April 2024 arXiv papers — page 116

Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia

Neurosymbolic artificial intelligence is a growing field of research aiming to combine neural network learning capabilities with the reasoning abilities of symbolic systems. Informed multi-label classification is a sub-field of neurosymbolic AI which studies how to leverage prior knowledge to improve neural classification systems. Recently, a family of neuro

Sheng Wu, Jiaxing Liu, Longbiao Wang, Dongxiao He

Emotion Recognition in Conversations (ERC) is a popular task in natural language processing, which aims to recognize the emotional state of the speaker in conversations. While current research primarily emphasizes contextual modeling, there exists a dearth of investigation into effective multimodal fusion methods. We propose a novel framework called AIMDiT t

Ching-Hsuan Yen

The Casimir effect describes the attractive force arising due to quantum fluctuations of the vacuum electromagnetic field between closely spaced conducting plates. Traditionally, zeta-regularization is employed in calculations to address infinities that emerge during the derivation. This paper presents a novel derivation of the Casimir effect that circumvent

Rita González-Márquez, Dmitry Kobak

The ICLR conference is unique among the top machine learning conferences in that all submitted papers are openly available. Here we present the ICLR dataset consisting of abstracts of all 24 thousand ICLR submissions from 2017-2024 with meta-data, decision scores, and custom keyword-based labels. We find that on this dataset, bag-of-words representation outp

Yun Fan, Yue Leng

Let $F$ be a field with cardinality $p^\ell$ and $0\neq \lambda\in F$, and $0\le h<\ell$. Extending Euclidean and Hermitian inner products, Fan and Zhang introduced Galois $p^h$-inner product (DCC, vol.84, pp.473-492). In this paper, we characterize the structure of $2$-quasi $\lambda$-constacyclic codes over $F$; and exhibit necessary and sufficient conditi

Marc Gutiérrez-Pérez, Antonio Agudo

Camera calibration in broadcast sports videos presents numerous challenges for accurate sports field registration due to multiple camera angles, varying camera parameters, and frequent occlusions of the field. Traditional search-based methods depend on initial camera pose estimates, which can struggle in non-standard positions and dynamic environments. In re

Alexandre Audibert, Aurélien Gauffre, Massih-Reza Amini

Learning an effective representation in multi-label text classification (MLTC) is a significant challenge in NLP. This challenge arises from the inherent complexity of the task, which is shaped by two key factors: the intricate connections between labels and the widespread long-tailed distribution of the data. To overcome this issue, one potential approach i

Ashleigh Wilcox

Monograph "B. Grechuk, Polynomial Diophantine equations. A systematic approach" suggests solving Diophantine equations systematically in certain order. Many hundreds of the equations are left to the reader. Here, we provide complete solutions to all these equations. The difficulties of solved equations range from elementary to research level. In the last sec

Payal Vasoya, Devsi Bantva

A radio labelling of a graph $G$ is a mapping $f : V(G) \rightarrow \{0, 1, 2,\ldots\}$ such that $|f(u)-f(v)|\geq diam(G) + 1 - d(u,v)$ for every pair of distinct vertices $u,v$ of $G$, where $diam(G)$ is the diameter of $G$ and $d(u,v)$ is the distance between $u$ and $v$ in $G$. The radio number $rn(G)$ of $G$ is the smallest integer $k$ such that $G$ adm

Miguel Ortiz del Castillo, Jonathan Morgan, Jack McRobbie, Clint Therakam

Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control. Here, we present the hardware and software design of an onboard AI subsystem hosted on SpIRIT. The syste

Önder Gürcan

We present a novel multi-agent simulator named Multi-Agent eXperimenter (MAX) that is designed to simulate blockchain experiments involving large numbers of agents of different types acting in one or several environments. The architecture of MAX is highly modular, enabling easy addition of new models.

