May 2024 arXiv papers — page 127

James Yang, Trevor Hastie

We develop fast and scalable algorithms based on block-coordinate descent to solve the group lasso and the group elastic net for generalized linear models along a regularization path. Special attention is given when the loss is the usual least squares loss (Gaussian loss). We show that each block-coordinate update can be solved efficiently using Newton's met

Giovanni Pecci, Ruiyi Wang, Pietro Torta, Glen Bigan Mbeng

Quantum Annealing (QA) relies on mixing two Hamiltonian terms, a simple driver and a complex problem Hamiltonian, in a linear combination. The time-dependent schedule for this mixing is often taken to be linear in time: improving on this linear choice is known to be essential and has proven to be difficult. Here, we present different techniques for improving

J. Wang, J. P. W. Verbiest, G. M. Shaifullah, I. Cognard

The measurement of pulsar pulse times-of-arrival (ToAs) is a crucial step in detecting low-frequency gravitational waves. To determine ToAs, we can use template-matching to compare each observed pulse profile with a standard template. However, using different combinations of templates and template-matching methods (TMMs) without careful consideration may lea

Killian Sheriff, Yifan Cao, Rodrigo Freitas

Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale properties. Establishing chemistry-microstructure relationships in such materials requires proper characterization of these chemical fluctuations. Yet, current characterization approaches (e.g., Warren-Cowley parameters) make only partial use of

Mauri J. Valtonen, Staszek Zola, Alok C. Gupta, Shubham Kishore

We report the study of a huge optical intraday flare on November 12, 2021, at 2 am UT, in the blazar OJ287. In the binary black hole model it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact, based on a prediction made e

Nicole Neis, Juergen Beyerer

The increasing complexity of automated driving functions and their growing operational design domains imply more demanding requirements on their validation. Classical methods such as field tests or formal analyses are not sufficient anymore and need to be complemented by simulations. For simulations, the standard approach is scenario-based testing, as oppose

Daniella Bar-Lev, Adir Kobovich, Orian Leitersdorf, Eitan Yaakobi

This paper presents a novel approach to address the constrained coding challenge of generating almost-balanced sequences. While strictly balanced sequences have been well studied in the past, the problem of designing efficient algorithms with small redundancy, preferably constant or even a single bit, for almost balanced sequences has remained unsolved. A se

Alexandre Arbey

With the direct discovery of gravitational waves, black holes have regain interest in the recent years. In particular primordial black holes (PBHs), which originate from the very early Universe, may constitute (at least in part) dark matter. The possibility that dark matter is made of black holes is particularly appealing, and multi-messenger searches are im

Sebastian Allmeier, Nicolas Gast

We consider a system of $N$ particles whose interactions are characterized by a (weighted) graph $G^N$. Each particle is a node of the graph with an internal state. The state changes according to Markovian dynamics that depend on the states and connection to other particles. We study the limiting properties, focusing on the dense graph regime, where the numb

Dmitry Golovaty, Alberto Montero, Etienne Sandier, Peter Sternberg

In this work we extend some of the results of Ignat and Jerrard for Ginzburg-Landau vortices of tangent vector fields on two-dimensional Riemannian manifolds to the setting of complex hermitian line bundles. In particular, we elucidate the locations of vortices for the cases of Q-tensors and their higher-rank analogs on a sphere.

Tianhao Peng, Chen Feng, Duolikun Danier, Fan Zhang

With recent advances in deep learning, numerous algorithms have been developed to enhance video quality, reduce visual artifacts, and improve perceptual quality. However, little research has been reported on the quality assessment of enhanced content - the evaluation of enhancement methods is often based on quality metrics that were designed for compression

Ivan Sechin, Mikhail Vasilev

We use the Hamiltonian reduction method to construct the Ruijsenaars dual systems to generalized Toda chains associated with the classical Lie algebras of types $B, C, D$. The dual systems turn out to be the $B, C$ and $D$ analogues of the rational Goldfish model, which is, as in the type $A$ case, the strong coupling limit of rational Ruijsenaars systems. W

Dimitris Gkoumas

The field of chemistry and Artificial Intelligence (AI) intersection is an area of active research that aims to accelerate scientific discovery. The integration of large language models (LLMs) with scientific modalities has shown significant promise in this endeavour. However, challenges persist in effectively addressing training efficacy and the out-of-dist

