October 2022 arXiv papers — page 32

Kejiang Chen, Hang Zhou, Yaofei Wang, Menghan Li

Whereas cryptography easily arouses attacks by means of encrypting a secret message into a suspicious form, steganography is advantageous for its resilience to attacks by concealing the message in an innocent-looking cover signal. Minimal distortion steganography, one of the mainstream steganography frameworks, embeds messages while minimizing the distortion

Wenhao Wang

A bi-order on a group $G$ is a total, bi-multiplication invariant order. A subset $S$ in an ordered group $(G,\leqslant)$ is convex if for all $f\leqslant g$ in $S$, every element $h\in G$ satisfying $f\leqslant h \leqslant g$ belongs to $S$. In this paper, we show that the derived subgroup of the free metabelian group of rank 2 is convex with respect to any

Daniel Lokshtanov, Marcin Pilipczuk, Michał Pilipczuk, Saket Saurabh

A set $X \subseteq V(G)$ in a graph $G$ is $(q,k)$-unbreakable if every separation $(A,B)$ of order at most $k$ in $G$ satisfies $|A \cap X| \leq q$ or $|B \cap X| \leq q$. In this paper, we prove the following result: If a graph $G$ excludes a fixed complete graph $K_h$ as a minor and satisfies certain unbreakability guarantees, then $G$ is almost rigid in

Jian-Feng Cai, Jingyang Li, Juntao You

We study the sparse phase retrieval problem, recovering an $s$-sparse length-$n$ signal from $m$ magnitude-only measurements. Two-stage non-convex approaches have drawn much attention in recent studies for this problem. Despite non-convexity, many two-stage algorithms provably converge to the underlying solution linearly when appropriately initialized. Howev

Lingrui Zhang, Yuxing Han, Qiong Wang, Wei Chen

Successive relaying can improve the transmission rate by allowing the source and relays to transmit messages simultaneously, but it may cause severe inter-relay interference (IRI). IRI cancellation schemes have been proposed to mitigate IRI. However, interference cancellation methods have a high risk of error propagation, resulting in a severe transmission r

Qingyan Wu, Shoulan Gao, Dong Liu

The present paper is devoted to studying local derivations on the Lie algebra $W(2,2)$ which has some outer derivations. Using some linear algebra methods in \cite{CZZ} and a key construction for $W(2,2)$ we prove that every local derivation on $W(2, 2)$ is a derivation. As an application, we determine all local derivations on the deformed $\mathfrak{bms}_3$

William J. Wolf, Karim P. Y. Thébault

We develop and apply a multi-dimensional account of explanatory depth towards a comparative analysis of inflationary and bouncing paradigms in primordial cosmology. Our analysis builds on earlier work due to Azhar and Loeb (2021) that establishes initial conditions fine-tuning as a dimension of explanatory depth relevant to debates in contemporary cosmology.

Priyash Bhugra, Benjamin Bischke, Christoph Werner, Robert Syrnicki

In the remote sensing community, Land Use Land Cover (LULC) classification with satellite imagery is a main focus of current research activities. Accurate and appropriate LULC classification, however, continues to be a challenging task. In this paper, we evaluate the performance of multi-temporal (monthly time series) compared to mono-temporal (single time s

Bojan Žunkovič, Enej Ilievski

We discuss two solvable grokking (generalisation beyond overfitting) models in a rule learning scenario. We show that grokking is a phase transition and find exact analytic expressions for the critical exponents, grokking probability, and grokking time distribution. Further, we introduce a tensor-network map that connects the proposed grokking setup with the

Dino Festi, Wim Nijgh, Daniel Platt

Let $X$ be a complex algebraic K3 surface of degree $2d$ and with Picard number $\rho$. Assume that $X$ admits two commuting involutions: one holomorphic and one anti-holomorphic. In that case, $\rho \geq 1$ when $d=1$ and $\rho \geq 2$ when $d \geq 2$. For $d=1$, the first example defined over $\mathbb{Q}$ with $\rho=1$ was produced already in 2008 by Elsen

Ifeoluwapo Aribilola, Mamoona Naveed Asghar, Brian Lee

The monitoring of individuals/objects has become increasingly possible in recent years due to the convenience of integrated cameras in many devices. Due to the important moments or activities of people captured by these devices, it has made it a great asset for attackers to launch attacks against by exploiting the weaknesses in these devices. Different studi

