December 2023 arXiv papers — page 42

Radu Ioan Boţ, Guoyin Li, Min Tao

In this paper, we consider a class of nonconvex and nonsmooth fractional programming problems, that involve the sum of a convex, possibly nonsmooth function composed with a linear operator and a differentiable, possibly nonconvex function in the numerator and a convex, possibly nonsmooth function composed with a linear operator in the denominator. These prob

Jackson A. Mickley, Waseem Kamleh, Derek B. Leinweber

The continued development of models that propose the existence of fractional topological objects in the Yang-Mills vacuum has called for a quantitative method to study the topological structure of $\mathrm{SU}(N)$ gauge theory. We present an original numerical algorithm that can identify distinct topological objects in the nontrivial ground-state fields and

Aaron J. Yeiser, Emma F. Wawrzynek, John Z. Zhang, Lukas Graf

Objective: We present the "UmboMic," a prototype piezoelectric cantilever microphone designed for future use with totally-implantable cochlear implants. Methods: The UmboMic sensor is made from polyvinylidene difluoride (PVDF) because of its low Young's modulus and biocompatibility. The sensor is designed to fit in the middle ear and measure the motion of th

Arun Debray

We give an overview of differential cohomology from the point of view of algebraic topology. This includes a survey of several different definitions of differential cohomology groups, a discussion of differential characteristic classes, an introduction to differential generalized cohomology theory, and some applications in physics.

Tsukasa Iwabuchi, Ryoma Ueda

We study the two-dimensional surface quasi-geostrophic equation. Motivated by the uniqueness for the three-dimensional incompressible Navier-Stokes equations, we demonstrate that the uniqueness of the mild solution of the two-dimensional surface quasi-geostrophic equation holds in the scaling critical Lebesgue space with a unique structure of the non-linear

Julia Briden, Changrak Choi, Kyongsik Yun, Richard Linares

Future spacecraft and surface robotic missions require increasingly capable autonomy stacks for exploring challenging and unstructured domains, and trajectory optimization will be a cornerstone of such autonomy stacks. However, the nonlinear optimization solvers required remain too slow for use on relatively resource-constrained flight-grade computers. In th

Zhichao Xu

Query-focused summarization (QFS) aims to provide a summary of a single document/multi documents that can satisfy the information needs of a given query. It is useful for various real-world applications, such as abstractive snippet generation or more recent retrieval augmented generation (RAG). A prototypical QFS pipeline consists of a retriever (sparse or d

Qiaoyue Tang, Frederick Shpilevskiy, Mathias Lécuyer

The Adam optimizer is a popular choice in contemporary deep learning, due to its strong empirical performance. However we observe that in privacy sensitive scenarios, the traditional use of Differential Privacy (DP) with the Adam optimizer leads to sub-optimal performance on several tasks. We find that this performance degradation is due to a DP bias in Adam

Gaël Gendron, Yang Chen, Mitchell Rogers, Yiping Liu

Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is essential to better understand the causes behind animal behaviour and what interventions are responsible for changes in their behaviours. This can help to predict unusual behaviours,

Jennifer Pi, Christopher Davis, Yasmeen Baki, Alessandra Pantano

We discuss two proof evaluation activities meant to promote the acquisition of learning behaviors of professional mathematics within an introductory undergraduate proof-writing course. These learning behaviors include the ability to read and discuss mathematics critically, reach a consensus on correctness and clarity as a group, and verbalize what qualities

Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon

Generative Flow Networks (GFNs) have emerged as a powerful tool for sampling discrete objects from unnormalized distributions, offering a scalable alternative to Markov Chain Monte Carlo (MCMC) methods. While GFNs draw inspiration from maximum entropy reinforcement learning (RL), the connection between the two has largely been unclear and seemingly applicabl

John E. McCarthy, Hazel T. McCarthy

We consider pairs of anti-commuting $2p$-by-$2p$ Hermitian matrices that are chosen randomly with respect to a Gaussian measure. Generically such a pair decomposes into the direct sum of $2$-by-$2$ blocks on which the first matrix has eigenvalues $\pm x_j$ and the second has eigenvalues $\pm y_j$. We call $\{ (x_j, y_j) \}$ the skew spectrum of the pair. We

