October 2024 arXiv papers — page 11

Alfonso Ballon-Bayona, Sean Bartz, Luis A. H. Mamani, Diego M. Rodrigues

We describe the chiral transition for the quark condensate and the melting of scalar and vector mesons in two-flavor holographic QCD. This is done by extending the improved holographic soft wall models proposed in [1] to finite temperature, by means of introducing an asymptotically AdS black brane. We find that the chiral transition is second order in the ch

Eugene Rotherham, Connor Casey, Eva Fernandez Rodriguez, Karen Wendy Vidaurre Torrez

This study discusses the current state of FSO technology, as well as global trends and developments in the industrial ecosystem to identify obstacles to the full realization of optical space-to-ground communication networks. Additionally, link performance and network availability trade-off studies are presented, comparing overall system performance between p

Douglas Q. Adams, Joshua Bautista, Marija Cuic, Adil Khawaja

A likelihood analysis of the observables in deeply virtual exclusive photoproduction off a proton target, $ep \rightarrow e' p' \gamma'$, is presented. Two processes contribute to the reaction: deeply virtual Compton scattering, where the photon is produced at the proton vertex, and the Bether-Heitler process, where the photon is radiated from the electron.

Xiao Hu, Pedro M. Lozano, Feng Ye, Qiang Li

The detection of static charge density waves (CDWs) in La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) with x $\sim$ 0.12 at relatively high temperatures has raised the question of what lattice feature pins the CDWs. Some recent structural studies have concluded that some form of monoclinic distortion, indicated by the appearance of certain weak Bragg peaks (type M peaks) at

Darian Rodriguez Vasquez, Afroditi Papadaki

Despite the impressive performance of large language models across various tasks, they often struggle with reasoning under negated statements. Negations are important in real-world applications as they encode negative polarity in verb phrases, clauses, or other expressions. Nevertheless, they are underrepresented in current benchmarks, which mainly include b

Daniel V. Cotton, Jeremy Bailey, Lucyna Kedziora-Chudczer, Kimberly Bott

Observations of polarization position angle ($\theta$) standards made from 2014 to 2023 with the High Precision Polarimetric Instrument (HIPPI) and other HIPPI-class polarimeters in both hemispheres are used to investigate their variability. Multi-band data were first used to thoroughly recalibrate the instrument performance by bench-marking against carefull

José-Fabian Villa-Vásquez, Marco Pedersoli

Unsupervised object discovery is commonly interpreted as the task of localizing and/or categorizing objects in visual data without the need for labeled examples. While current object recognition methods have proven highly effective for practical applications, the ongoing demand for annotated data in real-world scenarios drives research into unsupervised appr

Alim Ablat, Sayipjamal Dulat, Marco Guzzi, Tie-Jiun Hou

We discuss the impact of eligible top-quark pair production differential cross-section measurements at the LHC with a collision energy of 13 TeV on the parton distribution functions (PDFs) of the proton as well as the impact of approximate next-to-next-to-next-to-leading order (aN$^3$LO) QCD corrections combined with next-to-leading order (NLO) electroweak (

Sergey Ostapchenko

The physics content of the QGSJET-III Monte Carlo model of high energy hadronic interactions is briefly described. The predictions of the model for extensive air shower characteristics are presented in comparison to the corresponding results of other Monte Carlo generators of cosmic ray interactions. The results of a recent quantitative analysis of uncertain

Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan, Huaxia Wang

Modulation classification is a very challenging task since the signals intertwine with various ambient noises. Methods are required that can classify them without adding extra steps like denoising, which introduces computational complexity. In this study, we propose a vision transformer (ViT) based model named NMformer to predict the channel modulation image

Ollie Wiltshire, Seyed Amir Tafrishi

Reconfigurable robots are at the forefront of robotics innovation due to their unmatched versatility and adaptability in addressing various tasks through collaborative operations. This paper explores the design and implementation of a novel pendulum-based magnetic coupling system within a reconfigurable disk robot. Diverging from traditional designs, this sy

Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, Idan Szpektor

Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved at processing long inputs, MD contexts still present unique difficulties, including management of inter-document dependencies, redundancy, and incoherent structures. To address thi

