March 2024 arXiv papers — page 75

Tsuyoshi Yamamoto, Leonid I. Glazman, Manuel Houzet

At zero temperature, a Josephson junction coupled to an ohmic environment displays a quantum phase transition between superconducting and insulating phases, depending whether the resistance of the environment is below or above the resistance quantum. At finite temperature, this so-called Schmid transition turns into a crossover. We determine the conditions u

Hangeol Chang, Jinho Chang, Jong Chul Ye

Despite recent advancements in text-to-image diffusion models facilitating various image editing techniques, complex text prompts often lead to an oversight of some requests due to a bottleneck in processing text information. To tackle this challenge, we present Ground-A-Score, a simple yet powerful model-agnostic image editing method by incorporating ground

H. Zhang, B. Duan, H. Wang, Z. Qiao

This paper proposes a social regulation model for dynamic adaptation according to user characteristics in virtual interactive environments, namely the tribal theater model. The model focuses on organizational regulation and builds an interaction scheme with more resilient user performance by improving the subjectivity of the user. This paper discusses the so

Dongfen Bian, Emmanuel Grenier

In this article, thanks to a new and detailed study of the Green's function of Rayleigh equation near the extrema of the velocity of a shear layer, we obtain optimal bounds on the asymptotic behaviour of solutions to the linearized incompressible Euler equations both in the whole plane, the half plane and the periodic case, and improve the description of the

Jiwoo Chung, Sangeek Hyun, Sang-Heon Shim, Jae-Pil Heo

StyleGAN has shown remarkable performance in unconditional image generation. However, its high computational cost poses a significant challenge for practical applications. Although recent efforts have been made to compress StyleGAN while preserving its performance, existing compressed models still lag behind the original model, particularly in terms of sampl

Artur Grigorev, Khaled Saleh, Yuming Ou, Adriana-Simona Mihaita

This research showcases the innovative integration of Large Language Models into machine learning workflows for traffic incident management, focusing on the classification of incident severity using accident reports. By leveraging features generated by modern language models alongside conventional data extracted from incident reports, our research demonstrat

Masashi Aiki

We consider a nonlinear model equation, known as the Localized Induction Equation, describing the motion of a vortex filament immersed in an incompressible and inviscid fluid. We show stability estimates for an arc-shaped vortex filament, which is an exact solution to an initial-boundary value problem for the Localized Induction Equation. An arc-shaped filam

Konstantinos Alexis, Stella Girtsou, Alexis Apostolakis, Giorgos Giannopoulos

In this paper we present a deep learning pipeline for next day fire prediction. The next day fire prediction task consists in learning models that receive as input the available information for an area up until a certain day, in order to predict the occurrence of fire for the next day. Starting from our previous problem formulation as a binary classification

Gustavo H. A. Pereira, Jianwen Cai

Regression models for compositional data are common in several areas of knowledge. As in other classes of regression models, it is desirable to perform diagnostic analysis in these models using residuals that are approximately standard normally distributed. However, for regression models for compositional data, there has not been any multivariate residual th

Himanshu Murari, Swati Shaw, Subhradip Ghosh

Surface and strain engineering are among the cheaper ways to modulate structure property relations in materials. Due to their compositional flexibilities, MXenes, the family of two-dimensional materials, provide enough opportunity for surface engineering. In this work, we have explored the possibility of improving thermoelectric efficiency of MXenes through

Jeongjin Shin

Deep learning models have achieved unprecedented performance in the domain of object detection, resulting in breakthroughs in areas such as autonomous driving and security. However, deep learning models are vulnerable to backdoor attacks. These attacks prompt models to behave similarly to standard models without a trigger; however, they act maliciously upon

Sergey N. Filippov, Sabrina Maniscalco, Guillermo García-Pérez

Error mitigation has elevated quantum computing to the scale of hundreds of qubits and tens of layers; however, yet larger scales (deeper circuits) are needed to fully exploit the potential of quantum computing to solve practical problems otherwise intractable. Here we demonstrate three key results that pave the way for the leap from quantum utility to quant

Julian Kaduk, Müge Cavdan, Knut Drewing, Heiko Hamann

In robotics, understanding human interaction with autonomous systems is crucial for enhancing collaborative technologies. We focus on human-swarm interaction (HSI), exploring how differently sized groups of active robots affect operators' cognitive and perceptual reactions over different durations. We analyze the impact of different numbers of active robots

