March 2024 arXiv papers — page 87

Mario Bravo, Juan Pablo Contreras

We analyze the oracle complexity of the stochastic Halpern iteration with minibatch, where we aim to approximate fixed-points of nonexpansive and contractive operators in a normed finite-dimensional space. We show that if the underlying stochastic oracle has uniformly bounded variance, our method exhibits an overall oracle complexity of $\tilde{O}(\varepsilo

Wang Sang Koon, Houman Owhadi, Molei Tao, Tomohiro Yanao

We study the metastability, internal frequencies, activation mechanism, energy transfer, and the collective base-flipping in a mesoscopic DNA via resonance with specific electric fields. Our new mesoscopic DNA model takes into account not only the issues of helicity and the coupling of an electric field with the base dipole moments, but also includes environ

Evan Saraivanov, Kunhao Zhong, Vivian Miranda, Supranta S. Boruah

The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge to analyzing theoretical models of cosmology. Additionally, new mitigation techniques of baryonic effects, intrinsic al

Abdon Moutinho

We study the global dynamics of the collision of two solitons having the same mass for one-dimensional Nonlinear Schr\"odinger models with multi-power nonlinearity. For any natural number k, it is verified that if the incoming speed v between the two solitary waves is small enough, then, after the collision, the two solitons will move away with an outcoming

Indranil Nayak, Ananda Chakrabarty, Mrinal Kumar, Fernando Teixeira

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-o

Wen-Xiang Chen, Yao-Guang Zheng

In this article, we explore the RN black hole under f(R) gravity and its thermodynamic properties. We begin by examining the small fluctuations around the equilibrium state and summarizing the expression for the modified thermodynamic entropy of this black hole. Additionally, we delve into the geometric thermodynamics (GTD) of black holes and investigate the

Kostiantyn Lyman, Rounak Meyur, Bala Krishnamoorthy, Mahantesh Halappanavar

We study the problem of comparing a pair of geometric networks that may not be similarly defined, i.e., when they do not have one-to-one correspondences between their nodes and edges. Our motivating application is to compare power distribution networks of a region. Due to the lack of openly available power network datasets, researchers synthesize realistic n

Mark Freidlin, Leonid Koralov

We study diffusion processes in $\mathbb{R}^d$ that leave invariant a finite collection of manifolds (surfaces or points) in $\mathbb{R}^d$ and small perturbations of such processes. Assuming certain ergodic properties at and near the invariant surfaces, we describe the rate at which the process gets attracted to or repelled from the surface, based on the lo

Hai-Bin Zhang, Tai-Fu Feng

Basing on Mellin-Barnes representations and Miller's transformation, we present the Gel'fand-Kapranov-Zelevinsky (GKZ) hypergeometric systems of 4-loop vacuum Feynman integrals with arbitrary masses. Through the GKZ hypergeometric systems, the analytical hypergeometric solutions of 4-loop vacuum Feynman integrals with arbitrary masses can be obtained in neig

Aishwarya Bhaskaran, Ding Ma, Benoit Liquet, Angela Hong

Accelerated failure time (AFT) models are frequently used to model survival data, providing a direct quantification of the relationship between event times and covariates. These models allow for the acceleration or deceleration of failure times through a multiplicative factor that accounts for the effect of covariates. While existing literature provides nume

Mengzhou Li, Chuang Niu, Ge Wang, Maya R Amma

X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap i

Yuuya Chiba, Yasushi Yoneta

The eigenstate thermalization hypothesis (ETH) plays a major role in explaining thermalization of isolated quantum many-body systems. However, there has been no proof of the ETH in realistic systems due to the difficulty in the theoretical treatment of thermal energy eigenstates of nonintegrable systems. Here, we write down analytically thermal eigenstates o

Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi

Standard federated learning (FL) algorithms typically require multiple rounds of communication between the server and the clients, which has several drawbacks, including requiring constant network connectivity, repeated investment of computational resources, and susceptibility to privacy attacks. One-Shot FL is a new paradigm that aims to address this challe

Samuel Panitch, Sunghyuk Park

We construct the 3d quantum trace map, a homomorphism from the Kauffman bracket skein module of an ideally triangulated 3-manifold to its (square root) quantum gluing module, thereby giving a precise relationship between the two quantizations of the character variety of ideally triangulated 3-manifolds. This map, whose existence was conjectured earlier by Ag

