March 2023 arXiv papers — page 38

Liying Han, Yang Li, Hao Tan, Weiyang Zhang

Quantum key distribution (QKD) based on the fundamental laws of quantum physics can allow the distribution of secure keys between distant users. However, the imperfections in realistic devices may lead to potential security risks, which must be accurately characterized and considered in practical security analysis. High-speed optical modulators, being as one

Marco Aymone

We study two models of random multiplicative functions: Rademacher random multiplicative functions supported on the squarefree integers $f$, and Rademacher random completely multiplicative functions $f^*$. We prove that the partial sums $\sum_{n\leq x}f^*(n)$ and $\sum_{n\leq x}\frac{f(n)}{\sqrt{n}}$ change sign infinitely often as $x\to\infty$, almost surel

Luca Butera, Andrea Cini, Alberto Ferrante, Cesare Alippi

Conditioning image generation on specific features of the desired output is a key ingredient of modern generative models. However, existing approaches lack a general and unified way of representing structural and semantic conditioning at diverse granularity levels. This paper explores a novel method to condition image generation, based on object-centric rela

S. B. Dubovichenko, A. S. Tkachenko, R. Ya. Kezerashvili, N. A. Burkova

We study a radiative $p^{15}$N capture on the ground state of $^{16}$O at stellar energies within the framework of a modified potential cluster model (MPCM) with forbidden states, including low-lying resonances. The investigation of the $^{15}$N($p,\gamma _{0}$)$^{16}$O reaction includes the consideration of $^{3}S_{1}$ resonances due to $E1$ transitions and

Yongqi An, Xu Zhao, Tao Yu, Haiyun Guo

Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophist

Simon van Mourik, Bert van't Ooster, Michel Vellekoop

This paper presents a risk mitigating, time-varying feedback control algorithm for crop production when state dynamics are subject to uncertainty. The model based case study concerns a 40 day production round of lettuce in a greenhouse where control input consists of daily and nightly temperature set points. The control problem was formulated in terms of a s

A. I. Fedyushkin, N. G. Bourago

By using the finite element code ASTRA, the effect of vibrations on the melt flow in Bridgman crystal growth is investigated for the Earth and Space gravity conditions. It is found that the vibrations significantly decrease the width of interfacial boundary layers in Bridgman crystal growth with submerged vibrator. The influence of the geometrical arrangemen

Hanlin Wang, Yilu Wu, Sheng Guo, Limin Wang

In this paper, we study the problem of procedure planning in instructional videos, which aims to make a plan (i.e. a sequence of actions) given the current visual observation and the desired goal. Previous works cast this as a sequence modeling problem and leverage either intermediate visual observations or language instructions as supervision to make autore

Naveen S. Prabhakar, Martin Roček

We describe the projective superspace approach to supersymmetric models with off-shell $(0,4)$ supersymmetry in two dimensions. In addition to the usual superspace coordinates, projective superspace has extra bosonic variables -- one doublet for each $\text{SU}(2)$ in the R-symmetry $\text{SU}(2) \times \text{SU}(2)$ which are interpreted as homogeneous coor

Yuting Liu, Shuo Cao, Marek Biesiada, Yujie Lian

The possible time variation of the fundamental constants of nature has been an active subject of research in modern physics. In this paper, we propose a new method to investigate such possible time variation of the speed of light $c$ using the updated Hubble diagram of high-redshift standard candles including Type Ia Supernovae (SNe Ia) and high-redshift qua

M. C. Diamantini, C. A. Trugenberger, Sheng-Zong Chen, Yu-Jung Lu

Superconductivity remains one of most fascinating quantum phenomena existing on a macroscopic scale. Its rich phenomenology is usually described by the Ginzburg-Landau (GL) theory in terms of the order parameter, representing the macroscopic wave function of the superconducting condensate. The GL theory addresses one of the prime superconducting properties,

Ming Qian, Jincheng Xiong, Gui-Song Xia, Nan Xue

This paper aims to develop an accurate 3D geometry representation of satellite images using satellite-ground image pairs. Our focus is on the challenging problem of 3D-aware ground-views synthesis from a satellite image. We draw inspiration from the density field representation used in volumetric neural rendering and propose a new approach, called Sat2Densit

