November 2021 arXiv papers

Tiago P. Peixoto

Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental building blocks, with the objective of providing a summary of its large-scale structure. Despite its importanc

Qiushi Bai, Sadeem Alsudais, Chen Li, Shuang Zhao

We consider data-visualization systems where a middleware layer translates a frontend request to a SQL query to a backend database to compute visual results. We study the problem of answering a visualization request within a limited time constraint due to the responsiveness requirement. We explore the optimization options of rewriting an original query by ad

B. Pahlevanzadeh, P. Sahebsara, D. Sénéchal

We study the possibility of charge order at quarter filling and antiferromagnetism at half-filling in a tight-binding model of magic angle twisted bilayer graphene. We build on the model proposed by Kang and Vafek [Physical Review X 8(3), 031088 (2018)], relevant to a twist angle of $1.30^\circ$, and add on-site and extended density-density interactions. App

Iván León, Diego Pazó

The emergence of collective synchrony from an incoherent state is a phenomenon essentially described by the Kuramoto model. This canonical model was derived perturbatively, by applying phase reduction to an ensemble of heterogeneous, globally coupled Stuart-Landau oscillators. This derivation neglects nonlinearities in the coupling constant. We show here tha

Alexander Montgomerie-Corcoran, Zhewen Yu, Christos-Savvas Bouganis

Significant effort has been placed on the development of toolflows that map Convolutional Neural Network (CNN) models to Field Programmable Gate Arrays (FPGAs) with the aim of automating the production of high performing designs for a diverse set of applications. However, within these toolflows, the problem of finding an optimal mapping is often overlooked,

Fangzhou Mu, Jian Wang, Yicheng Wu, Yin Li

Visual content creation has spurred a soaring interest given its applications in mobile photography and AR / VR. Style transfer and single-image 3D photography as two representative tasks have so far evolved independently. In this paper, we make a connection between the two, and address the challenging task of 3D photo stylization - generating stylized novel

David Harry Richman

In analogy with the Manin-Mumford conjecture for algebraic curves, one may ask how a metric graph under the Abel-Jacobi embedding intersects torsion points of its Jacobian. We show that the number of torsion points is finite for metric graphs of genus $g\geq 2$ which are biconnected and have edge lengths which are "sufficiently irrational" in a preci

Lei Sun, Christos Sakaridis, Jingyun Liang, Qi Jiang

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time. In this paper, we rethink the eventbased image deblurring

Suraj Kothawade, Saikat Ghosh, Sumit Shekhar, Yu Xiang

Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc. It is known that their success depends on a large amount of data from the domain of interest. While deep models often perform well in terms of overall accuracy, they often struggle in performance on rare yet critical

Enrica Rossi, Marco Tognon, Luca Ballotta, Ruggero Carli

In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perform trajectory tracking in a coordinated way when the sampling time of communications is much larger than the sampling time of low-level controllers, disrupting theoretical convergen

Nivedan Vishwanath, Aditya Karthik Saravanakumar, Kush Dwivedi, K. Ram Chandra Murthy

Tall buildings are often subjected to steady and unsteady forces due to external wind flows. Measurement and mitigation of these forces becomes critical to structural design in engineering applications. Over the last few decades, many approaches such as modification of the external geometry of structures have been investigated to mitigate wind-induced load.

Jacopo Borga

We recently introduced a new universal family of permutons, depending on two parameters, called skew Brownian permuton. For some specific choices of the parameters, the skew Brownian permuton coincides with some previously studied permutons: the biased Brownian separable permuton and the Baxter permuton. The latter two permutons are degenerate cases of the s

Sundararajan Srinivasan, Zhaocheng Huang, Katrin Kirchhoff

Generic pre-trained speech and text representations promise to reduce the need for large labeled datasets on specific speech and language tasks. However, it is not clear how to effectively adapt these representations for speech emotion recognition. Recent public benchmarks show the efficacy of several popular self-supervised speech representations for emotio

Yogesh S. S. Patil, Judith Höller, Parker A. Henry, Chitres Guria

Any system of coupled oscillators may be characterized by its spectrum of resonance frequencies (or eigenfrequencies), which can be tuned by varying the system's parameters. The relationship between control parameters and the eigenfrequency spectrum is central to a range of applications. However, fundamental aspects of this relationship remain poorly und

