August 2022 arXiv papers — page 82

Yanqing Wang, Gang Wu

The main objective of this paper is to answer the questions posed by Robinson and Sadowski [21, p. 505, Comm. Math. Phys., 2010]{[RS3]} for the Navier-Stokes equations. Firstly, we prove that the upper box dimension of the potential singular points set $\mathcal{S}$ of suitable weak solution $u$ belonging in $ L^{q}(0,T;L^{p}(\mathbb{R}^{3}))$ for $1\leq\fra

Seyed Ali Hashemian, Farid Ashtiani

The emerging 5G technology needs to support simultaneously running incompatible service types on a common infrastructure. Network slicing is a solution that corresponds a slice of the network to each service type. Ensuring that user activity in one slice does not affect other slices, i.e., inter-slice isolation, is a key requirement of slicing. Since due to

Lin Zhu, Junho Yang, Mikyoung Jun, Scott Cook

Compared to widely used likelihood-based approaches, the minimum contrast (MC) method offers a computationally efficient method for estimation and inference of spatial point processes. These relative gains in computing time become more pronounced when analyzing complicated multivariate point process models. Despite this, there has been little exploration of

Dan-Radu Grigore

We extend the general framework of perturbative quantum field theory developped for the pure Yang-Mills model to gravity. First we present a variant of the elimination procedure of the anomalies in the second order of perturbation theory. After that we prove that gauge invariance restricts severely the possible finite renormalizations, so at least in the sec

Sebastian Schäfer, Ulrike Meyer

In this paper, we present the modular design and implementation of DONUT, a novel tool for identifying software running on a device. Our tool uses a rule-based approach to detect software-specific DNS fingerprints (stored in an easily extendable database) in passively monitored DNS traffic. We automated the rule extraction process for DONUT with the help of

Kirstin Peters, Nobuko Yoshida

Session types provide a flexible programming style for structuring interaction, and are used to guarantee a safe and consistent composition of distributed processes. Traditional session types include only one-directional input (external) and output (internal) guarded choices. This prevents the session-processes to explore the full expressive power of the pi-

Sanjib Basu, Abhit Chandra Pramanik

Michalski gave a short and elegant proof of a theorem of A. Kumar which states that for each set A in R, there exists a subset B of A which is full in A and such that no distance between points in B is a rational number. He also proved a similar theorem for sets in the Euclidean plane. In this paper, we generalize these results in some special types of categ

Dongwoo Park, Jong-Min Lee

Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and query images owing to structural limitations. We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images. In add

Bridget Smart, Joshua Watt, Sara Benedetti, Lewis Mitchell

The 2022 Russian invasion of Ukraine emphasises the role social media plays in modern-day warfare, with conflict occurring in both the physical and information environments. There is a large body of work on identifying malicious cyber-activity, but less focusing on the effect this activity has on the overall conversation, especially with regards to the Russi

Firas Bayram, Bestoun S. Ahmed, Erik Hallin, Anton Engman

Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learning (ML) software systems to the new conditions. In this paper, we investigate an Electroslag Remelting (ESR) use case process from the Uddeholms AB steel company. The use case invol

Qing-Geng Yang, Da Wang, Qiang-Hua Wang

We study the Hubbard model on the square lattice coupled in addition to the optical Su-Schrieffer-Heeger (SSH) phonons, using the singular-mode functional renormalization group method. At half-filling and in the absence of the Hubbard interaction $U$, we find the degenerate spin-density-wave (SDW)/charge-density-wave (CDW)/s-wave superconductivity (sSC) stat

Kevin Haninger, Christian Hegeler, Luka Peternel

Robotic tasks which involve uncertainty--due to variation in goal, environment configuration, or confidence in task model--may require human input to instruct or adapt the robot. In tasks with physical contact, several existing methods for adapting robot trajectory or impedance according to individual uncertainties have been proposed, e.g., realizing intenti

Rituraj Borah, Jorid Smets, Rajeshreddy Ninakanti, Max L. Tietze

Films of close-packed Au nanoparticles are coupled electrodynamically through their collective plasmon resonances. This collective optical response results in enhanced light-matter interactions, which can be exploited in various applications. Here, we demonstrate their application in sensing volatile organic compounds, using methanol as a test-case. Ordered

