April 2024 arXiv papers — page 45

Liang Qu, Cunze Wang, Yuhui Shi

The application of deep learning techniques to medical problems has garnered widespread research interest in recent years, such as applying convolutional neural networks to medical image classification tasks. However, data in the medical field is often highly private, preventing different hospitals from sharing data to train an accurate model. Federated lear

Rui Wang, Xiaoqing Bai, Shengquan Huang, Shoupu Wei

As power systems become more complex and uncertain, low-voltage distribution networks face numerous challenges, including three-phase imbalances caused by asymmetrical loads and distributed energy resources. We propose a robust stochastic optimization (RSO) based optimal power flow (OPF) control method for three-phase, four-wire low-voltage distribution netw

Sarah Keren, Chaimaa Essayeh, Stefano V. Albrecht, Thomas Morstyn

The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving na

Horatiu Cirstea, Markus A. Kuppe, Benjamin Loillier, Stephan Merz

TLA+ is a formal language for specifying systems, including distributed algorithms, that is supported by powerful verification tools. In this work we present a framework for relating traces of distributed programs to high-level specifications written in TLA+. The problem is reduced to a constrained model checking problem, realized using the TLC model checker

Gwantae Kim, Bokyeung Lee, Donghyeon Kim, Hanseok Ko

In recent times, there has been a growing interest in utilizing personalized large models on low-spec devices, such as mobile and CPU-only devices. However, utilizing a personalized large model in the on-device is inefficient, and sometimes limited due to computational cost. To tackle the problem, this paper presents the weights separation method to minimize

Xiaolin Zhang, Chenghao Chen, Kailun Qin, Yuxuan Wang

The signing key protection of Certificate Authorities (CAs) remains a critical challenge in PKI. Traditional approaches struggle to eliminate the risk of key exposure due to those (un)intentional human errors. This long-standing dilemma motivates us to propose Armored Core, a novel PKI security extension using the trusted binding of Physically Unclonable Fun

Sina Sanjari, Naci Saldi, Serdar Yüksel

We study a class of stochastic exchangeable teams comprising a finite number of decision makers (DMs) as well as their mean-field limits involving infinite numbers of DMs. In the finite population regime, we study exchangeable teams under the centralized information structure. For the infinite population setting, we study exchangeable teams under the decentr

Jiaxin Zhuang, Linshan Wu, Qiong Wang, Peng Fei

The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised Learning (SSL) for 3D medical image analysis. Masked AutoEncoder (MAE) for feature pre-training can further unleash the potential of ViT on various medical vision tasks. However, due to large spatial sizes with much higher dimensions of 3D medical images, the lack of hier

Jinil Lee, Wooyeong Song, Donghwa Lee, Yosep Kim

Variational quantum eigensolver (VQE), which combines quantum systems with classical computational power, has been arisen as a promising candidate for near-term quantum computing applications. However, the experimental resources such as the number of measurements to implement VQE rapidly increases as the Hamiltonian problem size grows. Applying entanglement

Hossein Salami, Brandye Smith-Goettler, Vijay Yadav

General purpose Large Language Models (LLM) such as the Generative Pretrained Transformer (GPT) and Large Language Model Meta AI (LLaMA) have attracted much attention in recent years. There is strong evidence that these models can perform remarkably well in various natural language processing tasks. However, how to leverage them to approach domain-specific u

Hanzhang Wang, Gowtham Kumar Tangirala, Gilkara Pranav Naidu, Charles Mayville

We present a machine learning-based anomaly detection product, AI Detect and Respond (AIDR), that monitors Walmart's business and system health in real-time. During the validation over 3 months, the product served predictions from over 3000 models to more than 25 application, platform, and operation teams, covering 63\% of major incidents and reducing the me

Angel Santarossa, Olfa D'Angelo, Achim Sack, Thorsten Pöschel

Granular grippers can manipulate a wide variety of objects, but need to be pressed on the object to conform to it. If the object is placed on unstable ground, e.g., on sand or water, this step might cause the object to sink or move away from the gripper, hindering proper operation. We introduce a granular gripper with an integrated suction cup, where suction

