May 2023 arXiv papers — page 144

Konstantinos Dareiotis, Benjamin Gess, Manuel V. Gnann, Max Sauerbrey

The stochastic thin-film equation with mobility exponent $n\in [\frac{8}{3},3)$ on the one-dimensional torus with multiplicative Stratonovich noise is considered. We show that martingale solutions exist for non-negative initial values. This advances on existing results in three aspects: (1) Non-quadratic mobility with not necessarily strictly positive initia

Ran Shen, Gang Sun, Hao Shen, Yiling Li

Converting text into the structured query language (Text2SQL) is a research hotspot in the field of natural language processing (NLP), which has broad application prospects. In the era of big data, the use of databases has penetrated all walks of life, in which the collected data is large in scale, diverse in variety, and wide in scope, making the data query

Bao Zhao, Jiahua Wang, Guobiao Hu, Andrea Colombi

Nonlinearity has enabled energy harvesting to advance towards higher power output and broader bandwidth in monostable, bistable, and multistable systems. However, challenges in operating in the high energy orbit (HEO) rather than low energy orbit (LEO) have restricted their applications. Based on a monostable nonlinear system, this paper proposes a self-cont

Shu-Ichiro Suzuki, Takumi Sato, Alexander A. Golubov, Yasuhiro Asano

We study theoretically the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) states appearing locally in a superconducting thin film with a small circular magnetic cluster. The pair potential, the pairing correlations, the free-energy density, and the quasiparticle density of states are calculated for several cluster sizes and the exchange potentials by solving the Ei

Giovanni Picogna, Carolina Schäfer, Barbara Ercolano, Christian Rab

Photoevaporative disc winds play a key role in our understanding of circumstellar disc evolution, especially in the final stages, and they might affect the planet formation process and the final location of planets. The study of transition discs (i.e. discs with a central dust cavity) is central for our understanding of the photoevaporation process and disc

Harshad Khadilkar

We present a simple, sample-efficient algorithm for introducing large but directed learning steps in reinforcement learning (RL), through the use of evolutionary operators. The methodology uses a population of RL agents training with a common experience buffer, with occasional crossovers and mutations of the agents in order to search efficiently through the

Ankit Kumar Singh, Zhan-Hong Lin, Min Jiang, Thomas G. Mayerhöfer

Chiral molecules show differences in their chemical and optical properties due to different spatial arrangements of the atoms in the two enantiomers. A common way to optically differentiate them is to detect the disparity in the absorption of light by the two enantiomers, i.e. the absorption circular dichroism (CD). However, the CD of typical molecules is ve

Wenhao He, Wenhao Zhang, Xiao Yuan, Yangchao Shen

Trapped-ion has shown great advantages in building quantum computers. While high fidelity entangling-gate has been realized for few ions, how to maintain the high fidelity for large scale trapped-ions still remains an open problem.Here, we present an analysis on arbitrary scale ion chain and focus on motional-related errors, reported as one of the leading er

Marcin Kępa, Łukasz Cywiński, Jan A. Krzywda

Fluctuations of electric fields can change the position of a gate-defined quantum dot in a semiconductor heterostructure. In the presence of magnetic field gradient, these stochastic shifts of electron's wavefunction lead to fluctuations of electron's spin splitting. The resulting spin dephasing due to charge noise limits the coherence times of spin qubits i

Kyoya Hashibori

We define singular points of the first kind and singular points of the second kind as singular points of mappings between surfaces. Typical examples of these singular points are fold singular points and cusp singular points, respectively. We can construct coherent tangent bundles, which are natural intrinsic formulations of wave fronts, by using mappings bet

Jintao Sun, Michael Cantoni

The closed-loop stability and infinite-horizon performance of receding-horizon approximations are studied for non-stationary linear-quadratic regulator (LQR) problems. The approach is based on a lifted reformulation of the optimal control problem, under assumed uniform controllability and observability, leading to a strict contraction property of the corresp

Artur Avila, Alex Eskin, Marcelo Viana

We prove that the Lyapunov exponents of random products in a (real or complex) matrix group depends continuously on the matrix coefficients and probability weights. More generally, the Lyapunov exponents of the random product defined by any compactly supported probability distribution on $GL(d)$ vary continuously with the distribution, in a natural topology

