March 2023 arXiv papers — page 34

Eric Wulff, Maria Girone, David Southwick, Juan Pablo García Amboage

Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as well as scalable and resource efficient hyperparameter search algorithms. This work studies the potential of using model performance prediction to aid the HPO process carried out on

B. S. Ham

Quantum entanglement is known as a unique quantum feature that cannot be obtained by classical physics. Over the last several decades, however, such an understanding on quantum entanglement might have confined us in a limited world of weird quantum mechanics. Unlike a single photon, a definite phase relation between paired photons is the key to understanding

Renchunzi Xie, Hongxin Wei, Lei Feng, Yuzhou Cao

Estimating the generalization performance is practically challenging on out-of-distribution (OOD) data without ground-truth labels. While previous methods emphasize the connection between distribution difference and OOD accuracy, we show that a large domain gap not necessarily leads to a low test accuracy. In this paper, we investigate this problem from the

Fabio Paparella, Leonardo Pedroso, Theo Hofman, Mauro Salazar

This paper presents a time-invariant network flow model capturing two-person ride-pooling that can be integrated within design and planning frameworks for Mobility-on-Demand systems. In these type of models, the arrival process of travel requests is described by a Poisson process, meaning that there is only statistical insight into request times, including t

Sayan Jana, Lea Sirota

We study the interplay of two distinct non-Hermitian parameters: directional coupling and onsite gain-loss, together with topology, in coupled one-dimensional (1D) non-Hermitian Su-Schrieffer-Heeger (SSH) chains. The SSH model represents one of the simplest two-band models that features boundary localized topological modes. Our study shows how the merging of

Zihao Wang, Nathan Keyes, Terry Crawford, Jinho D. Choi

We present the InterviewBot that dynamically integrates conversation history and customized topics into a coherent embedding space to conduct 10 mins hybrid-domain (open and closed) conversations with foreign students applying to U.S. colleges for assessing their academic and cultural readiness. To build a neural-based end-to-end dialogue model, 7,361 audio

Tapan K. Sengupta, Shivam K. Jha, Aditi Sengupta, Bhavna Joshi

Here, the perturbation equation for a dissipative medium is derived from the first principle from the linearized compressible Navier-Stokes equation without Stokes's hypothesis. The dispersion relations of this generic governing equation are obtained for one and three-dimensional perturbations, which exhibit both the dispersive and dissipative nature of the

Juan Manuel Rosselló, Hendrik Reese, K. Ashoke Raman, Claus-Dieter Ohl

In this work, we present experiments and simulations on the nucleation and successive dynamics of laser-induced bubbles inside liquid droplets in free-fall motion, i.e. a case with a free boundary in all directions. The droplets of a millimetric size have a nearly spherical shape by the moment the bubble is nucleated. We have investigated the nucleation of s

Yuekun Dai, Yihang Luo, Shangchen Zhou, Chongyi Li

Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, but they often fail to identify reflecti

Athanassios Tzouvaras

It is shown that Vop\v{e}nka's Principle (VP) can restore almost the entire ZF over a weak fragment of it. Namely, if EST is the theory consisting of the axioms of Extensionality, Empty Set, Pairing, Union, Cartesian Product, $\Delta_0$-Separation and Induction along $\omega$, then ${\rm EST+VP}$ proves the axioms of Infinity, Replacement (thus also Separati

Philippe Laurençot

Global weak solutions to a chemotaxis model with local sensing and consumption are shown to converge to spatially homogeneous steady states in the large time limit, when the motility is assumed to be positive and $C^1$-smooth on $[0,\infty)$. The result is valid in arbitrary space dimension $n\ge 1$ and extends a previous result which only deals with space d

Wei Shang, Dongwei Ren, Yi Yang, Hongzhi Zhang

Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and less than ideal exposure setting. As a result, computational methods that jointly perform video frame interpolation and deblurring begin to emerge with the unrealistic assumption tha

