October 2020 arXiv papers — page 126

Joonas A. Autio, Qi Zhu, Xiaolian Li, Matthew F. Glasser

Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the PRIME-DE initiative has increased the availability of NHP MRI data and the need for robust multi-subject multi-center analyses. Streamlined acquisition and analysis protocols would accelerate and impr

Xiangdong Chen, Meng Li, Xi Lin, Yafeng Yin

Leveraging the accuracy and consistency of vehicle motion control enabled by the connected and automated vehicle technology, we propose the rhythmic control (RC) scheme that allows vehicles to pass through an intersection in a conflict-free manner with a preset rhythm. The rhythm enables vehicles to proceed at a constant speed without any stop. The RC is cap

Alexandra DeLucia, Elisabeth Moore

High performance computing (HPC) user support teams are the first line of defense against large-scale problems, as they are often the first to learn of problems reported by users. Developing tools to better assist support teams in solving user problems and tracking issue trends is critical for maintaining system health. Our work examines the Los Alamos Natio

Pengyong Ding

There are two questions in analytic number theory which have attracted much attention over the years. The first one is about the asymptotic formula for the variance associated with the distribution of a real sequence in arithmetic progressions, which origins in the work of Barban. The second one is about the function $r_3(n)$, the number of ordered represent

Anthony Tompkins, Rafael Oliveira, Fabio Ramos

We establish a general form of explicit, input-dependent, measure-valued warpings for learning nonstationary kernels. While stationary kernels are ubiquitous and simple to use, they struggle to adapt to functions that vary in smoothness with respect to the input. The proposed learning algorithm warps inputs as conditional Gaussian measures that control the s

Xiaomian Kang, Yang Zhao, Jiajun Zhang, Chengqing Zong

Document-level neural machine translation has yielded attractive improvements. However, majority of existing methods roughly use all context sentences in a fixed scope. They neglect the fact that different source sentences need different sizes of context. To address this problem, we propose an effective approach to select dynamic context so that the document

Alemayehu Solomon Abrar, Anh Luong, Gregory Spencer, Nathan Genstein

The ability to predict, and thus react to, oncoming collisions among a set of mobile agents is a fundamental requirement for safe autonomous movement, both human and robotic. This paper addresses systems that use range measurements between mobile agents for the purpose of collision prediction, which involves prediction of the agents' future paths to know

Erika Hunhoff, Shazal Irshad, Vijay Thurimella, Ali Tariq

This paper introduces a new primitive to serverless language runtimes called freshen. With freshen, developers or providers specify functionality to perform before a given function executes. This proactive technique allows for overheads associated with serverless functions to be mitigated at execution time, which improves function responsiveness. We show var

Yuanyuan Gao, Lora Cavuoto, Anirban Dutta, Uwe Kruger

Acquisition of fine motor skills is a time-consuming process as it requires frequent repetitions. Transcranial electrical stimulation is a promising means of enhancing simple motor skill development via neuromodulatory mechanisms. Here, we report that non-invasive neurostimulation facilitates the learning of complex fine bimanual motor skills associated with

Wei Dai, Daoyuan Fang, Chengbo Wang

By assuming certain local energy estimates on $(1+3)$-dimensional asymptotically flat space-time, we study the existence portion of the \emph{Strauss} type wave system. Firstly we give a kind of space-time estimates which are related to the local energy norm that appeared in \cite{MR2944027}. These estimates can be used to prove a series of weighted \emph{St

Enes Krijestorac, Ghaith Hattab, Petar Popovski, Danijela Cabric

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing bands and transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one mu

Barinder Thind, Kevin Multani, Jiguo Cao

In recent years, there has been considerable innovation in the world of predictive methodologies. This is evident by the relative domination of machine learning approaches in various classification competitions. While these algorithms have excelled at multivariate problems, they have remained dormant in the realm of functional data analysis. We extend notabl

Daniele Reda, Tianxin Tao, Michiel van de Panne

Learning to locomote is one of the most common tasks in physics-based animation and deep reinforcement learning (RL). A learned policy is the product of the problem to be solved, as embodied by the RL environment, and the RL algorithm. While enormous attention has been devoted to RL algorithms, much less is known about the impact of design choices for the RL