Alberto Imparato, Nicholas Chancellor, Gabriele De Chiara

We consider the problem of finding the energy minimum of a complex quantum Hamiltonian by employing a non-Markovian bath prepared in a low energy state. The energy minimization problem is thus turned into a thermodynamic cooling protocol in which we repeatedly put the system of interest in contact with a colder auxiliary system. By tuning the internal parame

Kyoya Hashibori

We define a frontal bundle by imposing a compatibility condition on two types of coherent tangent bundles over a surface with boundary. Since it is known that there are two Gauss-Bonnet type formulas for coherent tangent bundles, we obtain four Gauss-Bonnet type formulas for frontal bundles. Furthermore, since two coherent tangent bundles over the surface ar

Bastian Heinlein, Lukas Brand, Malcolm Egan, Maximilian Schäfer

In order to fully exploit the potential of molecular communication (MC) for intra-body communication, practically implementable cellular receivers are an important long-term goal. A variety of receiver architectures based on chemical reaction networks (CRNs) and gene-regulatory networks (GRNs) has been introduced in the literature, because cells use these co

Agnieszka Anna Corley-Wiciak, Shunda Chen, Omar Concepción, Marvin Hartwig Zoellner

The local ordering of atoms in alloys directly has a strong impact on their electronic and optical properties. This is particularly relevant in nonrandom alloys, especially if they are deposited using far from the equilibrium processes, as is the case of epitaxial Ge1-xSnx layers. In this work, we investigate the arrangement of Ge and Sn atoms in optoelectro

Yongkyu Cho, Seung Min Baik, Young Myoung Ko

This paper considers a circular k-out-of-n: G balance system equipped with homogeneous and stationary units. Building on previous research by Endharta et al. (Reliability Engineering & System Safety, 2018), we propose a new balance definition in circular k-out-of-n: G balance systems based on the concept of center of gravity. According to this condition, a c

Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto

We present an approach for safe motion planning under robot state and environment (obstacle and landmark location) uncertainties. To this end, we first develop an approach that accounts for the landmark uncertainties during robot localization. Existing planning approaches assume that the landmark locations are well known or are known with little uncertainty.

Jintao Sun, Michael Cantoni

Contraction properties of the Riccati operator are studied within the context of non-stationary linear-quadratic optimal control. A lifting approach is used to obtain a bound on the rate of strict contraction, with respect to the Riemannian metric, across a sufficient number of iterations. This number of iterations is related to an assumed uniform controllab

Anwen Hu, Shizhe Chen, Liang Zhang, Qin Jin

Automatic image captioning evaluation is critical for benchmarking and promoting advances in image captioning research. Existing metrics only provide a single score to measure caption qualities, which are less explainable and informative. Instead, we humans can easily identify the problems of captions in details, e.g., which words are inaccurate and which sa

Hannes Waclawek, Georg Schäfer, Christoph Binder, Eduard Hirsch

The vision of Industry 4.0 introduces new requirements to Operational Technology (OT) systems. Solutions for these requirements already exist in the Information Technology (IT) world, however, due to the different characteristics of both worlds, these solutions often cannot be directly used in the world of OT. We therefore propose an Industrial Business Proc

Deqing Jiang, Justin Sirignano, Samuel N. Cohen

Numerically solving high-dimensional partial differential equations (PDEs) is a major challenge. Conventional methods, such as finite difference methods, are unable to solve high-dimensional PDEs due to the curse-of-dimensionality. A variety of deep learning methods have been recently developed to try and solve high-dimensional PDEs by approximating the solu

C. Mondal, M. Antonelli, F. Gulminelli, M. Mancini

Possible strong first-order hadron-quark phase transitions in neutron star interiors leave an imprint on gravitational waves, which could be detected with planned third-generation interferometers. Given a signal from the late inspiral of a binary neutron star (BNS) coalescence, %the possibility of assessing the presence of such a phase transition depends on

Koichiro Moriya, Akihiko Noda

This paper is the first study to examine the time instability of the APT in the Japanese stock market. In particular, we measure how changes in each risk factor affect the stock risk premiums to investigate the validity of the APT over time, applying the rolling window method to Fama and MacBeth's (1973) two-step regression and Kamstra and Shi's (2023) gener

Paola Lemus-Basilio, Haret C. Rosu

A novel formulation of the Lie-Darboux method of obtaining the Riccati equations for the spatial curves in Euclidean three-dimensional space is presented. It leads to two Riccati equations that differ by the sign of torsion. The case of cylindrical helices is used as an illustrative example. Possible applications in Physics are suggested.