Huajian Fang, Niklas Wittmer, Johannes Twiefel, Stefan Wermter

Human-robot interaction relies on a noise-robust audio processing module capable of estimating target speech from audio recordings impacted by environmental noise, as well as self-induced noise, so-called ego-noise. While external ambient noise sources vary from environment to environment, ego-noise is mainly caused by the internal motors and joints of a rob

Amanda Lenzi, Haavard Rue

Deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging. But the success of these approaches depends on simulating parameters that sufficiently reproduce the observed data, and, at present, there is a lack of efficient methods t

D. Benisty, J. Mifsud, J. Levi Said, D. Staicova

Extended gravity is widely constrained in different astrophysical and astronomical systems. Since these different systems are based on different scales it is not trivial to get a combined constraint that is based on different phenomenology. Here, for the first time (to the best of our knowledge), we combine constraints for $f(R)$ gravity from late time Cosmo

Alessandro Columbu, Silvia Frassu, Giuseppe Viglialoro

This paper deals with unbounded solutions to a class of chemotaxis systems. In particular, for a rather general attraction-repulsion model, with nonlinear productions, diffusion, sensitivities and logistic term, we detect Lebesgue spaces where given unbounded solutions blow-up also in the corresponding norms of those spaces; subsequently, estimates for the b

Haoxuan Che, Siyu Chen, Hao Chen

Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge

Vittorio De Falco

The general relativistic Poynting-Robertson (PR) effect is a very important dissipative phenomenon occurring in high-energy astrophysics. Recently, it has been proposed a new model, which upgrades the two-dimensional (2D) description in the three-dimensional (3D) case in Kerr spacetime. The radiation field is considered as constituted by photons emitted from

Dwaipayan Roy, Zeljko Carevic, Philipp Mayr

Retrievability measures the influence a retrieval system has on the access to information in a given collection of items. This measure can help in making an evaluation of the search system based on which insights can be drawn. In this paper, we investigate the retrievability in an integrated search system consisting of items from various categories, particul

David Chavalarias, Paul Bouchaud, Maziyar Panahi

As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed at large scale by digital pla

S. K. Rajput

This article explores how a rigid top surface with diffuse and oblique collimated irradiation affect isotropic scattering algal suspensions. When the fluid flow becomes zero, the suspension reaches a steady (basic) state where up-and-down swimming occurs due to interplay of phototaxis and diffusion. For purely scattering suspensions, a bimodal steady state o

Cataldo Basile, Bjorn De Sutter, Daniele Canavese, Leonardo Regano

The last years have seen an increase in Man-at-the-End (MATE) attacks against software applications, both in number and severity. However, software protection, which aims at mitigating MATE attacks, is dominated by fuzzy concepts and security-through-obscurity. This paper presents a rationale for adopting and standardizing the protection of software as a ris

Silviu Balanescu, Mircea Cimpoeas

Let $J_{n,m}:=(x_1x_2\cdots x_m,\; x_2x_3\cdots x_{m+1},\; \ldots,\; x_{n-m+1}\cdots x_n,\; x_{n-m+2}\cdots x_nx_1, \ldots, x_nx_1\cdots x_{m-1})$ be the $m$-path ideal of the cycle graph of length $n$, in the ring $S=K[x_1,\ldots,x_n]$. Let $d=\gcd(n,m)$. We prove that $\operatorname{depth}(S/J_{n,m}^t)\leq d-1$ for all $t\geq n-1$. We show that $\operatorn

Athanassios Tzouvaras

We investigate how the sentence choice semantics (SCS) for propositional superposition logic (PLS) developed in \cite{Tz17} could be extended so as to successfully apply to first-order superposition logic(FOLS). There are two options for such an extension. The apparently more natural one is the formula choice semantics (FCS) based on choice functions for pai