Agnieszka Pregowska, Agata Roszkiewicz, Magdalena Osial, Michael Giersig

We focus on the potential possibilities for supporting Scanning Probe Microscopy measurements, emphasizing the application of Artificial Intelligence, especially Machine Learning as well as quantum computing. It turned out that Artificial Intelligence can be helpful in the experimental processes automation in routine operations, the algorithmic search for go

Ulrike Bücking, Daniel Matthes

The Bj\"orling problem amounts to the construction of a minimal surface from a real-analytic curve with a given real-analytic normal vector field. We approximate that solution locally by discrete minimal surfaces as special discrete isothermic surfaces (as defined by Bobenko and Pinkall in 1996). The main step in our construction is the approximation of the

Simone Ciani, Eurica Henriques, Igor i. Skrypnik

In this work we prove that the non-negative functions $u \in L^s_{loc}(\Omega)$, for some $s>0$, belonging to the De Giorgi classes \begin{equation}\label{eq0.1} \fint\limits_{B_{r(1-\sigma)}(x_{0})} \big|\nabla \big(u-k\big)_{-}\big|^{p}\, dx \leqslant \frac{c}{\sigma^{q}} \,\Lambda\big(x_{0}, r, k\big)\bigg(\frac{k}{r}\bigg)^{p}\bigg(\frac{\big|B_{r}(x_{0}

Soyeon Kim, Jihyeon Seong, Hyunkyung Han, Jaesik Choi

Capsule Neural Networks utilize capsules, which bind neurons into a single vector and learn position equivariant features, which makes them more robust than original Convolutional Neural Networks. CapsNets employ an affine transformation matrix and dynamic routing with coupling coefficients to learn robustly. In this paper, we investigate the effectiveness o

Pedro Sancho

We analyze the electronic correlation contribution to the time delay in the photo-ionization of the excited ortho- and para-Helium states. A simple estimation, based on the ionization probability amplitudes, shows that the different form of anti-symmetrising both states can in principle lead to very different values of the correlation time delay. This result

Paloma García-de-Herreros, Vagrant Gautam, Philipp Slusallek, Dietrich Klakow

ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning, i.e., applying pre-trained transformer models to modalities beyond their training data. The technique consists primarily of training an embedder and fine-tuning the embedder and model. Despite its high performance on a variety of downstream tasks, we do not understand precisely how e

Joshua Ebere Chukwuere

Although there are obstacles related to obtaining data, ensuring model precision, and upholding ethical standards, the advantages of utilizing machine learning to generate predictive models for unemployment rates in developing nations amid the implementation of Industry 4.0 (I4.0) are noteworthy. This research delves into the concept of utilizing machine lea

Siying Cui, Jia Guo, Xiang An, Jiankang Deng

Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts. However, existing personalization methods face challenges, including test-time fine-tuning, the requirement of multiple input images, low pres

Emmanouil G. Kakouris, Manolis N. Chatzis, Savvas P. Triantafyllou

A novel Material Point Method (MPM) is introduced for addressing frictional contact problems. In contrast to the standard multi-velocity field approach, this method employs a penalty method to evaluate contact forces at the discretised boundaries of their respective physical domains. This enhances simulation fidelity by accurately considering the deformabili

Sai Teja Somu, Duc Van Khanh Tran

Practical numbers are positive integers $n$ such that every positive integer less than or equal to $n$ can be written as a sum of distinct positive divisors of $n$. In this paper, we show that all positive integers can be written as a sum of a practical number and a triangular number, resolving a conjecture by Sun. We also show that all sufficiently large na

Fu-Lai Wang, Si-Qiang Luo, Xiang Liu

In order to unravel the composition of the isoscalar $DN$, $D^*N$, $D_1N$, and $D_2^*N$ molecular pentaquarks, we carry out a systematic investigation of their M1 and E1 radiative decays and magnetic moment properties. Using the constituent quark model and taking into account the $S$-$D$ wave mixing effect and the coupled channel effect, our analysis yields