Jacopo Borga

We construct a new family of random permutons, called skew Brownian permuton, which describes the limits of several models of random constrained permutations. This family is parametrized by two real parameters. For a specific choice of the parameters, the skew Brownian permuton coincides with the Baxter permuton, i.e., the permuton limit of Baxter permutatio

Philipp Kopp, Victor Calo, Ernst Rank, Stefan Kollmannsberger

The direct numerical simulation of metal additive manufacturing processes such as laser powder bed fusion is challenging due to the vast differences in spatial and temporal scales. Classical approaches based on locally refined finite elements combined with time-stepping schemes can only address the spatial multi-scale nature and provide only limited scaling

Norman Christ, Xu Feng, Joseph Karpie, Tuan Nguyen

The relationship between finite volume multi-hadron energy levels and matrix elements and two particle scattering phase shifts and decays is well known, but the inclusion of long range interactions such as QED is non-trivial. Inclusion of QED is an important systematic error correction to $K\toππ$ decays. In this talk, we present a method of including a trun

Danny Nam, Allan Sly, Youngtak Sohn

Continuing our earlier work in \cite{nss20a}, we study the random regular k-NAE-SAT model in the condensation regime. In \cite{nss20a}, the 1RSB properties of the model were established with positive probability. In this paper, we improve the result to probability arbitrarily close to one. To do so, we introduce a new framework which is the synthesis of two

Ali Esmaeily, Katina Kralevska, Toktam Mahmoodi

One of the most challenging services fifth-generation (5G) mobile network is designed to support, is the critical services in-need of very low latency, and/or high reliability. It is now clear that such critical services will also be at the core of beyond 5G (B5G) networks. While 5G radio design accommodates such supports by introducing more flexibility in t

D. Gadkari, K. S. Shanks, H. Hu, H. T. Philipp

We characterize a new x-ray Mixed-Mode Pixel Array Detector (MM-PAD-2.1) Application Specific Integrated Circuit (ASIC). Using an integrating pixel front-end with dynamic charge removal architecture, the MM-PAD-2.1 ASIC extends the maximum measurable x-ray signal (in 20 keV photon units) to > 10$^{7}$ x-rays/pixel/frame while maintaining a low read noise acr

Peyton T. Johnson, Michael W. Coughlin, Ashlie Hamilton, María José Bustamante-Rosell

The upcoming Laser Interferometer Space Antenna (LISA) will detect a large gravitational-wave foreground of Galactic white dwarf binaries. These sources are exceptional for their probable detection at electromagnetic wavelengths, some long before LISA flies. Studies in both gravitational and electromagnetic waves will yield strong constraints on system param

William Bains, Oliver Shorttle, Sukrit Ranjan, Paul B. Rimmer

The initial reports of the presence of phosphine in the cloud decks of Venus has led to the suggestion that volcanism was the source of phosphine, through volcanic phosphides ejected into the clouds. Here we examine the idea that mantle plume volcanism, bringing material from the deep mantle to the surface, could generate observed amounts of phosphine throug

P. Amaro, A. Adamczak, M. Abdou Ahmed, L. Affolter

The CREMA collaboration is pursuing a measurement of the ground-state hyperfine splitting (HFS) in muonic hydrogen ($μ$p) with 1 ppm accuracy by means of pulsed laser spectroscopy to determine the two-photon-exchange contribution with $2\times10^{-4}$ relative accuracy. In the proposed experiment, the $μ$p atom undergoes a laser excitation from the singlet h

Linda M. Carpenter, Matthew J. Smylie, Jesus Manuel Caridad Ramirez, Cameron McDowell

We present a study of the sensitivity to models of new physics of proton collisions resulting in three electroweak bosons. As a benchmark, we analyze models in which an exotic scalar field $ϕ$ is produced in association with a gauge boson ($V=γ$ or $Z$). The scalar then decays to a pair of bosons, giving the process $pp\rightarrow ϕV\rightarrow V'V"V

I. Alekseev, M. Danilov, V. Rusinov, E. Samigullin

Wavelength shifting fibers are widely used for light collection from scintillation counters, which allow connection of various scintillation planes to relatively small photocathodes of photodetectors and especially tiny photocathodes of silicon photo-multipliers. In October 2020 Kuraray announced production of a new branch of faster fibers. We performed a co