Janina E. Schütte, Martin Eigel

To solve high-dimensional parameter-dependent partial differential equations (pPDEs), a neural network architecture is presented. It is constructed to map parameters of the model data to corresponding finite element solutions. To improve training efficiency and to enable control of the approximation error, the network mimics an adaptive finite element method

Hai-Liang Wu, Ning-Liu Wei, Yu-Bo Li

In this paper, we study some topics concerning the additive decompositions of the set $D_k$ of all $k$th power residues modulo a prime $p$. For example, given a positive integer $k\ge2$, we prove that $$\lim_{x\rightarrow+\infty}\frac{B(x)}{π(x)}=0,$$ where $π(x)$ is the number of primes $p\le x$ and $B(x)$ denotes the cardinality of the set $$\{p\le x: p\eq

Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto, Jean Paul Barddal

Systems and individuals produce data continuously. On the Internet, people share their knowledge, sentiments, and opinions, provide reviews about services and products, and so on. Automatically learning from these textual data can provide insights to organizations and institutions, thus preventing financial impacts, for example. To learn from textual data ov

Howard Zhang, Yunhao Ba, Ethan Yang, Rishi Upadhyay

This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023. The aim of this competition is to study the rainy weather phenomenon in real world scenarios, provide a novel real world rainy image dataset, and to spark innovative ideas that will further the development of single image deraining methods on

Anh Bui, Khanh Doan, Trung Le, Paul Montague

Diffusion models have demonstrated remarkable capability in generating high-quality visual content from textual descriptions. However, since these models are trained on large-scale internet data, they inevitably learn undesirable concepts, such as sensitive content, copyrighted material, and harmful or unethical elements. While previous works focus on perman

Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto, Jean Paul Barddal

The proliferation of textual data on the Internet presents a unique opportunity for institutions and companies to monitor public opinion about their services and products. Given the rapid generation of such data, the text stream mining setting, which handles sequentially arriving, potentially infinite text streams, is often more suitable than traditional bat

Marko Ljubotina, Elena Petrova, Norbert Schuch, Maksym Serbyn

The advancement of quantum simulators motivates the development of a theoretical framework to assist with efficient state preparation in quantum many-body systems. Generally, preparing a target entangled state via unitary evolution with time-dependent couplings is a challenging task and very little is known about the existence of solutions and their properti

Edward D. Weinberger

Standard information theory says nothing about how much meaning is conveyed by a message. We fill this gap with a quantitative definition of ``pragmatic information'', the amount of meaning in a message relevant to a particular decision, a definition that is close to, but not identical with previous definitions. Here, we posit that such a message upd

Manuel E. Segura, Pere Verges, Justin Tian Jin Chen, Ramesh Arangott

Alcohol consumption has a significant impact on individuals' health, with even more pronounced consequences when consumption becomes excessive. One approach to promoting healthier drinking habits is implementing just-in-time interventions, where timely notifications indicating intoxication are sent during heavy drinking episodes. However, the complexity or i

Tony Danjun Wang

We propose a novel method that leverages 3D information to automatically anonymize multi-view RGB-D video recordings of operating rooms (OR). Our anonymization method preserves the original data distribution by replacing the faces in each image with different faces so that the data remains suitable for further downstream tasks. In contrast to established ano

Paul H. Frampton, Thomas W. Kephart

The Amaterasu cosmic ray particle appears to have come from the direction of the local cosmic void. We take this as evidence that it is a magnetic monopole rather than a proton or nucleus. This in turn strongly suggests physics at high energy is described by a quiver gauge theory.