Javid Ebrahimi, Dhruv Gelda, Wei Zhang

We focus on the recognition of Dyck-n ($\mathcal{D}_n$) languages with self-attention (SA) networks, which has been deemed to be a difficult task for these networks. We compare the performance of two variants of SA, one with a starting symbol (SA$^+$) and one without (SA$^-$). Our results show that SA$^+$ is able to generalize to longer sequences and deeper

Diego Cifuentes

Given an affine space of matrices $\mathcal{L}$ and a matrix $Θ\in \mathcal{L}$, consider the problem of computing the closest rank deficient matrix to $Θ$ on $\mathcal{L}$ with respect to the Frobenius norm. This is a nonconvex problem with several applications in control theory, computer algebra, and computer vision. We introduce a novel semidefinite progr

Jonathan Shen, Ye Jia, Mike Chrzanowski, Yu Zhang

This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, replacing the attention mechanism with an explicit duration predictor. This improves robustness significantly as measured by unaligned duration ratio and word deletion rate, two metrics introduced in this paper for large-scale robustness evaluation using a pre-trained sp

Dylan Lewis, Asmae Benhemou, Natasha Feinstein, Leonardo Banchi

Continuous-time quantum walks can be used to solve the spatial search problem, which is an essential component for many quantum algorithms that run quadratically faster than their classical counterpart, in $\mathcal O(\sqrt n)$ time for $n$ entries. However the capability of models found in nature is largely unexplored - e.g., in one dimension only nearest-n

Thibault Sellam, Amy Pu, Hyung Won Chung, Sebastian Gehrmann

The quality of machine translation systems has dramatically improved over the last decade, and as a result, evaluation has become an increasingly challenging problem. This paper describes our contribution to the WMT 2020 Metrics Shared Task, the main benchmark for automatic evaluation of translation. We make several submissions based on BLEURT, a previously

Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz

Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environment is a simulation of an open-source r

Bhargav Srinivasa Desikan, Tasker Hull, Ethan O. Nadler, Douglas Guilbeault

Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we introduce the Python package comp-syn, which provides grounded word embeddings based on the perceptually uniform color distributions of Google Image search results. We demonstrate t

Nishant Agrawal, Yaozhong Hu

In this paper, we obtain the existence, uniqueness and positivity of the solution to delayed stochastic differential equations with jumps. This equation is then applied to model the price movement of the risky asset in a financial market and the Black-Scholes formula for the price of European options is obtained together with the hedging portfolios. The opti

L. Chauvin, M. Ben Lazreg, J. B. Carluer, W. Wells

We propose a unified appearance model accounting for traditional shallow (i.e. 3D SIFT keypoints) and deep (i.e. CNN output layers) image feature representations, encoding respectively specific, localized neuroanatomical patterns and rich global information into a single indexing and classification framework. A novel Bayesian model combines shallow and deep

Patrick Rodler

Various model-based diagnosis scenarios require the computation of most preferred fault explanations. Existing algorithms that are sound (i.e., output only actual fault explanations) and complete (i.e., can return all explanations), however, require exponential space to achieve this task. As a remedy, we propose two novel diagnostic search algorithms, called

Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David Sontag

Reinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to subtle choices in study design. We highlight a simple approach, trajectory inspection, to bring clinicians into an iterative design process for model-based RL studies. We identify

James R. Wright, Riccardo Balin, John W. Patterson, John A. Evans

The turbulent boundary layer over a flat plate is computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations as a test bed for a synthetic turbulence generator (STG) inflow boundary condition. The inlet momentum thickness Reynolds number is approximately 1,000. The study provides validation of the ability of the STG to develo

Shaun Bullett, Luna Lomonaco

We prove that there exists a homeomorphism $χ$ between the connectedness locus $\mathcal{M}_Γ$ for the family $\mathcal{F}_a$ of $(2:2)$ holomorphic correspondences introduced by Bullett and Penrose, and the parabolic Mandelbrot set $\mathcal{M}_1$. The homeomorphism $χ$ is dynamical ($\mathcal{F}_a$ is a mating between $PSL(2,\mathbb{Z})$ and $P_{χ(a)}$), i

Hanul Jeon

The main goal of this paper is to formulate a constructive analogue of Ackermann's observation about finite set theory and arithmetic. We will see that Heyting arithmetic is bi-interpretable with $\mathsf{CZF^{fin}}$, the finitary version of $\mathsf{CZF}$. We also examine bi-interpretability between subtheories of finitary $\mathsf{CZF}$ and Heyting ari