Yan-Ting Xie, Yong-De Feng, Shou-Jun Xu

Partial cubes are the graphs which can be embedded into hypercubes. The {\em cube polynomial} of a graph $G$ is a counting polynomial of induced hypercubes of $G$, which is defined as $C(G,x):=\sum_{i\geqslant 0}\alpha_i(G)x^i$, where $\alpha_i(G)$ is the number of induced $i$-cubes (hypercubes of dimension $i$) of $G$. The {\em clique polynomial} of $G$ is

Dmytro Kaliuzhnyi-Verbovetskyi, Vyacheslav Pivovarchik

We show how to find the shape of an equilateral caterpillar tree using the spectra of the Neumann and the Dirichlet problems generated by the Sturm-Liouville equation on this tree. We prove that in the case of a caterpillar tree the spectra of the Neumann and Dirichlet problems uniquely determine the shape of the tree.

Ivar Lyberg, Vladimir Korepin, Jacopo Viti

We consider the six-vertex model with Domain Wall Boundary Conditions on a $N\times N$ square lattice. Our main interest is the study of the fluctuations of the extremal lattice path about the arctic curves. We address the problem through Monte Carlo simulations. At $\Delta = 0$, the fluctuations of the extremal path along any line parallel to the square dia

Tri Dung Duong, Qian Li, Guandong Xu

Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation methods are proposed to use variational

Rong An, Shuai Sun, Li-Gang Cao, Feng-Shou Zhang

The nuclear charge radius plays a vital role in determining the equation of state of isospin asymmetric nuclear matter. Based on the correlation between the differences in charge radii of mirror-partner nuclei and the slope parameter ($L$) of symmetry energy at the nuclear saturation density, an analysis of the calibrated slope parameter $L$ was performed in

Tianli Zhang, Mengqi Xue, Jiangtao Zhang, Haofei Zhang

Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability. In this paper, we strive to fully utilize multi-model settings instead of well-designed modules to achieve a distillation effect with excellent generalization performance. Generally,

Tri Dung Duong, Qian Li, Guandong Xu

Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i.e., what if the individual belongs to other sensitive groups). The existing studies need to pre-define the structural causal model that captures the correlations among variables f

Felix Christian Clemen, Adam Zsolt Wagner

A balanced edge-coloring of the complete graph is an edge-coloring such that every vertex is incident to each color the same number of times. In this short note, we present a construction of a balanced edge-coloring with six colors of the complete graph on $n=13^k$ vertices, for every positive integer $k$, with no rainbow $K_4$. This solves a problem by Erd\

Kristoffer Kortsen, Siobhan Kilbride, Stephen R. Lowe, Adam Peirce

Plastics are ubiquitous in modern society, but the linear model of produce, use, and dispose results in massive amounts of resource consumption and pollution. Landfill and incineration of plastic waste is endemic, with no consensus on a clear path towards a more sustainable model. Progress is often hampered by a lack of clarity on what choices will enable a

József Balogh, Felix Christian Clemen, Adrian Dumitrescu

Almost $50$ years ago Erd\H{o}s and Purdy asked the following question: Given $n$ points in the plane, how many triangles can be approximate congruent to equilateral triangles? They pointed out that by dividing the points evenly into three small clusters built around the three vertices of a fixed equilateral triangle, one gets at least $\left\lfloor \frac{n}

Zhiyuan Ma, Xiangyu Zhu, Guojun Qi, Zhen Lei

Controllability, generalizability and efficiency are the major objectives of constructing face avatars represented by neural implicit field. However, existing methods have not managed to accommodate the three requirements simultaneously. They either focus on static portraits, restricting the representation ability to a specific subject, or suffer from substa

Duong Trong Luyen, Nguyen Minh Tri, Dang Anh Tuan

In this article, we study the existence of non-trivial weak solutions for the following boundary-value problem \begin{gather*} -\frac{\partial^2 u}{\partial x^2} -\left|x\right|^{2k}\frac{\partial^2 u}{\partial y^2}=f(x,y,u) \quad\text{ in }\Omega, \ u=0 \quad\text{ on }\partial\Omega, \end{gather*} where $\Omega$ is a bounded domain with smooth boundary in