A. Elmaizi, E. Sarhrouni, A. Hammouch, C. Nacir

The high dimensionality of hyperspectral images consisting of several bands often imposes a big computational challenge for image processing. Therefore, spectral band selection is an essential step for removing the irrelevant, noisy and redundant bands. Consequently increasing the classification accuracy. However, identification of useful bands from hundreds

Jonas Rønning, Luiza Angheluta

We apply the Halperin-Mazenco formalism within the Gross-Pitaevskii theory to characterise the kinematics and nucleation of quantum vortices in a two-dimensional stirred Bose Einstein condensate. We introduce a smooth defect density field measuring the superfluid vorticity and is a topologically conserved quantity. We use this defect density field and its as

Xiangwang Hou, Jingjing Wang, Tong Bai, Yansha Deng

The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices' wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the

Junliang Chen, Xiaodong Zhao, Cheng Luo, Linlin Shen

Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based image classifier. In this paper, we propose a novel transformer-based framework, named Semantic Guided Activation Transformer (SemFormer), for WSSS. We design a transformer-based Cl

José M. Rodríguez, Raquel Taboada-Vázquez

We propose a two-dimensional flow model of a viscous fluid between two close moving surfaces. We show, using a formal asymptotic expansion of the solution, that its asymptotic behavior, when the distance between the two surfaces tends to zero, is the same as that of the the Navier-Stokes equations. The leading term of the formal asymptotic expansions of the

Zhibo Zhang, Ernesto Damiani, Hussam Al Hamadi, Chan Yeob Yeun

In recent years, spammers are now trying to obfuscate their intents by introducing hybrid spam e-mail combining both image and text parts, which is more challenging to detect in comparison to e-mails containing text or image only. The motivation behind this research is to design an effective approach filtering out hybrid spam e-mails to avoid situations wher

Mahboobeh Jafari, Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrh

Carlos S. Sepúlveda, Eric Goles, Martín Ríos-Wilson, Andrew Adamatzky

Cells in a fungal hyphae are separated by internal walls (septa). The septa have tiny pores that allow cytoplasm flowing between cells. Cells can close their septa blocking the flow if they are injured, preventing fluid loss from the rest of filament. This action is achieved by special organelles called Woronin bodies. Using the controllable pores as an insp

Sahil Maharaj, Mobin Yunus Malik, Olivier Allegre, Katharine Lucy Smith

A new method for determining the onset voltage of electrospray thrusters is proposed, which specifically focuses on electrospray thrusters manufactured by laser drilling through flat plates. The novelty of this method is that it accounts for the effect of the thickness of the plate on the electrospray onset voltage requirements, while traditional methods do

Michael J. W. Hall

An uncertainty relation for the R\'enyi entropies of conjugate quantum observables is used to obtain a strong Heisenberg limit of the form ${\rm RMSE} \geq f(\alpha)/(\langle N\rangle+\frac12)$, bounding the root mean square error of any estimate of a random optical phase shift in terms of average photon number, where $f(\alpha)$ is maximised for non-Shannon

Marc Uecker, Tobias Fleck, Marcel Pflugfelder, J. Marius Zöllner

LiDAR sensors are an integral part of modern autonomous vehicles as they provide an accurate, high-resolution 3D representation of the vehicle's surroundings. However, it is computationally difficult to make use of the ever-increasing amounts of data from multiple high-resolution LiDAR sensors. As frame-rates, point cloud sizes and sensor resolutions increas

Jianye Hao, Pengyi Li, Hongyao Tang, Yan Zheng

Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research direction is integrating Deep RL and EA to devise new methods by fusing their complementary advantages. However, existing works on combining Dee

Mahboobeh Jafari, Afshin Shoeibi, Navid Ghassemi, Jonathan Heras

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scan

Boris Benedikter, Alessandro Zavoli, Guido Colasurdo, Simone Pizzurro

This paper presents a novel synthesis method for designing an optimal and robust guidance law for a non-throttleable upper stage of a launch vehicle, using a convex approach. In the unperturbed scenario, a combination of lossless and successive convexification techniques is employed to formulate the guidance problem as a sequence of convex problems that yiel

