July 2023 arXiv papers — page 133

Mohammad Mahafuzur Rahamana, Md. Abdul Momin, Abhijit Majumdar, Mohammad Jellur Rahman

In this paper, the structural, electronic and optical properties of tin-monoxide and the impact of Indium (In) doping into tin-monoxide are computed by Local Density Approximation (LDA) under density function theory (DFT) framework. The calculated bond length of Sn-O in tin-monoxide is 2.285 angstrom and that deviates greater than 3 percent from the experime

Hanlin Fang, Qiaoling Lin, Yi Zhang, Joshua Thompson

Transition metal dichalcogenide (TMD) heterobilayers provide a versatile platform to explore unique excitonic physics via properties of the constituent TMDs and external stimuli. Interlayer excitons (IXs) can form in TMD heterobilayers as delocalized or localized states. However, the localization of IX in different types of potential traps, the emergence of

Jacob Boyd, Sabin Thapa, Michael Strickland

We extract transverse momentum dependent feed-down fractions for bottomonium production using a data-driven approach. We use data published by the ATLAS, CMS, and LHCb Collaborations for sqrt(s) = 7 TeV proton-proton collisions. Based on this collected data, we produce fits to the differential cross sections for the production of both S- and P-wave bottomoni

Elsa Andres, Alain Barrat, Márton Karsai

Temporal networks are commonly used to represent dynamical complex systems like social networks, simultaneous firing of neurons, human mobility or public transportation. Their dynamics may evolve on multiple time scales characterising for instance periodic activity patterns or structural changes. The detection of these time scales can be challenging from the

Jessica Yin, Paarth Shah, Naveen Kuppuswamy, Andrew Beaulieu

Visuotactile sensors are a popular tactile sensing strategy due to high-fidelity estimates of local object geometry. However, existing algorithms for processing raw sensor inputs to useful intermediate signals such as contact patches struggle in high-deformation regimes. This is due to physical constraints imposed by sensor hardware and small-deformation ass

Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho

Recent advances in large language models (LLMs) and the intensifying popularity of ChatGPT-like applications have blurred the boundary of high-quality text generation between humans and machines. However, in addition to the anticipated revolutionary changes to our technology and society, the difficulty of distinguishing LLM-generated texts (AI-text) from hum

Andreas Knauf, Richard Montgomery

For n bodies moving in Euclidean d-space under the influence of a homogeneous pair interaction we compactify every center-of-mass energy surface, obtaining a 2d(n -1)-1 - dimensional manifold with corners in the sense of Melrose. After a time change, the flow on this manifold is globally defined and non-trivial on the boundary.

Tiberius Berndsen, Imran M. Mirza

We study single-photon induced electromagnetically induced transparency (EIT) in many-emitter waveguide quantum electrodynamics (wQED) with linear and nonlinear waveguide dispersion relations. In the single-emitter problem, in addition to the robustness of the EIT spectral features in the over-coupled regime of wQED, we find that the nonlinear dispersion res

Sezer Sorgun, Esma Elyemani

We consider the eccentric graph of a graph $G$, denoted by $ecc(G)$, which has the same vertex set as $G$, and two vertices in the eccentric graph are adjacent iff their distance in $G$ is equal to the eccentricity of one of them. In this paper, we present a fundamental requirement for the isomorphism between $ecc(G)$ and the complement of $G$, and show that

Boris Kunyavskii, Andrei Lavrenov, Eugene Plotkin, Nikolai Vavilov

The main result of the present paper is bounded elementary generation of the Steinberg groups $\mathrm{St}(\Phi,R)$ for simply laced root systems $\Phi$ of rank $\ge 2$ and arbitrary Dedekind rings of arithmetic type. Also, we prove bounded generation of $\mathrm{St}(\Phi,\mathbb F_{q}[t,\,t^{-1}])$ for all root systems $\Phi$, and bounded generation of $\ma

Ming Geng, Chris E. Mohn

The melting curve of pure silica (SiO$_2$) was determined using {\it ab initio} density functional theory together with the solid-liquid coexisting approach, thermodynamic integration and the Z method. The melting curves are consistent with a smooth slow increase in a large region from 50 GPa (dT/dP $\approx$ 15 K/GPa) to about 500 GPa (dT/dP $\approx$ 5 K/G

Waldemar Barrera, Angel Cano, Juan Pablo Navarrete, José Seade

In this paper, we give a classification of the subgroups of $\textrm{PSL}(3, \mathbb{C})$ that act on $\mathbb{P}_{\mathbb{C}}^2$ in such a way that their Kulkarni limit set has finitely many lines in general position lines. These are the elementary groups.