João B. S. Carvalho, Mengtao Zhang, Robin Geyer, Carlos Cotrini

Anomaly detection (AD) is the machine learning task of identifying highly discrepant abnormal samples by solely relying on the consistency of the normal training samples. Under the constraints of a distribution shift, the assumption that training samples and test samples are drawn from the same distribution breaks down. In this work, by leveraging tools from

Stefano Piccardo, Matteo Giacomini, Antonio Huerta

A high-order, degree-adaptive hybridizable discontinuous Galerkin (HDG) method is presented for two-fluid incompressible Stokes flows, with boundaries and interfaces described using NURBS. The NURBS curves are embedded in a fixed Cartesian grid, yielding an unfitted HDG scheme capable of treating the exact geometry of the boundaries/interfaces, circumventing

Harleen Kaur, Jan Mendling, Christoffer Rubensson, Timotheus Kampik

A key concern of automatic process discovery is to provide insights into performance aspects of business processes. Waiting times are of particular importance in this context. For that reason, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models, but often miss the opportunity

Katrin Tomanek, Shanqing Cai, Subhashini Venugopalan

Abbreviation expansion is a strategy used to speed up communication by limiting the amount of typing and using a language model to suggest expansions. Here we look at personalizing a Large Language Model's (LLM) suggestions based on prior conversations to enhance the relevance of predictions, particularly when the user data is small (~1000 samples). Specific

Wolfgang Messner, Tatum Greene, Josephine Matalone

Large language models (LLMs) are able to engage in natural-sounding conversations with humans, showcasing unprecedented capabilities for information retrieval and automated decision support. They have disrupted human-technology interaction and the way businesses operate. However, technologies based on generative artificial intelligence (GenAI) are known to h

Jia Wang, Leander Hemelhof, Ivan Markovsky, Panagiotis Patrinos

This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints. Our main contributions are: 1) using a non-parametric data-driven representation of the system dynamics, for dealing with the unknown system dynamics in the context of ILC, 2) design of

Aaron Maiwald, Leon Ackermann, Maximilian Kalcher, Daniel J. Wu

Alternative data representations are powerful tools that augment the performance of downstream models. However, there is an abundance of such representations within the machine learning toolbox, and the field lacks a comparative understanding of the suitability of each representation method. In this paper, we propose artifact detection and classification wit

Jiong Liu, Hamed Farahani, R. A. Serota

We use house prices (HP) and house price indices (HPI) as a proxy to income distribution. Specifically, we analyze sale prices in the 1970-2010 window of over 116,000 single-family homes in Hamilton County, Ohio, including Cincinnati metro area of about 2.2 million people. We also analyze HPI, published by Federal Housing Finance Agency (FHFA), for nearly 18

Shashank Kumar Anand, Matteo B. Bertagni, Theodore D. Drivas, Amilcare Porporato

Complex topographies exhibit universal properties when fluvial erosion dominates landscape evolution over other geomorphological processes. Similarly, we show that the solutions of a minimalist landscape evolution model display invariant behavior as the impact of soil diffusion diminishes compared to fluvial erosion at the landscape scale, yielding complete

Oliver Lunding Sandqvist

Disability insurance claims are often affected by lengthy reporting delays and adjudication processes. The classic multistate life insurance modeling framework is ill-suited to handle such information delays since the cash flow and available information can no longer be based on the biometric multistate process determining the contractual payments. We propos

Francisco Gancedo, Eduardo García-Juárez, Neel Patel, Robert M. Strain

In this paper we consider gravity-capillarity Muskat bubbles in 2D. We obtain a new approach to improve our result in [25]. Due to a new bubble-adapted formulation, the improvement is two fold. We significantly condense the proof and we now obtain the global well-posedness result for Muskat bubbles in critical regularity.

Justyna P. Zwolak, Jacob M. Taylor, Reed W. Andrews, Jared Benson

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasi

Krishn Kumar Gupt, Meghana Kshirsagar, Douglas Mota Dias, Joseph P. Sullivan

Computational cost in metaheuristics such as Evolutionary Algorithms (EAs) is often a major concern, particularly with their ability to scale. In data-based training, traditional EAs typically use a significant portion, if not all, of the dataset for model training and fitness evaluation in each generation. This makes EAs suffer from high computational costs

Aseel Farhat, Zoran Grujic

The goal of this note is to demonstrate that as soon as the hyper-diffusion exponent is greater than one, a class of finite time blow-up scenarios consistent with the analytic structure of the flow (prior to the possible blow-up time) can be ruled out. The argument is self-contained, in spirit of the regularity theory of the hyper-dissipative Navier-Stokes s

