October 2023 arXiv papers — page 10

Victor L. Knoop, Wouter J. Schakel, Tim P. van Oijen, Ludovic Leclercq

An excessive demand of vehicles to a motorway bottleneck leads to traffic jams. Motorbikes are narrow and can drive next to each other in a lane, or in-between lanes in low speeds. This paper analyses the resulting traffic characteristics and presents numerical scheme for a macroscopic traffic flow model for these two classes. The behavior included is as fol

Vladislav Koloskov, Wyger Brink, Andrew Webb, Alena Shchelokova

Ultra-high field MRI offers unprecedented detail for non-invasive visualization of the human brain. However, brain imaging is challenging at 7T due to the B$_1^+$ field inhomogeneity, which results in signal intensity drops in temporal lobes and a bright region in the brain center. This study aims to evaluate using a metasurface to improve brain imaging at 7

Hui Li, Eli Halperin, Shai Ronen, John L. Bohn

We theoretically investigate the merging behaviour of two identical supersolids through dipolar Bose-Einstein condensates confined within a double-well potential. By adiabatically tuning the barrier height and the spacing between the two wells for specific trap aspect ratios, the two supersolids move toward each other and lead to the emergence of a variety o

Donald M. Evans, Michele Conroy, Lukas Puntigam, Dorina Croitori

Superconductivity is identified by the emergence of a macroscopic zero-resistance state, typically inferred from a vanishing four-probe voltage at finite current. That inference assumes spatially uniform conduction-e.g., at least one continuous superconducting path between the current leads and voltage electrodes that sample a finite potential gradient-and c

L. A. Melnikovsky

Andreev-Bashkin entrainment makes the hydrodynamics of the binary superfluid solution particularly interesting. We investigate stability and motion of quantum vortices in such system.

Shibu Saw, Lorenzo Costigliola, Jeppe C. Dyre

In both experiments and simulations the most commonly studied kind of parameter polydispersity is that of varying particles size. This paper investigates by simulations the effects of introducing polydispersity in other parameters for two-dimensional Active Brownian Particles with Yukawa pair interactions. Polydispersity is studied separately in the translat

Ángel Crespo-Blanco, Leszek Gasiński, Patrick Winkert

In this paper we study the following nonlocal Dirichlet equation of double phase type \begin{align*} -\psi \left [ \int_\Omega \left ( \frac{|\nabla u |^p}{p} + \mu(x) \frac{|\nabla u|^q}{q}\right)\,\mathrm{d} x\right] \mathcal{G}(u) = f(x,u)\quad \text{in } \Omega, \quad u = 0\quad \text{on } \partial\Omega, \end{align*} where $\mathcal{G}$ is the double ph

Jeff Shen, Peter Melchior

Machine learning techniques can automatically identify outliers in massive datasets, much faster and more reproducible than human inspection ever could. But finding such outliers immediately leads to the question: which features render this input anomalous? We propose a new feature attribution method, Inverse Multiscale Occlusion, that is specifically design

Yaïr Hein, Farshid Jafarpour

It has been recently shown that the exponential growth rate of a population of bacterial cells starting from a single cell shows transient oscillations due to early synchronized bursts of division. These oscillations are enhanced by cell size regulation and contain information about single-cell growth statistics. Here, we report a phase transition in these o

Minghao Song, Timur Shaftan

Light sources worldwide have experienced rapid growth in the last decades, pushing towards higher brightness with lower emittance to meet growing demands from the user community. The quest for higher brightness motivates the development of low-emittance ring lattices. At this point, all fourth-generation storage ring light sources employ variations of the Mu

Brady Bateman, Ming Xin, H. Eric Tseng, Mushuang Liu

This paper studies game-theoretic decision-making for autonomous vehicles (AVs). A receding horizon multi-player game is formulated to model the AV decision-making problem. Two classes of games, including Nash game and Stackelber games, are developed respectively. For each of the two games, two solution settings, including pairwise games and multi-player gam

Lana Bertoldo Rossato, Leonardo Boaventura Bombardelli, Anderson Rocha Tavares

Creating and evaluating games manually is an arduous and laborious task. Procedural content generation can aid by creating game artifacts, but usually not an entire game. Evolutionary game design, which combines evolutionary algorithms with automated playtesting, has been used to create novel board games with simple equipment; however, the original approach

Ahmadreza Moradipari, Mohammad Pedramfar, Modjtaba Shokrian Zini, Vaneet Aggarwal