Yue Lyu, Pengcheng An, Huan Zhang, Keiko Katsuragawa

Children with autism spectrum disorder (ASD) experience challenges in grasping social-emotional cues, which can result in difficulties in recognizing emotions and understanding and responding to social interactions. Social-emotional intervention is an effective method to improve emotional understanding and facial expression recognition among individuals with

Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong

Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models. A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence, as well as task outcomes and costs. The emergence of data-centric AI represents a shift in the landscape from

Chase Urasaki, Frances Zhu, Michael Bottom, Miguel Nunes

The Hyperspectral Thermal Imager (HyTI) is a technology demonstration mission that will obtain high spatial, spectral, and temporal resolution long-wave infrared images of Earth's surface from a 6U cubesat. HyTI science requires that the pointing accuracy of the optical axis shall not exceed 2.89 arcsec over the 0.5 ms integration time due to microvibration

Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng

Despite the recent progress in long-context language models, it remains elusive how transformer-based models exhibit the capability to retrieve relevant information from arbitrary locations within the long context. This paper aims to address this question. Our systematic investigation across a wide spectrum of models reveals that a special type of attention

Peng-Cheng Hang

The matrix analogues of Laplace's method and Watson's lemma are derived via the approach described by Williams and Wong [J. Approx. Theory 24 (4) (1974), 378-384]. Some examples are also given.

Alexander C. Murph, G. Casey Gibson, Lauren J. Beesley, Nishant Panda

Infectious disease modeling and forecasting have played a key role in helping assess and respond to epidemics and pandemics. Recent work has leveraged data on disease peak infection and peak hospital incidence to fit compartmental models for the purpose of forecasting and describing the dynamics of a disease outbreak. Incorporating these data can greatly sta

M. V. Dolgopolik

The goal of this note is to point out an erroneous formula for the generalised Hessian of the least squares associated with a system of linear inequalities, that was given in the paper "A finite Newton method for classification" by O.L. Mangasarian (Optim. Methods Softw. 17: 913--929, 2002) and reproduced multiple times in other publications. We also provide

Nicholas C. Orndorff, John T. Hwang

To fulfill the vision for large-scale urban air mobility, air-taxi concepts must be carefully designed and optimized for their intended mission. Proposed air-taxi missions contain dynamic segments that are dominated by nonlinear dynamics. One such segment is the transition to and from hover and cruise that occurs at the start and end of the mission. Because

Jasper Roe, Mike Perkins

The availability of software which can produce convincing yet synthetic media poses both threats and benefits to tertiary education globally. While other forms of synthetic media exist, this study focuses on deepfakes, which are advanced Generative AI (GenAI) fakes of real people. This conceptual paper assesses the current literature on deepfakes across mult

Mike Perkins, Leon Furze, Jasper Roe, Jason MacVaugh

Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this

Somak Basistha, Soumyajit Mandal, John Jesudasan, Vivas Bagwe

We investigate the low-frequency electrodynamics in the vortex state of two type-II superconducting films, namely, a moderate-to-strongly pinned Niobium Nitride (NbN) and a very weakly pinned amorphous Molybdenum Germanium (a-MoGe). We employ a two-coil mutual inductance technique to extract the complex penetration depth, $\tildeλ$. The sample response is st

Michel J. G. Weber

In this paper we study the divisibility and primality properties of the Bernoulli random walk. We improve or extend some of our divisibility results to wide classes of iid or independent non iid random walks. We also obtain new primality results for the Rademacher random walk. We study the value distribution of divisors of the random walk in the Cramér model

Adam Dearing, Jason R. Blevins

We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for dynamic discrete choice games of incomplete information. k-EPL considers the joint behavior of multiple players simultaneously, as opposed to individual responses to other agents' equilibrium play. This, in addition to reframing the problem from conditional choice probability (

Cristóbal Corral, Daniel Flores-Alfonso, Gastón Giribet, Julio Oliva

We study self-gravitating solutions of 3-dimensional massive gravity coupled to Yang-Mills-Chern-Simons gauge theory. Among these, there is a family of asymptotically Warped-Anti de Sitter black holes that come to generalize previous solutions found in the literature and studied in the context of WAdS$_3$/CFT$_2$. We also present self-gravitating solutions t