Abdellatif Lfounoune, Hafida Massit, Abdelilah Karara, Mohamed Rossafi

In this article, we study g-frames in Hilbert $C^*$-modules and investigate conditions under which the sum of two g-frames (or a g-frame and a g-Bessel sequence) remains a g-frame. We also address the stability of g-frames under certain perturbations and provide illustrative examples in the context of $C^*$-algebras. Our results unify and extend many of the

Xinchao Zhou, Hikaru Tamura, Tzu-Han Chang, Chen-Lung Hung

Interfacing cold atoms with integrated nanophotonic devices could offer new paradigms for engineering atom-light interactions and provide a potentially scalable route for quantum sensing, metrology, and quantum information processing. However, it remains a challenging task to efficiently trap a large ensemble of cold atoms on an integrated nanophotonic circu

Jorge Arvesú Carballo, Alejandro J. Quintero Roba

We study two families of type II discrete multiple orthogonal polynomials on an $r$-legged star-like set with respect to $r$ weight functions of Charlier (Poisson distributions) and Meixner (negative binomial distributions), respectively. We focus our attention on the structural properties such as the raising operators, the Rodrigues-type formulas, and the e

Zhihao Wang

For any marked three manifold $(M,\mathcal N)$ and any quantum parameter $q^{\frac{1}{2}}$ (a nonzero complex number), we use $\mathscr{S}_{q^{1/2}}(M,\mathcal{N})$ to denote the stated skein module of $(M,\mathcal{N})$. When $q^{\frac{1}{2}}$ is a root of unity of odd order, the commutative algebra $\mathscr{S}_1(M,\mathcal{N})$ acts on $\mathscr{S}_{q^{1/2

Maria Manuel Clementino, George Janelidze

We characterize effective descent morphisms of what we call filtered preorders, and apply these results to slightly improve a known result, due to the first author and F. Lucatelli Nunes, on the effective descent morphisms in lax comma categories of preorders. A filtered preorder, over a fixed preorder $X$, is defined as a preorder $A$ equipped with a profun

D. V. Belousov, A. K. Pavlov

Comet nuclei in the outer Solar system are constantly irradiated by cosmic rays at low temperatures. Accumulated high concentrations of radicals can undergo fast recombination with significant heating of cometary surface layers. We present the model of comet activity at large heliocentric distances caused by the recombination of radicals. We found that the c

Caleb G. Wagner

Tensorial neural networks (TNNs) combine the successes of multilinear algebra with those of deep learning to enable extremely efficient reduced-order models of high-dimensional problems. Here, I describe a deep neural network architecture that fuses multiple TNNs into a larger network, intended to solve a broader class of problems than a single TNN. I evalua

Rigel Zifkin, César Daniel Rodríguez Rosenblueth, Erika Janitz, Yannik Fontana

Coupling between a single quantum emitter and an optical cavity presents a key capability for future quantum networking applications. Here, we explore interactions between individual germanium-vacancy (GeV) defects in diamond and an open microcavity at cryogenic temperatures. Exploiting the tunability of our microcavity system to characterize and select emit

Mladen Kovačević

Basic algebraic and combinatorial properties of finite vector spaces in which individual vectors are allowed to have multiplicities larger than $ 1 $ are derived. An application in coding theory is illustrated by showing that multispace codes that are introduced here may be used in random linear network coding scenarios, and that they generalize standard sub

Isaiah A. Moses, Chengyin Wu, Wesley F. Reinhart

Materials characterization remains a labor-intensive process, with a large amount of expert time required to post-process and analyze micrographs. As a result, machine learning has become an essential tool in materials science, including for materials characterization. In this study, we perform an in-depth analysis of the prediction of crystal coverage in WS

Abhishek Shukla, Boo Carmans, Michael Petrov, Daan Vrancken

We report here on a major improvement of the control and characterization capabilities of 14N nuclear spin of single NV centers in diamond, as well as on a new method that we have devised for characterizing quantum states, i.e. quantum state tomography using Rabi experiments. Depending on whether we use amplitude information or phase information from Rabi ex

Mahdi Chehimi, Samuel Yen-Chi Chen, Walid Saad, Shinjae Yoo

Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification while leveraging several data types, no prior work has focused on developing a QFL framework that utilizes temporal

