May 2023 arXiv papers — page 166

Alexander Mariona, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Muriel Médard

The problem of mismatched guesswork considers the additional cost incurred by using a guessing function which is optimal for a distribution $q$ when the random variable to be guessed is actually distributed according to a different distribution $p$. This problem has been well-studied from an asymptotic perspective, but there has been little work on quantifyi

Andrey Smirnov, Alexander Varchenko

In this note we discuss an integral representation for the vertex function of the cotangent bundle over the Grassmannian, $X=T^{*} Gr(k,n)$. This integral representation can be used to compute the $\hbar\to \infty$ limit of the vertex function, where $\hbar$ denotes the equivariant parameter of a torus acting on $X$ by dilating the cotangent fibers. We show

Aqil Sajjad, Michael R Grace, Saikat Guha

Distributed aperture telescopes are a well-established approach for boosting resolution in astronomical imaging. However, theoretical limits on quantitative imaging precision, and the fundamentally best possible beam-combining and detection schemes to use with such arrays, remain largely unexplored. Using mathematical tools of the quantum and classical Crame

Etera R. Livine

An effective operational approach to quantum mechanics is to focus on the evolution of wave-packets, for which the wave-function can be seen in the semi-classical regime as representing a classical motion dressed with extra degrees of freedom describing the shape of the wave-packet and its fluctuations. These quantum dressing are independent degrees of freed

Nicholas Sharp, Cristian Romero, Alec Jacobson, Etienne Vouga

Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrated on much lower-dimensional subspaces. This work addresses the challenge of identifying such subspaces automatically: given as input an energy function for a high-dimensional syste

Harsh Verma, Sabine Bergler

This paper summarizes the CLaC submission for the MultiCoNER 2 task which concerns the recognition of complex, fine-grained named entities. We compare two popular approaches for NER, namely Sequence Labeling and Span Prediction. We find that our best Span Prediction system performs slightly better than our best Sequence Labeling system on test data. Moreover

Jinwei Zhang, Alexey Dimov, Chao Li, Hang Zhang

Purpose: To improve the generalization ability of deep learning based predictions of quantitative susceptibility mapping (QSM) from high-pass filtered phase (HPFP) data. Methods: A network fine-tuning step called HP-FINE is proposed, which is based on the high-pass filtering forward model with low-frequency preservation regularization. Several comparisons we

Anthony Saieva, Saikat Chakraborty, Gail Kaiser

This paper introduces a novel code-to-code search technique that enhances the performance of Large Language Models (LLMs) by including both static and dynamic features as well as utilizing both similar and dissimilar examples during training. We present the first-ever code search method that encodes dynamic runtime information during training without the nee

Siyuan Xia, Zhiru Zhu, Chris Zhu, Jinjin Zhao

Pooling and sharing data increases and distributes its value. But since data cannot be revoked once shared, scenarios that require controlled release of data for regulatory, privacy, and legal reasons default to not sharing. Because selectively controlling what data to release is difficult, the few data-sharing consortia that exist are often built around dat

Hong-Yi Zhang, Siyang Ling

We study three astrophysical/cosmological consequences of nonminimal couplings to gravity in wavelike vector dark matter. In the nonrelativistic limit, the nonminimal coupling with the lowest mass dimension leads to effective self-interactions that affect the mass-radius relation of vector solitons, growth of linear perturbations during structure formation,

Robert Ellis, Joshua Bowman, Matthew Allmark, Shanti Bhushan

For tidal energy to become an alternative energy resource to fossil fuels then there needs to be confidence in the predicted performance of horizontal axis tidal turbine devices. A number of computational fluids dynamics packages are now available to assist in the design and testing of new devices. The work in this paper describes a comparative study as a sc

Arun K. Pati, Brij Mohan, Sahil, Samuel L. Braunstein

The traditional quantum speed limits are not attainable for many physical processes, as they tend to be loose and fail to determine the exact time taken by quantum systems to evolve. To address this, we derive exact quantum speed limits for the unitary dynamics of pure-state quantum system that outperform the existing quantum speed limits. Using these exact

Niklas Küchler, Jürgen Horbach

The dynamics of a polydisperse model glassformer are investigated by augmenting molecular dynamics (MD) simulation with swap Monte Carlo (SMC). Three variants of the SMC algorithm are analyzed with regard to convergence and performance. We elucidate the microscopic mechanism responsible for the drastic speed-up of structural relaxation at low temperature. It

