April 2024 arXiv papers — page 9

Chaojie Zhang, Doug Storey, Pablo San Miguel Claveria, Zan Nie

This study documents several correlations observed during the first run of the plasma wakefield acceleration experiment E300 conducted at FACET-II, using a single drive electron bunch. The established correlations include those between the measured maximum energy loss of the drive electron beam and the integrated betatron x-ray signal, the calculated total b

Kevin O'Bryant

A set $A$ of nonnegative integers is called a $B_h$-set if every solution to $a_1+\dots+a_h = b_1+\dots+b_h$, where $a_i,b_i \in A$, has $\{a_1,\dots,a_h\}=\{b_1,\dots,b_h\}$ (as multisets). Let $γ_k(h)$ be the $k$-th positive element of the greedy $B_h$-set. We give a nontrivial lower bound on $γ_5(h)$, and a nontrivial upper bound on $γ_k(h)$ for $k\ge 5$.

Marco Sutti, Jan S. Hesthaven

We study the stability and sensitivity of an absorbing layer for the Boltzmann equation by examining the Bhatnagar-Gross-Krook (BGK) approximation and using the perfectly matched layer (PML) technique. To ensure stability, we discard some parameters in the model and calculate the total sensitivity indices of the remaining parameters using the ANOVA expansion

Zhidan Feng, Henning Fernau, Binhai Zhu

Given a road network modelled as a planar straight-line graph $G=(V,E)$ with $|V|=n$, let $(u,v)\in V\times V$, the shortest path (distance) between $u,v$ is denoted as $\delta_G(u,v)$. Let $\delta(G)=\max_{(u,v)}\delta_G(u,v)$, for $(u,v)\in V\times V$, which is called the diameter of $G$. Given a disconnected road network modelled as two disjoint trees $T_

Mehmet Can Yavuz, Yang Yang

Deep learning classifiers face significant challenges when dealing with heterogeneous multi-modal and multi-organ biomedical datasets. The low-level feature distinguishability limited to imaging-modality hinders the classifiers' ability to learn high-level semantic relationships, resulting in sub-optimal performance. To address this issue, image augmentation

Adela Habib, Joshua Finkelstein, Anders M. N. Niklasson

In recent years, a new kind of accelerated hardware has gained popularity in the Artificial Intelligence (AI) and Machine Learning (ML) communities which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI/ML hardwa

Takeshi Chiba, Tsuyoshi Houri

A scalar field with an exponential potential has been proposed as a model of inflation (called power-law inflation). Although it admits an exact solution, the integrability of the system has not been shown.We uncover the hidden symmetries behind the system by utilising the Eisenhart lift of field theories. We find that a conformal Killing vector field in the

Ben T. McAllister, Zijun C. Zhao, Jeremy F. Bourhill, Maxim Goryachev

Silicon is a key semiconducting material for electrical devices and hybrid quantum systems where low temperatures and zero-spin isotopic purity can enhance quantum coherence. Electrical conductivity in Si is characterised by carrier freeze out at around 40 K allowing microwave transmission which is a key component for addressing spins efficiently in silicon

Tomohiro Matsuda

The physical phenomena seen by an observer are defined for a local inertial system that is subjective to the observer. Such a coordinate system is called a ``moving frame'' because it changes from time to time. However, unlike the Thomas precession, the Unruh-DeWitt detector has been discussed for a fixed frame. We discuss the Unruh-DeWitt detector by defini

Sina Bagheri Nezhad, Ameeta Agrawal

This study investigates the factors influencing the performance of multilingual large language models (MLLMs) across diverse languages. We study 6 MLLMs, including masked language models, autoregressive models, and instruction-tuned LLMs, on the SIB-200 dataset, a topic classification dataset encompassing 204 languages. Our analysis considers three scenarios

Mason N. Rouches, Benjamin B. Machta

The three-dimensional organization of chromatin is thought to play an important role in controlling gene expression. Specificity in expression is achieved through the interaction of transcription factors and other nuclear proteins with particular sequences of DNA. At unphysiological concentrations many of these nuclear proteins can phase-separate in the abse

Javier Antoran

Large neural networks trained on large datasets have become the dominant paradigm in machine learning. These systems rely on maximum likelihood point estimates of their parameters, precluding them from expressing model uncertainty. This may result in overconfident predictions and it prevents the use of deep learning models for sequential decision making. Thi

