March 2023 arXiv papers — page 87

Amin Jabini, Erik A. Johnson

Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many uncertainties in system input and parameters that affect system response sensor measurements. Accounting for the uncertainties in

Takuro Usui, Suguru Shiratori, Kohei Tanimoto, Shumpei Ozawa

Fluid flow and heat transfer in levitated droplets were numerically investigated. Three levitation methods: electro-magnetic levitation (EML), aerodynamic levitation (ADL), and electro-static levitation (ESL) were considered, and conservative laws of mass, momentum, and energy were applied as common models. The Marangoni effect was applied as a velocity boun

Junu Jeong, Younggeun Kim, Sungjae Bae, Sungwoo Youn

The axion is a hypothetical particle motivated to address the strong CP problem, and is one of the appealing dark matter candidates. Numerous experimental searches for dark matter axions have been proposed relying on their coupling with photons. The classical equations of motion for the axion-photon coupling are well known but need to be fully computed for c

T. Wakamura, M. Hashisaka, S. Hoshino, M. Bard

We demonstrate gate-tunable giant field-dependent nonreciprocal transport (magnetochiral anisotropy) in a noncentrosymmetric superconductor $T_{\rm d}$-MoTe$_2$ in the thin limit. Giant magnetochiral anisotropy (MCA) with a rectification coefficient $\gamma$ = $3.1 \times 10^6$ T$^{-1}$ A$^{-1}$, is observed at 230 mK, below the superconducting transition te

Binbin Lina, Lei Zoua, Bo Zhao, Xiao Huang

Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without pro

Jinggang Chen, Xiaoyang Qu, Junjie Li, Jianzong Wang

Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data. Previous progress has introduced various approaches where the in-distribution training data and even several OOD examples are prerequisites. However, due to privacy and security, auxiliary data tends to be impractic

Fernando F. Dall'Agnol, Felipe Vieira, Francisco T. Degasperi

We derived the geometrical parameters on the tube connections that homogenize the pressure drop in a multi-chamber vacuum system, where each chamber has a distinct volume and all are connected to the same vacuum pump. We start deriving the pressure drop in a single chamber for a tube with finite conductance. Next, we derive a solution that provides the radiu

Tianyu Qiu, David Fridovich-Keil

In mobile robotics and autonomous driving, it is natural to model agent interactions as the Nash equilibrium of a noncooperative, dynamic game. These methods inherently rely on observations from sensors such as lidars and cameras to identify agents participating in the game and, therefore, have difficulty when some agents are occluded. To address this limita

Tzu-Sheng Kuo, Hong Shen, Jisoo Geum, Nev Jones

Recent years have seen growing adoption of AI-based decision-support systems (ADS) in homeless services, yet we know little about stakeholder desires and concerns surrounding their use. In this work, we aim to understand impacted stakeholders' perspectives on a deployed ADS that prioritizes scarce housing resources. We employed AI lifecycle comicboarding, an

Yanna Wang, Bo Zhou

A cactus is a connected graph in which any two cycles have at most one common vertex. We determine the unique graph that maximizes the distance spectral radius over all cacti with fixed numbers of vertices and cycles, and thus prove a conjecture on the distance spectral radius of cacti in [S.S. Bose, M. Nath, S. Paul, On the distance spectral radius of cacti

Leyou Xu, Chengli Li, Bo Zhou

Given a graph $H$, a graph $G$ is $H$-free if $G$ does not contain $H$ as an induced subgraph. For a positive real number $t$, a non-complete graph $G$ is said to be $t$-tough if for every vertex cut $S$ of $G$, the ratio of $|S|$ to the number of components of $G-S$ is at least $t$. A complete graph is said to be $t$-tough for any $t>0$. Chv\'{a}tal's tough

J. Guo, A. Sarikhani, P. Ghosh, T. Heitmann

Magnetic semiconductors are at the core of recent spintronics research endeavors. Chemically doped II-VI diluted magnetic semiconductors, such as (Zn$_{1-x}$Cr$_{x}$)Te, provide promising platform in this quest. However, a detailed knowledge of the microscopic nature of magnetic ground state is necessary for any practical application. Here, we report on the