Shivam Sharma

We obtain error approximation bounds between expected suprema of canonical processes that are generated by random vectors with independent coordinates and expected suprema of Gaussian processes. In particular, we obtain a sharper proximity estimate for Rademacher and Gaussian complexities. Our estimates are dimension-free, and depend only on the geometric pa

M. T. Leist, C. Packham, D. J. V. Rosario, D. A. Hope

The superb image quality, stability and sensitivity of the JWST permit deconvolution techniques to be pursued with a fidelity unavailable to ground-based observations. We present an assessment of several deconvolution approaches to improve image quality and mitigate effects of the complex JWST point spread function (PSF). The optimal deconvolution method is

Behafarid Mohammad Jafari, Xiao Luo, Ali Jafari

Social recommendations have been widely adopted in substantial domains. Recently, graph neural networks (GNN) have been employed in recommender systems due to their success in graph representation learning. However, dealing with the dynamic property of social network data is a challenge. This research presents a novel method that provides social recommendati

Prosenjit Bose, Jean-Lou De Carufel, Sandrine Njoo

Finding the exact spanning ratio of a Delaunay graph has been one of the longstanding open problems in Computational Geometry. Currently there are only four convex shapes for which the exact spanning ratio of their Delaunay graph is known: the equilateral triangle, the square, the regular hexagon and the rectangle. In this paper, we show the exact spanning r

Cristina Balan, Carlos Granja, Gennady Mytsin, Sergey Shvidky

This study aims to assess the composition of scattered particles generated in proton therapy for tumours situated proximal to titanium dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumour. A conformal proton bea

Yiming Li, Zeyu Li, Zhihui Gao, Tingjun Chen

Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the RF signal strength and distribution across specific areas, is crucial for cellular network planning and deployment. Traditional approaches to RF signal mapping rely on statistical models constructed based on measurement data, which offer low complexity but often lack ac

Kellin Pelrine, Mohammad Taufeeque, Michał Zając, Euan McLean

Language model attacks typically assume one of two extreme threat models: full white-box access to model weights, or black-box access limited to a text generation API. However, real-world APIs are often more flexible than just text generation: these APIs expose "gray-box" access leading to new threat vectors. To explore this, we red-team three new functional

Enoch Solomon, Abraham Woubie, Eyael Solomon Emiru

The primary objective of this work is to present an alternative approach aimed at reducing the dependency on labeled data. Our proposed method involves utilizing autoencoder pre-training within a face image recognition task with two step processes. Initially, an autoencoder is trained in an unsupervised manner using a substantial amount of unlabeled training

Zebo Yang, Ali Ghubaish, Raj Jain, Hassan Shapourian

With the emergence of the Quantum Internet, the need for advanced quantum networking techniques has significantly risen. Various models of quantum repeaters have been presented, each delineating a unique strategy to ensure quantum communication over long distances. We focus on repeaters that employ entanglement generation and swapping. This revolves around e

Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong

Submodular maximization over a matroid constraint is a fundamental problem with various applications in machine learning. Some of these applications involve decision-making over datapoints with sensitive attributes such as gender or race. In such settings, it is crucial to guarantee that the selected solution is fairly distributed with respect to this attrib

Cheryl Grood, Ruth Haas, Bonnie Jacob, Erika King

A graph in which all minimal zero forcing sets are in fact minimum size is called ``well-forced." This paper characterizes well-forced trees and presents an algorithm for determining which trees are well-forced. Additionally, we characterize which vertices in a tree are contained in no minimal zero forcing set.

Shinhyuk Choi, Jiawei Zuo, Nabasindhu Das Yu Yao, Chao Wang

Optical metasurfaces, consisting of subwavelength-scale meta-atom arrays, hold great promise to overcome fundamental limitations of conventional optics. Scalable nanomanufacturing of metasurfaces with high uniformity and reproducibility is key to transferring technology from laboratory demonstrations to commercialization. Recently, nanoimprint lithography (N

Hermès Lajoinie-Dodel

We prove that relatively hyperbolic groups do not have Lafforgue strong Property $(T)$ with respect to Hilbert spaces. To do so we construct an unbounded affine representation of such groups, whose linear part is of polynomial growth of degree $2$. Moreover, this representation is proper for the metric of the coned-off graph.

