July 2023 arXiv papers — page 20

Xinmeng Xu, Weiping Tu, Yuhong Yang

Convolutional neural networks (CNN) and Transformer have wildly succeeded in multimedia applications. However, more effort needs to be made to harmonize these two architectures effectively to satisfy speech enhancement. This paper aims to unify these two architectures and presents a Parallel Conformer for speech enhancement. In particular, the CNN and the se

Georg Wenzel, Adam Jatowt

Temporal commonsense reasoning refers to the ability to understand the typical temporal context of phrases, actions, and events, and use it to reason over problems requiring such knowledge. This trait is essential in temporal natural language processing tasks, with possible applications such as timeline summarization, temporal question answering, and tempora

Da Wei, Yaochen Yang, Xuefeng Wei, Ramin Golestanian

Turbulent flows are observed in low-Reynolds active fluids. They are intrinsically different from the classical inertial turbulence and behave distinctively in two- and three-dimensions. Understanding the behaviors of this new type of turbulence and their dependence on the system dimensionality is a fundamental challenge in non-equilibrium physics. We experi

Bach-Thuan Bui, Huy-Hoang Bui, Dinh-Tuan Tran, Joo-Ho Lee

State-of-the-art visual localization methods mostly rely on complex procedures to match local descriptors and 3D point clouds. However, these procedures can incur significant costs in terms of inference, storage, and updates over time. In this study, we propose a direct learning-based approach that utilizes a simple network named D2S to represent complex loc

Nengxin Bao, Tong Zhang, Ruizhi Huang, Suryakanta Biswal

Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data is almost always insufficient, because the real-time monitoring data of a structure only reflects a limited number of structural conditions, while the number of possible structural conditions is infi

Guy Henniart, Masao Oi

Let $F$ be a local non-Archimedean field and $E$ a finite Galois extension of $F$, with Galois group $G$. If $\rho$ is a representation of $G$ on a complex vector space $V$, we may compose it with any tensor operation $R$ on $V$, and get another representation $R\circ\rho$. We study the relation between the Swan exponents $\mathrm{Sw}(\rho)$ and $\mathrm{Sw}

Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick

In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous populations and satisfy strong notions of algorithmic fairness. However, the task of auditing a model for strong calibration is well-known to be difficult -- particularly for machi

Jan Zemen

Simulations of magneto-optical Kerr effect in biaxially strained Mn$_4$N are performed using density functional theory and linear response theory. We consider three ferrimagnetic phases, two collinear and one noncollinear, which have been corroborated separately by earlier studies. The simulated spectra are compared to magneto-optical data available in recen

Mahdi Morafah, Weijia Wang, Bill Lin

Federated Learning (FL) has been an area of active research in recent years. There have been numerous studies in FL to make it more successful in the presence of data heterogeneity. However, despite the existence of many publications, the state of progress in the field is unknown. Many of the works use inconsistent experimental settings and there are no comp

Jie Liu, Mengting He, Xuequn Shang, Jieming Shi

Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, thes

Lin Yan, Hanqi Guo, Thomas Peterka, Bei Wang

Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness of critical poi

Mingjian Wen, Matthew K. Horton, Jason M. Munro, Patrick Huck

The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid a

Marcus O. Thomas, Ohad Shemmer, Benny Trakhtenbrot, Paulina Lira

We present rest-frame ultraviolet (UV) images of six luminous quasars at $z \sim 4.8$ obtained with the Hubble Space Telescope (HST). These quasars exhibit a wide range of star formation rates (SFRs) and lie in a wide range of environments. We carefully model and subtract the point-like quasar emission and investigate the morphology of the underlying host ga

Robert Laterveer

We prove that the Beauville-Voisin conjecture is true for any double EPW sextic, i.e. the subalgebra of the Chow ring generated by divisors and Chern classes of the tangent bundle injects into cohomology.