Zhongyu Jiang, Zhuoran Zhou, Lei Li, Wenhao Chai

Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with significantly better performance in most benchmarks than traditional optimization-based methods. Nonetheless, 3D HPE in the wild is still the biggest challenge for learning-based models, whether with 2D-3D lifting, image-to-3D, or diffusion-based methods, since the trained ne

Jiachen Lu, Qian Xiao, Cici Bauer

24-hour actigraphy data collected by wearable devices offer valuable insights into physical activity types, intensity levels, and rest-activity rhythms (RAR). RARs, or patterns of rest and activity exhibited over a 24-hour period, are regulated by the body's circadian system, synchronizing physiological processes with external cues like the light-dark cycle.

Michael Bishop, Joey Contreras, Peter Martin, Piero Nicolini

We investigate an extension of the Generalized Uncertainty Principle (GUP) in three dimensions by modifying the three dimensional position and momentum operators in a manner that remains coordinate-independent and retains as much of the standard position-momentum commutators as possible. Moreover, we bound the physical momentum which leads to an effective mi

Mauricio Leston, Andrés Goya, Guillem Pérez-Nadal, Mario Passaglia

The partition function of 3-dimensional quantum gravity has been argued to be 1-loop exact. Here, we verify the vanishing of higher-orders in perturbation theory by explicit computation in the second-order, metric formulation at 3-loops. The number of 1-particle irreducible Feynman diagrams involving both gravitons and ghosts turns out to be 17. Using dimens

Rojin Zandi, Hojjat Salehinejad, Kian Behzad, Elaheh Motamedi

Autonomous robotic systems have gained a lot of attention, in recent years. However, accurate prediction of robot motion in indoor environments with limited visibility is challenging. While vision-based and light detection and ranging (LiDAR) sensors are commonly used for motion detection and localization of robotic arms, they are privacy-invasive and depend

Patryk Lipka-Bartosik, Giovanni Francesco Diotallevi, Pharnam Bakhshinezhad

In classical thermodynamics energy always flows from the hotter system to the colder one. However, if these systems are initially correlated, the energy flow can reverse, making the cold system colder and the hot system hotter. This intriguing phenomenon is called ``anomalous energy flow'' and shows the importance of initial correlations in determining physi

Abdollah Ghazvanchahi, Pejman Jahbedar Maralani, Alan R. Moody, April Khademi

Deep learning (DL) methods for white matter lesion (WML) segmentation in MRI suffer a reduction in performance when applied on data from a scanner or centre that is out-of-distribution (OOD) from the training data. This is critical for translation and widescale adoption, since current models cannot be readily applied to data from new institutions. In this wo

Robert Capra, Jaime Arguello

Generative AI tools such as chatGPT are poised to change the way people engage with online information. Recently, Microsoft announced their "new Bing" search system which incorporates chat and generative AI technology from OpenAI. Google has announced plans to deploy search interfaces that incorporate similar types of technology. These new technologies will

Ludmila Viotti

This thesis consists of several studies performed over different few-dof quantum systems exposed to the effect of an uncontrolled environment. The primary focus of the work is to explore the relation between decoherence and environmentally-induced dissipative effects, and the concept known as geometric phases. The first mention of such an object in the conte

Elia Brue, Aaron Naber, Daniele Semola

Let $(M^n_i, g_i)\to (X,d_X)$ be a Gromov-Hausdorff converging sequence of Riemannian manifolds with ${\rm Sec}_{g_i} \ge -1$, ${\rm diam}\, (M_i)\le D$, and such that the $M^n_i$ are all homeomorphic to tori $T^n$. Then $X$ is homeomorphic to a $k$-dimensional torus $T^k$ for some $0\leq k\leq n$. This answers a question of Petrunin in the affirmative. We s

Sireesh Gururaja, Ritam Dutt, Tinglong Liao, Carolyn Rose

Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolding on the in-domain performance of several NLP tasks. We extend this work by exploring the impact of linguistic representations on cross-domain performance in a few-shot transfer setting. An important question is whether linguistic

