February 2024 arXiv papers — page 105

Anton Semenkin, Yaroslav Sokolov, Evgeniia Vu

Code Completion is one of the most used Integrated Development Environment (IDE) features, which affects the everyday life of a software developer. Modern code completion approaches moved from the composition of several static analysis-based contributors to pipelines that involve neural networks. This change allows the proposal of longer code suggestions whi

Cecilia Herrera, Marcos Origlia

We describe completely conformal Killing or conformal Killing-Yano (CKY) $p$-forms on almost abelian metric Lie algebras. In particular we prove that if a $n$-dimensional almost abelian metric Lie algebra admits a non-parallel CKY $p$-form, then $p=1$ or $p=n-1$. In other words, any CKY $p$-form on a metric almost abelian Lie algebra is parallel for $2\leq p

Yongshan Liu, Yong Xu, Albert Fert, Henri-Yves Jaffres

Orbitronics devices operate by manipulating orbitally-polarized currents. Recent studies have shown that these orbital currents can be excited by femtosecond laser pulses in ferromagnet as Ni and converted into ultrafast charge current via orbital-to-charge conversion. However, the terahertz emission from orbitronic terahertz emitter based on Ni is still muc

Mahdi Karder, Tatjana Petek

In this paper, we give the complete description of maps on self-adjoint bounded operators on Hilbert space which preserve a triadic relation involving the difference of operators and either commutativity or quasi-commutativity in both directions. We show that those maps are implemented by unitary or antiunitary equivalence and possible additive perturbation

Akshay Kumar, Jarvis Haupt

This paper examines gradient flow dynamics of two-homogeneous neural networks for small initializations, where all weights are initialized near the origin. For both square and logistic losses, it is shown that for sufficiently small initializations, the gradient flow dynamics spend sufficient time in the neighborhood of the origin to allow the weights of the

Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera

This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifically, we propose two MINT architectures designed to learn the distinct activation patterns that emerge when an Audited Model is exposed to data used during its training process. The

Bogdan Petraszczuk

Inspired by Frehse's [1] 1973 work, we show that his elliptic system $\Delta u = F(u, \nabla u)$ in the plane has bounded weak solutions $u$ with arbitrarily prescribed singular sets.

Bob Holdom

A scalar field theory with 4-derivative kinetic terms and 4-derivative cubic and quartic couplings is presented as a proxy for quantum quadratic gravity (QQG). The scalar theory is renormalizable and asymptotically free and the remaining key issue is unitarity, or more precisely positivity, just as it is in QQG. We have extended calculations for the optical

Xingfu Wu, John R. Tramm, Jeffrey Larson, John-Luke Navarro

ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maxi

Nicola Cancedda

Projecting intermediate representations onto the vocabulary is an increasingly popular interpretation tool for transformer-based LLMs, also known as the logit lens. We propose a quantitative extension to this approach and define spectral filters on intermediate representations based on partitioning the singular vectors of the vocabulary embedding and unembed

Mathilde Auxois, Marine Minière, Chloé Bertrand-Drira, Fabien Salvatori

The efficiency of supported catalysts depends on the porous microstructure of their solid support, which regulates mass transfer, exposure to the active phase, and mechanical strength. Here, we focus on the manufacturing of a specific type of catalytic support, $\gamma$-alumina extrudates, by a kneading-extrusion process. In this process, a paste is initiall

José Alberto Benítez-Andrades, Tania Fernández-Villa, Carmen Benavides, Andrea Gayubo-Serrenes

The COVID-19 pandemic has meant that young university students have had to adapt their learning and have a reduced relational context. Adversity contexts build models of human behaviour based on relationships. However, there is a lack of studies that analyse the behaviour of university students based on their social structure in the context of a pandemic. Th

Victor Buchstaber

We establish differential-algebraic theory of the Mumford dynamical system. In the framework of this theory, we introduce the $(P,Q)$-recursion, which defines a sequence of functions $P_1,P_2,\ldots$ given the first function of this sequence $P_1$ and a sequence of parameters $h_1,h_2,\ldots$. The general solution of the $(P,Q)$-recursion is shown to give a

