October 2023 arXiv papers — page 115

Koichi Yamawaki

The rho meson has long been successfully identified with a dynamical gauge boson of the Hidden Local Symmetry (HLS) $H_{\rm local}$ in the nonlinear sigma model $G/H$ gauge equivalent to the model having the symmetry $G_{\rm global}\times H_{\rm local}$, with $G= [SU(2)_L \times SU(2)_R]\simeq O(4), H=SU(2)_{V}\simeq O(3)$, however under a hitherto unproven

Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana, Sayan Ranu

GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments. Graph distillation is an effort in that direction with the aim to construct a smaller synthetic training set from the original training data without significantly comp

Jai Pal, Bryan Hong

Artificial intelligence (AI) is a powerful tool for reshaping healthcare systems. In healthcare, AI is invaluable for its capacity to manage vast amounts of data, which can lead to more accurate and speedy diagnoses, ultimately easing the workload on healthcare professionals. As a result, AI has proven itself to be a power tool across various industries, sim

Zander W. Blasingame, Chen Liu

Diffusion Morphs (DiM) are a recent state-of-the-art method for creating high quality face morphs; however, they require a high number of network function evaluations (NFE) to create the morphs. We propose a new DiM pipeline, Fast-DiM, which can create morphs of a similar quality but with fewer NFE. We investigate the ODE solvers used to solve the Probabilit

Chris Trevisan

We continue the study of selection and sorting of $n$ numbers under the adversarial comparator model, where comparisons can be adversarially tampered with if the arguments are sufficiently close. We derive a randomized sorting algorithm that does $O(n \log^2 n)$ comparisons and gives a correct answer with high probability, addressing an open problem of Ajtai

Siddhartha G Jena, Archit Verma, Barbara E Engelhardt

Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shape, relative locations of, movement of, and interactions between cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new

Sirak M. Mekonen, Deepti Jain, Seongshik Oh, N. P. Armitage

We report terahertz time-domain spectroscopy (TDTS) experiments demonstrating strong light-matter coupling in a terahertz (THz) LC-metamaterial in which the phonon resonance of a topological insulator (TI) thin film is coupled to the photonic modes of an array of electronic split-ring resonators. As we tune the metamaterial resonance frequency through the fr

Lichen Ding, Kazumune Hashimoto, Shigemasa Takai

In this paper, we investigate the problem of mitigating epidemics by applying an event-triggered control strategy. We consider a susceptible-infected-removed-susceptible (SIRS) model, which builds upon the foundational SIR model by accounting for reinfection cases. The event-triggered control strategy is formulated based on the condition in which the control

Rahul Vishwakarma, Amin Rezaei

As the semiconductor industry has shifted to a fabless paradigm, the risk of hardware Trojans being inserted at various stages of production has also increased. Recently, there has been a growing trend toward the use of machine learning solutions to detect hardware Trojans more effectively, with a focus on the accuracy of the model as an evaluation metric. H

Ruinan Ma, Yu-an Tan, Shangbo Wu, Tian Chen

Deep learning techniques have implemented many unconditional image generation (UIG) models, such as GAN, Diffusion model, etc. The extremely realistic images (also known as AI-Generated Content, AIGC for short) produced by these models bring urgent needs for intellectual property protection such as data traceability and copyright certification. An attacker c

Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li

Large language models have shown their remarkable capabilities as a general interface for various language-related applications. Motivated by this, we target to build a unified interface for completing many vision-language tasks including image description, visual question answering, and visual grounding, among others. The challenge is to use a single model

Lili Du, Chunlei Yang

This is the sequel of the recent work (Du, Huang, Pu, Commun. Math. Phys, 2023, doi: 10.1007/s00220-023-04651-7) on axially symmetric gravity water waves with general vorticities, which has investigated the singular wave profile of the free boundary near the degenerate points. In this companion paper, we are interested in the regularity of the free surface o

Yu-Ting Rong

The relativistic mean-field model, augmented with three types of center-of-mass corrections and two types of rotational corrections, is employed to investigate the ground-state properties of helium, beryllium, and carbon isotopes. The efficacy of the mean-field approach in describing the binding energies, quadrupole deformations, root-mean-square charge radi