Nilaksh Das, Monica Sunkara, Sravan Bodapati, Jinglun Cai

End-to-end ASR models trained on large amount of data tend to be implicitly biased towards language semantics of the training data. Internal language model estimation (ILME) has been proposed to mitigate this bias for autoregressive models such as attention-based encoder-decoder and RNN-T. Typically, ILME is performed by modularizing the acoustic and languag

Ilia M. Mazin, Alexander Yu. Sokolov

We report the development and benchmark of multireference algebraic diagrammatic construction theory (MR-ADC) for the simulations of core-excited states and X-ray absorption spectra (XAS). Our work features an implementation that incorporates core-valence separation into the strict and extended second-order MR-ADC approximations (MR-ADC(2) and MR-ADC(2)-X),

Daniel Boyle, Jugal Kalita

Financial markets are an intriguing place that offer investors the potential to gain large profits if timed correctly. Unfortunately, the dynamic, non-linear nature of financial markets makes it extremely hard to predict future price movements. Within the US stock exchange, there are a countless number of factors that play a role in the price of a company's

Héctor Hernández-Toledo, Edgar Cortes-Suárez, Jose Antonio Vázquez-Mata, Rebecca Nevin

We present a study on the incidence of major mergers and their impact on the triggering of nuclear activity in 47 type I and 236 type II optically-selected AGN from the MaNGA DR15 sample. From an estimate of non-parametric image predictors ($Gini$, M$_{20}$, concentration (C), asymmetry (A), clumpiness (S), S\'ersic index (n), and shape asymmetry($A_S$)) usi

M. Epstein, D. L. Kreher, S. S. Magliveras

A $3$-$(v,\{4,6\},1)$ design is a configuration of $v$ points and a collection of $4$- and $6$-element subsets called blocks, that jointly contain every 3-element subset exactly once. Using an exhaustive computer search on $v\leq 28$ points we investigate the $3$-$(v,\{4,6\},1)$ designs that have a transitive automorphism group and where the blocks of size 6

Fabio Gadducci, Andrea Laretto, Davide Trotta

We present a first-order linear-time temporal logic for reasoning about the evolution of directed graphs. Its semantics is based on the counterpart paradigm, thus allowing our logic to represent the creation, duplication, merging, and deletion of elements of a graph as well as how its topology changes over time. We then introduce a positive normal forms pres

Vaibhav Kumar, Marwa Chafii, A. Lee Swindlehurst, Le-Nam Tran

Integrated sensing and communication (ISAC) is expected to be offered as a fundamental service in the upcoming sixth-generation (6G) communications standard. However, due to the exposure of information-bearing signals to the sensing targets, ISAC poses unique security challenges. In recent years, intelligent reflecting surfaces (IRSs) have emerged as a novel

Shubhendu Shekhar Khali, Fernando Peruani, Debasish Chaudhuri

Active baths are characterized by a non-Gaussian velocity distribution and a quadratic dependence with active velocity $v_0$ of the kinetic temperature and diffusion coefficient. While these results hold in over-damped active systems, inertial effects lead to normal velocity distributions, with kinetic temperature and diffusion coefficient increasing as $\si

Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta, Douglas L. Arnold

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertain

S. A. Moses, C. H. Baldwin, M. S. Allman, R. Ancona

We describe and benchmark a new quantum charge-coupled device (QCCD) trapped-ion quantum computer based on a linear trap with periodic boundary conditions, which resembles a race track. The new system successfully incorporates several technologies crucial to future scalability, including electrode broadcasting, multi-layer RF routing, and magneto-optical tra

Yufei Li, Xiao Yu, Yanchi Liu, Haifeng Chen

Jointly extracting entity pairs and their relations is challenging when working on distantly-supervised data with ambiguous or noisy labels. To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the gro

Mariya Iv. Trukhanova, Pavel Andreev

There is a version of the Landau-Lifshitz equation that takes into account the Coulomb exchange interactions between atoms, expressed by the term $\sim\bm{s}\times\triangle\bm{s}$. On the other hand, ions in the magnetic materials have several valence electrons on the $d$-shell, and therefore the Hamiltonian of many-electron atoms with spins $S>1$ should inc

