March 2024 arXiv papers — page 184

Spiridon Kasapis, Irina N. Kitiashvili, Paul Kosovich, Alexander G. Kosovichev

Prediction of the Solar Energetic Particle (SEP) events garner increasing interest as space missions extend beyond Earth's protective magnetosphere. These events, which are, in most cases, products of magnetic reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, sp

Z. Wu, T. I. Weinberger, A. J. Hickey, D. V. Chichinadze

Quantum critical phenomena are widely studied across various materials families, from high temperature superconductors to magnetic insulators. They occur when a thermodynamic phase transition is suppressed to zero temperature as a function of some tuning parameter such as pressure or magnetic field. This generally yields a point of instability - a so-called

Kseniia Kuvshinova, Olga Tsymboi, Alina Kostromina, Dmitry Simakov

The industry is rich in cases when we are required to make forecasting for large amounts of time series at once. However, we might be in a situation where we can not afford to train a separate model for each of them. Such issue in time series modeling remains without due attention. The remedy for this setting is the establishment of a foundation model. Such

Gustav Holzegel, Georgios Mavrogiannis, Renato Velozo Ruiz

We present short proofs of integrated local energy decay estimates on Schwarzschild, extremal Reissner-Nordstr\"om, and Schwarzschild-de Sitter spacetimes. The proofs employ novel global physical space multipliers, which, besides their remarkable simplicity, (a) are directly derivable from the geodesic flow, (b) do not require decomposition into spherical ha

Roozbeh Bassirian, Kunal Marwaha

We prove that QMA where the verifier may also make a single non-collapsing measurement is equal to NEXP, resolving an open question of Aaronson. We show this is a corollary to a modified proof of QMA+ = NEXP [arXiv:2306.13247]. At the core of many results inspired by Blier and Tapp [arXiv:0709.0738] is an unphysical property testing problem deciding whether

Bardia Yousefi, Mélina Khansari, Ryan Trask, Patrick Tallon

The isometric mapping method employs the shortest path algorithm to estimate the Euclidean distance between points on High dimensional (HD) manifolds. This may not be sufficient for weakly uniformed HD data as it could lead to overestimating distances between far neighboring points, resulting in inconsistencies between the intrinsic (local) and extrinsic (gl

Guillaume Olikier, Irène Waldspurger

This paper considers the projected gradient descent (PGD) algorithm for the problem of minimizing a continuously differentiable function on a nonempty closed subset of a Euclidean vector space. Without further assumptions, this problem is intractable and algorithms are only expected to find a stationary point. PGD generates a sequence in the set whose accumu

Yingbo Hua, Ahmed Maksud

Revealing expressions of secret-key capacity (SKC) based on data sets from Gaussian MIMO channel probing are presented. It is shown that Maurer's upper and lower bounds on SKC coincide when the used data sets are produced from one-way channel probing. As channel coherence time increases, SKC in bits per probing channel use is always lower bounded by a positi

Xueqing Wu, Rui Zheng, Jingzhen Sha, Te-Lin Wu

Data analysis is a crucial analytical process to generate in-depth studies and conclusive insights to comprehensively answer a given user query for tabular data. In this work, we aim to propose new resources and benchmarks to inspire future research on this crucial yet challenging and under-explored task. However, collecting data analysis annotations curated

Amariah Condon, Bailey Buscarino, Eric Moch, William J. Sehnert

Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a varie

Omran Abbas, Anas Chaaban, Loic Markley

A reconfigurable intelligent surface (RIS) is an essential component in the architecture of the next generation of wireless communication systems. An RIS is deployed to provide a controllability to the multi-path environment between the transmitter and the receiver, which becomes critical when the line-of-sight signal between them is blocked. In this work, w

Tarun Chitra, Kshitij Kulkarni, Mallesh Pai, Theo Diamandis

Mechanisms for decentralized finance on blockchains suffer from various problems, including suboptimal price execution for users, latency, and a worse user experience compared to their centralized counterparts. Recently, off-chain marketplaces, colloquially called `intent markets,' have been proposed as a solution to these problems. In these markets, agents