Ciro Ciliberto, Claudio Fontanari

Motivated by the weighted Bounded Negativity Conjecture, we prove that all but finitely many reduced and irreducible curves $C$ on the blow-up of $\mathbb{P}^2$ at $n$ points satisfy the inequality $C^2 \ge \min \{-\frac{1}{12} n (C.L +27), -2 \}$, where $L$ is the pull-back of a line. This partially improves on some result by Laface and Pokora.

Xinrui Liu, Yajie Wang, Yu-an Tan, Kefan Qiu

Deep neural networks (DNNs) have made tremendous progress in the past ten years and have been applied in various critical applications. However, recent studies have shown that deep neural networks are vulnerable to backdoor attacks. By injecting malicious data into the training set, an adversary can plant the backdoor into the original model. The backdoor ca

Armen Poghosyan, Hasmik Poghosyan

We investigate the RG domain wall between neighboring $A_2^{(p)}$ minimal CFT models and establish the map between UV and IR fields (matrix of mixing coefficients). A particular RG invariant set of six primary and four descendant fields is analyzed in full details. Using the algebraic construction of the RG domain wall we compute the UV/IR mixing matrix. To

Shabhrish Reddy Uddehal, Tilo Strutz, Hannah Och, André Kaup

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic

Ying Wang, Zihao Zhang

By using Sulanke-Xin continued fractions method, Xin proposed a recursion system to solve the Somos 4 Hankel determinant conjecture. We find Xin's recursion system indeed give a sufficient condition for $(\alpha, \beta)$ Somos $4$ sequences. This allows us to prove 4 conjectures of Barry on $(\alpha, \beta)$ Somos $4$ sequences in a unified way.

Xinrui Liu, Yu-an Tan, Yajie Wang, Kefan Qiu

Deep neural networks (DNNs) have gain its popularity in various scenarios in recent years. However, its excellent ability of fitting complex functions also makes it vulnerable to backdoor attacks. Specifically, a backdoor can remain hidden indefinitely until activated by a sample with a specific trigger, which is hugely concealed. Nevertheless, existing back

Siyu Yuan, Jiangjie Chen, Changzhi Sun, Jiaqing Liang

Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing know

Piotr Sawicki, Marek Grzes, Fabricio Goes, Dan Brown

This study examines the ability of GPT-3.5, GPT-3.5-turbo (ChatGPT) and GPT-4 models to generate poems in the style of specific authors using zero-shot and many-shot prompts (which use the maximum context length of 8192 tokens). We assess the performance of models that are not fine-tuned for generating poetry in the style of specific authors, via automated e

René Bødker Christensen, Petar Popovski

In this work, we show that a pair of entangled qubits can be used to compute a product privately. More precisely, two participants with a private input from a finite field can perform local operations on a shared, Bell-like quantum state, and when these qubits are later sent to a third participant, the third participant can determine the product of the input

Jianbin Zheng, Daqing Liu, Chaoyue Wang, Minghui Hu

Existing multimodal conditional image synthesis (MCIS) methods generate images conditioned on any combinations of various modalities that require all of them must be exactly conformed, hindering the synthesis controllability and leaving the potential of cross-modality under-exploited. To this end, we propose to generate images conditioned on the compositions

Chu Chen, Yanqi Ma, Bingcheng Dong, Junjie Cao

LiDAR sensors are critical for autonomous driving and robotics applications due to their ability to provide accurate range measurements and their robustness to lighting conditions. However, airborne particles, such as fog, rain, snow, and dust, will degrade its performance and it is inevitable to encounter these inclement environmental conditions outdoors. I

Honghao Yu, Robert L. Jack

We consider mixtures of oppositely driven particles, showing that their non-equilibrium steady states form lanes parallel to the drive, which coexist with transient jammed clusters where particles are temporarily immobilised. We analyse the interplay between these two types of non-equilibrium pattern formation, including their implications for macroscopic de