Roberto C. Díaz, Ana I. Julio

All graphs considered are simple and undirected. The Inverse Eigenvalue Problem of a Graph $G$ (IEP-G) aims to find all possible spectra for matrices whose $(i,j)-$entry, for $i\neq j$, is nonzero precisely when $i$ is adjacent to $j$. A cluster in a graph $G$ is a pair of vertex subsets $(C, S)$, where $C$ is a maximal set of cardinality $\vert C\vert\geq 2

Kai Chen, Ling Liang, Shaohua Pan

One-bit compressed sensing is very popular in signal processing and communications due to its low storage costs and low hardware complexity, but it is a challenging task to recover the signal by using the one-bit information. In this paper, we propose a zero-norm regularized smooth difference of convexity (DC) loss model and derive a family of equivalent non

Mengdong Shang, Xia Chen

We propose the so-called jackknife empirical likelihood approach for the survey data of general unequal probability sampling designs, and analyze parameters defined according to U-statistics. We prove theoretically that jackknife pseudo-empirical likelihood ratio statistic is asymptotically distributed as a chi-square random variable, and can be used to cons

Jun-Hyung Park, Yeachan Kim, Junho Kim, Joon-Young Choi

Structure pruning is an effective method to compress and accelerate neural networks. While filter and channel pruning are preferable to other structure pruning methods in terms of realistic acceleration and hardware compatibility, pruning methods with a finer granularity, such as intra-channel pruning, are expected to be capable of yielding more compact and

Yupeng Zhou, Zhen Li, Chun-Le Guo, Li Liu

Previous works have shown that increasing the window size for Transformer-based image super-resolution models (e.g., SwinIR) can significantly improve the model performance. Still, the computation overhead is also considerable when the window size gradually increases. In this paper, we present SRFormer, a simple but novel method that can enjoy the benefit of

Kiran Tomlinson, Johan Ugander, Jon Kleinberg

Instant runoff voting (IRV) has recently gained popularity as an alternative to plurality voting for political elections, with advocates claiming a range of advantages, including that it produces more moderate winners than plurality and could thus help address polarization. However, there is little theoretical backing for this claim, with existing evidence f

Samin Poudel, Marwan Bikdash

The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent studies significantly explained the variation in the performances of CF methods resulted due to the change in URM using six or more RDC. Here,

Zhengyi Liu, Xiaoshen Huang, Guanghui Zhang, Xianyong Fang

Salient object detection segments attractive objects in scenes. RGB and thermal modalities provide complementary information and scribble annotations alleviate large amounts of human labor. Based on the above facts, we propose a scribble-supervised RGB-T salient object detection model. By a four-step solution (expansion, prediction, aggregation, and supervis

Yifan Yan, Xudong Pan, Mi Zhang, Min Yang

Copyright protection for deep neural networks (DNNs) is an urgent need for AI corporations. To trace illegally distributed model copies, DNN watermarking is an emerging technique for embedding and verifying secret identity messages in the prediction behaviors or the model internals. Sacrificing less functionality and involving more knowledge about the target

Qifan Xiao, Xudong Pan, Yifan Lu, Mi Zhang

Automated driving systems rely on 3D object detectors to recognize possible obstacles from LiDAR point clouds. However, recent works show the adversary can forge non-existent cars in the prediction results with a few fake points (i.e., appearing attack). By removing statistical outliers, existing defenses are however designed for specific attacks or biased b

Chen Tang, Li Lyna Zhang, Huiqiang Jiang, Jiahang Xu

Neural Architecture Search (NAS) has shown promising performance in the automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing lightweight and low-latency ViT models for diverse mobile devices remains a big challenge. In this work, we propose ElasticViT, a two-stage NAS approach that trains a high-quality ViT supernet over a ver

T. Rubin, J. M. Rax, N. J. Fisch

A new end-plugging method for rotating plasmas is identified and analyzed. It uses the ponderomotive potential associated with an azimuthal magnetostatic wiggler. Studied both analytically and numerically, this process compares favorably to other end-plugging methods in open field line magnetized plasma devices.