Haozhi Wang, Fangfei Yin, Linyun Li, Mingqiang Li

DNA monolayers with inherent chirality play a pivotal role across various domains, including biosensors, DNA chips, and bioelectronics. Nonetheless, conventional DNA chiral monolayers, typically constructed from single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA), often lack structural orderliness and design flexibility at the interface. Structural DN

Xuefei Yang, Emilia Fridman

In this paper, we study gradient-based classical extremum seeking (ES) for uncertain n-dimensional (nD) static quadratic maps in the presence of known large constant distinct input delays and large output constant delay with a small time-varying uncertainty. This uncertainty may appear due to network-based measurements. We present a quantitative analysis via

Ashley Jisue Hong, David DiStefano, Sejal Dua

Emotional Artificial Intelligences are currently one of the most anticipated developments of AI. If successful, these AIs will be classified as one of the most complex, intelligent nonhuman entities as they will possess sentience, the primary factor that distinguishes living humans and mechanical machines. For AIs to be classified as "emotional," they should

Sara Vannah, Marcelo Gleiser, Lisa Kaltenegger

Can information theory provide insights into whether exoplanets are habitable? Here we apply information theory to a range of simulated exoplanet transmission spectra as a diagnostic tool to search for potential signatures of life on Earth-analog planets. We test the algorithms on three epochs of evolution for Earth-like planets orbiting a range of host star

Qianyu Guo, Huifang Du, Xing Jia, Shuyong Gao

Few-shot learning (FSL) presents immense potential in enhancing model generalization and practicality for medical image classification with limited training data; however, it still faces the challenge of severe overfitting in classifier training due to distribution bias caused by the scarce training samples. To address the issue, we propose MedMFG, a flexibl

Adam Seewald, Cameron J. Lerch, Marvin Chancán, Aaron M. Dollar

Continuous exploration without interruption is important in scenarios such as search and rescue and precision agriculture, where consistent presence is needed to detect events over large areas. Ergodic search already derives continuous trajectories in these scenarios so that a robot spends more time in areas with high information density. However, existing l

Mengfei Xia, Yujun Shen, Changsong Lei, Yu Zhou

A diffusion model, which is formulated to produce an image using thousands of denoising steps, usually suffers from a slow inference speed. Existing acceleration algorithms simplify the sampling by skipping most steps yet exhibit considerable performance degradation. By viewing the generation of diffusion models as a discretized integral process, we argue th

Lucas Tecot, Cho-Jui Hsieh

In the field of quantum information, classical optimizers play an important role. From experimentalists optimizing their physical devices to theorists exploring variational quantum algorithms, many aspects of quantum information require the use of a classical optimizer. For this reason, there are many papers that benchmark the effectiveness of different opti

Changqing Su, Zihan Lin, You Zhou, Shuai Wang

Light-field fluorescence microscopy (LFM) is a powerful elegant compact method for long-term high-speed imaging of complex biological systems, such as neuron activities and rapid movements of organelles. LFM experiments typically generate terabytes image data and require a huge number of storage space. Some lossy compression algorithms have been proposed rec

Kaizhi Huang, Wenyu Jiang, Yajun Chen, Liang Jin

In the future commercial and military communication systems, anti-jamming remains a critical issue. Existing homogeneous or heterogeneous arrays with a limited degrees of freedom (DoF) and high consumption are unable to meet the requirements of communication in rapidly changing and intense jamming environments. To address these challenges, we propose a recon

Zhiqing Liu, Ryan E. Mitchell

The BESIII experiment starts to run at 2009 and has submitted 500 publications during the past 15 years. This article reviews the 26 new hadrons discovered at BESIII, dedicated to the celebration of this event.

