January 2022 arXiv papers — page 19

Alexander Varchenko

It is known that solutions of the KZ equations can be written in the form of multidimensional hypergeometric integrals. In 2017 in a joint paper of the author with V. Schechtman the construction of hypergeometric solutions was modified, and solutions of the KZ equations modulo a prime number $p$ were constructed. These solutions modulo $p$, called the $p$-hy

Michal Piovarci, Michael Foshey, Jie Xu, Timothy Erps

Enabling additive manufacturing to employ a wide range of novel, functional materials can be a major boost to this technology. However, making such materials printable requires painstaking trial-and-error by an expert operator, as they typically tend to exhibit peculiar rheological or hysteresis properties. Even in the case of successfully finding the proces

Samuel T. Badman, David H. Brooks, Nicolas Poirier, Harry P. Warren

Global coronal models seek to produce an accurate physical representation of the Sun's atmosphere which can be used, for example, to drive space weather models. Assessing their accuracy is a complex task and there are multiple observational pathways to provide constraints and tune model parameters. Here, we combine several such independent constraints, defin

Marcel Binz, Eric Schulz

Equipping artificial agents with useful exploration mechanisms remains a challenge to this day. Humans, on the other hand, seem to manage the trade-off between exploration and exploitation effortlessly. In the present article, we put forward the hypothesis that they accomplish this by making optimal use of limited computational resources. We study this hypot

Yimin Lin, Jesse Chan, Ignacio Tomas

High-order entropy-stable discontinuous Galerkin methods for the compressible Euler and Navier-Stokes equations require the positivity of thermodynamic quantities in order to guarantee their well-posedness. In this work, we introduce a positivity limiting strategy for entropy-stable discontinuous Galerkin discretizations constructed by blending high order so

Katarzyna Woźnica, Mateusz Grzyb, Zuzanna Trafas, Przemysław Biecek

For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance. Prevalent meta-learning approaches focus on obtaining good hyperparameters configurations with a limited computational budget for a completely new task based on the results obtained from the prior tasks. This paper proposes a new formulation of

Chen Jiang, Yu Zou

For a terminal weak $\mathbb{Q}$-Fano $3$-fold $X$, we show that the $m$-th anti-canonical map defined by $|-mK_X|$ is birational for all $m\geq59$.

Benjamin Dees, Susama Agarwala, Corey Lowman

In this paper, we investigate the evolution of autoencoders near their initialization. In particular, we study the distribution of the eigenvalues of the Jacobian matrices of autoencoders early in the training process, training on the MNIST data set. We find that autoencoders that have not been trained have eigenvalue distributions that are qualitatively dif

Li Yang, Abdallah Shami

Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication a

Hichan Agueny

This documentation is designed for beginners in Graphics Processing Unit (GPU)-programming and who want to get familiar with OpenACC and OpenMP offloading models. Here we present an overview of these two programming models as well as of the GPU-architectures. Specifically, we provide some insights into the functionality of these models and perform experiment

Julian Amette Estrada, Marc E. Brachet, Pablo D. Mininni

Since the idea of quantum turbulence was first proposed by Feynman, and later realized in experiments of superfluid helium and Bose-Einstein condensates, much emphasis has been put in finding signatures that distinguish quantum turbulence from its classical counterpart. Here we show that quantum turbulence in rotating condensates is fundamentally different f

Andrew Ahn

We establish universality for the largest singular values of products of random matrices with right unitarily invariant distributions, in a regime where the number of matrix factors and size of the matrices tend to infinity simultaneously. The behavior of the largest log singular values coincides with the large N limit of Dyson Brownian motion with a charact

Rassa Ghavami Modegh, Ahmad Salimi, Alireza Dizaji, Hamid R. Rabiee

Despite the state-of-the-art performance of deep convolutional neural networks, they are susceptible to bias and malfunction in unseen situations. Moreover, the complex computation behind their reasoning is not human-understandable to develop trust. External explainer methods have tried to interpret network decisions in a human-understandable way, but they a

Alejandro Mazuera-Rozo, Camilo Escobar-Velásquez, Juan Espitia-Acero, David Vega-Guzmán

