November 2022 arXiv papers — page 93

Denitsa Staicova

It has been theorized that Dynamical Dark Energy (DDE) could be a possible solution to the Hubble tension. To avoid the degeneracy between the Hubble parameter $H_0$ and the sound horizon scale $r_d$, in this article we use their multiplication as one parameter $c/\left(H_0 r_d\right)$ and we use it to infer cosmological parameters for 6 different models - $

Zi-Yu Tang, Xiao-Mei Kuang, Bin Wang, Wei-Liang Qian

To explore the possible clues for the extra dimension from the Event Horizon Telescope (EHT) observations, we study the shadow of the rotating 5D black string in General Relativity (GR). Instead of investigating the shadow in the effective 4D theory, we concern the motion of photons along the extra dimension $z$ with a conserved momentum $P_z$, which appears

Gui-Jun Ding, F. R. Joaquim, Jun-Nan Lu

Texture zeros in fermion mass matrices have been widely considered in tackling the Standard Model flavour puzzle. In this work, we perform a systematic analysis of texture zeros in lepton mass matrices in the framework of $\Gamma_{3}'\cong T'$ modular symmetry. Assuming that the lepton fields transform as irreducible representations of $T'$, we obtain all po

YongLiang Sun, Jinbi Zhang

For any Artin algebra, we construct a related algebra that increases the delooping level on one side while decreasing it to zero on the opposite side. This dual construction corresponds to Cummings' original work on finite dimensional algebras, later extended to rings by Henning Krause. As an application, we show that the finite delooping level is not left-r

Anton Angwald, Kalle Areskoug, Alan Said

The tech industry has been criticised for designing applications that undermine individuals' autonomy. Recommender systems, in particular, have been identified as a suspected culprit that might exercise unwanted control over peoples' lives. In this article we try to assess the objectives of recommender system research and offer a nuanced discussion of how th

Robyn L. Munoz, Marco Bruni

In order to invariantly characterise spacetimes resulting from cosmological simulations in numerical relativity, we present two different methodologies to compute the electric and magnetic parts of the Weyl tensor, $E_{\alpha\beta}$ and $B_{\alpha\beta}$, from which we construct scalar invariants and the Weyl scalars. The first method is geometrical, computi

Jin Min Yang, Yang Zhang, Pengxuan Zhu, Rui Zhu

We present a set of Lorentz invariant kinematic variables for reconstructing mass of semi-invisible decaying particles pair-produced at lepton colliders, $m_{\rm RC}^{\rm min}$, $m_{\rm RC}^{\rm max}$ and $m_{\rm LSP}^{\rm max}$, with analytical formulas. They give the minimal and maximum bounds of the decaying particle mass and upper bound of the invisible

Antoine Godichon-Baggioni, Stéphane Robin

Grouping observations into homogeneous groups is a recurrent task in statistical data analysis. We consider Gaussian Mixture Models, which are the most famous parametric model-based clustering method. We propose a new robust approach for model-based clustering, which consists in a modification of the EM algorithm (more specifically, the M-step) by replacing

Wibson W. G. Silva, Sérgio V. B. Degiorgi, José Holanda

We observed a magnetic interfacial effect due to the coupling between two interfaces of different materials. The interface is compoust of an antiferromagnetic and other quasi-ferromagnetic material. This effect we measured through the ferromagnetic resonance technique without and with electric current.

Chao Tao, Ji Qi, Mingning Guo, Qing Zhu

Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between feature learning paradigms (e.g., unsupervised feature learning (USFL), supervised feature learning (SFL), and self-supervised feature learning (SSFL)), this paper analyzes and compares them from the perspective

Takeo Sasai, Etsushi Yamazaki, Yoshiaki Kisaka

This paper presents analytical results on longitudinal power profile estimation (PPE) methods, which visualize signal power evolution in optical fibers at a coherent receiver. The PPE can be formulated as an inverse problem of the nonlinear Schr\"odinger equation, where the nonlinear coefficient (and thus signal power) is reconstructed from boundary conditio

Yang Qu, Jun Tao, Huan Yang

In this paper, we investigate the thermodynamics of D-dimensional charged Gauss-Bonnet black holes in anti-de Sitter spacetime. Varying the cosmological constant, Newton constant and Gauss-Bonnet coupling constant in the bulk, one can rewrite the first law of thermodynamics for black holes. Furthermore, we introduce the central charge and study the critical