Emanuel Wallison de Oliveira Costa, Raheleh Jalalzadeh, Pedro Felix da Silva Júnior, Seyed Meraj Mousavi Rasouli

Our proposed cosmological framework, which is based on fractional quantum cosmology, aims to address the issue of synchronicity in the age of the universe. To achieve this, we have developed a new fractional $\Lambda$CDM cosmological model. We obtained the necessary formalism by obtaining the fractional Hamiltonian constraint in a general minisuperspace. Thi

Vasileios Vasilopoulos, Suveer Garg, Jinwook Huh, Bhoram Lee

A good representation of a large, complex mobile robot workspace must be space-efficient yet capable of encoding relevant geometric details. When exploring unknown environments, it needs to be updatable incrementally in an online fashion. We introduce HIO-SDF, a new method that represents the environment as a Signed Distance Field (SDF). State of the art rep

Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee

Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This research focuses on developing a reinforcement learning (RL) framework to tackle the complexities of trading five prominent altcoins: Binance Coin, Ethereum, Litecoin, R

Brendan Conway-Smith, Robert L. West

This paper will explore ways of computationally accounting for the metacognitive threshold -- the minimum amount of stimulus needed for a mental state to be perceived -- and discuss potential cognitive mechanisms by which this threshold can be influenced through metacognitive training and meditation.

Yutian Lei, Jun Liu, Dong Huang

The flourishing success of Deep Neural Networks(DNNs) on RGB-input perception tasks has opened unbounded possibilities for non-RGB-input perception tasks, such as object detection from wireless signals, lidar scans, and infrared images. Compared to the matured development pipeline of RGB-input (source modality) models, developing non-RGB-input (target-modali

Tao Feng, Qing Xiang

In this paper, we classify the groups of semisimilarities of finite classical polar spaces with exactly two orbits on the singular or isotropic points. As a byproduct, we obtain many highly symmetric regular sets in the point graphs of finite classical polar spaces.

Burcu Çınarcı, Thomas Michael Keller

In 2003, H\'{e}thelyi and K\"{u}lshammer proposed that if $G$ is a finite group and $p$ is a prime dividing the group order, then $k(G)\geq 2\sqrt{p-1}$, and they proved this conjecture for solvable $G$ and showed that it is sharp for those primes $p$ for which $\sqrt{p-1}$ is an integer. This initiated a flurry of activity, leading to many generalizations a

Jianhui Yu, Hao Zhu, Liming Jiang, Chen Change Loy

Recent advances in zero-shot text-to-3D human generation, which employ the human model prior (eg, SMPL) or Score Distillation Sampling (SDS) with pre-trained text-to-image diffusion models, have been groundbreaking. However, SDS may provide inaccurate gradient directions under the weak diffusion guidance, as it tends to produce over-smoothed results and gene

Yuanshun Yao, Xiaojun Xu, Yang Liu

We study how to perform unlearning, i.e. forgetting undesirable misbehaviors, on large language models (LLMs). We show at least three scenarios of aligning LLMs with human preferences can benefit from unlearning: (1) removing harmful responses, (2) erasing copyright-protected content as requested, and (3) reducing hallucinations. Unlearning, as an alignment

Chunyu Yuan, Dongfang Zhao, Sos S. Agaian

Skin cancer poses a significant public health challenge, necessitating efficient diagnostic tools. We introduce UCM-Net, a novel skin lesion segmentation model combining Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). This lightweight, efficient architecture, deviating from traditional UNet designs, dramatically reduces computational d

Nana Geraldine Cabo Bizet, Josué Díaz-Correa, Hugo García-Compeán

Two dimensional gauged linear sigma models(GLSMs) with $(0,2)$ supersymmetry and $U(1)$ gauge group possesing global symmetries are considered. For the case obtained as a reduction from the $(2,2)$ supersymmetric GLSM, we find the Abelian T-dual, comparing with previous studies. Then, the Abelian T-dual model of the pure $(0,2)$ theory is found. Instanton co

Sahana H. Balasubramanya

This paper is a survey of results proved in recent years that pertain to classifying cobounded hyperbolic actions of any group $G$. In other words, we discuss results that allow us to describe the partially ordered set $\mathcal{H}(G)$, first introduced in by Abbott-Balasubramanya-Osin. In certain cases, a complete classification of the poset is possible. In