Ethan Akin, Morton Davis

With $P_t$ the price in current dollars of a dollar delivered $t$ time units from now, we assume that $P$ is a decreasing function defined for $t \in \mathbb{R}_+$ with $P_0 = 1$. The negative logarithmic derivative, $- \stackrel{\bullet}{P}_t/P_t$ defines the yield curve function $Y_t$. An $n$ parameter linear yield curve model selects as allowable yield cu

S. W. Schmid, L. Pósa, T. N. Török, B. Sánta

Beyond-Moore computing technologies are expected to provide a sustainable alternative to the von Neumann approach not only due to their down-scaling potential but also via exploiting device-level functional complexity at the lowest possible energy consumption. The dynamics of the Mott transition in correlated electron oxides, such as vanadium dioxide, has be

Nilavjyoti Hazarika, Paramita Deka, Kalpana Bora

In this work, we delve to investigate a feasible range of dark matter (DM) masses within a non-supersymmetric $SO(10)$ Grand Unified Theory (GUT) scalar dark matter model, in freeze-out scenario. This model includes a singlet scalar denoted as S and an inert doublet represented by $\phi$. Being part of SO(10), the quantum numbers of DM particles are assigned

Guillermo Aparicio-Estrems, Abel Gargallo-Peiró, Xevi Roca

We define a regularized size-shape distortion (quality) measure for curved high-order elements on a Riemannian space. To this end, we measure the deviation of a given element, straight-sided or curved, from the stretching, alignment, and sizing determined by a target metric. The defined distortion (quality) is suitable to check the validity and the quality o

Stefan Hoffelner

We show that given a reflecting cardinal, one can produce a model of $\mathsf{BPFA}$ where the $\Sigma^1_n$-uniformization property holds simultaneously for all $n \ge 2$.

A. Levy Yeyati, D. Subero, J. Pekola, R. Sánchez

Motivated by recent experiments (Subero et. al. Nature Comm. $\bf{14}$, 7924 (2023)) we analyze photonic heat transport through a Josephson junction in a dissipative environment. For this purpose we derive general expressions for the heat current in terms of non-equilibrium Green functions for the junction coupled in series or in parallel with two environmen

Chenghao Shen, Haiying Shan

The $f$ adjacency matrix is a type of edge-weighted adjacency matrix, whose weight of an edge $ij$ is $f(d_i,d_j)$, where $f$ is a real symmetric function and $d_i,d_j$ are the degrees of vertex $i$ and vertex $j$. The $f$-spectral radius of a graph is the spectral radius of its $f$-adjacency matrix. In this paper, the effect of subdividing an edge on $f$-sp

Bowen Zhang, Tianyu Yang, Yu Li, Lei Zhang

3D generation has witnessed significant advancements, yet efficiently producing high-quality 3D assets from a single image remains challenging. In this paper, we present a triplane autoencoder, which encodes 3D models into a compact triplane latent space to effectively compress both the 3D geometry and texture information. Within the autoencoder framework, w

Fabio De Gaspari, Dorjan Hitaj, Luigi V. Mancini

The unprecedented availability of training data fueled the rapid development of powerful neural networks in recent years. However, the need for such large amounts of data leads to potential threats such as poisoning attacks: adversarial manipulations of the training data aimed at compromising the learned model to achieve a given adversarial goal. This paper

Run He, Di Fang, Yizhu Chen, Kai Tong

Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning (CIL) without available historical training samples as exemplars. Compared with its exemplar-based CIL counterpart that stores exemplars, EFCIL suffers more from forgetting issues. Recently, a new EFCIL branch named Analytic Continual Learn

Joo Yong Shim, Jean Seong Bjorn Choe, Jong-Kook Kim

This article proposes auction-inspired multi-player generative adversarial networks training, which mitigates the mode collapse problem of GANs. Mode collapse occurs when an over-fitted generator generates a limited range of samples, often concentrating on a small subset of the data distribution. Despite the restricted diversity of generated samples, the dis

Jianzhi Chen, Aoqian Shi, Yuchen Peng, Peng Peng

The hybrid skin-topological effect (HSTE) in non-Hermitian systems exhibits both the skin effect and topological protection, offering a novel mechanism for the localization of topological edge states (TESs) in electrons, circuits, and photons. However, it remains unclear whether the HSTE can be realized in quasicrystals, and the unique structure of quasicrys

