March 2023 arXiv papers — page 142

Aayush Garg, Renzo Degiovanni, Mike Papadakis, Yves Le Traon

With the increasing release of powerful language models trained on large code corpus (e.g. CodeBERT was trained on 6.4 million programs), a new family of mutation testing tools has arisen with the promise to generate more "natural" mutants in the sense that the mutated code aims at following the implicit rules and coding conventions typically produced by pro

Natasha Dobrinen, Andy Zucker

We develop infinite-dimensional Ramsey theory for Fra\"iss\'e limits of finitely constrained free amalgamation classes in finite binary languages. We show that our approach is optimal and in particular, recovers the exact big Ramsey degrees proved in [2] for these structures. A crucial step in the work develops the new notion of an A.3(2)-ideal and shows tha

Yuchen Li, Yuanzhi Li, Andrej Risteski

While the successes of transformers across many domains are indisputable, accurate understanding of the learning mechanics is still largely lacking. Their capabilities have been probed on benchmarks which include a variety of structured and reasoning tasks -- but mathematical understanding is lagging substantially behind. Recent lines of work have begun stud

Robert T. Collins

We present a simple unsupervised method for learning an encoder mapping short 3D pose sequences into embedding vectors suitable for sequence-to-sequence alignment by dynamic time warping. Training samples consist of temporal windows of frames containing 3D body points such as mocap markers or skeleton joints. A light-weight, 3-layer encoder is trained using

Andres Sixtos, Aida Wofford, Andreas A. C. Sanders, Antonio Peimbert

The nebular He II {\lambda}1640 emission line is observed in star forming galaxies out to large distances and can be used to constrain the properties of sources of He+ ionizing photons. For this purpose, it is crucial to understand which are the main stellar sources of these photons. In some nearby metal poor starburst galaxies, nebular He II {\lambda}4686 (

Facundo Carrillo, Elaine Hu

Maximum extractable value (MEV) has been extensively studied. In most papers, the researchers have worked with the Ethereum blockchain almost exclusively. Even though, Ethereum and other blockchains have dynamic gas prices this is not the case for all blockchains; many of them have fixed gas prices. Extending the research to other blockchains with fixed gas

S. Chakrabarty, J. R. Gleason, Y. Han, A. T. Hipp

We investigate reentrant and dielectric loaded cavities for the purpose of extending the range of axion cavity haloscopes to lower masses, below the range where the Axion Dark Matter eXperiment (ADMX) has already searched. Reentrant and dielectric loaded cavities were simulated numerically to calculate and optimize their form factors and quality factors. A p

Max Cohen, Calin Belta

This paper presents an adaptive control approach for uncertain nonlinear systems subject to safety constraints that allows for modularity in the selection of the parameter estimation algorithm. Such modularity is achieved by unifying the concepts of input-to-state stability (ISS) and input-to-state safety (ISSf) via control Lyapunov functions (CLFs) and cont

Qizhen Lan, Qing Tian

Deep learning models have demonstrated remarkable success in object detection, yet their complexity and computational intensity pose a barrier to deploying them in real-world applications (e.g., self-driving perception). Knowledge Distillation (KD) is an effective way to derive efficient models. However, only a small number of KD methods tackle object detect

Christopher DuPre

In this paper we develop a quantitative Harris theorem with effective control over the constants. A benefit of our methodology is the decoupling of the small set and Lyapunov-Foster Drift conditions. Our methodology allows any small set and any set in the Lyapunov-Foster condition as long as the second satisfies a so-called ``quantitative petiteness" conditi

Raz Lapid, Eylon Mizrahi, Moshe Sipper

Adversarial attacks on deep learning models have received increased attention in recent years. Work in this area has mostly focused on gradient-based techniques, so-called 'white-box' attacks, where the attacker has access to the targeted model's internal parameters; such an assumption is usually untenable in the real world. Additionally, some attacks use th

Ilya Gekhtman, Arie Levit

We show that discrete stationary random subgroups of isometry groups of Gromov hyperbolic spaces have full limit sets as well as critical exponents bounded from below. This information is used to answer a question of Gelander and show that a rank one locally symmetric space for which the bottom of the spectrum of the Laplace-Beltrami operator is the same as

