April 2024 arXiv papers — page 156

David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo-Hermsdorff

Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong assumptions such as the $\textit{exclusion criterion}$, which states that instrumental effects must be entirely mediated by treatments. This assumption often fails in practice. When

Fahmida Alam, Md Asiful Islam, Robert Vacareanu, Mihai Surdeanu

We introduce a meta dataset for few-shot relation extraction, which includes two datasets derived from existing supervised relation extraction datasets NYT29 (Takanobu et al., 2019; Nayak and Ng, 2020) and WIKIDATA (Sorokin and Gurevych, 2017) as well as a few-shot form of the TACRED dataset (Sabo et al., 2021). Importantly, all these few-shot datasets were

Lisa Orii, Caryl Feldacker, Jacqueline Madalitso Huwa, Agness Thawani

eHealth has strong potential to advance HIV care in low- and middle-income countries. Given the sensitivity of HIV-related information and the risks associated with unintended HIV status disclosure, clients' privacy perceptions towards eHealth applications should be examined to develop client-centered technologies. Through focus group discussions with antire

Hossein Kazemi, Elham Sarbazi, Michael Crisp, Taisir E. H. El-Gorashi

In this paper, we put forward a proof of concept for sixth generation (6G) Terabit infrared (IR) laser-based indoor optical wireless networks. We propose a novel double-tier access point (AP) architecture based on an array of arrays of vertical cavity surface emitting lasers (VCSELs) to provide a seamless grid-of-beam coverage with multi-Gb/s per beam. We pr

Sanad Aburass, Maha Abu Rumman

The "Writer's Integrity" framework introduces a paradigm shift in maintaining the sanctity of human-generated text in the realms of academia, research, and publishing. This innovative system circumvents the shortcomings of current AI detection tools by monitoring the writing process, rather than the product, capturing the distinct behavioral footprint of hum

Saikat Barua

Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. These agents, proficient in human-like text comprehension and generation, have the potential to revolutionize sectors from customer service to healthcare. However, they face challenges such as multimodality, human

Baodi Shan, Mauricio Araya-Polo

Accelerated computing is widely used in high-performance computing. Therefore, it is crucial to experiment and discover how to better utilize GPUGPUs latest generations on relevant applications. In this paper, we present results and share insights about highly tuned stencil-based kernels for NVIDIA Ampere (A100) and Hopper (GH200) architectures. Performance

O. I. Utesov, S. V. Andreev

We extend our theory of slow magnons in a two-component Bose-Einstein condensate to the case of two spatial dimensions (2D). We provide a detailed discussion of polaronic corrections to the magnon branch of the elementary excitation spectrum in a weakly- and strongly-interacting regimes. In a dilute system, the latter may be achieved by adjusting inter-speci

Krishna Subramani, Paris Smaragdis, Takuya Higuchi, Mehrez Souden

Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i.e., data that can be stored in a matrix. For audio, this has led to numerous applications using time-frequency (TF) representations like the Short-Time Fourier Transform. However extending these applications to irregularly-spaced TF representations, like t

Ramesh Adhikari, Costas Busch, Dariusz R. Kowalski

Sharding is used to improve the scalability and performance of blockchain systems. We investigate the stability of blockchain sharding, where transactions are continuously generated by an adversarial model. The system consists of $n$ processing nodes that are divided into $s$ shards. Following the paradigm of classical adversarial queuing theory, transaction

Ryan D. Boyden, Josh A. Eisner

We present Karl G. Jansky Very Large Array observations covering the NGC 1977 region at 3.0, 6.4, and 15.0 GHz. We search for compact radio sources and detect continuum emission from 34 NGC 1977 cluster members and 37 background objects. Of the 34 radio-detected cluster members, 3 are associated with known proplyds in NGC 1977, 22 are associated with additio

Anirban Mukherjee, Hannah Hanwen Chang

We investigate whether modern AI can emulate expert creativity in complex scientific endeavors. We introduce novel methodology that utilizes original research articles published after the AI's training cutoff, ensuring no prior exposure, mitigating concerns of rote memorization and prior training. The AI are tasked with redacting findings, predicting outcome

Meghan Speckert, Philip Massey, Brian A. Skiff

We observed the Galactic open cluster Berkeley 50 in order to determine its stellar content, distance, and age. We obtained UBV photometry of 1145 stars in a 12.3' $\times$ 12.3' field, and used Gaia proper motions and parallaxes to identify 64 members, of which we obtained spectra of the 17 brightest members. The majority of the observed population we class

Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu

This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models. The paper makes three original contributions. First, we explore the unique characteristics of few-shot learning to ensemble multiple few-shot (FS) models by creating three a

Huizhong Ren, Georgios G. Pyrialakos, Fan O. Wu, Nikolaos K. Efremidis

We show that in highly multimoded nonlinear photonic systems, the optical thermodynamic pressures emerging from different species of the optical field obey Dalton's law of partial pressures. In multimode settings, the optical thermodynamic pressure is defined as the conjugate to the extensive variable associated with the system's total number of modes and is

Yufan Li, Yanbo Zhang, Yunqing Zhang

For two graphs $G_1$ and $G_2$, the size Ramsey number $\hat{r}(G_1,G_2)$ is the smallest positive integer $m$ for which there exists a graph $G$ of size $m$ such that for any red-blue edge-coloring of the graph $G$, $G$ contains either a red subgraph isomorphic to $G_1$, or a blue subgraph isomorphic to $G_2$. Let $P_n$ be a path with $n$ vertices, $nK_2$ a

Łukasz Mioduszewski, Jakub Bednarz, Mateusz Chwastyk, Marek Cieplak

We present a coarse-grained C$\alpha$-based protein model that can be used to simulate structured, intrinsically disordered and partially disordered proteins. We use a Go-like potential for the structured parts and two different variants of a transferable potential for the disordered parts. The first variant uses dynamic structure-based (DSB) contacts that f

Yufei Zhang, Jeffrey O. Kephart, Zijun Cui, Qiang Ji

While current methods have shown promising progress on estimating 3D human motion from monocular videos, their motion estimates are often physically unrealistic because they mainly consider kinematics. In this paper, we introduce Physics-aware Pretrained Transformer (PhysPT), which improves kinematics-based motion estimates and infers motion forces. PhysPT e

Sina Navidi, Adam Thelen, Tingkai Li, Chao Hu

Monitoring the health of lithium-ion batteries' internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditio

Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka

We investigate the challenge of multi-agent deep reinforcement learning in partially competitive environments, where traditional methods struggle to foster reciprocity-based cooperation. LOLA and POLA agents learn reciprocity-based cooperative policies by differentiation through a few look-ahead optimization steps of their opponent. However, there is a key l

Zhendong Zhang, Edward Robert Criscuolo, Yao Hao, Deshan Yang

Purpose: Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there's a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DI

Simon Marshall, Hiroaki Narita, Ameya Pitale

We give an explicit formula for the Petersson norms of theta lifts from Maass cusp forms of level one to cusp forms on orthogonal groups O(1,8n+1). Our formula explicitly determines archimedean local factors of the norms. As an application, we obtain the injectivity of the lifting of Maass forms and bounds on the sup-norm of cusp forms on these orthogonal gr

Danielle Van Boxel

We apply Bayesian Additive Regression Tree (BART) principles to training an ensemble of small neural networks for regression tasks. Using Markov Chain Monte Carlo, we sample from the posterior distribution of neural networks that have a single hidden layer. To create an ensemble of these, we apply Gibbs sampling to update each network against the residual ta

Annie Liang, Jay Lu

Algorithms are increasingly used to guide high-stakes decisions about individuals. Consequently, substantial interest has developed around defining and measuring the ``fairness'' of these algorithms. These definitions of fair algorithms share two features: First, they prioritize the role of a pre-defined group identity (e.g., race or gender) by focusing on h

R. Linehan, I. Hernandez, D. J. Temples, S. Q. Dang

In recent years, the lack of a conclusive detection of WIMP dark matter at the 10 GeV/c$^{2}$ mass scale and above has encouraged development of low-threshold detector technology aimed at probing lighter dark matter candidates. Detectors based on Cooper-pair-breaking sensors have emerged as a promising avenue for this detection due to the low (meV-scale) ene

Amornrat Aungwerojwit, Boris T. Gaensicke, Vikram S. Dhillon, Andrew Drake

Combining archival photometric observations from multiple large-area surveys spanning the past 17 years, we detect long-term variability in the light curves of ZTFJ032833.52-121945.27 (ZTFJ0328-1219), ZTFJ092311.41+423634.16 (ZTFJ0923+4236) and WD1145+017, all known to exhibit transits from planetary debris. ZTFJ0328-1219 showed an overall fading in brightne