In this paper, we prove the first Bayesian regret bounds for Thompson Sampling in reinforcement learning in a multitude of settings. We simplify the learning problem using a discrete set of surrogate environments, and present a refined analysis of the information ratio using posterior consistency. This leads to an upper bound of order $\widetilde{O}(H\sqrt{d

Tsao-Hsien Chen, Lingfei Yi

We study the singularities of closures of Iwahori orbits on loop spaces of symmetric varieties extending the celebrated work of Lusztig-Vogan to the affine setting. We show that the IC-complexes of orbit closures (with possible non-trivial coefficients) are pointwise pure and satisfy a parity vanishing property. We apply those geometric results to study the

Brandon Fields, Patrick K. Schelling

Building on recent simulation work, it is demonstrated using molecular-dynamics (MD) simulations of two-component fluid mixtures that the chemical contribution to the Soret effect in two-component non-ideal fluid mixtures arises due to differences in how the partial pressures of the components respond to temperature and density gradients. Further insight is

Moritz Clausnitzer, Timo Danner, Benedikt Prifling, Matthias Neumann

All-solid-state batteries (ASSBs) offer a promising route to safer batteries with superior energy density compared to conventional Li-ion batteries (LIBs). However, the design of the composite cathode and optimization of the underlying microstructure is one of the aspects requiring intensive research. Achieving both high energy and power density remains chal

Horacio Thompson, Marcelo Errecalde

MentalRiskES is a novel challenge that proposes to solve problems related to early risk detection for the Spanish language. The objective is to detect, as soon as possible, Telegram users who show signs of mental disorders considering different tasks. Task 1 involved the users' detection of eating disorders, Task 2 focused on depression detection, and Task 3

Federico Franceschini

We prove an $\varepsilon$-regularity theorem for $BV^\mathcal{B}$ minimizers of strongly $\mathcal{B}$-quasiconvex functionals with linear growth, where $\mathcal{B}$ is an elliptic operator of the first order. This generalises to the $BV^\mathcal{B}$ setting the analogous result for $BV$ functions by F. Gmeineder and J. Kristensen [Arch. Rational Mech. Anal

Fellype do Nascimento, Aline da Graça Sampaio, Noala Vicensoto Moreira Milhan, Aline Vidal Lacerda Gontijo

Plasma sources suitable to generate low temperature plasmas has been fundamental for the advances in plasma medicine. In this research field, plasma sources must comply with stringent conditions for clinical applications. The main requirement to be met is the patient and operator's safety and the ethical requirement of effectivity, which encompasses the elec

Alex Laguë, Bodo Schwabe, Renée Hložek, David J. E. Marsh

The era of precision cosmology allows us to test the composition of the dark matter. Mixed ultralight or fuzzy dark matter (FDM) is a cosmological model with dark matter composed of a combination of particles of mass $m\leq 10^{-20}\;\mathrm{eV}$, with an astrophysical de Broglie wavelength, and particles with a negligible wavelength sharing the properties o

Rita S. P. Maciel, Pedro H. Valle, Kécia S. Santos, Elisa Y. Nakagawa

Interoperability has been a focus of attention over at least four decades, with the emergence of several interoperability types (or levels), diverse models, frameworks, and solutions, also as a result of a continuous effort from different domains. The current heterogeneity in technologies such as blockchain, IoT and new application domains such as Industry 4

Markus J. Buehler

Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design and manufacturing, including their capacity to work effectively with both human language, symbols, code, and numerical data. Here we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied

Filip Surma, Anahita Jamshidnejad

Most real-world systems are affected by external disturbances, which may be impossible or costly to measure. For instance, when autonomous robots move in dusty environments, the perception of their sensors is disturbed. Moreover, uneven terrains can cause ground robots to deviate from their planned trajectories. Thus, learning the external disturbances and i

Michalis Lazarou, Yannis Avrithis, Guangyu Ren, Tania Stathaki

Few-shot learning addresses the issue of classifying images using limited labeled data. Exploiting unlabeled data through the use of transductive inference methods such as label propagation has been shown to improve the performance of few-shot learning significantly. Label propagation infers pseudo-labels for unlabeled data by utilizing a constructed graph t

Gianluigi Lopardo, Frederic Precioso, Damien Garreau

Interpretability is essential for machine learning models to be trusted and deployed in critical domains. However, existing methods for interpreting text models are often complex, lack mathematical foundations, and their performance is not guaranteed. In this paper, we propose FRED (Faithful and Robust Explainer for textual Documents), a novel method for int

