

In this work we extend the notion of what is meant by a meanfield. Meanfields are approximately maps - through some self consistency relation - of a complex, usually manybody, problem to a simpler more readily solvable problem. This mapping can then be solved to represent properties of the complex many body problem using some self consistency relations. Prot

A. Krieger, M. Kuffmeier, S. Reissl, C. P. Dullemond

Observations performed with high-resolution imaging techniques revealed the existence of shadows in circumstellar disks that can be explained by the misalignment of an inner with respect to an outer disk. The cause of misalignment, however, is still debated. In this study, we investigate the feasibility of observing shadows induced by one prominent scenario

Bahareh Azad, Jose Luis Blázquez-Salcedo, Fech Scen Khoo, Jutta Kunz

We consider slowly rotating Ellis-Bronnikov wormholes and investigate their radial perturbations ($\mathrm{l}=0$), expanding up to second order in rotation. We present the detailed derivations in the general case, including symmetric and non-symmetric wormholes. The calculations show that the unstable mode present in the static case becomes less unstable wit

Andrew Fuchs, Andrea Passarella, Marco Conti

We anticipate increased instances of humans and AI systems working together in what we refer to as a hybrid team. The increase in collaboration is expected as AI systems gain proficiency and their adoption becomes more widespread. However, their behavior is not error-free, making hybrid teams a very suitable solution. As such, we consider methods for improvi

Dario A. Leon, Cana Elgvin, Phuong Dan Nguyen, Øystein Prytz

We present a detailed study of the dielectric response of ZnO using a combination of low-loss momentum-resolved electron energy-loss spectroscopy (EELS) and first-principles calculations at several levels of theory, from the independent particle and the random phase approximation with different variants of density functional theory (DFT), including hybrid an

Gustaw Opiełka, Hannes Rosenbusch, Veerle Vijverberg, Claire E. Stevenson

The Abstraction Reasoning Corpus (ARC) is a visual analogical reasoning test designed for humans and machines (Chollet, 2019). We compared human and large language model (LLM) performance on a new child-friendly set of ARC items. Results show that both children and adults outperform most LLMs on these tasks. Error analysis revealed a similar "fallback" solut

M Rakesh Reddy, Shubham Mandloi, Aman Kumar

Moire pattern frequently appears in photographs captured with mobile devices and digital cameras, potentially degrading image quality. Despite recent advancements in computer vision, image demoire'ing remains a challenging task due to the dynamic textures and variations in colour, shape, and frequency of moire patterns. Most existing methods struggle to gene

Can Liu, Jin Wang, and Yipeng Zhou, Yachao Yuan

Federated learning (FL) empowers privacypreservation in model training by only exposing users' model gradients. Yet, FL users are susceptible to gradient inversion attacks (GIAs) which can reconstruct ground-truth training data such as images based on model gradients. However, reconstructing high-resolution images by existing GIAs faces two challenges: infer

Qiang Luo, Jize Zhao, Jinbin Li, Xiaoqun Wang

The higher-spin Kitaev magnets, in which the Kitaev interaction and off-diagonal exchange couplings are overwhelmingly large, have emerged as a fertile avenue to explore exotic phases and unusual excitations. In this work, we study the quantum phase diagram of the spin-1 Kitaev-$\Gamma$ model on the honeycomb lattice using density-matrix renormalization grou

Pengze Zhang, Hubery Yin, Chen Li, Xiaohua Xie

Most diffusion models assume that the reverse process adheres to a Gaussian distribution. However, this approximation has not been rigorously validated, especially at singularities, where t=0 and t=1. Improperly dealing with such singularities leads to an average brightness issue in applications, and limits the generation of images with extreme brightness or

Haoqing Li, Jinfu Yang, Yifei Xu, Runshi Wang

Infrared Small Target Detection (IRSTD) aims to segment small targets from infrared clutter background. Existing methods mainly focus on discriminative approaches, i.e., a pixel-level front-background binary segmentation. Since infrared small targets are small and low signal-to-clutter ratio, empirical risk has few disturbances when a certain false alarm and

