March 2025 arXiv papers — page 200

Sima Arasteh, Pegah Jandaghi, Nicolaas Weideman, Dennis Perepech

The software compilation process has a tendency to obscure the original design of the system and makes it difficult both to identify individual components and discern their purpose simply by examining the resulting binary code. Although decompilation techniques attempt to recover higher-level source code from the machine code in question, they are not fully

Matteo Grella

We propose leveraging chromostereopsis, a perceptual phenomenon inducing depth perception through color contrast, as a novel approach to visually differentiating conversational roles in text-based AI interfaces. This method aims to implicitly communicate role hierarchy and add a subtle sense of physical space.

Soya Park, J. D. Zamfirescu-Pereira, Chinmay Kulkarni

Training AI models is challenging, particularly when crafting behavior instructions. Traditional methods rely on machines (supervised learning) or manual pattern discovery, which results in not interpretable models or time sink. While Large Language Models (LLMs) simplify instruction writing through natural language, articulating intended model behavior stil

Luukas Nikkanen, Susi Lehtola

An attractive way to model nuclear quantum effects is to describe select nuclei quantum mechanically at the same level as the electrons. This non-Born-Oppenheimer (non-BO) method is known by many names including the nuclear-electronic orbital (NEO) and the multicomponent method. Two basis sets are typically used for such calculations: a nuclear basis set and

Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan

Diffusion models are the standard toolkit for generative modelling of 3D atomic systems. However, for different types of atomic systems -- such as molecules and materials -- the generative processes are usually highly specific to the target system despite the underlying physics being the same. We introduce the All-atom Diffusion Transformer (ADiT), a unified

R. C. H. Gomes, S. Sugiyama, B. Jain, M. Jarvis

We present a new pipeline designed for the robust inference of cosmological parameters using both second- and third-order shear statistics. We build a theoretical model for rapid evaluation of three-point correlations using our fastnc code and integrate it into the CosmoSIS framework. We measure the two-point functions $\xi_{\pm}$ and the full configuration-

Dimitris G. Giovanis, Ellis Crabtree, Roger G. Ghanem, Ioannis G. Kevrekidis

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are descr

Catherine Arnett, Tyler A. Chang, James A. Michaelov, Benjamin K. Bergen

Crosslingual transfer is crucial to contemporary language models' multilingual capabilities, but how it occurs is not well understood. We ask what happens to a monolingual language model when it begins to be trained on a second language. Specifically, we train small bilingual models for which we control the amount of data for each language and the order of l

William Merrill, Ashish Sabharwal

Recent theoretical results show transformers cannot express sequential reasoning problems over long inputs, intuitively because their computational depth is bounded. However, prior work treats the depth as a constant, leaving it unclear to what degree bounded depth may suffice for solving problems over short inputs, or how increasing the transformer's depth

In Hak Moon

This paper presents an in-depth analysis of the performance of seven different Large Language Models (LLMs) in solving a diverse set of math advanced calculus problems. The study aims to evaluate these models' accuracy, reliability, and problem-solving capabilities, including ChatGPT 4o, Gemini Advanced with 1.5 Pro, Copilot Pro, Claude 3.5 Sonnet, Meta AI,

Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang, Haimin Wang

We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). Unlike previous studies that focus primarily on spatial super-resolution of MDI magnetograms, our a

Rafael Engel

This Master's thesis examines the properties of large degree vertices in random recursive directed acyclic graphs (RRDAGs), a generalization of the well-studied random recursive tree (RRT) model. Using a novel adaptation of Kingman's coalescent, we extend results from RRTs to RRDAGs, focusing on different vertex properties. For large degrees, we establish th

Sijia Zhao, Rong Zhang, Wen O. Wang, Jixun K. Ding

Numerical evidence for superconductivity in the single-band Hubbard model is elusive or ambiguous despite extensive study, raising the question of whether the single-band Hubbard model is a faithful low energy effective model for cuprates, and whether explicitly including the oxygen ions will recover the properties necessary for a superconducting transition.