Isao Ishikawa, Yuka Hashimoto, Masahiro Ikeda, Yoshinobu Kawahara

This paper presents a novel approach for estimating the Koopman operator defined on a reproducing kernel Hilbert space (RKHS) and its spectra. We propose an estimation method, what we call Jet Extended Dynamic Mode Decomposition (JetEDMD), leveraging the intrinsic structure of RKHS and the geometric notion known as jets to enhance the estimation of the Koopm

Pierre Brugiere, Gabriel Turinici

The transformer models have been extensively used with good results in a wide area of machine learning applications including Large Language Models and image generation. Here, we inquire on the applicability of this approach to financial time series. We first describe the dataset construction for two prototypical situations: a mean reverting synthetic Ornste

Sebastien Baur, Zaid Nabulsi, Wei-Hung Weng, Jake Garrison

Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in the medical machine learning community. The existing deep learning systems for health acoustics are often narrowly trained and evaluated on a single task, which is limited by data

Kenta Kojin

In this paper, we will characterize those sets, over which every irreducible complete Nevanlinna--Pick space enjoys that its multiplier and supremum norms coincide. Moreover, we will prove that, if there exists an irreducible complete Nevanlinna--Pick space of holomorphic functions on a reduced complex space $X$ whose multiplier algebra is isometrically equa

Cem Komurcuoglu, Alan C. West, Alexander Urban

The phase transition layered Li$_{0.5}$NiO$_2$ to spinel Li(NiO$_2$)$_2$ is a potential degradation pathway in LiNiO$_2$-based lithium-ion battery cathodes. We investigated the mechanism of this phase transformation from first principles. Consistent with experimental observations reported in the literature, our results indicate a high energy barrier for the

B. Q. Song, J. D. H. Smith, J. Wang

Position operator $\hat{r}$ appears as $i{\partial_p}$ in wave mechanics, while its matrix form is well known diverging in diagonals, causing serious difficulties in basis transformation, observable yielding, etc. We aim to find a convergent $r$-matrix (CRM) to improve the existing divergent $r$-matrix (DRM), and investigate its influence at both the concept

Jad El Karchi, Hanze Chen, Ali TehraniJamsaz, Ali Jannesari

Identifying errors in parallel MPI programs is a challenging task. Despite the growing number of verification tools, debugging parallel programs remains a significant challenge. This paper is the first to utilize embedding and deep learning graph neural networks (GNNs) to tackle the issue of identifying bugs in MPI programs. Specifically, we have designed an

Ratimanasee Sahu, Mohit Sharma, Peter Schall, Sarika Maitra Bhattacharyya

Amorphous solids relax via slow molecular rearrangement induced by thermal fluctuations or applied stress. Although microscopic structural signatures predicting these structural relaxations have long been sought, a physically motivated structural measure relevant to diverse systems remains elusive. Here, we introduce a structural order parameter derived from

IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams

We report on a measurement of astrophysical tau neutrinos with 9.7 years of IceCube data. Using convolutional neural networks trained on images derived from simulated events, seven candidate $\nu_\tau$ events were found with visible energies ranging from roughly 20 TeV to 1 PeV and a median expected parent $\nu_\tau$ energy of about 200 TeV. Considering back

Jason Saied, Jeffrey Marshall, Namit Anand, Shon Grabbe

Quantum networking at many scales will be critical to future quantum technologies and experiments on quantum systems. Photonic links enable quantum networking. They will connect co-located quantum processors to enable large-scale quantum computers, provide links between distant quantum computers to support distributed, delegated, and blind quantum computing,

Gianluca Baldassarre, Richard J. Duro, Emilio Cartoni, Mehdi Khamassi

The rapid advancement of artificial intelligence is enabling the development of increasingly autonomous robots capable of operating beyond engineered factory settings and into the unstructured environments of human life. This shift raises a critical autonomy-alignment problem: how to ensure that a robot's autonomous learning focuses on acquiring knowledge an

Chen Zheng, Ke Sun, Hang Wu, Chenguang Xi

In recent advancements in Conversational Large Language Models (LLMs), a concerning trend has emerged, showing that many new base LLMs experience a knowledge reduction in their foundational capabilities following Supervised Fine-Tuning (SFT). This process often leads to issues such as forgetting or a decrease in the base model's abilities. Moreover, fine-tun