William McDonald, Danail Obreschkow, Lilian Garratt-Smithson

The partial spatial separation of cold dark matter (DM) and gas is a ubiquitous feature in the formation of cosmic large-scale structure. This separation, termed dissociation, is prominent in galaxy clusters that formed through collisions of massive progenitors, such as the famous `Bullet' cluster. A direct comparison of the incidence of such dissociated str

Jun Qi

The heart of Quantum Federated Learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm for the QFL is expected to minimize the communication overhead among different quantum participants. In this work, we put forth an efficient learning algorithm, namely federated quan

Henry W. Lin

The emergence of the bulk Hilbert space is a mysterious concept in holography. In arXiv:1811.02584, the SYK model was solved in the double scaling limit by summing chord diagrams. Here, we explicitly construct the bulk Hilbert space of double scaled SYK by slicing open these chord diagrams; this Hilbert space resembles that of a lattice field theory where th

Rishi Veerapaneni, Maxim Likhachev

Heuristic search has traditionally relied on hand-crafted or programmatically derived heuristics. Neural networks (NNs) are newer powerful tools which can be used to learn complex mappings from states to cost-to-go heuristics. However, their slow single inference time is a large overhead that can substantially slow down planning time in optimized heuristic s

Pierre-Cyril Aubin-Frankowski, Alain Bensoussan

It is often said that control and estimation problems are in duality. Recently, in (Aubin-Frankowski,2021), we found new reproducing kernels in Linear-Quadratic optimal control by focusing on the Hilbert space of controlled trajectories, allowing for a convenient handling of state constraints and meeting points. We now extend this viewpoint to estimation pro

Yuuka Kanakubo

I establish the dynamical core--corona initialization framework (DCCI2) as a state-of-the-art dynamical framework that is capable of describing small and large colliding systems at the LHC energies. Under the core--corona picture, contributions from both equilibrated (core) and non-equilibrated (corona) components are implemented. I describe the dynamical se

Claudio Pisani

It is shown how double categories provide a direct abstract approach to coloured operads; namely, product-preserving normal lax functors from (Pb C)^op (the opposite of the double category of pullback squares in C) to Cat (the double category of functors and profunctors) can be seen as generalized operads, the standard ones arising when C = Set_f. In this co

Duan Zhang, Ying Sun

The purposes of this paper are to classify lower triangular forms and to determine under what conditions a nonlinear system is equivalent to a specific type of lower triangular forms. According to the least multi-indices and the greatest essential multi-index sets, which are introduced as new notions and can be obtained from the system equations, two classif

Farshad Rostami Ghadi, F. Javier Lopez-Martinez

We characterize the capacity region of a two-user multiple access channel (MAC) in a reconfigurable intelligent surface (RIS)-aided communication system with side information (SI) at the transmitters. We consider two uplink communication scenarios: (i) a double dirty MAC where the interferences are known non-causally to both users; and (ii) a single dirty MA

Matthias Posselt, Hartmut Bracht, Mahdi Ghorbani-Asl, Drazen Radić

Based on recent calculations of the self-diffusion (SD) coefficient in amorphous silicon (a-Si) by classical Molecular Dynamics simulation [M. Posselt, H. Bracht, and D. Radi\'c, J. Appl. Phys. 131, 035102 (2022)] detailed investigations on atomic mechanisms are performed. For this purpose two Stillinger-Weber-type potentials are employed, one strongly overe

Xiangyang Li, Qing-Bo Liu, Yongsen Tang, Wei Li

Two-dimensional (2D) semiconductors integrated with two or more functions are the cornerstone for constructing multifunctional nanodevices, but remain largely limited. Here, by tuning the spin state of organic linkers and the symmetry/topology of crystal lattice, we predict a class of unprecedented multifunctional semiconductors in 2D Cr(II) five-membered he