Junyi He, Meimei Wu, Meng Li, Xiaobo Zhu

Multimodal emotion recognition has attracted much attention recently. Fusing multiple modalities effectively with limited labeled data is a challenging task. Considering the success of pre-trained model and fine-grained nature of emotion expression, it is reasonable to take these two aspects into consideration. Unlike previous methods that mainly focus on on

Menglin Li, Kwan Hui Lim, Teng Guo, Junhua Liu

POI-level geo-information of social posts is critical to many location-based applications and services. However, the multi-modality, complexity and diverse nature of social media data and their platforms limit the performance of inferring such fine-grained locations and their subsequent applications. To address this issue, we present a transformer-based gene

Asma Elmaizi, Elkebir Sarhrouni, Ahmed hammouch, Chafik Nacir

The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and complicates the learning process. In fact, the removal of irrelevant, noisy and redundant bands helps increase the classification accuracy. Band s

Jean Cardinal, Raphael Steiner

We consider the computational problem of finding short paths in the skeleton of the perfect matching polytope of a bipartite graph. We prove that unless $P=NP$, there is no polynomial-time algorithm that computes a path of constant length between two vertices at distance two of the perfect matching polytope of a bipartite graph. Conditioned on $P\neq NP$, th

Viet-Trung Tran, Hai-Nam Cao, Tuan-Dung Cao

Vietnamese labor market has been under an imbalanced development. The number of university graduates is growing, but so is the unemployment rate. This situation is often caused by the lack of accurate and timely labor market information, which leads to skill miss-matches between worker supply and the actual market demands. To build a data monitoring and anal

Frederic Kirstein, Jan Philip Wahle, Terry Ruas, Bela Gipp

Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of task grouping during the pre-finetuning stage to learn common skills, such as reading comprehension. To close this gap, we analyze the influ

Mostafa Shabani, Martin Magris, George Tzagkarakis, Juho Kanniainen

Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross-recurrence plot analysis as a nonlinear method for quantifying the multidimensional coupling in th

J. A. Gracey, R. H. Mason

We determine the anomalous dimensions of the gluon, Faddeev-Popov ghost and quark in the minimal MOM scheme to five loops for a general colour group when Quantum Chromodynamics is fixed in a linear covariant gauge. The quark mass anomalous dimension is also constructed in the same scheme.

F. Bossi, P. Branchini, B. Buonomo, V. Capirossi

The inclusive cross-section of annihilation in flight $e^+e^-\rightarrow\gamma\gamma$ of 430 MeV positrons with atomic electrons of a thin diamond target has been measured with the PADME detector at the Laboratori Nazionali di Frascati. The two photons produced in the process were detected by an electromagnetic calorimeter made of BGO crystals. This measurem

Andrew Gambardella, Youngjun Choi, Doyo Choi, Jinjoon Lee

We introduce an efficient algorithm for general data mosaicing, based on the simulation-based inference paradigm. Our algorithm takes as input a target datum, source data, and partitions of the target and source data into fragments, learning distributions over averages of fragments of the source data such that samples from those distributions approximate fra

Bruno Korbar, Andrew Zisserman

Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, that perform well on datasets where the object classes are known, to class-agnostic tracking that performs well also

Jyothish Kumar J, Subhankar Mishra, Amish Bibhu, Shreya Shivangi

Menstruation is the monthly shedding of the endometrium lining of a woman's uterus. The average age when girls start menstruating is around the age of 12 years (menarche), and the cycle continues until they attain menopause (about the age of 51). Medical research and analysis in this field reveal that most women have to go through a painful cycle of abdomina

Jaroslav Pešek, Dominik Soukup, Tomáš Čejka

Recent network traffic classification methods benefitfrom machine learning (ML) technology. However, there aremany challenges due to use of ML, such as: lack of high-qualityannotated datasets, data-drifts and other effects causing aging ofdatasets and ML models, high volumes of network traffic etc. Thispaper argues that it is necessary to augment traditional

Sitt Min Oo, Gerald Haesendonck, Ben De Meester, Anastasia Dimou

Stream-reasoning query languages such as CQELS and C-SPARQL enable query answering over RDF streams. Unfortunately, there currently is a lack of efficient RDF stream generators to feed RDF stream reasoners. State-of-the-art RDF stream generators are limited with regard to the velocity and volume of streaming data they can handle. To efficiently generate RDF