Tristan Trim, Triston Grayston

This paper explores the mechanistic interpretability of reinforcement learning (RL) agents through an analysis of a neural network trained on procedural maze environments. By dissecting the network's inner workings, we identified fundamental features like maze walls and pathways, forming the basis of the model's decision-making process. A significant observa

Paulo Custodio, Cristian R. Ghezzi, Nadja S. Magalhae, Carlos Frajuca

In inflationary cosmology, the form of the potential is still an open problem. In this work, second-order effects of the inflationary potential are evaluated and related to the known formula for the primordial perturbations at a wide range of scales. We found effects that may help to unravel the unknown inflationary potential form and impose new constraints

Omar Montasser, Han Shao, Emmanuel Abbe

Learning with identical train and test distributions has been extensively investigated both practically and theoretically. Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps. We

Edwin Barnes, Michael B. Bennett, Alexandra Boltasseva, Victoria Borish

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum educat

Laurence Boxer

We continue a discussion of published assertions that are incorrect, incorrectly proven, or trivial, in the theory of fixed points in digital topology.

Carolina Melo

Snake graphs and their perfect matchings play a key role in the description of cluster variables of cluster algebras associated to surfaces. In this paper, we introduce triangular snake graphs and establish a bijection between their routes (non-intersecting lattice paths), perfect matchings of their underlying snake graphs, and tilings. As an application, we

Marcin Bilski, Wojciech Kucharz

Given a finite simplicial complex $\mathcal{K}$ in $\mathbb{R}^n$ and a real algebraic variety $Y,$ by a $\mathcal{K}$-regular map $|\mathcal{K}|\rightarrow Y$ we mean a continuous map whose restriction to every simplex in $\mathcal{K}$ is a regular map. A simplified version of our main result says that if $Y$ is a uniformly retract rational variety and if $

Oleg Chalykh, Bradley Ryan

This paper studies the spherical subalgebra of the double affine Hecke algebra of type $C^\vee C_n$ and relates it, at the classical level $q = 1$, to a certain character variety of the four-punctured Riemann sphere. This establishes a conjecture from math.QA/0504089. As a by-product, we find a completed phase space for the trigonometric van Diejen system, e

Jacob Migdail, Stephan Wehrli

We extend the generalized Khovanov bracket to smooth link cobordisms in $\mathbb{R}^3\times I$ and prove that the resulting theory is functorial up to global invertible scalars. The generalized Khovanov bracket can be specialized to both even and odd Khovanov homology. Particularly by setting $\pi=-1$, we obtain that odd Khovanov homology is functorial up to

Nassim Bozorgnia, Joseph Bramante, James M. Cline, David Curtin

Astrophysical observations suggest that most of the matter in the cosmos consists of a new form that has not been observed on Earth. The nature and origin of this mysterious dark matter are among the most pressing questions in fundamental science. In this review we summarize the current state of dark matter research from two perspectives. First, we provide a

Vicky Dong, Hao Yu, Yao Chen

This study introduces a novel approach to sentence-level relation extraction (RE) that integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate contextually enriched support documents. By harnessing the power of LLMs to generate auxiliary information, our approach crafts an intricate graph representation of textual data. This gra

Jay Bear, Adam Prügel-Bennett, Jonathon Hare

Iterative algorithms solve problems by taking steps until a solution is reached. Models in the form of Deep Thinking (DT) networks have been demonstrated to learn iterative algorithms in a way that can scale to different sized problems at inference time using recurrent computation and convolutions. However, they are often unstable during training, and have n

Brandon Price, Jason Adleberg, Kaesha Thomas, Zach Zaiman

The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 83,011 patients, 40% of which are African American. This dataset provides imaging data in DICOM format along with detailed clinical information, including patient-reported pain scores, diagnostic codes, and procedural codes, which are not

Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou

We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. Our approach centers on return-conditioned supervised learning (RCSL), particularly focusing on Decision Transformer (DT) type frameworks, which can predict actions conditioned on d