S. Fassari, M. Gadella, J. T. Lunardi, L. M. Nieto

We study the Birman-Schwinger operator for a self-adjoint realisation of the one-dimensional Hamiltonian with the Coulomb potential. We study both the case in which this Hamiltonian is defined on the whole real line and when it is only defined on the positive semiaxis. In both cases, the Birman-Schwinger operator is Hilbert-Schmidt, even though it is not tra

Jessica Park, Susan Stepney, Irene D'Amico

Quantum Computing (QC) is undergoing a high rate of development, investment and research devoted to its improvement.However, there is little consensus in the industry and wider literature as to what improvement might consist of beyond ambiguous statements of "more qubits" and "fewer errors". Before one can decide how to improve something, it is first necessa

Markus Diehl, Oskar Grocholski

We present a method for the numerical computation of Fourier-Bessel transforms on a finite or infinite interval. The function to be transformed needs to be evaluated on a grid of points that is independent of the argument of the Bessel function. We demonstrate the accuracy of the algorithm for a wide range of functions, including those that appear in the con

Rounak Biswas, Falguni Roy

Consider a complex unital Banach algebra $\mathcal{A}.$ For $x_1,x_2,x_3\in\mathcal{A},$ in this paper, we establish that under certain assumptions on $x_1,x_2,x_3$, Drazin (resp. g-Drazin) invertibility of any three elements among $x_1,x_2,x_3$ and $x_1+x_2+x_3\text{ }(\text{or }x_1x_2+x_1x_3+x_2x_3)$ ensure the Drazin (resp. g-Drazin) invertibility of the

Jungyeon Kim, Jinseok Choi, Jeonghun Park, Ahmed Alkhateeb

In frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) for precoding is vastly challenging due to the tremendous feedback overhead with the growing number of antennas. Utilizing uplink pilots for downlink CSI estimation is a promising approach that can eliminate CSI fee

Ali Forootani, Harshit Kapadia, Sridhar Chellappa, Pawan Goyal

The sparse identification of nonlinear dynamical systems (SINDy) is a data-driven technique employed for uncovering and representing the fundamental dynamics of intricate systems based on observational data. However, a primary obstacle in the discovery of models for nonlinear partial differential equations (PDEs) lies in addressing the challenges posed by th

Zhe Wang, Shang-Qiang Ning, Zenan Liu, Junchen Rong

We design a (2+1))-dimensional [(2+1)D] quantum spin model in which spin-1/2 ladders are coupled through antiferromagnetic Ising interactions. The model hosts a quantum phase transition in the (2+1)D $Z_2$ universality class from the Haldane phase to the antiferromagnetic Ising ordered phase. We focus on studying the surface properties of three different sur

Jessica Park, Susan Stepney, Irene D'Amico

From available data, we show strong positive spatial correlations in the qubits of a D-Wave 2000Q quantum annealing chip that are connected to qubits outside their own unit cell. Then, by simulating the dynamics of three different spin networks and two different initial conditions, we then show that correlation between nodes is affected by a number of factor

R. Shakhmuratov, A. Zinnatullin, F. Vagizov

Secure communication that allows only the sender and intended recipient of a message to view its content has a long history. Quantum objects, such as single photons are ideal carriers for secure information transmission because, according to the no-cloning theorem [1], it is impossible to create an identical and independent copy of an arbitrary quantum state

Jinwei Lin

Neural Radiance Field(NeRF) is an novel implicit method to achieve the 3D reconstruction and representation with a high resolution. After the first research of NeRF is proposed, NeRF has gained a robust developing power and is booming in the 3D modeling, representation and reconstruction areas. However the first and most of the followed research projects bas

Shohei Satake

Constructing explicit RIP matrices is an open problem in compressed sensing theory. In particular, it is quite challenging to construct explicit RIP matrices that break the square-root bottleneck. On the other hand, providing explicit $2$-source extractors is a fundamental problem in theoretical computer science, cryptography and combinatorics. Nowadays, the

Keivan G. Stassun, Marina Kounkel

We have assembled a sample of $\sim$8200 stars with spectral types F5V-M5V, all having directly measured X-ray luminosities from eROSITA and rotation periods from TESS, and having empirically estimated ages via their membership in stellar clusters and groups identified in Gaia astrometry (ages 3-500 Myr). This is the largest such study sample yet assembled f