Ali Asadi, Amintor Dusko, Chae-Yeun Park, Vincent Michaud-Rioux

We introduce PennyLane's Lightning suite, a collection of high-performance state-vector simulators targeting CPU, GPU, and HPC-native architectures and workloads. Quantum applications such as QAOA, VQE, and synthetic workloads are implemented to demonstrate the supported classical computing architectures and showcase the scale of problems that can be simulat

Nailong Wu

A new type of interferometer, called High Order Harmonic Interferometer (HOHI), was proposed by Wu (1996) for imaging by aperture synthesis radio telescope. Its feasibility was proven by theoretical analysis. Before putting HOHI in practical use, computer simulation is a necessary intermediate stage. In this paper the theoretical analysis is reviewed. Then,

Vineet Dawara, Koushik Viswanathan

The transition from static to dynamic friction when an elastic body is slid over another is now known to result from the motion of interface rupture fronts. These fronts may be either crack-like or pulse-like, with the latter involving reattachment in the wake of the front. How and why these fronts occur remains a subject of active theoretical and experiment

Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das

In recent years, large language models (LLMs) have become increasingly prevalent, offering remarkable text generation capabilities. However, a pressing challenge is their tendency to make confidently wrong predictions, highlighting the critical need for uncertainty quantification (UQ) in LLMs. While previous works have mainly focused on addressing aleatoric

Tamas G. Molnar, Suresh K. Kannan, James Cunningham, Kyle Dunlap

Safety-critical failures often have fatal consequences in aerospace control. Control systems on aircraft, therefore, must ensure the strict satisfaction of safety constraints, preferably with formal guarantees of safe behavior. This paper establishes the safety-critical control of fixed-wing aircraft in collision avoidance and geofencing tasks. A control fra

Brian Block, Stephanie Stockar

In this paper, an extension of a linear control design for hyperbolic linear partial differential equations is presented for a first-order traffic flow model. Starting from the Lighthill-Whitham-Richards (LWR) model, variable speed limit control (VSL) is applied through a modification of Greenshield's equilibrium flow model. Then, an optimal linear quadratic

Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus

Differentially private (DP) machine learning is considered the gold-standard solution for training a model from sensitive data while still preserving privacy. However, a major barrier to achieving this ideal is its sub-optimal privacy-accuracy trade-off, which is particularly visible in DP representation learning. Specifically, it has been shown that under m

Asifur Rahman, Samuele Ferracin, Sujata Tank, Chris Zhang

In this study, we present a concept of morphing structure -- featuring an arch mounted on a compliant base -- that can be reconfigured via snap-through buckling and leverages bistability to retain its morphed shape. We show that one-dimensional arrays of such units yield beam-like structures that, upon localized snapping, can attain multiple, morphologically

Yu Wang, Wen Qu

Given that natural language serves as the primary conduit for expressing thoughts and emotions, text analysis has become a key technique in psychological research. It enables the extraction of valuable insights from natural language, facilitating endeavors like personality traits assessment, mental health monitoring, and sentiment analysis in interpersonal c

Chaim Goodman-Strauss

We describe a new method of weaving a model of the Klein quartic, a highly symmetric, but abstract genus-3 surface akin to a platonic polyhedron, with negatively-curved geometry. The Klein quartic cannot be realized in its fully symmetric form in three-dimensional space, but this model exhibits the most rigid symmetry that is possible. With remarkably little

Yifan Song, Da Yin, Xiang Yue, Jie Huang

Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory optimization approach, referred to as ETO. This learning method is designed to enhance the performance of open LLM agents. Contrary to previous studies that exclusively train on successful expert trajecto

Aghil Alaee, Pei-Ken Hung, Marcus Khuri

A classic result of Shi and Tam states that a 2-sphere of positive Gauss and mean curvature bounding a compact 3-manifold with nonnegative scalar curvature, must have total mean curvature not greater than that of the isometric embedding into Euclidean 3-space, with equality only for domains in this reference manifold. We generalize this result to 2-tori of G

Yilun Wang, Shengjie Guo

In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. This model offers improved handling of complex, nonlinear, and noisy market conditions compared to traditional static factor models. The advancement of machine learning, especially in dealing with nonlinear data, has furt