Brian Sweeney, Rosemary Renaut, Malena Español

$\ell_1$ regularization is used to preserve edges or enforce sparsity in a solution to an inverse problem. We investigate the Split Bregman and the Majorization-Minimization iterative methods that turn this non-smooth minimization problem into a sequence of steps that include solving an $\ell_2$-regularized minimization problem. We consider selecting the reg

Calvin McPhail-Snyder

Let $\rho$ be a representation of a knot group (or more generally, the fundamental group of a tangle complement) into $\operatorname{SL}_2(\mathbb{C})$ expressed in terms of the Wirtinger generators of a diagram $D$. This diagram also determines an ideal triangulation of the complement called the octahedral decomposition. $\rho$ induces a hyperbolic structur

Ning An, Lei Hei, Yong Jiang, Weiping Meng

Relational triple extraction is crucial work for the automatic construction of knowledge graphs. Existing methods only construct shallow representations from a token or token pair-level. However, previous works ignore local spatial dependencies of relational triples, resulting in a weakness of entity pair boundary detection. To tackle this problem, we propos

Mingran Xu, Axel J. M. Deenen, Huixin Guo, Dirk Grundler

Chiral magnets are materials which possess unique helical arrangements of magnetic moments, which give rise to nonreciprocal transport and fascinating physics phenomena. On the one hand, their exploration is guided by the prospects of unconventional signal processing, computation schemes and magnetic memory. On the other hand, progress in applications is hin

Guoliang Yu, Junyi Ji, Changsong Xu, H. J. Xiang

Traditional ferroelectrics are limited by Neumann's principle, which confines exploration of ferroelectrics within polar point groups. Our recent work [Nat. Commun. 15, 135, (2024)] proposes the concept of fractional quantum ferroelectricity (FQFE) that extend the playground of ferroelectricity to non-polar point groups. Here, we apply group theory and intro

A. Arellano Ferro, S. Muneer, Sunetra Giridhar, I. Bustos Fierro

Employing \emph{VI} images of NGC 2419 acquired over 17 years, light curves for most of the known variables in the field of the cluster are produced. A cluster membership analysis for about 3100 stars in the cluster field with proper motions from $Gaia$-DR3, revealed the presence of member stars as far as 140 pc from the cluster center and enabled the constr

Alexei N. Skorobogatov

We prove that the quotient of the Brauer group of a product of varieties over k by the sum of the images of the Brauer groups of factors has finite exponent. The bulk of the proof concerns p-primary torsion in characteristic p. Our approach gives a more direct proof of the boundedness of the p-primary torsion of the Brauer group of an abelian variety, as rec

Evangelos Ververas, Rolandos Alexandros Potamias, Jifei Song, Jiankang Deng

Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the way to real-time neural rendering overcoming the computational burden of volumetric methods. Following the pioneering work of 3D-GS, several methods have attempted to achieve compressible and high-fidelity performance alternatives. However, by employing a geometry-agnostic optimizatio

Carlos Eduardo G. R. Alves, Francisco de Assis Boldt, Thiago M. Paixão

Effective communication is paramount for the inclusion of deaf individuals in society. However, persistent communication barriers due to limited Sign Language (SL) knowledge hinder their full participation. In this context, Sign Language Recognition (SLR) systems have been developed to improve communication between signing and non-signing individuals. In par

Harold Blas

We study a particular deformation of the potential KdV model (pKdV) and construct the quasi-conservation laws by a direct method. The charge densities, differing from their integrable counterpart with homogeneous degree terms, exhibit mixed scale dimension terms. The modifications of the charges around the soliton interaction regions are examined by numerica

Qasem Exirifard, Alessio D'Errico, Ebrahim Karimi

We show that a charged sphere moving at a constant velocity $v$ exhibits a mass due to electromagnetic radiation, expressed as $4/(3+(v/c)^2) (E/c^2)$, where $E$ is the electromagnetic energy and $c$ the speed of light in vacuum. Our finding reconciles the longstanding mismatch between the electromagnetic mass calculated from the classical electrodynamics' $

Linyi Ding, Sizhe Zhou, Jinfeng Xiao, Jiawei Han

Despite widespread applications of knowledge graphs (KGs) in various tasks such as question answering and intelligent conversational systems, existing KGs face two major challenges: information granularity and deficiency in timeliness. These hinder considerably the retrieval and analysis of in-context, fine-grained, and up-to-date knowledge from KGs, particu