Rongguang Ye, Lei Chen, Weiduo Liao, Jinyuan Zhang

Pareto front learning is a technique that introduces preference vectors in a neural network to approximate the Pareto front. Previous Pareto front learning methods have demonstrated high performance in approximating simple Pareto fronts. These methods often sample preference vectors from a fixed Dirichlet distribution. However, no fixed sampling distribution

Trinh Van Chien, Mai Dinh Cong, Nguyen Cong Luong, Tri Nhu Do

In this paper, we investigate a joint computation offloading and target tracking in Integrated Sensing and Communication (ISAC)-enabled unmanned aerial vehicle (UAV) network. Therein, the UAV has a computing task that is partially offloaded to the ground UE for execution. Meanwhile, the UAV uses the offloading bit sequence to estimate the velocity of a groun

Francisco Freire-Fernández, Nathan G. Sinai, Max J. H. Tan, Sang-Min Park

This paper reports how CdSe core-only nanoplatelets coupled with plasmonic Al nanoparticle lattices can exhibit exciton-polariton lasing. By improving a procedure to synthesize monodisperse 4-monolayer CdSe nanoplatelets, we could resolve polariton decay dynamics and pathways. Experiment and theory confirmed that the system is in the strong coupling regime b

Martí Berenguer, Anshuman Dey, Javier Mas, Juan Santos-Suárez

We study the non-equilibrium dynamics of two coupled SYK models, conjectured to be holographically dual to an eternal traversable wormhole in AdS$_2$. We consider different periodic drivings of the parameters of the system. We analyze the energy flows in the wormhole and black hole phases of the model as a function of the driving frequency. Our numerical res

Jorge J. Garcés, Mykola Khrypchenko

Let $X$ be an arbitrary poset and $K$ an arbitrary field. We describe linear unital invertibility preservers of the finitary incidence algebra $FI(X,K)$ in terms of certain maps of the power set algebra $\mathcal{P}(X)$ and linear maps $FI(X,K)\to J(FI(X,K))$.

David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani

Despite their exceptional performance in vision tasks, deep learning models often struggle when faced with domain shifts during testing. Test-Time Training (TTT) methods have recently gained popularity by their ability to enhance the robustness of models through the addition of an auxiliary objective that is jointly optimized with the main task. Being strict

Sourabh Sarkar, S. P. Ram, Kavish Bhardwaj, Gunjan Verma

We have investigated the atom trapping geometry for trapping of $^{87}{Rb}$ atoms in a radio-frequency (rf) dressed potential generated after superposing a strong linearly polarized rf-field on a static magnetic trap. For this, laser cooled atoms in a magneto-optical trap (MOT) in an ultra-high vacuum (UHV) chamber (pressure $\sim$ 1.5 $\times$ $10^{-10}$ To

Thiemen Siemensma, Darren Chiu, Sneha Ramshanker, Radhika Nagpal

Robot swarms can effectively serve a variety of sensing and inspection applications. Certain inspection tasks require a binary classification decision. This work presents an experimental setup for a surface inspection task based on vibration sensing and studies a Bayesian two-outcome decision-making algorithm in a swarm of miniaturized wheeled robots. The ro

Yuki Miyazaki, Giacomo Marmorini, Nobuo Furukawa, Daisuke Yamamoto

We investigate the role of the permutationally invariant part of the density matrix (PIDM) in capturing the properties of the ground state of the system during a quantum phase transition. In the context of quantum state tomography, PIDM is known to be obtainable with only a low number of measurement settings, namely $\mathcal{O}(L^2)$, where $L$ is the syste

Reyhaneh Ghassemizadeh, Wolfgang Körner, Daniel F. Urban, Christian Elsässer

We investigate nitrogen-vacancy center (NV) ensembles in diamond under the influence of strongly-correlated electron-spin baths. We thoroughly calculate the decoherence properties of the NV central spin for bath concentrations of 0.1-100 ppm using the cluster-correlation expansion (CCE) method. We systematically analyze possible origins of the significant de

Ira W. Herbst, Jesper Møller, Anne Marie Svane

Let $X=\sum_{k=1}^\infty X_k \beta^{-k}$ be the base-$\beta$ expansion of a continuous random variable $X$ on the unit interval where $\beta$ is the positive solution to $\beta^n = 1 + \beta + \cdots + \beta^{n-1}$ for an integer $n\ge 2$ (i.e., $\beta$ is a generalization of the golden mean for which $n=2$). We study the asymptotic distribution and converge

Zenon Jan Jablonski, Il Bong Jung, Jan Stochel

Criteria for an algebraic operator $T$ on a complex Hilbert space $\mathcal{H}$ to be unitary are established. The main one is written in terms of the convergence of sequences of the form $\{\|T^nh\|\}_{n=0}^{\infty}$ with $h\in \mathcal{H}$. Related questions are also discussed.

