July 2023 arXiv papers — page 53

Jing Hao, Jingming Xie, Jinyuan Zhang, Moyun Liu

Fisheye lens, which is suitable for panoramic imaging, has the prominent advantage of a large field of view and low cost. However, the fisheye image has a severe geometric distortion which may interfere with the stage of image registration and stitching. Aiming to resolve this drawback, we devise a stronger stitching algorithm for fisheye images by combining

S Rahul, Nilanjan Roy, Ranjith R Kumar, Y R Kartik

We investigate the nature of quantum criticality and topological phase transitions near the critical lines obtained for the extended Kitaev chain with next nearest neighbor hopping parameters and non-Hermitian chemical potential. We surprisingly find multiple gap-less points, the locations of which in the momentum space can change along the critical line unl

Sheng-Xian Xiao, Ying Liang, Ya Zhang, Tao Wang

Super-Bloch oscillations(SBOs) is giant Bloch oscillations (BOs) when applying both static and periodically driving force to free atoms in lattice at the condition that Bloch oscillations are close to integer times of driving frequencies. Rather than observe SBOs in real space, this paper presents a method to observe it using Rabi spectroscopy of Optical lat

Chen Rui, Liang Guotao, Ma Chenrui, Han Qilong

Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal inf

Syed Rafiul Hussain, Patrick McDaniel, Anshul Gandhi, Kanad Ghose

Data centers have significant energy needs, both embodied and operational, affecting sustainability adversely. The current techniques and tools for collecting, aggregating, and reporting verifiable sustainability data are vulnerable to cyberattacks and misuse, requiring new security and privacy-preserving solutions. This paper outlines security challenges an

Carlos David Jiménez-Flores, Alejandro Ríos-Herrejón, Alejandro Darío Rojas-Sánchez, Elmer Enrique Tovar-Acosta

We say that a topological space $X$ is selectively highly divergent (SHD) if for every sequence of non-empty open sets $\{U_n\mid n\in\omega \}$ of $X$, we can find $x_n\in U_n$ such that the sequence $(x_n)$ has no convergent subsequences. We investigate the basic topological properties of SHD spaces and we will exhibit that this class of spaces is full of

Tin Lai, Yukun Shi, Zicong Du, Jiajie Wu

The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which has heightened the need for timely and professional mental health support. Online psychological counselling has emerged as the predominant mode of providing services in response to this demand. In this study, we propose t

Yagub N. Aliyev

In the paper, some special linear combinations of the terms of rational cycles of generalized Collatz sequences are studied. It is proved that if the coefficients of the linear combinations satisfy some conditions then these linear combinations are integers. The discussed results are demonstrated on some examples. In some particular cases the obtained result

Qixiang Zhang, Yi Li, Cheng Xue, Xiaomeng Li

Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required.

Refath Bari

Kipping (2017) discusses relativistic reflection from light sails. As an extension to this work, we present a new formula for relativistic reflection of sunlight from a light sail moving at a velocity inclined at an arbitrary angle, using an elementary two-body analysis. The formula is shown to reduce to the formulas of Euclid, Einstein, and Gjurchinovski in

Yogi Prasetyo, Novanto Yudistira, Agus Wahyu Widodo

The Vision Transformer architecture is a deep learning model inspired by the success of the Transformer model in Natural Language Processing. However, the self-attention mechanism, large number of parameters, and the requirement for a substantial amount of training data still make Vision Transformers computationally burdensome. In this research, we investiga

Rubing Han, Shuonan Wu, Hao Zhou

We introduce a novel monotone discretization method for addressing obstacle problems involving the integral fractional Laplacian with homogeneous Dirichlet boundary conditions over bounded Lipschitz domains. This problem is prevalent in mathematical finance, particle systems, and elastic theory. By leveraging insights from the successful monotone discretizat

Subangkar Karmaker Shanto, Shoumik Saha, Atif Hasan Rahman, Mohammad Mehedy Masud

In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with

Xinyue Hu, Lin Gu, Qiyuan An, Mengliang Zhang

To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them. This is consistent with the radiologist's diagnosis practi