Stefano Bolognesi, Kenichi Konishi, Andrea Luzio

We revisit the simplest Bars-Yankielowicz (BY) model (the $\psi\eta$ model), starting from a model with an additional Dirac pair of fermions in the fundamental representation, together with a complex color-singlet scalar $\phi$ coupled to them through a Yukawa interaction. This model possesses a color-flavor-locked 1-form ${\Bbb Z}_N$ symmetry, due to the in

Yixi Xu, Yi Zhao

This study introduces a mediation analysis framework when the mediator is a graph. A Gaussian covariance graph model is assumed for graph representation. Causal estimands and assumptions are discussed under this representation. With a covariance matrix as the mediator, parametric mediation models are imposed based on matrix decomposition. Assuming Gaussian r

S. J. Brooks

The Newton, Gauss--Newton and Levenberg--Marquardt methods all use the first derivative of a vector function (the Jacobian) to minimise its sum of squares. When the Jacobian matrix is ill-conditioned, the function varies much faster in some directions than others and the space of possible improvement in sum of squares becomes a long narrow ellipsoid in the l

Ritajit Majumdar, Pedro Rivero, Friederike Metz, Areeq Hasan

Digital zero-noise extrapolation (dZNE) has emerged as a common approach for quantum error mitigation (QEM) due to its conceptual simplicity, accessibility, and resource efficiency. In practice, however, properly applying dZNE to extend the computational reach of noisy quantum processors is rife with subtleties. Here, based on literature review and original

Jianing Yang

Given a Galois cover of curves $f$ over a field of characteristic $p$, the lifting problem asks whether there exists a Galois cover over a complete mixed characteristic discrete valuation ring whose reduction is $f$. In this paper, we consider the case where the Galois groups are elementary abelian $p$-groups. We prove a combinatorial criterion for lifting a

Nathan Cordner, George Kollios

Consensus clustering (or clustering aggregation) inputs $k$ partitions of a given ground set $V$, and seeks to create a single partition that minimizes disagreement with all input partitions. State-of-the-art algorithms for consensus clustering are based on correlation clustering methods like the popular Pivot algorithm. Unfortunately these methods have not

Zachary Englhardt, Richard Li, Dilini Nissanka, Zhihan Zhang

Large language models (LLMs) have shown remarkable abilities to generate code, however their ability to develop software for embedded systems, which requires cross-domain knowledge of hardware and software has not been studied. In this paper we develop an extensible, open source hardware-in-the-loop framework to systematically evaluate leading LLMs (GPT-3.5,

Tim Planken, Torsten Ueckerdt

A subset $M$ of the edges of a graph or hypergraph is hitting if $M$ covers each vertex of $H$ at least once, and $M$ is $t$-shallow if it covers each vertex of $H$ at most $t$ times. We consider the existence of shallow hitting edge sets and the maximum size of shallow edge sets in $r$-uniform hypergraph $H$ that are regular or have a large minimum degree.

Vinod Raman, Unique Subedi, Ambuj Tewari

We study the online learnability of hypothesis classes with respect to arbitrary, but bounded loss functions. No characterization of online learnability is known at this level of generality. We give a new scale-sensitive combinatorial dimension, named the sequential minimax dimension, and show that it gives a tight quantitative characterization of online lea

Chirine Saadi, Hai Son Nguyen, Sébastien Cueff, Lydie Ferrier

Moir\'e structures are receiving increasing attention in nanophotonics as they support intriguing optical phenomena. In the so-called "magic configuration", one-dimensional moir\'es give rise to fully dispersionless energy bands known as "flatbands", where the light is tightly localized within each supercell of the periodic moir\'e. The goal of this investig

Ethan Akin

The dynamics by iteration of a function on a compact metric space, sometimes called a cascade, can be extended to the dynamics of a closed relation on such a space. Here we apply this relation dynamics to study semiflows (and their relation extension) as well as hybrid dynamical systems which combine both continuous time and discrete time dynamics. In a unif

Mahdi Ghafariasl, Sarabpreet Singh, Sampath Gamage, Timothy Prusnick

Violet phosphorus (VP) has garnered attention for its appealing physical properties and potential applications in optoelectronics. We present a comprehensive investigation of the photo-degradation and thermal effects of exfoliated VP on SiO2 substrate. The degradation rate of VP was found to be strongly influenced by the excitation wavelength and light expos

