April 2024 arXiv papers — page 137

Kim L. Kreienkamp, Sabine H. L. Klapp

Non-reciprocal systems exhibit diverse dynamical phases whose character depends on the type and degree of non-reciprocity. In this study, we theoretically investigate dynamical structures in a mixture of non-reciprocally aligning polar active particles with repulsion, focusing on the performance on (and connection between) different levels of description. Li

C. E. Brasseur, M. M. Jardine, G. A. J. Hussain

We present a multiwavelength study of AB Doradus, combining modelling that incorporates a spectropolarimetric magnetic field map with 8.4 GHz radio interferometry to measure the coronal extent and density of this young star. We use the surface magnetic field map to produce a 3D extrapolation of AB Dor's coronal magnetic field. From this model we create synth

Gan Gu, Xiaoma Wang, Xiaoyong Mu, Shuo Yuan

We develop methods to extract key dark energy information from cosmic distance measurements including the BAO scales and supernovae luminosity distances. Demonstrated using simulated datasets of the complete DESI, LSST and Roman surveys designed for BAO and SNe distance measurements, we show that using our method, the dynamical behaviour of the energy, press

Victor-Emmanuel Brunel, John Urschel

We consider the inverse problem of finding a magnitude-symmetric matrix (matrix with opposing off-diagonal entries equal in magnitude) with a prescribed set of principal minors. This problem is closely related to the theory of recognizing and learning signed determinantal point processes in machine learning, as kernels of these point processes are magnitude-

Matthew Hogancamp, David E. V. Rose, Paul Wedrich

To every compact oriented surface that is composed entirely out of 2-dimensional 0- and 1-handles, we construct a dg category using structures arising in Khovanov homology. These dg categories form part of the 2-dimensional layer (a.k.a. modular functor) of a categorified version of the sl(2) Turaev--Viro topological field theory. As a byproduct, we obtain a

Matías Chávez, Matthias Ernst

This article presents the application of continuous Floquet theory in solid-state NMR. Continuous Floquet theory extends traditional Floquet theory to non-continuous Hamiltonians, enabling the description of observable effects not fully captured by traditional Floquet theory due to its requirement for a periodic Hamiltonian. We present closed-form expression

Jan Gregorovič, Martin Kolář, David Sykes

We show that every point in a uniformly $2$-nondegenerate CR hypersurface is canonically associated with a model $2$-nondegenerate structure. The $2$-nondegenerate models are basic CR invariants playing the same fundamental role as quadrics do in the Levi nondegenerate case. We characterize all $2$-nondegenerate models and show that the moduli space of such

Christophe Nicolet, Antoine Béguin, Matthieu Dreyer, Sébastien Alligné

This paper is addressing the quantification and the comparison of pumped storage power plants, PSPP, contribution to synchronous inertia and synthetic inertia when fixed speed and variable speed motor-generators technologies are considered, respectively. Therefore, a grid stability study was conducted by means of 1D SIMSEN simulation for the 2 x 395 MW PSPP

Christian W. Bauer, Zohreh Davoudi, Natalie Klco, Martin J. Savage

Simulating key static and dynamic properties of matter -- from creation in the Big Bang to evolution into sub-atomic and astrophysical environments -- arising from the underlying fundamental quantum fields of the Standard Model and their effective descriptions, lies beyond the capabilities of classical computation alone. Advances in quantum technologies have

Yujie Di, Anzi Xu, Lian-Kuan Chen

Intensified underwater activities have driven the escalating demand for reliable, flexible, and high data-rate underwater communication links. Optical wireless communication (OWC) emerges as the most promising technology for short- to medium-range communication, facilitating the real-time high-speed transmission of information from undersea to an aerial vehi

S. Mironov, V. Volkova

We consider a general static, spherically symmetric background in the quadratic beyond Horndeski theory and analyse the behaviour of linear perturbations in both parity odd and parity even sectors. We derive a full set of stability conditions for an arbitrary static, spherically symmetric solution which guarantees absence of ghosts, gradient instabilities, t

Guangyu Wei, Matteo Valerio Falessi, Tao Wang, Fulvio Zonca

A workflow is developed based on the ideal MHD model to investigate the linear physics of various Alfv\'en eigenmodes in general axisymmetric toroidal geometry, by solving the coupled shear Alfv\'en wave (SAW) and ion sound wave (ISW) equations in ballooning space. The model equations are solved by the FALCON code in the singular layer, and the corresponding