Erin Marshall Seroka, Axel Schmidt

Short-range correlated (SRC) nucleon pairs, which are strongly interacting nucleons at short inter-particle distances, can reveal properties of the effective nucleon-nucleon (\textit{NN}) interaction at short distance scales. The relative abundance of proton-proton (\textit{pp}) pairs and proton-neutron (\textit{pn}) pairs, for example, is sensitive to the t

Lisa Schwetlick, Hendrik Graupner, Olaf Dimigen, Ralf Engbert

Pupil dynamics and fixational eye movements are primarily involuntary processes that actively support visual perception during fixations. Both measures are known to be sensitive to ongoing cognitive and affective processing. In a visual fixation experiment (N=116) we demonstrate that self-recognition, familiar faces, and unfamiliar faces elicit specific resp

Ricardo Ochoa-Armenta, Francisco S. Guzmán

The study of our Sun holds significant importance in Space Weather research, encompassing a diverse range of phenomena characterized by distinct temporal and spatial scales. To address these complexities, we developed CAFE-AMR, an implementation of an Adaptive Mesh Refinement (AMR) strategy coupled with a Magnetohydrodynamics (MHD) equation solver, aiming to

Samrat Acharya, Malini Ghosal, Travis Thurber, Casey D. Burleyson

Electrification of transport compounded with climate change will transform hourly load profiles and their response to weather. Power system operators and EV charging stakeholders require such high-resolution load profiles for their planning studies. However, such profiles accounting whole transportation sector is lacking. Thus, we present a novel approach to

Steve Macenski, Tom Moore, David Lu, Alexey Merzlyakov

The Robot Operating System 2 (ROS 2) is rapidly impacting the intelligent machines sector -- on space missions, large agriculture equipment, multi-robot fleets, and more. Its success derives from its focused design and improved capabilities targeting product-grade and modern robotic systems. Following ROS 2's example, the mobile robotics ecosystem has been f

Iwona Chlebicka, Minhyun Kim, Marvin Weidner

We study the gradient regularity of solutions to measure data elliptic systems with Uhlenbeck-type structure and Orlicz growth. For any bounded Borel measure, pointwise estimates for the gradient of solutions are provided in terms of the truncated Riesz potential. This allows us to show a precise transfer of regularity from data to solutions on various scale

Florian Grube, Thorben Hensiek, Waldemar Schefer

We prove Sobolev regularity for distributional solutions to the Dirichlet problem for generators of $2s$-stable processes and exterior data, inhomogeneity in weighted $L^2$-spaces. This class of operators includes the fractional Laplacian. For these rough exterior data the theory of weak variational solutions is not applicable. Our regularity estimate is rob

Danielle Wang

Using a relative trace formula approach, we prove the twisted global Gan-Gross-Prasad conjecture for $\operatorname{U}(V) \subseteq \operatorname{GL}(V)$, as well as its refinement, under some unramifiedness assumptions and local conditions on the quadratic extension and the automorphic representation.

Brian W. Blankenship, Zachary Jones, Naichen Zhao, Harpreet Singh

We present a novel method for fabricating highly customizable three-dimensional structures hosting quantum sensors based on Nitrogen Vacancy (NV) centers using two-photon polymerization. This approach overcomes challenges associated with structuring traditional single-crystal quantum sensing platforms and enables the creation of complex, fully three-dimensio

Adam Z. Foshie, James S. Plank, Garrett S. Rose, Catherine D. Schuman

RAVENS is a neuroprocessor that has been developed by the TENNLab research group at the University of Tennessee. Its main focus has been as a vehicle for chip design with memristive elements; however it has also been the vehicle for all-digital CMOS development, plus it has implementations on FPGA's, microcontrollers and software simulation. The software sim

Niladri Gomes, Jia Yin, Siyuan Niu, Chao Yang

We propose a hybrid approach to simulate quantum many body dynamics by combining Trotter based quantum algorithm with classical dynamic mode decomposition. The interest often lies in estimating observables rather than explicitly obtaining the wave function's form. Our method predicts observables of a quantum state in the long time by using data from a set of

Ali Hakem Alsaeedi, Suha Mohammed Hadi, Yarub Alazzawi

Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and new membership function to enhance san dust images. The proposed model consists of three phase