Clément Duval, Nicolas Cherroret

We present a quantitative description of the thermalization dynamics of far-from-equilibrium, two-dimensional (2D) Bose superfluids. Our analysis leverages a quantum kinetic formalism and allows us to identify two successive regimes of relaxation: an initial damping of quasi-particles due to Landau scattering processes, followed by the slower establishment o

Yongfan Lu, Zixiang Di, Bingdong Li, Shengcai Liu

Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to transform an MOCO problem into a series of singe-objective combinatorial optimization (SOCO) problems and train attention models based on a single-step and deterministic greedy rollout

Elliot Faugier, Frédéric Armetta, Angela Bonifati, Bruno Yun

We introduce ADBL2, an assisted debate builder tool. It is based on the capability of large language models to generalise and perform relation-based argument mining in a wide-variety of domains. It is the first open-source tool that leverages relation-based mining for (1) the verification of pre-established relations in a debate and (2) the assisted creation

Hanguang Xiao, Feizhong Zhou, Xingyue Liu, Tianqi Liu

Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have attracted widespread attention for their exceptional capabilities in understanding, reasoning, and generation, introducing transformative paradigms for integrating artificial intelligence into medicine. This survey provides a comprehensive o

Reilly Pickard, F. Wredenhagen, Y. Lawryshyn

This paper contributes to the existing literature on hedging American options with Deep Reinforcement Learning (DRL). The study first investigates hyperparameter impact on hedging performance, considering learning rates, training episodes, neural network architectures, training steps, and transaction cost penalty functions. Results highlight the importance o

Robert Worden

A model of the evolution of cognition is used to derive a Requirement Equation (RE), which defines what computations the fittest possible brain must make, or must choose actions as if it had made those computations. The terms in the RE depend on factors outside an animals brain, which can be modelled without making assumptions about how the brain works, from

Gabriel Velho, Jean Auriol, Riccardo Bonalli, Islam Boussaada

In this paper, we design a controller for an interconnected system consisting of a linear Stochastic Differential Equation (SDE) actuated through a linear hyperbolic Partial Differential Equation (PDE). Our approach aims to minimize the variance of the state of the SDE component. We leverage a backstepping technique to transform the original PDE into an unco

Yuanqiu Mo, He Wang

Unlike the classical distributed consensus protocols enabling the group of agents as a whole to reach an agreement regarding a certain quantity of interest in a distributed fashion, the distributed biased min-consensus protocol (DBMC) has been proven to generate advanced complexity pertaining to solving the shortest path problem. As such a protocol is common

Xiaolin Liu, Kuan Deng, Kuan Xu

We have developed a method for constructing spectral approximations for convolution operators of Fredholm type. The algorithm we propose is numerically stable and takes advantage of the recurrence relations satisfied by the entries of such a matrix approximation. When used for computing the Fredholm convolution of two given functions, such approximations pro

Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder

Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation, in particular from some of the major tech companies who

Xiaohui Zhang, Jiangyan Yi, Jianhua Tao

The rise of advanced large language models such as GPT-4, GPT-4o, and the Claude family has made fake audio detection increasingly challenging. Traditional fine-tuning methods struggle to keep pace with the evolving landscape of synthetic speech, necessitating continual learning approaches that can adapt to new audio while retaining the ability to detect old

Susanne Albers, G. Wessel van der Heijden

We consider the online busy time scheduling problem motivated by energy and cost minimization in cloud computing systems. The input is a set of jobs $J=\{1,\dots,n\}$ where each job $j\in J$ has a release time $r_j$, deadline $d_j$, and processing time $p_j$. $m$ homogeneous machines are given with a parallelism parameter $g\geq 1$, which is the maximal numb

Oktay K Pashaev, Aygul Kocak

The set of maximally fermion-boson entangled Bell super-coherent states is introduced. A superposition of these states with separable bosonic coherent states, represented by points on the super-Bloch sphere, we call the Bell based super-coherent states. Entanglement of bosonic and fermionic degrees of freedom in these states is studied by using displacement