I. Iyyappan, Jetin E. Thomas, Sibasish Ghosh

We investigate the performance of a Brownian heat engine working in a heterogeneous thermal bath where the mobility fluctuates. Brownian particle is trapped by the time-dependent harmonic potential, by changing the stiffness coefficient and the bath temperatures, we perform a Stirling cycle. We numerically evaluated the average work, power and efficiency. We

Kunyang Lin, Peihao Chen, Diwei Huang, Thomas H. Li

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3D environments using natural language instructions. Existing VLN methods suffer from training on small-scale environments or unreasonable path-instruction datasets, limiting the generalization to unseen environments. There are massive house tour videos on YouTube, provi

Xiutao Zhu, Yaojun Chen

For a family of graphs $\F$, a graph is called $\F$-free if it does not contain any member of $\F$ as a subgraph. The generalized Tur\'an number $\ex(n,K_r,\F)$ is the maximum number of $K_r$ in an $n$-vertex $\F$-free graph and $\ex(n,K_2,\F)=\ex(n,\F)$, i.e., the classical Tur\'an number. Let $M_{s+1}$ be a matching on $s+1$ edges and $F$ be any graph. In

Sulakashna, Rupam Barman

Let $D_\lambda^{d,k}$ denote the family of diagonal hypersurface over a finite field $\mathbb{F}_q$ given by \begin{align*} D_\lambda^{d,k}:X_1^d+X_2^d=\lambda dX_1^kx_2^{d-k}, \end{align*} where $d\geq2$, $1\leq k\leq d-1$, and $\gcd(d,k)=1$. Let $\#D^{d,k}_\lambda$ denote the number of points on $D_\lambda^{d,k}$ in $\mathbb{P}^{1}(\mathbb{F}_q)$. It is ea

Qiaoyu Tan, Xin Zhang, Xiao Huang, Hao Chen

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while the exploitation of node attributes is rather limited as they only serve as node features at the initial layer. This simple strategy impedes the potential of node attributes in augmentin

Eisuke Yamagata, Shunsuke Ono

Sparse index tracking is a prominent passive portfolio management strategy that constructs a sparse portfolio to track a financial index. A sparse portfolio is preferable to a full portfolio in terms of reducing transaction costs and avoiding illiquid assets. To achieve portfolio sparsity, conventional studies have utilized $\ell_p$-norm regularizations as a

Dayang Wang, Srivathsa Pasumarthi, Greg Zaharchuk, Ryan Chamberlain

Deep learning (DL) based contrast dose reduction and elimination in MRI imaging is gaining traction, given the detrimental effects of Gadolinium-based Contrast Agents (GBCAs). These DL algorithms are however limited by the availability of high quality low dose datasets. Additionally, different types of GBCAs and pathologies require different dose levels for

Kun Woo Kim, T. Pereg-Barnea

Inspired by recent advances in the fabrication of surface superlattices, and in particular the triangular lattice made of tin (Sn) atoms on silicon, we study an extended Hubbard mode on a triangular lattice. The observations of magnetism in these systems justify the inclusion of a strong on-site repulsion and the observation of superconductivity suggests inc

Cheng-En Wu, Yu Tian, Haichao Yu, Heng Wang

Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through few-shot prompt tuning. We find that such a prompt tuning process is highly robust to label noises. This intrigues us to study the key reasons contributing to the robustness of th

Aaron Calvin, Scott Eierman, Zeyun Peng, Merrell Brzeczek

We demonstrate a novel single molecule technique that is compatible with high precision measurements and obtain the spectrum of two molecular ion species. While the current result yields modest spectral resolution due to a broad light source, we expect the method to ultimately provide resolution comparable to quantum logic methods with significantly less str

Quinn Jarecki, Meredith Kupinski

Light-matter interactions within indoor environments are significantly depolarizing. Nonetheless, the relatively small polarization attributes are informative. To make use of this information, polarized-BRDF (pBRDF) models for common indoor materials are sought. Fresnel reflection and diffuse partial polarization are popular terms in pBRDF models, but the re