Melike Afşar, Zeynep Bozkurt, Gamze Böcek Topcu, Sergen Özdemir

Determining accurate effective temperatures of stars buried in the dust-obscured Galactic regions is extremely difficult from photometry. Fortunately, high-resolution infrared spectroscopy is a powerful tool for determining the temperatures of stars with no dependence on interstellar extinction. It has long been known that the depth ratios of temperature-sen

Mario Beraha, Stefano Favaro, Matteo Sesia

This paper introduces a Bayesian nonparametric approach to frequency recovery from lossy-compressed discrete data, leveraging all information contained in a sketch obtained through random hashing. By modeling the data points as random samples from an unknown discrete distribution endowed with a Poisson-Kingman prior, we derive the posterior distribution of a

E. Ferrer-Ribas, K. Altenmüller, B. Biasuzzi, J. F. Castel

The International AXion Observatory (IAXO) is a large scale axion helioscope that will look for axions and axion-like particles produced in the Sun with unprecedented sensitivity. BabyIAXO is an intermediate experimental stage that will be hosted at DESY (Germany) and that will test all IAXO subsystems serving as a prototype for IAXO but at the same time as

Uzma Hasan, Emam Hossain, Md Osman Gani

The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data with certain assumptions. Over the years, several methods have been developed primarily based on the statistical

Mingming Cao, Kai Li, Wending Zhao, Weixuan Guo

Non-Hermitian systems generically have complex energies, which may host topological structures, such as links or knots. While there has been great progress in experimentally engineering non-Hermitian models in quantum simulators, it remains a significant challenge to experimentally probe complex energies in these systems, thereby making it difficult to direc

Danny Weyns, Thomas Vogel

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature

Xiaoyu Bai, Yong Xia

Fully annotated large-scale medical image datasets are highly valuable. However, because labeling medical images is tedious and requires specialized knowledge, the large-scale datasets available often have missing annotation issues. For instance, DeepLesion, a large-scale CT image dataset with labels for various kinds of lesions, is reported to have a missin

Chen Li, Gim Hee Lee

Animal pose estimation is an important but under-explored task due to the lack of labeled data. In this paper, we tackle the task of animal pose estimation with scarce annotations, where only a small set of labeled data and unlabeled images are available. At the core of the solution to this problem setting is the use of the unlabeled data to compensate for t

Antonio Rago, Hengzhi Li, Francesca Toni

As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the outputs of) AI models are growing, but the state-of-the-art predominantly focuses on static explanations. In this paper, we focus instead on interactive explanations framed as conflict resolution between agents (i.e. AI models and/or humans) by leveraging on computa

Athanassios Tzouvaras

We extend classical Propositional Logic (PL) by adding a new primitive binary connective $\varphi|\psi$, intended to represent the "superposition" of sentences $\varphi$ and $\psi$, an operation motivated by the corresponding notion of quantum mechanics, but not intended to capture all aspects of the latter as they appear in physics. To interpret the new con

Naijun Zhan, Xiangyu Jin, Bohua Zhan, Shuling Wang

Deductive verification of hybrid systems (HSs) increasingly attracts more attention in recent years because of its power and scalability, where a powerful specification logic for HSs is the cornerstone. Often, HSs are naturally modelled by concurrent processes that communicate with each other. However, existing specification logics cannot easily handle such

Hongjia Chen, Hyun-MIn Kim, Jie Meng

A quasi-Toeplitz $M$-matrix $A$ is an infinite $M$-matrix that can be written as the sum of a semi-infinite Toeplitz matrix and a correction matrix. This paper is concerned with computing the square root of invertible quasi-Toeplitz $M$-matrices which preserves the quasi-Toeplitz structure. We show that the Toeplitz part of the square root can be easily comp

Chen Zhang, Cai-Xin Zhang, Su-Huai Wei, Haiqing Lin

Many issues concerning the origin of high-temperature superconductivity (HTS) are still under debate. For example, how the magnetic ordering varies with doping and its relationship with the superconducting temperature; and why the maximal Tc always occurs near the quantum critical point. In this paper, taking hole-doped La2CuO4 as a classical example, we emp