Yasaman Etesam, Özge Nilay Yalçın, Chuxuan Zhang, Angelica Lim

The emotional theory of mind problem requires facial expressions, body pose, contextual information and implicit commonsense knowledge to reason about the person's emotion and its causes, making it currently one of the most difficult problems in affective computing. In this work, we propose multiple methods to incorporate the emotional reasoning capabilities

I. A. Pshenichnov, U. A. Dmitrieva, S. D. Savenkov, A. O. Svetlichnyi

Arrays of minibeams of protons and $^{12}$C in tissue-like media were modeled with Geant4 toolkit. A set of beam energies was used in simulations to provide a Spead-out Bragg peak (SOBP) extended by 6 cm in depth for protons as well as for $^{12}$C. In both cases, beams of 0.3 mm or 0.5 mm FWHM were arranged at the entrance to a water phantom either on a rec

Aoife K. Hurley, Ruth F. Carden, Sally Cook, Irish Deer Commission

Accurate predictions of the populations and spatial distributions of wild animal species is critical from a species management and conservation perspective. Culling is a measure taken for various reasons, including when overpopulation of a species is observed or suspected. Thus accurate estimates of population numbers are essential for specifying, monitoring

Peter Reinhard Hansen, Yiyao Luo

Time-varying volatility is an inherent feature of most economic time-series, which causes standard correlation estimators to be inconsistent. The quadrant correlation estimator is consistent but very inefficient. We propose a novel subsampled quadrant estimator that improves efficiency while preserving consistency and robustness. This estimator is particular

Minghao Yan, Hongyi Wang, Shivaram Venkataraman

As neural networks (NN) are deployed across diverse sectors, their energy demand correspondingly grows. While several prior works have focused on reducing energy consumption during training, the continuous operation of ML-powered systems leads to significant energy use during inference. This paper investigates how the configuration of on-device hardware-elem

Ankur Nath, Alan Kuhnle

In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integ

Antonio Vassallo, Pedro Naranjo

Pure shape dynamics (PSD) is a novel implementation of the relational framework originally proposed by Julian Barbour and Bruno Bertotti. PSD represents a Leibnizian/Machian approach to physics in that it completely describes the dynamical evolution of a physical system without resorting to any structure external to the system itself. The chapter discusses h

Solvejg Wastvedt, Jared D Huling, Julian Wolfson

While methods for measuring and correcting differential performance in risk prediction models have proliferated in recent years, most existing techniques can only be used to assess fairness across relatively large subgroups. The purpose of algorithmic fairness efforts is often to redress discrimination against groups that are both marginalized and small, so

Garen Chiloyan

Let $N$ be a positive integer. Let $\operatorname{H}$ be a group of level $N$ and let $E$ be an elliptic curve defined over the rationals with $\textit{j}_{E} \neq 0, 1728$. Then the image $\overline{\rho}_{E,N}\left(\operatorname{Gal}\left(\overline{\mathbb{Q}}/\mathbb{Q}\right)\right)$, of the mod-$N$ Galois representation attached to $E$, is conjugate to

Preetam Prabhu Srikar Dammu, Yunhe Feng, Chirag Shah

Machine learning (ML) technologies are known to be riddled with ethical and operational problems, however, we are witnessing an increasing thrust by businesses to deploy them in sensitive applications. One major issue among many is that ML models do not perform equally well for underrepresented groups. This puts vulnerable populations in an even disadvantage

Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Tales Imbiriba

We study the task of gesture recognition from electromyography (EMG), with the goal of enabling expressive human-computer interaction at high accuracy, while minimizing the time required for new subjects to provide calibration data. To fulfill these goals, we define combination gestures consisting of a direction component and a modifier component. New subjec

Rayleigh Lei, XuanLong Nguyen

We propose models and algorithms for learning about random directions in simplex-valued data. The models are applied to the study of income level proportions and their changes over time in a geostatistical area. There are several notable challenges in the analysis of simplex-valued data: the measurements must respect the simplex constraint and the changes ex

Santiago Torres Paz, Jose Ricardo Arteaga Bejarano

In this paper, we use Time Scale Calculus (TSC) to formulate and solve pharmacokinetic models exploring multiple dose dynamics. TSC is a mathematical framework that allows the modeling of dynamical systems comprising continuous and discrete processes. This characteristic makes TSC particularly suited for multi-dose pharmacokinetic problems, which inherently

