April 2024 arXiv papers — page 56

Saumya Gandhi, Ritu Gala, Vijay Viswanathan, Tongshuang Wu

Despite recent advances in large language models, building dependable and deployable NLP models typically requires abundant, high-quality training data. However, task-specific data is not available for many use cases, and manually curating task-specific data is labor-intensive. Recent work has studied prompt-driven synthetic data generation using large langu

Stefan Höche, Frank Krauss, Daniel Reichelt

We introduce the Alaric parton shower for simulating QCD radiation at hadron colliders and present numerical results from an implementation in the event generator Sherpa. Alaric provides a consistent framework to quantify certain systematic uncertainties which cannot be eliminated by comparing the parton shower with analytic resummation. In particular, it al

Fatemeh Bagheri, Ramon E. Lopez, Amir Shahmoradi

There are several methods for indirectly detecting exoplanets, such as transit, radial velocity, astrometry, and the conventional gravitational microlensing approach. These methods rely on observing the effects of exoplanets on the emission or motion of observed stars. All these techniques have focused on the optical or infrared domains. However, an alternat

Chris Junchi Li

Stochastic versions of the alternating direction method of multiplier (ADMM) and its variants play a key role in many modern large-scale machine learning problems. In this work, we introduce a unified algorithmic framework called generalized stochastic ADMM and investigate their continuous-time analysis. The generalized framework widely includes many stochas

Ted Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi

This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes. The coverage and coverage robustness scores evaluate the effectiveness of the observer node placements. This

Shabnam Hassani, Mehrdad Sabetzadeh, Daniel Amyot, Jain Liao

As software-intensive systems face growing pressure to comply with laws and regulations, providing automated support for compliance analysis has become paramount. Despite advances in the Requirements Engineering (RE) community on legal compliance analysis, important obstacles remain in developing accurate and generalizable compliance automation solutions. Th

Vishruth Veerendranath, Vishwa Shah, Kshitish Ghate

Quantitative and numerical comprehension in language is an important task in many fields like education and finance, but still remains a challenging task for language models. While tool and calculator usage has shown to be helpful to improve mathematical reasoning in large pretrained decoder-only language models, this remains unexplored for smaller language

Scott Donaldson, Robert A. Lawrence, Matt I. J. Probert

Computationally efficient and automated generation of convex hulls is desirable for high throughput materials discovery of thermodynamically stable multi-species crystal structures. A convex hull genetic algorithm is proposed that uses methodology adapted from multi-objective optimisation techniques to optimise the convex hull itself as an object, enabling e

Praveen Sahu, Ignacio G. Camarillo, Pragatheiswar Giri, Raji Sundararajan

In this research, we investigated the efficacy of Metformin, the most commonly administered type-2 diabetes drug for triple negative breast cancer (TNBC) treatment, due to its various anticancer properties. It is a plant-based bio-compound, synthesized as a novel biguanide, called dimethyl biguanide or metformin. One of the ways it operates is by hindering e

A. J. Pan-Collantes, J. A. Alvarez-Garcia

A new class of vector fields enabling the integration of first-order ordinary differential equations (ODEs) is introduced. These vector fields are not, in general, Lie point symmetries. The results are based on a relation between 2-dimensional Riemannian manifolds and the integrability of first-order ODEs, which was established in a previous work of the auth

Eric Brachmann, Jamie Wynn, Shuai Chen, Tommaso Cavallari

We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental reconstruction: they repeat triangulation of sparse 3D points and registration of more camera views to the sparse point cloud. We re-interpret incremental structure-from-motion as a

Mikhail Bershtein, Boris Feigin, Aleksandr Trufanov

We revisit the classical Goddard-Kent-Olive coset construction. We find the formulas for the highest weight vectors in coset decomposition and calculate their norms. We also derive formulas for matrix elements of natural vertex operators between these vectors. This leads to relations on conformal blocks. Due to the AGT correspondence, these relations are equ

Achyuta Rajaram, Neil Chowdhury, Antonio Torralba, Jacob Andreas

To date, most discoveries of network subcomponents that implement human-interpretable computations in deep vision models have involved close study of single units and large amounts of human labor. We explore scalable methods for extracting the subgraph of a vision model's computational graph that underlies recognition of a specific visual concept. We introdu