Michelle Blom, Ronal Singh, Tim Miller, Liz Sonenberg

Research in cognitive psychology has established that whether people prefer simpler explanations to complex ones is context dependent, but the question of `simple vs. complex' becomes critical when an artificial agent seeks to explain its decisions or predictions to humans. We present a model for abstracting causal reasoning chains for the purpose of explana

Dejan Grubisic, Chris Cummins, Volker Seeker, Hugh Leather

We introduce a novel paradigm in compiler optimization powered by Large Language Models with compiler feedback to optimize the code size of LLVM assembly. The model takes unoptimized LLVM IR as input and produces optimized IR, the best optimization passes, and instruction counts of both unoptimized and optimized IRs. Then we compile the input with generated

Zeliang Zhang, Jinyang Jiang, Zhuo Liu, Susan Liang

Efficient and biologically plausible alternatives to backpropagation in neural network training remain a challenge due to issues such as high computational complexity and additional assumptions about neural networks, which limit scalability to deeper networks. The likelihood ratio method offers a promising gradient estimation strategy but is constrained by s

Kit Joll, Philipp Schienbein, Kevin M. Rosso, Jochen Blumberger

The interaction of condensed phase systems with external electric fields is crucial in myriad processes in nature and technology ranging from the field-directed motion of cells (galvanotaxis), to energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give impor

Antonio Pittelli

We introduce supersymmetric indices for four-dimensional gauge theories defined on $\mathscr O \times S^1$, where $\mathscr O $ is a circle bundle over the weighted complex projective line informally known as spindle. Trivial fibrations yield a four-dimensional version of the spindle index, which we obtain by applying localization to partition functions of t

Mahdi Rezapour

In this study, we explore the application of transformer-based models for emotion classification on text data. We train and evaluate several pre-trained transformer models, on the Emotion dataset using different variants of transformers. The paper also analyzes some factors that in-fluence the performance of the model, such as the fine-tuning of the transfor

Xinhao Xiang, Simon Dräger, Jiawei Zhang

The accuracy-speed-memory trade-off is always the priority to consider for several computer vision perception tasks. Previous methods mainly focus on a single or small couple of these tasks, such as creating effective data augmentation, feature extractor, learning strategies, etc. These approaches, however, could be inherently task-specific: their proposed m

Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin

The rapid development of Chinese large language models (LLMs) poses big challenges for efficient LLM evaluation. While current initiatives have introduced new benchmarks or evaluation platforms for assessing Chinese LLMs, many of these focus primarily on capabilities, usually overlooking potential alignment and safety issues. To address this gap, we introduc

M. H. Rayment, S. D. Hogan

N$_2$ molecules traveling in pulsed supersonic beams have been excited from their ${\mathrm{X\,^1\Sigma_{\mathrm{g}}^+}}$ ground electronic state to long-lived Rydberg states with principal quantum numbers between 39 and 48 using a resonance-enhanced two-color three-photon excitation scheme. The Rydberg states populated had static electric dipole moments exc

Joana Cirici, Scott O. Wilson

We show that the de Rham complex of any almost Hermitian manifold carries a natural commutative $BV_\infty$-algebra structure satisfying the degeneration property. In the almost K\"ahler case, this recovers Koszul's BV-algebra, defined for any Poisson manifold. As a consequence, both the Dolbeault and the de Rham cohomologies of any compact Hermitian manifol

Youbang Sun, Zitao Li, Yaliang Li, Bolin Ding

Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency. LoRA injects a product of two trainable rank decomposition matrices over the top of each frozen pre-trained model module. However, when applied in the setting

Anil Kumar Sahoo, Richard Schwarzl, Markus S. Miettinen, Roland R. Netz

On the microscopic level, biological signal transmission relies on coordinated structural changes in allosteric proteins that involve sensor and effector modules. The timescales and microscopic details of signal transmission in proteins are often unclear, despite a plethora of structural information on signaling proteins. Based on linear-response theory, we

Ján Šubjak, David W. Latham, Samuel N. Quinn, Perry Berlind

The present study reports the confirmation of BD-14 3065b, a transiting planet/brown dwarf in a triple-star system, with a mass near the deuterium burning boundary. BD-14 3065b has the largest radius observed within the sample of giant planets and brown dwarfs around post-main-sequence stars. Its orbital period is 4.3 days, and it transits a subgiant F-type

Benjamín Ojeda-Magaña, Rubén Ruelas, José Guadalupe Robledo-Hernández, Víctor Manuel Rangel-Cobián

3D sensors, also known as RGB-D sensors, utilize depth images where each pixel measures the distance from the camera to objects, using principles like structured light or time-of-flight. Advances in artificial vision have led to affordable 3D cameras capable of real-time object detection without object movement, surpassing 2D cameras in information depth. Th