Daniel Barrera Salazar, Mladen Dimitrov, Andrew Graham, Andrei Jorza

In this paper, we prove that a $\mathrm{GL}(2n)$-eigenvariety is \'etale over the (pure) weight space at non-critical Shalika points, and construct multi-variable $p$-adic $L$-functions varying over the resulting Shalika components. Our constructions hold in tame level 1 and Iwahori level at $p$, and give $p$-adic variation of $L$-values (of regular algebrai

Ali Khalatpour, Man Chun Tam, Sadhvikas J. Addamane, John Reno

Room temperature operation of Terahertz Quantum Cascade Lasers (THz QCLs) has been a long-pursued goal to realize compact semiconductor THz sources. The progress toward high-temperature operation in THz QCLs has been relatively slow compared to infrared QCLs owing to more significant challenges at THz frequencies. Recently, the maximum operating temperature

Mátyás Domokos, Botond Miklósi

It is shown that two vectors with coordinates in the finite $q$-element field of characteristic $p$ belong to the same orbit under the natural action of the symmetric group if each of the elementary symmetric polynomials of degree $p^k,2p^k,\dots,(q-1)p^k$, $k=0,1,2,\dots$ has the same value on them. This separating set of polynomial invariants for the natur

Diego Martín Duro

In this paper, we study if, for a given simple module over a Hopf algebra, there exists a virtual module such that their tensor product is the regular module. This is related to a conjecture by Donald Knutson, later disproved and refined by Savitskii, stating that for every irreducible character of a finite group, there exists a virtual character such that t

Krishna Jaiswal, Horia Metiu, Vishal Agarwal

We use a simple generic model to study the desorption of atoms from a solid surface in contact with a liquid, by using a combination of Monte Carlo and molecular dynamics simulations. The behavior of the system depends on two parameters: the strength $\epsilon_{LS}$ of the solid-liquid interaction energy and the strength $\epsilon_{LL}$ of the liquid-liquid

Giulia Ferrandi, Igor V. Kravchenko, Michiel E. Hochstenbach, M. Rosário Oliveira

We compare two different linear dimensionality reduction strategies for the multigroup classification problem: the trace ratio method and Fisher's discriminant analysis. Recently, trace ratio optimization has gained in popularity due to its computational efficiency, as well as the occasionally better classification results. However, a statistical understandi

Sipei Li, Jianzhong He, Tengfei Xue, Guoqiang Xie

The retinogeniculate pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that u

Julian Holstein, Andrey Lazarev

It is well-known that the category of small dg categories dgCat, though it is monoidal, does not form a monoidal model category. In this paper we construct a monoidal model structure on the category of pointed curved coalgebras ptdCoa* and show that the Quillen equivalence relating it to dgCat is monoidal. We also show that dgCat is a ptdCoa*-enriched model

M. Greta Ruppert, Yvonne Späck-Leigsnering, Julian Buschbaum, Herbert De Gersem

Many optimization problems in electrical engineering consider a large number of design parameters. A sensitivity analysis identifies the design parameters with the strongest influence on the problem of interest. This paper introduces the adjoint variable method as an efficient approach to study sensitivities of nonlinear electroquasistatic problems in time d

Rajjat Dadwal, Thorben Funke, Michael Nüsken, Elena Demidova

With the rise of data-driven methods for traffic forecasting, accident prediction, and profiling driving behavior, personal GPS trajectory data has become an essential asset for businesses and emerging data markets. However, as personal data, GPS trajectories require protection. Especially by data breaches, verification of GPS data ownership is a challenging

Julia Hornauer, Vasileios Belagiannis

Our work investigates out-of-distribution (OOD) detection as a neural network output explanation problem. We learn a heatmap representation for detecting OOD images while visualizing in- and out-of-distribution image regions at the same time. Given a trained and fixed classifier, we train a decoder neural network to produce heatmaps with zero response for in

Ning Tian, Zhe Huang, Bo Gyu Jang, Shuaifei Guo

When electron-electron interaction dominates over other electronic energy scales, exotic, collective phenomena often emerge out of seemingly ordinary matter. The strongly correlated phenomena, such as quantum spin liquid and unconventional superconductivity, represent a major research frontier and a constant source of inspiration. Central to strongly correla