Sunyuan Qiang, Xianfei Li, Yanyan Liang, Wenlong Liao

The nature of diversity in real-world environments necessitates neural network models to expand from closed category settings to accommodate novel emerging categories. In this paper, we study the open-vocabulary object detection (OVD), which facilitates the detection of novel object classes under the supervision of only base annotations and open-vocabulary k

Xuan-Ye Fu, Shao-Feng Ge, Zi-Yang Guo, Qi-Heng Wang

We explore the fermion oscillation in a degenerate environment. The direct consequence is introducing a Pauli blocking factor $1 - f_i$, where $f_i$ is the phase space distribution function, for each intermediate mass eigenstate during propagation. It is then much easier for a state with larger existing fraction or density to oscillate into other states with

Eduardo Sebastián, Mauro Franceschelli, Andrea Gasparri, Eduardo Montijano

This paper presents a novel accelerated distributed algorithm for unconstrained consensus optimization over static undirected networks. The proposed algorithm combines the benefits of acceleration from momentum, the robustness of the alternating direction method of multipliers, and the computational efficiency of gradient tracking to surpass existing state-o

Wei Lian, Zhesen Cui, Fei Ma, Hang Pan

Numerous applications require algorithms that can align partially overlapping point sets while maintaining invariance to geometric transformations (e.g., similarity, affine, rigid). This paper introduces a novel global optimization method for this task by minimizing the objective function of the Robust Point Matching (RPM) algorithm. We first reveal that the

Shufen Dong, Zinuo Cai, Chunfeng Wu, Changliang Ren

We propose a scheme for the sharing of quantum steering among three observers, Alice, Bob, and Charlie using standard projective measurements. We show that in the unilateral sequential scenario, Alice can steer Bob's and Charlie's states and conversely, Bob and Charlie can steer Alice's state. Unlike the quantum steering sharing achieved through weak measure

Md Abulkalam Azad, Artem Chernyshov, John Nyberg, Ingrid Tveten

Tissue tracking in echocardiography is challenging due to the complex cardiac motion and the inherent nature of ultrasound acquisitions. Although optical flow methods are considered state-of-the-art (SOTA), they struggle with long-range tracking, noise occlusions, and drift throughout the cardiac cycle. Recently, novel learning-based point tracking technique

Yingnan Liu, Yingtian Zou, Rui Qiao, Fusheng Liu

Domain generalization aims to develop models that are robust to distribution shifts. Existing methods focus on learning invariance across domains to enhance model robustness, and data augmentation has been widely used to learn invariant predictors, with most methods performing augmentation in the input space. However, augmentation in the input space has limi

Sadaf Syed, Dominik Semmler, Donia Ben Amor, Michael Joham

Reconfigurable intelligent surface (RIS) is a promising technology to enhance the spectral and energy efficiency in a wireless communication system. The design of the phase shifts of an RIS in every channel coherence interval demands a huge training overhead, making its deployment practically infeasible. The design complexity can be significantly reduced by

Dean Doron, Jonathan Mosheiff, Mary Wootters

The Gilbert--Varshamov (GV) bound is a classical existential result in coding theory. It implies that a random linear binary code of rate $\epsilon^2$ has relative distance at least $\frac{1}{2} - O(\epsilon)$ with high probability. However, it is a major challenge to construct explicit codes with similar parameters. One hope to derandomize the Gilbert--Vars

Tibor Kiss, Gergő Nagy

The aim of this paper is to characterize the so-called $\sigma$-balancing property in the class of generalized quasi-arithmetic means. In general, the question is whether those elements of a given family of means that possess this property are quasi-arithmetic. The first result in the latter direction is due to G. Aumann who showed that a balanced complex me

Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu

Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many users' hidden interests unexplored. Existing approaches typically mitigate the bias, increase recommendation diversity, or use bandit algorithms to balance exploration-exploitation

Ye-jun Xu, Long-hua Zhai, Peng Fu, Shou-jing Cheng

We investigate the nonreciprocal quantum phase transition in a cavity magnonic system driven by a parametric field, where an yttrium iron garnet (YIG) sphere is placed in a spinning microwave resonator. The system exhibits a rich phase diagram due to both magnon Kerr nonlinearity in YIG and parametric drive on the resonator. Especially, Sagnac-Fizeau shift c