Tony Lyons

Relative motion of particles is examined in the context of relational space-time. It is shown that de Broglie waves may be derived as a representation of the coordinate maps between the rest-frames of these particles. Energy and momentum are not absolute characteristics of these particles, they are understood as parameters of the coordinate maps between thei

Yulong Zhang, Shuhao Chen, Yu Zhang, Jiangang Lu

Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, unsupervised domain adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant features. However, large domain shifts and the sample scarcity in the target domain make existing UDA methods

Gongpei Zhao, Tao Wang, Yidong Li, Yi Jin

Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning. However, there exist some limitations associated with this algorithm, such as getting stuck in local minima and experiencing vanishing/exploding gradients, which have

Rita Garcia, Christoph Treude, Wendy La

The open-source community uses the GitHub platform to exchange and share software applications and services of interest. This paper aims to identify the open-source community's interest in gender-related projects on GitHub. Our findings create research opportunities and identify resources by the open-source community that promote diversity, equity, and inclu

Dongdong Shi, Xin Wang, XianZhong Zheng, Zheng Cai

We report the detection of a pair of massive quiescent galaxies likely in the process of merging at the center of the spectroscopically confirmed, extremely massive protocluster BOSS1244 at $z=2.24\pm0.02$. These galaxies, BOSS1244-QG1 and BOSS1244-QG2, were detected with Hubble Space Telescope (HST) grism slitless spectroscopic observations. These two quies

Mark Mansi, Michael M. Swift

"As many of us know from bitter experience, the policies provided in extant operating systems, which are claimed to work well and behave fairly 'on the average', often fail to do so in the special cases important to us" [Wulf et al. 1974]. Written in 1974, these words motivated moving policy decisions into user-space. Today, as warehouse-scale computers (WSC

Xi-guang Wang, Guang-hua Guo, V. K. Dugaev, J. Barnaś

Tools for controlling electrically the motion of magnetic skyrmions are important elements towards their use in spintronic devices. Here, we propose and demonstrate the transport of skyrmions via GHz and THz electric pulses. The method relies on using polarization textured pulses such that the skyrmion experiences (via its inherent magnetoelectricity) the ou

Yangcheng Li, Yong Zhang

Many authors studied the problem that rational triangle pairs (triangle-parallelogram pairs) with the same area and the same perimeter. They investigated this problem by solving the rational solutions of the corresponding Diophantine equations. In this paper, we give a unified description of this problem by using the affine transformation in a rectangular co

Dong-Hui Fan, Xing-Yu Zhang, Wei-Jun Zhang, Ruo-Yan Ma

We propose a method for coupling a tapered optical fiber to an inverted tapered SiN waveguide by fabricating a microfiber using 3D nanoprinting lithography. The microfiber consists of three parts: a tapered cladding cap, an S-bend, and a straight part, all composed of high-refractive-index material. Light is adiabatically coupled from the tapered fiber to th

Mayuka Ichihara, Daisuke Yoshida, Feng-Lei Hong, Tomoyuki Horikiri

The implementation of quantum repeaters needed for long-distance quantum communication requires the generation of quantum entanglement distributed among the elementary links. These entanglements must be swapped among the quantum repeaters through Bell-state measurements. This study aims to improve the entanglement generation rate by frequency multiplexing th

Hirotaka Goto

Individual participants in human society collectively exhibit aggregation behavior. In this study, we present a simple microscopic model of labor force migration based on the active Brownian particles framework. In particular, agent-based simulations show that the model produces clusters of agents from a random initial distribution. Furthermore, two empirica

Xiuying Chen, Mingzhe Li, Jiayi Zhang, Xiaoqiang Xia

As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples. However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between

BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $15.6$ $\rm fb^{-1}$ of $e^+e^-$ collision data collected at twenty-four center-of-mass energies from $4.0$ to $4.6$ GeV with the BESIII detector, the helicity amplitudes of the process $e^+e^-\to \pi^{+}\pi^{-}\omega$ are analyzed for the first time. Born cross section measurements of two-body intermediate resonance states with statistical significanc