Haibin Chen, Rong Wu

The rotation of granules shows thermal properties similar to the thermal motion of molecules, which effects convection criterion and solar granule size.The granular rotation generates the rotational additional pressure and corresponding rotational equivalent temperature in the spherically symmetric expansion of the granule.The rotation equivalent temperature

Sepideh Mamooler, Rémi Lebret, Stéphane Massonnet, Karl Aberer

Active Learning (AL) is a powerful tool for learning with less labeled data, in particular, for specialized domains, like legal documents, where unlabeled data is abundant, but the annotation requires domain expertise and is thus expensive. Recent works have shown the effectiveness of AL strategies for pre-trained language models. However, most AL strategies

Andrea Merlo, Daniel Böckenhoff, Jonathan Schilling, Samuel Aaron Lazerson

The computational cost of constructing 3D magnetohydrodynamic (MHD) equilibria is one of the limiting factors in stellarator research and design. Although data-driven approaches have been proposed to provide fast 3D MHD equilibria, the accuracy with which equilibrium properties are reconstructed is unknown. In this work, we describe an artificial neural netw

Dominik Janzing, Sergio Hernan Garrido Mejia

Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking 'what qualifies an action for being an intervention on the variable X' raises the question whether the action impacted all other variables only through X or dir

Liviu Ornea, Misha Verbitsky, Victor Vuletescu

An LCK (locally conformally Kahler) manifold is a Hermitian manifold which admits a Kahler cover with deck group acting by holomorphic homotheties with respect to the Kahler metric. The product of two LCK manifolds does not have a natural product LCK structure. It is conjectured that a product of two compact complex manifolds is never LCK. We classify all kn

Peiyan Dong, Mengshu Sun, Alec Lu, Yanyue Xie

While vision transformers (ViTs) have continuously achieved new milestones in the field of computer vision, their sophisticated network architectures with high computation and memory costs have impeded their deployment on resource-limited edge devices. In this paper, we propose a hardware-efficient image-adaptive token pruning framework called HeatViT for ef

Elena Tiukhova, Emiliano Penaloza, María Óskarsdóttir, Hernan Garcia

Leveraging network information for prediction tasks has become a common practice in many domains. Being an important part of targeted marketing, influencer detection can potentially benefit from incorporating dynamic network representation. In this work, we investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection and

Lech Longa, Michał Cieśla, Paweł Karbowniczek, Agnieszka Chrzanowska

We study the self-organization of flexible planar trimer particles on a structureless surface. The molecules are made up of two mesogenic units linked by a spacer, all of which are modeled as hard needles of the same length. Each molecule can dynamically adopt two conformational states: an achiral bent-shaped (cis-) and a chiral zigzag (trans-) one. Using co

Daniela Maier, Wolfgang Reichel, Guido Schneider

We consider the nonlinear Klein-Gordon equation $\partial_t^2u(x,t)-\partial_x^2u(x,t)+\alpha u(x,t)=\pm|u(x,t)|^{p-1}u(x,t)$ on a periodic metric graph (necklace graph) for $p>1$ with Kirchhoff conditions at the vertices. Under suitable assumptions on the frequency we prove the existence and regularity of infinitely many spatially localized time-periodic so

Alejandro Cholaquidis

The study of shape restrictions of subsets of $\mathbb{R}^d$ have several applications in many areas, being convexity, $r$-convexity, and positive reach, some of the most famous, and typically imposed in set estimation. The following problem was attributed to K. Borsuk, by J. Perkal in 1956: find an $r$-convex set which is not locally contractible. Stated in

Weimin Wu, Jiayuan Fan, Tao Chen, Hancheng Ye

The linear ensemble based strategy, i.e., averaging ensemble, has been proposed to improve the performance in unsupervised domain adaptation tasks. However, a typical UDA task is usually challenged by dynamically changing factors, such as variable weather, views, and background in the unlabeled target domain. Most previous ensemble strategies ignore UDA's dy

Asher Yahalom

In a previous paper we have shown that superluminal particles are allowed by the general relativistic theory of gravity provided that the metric is locally Euclidean. Here we calculate the probability density function of a canonical ensemble of superluminal particles as function of temperature. This is done for both space-times invariant under Lorentz symmet

