arXiv papers from September 2024

Dasong Li, Wenjie Li, Baili Lu, Hongsheng Li

Understanding and modeling the popularity of User Generated Content (UGC) short videos on social media platforms presents a critical challenge with broad implications for content creators and recommendation systems. This study delves deep into the intricacies of predicting engagement for newly published videos with limited user interactions. Surprisingly, ou

Xuan Hu, Hailong Chen, Yichi Zhang, Zening Wang

This paper presents a unified framework for bond-associated peridynamic material correspondence models that were proposed to inherently address the issue of material instability or existence of zero-energy modes in the conventional correspondence formulation. The conventional formulation is well-known for having the issue of material instability due to the n

Zeda Xu, John Liechty, Sebastian Benthall, Nicholas Skar-Gislinge

Volatility, which indicates the dispersion of returns, is a crucial measure of risk and is hence used extensively for pricing and discriminating between different financial investments. As a result, accurate volatility prediction receives extensive attention. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its succeeding varia

Levi Burner, Cornelia Fermüller, Yiannis Aloimonos

Imagine sitting at your desk, looking at objects on it. You do not know their exact distances from your eye in meters, but you can immediately reach out and touch them. Instead of an externally defined unit, your sense of distance is tied to your action's embodiment. In contrast, conventional robotics relies on precise calibration to external units, with whi

Anderson Chaves, Eduardo Ogasawara, Patrick Valduriez, Fabio Porto

Predictive queries over spatiotemporal (ST) stream data pose significant data processing and analysis challenges. ST data streams involve a set of time series whose data distributions may vary in space and time, exhibiting multiple distinct patterns. In this context, assuming a single machine learning model would adequately handle such variations is likely t

Eric Burns, Stephen Lesage, Adam Goldstein, Michael S. Briggs

The prompt spectra of gamma-ray bursts are known to follow broadband continuum behavior over decades in energy. GRB 221009A, given the moniker the brightest of all time (BOAT), is the brightest gamma-ray burst identified in half a century of observations, and was first identified by the Fermi Gamma-ray Burst Monitor (GBM). On behalf of the Fermi-GBM Team, Le

Mohssen E. Elshaar, Zeyad M. Manaa, Mohammed R. Elbalshy, Abdul Jabbar Siddiqui

Unmanned Aerial Vehicles (UAVs) are becoming more popular in various sectors, offering many benefits, yet introducing significant challenges to privacy and safety. This paper investigates state-of-the-art solutions for detecting and tracking quadrotor UAVs to address these concerns. Cutting-edge deep learning models, specifically the YOLOv5 and YOLOv8 series

Sylvio R. Bistafa

The present work examines and compares the approaches of Jacob Bernoulli and Leonhard Euler to the problem of ship propulsion generated by internal forces. Jacob Bernoulli's analysis, developed in the late 17th century, relies on geometric interpretations and algebraic relationships to estimate the impulse exerted by a pendulum within a ship. His results, ho

Sylvio R. Bistafa

The following translation of Leonhard Euler's "Examination of an Artifice for Propelling a Ship by the Principle of Internal Motion," originally published in 1750, offers a glimpse into a fascinating historical debate in the field of mechanics. This work critically examines a proposal by Jacob Bernoulli, one of the foremost mathematicians of the 17th century

Dongcheng Li, W. Eric Wong, Xiaodan Wang, Sean Pan

This paper introduces a method for detecting vulnerabilities in smart contracts using static analysis and a multi-objective optimization algorithm. We focus on four types of vulnerabilities: reentrancy, call stack overflow, integer overflow, and timestamp dependencies. Initially, smart contracts are compiled into an abstract syntax tree to analyze relationsh

Ricardo A. Podestá, Denis E. Videla

In this work we consider the class of Cayley graphs known as generalized Paley graphs (GP-graphs for short) given by $\Gamma(k,q) = Cay(\mathbb{F}_q, \{x^k : x\in \mathbb{F}_q^* \})$, where $\mathbb{F}_q$ is a finite field with $q$ elements, both in the directed and undirected case. Hence $q=p^m$ with $p$ prime, $m\in \mathbb{N}$ and one can assume that $k\m

Y. F. Adans, A. R. Aguirre, J. F. Gomes, G. V. Lobo

A systematic construction for supersymmetric negative graded (non-local) flows for mKdV and KdV based on $sl(2,1)$ with a principal gradation is proposed in this paper. We show that smKdV and sKdV can be mapped onto each other through a gauge super Miura transformation, together with an additional condition for the negative flows, which ensure the supersymme