Fernando Gotardo, Benjamin J. Carey, Hamish Greenall, Glen I. Harris

Optomechanical magnetometers enable highly sensitive magnetic field sensing. However, all such magnetometers to date have been optically excited and read-out either via free space or a tapered optical fiber. This limits their scalability and integrability, and ultimately their range of applications. Here, we present an optomechanical magnetometer that is exc

Ghizal fatima, Risala H. Allami, Maitham G. Yousif

Precision medicine, tailored to individual patients based on their genetics, environment, and lifestyle, shows promise in managing complex diseases like infections. Integrating artificial intelligence (AI) into precision medicine can revolutionize disease management. This paper introduces a novel approach using AI to advance precision medicine in infectious

Jinlei Dong, Fang Li

In this paper, we give presentations of the mapping class groups of marked surfaces stabilizing boundaries for any genus. Note that in the existing works, the mapping class groups of marked surfaces were the isotopy classes of homeomorphisms fixing boundaries pointwise. The condition for stabilizing boundaries of mapping class groups makes the requirement fo

Ashutosh Goswami, Mehdi Mhalla, Valentin Savin

A fault-tolerant way to prepare logical code-states of Q1 codes, i.e., quantum polar codes encoding one qubit, has been recently proposed. The fault tolerance therein is guaranteed by an error detection gadget, where if an error is detected during the preparation, one discards entirely the preparation. Due to error detection, the preparation is probabilistic

Sylvia Worlali Azumah, Victor Adewopo, Zag ElSayed, Nelly Elsayed

Cyberbullying has become a pervasive issue based on the rise of cell phones and internet usage affecting individuals worldwide. This paper proposes an open-source intelligence pipeline using data from Twitter to track keywords relevant to cyberbullying in social media to build dashboards for law enforcement agents. We discuss the prevalence of cyberbullying

Talia Crawford, Scott Duong, Richard Fueston, Ayorinde Lawani

Artificial Intelligence (AI) refers to the intelligence demonstrated by machines, and within the realm of AI, Machine Learning (ML) stands as a notable subset. ML employs algorithms that undergo training on data sets, enabling them to carry out specific tasks autonomously. Notably, AI holds immense potential in the field of software engineering, particularly

Jorge Paz-Ruza, Amparo Alonso-Betanzos, Berta Guijarro-Berdiñas, Brais Cancela

Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for r

Bao-Jun Cai, Bao-An Li, Zhen Zhang

The central speed of sound (SS) measures the stiffness of the Equation of State (EOS) of superdense neutron star (NS) matter. Its variations with density and radial coordinate in NSs in conventional analyses often suffer from uncertainties of the specific nuclear EOSs used. Using the central SS and NS mass/radius scaling obtained from solving perturbatively

Dipesh Bhandari, Michael Crescimanno

Introducing a radially dependent magnetic field into Newton's off-center circular orbits potential so as to preserve the $E=0$ dynamical symmetry leads to a unique choice of field that can be identified as the inclusion of a magnetic monopole in the inverse stereographically projected problem. One finds also a phenomenological correspondence with that of the

Jesse A. Rodriguez, Mark A. Cappelli

We apply inverse design methods to produce two-dimensional triangular-lattice plasma metamaterial (PMM) devices which are then constructed and demonstrated experimentally. Finite difference frequency domain simulations are used along with forward-mode automatic differentiation to optimize the plasma densities of each of the plasma elements in the PMM to perf

Kun Yuan, Vinkle Srivastav, Tong Yu, Joel L. Lavanchy

Recent advancements in surgical computer vision applications have been driven by vision-only models, which do not explicitly integrate the rich semantics of language into their design. These methods rely on manually annotated surgical videos to predict a fixed set of object categories, limiting their generalizability to unseen surgical procedures and downstr

Roberto Grimaudo, Davide Valenti, Bernardo Spagnolo, Antonio Troisi

The direct effect of an axion field on Josephson junctions is analyzed through the consequences on the effective potential barrier that prevents the junction from switching from the superconducting to the finite-voltage state. We describe a method to reliably compute the quasipotential with stochastic simulations, which allows to span the coupling parameter

Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec

We consider {\em bidding games}, a class of two-player zero-sum {\em graph games}. The game proceeds as follows. Both players have bounded budgets. A token is placed on a vertex of a graph, in each turn the players simultaneously submit bids, and the higher bidder moves the token, where we break bidding ties in favor of Player 1. Player 1 wins the game iff t

Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and

Mariia Krasikova, Aleksandra Pavliuk, Sergey Krasikov, Mikhail Kuzmin

Acoustic metamaterials and phononic crystals represent a promising platform for the development of noise-insulating systems characterized by a low weight and small thickness. Nevertheless, the operational spectral range of these structures is usually quite narrow, limiting their application as substitutions of conventional noise-insulating systems. In this w

Razvan C. Fetecau, Hansol Park

We consider an aggregation-diffusion energy on Cartan-Hadamard manifolds with sectional curvatures that can grow unbounded at infinity. The energy corresponds to a macroscopic aggregation model that involves nonlocal interactions and linear diffusion. We establish necessary and sufficient conditions on the growth at infinity of the attractive interaction pot

Jimmy C. Kotsakidis, Marc Currie, Antonija Grubišić-Čabo, Anton Tadich

Magnesium intercalated 'quasi-freestanding' bilayer graphene on 6H-SiC(0001) (Mg-QFSBLG) has many favorable properties (e.g., highly n-type doped, relatively stable in ambient conditions). However, intercalation of Mg underneath monolayer graphene is challenging, requiring multiple intercalation steps. Here, we overcome these challenges and subsequently incr

Donggun Kim, Kisung You

The research detailed in this paper scrutinizes Principal Component Analysis (PCA), a seminal method employed in statistics and machine learning for the purpose of reducing data dimensionality. Singular Value Decomposition (SVD) is often employed as the primary means for computing PCA, a process that indispensably includes the step of centering - the subtrac

Uwe Trittmann

We apply a method ("eLCQ") to find the asymptotic spectrum of a Hamiltonian from its symmetries to two-dimensional adjoint QCD. Streamlining the approach, we construct a complete set of asymptotic eigenfunctions in all parton sectors and use it in a basis-function approach to find the spectrum of the full theory. We are able to reproduce previous results inc

Sergei Chmutov

We prove that the partial-dual genus polynomial considered as a function on chord diagrams satisfies the four-term relation. Thus it is a weight system from the theory of Vassiliev knot invariants.

Andreas Fring, Bethan Turner

We discuss how a standard scattering theory a of multi-particle theory generalises to systems based on Hamiltonians that involve higher-order derivatives in their quantum mechanical formulation. As concrete examples, we consider Hamiltonian systems built from higher-order charges of Calogero and Calogero-Moser systems. Exploiting the integrability of these s

Kasia Jankiewicz

We give criteria for a graph of groups to have finite stature with respect to its collection of vertex groups, in the sense of Huang-Wise. We apply it to the triangle Artin groups that were previously shown to split as a graph of groups. This allows us to deduce residual finiteness, and expands the list of Artin groups known to be residually finite.

Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI

Scott M. Garner, Eric A. Haugen, Stephen R. Leone, Eric Neuscamman

We theoretically investigate the influence of diradical electron spin coupling on the time-resolved X-ray absorption spectra of the photochemical ring opening of furanone. We predict geometry dependent carbon K-edge signals involving transitions from core orbitals to both singly and unoccupied molecular orbitals. The most obvious features of the ring opening

Masato Kobayashi

Ramanujan (1916) and Shen (1999) discovered differential equations for classical Eisenstein series. Motivated by them, we derive new differential equations for Eisenstein series of level 2 from the second kind of Jacobi theta function. This gives a new characterization of a system of differential equations by Ablowitz-Chakravarty-Hahn (2006), Hahn (2008), Ka