Yukun Liu, Kunmo Koo, Zugang Mao, Xianbiao Fu

Palladium (Pd) catalysts have been extensively studied for the direct synthesis of H2O through the hydrogen oxidation reaction at ambient conditions. This heterogeneous catalytic reaction not only holds considerable practical significance but also serves as a classical model for investigating fundamental mechanisms, including adsorption and reactions between

Samuel E. Otto, Cassio M. Oishi, Fabio Amaral, Steven L. Brunton

The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in physical principles. In this study, we focus on the development of such metrics for viscoelastic fluid flows governed by a large class of linear and nonlinear stress models. To do

A. Hahn, R. Mirzoyan, A. Dettlaff, D. Fink

The peak photon detection efficiency (PDE) of silicon photomultipliers (SiPMs) can be as good or better than the PDE of photomultiplier tubes (PMTs). There are experiments where the signal is measured in the presence of a strong, steady background light emission. In these, one needs to accurately evaluate the signal-to-noise ratio. Imaging Atmospheric Cheren

Andrea Zanoni, Kostas Mouloudakis, Michael C. D. Tayler, Giacomo Corrielli

We demonstrate a sensitive optically-pumped magnetometer using rubidium vapor and 0.75 amg of nitrogen buffer gas in a sub-mm-width sensing channel excavated by femtosecond laser writing followed by chemical etching. The channel is buried less than 1 mm below the surface of its fused silica host material, which also includes reservoir chambers and micro-stra

Meyer Adrien, Mazellier Jean-Paul, Jeremy Dana, Nicolas Padoy

Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two corners makes the process inherently frame-by-frame. Given the scarcity of experts' time, efficient annotation me

Anjith George, Sebastien Marcel

Heterogeneous Face Recognition (HFR) aims to expand the applicability of Face Recognition (FR) systems to challenging scenarios, enabling the matching of face images across different domains, such as matching thermal images to visible spectra. However, the development of HFR systems is challenging because of the significant domain gap between modalities and

Robyn E. Sanderson, Ryan Hickox, Christopher M. Hirata, Matthew J. Holman

The Nancy Grace Roman Space Telescope (Roman), NASA's next flagship observatory, has significant mission time to be spent on surveys for general astrophysics in addition to its three core community surveys. We considered what types of observations outside the core surveys would most benefit from early definition, given 700 hours of mission time in the first

Kim V. Berghaus, Joshua A. Kable, Vivian Miranda

Quintessence scalar fields are a natural candidate for evolving dark energy. Unlike the phenomenological $w_0w_a$ parameterization of the dark energy equation of state, they cannot accommodate the phantom regime of dark energy $w(z) < -1$, or crossings into the phantom regime. Recent baryon acoustic oscillation (BAO) measurements by the Dark Energy Spectrosc

Pablo Barenbaum, Delia Kesner, Mariana Milicich

Intersection type systems have been independently applied to different evaluation strategies, such as call-by-name (CBN) and call-by-value (CBV). These type systems have been then generalized to different subsuming paradigms being able, in particular, to encode CBN and CBV in a unique unifying framework. However, there are no intersection type systems that e

Bharathi A, Arkaitz Zubiaga

Stance detection has been widely studied as the task of determining if a social media post is positive, negative or neutral towards a specific issue, such as support towards vaccines. Research in stance detection has however often been limited to a single language and, where more than one language has been studied, research has focused on few-shot settings,

Loïc Dumortier, Céline Chizallet, Benoit Creton, Theodorus de Bruin

ReaxFF is a computationally efficient model for reactive molecular dynamics simulations, which has been applied to a wide variety of chemical systems. When ReaxFF parameters are not yet available for a chemistry of interest, they must be (re)optimized, for which one defines a set of training data that the new ReaxFF parameters should reproduce. ReaxFF traini

Hongxuan Liu, Haoyu Yin, Zhiyao Luo, Xiaonan Wang

This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains. A benchmark dataset is curated to encapsulate the intricate physical-chemical properties of small molecules, their drugability for pharmacology, alongside the functional attributes

Agathe Sadeghi, Zachary Feinstein

In this paper, we introduce an impact centrality measure to evaluate shock propagation on financial networks capturing a notion of contagion and systemic risk contributions, permitting comparisons of these risks over time. In addition, we provide a statistical validation method when the network is estimated from data, as is done in practice. This statistical