Zi Wang, Shiyi Lan, Xinglong Sun, Nadine Chang

Autonomous vehicle safety is crucial for the successful deployment of self-driving cars. However, most existing planning methods rely heavily on imitation learning, which limits their ability to leverage collision data effectively. Moreover, collecting collision or near-collision data is inherently challenging, as it involves risks and raises ethical and pra

Andrey Pototsky, Uwe Thiele

We study the formation of propagating large-scale density waves of mixed polar-nematic symmetry in a colony of self-propelled agents that are bound to move along the planar surface of a thin viscous film. The agents act as an insoluble surfactant, i.e. the surface tension of the liquid depends on their density. Therefore, density gradients generate a Marango

Moritz Bensberg, Marco Eckhoff, F. Emil Thomasen, William Bro-Jørgensen

Binding free energies are a key element in understanding and predicting the strength of protein--drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs including transition metal atoms often require quantum chemical methods for an accurate description. We propose a general and automated workflow t

Zixuan Wang, Luis Zerkowski, Ilya Vidrin, Mariel Pettee

Existing AI-generated dance methods primarily train on motion capture data from solo dance performances, but a critical feature of dance in nearly any genre is the interaction of two or more bodies in space. Moreover, many works at the intersection of AI and dance fail to incorporate the ideas and needs of the artists themselves into their development proces

Aidan Marler, Yannik Roell, Steffen Knoblauch, Jane P. Messina

Static maps and animations remain popular in spatial epidemiology of dengue, limiting the analytical depth and scope of visualisations. Over half of the global population live in dengue endemic regions. Understanding the spatiotemporal dynamics of the four closely related dengue serotypes, and their immunological interactions, remains a challenge at a global

Yasuyuki Hatsuda

The Schur index in four-dimensional $\mathcal{N}=4$ super Yang-Mills theory with $U(N)$ gauge group has a natural two-parameter deformation. We find that a matrix integral in such a deformed Schur index can be exactly evaluated by using Macdonald polynomials. The resulting expression is a simple combinatorial summation over partitions. An extension to line o

Ildar Rakhmatulin

PiEEG kit is a multifunctional, compact, and mobile device that allows measure EEG, EMG, EOG, and EKG signals. The PiEEG Box incorporates the Raspberry Pi-based PiEEG shield, an EEG electrode cap, a display screen, additional sensors about body parameters and the environment, and other necessary peripherals, software, and an SDK course to learn signal proces

Susmita Haldar, Mary Pierce, Luiz Fernando Capretz

This innovative practice WIP paper describes a research study that explores the integration of ChatGPT into the software testing curriculum and evaluates its effectiveness compared to human-generated testing artifacts. In a Capstone Project course, students were tasked with generating preparatory testing artifacts using ChatGPT prompts, which they had previo

Pedro Márquez-Zacarías, Andrés Ortiz-Muñoz, Emma P. Bingham

Living systems are thermodynamically open but closed in their organization. In other words, even though their material components turn over constantly, a material-independent property persists, which we call organization. Moreover, organization comes from within organisms themselves, which requires us to explain how this self-organization is established and

E Graham, R Granger

Human language and logic abilities are computationally quantified within the well-studied grammar-automata hierarchy. We identify three hierarchical tiers and two corresponding transitions and show their correspondence to specific abilities in transformer-based language models (LMs). These emergent abilities have often been described in terms of scaling; we

Yi-Heng Tsai

We prove that the birational automorphism group of a general Calabi-yau complete intersection $X$ given by ample divisors in $\mathbb{P}^{n_1}\times\cdots\times\mathbb{P}^{n_l}$ is always Lorentzain. Applying the Kawamata-Morrison cone theorem on such $X$, we compute $\operatorname{vol}_X(D+sA)$ for any divisor $D\in \partial\overline{\operatorname{Eff}}(X)$

Ki Young Lee, Stephan Wong, Sachin Vaidya, Terry A. Loring

The moire flat bands in twisted bilayer graphene have attracted considerable attention not only because of the emergence of correlated phases but also due to their nontrivial topology. Specifically, they exhibit a new class of topology that can be nullified by the addition of trivial bands, termed fragile topology, which suggests the need for an expansion of

Aurelio Noca, Xianmei Lei, Jonathan Becktor, Jeffrey Edlund

Autonomous off-road navigation faces challenges due to diverse, unstructured environments, requiring robust perception with both geometric and semantic understanding. However, scarce densely labeled semantic data limits generalization across domains. Simulated data helps, but introduces domain adaptation issues. We propose COARSE, a semi-supervised domain ad