Antonio Martín Andrés, María Álvarez Hernández

Recently Mart\'in Andr\'es and \'Alvarez Hern\'andez (2024) have proposed new estimators of various kappa coefficients. These estimators are based on the unbiased estimator of the expected index of agreement of each population coefficient. In their article, these authors propose variance formulas based on the univariate delta method. Here new formulas are pr

Simon Mauras, Divyarthi Mohan, Rebecca Reiffenhäuser

We study online selection problems in both the prophet and secretary settings, when arriving agents have interdependent values. In the interdependent values model, introduced in the seminal work of Milgrom and Weber [1982], each agent has a private signal and the value of an agent is a function of the signals held by all agents. Results in online selection c

Iwona Christop

Speech emotion recognition has become increasingly important in recent years due to its potential applications in healthcare, customer service, and personalization of dialogue systems. However, a major issue in this field is the lack of datasets that adequately represent basic emotional states across various language families. As datasets covering Slavic lan

Lanjing Bao, Rachel Kuske, Daniil Yurchenko, Igor Belykh

We present a novel approach for studying the global dynamics of a vibro-impact pair, that is, a ball moving in a harmonically forced capsule. Motivated by a specific context of vibro-impact energy harvesting, we develop the method with broader non-smooth systems in mind. The seeming complications of the impacts of the ball with the capsule are exploited as u

Beichen Huang, Xingyu Wu, Yu Zhou, Jibin Wu

Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diverse optimization scenarios, there is also a rising trend of applying LLMs. However, whether the application of LLMs in the black-box optimization problems is genuinely beneficial re

Meng Wang, Eran Bouchbinder, Jay Fineberg

Material failure is mediated by the propagation of cracks, which in realistic 3D materials typically involve multiple coexisting fracture planes. Multiple fracture-plane interactions create poorly understood out-of-plane crack structures, such as step defects on tensile fracture surfaces. Steps form once a slowly moving, distorted crack front segments into d

Christian Reichenbächer, Jochen Hipp, Oliver Bringmann

Ensuring the safety of road vehicles at an acceptable level requires the absence of any unreasonable risk arising from all potential hazards linked to the intended au-tomated driving function and its implementation. The assurance that there are no unreasonable risks stemming from hazardous behaviours associated to functional insufficiencies is denoted as saf

Ming-Kun Xie, Jia-Hao Xiao, Pei Peng, Gang Niu

The key to multi-label image classification (MLC) is to improve model performance by leveraging label correlations. Unfortunately, it has been shown that overemphasizing co-occurrence relationships can cause the overfitting issue of the model, ultimately leading to performance degradation. In this paper, we provide a causal inference framework to show that t

Ibrahim Yazici, Emre Gures

The proliferation of connected vehicles and the advent of New Radio (NR) technologies have ushered in a new era of intelligent transportation systems. Ensuring reliable and lowlatency communication between vehicles and their surrounding environment is of utmost importance for the success of these systems. This paper presents a novel approach to predict Quali

Donghun Jung, Young-Wook Cho, Yosep Kim, Junghyun Lee

Constructing an integrated large-scale qubit system of realistic size requires addressing the challenge of physical crowding among qubits. This constraint poses an issue of coarse-grained (CG) measurement, wherein information from the multi-qubit system is collectively gathered. In this work, we introduce a novel approach to reconstruct the target density ma

Lórien MacEnulty, Matteo Giantomassi, Bernard Amadon, Gian-Marco Rignanese

Members of the DFT+U family of functionals are increasingly prevalent methods of addressing errors intrinsic to (semi-) local exchange-correlation functionals at minimum computational cost, but require their parameters U and J to be calculated in situ for a given system of interest, simulation scheme, and runtime parameters. The SCF linear response approach

Victoria Basmov, Yoav Goldberg, Reut Tsarfaty

The task of reading comprehension (RC), often implemented as context-based question answering (QA), provides a primary means to assess language models' natural language understanding (NLU) capabilities. Yet, when applied to large language models (LLMs) with extensive built-in world knowledge, this method can be deceptive. If the context aligns with the LLMs'

