May 2022 arXiv papers — page 32

David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek

Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects and potentia

Kolby Nottingham, Alekhya Pyla, Sameer Singh, Roy Fox

Generalization to out of distribution tasks in reinforcement learning is a challenging problem. One successful approach improves generalization by conditioning policies on task or environment descriptions that provide information about the current transition or reward functions. Previously, these descriptions were often expressed as generated or crowd source

Babhrubahan Bose

We study Birkhoff-James orthogonality and its pointwise symmetry in commutative $C^*$ algebras, i.e., the space of all continuous functions defined on a locally compact Hausdorff space that vanish at infinity. We use this characterization to obtain the characterization of Birkhoff-James orthogonality on $L_\infty$ space defined on any arbitrary measure space

Zheqi Shen, Zijin Wan, Yan Gu, Yihan Sun

To design efficient parallel algorithms, some recent papers showed that many sequential iterative algorithms can be directly parallelized but there are still challenges in achieving work-efficiency and high-parallelism. Work-efficiency can be hard for certain problems where the number of dependences is asymptotically more than optimal sequential work bound.

Amir Joudaki, Hadi Daneshmand, Francis Bach

Mean field theory is widely used in the theoretical studies of neural networks. In this paper, we analyze the role of depth in the concentration of mean-field predictions, specifically for deep multilayer perceptron (MLP) with batch normalization (BN) at initialization. By scaling the network width to infinity, it is postulated that the mean-field prediction

Martin Herdegen, Gechun Liang, Osian Shelley

The Laplace transforms of positive measures on $\mathbb{R}_{+}$ converge if and only if their distribution functions converge at continuity points of the limiting measure. We extend this classical continuity theorem to the case of generalised signed Radon measures. The result for the signed case requires some additional conditions, which follow from recent r

Ryan Shaffer, Hang Ren, Emiliia Dyrenkova, Christopher G. Yale

Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum circuits will be implemented directly using the physical native gate set of the device. These native gates often have a parameterization (e.g., rotation angles) which provide the abil

Emil Jacobsen

We prove that, on a smooth, connected variety in characteristic zero admitting a rational point, local systems of geometric origin are stable under extension in the category of all local systems. As a consequence of this, we obtain a (Nori) motivic strengthening of Hain's theorem on Malcev completions of monodromy representations. Our methods are Tannakian,

Oscar J. C. Dias, Mahdi Godazgar, Jorge E. Santos

The frequency spectra of the gravito-electromagnetic perturbations of the Kerr-Newman (KN) black hole with the slowest decay rate have been computed recently. It has been found that KN has two families $-$ the photon sphere and the near-horizon families $-$ of quasinormal modes (QNMs), which display the interesting phenomenon of eigenvalue repulsion. The per

Carlos Gómez-Huélamo, Marcos V. Conde, Miguel Ortiz

Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex environments, from social robots to self-driving cars. Current approaches tackle this problem using end-to-end networks, where the input data is usually a rendered top-view of the scene and the past trajectories of all the agents; leveraging this information is a must to obtai

Sethupathy Subramanian, Sujata Bhowmick

The finite element method is one of the widely employed numerical techniques in electrical engineering for the study of electric and magnetic fields. When applied to the moving conductor problems, the finite element method is known to have numerical oscillations in the solution. To resolve this, the upwinding techniques, which are developed for the transport

Sujit Narayanan, Malcolm P. Kennett

We study the collective excitations of fractional quantum Hall states in graphene. We focus on states which allow for chiral symmetry breaking (CSB) orders, specifically antiferromagnetism and charge density wave order. We investigate numerically how the collective excitation spectra depend on filling and the flux attachment scheme for two classes of variati

Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal

We develop a rigorous mathematical analysis of zero-shot learning with attributes. In this setting, the goal is to label novel classes with no training data, only detectors for attributes and a description of how those attributes are correlated with the target classes, called the class-attribute matrix. We develop the first non-trivial lower bound on the wor

Paula Maddigan, Teo Susnjak

Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to inadequate staffing levels. These delays have been linked with adverse clinical outcomes. Previous research into forecasting demand this domain has mostly used a collection of statistical techniques, with machine lear