Alexander V. Gheorghiu, Tao Gu, David J. Pym

In systems modelling, a 'system' typically comprises located resources relative to which processes execute. One important use of logic in informatics is in modelling such systems for the purpose of reasoning (perhaps automated) about their behaviour and properties. To this end, one requires an interpretation of logical formulae in terms of the resources and

Sankalan Pal Chowdhury, Vilém Zouhar, Mrinmaya Sachan

Large Language Models (LLMs) have found several use cases in education, ranging from automatic question generation to essay evaluation. In this paper, we explore the potential of using Large Language Models (LLMs) to author Intelligent Tutoring Systems. A common pitfall of LLMs is their straying from desired pedagogical strategies such as leaking the answer

Petr Girg, Lukáš Kotrla

We propose a new mathematical model of groundwater flow in porous medium layered over inclined impermeable bed. In its full generality, this is a free-surface problem. To obtain analytically tractable model, we use generalized Dupuit-Forchheimer assumption for inclined impermeable bed. In this way, we arrive at parabolic partial differential equation which i

Le Giang, Tran Van Tan, Nguyen Van Thin

In this paper, we establish a Schmidt's subspace theorem for moving hyeprplane targets in projective spaces over function fields.

Pilar Marqués-Sánchez, Arrate Pinto-Carral, Tania Fernández-Villa, Ana Vázquez-Casares

The aims: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students ha

Germán Benitez, Gustavo Costa, Lucas Q. Pinto

V. Bondarenko and Y. Drozd gives a description of all indecomposable objects in a category of representations of posets, nowadays known as the Bondarenko's category. This category was essential for V. Bekkert and H. Merklen classify all indecomposable objects of the derived category of gentle algebras. In view of this connection with the derived category, wh

Jan Soubusta, Antonín Černoch, Karel Lemr

The paper suggest employing machine learning for resource-efficient classification of quantum correlations in entanglement distribution networks. Specifically, artificial neural networks (ANN) are utilized to classify quantum correlations based on collective measurements conducted in the geometry of entanglement swapping. ANNs are trained to categorize two-q

Dongseok Yang, Jiho Kang, Lingni Ma, Joseph Greer

Full-body avatar presence is crucial for immersive social and environmental interactions in digital reality. However, current devices only provide three six degrees of freedom (DOF) poses from the headset and two controllers (i.e. three-point trackers). Because it is a highly under-constrained problem, inferring full-body pose from these inputs is challengin

Marc Gardegaront, Amelie Sas, Denis Brizard, Aurelie Levillain

Metastases increase the risk of fracture when affecting the femur. Consequently, clinicians need to know if the patients femur can withstand the stress of daily activities. The current tools used in clinics are not sufficiently precise. A new method, the CT-scan-based finite element analysis, gives good predictive results. However, none of the existing model

Lucas Hedström, Ralf Metzler, Ludvig Lizana

Cells regulate fates and complex body plans using spatiotemporal signaling cascades that alter gene expression. Enhancers, short DNA sequences (50-150 base pairs), help coordinate these cascades by attracting regulatory proteins to enhance the transcription of distal genes by binding to promoters. In humans, there are hundreds of thousands of enhancers dispe

Wei Yuan, Zi-Jie Yan, Hemian Yi, Zihao Wang

The interface of two materials can harbor unexpected emergent phenomena. One example is interface-induced superconductivity. In this work, we employ molecular beam epitaxy to grow a series of heterostructures formed by stacking together two non-superconducting antiferromagnetic materials, an intrinsic antiferromagnetic topological insulator MnBi2Te4 and an a

James A. Brotherston

We establish monoidal model structures on model categories of filtered chain complexes constructed by Cirici, Egas Santander, Livernet and Whitehouse whose weak equivalences are the quasi-isomorphisms on the $r$-page of the associated spectral sequences. In doing so we provide a partial classification of cofibrant objects and cofibrations of the model struct