Yichi Zhang, Zhiru Zhang, Lukasz Lew

Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the compute intensity but existing models suffer from low quality. To overcome this deficiency, we propose PokeConv, a binary convolution block which improves quality of BNNs by techn

Mrinmoy Roy, Venkata Devesh Reddy Seethi, Rami Lake, Pratool Bharti

Worldwide 2019 million people have been infected and 4.5 million have lost their lives in the ongoing Covid-19 pandemic. Until vaccines became widely available, precautions and safety measures like wearing masks, physical distancing, avoiding face touching were some of the primary means to curb the spread of virus. Face touching is a compulsive human begavio

E. A. Kudryavtseva, A. A. Oshemkov

In this paper, we study singularities of the Lagrangian fibration given by a completely integrable system. We prove that a non-degenerate singular fibre satisfying the so-called connectedness condition is structurally stable under (small enough) real-analytic integrable perturbations of the system. In other words, the topology of the fibration in a neighbour

Gabriele Grillo, Giulia Meglioli, Fabio Punzo

It is well known from the work of [2] that the Fujita phenomenon for reaction-diffusion evolution equations with power nonlinearities does not occur on the hyperbolic space $\mathbb{H}^N$, thus marking a striking difference with the Euclidean situation. We show that, on classes of manifolds in which the bottom $Λ$ of the $L^2$ spectrum of $-Δ$ is strictly po

Shamiul Alam, Md Mazharul Islam, Md Shafayat Hossain, Akhilesh Jaiswal

The scaling of the already-matured CMOS technology is steadily approaching its physical limit, motivating the quest for a suitable alternative. Cryogenic operation offers a promising pathway towards continued improvement in computing speed and energy efficiency without aggressive scaling. However, the memory wall bottleneck of the traditional von-Neumann arc

Zhi-Qiu Huang, John Kirk, Gwenael Giacinti, Brian Reville

Motivated by the detection of very high energy gamma-rays deep in the afterglow emission of a gamma-ray burst, we revisit predictions of the maximum energy to which electrons can be accelerated at a relativistic blast wave. Acceleration at the weakly-magnetized forward shock of a blast-wave can be limited either by the rapid damping of turbulence generated b

Radouane Gannouji, Yolbeiker Rodríguez Baez

We study the stability of static black holes in generalized Einstein-Maxwell-scalar theories. We derive the master equations for the odd and even parity perturbations. The sufficient and necessary conditions for the stability of black holes under odd-parity perturbations are derived. We show that these conditions are usually not similar to energy conditions

Eduardo Esteves, Renan Santos, Eduardo Vital

We give a local characterization for when certain quiver representations in semisimple Abelian categories are semisimple, among them those arising from degenerations of linear series. This paper is the first of two, aimed to describe all the schematic limits of families of divisors associated to a given family of linear series on a one-dimensional family of

Guram Mikaberidze, Raissa M. D'Souza

Cascading failures abound in complex systems and the BTW sandpile model provides a theoretical underpinning for their analysis. Yet, it does not account for the possibility of nodes having oscillatory dynamics such as in power grids and brain networks. Here we consider a network of Kuramoto oscillators upon which the BTW model is unfolding, enabling us to st

Lars Winther Christensen, Luigi Ferraro, Peder Thompson

Let p be a prime ideal in a commutative noetherian ring R and denote by k(p) the residue field of the local ring R_p. We prove that if an R-module M satisfies Ext_R^n(k(p),M) = 0 for some n >= dim R, then Ext_R^i(k(p),M) = 0 holds for all i >= n. This improves a result of Christensen, Iyengar, and Marley by lowering the bound on n. We also improve existing r

Stan Zachary

Future ``net-zero'' electricity systems in which all or most generation is renewable may require very high volumes of storage, provided jointly by a number of heterogeneous technologies, in order to manage the associated variability in the generation-demand balance. We consider the problems of scheduling and dimensioning such storage. We develop a va