Hugo E. Ramirez, Rafael Serrano

We study investment and insurance demand decisions for an agent in a theoretical continuous-time expected utility maximization model that combines risky assets with an (exogenous) insurable background risk. This risk takes the form of a jump-diffusion process with negative jumps in the return rate of the (self-financed) wealth. The main distinctive feature o

Takuya Hatomura

We study errors caused by digitization of shortcuts to adiabaticity by counterdiabatic driving. We find possibility of error scaling $\mathcal{O}(M^{-2})$ with the number of time slices $M$, whereas worse error scaling $\mathcal{O}(M^{-1})$ is predicted in the conventional theory of the first-order Suzuki-Trotter decomposition. We point out this possibility

Bettina Berendt, Özgür Karadeniz, Sercan Kıyak, Stefan Mertens

Libraries are increasingly relying on computational methods, including methods from Artificial Intelligence (AI). This increasing usage raises concerns about the risks of AI that are currently broadly discussed in scientific literature, the media and law-making. In this article we investigate the risks surrounding bias and unfairness in AI usage in classific

Wajdi Aljedaani, Mohamed Wiem Mkaouer, Anthony Peruma, Stephanie Ludi

To ensure the quality of a software system, developers perform an activity known as unit testing, where they write code (known as test cases) that verifies the individual software units that make up the system. Like production code, test cases are subject to bad programming practices, known as test smells, that hurt maintenance activities. An essential part

Nathan M. Dunfield, Sherry Gong, Thomas Hockenhull, Marco Marengon

For a ribbon knot, it is a folk conjecture that the rank of its knot Floer homology must be 1 modulo 8, and another folk conjecture says the same about reduced Khovanov homology. We give the first counter-examples to both of these folk conjectures, but at the same time present compelling evidence for new conjectures that either of these homologies must have

Costel Peligrad

An action of a compact, in particular finite group on a C*-algebra is called properly outer if no automorphism of the group that is distinct from identity is implemented by a unitary element of the algebra of local multipliers of the C*-algebra. In this paper I define the notion of strictly outer action (similar to the definition for von Neumann factors in [

Aina Ferrà, Gloria Cecchini, Fritz-Pere Nobbe Fisas, Carles Casacuberta

Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. By contrast, topological data analysis (T

Serik Sagitov, Alexey Lindo, Yerakhmet Zhumayev

Branching processes in a varying environment encompass a wide range of stochastic demographic models, and their complete understanding in terms of limit behaviour poses a formidable research challenge. In this paper, we conduct a thorough investigation of such processes within a continuous-time framework, assuming that the reproduction law of individuals adh

Marc Arnaudon, Xue-Mei Li, Benedikt Petko

We show that the generalized Ricci tensor of a weighted complete Riemannian manifold can be retrieved asymptotically from a scaled metric derivative of Wasserstein 1-distances between normalized weighted local volume measures. As an application, we demonstrate that the limiting coarse curvature of random geometric graphs sampled from Poisson point process wi

Matthew Kendall

We extend the crossing-change maps between grid complexes, defined by Ozsv\'ath-Szab\'o-Stipsicz, to filtered grid complexes and give a combinatorial formulation of the Alishahi-Eftekhary $\mathfrak{l}(K)$ knot invariant.

Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan

Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant attention from society. As a result, many individuals have become interested in related resources and are seeking to uncover the background and secrets behind its impressive performance. In fact, ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial

Jared J. Beard, R. Michael Butts, Yu Gu

Due to the complexity of many decision making problems, tree search algorithms often have inadequate information to produce accurate transition models. This results in ambiguities (uncertainties for which there are multiple plausible models). Faced with ambiguities, robust methods have been used to produce safe solutions--often by maximizing the lower bound

Yuri Bakhtin, Douglas Dow

For directed polymers, the shape function computes the limiting average energy accrued by paths with a given average slope. We prove that, for a large family of directed polymer models in discrete time and continuous space in dimension $1+1$, for positive and zero temperature, the shape function is differentiable with respect to the slope on the entire real

Md Deluair Hossen

In international trade, firms face lengthy ordering-producing-delivery times and make shipping frequency decisions based on the per-shipment costs and financing costs. In this paper, I develop a model of importer-exporter procurement where the importer procures international inputs from exporting firms in developing countries. The exporters are credit constr

Hannah Rose Kirk, Wenjie Yin, Bertie Vidgen, Paul Röttger

Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS).