Xufeng Xu, Aleksander A. Rebane, Laura R. Julia, Kathryn A. Rosowski

Liquid liquid phase separation (LLPS) of proteins is an intracellular process that is widely used by cells for many purposes. In living cells (in vivo), LLPS occurs in complex and crowded environments. Amino acids (AAs) are vital components of such environments, occupying a significant fraction of the cellular volume. In this work, we studied the effects of

Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng

Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adept at devising strategies and performing tasks, these agents struggle with seeking clarification and grasping precise user intentions. To bridge this gap, we introduce Intention-in-

Jiexin Wang, Jiahao Chen, Bing Su

Although deep neural networks yield high classification accuracy given sufficient training data, their predictions are typically overconfident or under-confident, i.e., the prediction confidences cannot truly reflect the accuracy. Post-hoc calibration tackles this problem by calibrating the prediction confidences without re-training the classification model.

Dhananjay Singh, Arvind Kumar

In the present work, we apply Tsallis non-extensive statistics to study the thermodynamic properties and phase diagram of quark matter in the Polyakov chiral SU(3) quark mean field model. Within this model, the properties of the quark matter are modified through the scalar fields $\sigma, \zeta, \delta, \chi$, the vector fields $\omega, \rho$, $\phi$, and th

Marco Bertenghi, Lucile Laulin

We introduce a variation of the step-reinforced random walk with general memory. For the diffusive regime, we establish a functional invariance principle and show that, given suitable conditions on the memory sequence, the arising limiting processes are always the sum of a noise reinforced Brownian motion and a (not independent) Brownian motion.

Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang

Let $f(\theta, X_1),$ $ \dots,$ $ f(\theta, X_n)$ be a sequence of random elements, where $f$ is a fixed scalar function, $X_1, \dots, X_n$ are independent random variables (data), and $\theta$ is a random parameter distributed according to some data-dependent posterior distribution $P_n$. In this paper, we consider the problem of proving concentration inequ

Cheng Wang, Christopher Redino, Abdul Rahman, Ryan Clark

Command and control (C2) channels are an essential component of many types of cyber attacks, as they enable attackers to remotely control their malware-infected machines and execute harmful actions, such as propagating malicious code across networks, exfiltrating confidential data, or initiating distributed denial of service (DDoS) attacks. Identifying these

Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi

With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution. This makes AI-generated text (AIGT) detection of great importance. Among existing methods, white-box methods are generally superior to black-box methods in terms

Henrique Gomes

In the philosophical literature, symmetries of physical theories are most often interpreted within the general doctrine called 'Sophistication'. Roughly speaking, it says that models related by symmetries can peacefully co-exist while representing the same physical possibility. But this interpretation still leaves open two main worries about Sophistication:

Ángel Delgado-Panadero, Beatriz Hernández-Lorca, María Teresa García-Ordás, José Alberto Benítez-Andrades

Gradient Boost Decision Trees (GBDT) is a powerful additive model based on tree ensembles. Its nature makes GBDT a black-box model even though there are multiple explainable artificial intelligence (XAI) models obtaining information by reinterpreting the model globally and locally. Each tree of the ensemble is a transparent model itself but the final outcome

V. Allard, C. Heidsieck, F. Bermond, C. Confavreux

Clinical use of finite element analysis requires validation and reproducibility studies. The current study compared two models of vertebral bodies including endplates, on the same experimental dataset and evaluated the influence of the operator on the failure load. Models used were strongly correlated (R2=0.91). The intra-operator reproducibility was 6.4% an

Massimo Blasone, Silvio De Siena, Gaetano Lambiase, Cristina Matrella

We exploit the complete complementarity relations (CCR) to fully characterize various aspects of quantumness in QED scattering processes at tree level. As a paradigmatic example, we consider Bhabha scattering in two different configurations: in the first case, the initial state is factorized in the spin and we study the generation of entanglement due to the

Marah-Lisanne Thormann, Phan Tu Vuong, Alain B. Zemkoho

A highly relevant problem of modern finance is the design of Value-at-Risk (VaR) optimal portfolios. Due to contemporary financial regulations, banks and other financial institutions are tied to use the risk measure to control their credit, market, and operational risks. Despite its practical relevance, the non-convexity induced by VaR constraints in portfol