Martin Magris, Mostafa Shabani, Alexandros Iosifidis

We propose an optimization algorithm for Variational Inference (VI) in complex models. Our approach relies on natural gradient updates where the variational space is a Riemann manifold. We develop an efficient algorithm for Gaussian Variational Inference whose updates satisfy the positive definite constraint on the variational covariance matrix. Our Manifold

Narasimharao Kowlagi, Huy Hoang Nguyen, Terence McSweeney, Simo Saarakkala

This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural changes in the spine, which is valuable for understanding low back pain. Multiple recent studies investigated different architecture designs, and the most recent success has been attr

Joern Ploennigs, Konstantinos Semertzidis, Fabio Lorenzi, Nandana Mihindukulasooriya

Digital Twins are digital representations of systems in the Internet of Things (IoT) that are often based on AI models that are trained on data from those systems. Semantic models are used increasingly to link these datasets from different stages of the IoT systems life-cycle together and to automatically configure the AI modelling pipelines. This combinatio

Yiwen Lu, Yilin Mo

Sustained research efforts have been devoted to learning optimal controllers for linear stochastic dynamical systems with unknown parameters, but due to the corruption of noise, learned controllers are usually uncertified in the sense that they may destabilize the system. To address this potential instability, we propose a "plug-and-play" modification to the

Catalin-Mihai Halati, Thierry Giamarchi

We consider interacting bosonic particles on a two-leg triangular ladder in the presence of an artificial gauge field. We employ density matrix renormalization group numerical simulations and analytical bosonization calculations to study the rich phase diagram of this system. We show that the interplay between the frustration induced by the triangular lattic

Matthew J. Filipovich, Alessandro Cappelli, Daniel Hesslow, Julien Launay

Alternatives to backpropagation have long been studied to better understand how biological brains may learn. Recently, they have also garnered interest as a way to train neural networks more efficiently. By relaxing constraints inherent to backpropagation (e.g., symmetric feedforward and feedback weights, sequential updates), these methods enable promising p

Da-Shuai Ma, Kejun Yu, Xiao-Ping Li, Xiaoyuan Zhou

Higher-order topological insulators (HOTIs) are described by symmetric exponentially decayed Wannier functions at some $necessary$ unoccupied Wyckoff positions and classified as obstructed atomic insulators (OAIs) in the topological quantum chemistry (TQC) theory. The boundary states in HOTIs reported so far are often fragile, manifested as strongly dependin

Hugo Witt, Srijani Mallik, Luis M. Vicente-Arche, Gerbold Ménard

The versatility of properties displayed by two-dimensional electron gases (2DEGs) at oxide interfaces has fostered intense research in hope of achieving exotic electromagnetic effects in confined systems. Of particular interest is the recently discovered superconducting state appearing in (111)-oriented KTaO$_3$ interfaces, with a critical temperature $T_c \

Dawid Hanrahan, Dariusz Kosz

We prove genuinely sharp estimates for the Jacobi heat kernels introduced in the context of the multidimensional cone $\mathbb{V}^{d+1}$ and its surface $\mathbb{V}^{d+1}_0$. To do so, we combine the theory of Jacobi polynomials on the cone explored by Xu with the recent techniques by Nowak, Sj\"ogren, and Szarek, developed to find genuinely sharp estimates

Jae Dong Noh

We investigate the eigenstate thermalization properties of the spin-1/2 $XXZ$ model in two-dimensional rectangular lattices of size $L_1\times L_2$ under periodic boundary conditions. Exploiting the symmetry property, we can perform an exact diagonalization study of the energy eigenvalues up to system size $4\times 7$ and of the energy eigenstates up to $4\t

Moritz Flaschel, Siddhant Kumar, Laura De Lorenzis

We extend the scope of our approach for unsupervised automated discovery of material laws (EUCLID) to the case of a material belonging to an unknown class of behavior. To this end, we leverage the theory of generalized standard materials, which encompasses a plethora of important constitutive classes. We show that, based only on full-field kinematic measurem