Tianyuan Cai, Aleena Gertrudes Niklaus, Michael Kraley, Bernard Kerr

Digital reading applications give readers the ability to customize fonts, sizes, and spacings, all of which have been shown to improve the reading experience for readers from different demographics. However, tweaking these text features can be challenging, especially given their interactions on the final look and feel of the text. Our solution is to offer re

Louis-Gregory Strolger, Jamila Pegues, Tegan King, Nathan Miles

With the start of a new Great Observatories era, there is renewed concern that the demand for these forefront facilities, through proposal pressure, will exceed conventional peer-review management's capacity for ensuring an unbiased and efficient selection. There is need for new methods, strategies, and tools to facilitate those reviews. Here, we describe PA

Domenic Rosati, Brian Simboli

Disagreements help drive science. How does one identify and track them in scholarly literature? We ask the research question will searching review articles (RA) will be more time efficient for this purpose than searching non-review ones (NRA). This is especially so to the extent NRAs exceed RAs in a given field. We also discuss a metric for whether RAs repor

Eivind Meyer, Lars Frederik Peiss, Matthias Althoff

Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural motion planners for autonomous vehicles. Instead, this work proposes to learn which features are task-relevant. Given its immediate relevance to motion planning, our proposed archite

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg

This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time- and spatial-scales, resulting in com

Pasquale Avella, Alice Calamita, Laura Palagi

This paper analyzes the performance of five well-known off-the-shelf optimization solvers on a set of congested capacitated facility location problems formulated as mixed-integer conic programs (MICPs). We aim to compare the computational efficiency of the solvers and examine the solution strategies they adopt when solving instances with different sizes and

D. Thirumalai, Abhinaw Kumar, Debayan Chakraborty, John E. Straub

The well known phenomenon of phase separation in synthetic polymers and proteins has become a major topic in biophysics because it has been invoked as a mechanism of compartment formation in cells, without the need for membranes. Most of the coacervates (or condensates) are composed of Intrinsically Disordered Proteins (IDPs) or regions that are structureles

Christopher A. Mizzi, Satya K. Kushwaha, Priscila F. S. Rosa, W. Adam Phelan

Beyond the quantum limit, many-body effects are expected to induce unusual electronic phase transitions. Materials possessing metallic ground states with strong interactions between localized and itinerant electronic states are natural candidates for the realization of such quantum phases. However, the electronic correlations responsible for increasing the l

M. Yadav, C. Hansel, B. Naranjo, G. Andonian

The characterization of plasma wakefield acceleration experiments using emitted photons from betatron radiation requires numerical models in support of instrumentation of single-shot, double-differential angular-energy spectra. Precision characterization for relevant experiments necessitates covering a wide energy range extending from tens of keV through 10~

Yue Meng, Sai Vemprala, Rogerio Bonatti, Chuchu Fan

Large-scale self-supervised models have recently revolutionized our ability to perform a variety of tasks within the vision and language domains. However, using such models for autonomous systems is challenging because of safety requirements: besides executing correct actions, an autonomous agent must also avoid the high cost and potentially fatal critical m

P. León, E. Fuenmayor, E. Contreras

We present a detailed analysis of a general relativistic static spherical symmetric distribution in which both the radial and tangential pressures follow a master polytropic equation of state that generalizes the standard treatment and avoids the appearance of singularities in the system. In particular, we find the corresponding Lane-Emden equation and integ

Rong Zhang, Vishvesh Kumar, Michael Ruzhansky

In this paper, we establish Liouville type results for semilinear subelliptic systems associated with the sub-Laplacian on the Heisenberg group $\mathbb{H}^{n}$ involving two different kinds of general nonlinearities. The main technique of the proof is the method of moving planes combined with some integral inequalities replacing the role of maximum principl

Joshua R. Loftus, Lucius E. J. Bynum, Sakina Hansen

Explaining artificial intelligence or machine learning models is increasingly important. To use such data-driven systems wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we develop Causal Dependence Plots (CDPs) to visualize how one variable--an outcome--depends on changes in another

Christopher Funk, Yanxi Liu

A deep learning model, EscherNet 101, is constructed to categorize images of 2D periodic patterns into their respective 17 wallpaper groups. Beyond evaluating EscherNet 101 performance by classification rates, at a micro-level we investigate the filters learned at different layers in the network, capable of capturing second-order invariants beyond edge and c