Sandra Cerrai, Zdzislaw Brzeźniak

We investigate the well-posedness of a class of stochastic second-order in time damped evolution equations in Hilbert spaces, subject to the constraint that the solution lie within the unitary sphere. Then, we focus on a specific example, the stochastic damped wave equation in a bounded domain of a $d$-dimensional Euclidean space, endowed with the Dirichlet

Anna Winnicki, R. Srikant

Optimal policies in standard MDPs can be obtained using either value iteration or policy iteration. However, in the case of zero-sum Markov games, there is no efficient policy iteration algorithm; e.g., it has been shown that one has to solve Omega(1/(1-alpha)) MDPs, where alpha is the discount factor, to implement the only known convergent version of policy

Alejandro Rodriguez Pascual, Ishan Mehta, Muhammad Khan, Frank Rodriz

Understanding player shooting profiles is an essential part of basketball analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive gameplans from their opponents; understanding where their players are most comfortable can lead them to developing more effective offensive strategies. An automatic tool that can p

Tong Bao, Zhiyuan Li, Zhongqun Cheng

We present a systematic study of periodic X-ray sources in the massive globular cluster 47 Tuc, utilizing deep archival Chandra observations that resolve the cluster core and recently available eROSITA observations that cover the cluster outskirt. By applying the Gregory-Loredo algorithm, we detect 20 periodic signals among 18 X-ray sources, ranging between

Seungju Han, Jack Hessel, Nouha Dziri, Yejin Choi

Visual information is central to conversation: body gestures and physical behaviour, for example, contribute to meaning that transcends words alone. To date, however, most neural conversational models are limited to just text. We introduce CHAMPAGNE, a generative model of conversations that can account for visual contexts. To train CHAMPAGNE, we collect and

Marc L. Whiting, Joshua B. Hill, Benjamin C. Bromley, Scott J. Kenyon

We describe a new catalog of accelerating star candidates with Gaia $G\le 17.5$ mag and distances $d\le 100$ pc. Designated as Gaia Nearby Accelerating Star Catalog (GNASC), it contains 29,684 members identified using a supervised machine-learning algorithm trained on the Hipparcos-Gaia Catalog of Accelerations (HGCA), Gaia Data Release 2, and Gaia Early Dat

Morgan H. Lynch

In this manuscript, we examine the gravitational radiation emitted by binary systems using an Unruh-DeWitt detector coupled to gravitons. Recoil is incorporated into the system via a kinetic energy term in the energy gap of the detector. We find a splitting of the gravitational wave frequency due to the recoil. Implications for the recoil velocity and force

Shashank Mehrotra, Jacob G Hunter, Matthew Konishi, Kumar Akash

The ever-increasing adoption of shared transportation modalities across the United States has the potential to fundamentally change the preferences and usage of different mobilities. It also raises several challenges with respect to the design and development of automated mobilities that can enable a large population to take advantage of this emergent techno

De-Jiang Yin, Li-Yun Zhang, Bao-Da Li, Ming-Hui Li

Up to November 2022, 267 pulsars have been discovered in 36 globular clusters (GCs). In this paper, we present our studies on the distribution of GC pulsar parameters and the detection efficiency. The power law relation between the average of dispersion measure ($\overline{\rm DM}$) and dispersion measure difference ($\Delta {\rm DM}$) of known pulsars in GC

Jeremy M. C. Brown, Lachlan Chartier, David Boardman, John Barnes

CLLBC(Ce) and TLYC(Ce) are novel scintillation materials capable of measuring mixed gamma ray and neutron radiation fields that have gained significant interest in the areas of space and nuclear safety/security science. To date Geant4, the world's most popular Monte Carlo radiation modelling toolkit, has yet to be effectively used to simulate the full respon

Yi-Dong Wu

Wilson-loop has been widely used to characterize the topological property of topological insulators, high order topological insulators and topological semimetals. Both bulk topological invariants and nontrivial boundary properties can be deduced from the topology of Wilson-loop spectrum. However, no attempt has been made to observe it. In this letter we demo

Kariane Calta, Cor Kraaikamp, Thomas A. Schmidt

We extend the results of our 2020 paper in the Annali della Scuola Normale Superiore di Pisa, Classe di Scienze. There, we associated to each of an infinite family of triangle Fuchsian groups a one-parameter family of continued fraction maps and showed that the matching (or, synchronization) intervals are of full measure. Here, we find planar extensions of e