Harrison Siegel, Maximiliano Isi, Will Farr

GW190521 is a short-duration, low-frequency gravitational-wave signal in the LIGO-Virgo catalogue. The signal is consistent with the ringdown and possibly some of the inspiral-merger of an intermediate-mass binary black-hole coalescence. We find that previous models of the quasinormal mode spectrum in the ringdown of GW190521 give remnant mass and spin estim

Tracy Beth Hoeg, Ambarish Chandra, Ram Duriseti, Shamez Ladhani

Background: A recent epidemiological analysis of staggered policy implementation reported a 29.4% reduction in COVID-19 cases by maintaining school mask mandates in the greater Boston area during the first half of 2022. The robustness of their results and the appropriateness of methodology are explored. Methods: Using data from the Massachusetts Department o

Yao Liu, Gangfeng Cui, Jiahui Luo, Xiaojun Chang

As a fundamental aspect of human life, two-person interactions contain meaningful information about people's activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased

Shoji Toyota, Kenji Fukumizu

Deep Neural Networks often inherit spurious correlations embedded in training data and hence may fail to generalize to unseen domains, which have different distributions from the domain to provide training data. M. Arjovsky et al. (2019) introduced the concept out-of-distribution (o.o.d.) risk, which is the maximum risk among all domains, and formulated the

Andrew Ying, Ronghui Xu

In this short communication, we describe the recent debate on whether the hazard function should be used for causal inference in time-to-event studies and consider three different potential outcomes frameworks (by Rubin, Robins, and Pearl, respectively) as well as use the single-world intervention graph to show mathematically that the hazard function has cau

Yuita Fujisawa, Anjana Krishnadas, Barnaby R. M. Smith, Markel Pardo-Almanza

Understanding the nature of vortices in type-II superconductors is crucial for comprehending exotic superconductors and advancing the application of superconducting materials in future electronic devices. This study uses spectroscopic scanning tunneling microscopy to visualize Josephson vortices along crystalline domain boundaries in the superconducting spin

Xiaoxu Xu

This paper is concerned with the uniqueness in inverse acoustic and electromagnetic scattering with phaseless near-field data generated by superpositions of two incident plane waves at a fixed frequency. It can be proved that the unknown scatterer can be uniquely determined by the phaseless near-field data. The proof is based on the analysis of the phase inf

Robert Griffin, James Foster, Stefan Fasano, Brandon Shrewsbury

For humanoid robots to live up to their potential utility, they must be able to robustly recover from instabilities. In this work, we propose a number of balance enhancements to enable the robot to both achieve specific, desired footholds in the world and adjusting the step positions and times as necessary while leveraging ankle and hip. This includes improv

Robert J. Wagner, Samuel Lamont, Zachary T. White, Franck J. Vernerey

Fire ants (Solenopsis invicta) cohesively aggregate via the formation of voluntary ant-to-ant attachments when under confinement or exposed to water. Once formed, these aggregations act as viscoelastic solids due to dynamic bond exchange between neighboring ants as demonstrated by rate-dependent mechanical response of 3D aggregations, confined in rheometers.

Yijiong Yu, Tao Wang, Kang Ran, Chang Li

Due to the inevitable presence of quality problems, quality inspection of remote sensing images is indeed an indispensable step between the acquisition and the application of them. However, traditional manual inspection suffers from low efficiency. Hence, we propose a novel deep learning-based two-step intelligent system consisting of multiple advanced compu

Xihua Yang, Mingfei Cheng, Min Xiao

In contrast to the general thought that the collisions are intrinsically dephasing in nature and detrimental to quantum entanglement at room or higher temperatures, here, we show that in the conventional ladder-type three-level electromagnetically induced transparency (EIT) configuration, when the probe field intensity is not very weak as compared to the pum

Leon D. Fairbanks

This paper presents expressions for sums of powers of sine and cosine in terms of the basis for the field extension obtained by adjoining the sine or cosine to the field of rational numbers.