Chulan Kwon, Ju-Yeon Gyhm

We investigate dynamics of a small quantum system open to a bath with thermostat. We introduce another bath, called super bath, weakly coupled with the bath to provide it with thermostat, which has either the Lindblad or Redfield type. We treat the interaction between the system and bath via a rigorous perturbation theory. Due to the thermostat, the bath beh

Erik Mainellis, Bouzid Mosbahi, Ahmed Zahari

The paper concerns the cohomology of (multiplicative) BiHom-associative trialgebras. We first detail the correspondence between central extensions and second cohomology. This is followed by a general cohomology theory that unifies those of BiHom-associative algebras and associative trialgebras. Finally, we introduce one-parameter formal deformations and clas

Dumitru Astefanesei, Romina Ballesteros, Paulina Cabrera, Gonzálo Casanova

We use the quasilocal formalism of Brown and York, supplemented with counterterms, to investigate the thermodynamics of asymptotically flat black holes. We consider two families of exact regular black hole solutions, which are thermodynamically stable. The first one consists of four-dimensional static charged hairy black holes in extended supergravity. The s

Nirupan Ananthamurugan, Dat Duong, Philip George, Ankita Gupta

Summarizing comparative opinions about entities (e.g., hotels, phones) from a set of source reviews, often referred to as contrastive summarization, can considerably aid users in decision making. However, reliably measuring the contrastiveness of the output summaries without relying on human evaluations remains an open problem. Prior work has proposed token-

Jun Huang, Yan Liu

This paper proposes a new gradient-based XAI method called Guided AbsoluteGrad for saliency map explanations. We utilize both positive and negative gradient magnitudes and employ gradient variance to distinguish the important areas for noise deduction. We also introduce a novel evaluation metric named ReCover And Predict (RCAP), which considers the Localizat

Colin Vendromin, Yan Liu, Zhenshan Yang, John E. Sipe

We present a multimode theory of squeezed state generation in resonant systems valid for arbitrary pump power and including pump depletion. The Hamiltonian is written in terms of asymptotic-in and -out fields from scattering theory, capable of describing a general interaction. As an example we consider the lossy generation of a highly squeezed state by an ef

Jan N. Fuhg, Asghar Jadoon, Oliver Weeger, D. Thomas Seidl

Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive constraints, which also allows them to generalize well to unseen data. In this work, we extend a polyconvex hyperelastic ne

R. Aros, F. Bugini, D. E. Díaz, C. Núñez-Barra

We elucidate the dependence of the Casimir energy on the trace anomaly coefficients for a six-dimensional CFT on $R\times S^5$. This extends the universal dependence on the central charge in 2D and the relation by Cappelli and Coste in 4D, unveiling the role of the trivial total derivatives in the anomaly that renders the Casimir energy scheme dependent. We

Daniel L. Vigil, Ting Ge, Michael Rubinstein, Thomas C. O'Connor

Polymers are an effective test-bed for studying topological constraints in condensed matter due to a wide array of synthetically-available chain topologies. When linear and ring polymers are blended together, emergent rheological properties are observed as the blend can be more viscous than either of the individual components. This emergent behavior arises s

Chetan Gupta, Janne H. Korhonen, Jan Studený, Jukka Suomela

In prior work, Gupta et al. (SPAA 2022) presented a distributed algorithm for multiplying sparse $n \times n$ matrices, using $n$ computers. They assumed that the input matrices are uniformly sparse--there are at most $d$ non-zeros in each row and column--and the task is to compute a uniformly sparse part of the product matrix. The sparsity structure is glob

Rayner Kay Jin Tan, Dilruk Perera, Salomi Arasaratnam, Yudara Kularathne

Artificial Intelligence applications have shown promise in the management of pandemics and have been widely used to assist the identification, classification, and diagnosis of medical images. In response to the global outbreak of Monkeypox (Mpox), the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases to develop a digital

S. Baid, A. Sáiz, L. Lamata, P. Pérez-Fernández

We investigate the Extended Lipkin Model (ELM), whose phase diagram mirrors that of the Interacting Boson Approximation model (IBA). Unlike the standard Lipkin model, the ELM (as the IBA) features both first- and second-order quantum shape phase transitions depending on the model parameters. Our goal is to implement the ELM on a quantum platform, leveraging