Kaizhao Liu, Jose Blanchet, Lexing Ying, Yiping Lu

Bootstrap is a popular methodology for simulating input uncertainty. However, it can be computationally expensive when the number of samples is large. We propose a new approach called \textbf{Orthogonal Bootstrap} that reduces the number of required Monte Carlo replications. We decomposes the target being simulated into two parts: the \textit{non-orthogonal

Lonjezo Sithole

I propose a locally robust semiparametric framework for estimating causal effects using the popular examiner IV design, in the presence of many examiners and possibly many covariates relative to the sample size. The key ingredient of this approach is an orthogonal moment function that is robust to biases and local misspecification from the first step estimat

Lexiang Huang, Anjaly Parayil, Jue Zhang, Xiaoting Qin

Today, cloud workloads are essentially opaque to the cloud platform. Typically, the only information the platform receives is the virtual machine (VM) type and possibly a decoration to the type (e.g., the VM is evictable). Similarly, workloads receive little to no information from the platform; generally, workloads might receive telemetry from their VMs or e

Peng Ju, Kunhong Shen, Stefan Püschel, Yuanbin Jin

Optical refrigeration of solids with anti-Stokes fluorescence has been widely explored as a vibration-free cryogenic cooling technology. A minimum temperature of 87 K has been demonstrated with rare-earth ion doped crystals using optical refrigeration. However, the depletion of the upper-lying energy levels in the ground state manifold hinders further coolin

Tonglong Wei, Youfang Lin, Yan Lin, Shengnan Guo

Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the constraints of the road network, could offer deep insights into users' moving behaviors in intelligent transportation systems. Although recent studies have demonstrated the advantages of achieving map-constrained trajectory recovery via an end-to-end manner, they still f

Saikat Sur, Pritam Chattopadhyay, Madhuparna Karmakar, Avijit Misra

Quantum many-body systems serve as a suitable working medium for realizing quantum thermal machines (QTMs) by offering distinct advantages such as cooperative many-body effects, and performance boost at the quantum critical points. However, the bulk of the existing literature exploring the criticality of many-body systems in the context of QTMs involves mode

Jaidev Shastri, Xiaoguang Wang, Basavesh Ammanaghatta Shivakumar, Freek Verbeek

Data race, a category of insidious software concurrency bugs, is often challenging and resource-intensive to detect and debug. Existing dynamic race detection tools incur significant execution time and memory overhead while exhibiting high false positives. This paper proposes HMTRace, a novel Armv8.5-A memory tag extension (MTE) based dynamic data race detec

Himani Sinhmar, Hadas Kress-Gazit

We present a decentralized control algorithm for a minimalist robotic swarm lacking memory, explicit communication, or relative position information, to encapsulate multiple diffusive target sources in a bounded environment. The state-of-the-art approaches generally require either local communication or relative localization to provide guarantees of converge

Kyohei Eguchi, Takeru Ito, Yoshiki J. Sato, Ryuji Okazaki

Low-dimensional materials, in which the electronic and transport properties are drastically modified in comparison to those of three-dimensional bulk materials, yield a key class of thermoelectric materials with high conversion efficiency. Among such materials, the organic compounds may serve peculiar properties owing to their unique molecular-based low-dime

Bertrand Teguia Tabuguia, James Worrell

It was recently conjectured that every component of a discrete-time rational dynamical system is a solution to an algebraic difference equation that is linear in its highest-shift term (a quasi-linear equation). We prove that the conjecture holds in the special case of holonomic sequences, which can straightforwardly be represented by rational dynamical syst

Stasiu Thomas Chyczewski, Hanwool Lee, Shuchen Li, Marwan Eladl

The scalable synthesis of strong spin orbit coupling (SOC) materials such as 1T${}^\prime$ phase MoTe${}_2$ is crucial for spintronics development. Here, we demonstrate wafer-scale growth of 1T${}^\prime$ MoTe${}_2$ using metal-organic chemical vapor deposition (MOCVD) with sputtered Mo and (C${}_4$H${}_9$)${}_2$Te. The synthesized films show uniform coverag