Alex Martsinkovsky

The notion of defect of a finitely presented functor on a module category is extended to arbitrary additive functors. The new defect and the contravariant Yoneda embedding form a right adjoint pair. The main result identifies the defect of the covariant Hom modulo projectives with the Bass torsion of the fixed argument. When applied to a linear control syste

Marko Kostić

In this paper, we investigate the abstract non-scalar Volterra difference equations. We employ the Poisson like transforms to connect the solutions of the abstract non-scalar Volterra integro-differential equations and the abstract non-scalar Volterra difference equations. We also investigate the existence, uniqueness and almost periodicity of solutions to t

Rituraj, Zhi Gang Yu, R. M. E. B. Kandegedara, Shanhui Fan

Photonic quantum technologies such as effective quantum communication require room temperature (RT) operating single- or few- photon sensors with high external quantum efficiency (EQE) at 1550 nm wavelength. The leading class of devices in this segment is avalanche photodetectors operating particularly in the Geiger mode. Often the requirements for RT operat

Ilan Ben-Noah, Juan J. Hidalgo, Marco Dentz

Conceptualizing a porous media as a network of conductors sets a compromise between the oversimplifying conceptualization of the media as a bundle of capillary tubes and the computationally expensive and unobtainable detailed description of the media's geometry needed for direct numerical simulations. These models are abundantly being used to evaluate single

Kunhang Li, Yansong Feng

The task of text2motion is to generate human motion sequences from given textual descriptions, where the model explores diverse mappings from natural language instructions to human body movements. While most existing works are confined to coarse-grained motion descriptions, e.g., "A man squats.", fine-grained descriptions specifying movements of relevant bod

Ying Yang, Tim Dwyer, Zachari Swiecki, Benjamin Lee

We delineate the development of a mind-mapping system designed concurrently for both VR and desktop platforms. Employing an iterative methodology with groups of users, we systematically examined and improved various facets of our system, including interactions, communication mechanisms and gamification elements, to streamline the mind-mapping process while a

Alexander Altland, Kun Woo Kim, Tobias Micklitz, Maedeh Rezaei

In recent years, the physics of many-body quantum chaotic systems close to their ground states has come under intensified scrutiny. Such studies are motivated by the emergence of model systems exhibiting chaotic fluctuations throughout the entire spectrum (the Sachdev-Ye-Kitaev (SYK) model being a renowned representative) as well as by the physics of hologra

Julio Urizarna-Carasa, Leon Schlegel, Daniel Ruprecht

The Maxey-Riley-Gatignol equations (MRGE) describe the motion of a finite-sized, spherical particle in a fluid. Because of wake effects, the force acting on a particle depends on its past trajectory. This is modelled by an integral term in the MRGE, also called Basset force, that makes its numerical solution challenging and memory intensive. A recent approac

Adnan Al Ali, Jindřich Libovický

Neural language models, which reach state-of-the-art results on most natural language processing tasks, are trained on large text corpora that inevitably contain value-burdened content and often capture undesirable biases, which the models reflect. This case study focuses on the political biases of pre-trained encoders in Czech and compares them with a repre

Junho Kim, Yeon Ju Kim, Yong Man Ro

This paper presents a way of enhancing the reliability of Large Multi-modal Models (LMMs) in addressing hallucination, where the models generate cross-modal inconsistent responses. Without additional training, we propose Counterfactual Inception, a novel method that implants counterfactual thinking into LMMs using self-generated counterfactual keywords. Our

Shicai Wei Chunbo Luo Yang Luo

Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However, it often suffers inferior performance compared to the feature knowledge distillation. In this paper, we argue that existing logit-based methods may be sub-optimal since they only leverage the global logit output that couples multiple semantic knowle

Zhimeng Chen, Jing Xu

In this note, we will define the formulas of curvature and it's covariant derivatives for holomorphic curves on C*-algebras for the multivariable case. As applications, the unitarily and similarly classification theorems for holomorphic bundle and commuting operator tuples in Cowen-Douglas class are given.