Katherine Mao, Sharon Ferguson, James Magarian, Alison Olechowski

The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research, meaning it is unclear how past findings from engineering will apply to this developing field. We conduct an exploratory study to gain an initial understanding of persistence in this field and identify fruitful directi

Joshua Davy, Peter Lloyd, James H. Chandler, Pietro Valdastri

Magnetic Soft Catheters (MSCs) are capable of miniaturization due to the use of an external magnetic field for actuation. Through careful design of the magnetic elements within the MSC and the external magnetic field, the shape along the full length of the catheter can be precisely controlled. However, modeling of the magnetic-soft material is challenging du

Sharon Ferguson, Katherine Mao, James Magarian, Alison Olechowski

Machine Learning (ML) and Artificial Intelligence (AI) are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms to recidivism algorithms, when they are designed without diversity in mind. Thus, we must take action to promote diver

Tim Planken, Torsten Ueckerdt

A range family $\mathcal{R}$ is a family of subsets of $\mathbb{R}^d$, like all halfplanes, or all unit disks. Given a range family $\mathcal{R}$, we consider the $m$-uniform range capturing hypergraphs $\mathcal{H}(V,\mathcal{R},m)$ whose vertex-sets $V$ are finite sets of points in $\mathbb{R}^d$ with any $m$ vertices forming a hyperedge $e$ whenever $e =

Sourojit Ghosh, Aylin Caliskan

We study stereotypes embedded within one of the most popular text-to-image generators: Stable Diffusion. We examine what stereotypes of gender and nationality/continental identity does Stable Diffusion display in the absence of such information i.e. what gender and nationality/continental identity is assigned to `a person', or to `a person from Asia'. Using

Giovanni Dematteis, Arnaud Le Boyer, Friederike Pollmann, Kurt L. Polzin

Across the stable density stratification of the abyssal ocean, deep dense water is slowly propelled upward by sustained, though irregular, turbulent mixing. The resulting mean upwelling determines large-scale oceanic circulation properties like heat and carbon transport. In the ocean interior, this turbulent mixing is caused mainly by breaking internal waves

Anthony Christiana, Huizheng Guo, Jozef H. Przytycki

In this paper we compute the Reduced Fox Coloring Group of the diagrams of Wheel Graphs which can also be represented at the closure of the braids $(\sigma_1 \sigma_2^{-1})^n$. In doing so, we utilize Fibonacci numbers and their properties. Following this, we generalize our result to compute the Alexander-Burau-Fox Module over the ring $\mathbb{Z}[t^{\pm 1}]

Edward Raff, Amol Khanna, Fred Lu

To the best of our knowledge, there are no methods today for training differentially private regression models on sparse input data. To remedy this, we adapt the Frank-Wolfe algorithm for $L_1$ penalized linear regression to be aware of sparse inputs and to use them effectively. In doing so, we reduce the training time of the algorithm from $\mathcal{O}( T D

Robert A. Handler, Richard J. Watkins, Silvia Matt, K. P. Judd

A model for the structure function constant associated with index of refraction fluctuations in Rayleigh-Benard turbulence is developed. The model is based upon the following assumptions: (1) the turbulence is homogeneous and isotropic at or near the mid-plane, (2) the rate of production is in balance with the rate of dissipation, (3) an inertial region exis

Dongryul M. Kim, Hee Oh, Yahui Wang

In this paper, we study the ergodicity of a one-parameter diagonalizable subgroup of a connected semisimple real algebraic group $G$ acting on a homogeneous space or, more generally, a homogeneous-like space, equipped with a Bowen-Margulis-Sullivan type measure. These flow spaces are associated with Anosov subgroups of $G$, or more generally, with transverse

Hieu Tran, Zhichao Yang, Zonghai Yao, Hong Yu

To enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its impact when combined with multi-task learning principles. We created the BioInstruct, comprising 25,005 instructions to instruction-tune LLMs(LLaMA 1 & 2, 7B & 13B version). The i

Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis

At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler since it minimizes bandwidth consumption, transmission delay, and power usage. Among existing text SemCom techniques, a popular text SemCom scheme -- that can reliably transmit semantic information in

Chendi Wang, Buxin Su, Jiayuan Ye, Reza Shokri

Differentially private (DP) machine learning algorithms incur many sources of randomness, such as random initialization, random batch subsampling, and shuffling. However, such randomness is difficult to take into account when proving differential privacy bounds because it induces mixture distributions for the algorithm's output that are difficult to analyze.