Jan Goedgebeur, Jorik Jooken, On-Hei Solomon Lo, Ben Seamone

We fully disprove a conjecture of Haythorpe on the minimum number of hamiltonian cycles in regular hamiltonian graphs, thereby extending a result of Zamfirescu, as well as correct and complement Haythorpe's computational enumerative results from [Experim. Math. 27 (2018) 426-430]. Thereafter, we use the Lov\'asz Local Lemma to extend Thomassen's independent

Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang

We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling and span prediction, into a unified multi-task framework. A

Lian Liu, Zhong-Bao Yin, Liang Zheng

In this paper, we perform the Tsallis Blast-Wave analysis on the transverse momentum spectra of identified hadrons produced in a wide range of collision systems at the Large Hadron Collider (LHC) including pp, pPb, XeXe and PbPb collisions. The kinetic freeze-out properties are investigated across these systems varying with the event multiplicity. We find th

Heejin Do, Yunsu Kim, Gary Geunbae Lee

Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels of granularity such as phoneme, word, and utterance, with diverse aspects such as accuracy, fluency, and completeness, is essential. However, existing multi-aspect multi-granularit

Yuval Meir, Shira Sardi, Shiri Hodassman, Karin Kisos

Power-law scaling, a central concept in critical phenomena, is found to be useful in deep learning, where optimized test errors on handwritten digit examples converge as a power-law to zero with database size. For rapid decision making with one training epoch, each example is presented only once to the trained network, the power-law exponent increased with t

Alexandre Didier, Melanie N. Zeilinger

This paper presents a synthesis method for the generalised dynamic regret problem, comparing the performance of a strictly causal controller to the optimal non-causal controller under a weighted disturbance. This framework encompasses both the dynamic regret problem, considering the difference of the incurred costs, as well as the competitive ratio, which co

Adam Chapman, Elad Paran

We study orbits and fixed points of polynomials in a general skew polynomial ring $D[x,\sigma, \delta]$. We extend results of the first author and Vishkautsan on polynomial dynamics in $D[x]$. In particular, we show that if $a \in D$ and $f \in D[x,\sigma,\delta]$ satisfy $f(a) = a$, then $f^{\circ n}(a) = a$ for every formal power of $f$. More generally, we

Haike Xu, Zongyu Lin, Jing Zhou, Yanan Zheng

Generative modeling has been the dominant approach for large-scale pretraining and zero-shot generalization. In this work, we challenge this convention by showing that discriminative approaches perform substantially better than generative ones on a large number of NLP tasks. Technically, we train a single discriminator to predict whether a text sample comes

Larisa Jonke

Homotopy Lie algebras are a generalization of differential graded Lie algebras encoding both the kinematics and dynamics of a given field theory. Focusing on kinematics, we show that these algebras provide a natural framework for the description of generalized gauge symmetries using two specific examples. The first example deals with the non-commutative gaug

Athanasios Chatzistavrakidis

Generalisations of geometry have emerged in various forms in the study of field theory and quantization. This mini-review focuses on the role of higher geometry in three selected physical applications. After motivating and describing some basic aspects of algebroid structures on bundles and (differential graded) Q-manifolds, we briefly discuss their relation

Zihao Yang, Shaoqi Zheng, Shangkun Shen, Fei Han

The fracture simulation of random particle reinforced composite structures remains a challenge. Current techniques either assumed a homogeneous model, ignoring the microstructure characteristics of composite structures, or considered a micro-mechanical model, involving intractable computational costs. This paper proposes a peridynamics-based statistical mult

Meng Huang, Xiucai Ye, Tetsuya Sakurai

In this paper, to unveil interpretable development-specific gene signatures in human PFC, we propose a novel gene selection method, named Interpretable Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to represent causality between multiple gene variables and a development variable. The proposed ICGS method combines the positive instance

Ryuichiro Hataya, Han Bao, Hiromi Arai

Recently proposed large-scale text-to-image generative models such as DALL$\cdot$E 2, Midjourney, and StableDiffusion can generate high-quality and realistic images from users' prompts. Not limited to the research community, ordinary Internet users enjoy these generative models, and consequently, a tremendous amount of generated images have been shared on th

Xiaopei Sun, Zhaozheng Lyu, Enna Zhuo, Bing Li

Majorana zero modes have been attracting considerable attention because of their prospective applications in fault-tolerant topological quantum computing. In recent years, some schemes have been proposed to detect and manipulate Majorana zero modes using superconducting qubits. However, manipulating and reading the Majorana zero modes must be kept in the tim