Weijieying Ren, Pengyang Wang, Xiaolin Li, Charles E. Hughes

In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift over time gradually; 4) the storage of historical streams is limited and model updating can only be achieved based on a very short lookback window. This lea

Hideki Tsuiki

The Sierpinski tetrahedron has a remarkable property: It is projected to squares in three orthogonal directions, and moreover, to sets with positive Lebesgue measures in numerous directions. This paper proposes a method for characterizing directions along which the Sierpinski tetrahedron and other similar fractal 3D objects are projected to sets with positiv

Joao Rulff, Fabio Miranda, Maryam Hosseini, Marcos Lage

Noise is one of the primary quality-of-life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low-cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytica

Carolina Arbeláez, Ricardo Cepedello, Juan Carlos Helo, Martin Hirsch

It is well-known that at tree-level the d=5 Weinberg operator can be generated in exactly three different ways, the famous seesaw models. In this paper we study the related question of how many phenomenologically consistent 1-loop models one can construct at d=5. First, we discuss that there are two possible classes of 1-loop neutrino mass models, that allow

Arran Fernandez, Joel E. Restrepo, Durvudkhan Suragan

Linear differential equations with variable coefficients and Prabhakar-type operators featuring Mittag-Leffler kernels are solved. In each case, the unique solution is constructed explicitly as a convergent infinite series involving compositions of Prabhakar fractional integrals. We also extend these results to Prabhakar operators with respect to functions.

Krithika Iyer, Riddhish Bhalodia, Shireen Elhabian

The manifold assumption for high-dimensional data assumes that the data is generated by varying a set of parameters obtained from a low-dimensional latent space. Deep generative models (DGMs) are widely used to learn data representations in an unsupervised way. DGMs parameterize the underlying low-dimensional manifold in the data space using bottleneck archi

Dipendra Jha, Ata Mahjoubfar, Anupama Joshi

On-Shelf Availability (OSA) of products in retail stores is a critical business criterion in the fast moving consumer goods and retails sector. When a product is out-of-stock (OOS) and a customer cannot find it on its designed shelf, this motivates the customer to store-switching or buying nothing, which causes fall in future sales and demands. Retailers are

Bruno Roso

In this article, the author defines an invariant of rational homology 3-spheres equipped with a contact structure as an element of a cohomotopy set of the Seiberg-Witten Floer spectrum as defined in Manolescu (2003). Furthermore, in light of the equivalence established in Lidman & Manolescu (2018a) between the Borel equivariant homology of said spectrum and

N. Hizawa

In nuclear theory, the generator coordinate method (GCM), a type of configuration mixing method, is often used for the microscopic description of collective motions. However, the GCM has a problem that a structure of the collective subspace, which is the Hilbert space spanned by the configurations, is not generally understood. In this paper, I investigate th

Cao Vien Phung, Admela Jukan

Future smart factories are expected to deploy an emerging dynamic Virtual Reality (VR) applications with high bandwidth wireless connections in the THz communication bands, where a factory worker can follow activities through 360{\deg}video streams with high quality resolution. THz communications, while promising as a high bandwidth wireless communication te

Adam Block, Max Simchowitz

Due to the drastic gap in complexity between sequential and batch statistical learning, recent work has studied a smoothed sequential learning setting, where Nature is constrained to select contexts with density bounded by 1/{\sigma} with respect to a known measure {\mu}. Unfortunately, for some function classes, there is an exponential gap between the stati

Weiwei Kong

This paper develops and analyzes an accelerated proximal descent method for finding stationary points of nonconvex composite optimization problems. The objective function is of the form $f+h$ where $h$ is a proper closed convex function, $f$ is a differentiable function on the domain of $h$, and $\nabla f$ is Lipschitz continuous on the domain of $h$. The ma

Zhenxiao Zhang, Zhidong Gao, Yuanxiong Guo, Yanmin Gong

Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework that relies on an edge server co-located with mobile base station for model aggregation has low communication latency but suffe