Shreyas Malpathak, Nandini Ananth

The vibrational relaxation of NO molecules scattering from an Au(111) surface has served as the focus of efforts to understand nonadiabatic energy transfer at metal-molecule interfaces. Experimental measurements and previous theoretical efforts suggest that multi-quantal NO vibrational energy relaxation occurs via electron hole pair excitations in the metal.

Andres Posada-Bedoya, Jason Olsthoorn, Leon Boegman

We investigated the stability of the bottom boundary layer (BBL) beneath periodic internal solitary waves (ISWs) of depression over a flat bottom through two-dimensional direct numerical simulations. We explored the effects of variation in wave Reynolds number $Re_{ISW}$ and wave period on the nature of the instability, and energy production in the separated

Horacio Thompson, Leticia Cagnina, Marcelo Errecalde

The CLEF eRisk Laboratory explores solutions to different tasks related to risk detection on the Internet. In the 2023 edition, Task 1 consisted of searching for symptoms of depression, the objective of which was to extract user writings according to their relevance to the BDI Questionnaire symptoms. Task 2 was related to the problem of early detection of pa

Qianyi Zhang, Antoine Boniface, Virendra K. Parashar, Martin A. M. Gijs

Light-based additive manufacturing holds great potential in the field of bioprinting due to its exceptional spatial resolution, enabling the reconstruction of intricate tissue structures. However, printing through biological tissues is severely limited due to the strong optical scattering within the tissues. The propagation of light is scrambled to form rand

Abhijit Talukdar, Sanjeev Kalita, Nirmali Das, Nandita Lahkar

Big Bang Nucleosynthesis (BBN) is a strong probe for constraining new physics including gravitation. $f(R)$ gravity theory is an interesting alternative to general relativity which introduces additional degrees of freedom known as scalarons. In this work we demonstrate the existence of black hole solutions in $f(R)$ gravity and develop a relation between sca

Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary

In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a

Bing Wang, Weizi Li, Anthony Bradlow, Antoni T. Y. Chan

Early detection of inflammatory arthritis (IA) is critical to efficient and accurate hospital referral triage for timely treatment and preventing the deterioration of the IA disease course, especially under limited healthcare resources. The manual assessment process is the most common approach in practice for the early detection of IA, but it is extremely la

The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta

The Auger Engineering Radio Array (AERA), part of the Pierre Auger Observatory, is currently the largest array of radio antenna stations deployed for the detection of cosmic rays, spanning an area of $17$ km$^2$ with 153 radio stations. It detects the radio emission of extensive air showers produced by cosmic rays in the $30-80$ MHz band. Here, we report the

Andrzej P. Kisielewicz

Two $d$-dimensional simplices in $R^d$ are neighborly if its intersection is a $(d-1)$-dimensional set. A family of $d$-dimensional simplices in $R^d$ is called neighborly if every two simplices of the family are neighborly. Let $S_d$ be the maximal cardinality of a neighborly family of $d$-dimensional simplices in $R^d$. Based on the structure of some codes

Benjamin J. Owen, Lee Lindblom, Luciano Soares Pinheiro, Binod Rajbhandari

We report on a new search for continuous gravitational waves from NS 1987A, the neutron star born in SN 1987A, using open data from Advanced LIGO and Virgo's third observing run (O3). The search covered frequencies from 35-1050 Hz, more than five times the band of the only previous gravitational wave search to constrain NS 1987A [B. J. Owen et al., ApJL 935,

The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta

We show, for the first time, radio measurements of the depth of shower maximum ($X_\text{max}$) of air showers induced by cosmic rays that are compared to measurements of the established fluorescence method at the same location. Using measurements at the Pierre Auger Observatory we show full compatibility between our radio and the previously published fluore

Julia Anabell Ziegler, Juhi Dutta, Jayita Lahiri, Cheng Li

Many different approaches have been made to explain the nature of dark matter (DM), but it remains and unsolved mystery of our universe. In this work we examine a type II two-Higgs-doublet model extended by a complex singlet (2HDMS), where the pseudo-scalar component of the singlet acts as a natural DM candidate. The DM candidate is stabilized by a Z'2 symme