Siyuan Xiang, Chin Tseng, Congcong Wen, Deshana Desai

We introduce the first work on benchmarking and evaluating deep clustering algorithms on large-scale non-categorical 3D CAD models. We first propose a workflow to allow expert mechanical engineers to efficiently annotate 252,648 carefully sampled pairwise CAD model similarities, from a subset of the ABC dataset with 22,968 shapes. Using seven baseline deep c

Yipeng Zhang, Laurent Charlin, Richard Zemel, Mengye Ren

We formulate a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that are specific to the present and the past data, encompassing stability, plasticity, and cross-task consolidation. The framework reveals that many existing UCL approaches overlook cross-task consolidation and try to balance plasticity and st

Daniel Cordier, Gerard Liger-Belair, David A. Bonhommeau, Thomas Seon

The Earth is no longer the only known celestial body containing one or more liquid phases. The Cassini spacecraft has discovered seas of hydrocarbons at the surface of Titan, while a series of corroborating evidences argue in favour of the existence of an aqueous ocean beneath the icy crust of several moons. Capillarity embraces a family of physical processe

Zihao Li, Yuyi Ao, Jingrui He

Knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expressive modeling and predictive embedding of KGs, most of the current KG representation learning methods, where each entity is embedded as a vector in the Euclidean space and each re

Breanna A. Binder, Rosalie Williams, Jacob Payne, Michael Eracleous

The population-wide properties and demographics of extragalactic X-ray binaries (XRBs) correlate with the star formation rates (SFRs), stellar masses ($M_{\star}$), and environmental factors (such as metallicity, $Z$) of their host galaxy. Although there is evidence that XRB scaling relations ($L_X$/SFR for high mass XRBs [HMXBs] and $L_X$/$M_{\star}$ for lo

Navid Rajabi, Jana Kosecka

Vision and Language Models (VLMs) continue to demonstrate remarkable zero-shot (ZS) performance across various tasks. However, many probing studies have revealed that even the best-performing VLMs struggle to capture aspects of compositional scene understanding, lacking the ability to properly ground and localize linguistic phrases in images. Recent VLM adva

Rakhi Singh

Subdata selection is a study of methods that select a small representative sample of the big data, the analysis of which is fast and statistically efficient. The existing subdata selection methods assume that the big data can be reasonably modeled using an underlying model, such as a (multinomial) logistic regression for classification problems. These method

Christopher J. Kymn, Sonia Mazelet, Annabel Ng, Denis Kleyko

We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequently factorized to parse scene content. The sparse feature representation is learned from image statistics via convolutional sparse coding, while scene parsing is performed by a r

Tsung-Ju Lee

In this article, we study the finite distance problem with respect to the period-map metric on the moduli of non-K\"{a}hler Calabi--Yau $\partial\bar{\partial}$-threefolds via Hodge theory. We extended C.-L. Wang's finite distance criterion for one-parameter degenerations to the present setting. As a byproduct, we also obtained a sufficient condition for a n

Davis Wertheimer, Joshua Rosenkranz, Thomas Parnell, Sahil Suneja

This technical report describes the design and training of novel speculative decoding draft models, for accelerating the inference speeds of large language models in a production environment. By conditioning draft predictions on both context vectors and sampled tokens, we can train our speculators to efficiently predict high-quality n-grams, which the base m

Nicolai Popov, William J. Briscoe, Igor Strakovsky

The strong CP violation problem has a long history emanating from its discovery 60 years ago in the decay of neutral kaons and subsequent experimental and theoretical studies over several decades. We review herein experimental data that observe indirect CP violation of the order of $\sim10^{-3}$, as well as the discovery of direct CP violation of the order o

Christian Brennecke, Adrien Schertzer, Chen Van Dam

Based on \cite{H}, it is well known that the rescaled two point correlation functions \[ \sqrt{N} \langle \sigma_i ; \sigma_j\rangle = \sqrt{N} \big( \langle \sigma_i \sigma_j\rangle -\langle \sigma_i\rangle \langle \sigma_j\rangle\big) \] in the Sherrington-Kirkpatrick spin glass model with non-zero external field admit at sufficiently high temperature an e

Anthony Kenyon, Lipika Deka, David Elizondo

Accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. One of the ways in which such anomalies can be detected is to analyse the infor