Grigorios Pavliotis, Renato Spacek, Gabriel Stoltz, Urbain Vaes

We propose a method utilizing physics-informed neural networks (PINNs) to solve Poisson equations that serve as control variates in the computation of transport coefficients via fluctuation formulas, such as the Green--Kubo and generalized Einstein-like formulas. By leveraging approximate solutions to the Poisson equation constructed through neural networks,

Alireza Ardalani, Joseph Antonucci, Iulian Neamtiu

Mobile apps are used in a variety of health settings, from apps that help providers, to apps designed for patients, to health and fitness apps designed for the general public. These apps ask the user for, and then collect and leak a wealth of Personal Information (PI). We analyze the PI that apps collect via their user interface, whether the app or third-par

Maru Sarazola, Brandon Shapiro, Inna Zakharevich

2-Segal spaces arise not only from $S_\dotp$-constructions associated to Waldhausen and (proto) exact categories, but also from $S_\dotp$-constructions associated to certain double-categorical structures. A major step in this direction is due to the work of Bergner--Osorno--Ozornova--Rovelli--Scheimbauer, who propose augmented stable double Segal objects as

Rohaifa Khaldi, Domingo Alcaraz-Segura, Ignacio Sánchez-Herrera, Javier Martinez-Lopez

Social media images provide valuable insights for modeling, mapping, and understanding human interactions with natural and cultural heritage. However, categorizing these images into semantically meaningful groups remains highly complex due to the vast diversity and heterogeneity of their visual content as they contain an open-world human and nature elements.

Fatemeh Lotfi, Fatemeh Afghah

As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes a crucial solution for real-time network data collection, analysis, and dynamic management of network resources including radio resource blocks and downlink power allocation. Util

Amina Kobenova, Cyan DeVeaux, Samyak Parajuli, Andrzej Banburski-Fahey

Generative artificial intelligence has shown promise in prompting virtual worlds into existence, yet little attention has been given to understanding how this process unfolds as social interaction. We present Social Conjurer, a framework for AI-augmented dynamic 3D scene co-creation, where multiple users collaboratively build and modify virtual worlds in rea

Shashank Subramanian, Ermal Rrapaj, Peter Harrington, Smeet Chheda

Generative AI, in particular large transformer models, are increasingly driving HPC system design in science and industry. We analyze performance characteristics of such transformer models and discuss their sensitivity to the transformer type, parallelization strategy, and HPC system features (accelerators and interconnects). We utilize a performance model t

Mike D. Schneider

Physicists and philosophers are increasingly prone to regarding our current physical theories as providing 'effective descriptions' of real-world systems. In the context of quantum gravity research, this fuels a common view that the classical spacetime theory of general relativity provides effective descriptions where it is successfully applied. That common

Zida Wu, Ankur Mehta

A crucial challenge in decentralized systems is state estimation in the presence of unknown inputs, particularly within heterogeneous sensor networks with dynamic topologies. While numerous consensus algorithms have been introduced, they often require extensive information exchange or multiple communication iterations to ensure estimation accuracy. This pape

Tuan Do, Bernie Boscoe, Evan Jones, Yun Qi Li

We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam Survey PDR2 in five imaging filters ($g,r,i,z,y$) with spectroscopically confirmed redshifts as ground truth. Such a da

Yuchen Chu, Zeshi Yang

In this work, we present a data-driven framework for generating diverse in-betweening motions for kinematic characters. Our approach injects dynamic conditions and explicit motion controls into the procedure of motion transitions. Notably, this integration enables a finer-grained spatial-temporal control by allowing users to impart additional conditions, suc

Qiyu Sha, Daniel Murnane, Max Fieg, Shelley Tong

Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in ML-based tracking allow for new inroads into previously inaccessible territory, such as reconstruction of tracks which do

Zhengxing Peng, Antoine Lainé, Ka Chon Ng, Mutian Hua

Chemical recycling of plastics to its constituent monomers is a promising solution to develop a sustainable circular plastic economy. An in-situ X-ray absorption spectra (XAS) characterization is an important way to understand the deconstruction process. However, radiation damage, and long acquisition time, prevent such characterization for fast chemical pro