Manasvi Lingam, Amedeo Balbi, Swadesh M. Mahajan

Current research indicates that (sub)surface ocean worlds essentially devoid of subaerial landmasses (e.g., continents) are common in the Milky Way, and that these worlds could host habitable conditions, thence raising the possibility that life and technological intelligence (TI) may arise in such aquatic settings. It is known, however, that TI on Earth (i.e

Pedro J. Martinez-Ferrer, Tufan Arslan, Vicenç Beltran

We propose fork-join and task-based hybrid implementations of four classical linear algebra iterative methods (Jacobi, Gauss-Seidel, conjugate gradient and biconjugate gradient stabilised) as well as variations of them. Algorithms are duly documented and the corresponding source code is made publicly available for reproducibility. Both weak and strong scalab

Ken Abe

We consider ($-\alpha$)-homogeneous solutions to the stationary incompressible Euler equations in $\mathbb{R}^{3}\backslash\{0\}$ for $\alpha\geq 0$ and in $\mathbb{R}^{3}$ for $\alpha<0$. Shvydkoy (2018) demonstrated the nonexistence of ($-1$)-homogeneous solutions and ($-\alpha$)-homogeneous solutions in the range $0\leq \alpha\leq 2$ for the Beltrami and

Jie Qiao, Ruichu Cai, Siyu Wu, Yu Xiang

Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes processes based methods, mostly boil down to learning the so-called Granger causality which assumes that the cause event happens strictly prior to its effect event. Such an assumpti

Aki Ikeda, Takeshi Takahashi

Yoshihara's definition of Galois points for irreducible plane curves is extended to reducible plane curves. We also define simultaneous Galois points, weakening the conditions of the definition. We studied the number of simultaneous Galois points for a reduced plane curve with nonsingular components.

Matin Hosseinzadeh, Anindo Saha, Joeran Bosma, Henkjan Huisman

Quality of deep convolutional neural network predictions strongly depends on the size of the training dataset and the quality of the annotations. Creating annotations, especially for 3D medical image segmentation, is time-consuming and requires expert knowledge. We propose a novel semi-supervised learning (SSL) approach that requires only a relatively small

Rakesh Mundlamuri, Omid Esrafilian, Rajeev Gangula, Rohan Kharade

In this work, we propose an UAV-aided Integrated Access and Backhaul (IAB) system design offering 5G connectivity to ground users. UAV is integrated with a distributed unit (DU) acting as an aerial DU, which can provide 5G wireless backhaul access to a terrestrial central unit (CU). The CU-DU interface fully complies with the 3GPP defined F1 application prot

Varun Nair, Elliot Schumacher, Anitha Kannan

A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient. An effective summary is required to be coherent and accurately capture all the medically relevant information in the dialogue, despite the complexity of patient-gen

Jelena Sedlar, Riste Škrekovski

In a (proper) edge-coloring of a bridgeless cubic graph G an edge e is rich (resp. poor) if the number of colors of all edges incident to end-vertices of e is 5 (resp. 3). An edge-coloring of G is is normal if every edge of G is either rich or poor. In this paper we consider snarks ~G obtained by a simple superposition of edges and vertices of a cycle C in a

Xiaorui Bai, Wenyong Wang, Jun Zhang, Yueqing Wang

Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods mainly based on global information and focused on 2D circumstance. Based on flow field theory, we propose novel flow field segmentation and classification deep learning methods in three-dimensional space. We const

Jens H. Nyhegn, Georg M. Bruun, Kristian K. Nielsen

Adding a dopant to an antiferromagnetic spin background disturbs the magnetic order and leads to the formation of a quasiparticle coined the magnetic polaron, which plays a central role in understanding strongly correlated materials. Recently, remarkably detailed insights into the spatial properties of such polarons have been obtained using atoms in optical

Yaqi Chen, Xianyi Zeng, Ben Niu

Circular domains frequently appear in the fields of ecology, biology and chemistry. In this paper, we investigate the equivariant Hopf bifurcation of partial functional differential equations with Neumann boundary condition on a two-dimensional disk. The properties of these bifurcations around equilibriums are analyzed rigorously by studying the equivariant

Kazuaki Toyoura

We propose an efficient and general strategy of metadynamics (MetaD) for investigating interstitial diffusion in a crystal by exploiting crystallographic symmetry. Assuming complete ignorance of the diffusion phenomenon of interest, the three-dimensional coordinates of the interstitial atom with the periodic boundaries are chosen as the collective variables