Shili Sheng, Pian Yu, David Parker, Marta Kwiatkowska

Online planning for partially observable Markov decision processes (POMDPs) provides efficient techniques for robot decision-making under uncertainty. However, existing methods fall short of preventing safety violations in dynamic environments. This work presents a novel safe POMDP online planning approach that maximizes expected returns while providing prob

Bahman Moraffah

The idea of the inheritance of biological processes, such as the developmental process or the life cycle of an organism, has been discussed in the biology literature, but formal mathematical descriptions and plausible data analysis frameworks are lacking. We introduce an extension of the nested Dirichlet Process (nDP) to a multiscale model to aid in understa

Tomohiro Asano

We construct partial symplectic quasi-states on a cotangent bundle with the use of microlocal sheaf theory. We also give criteria and characterization for heaviness/superheaviness with respect to the partial symplectic quasi-state.

Christoph Mordasini, Remo Burn

The goal of planet formation as a field of study is not only to provide the understanding of how planets come into existence. It is also an interdisciplinary bridge which links astronomy to geology and mineralogy. Recent observations of young stars accompanied by their protoplanetary disks (Manara et al. 2022) provide direct insights into the conditions at w

Marcin Bienkowski, Jarosław Byrka, Łukasz Jeż

In the online disjoint set covers problem, the edges of a hypergraph are revealed online, and the goal is to partition them into a maximum number of disjoint set covers. That is, n nodes of a hypergraph are given at the beginning, and then a sequence of hyperedges (subsets of [n]) is presented to an algorithm. For each hyperedge, an online algorithm must ass

Mostafa Tanhayi Ahari, Yaroslav Tserkovnyak

In superconductors that lack inversion symmetry, a supercurrent flow can lead to nondissipative magnetoelectric effects. We offer a straightforward formalism to obtain a supercurrent-induced magnetization in superconductors with broken inversion symmetry, which may have orbital, layer, sublattice, or valley degrees of freedom, multiband noncentrosymmetric su

Yi Li, Yunan Wu, Aggelos K. Katsaggelos

The advancement of The Laser Interferometer Gravitational-Wave Observatory (LIGO) has significantly enhanced the feasibility and reliability of gravitational wave detection. However, LIGO's high sensitivity makes it susceptible to transient noises known as glitches, which necessitate effective differentiation from real gravitational wave signals. Traditional

Ricardo J. C. Rosado, Adriano Cherchiglia, Marcos Sampaio, Brigitte Hiller

We examine the subtleties of regularization schemes in four-dimensional space ($4S$), related in particular to the introduction of the $\gamma_5$ matrix. To illustrate we use a "Bumblebee" model featuring dynamically induced Lorentz symmetry violation. The analysis centers on how different regularization methods affect the solutions to the gap equation in th

Xi Cen

Let $(X,d,\mu)$ is a space of homogeneous type, we establish a new class of fractional-type variable weights $A_{p(\cdot), q(\cdot)}(X)$. Then, we get the new weighted strong-type and weak-type characterizations for fractional maximal operators $M_\eta$ on weighted variable Lebesgue spaces over $(X,d,\mu)$. This study generalizes the results by Cruz-Uribe-Fi

Shashi Kant Gupta, Aditya Basu, Mauro Nievas, Jerrin Thomas

Clinical trial matching is the task of identifying trials for which patients may be potentially eligible. Typically, this task is labor-intensive and requires detailed verification of patient electronic health records (EHRs) against the stringent inclusion and exclusion criteria of clinical trials. This process is manual, time-intensive, and challenging to s

Eduardo Serrano-Ensástiga, Chryssomalis Chryssomalakos, John Martin

The efficiency of a quantum metrology protocol can be significantly diminished by the interaction of the system with its environment, leading to a loss of purity and, as a result, a mixed state for the probing system. An example is the measurement of a magnetic field through the rotation of a spin that is subject to decoherence due to its coupling to a surro