Alireza Mohammadi, Hafiz Malik

The safety-critical nature of vehicle steering is one of the main motivations for exploring the space of possible cyber-physical attacks against the steering systems of modern vehicles. This paper investigates the adversarial capabilities for destabilizing the interaction dynamics between human drivers and vehicle haptic shared control (HSC) steering systems

Robert M. Kent, Wendson A. S. Barbosa, Daniel J. Gauthier

In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic H\'enon map, including controlling the system between unstable fixed-po

Nafisa Labiba Ishrat Huda, Angona Biswas, MD Abdullah Al Nasim, Md. Fahim Rahman

Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are used to detect diseases brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease and man

Adel Khorramrouz, Sujan Dutta, Ashiqur R. KhudaBukhsh

In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death of Mahsa Amini in police custody. We present an ensemble active learning pipeline to train a stance classifier. Our novelty lies in the involvement of Iranian women in an active r

Degan Hao, Mohammadreza Negahdar

Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-term complications in COVID-19 could benefit the treatment planning and resource arrangement. However, due to the heterogeneous phenotype pre

Mostafa Adimy, Abdennasser Chekroun, Laurent Pujo-Menjouet, Mattia Sensi

We generalize the model proposed in [Adimy, Babin, Pujo-Menjouet, \emph{SIAM Journal on Applied Dynamical Systems} (2022)] for prion infection to a network of neurons. We do so by applying a so-called \emph{multigroup approach} to the system of Delay Differential Equations (DDEs) proposed in the aforementioned paper. We derive the classical threshold quantit

Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji

Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices,

Vidushi Sharma, Maxwell Giammona, Dmitry Zubarev, Andy Tek

Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the disco

Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh, Enkelejda Kasneci

Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not only provide embeddings but also transferable uncertainty estim

Furkan Sahbaz, James N. Eckstein, Dale J. Van Harlingen, Simeon I. Bogdanov

Transduction of quantum signals between the microwave and the optical ranges will unlock powerful hybrid quantum systems enabling information processing with superconducting qubits and low-noise quantum networking through optical photons. Most microwave-to-optical quantum transducers suffer from thermal noise due to pump absorption. We analyze the coupled th

Aditya Ramji, Hanif Tayarani

Global light duty electric vehicle (EV) sales exceeded 10.5 million units in 2022, with a year-on-year growth of 55%, but these trends differ regionally. Despite the robust growth, upfront purchase price remains a challenge for consumers in different regions, and thus, OEMs make technology choices to respond to market needs. This paper examines the electrifi

Sreemayee Aditya, Nilanjan Roy

The investigation of the dynamical universality classes of quantum systems is an important, and rather less explored, aspect of non-equilibrium physics. In this work, considering the out-of-equilibrium dynamics of spinless fermions in a one-dimensional quasiperiodic model with and without a periodic driving, we report the existence of the dynamical one-param

Emilia Pop, Manuela Petrescu

This study explores and analyzes the learning tendencies of second-year students enrolled in different lines of study related to the Databases course. There were 79 answers collected from 191 enrolled students that were analyzed and interpreted using thematic analysis. The participants in the study provided two sets of answers, anonymously collected (at the

Michael Jung, Thomas O. Rot

Using geometric arguments, we compute the group of homotopy classes of maps from a closed $(n+1)$-dimensional manifold to the $n$-sphere for $n \geq 3$. Our work extends results from Kirby, Melvin and Teichner for closed oriented 4-manifolds and from Konstantis for closed $(n+1)$-dimensional spin manifolds, considering possibly non-orientable and non-spinnab

Manuela Petrescu, Emilia Pop

This study explores and analyses the expectations of second-year students enrolled in different lines of study related to Database course, as their interest in having a carrier path in a database related domain and how it reflects the job demands from the market. The participants in the study provided two sets of answers, anonymously collected (in the beggin

Jovon Craig, Josh Andle, Theodore S. Nowak, Salimeh Yasaei Sekeh

The robustness of deep neural networks (DNNs) against adversarial attacks has been studied extensively in hopes of both better understanding how deep learning models converge and in order to ensure the security of these models in safety-critical applications. Adversarial training is one approach to strengthening DNNs against adversarial attacks, and has been