Chethan Krishnan, Ranjini Mondol, M. M. Sheikh-Jabbari

We show that the Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) framework has an instability towards the growth of fluid flow anisotropies, even if the Universe is accelerating. This flow (tilt) instability in the matter sector is invisible to Cosmic No-Hair Theorem-like arguments, which typically only flag shear anisotropies in the metric. We illustrate our

Richard A. N. Brooks, Kyle A. Oman, Carlos S. Frenk

The number density of extragalactic 21-cm radio sources as a function of their spectral line-widths -- the HI width function (HIWF) -- is a sensitive tracer of the dark matter halo mass function (HMF). The $\Lambda$ cold dark matter model predicts that the HMF should be identical everywhere provided it is sampled in sufficiently large volumes, implying that

Yan Gerard

We consider a class of problems of Discrete Tomography which has been deeply investigated in the past: the reconstruction of convex lattice sets from their horizontal and/or vertical X-rays, i.e. from the number of points in a sequence of consecutive horizontal and vertical lines. The reconstruction of the HV-convex polyominoes works usually in two steps, fi

Javier Jiménez-Garrido, David Nicolas Nenning, Gerhard Schindl

We show that the ultradifferentiable-like classes of smooth functions introduced and studied by S. Pilipovi\'c, N. Teofanov and F. Tomi\'c are special cases of the general framework of spaces of ultradifferentiable functions defined in terms of weight matrices in the sense of A. Rainer and the third author. We study classes "beyond geometric growth factors"

Yeying Jin, Wei Ye, Wenhan Yang, Yuan Yuan

Removing soft and self shadows that lack clear boundaries from a single image is still challenging. Self shadows are shadows that are cast on the object itself. Most existing methods rely on binary shadow masks, without considering the ambiguous boundaries of soft and self shadows. In this paper, we present DeS3, a method that removes hard, soft and self sha

Gaétan Leclerc

We exhibit a family of autosimilar H\"older maps that satisfies a fractal version of the Van Der Corput Lemma, despite not being absolutely continuous. The result is a direct consequence of a recent work of Sahlsten and Steven arXiv:2009.01703, which is based on a powerful theorem of Bourgain known as a sum-product phenomenon estimate. We give a substantiall

M. C. Crabb

We describe a connective $K$-theory Borsuk--Ulam/Bourgin--Yang theorem for cyclic groups of order a power of a prime $p$. Consider two finite dimensional complex representations $U$ and $V$ of the cyclic group $Z /p^{k+1}$ of order $p^{k+1}$, where $k\geq 0$. For $0\leq l\leq k$, we write $V_l$ for the subspace of $V$ fixed by the cyclic subgroup of order $p

Daniel Reich, Felix Putze, Tanja Schultz

Visual Grounding (VG) in Visual Question Answering (VQA) systems describes how well a system manages to tie a question and its answer to relevant image regions. Systems with strong VG are considered intuitively interpretable and suggest an improved scene understanding. While VQA accuracy performances have seen impressive gains over the past few years, explic

Nikita Barabash, Tatiana Levanova, Sergey Stasenko

Despite the fact that the phenomenon of bursting activity is important for functioning of living neural networks, the mechanisms of its origin are still not clear. In this paper, we propose a new phenomenological model that can explain the mechanisms of the formation of bursting activity based on short-term synaptic plasticity, recurrent connections, and neu

Meng Huang, Jiangtao Ma, Changzhou Long, Junpeng Zhang

Long non-coding RNAs (lncRNAs) are important regulators to modulate gene expression and cell proliferation in the developing human brain. Previous methods mainly use bulk lncRNA and mRNA expression data to study lncRNA regulation. However, to analyze lncRNA regulation regarding individual cells, we focus on single-cell RNA-sequencing (scRNA-seq) data instead

Pierre Apkarian, Dominikus Noll

We discuss strategies to bring $H_\infty$-control techniques into play when the system dynamics are modeled by hyperbolic partial differential equations, or more generally, by systems with non-sectorial pole pattern.