Ashley Lenau, Dennis M. Dimiduk, Stephen R. Niezgoda

Incorporating scientific knowledge into deep learning (DL) models for materials-based simulations can constrain the network's predictions to be within the boundaries of the material system. Altering loss functions or adding physics-based regularization (PBR) terms to reflect material properties informs a network about the physical constraints the simulation

Sergei Igonin

In this paper we explore interconnections of differential-difference matrix Lax representations (Lax pairs), gauge transformations, and discrete Miura-type transformations (MTs), which belong to the main tools in the theory of integrable differential-difference (lattice) equations. For a given equation, two matrix Lax representations (MLRs) are said to be ga

Hao Li, Lipo Wang, Tianyun Zhao, Wei Zhao

Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure. Typically, conventional image stitching techniques, other than deep learning, require complex computation and thus computational pricy, especially for stitching large raw images. In this study, inspired by the multiscale feature

Sanaz Soltani, Mohammad Shojafar, Ali Amanlou, Rahim Tafazolli

The Open Radio Access Network (Open RAN) framework, emerging as the cornerstone for Artificial Intelligence (AI)-enabled Sixth-Generation (6G) mobile networks, heralds a transformative shift in radio access network architecture. As the adoption of Open RAN accelerates, ensuring its security becomes critical. The RAN Intelligent Controller (RIC) plays a centr

Dinesh Beniwal, Arnab Laha, Adam Miranowicz, Somnath Ghosh

The fascinating realm of non-Hermitian physics with the interplay of parity (P) and time-reversal (T) symmetry has been witnessing immense attention in exploring unconventional physics at Exceptional Point (EP) singularities. Particularly, the physics of PT-symmetry, anti-PT (APT)-symmetry, and the emergence of EPs have ignited fervor in photonics. Beyond th

Jared Mejia, Victoria Dean, Tess Hellebrekers, Abhinav Gupta

Although pre-training on a large amount of data is beneficial for robot learning, current paradigms only perform large-scale pretraining for visual representations, whereas representations for other modalities are trained from scratch. In contrast to the abundance of visual data, it is unclear what relevant internet-scale data may be used for pretraining oth

Allison Wang

Sabok showed that the set of codes for $G_\delta$ Ramsey positive subsets of $[\omega]^\omega$ is $\mathbf{\Sigma}^1_2$-complete. We extend this result by providing sufficient conditions for the set of codes for $G_\delta$ Ramsey positive subsets of an arbitrary topological Ramsey space to be $\mathbf{\Sigma}^1_2$-complete.

Aubrey Condor

We explore whether the human ratings of open ended responses can be explained with non-content related features, and if such effects vary across different mathematics-related items. When scoring is rigorously defined and rooted in a measurement framework, educators intend that the features of a response which are indicative of the respondent's level of abili

Shenji Zhu, Miaoxin Hu, Tianya Pan, Yue Hong

Tooth segmentation is a key step for computer aided diagnosis of dental diseases. Numerous machine learning models have been employed for tooth segmentation on dental panoramic radiograph. However, it is a difficult task to achieve accurate tooth segmentation due to complex tooth shapes, diverse tooth categories and incomplete sample set for machine learning

Brandon Vu, Toki Migimatsu, Jeannette Bohg

Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks by integrating task planning with motion planning; the task planner proposes a sequence of actions towards a goal state and the motion planner verifies whether this action sequence is geometrically feasible for the robot. However, state-of-the-art TAMP algorithms do not scale well w

Igor Poboiko, Mikhail Feigel'man

Recent experimental studies on strongly disordered indium oxide films have revealed an unusual first-order quantum phase transition between the superconducting and insulating states (SIT). This transition is characterized by a discontinuous jump from non-zero to zero values of superfluid stiffness at the critical point, contradicting the conventional "scalin

Wei Wang, Zhaowei Li, Qi Xu, Yiqing Cai

The deployment of large language models (LLMs) faces considerable challenges concerning resource constraints and inference efficiency. Recent research has increasingly focused on smaller, task-specific models enhanced by distilling knowledge from LLMs. However, prior studies have often overlooked the diversity and quality of knowledge, especially the untappe

Yubo Song

This article explores the adaptive relationship between Encoder Layers and Decoder Layers using the SOTA model Helsinki-NLP/opus-mt-de-en, which translates German to English. The specific method involves introducing a bias-free fully connected layer between the Encoder and Decoder, with different initializations of the layer's weights, and observing the outc