CMS Collaboration

Measurements of the luminosity delivered to the CMS experiment during the lead-lead data-taking periods in 2015 and 2018 are presented for the first time. The collisions were recorded at a nucleon-nucleon center-of-mass energy of 5.02 TeV; the 2018 data sample is three times larger than the 2015 data sample. Three subdetectors are used: the pixel luminosity

Niklas M. Melton, Leonardo Enzo Brito da Silva, Sasha Petrenko, Donald. C. Wunsch

This paper presents Deep ARTMAP, a novel extension of the ARTMAP architecture that generalizes the self-consistent modular ART (SMART) architecture to enable hierarchical learning (supervised and unsupervised) across arbitrary transformations of data. The Deep ARTMAP framework operates as a divisive clustering mechanism, supporting an arbitrary number of mod

Aaron Clauset, Barbara F. Walter, Lars-Erik Cederman, Kristian Skrede Gleditsch

Although very large wars remain an enduring threat in global politics, we lack a clear understanding of how some wars become large and costly, while most do not. There are three possibilities: large conflicts start with and maintain intense fighting, they persist over a long duration, or they escalate in intensity over time. Using detailed within-conflict da

Yaopei Zeng, Yuanpu Cao, Lu Lin

The growing accessibility of diffusion models has revolutionized image editing but also raised significant concerns about unauthorized modifications, such as misinformation and plagiarism. Existing countermeasures largely rely on adversarial perturbations designed to disrupt diffusion model outputs. However, these approaches are found to be easily neutralize

Hafiz Usman Naseer, Daulet Izbassarov, Marco Edoardo Rosti, Metin Muradoglu

Interface-resolved direct numerical simulations are performed to investigate bubble-induced transition from laminar to elasto-inertial turbulent (EIT) state in a pressure-driven viscoelastic square channel flow. The Giesekus model is used to account for the viscoelasticity of the continuous phase while the dispersed phase is Newtonian. Simulations are perfor

Jing Zhang, Xiaowei Yu, Tong Chen, Chao Cao

The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD and LBD is crucial because they require different treatment approaches, but this is challenging due to significant clinical overlap, heterogeneity, complex pathogenesis, and the rarity of LBD. While recent advance

Devanish N. Kamtam, Joseph B. Shrager, Satya Deepya Malla, Xiaohan Wang

Background: We evaluate SAM 2 for surgical scene understanding by examining its semantic segmentation capabilities for organs/tissues both in zero-shot scenarios and after fine-tuning. Methods: We utilized five public datasets to evaluate and fine-tune SAM 2 for segmenting anatomical tissues in surgical videos/images. Fine-tuning was applied to the image enc

Talia Goldwasser, Meera Nadeem, Garcia Sun, Julianna Tymoczko

Springer fibers are a family of subvarieties of the flag variety parametrized by nilpotent matrices that are important in geometric representation theory and whose geometry encodes deep combinatorics. Two-row Springer fibers, which correspond to nilpotent matrices with two Jordan blocks, also arise in knot theory, in part because their components are indexed

Graham V. Weinberg, Martijn van der Merwe

Detection of undersea threats is a complex problem of considerable importance for maritime regional surveillance and security. Multistatic sonar systems can provide a means to monitor for underwater threats, where fixed sensors, towed arrays and dipping sonars may be utilised for this purpose. However, it is advantageous to deploy passive sensors to provide

Joseph S. Schafer

Sudden attention on social media, and how users navigate these contextual shifts, has been a focus of much recent work in social media research. Even when this attention is not harassing, some users experience this sudden growth as overwhelming. In this workshop paper, I outline how growth infuses the design of much of the modern social media platform landsc

Z. M. McIntyre, W. A. Coish

As quantum devices scale to larger numbers of qubits, entangling gates between distant stationary qubits will help provide flexible, long-range connectivity in modular architectures. In this work, we present protocols for implementing long-range two-qubit gates mediated by either Fock-state or time-bin qubits -- photonic encodings that are both compatible wi

S. V. Dordevic

The phenomena of superconductivity and superfluidity are believed to originate from the same underlying physics, namely the condensation of either bosons or pairs of fermions (Cooper pairs). In this work I complied and analyzed literature data for a number of quantum fluids and showed that indeed they all follow the same simple scaling law. The critical temp