Alessandro Niro, Michael Werner

Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However, many real-world process executions exhibit a graph-like structure, where events can be associated with multiple cases. Fla

Philipp Alexander Grzywaczyk, Arne Winterhof

For a finite field $\mathbf{F}_{q^r}$ with fixed $q$ and $r$ sufficiently large, we prove the existence of a primitive element outside of a set of $r$ many affine hyperplanes for $q=4$ and $q=5$. This complements earlier results by Fernandes and Reis for $q\ge 7$. For $q=3$ the analogous result can be derived from a very recent bound on character sums of Iye

Pedro Beltran Lopez, Pantaleone Nespoli, Manuel Gil Perez

Cybersecurity is developing rapidly, and new methods of defence against attackers are appearing, such as Cyber Deception (CYDEC). CYDEC consists of deceiving the enemy who performs actions without realising that he/she is being deceived. This article proposes designing, implementing, and evaluating a deception mechanism based on the stealthy redirection of T

Xin Zheng, Jianke Zhu

Nowadays, sensor suits have been equipped with redundant LiDARs and IMUs to mitigate the risks associated with sensor failure. It is challenging for the previous discrete-time and IMU-driven kinematic systems to incorporate multiple asynchronized sensors, which are susceptible to abnormal IMU data. To address these limitations, we introduce a multi-LiDAR mul

Rosa Maria Miró-Roig, Josep Pérez Díez

We deal with Perazzo hypersurfaces $X=V(f)$ in $\PP^{n+2}$ defined by a homogeneous polynomial $f(x_0,x_1,\dots,x_n,u,v)=p_0(u,v)x_0+p_1(u,v)x_1+\cdots +p_n(u,v)x_n+g(u,v)$, where $p_0,p_1,\dots ,p_n$ are algebraically dependent but linearly independent forms of degree $d-1$ in $K[u,v]$ and $g$ is a form in $K[u,v]$ of degree $d$. Perazzo hypersurfaces have

Jakub Rydval, Žaneta Semanišinová, Michał Wrona

The constraint satisfaction problem, parameterized by a relational structure, provides a general framework for expressing computational decision problems. Already the restriction to the class of all finite structures forms an interesting microcosm on its own, but to express decision problems in temporal reasoning one has to take a step beyond the finite-doma

Ravishankar Ramanathan

The Kochen-Specker (KS) theorem is a cornerstone result in quantum foundations, establishing that quantum correlations in Hilbert spaces of dimension $d \geq 3$ cannot be explained by (consistent) hidden variable theories that assign a single deterministic outcome to each measurement. Specifically, there exist finite sets of vectors in these dimensions such

Chen Dudai, Morris Alper, Hana Bezalel, Rana Hanocka

Internet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large-scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine-grained understanding

Roland Guttenberg

Vector addition systems (VAS), also known as Petri nets, are a popular model of concurrent systems. Many problems from many areas reduce to the reachability problem for VAS, which consists of deciding whether a target configuration of a VAS is reachable from a given initial configuration. One of the main approaches to solve the problem on practical instances

Armen Sargsyan, Rodolphe Momier, Claude Leroy, David Sarkisyan

The $N$-resonance process is an accessible and effective method for obtaining narrow (down to subnatural linewidth), and contrasted resonances, using two continuous lasers and an Rb vapor cell. In this article, we investigate the impact of buffer gas partial pressure on the contrast and linewidth of $N$-resonances formed in the $D_1$ line of an $^{85}$Rb the

Alysson F. Morais, Sambhu Radhakrishnan, Gavriel Arbiv, Dirk Dom

Introduction of a dielectric material in an NMR probe head modifies the frequency response of the probe circuit, a phenomenon revealed by the detuning of the probe. For NMR spectroscopy, this detuning is corrected for by tuning and matching the probe head prior to the NMR measurement. The magnitude of the probe detuning - the dielectric shift - provides dire