A. Samartzis, S. Chillal, H. O. Jeschke, D. J. Voneshen

The magnetic excitation spectrum and Hamiltonian of the quantum magnet BaCuTe2O6 is studied by inelastic neutron scattering (INS) and density functional theory (DFT). INS on powder and single crystal samples reveals overlapping spinon continuua - the spectrum of an antiferromagnetic spin-1/2 spin chain - due to equivalent chains running along the a, b, and c

Congwen Lu, Joel A. Paulson

This paper investigates the problem of efficient constrained global optimization of hybrid models that are a composition of a known white-box function and an expensive multi-output black-box function subject to noisy observations, which often arises in real-world science and engineering applications. We propose a novel method, Constrained Upper Quantile Boun

Shanti Bhushan, Greg W Burgreen, Joshua L Bowman, Ian D Dettwiller

The applicability of computational fluid dynamics (CFD) based design tools depend on the accuracy and complexity of the physical models, for example turbulence models, which remains an unsolved problem in physics, and rotor models that dictates the computational cost of rotorcraft and wind/hydro turbine farm simulations. The research focuses on investigation

Bin Gui

These are the lecture notes for a course taught at Tsinghua University in the spring of 2022. In these notes, we develop the basic theory of vertex operator algebras (VOAs) and their conformal blocks using complex-analytic methods. In particular, many well-known subtleties in VOA theory (about formal variables and, e.g., delta-functions) are presented in the

Joel E. Cohen

In 1845, Bertrand conjectured that twice any prime strictly exceeds the next prime. Tchebichef proved Bertrand's postulate in 1850. In 1934, Ishikawa proved a stronger result: the sum of any two consecutive primes strictly exceeds the next prime, except for the only equality $2+3=5$. This observation is a special case of a more general result, perhaps not pr

Tyler Hanks, Baike She, Matthew Hale, Evan Patterson

Model predictive control (MPC) is an optimal control technique which involves solving a sequence of constrained optimization problems across a given time horizon. In this paper, we introduce a category theoretic framework for constructing complex MPC problem formulations by composing subproblems. Specifically, we construct a monoidal category - called Para(C

Shijia Liu, David A. Smith

Brain-computer interfaces (BCI) are an important mode of alternative and augmentative communication for many people. Unlike keyboards, many BCI systems do not display even the 26 letters of English at one time, let alone all the symbols in more complex systems. Using language models to make character-level predictions, therefore, can greatly speed up BCI typ

Andres Mejia, Steven Simon, Jialin Zhang

Based on a result of Makeev, in 2012 Blagojevi\'c and Karasev proposed the following problem: given any positive integers $m$ and $1\leq \ell\leq k$, find the minimum dimension $d=\Delta(m;\ell/k)$ such that for any $m$ mass distributions on $\mathbb{R}^d$, there exist $k$ hyperplanes, any $\ell$ of which equipartition each mass. The $\ell=k$ case is a centr

Yannick J. Franzke, Werner M. Schosser, Fabian Pauly

The implementation of an efficient self-consistent field (SCF) method including both scalar relativistic effects and spin-orbit interaction in density functional theory (DFT) is presented. We make use of Gaussian-type orbitals (GTOs) and all integrals are evaluated in real space. Our implementation supports density functional approximations up to the level o

Marcelo V. Flamarion, Efim Pelinovsky

This study aims to investigate the interactions of solitons with an external force within the framework of the Schamel equation, both asymptotically and numerically. By utilizing asymptotic expansions, we demonstrate that the soliton interaction can be approximated by a dynamical system that involves the soliton amplitude and its crest position. To solve the

Ekta U. Samani, Ashis G. Banerjee

Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. To address this challenge, we propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. The descriptor employs a novel slicing-based approac

Ammar Ahmed Pallikonda Latheef, Sejal Ghate, Zhipeng Hui, Alberto Santamaria-Pang

Resting State Networks (RSNs) of the brain extracted from Resting State functional Magnetic Resonance Imaging (RS-fMRI) are used in the pre-surgical planning to guide the neurosurgeon. This is difficult, though, as expert knowledge is required to label each of the RSNs. There is a lack of efficient and standardized methods to be used in clinical workflows. A

C. E. Fernandez Noa, C. E. Fiore, F. F. S. Filho, B. Wijns

Sequential (or collisional) engines have been put forward as an alternative candidate for the realisation of reliable engine setups. Despite this, the role of the different stages and the influence of the intermediate reservoirs is not well understood. We introduce the idea of conveniently adjusting/choosing intermediate reservoirs at engine devices as a str