Mariam Ayman, Youssef El-harty, Ahmed Rashed, Ahmed Fathy

In today's digital age, online communities have become an integral part of our lives, fostering collaboration, knowledge sharing, and community engagement. Higher education institutions, in particular, can greatly benefit from dedicated platforms that facilitate academic discussions and provide incentives for active participation. This research paper present

Manuela Petrescu, Kuderna Bentasup

The scope of this paper was to find out how the students in Computer Science perceive different teaching styles and how the teaching style impacts the learning desire and interest in the course. To find out, we designed and implemented an experiment in which the same groups of students (86 students) were exposed to different teaching styles (presented by the

Miguel A. Bastarrachea-Magnani, David Villaseñor, Jorge Chávez-Carlos, Sergio Lerma-Hernández

We study the multifractal behavior of coherent states projected in the energy eigenbasis of the spin-boson Dicke Hamiltonian, a paradigmatic model describing the collective interaction between a single bosonic mode and a set of two-level systems. By examining the linear approximation and parabolic correction to the mass exponents, we find ergodic and multifr

Zhongliang Jiang, Chenyang Li, Xuesong Li, Nassir Navab

Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US examinations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic application

Aricia Rinkens, Clemens V. Verhoosel, Nick O. Jaensson

The calibration of rheological parameters in the modeling of complex flows of non-Newtonian fluids can be a daunting task. In this paper we demonstrate how the framework of Uncertainty Quantification (UQ) can be used to improve the predictive capabilities of rheological models in such flow scenarios. For this demonstration, we consider the squeeze flow of ge

Matthias Freiberger, Peter Kun, Christian Igel, Anders Sundnes Løvlie

Models leveraging both visual and textual data such as Contrastive Language-Image Pre-training (CLIP), are the backbone of many recent advances in artificial intelligence. In this work, we show that despite their versatility, such models are vulnerable to what we refer to as fooling master images. Fooling master images are capable of maximizing the confidenc

Run Chen, Tie Li, Xinran Wang, Shuai Huang

In the transportation sector, ammonia used as a power source plays a significant role in the scenario of carbon neutralization. However, the engine-out emissions correlations of ammonia-diesel dual-fuel (DF) engines are still unclear, especially the nitro-compounds of great concern and GHG. In this study, the engine-out emissions are evaluated by using a fou

Nelson Kyakutwika, Bruce Bartlett

Cross-series dependencies are crucial in obtaining accurate forecasts when forecasting a multivariate time series. Simultaneous Graphical Dynamic Linear Models (SGDLMs) are Bayesian models that elegantly capture cross-series dependencies. This study forecasts returns of a 40-dimensional time series of stock data from the Johannesburg Stock Exchange (JSE) usi

Kelly Shortridge

Traditional information security presents a poor ROI: payoffs only manifest when attacks are successfully prevented. In a reality where attacks are inevitable, subpar returns are therefore inevitable. The emerging paradigm of Security Chaos Engineering offers a more remunerative and reliable ROI by minimizing attack impacts and generating valuable evidence t

Xin An, Anton Artemyev, Vassilis Angelopoulos, Xiao-Jia Zhang

Electromagnetic ion cyclotron (EMIC) waves lead to rapid scattering of relativistic electrons in Earth's radiation belts, due to their large amplitudes relative to other waves that interact with electrons of this energy range. A central feature of electron precipitation driven by EMIC waves is deeply elusive. That is, moderate precipitating fluxes at energie

Haris Aziz, Gergely Csáji, Ágnes Cseh

We study deviations by a group of agents in the three main types of matching markets: the house allocation, the marriage, and the roommates models. For a given instance, we call a matching $k$-stable if no other matching exists that is more beneficial to at least $k$ out of the $n$ agents. The concept generalizes the recently studied majority stability. We p

Thomas A. Maier, Satoshi Okamoto

Inelastic neutron scattering provides a powerful probe of the magnetic excitations of quantum magnets. Altermagnets have recently emerged as a new class of magnets with vanishing net magnetization characteristic of antiferromagnets and with a spin-split electronic structure typical of ferromagnets. Here we introduce a minimal Hubbard model with two-sublattic

Gergely Ambrus, Barnabás Gárgyán

We study the $(n-1)$-dimensional volume of central hyperplane sections of the $n$-dimensional cube $Q_n$. Our main goal is two-fold: first, we provide an alternative, simpler argument for proving that the volume of the section perpendicular to the main diagonal of the cube is strictly locally maximal for every $n \geq 4$, which was shown before by L. Pournin