Deepak Prakash Kumar, Sivakumar Rathinam, Swaroop Darbha, Trevor Bihl

In this paper, a heuristic for a heterogeneous min-max multi-vehicle multi-depot traveling salesman problem is proposed, wherein heterogeneous vehicles start from given depot locations and need to cover a given set of targets. In the considered problem, vehicles can be structurally heterogeneous due to different vehicle speeds and/or functionally heterogeneo

Vinay Koshy, Frederick Choi, Yi-Shyuan Chiang, Hari Sundaram

Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including

Marcus Gawronsky, Chun-Sung Huang

Firm characteristics are commonly represented as fixed vectors, even though evidence about firms' operations arrives as heterogeneous collections of articles reports. We study how distances between distributions of firm characteristics restrict systematic covariance. For given latent exposure laws, quadratic Wasserstein geometry yields sharp covariance e

Marco de Cesare, Roberto Oliveri, Achilleas P. Porfyriadis

We study spherically symmetric spacetime perturbations induced by a neutral scalar in the near-horizon region of extreme Reissner-Nordstrom black holes. For the unperturbed black hole, the near-horizon region is given by another exact solution of the Einstein-Maxwell equations, namely the Bertotti-Robinson spacetime. Our aim is to extend this connection beyo

Hina Shaheen, Roderick Melnik

It is essential to understand the complex structure of the human brain to develop new treatment approaches for neurodegenerative disorders (NDDs). This review paper comprehensively discusses the challenges associated with modelling the complex brain networks and dynamic processes involved in NDDs, particularly Alzheimer's disease (AD), Parkinson's disease (P

Omar A. Alvarez-Gonzaga, Ulises A. Vergara-Beltran, Juan I. Rodriguez

Random Forest (RF) and Gradient Boosting Regression Trees (GBRT) regression models along with three cheminformatics data sets (RDkit, Mordred, Morgan) have been used to predict the power conversion efficiency (PCE) of organic solar cells (OSCs). The data consists of cheinformatics descriptors of the electron donor used in 433 OSCs for which the experimental

Fabio S. Dias, Grace A. Lauretta

This paper aims to review the radical role of AI and deep learning in various functional areas of the business, such as marketing, finance, operations, human resources and customer service. Thus, based on the overview of the latest research and practices focusing on AI technologies in different industries, the possibilities of improving organizational effici

Kuznetsov Evgeny

In this paper, we investigate the concept of local homeomorphism in Esakia spaces. We introduce the notion of etale Heyting H-algebra and establish category-theoretic duality for etale Heyting H-algebra in the case of finite Heyting algebra H. Furthermore, we give an identity that axiomatizes the variety of etale Heyting H-algebras when H is finite. We also

Lucas S. Marinho, Michelle O. Araújo, Wellington Martins, Daniel Felinto

We report the enhancement of quantum correlations for biphotons generated via spontaneous four-wave mixing in an ensemble of cold two-level atoms. This enhancement is based on the filtering of the Rayleigh linear component of the spectrum of the two emitted photons, favoring the quantum-correlated sidebands reaching the detectors. We provide direct measureme

Ben Adcock, Michael Griebel, Gregor Maier

Operator learning, the approximation of mappings between infinite-dimensional function spaces using machine learning, has gained increasing research attention in recent years. Approximate operators, learned from data, can serve as efficient surrogate models for problems in computational science and engineering, complementing traditional methods. However, des

Piotr Mitosek, Miriam Backens

The one-way model of quantum computation is an alternative to the circuit model. A one-way computation is driven entirely by successive adaptive measurements of a pre-prepared entangled resource state. For each measurement, only one outcome is desired; hence a fundamental question is whether some intended measurement scheme can be performed in a robustly det

Yunwei Ren, Zixuan Wang, Jason D. Lee

Transformers have excelled in natural language modeling and one reason behind this success is their exceptional ability to combine contextual informal and global knowledge. However, the theoretical basis remains unclear. In this paper, first we introduce the Sparse Contextual Bigram (SCB), a natural extension of the classical bigram model, where the next tok