Katsuhiko Sato

Many fundamental biological processes are dependent on cellular migration. Although the mechanical mechanisms of single-cell migration are relatively well understood, those underlying migration of multiple cells adhered to each other in a cluster, referred to as cluster migration, are poorly understood. A key reason for this knowledge gap is that many forces

Zexin Li, Xiaoxi He, Yufei Li, Wei Yang

Future intelligent robots are expected to process multiple inputs simultaneously (such as image and audio data) and generate multiple outputs accordingly (such as gender and emotion), similar to humans. Recent research has shown that multi-input single-output (MISO) deep neural networks (DNN) outperform traditional single-input single-output (SISO) models, r

Yan Wang, Hui-Lai Zhang, Jin-Lei Wu, Jie Song

The recently developed hybrid magnonics provides new opportunities for advances in both the study of magnetism and the development of quantum information processing. However, engineering coherent quantum state transfer between magnons and specific information carriers, in particular, mechanical oscillators and solid-state spins, remains challenging due to th

Haonan Wang, Xiaomeng Li

The volume-wise labeling of 3D medical images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL) is highly desirable for training with limited labeled data. Imbalanced class distribution is a severe problem that bottlenecks the real-world application of these methods but was not addressed much. Aiming to solve this issue, we prese

Zhixing Zhang, Ziwei Zhao, Dong Wang, Shishuang Zhao

Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases. For experienced radiologists, the anatomically predetermined connections are important for labeling the artery segments accurately, while this prior knowledge is barely explored in previous studies. In this paper, we present a new frame

Yuncheng Yang, Meng Wei, Junjun He, Jie Yang

Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance of medical image data, many research institutions release models trained on various datasets that can form a huge pool of candidate source models to choose from. Hence, it's vital

Fu Lin, Haonan Gong, Mingkang Li, Zitong Wang

Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem. The objective of this research is centered on the particular issue that how to detect abnormal graphs within a graph set. The previous works have observed that abnormal graphs mai

Guangyuan Zhao, Xin Shu, Renjie Zhou

Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps. We propose a gradient-based model-free optimization (G-MFO) method based on a Monte Carlo gradient estimation algorithm for computationally efficient in situ training of optical computing syst

Xinshu Zhang, Tyler Carbin, Adrian B. Culver, Kai Du

In a solid, the electronic subsystem can exhibit incipient order with lower point group symmetry than the crystal lattice. External fields that couple to electronic order parameters have rarely been investigated, however, despite their potential importance to inducing exotic effects. Here, we show that when inversion symmetry is broken by the antiferromagnet

Adam Sinclair, Kayla Kautai, Seyed Reza Shahamiri

Automated exercise repetition counting has applications across the physical fitness realm, from personal health to rehabilitation. Motivated by the ubiquity of mobile phones and the benefits of tracking physical activity, this study explored the feasibility of counting exercise repetitions in real-time, using only on-device inference, on smartphones. In this

Keyi Chen, Francesco Orabona

Due to its speed and simplicity, subgradient descent is one of the most used optimization algorithms in convex machine learning algorithms. However, tuning its learning rate is probably its most severe bottleneck to achieve consistent good performance. A common way to reduce the dependency on the learning rate is to use implicit/proximal updates. One such va

Nader Zare, Aref Sayareh, Omid Amini, Mahtab Sarvmaili

Soccer, also known as football in some parts of the world, involves two teams of eleven players whose objective is to score more goals than the opposing team. To simulate this game and attract scientists from all over the world to conduct research and participate in an annual computer-based soccer world cup, Soccer Simulation 2D (SS2D) was one of the leagues

A. I. Shtern

We prove that the discontinuity group of every locally bounded homomorphism of a Lie group into a Lie group is not only compact and connected, which is known, but is also commutative.

Hai Nguyen, Sammie Katt, Yuchen Xiao, Christopher Amato

Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expert information on robot dynamics. Bayesian reinforcement learning (BRL), thanks to its sample efficiency and ability to exploit prior knowledge, is uniquely positioned as such a so

M. E. Lower, S. Johnston, A. Karastergiou, P. R. Brook

We present a comprehensive study of the rotational and emission properties of PSR J0738$-$4042 using a combination of observations taken by the Deep Space Network, Hartebeesthoek, Parkes (Murriyang) and Molonglo observatories between 1972 and 2023. Our timing of the pulsar is motivated by previously reported profile/spin-down events that occurred in Septembe