Android is nowadays the most popular operating system in the world, not only in the realm of mobile devices, but also when considering desktop and laptop computers. Such a popularity makes it an attractive target for security attacks, also due to the sensitive information often manipulated by mobile apps. The latter are going through a transition in which th

Kalyani Bhopi, Will Tyndall, Pranav Sanghavi, Kevin Bandura

Foreground mitigation is critical to all next-generation radio interferometers that target cosmology using the redshifted neutral hydrogen 21 cm emission line. Attempts to remove this foreground emission have led to new analysis techniques as well as new developments in hardware specifically dedicated to instrument beam and gain calibration, including stabil

Adva Mond, Julien Portier

For a prime number $p$, let $A_3(p)= | \{ m \in \mathbb{N}: \exists m_1,m_2,m_3 \in \mathbb{N}, \frac{m}{p}=\frac{1}{m_1}+\frac{1}{m_2}+\frac{1}{m_3} \} |$. In 2019 Luca and Pappalardi proved that $x (\log x)^3 \ll \sum_{p \le x} A_{3}(p) \ll x (\log x)^5$. We improve the upper bound, showing $\sum_{p \le x} A_{3}(p) \ll x (\log x)^3 (\log \log x)^2$.

M. Khlopunov, D. V. Gal'tsov

We explore possible manifestations of an odd number of extra dimensions in gravitational radiation, which are associated with violation of Huygens' principle in flat odd-dimensional spacetime. Our setup can be regarded as the limit of an infinite compactification radius in ADD model and is not viable as realistic cosmology, but it still may be useful as a si

Hanhan Zhou, Tian Lan, Guru Venkataramani, Wenbo Ding

One of the biggest challenges in Federated Learning (FL) is that client devices often have drastically different computation and communication resources for local updates. To this end, recent research efforts have focused on training heterogeneous local models obtained by pruning a shared global model. Despite empirical success, theoretical guarantees on con

Jia Wang, Hongwei Zhu, Jiancheng Shen, Yu Cao

It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a

Xizhe Wang, Ning Zhang, Jia Wang, Jing Ni

In Vitro Fertilization (IVF) is the most widely used Assisted Reproductive Technology (ART). IVF usually involves controlled ovarian stimulation, oocyte retrieval, fertilization in the laboratory with subsequent embryo transfer. The first two steps correspond with follicular phase of females and ovulation in their menstrual cycle. Therefore, we refer to it a

Ben Gripaios

We show how the theory of characters can be used to analyse an anomaly corresponding to chiral fermions carrying an arbitrary representation of a gauge group that is finite, but otherwise arbitrary. By way of example, we do this for some groups of relevance for the study of quark and lepton masses and mixings.

Alberto Saldana-Lopez, Daniel Schaerer, John Chisholm, Sophia R. Flury

Combining 66 ultraviolet (UV) spectra and ancillary data from the Low-Redshift Lyman Continuum Survey (LzLCS) and 23 LyC observations by earlier studies, we form a statistical sample of star-forming galaxies at $z \sim 0.3$ to study the role of the cold interstellar medium (ISM) gas in the leakage of ionizing radiation. We first constrain the massive star co

Boning Li, Gunjan Verma, Santiago Segarra

We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non-convex nature of the problem, the proposed method consists of modular structures inspired by a classical iterative suboptimal approach and enhanced with learnable components. More

Ryan J. Downey, Daniel P. Ferris

Data recordings are often corrupted by noise, and it can be difficult to isolate clean data of interest. For example, mobile electroencephalography is commonly corrupted by motion artifact, which limits its use in real-world settings. Here, we describe a novel noise-canceling algorithm that uses canonical correlation analysis to find and remove subspaces of

Meenakshi L, Bhaskar Sen Gupta

When an amorphous solid is deformed homogeneously, the response exhibits heterogeneous plastic instabilities with localized cooperative rearrangement of cluster of particles. The heterogeneous behavior plays an important role in deciding the mechanical properties of amorphous solids. In this paper, we employ computer simulation to study the characteristics a

Shamiul Alam, Md Shafayat Hossain, Ahmedullah Aziz

Controlling the spread of infectious diseases, such as the ongoing SARS-CoV-2 pandemic, is one of the most challenging problems for human civilization. The world is more populous and connected than ever before, and therefore, the rate of contagion for such diseases often becomes stupendous. The development and distribution of testing kits cannot keep up with