Yang Zheng, Qingqing Zhao, Guandao Yang, Wang Yifan

Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to reconstruct clothed humans. We introduce PhysAvatar, a novel framework that combines inverse rendering with inverse physics to automatically estimate the shape and appearance of a

Igor Cardoso, Rubens O. Moraes, Lucas N. Ferreira

Neural models are one of the most popular approaches for music generation, yet there aren't standard large datasets tailored for learning music directly from game data. To address this research gap, we introduce a novel dataset named NES-VMDB, containing 98,940 gameplay videos from 389 NES games, each paired with its original soundtrack in symbolic format (M

Ehsan Nasiri, Long Wang

This paper proposes a hybrid force-motion framework that utilizes real-time surface normal updates. The surface normal is estimated via a novel method that leverages force sensing measurements and velocity commands to compensate the friction bias. This approach is critical for robust execution of precision force-controlled tasks in manufacturing, such as the

Jinye Shen, Heng Dai, Weizhang Huang

A moving mesh finite element method is studied for the numerical solution of Bernoulli free boundary problems. The method is based on the pseudo-transient continuation with which a moving boundary problem is constructed and its steady-state solution is taken as the solution of the underlying Bernoulli free boundary problem. The moving boundary problem is sol

Laura Albrecht, Karin Leiderman, Suzanne Sindi, Douglas Nychka

This study introduces a stochastic model of COVID-19 transmission tailored to the Colorado School of Mines campus and evaluates surveillance testing strategies within a university context. Enhancing the conventional SEIR framework with stochastic transitions, our model accounts for the unique characteristics of disease spread in a residential college, includ

Ehsan Nasiri, Long Wang

In laparoscopic robot-assisted minimally invasive surgery, the kinematic control of the robot is subject to the remote center of motion (RCM) constraint at the port of entry (e.g., trocar) into the patient's body. During surgery, after the instrument is inserted through the trocar, intrinsic physiological movements such as the patient's heartbeat, breathing

Xia Wang, Sobenna Onwumelu, Jonathan Sprinkle

This work describes the use of on-board vehicle data from cars with advanced driver assistance features as a trip summary, with the goal of helping drivers contextualize their driving habits in terms of sustainability. The approach is similar to recent advancements in fitness tracking apps, which leverage smartwatches and other wearable devices to characteri

Di Shu, Guangyong Zou

Most clinical trials conducted in drug development contain multiple endpoints in order to collectively assess the intended effects of the drug on various disease characteristics. Focusing on the estimation of the global win probability, defined as the average win probability (WinP) across endpoints that a treated participant would have a better outcome than

Patrick Ebel, Brandon Victor, Peter Naylor, Gabriele Meoni

Hurricanes and coastal floods are among the most disastrous natural hazards. Both are intimately related to storm surges, as their causes and effects, respectively. However, the short-term forecasting of storm surges has proven challenging, especially when targeting previously unseen locations or sites without tidal gauges. Furthermore, recent work improved

Jack Sayers, Camille Avestruz, Ritoban Basu Thakur, Elia Stefano Battistelli

OLIMPO is a proposed Antarctic balloon-borne Sunyaev-Zel'dovich effect (SZE) imager to study gas dynamics associated with structure formation along with the properties of the warm-hot intergalactic medium (WHIM) residing in the connective filaments. During a 25 day flight OLIMPO will image a total of 10 z~0.05 galaxy clusters and 8 bridges at 145, 250, 365,

Nils B. Tack, Sara Oliveira Santos, Brad J. Gemmell, Monica M. Wilhelmus

Copepods participate in large-scale diel vertical migrations (DVM) as primary consumers in marine ecosystems. Given that they are negatively buoyant, gravity facilitates their downward cruising but impedes their upward relocation. In principle, overcoming this retarding force using drag-based propulsion during upward swimming incurs extra energy costs that n

Heng-Tong Ding, Xiang Gao, Andrew D. Hanlon, Swagato Mukherjee

We report the first lattice QCD computation of pion and kaon electromagnetic form factors, $F_M(Q^2)$, at large momentum transfer up to 10 and 28 $\mathrm{GeV}^2$, respectively. Utilizing physical masses and two fine lattices, we achieve good agreement with JLab experimental results at $Q^2 \lesssim 4~\mathrm{GeV}^2$. For $Q^2 \gtrsim 4~\mathrm{GeV}^2$, our