Kexin Ding, Mu Zhou, Dimitris N. Metaxas, Shaoting Zhang

Survival outcome assessment is challenging and inherently associated with multiple clinical factors (e.g., imaging and genomics biomarkers) in cancer. Enabling multimodal analytics promises to reveal novel predictive patterns of patient outcomes. In this study, we propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into c

Weizhong Ding, Shengming Chang, Shudi Bao

Accurate and reliable localization is crucial for various wireless communication applications. Numerous studies have proposed accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may

Weizhong Ding, Shengming Chang, Shudi Bao, Meng Chen

Wireless sensor networks require accurate target localization, often achieved through received signal strength (RSS) localization estimation based on maximum likelihood (ML). However, ML-based algorithms can suffer from issues such as low diversity, slow convergence, and local optima, which can significantly affect localization performance. In this paper, we

Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine

Unsupervised pre-training has recently become the bedrock for computer vision and natural language processing. In reinforcement learning (RL), goal-conditioned RL can potentially provide an analogous self-supervised approach for making use of large quantities of unlabeled (reward-free) data. However, building effective algorithms for goal-conditioned RL that

Lawrence Wang, Stephen Roberts

Modern neural networks are undeniably successful. Numerous works study how the curvature of loss landscapes can affect the quality of solutions. In this work we study the loss landscape by considering the Hessian matrix during network training with large learning rates - an attractive regime that is (in)famously unstable. We characterise the instabilities of

Chen Cheng, Gary Cheng, John Duchi

We study the problem of collaboratively learning least squares estimates for $m$ agents. Each agent observes a different subset of the features$\unicode{x2013}$e.g., containing data collected from sensors of varying resolution. Our goal is to determine how to coordinate the agents in order to produce the best estimator for each agent. We propose a distribute

Di Wu, Baogang Xu

Let $G$ and $H$ be two vertex disjoint graphs. The {\em union} $G\cup H$ is the graph with $V(G\cup H)=V(G)\cup (H)$ and $E(G\cup H)=E(G)\cup E(H)$. The {\em join} $G+H$ is the graph with $V(G+H)=V(G)+V(H)$ and $E(G+H)=E(G)\cup E(H)\cup\{xy\;|\; x\in V(G), y\in V(H)$$\}$. We use $P_k$ to denote a {\em path} on $k$ vertices, use {\em fork} to denote the graph

Nathaniel Cooper, Maria Giovanna Dainotti, Aditya Narendra, Ioannis Liodakis

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data Release 3. Missing data hinders the use of machine learning to classify AGN. A previous paper found that Multiple Imputa

Chuqiao Chen, Viviana Palacio-Betancur, Sepideh Norouzi, Pablo F. Zubieta Rico

When viewed with a cross-polarized optical microscope (POM), liquid crystals display interference colors and complex patterns that depend on the material's microscopic orientation. That orientation can be manipulated by application of external fields, which provides the basis for applications in optical display and sensing technologies. The color patterns th

Kota Fukuzawa, Takeo Kato, Thibaut Jonckheere, Jérôme Rech

We study theoretically the effect of electronic interactions in 1d systems on electron injection using periodic Lorentzian pulses, known as Levitons. We consider specifically a system composed of a metallic single-wall carbon nanotube, described with the Luttinger liquid formalism, a scanning tunneling microscope (STM) tip, and metallic leads. Using the out-

Wei Cai

In this paper, we propose a neural network learning algorithm for solving eigenvalue problems and boundary value problems (BVPs) for elliptic operators and initial BVPs (IBVPs) of quasi-linear parabolic equations in high dimensions as well as optimal stochastic controls. The method is based on the Martingale property in the stochastic representation for the

Brian Gilbert, Abhirup Datta

We present a new method for constructing valid covariance functions of Gaussian processes for spatial analysis in irregular, non-convex domains such as bodies of water. Standard covariance functions based on geodesic distances are not guaranteed to be positive definite on such domains, while existing non-Euclidean approaches fail to respect the partially Euc