Sa Wang, Wei Dai, Enke Wang, Xin-Nian Wang

Reconstructed jets initiated from heavy quarks provide a powerful tool to probe the properties of the quark-gluon plasma (QGP) and to explore the mass hierarchy of jet quenching. In this article, we review the recent theoretical progresses on heavy-flavour jets in high-energy nuclear collisions at the RHIC and LHC. We focus on the yields and substructures of

Xin Yue, Guo-Jian Qiao, C. P. Sun

The edge state of the topological insulator coupled to a superconductor system is able to simulate the Majorana fermion in zero energy mode since the Kitaev-type pairing is induced by exchanging quasi-excitations in electron tunneling. However, the present study has revealed that this physical simulation is not valid for a larger surface gap, which is the en

Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu

The generalization error of a learning algorithm refers to the discrepancy between the loss of a learning algorithm on training data and that on unseen testing data. Various information-theoretic bounds on the generalization error have been derived in the literature, where the mutual information between the training data and the hypothesis (the output of the

Martin Donati

In this paper we construct solutions to the Euler and gSQG equations that are concentrated near unstable stationary configurations of point-vortices. Those solutions are themselves unstable, in the sense that their localization radius grows from order $\varepsilon$ to order $\varepsilon^\beta$ (with $\beta < 1$) in a time of order $|\ln\varepsilon|$. This pr

Kan Kitamura

We classify discrete quantum subgroups in the quantum double of the $q$-deformation of a compact semisimple Lie group, regarded as the complexification. We also record their classifications in some variants of quantum groups. Along the way, we show that quantum doubles of non-Kac type compact quantum groups do not admit the quantum analog of lattices conside

Ji Qi, Jifan Yu, Teng Tu, Kunyu Gao

Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i.e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative. In this p

Zhila Shaterabadi, Gholamreza Nabiyouni, Gerardo F Goya, Meysam Soleymani

We present a study on the magnetic behavior of dextran-coated magnetite nanoparticles (DM NPs) with sizes between 3 and 19 nm, synthesized by hydrothermal-assisted co-precipitation method. The decrease of saturation magnetization ($M_s$) with decreasing particle size has been modeled by assuming the existence of a spin-disordered layer at the particle surfac

Yingda Guan, Zhengyang Feng, Huiying Chang, Kuo Du

We present SDTracker, a method that harnesses the potential of synthetic data for multi-object tracking of real-world scenes in a domain generalization and semi-supervised fashion. First, we use the ImageNet dataset as an auxiliary to randomize the style of synthetic data. With out-of-domain data, we further enforce pyramid consistency loss across different

Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chenyao Wang

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic feature and prototype representation suffer from coarse segmentation granularity and train-set overfitting. In this work, we design Hierarchically Decoupled Matching Network (HDMNet) mi

Jie Hu, Linyan Huang, Tianhe Ren, Shengchuan Zhang

In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO predicts masks via dynamic convolutions between panoptic kernels and image feature maps, in which you only need to segment once for both instance and semantic segmentation tasks. To reduce the computational overhead, we design a feature pyramid aggregator for the feature map ex

Jesús A. Fuentes-García, Beatriz Sanz-Sagué, Reyes Mallada, M. Ricardo Ibarra

This work reports on the synthesis and characterization of a new type of electrospun magnetic nanofibers (MNFs), and their application for degradation of organic pollutants using remote magnetic inductive heating. We describe a simple protocol combining a fast (app. 5 min) synthesis of MnFe\textsubscript{2}O\textsubscript{4} magnetic nanoparticles (MNPs) by

Preeti Bhandari, Vikas Malik, Moshe Schechter

We propose a Monte Carlo simulation to understand electron transport in a non-equilibrium steady state (\textit{NESS}) for the lattice Coulomb Glass model, created by continuous excitation of single electrons to high energies followed by relaxation of the system. Around the Fermi level, the \textit{NESS} state approximately obeys the Fermi-Dirac statistics,