Ankur Mali, Alexander Ororbia, Daniel Kifer, Lee Giles

Recent advances in deep learning have led to superhuman performance across a variety of applications. Recently, these methods have been successfully employed to improve the rate-distortion performance in the task of image compression. However, current methods either use additional post-processing blocks on the decoder end to improve compression or propose an

Sebastian Monka, Lavdim Halilaj, Achim Rettinger

Recent approaches of computer vision utilize deep learning methods as they perform quite well if training and testing domains follow the same underlying data distribution. However, it has been shown that minor variations in the images that occur when using these methods in the real world can lead to unpredictable errors. Transfer learning is the area of mach

Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song

Many interesting tasks in image restoration can be cast as linear inverse problems. A recent family of approaches for solving these problems uses stochastic algorithms that sample from the posterior distribution of natural images given the measurements. However, efficient solutions often require problem-specific supervised training to model the posterior, wh

Kevin Schultz, Ryan LaRose, Andrea Mari, Gregory Quiroz

Zero-noise extrapolation is a quantum error mitigation technique that has typically been studied under the ideal approximation that the noise acting on a quantum device is not time-correlated. In this work, we investigate the feasibility and performance of zero-noise extrapolation in the presence of time-correlated noise. We show that, in contrast to white n

Andrei Bykov, Vadim Romansky, Sergei Osipov

Recent discovery of fast blue optical transients (FBOTs) as a new class of energetic transient sources can shed light on the long-standing problem of supernova-long gamma-ray burst connections. A distinctive feature of such objects is the presence of modestly relativistic outflows which place them in between the non-relativistic and relativistic supernovae-r

Friederike Metz, Marin Bukov

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols. Here, we present a novel framew

D. V. Giri, Frederick M. Tesche, Michael A. Morgan

Vector and scalar potentials are used for convenience in solving boundary value problems involving electromagnetic (EM) fields. The potentials are made unique by choosing a non-unique gauge relationship. The most commonly used gauges are those named for Lorenz and Coulomb, both of which may be defined as special cases of what is termed the velocity gauge, or

Piotr Pokora, Tim Römer

In the present paper we study algebraic properties of edge ideals associated with plane curve arrangements via their Levi graphs. Using combinatorial properties of such Levi graphs we are able to describe those monomial algebras being Cohen-Macaulay, Buchsbaum, and sequentially Cohen-Macaulay. We also condsider the projective dimension and the Castelnuovo-Mu

Ross Hyman, Nicolaus Tideman

How to fairly apportion congressional seats to states has been debated for centuries. We present an alternative perspective on apportionment, centered not on states but "families" of state, sets of states with "divisor-method" quotas with the same integer part. We develop ``impartial" and ``unbiased" apportionment methods. Impartial methods apportion the sam

Luca Dal Zilio, Betti Hegyi, Whitney Behr, Taras Gerya

A major goal in earthquake physics is to derive a constitutive framework for fault slip that captures the dependence of shear strength on fault rheology, sliding velocity, and pore-fluid pressure. In this study, we present H-MEC (Hydro-Mechanical Earthquake Cycles), a newly-developed two-phase flow numerical code - which couples solid rock deformation and pe

Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson, James Ostrowski

Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unweighted MaxCut problems by leveraging similarities in the objective landscape among different problem instances. However, we show that the more general weighted MaxCut problem has si

Kıvanç İ. Ünlütürk, Cem Yetişmişoğlu

Following earlier works of Dereli and collaborators, we study a three dimensional toy model where we extend the topologically massive gravity with electrodynamics by the most general $RF^2$-type non-minimal coupling terms. Here $R$ denotes the possible curvature terms and $F$ denotes the electromagnetic 2-form. We derive the variational field equations and l

Ramnath Kumar, Tristan Deleu, Yoshua Bengio

Advancements in reinforcement learning (RL) have been remarkable in recent years. However, the limitations of traditional training methods have become increasingly evident, particularly in meta-RL settings where agents face new, unseen tasks. Conventional training approaches are susceptible to failure in such situations as they need more robustness to advers