Ilan Tennenhouse, Netanel Raviv

As blockchains continue to seek to scale to a larger number of nodes, the communication complexity of protocols has become a significant priority as the network can quickly become overburdened. Several schemes have attempted to address this, one of which uses coded computation to lighten the load. Here we seek to address one issue with all such coded blockch

Jiangjie Chen, Wei Shi, Ziquan Fu, Sijie Cheng

Large language models (LLMs) have been widely studied for their ability to store and utilize positive knowledge. However, negative knowledge, such as "lions don't live in the ocean", is also ubiquitous in the world but rarely mentioned explicitly in the text. What do LLMs know about negative knowledge? This work examines the ability of LLMs to negative commo

Arsham Ghavasieh, Manlio De Domenico

Empirical complex systems must differentially respond to external perturbations and, at the same time, internally distribute information to coordinate their components. While networked backbones help with the latter, they limit the components' individual degrees of freedom and reduce their collective dynamical range. Here, we show that real-world networks ar

Petre Stoica, Prabhu Babu

The Pearson-Matthews correlation coefficient (usually abbreviated MCC) is considered to be one of the most useful metrics for the performance of a binary classification or hypothesis testing method (for the sake of conciseness we will use the classification terminology throughout, but the concepts and methods discussed in the paper apply verbatim to hypothes

Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin

We address the challenge of ensuring differential privacy (DP) guarantees in training deep retrieval systems. Training these systems often involves the use of contrastive-style losses, which are typically non-per-example decomposable, making them difficult to directly DP-train with since common techniques require per-example gradients. To address this issue,

Daniella Bar-Lev, Avi Mizrahi, Tuvi Etzion, Ori Rottenstreich

The Invertible Bloom Lookup Table (IBLT) is a probabilistic data structure for set representation, with applications in network and traffic monitoring. It is known for its ability to list its elements, an operation that succeeds with high probability for sufficiently large table. However, listing can fail even for relatively small sets. This paper extends re

Griffen Adams, Ilarion V. Melnikov

We study two-dimensional non-linear sigma models with (2,2) supersymmetry and a holomorphic superpotential that are believed to flow to unitary compact (2,2) superconformal theories with equal left and right central charges c=9. The SCFTs have a set of marginal deformations, and some of these can be realized as deformations of parameters of the UV theory, ma

Chunyang Cheng, Tianyang Xu, Xiao-Jun Wu, Hui Li

In recent years, numerous ideas have emerged for designing a mutually reinforcing mechanism or extra stages for the image fusion task, ignoring the inevitable gaps between different vision tasks and the computational burden. We argue that there is a scope to improve the fusion performance with the help of the FusionBooster, a model specifically designed for

Yusuke Oka, Erbol Zhanpeisov

We consider the Cauchy problem for a time fractional semilinear heat equation with initial data belonging to inhomogeneous/homogeneous Besov--Morrey spaces. We present sufficient conditions for the existence of local/global-in-time solutions to the Cauchy problem, which cover all existing results in the literature and can be applied to a large class of initi

Yun Luo, Zhen Yang, Xuefeng Bai, Fandong Meng

Representation forgetting refers to the drift of contextualized representations during continual training. Intuitively, the representation forgetting can influence the general knowledge stored in pre-trained language models (LMs), but the concrete effect is still unclear. In this paper, we study the effect of representation forgetting on the generality of pr

Federico Talamucci

We first present a way to formulate the equations of motion for a nonholonomic system with nonlinear constraints with respect to the velocities. The formulation is based on the Cetaev condition which aims to extend the practical method of virtual displacements from the holonomic case to the nonlinear nonholonomic one. The condition may appear in a certain se

Youri Davydov, Vladimir Rotar

We consider a limit theorem for the distribution of a r.v. $Y_n:=argmax {\{X_i, i= 1,..., n\}},$ where $X_i'$s are independent continuous non-negative random variables. The r.v.'s $\{X_i, i=1,..., n\}$, may be interpreted as the gains of $n$ players in a game, and the r.v. $Y_n$ itself as the number of a ``winner". In the case of i.i.d.r.v.'s, the distributi