Arihan Yadav, Alan McMillan

Retrieval-Augmented Generation (RAG) systems enhance text generation by incorporating external knowledge but often struggle when retrieving context across different text modalities due to semantic gaps. We introduce a generalized projection-based method, inspired by adapter modules in transfer learning, that efficiently bridges these gaps between various tex

Thomas D. Kennedy, Emily Rauscher, Isaac Malsky, Michael T. Roman

Cloud formation and magnetic effects are both expected to significantly impact the structures and observable properties of hot Jupiter atmospheres. For some hot Jupiters, thermal ionization and condensation can coexist in a single atmosphere, and both processes are important. We present a grid of general circulation models across a wide range of irradiation

Amirhossein Nazerian, Francesco Sorrentino, Zahra Aminzare

Reactivity, contractivity, and Lyapunov exponents are powerful tools for studying the stability properties of dynamical systems and have been extensively investigated in the literature for decades. In this paper, we review and extend the concepts of reactivity, contractivity, and finite-time Lyapunov exponents for discrete-time dynamical systems and establis

Stefan Stojanovic, Yassir Jedra, Alexandre Proutiere

We consider the problem of learning an $\varepsilon$-optimal policy in controlled dynamical systems with low-rank latent structure. For this problem, we present LoRa-PI (Low-Rank Policy Iteration), a model-free learning algorithm alternating between policy improvement and policy evaluation steps. In the latter, the algorithm estimates the low-rank matrix cor

Mostafa Rezapour, Aarthi Narayanan, Wyatt H. Mowery, Metin Nafi Gurcan

This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). We performed a detailed comparison of both platforms, demonstrating a strong correlation between them, with Spearman coefficients for 56 out of 62 samples ranging from 0.

Megan A. Brown, Andrew Gruen, Gabe Maldoff, Solomon Messing

Scientists across disciplines often use data from the internet to conduct research, generating valuable insights about human behavior. However, as generative AI relying on massive text corpora becomes increasingly valuable, platforms have greatly restricted access to data through official channels. As a result, researchers will likely engage in more web scra

Dániel Garamvölgyi

A graph matroid family $\mathcal{M}$ is a family of matroids $\mathcal{M}(G)$ defined on the edge set of each finite graph $G$ in a compatible and isomorphism-invariant way. We say that $\mathcal{M}$ has the Whitney property if there is a constant $c$ such that every $c$-connected graph $G$ is uniquely determined by $\mathcal{M}(G)$. Similarly, $\mathcal{M}$

Sivaprasad Omanakuttan, Vikas Buchemmavari, Michael J. Martin, Ivan H Deutsch

Optically trapped atoms in arrays of optical tweezers have emerged as a powerful platform for quantum information processing given the recent demonstrations of high-fidelity quantum logic gates and on-demand reconfigurable geometry. Both in gate operations and atomic transport, additional errors will occur due to leakage out of the computation space, atomic

Chaochen Wu, Meiyun Zuo, Lei Xie

Single-cell RNA-seq (scRNA-seq) technology is a powerful tool for unraveling the complexity of biological systems. One of essential and fundamental tasks in scRNA-seq data analysis is Cell Type Annotation (CTA). In spite of tremendous efforts in developing machine learning methods for this problem, several challenges remains. They include identifying Out-of-

Hina Shaheen, Roderick Melnik

Parkinson's disease (PD) belongs to the class of neurodegenerative disorders that affect the central nervous system. It is usually defined as the gradual loss of dopaminergic neurons in the substantia nigra pars compacta, which causes both motor and non-motor symptoms. Understanding the neuronal processes that underlie PD is critical for creating successful

Mingen Li, Changhyun Choi

Manipulation of deformable Linear objects (DLOs), including iron wire, rubber, silk, and nylon rope, is ubiquitous in daily life. These objects exhibit diverse physical properties, such as Young$'$s modulus and bending stiffness.Such diversity poses challenges for developing generalized manipulation policies. However, previous research limited their scope to

Smail Bougouffa, Mohamed Babiker

The interaction of atoms with higher-order Poincar\'e optical vortex modes of order $m\geq 0$ is explored for light close to resonance with atomic dipole transitions. It is well-known that atoms subject to optical vortex modes experience both translational and rotational forces acting on the atomic centre of mass, leading to atom dynamics and atom trapping.