Ankur Mali, Alexander Ororbia, Daniel Kifer, Lee Giles

Recent advances in deep learning have resulted in image compression algorithms that outperform JPEG and JPEG 2000 on the standard Kodak benchmark. However, they are slow to train (due to backprop-through-time) and, to the best of our knowledge, have not been systematically evaluated on a large variety of datasets. In this paper, we perform the first large-sc

Danial Ghamari, Philipp Hauke, Roberto Covino, Pietro Faccioli

Spontaneous structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, physics-based computer simulations like Molecular Dynamics (MD) enable us to investigate these thermally activated processes with an atomic level of resolution. However, rare conformational transitions are intrinsically ha

Matthias Fitzi, Xuechao Wang, Sreeram Kannan, Aggelos Kiayias

Resource-based consensus is the backbone of permissionless distributed ledger systems. The security of such protocols relies fundamentally on the level of resources actively engaged in the system. The variety of different resources (and related proof protocols, some times referred to as PoX in the literature) raises the fundamental question whether it is pos

Athur Michon, Fayçal Ait Aoudia, K. Pavan Srinath

In orthogonal frequency division multiplexing (OFDM)-based wireless communication systems, the bit error rate (BER) performance is heavily dependent on the accuracy of channel estimation. It is important for a good channel estimator to be capable of handling the changes in the wireless channel conditions that occur due to the mobility of the users. In recent

Yunshu Shi, David S. Parker, Eun Sang Choi, Kasey P. Devlin

We report on a solution-growth based method to synthesise single crystals of Y$_2$Co$_3$ and on its structural and magnetic properties. We find that Y$_2$Co$_3$ crystallizes in the La2Ni3-type orthorhombic structure with space group Cmce (No. 64), with Co forming distorted kagome lattices. Y$_2$Co$_3$ orders antiferromagnetically below $T_N$ = 252 K. Magneti

Heiko Dietrich, Willem A. de Graaf, Alessio Marrani, Marcos Origlia

We classify states of four rebits, that is, we classify the orbits of the group $\widehat{G}(\mathbb R) = \mathrm{\mathop{SL}}(2,\mathbb R)^4$ in the space $(\mathbb R^2)^{\otimes 4}$. This is the real analogon of the well-known SLOCC operations in quantum information theory. By constructing the $\widehat{G}(\mathbb R)$-module $(\mathbb R^2)^{\otimes 4}$ via

James E. Yoder, Todd E. Humphreys

A vehicular pose estimation technique is presented that tightly couples multi-antenna carrier-phase differential GNSS (CDGNSS) with a low-cost MEMS inertial sensor and vehicle dynamics constraints. This work is the first to explore the use of consumer-grade inertial sensors for tightly-coupled urban CDGNSS, and first to explore the tightly-coupled combinatio

Ramnath Kumar, Tristan Deleu, Yoshua Bengio

Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset o

Oskar Słowik, Adam Sawicki

Currently available quantum computers, so called Noisy Intermediate-Scale Quantum (NISQ) devices, are characterized by relatively low number of qubits and moderate gate fidelities. In such scenario, the implementation of quantum error correction is impossible and the performance of those devices is quite modest. In particular, the depth of circuits implement

Louigi Addario-Berry, Serte Donderwinkel

We obtain new non-asymptotic tail bounds for the height of uniformly random trees with a given degree sequence, simply generated trees and conditioned Bienaym\'e trees (the family trees of branching processes), in the process settling three conjectures of Janson (2012) and answering several other questions from the literature. Moreover, we define a partial o

Norberto Romanelli, Nahuel Andres, Gina DiBraccio

We present a statistical analysis on the variability of the incompressible energy cascade rate in the solar wind around Mars, making use of an exact relation for fully developed turbulence and more than five years of Mars Atmosphere and Volatile EvolutioN (MAVEN) observations. Using magnetic field and plasma data, we compute the energy cascade rate in the ma

Taichi Uyama, Garreth Ruane, Kellen Lawson, Takayuki Muto

We present Keck/NIRC2 $K_{\rm p}L_{\rm p}$ high-contrast imaging observations of a J0337 protoplanetary disk. The data discover the spatially-resolved large cavity, which is the second report among protoplanetary disks in the Perseus star forming region after the LkH$\alpha$~330 system. Our data and forward modeling using RADMC-3D suggests $\sim80$~au for th

Abraham Israeli, Oren Tsur

Social platforms such as Gab and Parler, branded as `free-speech' networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit. In this work we provide the first large scale analysis of hate-speech on Parler.