Jan Balewski, Milan Kornjača, Katherine Klymko, Siva Darbha

Aquila, an analog quantum simulation platform developed by QuEra Computing, supports control of the position and coherent evolution of up to 256 neutral atoms. This study details novel experimental protocols designed for analog quantum simulators that generate Bell state entanglement far away from the blockade regime, construct a $Z_2$ state with a defect in

Fernando Argentieri, Livia Corsi

We provide an arithmetic condition weaker then the Bryuno condition for which it is possible to apply a KAM scheme in dimension greater then one. The KAM scheme will be provided in the setting of linearization of analytic diffeomorphisms of the torus that are close to a rotation.

Debajyoti Choudhury, Kuldeep Deka, Lalit Kumar Saini

Scenarios seeking to address the issue of electroweak symmetry breaking often have heavy colored gauge bosons coupling preferentially to the top quark. Considering the bulk Randall-Sundrum as a typical example, we consider the prospects of the first Kaluza-Klein mode ($G^{(1)}$) of the gluon being produced at the LHC in association with a $t \bar{t}$ pair. T

A. Borković, M. H. Gfrerer, R. A. Sauer, B. Marussig

We derive a novel formulation for the interaction potential between deformable fibers due to short-range fields arising from intermolecular forces. The formulation improves the existing section-section interaction potential law for in-plane beams by considering an offset between interacting cross sections. The new law is asymptotically consistent, which is p

Jinye Shen, Bowen Shi, Weizhang Huang

A so-called grid-overlay finite difference method (GoFD) was proposed recently for the numerical solution of homogeneous Dirichlet boundary value problems of the fractional Laplacian on arbitrary bounded domains. It was shown to have advantages of both finite difference and finite element methods, including its efficient implementation through the fast Fouri

Hasan Farooq, Julien Forgeat, Shruti Bothe, Kristijonas Cyras

The realization of data-driven AI-native architecture envisioned for 6G and beyond networks can eventually lead to multiple machine learning (ML) workloads distributed at the network edges driving downstream tasks like secondary carrier prediction, positioning, channel prediction etc. The independent life-cycle management of these edge-distributed independen

Samuel J. Weisenthal, Matthew Eckard, Askhan Ertefaie, Marissa Sobolewski

In toxicology research, experiments are often conducted to determine the effect of toxicant exposure on the behavior of mice, where mice are randomized to receive the toxicant or not. In particular, in fixed interval experiments, one provides a mouse reinforcers (e.g., a food pellet), contingent upon some action taken by the mouse (e.g., a press of a lever),

Haiguang Li, Usama Pervaiz, Michał Matuszak, Robert Kamara

On-device machine learning (ODML) enables intelligent applications on resource-constrained devices. However, power consumption poses a major challenge, forcing a trade-off between model accuracy and power efficiency that often limits model complexity. The previously established Gated Compression (GC) layers offer a solution, enabling power efficiency without

Jiuyun Hu, Ziyue Li, Chen Zhang, Fugee Tsung

Tensor clustering has become an important topic, specifically in spatio-temporal modeling, due to its ability to cluster spatial modes (e.g., stations or road segments) and temporal modes (e.g., time of the day or day of the week). Our motivating example is from subway passenger flow modeling, where similarities between stations are commonly found. However,

Heinz H. Bauschke, Yuan Gao

It is well known that the iterates of an averaged nonexpansive mapping may only converge weakly to fixed point. A celebrated result by Baillon, Bruck, and Reich from 1978 yields strong convergence in the presence of linearity. In this paper, we extend this result to allow for flexible relaxation parameters. Examples are also provided to illustrate the result

Robert Hołyst, Paweł Jan Żuk, Anna Maciołek, Karol Makuch

The second law of equilibrium thermodynamics explains the direction of spontaneous processes in a system after removing internal constraints. When the system only exchanges energy with the environment as heat, the second law states that spontaneous processes at constant temperature satisfy: $\textrm{d} U - \delta Q \leq 0$. Here, $\textrm{d} U$ is the infini