Ajit C. Balram, Nicolas Regnault

We consider the fractional quantum Hall effect (FQHE) at the filling factor $8/17$, where signatures of incompressibility have been observed in the zeroth Landau level of bilayer graphene. We propose an Abelian state described by the ``$\overline{(8/3)}\bar{2}1^{3}$" parton wave function, where a parton itself forms an FQHE state. This state is topologically

Md Shajalal, Alexander Boden, Gunnar Stevens, Delong Du

Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about

Varsha Gupta

In this paper, we develop an alternate formulation of Asymmetric Traveling Salesman Problem (ATSP). The equivalent problem is to find the zeros of a holomorphic cusp form on the principal congruence subgroup, $\Gamma(4) $. The resultant Poincar{\'e} series gives a cusp form whose interior zeros are in bijection with the arc that constitute optimal Hamiltonia

Stephen Whitelam

We show that the time-resolved dynamics of an underdamped harmonic oscillator can be used to do multifunctional computation, performing distinct computations at distinct times within a single dynamical trajectory. We consider the amplitude of an oscillator whose inputs influence its frequency. The activity of the oscillator at fixed time is a nonmonotonic fu

Bela Bajnok

Spherical t-designs are Chebyshev-type averaging sets on the d-sphere S^d which are exact for polynomials of degree at most t. This concept was introduced in 1977 by Delsarte, Goethals, and Seidel, who also found the minimum possible size of such designs, in particular, that the number of points in a 3-design on S^d must be at least n>=2d+2. In this paper we

S. Stagraczynski, P. Balaz, M. Jafari, J. Barnas

We analyze magnetic properties of monolayers and bilayers of chromium trihalides, CrI$_3$, in two different stacking configurations: AA and rhombohedral ones. Our main focus is on the corresponding Curie temperatures, hysteresis curves, equilibrium spin structures, and spin wave excitations. To obtain all these magnetic characteristic, we employ the atomisti

Arka Karmakar, Abdullah Al-Mahboob, Natalia Zawadzka, Mateusz Raczyński

Heterostructures (HSs) formed by the transition-metal dichalcogenides (TMDCs) materials have shown great promise in next-generation optoelectronic and photonic applications. An artificially twisted HS, allows us to manipulate the optical, and electronic properties. With this work, we introduce the understanding of the complex energy transfer (ET) process gov

Plamen Stefanov

We study the scattering rigidity problem in Lorentzian geometry: recovery of a Lorentzian metric from the scattering relation known on a lateral timelike boundary. We show that one can recover the jet of the metric up to a gauge transformation near a lightlike strictly convex point. Assuming that the metric is real analytic, we show that one can recover the

Varsha Gupta

An optimization framework is presented for minimizing the energy functional developed around a generalized equation governing physical systems such as fluid dynamics, particle transport, phase transition, and other related systems. The convexity of the energy functional is investigated to derive the necessary conditions for a smooth and global optimum soluti

Evgeniia Filimonova, Viktor Ivanov, Timur Shakirov

We suggest new modification (we call it a noise reduction procedure) for Steinhardt parameters which are often used for detecting crystalline structures in computer simulation of solids and soft matter systems. We have also developed a new methodology how to reconstruct "ideal" lattice structure in the whole simulation box that would be most close to a real

Sam Earle, Filippos Kokkinos, Yuhe Nie, Julian Togelius

Procedural Content Generation (PCG) algorithms enable the automatic generation of complex and diverse artifacts. However, they don't provide high-level control over the generated content and typically require domain expertise. In contrast, text-to-3D methods allow users to specify desired characteristics in natural language, offering a high amount of flexibi

Ahmed Errahmani, Amine Bouali, Safae Dahmani, Imad El Bojaddaini

In this paper, we examine the acceleration of the Universe's expansion in $F(R,T)$ gravity, where $R$ denotes the Ricci scalar and $T$ the trace of energy-momentum tensor. Indeed, the unknown nature of the source controlling this acceleration in general relativity leads scientists to investigate its properties by means of some alternative theories to general

Federico Ferrari

We investigate the effect of axial compression on the optimal design of columns, for the maximization of the fundamental vibration frequency. The compression may be due to a force at the columns' tip or to a load distributed along its axis, which may act either independently or simultaneously. We discuss the influence of these contributions on the optimality