Pavel Putrov, Song Jin Ri

We test the efficiency of applying Geometric Deep Learning to the problems in low-dimensional topology in a certain simple setting. Specifically, we consider the class of 3-manifolds described by plumbing graphs and use Graph Neural Networks (GNN) for the problem of deciding whether a pair of graphs give homeomorphic 3-manifolds. We use supervised learning t

Federico Carrasco

This article study the average conditioning for a random underdetermined polynomial system. The expected value of the moments of the condition number are compared to the moments of the condition number of random matrices. An expression for these moments is given by studying the kernel finding problem for random matrices. Furthermore, the second moment of the

Anis Koubaa, Basit Qureshi, Adel Ammar, Zahid Khan

Since the release of ChatGPT, numerous studies have highlighted the remarkable performance of ChatGPT, which often rivals or even surpasses human capabilities in various tasks and domains. However, this paper presents a contrasting perspective by demonstrating an instance where human performance excels in typical tasks suited for ChatGPT, specifically in the

Hui Liu, Wenya Wang, Haoliang Li

Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information. While existing multimodal detection approaches have achieved high performance, the lack of interpretability hinders these systems' reliability

Prabhu Babu, Petre Stoica

In this paper we propose a new iterative algorithm to solve the fair PCA (FPCA) problem. We start with the max-min fair PCA formulation originally proposed in [1] and derive a simple and efficient iterative algorithm which is based on the minorization-maximization (MM) approach. The proposed algorithm relies on the relaxation of a semi-orthogonality constrai

Shaojing Fan, Ramesh C. Jain, Mohan S. Kankanhalli

Mobile health (mHealth) applications have become increasingly valuable in preventive healthcare and in reducing the burden on healthcare organizations. The aim of this paper is to investigate the factors that influence user acceptance of mHealth apps and identify the underlying structure that shapes users' behavioral intention. An online study that employed

Dorian Martino, Armin Schikorra

We show continuity of solutions $u \in W^{1,n}(B^n,\mathbb{R}^N)$ to the system \[ -{\rm div} (|\nabla u|^{n-2} \nabla u) = \Omega \cdot |\nabla u|^{n-2} \nabla u \] when $\Omega$ is an $L^n$-antisymmetric potential -- and additionally satisfies a Lorentz-space assumption. To obtain our result we study a rotated n-Laplace system \[ -{\rm div} (Q|\nabla u|^{n

Suzanne Lafon, Tiago Outerelo-Corvo, Marion Grzelka, Arnaud Hélary

Controlling the structure of polymer solutions near a solid surface is crucial for many industrial processes, as it significantly impacts solution flow and influences slip at the interface. To date, only a few techniques have been developed to experimentally investigate this type of interface at the nanometric scale of solid/liquid interaction. In this study

Yutao Xie, Jiayi Lin, Hande Dong, Lei Zhang

Code writing is repetitive and predictable, inspiring us to develop various code intelligence techniques. This survey focuses on code search, that is, to retrieve code that matches a given query by effectively capturing the semantic similarity between the query and code. Deep learning, being able to extract complex semantics information, has achieved great s

Thomas Wilding, Benjamin J. B. Deutschmann, Christian Nelson, Xuhong Li

This paper deals with propagation and channel modeling for physically large arrays. The focus lies on acquiring a spatially consistent model, which is essential, especially for positioning and sensing applications. Ultra-wideband, synthetic array measurement data have been acquired with large positioning devices to support this research. We present a modifie

Bohan Li, Diego Dupleich, Guoqing Xia, Huiyu Zhou

To make indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks free from wired fronthaul, this paper studies a multicarrier-division duplex (MDD)-enabled two-tier terahertz (THz) fronthaul scheme. More specifically, two layers of fronthaul links rely on the mutually orthogonal subcarreir sets in the same THz band, while access

Rubén Medina, Abraham Rueda Zoca

Let $M$ be a metric space and $X$ be a Banach space. In this paper we address several questions about the structure of $\mathcal F(M)\widehat{\otimes}_\pi X$ and $\mathop{Lip}(M,X)$. Our results are the following: (1) We prove that if $M$ is a length metric space then $\mathop{Lip}(M,X)$ has the Daugavet property. As a consequence, if $M$ is length we obtain