Jaume Abella, Francisco J. Cazorla, Sergi Alcaide, Michael Paulitsch

HPC (High Performance Computing) devices increasingly become the only alternative to deliver the performance needed in safety-critical autonomous systems (e.g., autonomous cars, unmanned planes) due to deploying large and powerful multicores along with accelerators such as GPUs. However, the support that those HPC devices offer to realize safety-critical sys

Toan N. Nguyen, Phuong Ha Nguyen, Lam M. Nguyen, Marten Van Dijk

Each round in Differential Private Stochastic Gradient Descent (DPSGD) transmits a sum of clipped gradients obfuscated with Gaussian noise to a central server which uses this to update a global model which often represents a deep neural network. Since the clipped gradients are computed separately, which we call Individual Clipping (IC), deep neural networks

Narender Khatri, Raymond Kapral

Synthetic nanomotors powered by chemical reactions have been designed to act as vehicles for active cargo transport, drug delivery as well as a variety of other uses. Collections of such motors, acting in consort, can self-assemble to form swarms or clusters, providing opportunities for applications on various length scales. While such collective behavior ha

Dionys Baeriswyl, Francesco Petocchi, Philipp Werner

We study a microscopic model for four spinless fermions on the square lattice which exhibits a quartet bound state in the strong coupling regime. The four-particle quantum states are analyzed using symmetry arguments and by introducing a zoo of relevant lattice animals. These considerations, as well as variational and exact diagonalization calculations demon

Yanyan Hu, Richard C. Kraaij, Fubao Xi

We study the large deviations for Cox-Ingersoll-Ross (CIR) processes with small noise and state-dependent fast switching via associated Hamilton-Jacobi equations. As the separation of time scales, when the noise goes to $0$ and the rate of switching goes to $\infty$, we get a limit equation characterized by the averaging principle. Moreover, we prove the lar

Andrew Campbell, Sean O'Rourke, David Renfrew

We extend the free convolution of Brown measures of $R$-diagonal elements introduced by K\"{o}sters and Tikhomirov [Probab. Math. Statist. 38 (2018), no. 2, 359--384] to fractional powers. We then show how this fractional free convolution arises naturally when studying the roots of random polynomials with independent coefficients under repeated differentiati

Shengnan Hu, Ce Zheng, Zixiang Zhou, Chen Chen

Human-centric visual understanding is an important desideratum for effective human-robot interaction. In order to navigate crowded public places, social robots must be able to interpret the activity of the surrounding humans. This paper addresses one key aspect of human-centric visual understanding, multi-person pose estimation. Achieving good performance on

Krishnanand Karthikeyan, Mathew Thomas Arun

The Einstein-Skyrme system is understood to defy the "no hair" conjecture by possessing black-hole solutions with fractional baryon number outside the event horizon. In this article, we extend the study of the Skyrme system to teleparallel gravity framework. We consider two scenarios, the Teleparallel Equivalent of General Relativity (TEGR) and generalized t

Isaac Kasahara, Shubham Agrawal, Selim Engin, Nikhil Chavan-Dafle

General scene reconstruction refers to the task of estimating the full 3D geometry and texture of a scene containing previously unseen objects. In many practical applications such as AR/VR, autonomous navigation, and robotics, only a single view of the scene may be available, making the scene reconstruction task challenging. In this paper, we present a metho

Félix Loubaton

This thesis is divided into two parts. In the first part, we study models of $(\infty,\omega)$-categories. The main result is to establish a Quillen equivalence between Rezk's complete Segal $\Theta$-spaces and Verity's complicial sets. In the second part, we study the $(\infty,1)$-category corresponding to these two model structures, denoted by $(\infty,\om

Dustin D. Nguyen, Todd A. Thompson, Evan E. Schneider, Ashley P. Tarrant

When cool clouds are ram-pressure accelerated by a hot supersonic galactic wind, some of the clouds may be shredded by hydrodynamical instabilities and incorporated into the hot flow. Recent one-dimensional steady-state calculations show how cool cloud entrainment directly affects the bulk thermodynamics, kinematics, and observational characteristics of the

Stephen Doty, Anthony Giaquinto

We give an historical survey of some of the original basic algebraic and combinatorial results on Temperley-Lieb algebras, with a focus on certain results that have become folklore.