Armin Karamzade, Kyungmin Kim, Montek Kalsi, Roy Fox

In standard reinforcement learning settings, agents typically assume immediate feedback about the effects of their actions after taking them. However, in practice, this assumption may not hold true due to physical constraints and can significantly impact the performance of learning algorithms. In this paper, we address observation delays in partially observa

Chao Chen, Christian Wagner, Jonathan M. Garibaldi

Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI. While the applications of fuzzy systems are diverse, there has been comparatively little advancement in their design from a

Pere Verges, Igor Nunes, Mike Heddes, Tony Givargis

Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a pivotal role. An impo

Maximilian Frey, Nico Neuber, Sascha Sebastian Riegler, Antoine Cornet

Modern detector technology and highly brilliant fourth-generation synchrotrons allow to improve the temporal resolution in time-resolved diffraction studies. Profiting from this, we applied temperature scanning X-ray photon correlation spectroscopy (XPCS) to probe the dynamics of a Pt-based metallic glass former in the glass, glass transition region, and sup

EL-Houcine Ouali, Azeddine Baalal, Mohamed Berghout

In this paper, we introduce a new fractional Musielak-Sobolev space $Ws,{\Phi}x,y({\Omega})$ where ${\Omega}$ is an open subset in RN and we show some density properties of smooth and compactly supported functions in this space.

Mehdi Lejmi, Scott O. Wilson

This short note provides a symplectic analogue of Vaisman's theorem in complex geometry. Namely, for any compact symplectic manifold satisfying the hard Lefschetz condition in degree 1, every locally conformally symplectic structure is in fact globally conformally symplectic, whenever there is a mutually compatible almost complex structure.

Timothy M. Chan, Pingan Cheng, Da Wei Zheng

Polynomial partitioning techniques have recently led to improved geometric data structures for a variety of fundamental problems related to semialgebraic range searching and intersection searching in 3D and higher dimensions (e.g., see [Agarwal, Aronov, Ezra, and Zahl, SoCG 2019; Ezra and Sharir, SoCG 2021; Agarwal, Aronov, Ezra, Katz, and Sharir, SoCG 2022]

Zakir Deniz

A vertex coloring of a graph $G$ is called a $2$-distance coloring if any two vertices at a distance at most $2$ from each other receive different colors. Recently, Bousquet et al. (Discrete Mathematics, 346(4), 113288, 2023) proved that $2\Delta+7$ colors are sufficient for the $2$-distance coloring of planar graphs with maximum degree $\Delta\geq 9$. In th

Qirui Wu, Sonia Raychaudhuri, Daniel Ritchie, Manolis Savva

We introduce the Reality-linked 3D Scenes (R3DS) dataset of synthetic 3D scenes mirroring the real-world scene arrangements from Matterport3D panoramas. Compared to prior work, R3DS has more complete and densely populated scenes with objects linked to real-world observations in panoramas. R3DS also provides an object support hierarchy, and matching object se

Jorge A. Combi, Federico A. Fogantini, Enzo A. Saavedra, Gustavo E. Romero

We present an X-ray analysis of three different XMM-Newton observations together with simultaneous NICER and NuSTAR observations of the ultraluminous X-ray source NGC 4190 ULX-1. Our goal is to constrain the structure of the accretion disk and the geometrical properties of the source. We performed a temporal and spectral analyses in the 0.4--30 keV energy ra

Mesut Arslandok, Helen Caines, Marian Ivanov

Measuring the energy loss and mass of highly ionizing particles predicted by theories from beyond the Standard Model pose considerable challenges to conventional detection techniques. Such particles are predicted to experience energy loss to matter they pass through that exceeds the dynamic range specified for most readout chips, leading to saturation of the

M. J. Rossewij, E. M. Okkinga, H. M. Naqvi, R. G. E. Barthel

The ITS3 upgrade baseline design employs MAPS (Monolithic Active Pixel Sensor) in bent state. Bending experiments with the existing ITS2 MAPS (=Alpide chip) show it remains functional but with relative large analog supply current changes. It is shown that by the piezoresistive effect, rotation of current mirror FETs can be responsible which was confirmed aft