K. Kade, K. K. Knudsen, W. Vlemmings, F. Stanley

Massive galaxies are expected to grow through different transformative evolutionary phases where high-redshift starburst galaxies and quasars are examples of such phases. The physical mechanisms driving these phases include companion galaxy interactions, active galactic nuclei feedback, and magnetic fields. Our aim is to characterize the physical properties

Benjamin Davidson, Jamie Brenner, Nimish Pujara

Marine debris pollution is a growing problem impacting aquatic ecosystems, coastal recreation, and human society. Beaches are known to be a sink for debris, and beaching needs to be accounted for in marine debris mass balances. The process of buoyant debris beaching is not sufficiently well understood in order to include this process yet. We develop a simpli

Jingcheng Li, Lina Yao, Binghao Li, Claude Sammut

Human Activity Recognition is an important task in many human-computer collaborative scenarios, whilst having various practical applications. Although uni-modal approaches have been extensively studied, they suffer from data quality and require modality-specific feature engineering, thus not being robust and effective enough for real-world deployment. By uti

Praphull Kumar, Dean M. Townsley

Accreting white dwarfs in Cataclysmic variables (CVs) show short-period (tens of minutes) brightness variations that are consistent with non-radial oscillations similar to gravity (g) modes observed in isolated white dwarfs (WDs). GW Librae, a dwarf nova, was the first CV in which non-radial oscillations were observed and continues to be the best studied acc

Michael Hahn, Mahboubeh Asgari-Targhi, Daniel Wolf Savin

We have measured line widths in active region coronal loops in order to determine whether the non-thermal broadening is anisotropic with respect to the magnetic field direction. These non-thermal velocities are caused by unresolved fluid motions. Our analysis method combines spectroscopic data and a magnetic field extrapolation. We analyzed spectra from the

Zhengyuan Jiang, Jinghuai Zhang, Neil Zhenqiang Gong

A generative AI model can generate extremely realistic-looking content, posing growing challenges to the authenticity of information. To address the challenges, watermark has been leveraged to detect AI-generated content. Specifically, a watermark is embedded into an AI-generated content before it is released. A content is detected as AI-generated if a simil

Shupeng Zhao, Bernhard Rauer, Lorenzo Valzania, Jonathan Dong

Imaging at depth in opaque materials has long been a challenge. Recently, wavefront shaping has enabled significant advance for deep imaging. Nevertheless, most non-invasive wavefront shaping methods require cameras, lack the sensitivity for deep imaging under weak optical signals, or can only focus on a single "guidestar". Here, we retrieve the transmission

Pulkit Garg, Timothy J. Rupert

Amorphous grain boundary complexions act as toughening features within a microstructure because they can absorb dislocations more efficiently than traditional grain boundaries. This toughening effect should be a strong function of the local internal structure of the complexion, which has recently been shown to be determined by grain boundary crystallography.

Yu Miyazaki

I present a novel equivariant neural network architecture for the large-scale spin dynamics simulation of the Kondo lattice model. This neural network mainly consists of tensor-product-based convolution layers and ensures two equivariances: translations of the lattice and rotations of the spins. I implement equivariant neural networks for two Kondo lattice m

Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed, Navid Ayoobi

In this work, we study literature in Explainable AI and Safe AI to understand poisoning of neural models of code. In order to do so, we first establish a novel taxonomy for Trojan AI for code, and present a new aspect-based classification of triggers in neural models of code. Next, we highlight recent works that help us deepen our conception of how these mod

Huan-Yu Bi, Manfred Kraus, Minos Reinartz, Malgorzata Worek

In view of the persisting tension between theoretical predictions and the LHC data for the $pp \to t\bar{t}W^\pm$ production process, we present the state-of-the-art full off-shell NLO QCD result for $pp \to t\bar{t}W^+\, j+X$. We concentrate on the multi-lepton decay channel at the LHC with $\sqrt{s}= 13$ TeV. In our calculation off-shell top quarks and gau

Zirui Deng, Netanel Raviv

Private inference refers to a two-party setting in which one has a model (e.g., a linear classifier), the other has data, and the model is to be applied over the data while safeguarding the privacy of both parties. In particular, models in which the weights are quantized (e.g., to 1 or -1) gained increasing attention lately, due to their benefits in efficien