Francisco Escudero Gutiérrez

We consider the problems of testing and learning an $n$-qubit $k$-local Hamiltonian from queries to its evolution operator with respect the 2-norm of the Pauli spectrum, or equivalently, the normalized Frobenius norm. For testing whether a Hamiltonian is $\epsilon_1$-close to $k$-local or $\epsilon_2$-far from $k$-local, we show that $O(1/(\epsilon_2-\epsilo

The IsoDAR Collaboration, Daniel Winklehner, Michel Abs, Jose R. Alonso

This Preliminary Design Report (PDR) describes the IsoDAR electron-antineutrino source in two volumes which are mostly site-independent and describe the cyclotron driver providing a 60 MeV, 10 mA proton beam (this Volume); and the medium energy beam transport line (MEBT) and target (Volume II). The IsoDAR driver and target will produce about 1.15e23 electron

Karim Abdel Sadek, Marek Elias

ML-augmented algorithms utilize predictions to achieve performance beyond their worst-case bounds. Producing these predictions might be a costly operation -- this motivated Im et al. '22 to introduce the study of algorithms which use predictions parsimoniously. We design parsimonious algorithms for caching and MTS with action predictions, proposed by Antonia

Ehsan Pajouheshgar, Yitao Xu, Sabine Süsstrunk

Neural Cellular Automata (NCA) is a class of Cellular Automata where the update rule is parameterized by a neural network that can be trained using gradient descent. In this paper, we focus on NCA models used for texture synthesis, where the update rule is inspired by partial differential equations (PDEs) describing reaction-diffusion systems. To train the N

Vitaly Bulgakov, Alec Segal

Dimensionality reduction in vector databases is pivotal for streamlining AI data management, enabling efficient storage, faster computation, and improved model performance. This paper explores the benefits of reducing vector database dimensions, with a focus on computational efficiency and overcoming the curse of dimensionality. We introduce a novel applicat

Anas Gouda, Max Schwarz, Christopher Reining, Sven Behnke

Foundation models are a strong trend in deep learning and computer vision. These models serve as a base for applications as they require minor or no further fine-tuning by developers to integrate into their applications. Foundation models for zero-shot object segmentation such as Segment Anything (SAM) output segmentation masks from images without any furthe

Junzhi Ye, Navendu Mondal, Ben P. Carwithen, Yunwei Zhang

Defect tolerance is a critical enabling factor for efficient lead-halide perovskite materials, but the current understanding is primarily on band-edge (cold) carriers, with significant debate over whether hot carriers (HCs) can also exhibit defect tolerance. Here, this important gap in the field is addressed by investigating how internationally-introduced tr

Christophe Nicolet, Matthieu Dreyer, Christian Landry, Sébastien Alligné

This paper presents the methodology and key results which enabled to establish the so-called Ancillary Service Matrix (ASM) presenting the ability to deliver the different ancillary services of each of the 6 demonstrators of the XFLEX HYDRO research project combined with the applicable technologies studied in this analysis. These technologies include i) the

Yuki Haruyama, Hiroyuki Takamura

This paper studies the upper bound of the lifespan of classical solutions of the initial value problems for one dimensional wave equations with quasilinear terms of space-, or time-derivatives of the unknown function. The results are same as those of the semilinear case. But it is quite meaningful to consider this kind of problems for the purpose to cover th

Binbin Wu, Jingyu Xu, Yifan Zhang, Bo Liu

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appr

Roman Malinowski, Emmanuelle Sarrazin, Loïc Dumas, Emmanuel Dubois

We propose a method for estimating disparity confidence intervals in stereo matching problems. Confidence intervals provide complementary information to usual confidence measures. To the best of our knowledge, this is the first method creating disparity confidence intervals based on the cost volume. This method relies on possibility distributions to interpre

Claudio Corianò, Mario Cretì, Stefano Lionetti, Riccardo Tommasi

We investigate the gravitational anomaly vertex $\langle TTJ_5\rangle$ (graviton - graviton - axial current) under conditions of finite density and temperature. Through a direct analysis of perturbative contributions, we demonstrate that neither finite temperature nor finite fermion density affects the gravitational chiral anomaly. These results find applica

Vasily Bolbachan

Let $\mathbb K$ be a field of characteristic zero. We prove that its motivic cohomology in degree $m-1$ and weight $m$ is rationally isomorphic to the cohomology of the polylogarithmic complex. This gives a partial extension of A. Suslin theorem describing the indecomposable $K_3$ of a field.