Chunjian Zhang, Jinhui Chen, Giuliano Giacalone, Shengli Huang

Investigating nucleon-nucleon correlations inherent to the strong nuclear force is one of the core goals in nuclear physics research. We showcase the unique opportunities offered by collisions of $^{16}$O nuclei at high-energy facilities to reveal detailed many-body properties of the nuclear ground state. We interface existing knowledge about the geometry of

Ying Wang, Fu-Quan Dou

Nuclear coherent population transfer (NCPT) plays an important role in the exploration and application of atomic nuclei. How to achieve high-fidelity NCPT remains so far challenging. Here, we investigate the complete population transfer of nuclear states. We first consider a cyclic three-level system, based on the mixed-state inverse engineering scheme by ad

Keyu Chen, Yunxin Zhang

The optimal transport and Wasserstein barycenter of Gaussian distributions have been solved. In literature, the closed form formulas of the Monge map, the Wasserstein distance and the Wasserstein barycenter have been given. Moreover, when Gaussian distributions extend more generally to elliptically contoured distributions, similar results also hold true. In

Xinpeng Wang, Chengzhi Hu, Bolei Ma, Paul Röttger

Multiple choice questions (MCQs) are commonly used to evaluate the capabilities of large language models (LLMs). One common way to evaluate the model response is to rank the candidate answers based on the log probability of the first token prediction. An alternative way is to examine the text output. Prior work has shown that first token probabilities lack r

Bhagya. R, Diganta Parai, Harsha Sreekumar, Suman Kumar Panja

We study a model of the neutron star in $\kappa$-deformed space-time in the presence of the cosmological constant ($\Lambda$). The Einstein tensor and the energy-momentum tensor are generalized to $\kappa$-deformed space-time and we construct the field equations with the cosmological constant. Considering the interior of the star to be a perfect fluid as in

Emanuel Carneiro, Micah B. Milinovich, Emily Quesada-Herrera, Antonio Pedro Ramos

By means of a Fourier optimization framework, we improve the current asymptotic bounds under GRH for two classical problems in number theory: the problem of estimating the least quadratic non-residue modulo a prime, and the problem of estimating the least prime in an arithmetic progression.

Aurélien Barrau, Juan García-Bellido, Killian Martineau, Martin Teuscher

In this brief article, we pursue the systematic investigation of possible gravitational wave sources in the gigahertz band. We focus on hyperbolic encounters of light black holes and evaluate precisely the expected signal when accounting for the detailed characteristics of haloscope experiments. Considering the GraHal setup as a benchmark, we insist on the c

Robert J. Chapman, Tristan Kuttner, Jost Kellner, Alessandra Sabatti

Generating and interfering non-classical states of light is fundamental to optical quantum information science and technology. Quantum photonic integrated circuits provide one pathway towards scalability by combining nonlinear sources of non-classical light and programmable circuits in centimeter-scale devices. The key requirements for quantum applications i

Mandeep Saini, Xiang Bin Chen, Stephane Zaleski, Daniel Fuster

In this article, we present direct numerical simulation results for the expansion of spherical cap bubbles attached to a rigid wall due to a sudden drop in the ambient pressure. The critical pressure drop beyond which the bubble growth becomes unstable is found to match well with the predictions from classical theory of heterogeneous nucleation imposing a qu

Andrea Ponti

Graphs are ubiquitous in various fields, and deep learning methods have been successful applied in graph classification tasks. However, building large and diverse graph datasets for training can be expensive. While augmentation techniques exist for structured data like images or numerical data, the augmentation of graph data remains challenging. This is prim

Zu-Cheng Chen, Lang Liu

The null energy condition (NEC) is a fundamental principle in general relativity, and its violation could leave discernible signatures in gravitational waves (GWs). A violation of the NEC during the primordial era would imprint a blue-tilted spectrum on the stochastic gravitational wave background (SGWB) at nanohertz frequencies, potentially accounting for t

Kerman Gallego-Lizarribar, Sergi Julià-Farré, Maciej Lewenstein, Christof Weitenberg

Spontaneously symmetry-broken (SSB) phases are locally ordered states of matter characterizing a large variety of physical systems. Because of their specific ordering, their presence is usually witnessed by means of local order parameters. Here, we propose an alternative approach based on statistical correlations of noise after the ballistic expansion of an

Silvia Chiacchiera, Patrick B. Warren, Andrew J. Masters, Michael A. Seaton

We critically examine a broad class of explicitly polarisable soft solvent models aimed at applications in dissipative particle dynamics. We obtain the dielectric permittivity using the fluctuating box dipole method in linear response theory, and verify the models in relation to several test cases including demonstrating ion desorption from an oil-water inte