Xingyao Yu, Benjamin Lee, Michael Sedlmair

Extended reality (XR) technologies are highly suited in assisting individuals in learning motor skills and movements -- referred to as motion guidance. In motion guidance, the "feedforward" provides instructional cues of the motions that are to be performed, whereas the "feedback" provides cues which help correct mistakes and minimize errors. Designing syner

Yutao Hu, Tianbin Li, Quanfeng Lu, Wenqi Shao

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in various multimodal tasks. However, their potential in the medical domain remains largely unexplored. A significant challenge arises from the scarcity of diverse medical images spanning various modalities and anatomical regions, which is essential in real-world medical applicati

Hyeonjun Yeo, Ha Eum Kim, Kabgyun Jeong

Rydberg atom arrays operated by a quantum adiabatic principle are among the most promising quantum simulating platforms due to their scalability and long coherence time. From the perspective of combinatorial optimization, they offer an efficient solution for an intrinsic maximum independent set problem because of the resemblance between the Rydberg Hamiltoni

Rui Zhang, Hongwei Li, Rui Wen, Wenbo Jiang

The increasing demand for customized Large Language Models (LLMs) has led to the development of solutions like GPTs. These solutions facilitate tailored LLM creation via natural language prompts without coding. However, the trustworthiness of third-party custom versions of LLMs remains an essential concern. In this paper, we propose the first instruction bac

Nicolas Chahine, Sira Ferradans, Javier Vazquez-Corral, Jean Ponce

Automated and robust portrait quality assessment (PQA) is of paramount importance in high-impact applications such as smartphone photography. This paper presents FHIQA, a learning-based approach to PQA that introduces a simple but effective quality score rescaling method based on image semantics, to enhance the precision of fine-grained image quality metrics

Yixin Cheng, Markos Georgopoulos, Volkan Cevher, Grigorios G. Chrysos

Large Language Models (LLMs) are susceptible to Jailbreaking attacks, which aim to extract harmful information by subtly modifying the attack query. As defense mechanisms evolve, directly obtaining harmful information becomes increasingly challenging for Jailbreaking attacks. In this work, inspired from Chomsky's transformational-generative grammar theory an

Feiran Huang, Yuanchen Bei, Zhenghang Yang, Junyi Jiang

Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely solely on content features, limiting their recommendation performance and impacting user experience and revenue. Current models generate synthetic behavioral embeddings from content f

Hantaek Bae, Jaeyong Shin

In this paper, we deal with some Oldroyd type models, which describe incompressible viscoelastic fluids. There are 3 parameters in these models: the viscous coefficient of fluid $\nu_{1}$, the viscous coefficient of the elastic part of the stress tensor $\nu_{2}$, and the damping coefficient of the elastic part of the stress tensor $\alpha$. In this paper, w

Amarjit Kundu, Shovan Chowdhury, Bidhan Modok

We propose some new results on the comparison of the minimum or maximum order statistic from a random number of non-identical random variables. Under the non-identical set-up, with certain conditions, we prove that random minimum (maximum) of one system dominates the other in hazard rate (reversed hazard rate) order. Further, we prove variation diminishing p

Yuanyu Wan, Tong Wei, Bo Xue, Mingli Song

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using only local computations and communications. Previous studies have established $O(n^{5/4}\rho^{-1/2}\sqrt{T})$ and ${O}(n^{3/2}\rho^{-1}\log T)$ regret bounds for convex and strongly convex functi

Larissa Brizhik

The influence of an external oscillating in time magnetic field on the dynamics of the Davydov's soliton is investigated. It is shown that it essentially depends not only on the amplitude and frequency of the magnetic field, but also on the field orientation with respect to the molecular chain axis. The soliton velocity and phase are calculated. They are osc

Nadia Alshahwan, Jubin Chheda, Anastasia Finegenova, Beliz Gokkaya

This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. We describe the deployment of Test

Yuanhao Jiang, Shidong Zhou, Xiaofeng Zhong

In this paper, we propose a modified Generalized Approximate Message Passing (GAMP) algorithm to estimate permittivity parameters using path loss data in ray-tracing model.