Tung Nguyen, Sudhanshu Agrawal, Aditya Grover

Experimental design is a fundamental problem in many science and engineering fields. In this problem, sample efficiency is crucial due to the time, money, and safety costs of real-world design evaluations. Existing approaches either rely on active data collection or access to large, labeled datasets of past experiments, making them impractical in many real-w

Hengrui Luo, Jisu Kim, Alice Patania, Mikael Vejdemo-Johansson

Topology can extract the structural information in a dataset efficiently. In this paper, we attempt to incorporate topological information into a multiple output Gaussian process model for transfer learning purposes. To achieve this goal, we extend the framework of circular coordinates into a novel framework of mixed valued coordinates to take linear trends

D. Cogollo, M. J. Neves, Tessio B. de Melo, Alvaro S. de Jesus

The charged Kaon meson ($K^+$) features several hadronic decay modes, but the most relevant contribution to its decay width stems from the leptonic decay $K^+ \rightarrow \mu^+ \nu_\mu $. Given the precision acquired on the rare decay mode $K^+ \rightarrow \mu^+ \nu_\mu + X$, one can use the data to set constraints on sub-GeV hidden sectors featuring light s

Tianyue Chu, Mengwei Yang, Nikolaos Laoutaris, Athina Markopoulou

Federated learning (FL) is a paradigm that allows several client devices and a server to collaboratively train a global model, by exchanging only model updates, without the devices sharing their local training data. These devices are often constrained in terms of communication and computation resources, and can further benefit from model pruning -- a paradig

Zhe Jiang

With advancements in GPS, remote sensing, and computational simulation, an enormous volume of spatiotemporal data is being collected at an increasing speed from various application domains, spanning Earth sciences, agriculture, smart cities, and public safety. Such emerging geospatial and spatiotemporal big data, coupled with recent advances in deep learning

Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta, Fei Sha

To process novel sentences, language models (LMs) must generalize compositionally -- combine familiar elements in new ways. What aspects of a model's structure promote compositional generalization? Focusing on transformers, we test the hypothesis, motivated by theoretical and empirical work, that deeper transformers generalize more compositionally. Simply ad

Clare Burrage, Bradley March, Aneesh P. Naik

Screening mechanisms allow light scalar fields to dynamically avoid the constraints that come from our lack of observation of a long-range fifth force. Galactic scale tests are of particular interest when the light scalar is introduced to explain the dark matter or dark energy that dominates our cosmology. To date, much of the literature that has studied scr

Wenya Shu, C. Nadir Kaplan, Justin R. Barone

Thin bilayers made of elastic sheets with different strain recoveries can be used for dynamic shape morphing through ambient stimuli, such as temperature, mass diffusion, and light. As a fundamentally different approach to designing temporal shape change, constituent polymer molecular features (rather than external fields) are leveraged, specifically the vis

David Neufeld, Sajad Fathi Hafshejani, Daya Gaur, Robert Benkoczi

The goal of the load flow study is to ensure that electrical power is delivered efficiently and reliably to end-users while maintaining the stability and security of the power system. Newton-Raphson is a numerical method used widely for load flow analysis. One of the most computationally expensive steps in this method is an equation-solving step. We propose

Matthew Baker, Oliver Lorscheid, Tianyi Zhang

In this sequel to "Foundations of matroids - Part 1", we establish several presentations of the foundation of a matroid in terms of small building blocks. For example, we show that the foundation of a matroid M is the colimit of the foundations of all embedded minors of M isomorphic to one of the matroids $U^2_4$, $U^2_5$, $U^3_5$, $C_5$, $C_5^\ast$, $U^2_4\

Arianna Pera, Gianmarco de Francisci Morales, Luca Maria Aiello

Exploring online behavior change is imperative for societal progress in the context of 21st-century challenges. We analyze 148 articles (2000-2023) focusing on behavior change in the digital space and build a map that categorizes behaviors, behavior change detection methodologies, platforms of reference, and theoretical frameworks that characterize the analy

Reza Bozorgpour

Cancer is a complex disease that is characterized by uncontrolled growth and division of cells. It involves a complex interplay between genetic and environmental factors that lead to the initiation and progression of tumors. Recent advances in molecular dynamics simulations have revolutionized our understanding of the molecular mechanisms underlying cancer i

Sandi Klavžar, Jing Tian, James Tuite

This paper considers a game version of the general position problem in which a general position set is built through adversarial play. Two players in a graph, Builder and Blocker, take it in turns to add a vertex to a set, such that the vertices of this set are always in general position. The goal of Builder is to create a large general position set, whilst