Steven A. King, Lukas J. Spieß, Peter Micke, Alexander Wilzewski

Optical atomic clocks are the most accurate measurement devices ever constructed and have found many applications in fundamental science and technology. The use of highly charged ions (HCI) as a new class of references for highest accuracy clocks and precision tests of fundamental physics has long been motivated by their extreme atomic properties and reduced

Abdellah Touati, Slimane Zaim

In this paper, we investigated the thermodynamic properties of Schwarzschild black hole (SBH) in the non-commutative (NC) gauge theory of gravity. According to our previous work, we modify the first law of the black hole (BH) thermodynamics by the physical quantity (NC potential) $\mathcal{A}$ which is the conjugate to the NC parameter $\Theta$, which leads

Jiaming Liu, Xiaojian Xu, Weijie Gan, Shirin Shoushtari

Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-used frameworks for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image priors. While traditional PnP/RED formulations have focused on priors specified using image denoisers, there is a growing interest in le

Khalil Idiab, Stijn J. van Tongeren

We define integrability preserving Yang-Baxter deformations of symmetric space sigma models with non-semi-simple symmetry group, in particular the flat space string, using only the essential structures of a symmetric space sigma model. For homogeneous deformations, the Lax connection is of the same form as the semi-simple case, although the R operator no lon

Shamsul Abedin, Yong Li, Abid Anjum Sifat, Khokan Roy

Surface-enhanced coherent anti-Stokes Raman scattering (SE-CARS) takes advantage of surface plasmon resonances supported on metallic nanostructures to amplify the coherent Raman response of target molecules. While these metallic antennas have found significant success in SE-CARS studies, photo-induced morphological changes to the nanoantenna under ultrafast

Yangyu Guo, Christophe Adessi, Manuel Cobian, Samy Merabia

The understanding and modeling of the heat transport across nanometer and sub-nanometer gaps where the distinction between thermal radiation and conduction become blurred remains an open question. In this work, we present a three-dimensional atomistic simulation framework by combining the molecular dynamics (MD) and phonon non-equilibrium Green's function (N

Manoj Kumar, Varsha Banerjee, Sanjay Puri, Martin Weigel

Enormous advances have been made in the past 20 years in our understanding of the random-field Ising model, and there is now consensus on many aspects of its behavior at least in thermal equilibrium. In contrast, little is known about its generalization to the random-field Potts model which has wide-ranging applications. Here we start filling this gap with a

Andreas Bjerre-Nielsen, Martin Benedikt Busch

We investigate how individuals form expectations about population behavior using statistical inference based on observations of their social relations. Misperceptions about others' connectedness and behavior arise from sampling bias stemming from the friendship paradox and uncertainty from small samples. In a game where actions are strategic complements, we

Liyu Chen, Haipeng Luo

We initiate the study of dynamic regret minimization for goal-oriented reinforcement learning modeled by a non-stationary stochastic shortest path problem with changing cost and transition functions. We start by establishing a lower bound $\Omega((B_{\star} SAT_{\star}(\Delta_c + B_{\star}^2\Delta_P))^{1/3}K^{2/3})$, where $B_{\star}$ is the maximum expected

Xian Shi

Entanglement is one of the important resources in quantum tasks. Recently, Yang $et$ $al.$ [arXiv:2205.08801] proposed an entanglement polygon inequalities (EPI) in terms of some entanglement measures for $n$-qudit pure states. Here we continue to consider the entanglement polygon inequalities. Specifially, we show that the EPI is valid for $n$-qudit pure st

Mehdi Nourelahi, Lars Kotthoff, Peijie Chen, Anh Nguyen

Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While these criteria have been studied independently, their relationship is unknown. For example, do CNNs that have a stronger out-of-distribution performance have also stronger explainability? Further

Tim Laux

In this note, we derive a stability and weak-strong uniqueness principle for volume-preserving mean curvature flow. The proof is based on a new notion of volume-preserving gradient flow calibrations, which is a natural extension of the concept in the case without volume preservation recently introduced by Fischer et al. [arXiv:2003.05478]. The first main res

C. Alexandros Psomas, Ariel Schvartzman, S. Matthew Weinberg

We consider a revenue-maximizing seller with $k$ heterogeneous items for sale to a single additive buyer, whose values are drawn from a known, possibly correlated prior $\mathcal{D}$. It is known that there exist priors $\mathcal{D}$ such that simple mechanisms -- those with bounded menu complexity -- extract an arbitrarily small fraction of the optimal reve