Shian Tang, Shanjin Wu, Huichao Song

Within the model A in the Hohenberg's dynamical universality classification, we investigate the critical slowing down effects on the critical fluctuations driven by the expanding quark-gluon plasma, using a trajectory and cooling rate obtained from hydrodynamics. We numerically solved the Langevin dynamics of the non-conserved order parameter field and find

Chunpu Xu, Jing Li

Social media is daily creating massive multimedia content with paired image and text, presenting the pressing need to automate the vision and language understanding for various multimodal classification tasks. Compared to the commonly researched visual-lingual data, social media posts tend to exhibit more implicit image-text relations. To better glue the cro

Kaituo Feng, Changsheng Li, Xiaolu Zhang, Jun Zhou

Graph neural networks (GNNs) for temporal graphs have recently attracted increasing attentions, where a common assumption is that the class set for nodes is closed. However, in real-world scenarios, it often faces the open set problem with the dynamically increased class set as the time passes by. This will bring two big challenges to the existing dynamic GN

Hyun Seok Seong, WonJun Moon, SuBeen Lee, Jae-Pil Heo

Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we

David Andrieux

We show that the current fluctuations and nonlinear response of Markovian dynamics can be obtained from a system of polynomial equations. This offers new opportunities for analytical and numerical results. As an example, we derive new expressions for the current nonlinear response coefficients.

Senmao Li, Joost van de Weijer, Yaxing Wang, Fahad Shahbaz Khan

Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multi-class image-to-image (3D-aware I2I) translation. Naively using 2D-I2I translation methods suffers from unrealistic shape/identity change. To perfor

Nick Hardy, Leon Bewersdorff, David Rowe, Russell Genet

Astronomers have been measuring the separations and position angles between the two components of binary stars since William Herschel began his observations in 1781. In 1970, Anton Labeyrie pioneered a method, speckle interferometry, that overcomes the usual resolution limits induced by atmospheric turbulence by taking hundreds or thousands of short exposure

Leonardo Carofiglio, Luigi De Filpo, Alessandro Gambini

We explore a conjecture posed by Eswarathasan and Levine on the distribution of $p$-adic valuations of harmonic numbers $H(n)=1+1/2+\cdots+1/n$ that states that the set $J_p$ of the positive integers $n$ such that $p$ divides the numerator of $H(n)$ is finite. We proved two results, using a modular-arithmetic approach, one for non-Wolstenholme primes and the

Mihaï Bostan, Anh-Tuan Vu

The subject matter of the paper concerns the Vlasov-Poisson-Fokker-Planck (VPFP) equations in the context of magnetic confinement. We study the long-time behavior of the VPFP system with an intense external magnetic field, when neglecting the curvature of the magnetic lines. When the intensity of the magnetic field tends to infinity, the long-time behavior o

P. F. L. Maxted

I describe the selection and initial characterisation of 20 eclipsing binary stars that are suitable for calibration and testing of stellar models and data analysis algorithms used by the PLATO mission and spectroscopic surveys. The binary stars selected are F-/G-type dwarf stars with M-type dwarf companions that contribute less than 2% of the flux at optica

The Cherenkov Telescope Array Consortium, F. Acero, A. Acharyya, R. Adam

The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3~PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been p

Wafa Aissa, Marin Ferecatu, Michel Crucianu

Visual Question Answering (VQA) is a complex task requiring large datasets and expensive training. Neural Module Networks (NMN) first translate the question to a reasoning path, then follow that path to analyze the image and provide an answer. We propose an NMN method that relies on predefined cross-modal embeddings to ``warm start'' learning on the GQA data

Andreea Postovan, Mădălina Eraşcu

Traffic signs support road safety and managing the flow of traffic, hence are an integral part of any vision system for autonomous driving. While the use of deep learning is well-known in traffic signs classification due to the high accuracy results obtained using convolutional neural networks (CNNs) (state of the art is 99.46\%), little is known about binar