Jacob Pettine, Prashant Padmanabhan, Teng Shi, Lauren Gingras

Controlled charge flows are fundamental to many areas of science and technology, serving as carriers of energy and information, as probes of material properties and dynamics, and as a means of revealing or even inducing broken symmetries. Emerging methods for light-based current control offer promising routes beyond the speed and adaptability limitations of

Sean M. Carroll

I propose a version of quantum mechanics featuring a discrete and finite number of states that is plausibly a model of the real world. The model is based on standard unitary quantum theory of a closed system with a finite-dimensional Hilbert space. Given certain simple conditions on the spectrum of the Hamiltonian, Schr\"odinger evolution is periodic, and it

Kai Zhao, Alex Ling Yu Hung, Kaifeng Pang, Haoxin Zheng

Denoising diffusion probabilistic models (DDPMs) have achieved impressive performance on various image generation tasks, including image super-resolution. By learning to reverse the process of gradually diffusing the data distribution into Gaussian noise, DDPMs generate new data by iteratively denoising from random noise. Despite their impressive performance

Karan Singh, Shovan Dan, A. Ptok, T. A. Zaleski

We report magnetic and transport properties of single-crystalline EuZn$_{2}$P$_{2}$, which has trigonal CaAl$_2$Si$_2$-type crystal structure and orders antiferromagnetically at $\approx$23~K. Easy $ab$-plane magneto-crystalline anisotropy was confirmed from the magnetization isotherms, measured with a magnetic field applied along different crystallographic

Velimir Todorovski, Miroslav Krstic

The topics of source seeking and Newton-based extremum seeking have flourished, independently, but never combined. We present the first Newton-based source seeking algorithm. The algorithm employs forward velocity tuning, as in the very first source seeker for the unicycle, and incorporates an additional Riccati filter for inverting the Hessian inverse and f

Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim, JB Lanier

Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities. However, previous work does little to explore what environment state information is provided to LLM actors via language. Exhaustively describing high-dimensional s

Simone Fobi, Manuel Cardona, Elliott Collins, Caleb Robinson

This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region. Our approach utilizes visual features obtained from a single-step featurization method applied to freely available 10m/px Sentinel-2 surface reflectance satellite imager

Elisa Crabu, Federica Pes, Giuseppe Rodriguez, Giuseppa Tanda

The standard photometric stereo model makes several assumptions that are rarely verified in experimental datasets. In particular, the observed object should behave as a Lambertian reflector and the light sources should be positioned at an infinite distance from it, along a known direction. Even when Lambert's law is approximately fulfilled, an accurate asses

Laurence Carassus, Massinissa Ferhoune

We study a robust utility maximization problem in a general discrete-time frictionless market under quasi-sure no-arbitrage. The investor is assumed to have a random and concave utility function defined on the whole real-line. She also faces model ambiguity on her beliefs about the market, which is modeled through a set of priors. We prove the existence of a

Conner Ganjavi, Michael B. Eppler, Asli Pekcan, Brett Biedermann

We aim to determine the extent and content of guidance for authors regarding the use of generative-AI (GAI), Generative Pretrained models (GPTs) and Large Language Models (LLMs) powered tools among the top 100 academic publishers and journals in science. The websites of these publishers and journals were screened from between 19th and 20th May 2023. Among th

Michael Wang, Michael Robinson

Fuzzing is a popular vulnerability automated testing method utilized by professionals and broader community alike. However, despite its abilities, fuzzing is a time-consuming, computationally expensive process. This is problematic for the open source community and smaller developers, as most people will not have dedicated security professionals and/or knowle

Xinghan Guo, Alexander M. Stramma, Zixi Li, William G. Roth

Robust spin-photon interfaces in solids are essential components in quantum networking and sensing technologies. Ideally, these interfaces combine a long-lived spin memory, coherent optical transitions, fast and high-fidelity spin manipulation, and straightforward device integration and scaling. The tin-vacancy center (SnV) in diamond is a promising spin-pho

Daniel Corey, Dante Luber

We study smoothness of realization spaces of matroids for small rank and ground set. For $\mathbb{C}$-realizable matroids, when the rank is $3$, we prove that the realization spaces are all smooth when the ground set has $11$ or fewer elements, and there are singular realization spaces for $12$ and greater elements. For rank $4$ and $9$ or fewer elements, we