Luca Giorgino, Andrea Vesco

This paper presents, for the first time, the Mediterraneous protocol. It is designed to support the development of an Internet of digital services, owned by their creators, and consumed by users by presenting their decentralised digital identity and a proof of service purchase. Mediterraneous is Self-Sovereign Identity (SSI) native, integrating the SSI model

Robert Turnbull, Simon Mutch

This paper outlines our submission for the 4th COV19D competition as part of the `Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis' (DEF-AI-MIA) workshop at the Computer Vision and Pattern Recognition Conference (CVPR). The competition consists of two challenges. The first is to train a classifier to detect the presence of COVID-1

Gerrit Schierholz

Quantum Chromodynamics admits a CP-violating contribution to the action, the $\theta$ term, which is expected to give rise to a nonvanishing electric dipole moment of the neutron. Despite intensive search, no CP violations have been found in the strong interaction. This puzzle is referred to as the strong CP problem. There is evidence that CP is conserved in

Jinmin Li, Kuofeng Gao, Yang Bai, Jingyun Zhang

Despite the remarkable performance of video-based large language models (LLMs), their adversarial threat remains unexplored. To fill this gap, we propose the first adversarial attack tailored for video-based LLMs by crafting flow-based multi-modal adversarial perturbations on a small fraction of frames within a video, dubbed FMM-Attack. Extensive experiments

Eita Shoji, Anis Maddi, Guillaume Penelet, Tetsushi Biwa

Phase-shifting interferometry is one of the optical measurement techniques that improves accuracy and resolution by incorporating a controlled phase shift into conventional optical interferometry. In this study, a four-step phase-shifting interferometer is developed to measure the spatio-temporal distribution of acoustic density oscillations of the gas next

Florian Honz, Nemanja Vokić, Michael Hentschel, Philip Walther

We demonstrate a novel transmitter concept for quantum key distribution based on the polarization-encoded BB84 protocol, which is sourced by the incoherent light of a forward-biased Ge-on-Si PIN junction. We investigate two architectures for quantum state preparation, including independent polarization encoding through multiple modulators and a simplified ap

Wojciech Rudno-Rudziński, Michał Gawełczyk, Paweł Podemski, Ramasubramanian Balasubramanian

Integrating light emitters based on III-V materials with silicon-based electronics is crucial for further increase in data transfer rates in communication systems since the indirect bandgap of silicon prevents its direct use as a light source. We investigate here InAs/InGaAlAs quantum dot (QD) structures grown directly on 5{\deg} off-cut Si substrate and emi

Florian Honz, Florian Prawits, Obada Alia, Hesham Sakr

As quantum key distribution has reached the maturity level for practical deployment, questions about the co-integration with existing classical communication systems are of utmost importance. To this end we demonstrate how the co-propagation of classical and quantum signals can benefit from the development of novel hollow-core fibers. We demonstrate a secure

Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy

Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for fault diagnosis i

Yumeng Li, William Beluch, Margret Keuper, Dan Zhang

Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. At the same time, scaling these models to enable

S. Ranchod, S. A. Mao, R. Deane, S. S. Sridhar

The S-band Polarisation All Sky Survey (SPASS/ATCA) rotation measure (RM) catalogue is the largest broadband RM catalogue to date, increasing the RM density in the sparse southern sky. Through analysis of this catalogue, we report a latitude dependency of the Faraday complexity of polarised sources in this catalogue within 10$^\circ$ of the Galactic plane to

Théophane Vallaeys, Mustafa Shukor, Matthieu Cord, Jakob Verbeek

The abilities of large language models (LLMs) have recently progressed to unprecedented levels, paving the way to novel applications in a wide variety of areas. In computer vision, LLMs can be used to prime vision-language tasks such image captioning and visual question answering when coupled with pre-trained vision backbones. While different approaches have

Joachim Frenkler

We link the QUMOND theory with the Helmholtz-Weyl decomposition and introduce a new formula for the gradient of the Mondian potential using singular integral operators. This approach allows us to demonstrate that, under very general assumptions on the mass distribution, the Mondian potential is well-defined, once weakly differentiable, with its gradient give

Dominic Laniewski, Eric Lanfer, Simon Beginn, Jan Dunker

Low Earth Orbit Satellite Networks such as Starlink promise to provide world-wide Internet access. While traditionally designed for stationary use, a new dish, released in April 2023 in Europe, provides mobile Internet access including in-motion usage, e.g., while mounted on a car. In this paper, we design and build a mobile measurement setup. Our goal is to