Jian-xiang Dong, Yu-feng Lu

Let $\mathcal{H}$ be a separable Hilbert space and let $A^{2}_{\varphi}(\mathcal{H})$ be the $\mathcal{H}$-valued Bergman spaces with exponential weights. In the present paper, we give the complete characterizations for the boundedness and compactness of Hankel operators on $A^{2}_{\varphi}(\mathcal{H})$. For $p\geq2$, the Schatten $p$-classes of the Hankel

Toni Ikonen

We prove that a Sobolev map from a Riemannian manifold into a complete metric space pushes forward almost every compactly supported integral current to an Ambrosio--Kirchheim integral current in the metric target, where "almost every" is understood in a modulus sense. As an application, we prove that when the target supports an isoperimetric inequality of Eu

Charles Leedham-Green, Klas Markström, Søren Riis

In this note, we report on a record-breaking Condorcet domain (CD) for n=8 alternatives. We show that there exists a CD of size 224, which is optimal and essentially unique (up to isomorphism). If we consider the underlying permutations and focus on Condorcet domains containing the identity permutation, 56 isomorphic such Condorcet domains exist. Our work sh

Yajuan Zang, Zihong Tian, Shao-Ming Fei, Hui-Juan Zuo

The quantum orthogonal arrays define remarkable classes of multipartite entangled states called $k$-uniform states whose every reductions to $k$ parties are maximally mixed. We present constructions of quantum orthogonal arrays of strength 2 with levels of prime power, as well as some constructions of strength 3. As a consequence, we give infinite classes of

Farhad Rezazadeh, Hatim Chergui, Josep Mangues-Bafalluy

The complexity of emerging sixth-generation (6G) wireless networks has sparked an upsurge in adopting artificial intelligence (AI) to underpin the challenges in network management and resource allocation under strict service level agreements (SLAs). It inaugurates the era of massive network slicing as a distributive technology where tenancy would be extended

Zhaofei Wang, Weijia Zhang, Min-Ling Zhang

Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. State-of-the-art WSOD detectors commonly rely on multi-instance learning (MIL) as the backbone of their detectors and assume that the bounding box proposals of an image are independent of each other. However, since such approaches onl

Bogyeong Kang, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo

In this work, we propose a multi-view image translation framework, which can translate contrast-enhanced T1 (ceT1) MR imaging to high-resolution T2 (hrT2) MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive cons

Ashot Aleksian, Pierre del Moral, Aline Kurtzmann, Julian Tugaut

We study a class of time-inhomogeneous diffusion: the self-interacting one. We show a convergence result with a rate of convergence that does not depend on the diffusion coefficient. Finally, we establish a so-called Kramers' type law for the first exit-time of the process from domain of attractions when the landscapes are uniformly convex.

Xin-Jian Xu, Chong Deng, Li-Jie Zhang

Many real-world systems involving higher-order interactions can be modeled by hypergraphs, where vertices represent the systemic units and hyperedges describe the interactions among them. In this paper, we focus on the problem of hyperlink prediction which aims at inferring missing hyperlinks based on observed hyperlinks. We propose three similarity indices

Carl A. Wesolowski

Nine models were evaluated as candidate glomerular filtration rate (GFR) reference standards in three datasets using [$^{51}$Cr(EDTA)]$^-$ or [$^{169}$Yb(DTPA)]$^{2-}$ anions in 98 studies. Noncompartmental methods formed an upper limit for estimating mass excreted and voluntary urine collection formed a lower limit. For current models and methods, reduced G

Probir Mondal, Pratyay Banerjee, Debranjan Pal, Krishnendu Basuli

We propose a new alignment-free algorithm by constructing a compact vector representation on $\mathbb{R}^{24}$ of a DNA sequence of arbitrary length. Each component of this vector is obtained from a representative sequence, the elements of which are the values realized by a function $\Gamma$. This function $\Gamma$ acts on neighborhoods of arbitrary radius t