Yash Shukla, Wenchang Gao, Vasanth Sarathy, Alvaro Velasquez

Recent advancements in reasoning abilities of Large Language Models (LLM) has promoted their usage in problems that require high-level planning for robots and artificial agents. However, current techniques that utilize LLMs for such planning tasks make certain key assumptions such as, access to datasets that permit finetuning, meticulously engineered prompts

Meijun Liu, Yi Bu, Daifeng Li, Ying Ding

Same-race mentorship preference refers to mentors or mentees forming connections significantly influenced by a shared race. Although racial diversity in science has been well-studied and linked to favorable outcomes, the extent and effects of same-race mentorship preferences remain largely underexplored. Here, we analyze 465,355 mentor-mentee pairs from more

Robin Armstrong, Alex Buzali, Anil Damle

Low-rank approximation is a task of critical importance in modern science, engineering, and statistics. Many low-rank approximation algorithms, such as the randomized singular value decomposition (RSVD), project their input matrix into a subspace approximating the span of its leading singular vectors. Other algorithms compress their input into a small subset

Tullio Ceccherini-Silberstein, Michel Coornaert, Xuan Kien Phung

Using algebraic geometry methods, the third author proved that the group ring of a surjunctive group with coefficients in a field is always stably finite. In other words, every group satisfying Gottschalk's conjecture also satisfies Kaplansky's stable finiteness conjecture. Here we present an alternative proof of this result based on first-order model theory

Tong Huang

This paper presents a non-intrusive, decentralized approach that stabilizes AC microgrids dominated by inverter-based resources (IBRs). By "non-intrusive" we mean that the approach does not require reprogramming IBRs' controllers to stabilize the microgrids. "Decentralized" is in the sense that the approach stabilizes the microgrids without communication amo

Yandong Wen, Weiyang Liu, Yao Feng, Bhiksha Raj

In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face recognition, speaker verification, image retrieval and person re-identification. The goal of PSL is to learn a pairwise similarity function assigning a higher similarity score to p

Alp Timucin Toymus, Umut Can Yener, Emine Bardakci, Ozgur Deniz Temel

Bladder volume measurement is critical for early detection and management of lower urinary tract dysfunctions. The current gold standard is invasive, and alternative technologies either require trained personnel or do not offer medical grade information. Here, we report an integrated wearable ultrasonic bladder volume monitoring (UBVM) device for accurate an

Markus Haltmeier, Daniel Obmann, Karoline Felbermayer, Florian Hinterleitner

We investigate resolution in photoacoustic tomography (PAT). Using Shannon theory, we investigate the theoretical resolution limit of sparse view PAT theoretically, and empirically demonstrate that all reconstruction methods used exceed this limit.

Diedre S. Carmo, Rosarie A. Tudas, Alejandro P. Comellas, Leticia Rittner

Automated segmentation of lung abnormalities in computed tomography is an important step for diagnosing and characterizing lung disease. In this work, we improve upon a previous method and propose S-MEDSeg, a deep learning based approach for accurate segmentation of lung lesions in chest CT images. S-MEDSeg combines a pre-trained EfficientNet backbone, bidir

Kyle Massingill, Brian Mason, Mark Lacy, Bjorn H. C. Emonts

We present continuum observations from the Atacama Large Millimeter/submillimeter Array (ALMA) of 10 high-redshift ($2.2 \le z \le 2.7$) ultraluminous quasars (QSOs) and constrain the presence of hot, ionized, circum-galactic gas in a stacking analysis. We measure a Compton-y parameter profile with a peak value of $(1.7 \pm 1.1) \times 10^{-6}$ at a radius o

Erfan Darzi, Yiqing Shen, Yangming Ou, Nanna M. Sijtsema

Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the performance of underrepresented clients. However, these methods often lead to lower overall model accuracy and slower convergence rates. In this paper, we demonstrate that using Vi

Haoyang Zhang, Yirui Eric Zhou, Yuqi Xue, Yiqi Liu

To break the GPU memory wall for scaling deep learning workloads, a variety of architecture and system techniques have been proposed recently. Their typical approaches include memory extension with flash memory and direct storage access. However, these techniques still suffer from suboptimal performance and introduce complexity to the GPU memory management,