Yejiong Zhu, Hao Chen

Dimensionality effects pose major challenges in high-dimensional and non-Euclidean data analysis. Graph-based two-sample tests and change-point detection are particularly attractive in this context, as they make minimal distributional assumptions and perform well across a wide range of scenarios. These methods rely on similarity graphs constructed from data,

Samirah Alzubaidi, Michael J. Higgins

Considerable recent work has focused on methods for analyzing experiments which exhibit treatment interference -- that is, when the treatment status of one unit may affect the response of another unit. Such settings are common in experiments on social networks. We consider a model of treatment interference -- the K-nearest neighbors interference model (KNNIM

Siyuan Chen, Yiqi Yang, Miguel Morales, Shiwei Zhang

Correlated sampling has wide-ranging applications in Monte Carlo calculations. When branching random walks are involved, as commonly found in many algorithms in quantum physics and electronic structure, population control is typically not applied with correlated sampling due to technical difficulties. This hinders the stability and efficiency of correlated s

Xiaozhen Yang, Erda Wen, Dinesh Bharadia, Daniel F. Sievenpiper

A varactor-based reconfigurable multifunctional metasurface capable of simultaneous beam steering, polarization conversion and phase offset is proposed in this paper. The unit cell is designed to naturally decompose the incident waves into two equal amplitude orthogonal linear components, and by integrating varactors, the reflection phase of the field compon

Seungchan Kim, Micah Corah, John Keller, Graeme Best

This work proposes an autonomous multi-robot exploration pipeline that coordinates the behaviors of robots in an indoor environment composed of multiple rooms. Contrary to simple frontier-based exploration approaches, we aim to enable robots to methodically explore and observe an unknown set of rooms in a structured building, keeping track of which rooms are

Alice-Agnes Gabriel, Dmitry I. Garagash, Kadek H. Palgunadi, P. Martin Mai

Earthquakes vary in size over many orders of magnitude, yet the scaling of the earthquake energy budget remains enigmatic. We propose that fundamentally different "small-slip" and "large-slip" fracture processes govern earthquakes. We combine seismological observations with a physics-based mechanical earthquake model under flash-heating friction. We find tha

Kiran Karra, Tom Mellan, Maria Silva, Juan P. Madrigal-Cianci

In this paper, we outline a framework for modeling utility-based blockchain-enabled economic systems using Agent Based Modeling (ABM). Our approach is to model the supply dynamics based on metrics of the cryptoeconomy. We then build autonomous agents that make decisions based on those metrics. Those decisions, in turn, impact the metrics in the next time-ste

Junhyeong Cho, Gilhyun Nam, Sungyeon Kim, Hunmin Yang

In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Also, a recent study has demonstrated the cross-modal transferability phenomenon of this joint space. From these observations, we propose PromptStyler which simulates various distribution shifts in

Yao Su, Zhentian Qian, Lei Ma, Lifang He

Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies. The aim is to extract the brain from raw imaging scans (i.e., extraction step), align it with a target brain image (i.e., registration step) and label the anatomical brain regions (i.e., segmentation step). Conventional studies typically focus on de

Yuanfang Ren, Yanjun Li, Tyler J. Loftus, Jeremy Balch

Initial hours of hospital admission impact clinical trajectory, but early clinical decisions often suffer due to data paucity. With clustering analysis for vital signs within six hours of admission, patient phenotypes with distinct pathophysiological signatures and outcomes may support early clinical decisions. We created a single-center, longitudinal EHR da

Aziz Guergachi, Javid Hakim

We investigate, at the fundamental level, the questions of `why', `when' and `how' one could or should reach out to poor and vulnerable people to support them in the absence of governmental institutions. We provide a simple and new approach that is rooted in linear algebra and basic graph theory to capture the dynamics of income circulation among economic ag

Runzhe Wang, Sadhika Malladi, Tianhao Wang, Kaifeng Lyu

Momentum is known to accelerate the convergence of gradient descent in strongly convex settings without stochastic gradient noise. In stochastic optimization, such as training neural networks, folklore suggests that momentum may help deep learning optimization by reducing the variance of the stochastic gradient update, but previous theoretical analyses do no

Nataliya Goncharuk, Michael Yampolsky

We study the global behavior of the renormalization operator on a specially constructed Banach manifold that has cubic critical circle maps on its boundary and circle diffeomorphisms in its interior. As an application, we prove results on smoothness of irrational Arnold tongues.