Elsa Rizk, Stefan Vlaski, Ali H. Sayed

We study the generation of dependent random numbers in a distributed fashion in order to enable privatized distributed learning by networked agents. We propose a method that we refer to as local graph-homomorphic processing; it relies on the construction of particular noises over the edges to ensure a certain level of differential privacy. We show that the a

Penghui Shen, Mouming Zhao, Jasper Landman, Feibai Zhou

Protein-inorganic hybrid nanoflowers (HNFs) possess unique properties in promoting surface reaction and have attracted wide-spread attention as a newly developed nanomaterial. However, the availability of protein sources has up to now been mostly limited to enzymes, which narrows the application of HNFs especially in food industry. Here we show that for many

A. G. Kusraev, S. S. Kutateladze

The aim of the present article is to extend the Stone--Weierstrass theorem to functions ranging in a lattice normed space and order rather than topological approximation. We proceed with the machinery of Boolean valued transfer from lattice normed space to normed space.

Margaret Duff, Ivor J. A. Simpson, Matthias J. Ehrhardt, Neill D. F. Campbell

Objective: This paper investigates how generative models, trained on ground-truth images, can be used \changes{as} priors for inverse problems, penalizing reconstructions far from images the generator can produce. The aim is that learned regularization will provide complex data-driven priors to inverse problems while still retaining the control and insight o

Stevell Muller

Given a complex vector space $V$ of finite dimension, its Grassmannian variety parametrizes all subspaces of $V$ of a given dimension. Similarly, if a finite group $G$ acts on $V$, its invariant Grassmannian parametrizes all the $G$-invariant subspaces of $V$ of a given dimension. Based on this fact, we develop an algorithm for computing $G$-invariant projec

Zacharaya Shabka, Georgios Zervas

Resource-disaggregated data centre architectures promise a means of pooling resources remotely within data centres, allowing for both more flexibility and resource efficiency underlying the increasingly important infrastructure-as-a-service business. This can be accomplished by means of using an optically circuit switched backbone in the data centre network

Filippos Christianos, Peter Karkus, Boris Ivanovic, Stefano V. Albrecht

Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predicting occluded agents based on the behaviour of nearby visible agents; however, as we show in experiments, these models are difficult to integrate into downstream planning. To this end

Reza Serati, Benyamin Teymuri, Nikolaos Athanasios Anagnostopoulos, Mehdi Rasti

The long-range and low energy consumption requirements in Internet of Things (IoT) applications have led to a new wireless communication technology known as Low Power Wide Area Network (LPWANs). In recent years, the Long Range (LoRa) protocol has gained a lot of attention as one of the most promising technologies in LPWAN. Choosing the right combination of t

Xiang Long, Yan Huang, Zhendong Liu, Lansheng Han

The feature diversity of different web systems in page elements, submission contents and return information makes it difficult to detect weak password automatically. To solve this problem, multi-factor correlation detection method as integrated in the DBKER algorithm is proposed to achieve automatic detection of web weak passwords and universal passwords. It

Qing Wang, Hang Chen, Ya Jiang, Zhe Wang

In this paper, we propose a deep learning based multi-speaker direction of arrival (DOA) estimation with audio and visual signals by using permutation-free loss function. We first collect a data set for multi-modal sound source localization (SSL) where both audio and visual signals are recorded in real-life home TV scenarios. Then we propose a novel spatial

Nicolas Aunai, Roch Smets, Andrea Ciardi, Philip Deegan

Modeling multi-scale collisionless magnetized processes constitutes an important numerical challenge. By treating electrons as a fluid and ions kinetically, the so-called hybrid Particle-In-Cell (PIC) codes represent a promising intermediary between fully kinetic codes, limited to model small scales and short durations, and magnetohydrodynamic codes used lar

Qi'an Guan, Zheng Yuan

In this article, we consider two classes of weighted Hardy spaces on products of planar domains and their corresponding kernel functions, and we prove product versions of Saitoh's conjecture related to the two classes of weighted Hardy spaces.