Yiyu Chen, Quan Nguyen

In the context of legged robots, adaptive behavior involves adaptive balancing and adaptive swing foot reflection. While adaptive balancing counteracts perturbations to the robot, adaptive swing foot reflection helps the robot to navigate intricate terrains without foot entrapment. In this paper, we manage to bring both aspects of adaptive behavior to quadru

Medha Sawhney, Bhas Karmarkar, Eric J. Leaman, Arka Daw

Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting mechanistic studies in physiologically relevant media (e.g., collagen), this challenge is exacerbated by the dense surrounding environments with feature size and shape comparable

Jessica Clark

How does the formulation of a target variable affect performance within the ML pipeline? The experiments in this study examine numeric targets that have been binarized by comparing against a threshold. We compare the predictive performance of regression models trained to predict the numeric targets vs. classifiers trained to predict their binarized counterpa

Yechan Noh, Alex Smolyanitsky

In living organisms, information is processed in interconnected symphonies of ionic currents spiking through protein ion channels. As a result of dynamically switching their conductive states, ion channels exhibit a variety of current-voltage nonlinearities and memory effects. Fueled by the promise of computing architectures entirely different from von Neuma

Daniel Obmann, Markus Haltmeier

We study the effect of using weaker forms of data-fidelity terms in generalized Tikhonov regularization accounting for model uncertainties. We show that relaxed data-consistency conditions can be beneficial for integrating available prior knowledge.

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

We study the approximation of a square-integrable function from a finite number of evaluations on a random set of nodes according to a well-chosen distribution. This is particularly relevant when the function is assumed to belong to a reproducing kernel Hilbert space (RKHS). This work proposes to combine several natural finite-dimensional approximations base

Zixuan Ke, Bing Liu, Wenhan Xiong, Asli Celikyilmaz

Continual learning (CL) has two main objectives: preventing catastrophic forgetting (CF) and encouraging knowledge transfer (KT). The existing literature mainly focused on overcoming CF. Some work has also been done on KT when the tasks are similar. To our knowledge, only one method has been proposed to learn a sequence of mixed tasks. However, these techniq

Harsh Kumar, Tong Li, Jiakai Shi, Ilya Musabirov

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextu

Liming Xu, Stephen Mak, Maria Minaricova, Alexandra Brintrup

Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts have significantly exposed vulnerabilities within traditional global supply chains. These events underscore the need for organisations to establish more resilient and flexible supply chains. To address these challenges, the concept of the autonomous supply chain (ASC), characterised by pre

Hardeep Bassi, Yuanran Zhu, Senwei Liang, Jia Yin

In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-differential equations (IDEs). The LSTM-RNN representation of the nonlinear integral operator allows us to turn a system of nonlinear integro-differential equations into a system of

Bernard J. Giron Castro, Christophe Peucheret, Darko Zibar, Francesco Da Ros

Microring resonators (MRRs) are promising devices for time-delay photonic reservoir computing, but the impact of the different physical effects taking place in the MRRs on the reservoir computing performance is yet to be fully understood. We numerically analyze the impact of linear losses as well as thermo-optic and free-carrier effects relaxation times on t

Davide Napolitano, Lorenzo Vaiani, Luca Cagliero

The Document-based Visual Question Answering competition addresses the automatic detection of parent-child relationships between elements in multi-page documents. The goal is to identify the document elements that answer a specific question posed in natural language. This paper describes the PoliTo's approach to addressing this task, in particular, our best

Aviv Gibali, Markus Haltmeier

Inverse problems are characterized by their inherent non-uniqueness and sensitivity with respect to data perturbations. Their stable solution requires the application of regularization methods including variational and iterative regularization methods. Superiorization is a heuristic approach that can steer basic iterative algorithms to have small value of ce

Qiming Bao, Gael Gendron, Alex Yuxuan Peng, Wanjun Zhong

Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-like levels. However, their generalisation and robustness when performing logical reasoning has not been sufficiently assessed. To comprehensively evaluate this ability, we develop t

Maciej Przanowski, Michał Dobrski, Jaromir Tosiek, Francisco J. Turrubiates

Wave function of a single linear graviton and its interpretation are proposed. The evolution equation for this function is given. A Hermitian operator with mutually commuting components canonically conjugated to the momentum operator of the linear graviton is found.