Miguel Gonçalves, Bruno Amorim, Flavio Riche, Eduardo V. Castro

We demonstrate that quasiperiodicity can radically change the ground state properties of 1D moir\'e systems with respect to their periodic counterparts. By studying an illustrative example we show that while narrow bands play a significant role in enhancing interactions both for commensurate and incommensurate structures, only quasiperiodicity is able to ext

Yasaman Moradi, Jerry SH Lee, Andrea M. Armani

The spatio-temporal organization of proteins within the cell membrane can affect numerous biological functions, including cell signaling, communication, and transportation. Deviations from normal spatial arrangements have been observed in various diseases, and better understanding this process is a key stepping-stone to advancing development of clinical inte

Sean Vaskov, Rien Quirynen, Marcel Menner, Karl Berntorp

This paper addresses the trajectory-tracking problem under uncertain road-surface conditions for autonomous vehicles. We propose a stochastic nonlinear model predictive controller (SNMPC) that learns a tire--road friction model online using standard automotive-grade sensors. Learning the entire tire--road friction model in real time requires driving in the n

Bhupesh Bishnoi

Materials informatics, data-enabled investigation, is a "fourth paradigm" in materials science research after the conventional empirical approach, theoretical science, and computational research. Materials informatics has two essential ingredients: fingerprinting materials proprieties and the theory of statistical inference and learning. We have researched t

Ta-Chung Chi, Ting-Han Fan, Alexander I. Rudnicky, Peter J. Ramadge

Unlike recurrent models, conventional wisdom has it that Transformers cannot perfectly model regular languages. Inspired by the notion of working memory, we propose a new Transformer variant named RegularGPT. With its novel combination of Weight-Sharing, Adaptive-Depth, and Sliding-Dilated-Attention, RegularGPT constructs working memory along the depth dimen

Jingfan Meng, Ziheng Liu, Yiwei Wang, Jun Xu

LT (Luby transform) codes are a celebrated family of rateless erasure codes (RECs). Most of existing LT codes were designed for applications in which a centralized encoder possesses all message blocks and is solely responsible for encoding them into codewords. Distributed LT codes, in which message blocks are physically scattered across multiple different lo

Andrea Di Carli, Christopher Parsonage, Arthur La Rooij, Lennart Koehn

Single-atom imaging resolution of many-body quantum systems in optical lattices is routinely achieved with quantum-gas microscopes. Key to their great versatility as quantum simulators is the ability to use engineered light potentials at the microscopic level. Here, we employ dynamically varying microscopic light potentials in a quantum-gas microscope to stu

Danilo Ribeiro, Omid Abdar, Jack Goetz, Mike Ross

Frame semantic parsing is an important component of task-oriented dialogue systems. Current models rely on a significant amount training data to successfully identify the intent and slots in the user's input utterance. This creates a significant barrier for adding new domains to virtual assistant capabilities, as creation of this data requires highly special

Charles D. Young, Patrick T. Corona, Anukta Datta, Matthew E. Helgeson

An overarching challenge in rheology is to develop constitutive models for complex fluids for which we lack accurate first principles theory. A further challenge is that most experiments probing dynamical structure and rheology do so only in very simple flow fields that are not characteristic of the complex deformation histories experienced by material in a

Anderson Luis Albuquerque de Araujo, Edir Junior Ferreira Leite

In this work, we will present variants Fixed Point Theorem for the affine and classical contexts, as a consequence of general Brouwer's Fixed Point Theorem. For instance, the affine results will allow working on affine balls, which are defined through the affine $L^{p}$ functional $\mathcal{E}_{p,\Omega}^p$ introduced by Lutwak, Yang and Zhang in the work $\

Timm Faulwasser, Jonas Kirchhoff, Volker Mehrmann, Friedrich Philipp

We study the problem of state transition on a finite time interval with minimal energy supply for linear port-Hamiltonian systems. While the cost functional of minimal energy supply is intrinsic to the port-Hamiltonian structure, the necessary conditions of optimality resulting from Pontryagin's maximum principle may yield singular arcs. The underlying reaso

Andras Gaspar, Schuyler Grace Wolff, George H. Rieke, Jarron M. Leisenring

Planetary debris disks around other stars are analogous to the Asteroid and Kuiper belts in the Solar System. Their structure reveals the configuration of small bodies and provides hints for the presence of planets. The nearby star Fomalhaut hosts one of the most prominent debris disks, resolved by HST, Spitzer, Herschel, and ALMA. Images of this system at m