Yili Shen, Xiao Liu, Cheng-Wei Ju, Jiaxu Yan

Subgraph representation learning based on Graph Neural Network (GNN) has exhibited broad applications in scientific advancements, such as predictions of molecular structure-property relationships and collective cellular function. In particular, graph augmentation techniques have shown promising results in improving graph-based and node-based classification t

Dmitry Ivanov, Aleksandr Chezhegov, Andrey Grunin, Mikhail Kiselev

Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. Next, it presents an overview of currently available neuromorphic AI projects in which these limitations are overcame by bringing

Toshiya Ueta

To understand the physical conditions of various gaseous systems, plasma diagnostics must be performed properly. To that end, it is equally important to have extinction correction performed properly. This means that the physical conditions of the target sources -- the very quantities to be derived via plasma diagnostics -- must be known even before performin

Grégoire Szymanski

We estimate the Hurst parameter $H \in (0,1)$ of a fractional Brownian motion from discrete noisy data, observed along a high frequency sampling scheme. When the intensity $\tau_n$ of the noise is smaller in order than $n^{-H}$ we establish the LAN property with optimal rate $n^{-1/2}$. Otherwise, we establish that the minimax rate of convergence is $(n/\tau

Amine M. Remita, Abdoulaye Baniré Diallo

Most evolutionary-oriented deep generative models do not explicitly consider the underlying evolutionary dynamics of biological sequences as it is performed within the Bayesian phylogenetic inference framework. In this study, we propose a method for a deep variational Bayesian generative model (EvoVGM) that jointly approximates the true posterior of local ev

Gary Koplik, Nathan Borggren, Sam Voisin, Gabrielle Angeloro

As Internet of Things (IoT) devices become both cheaper and more powerful, researchers are increasingly finding solutions to their scientific curiosities both financially and computationally feasible. When operating with restricted power or communications budgets, however, devices can only send highly-compressed data. Such circumstances are common for device

Anish Thite, Mohan Dodda, Pulak Agarwal, Jason Zutty

Deep Neural Networks (DNN's) are a widely-used solution for a variety of machine learning problems. However, it is often necessary to invest a significant amount of a data scientist's time to pre-process input data, test different neural network architectures, and tune hyper-parameters for optimal performance. Automated machine learning (autoML) methods auto

Abner J. Salgado, Ignacio Tomas

We study diagonally implicit Runge-Kutta (DIRK) schemes when applied to abstract evolution problems that fit into the Gelfand-triple framework. We introduce novel stability notions that are well-suited to this setting and provide simple, necessary and sufficient, conditions to verify that a DIRK scheme is stable in our sense and in Bochner-type norms. We use

Russell Avdek

We define a new algebra associated to a Legendrian submanifold $\Lambda$ of a contact manifold of the form $\mathbb{R}_{t} \times W$, called the planar diagram algebra and denoted $PDA(\Lambda, \mathcal{P})$. It is a non-commutative, filtered, differential graded algebra whose filtered stable tame isomorphism class is an invariant of $\Lambda$ together with

Marcelo P. L. Benedito, Lucas P. Melo, Lehilton L. C. Pedrosa

In the Multiple Allocation $k$-Hub Center (MA$k$HC), we are given a connected edge-weighted graph $G$, sets of clients $\mathcal{C}$ and hub locations $\mathcal{H}$, where ${V(G) = \mathcal{C} \cup \mathcal{H}}$, a set of demands $\mathcal{D} \subseteq \mathcal{C}^2$ and a positive integer $k$. A solution is a set of hubs $H \subseteq \mathcal{H}$ of size $k

Jimmy He

We formulate and establish symmetries of certain integrable half space models, analogous to recent results on symmetries for models in a full space. Our starting point is the colored stochastic six vertex model in a half space, from which we obtain results on the asymmetric simple exclusion process, as well as for the beta polymer through a fusion procedure

Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden

When trying to solve a computational problem, we are often faced with a choice between algorithms that are guaranteed to return the right answer but differ in their runtime distributions (e.g., SAT solvers, sorting algorithms). This paper aims to lay theoretical foundations for such choices by formalizing preferences over runtime distributions. It might seem

Lindsey Farris

Nilpotent Leibniz algebras with isomorphic maximal subalgebras are considered. The algebras are classified for coclass zero, one, and two. The results are field dependent.