Raoul Prisant, Federica Garin, Paolo Frasca

In this paper we make use of graphon theory to study opinion dynamics on large undirected networks. The opinion dynamics models that we take into consideration allow for negative interactions between the individuals, i.e. competing entities whose opinions can grow apart. We consider both the repelling model and the opposing model that are studied in the lite

Fabian Böhm, Daniel Bauer, Nils Kohl, Christie Alappat

This paper introduces a code generator designed for node-level optimized, extreme-scalable, matrix-free finite element operators on hybrid tetrahedral grids. It optimizes the local evaluation of bilinear forms through various techniques including tabulation, relocation of loop invariants, and inter-element vectorization - implemented as transformations of an

Svyatoslav Gryaznov, Sergei Ovcharov, Artur Riazanov

We consider the proof system Res($\oplus$) introduced by Itsykson and Sokolov (Ann. Pure Appl. Log.'20), which is an extension of the resolution proof system and operates with disjunctions of linear equations over $\mathbb{F}_2$. We study characterizations of tree-like size and space of Res($\oplus$) refutations using combinatorial games. Namely, we introduc

Chen Xuanbang, Liu Ziqi, Zhang Xun, Wang Yuhao

In addressing physical layer security issues, hardware fingerprinting has been proven to be a reliable method. Additionally, Visible Light Communication (VLC) technology offers a solution to the spectrum congestion in next-generation wireless communications and is noteworthy for its high security. However, there is currently a lack of a comprehensive and sys

Monica Romero, Sandra Gomez, Ivan G. Torre

Indigenous languages are a fundamental legacy in the development of human communication, embodying the unique identity and culture of local communities in America. The Second AmericasNLP (Americas Natural Language Processing) Competition Track 1 of NeurIPS (Neural Information Processing Systems) 2022 proposed the task of training automatic speech recognition

Felix Prause, Ralf Borndörfer

The rolling stock rotation problem with predictive maintenance (RSRP-PdM) involves the assignment of trips to a fleet of vehicles with integrated maintenance scheduling based on the predicted failure probability of the vehicles. These probabilities are determined by the health states of the vehicles, which are considered to be random variables distributed by

Beixiong Zheng, Xue Xiong, Tiantian Ma, Jie Tang

The ever-increasing reliance on wireless communication and sensing has led to growing concerns over the vulnerability of sensitive information to unauthorized detection and interception. Traditional anti-detection methods are often inadequate, suffering from limited adaptability and diminished effectiveness against advanced detection technologies. To overcom

Guohua Feng, Jiti Gao, Fei Liu, Bin Peng

Hierarchical panel data models have recently garnered significant attention. This study contributes to the relevant literature by introducing a novel three-dimensional (3D) hierarchical panel data model, which integrates panel regression with three sets of latent factor structures: one set of global factors and two sets of local factors. Instead of aggregati

Junyi Mei, Shixuan Sun, Chao Li, Cheng Xu

Dynamic graph random walk (DGRW) emerges as a practical tool for capturing structural relations within a graph. Effectively executing DGRW on GPU presents certain challenges. First, existing sampling methods demand a pre-processing buffer, causing substantial space complexity. Moreover, the power-law distribution of graph vertex degrees introduces workload i

Patrik Vacek, David Hurych, Tomáš Svoboda, Karel Zimmermann

We study the problem of self-supervised 3D scene flow estimation from real large-scale raw point cloud sequences, which is crucial to various tasks like trajectory prediction or instance segmentation. In the absence of ground truth scene flow labels, contemporary approaches concentrate on deducing optimizing flow across sequential pairs of point clouds by in

Sabrina Bodmer, Lukas Vogel, Simon Muntwiler, Alexander Hansson

This paper presents an open-source miniature car-like robot with low-cost sensing and a pipeline for optimization-based system identification, state estimation, and control. The overall robotics platform comes at a cost of less than \$\,700 and thus significantly simplifies the verification of advanced algorithms in a realistic setting. We present a modified

Dongbo Xi, Zhen Chen, Yuexian Wang, He Cui

Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even outside the application. Multi-domain learning is a typical solution in this regard. While considerable efforts have been made in this regar

Jingxin Zhang, Uzair Bin Tahir, Richard Manasseh

The reactive Power Take Off (PTO) force is the key to maximizing mechanical power absorption and electric power generation of Wave Energy Converters (WECs) from ocean waves with variable frequency, but its study is limited due to its difficulty in physical realization. This paper presents a simple yet effective $LC$-tuned WEC that generates a tunable reactiv