Manon Blanc, Olivier Bournez

Models of computations over the integers are equivalent from a computability and complexity theory point of view by the Church-Turing thesis. It is not possible to unify discrete-time models over the reals. The situation is unclear but simpler for continuous-time models, as there is a unifying mathematical model provided by ordinary differential equations (O

Ladislav Mišta, Matouš Mišta, Zdeněk Hradil

The quantum rotor represents, after the harmonic oscillator, the next obvious quantum system to study the complementary pair of variables: the angular momentum and the unitary shift operator in angular momentum. Proper quantification of uncertainties and the incompatibility of these two operators are thus essential for applications of rotor-like quantum syst

Krzysztof Skos, Albert Diez Comas, Josep Miquel Jornet, Pawel Kulakowski

Nano-machines circulating inside the human body, collecting data on tissue conditions, represent a vital part of next-generation medical diagnostic systems. However, for these devices to operate effectively, they need to relay not only their medical measurements but also their positions. This paper introduces a novel localization method for in-body nano-mach

Stephanie Day, Jin K. Hwang, Tracy Arner, Danielle McNamara

The purpose of this feasibility study was to examine the potential impact of reading digital interactive e-books on essential skills that support reading comprehension with third-fifth grade students. Students read two e-Books that taught word learning and comprehension monitoring strategies in the service of learning difficult vocabulary and targeted scienc

Huy Le, Philipp Schillinger, Miroslav Gabriel, Alexander Qualmann

The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scenarios. These scenarios may involve previously unseen objects, variations in camera perspectives, and bin configurations, among other factors. In this paper, we introduce a novel ap

Joachim Dahl Thomsen, Yaxian Wang, Henrik Flyvbjerg, Eugene Park

Diffusion processes govern fundamental phenomena such as phase transformations, doping, and intercalation in van der Waals (vdW) bonded materials. Here, we quantify the diffusion dynamics of W atoms by visualizing the motion of individual atoms at three different vdW interfaces: BN/vacuum, BN/BN, and BN/WSe2, by recording scanning transmission electron micro

Joshua Auld, Jamie Cook, Krishna Murthy Gurumurthy, Nazmul Khan

Rapid technological progress and innovation in the areas of vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated numerous questions and studies regarding the potential impact on key performance and equity metrics. Several

Alina Iacob

We prove that the class of Gorenstein injective modules, $\mathcal{GI}$, is special precovering if and only if it is covering if and only if it is closed under direct limits. This adds to the list of examples that support Enochs' conjecture:\\ "Every covering class of modules is closed under direct limits".\\ We also give a characterization of the rings for

Uri Andrews, Matthew Harrison-Trainor, Meng-Che "Turbo" Ho

We answer two questions on the complexities of decision problems of groups, each related to a classical result. First, C. Miller characterized the complexity of the isomorphism problem for finitely presented groups in 1971. We do the same for the isomorphism problem for recursively presented groups. Second, the fact that every Turing degree appears as the de

Steven A. Balbus

We show by direct calculation that the common Equivalence Principle explanation for why gravity must deflect light is quantitatively incorrect by a factor of three in Schwarzschild geometry. It is therefore possible, at least as a matter of principle, to tell the difference between local acceleration and a true gravitational field by measuring the local defl

Litao Yan, Alyssa Hwang, Zhiyuan Wu, Andrew Head

Programming assistants have reshaped the experience of programming into one where programmers spend less time writing and more time critically examining code. In this paper, we explore how programming assistants can be extended to accelerate the inspection of generated code. We introduce an extension to the programming assistant called Ivie, or instantly vis

Hong Chen, Siddhartha Sahi

Interpolation polynomials were introduced by Knop--Sahi in type $A$, and Okounkov in type $BC$. They are inhomogeneous polynomials whose top terms are Jack and Macdonald polynomials. Thus the expansion coefficients for the product of two interpolation polynomials, known as Littlewood--Richardson coefficients, generalize the corresponding coefficients for Jac

Daniel Hey, Daniel Huber, Joel Ong, Dennis Stello

The TESS mission has provided a wealth of asteroseismic data for solar-like oscillators. However, these data are subject to varying cadences, large gaps, and unequal sampling, which complicates analysis in the frequency domain. One solution is to model the oscillations in the time domain by treating them as stochastically damped simple harmonic oscillators t