Spencer J. Magnall, Daniel J. Price, Paul D. Lasky, Hayley J. Macpherson

We perform three-dimensional simulations of homogeneous and inhomogeneous cosmologies via the coupling of a numerical relativity code for spacetime evolution and smoothed particle hydrodynamics (SPH) code. Evolution of a flat dust and radiation dominated Friedmann-Lema\^itre-Roberston-Walker (FLRW) spacetime shows an agreement of exact solutions with residua

Rigel Galgana, Negin Golrezaei

Motivated by Carbon Emissions Trading Schemes, Treasury Auctions, Procurement Auctions, and Wholesale Electricity Markets, which all involve the auctioning of homogeneous multiple units, we consider the problem of learning how to bid in repeated multi-unit pay-as-bid auctions. In each of these auctions, a large number of (identical) items are to be allocated

Barbara Gatti, Gábor Korchmáros

A (projective, geometrically irreducible, non-singular) curve $\mathcal{X}$ defined over a finite field $\mathbb{F}_{q^2}$ is maximal if the number $N_{q^2}$ of its $\mathbb{F}_{q^2}$-rational points attains the Hasse-Weil upper bound, that is $N_{q^2}=q^2+2\mathfrak{g}q+1$ where $\mathfrak{g}$ is the genus of $\mathcal{X}$. An important question, also motiv

Tom Sanitz, Christian Wilms, Simone Frintrop

Traffic light detection is a challenging problem in the context of self-driving cars and driver assistance systems. While most existing systems produce good results on large traffic lights, detecting small and tiny ones is often overlooked. A key problem here is the inherent downsampling in CNNs, leading to low-resolution features for detection. To mitigate

Yuqiao Wen, Zichao Li, Wenyu Du, Lili Mou

Knowledge distillation (KD) is the process of transferring knowledge from a large model to a small one. It has gained increasing attention in the natural language processing community, driven by the demands of compressing ever-growing language models. In this work, we propose an f-DISTILL framework, which formulates sequence-level knowledge distillation as m

Michael Moor, Qian Huang, Shirley Wu, Michihiro Yasunaga

Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a first step in this direction and promise many exciting clinical applications. However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a s

Eamonn Corrigan, Martin Williams, Mary A. Wells

Students' high school decisions will always impact efforts to achieve gender parity in STEM at the university level and beyond. Without a comprehensive understanding of gendered disparities in high school course selection, it will be impossible to close completely the gender gap in many STEM disciplines. This study examines eleven years of detailed administr

Donniel Cruz, Emmanuel Rodulfo

This work studies the cosmology of $\chi^{3/2}$-MOND gravity by Bernal et. al. (2011). This theory is a modification to Einstein's General Relativity (GR) that uses a dimensionless curvature scalar $\chi$ by rescaling the Ricci scalar $R$ by some characteristic length scale $L_M$, as well as a set of modified field equations that follows from a $3/2$-power L

Eva Kilian, Marko Toroš, P. F. Barker, Sougato Bose

Exploiting quantum mechanics for sensing offers unprecedented possibilities. State of the art proposals for novel quantum sensors often rely on the creation of large superpositions and generally detect a field. However, what is the optimal superposition size for detecting an incident particle (or an incident stream of particles) from a specific direction? Th

Michael Coons, James Evans, Philipp Gohlke, Neil Mañibo

We extend the existence of ghost measures beyond nonnegative primitive regular sequences to a large class of nonnegative real-valued regular sequences. In the general case, where the ghost measure is not unique, we show that they can be parametrised by a compact abelian group. For a subclass of these measures, by replacing primitivity with a commutativity co

Yizhou Lu, Trevor Seets, Felipe Gutierrez-Barragan, Ehsan Ahmadi

The design of the camera and optical measurement is a crucial part of optimizing machine vision systems. However, camera designs are usually optimized to produce human-interpretable images. Moreover, camera optimization typically makes the assumption of additive noise, while modern optical imaging systems are mainly affected by photon noise which is not addi