Shantanu Ghosh, Kayhan Batmanghelich

Discovering a high-performing sparse network within a massive neural network is advantageous for deploying them on devices with limited storage, such as mobile phones. Additionally, model explainability is essential to fostering trust in AI. The Lottery Ticket Hypothesis (LTH) finds a network within a deep network with comparable or superior performance to t

Karthika Venkatesan, Sujit Kumar Chakrabarti

Statechart is a visual modelling language for systems. In this paper, we extend our earlier work on modular statecharts with local variables and present an updated operational semantics for statecharts with concurrency. Our variant of the statechart has local variables, which interact significantly with the remainder of the language semantics. Our semantics

Debasmita Pal, Arun Ross

Plant phenology and phenotype prediction using remote sensing data are increasingly gaining attention within the plant science community as a promising approach to enhance agricultural productivity. This work focuses on generating synthetic forestry images that satisfy certain phenotypic attributes, viz. canopy greenness. We harness a Generative Adversarial

Natalie Behague, Natasha Morrison, Jonathan A. Noel

A graph $H$ is common if the limit as $n\to\infty$ of the minimum density of monochromatic labelled copies of $H$ in an edge colouring of $K_n$ with red and blue is attained by a sequence of quasirandom colourings. We apply an information-theoretic approach to show that certain graphs obtained from odd cycles and paths via gluing operations are common. In fa

Nicolas Augier, Didier Henrion, Milan Korda, Victor Magron

We address the problem of symmetry reduction of optimal control problems under the action of a finite group from a measure relaxation viewpoint. We propose a method based on the moment-SOS aka Lasserre hierarchy which allows one to significantly reduce the computation time and memory requirements compared to the case without symmetry reduction. We show that

Haleh Damirchi, Michael Greenspan, Ali Etemad

We propose a novel solution for predicting future trajectories of pedestrians. Our method uses a multimodal encoder-decoder transformer architecture, which takes as input both pedestrian locations and ego-vehicle speeds. Notably, our decoder predicts the entire future trajectory in a single-pass and does not perform one-step-ahead prediction, which makes the

Eamon Quinlan-Gallego, Austyn Simpson, Anurag K. Singh

For each positive prime integer $p$ we construct a standard graded $F$-rational ring $R$, over a field $K$ of characteristic $p$, such that $R\otimes_K\overline{K}$ is not $F$-rational. By localizing we obtain a flat local homomorphism $(R, \mathfrak{m}) \to (S, \mathfrak{n})$ such that $R$ is $F$-rational, $S/\mathfrak{m} S$ is regular (in fact, a field), b

Arash Fayyazi, Mahdi Nazemi, Arya Fayyazi, Massoud Pedram

This paper introduces NeuroBlend, a novel neural network architecture featuring a unique building block known as the Blend module. This module incorporates binary and fixed-point convolutions in its main and skip paths, respectively. There is a judicious deployment of batch normalizations on both main and skip paths inside the Blend module and in between con

Rupak Majumdar, Mahmoud Salamati, Sadegh Soudjani

Abstraction-based techniques are an attractive approach for synthesizing correct-by-construction controllers to satisfy high-level temporal requirements. A main bottleneck for successful application of these techniques is the memory requirement, both during controller synthesis and in controller deployment. We propose memory-efficient methods for mitigating

Stuart White

In the 1970s Alain Connes identified the appropriate notion of amenabilty for von Neumann algebras, and used it to obtain a deep internal finite dimensional approximation structure for these algebras. This structure is exactly what is needed for classification, and one of many consequences of Connes' theorem is the uniqueness of amenable II$_1$ factors, and

Curtis J. Saxton, Brad Marston, Jeffrey S. Oishi, Steven M. Tobias

We discuss the applicability of quasilinear-type approximations for a turbulent system with a large range of spatial and temporal scales. We consider a paradigm fluid system of rotating convection with a vertical and horizontal temperature gradients. In particular, the interaction of rotating with the horizontal temperature gradient drives a ``thermal wind''

Kevin France, Nicole Arulanantham, Erin Maloney, P. Wilson Cauley

The spatial distribution and evolution of gas in the inner 10 au of protoplanetary disks form the basis for estimating the initial conditions of planet formation. Among the most important constraints derived from spectroscopic observations of the inner disk are the radial distributions of the major gas phase constituents, how the properties of the gas change