Pouria Paymard, Abolfazl Amiri, Troels E. Kolding, Klaus I. Pedersen

One of the rapidly emerging services for fifth-generation (5G)-Advanced is eXtended Reality (XR) which combines several immersive experiences and cloud gaming services. Those services are demanding as they call for relatively high data rates under tight latency constraints, sometimes also referred to as dependable real-time applications. Supporting as many X

Hengyu Zhang, Enming Yuan, Wei Guo, Zhicheng He

Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors. Unlike the standard autoregressive training strategy, future data (also available during training) has been used to facilitate model training as it provides richer signals a

Hanshan Zhang, Zhen Zhang, Hongfei Jiang, Yang Song

Active learning for sentence understanding attempts to reduce the annotation cost by identifying the most informative examples. Common methods for active learning use either uncertainty or diversity sampling in the pool-based scenario. In this work, to incorporate both predictive uncertainty and sample diversity, we propose Virtual Adversarial Perturbation f

Paulina Lewandowska, Łukasz Pawela, Zbigniew Puchała

The topic of causality has recently gained traction quantum information research. This work examines the problem of single-shot discrimination between process matrices which are an universal method defining a causal structure. We provide an exact expression for the optimal probability of correct distinction. In addition, we present an alternative way to achi

Marie-Noëlle Célérier

In an important series of articles published during the 70's, Krasi\'nski displayed a class of interior solutions of the Einstein field equations sourced by a stationary isentropic rotating cylinder of perfect fluid. However, these solutions depend on an unspecified arbitrary function, which lead the author to claim that the equation of state of the fluid co

Maximilian Kertel, Stefan Harmeling, Markus Pauly

Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in t

Batya Kenig, Nir Weinberger

Acyclic schemes posses known benefits for database design, speeding up queries, and reducing space requirements. An acyclic join dependency (AJD) is lossless with respect to a universal relation if joining the projections associated with the schema results in the original universal relation. An intuitive and standard measure of loss entailed by an AJD is the

Jonas Ricker, Simon Damm, Thorsten Holz, Asja Fischer

In the course of the past few years, diffusion models (DMs) have reached an unprecedented level of visual quality. However, relatively little attention has been paid to the detection of DM-generated images, which is critical to prevent adverse impacts on our society. In contrast, generative adversarial networks (GANs), have been extensively studied from a fo

Ruhul F. Hajjaj, Sjoerd A. L. de Ridder, Philip W. Livermore, Matteo Ravasi

Marchenko focusing functions are in their essence wavefields that satisfy the wave equation subject to a set of boundary, initial, and focusing conditions. Here, we show how Marchenko focusing functions can be modeled by finding the solution to a wavefield reconstruction inversion problem. Our solution yields all elements of the focusing function including e

Zhonghua LI, Shukun Wang

As a generalization of skew braces, the notion of skew trusses was introduced by T. Brzezinski. It was shown that every Rota-Baxter group has the structure of skew braces by V. G. Bardakov and V. Gubarev. To investigate an analogue of Rota-Baxter groups which has the structure of skew trusses, we define RotaBaxter systems. We study the relationship between R

Aleksandr Artemev, Vladimir Belavin

We present a method for the first principles calculation of tachyon one-point amplitudes in $(2,2p+1)$ minimal Liouville gravity defined on a torus. The method is based on the higher equations of motion in the Liouville CFT. These equations were earlier successfully applied for analytic calculations of the amplitudes in the spherical topology. We show that t

Hexin Liu, Haihua Xu, Leibny Paola Garcia, Andy W. H. Khong

Code-switching (CS) refers to the phenomenon that languages switch within a speech signal and leads to language confusion for automatic speech recognition (ASR). This paper aims to address language confusion for improving CS-ASR from two perspectives: incorporating and disentangling language information. We incorporate language information in the CS-ASR mode

Xuefei Li, Ru Li

The distributed structure of the Internet of things has gradually replaced the centralized structure because of its scalability, security, and single point of failure. The huge scale of information recording of the Internet of things brings challenges and opportunities to the trust management of the Internet of things. Through the analysis of a variety of ex

Debraj Kundu, Vivek Baruah Thapa, Monika Sinha

Recent observations of several massive pulsars, with masses near and above $2~M_\odot$, point towards the existence of matter at very high densities, compared to normal matter that we are familiar with in our terrestrial world. This leads to the possibility of appearance of exotic degrees of freedom other than nucleons inside the core of the neutrons stars (