Hazal Türkmen, Oğuz Dikenelli, Cenk Eraslan, Mehmet Cem Çallı

In recent years, major advancements in natural language processing (NLP) have been driven by the emergence of large language models (LLMs), which have significantly revolutionized research and development within the field. Building upon this progress, our study delves into the effects of various pre-training methodologies on Turkish clinical language models'

Angelica Sofia Valeriani

Surveillance capitalism is a concept that describes the practice of collecting and analyzing massive amounts of user data for the purpose of targeted advertising and other forms of monetization. The phenomenon has become increasingly prevalent in recent years, with tech companies like Google and Facebook using users' personal information to deliver personali

Max Fathi, Dan Mikulincer, Yair Shenfeld

We establish sufficient conditions for the existence of globally Lipschitz transport maps between probability measures and their log-Lipschitz perturbations, with dimension-free bounds. Our results include Gaussian measures on Euclidean spaces and uniform measures on spheres as source measures. More generally, we prove results for source measures on manifold

Francisco Romero, Caleb Winston, Johann Hauswald, Matei Zaharia

Advances in ML have motivated the design of video analytics systems that allow for structured queries over video datasets. However, existing systems limit query expressivity, require users to specify an ML model per predicate, rely on complex optimizations that trade off accuracy for performance, and return large amounts of redundant and low-quality results.

Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He

In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration trade-off in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confide

Mingen Pan

Prior research primarily examined differentially-private continual releases against data streams, where entries were immutable after insertion. However, most data is dynamic and housed in databases. Addressing this literature gap, this article presents a methodology for achieving differential privacy for continual releases in dynamic databases, where entries

Xiangtian Zheng, Le Xie, Kiyeob Lee, Dan Fu

The reliability of energy systems is strongly influenced by the prevailing climate conditions. With the increasing prevalence of renewable energy sources, the interdependence between energy and climate systems has become even stronger. This study examines the impact of different spatial resolutions in climate modeling on energy grid reliability assessment, w

Ivan Gnusov, Stepan Baryshev, Helgi Sigurðsson, Kirill Sitnik

External driving of spinor degrees of freedom by magnetic or optical fields in quantum systems underpin many applications ranging from nuclear magnetic resonance to coherent state control in quantum computing. Although spinor polariton condensates are offering a flexible platform for spinoptronic applications, strong inter-particle interactions limit their s

Anna Skorobogatova

Consider an $m$-dimensional area minimizing mod$(2Q)$ current $T$, with $Q\in\mathbb{N}$, inside a sufficiently regular Riemannian manifold of dimension $m + 1$. We show that the set of singular density-$Q$ points with a flat tangent cone is countably $(m-2)$-rectifiable and has locally finite $(m-2)$-dimensional upper Minkowski content. This complements the

Adeline P. Guthrie, Christopher T. Franck

Probability predictions are essential to inform decision making across many fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, i.e., spread out enough to be informative for decision making. However, there is a fundamental tension between calibration and boldness, since calibration metrics can be high when predict

Qinan Wang, Armin Rest, Georgios Dimitriadis, Ryan Ridden-harper

We present high-cadence ultraviolet through near-infrared observations of the Type Ia supernova (SN Ia) 2023bee in NGC~2708 ($D = 32 \pm 3$ Mpc), finding excess flux in the first days after explosion relative to the expected power-law rise from an expanding fireball. This deviation from typical behavior for SNe Ia is particularly obvious in our 10-minute cad

Qianhong Zhao, Gang Tao

This report presents the results of a simulation study of the linear model and bilinear model approximations of the Koopman system model of the nonlinear utility functions in optimal control of a 3-robot system. In such a control problem, the nonlinear utility functions are maximized to achieve the control objective of moving the robots to their target posit

Gang Tao, Qianhong Zhao

This paper presents a study of the Koopman operator theory and its application to optimal control of a multi-robot system. The Koopman operator, while operating on a set of observation functions of the state vector of a nonlinear system, produces a set of dynamic equations which, through a dynamic transformation, form a new dynamic system. As an operator, it

António P. Morais, Vinícius Oliveira, António Onofre, Roman Pasechnik

We study a simple extension of the Standard Model featuring a dark sector with an ultralight pseudo Nambu-Goldstone boson as dark matter candidate. We focus on the mass range $\mathcal{O}(10^{-20} - 10^{-10})$ eV, relevant for strong gravity applications, and explore its production and evolution in the early Universe. The model is formulated in such a way th