Zhicheng Lu, Xiang Guo, Le Hui, Tianrui Chen

In this paper, we propose a 3D geometry-aware deformable Gaussian Splatting method for dynamic view synthesis. Existing neural radiance fields (NeRF) based solutions learn the deformation in an implicit manner, which cannot incorporate 3D scene geometry. Therefore, the learned deformation is not necessarily geometrically coherent, which results in unsatisfac

Zihao Liu, Mohsen Ebrahimzadeh Hassanabadi, Daniel Dias-da-Costa

Recursive Bayesian filters have been widely deployed in structural system identification where output-only filters are of higher practicality. Unfortunately, the estimation obtained by instantaneous system inversion via filters can be compromised by an ill-conditionedness of the system, which is a consequence of the architecture of the sensor network. To sig

Syu Kato

For each integers $\ell > 1$ and $n \ge m \ge 1$, we prove an equivalence between the category of polynomial modules over a paraholic subalgebra $\mathfrak p$ of an affine Lie algebra of $\mathfrak{gl}(n\ell)$ and the module category of the smash product algebra $A$ of the complex reflection group $G(\ell,1,m)$ with $\mathbb C [X_1,\ldots,X_m]$. Then, we tra

Keyvan Amiri Elyasi, Han van der Aa, Heiner Stuckenschmidt

We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances. PGTNet consistently outperforms state-of-the-art deep learning approaches across a diverse range of 20 publicly available real-world event

Andrea Marino, Denise S. Christovam, Daisuke Takegami, Johannes Falke

A procedure for quantifying the U $5f$ electrons' covalence and degree of localization in U intermetallic compounds is presented. To this end, bulk sensitive hard and soft x-ray photoelectron spectroscopy were utilized in combination with density-functional theory (DFT) plus dynamical mean-field theory (DMFT) calculations. The energy dependence of the photoi

Deshui Miao, Xin Li, Zhenyu He, Huchuan Lu

Existing semi-supervised video object segmentation methods either focus on temporal feature matching or spatial-temporal feature modeling. However, they do not address the issues of sufficient target interaction and efficient parallel processing simultaneously, thereby constraining the learning of dynamic, target-aware features. To tackle these limitations,

Federico Gallina, Matteo Bruschi, Barbara Fresch

The unraveling of open quantum system dynamics in terms of stochastic quantum trajectories offers a picture of open system dynamics that consistently considers memory effects stemming from the finite correlation time of environment fluctuations. These fluctuations significantly influence the coherence and energy transport properties of excitonic systems. Whe

Bingqing Liu

Electronic Health Records (EHR) can be represented as temporal sequences that record the events (medical visits) from patients. Neural temporal point process (NTPP) has achieved great success in modeling event sequences that occur in continuous time space. However, due to the black-box nature of neural networks, existing NTPP models fall short in explaining

Erik Lindell

The IA-automorphism group is the group of automorphisms of the free group $F_n$ that act trivially on the abelianization $F_n^{\mathrm{ab}}$. This group is in many ways analoguous to Torelli groups of surfaces and their higher dimensional analogues. In recent work, the stable rational cohomology of such groups was studied by Kupers and Randal-Williams, using

Pedro L. Contreras E

In this numerical work, we find a self-consistent constant scattering superconducting lifetime for two different values of the disorder parameters, the inverse atomic strength, and the stoichiometric impurity in the triplet paired unconventional super-conductor strontium ruthenate. This finding is relevant for experimentalists given that the expressions for

Chuang-Wei Liu, Qijun Chen, Rui Fan

Stereo matching has become a key technique for 3D environment perception in intelligent vehicles. For a considerable time, convolutional neural networks (CNNs) have remained the mainstream choice for feature extraction in this domain. Nonetheless, there is a growing consensus that the existing paradigm should evolve towards vision foundation models (VFM), pa

Tom Gustafsson, Antti Hannukainen, Vili Kohonen

We extend a localized model order reduction method for the distributed finite element solution of elliptic boundary value problems in the cloud. We give a computationally efficient technique to compute the required inner product matrices and optimal reduced bases. A memory-efficient methodology is proposed to project the global finite element linear system o