Kyunghoon Hur, Jungwoo Oh, Junu Kim, Jiyoun Kim

Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models. To address this challenge, we propose Universal Healthcare Predictive Framework (UniHPF), which requires no medical domain knowledge and minimal pre-processing for multiple prediction tasks. Experimental res

Annika Junker, Niklas Fittkau, Julia Timmermann, Ansgar Trächtler

We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods, to have the robot autonomously learn to putt the ball from an arbitrary point on the green. Apart from the mechatronic control design of the robot, this task is accomplished by a camera system with image recognition and a neural network for predicti

Luca Nanu, Carlos Perez Montenegro, Luigi Colangelo, Carlo Novara

In Networked Control Systems (NCS), the absence of physical communication links in the loop leads to relevant issues, such as measurement delays and asynchronous execution of the control commands. These issues may lead to unwanted control behaviours. This ArXiv paper is intended to give additional results to the work presented in "Embedded Model Control of N

Kota Yoshioka

We shall study stability conditions and Fourier-Mukai transforms on an elliptic surface. In particular we shall explain duality of elliptic surfaces by Fourier-Mukai transforms.

Valeria Terrones, Richard S. J. Tol

Previous studies show that natural disasters decelerate economic growth, and more so in countries with lower financial development. We confirm these results with more recent data. We are the first to show that fiscal stability reduces the negative economic impact of natural disasters in poorer countries, and that catastrophe bonds have the same effect in ric

Elise Dumas, Anne-Sophie Hamy, Sophie Houzard, Eva Hernandez

While the emergence of large administrative claims data provides opportunities for research, their use remains limited by the lack of clinical annotations relevant to disease outcomes, such as recurrence in breast cancer (BC). Several challenges arise from the annotation of such endpoints in administrative claims, including the need to infer both the occurre

Maximilian Weigert, Andreas Beyerlein, Katharina Katz, Rickmer Schulte

Background: There is a lack of population-based studies on the effectiveness and durability of the SARS-CoV-2-induced immune protection during the Omicron wave. Methods: This retrospective study included 470 159 cases aged 60 years or older, who tested positive for SARS-CoV-2 between January 1 and June 30, 2022 in Bavaria, Germany. We examined time to death,

Guillermo García-Grao, Álvaro Carrera

The DevOps paradigm is taking over software development systems, helping businesses increase efficiency, accelerate production, and adapt quickly to market changes. However, adopting these principles can be challenging. Practitioners often face an important issue known as vendor lock-in caused by the cost of tool replacement. In addition, automating the proc

Ciheng Zhang, Decky Aspandi, Steffen Staab

World-wide-web, with the website and webpage as the main interface, facilitates the dissemination of important information. Hence it is crucial to optimize them for better user interaction, which is primarily done by analyzing users' behavior, especially users' eye-gaze locations. However, gathering these data is still considered to be labor and time intensi

Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li

Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase. However, the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks, limiting the real-world deployment of these methods. This paper

M. Georgopoulou, B. Fåk, D. Boldrin, J. R. Stewart

Neutron scattering measurements have been performed on deuterated powder samples of claringbullite and Zn-doped claringbullite (Zn$_x$Cu$_{4-x}$(OD)$_{6}$FCl). At low temperatures, claringbullite Cu$_4$(OD)$_{6}$FCl forms a distorted pyrochlore lattice with long-range magnetic order and spin-wave-like magnetic excitations. Partial Zn doping leads to the nomi

Yu Wang, Xin Li, Shengzhao Weng, Gang Zhang

DETR is a novel end-to-end transformer architecture object detector, which significantly outperforms classic detectors when scaling up. In this paper, we focus on the compression of DETR with knowledge distillation. While knowledge distillation has been well-studied in classic detectors, there is a lack of researches on how to make it work effectively on DET

Zhongda Zeng, Enderalp Yakaboylu, Mikhail Lemeshko, Tao Shi

The angulon, a quasiparticle formed by a quantum rotor dressed by the excitations of a many-body bath, can be used to describe an impurity rotating in a fluid or solid environment. Here we propose a coherent state ansatz in the co-rotating frame which provides a comprehensive theoretical description of angulons. We reveal the quasiparticle properties, such a

Takafumi D. Yamamoto, Akiko T. Saito, Hiroyuki Takeya, Kensei Terashima

Processing magnetocaloric materials into magnetic refrigerants with appropriate shapes is essential for the development of magnetic refrigeration systems. In this context, the impact of processing on the physical properties of magnetocaloric materials is one of the important issues. Here, we investigate the crystallographic, magnetic, and magnetocaloric prop