Meng-Che "Turbo" Ho, Julia Knight, Russell Miller

Let $\operatorname{TFAb}_r$ be the class of torsion-free abelian groups of rank $r$, and let $\operatorname{FD}_r$ be the class of fields of characteristic $0$ and transcendence degree~$r$. We compare these classes using various notions. Considering Scott complexity of the structures in the classes and the complexity of the isomorphism relations on the class

Bhanu Prasad Bhowmik, Christopher Ness

We study the absorbing state transition in particulate systems under spatially inhomogeneous driving using a modified random organization model. For smoothly varying driving the steady state results map onto the homogeneous absorbing state phase diagram, with the position of the boundary between absorbing and diffusive states being insensitive to the driving

Zhenyun Xie, David Sánchez-Jácome, Luis Torrijos-Morán, Daniel Pérez-López

Data center networks are experiencing unprecedented exponential growth, mostly driven by the continuous computing demands in machine learning and artificial intelligence algorithms. Within this realm, optical networking offers numerous advantages, including low latency, energy efficiency, and bandwidth transparency, positioning it as a compelling alternative

Oluwami Dosunmu-Ogunbi, Aayushi Shrivastava, Jessy W Grizzle

This work explores an innovative algorithm designed to enhance the mobility of underactuated bipedal robots across challenging terrains, especially when navigating through spaces with constrained opportunities for foot support, like steps or stairs. By combining ankle torque with a refined angular momentum-based linear inverted pendulum model (ALIP), our met

Romain Tessera, Matthew Tointon

We give sharp bounds in Breuillard, Green and Tao's finitary version of Gromov's theorem on groups with polynomial growth. Precisely, we show that for every non-negative integer d there exists $c=c(d)>0$ such that if $G$ is a group with finite symmetric generating set $S$ containing the identity and $|S^n|\le cn^{d+1}|S|$ for some positive integer $n$ then t

Yash Akhauri, Mohamed S. Abdelfattah

Predictor-based methods have substantially enhanced Neural Architecture Search (NAS) optimization. The efficacy of these predictors is largely influenced by the method of encoding neural network architectures. While traditional encodings used an adjacency matrix describing the graph structure of a neural network, novel encodings embrace a variety of approach

Luis Carlos F. Latoski, W. G. Dantas, Jeferson J. Arenzon

We consider the Persistent Voter model (PVM), a variant of the Voter model (VM) that includes transient, dynamically-induced zealots. Due to peer reinforcement, the internal confidence $\eta_i$ of a normal voter increases by steps of size $\Delta\eta$ and once it gets above a given threshold, it becomes a zealot. Then, its opinion remains frozen until enough

Rahul Patel, Elias B. Khalil, David Bergman

Decision diagrams (DDs) have emerged as a state-of-the-art method for exact multiobjective integer linear programming. When the DD is too large to fit into memory or the decision-maker prefers a fast approximation to the Pareto frontier, the complete DD must be restricted to a subset of its states (or nodes). We introduce new node-selection heuristics for co

Themba Hodge, Eric Mascot, Dan Crawford, Stephan Rachel

Qubits built out of Majorana zero modes (MZMs) have long been theorized as a potential pathway toward fault-tolerant topological quantum computation. Almost unavoidable in these processes is Majorana wavefunction overlap, known as hybridization, which arise throughout the process when Majorana modes get close to each other. This breaks the ground state degen

Brian Lehmann, David McKinnon, Matthew Satriano

Let $X$ be a smooth projective algebraic variety over a number field $k$ and $P$ in $X(k)$. In 2007, the second author conjectured that, in a precise sense, if rational points on $X$ are dense enough, then the best rational approximations to $P$ must lie on a curve. We present a strategy for deducing a slightly weaker conjecture from Vojta's conjecture, and

Philippe Di Francesco, Hieu Trung Vu

We study the T-system of type $A_\infty$, also known as the octahedron recurrence/equation, viewed as a 2+1-dimensional discrete evolution equation. Generalizing the study of [P. Di Francesco and R. Soto-Garrido. Arctic curves of the octahedron equation. J. Phys. A, 47(28):285204, 34, 2014], we consider initial data along parallel ``slanted" planes perpendic