Maria Mahbub, Gregory M. Dams, Sudarshan Srinivasan, Caitlin Rizy

Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by American insurance providers, like the Internation

Adam Samorzewski, Adrian Kliks

The paper considers energy consumption in 5G cellular networks powered by Renewable Energy Sources (RESs) and equipped with Reconfigurable Intelligent Surfaces (RISs) and tethered Unmanned Aerial Vehicles (UAVs), acting as mobile access points. The study was focused on the energy side of the Radio Access Network (RAN) located in the city of Pozna\'n in Polan

Ulysse Gazin, Ruth Heller, Ariane Marandon, Etienne Roquain

In supervised learning, including regression and classification, conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider here the case where such prediction sets come after a selection process. The selection process requires that the selected prediction sets be `informative'

Elliot Murphy, Jill de Villiers, Sofia Lucero Morales

In this study we compared how well DALL-E 2 visually represented the meaning of linguistic prompts also given to young children in comprehension tests. Sentences representing fundamental components of grammatical knowledge were selected from assessment tests used with several hundred English-speaking children aged 2-7 years for whom we had collected original

Yitong Zheng, Dong Wang, Garrett Beeghly, Claudia Fischbach

Breast cancer invasion into adipose tissue strongly influences disease progression and metastasis. The degree of cancer cell invasion into adipose tissue depends on numerous biochemical and physical properties of cancer cells, adipocytes, and other key components of adipose tissue. We model breast cancer invasion into adipose tissue as a physical process by

G. Huang, X. -J. Wang, Y. Zhou

In this paper, we prove the long time regularity of the interface in the $p$-Gauss curvature flow with flat side in all dimensions for $p>\frac1n$. Here the interface is the boundary of the flat part in the flow. In dimension $2$, this problem was solved in \cite{DL2004} for $p=1$ and in \cite{KimLeeRhee2013} for $p\in(1/2,1)$. We utilize the duality method

Hamidreza Akbari, Pankaj K. Jha, Kristina Malinowski, Benjamin E. C. Koltenbah

We report on the quantum photon statistics of hexagonal boron nitride (h-BN) quantum emitters by analyzing the Mandel Q parameter. We have measured the Mandel Q parameter for h-BN quantum emitters under various temperatures and pump power excitation conditions. Under pulsed excitation we can achieve a Mandel Q of -0.002 and under continuous-wave (CW) excitat

Adam Samorzewski, Adrian Kliks

This paper presents the examination of the 5G cellular network aware of Renewable Energy Sources (RESs) and supported by Reconfigurable Intelligent Surfaces (RISs) and Unmanned Aerial Vehicles working as mobile access nodes. The investigations have been focused on the energy side of the Radio Access Network (RAN) placed within the area of the city of Poznan

Rachaell Nihalaani, Tushar Kataria, Jadie Adams, Shireen Y. Elhabian

Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data. Slice propagation has emerged as an self-supervised approach that leverages slice

Paolo Testolina, Michele Polese, Pedram Johari, Tommaso Melodia

Digital twins are now a staple of wireless networks design and evolution. Creating an accurate digital copy of a real system offers numerous opportunities to study and analyze its performance and issues. It also allows designing and testing new solutions in a risk-free environment, and applying them back to the real system after validation. A candidate techn

Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick

Verbal autopsies (VAs) are extensively used to investigate the population-level distributions of deaths by cause in low-resource settings without well-organized vital statistics systems. Computer-based methods are often adopted to assign causes of death to deceased individuals based on the interview responses of their family members or caregivers. In this ar

I. I. Klimovskikh, S. V. Eremeev, D. A. Estyunin, S. O. Filnov

Meeting of non-trivial topology with magnetism results in novel phases of matter, such as Quantum Anomalous Hall (QAH) or axion insulator phases. Even more exotic states with high and tunable Chern numbers are expected at the contact of intrinsic magnetic topological insulators (IMTIs) and 2D topological insulators (TIs).Here we synthesize a heterostructures

P. C. Malta, C. A. D. Zarro

We study the effects of a finite mass for the photon on its propagation in a weak gravitational field. In particular, we analyse the gravitational time delay, also known as the Shapiro effect. We work in isotropic coordinates in the weak-field limit and find that the mass-dependent corrections enhance the gravitational time delay. Doppler-tracking data from