Mikko Majamaa, Lauri Sormunen, Verneri Rönty, Henrik Martikainen

The International Telecommunication Union defined the requirements for 5G in the International Mobile Telecommunications 2020 (IMT-2020) standard in 2017. Since then, advances in technology and standardization have made the ubiquitous deployment of 5G via satellite a practical possibility, for example, in locations where terrestrial networks (TNs) are not av

Gökhan Torun

Correlations may arise in quantum systems through various means, of which the most remarkable one is quantum entanglement. Additionally, there are systems that exhibit non-classical correlations even in the absence of entanglement. Quantum dissonance refers to how quantum discord (QD) -- the difference between the total correlation and the classical correlat

Georg Schäfer, Max Schirl, Jakob Rehrl, Stefan Huber

This paper proposes a framework for training Reinforcement Learning agents using Python in conjunction with Simulink models. Leveraging Python's superior customization options and popular libraries like Stable Baselines3, we aim to bridge the gap between the established Simulink environment and the flexibility of Python for training bleeding edge agents. Our

Alessandra Aimi, Giulia Di Credico, Heiko Gimperlein

This article studies a boundary element method for dynamic frictional contact between linearly elastic bodies. We formulate these problems as a variational inequality on the boundary, involving the elastodynamic Poincar\'{e}-Steklov operator. The variational inequality is solved in a mixed formulation using boundary elements in space and time. In the model p

Heiko Gimperlein, Fabian Meyer, Ceyhun Özdemir

Acoustic emission or scattering problems naturally involve uncertainties about the sound sources or boundary conditions. This article initiates the study of time domain boundary elements for such stochastic boundary problems for the acoustic wave equation. We present a space-time stochastic Galerkin boundary element method which is applied to sound-hard, sou

Chloé Sekkat, Fanny Leroy, Salima Mdhaffar, Blake Perry Smith

Recent works demonstrate that voice assistants do not perform equally well for everyone, but research on demographic robustness of speech technologies is still scarce. This is mainly due to the rarity of large datasets with controlled demographic tags. This paper introduces the Sonos Voice Control Bias Assessment Dataset, an open dataset composed of voice as

Emma Caizergues, François Durand, Fabien Mathieu

This paper addresses the anytime sorting problem, aiming to develop algorithms providing tentative estimates of the sorted list at each execution step. Comparisons are treated as steps, and the Spearman's footrule metric evaluates estimation accuracy. We propose a general approach for making any sorting algorithm anytime and introduce two new algorithms: mul

Soma Heydari, Kayoomars Karami

In this work, a new fast-roll (FR) mechanism to generate primordial black holes (PBHs) and gravitational waves (GWs) in generalized non-canonical natural inflation is introduced. In this model, choosing a suitable function for non-canonical mass scale parameter $M(\phi)$ gives rise to produce a cliff-like region in the field evolution path. When inflaton rol

Zehui Yu, Indira Sen, Dennis Assenmacher, Mattia Samory

Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may di

Tatiana Nikiforova

Sums of translates generalize logarithms of weighted algebraic polynomials. The paper presents the solution to the minimax and maximin problems on the real axis for sums of translates. We prove that there is a unique function that is extremal in both problems. The key in our proof is a reduction to the problem on a segment. For this, we work out an analogue

Kocher Omer Salih, Aram Akram Mohammed, Ibrahim Maaroof Noori

The aqueous extract of Salix spp contains many compounds which may act as root-promoting agents in cuttings. S. alba is a deciduous tree containing variable phytochemicals which are variable throughout the year. So, in this study, one- and two-year-old shoots of S. alba were collected on the 15th of each month in the year 2022, extracted in 2% ethanol at 9 g

Eric A. Wolf, Drew M. Finton, Vincent Zoutenbier, Ivan Biaggio

We observe quantum beats in the nanosecond-scale photoluminescence decay of rubrene single crystals after photoexcitation with short laser pulses in a magnetic field of 0.1 to 0.3 T. The relative amplitude of the quantum beats is of the order of 5\%. Their frequency is $1.3$ GHz when the magnetic field is oriented parallel to the two-fold rotation axis of th

Fusheng Deng, Jinjin Hu, Weiwen Jiang, Xiangsen Qin

We propose a way to connect complex analysis and convex analysis. As applications, we derive some results about $L^2$-estimate for $d$-equation and prove some curvature positivity related to convex analysis from well known $L^2$-estimate for $\bar\partial$-equation or the results we prove in complex analysis.