Tananun Songdechakraiwut, Bryan M. Krause, Matthew I. Banks, Kirill V. Nourski

The topological patterns exhibited by many real-world networks motivate the development of topology-based methods for assessing the similarity of networks. However, extracting topological structure is difficult, especially for large and dense networks whose node degrees range over multiple orders of magnitude. In this paper, we propose a novel and computatio

Ethan L Addison

We begin by defining a type of Kähler metric near the zero section of a trivial holomorphic open disk bundle $N$ over a compact Kähler manifold $X$ by incorporating flows generated by holomorphic vector fields on $X$. These metrics are then shown to deviate exponentially from Poincaré-type metrics on $N\setminus X$ in terms of the log-polar distance from $X$

Piotr T. Chruściel, Erwann Delay, Raphaela Wutte

We derive a formula for the energy of asymptotically locally hyperbolic (ALH) manifolds obtained by a gluing at infinity of two ALH manifolds. As an application we show that there exist three-dimensional conformally compact ALH manifolds either without boundary or with toroidal black hole boundary, with connected conformal infinity of higher genus, with cons

Chris McDonagh, Xi Chen

Designing models that produce accurate predictions is the fundamental objective of machine learning (ML). This work presents methods demonstrating that when the derivatives of target variables (outputs) with respect to inputs can be extracted from processes of interest, e.g., neural networks (NN) based surrogate models, they can be leveraged to further impro

Steve Mann, Cayden Pierce, Christopher Tong, Christina Mann

"Vironment" is a series of art pieces, social commentary, technology, etc., based on wearable health technologies of social-distancing, culminating in a social-distancing device that takes the familiar world of security and surveillance technologies that surround us and re-situates it on the body of the wearer (technologies that become part of us). T

Alexander F. Jercher, Daniele Oriti, Andreas G. A. Pithis

We study the cosmological sector of the Lorentzian Barrett-Crane (BC) model coupled to a free massless scalar field in its Group Field Theory (GFT) formulation, corresponding to the mean-field hydrodynamics obtained from coherent condensate states. The relational evolution of the condensate with respect to the scalar field yields effective dynamics of homoge

Jay Gopalakrishnan, Lukas Kogler, Philip L. Lederer, Joachim Schöberl

We introduce two new lowest order methods, a mixed method, and a hybrid Discontinuous Galerkin (HDG) method, for the approximation of incompressible flows. Both methods use divergence-conforming linear Brezzi-Douglas-Marini space for approximating the velocity and the lowest order Raviart-Thomas space for approximating the vorticity. Our methods are based on

Prashant Singh

Heterogeneous diffusion with spatially changing diffusion coefficient arises in many experimental systems like protein dynamics in the cell cytoplasm, mobility of cajal bodies and confined hard-sphere fluids. Here, we showcase a simple model of heterogeneous diffusion where the diffusion coefficient $D(x)$ varies in power-law way, i.e. $D(x) \sim |x|^{-α}$ w

Luben M. C. Cabezas, Rafael Izbicki, Rafael B. Stern

We propose methods for the analysis of hierarchical clustering that fully use the multi-resolution structure provided by a dendrogram. Specifically, we propose a loss for choosing between clustering methods, a feature importance score and a graphical tool for visualizing the segmentation of features in a dendrogram. Current approaches to these tasks lead to

Tuan-Phong Nguyen, Simon Razniewski, Julien Romero, Gerhard Weikum

Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O. This paper presents a method called ASCENT++ to

Barbara Kaltenbacher, William Rundell

This paper deals with the inverse problem of recovering an arbitrary number of fractional damping terms in a wave equation. We develop several approaches on uniqueness and reconstruction, some of them relying on Tauberian theorems on the relation between the asymptotics of solutions in time and Laplace domain. Also the possibility of additionally recovering

Samuel Carton, Surya Kanoria, Chenhao Tan

Learning from rationales seeks to augment model prediction accuracy using human-annotated rationales (i.e. subsets of input tokens) that justify their chosen labels, often in the form of intermediate or multitask supervision. While intuitive, this idea has proven elusive in practice. We make two observations about human rationales via empirical analyses: 1)

Kwok Kwan Wong

We show that Kaplansky's unit conjecture is true, under the assumption that the underlying field is complex.

József Kiss, Bálint Vető

We consider the elephant random walk with general step distribution. We calculate the first four moments of the limiting distribution of the position rescaled by $n^α$ in the superdiffusive regime where $α$ is the memory parameter. This extends the results obtained by Bercu.