Andre Maeder, Frederic Courbin

We study the path of light rays passing near a massive object, in the context of the scale invariant equation of the geodesics first obtained by Dirac (1973). Using the exterior Schwarzschild solution for the metric, we derive the complete equations of the geodesics in the scale invariant context. We find that scale invariance introduces two additional terms

Lu Jiang, Qi Wang, Yuhang Chang, Jianing Song

Category imbalance is one of the most popular and important issues in the domain of classification. Emotion classification model trained on imbalanced datasets easily leads to unreliable prediction. The traditional machine learning method tends to favor the majority class, which leads to the lack of minority class information in the model. Moreover, most exi

Fangqi Zhu, Yongqi Zhang, Lei Chen, Bing Qin

Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown adverse effects resulting from DDIs becomes a growing concern. Traditional computational methods for DDI prediction may fail to capture int

Eleni D. Koronaki, Luise F. Kaven, Johannes M. M. Faust, Ioannis G. Kevrekidis

Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in-line concentration monitoring. Recent approaches and some theoretical considerations show a correlation between Raman signals and particle sizes but do not determine polymer si

Ran Zmigrod, Salwa Alamir, Xiaomo Liu

Migrations of systems from on-site premises to the cloud has been a fundamental endeavor by many industrial institutions. A crucial component of such cloud migrations is the transition of databases to be hosted online. In this work, we consider the difficulties of this migration for SQL databases. While SQL is one of the prominent methods for storing databas

Pierre Civit, Muhammad Ayaz Dzulfikar, Seth Gilbert, Rachid Guerraoui

Byzantine agreement enables n processes to agree on a common L-bit value, despite up to t > 0 arbitrary failures. A long line of work has been dedicated to improving the bit complexity of Byzantine agreement in synchrony. This has culminated in COOL, an error-free (deterministically secure against a computationally unbounded adversary) solution that achieves

L. Wojciechowski, K. J. Kubiak, S. Boncel, A. Marek

Frictional losses are responsible for significant energy waste in many practical applications, and superlubricity with a coefficient of friction lower than 0.01 is the goal of tribologists. In this paper, metal-on-polymer contact was analysed and close to superlubricity conditions for this material configuration were explored. A new lubricant has been propos

Kohei Nishi

Recently, there has been significant attention on online political incivility. While previous research suggests that uncivil political comments lead people to be less willing to see more comments on the same issue, two critical questions have received limited exploration: (1) Are people exposed to uncivil political comments less willing to see other comments

Vu Nguyen Ha, Duy H. N. Nguyen, Juan C. -M. Duncan, Jorge L. Gonzalez-Rios

This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT) beamforming (BF) technique. Regarding serving users at their target SINRs and minimizing the total transmit power, the scheme aims to effic

H S V N S Kowndinya Renduchintala, Sumit Bhatia, Ganesh Ramakrishnan

Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Studies have shown the importance of balancing different task proportions during finetuning, but finding the right balance remains challenging. Unfortunately, there's currently no sys

Soumya Bera, Ishita Modak, Roderich Moessner

How a closed system thermalizes, especially in the absence of global conservation laws but in the presence of disorder and interactions, is one of the central questions in non-equilibrium statistical mechanics. We explore this for a disordered, periodically driven Ising chain. Our numerical results reveal inhomogeneous thermalization leading to a distributio

L. Papa, P. Russo, I. Amerini

Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth estimation have gained significant interest in recent years, owing to the potential benefits of this methodology in overcoming the limitations of active depth sensing systems. More

Laurent Berger

Fresnel and de Mathan proved that the p-adic Fourier transform is surjective. We reinterpret their result in terms of analytic boundaries, and extend it beyond the cyclotomic case. We also give some applications of their result to Schneider and Teitelbaum's p-adic Fourier theory, in particular to generalized Mahler expansions and to the geometry of the chara