Ranjeev Kumar Parashar, Suchita Kandpal, Prasanta Bandyopadhyay, Mainak Sadhukhan

The present era has seen tremendous demands for low-cost electrochromic materials for visible-region multicolor display technology, paper-based, flexible, and wearable electronic devices, smart windows, and optoelectronic applications. Towards this goal, we report large-scale polyelectrochromic devices fabricated on rigid to flexible ITO substrates comprisin

Najmeh Torabian, Behrouz Minaei-Bidgoli, Mohsen Jahanshahi

Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a low-dimensional representation of a kno

Kaijun Shen, Kewei Sun, Yang Zhao

The multimode Brownian oscillator model is employed to simulate the emission spectra of transition metal dichalcogenide monolayers. Good agreement is obtained between measured and simulated photoluminescence spectra of WSe2, WS2, MoSe2 and MoS2 at various temperatures. The Huang-Rhys factor extracted from the model can be associated with that from the modifi

Dacheng Wen, Yupeng Li, Francis C. M. Lau

Ride-hailing is a sustainable transportation paradigm where riders access door-to-door traveling services through a mobile phone application, which has attracted a colossal amount of usage. There are two major planning tasks in a ride-hailing system: (1) matching, i.e., assigning available vehicles to pick up the riders, and (2) repositioning, i.e., proactiv

Dianyi Yang, Jiadong Tang, Yu Gao, Yi Yang

Fisheye cameras suffer from image distortion while having a large field of view(LFOV). And this fact leads to poor performance on some fisheye vision tasks. One of the solutions is to optimize the current vision algorithm for fisheye images. However, most of the CNN-based methods and the Transformer-based methods lack the capability of leveraging distortion

Abdur Rab Dhruba, Kazi Nabiul Alam, Md. Shakib Khan, Sananda Saha

The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious

Joya Chen, Difei Gao, Kevin Qinghong Lin, Mike Zheng Shou

Humans excel at learning from expert demonstrations and solving their own problems. To equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to ground human hand interactions (i.e., affordances) from demonstration videos and apply them to a target image like a user's AR glass view. The video-to-image affordance groun

Yue Zhang, Suchen Wang, Shichao Kan, Zhenyu Weng

Pedestrian attribute recognition (PAR) aims to predict the attributes of a target pedestrian in a surveillance system. Existing methods address the PAR problem by training a multi-label classifier with predefined attribute classes. However, it is impossible to exhaust all pedestrian attributes in the real world. To tackle this problem, we develop a novel ped

Morgan H. Lynch

In this manuscript we confirm the presence of a Rindler horizon at CERN-NA63 by exploring its thermodynamics induced by the Unruh effect in their high energy channeling radiation experiments. By linking the entropy of the emitted radiation to the photon number, we find the measured spectrum to be a simple manifestation of the second law of Rindler horizon th

Ritu Gupta, Ankur Malik, Vincent Vivier, Prakash Chandra Mondal

Due to the shorter channel length allowing faster ion/charge movement, nanoscale molecular thin films can be attractive electronic components for next-generation high-performing energy storage devices. However, controlling chemical functionalization and achieving stable electrode-molecule interfaces at the nanoscale via covalent functionalization for low-vol

Sixian Wang, Jincheng Dai, Xiaoqi Qin, Kai Niu

Recent advances in deep learning have led to increased interest in solving high-efficiency end-to-end transmission problems using methods that employ the nonlinear property of neural networks. These techniques, we call neural joint source-channel coding (NeurJSCC), extract latent semantic features of the source signal across space and time, and design corres

Xihan Wang, Xi Xu, Yu Gao, Yi Yang

Modern object detectors take advantage of rectangular bounding boxes as a conventional way to represent objects. When it comes to fisheye images, rectangular boxes involve more background noise rather than semantic information. Although multi-point representation has been proposed, both the regression accuracy and convergence still perform inferior to the wi