Sachin Karmani, Thanushon Sivakaran, Gaurav Prasad, Mehmet Ali

Deep learning models often function as black boxes, providing no straightforward reasoning for their predictions. This is particularly true for computer vision models, which process tensors of pixel values to generate outcomes in tasks such as image classification and object detection. To elucidate the reasoning of these models, class activation maps (CAMs)

Aleyna Kütük, Tevfik Metin Sezgin

Scene sketch semantic segmentation is a crucial task for various applications including sketch-to-image retrieval and scene understanding. Existing sketch segmentation methods treat sketches as bitmap images, leading to the loss of temporal order among strokes due to the shift from vector to image format. Moreover, these methods struggle to segment objects f

Geoffrey Lovelace, Kyle C. Nelli, Nils Deppe, Nils L. Vu

Binary black holes are the most abundant source of gravitational-wave observations. Gravitational-wave observatories in the next decade will require tremendous increases in the accuracy of numerical waveforms modeling binary black holes, compared to today's state of the art. One approach to achieving the required accuracy is using spectral-type methods that

Chiara Coviello, Maria Luisa Chiofalo, Dario Grasso, Stefano Liberati

Phonons in Bose-Einstein condensates propagate as massless scalar particles on top of an emergent acoustic metric. This hydrodynamics/gravity analogy can be exploited to realize acoustic black holes, featuring an event horizon that traps phonons. We show that by an appropriate external potential, gravitational wave-like perturbations of the acoustic metric c

Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data. This study aims to bridge the gap by addressing issues regarding textual information loss in surgical lecture videos and the spatial-temporal challenges of surgical VLP. We propose a hierarchical knowledge augmentation appro

Jian Shi, Zhenyu Li, Peter Wonka

We introduce \textit{ImmersePro}, an innovative framework specifically designed to transform single-view videos into stereo videos. This framework utilizes a novel dual-branch architecture comprising a disparity branch and a context branch on video data by leveraging spatial-temporal attention mechanisms. \textit{ImmersePro} employs implicit disparity guidan

Kejia Ren, Gaotian Wang, Andrew S. Morgan, Lydia E. Kavraki

Nonprehensile actions such as pushing are crucial for addressing multi-object rearrangement problems. Many traditional methods generate robot-centric actions, which differ from intuitive human strategies and are typically inefficient. To this end, we adopt an object-centric planning paradigm and propose a unified framework for addressing a range of large-sca

Vinayak Arannil, Neha Narwal, Sourav Sanjukta Bhabesh, Sai Nikhil Thirandas

Large Language Models (LLMs) have shown remarkable ability to generalize effectively across numerous industry domains while executing a range of tasks. Many of these competencies are obtained from the data utilized during the pre-training phase of the Language Models (LMs). However, these models exhibit limitations when tasked with performing in specialized

Qingyuan Yang, Gregory S Elsaesser, Marcus Van Lier-Walqui, Trude Eidhammer

We present a new additive method, nicknamed sage for Simplified Additive Gaussian processes Emulator, to emulate climate model Perturbed Parameter Ensembles (PPEs). It estimates the value of a climate model output as the sum of additive terms. Each additive term is the mean of a Gaussian Process, and corresponds to the impact of a parameter or parameter grou

Jiangshan Zhang, Vivek Pradhan, Yuxi Zhao

The Binary Emax model is widely employed in dose-response analysis during drug development, where missing data often pose significant challenges. Addressing nonignorable missing binary responses, where the likelihood of missing data is related to unobserved outcomes, is particularly important, yet existing methods often lead to biased estimates. This issue i

Lancelot Da Costa, Tomáš Gavenčiak, David Hyland, Mandana Samiei

This paper offers a roadmap for the development of scalable aligned artificial intelligence (AI) from first principle descriptions of natural intelligence. In brief, a possible path toward scalable aligned AI rests upon enabling artificial agents to learn a good model of the world that includes a good model of our preferences. For this, the main objective is

Jeremy I Skipper, Joanna Kuc, Greg Cooper, Christopher Timmermann

How is language related to consciousness? Language functions to categorise perceptual experiences (e.g., labelling interoceptive states as 'happy') and higher-level constructs (e.g., using 'I' to represent the narrative self). Psychedelic use and meditation might be described as altered states that impair or intentionally modify the capacity for linguistic c

Qianwen Xing, Chang Yu, Sining Huang, Qi Zheng

In contemporary economic society, credit scores are crucial for every participant. A robust credit evaluation system is essential for the profitability of core businesses such as credit cards, loans, and investments for commercial banks and the financial sector. This paper combines high-performance models like XGBoost and LightGBM, already widely used in mod