Tiago F. T. Cerqueira, Yue-Wen Fang, Ion Errea, Antonio Sanna

We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition temperatures surpassing 20 K, and some even reaching above 70 K. These compounds have very different crystal structures, with

Kayn A. Forbes

In a recent article Babiker et al. [Optics Communications $\mathbf{554}$, 130185 (2024)] claim that cylindrical vector beams (CVBs), also referred to as higher-order Poincar\'e (HOP) beams, possess optical chirality densities which exhibit `superchirality'. Here we show that, on the contrary, CVBs possess less optical chirality density than a corresponding c

Doan Minh Luong, Phung Van Dong, Nguyen Huy Thao

We investigate a dark photon that arises from the UV model based upon $SU(3)_C\otimes SU(3)_L\otimes U(1)_X \otimes U(1)_G$ (3-3-1-1) gauge symmetry, where the last three factors enlarge the electroweak symmetry encompassing electric charge $Q=T_3 - 1/ \sqrt{3}T_8 +X$ and dark charge $D = -2/\sqrt{3} T_8 +G$. It is well-established that this model addresses

Sandra Cerrai, Wen-Tai Hsu

We investigate a class of stochastic partial differential equations of reaction-diffusion type defined on graphs, which can be derived as the limit of SPDEs on narrow planar channels. In the first part, we demonstrate that this limit can be achieved under less restrictive assumptions on the regularity of the noise, compared to [4]. In the second part, we est

Qiyao Luo, Yilei Wang, Wei Dong, Ke Yi

We present LINQ, the first join protocol with linear complexity (in both running time and communication) under the secure multi-party computation model (MPC). It can also be extended to support all free-connex queries, a large class of select-join-aggregate queries, still with linear complexity. This matches the plaintext result for the query processing prob

Zeyu Wang, Haoran Xiong, Qitong Wang, Zhenying He

Approximate Nearest Neighbor Search (ANNS) on high-dimensional vectors has become a fundamental and essential component in various machine learning tasks. Recently, with the rapid development of deep learning models and the applications of Large Language Models (LLMs), the dimensionality of the vectors keeps growing in order to accommodate a richer semantic

F. Pozzi, F. Calura, Q. D'Amato, M. Gavarente

Measurements of the size of dust continuum emission are an important tool for constraining the spatial extent of star formation and hence the build-up of stellar mass. Compact dust emission has generally been observed at Cosmic Noon (z~2-3). However, at earlier epochs, toward the end of the Reionization (z~4-6), only the sizes of a handful of IR-bright galax

Yuga Iguchi, Ajay Jasra, Mohamed Maama, Alexandros Beskos

We present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Inspired by recent works, we introd

Manel Rakez, Julien Guillaumin, Aurelien Chick, Gaelle Coureau

Mammographic density is a dynamic risk factor for breast cancer and affects the sensitivity of mammography-based screening. While automated machine and deep learning-based methods provide more consistent and precise measurements compared to subjective BI-RADS assessments, they often fail to account for the longitudinal evolution of density. Many of these met

Joshua Ebere Chukwuere

The integration of generative Artificial Intelligence (AI) chatbots in higher education institutions (HEIs) is reshaping the educational landscape, offering opportunities for enhanced student support, and administrative and research efficiency. This study explores the future implications of generative AI chatbots in HEIs, aiming to understand their potential

A. Termanova, Ar. Melnikov, E. Mamenchikov, N. Belokonev

Running quantum algorithms often involves implementing complex quantum circuits with such a large number of multi-qubit gates that the challenge of tackling practical applications appears daunting. To date, no experiments have successfully demonstrated a quantum advantage due to the ease with which the results can be adequately replicated on classical comput

Yijian Lu, Aiwei Liu, Dianzhi Yu, Jingjing Li

Text watermarking algorithms for large language models (LLMs) can effectively identify machine-generated texts by embedding and detecting hidden features in the text. Although the current text watermarking algorithms perform well in most high-entropy scenarios, its performance in low-entropy scenarios still needs to be improved. In this work, we opine that t

Sayantan Choudhury, Ahaskar Karde, Pankaj Padiyar, M. Sami

We present a study of the Effective Field Theory (EFT) generalization of stochastic inflation in a model-independent single-field framework and its impact on primordial black hole (PBH) formation. We show how the Langevin equations for the "soft" modes in quasi de Sitter background is described by the Infra-Red (IR) contributions of scalar perturbations, and