Thanos Konstantinidis, Giorgos Iacovides, Mingxue Xu, Tony G. Constantinides

There are multiple sources of financial news online which influence market movements and trader's decisions. This highlights the need for accurate sentiment analysis, in addition to having appropriate algorithmic trading techniques, to arrive at better informed trading decisions. Standard lexicon based sentiment approaches have demonstrated their power in ai

Jiajun Liang, Yue Liu, Doudou Zhou, Sinian Zhang

Graphical models find numerous applications in biology, chemistry, sociology, neuroscience, etc. While substantial progress has been made in graph estimation, it remains largely unexplored how to select significant graph signals with uncertainty assessment, especially those graph features related to topological structures including cycles (i.e., wreaths), cl

Adam Samorzewski, Margot Deruyck, Adrian Kliks

In this work, the impact of using Renewable Energy Source (RES) generators in next-generation (5G) cellular systems on total power consumption (PC) has been investigated. The paper highlights the gain related to the use of photovoltaic (PV) panels and wind turbines (WTs) in the form of two factors - the average extension of battery lifetime (AEBL) powering a

Tao E. Li

Developing theoretical frameworks for vibrational strong coupling (VSC) beyond the single-mode approximation is crucial for a comprehensive understanding of experiments with planar Fabry-P\'erot cavities. Herein, a generalized cavity molecular dynamics (CavMD) scheme is developed to simulate VSC of a large ensemble of realistic molecules coupled to an arbitr

Christian I. Eze, Gerald Handler

The combined strength of asteroseismology and empirical stellar basic parameter determinations for in-depth asteroseismic analysis of massive pulsators in eclipsing binaries shows great potential for treating the challenging and mysterious discrepancies between observations and models of stellar structure and evolution of massive stars. This paper compiles a

Jonathan Michaux, Qingyi Chen, Challen Enninful Adu, Jinsun Liu

Generating receding-horizon motion trajectories for autonomous vehicles in real-time while also providing safety guarantees is challenging. This is because a future trajectory needs to be planned before the previously computed trajectory is completely executed. This becomes even more difficult if the trajectory is required to satisfy continuous-time collisio

Vivek Pandey, Arash Amini, Guangyi Liu, Ufuk Topcu

We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features by anticipating their importance in robots localization by simulating trajectories of robots over a predictio

Gregory Dexter, Christos Boutsikas, Linkai Ma, Ilse C. F. Ipsen

Motivated by the popularity of stochastic rounding in the context of machine learning and the training of large-scale deep neural network models, we consider stochastic nearness rounding of real matrices $\mathbf{A}$ with many more rows than columns. We provide novel theoretical evidence, supported by extensive experimental evaluation that, with high probabi

E. T. Jensen

Study of the near-UV photodissociation dynamics for monolayer (ML) quantities of CH$_3$I on thin films of a series of fluorobenzenes and benzene (1--25ML) grown on a Cu(100) substrate finds that in addition to gas-phase-like neutral photodissociation, CH$_3$I dissociation can be enhanced via photoabsorption in several of the thin films studied. Distinct CH$_

Pankaj K. Agarwal, Esther Ezra, Micha Sharir

Let $P$ be a set of $m$ points in ${\mathbb R}^2$, let $\Sigma$ be a set of $n$ semi-algebraic sets of constant complexity in ${\mathbb R}^2$, let $(S,+)$ be a semigroup, and let $w: P \rightarrow S$ be a weight function on the points of $P$. We describe a randomized algorithm for computing $w(P\cap\sigma)$ for every $\sigma\in\Sigma$ in overall expected tim

Kemal Atalar, Yannic Rath, Rachel Crespo-Otero, George H. Booth

We build on the concept of eigenvector continuation to develop an efficient multi-state method for the rigorous and smooth interpolation of a small training set of many-body wavefunctions through chemical space at mean-field cost. The inferred states are represented as variationally optimal linear combinations of the training states transferred between the m