Ahmad Biniaz

We confirm the following conjecture of Fekete and Woeginger from 1997: for any sufficiently large even number $n$, every set of $n$ points in the plane can be connected by a spanning tour (Hamiltonian cycle) consisting of straight-line edges such that the angle between any two consecutive edges is at most $π/2$. Our proof is constructive and suggests a simpl

Daniel Fadel

We study the asymptotic behavior of finite energy $\rm{SU}(2)$ monopoles, and general critical points of the $\rm{SU}(2)$ Yang--Mills--Higgs energy, on asymptotically conical $3$-manifolds with only one end. Our main results generalize classical results due to Groisser and Taubes in the particular case of the flat $3$-dimensional Euclidean space $\mathbb{R}^

Sahar Abdelnabi, Rakibul Hasan, Mario Fritz

Misinformation is now a major problem due to its potential high risks to our core democratic and societal values and orders. Out-of-context misinformation is one of the easiest and effective ways used by adversaries to spread viral false stories. In this threat, a real image is re-purposed to support other narratives by misrepresenting its context and/or ele

J. Arrington, M. Battaglieri, A. Boehnlein, S. A. Bogacz

We summarize the ongoing scientific program of the 12 GeV Continuous Electron Beam Accelerator Facility (CEBAF) and give an outlook into future scientific opportunities. The program addresses important topics in nuclear, hadronic, and electroweak physics including nuclear femtography, meson and baryon spectroscopy, quarks and gluons in nuclei, precision test

Eric Boulter

Moduli spaces of stably irreducible sheaves on Kodaira surfaces belong to the short list of examples of smooth and compact holomorphic symplectic manifolds, and it is not yet known how they fit into the classification of holomorphic symplectic manifolds by deformation type. This paper studies a natural Lagrangian fibration on these moduli spaces to determine

Simon Caron-Huot, Andrzej Pokraka

The first paper of this series introduced objects (elements of twisted relative cohomology) that are Poincaré dual to Feynman integrals. We show how to use the pairing between these spaces -- an algebraic invariant called the intersection number -- to express a scattering amplitude over a minimal basis of integrals, bypassing the generation of integration-by

Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu Chen

Pre-training models on Imagenet or other massive datasets of real images has led to major advances in computer vision, albeit accompanied with shortcomings related to curation cost, privacy, usage rights, and ethical issues. In this paper, for the first time, we study the transferability of pre-trained models based on synthetic data generated by graphics sim

Narayan Khadka, Mary Loli Martínez-Aldama, Michal Zajaček, Bożena Czerny

We use 118 H$β$ quasar (QSO) observations in the redshift range $0.0023 \leq z \leq 0.89$ to simultaneously constrain cosmological model parameters and QSO 2-parameter radius-luminosity ($R-L$) relation parameters in six different cosmological models. We find that the $R-L$ relation parameters for these QSOs are independent of the assumed cosmology so these

F. Micena, J. S. C. Costa

In this work, we deal with a notion of partially hyperbolic endomorphism. We explore topological properties of this definition and we obtain, among other results, obstructions to get center leaf conjugacy with the linear part, for a class of partially hyperbolic endomorphism $C^1-$sufficiently close to a hyperbolic linear endomorphism. Indeed such obstructio

Tian Yan, Fang Liu

Background: The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media. Method: We collected Reddit social media data from 2019 (pre-pandemic) and 2020

Adam Freese

Noether's first and second theorems both imply conserved currents that can be identified as an energy-momentum tensor (EMT). The first theorem identifies the EMT as the conserved current associated with global spacetime translations, while the second theorem identifies it as a conserved current associated with local spacetime translations. This work obta

Anne-Catherine de la Hamette, Stefan L. Ludescher, Markus P. Mueller

In the quantization of gauge theories and quantum gravity, it is crucial to treat reference frames such as rods or clocks not as idealized external classical relata, but as internal quantum subsystems. In the Page-Wootters formalism, for example, evolution of a quantum system S is described by a stationary joint state of S and a quantum clock, where time-dep