Kwangmoon Park, Sündüz Keleş

Partial Least Squares (PLS) regression emerged as an alternative to ordinary least squares for addressing multicollinearity in a wide range of scientific applications. As multidimensional tensor data is becoming more widespread, tensor adaptations of PLS have been developed. In this paper, we first establish the statistical behavior of Higher Order PLS (HOPL

Yuki Fujimoto, Sanjay Reddy

We derive robust bounds on the equation of state (EoS) at finite baryon chemical potential using QCD inequalities and input from recent lattice-QCD calculations of thermodynamic properties of matter at nonzero isospin chemical potential. We use lattice data to deduce an upper bound on the baryon density of the symmetric nuclear matter at a given baryon chemi

Dmytro Korenkevych, Frank Cheng, Artsiom Balakir, Alex Nikulkov

The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constraint. Thus, advertising platforms typically provide automated agents to their customers, which act on their behalf to bid for impression opportunities in real time at scale. Because

Jintae Kim, Yun-Tak Oh, Daniel Bulmash, Jung Hoon Han

Some topological lattice models in two spatial dimensions exhibit intricate lattice size dependence in their ground state degeneracy (GSD). This and other features such as the position-dependent anyonic excitations are manifestations of UV/IR mixing. In the first part of this paper, we perform an exact calculation of the topological entanglement entropy (TEE

Liming Xu, Stephen Mak, Yaniv Proselkov, Alexandra Brintrup

Recent global disruptions, such as the pandemic and geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Autonomous supply chains (ASCs) have emerged as a potential solution, offering increased visibility, flexibility, and resilience in turbulent trade environments

Zhehuai Chen, He Huang, Andrei Andrusenko, Oleksii Hrinchuk

We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities. SALM comprises a frozen text LLM, a audio encoder, a modality adapter module, and LoRA layers to accommodate speech input and associated task instructions. The unified SALM not only achieves performance on par with task-specific Conforme

Xumiao Zhang, Shuowei Jin, Yi He, Ahmad Hassan

QUIC is expected to be a game-changer in improving web application performance. In this paper, we conduct a systematic examination of QUIC's performance over high-speed networks. We find that over fast Internet, the UDP+QUIC+HTTP/3 stack suffers a data rate reduction of up to 45.2% compared to the TCP+TLS+HTTP/2 counterpart. Moreover, the performance gap bet

Elena Bellomi, John ZuHone, Rainer Weinberger, Stephen Walker

The intracluster medium of the Perseus Cluster exhibits spiral-shaped X-ray surface brightness discontinuities known as ``cold fronts'', which simulations indicate are caused by the sloshing motion of the gas after the passage of a subcluster. Recent observations of Perseus have shown that these fronts extend to large radii. In this work, we present simulati

Sinan Allak

This paper presents the identification of a new transient ULX candidate (ULX-3) with reaching a peak luminosity of ~ 4e39 erg/s in NGC 4254 by using archival Chandra, Swift X-Ray Telescope (Swift/XRT), Hubble Space Telescope (HST), and James Webb Space Telescope (JWST) observations. From precise astrometric calculations, unique optical, near-infrared (NIR) a

Bowen Li, Jun Zou

A generalized unbalanced optimal transport distance ${\rm WB}_{\Lambda}$ on matrix-valued measures $\mathcal{M}(\Omega,\mathbb{S}_+^n)$ was defined in [arXiv:2011.05845] \`{a} la Benamou-Brenier, which extends the Kantorovich-Bures and the Wasserstein-Fisher-Rao distances. In this work, we investigate the convergence properties of the discrete transport prob

Joel Bierman, Yingzhou Li, Jianfeng Lu

We propose a state-averaged orbital optimization scheme for improving the accuracy of excited states of the electronic structure Hamiltonian for use on near-term quantum computers. Instead of parameterizing the orbital rotation operator in the conventional fashion as an exponential of an anti-hermitian matrix, we parameterize the orbital rotation as a genera