Alessio Franchi, Francesco Tarantelli

We consider an open quantum system composed of a $(1+1)$-dimensional Kitaev ring coupled with the environment via $n$ particle-loss dissipators in a \textit{sunburst} geometry. We describe the out-of-equilibrium dynamics of the whole apparatus in terms of Lindblad master equations and focus on the scaling behavior of the Liovillian gap $\Delta_\lambda$ with

Sanghyuk Moon, Woong-Tae Kim, Chang-Goo Kim, Eve C. Ostriker

Nuclear rings at the centers of barred galaxies are known to be strongly magnetized. To explore the effects of magnetic fields on star formation in these rings and nuclear gas flows, we run magnetohydrodynamic simulations in which there is a temporally-constant magnetized inflow to the ring, representing a bar-driven inflow. The mass inflow rate is $1\,M_\od

Marilia D. V. Braga, Leonie R. Brockmann, Katharina Klerx, Jens Stoye

Two genomes over the same set of gene families form a canonical pair when each of them has exactly one gene from each family. Different distances of canonical genomes can be derived from a structure called breakpoint graph, which represents the relation between the two given genomes as a collection of cycles of even length and paths. Then, the breakpoint dis

Qingyi Wang, Shenhao Wang, Yunhan Zheng, Hongzhou Lin

Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by both numeric data and urban imagery, thus necessitating a synergetic framework to combine them. This study creates a th

Xin Li

Cognition is not passive data accumulation but the active resolution of uncertainty through symmetry breaking. This paper argues that both cognitive evolution and development unfold via sequential symmetry-breaking transitions that disrupt innate regularities across space, time, self, and representation. First, spatial symmetry is broken through bilateral bo

Ronald Wilhelm, Kenneth Carrell, Hannah H. Means, Adam Popowicz

We present analysis of the RR Lyrae star, LS Her and confirm the previously reported modulation to its Blazhko cycles. We performed Fourier analysis on two sectors (Sector 24 & 25) of data from the Transiting Exoplanet Survey Satellite (TESS) spanning 53 days. We find LS Her to have a primary pulsation period of 0.2308 d and a Blazhko period of 12.7 d in kee

Shantanu Ghosh, Zheng Feng, Jiang Bian, Kevin Butler

Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects (ITE). We present the Doubly Robust Va

Ethan Ross

A stratified space is a kind of topological space together with a partition into smooth manifolds. These kinds of spaces naturally arise in the study of singular algebraic varieties, symplectic reduction, and differentiable stacks. In this paper, we introduce a particular class of stratified spaces called stratified vector bundles, and provide an alternate c

Adam D. Jozefiak, F. Bruce Shepherd

Good approximations have been attained for the sparsest cut problem by rounding solutions to convex relaxations via low-distortion metric embeddings. Recently, Bryant and Tupper showed that this approach extends to the hypergraph setting by formulating a linear program whose solutions are so-called diversities which are rounded via diversity embeddings into

Ziyu Chen, Wei Zhu

We study the implicit bias of gradient flow on linear equivariant steerable networks in group-invariant binary classification. Our findings reveal that the parameterized predictor converges in direction to the unique group-invariant classifier with a maximum margin defined by the input group action. Under a unitary assumption on the input representation, we

Shuyang Cao, Daniel Boyanovsky

We obtain the non-equilibrium condensate of the Chern Simons density induced by a misaligned homogeneous coherent axion field in linear response. The Chern-Simons dynamical susceptibility is simply related to the axion self-energy, a result that is valid to leading order in the axion coupling but to all orders in the couplings of the gauge fields to other fi

Thanasis Karakasis, George Koutsoumbas, Eleftherios Papantonopoulos

We study the geodesic motion of uncharged particles in the background of a magnetically charged Euler-Heisenberg black hole with a scalar hair. The spacetime can be asymptotically (A)dS or flat and we find, analysing the behavior of the effective potential of the radial motion that in all cases there exist stable and unstable orbits. Performing numerical int

Seth Neel

We introduce a new private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired by the common situation where a data analyst wants to perform a set of $l$ regressions while preserving privacy, where the features $X$ are shared across all $l$ regressions, and each regression $i \in [l]$ has a different vector of outcomes $y_i$.