Kexiang Yang, Ercai Chen, Zijie Lin, Xiaoyao Zhou

Symbolic dynamical theory plays an important role in the research of amenability with a countable group. Motivated by the deep results of Dougall and Sharp, we study the group extensions for topologically mixing random shifts of finite type. For a countable group $G$, we consider the potential connections between relative Gurevi\v{c} pressure (entropy), the

Yu-Hua Yao, Yi-Qing Guo, Wei Liu

The Sun stands out as the closest and clearest astrophysical accelerator of cosmic rays, while other objects within and beyond the galaxy remain enigmatic. It is probable that the cosmic ray spectrum and mass components from these celestial sources share similarities, offering a novel approach to study their origin. In this study, we analyze of spectra and m

Xuefeng Wei, Gaspard Junot, Ramin Golestanian, Xin Zhou

Magnetic gels with embedded micro/nano-sized magnetic particles in crosslinked polymer networks can be actuated by external magnetic fields, with changes in their internal microscopic structures and macroscopic mechanical properties. We investigate the responses of such magnetic gels to an external magnetic field, by means of coarse-grained molecular dynamic

Haochen Han, Minnan Luo, Huan Liu, Fang Nan

Cross-modal retrieval (CMR) aims to establish interaction between different modalities, among which supervised CMR is emerging due to its flexibility in learning semantic category discrimination. Despite the remarkable performance of previous supervised CMR methods, much of their success can be attributed to the well-annotated data. However, even for unimoda

Davide Alessandro Coccomini, Roberto Caldelli, Claudio Gennaro, Giuseppe Fiameni

Deepfake detectors are typically trained on large sets of pristine and generated images, resulting in limited generalization capacity; they excel at identifying deepfakes created through methods encountered during training but struggle with those generated by unknown techniques. This paper introduces a learning approach aimed at significantly enhancing the g

Abhishek Sharma, Saumya Gupta, Debasis Das, Ashwin. A. Tulapurkar

Spintronic nano-oscillators can generate tunable microwave signals that find a wide range of applications in the field of telecommunication to modern neuromorphic computing systems. Among other spintronic devices, a magnetic skyrmion is a promising candidate for the next generation of low-power devices due to its small size and topological stability. In this

Gesualdo Delfino, Marianna Sorba

We consider $d$-dimensional quantum systems which for positive times evolve with a time-independent Hamiltonian in a nonequilibrium state that we keep generic in order to account for arbitrary evolution at negative times. We show how the one-point functions of local operators depend on the coefficients of the expansion of the nonequilibrium state on the basi

Tim Hoffmann, Gudrun Szewieczek

We prove that all discrete isothermic nets with a family of planar or spherical lines of curvature can be obtained from special discrete holomorphic maps via lifted-folding. This novel approach is a generalization and discretization of a classical method to create planar curvature lines on smooth surfaces. In particular, this technique provides an efficient

Stefano Buccheri, Wojciech Górny

In this note we consider a generalisation to the metric setting of the recent work [Gu-Yung, JFA 281 (2021), 109075]. In particular, we show that under relatively weak conditions on a metric measure space $(X,d,\nu)$, it holds true that \[ \bigg[ \frac{u(x)-u(y)}{d(x,y)^{\frac{s}{p}}} \bigg]_{L^p_w(X \times X, \nu \otimes \nu)} \approx \| u \|_{L^p(X,\nu)},

Mehmetcan Kaymaz, Nazim Kemal Ure

Time-optimal obstacle avoidance is a prevalent problem encountered in various fields, including robotics and autonomous vehicles, where the task involves determining a path for a moving vehicle to reach its goal while navigating around obstacles within its environment. This problem becomes increasingly challenging as the number of obstacles in the environmen

Si Kheang Moeurn

The paper presented in this article deals with the issue of distributed cooperative formation of multi-agent systems (MASs). It proposes the use of appropriate neural network control methods to address formation requirements (uncertainties dynamic model). It considers an adaptive leader-follower distributed cooperative formation control based on neural netwo

Chao Yang, Zhujun Zhang

We give a proof of Ollinger's conjecture that the problem of tiling the plane with translated copies of a set of $8$ polyominoes is undecidable. The techniques employed in our proof include a different orientation for simulating the Wang tiles in polyomino and a new method for encoding the colors of Wang tiles.