Ahmad Biniaz, Prosenjit Bose, Jean-Lou De Carufel, Anil Maheshwari

We explore the concept of separating systems of vertex sets of graphs. A separating system of a set $X$ is a collection of subsets of $X$ such that for any pair of distinct elements in $X$, there exists a set in the separating system that contains exactly one of the two elements. A separating system of the vertex set of a graph $G$ is called a vertex-separat

Gourab Nath, Arav Sood, Aanchal Khanna, Savi Wilson

Typical investors start off the day by going through the daily news to get an intuition about the performance of the market. The speculations based on the tone of the news ultimately shape their responses towards the market. Today, computers are being trained to compute the news sentiment so that it can be used as a variable to predict stock market movements

Kweku Abraham, Neil Deo

We study the use of a deep Gaussian process (DGP) prior in a general nonlinear inverse problem satisfying certain regularity conditions. We prove that when the data arises from a true parameter $\theta^*$ with a compositional structure, the posterior induced by the DGP prior concentrates around $\theta^*$ as the number of observations increases. The DGP prio

Naina Balepur, Andy Lee, Hari Sundaram

In this paper we address how complex social communities emerge from local decisions by individuals with limited attention and knowledge. This problem is critical; if we understand community formation mechanisms, it may be possible to intervene to improve social welfare. We propose an interpretable, novel model for attributed community formation driven by res

Siddhant Bhambri, Mudit Verma, Upasana Biswas, Anil Murthy

Preference-based Reinforcement Learning (PbRL) has made significant strides in single-agent settings, but has not been studied for multi-agent frameworks. On the other hand, modeling cooperation between multiple agents, specifically, Human-AI Teaming settings while ensuring successful task completion is a challenging problem. To this end, we perform the firs

Vahid Ghadakchi, Mian Xie, Arash Termehchy, Bakhtiyar Doskenov

It is crucial to provide real-time performance in many applications, such as interactive and exploratory data analysis. In these settings, users often need to view subsets of query results quickly. It is challenging to deliver such results over large datasets for relational operators over multiple relations, such as join. Join algorithms usually spend a long

Stephan De Bievre, Marco Merkli, Paul E. Parris

Rigorous derivations of the approach of individual elements of large isolated systems to a state of thermal equilibrium, starting from arbitrary initial states, are exceedingly rare. This is particularly true for quantum mechanical systems. We demonstrate here how, through a mechanism of repeated scattering, an approach to equilibrium of this type actually o

Matt Clancy

Scientific and technological progress has historically been very beneficial to humanity but this does not always need to be true. Going forward, science may enable bad actors to cause genetically engineered pandemics that are more frequent and deadly than prior pandemics. I develop a quantitative economic model to assess the social returns to science, taking

Aniruddha Acharya, Enrique Perez, Miller Maddox-Mandolini, Hania De La Fuente

The release of heavy metals into the agricultural soil and waterbodies has been accelerated due to anthropogenic activities. They are not usually required for biological functions thus, their accumulation in biological system poses serious threat to health and environment globally. Phytoremediation offers a safe, inexpensive, and ecologically sustainable tec

Jun Nian

The black hole information paradox is a long-standing problem in theoretical physics. Despite some recent progress, many issues remain open and should be clarified. In this paper, we study the information paradox of Kerr black holes and propose a new resolution with precise physical meanings. We compute the time-dependent Hawking radiation rate during the Ke

C. Hekatelyne, Thaisa Storchi-Bergmann, Rogemar A. Riffel, Preeti Kharb

We present a two-dimensional study of the gas distribution, excitation and kinematics of the OH absorber galaxy IRAS 19154+2704 using Gemini GMOS-IFU observations. Its continuum image shows a disturbed morphology indicative of a past or on-going interaction. The ionised gas emission presents two kinematic components: a narrow ($\sigma\lesssim$300 km s$^{-1}$

Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy, Joel Dapello

Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches. However, concrete methodologies for bridging the gap between these levels of abstraction remain elusive. In this work, we investigate the internal

Ranjiangshang Ran, Paulo E. Arratia

We investigate the effects of bacterial activity on the mixing and transport properties of a passive scalar in time-periodic flows in experiments and in a simple model. We focus on the interactions between swimming E. coli and the Lagrangian Coherent Structures (LCSs) of the flow, which are computed from experimentally measured velocity fields. Experiments s