Sarah Dean, Jamie Morgenstern

Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not change with the content they see. Evidence suggests that individuals' preferences are directly shaped by what content they see -- radicalization, rabbit holes, polarization, and boredo

Lyndon Moore, Gertjan Verdickt

The Reconstruction Finance Corporation and Public Works Administration loaned 50 U.S. railroads over $1.1 billion between 1932 and 1939. The government goal was to decrease the likelihood of bond defaults and increase employment. Bailouts had little effect on employment, instead they increased the average wage of their employees. Bailouts reduced leverage, b

Debjoy Thakur, Ishapathik Das, Shubhashree Chakravarty

Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Maintaining spatial homogeneity and continuity around the observed random spatial point is also challenging, especially when interpolating along a spatial surface, focusing on the boundary points as a neighborhood. Otherwise, the

Chamani M. Gunasekera, Xihan Ji, Marios Chatzikos, Renbin Yan

The depletion of elements onto dust grains is characterized using a generalized depletion strength $F_*$ for any sightline, and trend-line parameters $A_X, B_X$ and $z_X$. The parameters $A_X, B_X$ and $z_X$ define the relative depletion pattern, for which values are published in previous works. The present study uses these parameters to calculate post-deple

Anh T. V. Dau, Thang Nguyen-Duc, Hoang Thanh-Tung, Nghi D. Q. Bui

Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is because there could be noise in the source code corpora used to train such models. We adapt data-influence methods to detect such noises in this paper. Data-influence methods are u

Hanwei Wang, Yun-Sheng Chen, Yang Zhao

Understanding the mechanism of photoacoustic generation at the nano-scale is key to developing more efficient photoacoustic devices and agents. Unlike the far-field photoacoustic effect that has been well employed in imaging, the near-field profile leads to a complex wave-tissue interaction but is under-studied. Here we show that the spatiotemporal profile o

Karthik Reddy Kanjula, Vishnu Vardhan Reddy, Jnanesh K P, Jeffy S Abraham

Developments in IoT applications are playing an important role in our day-to-day life, starting from business predictions to self driving cars. One of the area, most influenced by the field of AI and IoT is retail analytics. In Retail Analytics, Conversion Rates - a metric which is most often used by retail stores to measure how many people have visited the

Chris D. A. Blair

I study generalisations of U-duality transformations which do not rely on the existence of isometries. I start by providing more details of a recently proposed generalised U-duality map between solutions of type IIA supergravity of the form $\text{M}_7 \times \text{S}^3$, with NSNS flux, and solutions of 11-dimensional supergravity, in which the three-sphere

Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi

Computing-in-memory with emerging non-volatile memory (nvCiM) is shown to be a promising candidate for accelerating deep neural networks (DNNs) with high energy efficiency. However, most non-volatile memory (NVM) devices suffer from reliability issues, resulting in a difference between actual data involved in the nvCiM computation and the weight value traine

Elvira Lupoian

We give a practical method to compute the 2-torsion subgroup of the Jacobian of a non-hyperelliptic curve of genus $3$, $4$ or $5$. The method is based on the correspondence between the 2-torsion subgroup and the theta hyperplanes to the curve. The correspondence is used to explicitly write down a zero-dimensional scheme whose points correspond to elements o

Zechun Liu, Barlas Oguz, Aasish Pappu, Lin Xiao

Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine learning, but have also grown in parameters and computational complexity, making them increasingly difficult to deploy in resource-constrained environments. Binarization of the weights and activations of the network can significantly alleviate these issues, however, is tech

Marcell Howard, Arthur Kosowsky, Georgios Valogiannis

If the accelerated expansion of the universe is due to a modification of general relativity at late times, it is likely that the growth of structure on large scales would also display deviations from the standard cosmology. We investigate the statistics of the distribution of galaxy cluster-sized halos as a probe of gravity. We analyze the output of several