Ahmet Furkan Hacan, Bilal Kabas, Samet Oguten

Optimization of design parameters of a transformer is a crucial task to increase efficiency and lower the material cost. This research presents an approach to model a three-phase transformer and optimize design parameters to minimize the volume and loss. ANSYS Maxwell 2D is used to model the transformer and analyze it for different design parameters. The mul

Malena Rice, Songhu Wang, Gregory Laughlin

The obliquity of a star, or the angle between its spin axis and the average orbit normal of its companion planets, provides a unique constraint on that system's evolutionary history. Unlike the Solar System, where the Sun's equator is nearly aligned with its companion planets, many hot Jupiter systems have been discovered with large spin-orbit misalignments,

R. M. Ludlam, E. M. Cackett, J. A. García, J. M. Miller

We present a spectral analysis of NuSTAR and NICER observations of the luminous, persistently accreting neutron star (NS) low-mass X-ray binary Cygnus X-2. The data were divided into different branches that the source traces out on the Z-track of the X-ray color-color diagram; namely the horizontal branch, normal branch, and the vertex between the two. The X

Matthew Setzler, Scott Howland, Lauren Phillips

Compositional generalization is a troubling blind spot for neural language models. Recent efforts have presented techniques for improving a model's ability to encode novel combinations of known inputs, but less work has focused on generating novel combinations of known outputs. Here we focus on this latter "decode-side" form of generalization in the context

Adam Leszczyński

This doctoral thesis focuses on the issue of phase matching in a quantum memory operating on a cold cloud of $^{87}$Rb atoms, where non-resonant Raman scattering is used as an interface between light and atoms. Experimental results and considerations for controlling the memory readout process by using the Zeeman effect, ac-Stark effect, controlling the laser

Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo

Driving safety analysis has recently experienced unprecedented improvements thanks to technological advances in precise positioning sensors, artificial intelligence (AI)-based safety features, autonomous driving systems, connected vehicles, high-throughput computing, and edge computing servers. Particularly, deep learning (DL) methods empowered volume video

Stefan Hristozov, Moritz Wettermann, Manuel Huber

A major security challenge for modern Internet of Things (IoT) deployments is to ensure that the devices run legitimate firmware free from malware. This challenge can be addressed through a security primitive called attestation which allows a remote backend to verify the firmware integrity of the devices it manages. In order to accelerate broad attestation a

Jean-Christophe Aval, Karimatou Djenabou, Peter R. W. McNamara

A famous conjecture of Stanley states that his chromatic symmetric function distinguishes trees. As a quasisymmetric analogue, we conjecture that the chromatic quasisymmetric function of Shareshian and Wachs and of Ellzey distinguishes directed trees. This latter conjecture would be implied by an affirmative answer to a question of Hasebe and Tsujie about th

Efthymia Derezea, Alfred Kume, Dirk Froebrich

One of the main features of interest in analysing the light curves of stars is the underlying periodic behaviour. The corresponding observations are a complex type of time series with unequally spaced time points and are sometimes accompanied by varying measures of accuracy. The main tools for analysing these type of data rely on the periodogram-like functio

Avi Friedlander, Katherine J. Mack, Sarah Schon, Ningqiang Song

Theories of large extra dimensions (LEDs) such as the Arkani-Hamed, Dimopoulos & Dvali scenario predict a "true" Planck scale $M_\star$ near the TeV scale, while the observed $M_{pl}$ is due to the geometric effect of compact extra dimensions. These theories allow for the creation of primordial black holes (PBHs) in the early Universe, from the collisional f

Dewei Hu, Yuankai K. Tao, Ipek Oguz

Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-

Anatoly B. Kuklov, Lode Pollet, Nikolay V. Prokof'ev, Boris V. Svistunov

The unique superflow-through-solid effect observed in solid Helium-4 and attributed to the quasi-one-dimensional superfluidity along the dislocation cores exhibits two extraordinary features: (i) an exponentially strong suppression of the flow by a moderate increase in pressure, and (ii) an unusual temperature dependence of the flow rate with no analogy to a