Adam Samorzewski

The paper considers the characteristics of the energy budget for mobile base stations (BSs) in the form of Unmanned Aerial Vehicles (UAVs) equipped with Radio Frequency (RF) transceivers, Intelligent Reconfigurable Surfaces (IRSs), and Renewable Energy Sources (RESs). The obtained results highlight the benefits and challenges related to using the aforementio

Linus Nwankwo, Elmar Rueckert

In this paper, we extended the method proposed in [21] to enable humans to interact naturally with autonomous agents through vocal and textual conversations. Our extended method exploits the inherent capabilities of pre-trained large language models (LLMs), multimodal visual language models (VLMs), and speech recognition (SR) models to decode the high-level

Sergei Artemov

For Hilbert, the consistency of a formal theory T is an infinite series of statements "D is free of contradictions" for each derivation D and a consistency proof is i) an operation that, given D, yields a proof that D is free of contradictions, and ii) a proof that (i) works for all inputs D. Hilbert's two-stage approach to proving consistency naturally gene

Prashant Sharma

We develop a general transfer-matrix formalism for determining the growth rate of the Rayleigh-Taylor instability in a fluid system with spatially varying density and viscosity. We use this formalism to analytically and numerically treat the case of a stratified heterogeneous fluid. We introduce the inviscid-flow approximation in our transfer-matrix formalis

Marc Basquens, Antonio Lasanta, Emanuel Mompó, Valle Varo

We examine relativistic diffusion through the frame and observer bundles associated with a Lorentzian manifold $(M,g)$. Our focus is on spacetimes with a non-trivial isometry group, and we detail the conditions required to find symmetric solutions of the relativistic diffusion equation. Additionally, we analyze the conservation laws associated with the prese

Reiko Yamada, Antoine Reserbat-Plantey, Eloy Piñol, Maciej Lewenstein

In quantum mechanics, the Wigner function $\rho_W(\textbf{r},\textbf{p})$ serves as a phase-space representation, capturing information about both the position $\textbf{r}$ and momentum $\textbf{p}$ of a quantum system. The Wigner function facilitates the calculation of expectation values of observables, examination of quantum system dynamics, and analysis o

Zhongzhichao Wan, Jieao Zhu, Linglong Dai

Electromagnetic information theory (EIT) is one of the emerging topics for 6G communication due to its potential to reveal the performance limit of wireless communication systems. For EIT, the research foundation is reasonable and accurate channel modeling. Existing channel modeling works for EIT in non-line-of-sight (NLoS) scenario focus on far-field modeli

Siddharth Joshi, Arnav Jain, Ali Payani, Baharan Mirzasoleiman

Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption datasets learns representations that can achieve remarkable zero-shot generalization. However, such models require a massive amount of pre-training data. Improving the quality of the pre-training data has been shown to be much more effective in improving CLIP's performance than incre

Jessica Werk, Kirill Tchernyshyov, Hannah Bish, Yong Zheng

We present a sample of 305 QSO candidates having $|b| < 30^{\circ}$, the majority with GALEX magnitudes NUV < 18.75. To generate this sample, we apply UV-IR color selection criteria to photometric data from the Ultraviolet GAlactic Plane Survey (UVGAPS) as part of GALEX-CAUSE, the Million Quasars Catalog, Gaia DR2, and Pan-STARRS DR1. 165 of these 305 candid

Mingyu Yu, Sahani Amaya Iddawela, Jiayang Wang, Maria Hilse

GaSe is an important member of the post-transition metal chalcogenide family and is an emerging two-dimensional (2D) semiconductor material. Because it is a van der Waals material, it can be fabricated into atomic-scale ultrathin films, making it suitable for the preparation of compact, heterostructure devices. In addition, GaSe possesses unusual optical and

Soham Mandal, Paul C. Duffell, Abigail Polin, Dan Milisavljevic

Supernova remnants (SNRs) exhibit varying degrees of anisotropy, which have been extensively modeled using numerical methods. We implement a technique to measure anisotropies in SNRs by calculating power spectra from their high-resolution images. To test this technique, we develop 3D hydrodynamical models of supernova remnants and generate synthetic x-ray im

Katharina Schmermbeck, Oliver Ott, Lennart Ralfs, Robert Weidner

People with disabilities often face discrimination and lack of access in all areas of society. While improving the affordability and appropriateness of assistive technologies can pave the way for easier participation and independence, awareness and acceptance of disability as part of society are inevitable. The presented regional initiative strives to tackle