Nathan Steinle, Hannah Middleton, Christopher J. Moore, Siyuan Chen

Pulsar timing arrays (PTAs) and the Laser Interferometer Space Antenna (LISA) will open complementary observational windows on massive black-hole binaries (MBHBs), i.e., with masses in the range $\sim 10^6 - 10^{10}\,$ M$_{\odot}$. While PTAs may detect a stochastic gravitational-wave background from a population of MBHBs, during operation LISA will detect i

Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu

Deep spiking neural networks (SNNs) have drawn much attention in recent years because of their low power consumption, biological rationality and event-driven property. However, state-of-the-art deep SNNs (including Spikformer and Spikingformer) suffer from a critical challenge related to the imprecise gradient backpropagation. This problem arises from the im

Ze Yu Zhang, Weibo Gao

Quantum information processing and its subfield, quantum image processing, are rapidly growing fields as a result of advancements in the practicality of quantum mechanics. In this paper, we propose a quantum algorithm for processing information, such as one-dimensional time series and two-dimensional images, in the frequency domain. The information of intere

Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son

The detection of Maximal Extractable Value (MEV) in blockchain is crucial for enhancing blockchain security, as it enables the evaluation of potential consensus layer risks, the effectiveness of anti-centralization solutions, and the assessment of user exploitation. However, existing MEV detection methods face limitations due to their low recall rate, relian

Abbas Ali, Mohsin Ilahi

We construct the ADHM linear sigma model that is complementary to a model constructed by Edward Witten in 1995. Analysis of the corresponding moduli space leads to the resolution of a nearly three decade old mystery associated with the original construction. A number of problems are listed on which this construction has a bearing.

Tomoya Hayata, Yoshimasa Hidaka

We study the Hamiltonian lattice Yang-Mills theory based on spin networks that provide a useful basis to represent the physical states satisfying the Gauss law constraints. We focus on $\mathrm{SU}(2)$ Yang-Mills theory in $(2+1)$ dimensions. Following the string-net model, we introduce a regularization of the Kogut-Susskind Hamiltonian of lattice Yang-Mills

Kaifeng Huang, Yixuan Yan, Bihuan Chen, Zixin Tao

Call graph construction is the foundation of inter-procedural static analysis. PYCG is the state-of-the-art approach for constructing call graphs for Python programs. Unfortunately, PyCG does not scale to large programs when adapted to whole-program analysis where application and dependent libraries are both analyzed. Moreover, PyCG is flow-insensitive and d

Ye Lin, Shuhan Zhou, Yanyang Li, Anxiang Ma

For years the model performance in machine learning obeyed a power-law relationship with the model size. For the consideration of parameter efficiency, recent studies focus on increasing model depth rather than width to achieve better performance. In this paper, we study how model width affects the Transformer model through a parameter-efficient multi-path s

Rumeysa Bodur, Erhan Gundogdu, Binod Bhattarai, Tae-Kyun Kim

Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability in the output space of the generated images. We propose a novel learning method for text-guided image editing, namely \texttt{iEdit}, that generates images conditioned on a source image and a textual edit promp

Nikos I. Kavallaris, Çhristos V. Nikolopoulos, Athanasios N. Yannacopoulos

In this paper, we study a stochastic parabolic problem involving a nonlocal diffusion operator associated with nonlocal Robin-type boundary conditions. The stochastic dynamics under consideration are driven by a mixture of a classical Brownian and a fractional Brownian motions with Hurst index $H\in(\frac{1}{2}, 1).$ We first establish local in existence res

Zhiqiang Hu, Roy Ka-Wei Lee, Nancy F. Chen

Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled data required for the specific task. In this paper, we address this challenge by introducing AdapterTST, a framework that freezes the pre-trained model's original parameters and ena

Kenji Tojo, Ariel Shamir, Bernd Bickel, Nobuyuki Umetani

We present a technique to optimize the reflectivity of a surface while preserving its overall shape. The naive optimization of the mesh vertices using the gradients of reflectivity simulations results in undesirable distortion. In contrast, our robust formulation optimizes the surface normal as an independent variable that bridges the reflectivity term with

Juan Hu, Xin Liao, Difei Gao, Satoshi Tsutsui

Deepfake techniques have been widely used for malicious purposes, prompting extensive research interest in developing Deepfake detection methods. Deepfake manipulations typically involve tampering with facial parts, which can result in inconsistencies across different parts of the face. For instance, Deepfake techniques may change smiling lips to an upset li