Yue Huang, Zhengqing Yuan, Yujun Zhou, Kehan Guo

Large Language Models (LLMs) are increasingly employed for simulations, enabling applications in role-playing agents and Computational Social Science (CSS). However, the reliability of these simulations is under-explored, which raises concerns about the trustworthiness of LLMs in these applications. In this paper, we aim to answer ``How reliable is LLM-based

Marc Subirana-Granés, Jill Hoffman, Haoyu Zhang, Christina Akirtava

Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptom

Vineet Sunil Gattani, Junshan Zhang, Gautam Dasarathy

Large-scale federated learning (FL) over wireless multiple access channels (MACs) has emerged as a crucial learning paradigm with a wide range of applications. However, its widespread adoption is hindered by several major challenges, including limited bandwidth shared by many edge devices, noisy and erroneous wireless communications, and heterogeneous datase

Hung-Tien Huang, Maxwell Lennon, Shreyas Bhat Brahmavar, Sean Sylvia

We introduce Dynamic Information Sub-Selection (DISS), a novel framework of AI assistance designed to enhance the performance of black-box decision-makers by tailoring their information processing on a per-instance basis. Blackbox decision-makers (e.g., humans or real-time systems) often face challenges in processing all possible information at hand (e.g., d

Li Li

The transition of Ethereum from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism introduces a transformative approach to blockchain validation, offering enhanced scalability, energy efficiency, and security. However, this shift also presents significant challenges, including high barriers to becoming a validator, restrictions on the liquid

Alex Lee Melendez, Shekhar Das, Francisco Ayala Rodriguez, I-Hsuan Kao

Optical detection of magnetic resonance using quantum spin sensors (QSS) provides a spatially local and sensitive technique to probe spin dynamics in magnets. However, its utility as a probe of antiferromagnetic resonance (AFMR) remains an open question. We report the first experimental demonstration of optically detected AFMR in layered van der Waals antife

Jacob Haqq-Misra, Clément Vidal, George Profitiliotis

Earth remains the only known example of a planet with technology, and future projections of Earth's trajectory provide a basis and motivation for approaching the search for extraterrestrial technospheres. Conventional approaches toward projecting Earth's technosphere include applications of the Kardashev scale, which suggest the possibility that energy-inten

Philipp Gassert, Matthias Althoff

Reinforcement learning (RL) is not yet competitive for many cyber-physical systems, such as robotics, process automation, and power systems, as training on a system with physical components cannot be accelerated, and simulation models do not exist or suffer from a large simulation-to-reality gap. During the long training time, expensive equipment cannot be u

Tomohiro Oishi, Masaaki Kimura, Lorenzo Fortunato

One-proton emission from the $^{102}$Sb nucleus is discussed, assuming an inert $^{100}$Sn core and the valence proton and neutron. There are experimentally measured bound states in the $^{100}$Sn-neutron system, whereas no particle-bound $^{100}$Sn-proton state has been observed. With time-dependent three-body calculations, the $1^+$ ground state of $^{102}

Isaac Echols, Jon Harrison, Tori Hudgins

Periodic orbits (equivalence classes of closed paths up to cyclic shifts) play an important role in applications of graph theory. For example, they appear in the definition of the Ihara zeta function and exact trace formulae for the spectra of quantum graphs. Circulant graphs are Cayley graphs of $\mathbb{Z}_n$. Here we consider directed Cayley graphs with t

Benjamin Cookson, Soroush Ebadian, Nisarg Shah

We study temporal fair division, whereby a set of agents are allocated a (possibly different) set of goods on each day for a period of days. We study this setting, as well as a number of its special cases formed by the restrictions to two agents, same goods on each day, identical preferences, or combinations thereof, and chart out the landscape of achieving

Nirmal Joshua Kapu, Mihit Sreejith

Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding taskspecific contexts. To address these issues, we propose a system called DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent

Wenkang Wang, Xu Chu

This study presents an automatic differentiation (AD)-based optimization framework for flow control in compressible turbulent channel flows. We developed a fully differentiable boundary condition framework that allows for the precise calculation of gradients with respect to boundary control variables. This facilitates the efficient optimization of flow contr