Michele Pacenti, Dimitris Chytas, Bane Vasic

Quantum low-density parity-check codes are a promising approach to fault-tolerant quantum computation, offering potential advantages in rate and decoding efficiency. In this work, we introduce quantum Margulis codes, a new class of QLDPC codes derived from Margulis' classical LDPC construction via the two-block group algebra framework. We show that quantum M

Václav Jirkovský, Jiří Kubalík, Petr Kadera, Arnd Schirrmann

This paper presents a new complex optimization problem in the field of automatic design of advanced industrial systems and proposes a hybrid optimization approach to solve the problem. The problem is multi-objective as it aims at finding solutions that minimize CO2 emissions, transportation time, and costs. The optimization approach combines an evolutionary

Abdullah Mamun, Asiful Arefeen, Susan B. Racette, Dorothy D. Sears

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after consuming a meal, is a critical indicator of progression toward type 2 diabetes in people with prediabetes and in healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial area under the curve (AUC). Predicting post

João P. M. Pitelli, Bruno S. Felipe, Claudio Dappiaggi, Elizabeth Winstanley

We explore the quantum superposition of boundary conditions in the context of the Poincar\'e patch of the two-dimensional Anti-de Sitter space ($\mathrm{PAdS}_2$). Focusing on Robin (mixed) boundary conditions (RBC), we investigate the response function of the Unruh-DeWitt (UDW) detector interacting with two or more scalar fields, each respecting a different

Haotian Zhao

We show conditions on $k$ such that any number $x$ in the interval $[0, k/2]$ can be represented in the form $x_1^{a_1} x_2^{a_2} + x_3^{a_3} x_4^{a_4} + \cdots + x_{k-1}^{a_{k-1}} x_k^{a_k}$, where the exponents $a_{2i-1}$ and $a_{2i}$ are positive integers satisfying $a_{2i-1} + a_{2i} = s$ for $i = 1, 2, \dots, k/2$, and each $x_i$ belongs to the generali

Sabur Butt, Hector G. Ceballos, Diana P. Madera

Job application and assessment processes have evolved significantly in recent years, largely due to advancements in technology and changes in the way companies operate. Skill extraction and classification remain an important component of the modern hiring process as it provides a more objective way to evaluate candidates and automatically align their skills

Luke M. Schmidt, Ryan J. Oelkers, Erika Cook, Mary Anne Limbach

The Exoplanet Transmission Spectroscopy Imager (ETSI) amalgamates a low resolution slitless prism spectrometer with custom multi-band filters to simultaneously image 15 spectral bandpasses between 430 nm and 975 nm with an average spectral resolution of $R = \lambda/\delta\lambda \sim 20$. ETSI requires only moderate telescope apertures ($\sim2$ m) and is ca

Ryan J. Oelkers, Luke M. Schmidt, Erika Cook, Mary Anne Limbach

One of the most prolific methods of studying exoplanet atmospheres is transmission spectroscopy, which measures the difference between the depth of an exoplanet's transit signal at various wavelengths and attempts to correlate the depth changes to potential features in the exoplanet's atmosphere. Here we present reconnaissance observations of 21 exoplanet at

Mohammed Bachir

We generalize the classical Monge-Kantorovich duality--typically established for tight (Radon) probability measures--to separable Baire probability measures, which are strictly more general than tight measures on completely regular Hausdorff spaces. Within this broader framework, we also demonstrate the existence of solutions.

Moritz Bensberg, Marco Eckhoff, Raphael T. Husistein, Matthew S. Teynor

We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model

Jack West, Bengisu Cagiltay, Shirley Zhang, Jingjie Li

Machine learning models deployed locally on social media applications are used for features, such as face filters which read faces in-real time, and they expose sensitive attributes to the apps. However, the deployment of machine learning models, e.g., when, where, and how they are used, in social media applications is opaque to users. We aim to address this

S. G. Bobkov, G. P. Chistyakov, F. Götze

We give an overview of various results and methods related to information-theoretic distances of R\'enyi type in the light of their applications to the central limit theorem (CLT). The first part (Sections 1-9) is devoted to the total variation and the Kullback-Leibler distance (relative entropy). In the second part (Sections 10-15) we discuss general proper

Christoph Kawan

In recent years, attempts have been made to extend nonlinear small-gain theorems for input-to-state stability (ISS) from finite networks to countably infinite networks with finite indegrees. Under specific assumptions about the interconnection gains and the ISS formulation, corresponding infinite-dimensional small-gain results have been proven. However, conc