Xueru Wu, Yao Ma, Liangyun Chen

In this paper, we introduce the cohomology theory of relative Rota-Baxter operators on Leibniz triple systems. We use the cohomological approach to study linear and formal deformations of relative Rota-Baxter operators. In particular, formal deformations and extendibility of order $n$ deformations of a relative Rota-Baxter operators are also characterized in

Hongyu Fu, Yijing Yang, Yuhuai Liu, Joseph Lin

Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classification and regression tasks. Its performance improvement is reached at the expense of higher computational complexity. In this work, we investigate two ways to accelerate SLM. Firs

Bowen Li, Philip H. S. Torr, Thomas Lukasiewicz

We introduce a memory-driven semi-parametric approach to text-to-image generation, which is based on both parametric and non-parametric techniques. The non-parametric component is a memory bank of image features constructed from a training set of images. The parametric component is a generative adversarial network. Given a new text description at inference t

Chaofan Ling, Junpei Zhong, Weihua Li

Visual-frame prediction is a pixel-dense prediction task that infers future frames from past frames. Lacking of appearance details, low prediction accuracy and high computational overhead are still major problems with current models or methods. In this paper, we propose a novel neural network model inspired by the well-known predictive coding theory to deal

David A. Kalarkop, Pawaton Kaemawichanurat, Raghavachar Rangarajan

For a graph $G = (V(G), E(G))$, a dominating set $D$ is a vertex subset of $V(G)$ in which every vertex of $V(G) \setminus D$ is adjacent to a vertex in $D$. The domination number of $G$ is the minimum cardinality of a dominating set of $G$ and is denoted by $\gamma(G)$. A coloring of $G$ is a partition $C = (V_{1}, ... ,V_{k})$ such that each of $V_{i}$ in

Huican Mao, Yufeng Li, Qingyong Ren, Mihai Chu

We use neutron powder diffraction to investigate the magnetic and crystalline structure of Cr$_2$GaN. A magnetic phase transition is identified at $T \approx 170$ K, whereas no trace of structural transition is observed down to 6 K. Combining Rietveld refinement with irreducible representations, the spin configuration of Cr ions in Cr$_2$GaN is depicted as a

Zhengyi Zhou

We prove, by an ad hoc method, that exact fillings with vanishing rational first Chern class of flexibly fillable contact manifolds have unique integral intersection forms. We appeal to the special Reeb dynamics (stronger than ADC \`a la Lazarev) on the contact boundary, while a more systematic approach working for general ADC manifolds is developed independ

Omer San, Suraj Pawar, Adil Rasheed

Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine lear

Max van Haren, Lennart Blanken, Tom Oomen

Frequency-domain representations of multirate systems are essential for controller design and performance evaluation of multirate systems and sampled-data control. The aim of this paper is to develop a time-efficient closed-loop identification approach for multirate systems in the frequency-domain. The developed method utilizes local polynomial modeling for

Zhi-Qiang Li, Shou-Fu Tian, Jin-Jie Yang

In this work, we are devoted to study the Cauchy problem of the Camassa-Holm (CH) equation with weighted Sobolev initial data in space-time solitonic regions \begin{align*} m_t+2\kappa q_x+3qq_x=2q_xq_{xx}+qq_{xx},~~m=q-q_{xx}+\kappa,\\ q(x,0)=q_0(x)\in H^{4,2}(\mathbb R),~~x\in\mathbb R, ~~t>0, \end{align*} where $\kappa$ is a positive constant. Based on th

Andrew Ying

Many epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still surviving after the study terminates or move out of the study area, in which case right censoring is typically treated as independent or non-informative. Such an assumption can be f

Takashi Ichikawa

Using abelian differentials and periods of the universal Mumford curve, we study the universal expression and asymptotic behavior of tau functions defined for stably degenerating families of algebraic curves with additional data. Furthermore, we apply this study to constructing solutions to the KP hierarchy which are expressed by nonarchimedean theta functio

Wendong Bi, Lun Du, Qiang Fu, Yanlin Wang

Graph Neural Networks (GNNs) have shown expressive performance on graph representation learning by aggregating information from neighbors. Recently, some studies have discussed the importance of modeling neighborhood distribution on the graph. However, most existing GNNs aggregate neighbors' features through single statistic (e.g., mean, max, sum), which los