Lukas Burgholzer, Sarah Schneider, Robert Wille

Executing quantum circuits on currently available quantum computers requires compiling them to a representation that conforms to all restrictions imposed by the targeted architecture. Due to the limited connectivity of the devices' physical qubits, an important step in the compilation process is to map the circuit in such a way that all its gates are exe

Tobias Binder, Anastasiia Filimonova, Kalliopi Petraki, Graham White

The formation and decay of metastable bound states can significantly decrease the thermal-relic dark matter density, particularly for dark matter masses around and above the TeV scale. Incorporating bound-state effects in the dark matter thermal decoupling requires in principle a set of coupled Boltzmann equations for the bound and unbound species. However,

Enrico Andriolo, Neil Lambert, Tristan Orchard, Constantinos Papageorgakis

Starting from the recent action proposed by Sen [1,2], we evaluate the partition function of the compact chiral boson on a two-dimensional torus using a path-integral formulation. Crucially, we use a Wick-rotation procedure obtained from a complex deformation of the physical spacetime metric. This directly reproduces the expected result including general cha

Boxi Li, Tommaso Calarco, Felix Motzoi

Deriving effective Hamiltonian models plays an essential role in quantum theory, with particular emphasis in recent years on control and engineering problems. In this work, we present two symbolic methods for computing effective Hamiltonian models: the Non-perturbative Analytical Diagonalization (NPAD) and the Recursive Schrieffer-Wolff Transformation (RSWT)

Ouail Kitouni, Niklas Nolte, Mike Williams

The Lipschitz constant of the map between the input and output space represented by a neural network is a natural metric for assessing the robustness of the model. We present a new method to constrain the Lipschitz constant of dense deep learning models that can also be generalized to other architectures. The method relies on a simple weight normalization sc

Mattia Atzori Corona, Riccardo Murgia, Matteo Cadeddu, Maria Archidiacono

We reassess the viability of a cosmological model including a fourth additional sterile neutrino species that self-interacts through a new pseudoscalar degree of freedom. We perform a series of extensive analyses fitting various combinations of cosmic microwave background (CMB) data from Planck, the Atacama Cosmology Telescope (ACT) and the South Pole Telesc

Michael T. Roman, Leigh N. Fletcher, Glenn S. Orton, Thomas K. Greathouse

We present an analysis of all currently available ground-based imaging of Neptune in the mid-infrared. Dating between 2003 and 2020, the images reveal changes in Neptune's mid-infrared ($\sim 8-25μ$m) emission over time in the years surrounding Neptune's 2005 southern summer solstice. Images sensitive to stratospheric ethane ($\sim12μ$m), methane ($\

Tri Dao, Beidi Chen, Kaizhao Liang, Jiaming Yang

Overparameterized neural networks generalize well but are expensive to train. Ideally, one would like to reduce their computational cost while retaining their generalization benefits. Sparse model training is a simple and promising approach to achieve this, but there remain challenges as existing methods struggle with accuracy loss, slow training runtime, or

Sandro Tacchella, Aaron Smith, Rahul Kannan, Federico Marinacci

The nebular recombination line H$α$ is widely used as a star-formation rate (SFR) indicator in the local and high-redshift Universe. We present a detailed H$α$ radiative transfer study of high-resolution isolated Milky-Way and Large Magellanic Cloud simulations that include radiative transfer, non-equilibrium thermochemistry, and dust evolution. We focus on

Aseem Paranjape, Ravi K. Sheth

In general relativity (GR), the internal dynamics of a self-gravitating system under free-fall in an external gravitational field should not depend on the external field strength. Recent work has claimed a statistical detection of an `external field effect' (EFE) using galaxy rotation curve data. We show that large uncertainties in rotation curve analyse

Ziqi Yan, Matthew Yu

We continue our study of the Kawai-Lewellen-Tye (KLT) factorization of winding string amplitudes. In a toroidal compactification, amplitudes for winding closed string states factorize into products of amplitudes for open strings ending on an array of D-branes localized in the compactified directions; the specific D-brane configuration is determined by the cl

Farid Shahandeh

We show that a separation between the class of all problems that can efficiently be solved on a quantum computer and those solvable using probabilistic classical algorithms in polynomial time implies the generalized contextuality of quantum algorithms. Our result subsumes versions of Gottesman-Knill theorem as special cases.