David Lee

Non-hydrostatic atmospheric models often use semi-implicit temporal discretisations in order to negate the time step limitation of explicitly resolving the fast acoustic and gravity waves. Solving the resulting system to machine precision using Newton's method is considered prohibitively expensive, and so the non-linear solver is typically truncated to a fix

Pengfei Zhu, Mengshi Qi, Xia Li, Weijian Li

Predicting attention regions of interest is an important yet challenging task for self-driving systems. Existing methodologies rely on large-scale labeled traffic datasets that are labor-intensive to obtain. Besides, the huge domain gap between natural scenes and traffic scenes in current datasets also limits the potential for model training. To address thes

Yuta Nakahara, Toshiyasu Matsushima

Previously, we proposed a probabilistic data generation model represented by an unobservable tree and a sequential updating method to calculate a posterior distribution over a set of trees. The set is called a meta-tree. In this paper, we propose a more efficient batch updating method.

Utkarsha Agwan, Junjie Qin, Kameshwar Poolla, Pravin Varaiya

This paper examines the marginal value of mobile energy storage, i.e., energy storage units that can be efficiently relocated to other locations in the power network. In particular, we formulate and analyze the joint problem for operating the power grid and a fleet of mobile storage units. We use two different storage models: rapid storage, which disregards

Ahmed Shoyeb Raihan, Imtiaz Ahmed

Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but infrequent events. Prompt detection of such events can prevent potential losses in terms of finances, information, and human resources. With

Anna Lubiw, Anurag Murty Naredla

The geodesic edge center of a polygon is a point c inside the polygon that minimizes the maximum geodesic distance from c to any edge of the polygon, where geodesic distance is the shortest path distance inside the polygon. We give a linear-time algorithm to find a geodesic edge center of a simple polygon. This improves on the previous O(n log n) time algori

Indranil Biswas, Jacques Hurtubise, Vladimir Roubtsov

Let $X$ be a compact connected Riemann surface of genus $g$, with $g\, \geq\,2$, and let $\xi$ be a holomorphic line bundle on $X$ with $\xi^{\otimes 2}\,=\, {\mathcal O}_X$. Fix a theta characteristic $\mathbb L$ on $X$. Let ${\mathcal M}_X(r,\xi)$ be the moduli space of stable vector bundles $E$ on $X$ of rank $r$ such that $\bigwedge^r E\,=\, \xi$ and $H^

Han Zhang, Shangen Lu, Yixin Wang, Mihaela Curmei

The impacts of link recommendations on social networks are challenging to evaluate, and so far they have been studied in limited settings. Observational studies are restricted in the kinds of causal questions they can answer and naive A/B tests often lead to biased evaluations due to unaccounted network interference. Furthermore, evaluations in simulation se

Nicolas R. Bertini, Hermano Velten

The $f(R,T)$ gravity is a model whose action contains an arbitrary function of the Ricci scalar $R$ and the trace of the energy-momentum tensor $T$. We consider the separable model $f (R, T ) = \chi(R) + \varphi(T )$ and shown that, for perfect fluids, the dynamical equations are sufficient to determine how $\varphi$ depends on $T$, independently of the matt

Noah Walton, Jesse Brown, William Fritsch, Dave Brown

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nat

Pilar Valderrama-Baca, Wenceslao Arroyo-Machado, Daniel Torres-Salinas

There has been a proliferation of descriptive for COVID-19 papers using altmetrics. The main objective of this study is to analyse whether the altmetric mentions of COVID-19 medical studies are associated with the type of study and its level of evidence. Data were collected from PubMed and Altmetric.com databases. A total of 16,672 study types (e.g., Case re

Sarah Bahanshal, Ahmad Abdel-Qader, Anas Chaaban

The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD). First, the paper proposes a new, capacity-preserving vector binary channel (VBC) model, consisting of dependent binary noisy bit-pipes. Then,

Sauradip Nag, Anran Qi, Xiatian Zhu, Ariel Shamir

Garment pattern design aims to convert a 3D garment to the corresponding 2D panels and their sewing structure. Existing methods rely either on template fitting with heuristics and prior assumptions, or on model learning with complicated shape parameterization. Importantly, both approaches do not allow for personalization of the output garment, which today ha

Konstantin Grotov, Sergey Titov, Alexandr Suhinin, Yaroslav Golubev

In this paper, we present an approach for transferring an optimal lower size threshold for clone detection from one language to another by analyzing their clone distributions. We showcase this method by transferring the threshold from regular Python scripts to Jupyter notebooks for using in two JetBrains IDEs, Datalore and DataSpell.