Francisco J. Ordoñez Araujo, Juan Camilo Guevara Gómez, Benjamín Calvo Mozo

This paper introduces a method for aligning solar observations from different telescopes. We utilized helioprojective coordinates from the Solar Dynamics Observatory (SDO) as a reference to align images from ALMA and IRIS. The alignment is based on correlation analysis, employing both the Pearson Correlation Coefficient (PCC) and the Structural Similarity In

Christian Le Merdy, Safoura Zadeh

Let $M$ be a von Neumann algebra, let $\varphi$ be a normal faithful state on $M$ and let $L^p(M,\varphi)$ be the associated Haagerup non-commutative $L^p$-spaces, for $1\leq p\leq\infty$. Let $D\in L^1(M,\varphi)$ be the density of $\varphi$. Given a positive map $T\colon M\to M$ such that $\varphi\circ T\leq C_1\varphi$ for some $C_1\geq 0$, we study the b

Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu

We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer architecture with heterogeneous type embedding trained using temporal-difference learning. After obtaining an i

Rob Trangucci, Jesse Contreras, Jon Zelner, Joseph N. S. Eisenberg

Measuring the impact of an environmental point source exposure on the risk of disease, like cancer or childhood asthma, is well-developed. Modeling how an environmental health hazard that is extensive in space, like a wastewater canal, impacts disease risk is not. We propose a novel Bayesian generative semiparametric model for characterizing the cumulative s

Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens

An appropriate data basis grants one of the most important aspects for training and evaluating probabilistic trajectory prediction models based on neural networks. In this regard, a common shortcoming of current benchmark datasets is their limitation to sets of sample trajectories and a lack of actual ground truth distributions, which prevents the use of mor

David F. Anderson, Badal Joshi

Recent technological advances allow us to view chemical mass-action systems as analog computers. In this context, the inputs to a computation are encoded as initial values of certain chemical species while the outputs are the limiting values of other chemical species. In this paper, we design chemical systems that carry out the elementary arithmetic computat

Yumeng He, Matan Kotler-Berkowitz, Harry Liuson, Zeyu Nie

In this paper, we examine the claims made by the paper "A polynomial-time algorithm for 3-SAT" by Lizhi Du. The paper claims to provide a polynomial-time algorithm for solving the NP-complete problem 3-SAT. In examining the paper's argument, we find a flaw in one of the main sections of its algorithm. We argue that this flaw causes the paper's algorithm to i

Alexander Vedernikov, Zhaodong Sun, Virpi-Liisa Kykyri, Mikko Pohjola

Engagement measurement finds application in healthcare, education, services. The use of physiological and behavioral features is viable, but the impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of unsupervised remote photoplethysmography (rPPG) as an alternative for contact sen

Andy Yang, David Chiang

Deriving formal bounds on the expressivity of transformers, as well as studying transformers that are constructed to implement known algorithms, are both effective methods for better understanding the computational power of transformers. Towards both ends, we introduce the temporal counting logic $\textsf{K}_\text{t}$[#] alongside the RASP variant $\textsf{C

Yuxiao Li, Xin Liang, Bei Wang, Yongfeng Qiu

This research explores a novel paradigm for preserving topological segmentations in existing error-bounded lossy compressors. Today's lossy compressors rarely consider preserving topologies such as Morse-Smale complexes, and the discrepancies in topology between original and decompressed datasets could potentially result in erroneous interpretations or even

Divyanshu Kumar, Anurakt Kumar, Sahil Agarwal, Prashanth Harshangi

Large Language Models (LLMs) have gained widespread adoption across various domains, including chatbots and auto-task completion agents. However, these models are susceptible to safety vulnerabilities such as jailbreaking, prompt injection, and privacy leakage attacks. These vulnerabilities can lead to the generation of malicious content, unauthorized action

Paprapee Buason, Sidhant Misra, Jean-Paul Watson, Daniel K. Molzahn

The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide opera

Bo Yuan, Ezekiel Horsley, M. B. Stone, Nicholas P. Butch

We report field-dependent high-resolution inelastic neutron scattering (INS) measurements on the honeycomb lattice magnet, CoTiO$_3$, to study the evolution of its magnon excitations across a spin reorientation transition driven by an in-plane magnetic field. By carrying out elastic neutron scattering in a magnetic field, we show that the sample transitions

MurphyKate Montee

We use a combinatorial approximation of the hyperbolic plane to investigate properties of hyperbolic geometry such as exponential growth of perimeter and area of disks, and the linear isoperimetric inequality. This calculations give a surprising link to Fibonacci numbers.