Yizhou Xu, Pei Sun, Yang Tian

Interacting systems can be studied as the networks where nodes are system units and edges denote correlated interactions. Although percolation on network is a unified way to model the emergence and propagation of correlated behaviours, it remains unknown how the dynamics characterized by percolation is related to the thermodynamics of phase transitions. It i

Sixian Wang, Jincheng Dai, Xiaoqi Qin, Zhongwei Si

Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel coding (NTSCC), extract the semantic latent features of source signal, and learn entropy model to guide the joint source-channel coding with variable rate to transmit latent feature

Deshabrato Mukherjee, Benjamin Kalas, Sven Burger, Gyorgy Safran

Spectroscopic ellipsometry is a sensitive and optical model-supported quantitative tool to monitor interfaces. In this work, solid-liquid interfaces are studied using the Kretschmann-Raether configuration for biosensing applications. The interface layers support two purposes simultaneously: (i) chemical suitability for the adsorption of molecules to be detec

Chao Lei, Allan H. MacDonald

The topological magneto-electric effect (TME) is a characteristic property of topological insulators. In this article, we use a simplified coupled-Dirac-cone electronic structure model to theoretically evaluate the THz and far infrared Kerr and Faraday responses of thin films of MnBi$_2$Te$_4$ with up to $N=10$ septuple layers with the goal of clarifying the

Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since

Panagiotis Nikolaidis, Asim Zoulkarni, John Baras

We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases resource efficiency. On the other hand, the performance of each NS becomes susceptible to traffic surges in other NSs, which

Zhuoying Zhao, Ziling Tan, Pinghui Mo, Xiaonan Wang

This paper proposes a special-purpose system to achieve high-accuracy and high-efficiency machine learning (ML) molecular dynamics (MD) calculations. The system consists of field programmable gate array (FPGA) and application specific integrated circuit (ASIC) working in heterogeneous parallelization. To be specific, a multiplication-less neural network (NN)

Xiaoxuan Liu, Siddharth Jha, Alvin Cheung

As models continue to grow in size, the development of memory optimization methods (MOMs) has emerged as a solution to address the memory bottleneck encountered when training large models. To comprehensively examine the practical value of various MOMs, we have conducted a thorough analysis of existing literature from a systems perspective. Our analysis has r

Daniel Gonzalez Cedre, Sophia Abraham, Lucas Parzianello, Eric Tsai

How do we summarize dynamic behavioral interactions? We introduce a possible node-embedding-based solution to this question: temporal egonet subgraph transitions.

Tripta Bhatia

In the lyotropic phase of lipids with excess water, multilamellar tubules (MLTs) grow from defects. A phenomenological model for the stability of MLTs is developed that is universal and independent of the underlying growth mechanisms of MLTs. The stability of MLTs implies that they are in hydrostatic equilibrium and stable as elastic objects that have compre

P. K. Verma

We conduct first-principles theoretical studies to investigate the temperature-dependent phonon properties of orthorhombic SrZrO$_3$ (SZO) perovskite. Our calculations include the quasiharmonic approximation, in which we explored mode Gr\"uneisen parameters, thermal expansion, and frequency shifts for several optical modes. For most modes these shifts exhibi

Tatyana Barron, Manimugdha Saikia

We determine the $N\to \infty$ asymptotics of the expected value of entanglement entropy in $H_{1,N}\otimes H_{2,N}$, where $H_{1,N}$ and $H_{2,N}$ are the spaces of holomorphic sections of the $N$-th tensor powers of hermitian ample line bundles on compact complex manifolds.

Jiquan Zhong, Xiaolin Huang, Xiao Yu

Multi-frame methods improve monocular depth estimation over single-frame approaches by aggregating spatial-temporal information via feature matching. However, the spatial-temporal feature leads to accuracy degradation in dynamic scenes. To enhance the performance, recent methods tend to propose complex architectures for feature matching and dynamic scenes. I

Ryota Yambe, Satoru Hayami

Anisotropic magnetic interactions become the origins of intriguing magnetic structures, such as helical and skyrmion structures by the Dzyaloshinskii-Moriya interaction. In general, possible anisotropic exchange interactions are restricted by crystal symmetry. Meanwhile, by lowering the crystal symmetry with light, additional anisotropic magnetic interaction