H. Utsunomiya, S. Goriely, M. Kimura, N. Shimizu

Photoneutron emission cross sections were measured for $^{13}$C below $2n$ threshold using quasi-monochromatic $\gamma$-ray beams produced in laser Compton-scattering at the NewSUBARU synchrotron radiation facility. The data show fine structures in the low-energy tail of the giant-dipole resonance; the integrated strength of the fine structure below 18~MeV i

Qin Yang, Ramviyas Parasuraman

Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayes

Sharief Saleh, Qamar Bader, Mohamed Elhabiby, Aboelmagd Noureldin

Fifth-generation (5G) networks are expected to provide high-precision positioning estimation utilizing mmWave signals in urban and downtown areas. In such areas, 5G base stations (BSs) will be densely deployed, allowing for line-of-sight (LOS) communications between the user equipment (UE) and multiple BSs at the same time. Having access to a plethora of mea

Chris van der Ploeg, Michiel Braat, Beatrice Masini, Jochem Brouwer

The introduction of highly automated vehicles on the public road may improve safety and comfort, although its success will depend on social acceptance. This requires trajectory planning methods that provide safe, proactive, and comfortable trajectories that are risk-averse, take into account predictions of other road users, and comply with traffic rules, soc

Rostislav Grigorchuk, Jean-Francois Quint, Asif Shaikh

We investigate a multivariate growth series $\Gamma_L({\bf z}), {\bf z} \in \mathbb{C}^d$ associated with a regular language $L$ over an alphabet of cardinality $d.$ Our focus is on languages coming from subgroups of the free group and from subshifts of finite type. We develop a mechanism for computing the rate of growth $\varphi_L({\bf r})$ of $L$ in the di

Rahim Moosa

Motivated by the study of meromorphic vector fields, a model theory of "compact complex manifolds equipped with a generic derivation" is here proposed. This is made precise by the notion of a differential CCM-structure. A first-order axiomatisation of existentially closed differential CCM-structures is given. The resulting theory, DCCM, is a common expansion

Thomas Lang

In many applications of X-ray computed tomography, an unsupervised segmentation of the reconstructed 3D volumes forms an important step in the image processing chain for further investigation of the digitized object. Therefore, the goal is to train a clustering algorithm on the volume, which produces a voxelwise classification by assigning a cluster index to

Martin Sustek, Samik Sadhu, Lukas Burget, Hynek Hermansky

The recently proposed Joint Energy-based Model (JEM) interprets discriminatively trained classifier $p(y|x)$ as an energy model, which is also trained as a generative model describing the distribution of the input observations $p(x)$. The JEM training relies on "positive examples" (i.e. examples from the training data set) as well as on "negative examples",

Chenhao Lin, Pengbin Hu, Chao Shen, Qian Li

Taking full advantage of the excellent performance of StyleGAN, style transfer-based face swapping methods have been extensively investigated recently. However, these studies require separate face segmentation and blending modules for successful face swapping, and the fixed selection of the manipulated latent code in these works is reckless, thus degrading f

Azade Nova, Hanjun Dai, Dale Schuurmans

Large Language Models (LLMs) have achieved great success in solving difficult tasks across many domains, but such success comes with a high computation cost, and inference latency. As developers and third parties customize these models, the need to provide efficient inference has increased. Many efforts have attempted to reduce inference cost through model c

Manuel Pichardo Marcano, Liliana E. Rivera Sandoval, Thomas J. Maccarone, Rene D. Rohrmann

We report a peculiar variable blue star in the globular cluster NGC 6397, using Hubble Space Telescope optical imaging. Its position in the colour-magnitude diagrams, and its spectrum, are consistent with this star being a helium core white dwarf (He WD) in a binary system. The optical light curve shows a periodicity at 18.5 hours. We argue that this periodi

Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu

Lifelong learning (LL) aims to improve a predictive model as the data source evolves continuously. Most work in this learning paradigm has focused on resolving the problem of 'catastrophic forgetting,' which refers to a notorious dilemma between improving model accuracy over new data and retaining accuracy over previous data. Yet, it is also known that machi

Chilin Zhang

We establish a C^1,alpha Schauder estimate of a non-standard degenerate elliptic equation and use it to give another proof of the higher order boundary Harnack inequality. As an application, we obtain the analyticity of the free boundary in the classical obstacle problem based on iterating the boundary Harnack principle.