A. Calamida, V. Bajaj, J. Mack, M. Marinelli

We present a new photometric calibration of the WFC3-UVIS and WFC3-IR detectors based on observations collected from 2009 to 2020 for four white dwarfs, namely GRW+70~5824, GD~153, GD~71, G191B2B, and a G-type star, P330E. These calibrations include recent updates to the Hubble Space Telescope primary standard white dwarf models and a new reference flux for

Niklas Lauffer, Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte, Ameesh Shah

The identification of a deterministic finite automaton (DFA) from labeled examples is a well-studied problem in the literature; however, prior work focuses on the identification of monolithic DFAs. Although monolithic DFAs provide accurate descriptions of systems' behavior, they lack simplicity and interpretability; moreover, they fail to capture sub-tasks r

Anh-Duy Pham, Anastassia Kuestenmacher, Paul G. Ploeger

Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a dee

Dario Tscholl, Stephan-Daniel Gravert, Aurel X. Appius, Robert K. Katzschmann

Rapid and versatile object manipulation in air is an open challenge. An energy-efficient and adaptive soft gripper combined with an agile aerial vehicle could revolutionize aerial robotic manipulation in areas such as warehousing. This paper presents a bio-inspired gripper powered by hydraulically amplified electrostatic actuators mounted to a quadcopter tha

Samantha Wallace, Shaela I. Jones, C. Nick Arge, Nicholeen Viall

Parker Solar Probes's (PSP)'s unique orbital path allows us to observe the solar wind closer to the Sun than ever before. Essential to advancing our knowledge of solar wind and energetic particle formation is identifying the sources of PSP observations. We report on results for the first two PSP solar encounters derived using the Wang-Sheeley-Arge (WSA) mode

Talwinder Singh, Tae K. Kim, Nikolai V. Pogorelov, Charles N. Arge

Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for the prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 12-July-2012 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using t

Benjamin Lion, Farhad Arbab, Carolyn Talcott

We introduce a transition system based specification of cyber-physical systems whose semantics is compositional with respect to a family of algebraic products. We give sufficient conditions for execution of a product to be correctly implemented by a lazy expansion of the product construction. The transition system algebra is implemented in the Maude rewritin

Fabrizio Corelli, Marina De Amicis, Taishi Ikeda, Paolo Pani

As the only gravity theory with quadratic curvature terms and second-order field equations, Einstein-dilaton-Gauss-Bonnet gravity is a natural testbed to probe the high-curvature regime beyond General Relativity in a fully nonperturbative way. Due to nonperturbative effects of the dilatonic coupling, black holes in this theory have a minimum mass which separ

Fabrizio Corelli, Marina De Amicis, Taishi Ikeda, Paolo Pani

During the final stages of black hole evaporation, ultraviolet deviations from General Relativity eventually become dramatic, potentially affecting the end-state. We explore this problem by performing nonlinear simulations of wave packets in Einstein-dilaton-Gauss-Bonnet gravity, the only gravity theory with quadratic curvature terms which can be studied at

Ryan Kortvelesy, Amanda Prorok

In multi-agent reinforcement learning, the use of a global objective is a powerful tool for incentivising cooperation. Unfortunately, it is not sample-efficient to train individual agents with a global reward, because it does not necessarily correlate with an agent's individual actions. This problem can be solved by factorising the global value function into

Alex Kontorovich, Christopher Lutsko

Given a Zariski-dense, discrete group, $\Gamma$, of isometries acting on $(n + 1)$-dimensional hyperbolic space, we use spectral methods to obtain a sharp asymptotic formula for the growth rate of certain $\Gamma$-orbits. In particular, this allows us to obtain a best-known effective error rate for the Apollonian and (more generally) Kleinian sphere packing

Marc Oncins, Jordi Miralda-Escudé, Jordi L. Gutiérrez, Pilar Gil-Pons

Primordial black holes in the asteroid-mass window ($\sim 10^{-16}$ to $10^{-11} \rm M_{\odot}$), which might constitute all the dark matter, can be captured by stars when they traverse them at low enough velocity. After being placed on a bound orbit during star formation, they can repeatedly cross the star if the orbit happens to be highly eccentric, slow d