Siqi Xu, Xianghui Cao, Tianyang Hu, Yang Li

We investigate the gravitational form factors of charmonium. Our method is based on a Hamiltonian formalism on the light front known as basis light-front quantization. The charmonium mass spectrum and light-front wave functions were obtained from diagonalizing an effective Hamiltonian that incorporates confinement from holographic QCD and one-gluon exchange

Zhaohui Chen, Elyas Asadi Shamsabadi, Sheng Jiang, Luming Shen

Vision-based crack detection faces deployment challenges due to the size of robust models and edge device limitations. These can be addressed with lightweight models trained with knowledge distillation (KD). However, state-of-the-art (SOTA) KD methods compromise anti-noise robustness. This paper develops Robust Feature Knowledge Distillation (RFKD), a framew

Bolun Zhang, Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen

The End-to-end (E2E) learning-based approach has great potential to reshape the existing communication systems by replacing the transceivers with deep neural networks. To this end, the E2E learning approach needs to assume the availability of prior channel information to mathematically formulate a differentiable channel layer for the backpropagation (BP) of

Minh-Quan Dao, Holger Caesar, Julie Stephany Berrio, Mao Shan

Occlusion presents a significant challenge for safety-critical applications such as autonomous driving. Collaborative perception has recently attracted a large research interest thanks to the ability to enhance the perception of autonomous vehicles via deep information fusion with intelligent roadside units (RSU), thus minimizing the impact of occlusion. Whi

Amir Shahhosseini, Thomas Chaffey, Rodolphe Sepulchre

Splitting algorithms are well-established in convex optimization and are designed to solve large-scale problems. Using such algorithms to simulate the behavior of nonlinear circuit networks provides scalable methods for the simulation and design of neuromorphic systems. For circuits made of linear capacitors and inductors with nonlinear resistive elements, w

Jan Hendrik Bruinier, Eugenia Rosu, Shaul Zemel

We consider the generating series of appropriately completed 0-dimensional special cycles on a toroidal compactification of an orthogonal or unitary Shimura variety with values in the Chow group. We prove that it is a holomorphic Siegel, respectively Hermitian, modular form

Yitong Li, Tom Nuno Wolf, Sebastian Pölsterl, Igor Yakushev

Differential diagnosis of dementia is challenging due to overlapping symptoms, with structural magnetic resonance imaging (MRI) being the primary method for diagnosis. Despite the clinical value of computer-aided differential diagnosis, research has been limited, mainly due to the absence of public datasets that contain diverse types of dementia. This leaves

Pinyan Lu, Zihan Luo, Jialin Zhang

We focus on the problem of placing two facilities along a linear space to serve a group of agents. Each agent is committed to minimizing the distance between her location and the closest facility. A mechanism is an algorithm that maps the reported agent locations to the facility locations. We are interested in mechanisms without money that are deterministic,

Yixin Yang, Jiangxin Dong, Jinhui Tang, Jinshan Pan

How to effectively explore spatial-temporal features is important for video colorization. Instead of stacking multiple frames along the temporal dimension or recurrently propagating estimated features that will accumulate errors or cannot explore information from far-apart frames, we develop a memory-based feature propagation module that can establish reliab

Philip Preußler, Felix L. Schwenninger

In this note we discuss the difficulty of verifying $\mathrm{L}^p$-admissibility for $p\neq 2$ -- that even manifests in the presence of a self-adjoint semigroup generator on a Hilbert space -- and survey tests for $\mathrm{L}^p$-admissibility of given control operators. These tests are obtained by virtue of either mapping properties of boundary trace operat

Stefano Galanda

In this paper, we aim to extend to interacting massive and massless fermionic theories the recent perturbative construction of equilibrium states developed within the framework of perturbative algebraic quantum field theory on Lorentzian spacetime. We analyze the case of interactions which depend on time by a smooth switch-on function and on space by a suita

D. Bazeia, Elisama E. M. Lima

This work deals with the presence of localized static structures in the real line, described by relativistic real scalar fields in two spacetime dimensions. We consider models featuring both standard and modified kinematics, where we employ two intriguing potentials supporting defect solutions. The first potential can transform kink into compacton in the sta