Shilong Guo, Balsam Alkouz, Babar Shahzaad, Abdallah Lakhdari

We demonstrate formation flying for drone swarm services. A set of drones fly in four different swarm formations. A dataset is collected to study the effect of formation flying on energy consumption. We conduct a set of experiments to study the effect of wind on formation flying. We examine the forces the drones exert on each other when flying in a formation

Jiayi Wei, Greg Durrett, Isil Dillig

There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript. While prior methods have achieved impressive accuracy when predicting the most common types, they often perform poorly on rare or complex types. In this paper, we present a new type inference method that treats type prediction as

Edoardo Niccolai

A study on the notion of covariant derivatives in flat and curved space-time via It\^o-Wiener processes, when subjected to stochastic processes, is presented. Going into details, there is an analysis of the following topics: (i) Besov space, (ii) Schr\"odinger operators, (iii) Klein-Gordon and Dirac equations, (iv) Dirac operator via Clifford connection, (v)

Stephanie M. Ribet, Steven E. Zeltmann, Karen C. Bustillo, Rohan Dhall

In a scanning transmission electron microscope (STEM), producing a high-resolution image generally requires an electron beam focused to the smallest point possible. However, the magnetic lenses used to focus the beam are unavoidably imperfect, introducing aberrations that limit resolution. Modern STEMs overcome this by using hardware aberration correctors co

Kangfeng Ye, Jim Woodcock, Simon Foster

Probabilistic programming combines general computer programming, statistical inference, and formal semantics to help systems make decisions when facing uncertainty. Probabilistic programs are ubiquitous, including having a significant impact on machine intelligence. While many probabilistic algorithms have been used in practice in different domains, their au

Lirui Feng

We consider a smooth semiflow strongly focusing monotone with respect to a cone of rank k on a Banach space. We obtain its generic dynamics, that is, semiorbits with initial data from an open and dense subset of any bounded open set are either pseudo-ordered or convergent to an equilibrium. For the case k=1, it is the celebrated Hirsch's Generic Convergence

Victor Monzon Baeza, Rafael Arellano Garcia

The looming electromagnetic spectrum crisis -- due to the fact of the explosive growth in the increasing user data demand -- has encouraged the emergence of new wireless technologies. This paper surveys the state-of-the-art leading and rapid developments in the current Light Fidelity (LiFi) technology. First, an overview is shown to help readers understand t

Justo López-Sarrión, Carlos M. Reyes, César Riquelme

We investigate the preservation of unitarity in a Lorentz and CPT-violating QED model containing higher-order operators. In particular, we consider modifications in the fermion sector with dimension-five operators. The higher-order operators lead to an indefinite metric and a pseudo-unitarity relation for the $S$-matrix. However, we show that the pseudo-unit

Chenyang Zhong

Introduced by Mallows in statistical ranking theory, Mallows permutation model is a class of non-uniform probability measures on the symmetric group $S_n$ that depend on a distance metric $d(\sigma,\tau)$ on $S_n$ and a scale parameter $\beta$. Taking the distance metric to be the $L^1$ and $L^2$ distances--which are respectively known as Spearman's footrule

Khandaker Foysal Haque, Milin Zhang, Francesca Meneghello, Francesco Restuccia

In this paper, we propose BeamSense, a completely novel approach to implement standard-compliant Wi-Fi sensing applications. Wi-Fi sensing enables game-changing applications in remote healthcare, home entertainment, and home surveillance, among others. However, existing work leverages the manual extraction of channel state information (CSI) from Wi-Fi chips