Z. Fisk, J. L. Smith, J. D. Thompson

Reports of unconventional superconductivity in UBe13 in 1983 and soon thereafter of the possible coexistence of bulk superconductivity and spin fluctuations in UPt3 marked the beginning of a 40-year adventure in the study of strongly correlated quantum materials and phenomena at Los Alamos. The subsequent discovery and exploration of heavy-fermion magnetism,

Cemile Kurkoglu

Let $\bf{G}$ be a split connected reductive group over a finite extension $F$ of $\mathbb Q_p$, and let $\bf{T} \subset \bf{B} \subset \bf{G}$ be a maximal split torus and a Borel subgroup, respectively. Denote by $G = {\bf{G}}(F)$ and $B= {\bf{B}}(F)$ their groups of $F$-valued points and by $\mathfrak g = \rm Lie(G)$ and $\mathfrak b = \rm Lie(B)$ their Li

Daniel Canavello, Rubens H. Damascena, Leonardo R. E. Cabral, Clécio C. de Souza Silva

We investigate the collective behavior of sterically interacting self-propelled particles confined in a harmonic potential. Our theoretical and numerical study unveils the emergence of distinctive collective polar organizations, revealing how different levels of interparticle torques and noise influence the system. The observed phases include the shear-bande

Sepideh Koohfar, Laura Dietz

Time series forecasting is a challenging task due to the existence of complex and dynamic temporal dependencies. This can lead to incorrect predictions by even the best forecasting models. Using more training data is one way to improve the accuracy, but this source is often limited. In contrast, we are building on successful denoising approaches for image ge

Catherine F. Higham, Desmond J. Higham, Peter Grindrod

Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more general tools, including text-to-image generators and large language models. Diffusion models work by adding noise to the ava

Xingfang Wu, Eric Laufer, Heng Li, Foutse Khomh

With the rapid growth of the developer community, the amount of posts on online technical forums has been growing rapidly, which poses difficulties for users to filter useful posts and find important information. Tags provide a concise feature dimension for users to locate their interested posts and for search engines to index the most relevant posts accordi

Thibaut Divoux, Elisabeth Agoritsas, Stefano Aime, Catherine Barentin

Soft amorphous materials are viscoelastic solids ubiquitously found around us, from clays and cementitious pastes to emulsions and physical gels encountered in food or biomedical engineering. Under an external deformation, these materials undergo a noteworthy transition from a solid to a liquid state that reshapes the material microstructure. This yielding t

Basudha Pal, Arunkumar Kannan, Ram Prabhakar Kathirvel, Alice J. O'Toole

Diffusion models have achieved great progress in face generation. However, these models amplify the bias in the generation process, leading to an imbalance in distribution of sensitive attributes such as age, gender and race. This paper proposes a novel solution to this problem by balancing the facial attributes of the generated images. We mitigate the bias

Zoé Faure Beaulieu, Volker L. Deringer, Fausto Martelli

Amorphous ice phases are key constituents of water's complex structural landscape. This study investigates the polyamorphic nature of water, focusing on the complexities within low-density amorphous ice (LDA), high-density amorphous ice (HDA), and the recently discovered medium-density amorphous ice (MDA). We use rotationally-invariant, high-dimensional orde

Juncai He, Jinchao Xu

In this study, we establish that deep neural networks employing ReLU and ReLU$^2$ activation functions can effectively represent Lagrange finite element functions of any order on various simplicial meshes in arbitrary dimensions. We introduce two novel formulations for globally expressing the basis functions of Lagrange elements, tailored for both specific a

Gergő Roósz, Anna Kauch, Frederic Bippus, Daniel Wieser

Strongly correlated electron systems are challenging to calculate, and entanglement in such systems is not widely analyzed. We present an approach that can be used as a post-processing step for calculating the two-site reduced density matrix and from it entanglement measures such as the mutual information and entanglement negativity. Input is only the one- a

Christof Fehrman, C. Daniel Meliza

Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control, which employs a dynamical model of the system to find optimal control inputs, has promise for dealing with the nonlinear

Kukhokuhle Tsengwa, Stephen Paine, Fred Nicolls, Yumna Albertus

Surface electromyography (sEMG) is a widely used muscle activity monitoring technique. sEMG measures muscle activity through monopolar and bipolar, multi-electrode electrodes. The surface electrodes are placed on the surface of the skin above the target muscle and the received signal can be used to infer the state of the muscle - active, inactive or fatigued