Keisuke Sugie, Dimitri Loutchko, Tetsuya J. Kobayashi

Chemical reaction networks (CRNs) exhibit complex dynamics governed by their underlying network structure. In this paper, we propose a novel approach to study the dynamics of CRNs by representing them on species graphs (S-graphs). By scaling concentrations by conservation laws, we obtain a graph representation of transitions compatible with the S-graph, whic

David Kubiznak, Otakar Svítek, Tayebeh Tahamtan

Conformal electrodynamics is a particularly interesting example of power Maxwell non-linear electrodynamics, designed to possess conformal symmetry in all dimensions. In this paper, we propose a regularized version of Conformal electrodynamics, minimally regularizing the field of a point charge at the origin by breaking the conformal invariance of the theory

Chun-Fang Li, Zhi-Juan Hu

The interpretation of optical rotation in optically active media as circular birefringence has persisted for over two centuries, yet the inherent fallacy in this phenomenological theory remains unnoticed. Recently, we employed logical reasoning to demonstrate that isotropic chiral media, a kind of optically active media, do not exhibit circular birefringence

Amila Perera, Roshan Godaliyadda, Marcos Katz

The growing demand for Internet of Things (IoT) networks has sparked interest in sustainable, zero-energy designs through Energy Harvesting (EH) to extend the lifespans of IoT sensors. Visible Light Communication (VLC) is particularly promising, integrating signal transmission with optical power harvesting to enable both data exchange and energy transfer in

Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of d

Francesco Lin

Gromov used convex integration to prove that any closed orientable three-manifold equipped with a volume form admits three divergence-free vector fields which are linearly independent at every point. We provide an alternative proof of this (inspired by Seiberg-Witten theory) using geometric properties of eigenspinors in three dimensions. In fact, our proof s

Fatemeh Bagheri, Ramon E. Lopez

Intense currents produced during geomagnetic storms dissipate energy in the ionosphere through Joule heating. This dissipation has significant space weather effects, and thus it is important to determine the ability of physics-based simulations to replicate real events quantitatively. Several empirical models estimate Joule heating based on ionospheric curre

Tao Hu, Wenhang Ge, Yuyang Zhao, Gim Hee Lee

We introduce X-Ray, a novel 3D sequential representation inspired by the penetrability of x-ray scans. X-Ray transforms a 3D object into a series of surface frames at different layers, making it suitable for generating 3D models from images. Our method utilizes ray casting from the camera center to capture geometric and textured details, including depth, nor

Jie Cheng, Yingbing Chen, Qifeng Chen

We present PLUTO, a powerful framework that pushes the limit of imitation learning-based planning for autonomous driving. Our improvements stem from three pivotal aspects: a longitudinal-lateral aware model architecture that enables flexible and diverse driving behaviors; An innovative auxiliary loss computation method that is broadly applicable and efficien

Taro Langner

Imaging sites around the world generate growing amounts of medical scan data with ever more versatile and affordable technology. Large-scale studies acquire MRI for tens of thousands of participants, together with metadata ranging from lifestyle questionnaires to biochemical assays, genetic analyses and more. These large datasets encode substantial informati

Karim G. Habashy, Benjamin D. Evans, Dan F. M. Goodman, Jeffrey S. Bowers

Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explore the importance of temporal parameters, we trained spiking neural networks on tasks with varying temporal com

Federico Marocco, J. Davy Kirkpatrick, Adam C. Schneider, Aaron M. Meisner

We present the discovery of 13 new widely separated T dwarf companions to M dwarf primaries, identified using WISE/NEOWISE data by the CatWISE and Backyard Worlds: Planet 9 projects. This sample represents a $\sim$60% increase in the number of known M+T systems, and allows us to probe the most extreme products of binary/planetary system formation, a discover

Dariush Salami, Francesc Wilhelmi, Lorenzo Galati-Giordano, Mika Kasslin

The increasing cloudification and softwarization of networks foster the interplay among multiple independently managed deployments. An appealing reason for such an interplay lies in distributed Machine Learning (ML), which allows the creation of robust ML models by leveraging collective intelligence and computational power. In this paper, we study the applic

Josef Pichlmeier, Philipp Ross, Andre Luckow

Large Language Models (LLMs) have experienced widespread adoption across scientific and industrial domains due to their versatility and utility for diverse tasks. Nevertheless, deploying and serving these models at scale with optimal throughput and latency remains a significant challenge, primarily because of LLMs' high computational and memory demands. Spec