Steven Robert Lippold

Permutation Matrices are a well known class of matrices which encode the elements of the symmetric group on $d$ elements as a square $d\times d$ matrix. Motivated by [4], we define a similar class of matrices which are a generalization of Permutation Matrices. We give explicit formulas for the multiplication of these matrices. Lastly, we discuss the spectral

Taylor Roper, Harri Hakula, Troy Butler

This work presents novel extensions for combining two frameworks for quantifying both aleatoric (i.e., irreducible) and epistemic (i.e., reducible) sources of uncertainties in the modeling of engineered systems. The data-consistent (DC) framework poses an inverse problem and solution for quantifying aleatoric uncertainties in terms of pullback and push-forwa

Nicolas P. Alves, Wesley F. Alves, André C. A. Siqueira, Naudson L. L. Matias

We report the first experimental demonstration of the replica symmetry breaking (RSB) phenomenon in a fiber laser system supporting standard mode-locking (SML) regime. Though theoretically predicted, this photonic glassy phase remained experimentally undisclosed so far. We employ an ytterbium-based mode-locked fiber laser with a very rich phase diagram. Two

Aritra Mitra

We study the finite-time convergence of TD learning with linear function approximation under Markovian sampling. Existing proofs for this setting either assume a projection step in the algorithm to simplify the analysis, or require a fairly intricate argument to ensure stability of the iterates. We ask: \textit{Is it possible to retain the simplicity of a pr

Zixuan Liu, Xiaolin Sun, Zizhan Zheng

The rapidly increasing capabilities of large language models (LLMs) raise an urgent need to align AI systems with diverse human preferences to simultaneously enhance their usefulness and safety, despite the often conflicting nature of these goals. To address this important problem, a promising approach is to enforce a safety constraint at the fine-tuning sta

Krishnapriya Vishnubhotla, Adam Hammond, Graeme Hirst, Saif M. Mohammad

Stories are rich in the emotions they exhibit in their narratives and evoke in the readers. The emotional journeys of the various characters within a story are central to their appeal. Computational analysis of the emotions of novels, however, has rarely examined the variation in the emotional trajectories of the different characters within them, instead con

Sahan Ahmad, Gabriel Trahan, Aminul Islam

Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in computer vision tasks such as image classification, detection, segmentation, and medical image analysis. In general, an arbitrary number of epochs is used to train such neural networks. In a single epoch, the entire training data -- divided by batch size -- are fed to the netwo

Amit Das, Mostafa Rahgouy, Dongji Feng, Zheng Zhang

The widespread presence of hateful languages on social media has resulted in adverse effects on societal well-being. As a result, addressing this issue with high priority has become very important. Hate speech or offensive languages exist in both explicit and implicit forms, with the latter being more challenging to detect. Current research in this domain en

Eric Ling, Annachiara Piubello

The inflationary scenario, which states that the early universe underwent a brief but dramatic period of accelerated spatial expansion, has become the current paradigm of early universe cosmology. Although inflationary cosmology has its many successes, it does not (as of yet) have the status of an established physical theory. In this paper, we provide mathem

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

The IceCube Neutrino Observatory relies on an array of photomultiplier tubes to detect Cherenkov light produced by charged particles in the South Pole ice. IceCube data analyses depend on an in-depth characterization of the glacial ice, and on novel approaches in event reconstruction that utilize fast approximations of photoelectron yields. Here, a more accu

Iryna Hartsock, Ghulam Rasool

Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual and textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on models designed for medical report generation and visual question answering (VQA). We provide background on NLP an

Tingwei Meng, Siting Liu, Wuchen Li, Stanley Osher

Optimal control problems are crucial in various domains, including path planning, robotics, and humanoid control, demonstrating their broad applicability. The connection between optimal control and Hamilton-Jacobi (HJ) partial differential equations (PDEs) underscores the need for solving HJ PDEs to address these control problems effectively. While numerous

Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler

An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predicti

F. R. Kamal Youssef, Isabelle A. Grenier

A fairly uniform cosmic-ray (CR) distribution is observed near the Sun, except in the nearby Eridu cloud, which shows an unexplained 30-50% deficit in GeV to TeV CR flux. To explore the origin of this deficit, we studied the Reticulum cloud, which shares notable traits with Eridu: a comparable distance in the low-density region of the Local Valley and a fila