Bilyana L. Tomova

In this paper we study the dual charges of $\mathcal{N}=1$ supergravity in asymptotically flat space-time. The action considered is the usual supergravity action with a topological contribution. This is the Nieh-Yan term and the magnetic term of the free Rarita-Schwinger field. Through methods of the covariant phase space formalism we construct the charges c

Jarrett Lawrence Johnson, Phoebe R. Upton Sanderbeck

One of the most pressing questions in cosmology is how the black holes (BHs) powering quasars at high redshift grow to supermassive scales within a billion years of the Big Bang. Here we show that sustained super-Eddington accretion can be achieved for BHs with Eddington fractions $f_{\rm Edd}$ > 2/$\epsilon$, where $\epsilon$ is the efficiency with which ra

James Aird, Alison L. Coil, Dale D. Kocevski

The extent of black hole growth during different galaxy evolution phases and the connection between galaxy compactness and AGN activity remain poorly understood. We use Hubble Space Telescope imaging of the CANDELS fields to identify star-forming and quiescent galaxies at z=0.5-3 in both compact and extended phases and use Chandra X-ray imaging to measure th

Yoshiaki Kato, Toshikazu Ebisuzaki, Toshiki Tajima

We have investigated electro-magnetic (EM) wave pulses in a jet from a neutrino driven accretion flow (NDAF) around a black hole (BH). NDAFs are massive accretion disks whose accretion rates of $\dot{M}\approx 0.01 - 10 \mathrm{M}_\odot/\mathrm{s}$ for stellar-mass BHs. Such an extreme accretion may produce a collimated relativistic outflow like a magnetical

Daniele Barducci, Enrico Bertuzzo

We study an extension of the Standard Model with electroweak scale right-handed singlet fermions $N$ that induces neutrino masses, plus a generic new physics sector at a higher scale $\Lambda$. The latter is parametrized in terms of effective operators in the language of the $\nu$SMEFT. We study its phenomenology considering operators up to $d=6$, where addi

Nicholas Kaaz, Ariadna Murguia-Berthier, Koushik Chatterjee, Matthew Liska

A black hole (BH) travelling through a uniform, gaseous medium is described by Bondi-Hoyle-Lyttleton (BHL) accretion. If the medium is magnetized, then the black hole can produce relativistic outflows. We performed the first 3D, general-relativistic magnetohydrodynamics simulations of BHL accretion onto rapidly rotating black holes using the code H-AMR, wher

Federico Lelli

Dwarf galaxies are ideal laboratories to test dark matter models and alternative theories because their dynamical mass (from observed kinematics) largely outweighs their baryonic mass (from gas and stars). In most star-forming dwarfs, cold atomic gas forms regularly rotating disks extending beyond the stellar component, thus probing the gravitational potenti

T. Wevers, D. R. Pasham, P. Jalan, S. Rakshit

Quasi-periodic X-ray eruptions (QPEs) are a recently discovered phenomenon, the nature of which remains unclear. Based on their discovery in active galactic nuclei (AGN), explanations related to an AGN accretion disk, or potentially stellar tidal disruption event (TDE), were put forward. Following the report of QPEs in apparently passive galaxies, alternativ

Teymoor Saifollahi, Dennis Zaritsky, Ignacio Trujillo, Reynier F. Peletier

We present an analysis of Hubble Space Telescope observations of globular clusters (GCs) in six ultra-diffuse galaxies (UDGs) in the Coma cluster, a sample that represents UDGs with large effective radii ($R_{\rm e}$), and use the results to evaluate competing formation models. We eliminate two significant sources of systematic uncertainty in the determinati

Dean Johnstone, Patrik Öhberg, Callum W. Duncan

We study the ground state phases of interacting bosons in the presence of a 2D Aubry-Andr\'e potential. By using a a mean-field percolation analysis, we focus on several superlattice and quasicrystalline regimes of the 2D Aubry-Andr\'e model, including generalisations that account for a tilting or skewing of the potential. We show that barriers to the onset