Myunghun Jung, Hoirin Kim

Many recent loss functions in deep metric learning are expressed with logarithmic and exponential forms, and they involve margin and scale as essential hyper-parameters. Since each data class has an intrinsic characteristic, several previous works have tried to learn embedding space close to the real distribution by introducing adaptive margins. However, the

Daiki Yamasaki, Satoshi Inoue, Yumi Bamba, Jeongwoo Lee

The solar active region NOAA 12887 produced a strong X1.0 flare on 2021 October 28, which exhibits X-shaped flare ribbons and a circle-shaped erupting filament. To understand the eruption process with these characteristics, we conducted a data-constrained magnetohydrodynamics simulation using a nonlinear force-free field of the active region about an hour be

Nesrine Cherif, Wael Jaafar, Evgenii Vinogradov, Halim Yanikomeroglu

We propose the intermittently tethered unmanned aerial vehicle (iTUAV) as a tradeoff between the power availability of a tethered UAV (TUAV) and the flexibility of an untethered UAV. An iTUAV can provide cellular connectivity while being temporarily tethered to the most adequate ground anchor. Also, it can flexibly detach from one anchor, travel, then attach

Junyang Wang, Yi Zhang, Jitao Sang

The Vision-Language Pre-training (VLP) models like CLIP have gained popularity in recent years. However, many works found that the social biases hidden in CLIP easily manifest in downstream tasks, especially in image retrieval, which can have harmful effects on human society. In this work, we propose FairCLIP to eliminate the social bias in CLIP-based image

Max Nusspickel, Basil Ibrahim, George H. Booth

Quantum embedding is an appealing route to fragment a large interacting quantum system into several smaller auxiliary `cluster' problems to exploit the locality of the correlated physics. In this work we critically review approaches to recombine these fragmented solutions in order to compute non-local expectation values, including the total energy. Starting

Zhengjie Yang, Sen Fu, Wei Bao, Dong Yuan

In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration. Momentum is calculated and aggregated in the three tiers. We provide convergence analysis for HierMo, showing a convergence rate of O(1/T). In the analysis,

M. Montalto

I report the results of a new search for transiting planets on a set of 1.4 million lightcurves extracted from TESS Full Frame Images (FFIs) using the DIAmante pipeline. The data come from the first two years of observations of TESS (Sectors 1-26) and the study is focused on a sample of FGKM dwarf and subgiant stars optimized for the search of transiting pla

Qingyi Si, Yuanxin Liu, Zheng Lin, Peng Fu

Despite the excellent performance of vision-language pre-trained models (VLPs) on conventional VQA task, they still suffer from two problems: First, VLPs tend to rely on language biases in datasets and fail to generalize to out-of-distribution (OOD) data. Second, they are inefficient in terms of memory footprint and computation. Although promising progress h

S. Diehl, A. Kim, K. Joo, P. Achenbach

A multidimensional extraction of the structure function ratio $\sigma_{LT'}/\sigma_{0}$ from the hard exclusive $\vec{e} p \to e^\prime n \pi^+$ reaction above the resonance region has been performed. The study was done based on beam-spin asymmetry measurements using a 10.6 GeV incident electron beam on a liquid-hydrogen target and the CLAS12 spectrometer at

Ronghao Lin, Haifeng Hu

Multimodal representation learning is a challenging task in which previous work mostly focus on either uni-modality pre-training or cross-modality fusion. In fact, we regard modeling multimodal representation as building a skyscraper, where laying stable foundation and designing the main structure are equally essential. The former is like encoding robust uni

Samuel Asante Gyamerah, Henry Ofoe Agbi-Kaiser, Keziah Ewura Adjoa Amankwah, Patience Anipa

In this paper, we capture the dynamic behaviour of hourly consumption of electricity during the period of power crisis ("dumsor" period) in Ghana using two-state Markov switching autoregressive (MS-AR) and autoregressive (AR) models. Hourly data between the periods of January 1, 2014, and December 31, 2014 was obtained from the Ghana Grid company and used fo

Jian Liu, Yunsheng Wang, Yonghao Gao, Pawel Danielewicz

$Background:$ The determination of the nuclear neutron properties suffers from uncontrolled uncertainties, which attracted considerable attention recently, such as in the context of the PREX experiment. $Purpose:$ Our aim is to analyze the sensitivity of charge-exchange ($p, n$) reactions to the neutron density distribution $\rho_{n}$ and constrain the neutr