María Olalla Olea-Romacho

$\gamma$-ray emission of blazars infer the presence of large-scale magnetic fields in the intergalactic medium, but their origin remains a mystery. Using recent data from MAGIC, H.E.S.S. and $\textit{Fermi}$-LAT, we investigate whether the large-scale magnetic fields in the intergalactic medium could have been generated by a first-order electroweak phase tra

Raphael Brieger, Markus Heinrich, Ingo Roth, Martin Kliesch

Expectation values of observables are routinely estimated using so-called classical shadows$\unicode{x2014}$the outcomes of randomized bases measurements on a repeatedly prepared quantum state. In order to trust the accuracy of shadow estimation in practice, it is crucial to understand the behavior of the estimators under realistic noise. In this work, we pr

Lorenzo Casarin, Olaf Lechtenfeld, Maximilian Rupprecht

Nicolai maps offer an alternative description of supersymmetric theories via nonlinear and nonlocal transformations characterized by the so-called `free-action' and `determinant-matching' conditions. The latter expresses the equality of the Jacobian determinant of the transformation with the one obtained by integrating out the fermions, which so far have bee

D. Manzoni, F. Ziparo, S. Gallerani, A. Ferrara

The near infrared background (NIRB) is the collective light from unresolved sources observed in the band 1-10 $\mu$m. The measured NIRB angular power spectrum on angular scales $\theta \gtrsim 1$ arcmin exceeds by roughly two order of magnitudes predictions from known galaxy populations. The nature of the sources producing these fluctuations is still unknown

Faris Janjoš, Marcel Hallgarten, Anthony Knittel, Maxim Dolgov

The CVAE is one of the most widely-used models in trajectory prediction for AD. It captures the interplay between a driving context and its ground-truth future into a probabilistic latent space and uses it to produce predictions. In this paper, we challenge key components of the CVAE. We leverage recent advances in the space of the VAE, the foundation of the

Luca M. Schulze Buschoff, Eric Schulz, Marcel Binz

As children grow older, they develop an intuitive understanding of the physical processes around them. Their physical understanding develops in stages, moving along developmental trajectories which have been mapped out extensively in previous empirical research. Here, we investigate how the learning trajectories of deep generative neural networks compare to

Jatin Arora, Youngja Park

In this work, we address the NER problem by splitting it into two logical sub-tasks: (1) Span Detection which simply extracts entity mention spans irrespective of entity type; (2) Span Classification which classifies the spans into their entity types. Further, we formulate both sub-tasks as question-answering (QA) problems and produce two leaner models which

Joshua Alexander Osborne, Christopher Michael Bryant, Fatemeh Bagheri, Amir Shahmoradi

The existence of a plateau in the short-duration tail of the observed distribution of cosmological Long-soft Gamma Ray Bursts (LGRBs) has been argued as the first direct evidence of Collapsars. A similar plateau in the short-duration tail of the observed duration distribution of Short-hard Gamma Ray Bursts (SGRBs) has been suggested as evidence of compact bi

A. Ruiz, A. Georgakakis, I. Georgantopoulos, A. Akylas

The recent serendipitous discovery of a new population of short duration X-ray transients, thought to be associated with collisions of compact objects or stellar explosions in distant galaxies, has motivated efforts to build up statistical samples by mining X-ray telescope archives. Most searches to date however, do not fully exploit recent developments in t

Maria Manuela Saez

Core-collapse supernovae (SNe) are one of the most powerful cosmic sources of neutrinos, with energies of several MeV. The emission of neutrinos and antineutrinos of all flavors carries away the gravitational binding energy of the compact remnant and drives its evolution from the hot initial to the cold final states. Detecting these neutrinos from Earth and

Saiedeh Akbari, Emily J. Griffis, Omkar Sudhir Patil, Warren E. Dixon

Deep neural network (DNN)-based adaptive controllers can be used to compensate for unstructured uncertainties in nonlinear dynamic systems. However, DNNs are also very susceptible to overfitting and co-adaptation. Dropout regularization is an approach where nodes are randomly dropped during training to alleviate issues such as overfitting and co-adaptation.

Olivia Strahan

It is proved that a system of parameters is always a Q-sequence for several classes of mixed characteristic rings: rings in which the characteristic of the residue field is a nilpotent element, a mixed characteristic analogue of Stanley-Reisner rings, and a mixed characteristic analogue of semigroup rings and toric face rings.