Zhangying He, Thomas Nguyen, Tahereh Miari, Mehrdad Aliasgari

Artificial Intelligence (AI), with ChatGPT as a prominent example, has recently taken center stage in various domains including higher education, particularly in Computer Science and Engineering (CSE). The AI revolution brings both convenience and controversy, offering substantial benefits while lacking formal guidance on their application. The primary objec

Brahim Asfour, Aatifa Bargach, Yahya Ladghami, Ahmed Errahmani

In this paper, we study the post-inflationary era called reheating stage. For this purpose, we consider a model in which the inflaton is non-minimally coupled to the curvature within the hybrid metric-Palatini approach. Furthermore, to investigate the consistency of our results with the observational data, we relate reheating parameters to those of inflation

Daniel E. Killough

This study evaluates the usage of virtual reality (VR) technologies as a teaching tool in oral placement therapy, a subset of speech therapy. The researcher distributed instructional videos using traditional lecture and modified three-dimensional video to prompt responses. Data was gathered with a two-part Google Form: In "Section 1: Knowledge Test" particip

Mostafa Ameli, Sean Mcquade, Jonathan W. Lee, Matthew Bunting

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-t

Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang

This paper presents BattleAgent, an emulation system that combines the Large Vision-Language Model and Multi-agent System. This novel system aims to simulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time. It emulates both the decision-making processes of leaders and the viewpoints

Nicole Immorlica, Nicholas Wu, Brendan Lucier

We study the problem of a principal who wants to influence an agent's observable action, subject to an ex-post budget. The agent has a private type determining their cost function. This paper endogenizes the value of the resource driving incentives, which holds no inherent value but is restricted by finite availability. We characterize the optimal mechanism,

Stefano Buzzi, Carmen D'Andrea, Li Wang, Ahmet Hasim Gokceoglu

Cell-free massive MIMO (CF-mMIMO) systems represent a deeply investigated evolution from the conventional multicell co-located massive MIMO (MC-mMIMO) network deployments. Anticipating a gradual integration of CF-mMIMO systems alongside pre-existing MC-mMIMO network elements, this paper considers a scenario where both deployments coexist, in order to serve a

Anna M. Grudinina, Nina S. Voronova

Large exciton-polariton optical nonlinearities present a key mechanism for photonics-based communication, ultimately in the quantum regime. Enhanced nonlinear response from various materials hosting excitons and allowing for their strong coupling with light is therefore the topic of intense studies, both in theoretical and experimental domains. Reports on th

Steve B. Howell, Arturo O. Martinez, Douglas A. Hope, David R. Ciardi

We explore the possibility of detecting very faint, very close-in stellar companions using large aperture ground-based telescopes and the technique of optical speckle imaging. We examine the state of high angular resolution speckle imaging and contrast levels being achieved using current speckle cameras on the Gemini 8-m telescope. We then explore the use of

Randa Asa'd, S. Hernandez, Johina M. John, M. Alfaro-Cuello

We present the first application of the novel approach based on data-driven machine learning methods applied to Multi-Unit Spectroscopic Explorer (MUSE) field data to derive stellar abundances of star clusters. MUSE has been used to target more than 10,000 fields, and it is unique in its ability to study dense stellar fields such as stellar clusters providin

Daniela D. Doneva, Llibert Aresté Saló, Stoytcho S. Yazadjiev

Scalar-Gauss-Bonnet (sGB) gravity with an additional coupling between the scalar field and the Ricci scalar exhibits very interesting properties related to black hole stability, evasion of binary pulsar constraints, and general relativity as a late-time cosmology attractor. Furthermore, it was demonstrated that a spherically symmetric collapse is well-posed

Mebatsion S. Gebre, Rebecca K. Banner, Kisung Kang, Kejian Qu

Multiple recent studies have identified the metallic antiferromagnet Mn$_2$Au to be a candidate for spintronic applications due to apparent in-plane anisotropy, preserved magnetic properties above room temperature, and current-induced N\'eel vector switching. Crystal growth is complicated by the fact that Mn$_2$Au melts incongruently. We present a bismuth fl