GianCarlo Catalano, Alexander E. I. Brownlee, David Cairns, John McCall

Genetic Algorithms have established their capability for solving many complex optimization problems. Even as good solutions are produced, the user's understanding of a problem is not necessarily improved, which can lead to a lack of confidence in the results. To mitigate this issue, explainability aims to give insight to the user by presenting them with the

J. F. Parisi, J. W. Berkery, A. Sladkomedova, S. Guizzo

A technique, volumetric power optimization, is presented for enhancing the power output of magnetic confinement fusion devices. Applied to a tokamak, this approach involves shifting the burning plasma region to a larger plasma volume while introducing minimal perturbations to the plasma boundary shape. This edge perturbation -- squareness -- is analogous to

Yushu Wu, Xiao Quan, Mohammad Rasool Izadi, Chuan-Che Huang

If our noise-canceling headphones can understand our audio environments, they can then inform us of important sound events, tune equalization based on the types of content we listen to, and dynamically adjust noise cancellation parameters based on audio scenes to further reduce distraction. However, running multiple audio understanding models on headphones w

Luís Sousa, José Cecílio, Pedro Ferreira, Alan Oliveira

Industrial Control Systems (ICS) constitute the backbone of contemporary industrial operations, ranging from modest heating, ventilation, and air conditioning systems to expansive national power grids. Given their pivotal role in critical infrastructure, there has been a concerted effort to enhance security measures and deepen our comprehension of potential

Siamak Solat

This article examines the significant challenges encountered in implementing sharding within distributed replication systems. It identifies the impediments of achieving consensus among large participant sets, leading to scalability, throughput, and performance limitations. These issues primarily arise due to the message complexity inherent in consensus mecha

M. Tortarolo, D. Goijman, M. A. Barral, S. Di Napoli

We present an experimental study of the magnetoelectric coupling (MEC) in the Fe-Ga/PMN-PT thin film multiferroic composite by means of x-ray magnetic circular dichroism (XMCD) and ferromagnetic resonance (FMR). Our measurements show evidence for a charge mediated coupling mechanism, suggested by the asymmetric magnetic remanence (M$_{rem}$) behaviour under

Jacob Bernstein, Lu Wang

We prove lower bounds on the density of regular minimal cones of dimension less than seven provided the complements of the cones are topologically nontrivial.

Alberto Miguel-Gómez

We provide a model-theoretic classification of the countable homogeneous $\mathbf{H}_4$-free 3-hypertournament studied by Cherlin, Hubi\v{c}ka, Kone\v{c}n\'y, and Ne\v{s}et\v{r}il. Our main result is that the theory of this structure is $\mathrm{SOP}_3$, $\mathrm{TP}_2$, and $\mathrm{NSOP}_4$. We offer two proofs of this fact: one is a direct proof, and the

Trung Chau, Tài Huy Hà, Aryaman Maithani

Let $G$ be a connected graph and let $I(G)$ denote its edge ideal. We classify when $I(G)^n$, for $n \ge 1$, admits a minimal Lyubeznik resolution. We also give a characterization for when $I(G)^n$ is bridge-friendly, which, in turn, implies that $I(G)^n$ has a minimal Barile-Macchia cellular resolution.

Emily M. Silich, Elena Bellomi, Jack Sayers, John ZuHone

Galaxy cluster mergers are representative of a wide range of physics, making them an excellent probe of the properties of dark matter and the ionized plasma of the intracluster medium. To date, most studies have focused on mergers occurring in the plane of the sky, where morphological features can be readily identified. To allow study of mergers with arbitra

Margaret E. Carrington, Wade N. Cowie, Gabor Kunstatter, Christopher D. Phillips

We study numerically the evolution of an expanding strongly self-coupled real scalar field. We use a conformally invariant action that gives a traceless energy-momentum tensor and is better suited to model the early time behaviour of a system such as QCD, whose action is also conformally invariant.We consider asymmetric initial conditions and observe that wh

Kurran Singh, Tim Magoun, John J. Leonard

Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the ability to segment and identify nearly all objects in the world. However, utilizing such objects to localize the robot and build an open-set semantic map of the world remains an ope