Weitai Kang, Haifeng Huang, Yuzhang Shang, Mubarak Shah

Recent advancements in 3D Large Language Models (3DLLMs) have highlighted their potential in building general-purpose agents in the 3D real world, yet challenges remain due to the lack of high-quality robust instruction-following data, leading to limited discriminative power and generalization of 3DLLMs. In this paper, we introduce Robin3D, a powerful 3DLLM

Benjamin Fehrman, Benjamin Gess

The purpose of this paper is to establish a well-posedness theory for conservative stochastic partial differential equations on the whole space. This class of stochastic PDEs arises in fluctuating hydrodynamics, and includes the Dean--Kawasaki equation with correlated noise. In combination with the analysis of the authors and Heydecker [35], the connection b

Anna Deichler, Jim O'Regan, Jonas Beskow

In this paper, we present a novel dataset captured using a VR headset to record conversations between participants within a physics simulator (AI2-THOR). Our primary objective is to extend the field of co-speech gesture generation by incorporating rich contextual information within referential settings. Participants engaged in various conversational scenario

Michael Zemcov, Richard Feder, Ryan Wills

We have regenerated Herschel-SPIRE maps covering 360 square degrees near the celestial equator. These are the largest extragalactic surveys designed to overlap with cosmic microwave background legacy fields mapped at sub-mm wavelengths. We provide documentation detailing their construction and use. The maps are available on zenodo as https://doi.org/10.5281/

Cristián Peña, Christina Wang, Si Xie, Adolf Bornheim

We present the first detailed study of an 8-channel $2\times2$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV proton beam and 8 GeV electron and pion beam at the Fermilab Test Beam Facility. The SMSPD detection efficiency was measured for the first time for protons, electrons, and pions, enabled by the use of a

Marina Ribeiro, Bárbara Malcorra, Natália B. Mota, Rodrigo Wilkens

Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully understood. Recent advancements in Large Language Model (LLM) architectures have developed many tools to identify representative

Weiliang Qi, Jiahao Cao, Darsh Poddar, Sophia Li

With the rapid development and widespread use of advanced network systems, software vulnerabilities pose a significant threat to secure communications and networking. Learning-based vulnerability detection systems, particularly those leveraging pre-trained language models, have demonstrated significant potential in promptly identifying vulnerabilities in com

Benjamin Baily, Amichai Lampert

Let $f$ be a homogeneous polynomial over a field. For many fields, including number fields and function fields, we prove that the strength of $f$ is bounded above by a constant multiple of the Birch rank of $f.$ The constant depends only on the degree of $f$ and the absolute transcendence degree of the field. This is the first linear bound obtained for forms

Rahul Somasundaram, Isak Svensson, Soumi De, Andrew E. Deneris

Understanding the interactions between nucleons in dense matter is an important challenge in theoretical physics. Effective field theories have emerged as the dominant approach to address this problem at low energies, with many successful applications to the structure of nuclei and the properties of dense nucleonic matter. However, how far into the interior

Howard S. Cohl, Hans Volkmer

In the $q^{-1}$-symmetric Askey scheme, namely the $q^{-1}$-Askey--Wilson, continuous dual $q^{-1}$-Hahn, $q^{-1}$-Al-Salam--Chihara, continuous big $q^{-1}$-Hermite and continuous $q^{-1}$-Hermite polynomials, we compute bilateral discrete and continuous orthogonality relations. We also derive a $q$-beta integral which comes from the continuous orthogonalit

Aitor Iribar Lopez

We prove the Harder-Siegel formula for the Euler characteristic of $\mathcal{A}_g$ via the intersection theory of $\overline{\mathcal{M}}_g$ and a vanishing result for lambda classes on the boundary of the toroidal compactifications of $\mathcal{A}_g$, recently proven by Canning, Molcho, Oprea and Pandharipande.

Martin Kittel, Wolf-Peter Schill

Variable renewable energy droughts, so called Dunkelflaute events, emerge as a challenge for climate-neutral energy systems based on variable renewables. Here we characterize European drought events for on- and offshore wind power, solar photovoltaics, and renewable technology portfolios, using 38 historic weather years and an advanced identification method.