Ufuk Kaya, Gokhan Turan

In this paper, we consider the concept of limit, one of the basic concepts of mathematical analysis. At a point $a\in{\mathbb{R}}$, the limit of a function $f$ from $A\subset\mathbb{R}$ to $\mathbb{R}$ is $L\in{\mathbb{R}}$ if and only if there exists $\delta>0$ such that the set $$ \left\{x\in\left(\left(a-\delta,a+\delta\right)\backslash\left\{a\right\}\ri

Jonathan Wahl

A surface pair $(X,C)$ is a germ of a normal surface singularity $(X,0)$ and a sum $C=\sum c_iC_i$ of curves on $X$, with $c_i\in [0,1]$. An orbifold pair has $c_i=1/n_i$, as intersecting with a small sphere gives a $3$-dimensional orbifold $(\Sigma, \gamma_i,n_i)$. There are natural notions of morphism and log cover of surface pairs. We introduce a volume $

Catherine C. Blume, Bradley W. Hindman, Loren I. Matilsky

Recent observations of Rossby waves and other more exotic forms of inertial oscillations in the Sun's convection zone have kindled the hope that such waves might be used as a seismic probe of the Sun's interior. Here we present a 3D numerical simulation in spherical geometry that models the Sun's convection zone and upper radiative interior. This model featu

C. Di Maio, A. Petralia, G. Micela, A. F. Lanza

The intrinsic variability due to the magnetic activity of young active stars is one of the main challenges in detecting and characterising exoplanets. We present a method able to model the stellar photosphere and its surface inhomogeneities (starspots) in young/active and fast-rotating stars, based on the cross-correlation function (CCF) technique, to extrac

Enrique Navarro, Claudio Falcón

We report on the construction of a granular network of particles to study the formation, evolution and statistical properties of clusters of particles developing at the vicinity of a liquid-solid-like phase transition within a vertically vibrated quasi two-dimensional granular system. Using the data of particle positions and local order from Castillo et al [

Cristina Giossi, Jonathan E. Rubin, Aryn Gittis, Timothy Verstynen

For decades the external globus pallidus (GPe) has been viewed as a passive way-station in the indirect pathway of the cortico-basal ganglia-thalamic (CBGT) circuit, sandwiched between striatal inputs and basal ganglia outputs. According to this model, one-way descending striatal signals in the indirect pathway amplify the suppression of downstream thalamic

François Fillastre, Roman Prosanov

We first prove that given a hyperbolic metric $h$ on a closed surface $S$, any flat metric on $S$ with negative singular curvatures isometrically embeds as a convex polyhedral Cauchy surface in a unique future-complete flat globally hyperbolic maximal (2+1)-spacetime whose linear part of the holonomy is given by $h$. The Gauss map allows to translate this st

Blaise Delaney, Nicole Schulte, Gregory Ciezarek, Niklas Nolte

The operating conditions defining the current data taking campaign at the Large Hadron Collider, known as Run 3, present unparalleled challenges for the real-time data acquisition workflow of the LHCb experiment at CERN. To address the anticipated surge in luminosity and consequent event rate, the LHCb experiment is transitioning to a fully software-based tr

Yang Lv, Brandon R. Zink, Robert P. Bloom, Hüsrev Cılasun

Conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence, because much of the power and energy is consumed by constant data transfers between logic and memory modules. A new paradigm, called "computational random-access memory (CRAM)" has emerged to address this fun

Ekaterine Dadiani, Tiziana Di Matteo, Nianyi Chen, Patrick Lachance

We study dual AGN host galaxy morphologies at $z=2$ using the ASTRID simulation, selecting black hole (BH) pairs with small separation ($\Delta r<30\rm{kpc}$), high mass ($M_{\text{BH,12}}>10^7M_\odot$), and luminosity ($L_{\text{bol,12}}>10^{43}\rm{erg/s}$). We kinematically decompose (using MORDOR) $\sim1000$ dual AGN hosts into standard components - a `di

Filipe Calegario, Vanilson Burégio, Francisco Erivaldo, Daniel Moraes Costa Andrade

In the ever-evolving landscape of Artificial Intelligence (AI), the synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. Spanning from project management to support and updates, we meticulo