Zheng Zhang, Yuanwei Liu, Zhaolin Wang, Xidong Mu

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing of the analog beamformer, the baseband

Surya T. Sathujoda, Soham M. Sheth

The global push to advance Carbon Capture and Sequestration initiatives and green energy solutions, such as geothermal, have thrust new demands upon the current state-of-the-art subsurface fluid simulators. The requirement to be able to simulate a large order of reservoir states simultaneously, in a short period of time, has opened the door of opportunity fo

Cem Eröncel, Jay Hubisz, Seung. J. Lee, Gabriele Rigo

We describe cosmological solutions of the holographic dilaton with the aim of exploring alternatives to the commonly studied thermal Randall-Sundrum phase transition. It is well known that the thermal transition is typically strongly first order, with the requirement of a perturbative 5D gravity theory obstructing completion of the transition. This thermal t

Nicolò Cangiotti, Alessandro Linzi

Let $\mathcal{P}$ be the set of points of a finite-dimensional projective space over a local field $F$, endowed with the topology $\tau$ naturally induced from the canonical topology of $F$. Intuitively, continuous incidence abelian group structures on $\mathcal{P}$ are abelian group structures on $\mathcal{P}$ preserving both the topology $\tau$ and the inc

Sayak Datta

We compute the rate of change of mass and angular momentum of a black hole, namely tidal heating, in an eccentric orbit. The change is caused due to the tidal field of the orbiting companion. We compute the result for both the spinning and non-spinning black holes in the leading order of the mean motion, namely $\xi$. We demonstrate that the rates get enhanc

Jasmin Graf, Joshua Baxter, Sanchar Sharma, Silvia Viola Kusminskiy

We extend the Finite-Difference Time-Domain method to treat dispersive magnetic media by incorporating magneto-optical effects through a frequency-dependent permittivity tensor. For benchmarking our method, we consider the light scattering on a magnetic sphere in the Mie regime. We first derive the analytical scattering expressions which predict a peak broad

C. Breu, H. Peter, R. Cameron, S. K. Solanki

Vortex flows have been found in the photosphere, chromosphere and low corona in observations and simulations. It has been suggested that vortices play an important role for channeling energy and plasma into the corona, but the impact of vortex flows on the corona has not directly been studied in a realistic setup. We investigate the role vortices play for co

A. C. Lehum

This study examines on-shell supersymmetry breaking in the Abelian $\mathcal{N}=1$ Chern-Simons-matter model within a three-dimensional spacetime. The classical Lagrangian is scale-invariant, but two-loop radiative corrections to the effective potential break this symmetry, along with gauge and on-shell supersymmetry. To investigate this issue, the Renormali

Megan Newsome, Iair Arcavi, D. A. Howell, Jamison Burke

AT 2020mot is a typical UV/optical tidal disruption event (TDE) with no radio or X-ray signatures in a quiescent host. We find an i-band excess and re-brightening along the decline of the light curve which could be due to two consecutive dust echoes from a TDE. We model our observations following van Velzen et al. (2016) and find that the near-infrared light

Mohsin Iqbal, Nathanan Tantivasadakarn, Ruben Verresen, Sara L. Campbell

Non-Abelian topological order (TO) is a coveted state of matter with remarkable properties, including quasiparticles that can remember the sequence in which they are exchanged. These anyonic excitations are promising building blocks of fault-tolerant quantum computers. However, despite extensive efforts, non-Abelian TO and its excitations have remained elusi

Jose Barrientos, Adolfo Cisterna

This paper investigates the integrability properties of Einstein's theory of gravity in the context of accelerating Newman-Unti-Tamburino (NUT) spacetimes by utilizing Ernst's description of stationary and axially symmetric electrovacuum solutions. We employ Ehlers transformations, Lie point symmetries of the Einstein field equations, to efficiently endorse

A. Bahramian, E. Tremou, A. J. Tetarenko, J. C. A. Miller-Jones

Over the past decade, observations of relativistic outflows from outbursting X-ray binaries in the Galactic field have grown significantly. In this work, we present the first detection of moving and decelerating radio-emitting outflows from an X-ray binary in a globular cluster. MAXI J1848-015 is a recently discovered transient X-ray binary in the direction