S. Mau, E. O. Nadler, R. H. Wechsler, A. Drlica-Wagner

We use a recent census of the Milky Way (MW) satellite galaxy population to constrain the lifetime of particle dark matter (DM). We consider two-body decaying dark matter (DDM) in which a heavy DM particle decays with lifetime $\tau$ comparable to the age of the Universe to a lighter DM particle (with mass splitting $\epsilon$) and to a dark radiation specie

Hong Yang, Travis Desell

Neural networks are capable of learning powerful representations of data, but they are susceptible to overfitting due to the number of parameters. This is particularly challenging in the domain of time series classification, where datasets may contain fewer than 100 training examples. In this paper, we show that the simple methods of cutout, cutmix, mixup, a

Paul Wilson, Dan Ghica, Fabio Zanasi

Whereas string diagrams for strict monoidal categories are well understood, and have found application in several fields of Computer Science, graphical formalisms for non-strict monoidal categories are far less studied. In this paper, we provide a presentation by generators and relations of string diagrams for non-strict monoidal categories, and show how thi

Fernando Martín-Rodríguez, Fernando Isasi-de-Vicente

This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam/smartphone videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to manufacturing imperfections. This is an

David T. Hoffmann, Nadine Behrmann, Juergen Gall, Thomas Brox

This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contrast to the standard InfoNCE loss, which requires a strict binary separation of the training pairs into similar and dissimilar samples, RINCE can exploit information about a similar

Martins Kokainis, Krišjānis Prūsis, Jevgēnijs Vihrovs, Vyacheslavs Kashcheyevs

We show that the discrete time quantum walk on the Boolean hypercube of dimension $n$ has a strong dispersion property: if the walk is started in one vertex, then the probability of the walker being at any particular vertex after $O(n)$ steps is of an order $O(1.4818^{-n})$. This improves over the known mixing results for this quantum walk which show that th

Dmitry Faifman

We investigate the support of a distribution $f$ on the real grassmannian $\mathrm{Gr}_k(\mathbb R^n)$ whose spectrum, namely its nontrivial $\mathrm O(n)$-components, is restricted to a subset $\Lambda$ of all $\mathrm O(n)$-types. We prove that unless $\Lambda$ is co-sparse, $f$ cannot be supported at a point. We utilize this uncertainty principle to prove

Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy

Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings t

Laurent Bulteau, Konrad K. Dabrowski, Noleen Köhler, Sebastian Ordyniak

A homomorphism $f$ from a guest graph $G$ to a host graph $H$ is locally bijective, injective or surjective if for every $u\in V(G)$, the restriction of $f$ to the neighbourhood of $u$ is bijective, injective or surjective, respectively. The corresponding decision problems, LBHOM, LIHOM and LSHOM, are well studied both on general graphs and on special graph

Chris Akers, Thomas Faulkner, Simon Lin, Pratik Rath

We study the reflected entropy $S_R$ in the West Coast Model, a toy model of black hole evaporation consisting of JT gravity coupled to end-of-the-world branes. We demonstrate the validity of the holographic duality relating it to the entanglement wedge cross section away from phase transitions. Further, we analyze the important non-perturbative effects that

Noam Razin, Asaf Maman, Nadav Cohen

In the pursuit of explaining implicit regularization in deep learning, prominent focus was given to matrix and tensor factorizations, which correspond to simplified neural networks. It was shown that these models exhibit an implicit tendency towards low matrix and tensor ranks, respectively. Drawing closer to practical deep learning, the current paper theore

R. Inanc Baykur, Mustafa Korkmaz, Jonathan Simone

We produce simply connected, minimal, symplectic Lefschetz fibrations realizing all the lattice points in the symplectic geography plane below the Noether line. This provides a symplectic extension of the classical works populating the complex geography plane with holomorphic Lefschetz fibrations. Our examples are obtained by rationally blowing down Lefschet

Zhiyuan Yao, Zihan Ding, Thomas Clausen

This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods. Traditional heuristic solutions like Weighted-Cost Multi-Path (WCMP) and Local Shortest Queue (LSQ) are less flexible to the changing workload distributions and arrival rates, with a poor balance among multiple load bal