Hilmil Pradana, Keiichi Horio

Controlling fish feeding machine is challenging problem because experienced fishermen can adequately control based on assumption. To build robust method for reasonable application, we propose automatic controlling fish feeding machine based on computer vision using combination of counting nutriments and estimating ripple behavior using regression and textura

Yuchen Guo, Qiguang Chen, Gary P. T. Choi, Lok Ming Lui

In medical imaging, surface registration is extensively used for performing systematic comparisons between anatomical structures, with a prime example being the highly convoluted brain cortical surfaces. To obtain a meaningful registration, a common approach is to identify prominent features on the surfaces and establish a low-distortion mapping between them

Hang-Hyun Jo, Eun Lee, Young-Ho Eom

A heterogeneous structure of social networks induces various intriguing phenomena. One of them is the friendship paradox, which states that on average your friends have more friends than you do. Its generalization, called the generalized friendship paradox (GFP), states that on average your friends have higher attributes than yours. Despite successful demons

Tenzan Araki, Franco Nori, Clemens Gneiting

The theory of optimal quantum control serves to identify time-dependent control Hamiltonians that efficiently produce desired target states. As such, it plays an essential role in the successful design and development of quantum technologies. However, often the delivered control pulses are exceedingly sensitive to small perturbations, which can make it hard

Shoya Sakamoto, Takayuki Nozaki, Shinji Yuasa, Kenta Amemiya

The Fe/MgO interface is an essential ingredient in spintronics as it shows giant tunneling magnetoresistance and strong perpendicular magnetic anisotropy (PMA). A recent study demonstrated that the insertion of an ultra-thin LiF layer between the Fe and MgO layers enhances PMA significantly. In this study, we perform x-ray magnetic circular dichroism measure

Andrew Critch, Michael Dennis, Stuart Russell

It is increasingly possible for real-world agents, such as software-based agents or human institutions, to view the internal programming of other such agents that they interact with. For instance, a company can read the bylaws of another company, or one software system can read the source code of another. Game-theoretic equilibria between the designers of su

Li Wang, Jiaqun Wei, Haicheng Zhang, Peiyu Zhang

Let $\mathscr{C}$ be an extriangulated category and $\mathcal{X}$ be a semibrick in $\mathscr{C}$. Let $\mathcal{T}$ be the filtration subcategory generated by $\mathcal{X}$. We introduce the weak Jordan-H\"{o}lder property (WJHP) and Jordan-H\"{o}lder property (JHP) in $\mathscr{C}$ and show that $\mathcal{T}$ satisfies (WJHP). Furthermore, $\mathcal{T}$ sa

Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and r

Jingbo Zhang, Ziyu Wan, Jing Liao

Due to inevitable noises introduced during scanning and quantization, 3D reconstruction via RGB-D sensors suffers from errors both in geometry and texture, leading to artifacts such as camera drifting, mesh distortion, texture ghosting, and blurriness. Given an imperfect reconstructed 3D model, most previous methods have focused on the refinement of either g

Giancarlos Parady, Keita Suzuki, Yuki Oyama, Makoto Chikaraishi

This study evaluates the effectiveness of using Google Maps Location History data to identify joint activities in social networks. To do so, an experiment was conducted where participants were asked to execute daily schedules designed to simulate daily travel incorporating joint activities. For Android devices, detection rates for 4-person group activities r

Mingshan Sun, Ye Zheng, Tianpeng Bao, Jianqiu Chen

Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images. We discover that the principal reasons of Uni6D performance limitations are Instance-Outside and Instance-Inside noise. Uni6D's simple pipeline design inherently introduces Instance-Outside noise from background pixels in th

Xin Wang, Xu-Yang Hou, Zheng Zhou, Hao Guo

We first compare the geometric frameworks behind the Uhlmann and Berry phases in a fiber-bundle language and then evaluate the Uhlmann phases of bosonic and fermionic coherent states. The Uhlmann phases of both coherent states are shown to carry geometric information and decrease smoothly with temperature. Importantly, the Uhlmann phases approach the corresp