Riccardo Grazi, Daniel Sacco Shaikh, Maura Sassetti, Niccolò Traverso Ziani

We investigate the performance of a one-dimensional dimerized XY chain as a spin quantum battery. Such integrable model shows a rich quantum phase diagram that emerges through a mapping of the spins onto auxiliary fermionic degrees of freedom. We consider a charging protocol relying on the double quench of an internal parameter, namely the strength of the di

Stephan Felber, Hugo Rincon Galeana

A substantial portion of distributed computing research is dedicated to terminating problems like consensus and similar agreement problems. However, non-terminating problems have been intensively studied in the context of self-stabilizing distributed algorithms, where processes may start from arbitrary initial states and can tolerate arbitrary transient faul

J. Senthilnath, Adithya Bhattiprolu, Ankur Singh, Bangjian Zhou

A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet. The proposed ERBM-KNet efficiently handles streaming data in a single-pass mode using the ERBM, employing a bias-variance strategy for neuron growing and pruning, as well as online clustering based on a c

Jean Pinsolle, Olivier Goudet, Cyrille Enderli, Sylvain Lamprier

In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exploits all available information about both the sequence of the different symbols and their arrival times. A theoretical analysis is carried out to prove that minimizing this score

Xuexin Chen, Ruichu Cai, Kaitao Zheng, Zhifan Jiang

Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has a massive of real-world applications. One of the most mainstream methods is to extract the invariant subgraph by aligning the original and augmented data with the help of environment augmentation. However, these solutions might lead to the lo

Ruoyu Chen, Hua Zhang, Siyuan Liang, Jingzhi Li

Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face the following challenges: 1) existing attribution methods generate inaccurate small regions thus misleading the direction of correct attributi

Péter Ozsvárt, Emma Kun

Radiolarians are significant contributors to the oceanic primary productivity and the global silica cycle in the last 500 Myr. Their diversity throughout the Phanerozoic shows periodic fluctuations. We identify a possible abiotic candidate for driving these patterns which seems to potentially influence radiolarian diversity changes during this period at a si

Vyacheslav Lysov

We present a new proof for the Chern-Gauss-Bonnet theorem. We represent the Euler class integral as the partition function for zero-dimensional field theory with on-shell supersymmetry. We rewrite the supersymmetric partition function as a BV integral and deform the Lagrangian submanifold. The new Lagrangian submanifold localizes the BV integral to the criti

Rita Stampfl, Igor Ivkić, Barbara Geyer

Since the COVID-19 pandemic, educational institutions have embarked on digital transformation projects. The success of these projects depends on integrating new technologies and understanding the needs of digitally literate students. The "learning by doing" approach suggests that real success in learning new skills is achieved when students can try out and p

Lies Beers, Raffaella Mulas

For a graph with largest normalized Laplacian eigenvalue $\lambda_N$ and (vertex) coloring number $\chi$, it is known that $\lambda_N\geq \chi/(\chi-1)$. Here we prove properties of graphs for which this bound is sharp, and we study the multiplicity of $\chi/(\chi-1)$. We then describe a family of graphs with largest eigenvalue $\chi/(\chi-1)$. We also study

J. I. García-García, R. Tapia-Ramos, A. Vigneron-Tenorio

This work delves into the {\it quotient of an affine semigroup by a positive integer}, exploring its intricate properties and broader implications. We unveil an {\it associated tree} that serves as a valuable tool for further analysis. Moreover, we successfully generalize several key irreducibility results, extending their applicability to the more general c

Tomás Mestre Santos, Rui Neto Marinheiro, Fernando Brito e Abreu

Overtourism occurs when the number of tourists exceeds the carrying capacity of a destination, leading to negative impacts on the environment, culture, and quality of life for residents. By monitoring overtourism, destination managers can identify areas of concern and implement measures to mitigate the negative impacts of tourism while promoting smarter tour