Yashasvi Aulak

In this paper we construct a family of topological conformal field theories (TCFTs) associated to a Calabi-Yau space by modifying the heat kernel and sections of the Calabi-Yau space. This is done by restricting to certain eigenspaces of the Laplacian. We then present two a-priori distinct ways to regularize the Calabi-Yau TCFT by using these modified heat k

Sekeun Kim, Pengfei Jin, Sifan Song, Cheng Chen

Foundation models have recently gained significant attention because of their generalizability and adaptability across multiple tasks and data distributions. Although medical foundation models have emerged, solutions for cardiac imaging, especially echocardiography videos, are still unexplored. In this paper, we introduce EchoFM, a foundation model specifica

Ziren Jiang, Gen Li, Eric F. Lock

Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, existing met

Sanjay Mallick, Debmalya Sain

We study the Gateaux differentiability in the Banach space of meromorphic functions and obtain a complete characterization of the same, by using Birkhoff-James orthogonality techniques. We introduce the concept of extended orthogonality covering set (EOCS), which allows us to present refinements of some earlier results on the Gateaux differentiability of ana

Tomislav Terzić

Lorentz invariance violation (LIV) in gamma rays can have multiple consequences, such as energy-dependent photon group velocity, photon instability, vacuum birefringence, and modified electromagnetic interaction. Depending on how LIV is introduced, several of these effects can occur simultaneously. Nevertheless, in experimental tests of LIV, each effect is t

Alessandro D'Amelio, Giuseppe Cartella, Vittorio Cuculo, Manuele Lucchi

Attention guides our gaze to fixate the proper location of the scene and holds it in that location for the deserved amount of time given current processing demands, before shifting to the next one. As such, gaze deployment crucially is a temporal process. Existing computational models have made significant strides in predicting spatial aspects of observer's

L. Lober, M. S. Palmero, F. A. Rodrigues

Inferring control parameters in non-linear dynamical systems is an important task in analysing general dynamical behaviours, particularly in the presence of inherently deterministic chaos. Traditional approaches often rely on system-specific models and involve heavily parametrised formulations, which can limit their general applicability. In this study, we p

Iuliu Cuceu, Sandra Robles

We utilize the now substantial amount of astrophysical observations of neutron stars (NSs), along with perturbative quantum chromodynamics (pQCD) calculations at high density, to directly constrain the NS equation of state (EOS). To this end, we construct nonparametric EOS priors by using Gaussian processes trained on 75 EOSs, which include models with eithe

Puskar Mondal

I prove a scalar curvature rigidity theorem for spheres. In particular, I prove that geodesic balls of radii strictly less than $\frac{\pi}{2}$ in $n+1~(n\geq 2)$ dimensional unit sphere can be rigid under smooth deformations that increase scalar curvature preserving the intrinsic geometry and the mean curvature of the boundary, and such rigidity result fail

Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood

Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the immense scale of the chemical space makes it challenging to explore all possible materials experimentally. In this paper, we introduce FlowLLM, a novel generative model that combines l

Matthew Willetts, Christian Harrington

Automated Market Makers (AMMs) hold assets and are constantly being rebalanced by external arbitrageurs to match external market prices. Loss-versus-rebalancing (LVR) is a pivotal metric for measuring how an AMM pool performs for its liquidity providers (LPs) relative to an idealised benchmark where rebalancing is done not via the action of arbitrageurs but

Rodrigo Itzamná Becerra-Deana, Martin Poinsinet de Sivry-Houle, Stéphane Virally, Caroline Boudoux

We present the design, fabrication, and characterization of mode-selective photonic lanterns using double-clad fibers. Here, we exploited several custom-pulled double-clad fibers to achieve the symmetry break required to excite higher-order modes. The resulting components are short and exhibit high modal isolation and low excess loss. They address some of th

Andrew Estornell, Jean-Francois Ton, Yuanshun Yao, Yang Liu

Large language models (LLMs) have demonstrated a remarkable ability to serve as general-purpose tools for various language-based tasks. Recent works have demonstrated that the efficacy of such models can be improved through iterative dialog between multiple models. While these paradigms show promise in improving model efficacy, most works in this area treat