M. Malnou, J. A. B. Mates, M. R. Vissers, L. R. Vale

We report on the use of a kinetic-inductance traveling-wave parametric amplifier (KITWPA) as the first amplifier in the readout chain of a microwave superconducting quantum interference device (SQUID) multiplexer (umux). This umux is designed to multiplex signals from arrays of low temperature detectors such as superconducting transition-edge sensor microcal

Zachary Atkins, Adriaan J. Duivenvoorden, William R. Coulton, Frank J. Qu

The increasing statistical power of cosmic microwave background (CMB) datasets requires a commensurate effort in understanding their noise properties. The noise in maps from ground-based instruments is dominated by large-scale correlations, which poses a modeling challenge. This paper develops novel models of the complex noise covariance structure in the Ata

Konstantin Rink, Tristan Gruschka, Patrick Palsbröker, Marcos Baez

In this paper we describe the design and development of a route training system for individuals with cognitive impairments (CIs) living in residential care facilities. Learning to move autonomously in public spaces is a fundamental skill for people with CI, who face several challenges to independently and safely move around. Yet, exploring opportunities for

Peter Senchyna, Adele Plat, Daniel P. Stark, Gwen C. Rudie

The first JWST spectroscopy of the luminous galaxy GN-z11 simultaneously both established its redshift at $z=10.6$ and revealed a rest-ultraviolet spectrum dominated by signatures of highly-ionized nitrogen, which has so far defied clear interpretation. Here we present a reappraisal of this spectrum in the context of both detailed nebular modeling and nearby

Cathy Li, Jana Sotáková, Emily Wenger, Mohamed Malhou

Learning with Errors (LWE) is a hard math problem underpinning many proposed post-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism (KEM) standardized by NIST is based on module~LWE, and current publicly available PQ Homomorphic Encryption (HE) libraries are based on ring LWE. The security of LWE-based PQ cryptosystems is critical, but

Aswin P. Vijayan, Peter A. Thomas, Christopher C. Lovell, Stephen M. Wilkins

Using the First Light And Reionisation Epoch Simulations (${\rm F{\small LARES}}$), a suite of hydrodynamical simulations we explore the consequences of a realistic model for star--dust geometry on the observed properties of galaxies. We find that the UV attenuation declines rapidly from the central regions of galaxies, and bright galaxies have spatially ext

Gal Shavit, Yuval Oreg

External magnetic fields conventionally suppress superconductivity, both by orbital and paramagnetic effects. A recent experiment has shown that in a Bernal stacked bilayer graphene system, the opposite occurs -- a finite critical magnetic field is necessary to observe superconducting features occurring in the vicinity of a magnetic phase transition. We prop

Aranya Bhattacharya, Pratik Nandy, Pingal Pratyush Nath, Himanshu Sahu

Continuing the previous initiatives arXiv: 2207.05347 and arXiv: 2212.06180, we pursue the exploration of operator growth and Krylov complexity in dissipative open quantum systems. In this paper, we resort to the bi-Lanczos algorithm generating two bi-orthogonal Krylov spaces, which individually generate non-orthogonal subspaces. Unlike the previously studie

Mustafa Gündoğan, Jasminder S. Sidhu, Markus Krutzik, Daniel K. L. Oi

Global-scale quantum networking faces significant technical and scientific obstacles. Quantum repeaters (QRs) have been proposed to overcome the inherent direct transmission range limit through optical fibre. However, QRs are typically limited to a total distance of a few thousand kilometres and/or require extensive hardware overhead. Recent proposals sugges

Jan L. Hellmann, Jonas M. Schneider, Elias Wölfer, Joanna Drążkowska

Carbonaceous chondrites are some of the most primitive meteorites and derive from planetesimals that formed a few million years after the beginning of the solar system. Here, using new and previously published Cr, Ti, and Te isotopic data, we show that carbonaceous chondrites exhibit correlated isotopic variations that can be accounted for by mixing among th

R. Skalidis, K. Gkimisi, K. Tassis, G. V. Panopoulou

The formation of molecular gas in interstellar clouds is a slow process, but is enhanced by gas compression. Magnetohydrodynamic (MHD) waves create compressed quasiperiodic linear structures, referred to as striations. Striations are observed at column densities where the atomic to molecular gas transition takes place. We explore the role of MHD waves in the