Zhiping Liu, Chengkai Zhu, Hua-Lei Yin, Xin Wang

Wave-particle duality, a fundamental principle of quantum mechanics, encapsulates the complementary relationship between the wave and particle behaviors of quantum systems. In this paper, we treat quantum coherence and classical distinguishability as complementary resources and uncover a novel duality relation, which is explored through quantum state discrim

Mohammad Ali Labbaf Khaniki, Mohammad Manthouri

Lung segmentation in chest X-ray images is of paramount importance as it plays a crucial role in the diagnosis and treatment of various lung diseases. This paper presents a novel approach for lung segmentation in chest X-ray images by integrating U-net with attention mechanisms. The proposed method enhances the U-net architecture by incorporating a Convoluti

Ioanna Stamou

In this study, we present an analysis of the fine-tuning required in various inflationary models in order to explain the production of Primordial Black Holes (PBHs). We specifically examine the degree of fine-tuning necessary in two prominent single field inflationary models: those with an inflection point and those with step-like features in the potential.

David R. Nickel, Anindya Bijoy Das, David J. Love, Christopher G. Brinton

Opportunistic spectrum access has the potential to increase the efficiency of spectrum utilization in cognitive radio networks (CRNs). In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput while minimizing collisions of secondary users with the primary network. However, many works in dynamic spectrum acces

Víctor E. Fernández, Natalie M. Paquette, Brian R. Williams

In this work, we revisit and elaborate on twisted holography for AdS$_3 \times S^3 \times X$ with $X= T^4$, K3, with a particular focus on K3. We describe the twist of supergravity, identify the corresponding (generalization of) BCOV theory, and enumerate twisted supergravity states. We use this knowledge, and the technique of Koszul duality, to obtain the $

Raj K. Sah, Michael J. Zdilla, Eric Borguet, John P. Perdew

Defects in a material can significantly tune properties and enhance utility. Hybrid functionals like HSE06 are often used to describe solids with such defects. However, geometry optimization (including accounting for effects such as Jahn-Teller distortion) using hybrid functionals is challenging for the large supercells needed for defect study. The proposed

Shashank Sonkar, Kangqi Ni, Lesa Tran Lu, Kristi Kincaid

We introduce a new area of study in the field of educational Natural Language Processing: Automated Long Answer Grading (ALAG). Distinguishing itself from Automated Short Answer Grading (ASAG) and Automated Essay Grading (AEG), ALAG presents unique challenges due to the complexity and multifaceted nature of fact-based long answers. To study ALAG, we introduc

Claudio Gonzalez-Fuentes, Maria Abellan, Simon Oyarzun, Christian Orellana

We have developed a novel, compact and cost-effective magneto-optical method for quantifying the spin-orbit torque (SOT) effective field vector (hSO) in magnetic thin films subjected to spin current injection. The damping-like (hSODL) component of the vector is obtained by the polar Kerr response arising from the out-of-plane magnetization tilting, whereas t

Ping Che, Riccardo Ciola, Markus Garst, Volodymyr Kravchuk

Topological magnon bands enable uni-directional edge transport without backscattering, enhancing the robustness of magnonic circuits and providing a novel platform for exploring quantum transport phenomena. Magnetic skyrmion lattices, in particular, host a manifold of topological magnon bands with multipole character and non-reciprocal dispersions. These mod

Jan-Philipp Fränken, Eric Zelikman, Rafael Rafailov, Kanishk Gandhi

When prompting a language model (LM), users often expect the model to adhere to a set of behavioral principles across diverse tasks, such as producing insightful content while avoiding harmful or biased language. Instilling such principles (i.e., a constitution) into a model is resource-intensive, technically challenging, and generally requires human prefere

Steffen Schotthöfer, M. Paul Laiu, Martin Frank, Cory D. Hauck

The main challenge of large-scale numerical simulation of radiation transport is the high memory and computation time requirements of discretization methods for kinetic equations. In this work, we derive and investigate a neural network-based approximation to the entropy closure method to accurately compute the solution of the multi-dimensional moment system

Moha Naeimi, Katharina Engster, Ingo Barke, Sylvia Speller

Crystallization of rubrene, progressing from an amorphous phase to a triclinic meta-stable and ultimately to the orthorhombic stable phase, offers broad applications not only in organic electronic devices but also for in-depth studies of optical and electronic properties, including exciton distribution and dynamics. We investigate the crystallization of rubr