Hiroshi Yamashita, Ken-ichi Okubo, Suguru Shimomura, Yusuke Ogura

The spatial photonic Ising machine (SPIM) [D. Pierangeli et al., Phys. Rev. Lett. 122, 213902 (2019)] is a promising optical architecture utilizing spatial light modulation for solving large-scale combinatorial optimization problems efficiently. The primitive version of the SPIM, however, can accommodate Ising problems with only rank-one interaction matrices

NaiChung Conan Leung, YuTung Yau

For a fixed prequantum line bundle $L$ over a hyperK\"ahler manifold $X$, we find a natural $\operatorname{Sp}(1)$-action on $\Omega^*(X, L)$ intertwining a twistor family of $\operatorname{Spin}^{\operatorname{c}}$-Dirac Laplacians on the spaces of $L$-valued $(0, *)$-forms on $X$, noting that $L$ is holomorphic for only one complex structure in the twistor

Houxing Ren, Linjun Shou, Ning Wu, Ming Gong

In monolingual dense retrieval, lots of works focus on how to distill knowledge from cross-encoder re-ranker to dual-encoder retriever and these methods achieve better performance due to the effectiveness of cross-encoder re-ranker. However, we find that the performance of the cross-encoder re-ranker is heavily influenced by the number of training samples an

James Lofty, Daniel Valero, Catherine A. M. E. Wilson, Mário J. Franca

Microplastic (MP) pollution is a well document threat to our aquatic and terrestrial ecosystems, however, the mechanisms by which MPs are transported in river flows are still unknown. The transport of MPs and natural sediment in aquatic flows could be somewhat comparable, as particles are similar in size. However, it is unknown how the lower density of MPs a

Marco Viceconti, Maarten De Vos, Sabato Mellone, Liesbet Geris

The idea of a systematic digital representation of the entire known human pathophysiology, which we could call the Virtual Human Twin, has been around for decades. To date, most research groups focused instead on developing highly specialised, highly focused patient-specific models able to predict specific quantities of clinical relevance. While it has facil

Pierre Houdouin, Esa Ollila, Frederic Pascal

Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing (local) maximum likelihood estimate (MLE). It can be used in an extensive range of problems, including the clustering of data based on the Gaussian mixture model (GMM). Numerical instability and convergence problems may arise in situations where the sample size is not

Naoki Kitazawa

As a pioneering work we construct explicit real algebraic functions which may have both compact and non-compact preimages. The author has obtained explicit real algebraic functions with preimages satisfying some nice conditions. More precisely, we have given answers to a considerably revised version of Sharko's question. Sharko originally asked whether we ca

Ioan Bucataru, Oana Constantinescu

We prove that various Finsler metrizability problems for sprays can be reformulated in terms of the geodesic invariance of two tensors (metric and angular). We show that gyroscopic sprays is the the largest class of sprays with geodesic invariant angular metric. Scalar functions associated to these geodesically invariant tensors will be invariant as well and

Niharika S. D'Souza, Archana Venkataraman

Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks ignore the underlying data geometry. This is largely because simple operations, such as mean estimation, do not have eas

Emil Horobet, Ettore Teixeira Turatti

Subtracting a critical rank-one approximation from a matrix always results in a matrix with a lower rank. This is not true for tensors in general. Motivated by this, we ask the question: what is the closure of the set of those tensors for which subtracting some of its critical rank-one approximation from it and repeating the process we will eventually get to

Laurent Seppecher

The aim of this short note is to give a synthetic presentation of the mathematical elements that are used to solve the elastic wave system of equations in a bounded anisotropic elastic body, in a general framework. In particular, the proof of existence of a basis of orthogonal modes is given. We explain how these modes can by used to efficiently approach dyn