Kathleen A. Hamilton-Campos, Raymond C. Simons, Molly S. Peeples, Gregory F. Snyder

In local disk galaxies such as our Milky Way, older stars generally inhabit a thicker disk than their younger counterparts. Two competing models have attempted to explain this result: one in which stars first form in thin disks that gradually thicken with time through dynamical heating, and one in which stars form in thick disks at early times and in progres

Marco Stein Muzio, M. Unger, Stephanie Wissel

We consider the prospects for future ultrahigh energy cosmic ray and neutrino observations to constrain the evolution of sources producing a proton flux above 10 EeV (1 EeV = 10^18 eV). We find that strong constraints on the source evolution can be obtained by combining measurements of the cosmic ray proton fraction above 30 EeV with measurement of the neutr

Sreekar Voleti, F David Wandler, Arun Paramekanti

The quest for exotic quantum magnetic ground states, including the Kitaev spin liquid and quantum spin-ices, has led to the discovery of several quantum materials where low energy pseudospin-$1/2$ doublets arise from the splitting of spin-orbit entangled multiplets with higher degeneracy. Such systems include $d$-orbital and $f$-orbital Mott insulators. When

Wouter Dekens, Jordy de Vries, Emanuele Mereghetti, Javier Menéndez

We investigate neutrinoless double-beta decay ($0\nu\beta\beta$) in the minimal extension of the standard model of particle physics, the $\nu$SM, where gauge-singlet right-handed neutrinos give rise to Dirac and Majorana neutrino mass terms. We focus on the associated sterile neutrinos and argue that the usual evaluation of their contributions to $0\nu\beta\

Susan Born, Rohith Karur, Simon Knapen, Jessie Shelton

We assess the capabilities of the CMS and LHCb searches for low-$p_T$ displaced dimuon pairs to discover hidden valley models, using a newly-developed benchmark model that realizes a range of dimuon vertex topologies. We show that the data scouting techniques used in these searches provide unique sensitivity and we make some additional suggestions to further

S. E. Thomas, L. Wagner, R. Joos, R. Sittig

A hybrid interface of solid state single-photon sources and atomic quantum memories is a long sought-after goal in photonic quantum technologies. Here we demonstrate deterministic storage and retrieval of photons from a semiconductor quantum dot in an atomic ensemble quantum memory at telecommunications wavelengths. We store single photons from an InAs quant

Shannon G. Patel, Daniel D. Kelson, Louis E. Abramson, Zahra Sattari

We study the recent star formation histories (SFHs) of 575 intermediate-mass galaxies (IMGs, $10^{9} \leq M/M_{\odot} \leq 10^{10}$) in COSMOS at $0.3<z<0.4$ by comparing their H$\alpha$ and UV luminosities. These two measurements trace star formation rates (SFRs) on different timescales and together reveal fluctuations in recent activity. We compute $L_{{\r

Paul A. Draghis, Mayura Balakrishnan, Jon M. Miller, Edward Cackett

The origin and distribution of stellar-mass black hole spins are a rare window into the progenitor stars and supernova events that create them. Swift J1728.9-3613 is an X-ray binary, likely associated with the supernova remnant G351.9-0.9 (Balakrishnan et al. 2023). A NuSTAR X-ray spectrum of this source during its 2019 outburst reveals reflection from an ac

Noah Kubli, Lucio Mayer, Hongping Deng

We study the initial development, structure and evolution of protoplanetary clumps formed in 3D resistive MHD simulations of self-gravitating disks. The magnetic field grows by means of the recently identified gravitational instability dynamo (Riols & Latter 2018; Deng et al. 2020). Clumps are identified and their evolution is tracked finely both backward an

Niccolò Cribiori, Carmine Montella

We give evidence that supersymmetric anti-de Sitter vacua of five-dimensional supergravity cannot be scale separated as a consequence of quantum gravity constraints, such as the weak gravity conjecture or the species scale. We show this in a model-independent way for the minimal and the maximal theory and we believe that the argument can be extended to any a

Giulia Santucci, Sarah Brough, Jesse van de Sande, Richard McDermid

Most dynamical models of galaxies to date assume axisymmetry, which is not representative of a significant fraction of massive galaxies. We have built triaxial orbit-superposition Schwarzschild models of galaxies observed by the SAMI Galaxy Survey, in order to reconstruct their inner orbital structure and mass distribution. The sample consists of 153 passive