Jianlang Chen, Xuhong Ren, Qing Guo, Felix Juefei-Xu

Visual object tracking plays a critical role in visual-based autonomous systems, as it aims to estimate the position and size of the object of interest within a live video. Despite significant progress made in this field, state-of-the-art (SOTA) trackers often fail when faced with adversarial perturbations in the incoming frames. This can lead to significant

Arnab Dey, Di Yang, Rohith Agaram, Antitza Dantcheva

Recent advances in Neural Radiance Fields (NeRF) have demonstrated promising results in 3D scene representations, including 3D human representations. However, these representations often lack crucial information on the underlying human pose and structure, which is crucial for AR/VR applications and games. In this paper, we introduce a novel approach, termed

Andrey A. Dobrynin, Hamidreza Golmohammadi

A coalition in a graph $G$ with vertex set $V$ consists of two disjoint sets $V_1, V_2\subset V$ such that neither $V_1$ nor $V_2$ is a dominating set, but the union $V_1\cup V_2$ is a dominating set in $G$. A partition of graph vertices is called a coalition partition $\mathcal{P}$ if every non-dominating set of $\mathcal{P}$ is a member of a coalition and

Jinwei Han, Zhiwen Lin, Zhongyisun Sun, Yingguo Gao

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after fin

Sharana Dharshikgan Suresh Dass, Hrishav Bakul Barua, Ganesh Krishnasamy, Raveendran Paramesran

Human action or activity recognition in videos is a fundamental task in computer vision with applications in surveillance and monitoring, self-driving cars, sports analytics, human-robot interaction and many more. Traditional supervised methods require large annotated datasets for training, which are expensive and time-consuming to acquire. This work propose

Sofie Marie Koksbang, Asta Heinesen, Hayley J. Macpherson

Measurements of the cosmic redshift drift - the change in redshift of a source over time - will enable independent detection of cosmological expansion thanks to the immense precision soon reached by new facilities such as the Square Kilometer Array Observatory and the Extremely Large Telescope. We conduct the first ever redshift drift computation in fully re

Michael Joswig, Lars Kastner, Benjamin Lorenz

We discuss what is special about the reproducibility of workflows in computer algebra. It is emphasized how the programming language Julia and the new computer algebra system OSCAR support such a reproducibility, and how users can benefit for their own work.

Alexander Chebykin, Peter A. N. Bosman, Tanja Alderliesten

Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for medical image synthesis must be manually adjusted when they are applied to unseen data. To remove this manual burden, we introduce a Hyperparameter-Free distributed learning method fo

Rajat Chawla, Adarsh Jha, Muskaan Kumar, Mukunda NS

In this paper, we introduce GUIDE, a novel dataset tailored for the advancement of Multimodal Large Language Model (MLLM) applications, particularly focusing on Robotic Process Automation (RPA) use cases. Our dataset encompasses diverse data from various websites including Apollo(62.67\%), Gmail(3.43\%), Calendar(10.98\%) and Canva(22.92\%). Each data entry

Joseph P. Romano, Marius A. Tirlea

In this paper, we consider the fundamental problem of testing for monotone trend in a time series. While the term "trend" is commonly used and has an intuitive meaning, it is first crucial to specify its exact meaning in a hypothesis testing context. A commonly used well-known test is the Mann-Kendall test, which we show does not offer Type 1 error control e

Joseph P. Romano, Marius A. Tirlea

This paper studies permutation tests for regression parameters in a time series setting, where the time series is assumed stationary but may exhibit an arbitrary (but weak) dependence structure. In such a setting, it is perhaps surprising that permutation tests can offer any type of inference guarantees, since permuting of covariates can destroy their relati

Asbjørn Tornøe Andersen, Simon Vendelbo Bylling Jensen, Lars Bojer Madsen

We study intraband high-order harmonic generation arising from a band-gap material driven by a linearly polarized laser field. We factorize the intraband high-order harmonic-generation signal into intracycle and intercycle terms. The intracycle term uniquely determines the spectral characteristics whereas the intercycle term merely modulates the spectral fea

Arthur Drichel, Marc Meyer, Ulrike Meyer

In this work, we conduct a comprehensive study on the robustness of domain generation algorithm (DGA) classifiers. We implement 32 white-box attacks, 19 of which are very effective and induce a false-negative rate (FNR) of $\approx$ 100\% on unhardened classifiers. To defend the classifiers, we evaluate different hardening approaches and propose a novel trai