Juraj Vladika, Florian Matthes

In today's digital world, seeking answers to health questions on the Internet is a common practice. However, existing question answering (QA) systems often rely on using pre-selected and annotated evidence documents, thus making them inadequate for addressing novel questions. Our study focuses on the open-domain QA setting, where the key challenge is to firs

John Abbott, Claus Fieker

We present a new, practical algorithm for computing the determinant of a non-singular dense, uniform matrix over Z; the aim is to achieve better practical efficiency, which is always at least as good as currently known methods. The algorithm uses randomness internally, but the result is guaranteed correct. The main new idea is to use a modular HNF in cases w

Enric Nart, Josnei Novacoski, Giulio Peruginelli

In this paper we present characterizations of the sets of key polynomials and abstract key polynomials for a valuation $\mu$ of $K(x)$, in terms of (ultrametric) balls in the algebraic closure $\overline K$ of $K$ with respect to $v$, a fixed extension of $\mu_{\mid K}$ to $\overline K$. In particular, we show that the ways of augmenting $\mu$, in the sense

Andrés A León Baldelli, Pierluigi Cesana

We study irreversible evolutionary processes with a general energetic notion of stability. We dedicate this contribution to releasing three nonlinear variational solvers as modular components (based on FEniCSx/dolfinx) that address three mathematical optimisation problems. They are general enough to apply, in principle, to evolutionary systems with instabili

Qianqian Jiang, Wenbo Li, Zeng Li

We investigate one/two-sample mean tests for high-dimensional compositional data when the number of variables is comparable with the sample size, as commonly encountered in microbiome research. Existing methods mainly focus on max-type test statistics which are suitable for detecting sparse signals. However, in this paper, we introduce a novel approach using

Xiao Zhang, Chunliu Wang, Rik van Noord, Johan Bos

The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation. Currently, we witness an excellent performance of neural parsers and generators on the PMB. This might suggest that such semantic processing tasks have by and large been solved. We argue that this is not the case and that performan

Shiwei Lian, Feitian Zhang

The generalization of the end-to-end deep reinforcement learning (DRL) for object-goal visual navigation is a long-standing challenge since object classes and placements vary in new test environments. Learning domain-independent visual representation is critical for enabling the trained DRL agent with the ability to generalize to unseen scenes and objects. I

A. Martín Andrés, I. Herranz Tejedor

The article by Hawila & Berg (2023) that is going to be commented presents four relevant problems, apart from other less important ones that are also cited. First, the title is incorrect, since it leads readers to believe that the confidence interval defined is exact when in fact it is asymptotic. Second, contrary to what is assumed by the authors of the art

Guillaume Astruc, Nicolas Gonthier, Clement Mallet, Loic Landrieu

The diversity and complementarity of sensors available for Earth Observations (EO) calls for developing bespoke self-supervised multimodal learning approaches. However, current multimodal EO datasets and models typically focus on a single data type, either mono-date images or time series, which limits their impact. To address this issue, we introduce OmniSat

Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang

Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruction. In this work, we introduce the concept of parallel imaging-inspired self-consistency (PISCO), which we in

Joaquim Bruna, Julià Cufí, Agustí Reventós

We establish some relations between the perimeter, the area and the visual angle of a planar compact convex set. Our first result states that Crofton's formula is the unique universal formula relating the visual angle, length and area. After that we give a characterization of convex sets of constant width by means of the behaviour of its isotopic sets at inf

Hollis Williams

We study the behavior of the Cheeger isoperimetric constant under the Ricci flow on compact surfaces. For metrics on a surface diffeomorphic to $S^2$, we show that the Cheeger constant is non-decreasing along the flow. The proof uses evolution identities for parallel curves together with a viscosity formulation of the evolution of $\log h$ which accommodates

Thomas K. Bracht, Florian Kappe, Moritz Cygorek, Tim Seidelmann

Entangled photon pairs form the foundation for many applications in the realm of quantum communication. For fiber-optic transfer of entangled photon pairs, time-bin encoding can potentially offer an improved stability compared to polarization encoded qubits. Here, we lay the theoretical foundations to describe the measurement of time-bin entangled photons. W