Simone Brivio, Stefania Fresca, Andrea Manzoni

The coupling of Proper Orthogonal Decomposition (POD) and deep learning-based ROMs (DL-ROMs) has proved to be a successful strategy to construct non-intrusive, highly accurate, surrogates for the real time solution of parametric nonlinear time-dependent PDEs. Inexpensive to evaluate, POD-DL-ROMs are also relatively fast to train, thanks to their limited comp

Ruth Kelleher, Federico Lelli

We study the mass distribution of galaxy clusters in Milgromian dynamics, or modified Newtonian dynamics (MOND). We focus on five galaxy clusters from the X-COP sample, for which high-quality data are available on both the baryonic mass distribution (gas and stars) and internal dynamics (from the hydrostatic equilibrium of hot gas and the Sunyaev-Zeldovich e

Rezkellah Noureddine Khiati, Pierre-Yves Brillet, Aurélien Justet, Radu Ispas

This paper addresses the problem of pathological lung segmentation, a significant challenge in medical image analysis, particularly pronounced in cases of peripheral opacities (severe fibrosis and consolidation) because of the textural similarity between lung tissue and surrounding areas. To overcome these challenges, this paper emphasizes the use of CycleGA

Wei Sun, Weixia Zhang, Yanwei Jiang, Haoning Wu

Portrait images typically consist of a salient person against diverse backgrounds. With the development of mobile devices and image processing techniques, users can conveniently capture portrait images anytime and anywhere. However, the quality of these portraits may suffer from the degradation caused by unfavorable environmental conditions, subpar photograp

Takehito Yokoyama

We study interaction between two magnetic impurities on top of a two dimensional insulator in the presence of chiral phonons by second-order perturbation theory. We show that this exchange interaction arises from angular momentum of phonons through spin-chiral phonon interaction of the form of spin-orbit coupling. Analytical expressions of the interaction fo

Da Xiao, Qingye Meng, Shengping Li, Xingyuan Yuan

Multi-Head Attention (MHA) is a key component of Transformer. In MHA, attention heads work independently, causing problems such as low-rank bottleneck of attention score matrices and head redundancy. We propose Dynamically Composable Multi-Head Attention (DCMHA), a parameter and computation efficient attention architecture that tackles the shortcomings of MH

Jie Li, Hai-Liang Wu

Motivated by the recent work of Zhi-Wei Sun on determinants involving the Legendre symbol, in this paper, we study some matrices concerning subgroups of finite fields. For example, let $q\equiv 3\pmod 4$ be an odd prime power and let $\phi$ be the unique quadratic multiplicative character of the finite field $\mathbb{F}_q$. If set $\{s_1,\cdots,s_{(q-1)/2}\}

Anna Guseva, Ludovic Petitdemange, Steven M. Tobias

Planets and stars are able to generate coherent large-scale magnetic fields by helical convective motions in their interiors. This process, known as hydromagnetic dynamo, involves nonlinear interaction between the flow and magnetic field. Nonlinearity facilitates existence of bi-stable dynamo branches: a weak field branch where the magnetic field is not stro

Jannis Neuhaus-Steinmetz, Tim Matthies, Elena Y. Vedmedenko, Thore Posske

We classify the magnetic ground states of a 2D lattice of localized magnetic moments which are coupled to a superconducting substrate with Rashba-spin-orbit coupling. We discover a rich magnetic phase diagram with surprisingly complex structures including 2q-spin-spirals, a 2x2-periodic pattern, and skyrmion lattices, self-consistently, using an effective cl

Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao

In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enhance coordination among these agents, a distributed communication framework is often employed. However, information sharing among all agents proves to be resource-intensive, while

Yu-Chen Cheng, Christian Klingenberg, Rony Touma

In this paper, we propose a new MUSCL scheme by combining the ideas of the Kurganov and Tadmor scheme and the so-called Deviation method which results in a well-balanced finite volume method for the hyperbolic balance laws, by evolving the difference between the exact solution and a given stationary solution. After that, we derive a semi-discrete scheme from