Jose Valencia, George Iskander, Nicholas V. Nardelli, David R. Leibrandt

The frequency stability of a laser locked to an optical reference cavity is fundamentally limited by thermal noise in the cavity length. These fluctuations are linked to material dissipation, which depends both on the temperature of the optical components and the material properties. Here, the design and experimental characterization of a sapphire optical ca

Yiming Liu, Kezhao Liu, Yao Xiao, Ziyi Dong

Diffusion-Based Purification (DBP) has emerged as an effective defense mechanism against adversarial attacks. The success of DBP is often attributed to the forward diffusion process, which reduces the distribution gap between clean and adversarial images by adding Gaussian noise. While this explanation is theoretically sound, the exact role of this mechanism

Timothy Edwards, Pablo Soberón

We prove extensions of Halman's discrete Helly theorem for axis-parallel boxes in $\mathbb{R}^d$. Halman's theorem says that, given a set $S$ in $\mathbb{R}^d$, if $F$ is a finite family of axis-parallel boxes such that the intersection of any $2d$ contains a point of $S$, then the intersection of $F$ contains a point of $S$. We prove colorful, fractional, a

Andre Dubovskiy, Troy Criss, Ahmed Sidi El Valli, Laura Rehm

Large quantities of random numbers are crucial in a wide range of applications. We have recently demonstrated that perpendicular nanopillar magnetic tunnel junctions (pMTJs) can produce true random bits when actuated with short pulses. However, our implementation used high-end and expensive electronics, such as a high bandwidth arbitrary waveform generator a

Tenzin Norden, Luis M. Martinez, Nehan Tarefder, Kevin W. C. Kwock

In addition to a plethora of emergent phenomena, the spatial topology of optical vortices enables an array of applications spanning communications to quantum photonics. Nonlinear optics is essential in this context, providing access to an infinitely large set of quantum states associated with the orbital angular momentum of light. Nevertheless, the realizati

Yao Lyu, Jie Cai, Bryan Dosono, Davis Yadav

Identity work in Human-Computer Interaction (HCI) has focused on the marginalized group to explore designs to support their asset (what they have). However, little has been explored specifically on the identity work of people with disabilities, specifically, visual impairments. In this study, we interviewed 45 BlindTokers (blind users on TikTok) from various

Xiang Yin, Potyka Nico, Francesca Toni

Quantitatively explaining the strength of arguments under gradual semantics has recently received increasing attention. Specifically, several works in the literature provide quantitative explanations by computing the attribution scores of arguments. These works disregard the importance of attacks and supports, even though they play an essential role when exp

Ruymán Cruz-Barroso, Lidia Fernández

In this paper we consider an appropriate ordering of the Laurent monomials $x^{i}y^{j}$, $i,j \in \mathbb{Z}$ that allows us to study sequences of orthogonal Laurent polynomials of the real variables $x$ and $y$ with respect to a positive Borel measure $\mu$ defined on $\mathbb{R}^2$ such that $\{ x=0 \}\cup \{ y=0 \} \not\in \textrm{supp}(\mu)$. This orderi

Andreas Haufler, Hayato Kato

The Global Minimum Tax (GMT) is applied only to firms above a certain size threshold, permitting countries to set differential tax rates for small and large firms. We analyse tax competition among multiple tax havens and a non-haven country for heterogeneous multinationals to evaluate the effects of this partial coverage of GMT. Upon the introduction of a lo

Shashank Sonkar, Naiming Liu, Debshila B. Mallick, Richard G. Baraniuk

In this paper, we introduce "Marking", a novel grading task that enhances automated grading systems by performing an in-depth analysis of student responses and providing students with visual highlights. Unlike traditional systems that provide binary scores, "marking" identifies and categorizes segments of the student response as correct, incorrect, or irrele

Jared Coleman, Dmitry Ivanov, Evangelos Kranakis, Danny Krizanc

We consider linear search for an escaping target whose speed and initial position are unknown to the searcher. A searcher (an autonomous mobile agent) is initially placed at the origin of the real line and can move with maximum speed $1$ in either direction along the line. An oblivious mobile target that is moving away from the origin with an unknown constan