Tal Bar, Baruch Meerson

Geometrical optics provides an instructive insight into Brownian motion, ``pushed" into a large-deviations regime by imposed constraints. Here we extend geometrical optics of Brownian motion by accounting for diffusion inhomogeneity in space. We consider three simple model problems of Brownian motion on the line or in the plane in situations where the diffus

Long Li, Jiansheng Yang, Bo Zhang, Haiwen Zhang

This paper is concerned with the problem of inverse scattering of time-harmonic acoustic plane waves by a two-layered medium with a locally rough interface in 2D. A direct imaging method is proposed to reconstruct the locally rough interface from the phaseless total-field data measured on the upper half of the circle with a large radius at a fixed frequency

Eshaan Tanwar, Subhabrata Dutta, Manish Borthakur, Tanmoy Chakraborty

In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update. ICL-enabled large language models provide a promising step forward toward bypassing recurrent annotation costs in a low-resource setting. Yet, only a handful of past studies have explored ICL in

Mathis Fricke, El Assad Ouro-Koura, Suraj Raju, Regine von Klitzing

The rise of a liquid column inside a thin capillary against the action of gravity is a prototypical example of a dynamic wetting process and plays an important role for applications but also for fundamental research in the area of multiphase fluid dynamics. Since the pioneering work by Lucas and Washburn, many research articles have been published which aim

Haibao Yu, Wenxian Yang, Hongzhi Ruan, Zhenwei Yang

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this issue, we introduce V2X-Seq, the first large-scale sequentia

Mukul Kumar, Sunil Kumar, Virender Thakur, Raj Kumar

We investigate the implications of parity-violating electron scattering experiment on neutron skin thickness of $^{48}$Ca (CREX) and $^{208}$Pb (PREX-II) data on the bulk properties of finite nuclei, nuclear matter, and neutron stars. The neutron skin thickness from the CREX and PREX-II data is employed to constrain the parameters of relativistic mean field

Xin Guan, Biwei Cao, Qingqing Gao, Zheng Yin

Commonsense question answering (QA) research requires machines to answer questions based on commonsense knowledge. However, this research requires expensive labor costs to annotate data as the basis of research, and models that rely on fine-tuning paradigms only apply to specific tasks, rather than learn a general commonsense reasoning ability. As a more rob

Shihao Shen, Yiwen Han, Xiaofei Wang, Shiqiang Wang

Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud networks. Besides, the hierarchical distribution of heterogeneous resources makes the modeling and scheduling of k8s-oriented edge-cloud network particularly challenging. In this pa

Jie Wei, Yonghui Zhang

This paper studies the principal component (PC) method-based estimation of weak factor models with sparse loadings. We uncover an intrinsic near-sparsity preservation property for the PC estimators of loadings, which comes from the approximately upper triangular (block) structure of the rotation matrix. It implies an asymmetric relationship among factors: th

Zhanwei Wang, Kaibin Huang, Yonina C. Eldar

Federated Learning (FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks can be compromised by exposure to interference from neighboring cells or jammers. Existing interference mitigation techniques require multi-cell cooperation or at least interference channe

S. S. Jensen, S. Spezzano, P. Caselli, T. Grassi

Pre-stellar cores represent the earliest stage of the star- and planet-formation process. By characterizing the physical and chemical structure of these cores we can establish the initial conditions for star and planet formation and determine to what degree the chemical composition of pre-stellar cores is inherited to the later stages. A 3D MHD model of a pr

Akın Alkan, Mehmet Önder

Some new kinds of special curves called $\overset{\_}{\xi}$-helix, $\overset{% \_}{\xi}_{1}$-helix, $\overset{\_}{\mu}$-helix, $\overset{\_}{\nu}$-helix and $W_{k}$-Darboux helices $(k\in \left\{ {n,r,o}\right\} )$ in the Myller configuration $M(C,\overset{\_}{\xi },\pi )$ are defined and studied. The necessary and sufficient conditions for these curves are

Marcos Tapia Costa, Ioannis Kontoyiannis, Simon Godsill

We consider the problem of obtaining effective representations for the solutions of linear, vector-valued stochastic differential equations (SDEs) driven by non-Gaussian pure-jump L\'evy processes, and we show how such representations lead to efficient simulation methods. The processes considered constitute a broad class of models that find application acros