Prakash Shukla, Phuong Bui, Sean S Levy, Max Kowalski

The rapid adoption of generative AI (GenAI) in design has sparked discussions about its benefits and unintended consequences. While AI is often framed as a tool for enhancing productivity by automating routine tasks, historical research on automation warns of paradoxical effects, such as de-skilling and misplaced responsibilities. To assess UX practitioners'

Hongjie Chen, Jingqiu Ding, Yiding Hua, Stefan Tiegel

We study the problem of robustly estimating the edge density of Erd\H{o}s-R\'enyi random graphs $G(n, d^\circ/n)$ when an adversary can arbitrarily add or remove edges incident to an $\eta$-fraction of the nodes. We develop the first polynomial-time algorithm for this problem that estimates $d^\circ$ up to an additive error $O([\sqrt{\log(n) / n} + \eta\sqrt

Ellis Rae Kennedy, Stephanie M. Ribet, Ian S. Winter, Caitlin A. Kohnert

While amorphous materials are often approximated to have a statistically homogeneous atomic structure, they frequently exhibit localized structural heterogeneity that challenges simplified models. This study uses 4D scanning transmission electron microscopy to investigate the strain and structural modifications around gas bubbles in amorphous Bi$_2$O$_3$ ind

Arthur Zhang, Harshit Sikchi, Amy Zhang, Joydeep Biswas

We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to achieving this is learning perceptual representations that generalize to open-set factors (e.g. novel semantic classes, terrains, dynamic entities) and inferring expert-aligned navi

Jie Hao, Yuman Wu, Ali Payani, Myungjin Lee

We study the task of personalized federated fine-tuning with heterogeneous data in the context of language models, where clients collaboratively fine-tune a language model (e.g., BERT, GPT) without sharing their local data, achieving personalization simultaneously. While recent efforts have applied parameter-efficient fine-tuning techniques like low-rank ada

Hortense Doms, Philippe Lambert, Catherine Legrand

In cancer clinical trials, health-related quality of life (HRQoL) is an important endpoint, providing information about patients' well-being and daily functioning. However, missing data due to premature dropout can lead to biased estimates, especially when dropouts are informative. This paper introduces the extJMIRT approach, a novel tool that efficiently an

Miguel Alcubierre

I present a review of the Dirac equation in general relativity. Although the generalization of the Dirac equation to a curved spacetime is well known, it is not usually part of the standard toolkit of techniques known to people working on classical general relativity. Recently, there has been some renewed interest in studying solutions of the Einstein--Dirac

Arnaud P. Nono Tchiomo, Anand Sharma, Sethulakshmi Sajeev, Anna Scheid

We report on a combined structural and magnetotransport study of Hall bar devices of various lateral dimensions patterned side-by-side on epitaxial PdCoO$_2$ thin films. We study the effects of both the thickness of the PdCoO$_2$ film and the width of the channel on the electronic transport and the magnetoresistance properties of the Hall bar devices. All th

Peter J. Haine, Maxime Ramzi, Jan Steinebrunner

We study stability properties of fully faithful functors, and compute mapping anima in pushouts of $\infty$-categories along fully faithful functors. We provide applications of these calculations to pushouts along Dwyer functors and Reedy categories.

Mario G. Santos, Stefano Camera, Zhaoting Chen, Steven Cunnington

We discuss the possible synergies for cosmology between SKAO and ESO facilities, focusing on the combinations SKA-Mid with the Multi-Object Spectrograph Telescope (4MOST) instrument built for ESO's Visible and Infrared Survey Telescope for Astronomy (VISTA), and SKA-Low with ESO's Extremely Large Telescope (ELT) multi-object spectrograph MOSAIC. Combining mu

Andrei Mesinger, Benedetta Ciardi, James E. Davies, Samuel Gagnon-Hartman

Mapping out the first billion years using the 21-cm line with the Square Kilometer Array (SKA) will revolutionize our understanding of the cosmic dawn, reionization and the galaxies that drove these milestones. However, synergies with other telescopes in the form of cross correlations will be fundamental in making and confirming initial, low signal-to-noise