Hemanth Manjunatha, Shrey Pareek, Amirhossein H. Memar, Thenkurussi Kesavadas

This study investigates the effect of haptic control strategies on a subject's mental engagement during a fine motor handwriting rehabilitation task. The considered control strategies include an error-reduction (ER) and an error-augmentation (EA), which are tested on both dominant and non-dominant hand. A non-invasive brain-computer interface is used to moni

Miqing Li, Manuel López-Ibáñez, Xin Yao

Most multi-objective optimisation algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e.g., as the population in evolutionary computation). Over the last two decades, archiving,

Christian G. Fink, Kelly Fullin, Guillermo Gutierrez, Nathan Omodt

While many centrality measures for complex networks have been proposed, relatively few have been developed specifically for weighted, directed (WD) networks. Here we propose a centrality measure for spread (of information, pathogens, etc.) through WD networks based on the independent cascade model (ICM). While deriving exact results for the ICM requires Mont

Annie Thomas

A new three dimensional approach to the chaos game representation of protein sequences is explored in this thesis. The basics of DNA, the synthesis of proteins from DNA, protein structure and functionality and sequence alignment techniques are presented. The mathematical background needed for understanding the chaos game representation and fractal analysis a

Titos Matsakos, Stuart Nield

Monte Carlo (MC) simulations are widely used in financial risk management, from estimating value-at-risk (VaR) to pricing over-the-counter derivatives. However, they come at a significant computational cost due to the number of scenarios required for convergence. If a probability distribution is available, Quantum Amplitude Estimation (QAE) algorithms can pr

Shihao Zou, Yuxuan Mu, Wei Ji, Zi-An Wang

Event camera, as an asynchronous vision sensor capturing scene dynamics, presents new opportunities for highly efficient 3D human pose tracking. Existing approaches typically adopt modern-day Artificial Neural Networks (ANNs), such as CNNs or Transformer, where sparse events are converted into dense images or paired with additional gray-scale images as input

Joel L. Horowitz, Ahnaf Rafi

We consider penalized extremum estimation of a high-dimensional, possibly nonlinear model that is sparse in the sense that most of its parameters are zero but some are not. We use the SCAD penalty function, which provides model selection consistent and oracle efficient estimates under suitable conditions. However, asymptotic approximations based on the oracl

Marcin Lara, Vasudevan Srinivas, Jakob Stix

It was recently proven by Esnault, Shusterman and the second named author, that the \'etale fundamental group of a connected smooth projective variety over an algebraically closed field $k$ is finitely presented. In this note, we extend this result to all connected proper schemes over $k$.

Ifeyinwa Linda Anene, Yongmin Li

The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases, namely

Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami

Stabilizing controller design and region of attraction (RoA) estimation are essential in nonlinear control. Moreover, it is challenging to implement a control Lyapunov function (CLF) in practice when only partial knowledge of the system is available. We propose a learning framework that can synthesize state-feedback controllers and a CLF for control-affine n

Pietro Astolfi, Arantxa Casanova, Jakob Verbeek, Pascal Vincent

Data augmentation has become a crucial component to train state-of-the-art visual representation models. However, handcrafting combinations of transformations that lead to improved performances is a laborious task, which can result in visually unrealistic samples. To overcome these limitations, recent works have explored the use of generative models as learn

Frieder Ladisch

Let $V$ be a finite abelian group of odd order, equipped with a non-degenerate, alternating form $\omega\colon V\times V \to \mathbb{Z}/m\mathbb{Z}$. We give closed formulas for the character values of the Weil representation associated with $(V,\omega)$. These formulas generalize the ones given by S. Gurevich and R. Hadani (2007) and by T. Thomas (2008, 201

Ian Ball

I study the optimal provision of information in a long-term relationship between a sender and a receiver. The sender observes a persistent, evolving state and commits to send signals over time to the receiver, who sequentially chooses public actions that affect the welfare of both players. I solve for the sender's optimal policy in closed form: the sender re

Jiawei Ma, Yulei Niu, Jincheng Xu, Shiyuan Huang

Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approaches enhance few-shot generalization with the sacrifice of base-class performance, or maintain high precision in base-class detection with limited improvement in novel-class adapta