Zhihao Zhu, Chenwang Wu, Min Zhou, Hao Liao

Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications. In this work, we focus on the emerging but critical attack, namely, Graph Injection Attack(GIA), in which the adversary poisons the graph by injecting f

Pooja Bhattacharjee, Francesca Calore, Pasquale Dario Serpico

Brown dwarfs (BDs) are celestial objects representing the link between the least massive main-sequence stars and giant gas planets. In the first part of this article, we perform a model-independent search of a gamma-ray signal from the direction of nine nearby BDs in 13 years of \Fermi-LAT data. We find no significant excess of gamma rays, and we, therefore,

Barbara Brandolini, Ida de Bonis, Vincenzo Ferone, Bruno Volzone

We provide symmetrization results in the form of mass concentration comparisons for fractional singular elliptic equations in bounded domains, coupled with homogeneous external Dirichlet conditions. Two types of comparison results are presented, depending on the summability of the right-hand side of the equation. The maximum principle arguments employed in t

Qing-Hong Cao, Naoto Kan, Daiki Ueda

We study constraints on the effective field theory (EFT) from the relative entropy between two theories: we refer to these as target and reference theories. The consequence of the non-negativity of the relative entropy is investigated by choosing some reference theories for a given target theory involving field theories, quantum mechanical models, etc. It is

Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying

Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are governed by physical laws. Recent work shows that it provides potential benefits for machine learning models by incorpo

Yin Fang, Wen-Bo Bo, Ru-Ru Wang, Yue-Yue Wang

The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the information of compound derivative embedded into the soft-constraint of physics-informed neural network(PINN). It is used to predict nonlinear dynamics and the formation process of bright and dark picosecond optical solitons, and femtosecond soliton molecule in the si

Alejandro Moreo, Manuel Francisco, Fabrizio Sebastiani

Quantification, variously called "supervised prevalence estimation" or "learning to quantify", is the supervised learning task of generating predictors of the relative frequencies (a.k.a. "prevalence values") of the classes of interest in unlabelled data samples. While many quantification methods have been proposed in the past for binary problems and, to a l

Shi-Ping He

Leptoquark (LQ) models are well motivated solutions to the $(g-2)_{\mu}$ anomaly. In the minimal LQ models, only specific representations can lead to the chiral enhancements. For the scalar LQs, the $R_2$ and $S_1$ can lead to the top quark chiral enhancement. For the vector LQs, the $V_2$ and $U_1$ can lead to the bottom quark chiral enhancement. When we co

H. V. Ovcharenko, O. B. Zaslavskii

We consider the metric of an axially symmetric rotating black hole. We do not specify the concrete form of a metric and rely on its behavior near the horizon only. Typically, it is characterized (in the coordinates that generalize the Boyer-Lindquist ones) by two integers $p$ and $q$ that enter asymptotic expansions of the time and radial metric coefficients

Dmitry Demidov, Rushali Grandhe, Salem AlMarri

Object detection in natural images has achieved remarkable results over the years. However, a similar progress has not yet been observed in aerial object detection due to several challenges, such as high resolution images, instances scale variation, class imbalance etc. We show the performance of two-stage, one-stage and attention based object detectors on t

Thomas Thersleff, Jordi Jacas Biendicho, Kunkanadu Prakasha, Elias Martinez Moreno

Despite significant potential as energy storage materials for electric vehicles due to their combination of high energy density per unit cost and reduced environmental and ethical concerns, Co-free lithium ion batteries based off layered Mn oxides presently lack the longevity and stability of their Co-containing counterparts. Here, we demonstrate a reduction

Toby P Jones

The financial losses from extreme weather events can have a disastrous effect, often costing billions of pounds. While changes in the disposition of individual events is of importance to both the insurance and re-insurance industries, these companies are often concerned with the aggregate risk posed in a season. This project explores how the statistical prop

Amar Bapić

In \cite{Bapic, Tang, Zheng} a new method for the secondary construction of vectorial/Boolean bent functions via the so-called $(P_U)$ property was introduced. In 2018, Qi et al. generalized the methods in \cite{Tang} for the construction of $p$-ary weakly regular bent functions. The objective of this paper is to further generalize these constructions, follo