Katherine J. Pearce, Chao Chen, Yijun Dong, Per-Gunnar Martinsson

Interpolative and CUR decompositions involve "natural bases" of row and column subsets, or skeletons, of a given matrix that approximately span its row and column spaces. These low-rank decompositions preserve properties such as sparsity or non-negativity, and are easily interpretable in the context of the original data. For large-scale problems, randomized

Margarita Akhmejanova, Vladislav Kozhevnikov

The celebrated Frieze's result about the independence number of $G(n,p)$ states that it is concentrated in an interval of size $o(1/p)$ for all $C_{\varepsilon}/n<p=o(1)$. We show concentration in an interval of size $o(1/p)$ for the maximum size (number of vertices) of an induced forest in $G(n,p)$ for all $C_{\varepsilon}/n<p<1-\varepsilon$. Presumably, it

Puspita Parui, Sovan Ghosh, Bheema Lingam Chittari

We study the effect of Haldane flux in the bilayer $\alpha$-$\mathcal{T}_3$ lattice system, considering possible non-equivalent, commensurate stacking configurations with a tight-binding formalism. The bilayer $\alpha$-$\mathcal{T}_3$ lattice comprises six sublattices in a unit cell, and its spectrum consists of six bands. In the absence of Haldane flux, thr

Chenoa D. Tremblay, Savin Shynu Varghese, Jack Hickish, Paul Demorest

The primary goal of the search for extraterrestrial intelligence (SETI) is to gain an understanding of the prevalence of technologically advanced beings (organic or inorganic) in the Galaxy. One way to approach this is to look for technosignatures: remotely detectable indicators of technology, such as temporal or spectral electromagnetic emissions consistent

Viraj Nadkarni, Jiachen Hu, Ranvir Rana, Chi Jin

Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity providers (LPs) incentivized to invest their assets into a liquidity pool. However, the prices at which a pooled asset is traded is often more stale than the prices on centralized a

Harsh Patel, Yuan Zhou, Alexander P Lamb, Shu Wang

This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs). Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, of

Guanghua Wang, Weili Wu

In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks,

Minseok Ryu, Geunyeong Byeon, Kibaek Kim

We propose a GPU-based distributed optimization algorithm, aimed at controlling optimal power flow in multi-phase and unbalanced distribution systems. Typically, conventional distributed optimization algorithms employed in such scenarios rely on parallel execution with multiple CPUs. However, this often leads to significant computation time primarily due to

Minseok Ryu, Ahmed Attia, Arthur Barnes, Russell Bent

We propose a new heuristic approach for solving the challenge of determining optimal placements for geomagnetically induced current blocking devices on electrical grids. Traditionally, these determinations are approached by formulating the problem as mixed-integer nonlinear programming models and solving them using optimization solvers based on the spatial b

Trung Dang, Walter McKelvie, Paul Valiant, Hongao Wang

Pearson's chi-squared test, from 1900, is the standard statistical tool for "hypothesis testing on distributions": namely, given samples from an unknown distribution $Q$ that may or may not equal a hypothesis distribution $P$, we want to return "yes" if $P=Q$ and "no" if $P$ is far from $Q$. While the chi-squared test is easy to use, it has been known for a

John Dougherty

A manifold is a space that locally looks like the smooth space $\mathbf{R}^{n}$. It is usually also assumed that the underlying topological space of a manifold is hausdorff. However, there are natural examples of manifolds for which the hausdorff conditions fails. Some but not all of these examples contain bifurcate pairs of curves: pairs of curves that agre

Dawid Paszko, Dominic C. Rose, Marzena H. Szymańska, Arijeet Pal

Topological order offers possibilities for processing quantum information which can be immune to imperfections. However, the question of its stability out of equilibrium is relevant for experiments, where coupling to an environment is unavoidable. In this work we demonstrate the robustness of certain aspects of $Z_2 \times Z_2$ symmetry-protected topological