Caterina Caccavella, Federico Paredes-Vallés, Marco Cannici, Lyes Khacef

The rise of mobility, IoT and wearables has shifted processing to the edge of the sensors, driven by the need to reduce latency, communication costs and overall energy consumption. While deep learning models have achieved remarkable results in various domains, their deployment at the edge for real-time applications remains computationally expensive. Neuromor

Janvi Thakkar, Giulio Zizzo, Sergio Maffeis

Machine learning models are being used in an increasing number of critical applications; thus, securing their integrity and ownership is critical. Recent studies observed that adversarial training and watermarking have a conflicting interaction. This work introduces a novel framework to integrate adversarial training with watermarking techniques to fortify a

Osama A. Hanna, Merve Karakas, Lin F. Yang, Christina Fragouli

Multi-Armed Bandit (MAB) systems are witnessing an upswing in applications within multi-agent distributed environments, leading to the advancement of collaborative MAB algorithms. In such settings, communication between agents executing actions and the primary learner making decisions can hinder the learning process. A prevalent challenge in distributed lear

J. A. V. Campos, M. A. Anacleto, F. A. Brito, E. Passos

In the present work, we study the scattering for a black hole described by the canonical acoustic metric with Lorentz violation using asymptotic and numerical methods. In this scenario, we also check the effects of quasinormal modes and the acoustic shadow radius. In the eikonal limit the relationship between the shadow radius and the real part of the quasin

Miguel Cruz, Samuel Lepe, Joel Saavedra

In the framework of Einstein's gravity, we study the thermodynamic equation state, $P=P(V,T)$, associated with a flat Friedmann-Lemaitre-Robertson-Walker (FLRW) universe. In this scenario, we consider the components of the dark sector as non-interacting fluids that dominate the universe's energy content at late times. Under these circumstances, the functiona

Wesley H. Holliday, Eric Pacuit

May's Theorem [K. O. May, Econometrica 20 (1952) 680-684] characterizes majority voting on two alternatives as the unique preferential voting method satisfying several simple axioms. Here we show that by adding some desirable axioms to May's axioms, we can uniquely determine how to vote on three alternatives (setting aside tiebreaking). In particular, we add

Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz, Maria Lavzin

Feature selection is a crucial tool in machine learning and is widely applied across various scientific disciplines. Traditional supervised methods generally identify a universal set of informative features for the entire population. However, feature relevance often varies with context, while the context itself may not directly affect the outcome variable. H

Atish Dabholkar, Upamanyu Moitra

We compute the exact one-loop partition function of $\mathbb{Z}_N$ orbifolds of Euclidean BTZ black hole with the aim to compute the entanglement entropy of the black hole horizon in string theory as a function of the mass and spin of the black hole and the $\mathrm{AdS}_3$ radius. We analyze the tachyonic contribution to the modular integrand for the partit

Axel Klawonn, Martin Lanser, Janine Weber

While linear FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) is an efficient iterative domain decomposition solver for discretized linear PDEs (partial differential equations), nonlinear FETI-DP is its consequent extension to the nonlinear case. In both methods, the parallel efficiency of the method results from a decomposition of the comp

Mingyu Xu, Shuyuan Huyan, Haozhe Wang, S. L. Bud'ko

Recently, superconductivity at high temperatures has been observed in bulk La$_3$Ni$_2$O$_{7-{\delta}}$ under high pressure. However, the attainment of high-purity La$_3$Ni$_2$O$_{7-{\delta}}$ single crystals, exhibiting controlled and homogeneous stoichiometry through the post-annealing process in an oxygen-rich floating zone furnace, remains a formidable c

Mirko Günther, Lars Schütze, Kilian Becher, Thorsten Strufe

Privacy-preserving analysis of confidential data can increase the value of such data and even improve peoples' lives. Fully homomorphic encryption (FHE) can enable privacy-preserving analysis. However, FHE adds a large amount of computational overhead and its efficient use requires a high level of expertise. Compilers can automate certain aspects such as par

Yingzhou Lu, Minjie Shen, Ling Yue, Chenhao Li

The surge in high-throughput omics data has reshaped the landscape of biological research, underlining the need for powerful, user-friendly data analysis and interpretation tools. This paper presents GenoCraft, a web-based comprehensive software solution designed to handle the entire pipeline of omics data processing. GenoCraft offers a unified platform feat