Elaine Zosa, Lidia Pivovarova

This paper presents M3L-Contrast -- a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space. Our model is trained jointly on texts and images and takes advantage of pretrained document and image embeddings to abstract the complexities between different languages

Anjo Vahldiek-Oberwagner, Mona Vij

Motivated by developer productivity, serverless computing, and microservices have become the de facto development model in the cloud. Microservices decompose monolithic applications into separate functional units deployed individually. This deployment model, however, costs CSPs a large infrastructure tax of more than 25%. To overcome these limitations, CSPs

Hao Liu, Zhuoran Xu, Dan Wang, Baofeng Zhang

3D object detection is a critical task in autonomous driving. Recently multi-modal fusion-based 3D object detection methods, which combine the complementary advantages of LiDAR and camera, have shown great performance improvements over mono-modal methods. However, so far, no methods have attempted to utilize the instance-level contextual image semantics to g

Octavio Arizmendi, Marwa Banna, Pei-Lun Tseng

We provide Berry-Esseen bounds for sums of operator-valued Boolean and monotone independent variables, in terms of the first moments of the summands. Our bounds are on the level of Cauchy transforms as well as the L\'evy distance. As applications, we obtain quantitative bounds for the corresponding CLTs, provide a quantitative "fourth moment theorem" for mon

Lins Denaux, Jozefien D'haeseleer, Geertrui Van de Voorde

In this paper, we investigate the Andr\'e/Bruck-Bose representation of certain $\mathbb{F}_q$-linear sets contained in a line of $\text{PG}(2,q^t)$. We show that scattered $\mathbb{F}_q$-linear sets of rank $3$ in $\text{PG}(1,q^3)$ correspond to particular hyperbolic quadrics and that $\mathbb{F}_q$-linear clubs in $\text{PG}(1,q^t)$ are linked to subspaces

David Fajman, Liam Urban

We consider general initial data for the Einstein scalar-field system on a closed $3$-manifold $(M,\gamma)$ which is close to data for a Friedman-Lema\^itre-Robertson-Walker solution with homogeneous scalar field matter and a negative Einstein metric $\gamma$ as spatial geometry. We prove that the maximal globally hyperbolic development of such initial data

Neil Deo

We study the empirical process arising from a multi-dimensional diffusion process with periodic drift and diffusivity. The smoothing properties of the generator of the diffusion are exploited to prove the Donsker property for certain classes of smooth functions. We partially generalise the finding from the one-dimensional case studied in [van der Vaart & van

Davi A. D. Chaves, Lukas Nulens, Heleen Dausy, Bart Raes

With the development of novel computing schemes working at cryogenic temperatures, superconducting memory elements have become essential. In this context, superconducting quantum interference devices (SQUIDs) are promising candidates, as they may trap different discrete amounts of magnetic flux. We demonstrate that a field-assisted writing scheme allows such

Andrea Ciamarra, Federico Becattini, Lorenzo Seidenari, Alberto Del Bimbo

For an autonomous vehicle it is essential to observe the ongoing dynamics of a scene and consequently predict imminent future scenarios to ensure safety to itself and others. This can be done using different sensors and modalities. In this paper we investigate the usage of optical flow for predicting future semantic segmentations. To do so we propose a model

A. Frej, I. Razdolski, A. Maziewski, A. Stupakiewicz

We analyze, both experimentally and numerically, the nonlinear regime of the photo-induced coherent magnetization dynamics in cobalt-doped yttrium iron garnet films. Photo-magnetic excitation with femtosecond laser pulses reveals a strongly nonlinear response of the spin subsystem with a significant increase of the effective Gilbert damping. By varying both

Siddhant Prakash, Gilles Rainer, Adrien Bousseau, George Drettakis

The movie and video game industries have adopted photogrammetry as a way to create digital 3D assets from multiple photographs of a real-world scene. But photogrammetry algorithms typically output an RGB texture atlas of the scene that only serves as visual guidance for skilled artists to create material maps suitable for physically-based rendering. We prese

Saideep Sreekumar, Mohammed Ashraf, Mohammed Nabeel, Ozgur Sinanoglu

Power side-channel (PSC) attacks are well-known threats to sensitive hardware like advanced encryption standard (AES) crypto cores. Given the significant impact of supply voltages (VCCs) on power profiles, various countermeasures based on VCC tuning have been proposed, among other defense strategies. Driver strengths of cells, however, have been largely over