Florent Becker

This paper gives a (polynomial time) algorithm to decide whether a given Discrete Self-Similar Fractal Shape can be assembled in the aTAM model.In the positive case, the construction relies on a Self-Assembling System in the aTAM which strictly assembles a particular self-similar fractal shape, namely a variant $K^\infty$ of the Sierpinski Carpet. We prove t

Zhiwei Wang, Jun Huang, Longhua Ma, Chengyu Wu

In visual tasks, large teacher models capture essential features and deep information, enhancing performance. However, distilling this information into smaller student models often leads to performance loss due to structural differences and capacity limitations. To tackle this, we propose a distillation framework based on graph knowledge, including a multi-l

Gökhan Kuşçu, Engin Erzin

Continuous emotion recognition (CER) aims to track the dynamic changes in a person's emotional state over time. This paper proposes a novel approach to translating CER into a prediction problem of dynamic affect-contour clusters from speech, where the affect-contour is defined as the contour of annotated affect attributes in a temporal window. Our approach d

Esam Ghaleb, Marlou Rasenberg, Wim Pouw, Ivan Toni

Conversation requires a substantial amount of coordination between dialogue participants, from managing turn taking to negotiating mutual understanding. Part of this coordination effort surfaces as the reuse of linguistic behaviour across speakers, a process often referred to as alignment. While the presence of linguistic alignment is well documented in the

L. Maiani, A. D. Polosa, V. Riquer

Prompted by a recent lattice QCD calculation, we review the SU(3) light quark flavor structure of charmed tetraquarks with spin-0 diquarks. Fermi statistics forces the three light quarks to be in the representation 3*x3*= 3+6*. This agrees with the weak repulsion in the 15 of the 3x8 in Dbar K scattering studied on the lattice. We analyze the 3+6* multiplet

Sayed Mohammad Reza Hashemi

We present a series of results, including local characterizations of $(\lambda,m+n)$-Einstein metrics in the context of warped product Einstein spaces. Using these local properties, we restate already known global characterizations of $(\lambda,m+n)$-Einstein manifolds from He, Petersen and Wylie.

S. Mahesh Chandran, S. Shankaranarayanan

Cosmological inflation is a popular paradigm for understanding Cosmic Microwave Background Radiation (CMBR); however, it faces many conceptual challenges. An alternative mechanism to inflation for generating an almost scale-invariant spectrum of perturbations is a \emph{bouncing cosmology} with an initial matter-dominated contraction phase, during which the

Shiying Zhang, Jun Li, Long Shi, Ming Ding

As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, product

Weibo Liu, Nanjia Li, Yongchao Ma, Ruo Hu

The Kibble balance requires a measurement of the local gravitational acceleration, $g$, with a typical relative measurement uncertainty of $10^{-9}$. In this paper, the determination of $g$ for the Tsinghua tabletop Kibble balance is presented. A polynomial fitting method is proposed for blind transfers of the absolute gravitational acceleration using relati

Rui Li, Chaozhuo Li, Yanming Shen, Zeyu Zhang

Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures. However, existing approaches are confined to rigid relational orthogonalization with restricted dimension and homogeneous geometry, leading to deficient modeling capability. In thi

Miguel Santana, Vinicius V. Cogo, Alan Oliveira de Sá

Background: Timely prioritising and remediating vulnerabilities are paramount in the dynamic cybersecurity field, and one of the most widely used vulnerability scoring systems (CVSS) does not address the increasing likelihood of emerging an exploit code. Aims: We present SecScore, an innovative vulnerability severity score that enhances CVSS Threat metric gr

Tong Yu, Lei Cheng, Ruslan Khalitov, Erland Brandser Olsson

Self-supervised pretraining (SSP) has been recognized as a method to enhance prediction accuracy in various downstream tasks. However, its efficacy for DNA sequences remains somewhat constrained. This limitation stems primarily from the fact that most existing SSP approaches in genomics focus on masked language modeling of individual sequences, neglecting th

Yaohua Liu

With the rapid development of drone technology, the application of multi-drones is becoming increasingly widespread in various fields. However, the task planning technology for multi-drones still faces challenges such as the complexity of remote operation and the convenience of human-machine interaction. To address these issues, this paper proposes a prompt-