ChaoXia Zhang, YongLang Lai, RongGuo Yang, Kui Liu

We experimentally realize a great precision enhancement in the small tilt measurement by using a Sagnac interferometer and balanced homodyne detection (BHD) of high-order optical modes, together with the weak value amplification (WVA) technique. Smaller minimum measurable tilt (MMT) and higher signal-to-noise ratio (SNR) can be obtained by using BHD, compare

Eddie L. Ungless, Amy Rafferty, Hrichika Nag, Björn Ross

The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both. In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings, then use these results to evaluate a fine-tuning process designed to drive a language model away from stereotyped portr

Manoj Kumar, Hyung Seon Song, Jaeho Lee, Dohyun Park

We present a novel scheme to obtain robust, narrowband, and tunable THz emission by using a nano-dimensional overdense plasma target that is irradiated by two counter-propagating detuned laser pulses. So far, no narrowband THz sources with a field strength of GV/m-level have been reported from laser-solid interaction. We report intense THz pulses at beat-fre

Jon-Lark Kim

The hull of a linear code $C$ is the intersection of $C$ with its dual. To the best of our knowledge, there are very few constructions of binary linear codes with the hull dimension $\ge 2$ except for self-orthogonal codes. We propose a building-up construction to obtain a plenty of binary $[n+2, k+1]$ codes with hull dimension $\ell, \ell +1$, or $\ell +2$

Daniele Amato, Paolo Facchi, Arturo Konderak

In this Article, several aspects of the asymptotic dynamics of finite-dimensional open quantum systems are explored. First, after recalling a structure theorem for the peripheral map, we discuss sufficient conditions and a characterization for its unitarity. Interestingly, this is not always guaranteed due to the presence of permutations in the structure of

Pierre-Loïc Méliot, Ashkan Nikeghbali, Gabriele Visentin

We introduce a new numerical approximation method for functionals of factor credit portfolio models based on the theory of mod-$\phi$ convergence and mod-$\phi$ approximation schemes. The method can be understood as providing correction terms to the classic Poisson approximation, where higher order corrections lead to asymptotically better approximations as

Guido Carnevale, Filippo Fabiani, Filiberto Fele, Kostas Margellos

We propose fully-distributed algorithms for Nash equilibrium seeking in aggregative games over networks. We first consider the case where local constraints are present and we design an algorithm combining, for each agent, (i) the projected pseudo-gradient descent and (ii) a tracking mechanism to locally reconstruct the aggregative variable. To handle couplin

Krzysztof Bisewski, H. M. Jansen, Yoni Nazarathy

In this paper we introduce the idea of partially sorting data to design nonparametric tests. This approach gives rise to tests that are sensitive to both the order and the underlying distribution of the data. We focus in particular on a test that uses the bubble sort algorithm to partially sort the data. We show that a function of the data, referred to as th

Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux, Jean-Christophe Pesquet

Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, the classes effectively present in the unlabeled query set are known a priori, and correspond exactly to the set of classes represented in the labeled support set. We relax these ass

Anton Galajinsky

The method of nonlinear realizations is a convenient tool for building dynamical realizations of a Lie group, which relies solely upon structure relations of the corresponding Lie algebra. The goal of this work is to discuss advantages and limitations of the method, which is here applied to construct perfect fluid equations with conformal symmetry. Four case

Zheyu Wu, Jiageng Wu, Wei-Kun Chen, Ya-Feng Liu

Quantized constant envelope (QCE) transmission is a popular and effective technique to reduce the hardware cost and improve the power efficiency of 5G and beyond systems equipped with large antenna arrays. It has been widely observed that the number of quantization levels has a substantial impact on the system performance. This paper aims to quantify the imp

Mohamad Niknam, Louis-S. Bouchard

The temperature dependence of the nuclear free induction decay in the presence of a magnetic-field gradient was found to exhibit motional narrowing in gases upon heating, a behavior that is opposite to that observed in liquids. This has led to the revision of the theoretical framework to include a more detailed description of particle trajectories, since dec