Dario A. Bahamon, Guillermo Gómez-Santos, Dmitri K. Efetov, Tobias Stauber

We propose minimal transport experiments in the coherent regime that can probe the chirality of twisted moir\'e structures. We show that only with a third contact and in the presence of an in-plane magnetic field (or other time-reversal symmetry breaking effect), a chiral system may display non-reciprocal transport in the linear regime. We then propose to us

Anna Niemiec, Mathilde Jauzac, Dominique Eckert, David Lagattuta

The HST treasury program BUFFALO provides extended wide-field imaging of the six Hubble Frontier Fields galaxy clusters. Here we present the combined strong and weak-lensing analysis of Abell 370, a massive cluster at z=0.375. From the reconstructed total projected mass distribution in the 6arcmin x 6arcmin BUFFALO field-of-view, we obtain the distribution o

G. V. Panopoulou, L. Markopoulioti, F. Bouzelou, M. A. Millar-Blanchaer

Polarimetry of stars at optical and near-infrared wavelengths is an invaluable tool for tracing interstellar dust and magnetic fields. Recent studies have demonstrated the power of combining stellar polarimetry with distances from the Gaia mission, in order to gain accurate, three-dimensional information on the properties of the interstellar magnetic field a

Mark S. Graham, Walter Hugo Lopez Pinaya, Paul Wright, Petru-Daniel Tudosiu

Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perform reconstruction-based OOD detection on 2D datasets, but do not trivially scale to 3D data. In this work, we

Ben Zager, Shang-Shun Zhang, Hana Schiff, Raymond Fan

We present fine momentum space resolution resonant elastic x-ray scattering measurements of the magnetic structure of the metallic antiferromagnet CoNb$_3$S$_6$. Using circular dichroism and full linear polarization analysis of the magnetic scattering, we reveal a non-coplanar double-$Q$ ($2Q$) order, with a non-collinear commensurate component and a long-wa

Christopher Tiede, Daniel J. D'Orazio

Modern numerical hydrodynamics tools have recently enabled detailed examinations of binaries accreting from prograde circumbinary disks. These have re-framed the current understanding of binary-disk interactions and disk driven orbital evolution. We present the first full-domain grid-based hydrodynamics simulations of equal-mass, eccentric binaries accreting

Giorgio Laverda, Javier Rubio

A Hubble-induced phase transition is a natural spontaneous symmetry breaking mechanism allowing for explosive particle production in non-oscillatory models of inflation involving non-minimally coupled spectator fields. In this work, we perform a comprehensive characterisation of this type of transitions as a tachyonic Ricci-heating mechanism, significantly e

A. S. Dotdaev, Ya. I. Rodionov, K. I. Kugel, B. A. Aronzon

We study the effects of anisotropy on the magnetoresistance of Weyl semimetals (WSMs) in the ultraquantum regime. We utilize the fact that many Weyl semimetals are approximately axially anisotropic. We find that anisotropy manifests itself in the strong dependence of the magnetoresistance on the polar and azimuthal angles determining the orientation of the a

Louise Welsh, Ryan Cooke, Michele Fumagalli, Max Pettini

We present new Keck/HIRES data of the most metal-poor damped Lyman-alpha (DLA) system currently known. By targeting the strongest accessible Fe II features, we have improved the upper limit of the [Fe/H] abundance determination by ~1 dex, finding [Fe/H]<-3.66 (2 sigma). We also provide the first upper limit on the relative abundance of an odd-atomic number e

C. M. Harrison, A. Girdhar, S. R. Ward

Cosmological simulations fail to reproduce realistic galaxy populations without energy injection from active galactic nuclei (AGN) into the interstellar medium (ISM) and circumgalactic medium (CGM); a process called `AGN feedback'. Consequently, observational work searches for evidence that luminous AGN impact their host galaxies. Here, we review some of thi

Tanmay Bhore, Jean-Yves Desaules, Zlatko Papić

The concept of "deep thermalization" has recently been introduced to characterize moments of an ensemble of pure states, resulting from projective measurements on a subsystem, which lie beyond the purview of conventional Eigenstate Thermalization Hypothesis (ETH). In this work, we study deep thermalization in systems with kinetic constraints, such as the qua