Yukang Zhang, Yan Yan, Jie Li, Hanzi Wang

Visible-infrared person re-identification (VI-ReID), which aims to search identities across different spectra, is a challenging task due to large cross-modality discrepancy between visible and infrared images. The key to reduce the discrepancy is to filter out identity-irrelevant interference and effectively learn modality-invariant person representations. I

Norihiro Hanihara

We study commutative Cohen-Macaulay rings whose Cohen-Macaulay representation theory are controlled by representations of quivers, which we call hereditary representation type. Based on tilting theory and cluster tilting theory, we construct some commutative Cohen-Macaulay rings of hereditary representation type. First we give a general existence theorem of

Jiacheng Wang, Hongyang Du, Dusit Niyato, Zehui Xiong

Recent advances in artificial intelligence (AI), coupled with a surge in training data, have led to the widespread use of AI for digital content generation, with ChatGPT serving as a representative example. Despite the increased efficiency and diversity, the inherent instability of AI models poses a persistent challenge in guiding these models to produce the

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) problem as a subroutine. This is counter-intui

Ao Shen, Xiao-Yu Cao, Yang Wang, Yao Fu

Quantum secret sharing (QSS) is one of the basic communication primitives in future quantum networks which addresses part of the basic cryptographic tasks of multiparty communication and computation. Nevertheless, it is a challenge to provide a practical QSS protocol with security against general attacks. A QSS protocol that balances security and practicalit

J. Dulangi Kanchana, Gayashan Amarasinghe, Vishaka Nanayakkara, Amal Shehan Perera

Distance learning is not a novel concept. Education or learning conducted online is a form of distance education. Online learning presents a convenient alternative to traditional learning. Numerous researchers have investigated the usage of online education in educational institutions and across nations. A set of essentials for effective online learning are

Shuaiyu Xie, Jian Wang, Bing Li, Zekun Zhang

Autoscaling is critical for ensuring optimal performance and resource utilization in cloud applications with dynamic workloads. However, traditional autoscaling technologies are typically no longer applicable in microservice-based applications due to the diverse workload patterns and complex interactions between microservices. Specifically, the propagation o

Minghan Li, Lei Zhang

It is expensive and labour-extensive to label the pixel-wise object masks in a video. As a result, the amount of pixel-wise annotations in existing video instance segmentation (VIS) datasets is small, limiting the generalization capability of trained VIS models. An alternative but much cheaper solution is to use bounding boxes to label instances in videos. I

Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu

Complex logical query answering (CLQA) is a recently emerged task of graph machine learning that goes beyond simple one-hop link prediction and solves a far more complex task of multi-hop logical reasoning over massive, potentially incomplete graphs in a latent space. The task received a significant traction in the community; numerous works expanded the fiel

Seong-Yeop Jeong, Clare Watt

The wave instability acts in astrophysical plasmas to redistribute energy and momentum in the absence of frequent collisions. There are many different types of waves, and it is important to quantify the wave energy density and growth rate for understanding what type of wave instabilities are possible in different plasma regimes. There are many situations thr

Ruitong Sun, Mohammad Rostami

Melanoma is a prevalent lethal type of cancer that is treatable if diagnosed at early stages of development. Skin lesions are a typical indicator for diagnosing melanoma but they often led to delayed diagnosis due to high similarities of cancerous and benign lesions at early stages of melanoma. Deep learning (DL) can be used as a solution to classify skin le

Kai Niu, Ping Zhang, Jincheng Dai, Zhongwei Si

After the pursuit of seventy years, the invention of polar codes indicates that we have found the first capacity-achieving coding with low complexity construction and decoding, which is the great breakthrough of the coding theory in the past two decades. In this survey, we retrospect the history of polar codes and summarize the advancement in the past ten ye

Tenglong Ao, Zeyi Zhang, Libin Liu

The automatic generation of stylized co-speech gestures has recently received increasing attention. Previous systems typically allow style control via predefined text labels or example motion clips, which are often not flexible enough to convey user intent accurately. In this work, we present GestureDiffuCLIP, a neural network framework for synthesizing real

Shahroz Tariq, Alsharif Abuadbba, Kristen Moore

The metaverse has gained significant attention from various industries due to its potential to create a fully immersive and interactive virtual world. However, the integration of deepfakes in the metaverse brings serious security implications, particularly with regard to impersonation. This paper examines the security implications of deepfakes in the metaver

Alexander Shnirelman

The story on the early days of the Quantum Ergodic Theorem. Alternative proof, underlying heuristics, physical analogies and interpretations.