Quentin Bouniot, Angélique Loesch, Romaric Audigier, Amaury Habrard

For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives. While in the few-shot setup we observe that transformer-based object detectors perform better than convolution-based two-stage models for a similar amoun

Christian Scharrer

A model describing cell membranes as optimal shapes with regard to the $L^2$-deficit of their mean curvature to a given constant called spontaneous curvature is considered. It is shown that the corresponding energy functional is lower semi-continuous with respect to oriented varifold convergence on a space of surfaces whose second fundamental form is uniform

Benjamin N. L. Sharkey, Vishnu Reddy, Olga Kuhn, Juan A. Sanchez

We collect near-infrared spectra ($\sim0.75-2.55\ \mu m$) of four Jovian irregular satellites and visible spectra ($\sim0.32-1.00\ \mu m$) of two Jovian irregular satellites, two Uranian irregular satellites, and four Neptune Trojans. We find close similarities between observed Jovian irregular satellites and previously characterized Jovian Trojans. However,

Tommaso Lorenzi, Fiona R. Macfarlane, Kevin J. Painter

We formulate haptotaxis models of cancer invasion wherein the infiltrating cancer cells can occupy a spectrum of states in phenotype space, ranging from `fully mesenchymal' to `fully epithelial'. The more mesenchymal cells are those that display stronger haptotaxis responses and have greater capacity to modify the extracellular matrix (ECM) through enhanced

Jonas Scholz, Tom R. Andersson, Anna Vaughan, James Requeima

Machine learning (ML)-based weather models have recently undergone rapid improvements. These models are typically trained on gridded reanalysis data from numerical data assimilation systems. However, reanalysis data comes with limitations, such as assumptions about physical laws and low spatiotemporal resolution. The gap between reanalysis and reality has sp

Tsung-Sheng Huang, Peter Lunts, Mohammad Hafezi

Optical excitations in moir\'e transition metal dichalcogenide bilayers lead to the creation of excitons, as electron-hole bound states, that are generically considered within a Bose-Hubbard framework. Here, we demonstrate that these composite particles obey an angular momentum commutation relation that is generally non-bosonic. This emergent spin descriptio

Philipp Bringmann

A convincing feature of least-squares finite element methods is the built-in a posteriori error estimator for any conforming discretization. In order to generalize this property to discontinuous finite element ansatz functions, this paper introduces a least-squares principle on piecewise Sobolev functions by the example of the Poisson model problem with mixe

Olivera Miskovic, Rodrigo Olea, P. Marios Petropoulos, David Rivera-Betancour

In three-dimensional pseudo-Riemannian manifolds, the Cotton tensor arises as the variation of the gravitational Chern-Simons action with respect to the metric. It is Weyl-covariant, symmetric, traceless and covariantly conserved. Performing a reduction of the Cotton tensor with respect to Carrollian diffeomorphisms in a suitable frame, one discloses four se

Noelia R. Perez, Luis A. Pereyra, Georgina Coldwell, Facundo Rodriguez

The standard paradigm for the formation of the Universe suggests that large structures are formed from hierarchical clustering by the continuous accretion of less massive galaxy systems through filaments. In this context, filamentary structures play an important role in the properties and evolution of galaxies by connecting high-density regions, such as node

Shenao Zhang, Boyi Liu, Zhaoran Wang, Tuo Zhao

ReParameterization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. However, recent studies have revealed that, when applied to long-term reinforcement learning problems, model-based RP PGMs may experience chaotic and non-smooth optimization landscapes with exploding gradient varianc

H. Dutrieux, J. Karpie, C. Monahan, K. Orginos

Lattice QCD offers the possibility of computing parton distributions from first principles, although not in the usual $\overline{MS}$ factorization scheme. We study in this paper the evolution of non-singlet parton distribution functions (PDFs) in the short-distance factorization scheme which notably arises in lattice calculations in the pseudo-distribution

Shihab Shahriar Khan, Bryce Palmer, Christopher Edelmaierd, Hasan Metin Aktulga

We introduce OpenRAND, a C++17 library aimed at facilitating reproducible scientific research through the generation of statistically robust and yet replicable random numbers. OpenRAND accommodates single and multi-threaded applications on CPUs and GPUs and offers a simplified, user-friendly API that complies with the C++ standard's random number engine inte