J. A. Aguilar-Saavedra, H. B. Câmara, F. R. Joaquim, J. F. Seabra

We consider the small excesses around 95 GeV found in several searches for a new scalar in $\gamma \gamma$, $\tau \tau$ and $b \bar b$ final states. Instead of trying to accommodate them all, as is usually done in the literature, in the context of a given Standard Model~(SM) extension, we investigate whether it would be possible that one or two of these exce

Chiara Damiolini, Angela Gibney, Daniel Krashen

Here we define a series of associative algebras attached to a vertex operator algebra $V$, called mode transition algebras, showing they reflect both algebraic properties of $V$ and geometric constructions on moduli of curves. One can define sheaves of coinvariants on pointed coordinatized curves from $V$-modules. We show that if the mode transition algebras

T. Eftekhari, W. Fong, A. C. Gordon, N. Sridhar

We present the first X-ray census of fast radio burst (FRB) host galaxies to conduct the deepest search for AGN and X-ray counterparts to date. Our sample includes seven well-localized FRBs with unambiguous host associations and existing deep Chandra observations, including two events for which we present new observations. We find evidence for AGN in two FRB

Ilya Shpitser

The ID algorithm solves the problem of identification of interventional distributions of the form p(Y | do(a)) in graphical causal models, and has been formulated in a number of ways [12, 9, 6]. The ID algorithm is sound (outputs the correct functional of the observed data distribution whenever p(Y | do(a)) is identified in the causal model represented by th

Evangelos Ntavelis, Aliaksandr Siarohin, Kyle Olszewski, Chaoyang Wang

We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space, which can then be decoded into a volumetric representation for rendering view-consistent appearance and geometry. We then identify the appr

Clara Meister, Tiago Pimentel, Luca Malagutti, Ethan G. Wilcox

Sampling is a common strategy for generating text from probabilistic models, yet standard ancestral sampling often results in text that is incoherent or ungrammatical. To alleviate this issue, various modifications to a model's sampling distribution, such as nucleus or top-k sampling, have been introduced and are now ubiquitously used in language generation

Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff

Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing different incentives. To maintain scientific rigor, statistical methods should acknowledge this state of affairs. To t

Bichu Li, Chao Chen, Bo Wang

We examine the backreaction effect of the enhanced small-scale scalar perturbations from the sound speed resonance (SSR) mechanism for primordial black hole formation in Dirac-Born-Infeld (DBI) inflation on background as well as curvature perturbation, which can generate a considerable amount of primordial black holes (PBH) in the radiation-dominated epoch.

David Corlin Marchand

We study oriented percolation on random causal triangulations, those are random planar graphs obtained roughly speaking by adding horizontal connections between vertices of an infinite tree. When the underlying tree is a geometric Galton--Watson tree with mean $m>1$, we prove that the oriented percolation undergoes a phase transition at $p_c(m)$, where $p_c(

Bhargav Bhatt, Manuel Blickle, Gennady Lyubeznik, Anurag K. Singh

We investigate the injectivity of the Frobenius map on thickenings of smooth varieties in projective space over a field of positive characteristic. We obtain uniform bounds -- i.e., independent of the characteristic -- on the thickening that ensures an injective Frobenius map when the projective variety is a smooth complete intersection or an arbitrary proje

Sofia Eleni Spatharioti, David M. Rothschild, Daniel G. Goldstein, Jake M. Hofman

Recent advances in the development of large language models are rapidly changing how online applications function. LLM-based search tools, for instance, offer a natural language interface that can accommodate complex queries and provide detailed, direct responses. At the same time, there have been concerns about the veracity of the information provided by LL

A. Maragkoudakis, E. Peeters, A. Ricca, C. Boersma

We examine the dependence of polycyclic aromatic hydrocarbon (PAH) band intensity ratios as a function of the average number of carbon atoms and assess their effectiveness as tracers for PAH size, utilising the data, models, and tools provided by the NASA Ames PAH Infrared Spectroscopic Database. To achieve this, we used spectra from mixtures of PAHs of diff

Vitoriano Ruas

The theory of mixed finite element methods for solving different types of elliptic partial differential equations in saddle point formulation is well established since many decades. This topic was mostly studied for variational formulations defined upon the same product spaces of both shape- and test-pairs of primal variable-multiplier. Whenever either these