Yang Yang, Hongpeng Pan, Qing-Yuan Jiang, Yi Xu

Multi-modal learning aims to enhance performance by unifying models from various modalities but often faces the "modality imbalance" problem in real data, leading to a bias towards dominant modalities and neglecting others, thereby limiting its overall effectiveness. To address this challenge, the core idea is to balance the optimization of each modality to

Reinoud Jan Slagter

We investigated an exact solution in a conformal invariant Randall-Sundrum 5D warped brane world model on a time dependent Kerr-like spacetime. The singular points are determined by a quintic polynomial in the complex plane and fulfills Cauchy's theorem on holomorphic functions. The solution, which is determined by a first-degree differential equation, shows

Marta Bañón, Jaume Zaragoza-Bernabeu, Gema Ramírez-Sánchez, Sergio Ortiz-Rojas

Language identification is a crucial component in the automated production of language resources, particularly in multilingual and big data contexts. However, commonly used language identifiers struggle to differentiate between similar or closely-related languages. This paper introduces FastSpell, a language identifier that combines fastText (a pre-trained l

Rudi Coppola, Andrea Peruffo, Licio Romao, Alessandro Abate

The abstraction of dynamical systems is a powerful tool that enables the design of feedback controllers using a correct-by-design framework. We investigate a novel scheme to obtain data-driven abstractions of discrete-time stochastic processes in terms of richer discrete stochastic models, whose actions lead to nondeterministic transitions over the space of

Chongjun Ouyang, Zhaolin Wang, Boqun Zhao, Xingqi Zhang

The near-field channel gain is analyzed by considering both radiating and reactive components of the electromagnetic field. Novel expressions are derived for the channel gains of spatially-discrete (SPD) and continuous-aperture (CAP) arrays, which are more accurate than conventional results that neglect the reactive region. To gain further insights, asymptot

Yu-Chen Liu, Yuan-Bin Cheng, Xing-Bo Pan, Ze-Zhou Sun

Quantum communication and quantum metrology are widely compelling applications in the field of quantum information science, and quantum remote sensing is an intersection of both. Despite their differences, there are notable commonalities between quantum communication and quantum remote sensing, as they achieve their functionalities through the transmission o

Yang Li, Songlin Yang, Wei Wang, Ziwen He

Highly realistic AI generated face forgeries known as deepfakes have raised serious social concerns. Although DNN-based face forgery detection models have achieved good performance, they are vulnerable to latest generative methods that have less forgery traces and adversarial attacks. This limitation of generalization and robustness hinders the credibility o

Stefano Vitale, Lorenzo Sala, Valerio Ferroni, William Joseph Weber

This paper discusses force noise in LISA and LISA Pathfinder arising from the interaction of patch potentials on the test mass and surrounding electrode housing surfaces with their own temporal fluctuations. We aim to estimate the contribution of this phenomenon to the force noise detected in LISA Pathfinder in excess of the background from Brownian motion.

Xingchi Yan, Siyuan Song, Gerald J. Diebold

Although the photoacoustic effect is most commonly generated by pulsed or amplitude modulated continuous optical sources, it is possible to generate acoustic waves by moving a constant amplitude, continuous light beam. If the light beam moves at the speed of sound, an amplification effect takes place which can be used in trace gas detection. Here, the proper

Cesare Malosso, Natalia Manko, Maria Grazia Izzo, Stefano Baroni

Over the last decade, an increasing body of evidence has emerged, supporting the existence of a metastable liquid-liquid critical point in supercooled water, whereby two distinct liquid phases of different densities coexist. Analysing long molecular dynamics simulations performed using deep neural-network force fields trained to accurate quantum mechanical d

Meiram Akhymbek, Michael Ruzhansky

In this paper, we study geometric properties of $\ell^{p}$-spaces associated with the unitary dual of a compact group. More precisely, we prove uniform smoothness, uniform convexity, Clarkson type inequalities, Kadec-Klee property, as well as type and cotype properties of such spaces. We also present duality and complex interpolation results.