Ashley R. Bemis, Christine D. Wilson, Piyush Sharda, Ian D. Roberts

We model emissivities of the HCN and CO $J=1-0$ transitions using measured properties of clouds found in normal star forming galaxies and more extreme systems. These models are compared with observations of HCN and CO $J=1-0$ transitions. We combine these model emissivities with predictions of gravoturbulent models of star formation, explore the impact of ex

Tomas Ortega, Hamid Jafarkhani

Recent advances in federated learning have shown that asynchronous variants can be faster and more scalable than their synchronous counterparts. However, their design does not include quantization, which is necessary in practice to deal with the communication bottleneck. To bridge this gap, we develop a novel algorithm, Quantized Asynchronous Federated Learn

Nicolò D'Anna, Jamie Bragg, Elizabeth Skoropata, Nazareth Ortiz Hernández

Fabrication of semiconductor heterostructures is now so precise that metrology has become a key challenge for progress in science and applications. It is now relatively straightforward to characterize classic III-V and group IV heterostructures consisting of slabs of different semiconductor alloys with thicknesses of $\sim$5 nm and greater using sophisticate

Rithvik Prakki

Active inference is a mathematical framework for understanding how agents (biological or artificial) interact with their environments, enabling continual adaptation and decision-making. It combines Bayesian inference and free energy minimization to model perception, action, and learning in uncertain and dynamic contexts. Unlike reinforcement learning, active

Hamid Jafarkhani, Hossein Maleki, Mojtaba Vaezi

Next-generation multiple access (NGMA) serves as an umbrella term for transmission schemes distinct from conventional orthogonal methods. A key candidate of NGMA, non-orthogonal multiple access (NOMA), emerges as a solution to enhance connectivity by allowing multiple users to share time, frequency, and space concurrently. However, NOMA faces challenges in i

I. Bediaga, T. Frederico, P. C. Magalhães, M. A. Shalchi

We explore the contribution of hadronic final state interactions (FSI) to propose a production mechanism and interpret the puzzle on helicity angle distributions in $B^+\to p\bar p \pi^+$ and $B^+\to p\bar p K^+$ decays. Experimental results indicate opposite helicity angle $\theta_p$ distributions in those two channels with the difference presenting a remar

Evgeny Saveliev, Tim Schubert, Thomas Pouplin, Vasilis Kosmoliaptsis

Despite its significant promise and continuous technical advances, real-world applications of artificial intelligence (AI) remain limited. We attribute this to the "domain expert-AI-conundrum": while domain experts, such as clinician scientists, should be able to build predictive models such as risk scores, they face substantial barriers in accessing state-o

Denise Hung, Brian C. Lemaux, Olga Cucciati, Ben Forrest

The Charting Cluster Construction with VUDS and ORELSE (C3VO) survey is an ongoing imaging and spectroscopic campaign aiming to map out the growth of structure up to $z\sim5$ and was born from the combination of the Visible Multi-Object Spectrograph Ultra Deep Survey and the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. As w

Kaelee S. Parker, Danielle A. Berg, Simon Gazagnes, John Chisholm

Rest-frame far-ultraviolet (FUV) observations from JWST are revolutionizing our understanding of the high-z galaxies that drove reionization and the mechanisms by which they accomplished it. To fully interpret these observations, we must be able to diagnose how properties of the interstellar medium (ISM; e.g., column density, covering fraction, outflow veloc

Daniele Rosso, Neil Saunders

We define a new family of algebraic varieties, called exotic Spaltenstein varieties. These generalise the notion of Spaltenstein varieties (which are the partial flag analogues to classical Springer fibres) to the case of exotic Springer fibres. We show that, for self-adjoint nilpotent endomorphisms of order two, the top-dimensional irreducible components ar

Francisco Ricardo Torres Arvizu, Adrian Ortega, Hernán Larralde

We study the spectrum, eigenstates and transport properties of a simple $\mathcal{P}\mathcal{T}$-symmetric model consisting in a finite, complex, square well potential with a delta potential at the origin. We show that as the strength of the delta potential increases, the system exhibits exceptional points accompanied by an accumulation of density associated

Martin A. Achondo, Jehanzeb H. Chaudhry, Christopher D. Cooper

Physics-informed neural networks (PINN) is a machine learning (ML)-based method to solve partial differential equations that has gained great popularity due to the fast development of ML libraries in the last few years. The Poisson-Boltzmann equation (PBE) is widely used to model mean-field electrostatics in molecular systems, and in this work we present a d