Cuong Chi Le, Hoang-Chau Truong-Vinh, Huy Nhat Phan, Dung Duy Le

Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for code analysis. While these models excel at understanding static syntax, they often struggle with dynamic reasoning tasks. We introduce VisualCoder, a simple yet effective approach tha

Jon Henshaw, Aviv Gibali, Thomas Humphries

The superiorization methodology (SM) is an optimization heuristic in which an iterative algorithm, which aims to solve a particular problem, is ``superiorized'' to promote solutions that are improved with respect to some secondary criterion. This superiorization is achieved by perturbing iterates of the algorithm in nonascending directions of a prescribed fu

Sammy Benzaira, Ian Short, Matty van Son, Andrei Zabolotskii

We use a class of Farey graphs introduced by the final three authors to enumerate the tame friezes over $\mathbb{Z}/n\mathbb{Z}$. Using the same strategy we enumerate the tame regular friezes over $\mathbb{Z}/n\mathbb{Z}$, thereby reproving a recent result of B\"ohmler, Cuntz, and Mabilat.

Alexandra Muresan, Carina Persson, Malcolm Fridlund

The rich diversity of multi-planetary systems and their architectures is greatly contrasted by the uniformity exhibited within many of these systems. Previous studies have shown that compact Kepler systems tend to exhibit a peas-in-a-pod architecture: Planets in the same system tend to have similar sizes and masses and be regularly spaced in orbits with low

Zhiyuan Fan, Christian Kroer, Gabriele Farina

First-order methods (FOMs) are arguably the most scalable algorithms for equilibrium computation in large extensive-form games. To operationalize these methods, a distance-generating function, acting as a regularizer for the strategy space, must be chosen. The ratio between the strong convexity modulus and the diameter of the regularizer is a key parameter i

Pablo Basteiro, Giuseppe Di Giulio, Johanna Erdmenger, René Meyer

Inhomogeneous quantum chains have recently been considered in the context of developing novel discrete realizations of holographic dualities. To advance this programme, we explore the ground states of infinite chains with large number $N$ of Majorana fermions on each site, which interact via on-site $q$-body Sachdev-Ye-Kitaev (SYK) couplings, as well as via

Qiliang Chen, Babak Heydari

Effective governance and steering of behavior in complex multi-agent systems (MAS) are essential for managing system-wide outcomes, particularly in environments where interactions are structured by dynamic networks. In many applications, the goal is to promote pro-social behavior among agents, where network structure plays a pivotal role in shaping these int

Marco S. Bianchi

We compute the two-point function of protected dimension-1 operators in ABJM up to two loops in dimensional regularization. The result exhibits uniform transcendentality empirically, which we conjecture to hold at all orders. We leverage this property to streamline the reconstruction of the dimensional regularization expansion of master integrals in terms of

Basileal Imana, Aleksandra Korolova, John Heidemann

Auditing social-media algorithms has become a focus of public-interest research and policymaking to ensure their fairness across demographic groups such as race, age, and gender in consequential domains such as the presentation of employment opportunities. However, such demographic attributes are often unavailable to auditors and platforms. When demographics

Qiliang Chen, Babak Heydari

We introduce a framework that integrates variational autoencoders (VAE) with reinforcement learning (RL) to balance system performance and resource usage in multi-agent systems by dynamically adjusting network structures over time. A key innovation of this method is its capability to handle the vast action space of the network structure. This is achieved by

Kotomi Taniguchi, Prasanta Gorai, Jonathan C. Tan, Miguel Gomez-Garrido

Evidence for similar chemical characteristics around low- and high-mass protostars has been found: in particular, a variety of carbon-chain species and complex organic molecules (COMs) are formed around them. On the other hand, the chemical compositions around intermediate-mass (IM; $2 M_{\odot} < m_* <8 M_{\odot}$) protostars have not been studied with larg