Srinivisan Mahendran, Jesús Carrete, Andreas Isacsson, Georg K. H. Madsen

Transition metal dichalcogenides are investigated for various applications at the nanoscale thanks to their unique combination of properties and dimensionality. For many of the anticipated applications, heat conduction plays an important role. At the same time, these materials often contain relatively large amounts of point defects. Here, we provide a system

Hashim Ali, Dhimant Khuttan, Rafi Ud Daula Refat, Hafiz Malik

Voice-Controllable Devices (VCDs) have seen an increasing trend towards their adoption due to the small form factor of the MEMS microphones and their easy integration into modern gadgets. Recent studies have revealed that MEMS microphones are vulnerable to audio-modulated laser injection attacks. This paper aims to develop countermeasures to detect and preve

Mario W. Barela, V. Pleitez

This note discusses the matter of probing Beyond the Standard Model physics and how, to succeed in this quest, the interpretations of the Standard Model regarding observed phenomena must be utilized with caution. We give several specific examples of why this is necessary and assess general scenarios in which it is specially important. In particular, we call

S. Cruz y Cruz, Z. Gress, P. Jimenez-Macias, O. Rosas-Ortiz

We present a novel approach to generate Bessel-Gauss modes of arbitrary integer order and well-defined optical angular momentum in a gradient index medium of transverse parabolic profile. The propagation and coherence properties, as well as the quality factor, are studied using algebraic techniques that are widely used in quantum mechanics. It is found that

Yunyong Ko, Seongeun Ryu, Sang-Wook Kim

Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload problem. Although many recent works have been studied for better personalized news recommendation, the following challenges should be explored more: (C1) Comprehending manifold in

Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu

Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced rich semantic modeling of item descriptions. However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic repre

Abraham Loeb, Morgan MacLeod

Low-mass stars appear to frequently host planetary systems. When these rocky planets develop high eccentricities as a result of secular torques or dynamical scatterings, they occasionally pass close to the host star. In these close passages, planets can be tidally disrupted, and sheared into bound and unbound debris tails. To suffer such a disruption the ste

Ron S. Jarmin, John M. Abowd, Robert Ashmead, Ryan Cumings-Menon

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. Following long-established precedent in economics and statistics, we argue that any proposal for q

Spencer L. Gordon, Manav Kant, Eric Ma, Leonard J. Schulman

Product of experts (PoE) are layered networks in which the value at each node is an AND (or product) of the values (possibly negated) at its inputs. These were introduced as a neural network architecture that can efficiently learn to generate high-dimensional data which satisfy many low-dimensional constraints -- thereby allowing each individual expert to pe

Romain Veyron, Jean-Baptiste Gérent, Guillaume Baclet, Vincent Mancois

In this work, we implement a new method for imaging ultracold atoms with subwavelength resolution capabilities and determine its regime of validity. It uses the laser driven interaction between excited states to engineer hyperfine ground state population transfer in a three-level system on scales much smaller than the optical resolution. Subwavelength imagin

Minghao Guo, Bohan Wang, Wojciech Matusik

We propose the Medial Skeletal Diagram, a novel skeletal representation that tackles the prevailing issues around skeleton sparsity and reconstruction accuracy in existing skeletal representations. Our approach augments the continuous elements in the medial axis representation to effectively shift the complexity away from the discrete elements. To that end,

Jinhyuk Choi, Jihong Park, Seung-Woo Ko, Jinho Choi

Recent studies on semantic communication commonly rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC). Unlike traditional transceivers, these neural transceivers are trainable using actual source data and channels, enabling them to extract and communicate semantics. On the flip side, each neural transceiver

Pei Xiong, Daniel K. Nikolov, Fei Cheng, Jannick P. Rolland

Metasurfaces are a promising technology that can serve as a compact alternative to conventional optics while providing multiple functions depending on the properties of the incident light, such as the wavelength, polarization, and incident angle. Here, we demonstrate a hybrid VIS-NIR dielectric metasurface that can reflect 940 nm light into a specified direc

Randy J. Chase, Amy McGovern, Cameron Homeyer, Peter Marinescu

The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This stud