Abdulmajeed Alsubhi, Rosemary Renaut

We consider the solution of the $\ell_1$ regularized image deblurring problem using isotropic and anisotropic regularization implemented with the split Bregman algorithm. For large scale problems, we replace the system matrix $A$ using a Kronecker product approximation obtained via an approximate truncated singular value decomposition for the reordered matri

Qiang Ye

Accelerated training algorithms, such as adaptive learning rates (or preconditioning) and various normalization methods, are widely used but not fully understood. When regularization is introduced, standard optimizers like adaptive learning rates may not perform effectively. This raises the need for alternative regularization approaches such as AdamW and the

Qi Wu, Zipeng Fu, Xuxin Cheng, Xiaolong Wang

Learning-based methods have achieved strong performance for quadrupedal locomotion. However, several challenges prevent quadrupeds from learning helpful indoor skills that require interaction with environments and humans: lack of end-effectors for manipulation, limited semantic understanding using only simulation data, and low traversability and reachability

Kamil Nalikowski, Valera Veryazov, Kjeld Beeks, Thorsten Schumm

Building on recent advances of the embedded cluster approach combined with multiconfigurational theory, this work investigates the electronic states in thorium-doped CaF2 crystals. Th:CaF2 is currently establishing as a promising material for solid-state nuclear clocks, which utilize the laser-accessible isomeric state in thorium-229. By comparing simulated

Qin Li, Maria Oprea, Li Wang, Yunan Yang

Inverse problems in physical or biological sciences often involve recovering an unknown parameter that is random. The sought-after quantity is a probability distribution of the unknown parameter, that produces data that aligns with measurements. Consequently, these problems are naturally framed as stochastic inverse problems. In this paper, we explore three

Arturo Rodriguez, Piyush Kumar, Cesar Diaz-Caraveo, Richard O. Adansi

In this aerothermal study, we performed a two-dimensional steady-state Computational Fluid Dynamics (CFD) and heat conduction simulation at Mach 6. The key to our methodology was a one-way coupling between CFD surface temperature as a boundary condition and the calculation of the heat transfer flux and temperatures inside the solid stainless-steel body of a

Mang Hei Gordon Lee, Enrico Pajer, Mathieu Giroux, Holmfridur S. Hannesdottir

By directly probing the initial conditions of our universe, cosmological surveys offer us a unique observational handle on quantum field theory in curved spacetime with dynamical gravity and might even allow us to glean information about a full theory of quantum gravity. Here we report on recent progress to study the natural observables in the problem, namel

Gyula Lakos

We approach the convergence of the Magnus, Wilcox, and symmetric Wilcox expansions by a non-commutative heat equation derived from the Maurer-Cartan equation.

Md Rejwanur Rahman, Adrian Rodriguez-Marek, Nina Stark, Grace Massey

The geotechnical evaluation of seabed sediments is important for engineering projects and naval applications, offering valuable insights into sediment properties, behavior, and strength. Obtaining high-quality seabed samples can be a challenging task, making in situ testing an essential part of site characterization. Free-fall penetrometers (FFPs) are robust

Panayotis G. Papaioannou, George P. Papaioannou, George Evangelidis, George Gavalakis

We investigate the impact of several critical events associated with the Russo Ukrainian war, started officially on 24 February 2022 with the Russian invasion of Ukraine, on ten European electricity markets, two natural gas markets (the European reference trading hub TTF and N.Y. NGNMX market) and how these markets interact to each other and with USDRUB exch

Katherine Slyman, Emmanuel Fleurantin, Christopher K. R. T. Jones

Rate-induced tipping (R-tipping) occurs when a ramp parameter changes rapidly enough to cause the system to tip between co-existing, attracting states, while noise-induced tipping (N-tipping) occurs when there are random transitions between two attractors of the underlying deterministic system. This work investigates R-tipping and N-tipping events in a carbo

Wentao Tang

The mathematical properties and data-driven learning of the Koopman operator, which represents nonlinear dynamics as a linear mapping on a properly defined functional spaces, have become key problems in nonlinear system identification and control. However, Koopman operators that are approximately learned from snapshot data may not always accurately predict t

Oliver J. Shindell, Aaron C. Davis, David J. Cappelleri

This paper presents a contact-based micromanipulation system for the alignment and installment of microscale magnets into micro robots and devices. Affixing tweezers to a three degree of freedom micromanipulator allows for precise movement of objects. The use of non-magnetic tweezers permits the assembly of magnetized robots, and a magnetic rotating stage al