Changhyun Ahn

For the vanishing deformation parameter $\lambda$, the full structure of the (anti)commutator relations in the ${\cal N}=4$ supersymmetric linear $W_{\infty}[\lambda=0]$ algebra is obtained for arbitrary weights $h_1$ and $h_2$ of the currents appearing on the left hand sides in these (anti)commutators. The $w_{1+\infty}$ algebra can be seen from this by tak

Wenchao Ma, Bin Tan, Nan Xue, Tianfu Wu

This paper studies the problem of holistic 3D wireframe perception (HoW-3D), a new task of perceiving both the visible 3D wireframes and the invisible ones from single-view 2D images. As the non-front surfaces of an object cannot be directly observed in a single view, estimating the non-line-of-sight (NLOS) geometries in HoW-3D is a fundamentally challenging

Yang Su, Jianqiang Yang

We show some results of compact Kahler manifolds with elliptic homotopy type. In complex dimension 4 we list the Hodge diamonds of compact Kahler manifolds with elliptic homotopy type. In general dimension we obtain a partial characterization of the Hodge diamonds.

Jong-Ping Hsu, Leonardo Hsu

General Yang-Mills symmetry and Yang-Mills gravity in inertial frames presents a possible avenue for understanding dark energy phenomenon in terms of the extremely small baryon charges of protons and neutrons. General gauge transformations involve vector gauge functions and Hamilton's characteristic phase functions rather than scalar gauge functions and usua

Weipan Xu, Yu Gu, Yifan Chen, Yongtian Wang

Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling rural development status and providing political proposals. However,present rural house quality data highly depends on a top-down, time-consuming survey at the national or provincial level but fails to unp

Minhui Xiong, Ren Wang, Qinyu Zhou, Bing-Zhong Wang

In structured light with controllable degrees of freedom (DoFs), the vortex beams carrying orbital angular momentum (OAM) give access to provide additional degrees of freedom for information transfer, and in classic field, the propagation invariant space time electromagnetic pulses are the possible approach to high dimensional states. This paper arose an ide

Changcun Huang

This paper proposes a theoretical framework on the mechanism of autoencoders. To the encoder part, under the main use of dimensionality reduction, we investigate its two fundamental properties: bijective maps and data disentangling. The general construction methods of an encoder that satisfies either or both of the above two properties are given. The general

Yuefan Ji, Clarke Palmer, Emily E. Foley, Raynald Giovine

While low-cost natural gas remains abundant, the energy content of this fuel can be utilized without greenhouse gas emissions through the production of molecular hydrogen and solid carbon via methane pyrolysis. In the absence of a carbon tax, methane pyrolysis is not economically competitive with current hydrogen production methods unless the carbon byproduc

Yating Wang, Z. C. Tu

We discuss escape problem with the consideration of both the activity of particles and the roughness of potentials. we derive analytic expressions for the escape rate of a Brownian particle (ABP) in two types of rough potentials by employing the effective equilibrium approach and the Zwanzig method. We find that activity enhances the escape rate, but both th

Cong Xu, Zhaoqi Wu, Shao-Ming Fei

We use a novel formation to illustrate the ($\alpha,\beta,\gamma$) modified weighted Wigner-Yanase-Dyson (($\alpha,\beta,\gamma$) MWWYD) skew information of quantum channels. By using operator norm inequalities, we explore the sum uncertainty relations for arbitrary $N$ quantum channels and for unitary channels. These uncertainty inequalities are shown to be

Jathurshan Pradeepkumar, Mithunjha Anandakumar, Vinith Kugathasan, Dhinesh Suntharalingham

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed , and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorith

Christeen T Jose

This paper presents our results and findings on the use of temporal images for deepfake detection. We modelled temporal relations that exist in the movement of 468 facial landmarks across frames of a given video as spatial relations by constructing an image (referred to as temporal image) using the pixel values at these facial landmarks. CNNs are capable of

Harold R. Parks, Dean C. Wills

We present an elementary proof of the generalization of the $k$-bonacci Binet formula, a closed form calculation of the $k$-bonacci numbers using the roots of the characteristic polynomial of the $k$-bonacci recursion.