Vedant Basu, Aswathi Balagopal

The IceCube Neutrino Observatory is a cubic-kilometer Cherenkov detector at the South Pole, designed to study neutrinos of astrophysical origin. We present an analysis of the Medium Energy Starting Events (MESE) sample, a veto-based event selection that selects neutrinos and efficiently rejects a background of cosmic ray-induced muons This is an extension of

Mark-David Hosale, Jim Madsen, Vedant Basu

The annual GLEAM outdoor art exhibition features curated, large-scale light installations in the Olbrich Botanical Gardens in Madison, Wisconsin. With submissions from local, regional, and international artists, the annual two-month long event draws tens of thousands of visitors each fall. "Tidal Disruption", an art-science light sculpture representative of

Anderson Almeida, Hektor Monteiro, Wilton S. Dias

The publication of the \emph{Gaia} catalogue and improvements in methods to determine memberships and fundamental parameters of open clusters has led to major advances in recent years. However, important parameters such as the masses of these objects, although being studied mostly in some isolated cases, have not been addressed in large homogeneous samples b

Yuehao Bai, Jizhou Liu, Azeem M. Shaikh, Max Tabord-Meehan

This paper studies the use of highly stratified designs for the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments. By a "highly stratified" design, we mean experiments in which units are divided into blocks of a fixed size and a proportion within each block is assigned to a binary treatment uniform

Duc C. Hoang, Behzad Ousat, Amin Kharraz, Cuong V. Nguyen

The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its failure cases and subsequently making CAPTCHAs more secure. Recently proposed solvers, built on advances in deep learning, are able to crack even the very challenging CAPTCHAs with

Owen Melia, Eric Jonas, Rebecca Willett

Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant machine learning methods set the state of the art for many tasks, including molecular property prediction and 3D shape classification. These methods generally either rely on task-spe

Masatoshi Imanishi, Shunsuke Baba, Kouichiro Nakanishi, Takuma Izumi

We present the results of our ALMA $\lesssim$0.5 kpc-resolution dense molecular line (HCN and HCO$^{+}$ J=2-1, J=3-2, and J=4-3) observations of 12 nearby (ultra)luminous infrared galaxies ([U]LIRGs). After matching beam sizes of all molecular line data to the same values in all (U)LIRGs, we derive molecular line flux ratios, by extracting spectra in the cen

Nuno Morujão, Carlos Correia, Paulo Andrade, Julien Woillez

Monitoring turbulence parameters is crucial in high-angular resolution astronomy for various purposes, such as optimising adaptive optics systems or fringe trackers. The former are present at most modern observatories and will remain significant in the future. This makes them a valuable complementary tool for the estimation of turbulence parameters. The feas

Jacob R. Taylor, Nathan L. Foulk, Sankar Das Sarma

Advanced tensor network numerical methods are used to explore the fidelity of repeated SWAP operations on a system comprising 20-100 quantum dot spin qubits in the presence of valley leakage and electrostatic crosstalk. The fidelity of SWAP gates is largely unaffected by Zeeman splitting and valley splitting, except when these parameters come into resonance.

Katherine A. Keith, Sergey Feldman, David Jurgens, Jonathan Bragg

Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics, or the behavioral social sciences -- researchers have proposed methods to adjust for confounding by adapting machine learning methods to the goal of causal estimation. However, e

Samrat Acharya, Yury Dvorkin, Ramesh Karri

Power utilities are adopting Automated Demand Response (ADR) to replace the costly fuel-fired generators and to preempt congestion during peak electricity demand. Similarly, third-party Demand Response (DR) aggregators are leveraging controllable small-scale electrical loads to provide on-demand grid support services to the utilities. Some aggregators and ut

Tingyuan Jiang, Xiaoge Xu, Junji Jia

This paper investigates off-equatorial plane deflections and gravitational lensing of both null signals and massive particles in Kerr spacetime in the weak deflection limit, with the finite distance effect of the source and detector taken into account. This is the effect caused by the fact that both the source and detector are located at finite distances fro