David Grangier, Simin Fan, Skyler Seto, Pierre Ablin

Specialist language models (LMs) focus on a specific task or domain on which they often outperform generalist LMs of the same size. However, the specialist data needed to pretrain these models is only available in limited amount for most tasks. In this work, we build specialist models from large generalist training sets instead. We propose a novel method, Cl

Zarif Ahsan, Xiran Liu, Noah A. Rosenberg

Motivated by a problem in population genetics, we examine the combinatorics of dissimilarity for pairs of random unordered draws of multiple objects, with replacement, from a collection of distinct objects. Consider two draws of size $K$ taken with replacement from a set of $I$ objects, where the two draws represent samples from potentially distinct probabil

Cesar Ayala, Gorazd Cvetic

We construct a QCD coupling ${\mathcal{A}}(Q^2)$ in the Effective Charge (ECH) scheme of the canonical part $d(Q^2)$ of the (inelastic) polarised Bjorken Sum Rule (BSR) ${\overline \Gamma}_1^{{\rm p-n}}(Q^2)$. In the perturbative domain, the coupling ${\mathcal{A}}(Q^2)$ practically coincides with the perturbative coupling $a(Q^2)$ [$\equiv \alpha_s(Q^2)/\pi

Subhadip Boral, Rikathi Pal, Ashish Ghosh

Intersecting manifold segmentation has been a focus of research, where individual manifolds, that intersect with other manifolds, are separated to discover their distinct properties. The proposed method is based on the intuition that when a manifold in $D$ dimensional space with an intrinsic dimension of $d$ intersects with another manifold, the data varianc

Min Xian, Tao Wang, Sai Zhang, Fei Xu

Identifying and classifying shutdown initiating events (SDIEs) is critical for developing low power shutdown probabilistic risk assessment for nuclear power plants. Existing computational approaches cannot achieve satisfactory performance due to the challenges of unavailable large, labeled datasets, imbalanced event types, and label noise. To address these c

Stanislav Minsker, Yinan Shen

Is there a natural way to order data in dimension greater than one? The approach based on the notion of data depth, often associated with John Tukey, is among the most popular. Tukey's depth has found applications in robust statistics, graph theory, and the study of elections and social choice. We present improved performance guarantees for empirical Tukey's

Chenhao Fang, Derek Larson, Shitong Zhu, Sophie Zeng

This paper presents new methods that have the potential to improve privacy process efficiency with LLM and RAG. To reduce hallucination, we continually pre-train the base LLM model with a privacy-specific knowledge base and then augment it with a semantic RAG layer. Our evaluations demonstrate that this approach enhances the model performance (as much as dou

Lan Li, Liri Fang, Yiren Liu, Vetle I. Torvik

Entity resolution (ER) is the process of determining whether two representations refer to the same real-world entity and plays a crucial role in data curation and data cleaning. Recent studies have introduced the KAER framework, aiming to improve pre-trained language models by augmenting external knowledge. However, identifying and documenting the external k

Vedant Vohra

Economists often estimate causal effects of policies on multiple outcomes and summarize them into scalar measures of cost-effectiveness or welfare, such as the Marginal Value of Public Funds (MVPF). In many settings, microdata underlying these estimates are unavailable, leaving researchers with only published estimates and their standard errors. We develop t

Jie Chen

We use matchings on Lyndon words to classify flat knots up to 8 crossings. Using flat knots invariants such as the based matrix, the $\phi$-invariant, the flat arrow polynomial, and the flat Jones-Krushkal polynomial, we distinguish all flat knots up to 7 crossings except for five pairs. Among the many flat knots considered, we find examples that are: (i) al

Yejin Lee, Anna Sun, Basil Hosmer, Bilge Acun

Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system design and optimization opportunities. The technology is capable of understanding and responding in multiple modalities. However, the advanced capability currently comes with signi

Dimitris Diamantidis, Takis Konstantopoulos, Linglong Yuan

We consider two independent Erd\H{o}s-R\'enyi random graphs, with possibly different parameters, and study two isomorphism problems, a graph embedding problem and a common subgraph problem. Under certain conditions on the graph parameters we show a sharp asymptotic phase transition as the graph sizes tend to infinity. This extends known results for the case