Mikkel Bennedsen, Eric Hillebrand, Siem Jan Koopman, Kathrine By Larsen

This study presents a statistical time-domain approach for identifying transitions between climate states, referred to as breakpoints, using well-established econometric tools. We analyze a 67.1 million year record of the oxygen isotope ratio delta-O-18 derived from benthic foraminifera. The dataset is presented in Westerhold et al. (2020), where the authors

Nived Rajaraman, Jiantao Jiao, Kannan Ramchandran

While there has been a large body of research attempting to circumvent tokenization for language modeling (Clark et al., 2022; Xue et al., 2022), the current consensus is that it is a necessary initial step for designing state-of-the-art performant language models. In this paper, we investigate tokenization from a theoretical point of view by studying the be

Frank J. Jiang, Kaj Munhoz Arfvidsson, Chong He, Mo Chen

In this paper, we present an approach for guaranteeing the completion of complex tasks with cyber-physical systems (CPS). Specifically, we leverage temporal logic trees constructed using Hamilton-Jacobi reachability analysis to (1) check for the existence of control policies that complete a specified task and (2) develop a computationally-efficient approach

Preety Priya, Yi Hong, Emanuele Viterbo

In low latency applications and in general, for overspread channels, channel delay spread is a large percentage of the transmission frame duration. In this paper, we consider OTFS in an overspread channel exhibiting a delay spread that exceeds the block duration in a frame, where traditional channel estimation (CE) fails. We propose a two-stage CE method bas

Elena Cordero, Gianluca Giacchi, Luigi Rodino

We exhibit the connection between the Wigner kernel and the Gabor matrix of a linear bounded operator T : $\mathcal{S}(\mathbb{R}^d) \to \mathcal{S}' (\mathbb{R}^d)$. The smoothing effect of the Gabor matrix is highlighted by basic examples. This connection allows a comparison between the classes of Fourier integral operators defined by means of the Gabor ma

Alexandra Daub, Andreas Mayr, Boyao Zhang, Elisabeth Bergherr

Component-wise gradient boosting algorithms are popular for their intrinsic variable selection and implicit regularization, which can be especially beneficial for very flexible model classes. When estimating generalized additive models for location, scale and shape (GAMLSS) by means of a component-wise gradient boosting algorithm, an important part of the es

Hyesong Choi, Hunsang Lee, Seyoung Joung, Hyejin Park

Driven by the success of Masked Language Modeling (MLM), the realm of self-supervised learning for computer vision has been invigorated by the central role of Masked Image Modeling (MIM) in driving recent breakthroughs. Notwithstanding the achievements of MIM across various downstream tasks, its overall efficiency is occasionally hampered by the lengthy dura

Jochem Kip, Ronald Kleiss

We present a pedagogical treatment of the electroweak Higgs mechanism based solely on Feynman diagrams and S-matrix elements, without recourse to (gauge) symmetry arguments. Throughout, the emphasis is on Feynman rules and the Schwinger-Dyson equations; it is pointed out that particular care is needed in the treatment of tadpole diagrams and their symmetry f

Tao Zhang, Chaoying Zhao

In recent years, the tunable coupling scheme has become the mainstream scheme for designing superconducting quan tum circuits. By working in the dispersive regime, the ZZ coupling and high-energy level leakage can be effectively suppressed and realize a high fidelity quantum gate. We propose a tunable fluxonium-transmon-transmon (FTT) cou pling scheme. In ou

Hyesong Choi, Hyejin Park, Kwang Moo Yi, Sungmin Cha

In this paper, we introduce Saliency-Based Adaptive Masking (SBAM), a novel and cost-effective approach that significantly enhances the pre-training performance of Masked Image Modeling (MIM) approaches by prioritizing token salience. Our method provides robustness against variations in masking ratios, effectively mitigating the performance instability issue

Xin Tong, Qingpeng Ding, Haiyang Fang, Shing Shin Cheng

This paper investigates the robust global attitude stabilization problem for a rigid-body system using quaternion-based feedback. We propose a novel synergistic hybrid feedback with the following notable features: (1) It demonstrates central synergism by utilizing a minimal number of potential functions; (2) It ensures consistency with respect to the unit qu

Nils Lehmann, Nina Maria Gottschling, Stefan Depeweg, Eric Nalisnick

Deep neural networks (DNNs) have been successfully applied to earth observation (EO) data and opened new research avenues. Despite the theoretical and practical advances of these techniques, DNNs are still considered black box tools and by default are designed to give point predictions. However, the majority of EO applications demand reliable uncertainty est

Liwei Wang, Jun Li, Wen Chen, Qingqing Wu

Federated Learning (FL) facilitates collaborative machine learning by training models on local datasets, and subsequently aggregating these local models at a central server. However, the frequent exchange of model parameters between clients and the central server can result in significant communication overhead during the FL training process. To solve this p

Ahmed Abouelazm, Jonas Michel, J. Marius Zoellner

Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable

Carlo Bellavita, Vasilis Daskalogiannis, Georgios Nikolaidis, Georgios Stylogiannis