Allen Isaac Jose, Sicong Pan, Tobias Zaenker, Rohit Menon

Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to

Ahmad Hafez, Alireza Naderi Akhormeh, Amr Hegazy, Amr Alanwar

The deployment of Large Language Models (LLMs) in robotic systems presents unique safety challenges, particularly in unpredictable environments. Although LLMs, leveraging zero-shot learning, enhance human-robot interaction and decision-making capabilities, their inherent probabilistic nature and lack of formal guarantees raise significant concerns for safety

Kasymkhan Khubiev, Mikhail Semenov

Classical asset price forecasting methods primarily rely on numerical data, such as price time series, trading volumes, limit order book data, and technical analysis indicators. However, the news flow plays a significant role in price formation, making the development of multimodal approaches that combine textual and numerical data for improved prediction ac

Daniel Appelo, Yingda Cheng

This paper proposes a new framework for computing low-rank solutions to nonlinear matrix equations arising from spatial discretization of nonlinear partial differential equations: low-rank Anderson acceleration (lrAA). lrAA is an adaptation of Anderson acceleration (AA), a well-known approach for solving nonlinear fixed point problems, to the low-rank format

Xiaochuan Gong, Jie Hao, Mingrui Liu

Adam has become one of the most popular optimizers for training modern deep neural networks, such as transformers. However, its applicability is largely restricted to single-level optimization problems. In this paper, we aim to extend vanilla Adam to tackle bilevel optimization problems, which have important applications in machine learning, such as meta-lea

Gal Yona, Roy Velich, Ron Kimmel, Ehud Rivlin

Classical shape descriptors such as Heat Kernel Signature (HKS), Wave Kernel Signature (WKS), and Signature of Histograms of OrienTations (SHOT), while widely used in shape analysis, exhibit sensitivity to mesh connectivity, sampling patterns, and topological noise. While differential geometry offers a promising alternative through its theory of differential

Mithun Ravisankar, Madeline Federle, Mina Bahadori, Asimanshu Das

Squash is a widely popular racket sport, practiced by millions of people worldwide, played inside a walled court. When played well, players can last for several minutes before the ball bounces twice on the floor. There is, however, an unanswerable shot. When the ball hits the nick between a vertical wall and the floor, under certain conditions, it rolls with

Gabor P. Nagy

In this paper, we study the Hamming distance between vectorial Boolean functions and affine functions. This parameter is known to be related to the non-linearity and differential uniformity of vectorial functions, while the calculation of it is in general difficult. In 2017, Liu, Mesnager and Chen conjectured an upper bound for this metric. We prove this bou

Nikolaos Nakis, Chrysoula Kosma, Anastasia Brativnyk, Michail Chatzianastasis

Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been mad

Dora Woodruff

We give a pattern-avoidance characterization of $w \in S_n$ such that the Schubert polynomial $\mathfrak{S}_w$ is a standard elementary monomial. This characterization tells us which quantum Schubert polynomials are easiest to compute. We solve a similar problem for complete homogeneous monomials.

Siqi Qu, Mathias Staudigl, Juan Peypouquet

We propose a comprehensive framework for solving constrained variational inequalities via various classes of evolution equations displaying multi-scale aspects. In an infinite-dimensional Hilbertian framework, the class of dynamical systems we propose combine Tikhonov regularization and exterior penalization terms in order to induce strong convergence of tra

Manju Johny, Jonathan Hobbs, Vineet Yadav, Margaret Johnson

Solar-induced chlorophyll fluorescence (SIF) has emerged as an effective indicator of vegetation productivity and plant health. The global quantification of SIF and its associated uncertainties yields many important capabilities, including improving carbon flux estimation, improving the identification of carbon sources and sinks, monitoring a variety of ecos

Marco A. Muñoz-Gutiérrez, Sebastián Ramírez, Antonio Peimbert, Angeles Pérez-Villegas

The current populations trapped in Neptune's main mean motion resonances in the Kuiper belt, Plutinos in the 3:2 and Twotinos in the 2:1, contain some of the best-characterized minor objects in the Solar System, given their dynamical importance. In particular, Twotinos may hide evidence of Neptune's early migration. However, these populations vary in time, d

Walter Bridges

Given an integer partition of $n$ into distinct parts, the sum of the reciprocal parts is an example of an egyptian fraction. We study this statistic under the uniform measure on distinct parts partitions of $n$ and prove that, as $n \to \infty$, the sum of reciprocal parts is distributed away from its mean like a random harmonic sum.