Huck Stepanyants, Alan Beardon, Jeremy Paton, Dmitri Krioukov

Riemann surfaces are among the simplest and most basic geometric objects. They appear as key players in many branches of physics, mathematics, and other sciences. Despite their widespread significance, how to compute distances between pairs of points on compact Riemann surfaces is surprisingly unknown, unless the surface is a sphere or a torus. This is becau

Arif Ahmed, Gondy Leroy, Stephen A. Rains, Philip Harber

Health literacy is crucial to supporting good health and is a major national goal. Audio delivery of information is becoming more popular for informing oneself. In this study, we evaluate the effect of audio enhancements in the form of information emphasis and pauses with health texts of varying difficulty and we measure health information comprehension and

S. El bourkadi, M. W. Mansouri

We introduce the notion of cosymplectic structure on Jacobi-Jordan algebras, and we state that they are related to symplectic Jacobi-Jordan algebras. We show, in particular, that they support a right-skew-symmetric product. We also study the double extension constructions of cosymplectic Jacobi-Jordan algebras and give a complete classification in dimension

Michele Santacatterina, Federico Macchiavelli Giron, Xinyi Zhang, Ivan Diaz

Platform trials are multi-arm designs that simultaneously evaluate multiple treatments for a single disease within the same overall trial structure. Unlike traditional randomized controlled trials, they allow treatment arms to enter and exit the trial at distinct times while maintaining a control arm throughout. This control arm comprises both concurrent con

Sergi Liesegang, Stefano Buzzi

This paper tackles the problem of designing proper uplink multiple access schemes for coexistence between enhanced mobile broadband+ (eMBB+) users and massive machine-type communications+ (mMTC+) devices in a terminal-centric cell-free massive MIMO system. Specifically, the use of a time-frequency spreading technique for the mMTC+ devices has been proposed.

Nisha Pillai, Bindu Nanduri, Michael J Rothrock, Zhiqian Chen

Synthetic microbiomes offer new possibilities for modulating microbiota, to address the barriers in multidtug resistance (MDR) research. We present a Bayesian optimization approach to enable efficient searching over the space of synthetic microbiome variants to identify candidates predictive of reduced MDR. Microbiome datasets were encoded into a low-dimensi

Alessandro Lizzeri, Eran Shmaya, Leeat Yariv

Starting from Robbins (1952), the literature on experimentation via multi-armed bandits has wed exploration and exploitation. Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined: a policymaker may assess new policies different than the status quo; an investor may evaluate projects outside her portfolio. We characte

Daniela Calvetti, Monica Pragliola, Erkki Somersalo

The electrical impedance tomography (EIT) problem of estimating the unknown conductivity distribution inside a domain from boundary current or voltage measurements requires the solution of a nonlinear inverse problem. Sparsity promoting hierarchical Bayesian models have been shown to be very effective in the recovery of almost piecewise constant solutions in

Afsaneh Mahanipour, Hana Khamfroush

The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher risk of cyberattacks because of the expanded attack surface from distributed edge and cloud services, the vulnerability of IoT devices, and challe

Andy He, Darren Key, Mason Bulling, Andrew Chang

Graphics Processing Units (GPUs) have become the leading hardware accelerator for deep learning applications and are used widely in training and inference of transformers; transformers have achieved state-of-the-art performance in many areas of machine learning and are especially used in most modern Large Language Models (LLMs). However, GPUs require large a

Alexis Guichemerre, Soufiane Belharbi, Tsiry Mayet, Shakeeb Murtaza

Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotation

Aditya Biswas

We present PSiLON Net, an MLP architecture that uses $L_1$ weight normalization for each weight vector and shares the length parameter across the layer. The 1-path-norm provides a bound for the Lipschitz constant of a neural network and reflects on its generalizability, and we show how PSiLON Net's design drastically simplifies the 1-path-norm, while providi

Wontae Kim, Kristian Moring, Lauri Särkiö

We prove that bounded weak solutions to degenerate parabolic double-phase equations of $p$-Laplace type are locally H\"older continuous. The proof is based on phase analysis and methods for the $p$-Laplace equation. In particular, the phase analysis determines whether the double-phase equation is locally similar to the $p$-Laplace or the $q$-Laplace equation

Nikita Drobyshev, Antoni Bigata Casademunt, Konstantinos Vougioukas, Zoe Landgraf

Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this mod

Claudio Bellei, Muhua Xu, Ross Phillips, Tom Robinson

Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applic

Hongrui Zhao, Michael F. Lembeck, Adrian Zhuang, Riya Shah

Star trackers are one of the most accurate celestial sensors used for absolute attitude determination. The devices detect stars in captured images and accurately compute their projected centroids on an imaging focal plane with subpixel precision. Traditional algorithms for star detection and centroiding often rely on threshold adjustments for star pixel dete

Dirk Grupe, S. Komossa, Salem Wolsing

We report on follow-up observations of the Seyfert 1.9 galaxy IC 3599 with the NASA Neil Gehrels Swift mission. The detection of a second X-ray outburst in 2010 by Swift after the first discovery of a bright X-ray outburst in 1990 by ROSAT led to the suggestion of two very different explanations: The first one assumed that IC 3599 exhibits outbursts due to r

Gondy Leroy, David Kauchak, Philip Harber, Ankit Pal

Text and audio simplification to increase information comprehension are important in healthcare. With the introduction of ChatGPT, an evaluation of its simplification performance is needed. We provide a systematic comparison of human and ChatGPT simplified texts using fourteen metrics indicative of text difficulty. We briefly introduce our online editor wher

David J. Fernández C., O. Pavón-Torres

Starting from the effective Hamiltonian arising from the tight binding model, we study the behaviour of low-lying excitations for bilayer graphene placed in periodic external magnetic fields by using irreducible second order supersymmetry transformations. The coupled system of equations describing these excitations is reduced to a pair of periodic Schr\"odin

Sitan Chen, Weiyuan Gong, Qi Ye

We revisit the problem of Pauli shadow tomography: given copies of an unknown $n$-qubit quantum state $\rho$, estimate $\text{tr}(P\rho)$ for some set of Pauli operators $P$ to within additive error $\epsilon$. This has been a popular testbed for exploring the advantage of protocols with quantum memory over those without: with enough memory to measure two co

David Dingel, Fabian Egidy, Christian Glaßer

We construct an oracle relative to which $\mathrm{NP} = \mathrm{PSPACE}$, but $\mathrm{UP}$ has no many-one complete sets. This combines the properties of an oracle by Hartmanis and Hemachandra [HH88] and one by Ogiwara and Hemachandra [OH93]. The oracle provides new separations of classical conjectures on optimal proof systems and complete sets in promise c

Valentin A. Skoutnev, Andrei M. Beloborodov

Tayler instability of toroidal magnetic fields $B_\phi$ is broadly invoked as a trigger for turbulence and angular momentum transport in stars. This paper presents a systematic revision of the linear stability analysis for a rotating, magnetized, and stably stratified star. For plausible configurations of $B_\phi$, instability requires diffusive processes: v

Martín F. Mestre, Carlos R. Argüelles, Daniel D. Carpintero, Valentina Crespi

Traditional studies on stellar streams typically involve phenomenological $\Lambda$CDM halos or ad hoc dark matter (DM) profiles with different degrees of triaxiality, which preclude to gain insights into the nature and mass of the DM particles. Recently, a Maximum Entropy Principle of halo formation has been applied to provide a DM halo model which incorpor

Ka Ho Yuen, Ka Wai Ho, Chi Yan Law, Avi Chen

The trending term "filament" is extensively used in the interstellar medium (ISM) and the star formation community, and is believed to be one of the most important objects that gauge molecular cloud and star formation. However, the physical definition of these ubiquitous, elongated, high contrast features is poorly defined and still actively debated. Despite

Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi

This paper investigates the relationships between hyperparameters of machine learning and fairness. Data-driven solutions are increasingly used in critical socio-technical applications where ensuring fairness is important. Rather than explicitly encoding decision logic via control and data structures, the ML developers provide input data, perform some pre-pr

Amrane Houas, Fateh Merahi, Mustafa Moumni

In this work, we are interested in problems that are related to the physical phenomena of diffusion. We will focus on the theoretical aspect of the study, such as existence, uniqueness and non-explosive solutions. We will weaken the conditions imposed on the coefficients of the stochastic differential equations (SDE) that model some diffusion phenomena of me

Shuang Lin, Shun-Li Shang, John D. Shimanek, Yi Wang

In the present work, the ideal shear strength ({\tau}_is) of dilute Ni34XZ ternary alloys (X or Z = Al, Co, Cr, Fe, Mn, Mo, Nb, Si, Ti) are predicted by first-principles calculations based on density functional theory (DFT) in terms of pure alias shear deformations. The {\tau}_is results show that within the concentration up to 8.3% of alloying elements, {\t