Arash Yavari

For a given class of materials, \emph{universal deformations} are those deformations that can be maintained in the absence of body forces and by applying solely boundary tractions. For inhomogeneous bodies, in addition to the universality constraints that determine the universal deformations, there are extra constraints on the form of the material inhomogene

Quentin Le Boulc'h, Fabio Hernandez, Gabriele Mainetti

The Vera C. Rubin Observatory, currently in construction in Chile, will start performing the Legacy Survey of Space and Time (LSST) in 2025 for 10 years. Its 8.4-meter telescope will survey the southern sky in less than 4 nights in six optical bands, and repeatedly generate about 2 000 exposures per night, corresponding to a data volume of about 20 TiB every

Robert Atkey, Wen Kokke

Multiplicative-Additive System Virtual (MAV) is a logic that extends Multiplicative-Additive Linear Logic with a self-dual non-commutative operator expressing the concept of "before" or "sequencing". MAV is also an extenson of the the logic Basic System Virtual (BV) with additives. Formulas in BV have an appealing reading as processes with parallel and seque

Jiashi Yang

This paper establishes a one-dimensional theoretical model for an elastic beam with embedded point magnets in extensional, bending and torsional motions. The beam has a circular cross-section. The point magnets may be in the interior or at the ends of the beam. They are assumed to be small and rigid, and may be ferromagnetic with spontaneous magnetic moments

F. Ferrente, C. Quintero Noda, F. Zuccarello, S. L. Guglielmino

We analyse the spatial distribution and vertical stratification of the physical parameters of the solar atmosphere when an X-class flare occurs. We made use of observations acquired by the Interferometric Bidimensional Spectropolarimeter instrument when observing the full Stokes parameters for the Fe I 6173 A and Ca II 8542 A transitions. We analysed the obs

Igor G. Smit, Zaharah Bukhsh, Mykola Pechenizkiy, Kostas Alogariastos

In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pickers load items onto the AMRs. In this paper, we consider an optimization problem in such systems where we allocate pickers to AMRs in a stochastic environment. We propose a novel m

Emre Ozfatura, Kerem Ozfatura, Baturalp Buyukates, Mert Coskuner

In federated learning (FL), profiling and verifying each client is inherently difficult, which introduces a significant security vulnerability: malicious clients, commonly referred to as Byzantines, can degrade the accuracy of the global model by submitting poisoned updates during training. To mitigate this, the aggregation process at the parameter server mu

Dianzhao Li, Paul Auerbach, Ostap Okhrin

While engaging with the unfolding revolution in autonomous driving, a challenge presents itself, how can we effectively raise awareness within society about this transformative trend? While full-scale autonomous driving vehicles often come with a hefty price tag, the emergence of small-scale car platforms offers a compelling alternative. These platforms not

Katharina Hämmerl, Jindřich Libovický, Alexander Fraser

Cross-lingual alignment, the meaningful similarity of representations across languages in multilingual language models, has been an active field of research in recent years. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field. We present different unders

Jiajing Chen, Weihang Xu, Haiming Cao, Zihuan Xu

With the increasing popularity of ChatGPT, large language models (LLMs) have demonstrated their capabilities in communication and reasoning, promising for transportation sector intelligentization. However, they still face challenges in domain-specific knowledge. This paper aims to leverage LLMs' reasoning and recognition abilities to replace traditional user

J. Karls, H. Cederquist, N. D. Gibson, J. Grumer

High-precision measurements of the electron affinities (EA) of the three stable isotopes of silicon, $^{28}$Si, $^{29}$Si and $^{30}$Si, have been performed at the cryogenic electrostatic ion-beam storage ring DESIREE. The quantum states of the ions were manipulated using laser depletion, and the ions were photodetached by laser photodetachment threshold spe

Qunlong Ma, Zhi Ma, Jinlong Xu, Hairui Zhang

Many deep neural networks have been used to solve Ising models, including autoregressive neural networks, convolutional neural networks, recurrent neural networks, and graph neural networks. Learning a probability distribution of energy configuration or finding the ground states of a disordered, fully connected Ising model is essential for statistical mechan

Bill Cai, Clarence Boon Liang Ng, Daniel Tan, Shelvia Hotama

Dictionary example sentences play an important role in illustrating word definitions and usage, but manually creating quality sentences is challenging. Prior works have demonstrated that language models can be trained to generate example sentences. However, they relied on costly customized models and word sense datasets for generation and evaluation of their