Guillermo Serrera, Javier González-Colsa, Pablo Albella

The interaction between chiral molecules and circularly polarized light is largely influenced by the local optical chirality density. This interaction prompts substantial demand of the design of nanophotonic platforms capable of enhancing such effects across large and accessible volumes. Such a magnification requires nanostructures that provide strong electr

Xinyi Chen, Yichen Zhang, Boyu Zhou, Shaojie Shen

Various perception-aware planning approaches have attempted to enhance the state estimation accuracy during maneuvers, while the feature matchability among frames, a crucial factor influencing estimation accuracy, has often been overlooked. In this paper, we present APACE, an Agile and Perception-Aware trajeCtory gEneration framework for quadrotors aggressiv

Zhuoxin Chen, Zhenyu Wu, Yang Ji

Federated learning is designed to enhance data security and privacy, but faces challenges when dealing with heterogeneous data in long-tailed and non-IID distributions. This paper explores an overlooked scenario where tail classes are sparsely distributed over a few clients, causing the models trained with these classes to have a lower probability of being s

Karthikeya Puttur Venkatraj, Wo Meijer, Monica Perusquía-Hernández, Gijs Huisman

Virtual co-embodiment enables two users to share a single avatar in Virtual Reality (VR). During such experiences, the illusion of shared motion control can break during joint-action activities, highlighting the need for position-aware feedback mechanisms. Drawing on the perceptual crossing paradigm, we explore how haptics can enable non-verbal coordination

Marcus Häggbom, Morten Karlsmark, Joakim Andén

Microcanonical gradient descent is a sampling procedure for energy-based models allowing for efficient sampling of distributions in high dimension. It works by transporting samples from a high-entropy distribution, such as Gaussian white noise, to a low-energy region using gradient descent. We put this model in the framework of normalizing flows, showing how

Xiaofeng Shang, Abdusalam Abdukerim, Zihao Bo, Wei Chen

We report the first search for the elastic scatterings between cosmic-ray boosted sub-MeV dark matter and electrons in the PandaX-4T liquid xenon experiment. Sub-MeV dark matter particles can be accelerated by scattering with electrons in the cosmic rays and produce detectable electron recoil signals in the detector. Using the commissioning data from PandaX-

Luyuan Peng, Hari Vishnu, Mandar Chitre, Yuen Min Too

We investigate the performance of image-based pose regressor models in underwater environments for relocalization. Leveraging PoseNet and PoseLSTM, we regress a 6-degree-of-freedom pose from single RGB images with high accuracy. Additionally, we explore data augmentation with stereo camera images to improve model accuracy. Experimental results demonstrate th

Katarzyna Skowronek, Tomasz Barszcz, Jerome Antoni, Radosław Zimroz

Any measurement in condition monitoring applications is associated with disturbing noise. Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the noise. This paper shares a novel perspective to the problem of local damage detection. The acquired vector of observations is considered as an additive mixture of signal of intere

Rares Dolga, Ran Zmigrod, Rui Silva, Salwa Alamir

Log analysis and monitoring are essential aspects in software maintenance and identifying defects. In particular, the temporal nature and vast size of log data leads to an interesting and important research question: How can logs be summarised and monitored over time? While this has been a fundamental topic of research in the software engineering community,

Yang Zhang, Xianggong Bo, Jimeng Jing, Lixia Wang

Due to the high magnetic coupling strength between the Cr elements, the bulk phase CrO2 is one of several ferromagnetic oxides known to have the highest Curie temperature. When the dimensionality of the material is reduced from 3D to 2D, the 2D CrO2 system material is expected to maintain a high Curie temperature. In this work, we predict two new phases of C