Zhongzheng Xu, Emily Wall

Data visualizations help extract insights from datasets, but reaching these insights requires decomposing high level goals into low-level analytic tasks that can be complex due to varying degrees of data literacy and visualization experience. Recent advancements in large language models (LLMs) have shown promise for lowering barriers for users to achieve tas

Yifan Xie, Julian Berberich, Frank Allgöwer

Data-driven controllers design is an important research problem, in particular when data is corrupted by the noise. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme using noisy input-state data for unknown linear time-invariant (LTI) system. The unknown system matrices are characterized by a set-membership representation

Sparsh Srivastava, Rohan Arora

We create an innovative mixed reality-first social recommendation model, utilizing features uniquely collected through mixed reality (MR) systems to promote social interaction, such as gaze recognition, proximity, noise level, congestion level, and conversational intensity. We further extend these models to include right-time features to deliver timely notif

Matteo Merler, Katsiaryna Haitsiukevich, Nicola Dainese, Pekka Marttinen

State of the art Symbolic Regression (SR) methods currently build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. In this work, we introduce the first comprehensive framework that utilizes LLMs for the task of SR. We propose In-Context Symbolic Regression (ICSR), an SR method which iteratively refines a f

Ruixuan Sun, Xinyi Li, Avinash Akella, Joseph A. Konstan

This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment. Our study involves a combination of between-subject prompt and historic consumption assessments, along with within-subject recommendation scenario evaluations. By examining conversation and

Bruno de Mendonça Braga, Javier Alejandro Chávez-Domínguez

We initiate the study of the small scale geometry of operator spaces. The authors have previously shown that a map between operator spaces which is completely coarse (that is, the sequence of its amplifications is equi-coarse) must be $\mathbb R$-linear. We obtain a generalization of the aforementioned result to completely coarse maps defined on the unit bal

Zhicheng Han

In this paper, we prove Schwartz estimates for Hodge Laplacian and Dirac operators on semisimple Lie groups. Alongside, we gives a version of Kuga lemma for its Lie algebra cohomology. This is a generalization of similar results on symmetric spaces. The main purpose of such estimates is to study the heat problem not only in the scalar case, but also for sect

S. Zargari, D. Galappaththige, C. Tellambura

Ambient Internet of Things networks use low-cost, low-power backscatter tags in various industry applications. By exploiting those tags, we introduce the integrated sensing and backscatter communication (ISABC) system, featuring multiple backscatter tags, a user (reader), and a full-duplex base station (BS) that integrates sensing and (backscatter) communica

Harrison LaBollita, Soumen Bag, Jesse Kapeghian, Antia S. Botana

We employ a density-functional theory plus dynamical mean-field theory framework to investigate the correlated electronic structure of the alternating single-layer trilayer (1313) polymorph of La$_3$Ni$_2$O$_7$ under pressure. At ambient pressure, the single-layer is in a Mott insulating regime and the low-energy physics is dominated by the trilayer block. U

Dianwei Chen, Yaobang Gong, Xianfeng Yang

Existing Advanced Driver Assistance Systems primarily focus on the vehicle directly ahead, often overlooking potential risks from following vehicles. This oversight can lead to ineffective handling of high risk situations, such as high speed, closely spaced, multi vehicle scenarios where emergency braking by one vehicle might trigger a pile up collision. To

Ása Skúladóttir, Ioanna Koutsouridou, Irene Vanni, Anish M. Amarsi

The first (Pop III) stars formed only out of H and He and were likely more massive than present-day stars. Massive Pop III stars in the range 140-260 M$_\odot$ are predicted to end their lives as pair-instability supernovae (PISNe), enriching the environment with a unique abundance pattern, with high ratios of odd to even elements. Recently, the most promisi

Nikolas Ditz, Antonio M. Puertas, M. Fuchs

This work presents a theoretical analysis of the motion of a tracer colloid driven by a time-dependent force through a viscoelastic fluid. The recoil of the colloid after application of a strong force is determined. It provides insights into the elastic forces stored locally in the fluid and their weakening by plastic processes. We generalize the mode coupli