L. A. Ferreira, L. R. Livramento

It has been recently proposed a modification of the Skyrme model which admits an exact self-dual sector by the introduction of six scalar fields assembled in a symmetric, positive and invertible 3x3 matrix h. In this paper we study soft manners of breaking the self-duality of that model. The crucial observation is that the self-duality equations impose disti

Jingbo Wang, Yu Rong, Jingyuan Liu, Sijie Yan

The ability to synthesize long-term human motion sequences in real-world scenes can facilitate numerous applications. Previous approaches for scene-aware motion synthesis are constrained by pre-defined target objects or positions and thus limit the diversity of human-scene interactions for synthesized motions. In this paper, we focus on the problem of synthe

P. S. Tarabunga, F. M. Surace, R. Andreoni, A. Angelone

Recent atomic physics experiments and numerical works have reported complementary signatures of the emergence of a topological quantum spin liquid in models with blockade interactions. However, the specific mechanism stabilizing such a phase remains unclear. Here, we introduce an exact relation between an Ising-Higgs lattice gauge theory on the kagome lattic

Eddie H. Chang, Jared Rivera, Brian Bostwick, Christian Schneider

We present the design, construction, and simulation of a simple, low-cost external cavity diode laser with a measured free-running frequency drift rate of 1.4(1)~MHz/h at 852 nm. This performance is achieved via a compact, nearly monolithic aluminum structure to minimize temperature gradients across the laser cavity. We present thermal finite element method

Grecco A. Oyarzun, Kevin Bundy, Kyle B. Westfall, Jeremy L. Tinker

We analyze spatially resolved and co-added SDSS-IV MaNGA spectra with signal-to-noise ~100 from 2200 passive central galaxies (z~0.05) to understand how central galaxy assembly depends on stellar mass (M*) and halo mass (Mh). We control for systematic errors in Mh by employing a new group catalog from Tinker (2020a,b) and the widely-used Yang et al. (2007) c

Yifan Hong, Jeremy T. Young, Adam M. Kaufman, Andrew Lucas

We describe a simple quantum error correcting code built out of a time-dependent transverse field Ising model. The code is similar to a repetition code, but has two advantages: an $N$-qubit code can be implemented with a finite-depth spatially local unitary circuit, and it can subsequently protect against both $X$ and $Z$ errors if $N\ge 10$ is even. We prop

James Dborin, Vinul Wimalaweera, Fergus Barratt, Eric Ostby

We optimise a translationally invariant, sequential quantum circuit on a superconducting quantum device to simulate the groundstate of the quantum Ising model through its quantum critical point. We further demonstrate how the dynamical quantum critical point found in quenches of this model across its quantum critical point can be simulated. Our approach avoi

Alexey Milekhin

It has been proposed in the literature that the volume of Einstein-Rosen bridge is equal to complexity of state preparation ("Complexity=Volume" conjecture). Taking this statement outside the horizon, one might be tempted to propose "Complexity=Time" correspondence. In this Essay we argue that in a blockchain protocol, which is the foundation of all modern c

Dirk Heumann, Dimitrios Psaltis

Recent advancements in observational techniques have led to new tests of the general relativistic predictions for black-hole spacetimes in the strong-field regime. One of the key ingredients for several tests is a metric that allows for deviations from the Kerr solution but remains free of pathologies outside its event horizon. Existing metrics that have bee

Hideki Maeda, Tomohiro Harada

In model building studies, it is important to check the energy conditions for the corresponding energy-momentum tensor determined by the gravitational field equations in order to single out physically reasonable models. In this process, one often encounters a situation where the energy-momentum tensor has one off-diagonal "space-time" component in the frame

Shachar Fraenkel, Moshe Goldstein

Entanglement measures constitute powerful tools in the quantitative description of quantum many-body systems out of equilibrium. We study entanglement in the current-carrying steady state of a paradigmatic one-dimensional model of noninteracting fermions at zero temperature in the presence of a scatterer. We show that disjoint intervals located on opposite s