Gregory Taroyan

In the present paper, we give a complete description of the group of holomorphic automorphisms of the Cox construction of a simplicial fan equivariant with respect to a large enough connected complex Lie subgroup of the large torus acting on the Cox construction. We then apply this description to find the groups of holomorphic automorphisms of rational momen

Ernold Thompson, Andres Goza

This study computes the optimal normal actuation on the surface of a NACA0012 airfoil at an angle of attack of 15{\deg} and a Reynolds number of Re = 1000, using costs defined for minimal drag and maximal lift. To allow for a general actuation profile, non-zero actuation is permissible on both the suction and pressure surfaces. This approach of optimal actua

Tongjiang Wang, Leon Ofman, Stephen J. Bradshaw

Recent observations of reflected propagating and standing slow-mode waves in hot flaring coronal loops have spurred our investigation into their underlying excitation and damping mechanisms. To understand these processes, we conduct 2.5D magnetohydrodynamic (MHD) simulations using an arcade active region model that includes a hot and dense loop. Our simulati

Changjiang Yi, Xiaolong Feng, Nitesh Kumar, Claudia Felser

Compounds with a kagome lattice exhibit intriguing properties and the charge density wave (CDW) adds an additional layer of interest to research on them. In this study, we investigate the temperature and magnetic field dependent electrical properties under a chemical substitution and hydrostatic pressure of ScV6Sn6, a non-magnetic charge density wave (CDW) c

Camilo Gómez Araya, David Gontier, Hanne Van Den Bosch

We study one- and two-dimensional periodic tight-binding models under the presence of a potential that grows to infinity in one direction, hence preventing the particles to escape in this direction (the soft wall). We prove that a spectral flow appears in these corresponding edge models, as the wall is shifted. We identity this flow as a number of Bloch band

Isabella Masina, Mariano Quiros

Extrapolating the Standard Model Higgs potential at high energies, we study the barrier between the electroweak and Planck scale minima. The barrier arises by taking the central values of the relevant experimental inputs, that is the strong coupling constant and the top quark and Higgs masses. We then extend the Standard Model by including a non-minimal coup

Amir Barda, Vladimir G. Kim, Noam Aigerman, Amit H. Bermano

The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive c

Simon Vrhovec, Igor Bernik, Damjan Fujs, Damjan Vavpotič

This work reports on a cross-sectional study on device proficiency, support availability and cybersecurity competence of older adult users of smartphones and/or tablets. Results indicate that cybersecurity competence is associated with both device proficiency and support availability although the variance explained is relatively low. There were no difference

Alexander Clifton, Nika Salia

In this work, we study how far one can deviate from optimal behavior when embedding a planar graph. For a planar graph $G$, we say that a plane subgraph $H\subseteq G$ is a \textit{plane-saturated subgraph} if adding any edge (possibly with new vertices) to $H$ would either violate planarity or make the resulting graph no longer a subgraph of $G$. For a plan

Cole A Tamburri, Maria D Kazachenko, Adam F Kowalski

We develop the impulsiveness index, a new classification system for solar flares using the SDO/EVE 304 {\AA} Sun-as-a-star light curves. Impulsiveness classifies events based on the duration and intensity of the initial high-energy deposition of energy into the chromosphere. In stellar flare U-band light curves, Kowalski et al. (2013) found that impulsivenes

S. D. Reyes-Reyes, A. M. Stutz, S. T. Megeath, Fengwei Xu

While intensively studied, it remains unclear how the star formation (SF) in Infrared Dark Clouds (IRDCs) compares to that of nearby clouds. We study G351.77-0.53 (henceforth G351), a cluster-forming filamentary IRDC. We begin by characterizing its young stellar object (YSO) content. Based on the average parallax of likely members, we obtain a Gaia distance

ATLAS Collaboration

The search for supersymmetry with the ATLAS experiment at the CERN Large Hadron Collider intensified after the discovery of the Higgs boson in 2012. The search programme expanded in both breadth and depth, profiting from the increased integrated luminosity and higher centre-of-mass energy of Run 2, and gaining new sensitivity to unexplored areas of supersymm