Kenneth M. Peterson, Mustafa Rifat, Edward C. DeMeter, Saurabh Basu

Surface characteristics are a major contributor to the in-service performance, particularly fatigue life, of additively manufactured (AM) components. Centrifugal disk finishing (CDF) is one of many rigid media, abrasive machining processes employed to smooth the surfaces and edges of AM components. Within the general family of abrasive machining processes cu

Batuhan K. Karaman, Katerina Dodelzon, Gozde B. Akar, Mert R. Sabuncu

Breast cancer is one of the leading causes of mortality among women worldwide. Early detection and risk assessment play a crucial role in improving survival rates. Therefore, annual or biennial mammograms are often recommended for screening in high-risk groups. Mammograms are typically interpreted by expert radiologists based on the Breast Imaging Reporting

Samudra Dasgupta

Quantum computing's potential is immense, promising super-polynomial reductions in execution time, energy use, and memory requirements compared to classical computers. This technology has the power to revolutionize scientific applications such as simulating many-body quantum systems for molecular structure understanding, factorization of large integers, enha

Maxime Flin, Parth Mittal

We study the communication complexity of $(\Delta + 1)$ vertex coloring, where the edges of an $n$-vertex graph of maximum degree $\Delta$ are partitioned between two players. We provide a randomized protocol which uses $O(n)$ bits of communication and ends with both players knowing the coloring. Combining this with a folklore $\Omega(n)$ lower bound, this s

J. Kluson

We study space-time filling unstable D-brane coupled to gravity. We find Hamiltonian for this D-brane and then we implement Brown-Kuchar formalism for this system. We calculate algebra of constraints of deparametrized theory and we show that for large $T$ the Poisson brackets between new constraints are zero. Then we define gauge invariant functions on phase

Chris Hull, Maxim Zabzine

We take a fresh look at the relation between generalised K\"ahler geometry and $N=(2,2)$ supersymmetric sigma models in two dimensions formulated in terms of $(2,2)$ superfields. Dual formulations in terms of different kinds of superfield are combined to give a formulation with a doubled target space and both the original superfield and the dual superfield.

Vladimir Dragović, Marko Stošić

Starting from billiard partitions which arose recently in the description of periodic trajectories of ellipsoidal billiards in $d$-dimensional Euclidean space, we introduce a new type of separable integer partition classes, called type B. We study the numbers of basis partitions with $d$ parts and relate them to the Fibonacci sequence and its natural general

Thomas Buchner, Stefan Weirich, Alexander M. Kübler, Wojciech Matusik

The functional replication and actuation of complex structures inspired by nature is a longstanding goal for humanity. Creating such complex structures combining soft and rigid features and actuating them with artificial muscles would further our understanding of natural kinematic structures. We printed a biomimetic hand in a single print process comprised o

Darren Lage, Riley Pruitt, Jason Ross Arnold

This study examines the extent to which U.S. federal agencies responded to and implemented the principles outlined in the White House's October 2022 "Blueprint for an AI Bill of Rights." The Blueprint provided a framework for the ethical governance of artificial intelligence systems, organized around five core principles: safety and effectiveness, protection

K. Aditya Mohan, Massimiliano Ferrucci, Chuck Divin, Garrett A. Stevenson

4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an important inverse problem in non-destructive evaluation. Conventional back-projection based reconstruction methods assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruct

Anton Marius Graf, Ke Lin, MyeongSeo Kim, Joonas Keski-Rahkonen

Recent theoretical investigations have revealed unconventional transport mechanisms within high Brilliouin zones of two-dimensional superlattices. Electrons can navigate along channels we call superwires, gently guided without brute force confinement. Such dynamical confinement is caused by weak superlattice deflections, markedly different from the static or

Jitendra K Tugnait

We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional, stationary matrix-variate Gaussian time series. All past work on high-dimensional matrix graphical models assumes that independent and identically distributed (i.i.d.) observations of the matrix-variate are available. Here we allow dependent observati

Abigale Snortland, Aidan Hunt, Owen Williams, Brian Polagye

Cross-flow turbine (known as vertical-axis wind turbines or ``VAWTs'' in wind) blades encounter a relatively undisturbed inflow for the first half of each rotational cycle (``upstream sweep'') and then pass through their own wake for the latter half (``downstream sweep''). While most research on cross-flow turbine optimization focuses on the power-generating