Kenneth Bogert, Yikang Gui, Prashant Doshi

The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically estimated feature expectations. However, in many real-world applications that use noisy sensors computing the feature expectations may be challenging due to partial observation of

Zihao Wang, Victor Veitch

Machine learning methods can be unreliable when deployed in domains that differ from the domains on which they were trained. There are a wide range of proposals for mitigating this problem by learning representations that are ``invariant'' in some sense.However, these methods generally contradict each other, and none of them consistently improve performance

Guangyu Li, Baoyi Chen, Yunpeng Liu

By solving two body Dirac equations with potentials at finite temperature, we calculated the dissociation temperature T_d of B_c mesons in the quark-gluon plasma. It is found that the T_d becomes higher with the relativistic correction than the T_d from the Schroedinger equation. Both the short range interaction and the constant term of the potential at the

Ian Dobson, Svetlana Ekisheva

We discuss ways to measure duration in a power transmission system resilience event by modeling outage and restore processes from utility data. We introduce novel Poisson process models that describe how resilience events progress and verify that they are typical using extensive outage data collected across North America. Some usual duration metrics show imp

Jialiang Sun, Wen Yao, Tingsong Jiang, Xiaoqian Chen

The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. However, with the rapid development of defense technologies, it also tends to be more difficult to evaluate the robustness of the

Ferenc Cole Thierrin, Fady Alajaji, Tamás Linder

Two R\'{e}nyi-type generalizations of the Shannon cross-entropy, the R\'{e}nyi cross-entropy and the Natural R\'{e}nyi cross-entropy, were recently used as loss functions for the improved design of deep learning generative adversarial networks. In this work, we build upon our results in [1] by deriving the R\'{e}nyi and Natural R\'{e}nyi differential cross-e

Dmitry V. Apeksimov, Andrey V. Bulygin, Yury E. Geints, Andrey M. Kabanov

High-power femtosecond laser radiation propagates nonlinearly in air exhibiting pulse self-focusing and strong multiphoton medium ionization, which leads to the spatial fragmentation of laser pulse into highly-localized light channels usually called the filaments. The filaments are characterized by high optical intensity, reduced (even zero) angular spreadin

Harry Rodger, Andrew Lensen, Marcin Betkier

The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction. This paper investigates by experimentation the potential use of explainable artificial intelligence for predicting imprisonment sentences in assault ca

Alexander Wong, Mohammad Javad Shafiee, Saad Abbasi, Saeejith Nair

With the growing adoption of deep learning for on-device TinyML applications, there has been an ever-increasing demand for efficient neural network backbones optimized for the edge. Recently, the introduction of attention condenser networks have resulted in low-footprint, highly-efficient, self-attention neural networks that strike a strong balance between a

Jizhou Huang, Zhengjie Huang, Xiaomin Fang, Shikun Feng

Estimated time of arrival (ETA) prediction, also known as travel time estimation, is a fundamental task for a wide range of intelligent transportation applications, such as navigation, route planning, and ride-hailing services. To accurately predict the travel time of a route, it is essential to take into account both contextual and predictive factors, such

J. M. Chen, J. Y. Chen

We study the Frobenius-Perron dimension of representation-directed algebras and quotient algebras of canonical algebras of type ADE, prove that the Frobenius-Perron dimension of a representation-directed algebra is always zero and the Frobenius-Perron dimension of a quotient algebra of canonical algebras of type ADE is 0 or 1. Moreover, we give a sufficient

Xinyue Hu, Yibo Yi, Kun Li, Hongwei Zhang

This paper investigates the physical-layer security in a Virtual Antenna Array (VAA)-aided Device- to-Device Multicast (D2DM) communication system, where the User Equipments (UEs) who cache the common content will form a VAA system and cooperatively multicast the content to UEs who desire it. Specifically, with the target of securing the VAA-aided D2DM commu

Yan Zheng, Stephan Bohacek

In the cloud environment, data centers are efficiently manipulated by cloud service providers (CSPs) in terms of energy consumption. Consequently, migrating workloads to clouds can result in lower energy consumption. This paper demonstrates that the Lift-and-Shift migration with optimal selections of cloud instances can provide significant energy savings, an

Wei Li, Ruxuan Li, Yuzhe Ma, Siu On Chan

Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible. However, we observe that state-of-the-art GNNs are less successful in the graph coloring problem. We analyze the reasons from two perspectives. First, most GNNs fail to generalize the task under homophily to heterophily, i.e.