Huxiao Luo

In this article, we study the uniqueness and nondegeneracy of ground states to a fractional Choquard equation of the form: $(-\Delta)^su+u=2(I_2\star u^2)u$ where $s\in(0,1)$ is sufficiently close to $1$. Our method is to make a continuation argument with respect to the power $s\in(0,1)$ appearing in $(-\Delta)^s$. This approach is based on [M. M. Fall and E

Soyoun Won, Sung-Ho Bae, Seong Tae Kim

Mixed sample data augmentation strategies are actively used when training deep neural networks (DNNs). Recent studies suggest that they are effective at various tasks. However, the impact of mixed sample data augmentation on model interpretability has not been widely studied. In this paper, we explore the relationship between model interpretability and mixed

Yasuaki Fujitani

The behavior of harmonic functions on Riemannian manifolds under lower bounds of the Ricci curvature has been studied from both analytic and geometric viewpoints. For example, some Liouville type theorems are obtained under lower bounds of the Ricci curvature. Recently, those results are generalized under lower bounds of the $N$-weighted Ricci curvature $\mb

Xiang-Rui Liu, Hanbin Deng, Yuntian Liu, Zhouyi Yin

The century-long development of surface sciences has witnessed the discoveries of a variety of quantum states. In the recently proposed "obstructed atomic insulators", insulators with symmetric charges pinned at virtual sites where no real atoms reside, the cleavage through these sites could lead to a set of obstructed surface states with partial occupation.

Irad Mwendo, Kinyua Gikunda, Anthony Maina

Chest X-rays remains to be the most common imaging modality used to diagnose lung diseases. However, they necessitate the interpretation of experts (radiologists and pulmonologists), who are few. This review paper investigates the use of deep transfer learning techniques to detect COVID-19, pneumonia, and tuberculosis in chest X-ray (CXR) images. It provides

Henglin Pu, Xingqi Wu

5G mmWave, as a revolutionary cellular technology, holds monumental potential for innovations in many academic and industrial areas. However, widespread adoption of this technology is hindered by the severe overheating issues experienced by current Commercial Off-The-Shelf (COTS) mmWave smartphones. This study aims to identify the root causes of device skin

Victor Reis, Thomas Rothvoss

In a seminal paper, Kannan and Lov\'asz (1988) considered a quantity $\mu_{KL}(\Lambda,K)$ which denotes the best volume-based lower bound on the covering radius $\mu(\Lambda,K)$ of a convex body $K$ with respect to a lattice $\Lambda$. Kannan and Lov\'asz proved that $\mu(\Lambda,K) \leq n \cdot \mu_{KL}(\Lambda,K)$ and the Subspace Flatness Conjecture by D

Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh

The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequence, the amount of compute used in training state-of-the-art models is exponentially increasing (doubling every 10 months between 2015 and 2022), resulting in a large carbon footprint. Federated Learning (FL) - a collabora

Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened

Hina Qayyum, Benjamin Zi Hao Zhao, Ian D. Wood, Muhammad Ikram

Toxicity is endemic to online social networks including Twitter. It follows a Pareto like distribution where most of the toxicity is generated by a very small number of profiles and as such, analyzing and characterizing these toxic profiles is critical. Prior research has largely focused on sporadic, event centric toxic content to characterize toxicity on th

Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari

AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare. A growing body of research has called for increased scrutiny around the validity of AI system designs. However, in real-world settings, it is often not possible to fully address questions around the validity