Pavel P. Popov, Michael Meth, Maciej Lewenstein, Philipp Hauke

Lattice gauge theories are fundamental to various fields, including particle physics, condensed matter, and quantum information theory. Recent progress in the control of quantum systems allows for studying Abelian lattice gauge theories in table-top experiments. However, several challenges remain, such as implementing dynamical fermions in higher spatial dim

Songlin Xu, Xinyu Zhang

Increasing individuals' awareness of their own body signals can lead to improved interoception, enabling the brain to estimate current body states more accurately and in a timely manner. However, certain body signals, such as eye movements, often go unnoticed by individuals themselves. This study aimed to test the hypothesis that providing eye-movement-corre

Thomas Boettcher

The LHCb detector's forward geometry provides unprecedented kinematic coverage at low Bjorken-$x$. LHCb's excellent momentum resolution, vertex reconstruction, and particle identification enable precision measurements at low transverse momentum and high rapidity in proton-lead collisions, probing $x$ as small as $10^{-6}$. In this contribution, we present re

Ryan S Gallagher, Nishant Sinha, Akash R Pattnaik, William K. S. Ojemann

Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale

Thomas S. -Y. Lai, Lee Armus, Marina Bianchin, Tanio Diaz-Santos

We present James Webb Space Telescope (JWST) Near Infrared Spectrograph (NIRSpec) integral-field spectroscopy of the nearby luminous infrared galaxy, NGC 7469. We take advantage of the high spatial/spectral resolution and wavelength coverage of JWST /NIRSpec to study the 3.3 um neutral polycyclic aromatic hydrocarbon (PAH) grain emission on ~60 pc scales. We

Matthew T. Pisano, Connor J. Patterson, Oshani Seneviratne

Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models. Although numerous publicly available machine learning models exist, they are often unhosted, necessitating end-users to establish their computational infrastructure. Alternatively, the

Zheng Zhang, Zheng Ning, Chenliang Xu, Yapeng Tian

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downstream tasks, such as video retrieval, AR/VR, and accessibility, the performance and adoption of existing audio-visual models have been impeded by the availability of high-quality da

Elizabeth Gross, Robert Krone, Samuel Martin

Phylogenetic networks represent evolutionary histories of sets of taxa where horizontal evolution or hybridization has occurred. Placing a Markov model of evolution on a phylogenetic network gives a model that is particularly amenable to algebraic study by representing it as an algebraic variety. In this paper, we give a formula for the dimension of the vari

Alex Ushveridze

The quest to comprehend the origins of intelligence raises intriguing questions about the evolution of learning abilities in natural systems. Why do living organisms possess an inherent drive to acquire knowledge of the unknown? Is this motivation solely explicable through natural selection, favoring systems capable of learning due to their increased chances

Leo Schnitzspan, Mathias Kläui, Gerhard Jakob

In this work, the effect of electrical coupling on stochastic switching of two in-plane superparamagnetic tunnel junctions (SMTJs) is studied, using experimental measurements as well as simulations. The coupling mechanism relies on the spin-transfer-torque (STT) effect, which enables the manipulation of the state probability of an SMTJ. Through the investiga

Vivek Kumar, Sushmita Singh, Prayag Tiwari

Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word

Ramiro Cayuso

This study explores the gravitational collapse of a massless scalar field within Quadratic Gravity treated as a dimension-four operator Effective Field Theory extension to General Relativity. The additional degrees of freedom associated with the higher derivatives in this theory are removed by an Order Reduction approach, where the truncated expansion nature

Hamed Vakili, Moaz Ali, Mohamed Elekhtiar, Alexey A. Kovalev

We study phase-controlled planar Josephson junctions comprising a two-dimensional electron gas with strong spin-orbit coupling and d-wave superconductors, which have an advantage of high critical temperature. We show that a region between the two superconductors can be tuned into a topological state by the in-plane Zeeman field, and can host Majorana bound s