Shuaiyi Huang, Luyu Yang, Bo He, Songyang Zhang

Finding dense semantic correspondence is a fundamental problem in computer vision, which remains challenging in complex scenes due to background clutter, extreme intra-class variation, and a severe lack of ground truth. In this paper, we aim to address the challenge of label sparsity in semantic correspondence by enriching supervision signals from sparse key

Jiaqian Ren, Lei Jiang, Hao Peng, Yuwei Cao

State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in social event detection tasks when restricted to a closed set of events. However, considering the large amount of data needed for training a neural network and the limited ability of a neural network in handling previously unknown data, it remains a challenge for existing GNN-ba

Darwin Zhou

There has been a consistent criticism over the past decade of the NFL franchise tag's monetary limitations due to its biased institutions in favor of the team rather than the player. But the question whether the NFL's franchise tag is fair or unfair to players has never been systematically studied. In this paper, I investigate the effects of NFL players' con

Afsoon Alidadi Shamsabadi, Animesh Yadav, Omid Abbasi, Halim Yanikomeroglu

Vertical heterogeneous networks (vHetNets) are promising architectures to bring significant advantages for 6G and beyond mobile communications. High altitude platform station (HAPS), one of the nodes in the vHetNets, can be considered as a complementary platform for terrestrial networks to meet the ever-increasing dynamic capacity demand and provide sustaina

Tyson Neuroth, Martin Rieth, Konduri Aditya, Myoungkyu Lee

Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and

Daniel V. Mathews, Jessica S. Purcell

In the 1980s, Neumann and Zagier introduced a symplectic vector space associated to an ideal triangulation of a cusped 3-manifold, such as a knot complement. We give a geometric interpretation for this symplectic structure in terms of the topology of the 3-manifold, via intersections of certain curves on a Heegaard surface. We also give an algorithm to const

Christopher T. Kenny, Cory McCartan, Tyler Simko, Shiro Kuriwaki

Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of alternati

Wencai Liu

Let $\Gamma=q_1\mathbb{Z}\oplus q_2 \mathbb{Z} $ with $q_1\in \mathbb{Z}_+$ and $q_2\in\mathbb{Z}_+$. Let $\Delta+X$ be the discrete periodic Schr\"odinger operator on $\mathbb{Z}^2$, where $\Delta$ is the discrete Laplacian and $X:\mathbb{Z}^2\to \mathbb{C}$ is $\Gamma$-periodic. In this paper, we develop tools from complex analysis to study the isospectral

Guoping Zhao, Bingqing Zhang, Mingyu Zhang, Yaxian Li

We propose a video feature representation learning framework called STAR-GNN, which applies a pluggable graph neural network component on a multi-scale lattice feature graph. The essence of STAR-GNN is to exploit both the temporal dynamics and spatial contents as well as visual connections between regions at different scales in the frames. It models a video

Yi-Min Huang, Amitava Bhattacharjee

Over the past decade, Boozer has argued that three-dimensional (3D) magnetic reconnection fundamentally differs from two-dimensional (2D) reconnection due to the fact that the separation between any pair of neighboring field lines almost always increases exponentially over distance in a 3D magnetic field. According to Boozer, this feature makes 3D field-line

Inkang Kim, Xueyuan Wan, Genkai Zhang

For a holomorphic vector bundle $E$ over a Hermitian manifold $M$ there are two important notions of curvature positivity, the Griffiths positivity and Nakano positivity. We study the consequence of these positivities and the relevant estimates. If $E$ is Griffiths negative over K\"ahler manifold, then there is a K\"ahler metric on its total space $E$, and w