Constantine Yannouleas, Uzi Landman

The few-body problem for $N=4$ fermionic charge carriers in a double-well moir\'{e} quantum dot (MQD), representing the first step in a bottom-up strategy to investigate formation of molecular supercrystals in transition metal dichalcogenide (TMD) moir\'e superlattices with integral fillings, $\nu > 1$, is solved exactly by employing large-scale exact-diagon

Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes

In robust decision-making under non-Bayesian uncertainty, different robust optimization criteria, such as maximin performance, minimax regret, and maximin ratio, have been proposed. In many problems, all three criteria are well-motivated and well-grounded from a decision-theoretic perspective, yet different criteria give different prescriptions. This paper i

Yang Zhang, Jason R. Trelewicz

This study explores the impact of temperature on defect dynamics in tungsten, emphasizing its application in nuclear fusion reactors as Plasma Facing Components (PFCs). Through atomistic simulations, the research elucidates the intricate interplay of defect production, annihilation, and redistribution under irradiation at room (300K) and elevated temperature

Yue Qiao, Ryan Miguel Gonzales, Edgar Choueiri

Data report for the 3D3A Lab Binaural Room Impulse Response (BRIR) Dataset (https://doi.org/10.34770/6gc9-5787).

Yang Zhang, Blas P. Uberuaga, Enrique Martinez Saez, Jason R. Trelewicz

This study explores the intricate interactions between grain boundaries (GBs) and irradiation-induced defects in nanocrystalline iron, highlighting the role of dopants like copper. Utilizing molecular dynamics simulations, the research delineates how GB properties, such as GB energy and defect formation energies, influence the formation and evolution of defe

Brown Zaz, Mikhail Nesterenko, Gokarna Sharma

We present BeRGeR: the first asynchronous geometric routing algorithm that guarantees delivery of a message despite a Byzantine fault without relying on cryptographic primitives or randomization. The communication graph is a planar embedding that remains three-connected if all edges intersecting the source-target line segment are removed. We prove the algori

Yang Zhang, Jason Trelewicz

This manuscript embarks on an inquiry into the influence of helium implantation on nanocrystalline tungsten, a contender for plasma-facing components (PFCs) in nuclear fusion reactors. The study underscores the inevitability of helium retention in tungsten due to the anticipated high flux of helium atoms from D-T fusion reactions in future reactors such as I

Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton

Many definitions of fairness or inequity involve unobservable causal quantities that cannot be directly estimated without strong assumptions. For instance, it is particularly difficult to estimate notions of fairness that rely on hard-to-measure concepts such as risk (e.g., quantifying whether patients at the same risk level have equal probability of treatme

Matthew R. Ziemann, Christopher A. Metzler

We propose a learning-based method for adaptively generating low probability of detection (LPD) radar waveforms that blend into their operating environment. Our waveforms are designed to follow a distribution that is indistinguishable from the ambient radio frequency (RF) background -- while still being effective at ranging and sensing. To do so, we use an u

Michael te Vrugt, Leon Topp, Raphael Wittkowski, Andreas Heuer

The hydrodynamics of thin films is typically described using phenomenological models whose connection to the microscopic particle dynamics is a subject of ongoing research. Existing methods based on density functional theory provide a good description of static thin films, but are not sufficient for understanding nonequilibrium dynamics. In this work, we pre

Maryam Saadat Safa, Patrick Schaumont, Shahin Tajik

Printed circuit boards (PCBs) are an integral part of electronic systems. Hence, verifying their physical integrity in the presence of supply chain attacks (e.g., tampering and counterfeiting) is of utmost importance. Recently, tamper detection techniques grounded in impedance characterization of PCB's Power Delivery Network (PDN) have gained prominence due

William P. Heath, Joaquin Carrasco

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closed-loop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system

Dayi Li, Ziang Zhang

Approximate Bayesian inference based on Laplace approximation and quadrature methods have become increasingly popular for their efficiency at fitting latent Gaussian models (LGM), which encompass popular models such as Bayesian generalized linear models, survival models, and spatio-temporal models. However, many useful models fall under the LGM framework onl