Harrison Espino, Robert Bain, Jeffrey L. Krichmar

Mapping traversal costs in an environment and planning paths based on this map are important for autonomous navigation. We present a neurobotic navigation system that utilizes a Spiking Neural Network Wavefront Planner and E-prop learning to concurrently map and plan paths in a large and complex environment. We incorporate a novel method for mapping which, w

Yun Yue, Fangzhou Lin, Guanyi Mou, Ziming Zhang

In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision. While these methods have achieved state-of-the-art performance on various metric learning tasks using hyperbolic distance measurements, the underlying theoretical analysis supporting this superior performance remains under-exploited. In this stu

Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura

Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks. But, can they really "reason" over the natural language? This question has been receiving significant research attention and many reasoning skills such as commonsense, numerical, and qualitative have been studied. Howeve

P. Fierlinger, M. Holl, D. Milstead, V. Santoro

High-intensity neutron beams, such as those available at the European Spallation Source (ESS), provide new opportunities for fundamental discoveries. Here we discuss a novel Ramsey neutron-beam experiment to search for ultralight axion dark matter through its coupling to neutron spins, which would cause the neutron spins to rotate about the velocity of the n

Florian Daval

We study in an explicit manner the partial sums of the multiplicative inverse of the Riemann zeta function and its derivative.

Romero-Gómez, J., Reynier F. Peletier, J. A. L. Aguerri

Galaxies undergo processes throughout their lifetimes that ultimately lead to the expulsion of the gas and the cessation of the star-forming activity. This phenomenon commonly known as quenching, can be caused by environmental processes. For this we use the results of Romero-G\'omez et al. (2024), who analyzed galaxies from the SAMI-Fornax and ATLAS$^{3D}$ s

Yangchen Pan, Junfeng Wen, Chenjun Xiao, Philip Torr

In traditional statistical learning, data points are usually assumed to be independently and identically distributed (i.i.d.) following an unknown probability distribution. This paper presents a contrasting viewpoint, perceiving data points as interconnected and employing a Markov reward process (MRP) for data modeling. We reformulate the typical supervised

Jitai Yang, Ke Li, Jia Liu, Jia Nie

In condensed matter physics, particularly in perovskite materials, the rotational motion of molecules and ions is associated with important issues such as ion conduction mechanism. Constrained Molecular Dynamics (MD) simulations offer a means to separate translational, vibrational, and rotational motions, enabling the independent study of their effects. In t

Young Kyun Jang, Donghyun Kim, Zihang Meng, Dat Huynh

Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification. Current techniques rely on supervised learning for CIR models using labeled triplets of the reference image, text, target image. These specific triplets are not as commonly available as simple image-text pairs, limiting the widespread

Weizhi Tang, Vaishak Belle

Theory of Mind (ToM) refers to the ability of individuals to attribute mental states to others. While Large Language Models (LLMs) have shown some promise with ToM ability, they still struggle with complex ToM reasoning. Our approach leverages an external symbolic executor, specifically the SMCDEL model checker, and fine-tuning to improve the ToM reasoning a

Tsorng-Whay Pan, Ang Li, Shang-Huan Chiu

In this article, three-dimensional (3D) lid-driven flows in semicircular cavities are studied. The numerical solution of the Navier-Stokes equations modeling incompressible viscous fluid flow in cavities is obtained via a methodology combining a first-order accurate operator-splitting scheme, a fictitious domain formulation, and finite element space approxim

Zuoyong Zhang, Chuang Deng

Solute segregation along grain boundaries (GBs) profoundly affects their thermodynamic and kinetic behaviors in polycrystalline materials. Recently, the spectral approach has emerged as a powerful tool to predict GB segregation. However, previous GB segregation predictions using this method relied heavily on single-solute segregation energy spectrum without

Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes

Willems' fundamental lemma enables a trajectory-based characterization of linear systems through data-based Hankel matrices. However, in the presence of measurement noise, we ask: Is this noisy Hankel-based model expressive enough to re-identify itself? In other words, we study the output prediction accuracy from recursively applying the same persistently ex

Bela Bajnok

A brief overview and history of the American Mathematics Competitions.