Ernesto A. Matute

Sterile neutrinos as source of mass and flavor mixing of active neutrinos as well as genesis of dark matter (DM) and matter-antimatter asymmetry have gained special interest. Here we study the case of the Standard Model (SM) extended with three right-handed (RH) neutrinos and a dark sector with two extra sterile neutrinos, odd under a discrete $Z_2$ symmetry

J. Karls, J. Grumer, S. Schiffmann, N. D. Gibson

The radiative decay of excited states of the negative ion of rhodium, Rh$^-$, has been investigated experimentally and theoretically. The experiments were conducted at the Double ElectroStatic Ion Ring Experiment (DESIREE) facility at Stockholm University using selective photodetachment from a stored ion beam to monitor the time evolution of the excited stat

Kaiwen Chen, Yiqi Geng, Yichao Jin, Zhicheng Yan

Motivated by the very recent observation of the $B^+_c\to J/\psi+\pi^+ +\pi^0$ decay using proton-proton collision data by the LHCb collaboration, we study the four-body angular distributions and the quantum entanglement effects in the $B^+_c\to J/\psi+\pi^+ +\pi^0$ associated with $J/\psi\to \mu^++\mu^-$. The helicity angular distributions are given in the

Rui Cai, Shichao Pei, Xiangliang Zhang

Relational learning is an essential task in the domain of knowledge representation, particularly in knowledge graph completion (KGC). While relational learning in traditional single-modal settings has been extensively studied, exploring it within a multimodal KGC context presents distinct challenges and opportunities. One of the major challenges is inference

Bach Ha, Birgit Schalter, Laura White, Joachim Koehler

Maintaining sewer systems in large cities is important, but also time and effort consuming, because visual inspections are currently done manually. To reduce the amount of aforementioned manual work, defects within sewer pipes should be located and classified automatically. In the past, multiple works have attempted solving this problem using classical image

Yuka Hashimoto, Ryuichiro Hataya

This paper introduces quantum circuit $C^*$-algebra net, which provides a connection between $C^*$-algebra nets proposed in classical machine learning and quantum circuits. Using $C^*$-algebra, a generalization of the space of complex numbers, we can represent quantum gates as weight parameters of a neural network. By introducing additional parameters, we ca

Li-Ming Zhan, Bo Liu, Xiao-Ming Wu

Out-of-distribution (OOD) detection plays a crucial role in ensuring the safety and reliability of deep neural networks in various applications. While there has been a growing focus on OOD detection in visual data, the field of textual OOD detection has received less attention. Only a few attempts have been made to directly apply general OOD detection method

Suleyman Ozdel, Efe Bozkir, Enkelejda Kasneci

As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce

Aleksandr V. Pukhlikov

We construct a new class of affine complements ${\mathbb P}^M\setminus S$ with the trivial group of automorphisms, where $S\subset {\mathbb P}^M$ is a rational hypersurface, $M$ is odd and $M\geqslant 5$.

Milad Yousefi, Shadi Farabi Maleki, Ali Jafarizadeh, Mahya Ahmadpour Youshanlui

Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023. A combination of keywords led to the discovery of an English academic

Leshem Choshen, Ryan Cotterell, Michael Y. Hu, Tal Linzen

After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the competition rules will be different. The big changes for this year's competition are as follows: First, we replace the loose track with a paper track, which allows (for example) non-mod

Bruno S. Felipe, João P. M. Pitelli

It is well known that (possibly non-unique) suitable field dynamics can be prescribed in spacetimes with timelike boundaries by means of appropriate boundary conditions. In Ref. [J. Math. Phys. {\bf 21}, 2802 (1980)], Wald derived a conserved energy functional for each prescribed dynamics. This conserved energy is related to the positive self-adjoint extensi

Elizaveta Goncharova, Anton Razzhigaev, Matvey Mikhalchuk, Maxim Kurkin

Last year, multimodal architectures served up a revolution in AI-based approaches and solutions, extending the capabilities of large language models (LLM). We propose an \textit{OmniFusion} model based on a pretrained LLM and adapters for visual modality. We evaluated and compared several architecture design principles for better text and visual data couplin