For g in BMOA, we consider the generalized Volterra operator T_g acting on Hardy spaces H^p. This article aims to study the largest space of analytic functions, which is mapped by T_g into the Hardy space H^p. We call this space the optimal domain of T_g and we describe its structural properties. Motivation for this comes from the work of G. Curbera and W. R

Yuqing Cheng, Bo Chen, Fanjin Zhang, Jie Tang

From-scratch name disambiguation is an essential task for establishing a reliable foundation for academic platforms. It involves partitioning documents authored by identically named individuals into groups representing distinct real-life experts. Canonically, the process is divided into two decoupled tasks: locally estimating the pairwise similarities betwee

Marco Donatelli, Davide Furchì

The iterated Arnoldi-Tikhonov (iAT) method is a regularization technique particularly suited for solving large-scale ill-posed linear inverse problems. Indeed, it reduces the computational complexity through the projection of the discretized problem into a lower-dimensional Krylov subspace, where the problem is then solved. This paper studies iAT under an ad

Ada J. Ellingsrud, Pietro Benedusi, Miroslav Kuchta

Mathematical models for excitable tissue with explicit representation of individual cells are highly detailed and can, unlike classical homogenized models, represent complex cellular geometries and local membrane variations. However, these cell-based models are challenging to approximate numerically, partly due to their mixed-dimensional nature with unknowns

David Alonso-Gutiérrez, Francisco Marín Sola, Javier Martín Goñi, Jesús Yepes Nicolás

A classical inequality by Gr\"unbaum provides a sharp lower bound for the ratio $\mathrm{vol}(K^{-})/\mathrm{vol}(K)$, where $K^{-}$ denotes the intersection of a convex body with non-empty interior $K\subset\mathbb{R}^n$ with a halfspace bounded by a hyperplane $H$ passing through the centroid $\mathrm{g}(K)$ of $K$. In this paper we extend this result to t

Jinn-Ouk Gong, Naoya Kitajima

We study the effects of non-Gaussianity from primordial black holes (PBHs). The formation of PBHs is in general a rare event and the number of PBHs fluctuates following the Poisson distribution function, which is independent from the pre-existing inflationary adiabatic fluctuations. Such fluctuations can dominate over the adiabatic mode on small scales. We f

The SPD Collaboration, V. Abazov, V. Abramov, L. Afanasyev

The Spin Physics Detector collaboration proposes to install a universal detector in the second interaction point of the NICA collider under construction (JINR, Dubna) to study the spin structure of the proton and deuteron and other spin-related phenomena using a unique possibility to operate with polarized proton and deuteron beams at a collision energy up t

Berenice Anne Neumann, Frank T. Seifried

We present a novel framework for mean field games with finite state space and common noise, where the common noise is given through shocks that occur at random times. We first analyze the game for up to $n$ shocks, in which case we are able to characterize mean field equilibria through a system of parameterized and coupled forward-backward equations. We esta

Roy D. Williams, Gareth P. Francis, Andy Lawrence, Terence M. Sloan

Lasair is the UK Community Broker for transient alerts from the Legacy Survey of Space and Time (LSST) from the Vera C. Rubin Observatory. We explain the system's capabilities, how users can achieve their scientific goals, and how Lasair is implemented. Lasair offers users a kit of parts that they can use to build filters to concentrate their desired alerts.

Hafsa Maryam, Tania Panayiotou, Georgios Ellinas

A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic

Muzhi Li, Minda Hu, Irwin King, Ho-fung Leung

The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the \textit{\textbf{structural knowledge}} in the local neighborhood of entities, disregarding \textit{\textbf{semantic knowledge}} in the textual representations of entities, relations, and types that are also cr

Haipeng Wang

Scanning real-life scenes with modern registration devices typically gives incomplete point cloud representations, primarily due to the limitations of partial scanning, 3D occlusions, and dynamic light conditions. Recent works on processing incomplete point clouds have always focused on point cloud completion. However, these approaches do not ensure consiste

Haoyi Zhang, Jiahao Song, Xiaohan Gao, Xiyuan Tang

Analog Computing-in-Memory (ACIM) is an emerging architecture to perform efficient AI edge computing. However, current ACIM designs usually have unscalable topology and still heavily rely on manual efforts. These drawbacks limit the ACIM application scenarios and lead to an undesired time-to-market. This work proposes an end-to-end automated ACIM based on a