Tomoyoshi Ibukiyama

For a quadratic extension $K$ of ${\mathbb Q}$, we consider orders $O$ in $K$ that are not necessarily maximal and the ideal class group $Cl^+(O)$ in the narrow sense of proper ideals of $O$. Characters of $Cl^+(O)$ of order at most two are traditionally called genus characters. Explicit description of such characters is known classically, but explicit $L$-f

Jerzy Cioslowski, Berthold-Georg Englert, Martin-Isbjörn Trappe, Jun Hao Hue

At the limit of an infinite confinement strength $\omega$, the ground state of a system that comprises two fermions or bosons in a harmonic confinement interacting through the Fermi--Huang pseudopotential remains strongly correlated. A detailed analysis of the one-particle description of this ``contactium'' reveals several peculiarities that are not encounte

Lena Baumann, Marlies Pirner

This paper concerns the linear Landau damping for the two species Vlasov-Poisson system for ions and electrons near Penrose stable equilibria. The result is an extension of the result on the one species Vlasov-Poisson equation by Mouhout and Villani. Different from their work we do not describe the ions as a background species but as a species which is also

Bingfeng Wei, Zhenghe Bai, Jiajie Tan, Lin Wang

Dynamic aperture (DA) is an important nonlinear property of a storage ring lattice, which has a dominant effect on beam injection efficiency and beam lifetime. Generally, minimizing both resonance driving terms (RDTs) and amplitude dependent tune shifts is an essential condition for enlarging the DA. In this paper, we study the correlation between the fluctu

Houxing Ren, Linjun Shou, Jian Pei, Ning Wu

Recent multilingual pre-trained models have shown better performance in various multilingual tasks. However, these models perform poorly on multilingual retrieval tasks due to lacking multilingual training data. In this paper, we propose to mine and generate self-supervised training data based on a large-scale unlabeled corpus. We carefully design a mining m

Jinming Liu, Heming Sun, Jiro Katto

Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages. Exploiting both advantages is a point worth exploring, whic

Xiaohui Guo

Small object detection presents a significant challenge in computer vision and object detection. The performance of small object detectors is often compromised by a lack of pixels and less significant features. This issue stems from information misalignment caused by variations in feature scale and information loss during feature processing. In response to t

Eric Goubault, Roman Kniazev, Jérémy Ledent, Sergio Rajsbaum

In recent years, a new class of models for multi-agent epistemic logic has emerged, based on simplicial complexes. Since then, many variants of these simplicial models have been investigated, giving rise to different logics and axiomatizations. In this paper, we present a further generalization, where a group of agents may distinguish two worlds, even though

Svitlana Bilun, Maria Loseva, Olena Myshnova, Alexandr Prishlyak

We describe all possible topological structures of typical one-parameter bifurcations of gradient flows on the 2-sphere with holes in the case that the number of singular point of flows is at most six. To describe structures, we separatrix diagrams of flows. The saddle-node singularity is specified by selecting a separatrix in the diagram of the flow befor t

Mike D. Schneider

I discuss empty space, as it appears in the physical foundations of relativistic field theories and in the semiclassical study of isolated systems. Of particular interest is the relationship between empirical measurements of the cosmological constant and the question of appropriate representation of empty space by spacetimes, or models of general relativity.

Jiwon Jeon, Jiho Jang, Hoil Kim, Taesu Park

SrAs$_3$ is a unique nodal-line semimetal that contains only a single nodal ring in the Brillouin zone, uninterrupted by any trivial bands near the Fermi energy. We performed axis-resolved optical reflection measurements on SrAs$_3$ and observed that the optical conductivity exhibits flat absorption up to 129 meV in both the radial and axial directions, conf

Victor Réville, Nicolas Poirier, Athanasios Kouloumvakos, Alexis P. Rouillard

We present a new 3D MHD heliospheric model for space-weather forecasting driven by boundary conditions defined from white-light observations of the solar corona. The model is based on the MHD code PLUTO, constrained by an empirical derivation of the solar wind background properties at 0.1au. This empirical method uses white-light observations to estimate the