Alec Helbling, Seongmin Lee, Polo Chau

Recently, researchers have proposed powerful systems for generating and manipulating images using natural language instructions. However, it is difficult to precisely specify many common classes of image transformations with text alone. For example, a user may wish to change the location and breed of a particular dog in an image with several similar dogs. Th

Yuezhu Xu, S. Sivaranjani

The Lipschitz constant plays a crucial role in certifying the robustness of neural networks to input perturbations. Since calculating the exact Lipschitz constant is NP-hard, efforts have been made to obtain tight upper bounds on the Lipschitz constant. Typically, this involves solving a large matrix verification problem, the computational cost of which grow

Amahury J. López-Díaz, Carlos Gershenson

This manuscript explores the evolutionary emergence of semantic closure -- the self-referential mechanism through which symbols actively construct and interpret their own functional contexts -- by integrating concepts from relational biology, physical biosemiotics, and ecological psychology into a unified computational enactivist framework. By extending Hofm

Charles E. Romero, Massimo Gaspari, Gerrit Schellenberger, Bradford A. Benson

Studies of surface brightness fluctuations in the intracluster medium (ICM) present an indirect probe of turbulent properties such as the turbulent velocities, injection scales, and the slope of the power spectrum of fluctuations towards smaller scales. With the advancement of Sunyaev-Zel'dovich (SZ) studies and surveys relative to X-ray observations, we see

Roy Zektzer, Xiyuan Lu, Khoi Tuan Hoang, Rahul Shrestha

Cavity quantum electrodynamics (cQED), the interaction of a two-level system with a high quality factor (Q) cavity, is a foundational building block in different architectures for quantum computation, communication, and metrology. The strong interaction between the atom and the cavity enables single photon operation which is required for quantum gates and so

Francis Dunn

We construct Rankin-Cohen type differential operators on Hermitian modular forms of signature $(n,n)$. The bilinear differential operators given here specialize to the original Rankin-Cohen operators in the case $n=1$, and more generally satisfy some analogous properties, including uniqueness. Our approach builds on previous work by Eholzer-Ibukiyama in the

Ruiqi Gao, Yifan Li, Roberto Car

In molecular simulations, neural network force fields aim at achieving \emph{ab initio} accuracy with reduced computational cost. This work introduces enhancements to the Deep Potential network architecture, integrating a message-passing framework and a new lightweight implementation with various improvements. Our model achieves accuracy on par with leading

Karl Bringmann, Egor Gorbachev

In an $m$-edge host graph $G$, all triangles can be listed in time $O(m^{1.5})$ [Itai, Rodeh '78], and all $k$-cycles can be listed in time $O(m^{2-1/{\lceil k/2 \rceil}} + t)$ where $t$ is the output size [Alon, Yuster, Zwick '97]. These classic results also hold for the colored problem variant, where the nodes of the host graph $G$ are colored by nodes in

Tal Horesh, Frédéric Paulin

Let $\nu$ be a place of a global function field $K$ over a finite field, with associated affine function ring $R_\nu$ and completion $K_\nu$, and let $1 \leq \mathfrak{m}<\textbf{d}$. The aim of this paper is to prove an effective triple joint equidistribution result for primitive partial $R_\nu$-lattices $\Lambda$ of rank $\mathfrak{m}$ in $K_\nu^{\;\textbf

Herve Zwirn

In two recent papers Khrennikov uses what he calls Ozawa intersubjectivity theorem to claim that intersubjectivity is necessarily verified in quantum mechanics and to criticize QBism and more generally all interpretations that are perspectival. In agreement with two previous QBist papers, I explain here why Khrennikov proof is not valid but in contrast with

Minoh Jeong, Alex Dytso, Martina Cardone

This paper explores Bayesian lower bounds on the minimum mean squared error (MMSE) that belong to the Ziv-Zakai (ZZ) family. The ZZ technique relies on connecting the bound to an M-ary hypothesis testing problem. Three versions of the ZZ bound (ZZB) exist: the first relies on the so-called valley-filling function (VFF), the second omits the VFF, and the thir

Zhuochun Li, Bo Xie, Robin Hilsabeck, Alyssa Aguirre

Evidence suggests that different prompts lead large language models (LLMs) to generate responses with varying quality. Yet, little is known about prompts' effects on response quality in healthcare domains. In this exploratory study, we address this gap, focusing on a specific healthcare domain: dementia caregiving. We first developed an innovative prompt tem