Kaustubh Shivdikar, Nicolas Bohm Agostini, Malith Jayaweera, Gilbert Jonatan

Graph Neural Networks (GNNs) are emerging as a formidable tool for processing non-euclidean data across various domains, ranging from social network analysis to bioinformatics. Despite their effectiveness, their adoption has not been pervasive because of scalability challenges associated with large-scale graph datasets, particularly when leveraging message p

Utkarshani Jaimini, Cory Henson, Amit P. Sheth

Causal networks are useful in a wide variety of applications, from medical diagnosis to root-cause analysis in manufacturing. In practice, however, causal networks are often incomplete with missing causal relations. This paper presents a novel approach, called CausalLP, that formulates the issue of incomplete causal networks as a knowledge graph completion p

Lynnette Hui Xian Ng, Samantha C. Phillips, Kathleen M. Carley

Web 3.0 focuses on the decentralization of the internet and creating a system of interconnected and independent computers for improved privacy and security. We extend the idea of the decentralization of the web to the social media space: whereby we ask: in the context of the social media space, what does "decentralization" mean? Does decentralization of soci

Luke Jacobs, Mohamad Alipour, Adam Watts, Elahe Soltanaghai

Soil moisture sensing through biomass or vegetation canopy has challenged researchers, even those who use SAR sensors with penetration capabilities. This is mainly due to the imposed extra time and phase offsets on Radio Frequency (RF) signals as they travel through the canopy. These offsets depend on the vegetation canopy moisture and height, both of which

Raphael Jauberteau, Alessandro Tonello, Yifan Sun, Mario Zitelli

We study the diffraction of a particular class of beams, composed only by a combination of azimuthally invariant guided modes of an optical fiber. We demonstrate that such beams can be obtained by injecting a Gaussian beam in a small piece of silica graded-index multimode fiber. This minimalistic low-cost method is applied for improving the axial resolution

Arianna Saba, Alexandra Thompson, Kai Hou Yip, Sushuang Ma

We present a population study of 20 exoplanets, ranging from Neptune-like to inflated hot-Jupiter planets, observed during transit with the STIS and WFC3 instruments aboard the Hubble Space Telescope. To obtain spectral information from the near-UV to the near-infrared, we reanalysed sixteen WFC3 and over fifty STIS archival data sets with our dedicated HST

Maureen van den Berg, Liliana Rivera Sandoval, Craig O. Heinke, Haldan N. Cohn

We present simultaneous Chandra X-ray Observatory and Hubble Space Telescope observations of three certain (X5, X7, W37) and two likely (X4, W17) quiescent neutron-star low-mass X-ray binaries (qLMXBs) in the globular cluster 47 Tuc. We study these systems in the X-ray, optical and near-ultraviolet (NUV) using the simultaneous data and additional non-contemp

Ali Abbasi, Fan Dong, Xin Wang, Henry Leung

Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process. Cloud and edge servers hosting FL clients may exhibit diverse carbon footprints influenced by their geographical locations with varying power sources, offering opportunities to reduc

Simranjit Singh, Michael Fore, Dimitrios Stamoulis

Tool-augmented Large Language Models (LLMs) have shown impressive capabilities in remote sensing (RS) applications. However, existing benchmarks assume question-answering input templates over predefined image-text data pairs. These standalone instructions neglect the intricacies of realistic user-grounded tasks. Consider a geospatial analyst: they zoom in a

Tangrui Li, Jun Zhou

This paper develops an innovative method that enables neural networks to generate and utilize knowledge graphs, which describe their concept-level knowledge and optimize network parameters through alignment with human-provided knowledge. This research addresses a gap where traditionally, network-generated knowledge has been limited to applications in downstr

Kylie E. Hall, Jennifer C. Yee, In-Gu Shin, Hongjing Yang

The gravitational microlensing method of discovering exoplanets and multi-star systems can produce degenerate solutions, some of which require in-depth analysis to uncover. We propose a new parameter space that can be used to sample potential solutions more efficiently and is more robust at finding all degenerate solutions. We identified two new parameters,

Zhuoyuan Wang, Haoming Jing, Christian Kurniawan, Albert Chern

This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesimal time intervals may exhibit significant long-term risks due to cumulative uncertainties/r