Elaine Wong, Vicente Leyton-Ortega, Daniel Claudino, Seth R. Johnson

Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple h

Yuhito Shibaike, Christoph Mordasini

The young T Tauri star PDS 70 has two gas accreting planets sharing one large gap in a pre-transitional disc. Dust continuum emission from PDS 70 c has been detected by Atacama Large Millimeter/submillimeter Array (ALMA) Band 7, considered as the evidence of a circumplanetary disc, but the emission from PDS 70 b has not. We constrain the planet mass and the

Hermann Erk, Carl Eric Jensen, Stephan Jauernik, Michael Bauer

An ultrafast photoemission-based low-energy electron diffraction experiment with monolayer surface sensitivity is presented. In a first experiment on tin-phthalocyanine adsorbed on graphite, we demonstrate a time resolution of approx. 100 fs. Analysis of the transient photoelectron diffraction signal indicates a heating of the adsorbate layer on a time scale

Yao Wan, Guanghua Wan, Shijie Zhang, Hongyu Zhang

Recent years have witnessed significant progress in developing deep learning-based models for automated code completion. Although using source code in GitHub has been a common practice for training deep-learning-based models for code completion, it may induce some legal and ethical issues such as copyright infringement. In this paper, we investigate the lega

Andrea Palermo, Eduardo Grossi, Iurii Karpenko, Francesco Becattini

We have studied the spin polarization of $\Lambda$ hyperons in heavy ion collisions at center-of-mass energies $\sqrt{s_{NN}} = 200$ GeV and $\sqrt{s_{NN}} = 5.02$ TeV carried out at RHIC and LHC colliders. We have calculated the mean spin vector at local thermodynamic equilibrium, including all known first-order terms in the gradients of the thermo-hydrodyn

Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu

Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing

Hanghang Ma, Liwei Tan, Suming Weng, Wenjun Ying

Laser plasma instabilities (LPIs) have significant influences on the laser energy deposition efficiency, hot electron generation, and uniformity of irradiation in inertial confined fusion (ICF). In contrast to theoretical analysis of linear development of LPIs, numerical simulations play a more and more important role in revealing the complex physics of LPIs

Maciej Łebek, Miłosz Panfil

The Navier-Stokes equations are paradigmatic equations describing hydrodynamics of an interacting system with microscopic interactions encoded in transport coefficients. In this work we show how the Navier-Stokes equations arise from the microscopic dynamics of nearly integrable $1d$ quantum many-body systems. We build upon the recently developed hydrodynami

Chin Hei Chan, Maosheng Xiong

Let $p$ be an odd prime, $k,\ell$ be positive integers, $q=p^k, Q=p^{\ell}$. In this paper we characterise planar functions of the form $f_{\underline{c}}(X)=c_0X^{qQ+q}+c_1X^{qQ+1}+c_2X^{Q+q}+c_3X^{Q+1}$ over $\mathbb{F}_{q^2}$ for any $\underline{c}=(c_0,c_1,c_2,c_3) \in \mathbb{F}_{q^2}^4$ in terms of linear equivalence.

B. S. D. Ch. S. Varaprasad, Chengchao Xu, Brandon Reese, David E. Laughlin

Granular $\text{L}1_0$-FePt thin films with small columnar grains are essential for heat-assisted magnetic recording media. While hexagonal boron nitride(h-BN) has proven effective for promoting columnar FePt grains, we explored multilayer graphene as an alternative grain boundary material leveraging its structural similarity to h-BN. The FePt granular thin

A. V. Syuy, I. V. Martynov, I. A. Zavidovskiy, D. V. Dyubo

Dyes used in industries such as textile, paper, and leather are known to be harmful to both human health and aquatic ecosystems. Therefore, finding effective and sustainable methods to remove dyes from wastewater is crucial for mitigating the detrimental effects of pollution.TiN nanoparticles have good absorption and conversion of light energy into thermal e

Raelynn Wonnacott, Dallin Stewart, Jared P Whitehead, Ronald A Harris

Indonesia is one of the world's most densely populated regions and lies among the epicenters of Earth's greatest natural hazards. Effectively reducing the disaster potential of these hazards through resource allocation and preparedness first requires an analysis of the risk factors of the region. Since destructive tsunamis present one of the most eminent dan

Scipio Cuccagna, Masaya Maeda

For exponents $p$ satisfying $0<|p-3|\ll 1$ and only in the context of spatially even solutions we prove that the ground states of the nonlinear Schr\"odinger equation (NLS) with pure power nonlinearity of exponent $p$ in the line are asymptotically stable. The proof is similar to a related result of Martel, preprint arXiv:2312.11016, for a cubic quintic NLS