Farukh Abdulkhamidov, Petya Nedkova, Javlon Rayibaev, Jutta Kunz

{\it Hunting} compact astrophysical objects such as black holes and wormholes, as well as testing gravity theories, are important issues in relativistic astrophysics. In this sense, theoretical and observational studies of quasiperiodic oscillations (QPOs) observed in (micro)quasars become helpful in exploring their central object, which can be a black hole

Ran Xu, Yan Shen, Xiaoqi Li, Ruihai Wu

Enabling home-assistant robots to perceive and manipulate a diverse range of 3D objects based on human language instructions is a pivotal challenge. Prior research has predominantly focused on simplistic and task-oriented instructions, i.e., "Slide the top drawer open". However, many real-world tasks demand intricate multi-step reasoning, and without human i

Jesse Campion Loth, Amarpreet Rattan

A factorisation problem in the symmetric group is central if conjugate permutations always have the same number of factorisations. We give the first fully combinatorial proof of the centrality of transitive star factorisations that is valid in all genera, which answers a natural question of Goulden and Jackson from 2009. We begin by showing that the set of s

Johannes Fiedler, Bodil Holst

Atom and, of late, molecule interferometers find application in both the crucible of fundamental research and industrial pursuits. A prevalent methodology in the construction of atom interferometers involves the utilisation of gratings fashioned from laser beams. While this approach imparts commendable precision, it is hampered by its incapacity to attain ex

Alhassan Mumuni, Fuseini Mumuni

Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the application of appropriate data transformation operations to create new data samples with desired properties. Despite its effectiveness, the process is often challenging because of

Jiangdong Ai, Fankang He, Yihang Liu

We call a partition of a $c$-partite tournament into tournaments of order $c$ is strong if each tournament is strongly connected. The strong partition number denoted as $ST(r)$, represents the minimum integer $c'$ such that every regular $r$-balanced $c$-partite tournament has a strong partition with $c\geq c'$. Figueroa, Montellano-Ballesteros and Olsen sho

Cheng Chen, Junchen Zhu, Xu Luo, Hengtao Shen

Instruction tuning represents a prevalent strategy employed by Multimodal Large Language Models (MLLMs) to align with human instructions and adapt to new tasks. Nevertheless, MLLMs encounter the challenge of adapting to users' evolving knowledge and demands. Therefore, how to retain existing skills while acquiring new knowledge needs to be investigated. In t

WonTae Hwang, Ei Thu Thu Kyaw

We provide a construction of the induced subgraphs of the zero-divisor graph of $M_2(R)$ for the ring $R$ of algebraic integers of some number fields that are neither complete nor connected, and study the structure of the induced subgraphs explicitly. As an application, we prove that the automorphism group of the zero-divisor graph of $M_2(R)$ is not a Jorda

Tianxiang Dai, Anqi Ma, Jun Mao, Yutian Ao

Controlling topological phases of light has allowed experimental observations of abundant topological phenomena and development of robust photonic devices. The prospect of more sophisticated controls with topological photonic devices for practical implementations requires high-level programmability. Here, we demonstrate a fully programmable topological photo

Weijun Zeng, Ilari Lilja, Ekaterina Mukhanova, Elica Heredia

Cobalt disilicide provides a promising nearly-epitaxial superconducting material on silicon, which is compatible with high-density integrated circuit technology. We have characterized CoSi$_{2}$ superconducting microwave cavities around 5.5 GHz for resonance frequency fluctuations at temperatures 10 - 200 mK. We found relatively weak fluctuations $(\delta f/

Annika Schiemann, Paul Manns

We introduce discretizations of infinite-dimensional optimization problems with total variation regularization and integrality constraints on the optimization variables. We advance the discretization of the dual formulation of the total variation term with Raviart--Thomas functions which is known from literature for certain convex problems. Since we have an

Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel

The conventional process of building Ontologies and Knowledge Graphs (KGs) heavily relies on human domain experts to define entities and relationship types, establish hierarchies, maintain relevance to the domain, fill the ABox (or populate with instances), and ensure data quality (including amongst others accuracy and completeness). On the other hand, Large