Haixiang Sun, Ye Shi

Deep Equilibrium Model (DEQ), which serves as a typical implicit neural network, emphasizes their memory efficiency and competitive performance compared to explicit neural networks. However, there has been relatively limited theoretical analysis on the representation of DEQ. In this paper, we utilize the Neural Collapse ($\mathcal{NC}$) as a tool to systemat

Iago Bischoff Montenegro, Konrad Prikoszovich, Subin Lee, Kilian Quiring

Scanning Electron Microscopy (SEM) is a widely used tool for nanoparticle characterization, but long-term directional drift can compromise image quality. We present a novel algorithm for post-imaging drift correction in SEM nanoparticle imaging. Our approach combines multiple rapidly acquired, noisy images to produce a single high-quality overlay through red

Joost Mertens, Stefan Klikovits, Francis Bordeleau, Joachim Denil

Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user's needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which gene

Efraín Magaña, Simone Pezzuto, Francisco Sahli Costabal

The anisotropic structure of the myocardium is a key determinant of the cardiac function. To date, there is no imaging modality to assess in-vivo the cardiac fiber structure. We recently proposed Fibernet, a method for the automatic identification of the anisotropic conduction -- and thus fibers -- in the atria from local electrical recordings. Fibernet uses

Neman Karimi, Burak Kocuk, Tugce Yuksel

In recent years, the transition to clean bus fleets has accelerated. Although this transition might bring environmental and economic benefits, it requires a long-term strategic plan due to the large investment costs involved. This paper proposes a multi-stage stochastic program to optimize strategic plans for the clean bus fleet transition that explicitly co

Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and partially subjective. Automatic, data-driven detection methods exist but show limited performance. Still, the rise of de

Christoph Hansknecht, Bernhard Heinzelreiter, John W. Pearson, Andreas Potschka

We consider the solution of saddle-point systems with a tree-based block structure, introducing a parallelizable direct method for their solution. As our key contribution, we then propose several structure-exploiting preconditioners to be used during applications of the MINRES and GMRES algorithms and analyze their properties. We adapt several concepts origi

Gabriel Vidal, Gabriel M. Dantas, Riccardo Sturani, Gabriel Menezes

The linear response of a Schwarzschild black hole to an external quadrupolar perturbation is studied in analogy to a mechanical electrodynamical system, with the goal to describe the gravitational polarizability. Its causality properties imply dispersion relations that relate fluctuation and dissipative properties. We review and combine results obtained via

Amr Elmasry

In this paper we give a single-source shortest-path algorithm that breaks, after over 65 years, the $O(n \cdot m)$ bound for the running time of the Bellman-Ford-Moore algorithm, where $n$ is the number of vertices and $m$ is the number of arcs of the graph. Our algorithm converts the input graph to a graph with nonnegative weights by performing at most $\mi

Abulikemu Abuduweili, Changliu Liu

Ensuring neural network robustness is essential for the safe and reliable operation of robotic learning systems, especially in perception and decision-making tasks within real-world environments. This paper investigates the robustness of neural networks in perception systems, specifically examining their sensitivity to targeted, small-scale perturbations. We

Kalina Borkiewicz, Eric Jensen, Yiwen Miao, Stuart Levy

Quantifying the global reach of planetarium dome shows presents significant challenges due to the lack of standardized viewership tracking mechanisms across diverse planetarium venues. We present an analysis of the global impact of dome shows, presenting data regarding four documentary films from a single visualization lab. Specifically, we designed and admi

Dustin R. Madison

For pulsar timing arrays (PTAs), the telltale signature of an isotropic stochastic background of gravitational waves is a pattern of pairwise interpulsar timing correlations approximately following the Hellings & Downs (HD) curve. Certain systematic errors and new physics processes also lead to interpulsar correlations with different patterns that can be dis

Monish Reddy Kotturu, Saniya Vahedian Movahed, Paul Robinette, Kshitij Jerath

We introduce an approach to improve team performance in a Multi-Agent Multi-Armed Bandit (MAMAB) framework using Fastest Mixing Markov Chain (FMMC) and Fastest Distributed Linear Averaging (FDLA) optimization algorithms. The multi-agent team is represented using a fixed relational network and simulated using the Coop-UCB2 algorithm. The edge weights of the c