Anke Biekötter, Benjamin D. Pecjak, Darren J. Scott, Tommy Smith

The choice of an electroweak (EW) input scheme is an important component of perturbative calculations in Standard Model Effective Field Theory (SMEFT). In this paper we perform a systematic study of three different EW input schemes in SMEFT, in particular those using the parameter sets $\{M_W, \, M_Z, \, G_F\}$, $\{M_W, \, M_Z, \, \alpha\}$, or $\{\alpha, \,

Fabio Rigamonti, Massimo Dotti, Stefano Covino, Francesco Haardt

From a purely photometric perspective galaxies are generally decomposed into a bulge+disc system, with bulges being dispersion-dominated and discs rotationally-supported. However, recent observations have demonstrated that such a framework oversimplifies complexity, especially if one considers galaxy kinematics. To address this issue we introduced with the G

Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih

Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels (CWoLa), a weakly-supervised anomaly detection method, to identify cold stellar streams within the more than one billion Milk

R. Scott Barrows, Julia M. Comerford, Daniel Stern, Roberto J. Assef

Pairs of galaxies hosting active galactic nuclei (AGN) are powerful probes of merger-driven supermassive black hole (SMBH) growth as they can resolve individual AGN and trace mergers over a large range of physical separations. To exploit this on a large scale for the first time for both obscured and unobscured AGN, we use photometric redshifts of AGN selecte

Yahui Li, Pablo Sala, Frank Pollmann

We investigate the phenomenon of Hilbert space fragmentation (HSF) in open quantum systems and find that it can stabilize highly entangled steady states. For concreteness, we consider the Temperley-Lieb model, which exhibits quantum HSF in an entangled basis, and investigate the Lindblad dynamics under two different couplings. First, we couple the system to

Maria Flors Mor-Ruiz, Wolfgang Dür

We consider entanglement-based quantum networks, where multipartite entangled resource states are distributed and stored among the nodes and locally manipulated upon request to establish the desired target configuration. Separating the generation process from the requests enables a pre-preparation of resources, hence a reduced network latency. It also allows

Andrea Kulier, Bianca Poggianti, Stephanie Tonnesen, Rory Smith

Ram pressure stripping of satellite galaxies is thought to be a ubiquitous process in galaxy clusters, and a growing number of observations reveal satellites at different stages of stripping. However, in order to determine the fate of any individual galaxy, we turn to predictions from either simulations or analytic models. It is not well-determined whether s

Leonard Weihao Cao, Chen Wu, Rajarshi Bhattacharyya, Ruolun Zhang

Microwave impedance microscopy (MIM) is a near-field imaging technique that has been used to visualize the local conductivity of materials with nanoscale resolution across the GHz regime. In recent years, MIM has shown great promise for the investigation of topological states of matter, correlated electronic states and emergent phenomena in quantum materials

Aleksandr Chatrchyan, Cem Eröncel, Matthias Koschnitzke, Géraldine Servant

Axion-like particles (ALPs) are leading candidates to explain the dark matter in the universe. Their production via the misalignment mechanism has been extensively studied for cosine potentials characteristic of pseudo-Nambu-Goldstone bosons. In this work we investigate ALPs with non-periodic potentials, which allow for large misalignment of the field from t

D. J. Fritzewski, S. A. Barnes, J. Weingrill, T. Granzer

Cool star rotation periods have become an important tool in determining ages of open clusters. We aim to estimate the age of the open cluster NGC 2281 based on the rotational properties of its low-mass members. Previous age estimates for this open cluster range from 275 Myr to 630 Myr. Based on an eight month-long photometric time series obtained at the 1.2

Alfredo Luminari, Andrea Marinucci, Stefano Bianchi, Barbara de Marco

We report on the 2019 XMM-Newton+NuSTAR monitoring campaign of the Seyfert galaxy NGC 2992, observed at one of its highest flux levels in the X-rays. The time-averaged spectra of the two XMM-Newton orbits show Ultra Fast Outflows (UFOs) absorbing structures above 9 keV with $> 3 \sigma$ significance. A detailed investigation of the temporal evolution on a $\

Michaela Hirschmann, Stephane Charlot, Rachel S. Somerville

Optical emission-line ratios are traditionally used to estimate gas metallicities from observed galaxy spectra. While such estimators have been calibrated primarily at low redshift, they are commonly used to study high-redshift galaxies, where their applicability may be questioned. We use comprehensive emission-line catalogues of galaxies from the IllustrisT