Nihar Gupte, Antoni Ramos-Buades, Alessandra Buonanno, Jonathan Gair

Binary black holes (BBHs) in eccentric orbits produce distinct modulations in gravitational waves (GWs); measuring orbital eccentricity provides evidence for dynamical binary formation channels. We analyze 57 GW events from the LIGO-Virgo-KAGRA (LVK) O1-O3 observing runs using a multipolar aligned-spin inspiral-merger-ringdown waveform with two eccentric par

Dongge Han, Trevor McInroe, Adam Jelley, Stefano V. Albrecht

Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation capabilities. However, in applications such as household robotics, a critical gap remains in the personalization of these models to individual user preferences. We introduce LLM-Pe

M. D. Bobb, James Farre

We study surface subgroups of $\mathrm{SL}(4,\mathbb R)$ acting convex cocompactly on $\mathbb R \textrm P^3$ with image in the coaffine group. The boundary of the convex core is stratified, and the one dimensional strata form a pair of bending laminations. We show that the bending data on each component consist of a convex $\mathbb R \textrm P^2$ structure

Ren-Jie Xiong, Xiao-Li Huang, Ze-Rui Wang

Chromatic break and/or plateau observed in the early optical and X-ray afterglow lightcurves challenge the conventional external shock models of gamma-ray bursts (GRBs). Detection of TeV gamma-ray afterglows indicates strong gamma-ray production within the afterglow jets. We investigate the cascade radiations of the $e^\pm$ production via the $\gamma\gamma$

Matija Piškorec, Anton Ivashkevich, Said Haji Abukar, Lundrim Azemi

In this paper we describe a prototype of a blockchain-in-a-box system which allows users to easily bootstrap the whole Ethereum Proof-of-Work (PoW) network running on multiple Raspberry Pi nodes - an inexpensive modular computers. Users are able to orchestrate the whole blockchain network using a single web based interface, for example they are able to set t

Igor Bogoslavskyi, Konstantinos Zampogiannis, Raymond Phan

Light Detection and Ranging (LiDAR) technology has proven to be an important part of many robotics systems. Surface normals estimated from LiDAR data are commonly used for a variety of tasks in such systems. As most of the today's mechanical LiDAR sensors produce sparse data, estimating normals from a single scan in a robust manner poses difficulties. In thi

Fadi Khatib, Yoni Kasten, Dror Moran, Meirav Galun

Multiview Structure from Motion is a fundamental and challenging computer vision problem. A recent deep-based approach utilized matrix equivariant architectures for simultaneous recovery of camera pose and 3D scene structure from large image collections. That work, however, made the unrealistic assumption that the point tracks given as input are almost clean

Baoheng Zhang, Yizhao Gao, Jingyuan Li, Hayden Kwok-Hay So

Eye-tracking technology is integral to numerous consumer electronics applications, particularly in the realm of virtual and augmented reality (VR/AR). These applications demand solutions that excel in three crucial aspects: low-latency, low-power consumption, and precision. Yet, achieving optimal performance across all these fronts presents a formidable chal

Yanick Ricard, Frédéric Chambat

Condensed planets contract or expand as their temperature changes. With the exception of the effect of phase changes, this phenomenon is generally interpreted as being solely related to the thermal expansivity of the planet's components. However, changes in density affect pressure and gravity and, consequently, the planet's compressibility. A planet's radius

Till Böhmer, Florian Pabst, Jan P. Gabriel, Thomas Blochowicz

The dielectric response of liquids reflects both, reorientation of single molecular dipoles and collective modes, i.e., dipolar cross-correlations. A recent theory predicts the latter to produce an additional slow peak in the dielectric loss spectrum. Following this idea we argue that in supercooled liquids the high-frequency power law exponent of the dielec

Mark McLeod, Bernardo Perez-Orozco, Nika Lee, Davide Zilli

Automotive insurers increasingly have access to telematic information via black-box recorders installed in the insured vehicle, and wish to identify undesirable behaviour which may signify increased risk or uninsured activities. However, identification of such behaviour with machine learning is non-trivial, and results are far from perfect, requiring human i