Catayoun Azarm, Erman Acar, Mickey van Zeelt

Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace with evolving fraud tactics, leading to high false positive rates and missed detections. Machine learning techniques offer a promising solution by leveraging historical data to ident

Jean-Christophe Pain

In this article, we would like to pay tribute to Gabor Kalman, outlining his contribution to a model widely used in dense plasma physics: the high-temperature Thomas-Fermi model. The approach of Ruoxian Ying and Kalman relies on the separation of the bound and free electrons, a physically reasonable definition of the bound electrons, a description of the sou

Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney

Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary

Jinseok Choi, Jeonghun Park, Namyoon Lee, Ahmed Alkhateeb

Integrated sensing and communication (ISAC) is widely recognized as a fundamental enabler for future wireless communications. In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user an

Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger

Current LLM alignment methods are readily broken through specifically crafted adversarial prompts. While crafting adversarial prompts using discrete optimization is highly effective, such attacks typically use more than 100,000 LLM calls. This high computational cost makes them unsuitable for, e.g., quantitative analyses and adversarial training. To remedy t

Rui-Qi Wang, Tian-Min Lei, Yue-Wen Fang

The capacity to externally manipulate magnetic properties is highly desired from both fundamental and technological perspectives, particularly in the development of magnetoelectronics and spintronics devices. Here, using first-principles calculations, we have demonstrated the ability of controlling the magnetism of magnetized graphene monolayers by interfaci

Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang

We investigate bandit convex optimization (BCO) with delayed feedback, where only the loss value of the action is revealed under an arbitrary delay. Let $n,T,\bar{d}$ denote the dimensionality, time horizon, and average delay, respectively. Previous studies have achieved an $O(\sqrt{n}T^{3/4}+(n\bar{d})^{1/3}T^{2/3})$ regret bound for this problem, whose del

Wei Zhai, Hongzhi Qi, Qing Zhao, Jianqiang Li

In the current environment, psychological issues are prevalent and widespread, with social media serving as a key outlet for individuals to share their feelings. This results in the generation of vast quantities of data daily, where negative emotions have the potential to precipitate crisis situations. There is a recognized need for models capable of efficie

Constant Bonard

Can AI and humans genuinely communicate? In this article, after giving some background and motivating my proposal (sections 1 to 3), I explore a way to answer this question that I call the "mental-behavioral methodology" (sections 4 and 5). This methodology follows the following three steps: First, spell out what mental capacities are sufficient for human co

Yaowei Long, Yunfan Wang

We study the \emph{sensitivity oracles problem for subgraph connectivity} in the \emph{decremental} and \emph{fully dynamic} settings. In the fully dynamic setting, we preprocess an $n$-vertices $m$-edges undirected graph $G$ with $n_{\rm off}$ deactivated vertices initially and the others are activated. Then we receive a single update $D\subseteq V(G)$ of s

Pınar Kirezli, Nilhan Özceylan

In this paper we consider a static, cylindrically symmetric spacetime with coincident f(Q) gravity. Since the field equation of this spacetime in symmetric teleparallel gravity is suitable for choosing the function of f(Q) in the form of power series and exponential forms, perfect fluid solutions of these forms are discussed. Energy densities, directional pr

Qianyu Liu, Haoran Jiang, Zihao Pan, Qiushi Han

In the process of evaluating competencies for job or student recruitment through material screening, decision-makers can be influenced by inherent cognitive biases, such as the screening order or anchoring information, leading to inconsistent outcomes. To tackle this challenge, we conducted interviews with seven experts to understand their challenges and nee

Teddy Ferdinan, Jan Kocoń, Przemysław Kazienko

We address the main problem of self-learning LLM: the question of what to learn. We propose a self-learning LLM framework that enables an LLM to independently learn previously unknown knowledge through self-assessment of their own hallucinations. We introduce a concept called Point in the Unknown (PiU) to identify atomic knowledge unknown to a model, along w

Muhammad Kashif, Muhammad Shafique

In this paper, we present a novel framework for enhancing the performance of Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional layers and addressing the critical challenges associated with them. Traditional quanvolutional layers, although beneficial for feature extraction, have largely been static, offering limited adaptability.