Hong Qiao, Zhaoyou Wang, Gustav Andersson, Alexander Anferov

Linear optical quantum computing (LOQC) provides a compelling approach to quantum information processing, with a short list of physical requirements; however, experimental implementations have faced significant challenges. Itinerant phonons in quantum acoustics, combined with superconducting qubits, offer a compelling alternative to the quantum optics approa

Maria Parilli, Sergi Martinez, Carlos Mastalli

We introduce a novel method for handling endpoint constraints in constrained differential dynamic programming (DDP). Unlike existing approaches, our method guarantees quadratic convergence and is exact, effectively managing rank deficiencies in both endpoint and stagewise equality constraints. It is applicable to both forward and inverse dynamics formulation

Nestor Barraza, Gabriel Pena, Juliana Gambini, Florencia Carusela

A novel probabilistic framework for modelling anomalous diffusion is presented. The resulting process is Markovian, non-homogeneous, non-stationary, non-ergodic, and state-dependent. The fundamental law governing this process is driven by two opposing forces: one proportional to the current state, representing the intensity of autocorrelation or contagion, a

Matthew M. Murphy, Thomas G. Beatty, Luis Welbanks, Guangwei Fu

Although Neptunian-sized (2 - 5 R$_{Earth}$) planets appear to be extremely common in the Galaxy, many mysteries remain about their overall nature. To date, only 11 Neptunian-sized planets have had their atmospheres spectroscopically characterized, and these observations hint at interesting diversity within this class of planets. Much of our understanding of

Alexandre I. Danilenko, Artem Dudko

Let $G$ be a countable branch group of automorphisms of a spherically homogeneous rooted tree. Under some assumption on finitarity of $G$, we construct, for each sequence $\omega\in\{0,1\}^\Bbb N$, an irreducible unitary representation $\kappa_\omega$ of $G$. Every two representations $\kappa_\omega$ and $\kappa_{\omega'}$ are weakly equivalent. They are uni

Yu Liang, Hong Hu

Database Management System (DBMS) is the key component for data-intensive applications. Recently, researchers propose many tools to comprehensively test DBMS systems for finding various bugs. However, these tools only cover a small subset of diverse syntax elements defined in DBMS-specific SQL dialects, leaving a large number of features unexplored. In this

Denvir Higgins, Travis Yeager, Peter McGill, James Buchanan

Cislunar space is the volume between Earth's geosynchronous orbit and beyond the Moon, including the lunar Lagrange points. Understanding the stability of orbits within this space is crucial for the successful planning and execution of space missions. Orbits in cislunar space are influenced by the gravitational forces of the Sun, Earth, Moon, and other Solar

Cheng Wang, Sander G. Huisman, Thomas Basset, Romain Volk

We report on Lagrangian flow statistics from experimental measurements of homogeneous isotropic turbulence. The investigated flow is driven by 12 impellers inside an icosahedral volume. Seven impeller rotation rates are considered resulting in seven Reynolds numbers with $205 \leq \text{Re}_\lambda \leq 602$. We perform high-speed imaging using 3 cameras to

Qian Feng, David S. Martinez Lema, Jianxiang Feng, Zhaopeng Chen

Learning dexterous manipulation from few-shot demonstrations is a significant yet challenging problem for advanced, human-like robotic systems. Dense distilled feature fields have addressed this challenge by distilling rich semantic features from 2D visual foundation models into the 3D domain. However, their reliance on neural rendering models such as Neural

Muhammad Waseem, Kshitij Bhatta, Chen Li, Qing Chang

The manufacturing industry is undergoing a transformative shift, driven by cutting-edge technologies like 5G, AI, and cloud computing. Despite these advancements, effective system control, which is crucial for optimizing production efficiency, remains a complex challenge due to the intricate, knowledge-dependent nature of manufacturing processes and the reli

Federico Petrovich, R. Rossignoli

We determine the necessary and sufficient conditions which ensure that an $N=2m$-particle fermionic or bosonic state has the form $|\Psi\rangle\propto(A^{\dagger})^{m}|0\rangle$, where $A^{\dagger}=\tfrac{1}{2}\sum_{i,j}A_{ij}c_{i}^{\dagger}c_{j}^{\dagger}$ is a general pair creation operator. These conditions can be cast as an eigenvalue equation for a modi