Zijun Guo, Wenwen Meng, Dongfeng Shi, Linbin Zha

When imaging moving objects, single-pixel imaging produces motion blur. This paper proposes a new single-pixel imaging method, which can achieve anti-motion blur imaging of a fast-moving object. The geometric moment patterns and Hadamard patterns are used to alternately encode the position information and the image information of the object with time-divisio

Mengyuan Lee, Guanding Yu, Huaiyu Dai

As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems. Given that the inference stage of GNNs can be naturally implemented in a decentralized manner, GNN is a potential enabler for decentralized control/management in the next-generation wireless commu

Yaxian Li, Bingqing Zhang, Guoping Zhao, Mingyu Zhang

After a survey for person-tracking system-induced privacy concerns, we propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee. The method learns printable adversarial patterns for T-shirts that cloak wearers in the physical world in front of person-tracking systems. We design an angle-agnostic learning s

Feng Wang Peifeng Li, Qiaoming Zhu

Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information. To address the above issue, we first view spatial relation extraction as a generation task and propose a novel hybrid mod

Hyeongock Yun, Daeho Park, Sungsik Noh, Aaron Park

We argue why the recently observed $T_{cc}$ could either be a compact multiquark configuration or a loosely bound molecular configuration composed of charmed mesons, whereas the $X(3872)$ is most likely a molecular configuration. The argument is based on different short range interactions for these tetraquark states coming from the color-color and color-spin

Nima Sadri

Although representational retrieval models based on Transformers have been able to make major advances in the past few years, and despite the widely accepted conventions and best-practices for testing such models, a $\textit{standardized}$ evaluation framework for testing them has not been developed. In this work, we formalize the best practices and conventi

Feiping Xiao, Qingjun Tong

We show that anisotropic energy of a 2D antiferromagnet is greatly enhanced via stacking on a magnetic substrate layer, arising from the sublattice-dependent interlayer magnetic interaction that defines an effective anisotropic energy. Interestingly, this effective energy couples strongly with the interlayer stacking order and the magnetic order of the subst

Arie Pratama Sutiono, Gus Hahn-Powell

In low resource settings, data augmentation strategies are commonly leveraged to improve performance. Numerous approaches have attempted document-level augmentation (e.g., text classification), but few studies have explored token-level augmentation. Performed naively, data augmentation can produce semantically incongruent and ungrammatical examples. In this

Shengyu Feng, Baoyu Jing, Yada Zhu, Hanghang Tong

Contrastive learning is an effective unsupervised method in graph representation learning, and the key component of contrastive learning lies in the construction of positive and negative samples. Previous methods usually utilize the proximity of nodes in the graph as the principle. Recently, the data-augmentation-based contrastive learning method has advance

Nima Sadri, Gordon V. Cormack

Pre-trained and fine-tuned transformer models like BERT and T5 have improved the state of the art in ad-hoc retrieval and question-answering, but not as yet in high-recall information retrieval, where the objective is to retrieve substantially all relevant documents. We investigate whether the use of transformer-based models for reranking and/or featurizatio

Jia Li, Shiva Nejati, Mehrdad Sabetzadeh, Michael McCallen

The Internet of things (IoT) is increasingly prevalent in domains such as emergency response, smart cities and autonomous vehicles. Simulation plays a key role in the testing of IoT systems, noting that field testing of a complete IoT product may be infeasible or prohibitively expensive. In this paper, we propose a domain-specific language (DSL) for generati

Kenneth M. Mackenthun

Consider any sequence of finite groups $A^t$, where $t$ takes values in an integer index set $\mathbf{Z}$. A group system $A$ is a set of sequences with components in $A^t$ that forms a group under componentwise addition in $A^t$, for each $t\in\mathbf{Z}$. As shown previously, any strongly controllable complete group system $A$ can be decomposed into genera

Yarden Livnat, Dan Maljovec, Attila Gyulassy, Dr Baptiste Mouginot

Understanding the response of an output variable to multi-dimensional inputs lies at the heart of many data exploration endeavours. Topology-based methods, in particular Morse theory and persistent homology, provide a useful framework for studying this relationship, as phenomena of interest often appear naturally as fundamental features. The Morse-Smale comp