Sergey Afonin, Timofey Solomko

We propose a general method for finding a string-like meson spectrum which is based on a certain condition for the breaking of closed gluon string. A model is constructed that successfully realizes the proposed method. The model is based on the holographic Soft Wall model for QCD and the use of the Wilson confinement criterion. We applied our approach to the

Giacomo Cacciapaglia, Thomas Flacke, Manuel Kunkel, Werner Porod

We consider a particular composite Higgs model which contains SU(3) color octet top partners besides the usually considered triplet representations. Moreover, color singlet top partners are present as well which can in principle serve as dark matter candidates. We investigate the LHC phenomenology of these unusual top partners. Some of these states could be

Pavel E. Mancera Piña, Filippo Fraternali, Tom Oosterloo, Elizabeth A. K. Adams

We present new HI interferometric observations of the gas-rich ultra-diffuse galaxy AGC 114905, which previous work, based on low-resolution data, identified as an outlier of the baryonic Tully-Fisher relation. The new observations, at a spatial resolution $\sim 2.5$ times higher than before, reveal a regular HI disc rotating at about 23 km/s. Our kinematic

Jessica Craven, Mark Hughes, Vishnu Jejjala, Arjun Kar

We use deep neural networks to machine learn correlations between knot invariants in various dimensions. The three-dimensional invariant of interest is the Jones polynomial $J(q)$, and the four-dimensional invariants are the Khovanov polynomial $\text{Kh}(q,t)$, smooth slice genus $g$, and Rasmussen's $s$-invariant. We find that a two-layer feed-forward

H. F. Stevance, S. G. Parsons, J. J. Eldridge

We use the self-consistent stellar populations in the Binary Population A Spectral Synthesis (BPASS) models to assess whether NGC1850-BH1 is a black hole. Using search criteria based on reported physical properties in the literature we purposefully search for suitable systems with a black hole (or compact object) companion: we do not find any. Good matches t

Nickolas Kokron, Joseph DeRose, Shi-Fan Chen, Martin White

We investigate the stochastic properties of typical red galaxy samples in a controlled numerical environment. We use Halo Occupation Distribution (HOD) modelling to create mock realizations of three separate bright red galaxy samples consistent with datasets used for clustering and lensing analyses in modern galaxy surveys. Second-order Hybrid Effective Fiel

Andras Szabo, Bitan Roy

Motivated by recent experiments [H. Zhou, \emph{et al.}, Science {\bf 375}, 774 (2022) and S. C. de la Barrera, \emph{et al.}, arXiv:2110.13907], here we propose a general mechanism for valley and/or spin degeneracy lifting of the electronic bands in doped Bernal bilayer graphene, subject to electric displacement ($D$) fields. A $D$-field induced layer polar

Ioannis Lavdas, Bruno Le Floch

We analyze exactly marginal deformations of 3d N=4 Lagrangian gauge theories, especially mixed-branch operators with both electric and magnetic charges. These mixed-branch moduli can either belong to products of electric and magnetic current supermultiplets, or be single-trace (non-factorizable). Apart from some exceptional quivers that have additional modul

Manuel Rey-Area, Mingze Yuan, Christian Richardt

360° cameras can capture complete environments in a single shot, which makes 360° imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360° data, particularly for high resolutions like 2K (2048x1024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-base

Mohsen Fayyaz, Soroush Abbasi Koohpayegani, Farnoush Rezaei Jafari, Sunando Sengupta

While state-of-the-art vision transformer models achieve promising results in image classification, they are computationally expensive and require many GFLOPs. Although the GFLOPs of a vision transformer can be decreased by reducing the number of tokens in the network, there is no setting that is optimal for all input images. In this work, we therefore intro

Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal

The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typically suffer from degraded semantic cont

Maíra Dutra, Yongcheng Wu

We present EvoEMD, a framework to calculate the evolution of cosmic relics in a Universe with an early matter-dominated (EMD) era. There are mainly two aspects to consider in this regard. First, an EMD era changes the Hubble expansion rate with respect to the standard radiation-dominated (RD) universe. Second, when the EMD era ends, the out-of-equilibrium de

Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam

Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding

Rebecca Goldin, Rahul Singh

We present a formula for the Poincaré dual in the flag manifold of the equivariant fundamental class of any regular nilpotent or regular semisimple Hessenberg variety as a polynomial in terms of certain Chern classes. We then develop a type-independent proof of the Giambelli formula for the Peterson variety, and use this formula to compute the intersection m

Ajda Pretnar Žagar, Tomaž Hočevar, Tomaž Curk

What kind of questions about human mobility can computational analysis help answer? How to translate the findings into anthropology? We analyzed a publicly available data set of road traffic counters in Slovenia to answer these questions. The data reveals interesting information on how a nation drives, how it travels for tourism, which locations it prefers,

Oliver Singh

A diffeomorphism $f$ of a compact manifold $X$ is pseudo-isotopic to the identity if there is a diffeomorphism $F$ of $X\times I$ which restricts to $f$ on $X\times 1$, and which restricts to the identity on $X\times 0$ and $\partial X\times I$. We construct examples of diffeomorphisms of 4-manifolds which are pseudo-isotopic but not isotopic to the identity

Deepti Hegde, Vishal M. Patel

3D object detection networks tend to be biased towards the data they are trained on. Evaluation on datasets captured in different locations, conditions or sensors than that of the training (source) data results in a drop in model performance due to the gap in distribution with the test (or target) data. Current methods for domain adaptation either assume acc

Gábor Bíró, Bence Tankó-Bartalis, Gergely Gábor Barnaföldi

Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and

Jiachen Li, Zehua Chen, Weitao Yang

We applied renormalized singles (RS) in the multireference density functional theory (DFT) to calculate accurate energies of ground and excited states. The multireference DFT approach determines the total energy of the $N$-electron system as the sum of the ($N-2$)-electron energy from a density functional approximation (DFA) and the two-electron addition ene

Soumyadip Das, Snehajit Misra

In this article, we study the behavior of the stability of pullback of a vector bundle under a finite morphism from a (not necessarily smooth) stacky curve to an orbifold curve. We establish a categorical equivalence between proper formal orbifold curves and proper orbifold curves in the sense of Deligne-Mumford stacks. Using this identification, we define t

J. D. Denlinger, J. -S. Kang, L. Dudy, J. W. Allen

Previous high-resolution angle-resolved photoemission (ARPES) studies of URu$_2$Si$_2$ have characterized the temperature-dependent behavior of narrow-band states close to the Fermi level ($E_\mathrm{F}$) at low photon energies near the zone center, with an emphasis on electronic reconstruction due to Brillouin zone folding. A substantial challenge to a prop

Andreas Brandhuber, Gang Chen, Henrik Johansson, Gabriele Travaglini

We present a closed formula for all Bern-Carrasco-Johansson (BCJ) numerators describing $D$-dimensional tree-level scattering amplitudes in a heavy-mass effective field theory with two massive particles and an arbitrary number of gluons. The corresponding gravitational amplitudes obtained via the double copy directly enter the computation of black-hole scatt

Stefan Dawydiak

We give a partial coherent categorification of $J_0$, the based ring of the lowest two sided cell of an affine Weyl group, equipped with a monoidal functor from the category of coherent sheaves on the derived Steinberg variety. We show that our categorification acts on natural coherent categorifications of the Iwahori invariants of the Schwartz space of the

Martin Koppenhöfer, Peter Groszkowski, Hoi-Kwan Lau, A. A. Clerk

Quantum metrology protocols exploiting ensembles of $N$ two-level systems and Ramsey-style measurements are ubiquitous. However, in many cases excess readout noise severely degrades the measurement sensitivity; in particular in sensors based on ensembles of solid-state defect spins. We present a dissipative "spin amplification" protocol that allows o

Griffin Floto, Stefan Kremer, Mihai Nica

An important property for deep neural networks is the ability to perform robust out-of-distribution detection on previously unseen data. This property is essential for safety purposes when deploying models for real world applications. Recent studies show that probabilistic generative models can perform poorly on this task, which is surprising given that they

X. Y. Han, Adrian S. Lewis

For strongly convex objectives that are smooth, the classical theory of gradient descent ensures linear convergence relative to the number of gradient evaluations. An analogous nonsmooth theory is challenging. Even when the objective is smooth at every iterate, the corresponding local models are unstable and the number of cutting planes invoked by traditiona