Casey L. Brinkman, Lauren M. Weiss, Daniel Huber, Rena A. Lee

Hundreds of exoplanets between 1-1.8 times the size of the Earth have been discovered on close in orbits. However, these planets show such a diversity in densities that some appear to be made entirely of iron, while others appear to host gaseous envelopes. To test this diversity in composition, we update the masses of 5 rocky exoplanets (HD 93963 A b, Kepler

Pierre Monmarché, Renato Spacek, Gabriel Stoltz

In molecular dynamics, transport coefficients measure the sensitivity of the invariant probability measure of the stochastic dynamics at hand with respect to some perturbation. They are typically computed using either the linear response of nonequilibrium dynamics, or the Green--Kubo formula. The estimators for both approaches have large variances, which mot

A. K. Pogrebkov

In \textit{SIGMA} \textbf{17} (2021), 091, 12 p.p.\ we have presented an integrable system with a negative time variable number for the Davey-Stewartson hierarchy. Here we develop this approach to construct an integrable equation with a lower time variable number. In addition, we show that the system reduced by this time is a new integrable equation in the d

Tim Beyer, Angela Dai

The automated creation of accurate musical notation from an expressive human performance is a fundamental task in computational musicology. To this end, we present an end-to-end deep learning approach that constructs detailed musical scores directly from real-world piano performance-MIDI files. We introduce a modern transformer-based architecture with a nove

Dmitry Kosolobov

Much research in stringology focuses on structures that can, in a way, ``grasp'' repeats (substrings that occur multiple times) as, for example, the so-called runs, a.k.a. maximal repetitions, compactly describe all tandem repeats. In this paper we introduce closed repeats: given a string $s$, its non-empty substring $s[i\,..\,j]$ is a right (left) closed re

Tin Yuet Chung, Majid Latifi

This research investigates the classification of Environmental, Social, and Governance (ESG) information within textual disclosures. The aim is to develop and evaluate binary classification models capable of accurately identifying and categorizing E, S and G-related content respectively. The motivation for this research stems from the growing importance of E

Éric Colin de Verdière, Petr Hliněný

The basic (and traditional) crossing number problem is to determine the minimum number of crossings in a topological drawing of an input graph in the plane. We develop a unified framework yielding fixed-parameter tractable (FPT) algorithms for many generalized crossing number problems. Our framework takes the following form. We fix a surface S and a class D

Seyede Zahra Zarei, Fatemeh Tarighi Tabesh, Mehdi Abdi

Interfering-or-not-interfering quantum key distribution (INI-QKD) is an innovative protocol whose performance surpasses existing twin-field protocol variants. In this study, we introduce an additional step of advantage distillation (AD) after the quantum communication phase to further enhance its performance. Through the AD the raw key is partitioned into sm

Saihui Hou, Panjian Huang, Zengbin Wang, Yuan Liu

This paper addresses the challenge of animal re-identification, an emerging field that shares similarities with person re-identification but presents unique complexities due to the diverse species, environments and poses. To facilitate research in this domain, we introduce OpenAnimals, a flexible and extensible codebase designed specifically for animal re-id

Tuan Anh Nguyen

We prove that multilevel Picard approximations are capable of approximating solutions of semilinear heat equations in $L^{p}$-sense, ${p}\in [2,\infty)$, in the case of gradient-dependent, Lipschitz-continuous nonlinearities, in the sense that the computational effort of the multilevel Picard approximations grow at most polynomially in both the dimension $d$

Yichen Guo, Paul Fischer, Misun Min

A spectral-element-based formulation of incompressible MHD is presented in the context of the open-source fluid-thermal code, Nek5000/RS. The formulation supports magnetic fields in a solid domain that surrounds the fluid domain. Several steady-state and time-transient model problems are presented as part of the code verification process. Nek5000/RS is desig

Yi-Hao Peng, Faria Huq, Yue Jiang, Jason Wu

Enabling machines to understand structured visuals like slides and user interfaces is essential for making them accessible to people with disabilities. However, achieving such understanding computationally has required manual data collection and annotation, which is time-consuming and labor-intensive. To overcome this challenge, we present a method to genera

Clement Yung

We introduce the notion of a weak A2 space (or wA2-space), which generalises spaces satisfying Todor\v{c}evi\'c's axioms A1-A4 and countable vector spaces. We show that in any Polish weak A2 space, analytic sets are Kastanas Ramsey, and discuss the relationship between Kastanas Ramsey sets and sets in the projective hierarchy. We also show that in all spaces