Giorgia Disarò, Maria Elena Valcher

In this paper we propose a data-driven approach to the design of reduced-order unknown-input observers (rUIOs). We first recall the model-based solution, by assuming a problem set-up slightly different from those traditionally adopted in the literature, in order to be able to easily adapt it to the data-driven scenario. Necessary and sufficient conditions fo

Lucas Nunes, Rodrigo Marcuzzi, Benedikt Mersch, Jens Behley

Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D LiDAR sensors are commonly used to collect sparse 3D point clouds from the scene. However, compared to human perception, such systems struggle to deduce the unseen parts of the sce

Zhen Yu, Yang Liu, Qingchao Chen

It is essential but challenging to share medical image datasets due to privacy issues, which prohibit building foundation models and knowledge transfer. In this paper, we propose a novel dataset distillation method to condense the original medical image datasets into a synthetic one that preserves useful information for building an analysis model without acc

Pranav Kasela, Gabriella Pasi, Raffaele Perego, Nicola Tonellotto

Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query types and topics. To deal with such topic heterogeneity through a unique model, we propose DESIRE-ME, a neural information retrieval model that leverages the Mixture-of-Experts fra

Pablo Pueyo, Eduardo Montijano, Ana C. Murillo, Mac Schwager

This paper introduces CLIPSwarm, a new algorithm designed to automate the modeling of swarm drone formations based on natural language. The algorithm begins by enriching a provided word, to compose a text prompt that serves as input to an iterative approach to find the formation that best matches the provided word. The algorithm iteratively refines formation

Gowravi Malalur Rajegowda, Yannis Spyridis, Barbara Villarini, Vasileios Argyriou

In recent years, there has been an increasing interest in the use of artificial intelligence (AI) and extended reality (XR) in the beauty industry. In this paper, we present an AI-assisted skin care recommendation system integrated into an XR platform. The system uses a convolutional neural network (CNN) to analyse an individual's skin type and recommend per

Mir Sayeed Mohammad, Azizul Zahid, Md Asif Iqbal

Automatic speech recognition (ASR) converts the human voice into readily understandable and categorized text or words. Although Bengali is one of the most widely spoken languages in the world, there have been very few studies on Bengali ASR, particularly on Bangladeshi-accented Bengali. In this study, audio recordings of spoken digits (0-9) from university s

Samuel Brem, Ermin Malic

Atomically thin heterostructures formed by twisted transition metal dichalcogenides can be used to create periodic moir\'e patterns. The emerging moir\'e potential can trap interlayer excitons into arrays of strongly interacting bosons, which form a unique platform to study strongly correlated many-body states. In order to create and manipulate these exotic

Raymond Cheng, Alexander Perry, Xiaolei Zhao

We construct singular quartic double fivefolds whose Kuznetsov component admits a crepant categorical resolution of singularities by a twisted Calabi--Yau threefold. We also construct rational specializations of these fivefolds where such a resolution exists without a twist. This confirms an instance of a higher-dimensional version of Kuznetsov's rationality

James Vo

The advancement of Large Language Models (LLMs) has significantly transformed the field of natural language processing, although the focus on English-centric models has created a noticeable research gap for specific languages, including Vietnamese. To address this issue, this paper presents vi-mistral-x, an innovative Large Language Model designed expressly

Kirill Lukyanov, Mikhail Drobyshevskiy, Danil Shaikhelislamov, Denis Turdakov

Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper we adopt graph neural networks for this

Ilan Ben-Noah, Juan J. Hidalgo, Marco Dentz

Pore networks play a key role in understanding and quantifying flow and transport processes in complex porous media. Realistic pore-spaces may be characterized by singular regions, i.e., isolated subnetworks that do not connect inlet and outlet, resulting from unconnected porosity or multiphase configurations. The robust identification of these features is c

Alexander Pechen

The ability to control quantum systems is necessary for many applications of quantum technologies ranging from gate generation in quantum computation to NMR and laser control of chemical reactions. In many practical situations, the controlled quantum systems are open, i.e., interacting with the environment. While often influence of the environment is conside