Yuri Lavinas, Claus Aranha, Gabriela Ochoa

Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We s

Romain Dubreoucq, Lucile Rutkowski

We demonstrate broadband and sensitive cavity ring-down spectroscopy using a near infrared frequency comb and a time-resolved Fourier transform spectrometer. The cavity decays are measured simultaneously and spectrally sorted, leading to purely exponential decays for each spectral element. The absorption spectra of atmospheric water and carbon dioxide are re

J. D. Wagenveld, A. Saxena, K. J. Duncan, H. J. A. Röttgering

We present a novel method to identify candidate high redshift quasars (HzQs; ($z\gtrsim5.5$), which are unique probes of supermassive black hole growth in the early Universe, from large area optical/infrared photometric surveys. Using Gaussian Mixture Models to construct likelihoods and incorporate informed priors based on population statistics, our method u

S. S. Babkin, A. A. Lyublinskaya, I. S. Burmistrov

The interplay of a potential and magnetic disorder in superconductors remains an active field of research for decades. Within the framework of the Usadel equation, we study the local density of states near a solitary classical magnetic impurity in a dirty superconducting film. We find that a potential disorder results in broadening of the delta-function in t

Hao Chen, Jiacheng Tang, Abhishek Gupta

In this paper, we develop a new change detection algorithm for detecting a change in the Markov kernel over a metric space in which the post-change kernel is unknown. Under the assumption that the pre- and post-change Markov kernel is uniformly ergodic, we derive an upper bound on the mean delay and a lower bound on the mean time between false alarms. A nume

Erik Duse

We give sufficient conditions for when an autonomous elliptic Leray-Lions equation in the plane has a conformal structure. This allows the Leray-Lions equation to be linearised in a special form through the hodograph transform.

Jorge Medina, Roberto Rojas-Cessa, Vatcharapan Umpaichitra

Because a fast vaccination rollout against coronavirus disease 2019 (COVID-19) is critical to restore daily life and avoid virus mutations, it is tempting to have a relaxed vaccination-administration management system. However, a robust management system can support the enforcement of preventive measures, and in turn, reduce incidence and deaths. Here, we mo

Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus

We study linear filters for processing signals supported on abstract topological spaces modeled as simplicial complexes, which may be interpreted as generalizations of graphs that account for nodes, edges, triangular faces etc. To process such signals, we develop simplicial convolutional filters defined as matrix polynomials of the lower and upper Hodge Lapl

M. Zhu, D. J. Voneshen, S. Raymond, O. J. Lipscombe

Theories of the origin of superconductivity in cuprates are dependent on an understanding of their normal state which exhibits various competing orders. Transport and thermodynamic measurements on La$_{2-x}$Sr$_x$CuO$_4$ show signatures of a quantum critical point, including a peak in the electronic specific heat $C$ versus doping $p$, near the doping $p^{\s

N. Ahmadiniaz, M. Geller, J. König, P. Kratzer

We investigate whether and how the quantum Zeno effect, i.e., the inhibition of quantum evolution by frequent measurements, can be employed to isolate a quantum dot from its surrounding electron reservoir. In contrast to the often studied case of tunneling between discrete levels, we consider the tunnelling of an electron from a continuum reservoir to a disc

Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci

The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency. However, most biological models are composed of simple single compartment neurons and cannot achieve the state-of-art performances of artificial intelligence. We propose a multi-compartment model of pyramidal neuron, in which bursts and dendritic input segregation

Sophia R. Flury, Anne E. Jaskot, Harry C. Ferguson, Gabor Worseck

The origins of Lyman continuum (LyC) photons responsible for the reionization of the universe are as of yet unknown and highly contested. Detecting LyC photons from the epoch of reionization is not possible due to absorption by the intergalactic medium, which has prompted the development of several indirect diagnostics to infer the rate at which galaxies con

David Benson, Radha Kessar, Markus Linckelmann

Let $k$ be an algebraically closed field of prime characteristic $p$. Let $kGe$ be a block of a group algebra of a finite group $G$, with normal defect group $P$ and abelian $p'$ inertial quotient $L$. Then we show that $kGe$ is a matrix algebra over a quantised version of the group algebra of a semidirect product of $P$ with a certain subgroup of $L$. To do