Junior da Silva Bessa, João Vitor da Silva, Ginaldo de Santana Sá

In this manuscript, we establish the existence and sharp geometric regularity estimates for bounded solutions of a class of quasilinear parabolic equations in non-divergence form with non-homogeneous degeneracy. The model equation in this class is given by \[ \partial_{t} u = \left(|\nabla u|^{\mathfrak{p}} + \mathfrak{a}(x, t)|\nabla u|^{\mathfrak{q}}\right

Edoardo Zorzi, Alberto Castellini, Leonidas Bakopoulos, Georgios Chalkiadakis

Most offline RL algorithms return optimal policies but do not provide statistical guarantees on desirable behaviors. This could generate reliability issues in safety-critical applications, such as in some multiagent domains where agents, and possibly humans, need to interact to reach their goals without harming each other. In this work, we propose a novel of

Santosh Bhupathi

Generative AI (GenAI) is transforming industries by enabling intelligent content generation, automation, and decision-making. However, the effectiveness of GenAI applications depends significantly on efficient data storage, retrieval, and contextual augmentation. This paper explores the critical role of databases in GenAI workflows, emphasizing the importanc

Shuangbao Wang

This article presents a novel network protocol that incorporates a quantum photonic channel for symmetric key distribution, a Dilithium signature to replace factor-based public key cryptography for enhanced authentication, security, and privacy. The protocol uses strong hash functions to hash original messages and verify heightened data integrity at the dest

Jingyun Chen, Yading Yuan

Federated Learning (FL) presents a promising avenue for collaborative model training among medical centers, facilitating knowledge exchange without compromising data privacy. However, vanilla FL is prone to server failures and rarely achieves optimal performance on all participating sites due to heterogeneous data distributions among them. To overcome these

Jiangtong Zhu, Zhao Yang, Yinan Shi, Jianwu Fang

Online vector map construction based on visual data can bypass the processes of data collection, post-processing, and manual annotation required by traditional map construction, which significantly enhances map-building efficiency. However, existing work treats the online mapping task as a local range perception task, overlooking the spatial scalability requ

Domenec Espriu

Alexander Andreevich Andrianov was a remarkable personality in Russian physics during the last decades. A member of th prestigious school of theoretical physics in Saint Petersburg, he made relevant contributions to a number of topics along his life. His activity ran in parallel with profound changes in his home country and the international arena. He was ve

Priyesh Kakka, Jonathan F. MacArt

Extending gradient-type turbulence closures to turbulent premixed flames is challenging due to the significant influence of combustion heat release. We incorporate a deep neural network (DNN) into Reynolds-averaged Navier--Stokes (RANS) models for the turbulent viscosity and thermal conductivity as nonlinear functions of the local flow state and thermochemic

Saif R. Kazi, Kexin Wang, Lorenz T. Biegler

Optimal control for switch-based dynamical systems is a challenging problem in the process control literature. In this study, we model these systems as hybrid dynamical systems with finite number of unknown switching points and reformulate them using non-smooth and non-convex complementarity constraints as a mathematical program with complementarity constrai

Arsalan Ali Malik, Harshvadan Mihir, Aydin Aysu

Fault injection attacks (FIA) pose significant security threats to embedded systems as they exploit weaknesses across multiple layers, including system software, instruction set architecture (ISA), microarchitecture, and physical hardware. Early detection and understanding of how physical faults propagate to system-level behavior are essential to safeguardin

Edson Luiz Ursini, Paulo S. Martins

The evaluation of the probability of union of a large number of independent events requires several combinations involving the factorial and the use of high performance computers with several hours of processing. Bounds and simplifications on the probability of the union are useful in the analysis of stochastic problems across various areas including (but no

Akshay Korpe, Osman El-Atwani, Enrique Martinez Saez

Tungsten (W) exhibits desirable properties for extreme applications, such as the divertor in magnetic fusion reactors, but its practicality remains limited due to poor formability and insufficient irradiation resistance. In this work, we study the intrinsic ductility of body-centered cubic WTaCrV based high entropy alloys (HEAs), which are known to exhibit e

Hetarth Chopra, Vidhi Rambhia, Vikram Adve

As specialized large language models (LLMs) become increasingly prevalent, model merging methods are being used to combine them to create a single multi-task model without requiring any additional data or training. However, these approaches fall short when the objective of merging is to increase the downstream model's performance on a particular task-specifi