Gabriele Masina, Giuseppe Spallitta, Roberto Sebastiani

Modern SAT and SMT solvers are designed to handle problems expressed in Conjunctive Normal Form (CNF) so that non-CNF problems must be CNF-ized upfront, typically by using variants of either Tseitin or Plaisted and Greenbaum transformations. When passing from plain solving to enumeration, however, the capability of producing partial satisfying assignments th

Vida Dujmović, Robert Hickingbotham, Gwenaël Joret, Piotr Micek

We prove that for every tree $T$ of radius $h$, there is an integer $c$ such that every $T$-minor-free graph is contained in $H\boxtimes K_c$ for some graph $H$ with pathwidth at most $2h-1$. This is a qualitative strengthening of the Excluded Tree Minor Theorem of Robertson and Seymour (GM I). We show that radius is the right parameter to consider in this s

Donggyun Kim, Jinwoo Kim, Seongwoong Cho, Chong Luo

Dense prediction tasks are a fundamental class of problems in computer vision. As supervised methods suffer from high pixel-wise labeling cost, a few-shot learning solution that can learn any dense task from a few labeled images is desired. Yet, current few-shot learning methods target a restricted set of tasks such as semantic segmentation, presumably due t

Weixia Zhang, Guangtao Zhai, Ying Wei, Xiaokang Yang

We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically.

Naoki Hara, Masaya Nakagawa

We investigate the effect of two-body loss due to chemical reactions on quantum magnetism of fermionic polar molecules in an optical lattice. We show that an interplay between dissipation and strong long-range interactions leads to formation of metastable ferromagnetic clusters. The spin states of clusters are controlled by interaction parameters and reflect

Shensheng Zheng, Wenhao Yuan, Xuehe Wang, Lingjie Duan

Federated Learning (FL) has emerged as a prominent distributed machine learning framework that enables geographically discrete clients to train a global model collaboratively while preserving their privacy-sensitive data. However, due to the non-independent-and-identically-distributed (Non-IID) data generated by heterogeneous clients, the performances of the

Xinkun Ai, Wei Zheng, Ming Zhang, Dalong Chen

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging tas

Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun

Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities. Constructing CD formulae for photographic images is still an active research topic in imaging/illumination, vision scienc

Luisa Werner, Nabil Layaïda, Pierre Genevès, Sarah Chlyah

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and incomplete. As a result, graph completion tasks, such as node classification or link prediction, have gained attention. On one hand, neural methods, such as gr

Jonathan Dunn

This paper measures variation in embedding spaces which have been trained on different regional varieties of English while controlling for instability in the embeddings. While previous work has shown that it is possible to distinguish between similar varieties of a language, this paper experiments with two follow-up questions: First, does the variety represe

Haeyong Kang, Jaehong Yoon, Sultan Rizky Madjid, Sung Ju Hwang

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we investigate two proposed architecture-based continual learning methods which sequentially learn and select adaptive binary- (WSN) and non-binary Soft-Subnetworks (SoftNet) for eac

Nazmus Sakib, G. M. Shahariar, Md. Mohsinul Kabir, Md. Kamrul Hasan

Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could be a solution. In this study, we presen

Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to mak

Chang Liu, Weiming Zhang, Xiangru Lin, Wei Zhang

With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localiz

Javier Sanchez-Rivero, Daniel Talaván, Jose Garcia-Alonso, Antonio Ruiz-Cortés

The evolution of quantum hardware is highlighting the need for advances in quantum software engineering that help developers create quantum software with good quality attributes. Specifically, reusability has been traditionally considered an important quality attribute in terms of efficiency of cost and effort. Increasing the reusability of quantum software

Tianfu Li, Chuang Sun, Olga Fink, Yuangui Yang

Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also been introduced in the field of fault diagnosis with the goal to make better use of the inductive bias of the interdependencies between the different sensor measurements. However, there