Orazio Scarlatella, Aashish A. Clerk, Marco Schirò

Reservoir engineering is a powerful approach for using controlled driven-dissipative dynamics to prepare target quantum states and phases. In this work, we study a paradigmatic model that can realize a Mott insulator of photons in its steady-state. We show that, while in some regimes its steady state approximates a Mott-insulating ground state, this phase ca

Ignacio Tapia, Gonzalo Gutiérrez, Sergio Davis

A statistical, path-dependent framework to describe time-dependent macroscopic theories using the Principle of Maximum Caliber is presented. By means of this procedure, it is possible to infer predictive non-equilibrium statistical mechanical models from a variational principle, provided that the adequate time-dependent constraints and the state of the syste

Arnab Mukherjee, James A. Warren, Peter W. Voorhees

The finite solid-liquid interface width in phase field models results in non-equilibrium effects, including solute trapping. Prior phase field modeling has shown that this extra degree of freedom, when compared to sharp-interface models, results in solute trapping that is well captured when realistic parameters, such as interface width, are employed. However

Alain Bruguières, Mariana Haim, Ignacio López Franco

Hopf monads generalise Hopf algebras. They clarify several aspects of the theory of Hopf algebras and capture several related structures such as weak Hopf algebras and Hopf algebroids. However, important parts of Hopf algebra theory are not reached by Hopf monads, most noticeably Drinfeld's quasi-Hopf algebras. In this paper we introduce a generalisation of

Thomas M. Bury, Daniel Dylewsky, Chris T. Bauch, Madhur Anand

Many natural and man-made systems are prone to critical transitions -- abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal (EWS) for critical transitions by learning generic features of bifurcations (dynamical instabilities) from large simulated training data sets. So far, classifiers have onl

Yukuan Zhang, Yunhua Jia, Housheng Xie, Mengzhen Li

Object tracking is divided into single-object tracking (SOT) and multi-object tracking (MOT). MOT aims to maintain the identities of multiple objects across a series of continuous video sequences. In recent years, MOT has made rapid progress. However, modeling the motion and appearance models of objects in complex scenes still faces various challenging issue

Sofiane Chalal, Nina H. Amini, Gaoyue Guo

Following Kolokoltsov's work [1], we present an extension of mean-field control theory in quantum framework. In particular such an extension is done naturally by considering the Belavkin quantum filtering and control theory in a mean-field setting. In this setting, the dynamics is described by a controlled Belavkin equation of McKean-Vlasov type. We prove th

Jocelyn Étienne, Pierre Recho

The conditions under which biological cells switch from a static to a motile state are fundamental to the understanding of many healthy and pathological processes. In this paper, we show that even in the presence of a fully symmetric protrusive activity at the cell edges, such a spontaneous transition can result solely from the mechanical interaction of the

Gen Li, Varun Jampani, Deqing Sun, Laura Sevilla-Lara

Humans excel at acquiring knowledge through observation. For example, we can learn to use new tools by watching demonstrations. This skill is fundamental for intelligent systems to interact with the world. A key step to acquire this skill is to identify what part of the object affords each action, which is called affordance grounding. In this paper, we addre

Yongsu Ahn, Muheng Yan, Yu-Ru Lin, Wen-Ting Chung

With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a group's shared characteristics; in others, the group-level analysis can lead to problems including stereo

Sara Shoouri, Mingyu Yang, Zichen Fan, Hun-Seok Kim

Solving multiple visual tasks using individual models can be resource-intensive, while multi-task learning can conserve resources by sharing knowledge across different tasks. Despite the benefits of multi-task learning, such techniques can struggle with balancing the loss for each task, leading to potential performance degradation. We present a novel computa

Isaac M. Craig, Madeline Van Winkle, Catherine Groschner, Kaidi Zhang

Moir\'e superlattices formed from twisting trilayers of graphene are an ideal model for studying electronic correlation, and offer several advantages over bilayer analogues, including more robust and tunable superconductivity and a wide range of twist angles associated with flat band formation. Atomic reconstruction, which strongly impacts the electronic str

Martin Escardo

The set-theoretical model of Goedel's system T is not fully abstract. We also briefly discuss fully abstract models of system T.