Bitan De, Gabriela Wojtowicz, Jakub Zakrzewski, Michael Zwolak

Extended reservoirs provide a framework for capturing macroscopic, continuum environments, such as metallic electrodes driving a current through a nanoscale contact, impurity, or material. We examine the application of this approach to periodically driven systems, specifically in the context of quantum transport. As with non--equilibrium steady states in tim

Mayura Balakrishnan, Paul A. Draghis, Jon M. Miller, Joe Bright

A number of neutron stars have been observed within the remnants of the core-collapse supernova explosions that created them. In contrast, black holes are not yet clearly associated with supernova remnants. Indeed, some observations suggest that black holes are ``born in the dark'', i.e. without a supernova explosion. Herein, we present a multi-wavelength an

Yang-Zhi Chou, Jay D. Sau

We demonstrate slow dynamics and constrained motion of domain walls in one-dimensional (1D) interacting bosons with double-well dispersion. In the symmetry-broken regime, the domain-wall motion is ``fractonlike'' -- a single domain wall cannot move freely, while two nearby domain walls can move collectively. Consequently, we find an Ohmic-like linear respons

Francesco D'Eugenio, Arjen van der Wel, Caro Derkenne, Josha van Houdt

We present the first statistical study of spatially integrated non-Gaussian stellar kinematics spanning 7 Gyr in cosmic time. We use deep, rest-frame optical spectroscopy of massive galaxies (stellar mass $M_\star > 10^{10.5} {\rm M}_\odot$) at redshifts z = 0.05, 0.3 and 0.8 from the SAMI, MAGPI and LEGA-C surveys, to measure the excess kurtosis $h_4$ of th

Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu

Modern deep learning models with great expressive power can be trained to overfit the training data but still generalize well. This phenomenon is referred to as \textit{benign overfitting}. Recently, a few studies have attempted to theoretically understand benign overfitting in neural networks. However, these works are either limited to neural networks with

Christian G. Prosko, Ivan Kulesh, Michael Chan, Lin Han

A single electron shared between two levels threaded by a magnetic flux is an irreducibly simple quantum system in which interference is predicted to occur. We demonstrate tuning of the tunnel coupling between two such electronic levels with flux, implemented in a loop comprising two quantum dots. Using radio-frequency reflectometry of the dots' gate electro

Boris Knyazev, Doha Hwang, Simon Lacoste-Julien

Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communities with large-resources. We aim at an ambitious goal of democratizing pretraining. Towards that goal, we train and release a single neural network that can predict high quality ImageNet parameters of other neural netwo

Rohith Pudari, Neil A. Ernst

AI-supported programming has arrived, as shown by the introduction and successes of large language models for code, such as Copilot/Codex (Github/OpenAI) and AlphaCode (DeepMind). Above human average performance on programming challenges is now possible. However, software engineering is much more than solving programming contests. Moving beyond code completi

Katarina Doctor, Christine Task, Eric Kildebeck, Mayank Kejriwal

Artificial Intelligence (AI) systems planned for deployment in real-world applications frequently are researched and developed in closed simulation environments where all variables are controlled and known to the simulator or labeled benchmark datasets are used. Transition from these simulators, testbeds, and benchmark datasets to more open-world domains pos

Tsung-Han Yeh, Keith A. Olive, Brian D. Fields

We explore the effect of neutron lifetime and its uncertainty on standard big-bang nucleosynthesis (BBN). BBN describes the cosmic production of the light nuclides $^1{\rm H}$, ${\rm D}$, $^3{\rm H}$+$^3{\rm He}$, $^4{\rm He}$, and $^7{\rm Li}$+$^7{\rm Be}$ in the first minutes of cosmic time. The neutron mean life $\tau_n$ has two roles in modern BBN calcul

Blair W. Lebert, Benjamin Bacq-Labreuil, Mark P. M. Dean, Kari Ruotsalainen

Using Resonant Inelastic X-ray Scattering, we measure the paramagnon dispersion and damping of undoped, antiferromagnetic Ca$_2$CuO$_2$Cl$_2$ as well as doped, superconducting Na$_{x}$Ca$_{2-x}$CuO$_2$Cl$_2$. Our estimation of the spin-exchange parameter and width of the paramagnon peak at the zone boundary $X=(0.5,0)$ confirms that no simple relation can be