Samiul Based Shuvo, Syed Samiul Alam, Taufiq Hasan

Objective: Lung auscultation is a valuable tool in diagnosing and monitoring various respiratory diseases. However, lung sounds (LS) are significantly affected by numerous sources of contamination, especially when recorded in real-world clinical settings. Conventional denoising models prove impractical for LS denoising, primarily owing to spectral overlap co

Lev Borisov, Xavier Roulleau

For a complex elliptic curve $E$ and a point $p$ of order $n$ on it, the images of the points $p_k=kp$ under the Weierstrass embedding of $E$ into $\mathbb{C}\mathbb{P}^2$ are collinear if and only if the sum of indices is divisible by $n$. Thus, it provides a realization of a certain matroid. We study this matroid in detail and prove that its realization sp

Junhao Chen, Xiang Li, Xiaojun Ye, Chao Li

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still do not fully unleash human creativity. We often imagine 3D content made fr

Robert J. Deissler, Rose Al Helo, Robert Brown

In view of the growing role of magnetic particles under magnetic field influence in medical and other applications, and perforce the bead chaining, it is important to understand more generally the chain dynamics. As is well known, in the presence of a magnetic field, magnetic beads tend to form chains that are aligned with the magnetic field vector. In addit

Alapan Kuila, Sudeshna Sarkar

Sentiment analysis plays a pivotal role in understanding public opinion, particularly in the political domain where the portrayal of entities in news articles influences public perception. In this paper, we investigate the effectiveness of Large Language Models (LLMs) in predicting entity-specific sentiment from political news articles. Leveraging zero-shot

Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang

Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language models (LLMs) trained on public data can improve the quality of pre-training data for the on-device language models trained with DP and FL. We carefully design LLM prompts to filt

Nancy Lynch

Brain networks exhibit complications such as noise, neuron failures, and partial synaptic connectivity. These can make it difficult to model and analyze their behavior. This paper describes a way to address this difficulty, namely, breaking down the models and analysis using levels of abstraction. We describe the approach for the problem of recognizing hiera

Alexandru-Gabriel Mitrut

In this paper was proved (i) the equivalence of Dirac equation for mass $\mathsf{m}$ with two entangled Proca fields of mass $\mathsf{2m}$ and (ii) is proposed an equation and (iii) an 12-dimensional Lie algebra for the entangled spin fields.

Zehui Lu, Hao Tu, Huazhen Fang, Yebin Wang

This paper presents an integrated control strategy for optimal fast charging and active thermal management of Lithium-ion batteries in extreme ambient temperatures, striking a balance between charging speed and battery health. A control-oriented thermal-NDC (nonlinear double-capacitor) battery model is proposed to describe the electrical and thermal dynamics

Jing An, Jianfeng Lu, Yue Wu, Yang Xiang

We revisit the unified two-timescale Q-learning algorithm as initially introduced by Angiuli et al. \cite{angiuli2022unified}. This algorithm demonstrates efficacy in solving mean field game (MFG) and mean field control (MFC) problems, simply by tuning the ratio of two learning rates for mean field distribution and the Q-functions respectively. In this paper

Mo Kordzanganeh, Danial Keshvary, Nariman Arian

Latent diffusion models are the state-of-the-art for synthetic image generation. To align these models with human preferences, training the models using reinforcement learning on human feedback is crucial. Black et. al 2024 introduced denoising diffusion policy optimisation (DDPO), which accounts for the iterative denoising nature of the generation by modell

Marek Kwiek, Wojciech Roszka

In this research, the contributions of a highly productive minority of scientists to the national Polish research output over the past three decades (1992-2021) is explored. In almost all previous research, the approaches to high research productivity are missing the time component. Cross-sectional studies were not complemented by longitudinal studies: Scien

S. S. Agaev, K. Azizi, H. Sundu

The mass and width of the tensor tetraquark $T=bb\overline{c}\overline{c}$ with spin-parity $J^{\mathrm{P}}=2^{+}$ are calculated in the context of the QCD sum rule method. The tetraquark $T$ is modeled as a diquark-antidiquark state built of components $b^{T}C\gamma _{\mu }b$ and $\overline{c}\gamma _{\nu }C\overline{c}^{T}$ with $C$ being the charge conjug