Jinyuan Jia, Yupei Liu, Yuepeng Hu, Neil Zhenqiang Gong

Data poisoning attacks spoof a recommender system to make arbitrary, attacker-desired recommendations via injecting fake users with carefully crafted rating scores into the recommender system. We envision a cat-and-mouse game for such data poisoning attacks and their defenses, i.e., new defenses are designed to defend against existing attacks and new attacks

Paul Pollack, Akash Singha Roy

An integer-valued multiplicative function $f$ is said to be polynomially-defined if there is a nonconstant separable polynomial $F(T)\in \mathbb{Z}[T]$ with $f(p)=F(p)$ for all primes $p$. We study the distribution in coprime residue classes of polynomially-defined multiplicative functions, establishing equidistribution results allowing a wide range of unifo

Kenta Sato

In this paper, we prove that if a $3$-dimensional quasi-projective variety $X$ over an algebraically closed field of characteristic $p>3$ has only log canonical singularities, then so does a general hyperplane section $H$ of $X$. We also show that the same is true for klt singularities, which is a slight extension of \cite{ST20}. In the course of the proof,

Yi Tian, Li Zhao, Meihong Zhu

This study investigates the dynamic game behaviors of dual-channel supply chains involving an oligopoly manufacturer selling low-carbon products to online and offline retailers. The price game models under government subsidy are discussed under three scenarios: (1) simultaneous decision, (2) manufacturer dominates the market, and (3) retailer dominates the m

Mahadee Al Mobin, Md. Kamrujjaman

Data scarcity and discontinuity are common occurrences in the healthcare and epidemiological dataset and often need help in forming an educative decision and forecasting the upcoming scenario. Often, these data are stored as monthly/yearly aggregate where the prevalent forecasting tools like Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregr

Sangchul Park

Cities are becoming smarter and more resilient by integrating urban infrastructure with information technology. However, concerns grow that smart cities might reverse progress on civil liberties when sensing, profiling, and predicting citizen activities; undermining citizen autonomy in connectivity, mobility, and energy consumption; and deprivatizing digital

Thomas Wong, Mauricio Barahona

This paper is a work in progress. We are looking for collaborators to provide us financial datasets in Equity/Futures market to conduct more bench-marking studies. The authors have papers employing similar methods applied on the Numerai dataset, which is freely available but obfuscated. We apply different feature engineering methods for time-series to US mar

Rafael D. Sorkin

Given a vector-space $~V~$ which is the tensor product of vector-spaces $A$ and $B$, we reconstruct $A$ and $B$ from the family of simple tensors $a{\otimes}b$ within $V$. In an application to quantum mechanics, one would be reconstructing the component subsystems of a composite system from its unentangled pure states. Our constructions can be viewed as inst

Qiao Gu, Dongsub Shim, Florian Shkurti

Catastrophic forgetting has been a major challenge in continual learning, where the model needs to learn new tasks with limited or no access to data from previously seen tasks. To tackle this challenge, methods based on knowledge distillation in feature space have been proposed and shown to reduce forgetting. However, most feature distillation methods direct

S. D. Odintsov, V. K. Oikonomou, F. P. Fronimos

In this article we present an alternative formalism for the inflationary phenomenology of rescaled Einstein-Gauss-Bonnet models which are in agreement with the GW170817 event. By constraining the propagation velocity of primordial tensor perturbations, an approximate form for the time derivative of the scalar field coupled to the Gauss-Bonnet density is extr

Hao Shi, Masato Mimura, Longbiao Wang, Jianwu Dang

Time-domain speech enhancement (SE) has recently been intensively investigated. Among recent works, DEMUCS introduces multi-resolution STFT loss to enhance performance. However, some resolutions used for STFT contain non-stationary signals, and it is challenging to learn multi-resolution frequency losses simultaneously with only one output. For better use of

Brad Windsor, Brandon O'Shea, Mengxi Wu

The Reinforcement Learning field is strong on achievements and weak on reapplication; a computer playing GO at a super-human level is still terrible at Tic-Tac-Toe. This paper asks whether the method of training networks improves their generalization. Specifically we explore core quality diversity algorithms, compare against two recent algorithms, and propos