Ruisheng Wang, Shangfeng Huang, Hongxin Yang

Urban modeling from LiDAR point clouds is an important topic in computer vision, computer graphics, photogrammetry and remote sensing. 3D city models have found a wide range of applications in smart cities, autonomous navigation, urban planning and mapping etc. However, existing datasets for 3D modeling mainly focus on common objects such as furniture or car

Anupam Gupta, Amit Kumar, Debmalya Panigrahi

In this paper, we study the weighted $k$-server problem on the uniform metric in both the offline and online settings. We start with the offline setting. In contrast to the (unweighted) $k$-server problem which has a polynomial-time solution using min-cost flows, there are strong computational lower bounds for the weighted $k$-server problem, even on the uni

Jonathan Smucker, John A. Montgomery, Mitchell Bredice, Michael G. Rozman

We present a theoretical description of resonant charge transfer in collisions of nano-particles, specifically for $C_{60} + C_{60}^+$ collisions. We predict that transient bonds between colliding fullerenes can significantly extend the interaction time, allowing for a greater probability of charge transfer. In our model, the dumbbell-shaped $(C_{60}-C_{60})

Piotr B. Mucha, Sarka Necasova, Maja Szlenk

We investigate the existence of weak solutions to a multi-component system, consisting of compressible chemically reacting components, coupled with the compressible Stokes equation for the velocity. Specifically, we consider the case of irreversible chemical reactions and assume a nonlinear relation between the pressure and the particular densities. These as

Benjamin Vermette, Ciro Salcedo, Kaya Mori, Julian Gerber

We report a broad-band analysis of a Galactic X-ray source, CXOGBS J174517.0-321356 (J1745), with a 614-second periodicity. Chandra discovered the source in the direction of the Galactic Bulge. Gong (2022) proposed J1745 was either an intermediate polar (IP) with a mass of ~1 $M_{\odot}$, or an ultra-compact X-ray binary (UCXB). By jointly fitting XMM-Newton

Alvaro Lopez Paredes, Miguel Heredia Conde

An indirect Pulse-based Time-of-Flight camera can be modelled as a linear sensing system in which the target's depth is recovered from few measurements through a sensing matrix formed by a set of demodulation functions. Each demodulation function is the result of the convolution of a (0,1)-binary code and a cross-correlation function which models the entire

Sara Pollock, Rhea Shroff

Efficient solvers for tensor eigenvalue problems are important tools for the analysis of higher-order data sets. Here we introduce, analyze and demonstrate an extrapolation method to accelerate the widely used shifted symmetric higher order power method for tensor $Z$-eigenvalue problems. We analyze the asymptotic convergence of the method, determining the r

Adam D. Wright, Jane C. Nelson, Marissa L. Weichman

Strong cavity coupling of gas-phase molecules will enable studies of benchmark chemical processes under strong light-matter interactions with a high level of experimental control and no solvent effects. We recently demonstrated the formation of gas-phase molecular polaritons by strongly coupling the bright $\nu_3$, $J=3 \rightarrow 4$ rovibrational transitio

Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Eric Chan-Tin

Deep learning has been rapidly employed in many applications revolutionizing many industries, but it is known to be vulnerable to adversarial attacks. Such attacks pose a serious threat to deep learning-based systems compromising their integrity, reliability, and trust. Interpretable Deep Learning Systems (IDLSes) are designed to make the system more transpa

Philip Taranto, Marco Túlio Quintino, Mio Murao, Simon Milz

Memory is the fundamental form of temporal complexity: when present but uncontrollable, it manifests as non-Markovian noise; conversely, if controllable, memory can be a powerful resource for information processing. Memory effects arise from/are transmitted via interactions between a system and its environment; as such, they can be either classical or quantu

Mohammad Jani, Jamil Fayyad, Younes Al-Younes, Homayoun Najjaran

Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the purpose of improving its accuracy and efficiency. While the evident merits of YOLO have yielded to its extensive use in many areas, deploying it on resource-limited devices poses c