Z. H. Sun, C. A. Bell, G. Hagen, T. Papenbrock

Coupled cluster theory is an attractive tool to solve the quantum many-body problem because its singles and doubles (CCSD) approximation is computationally affordable and yields about 90% of the correlation energy. Capturing the remaining 10%, e.g. via including triples, is numerically expensive. Here we assume that short-range three-body correlations domina

Hyungjin Kim, Gilad Perez

Axion-gluon interaction induces quadratic couplings between the axion and the matter fields. We find that, if the axion is an ultralight dark matter field, it induces small oscillations of the mass of the hadrons as well as other nuclear quantities. As a result, atomic energy levels oscillate. We use currently available atomic spectroscopy data to constrain

Håvard H. Haugen, Asle Sudbø

Motivated by the recent experimental observation of an intermediate bosonic metallic state in the two-dimensional superconductor-insulator transition at $T=0$, we study an extended Bose Hubbard model in the limit of large number of particles per site. Using a representation of this in terms of two coupled $XY$ models, we find, in addition to an insulating ph

Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou

Sentence scoring aims at measuring the likelihood score of a sentence and is widely used in many natural language processing scenarios, like reranking, which is to select the best sentence from multiple candidates. Previous works on sentence scoring mainly adopted either causal language modeling (CLM) like GPT or masked language modeling (MLM) like BERT, whi

Maurice L. Wilson, John C. Raymond

Solar coronal mass ejections (CMEs) have a strong association with solar flares that is not fully understood. This characteristic of our Sun's magnetic activity may also occur on other stars, but the lack of successfully detected stellar CMEs makes it difficult to perform statistical studies that might show a similar association between CMEs and flares. Beca

Rubén García-Benito, Enrique Pérez-Montero

CosMOnic (COSmos harMONIC) is a sonification project with a triple purpose: analysis (by means of sounds) of any type of data, source of inspiration for artistic creations, and pedagogical and dissemination purposes. In this contribution we present the work recently produced by CosMonic in the latter field, creating specific cases for the inclusive astronomy

Azadeh Maleknejad, Evan McDonough

We consider a dark confining gauge theory with millicharged Ultra-Light Pions (ULP) and heavy baryons as dark matter candidates. The model simultaneously realizes the ultra-light (STrongly-interacting Ultralight Millicharged Particle or "STUMP") and superheavy ("WIMPzilla") dark matter paradigms, connected by the confinement scale of the dark QCD. It is a re

Laura Engelbrecht, Callum R. T. Jones, Shruti Paranjape

We initiate a systematic study of the self-interactions of a massive spin-2 "graviton" consistent with up to $\mathcal{N}=4$ supersymmetry. Using a recently developed massive on-shell superspace formalism, we construct the most general set of cubic massive graviton amplitudes in a form with all supersymmetry and Lorentz invariance manifest. We find that for

K. Arur, T. J. Maccarone

We present the results of bicoherence analysis on observations of GRS 1915+105 that exhibit quasi-periodic oscillations (QPOs). The bicoherence is a higher order statistic that can be used to probe the relation between the phases of a triplet of Fourier frequencies. Despite showing very similar power spectra, the observations exhibit different patterns in th

Micaela B. Bagley, Steven L. Finkelstein, Sofía Rojas-Ruiz, James Diekmann

The abundance of bright galaxies at z>8 can provide key constraints on models of galaxy formation and evolution, as the predicted abundance varies greatly when different physical prescriptions for gas cooling and star formation are implemented. We present the results of a search for bright z=9-10 galaxies selected from pure-parallel Hubble Space Telescope im

Philip W. Phillips, Nigel E. Hussey, Peter Abbamonte

Although the resistivity in traditional metals increases with temperature, its $T$ dependence vanishes at low or high temperature, albeit for different reasons. Here, we review a class of materials, known as \lq strange' metals, that can violate both principles. In materials exhibiting such behavior, the change in slope of the resistivity as the mean free pa

Shai M. Chester

We derive exact relations between certain integrals of the conserved flavor current four point function in 4d $\mathcal{N}=2$ conformal field theories (CFTs) and derivatives of the mass deformed sphere free energy, which can be computed exactly for gauge theories using supersymmetric localization. For conformal gauge theories with flavor groups of rank great