Romina Wild, Emanuela Sozio, Riccardo G. Margiotta, Fabiana Dellai

Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different

Wei Zeng, Qian Wang

Numerical simulations of ideal compressible magnetohydrodynamic (MHD) equations are challenging, as the solutions are required to be magnetic divergence-free for general cases as well as oscillation-free for cases involving discontinuities. To overcome these difficulties, we develop a locally divergence-free oscillation-eliminating discontinuous Galerkin (LD

Luca Traini, Jessica Leone, Giovanni Stilo, Antinisca Di Marco

Analysis of microservices' performance is a considerably challenging task due to the multifaceted nature of these systems. Each request to a microservices system might raise several Remote Procedure Calls (RPCs) to services deployed on different servers and/or containers. Existing distributed tracing tools leverage swimlane visualizations as the primary mean

B. Szafran, M. Zegrodnik, M. P. Nowak, R. Citro

The spin dynamics in two electron double quantum dots embedded in two dimensional electron gas at the interface between SrTiO$_3$ and LaAlO$_3$ is studied by an exact numerical solution of the time-dependent Schr\"odinger equation, in the context of the electric dipole spin resonance experiment. Based on the three band model of $3d$-electrons localized at Ti

Paulo Yanez Sarmiento, Simon Witzke, Nadja Klein, Bernhard Y. Renard

Explainability is a key component in many applications involving deep neural networks (DNNs). However, current explanation methods for DNNs commonly leave it to the human observer to distinguish relevant explanations from spurious noise. This is not feasible anymore when going from easily human-accessible data such as images to more complex data such as geno

Jue Hou, Anisia Katinskaia, Lari Kotilainen, Sathianpong Trangcasanchai

This paper investigates what insights about linguistic features and what knowledge about the structure of natural language can be obtained from the encodings in transformer language models.In particular, we explore how BERT encodes the government relation between constituents in a sentence. We use several probing classifiers, and data from two morphologicall

Martin Willame, Hasan Can Yildirim, Laurent Storrer, Francois Horlin

Passive Wi-Fi-based radars (PWRs) are devices that enable the localization of targets using Wi-Fi signals of opportunity transmitted by an access point. Unlike active radars that optimize their transmitted waveform for localization, PWRs align with the 802.11 amendments. Specifically, during the channel sounding session preceding a multi-user multiple-input

Surajit Mondal, Andrea F. Battaglia, Bin Chen, Sijie Yu

The standard flare model, despite its success, is limited in comprehensively explaining the various processes involving nonthermal particles. One such missing ingredient is a detailed understanding of the various processes involved during the transport of accelerated electrons from their site of acceleration to different parts of the flare region. Here we us

Lopamudra Nayak, Sonali Patnaik, Priyanka Sadangi, Sanjay Kumar Swain

We analyze the exclusive two-body nonleptonic decays of $B_s$ meson to ground as well as radially excited $2S$ charmonium state with a light meson $K_s$, induced by the $b\to c\bar{c}d$ transition. Within the framework of relativistic independent quark (RIQ) model based on a flavor-independent interaction potential in scalar-vector harmonic form, we calculat

Andrea Addazi, Qingyu Gan

We explore superoscillations within the context of classical and quantum field theories, presenting novel solutions to Klein-Gordon's, Dirac's, Maxwell's and Einstein's equations. In particular, we illustrate a procedure of second quantization of fields and how to construct a Fock space which encompasses Superoscillating states. Furthermore, we extend the ap

Anthony Baptista, Alessandro Barp, Tapabrata Chakraborti, Chris Harbron

Deep neural networks (DNNs) are powerful tools for approximating the distribution of complex data. It is known that data passing through a trained DNN classifier undergoes a series of geometric and topological simplifications. While some progress has been made toward understanding these transformations in neural networks with smooth activation functions, an

Pralay Chakraborty, Tanmay Dev, Subhankar Roy

We propose two new predictive neutrino mass matrix textures that deviate from $\mu$-$\tau$ symmetry and explore their phenomenological implications. These textures are significant due to their predictive nature and the unique approach they introduce for breaking the $\mu$-$\tau$ symmetry. Both textures predict six physical parameters and impose sharp constra

P. V. Buividovich

The status of the Chiral Magnetic Effect (CME) response in full Quantum Chromodynamics (QCD) has been controversial so far, with previous lattice QCD studies indicating either its strong suppression or vanishing in thermal equilibrium state. We introduce the Euclidean-time correlator of axial charge and electric current as an observable that can be used to s