Asad Anjum, Yuting Li, Noelle Law, M Charity

This paper studies how large language models (LLMs) can act as effective, high-level creative collaborators and ``muses'' for game design. We model the design of this study after the exercises artists use by looking at amorphous ink splotches for creative inspiration. Our goal is to determine whether AI-assistance can improve, hinder, or provide an alternati

Edward H. Hellen

A nonlinear RLC resonator is investigated experimentally and numerically using bifurcation analysis. The nonlinearity is due to the parallel combination of a semiconductor rectifier diode and a fixed capacitor. The diode's junction capacitance, diffusion capacitance, and DC current-voltage relation each contribute to the nonlinearity. The closely related RL-

Lauro B. Braz, George B. Martins, Luis G. G. V. Dias da Silva

Multiband superconductors have featured one of the main challenges to achieve a comprehensive understanding of unconventional superconductivity. Here, the multiband character is studied separately as orbital and sublattice degrees of freedom, as they have different effects for the superconducting and magnetic or charge orders. We build on the framework of th

Yi Zhou, Jih-Liang Hsieh, Ilker Oguz, Mustafa Yildirim

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic coun

Adil Soubki, John Murzaku, Arash Yousefi Jordehi, Peter Zeng

Evaluating the theory of mind (ToM) capabilities of language models (LMs) has recently received a great deal of attention. However, many existing benchmarks rely on synthetic data, which risks misaligning the resulting experiments with human behavior. We introduce the first ToM dataset based on naturally occurring spoken dialogs, Common-ToM, and show that LM

Eugene T. Hamzezadeh, John G. Rogers, Neil T. Dantam, Andrew J. Petruska

While maximizing line-of-sight coverage of specific regions or agents in the environment is a well-explored path planning objective, the converse problem of minimizing exposure to the entire environment during navigation is especially interesting in the context of minimizing detection risk. This work demonstrates that minimizing line-of-sight exposure to the

Mahmoud Afifi, Zhenhua Hu, Liang Liang

High dynamic range (HDR) imaging involves capturing a series of frames of the same scene, each with different exposure settings, to broaden the dynamic range of light. This can be achieved through burst capturing or using staggered HDR sensors that capture long and short exposures simultaneously in the camera image signal processor (ISP). Within camera ISP p

Erik Berglund, Jiaojiao Zhang, Mikael Johansson

We propose a novel algorithm, termed soft quasi-Newton (soft QN), for optimization in the presence of bounded noise. Traditional quasi-Newton algorithms are vulnerable to such perturbations. To develop a more robust quasi-Newton method, we replace the secant condition in the matrix optimization problem for the Hessian update with a penalty term in its object

Maria Andrade, Halyson Baltazar, Christopher Queiroz

In this paper, we deduce a Bochner-type identity for compact gradient Einstein-type manifolds with boundary. As consequence, we are able to show a rigidity result for Einstein-type manifolds assuming the parallel Ricci curvature condition. Moreover, we provide a condition on the norm of the gradient of the potential function in order to classify such structu

Yash Akhauri, Mohamed S. Abdelfattah

Efficient deployment of neural networks (NN) requires the co-optimization of accuracy and latency. For example, hardware-aware neural architecture search has been used to automatically find NN architectures that satisfy a latency constraint on a specific hardware device. Central to these search algorithms is a prediction model that is designed to provide a h

Naif Mehanna, Walter Rudametkin, Pierre Laperdrix, Antoine Vastel

Free-proxies have been widespread since the early days of the Web, helping users bypass geo-blocked content and conceal their IP addresses. Various proxy providers promise faster Internet or increased privacy while advertising their lists comprised of hundreds of readily available free proxies. However, while paid proxy services advertise the support of encr

Camilo Calixto, Camilo Jaimes, Matheus D. Soldatelli, Simon K. Warfield

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and th

Alex Kolmus, Justin Janquart, Tomasz Baka, Twan van Laarhoven

Modern simulation-based inference techniques use neural networks to solve inverse problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a neural network parameterizes a distribution to approximate the posterior. This approach is particularly advantageous for tackling low-latency or high-volume inverse problems. However, the