Xuexin Xu, Manabputra, Chloé Vignes, Mohammad H. Ansari

Developing Hamiltonian models for quantum processors with many qubits on the same chip is crucial for advancing quantum computing technologies. Stray couplings between qubits lead to errors in gate operations. This study underscores the importance of incorporating lattice Hamiltonians into quantum circuit design. By comparing many-body effects with two-body

Ryan Griffiths, Lisa Bardou, Timothy Butterley, James Osborn

A six-night optical turbulence monitoring campaign has been carried at Cerro Paranal observatory in February and March, 2023 to facilitate the development and characterisation of two novel atmospheric site monitoring instruments - the ring-image next generation scintillation sensor (RINGSS) and 24-hour Shack Hartmann image motion monitor (24hSHIMM) in the co

Paul Z. Wang

This paper aims at developing model-theoretic tools to study interpretable fields and definably amenable groups, mainly in $\mathrm{NIP}$ or $\mathrm{NTP_2}$ settings. An abstract theorem constructing definable group homomorphisms from generic data is proved. It relies heavily on a stabilizer theorem of Montenegro, Onshuus and Simon. The main application is

Loek van Rossem, Andrew M. Saxe

Deep neural networks come in many sizes and architectures. The choice of architecture, in conjunction with the dataset and learning algorithm, is commonly understood to affect the learned neural representations. Yet, recent results have shown that different architectures learn representations with striking qualitative similarities. Here we derive an effectiv

Himmet Toprak Kesgin, Mehmet Fatih Amasyali

This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable, generalized evidence for these methods. It examines the effectiveness of these techniques in augmenting training sets to improve performance in tasks such as topic classification,

Julien Eng, Conor Rankine, Thomas Penfold

Owing to ring-strain, cyclic ketones exhibit complex excited-state dynamics with multiple competing photochemical channels active on the ultrafast timescale. While the excited-state dynamics of cyclobutanone after $\pi^{\ast}\leftarrow n$ excitation into the lowest-energy excited singlet state (S$_1$) has been extensively studied, the dynamics following 3$s\

Isolde Adler, Eva Fluck

We study a variation of the cops and robber game characterising treewidth, where in each play at most q cops can be placed in order to catch the robber, where q is a parameter of the game. We prove that if k cops have a winning strategy in this game, then k cops have a monotone winning strategy. As a corollary we obtain a new characterisation of bounded dept

Flavien Breuvart, Marie Kerjean, Simon Mirwasser

Linear Logic refines Intuitionnistic Logic by taking into account the resources used during the proof and program computation. In the past decades, it has been extended to various frameworks. The most famous are indexed linear logics which can describe the resource management or the complexity analysis of a program. From an other perspective, Differential Li

Ayodeji Ijishakin, Sophie Martin, Florence Townend, Federica Agosta

Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervised diffusion model, obtaining a 0.83(p<0.01) correlation between chronological and predicted age on low quality

Yejie Wang, Keqing He, Guanting Dong, Pei Wang

Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Several instruction tuning approaches have been proposed to boost the code generation performance of pre-trained Code LLMs. In this paper, we introduce a diverse instruction model (DolphCoder) with self-evaluating for code generation. It learns diverse ins

Kostas Sozos, Stavros Deligiannidis, Charis Mesaritakis, Adonis Bogris

In this work we numerically analyze a photonic unconventional accelerator based on the four-wave mixing effect in highly nonlinear waveguides. The proposed scheme can act as a fully analogue system for nonlinear signal processing directly in the optical domain. By exploiting the rich Kerr-induced nonlinearities, multiple nonlinear transformations of an input

Pål Forr Austnes, Signe Riemer-Sørensen, David Andreas Bordvik, Christian Andre Andresen

The balancing market for power is designed to account for the difference between predicted supply/demand of electricity and the realised supply/demand. However, increased electrification of society changes the consumption patterns, and increased production from renewable sources leads to larger un-predicted fluctuations in production, both effects potentiall