Melia E. Bonomo, Anthony K. Brandt, J. Todd Frazier, Christof Karmonik

Music listening involves many simultaneous neural operations, including auditory processing, working memory, temporal sequencing, pitch tracking, anticipation, reward, and emotion, and thus, a full investigation of music cognition would benefit from whole-brain analyses. Here, we quantify whole-brain activity while participants listen to a variety of music a

Ajaharul Islam, Michael Strickland

We introduce a framework called Heavy Quarkonium Quantum Dynamics (HQQD) which can be used to compute the dynamical suppression of heavy quarkonia propagating in the quark-gluon plasma using real-time in-medium quantum evolution. Using HQQD we compute large sets of real-time solutions to the Schrödinger equation using a realistic in-medium complex-valued pot

Javier F. Troncoso

ClasSOMfier is a software package to classify atoms into a given number of disconnected groups (or clusters) and detect lattice defects, such as vacancies, interstitials, dislocations, voids and grain boundaries. Each cluster is formed by atoms whose atomic environment can be described by a common pattern. Unlike many methods available in the literature, whe

Takaaki Nomura, Prasenjit Sanyal

We investigate a two Higgs doublet model with extra flavour depending $U(1)_X$ gauge symmetry where $Z'$ boson interactions can explain the Atomki anomaly by choosing appropriate charge assignment for the SM fermions. For parameter region explaining the Atomki anomaly we obtain light scalar boson with $\mathcal{O}(10)$ GeV mass, and we explore scalar sec

Gino Biondini, Jeffrey Oregero, Alexander Tovbis

The spectrum of the focusing Zakharov-Shabat operator on the circle is studied, and its explicit dependence on the presence of a semiclassical parameter is also considered. Several new results are obtained. In particular: (i) it is proved that the resolvent set is comprised of two connected components, (ii) new bounds on the location of the Floquet and Diric

Xinyang Zhou, Chin-Yao Chang, Andrey Bernstein, Changhong Zhao

The increasing penetration of distributed energy resources (DERs) in the distribution networks has turned the conventionally passive load buses into active buses that can provide grid services for the transmission system. To take advantage of the DERs in the distribution networks, this letter formulates a transmission-and-distribution (T&D) systems co-optimi

Yikai Wu, Xingyu Zhu, Chenwei Wu, Annie Wang

Hessian captures important properties of the deep neural network loss landscape. Previous works have observed low rank structure in the Hessians of neural networks. In this paper, we propose a decoupling conjecture that decomposes the layer-wise Hessians of a network as the Kronecker product of two smaller matrices. We can analyze the properties of these sma

Michell Guzman, Oliviero Riganelli, Daniela Micucci, Leonardo Mariani

Software enforcers can be used to modify the runtime behavior of software applications to guarantee that relevant correctness policies are satisfied. Indeed, the implementation of software enforcers can be tricky, due to the heterogeneity of the situations that they must be able to handle. Assessing their ability to steer the behavior of the target system wi

Julia B. Nakhleh, M. Giselle Fernández-Godino, Michael J. Grosskopf, Brandon M. Wilson

Building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their pred

Antti Kuusisto

We discuss partial specifications in first-order logic FO and also in a Turing-complete extension of FO. We compare the compositional and game-theoretic approaches to the systems.

Mykael Cardoso, Luiz Gustavo Farah

We consider the inhomogeneous nonlinear Schrödinger (INLS) equation in $\mathbb{R}^N$ $$i \partial_t u +Δu +|x|^{-b} |u|^{2σ}u = 0,$$ where $N\geq 3$, $0<b<\min\left\{\frac{N}{2},2\right\}$ and $\frac{2-b}{N}<σ<\frac{2-b}{N-2}$. The scaling invariant Sobolev space is $\dot{H}^{s_c}$ with $s_c=\frac{N}{2}-\frac{2-b}{2σ}$. The restriction on $σ$ implies $0<s_c

Juraj Lörinčík, Jaroslav Dudík, Guillaume Aulanier, Brigitte Schmieder

We report on the Atmospheric Imaging Assembly (AIA) observations of plasma outflows originating in a coronal dimming during the 2015 April 28th filament eruption. After the filament started to erupt, two flare ribbons formed, one of which had a well-visible hook enclosing a core (twin) dimming region. Along multiple funnels located in this dimming, a motion

Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky

Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dynamic Mode Decomposition (DMD) in time delay coordinates. Central to this approach is the insight that grid load, like many observables on compl

Jeremie Fish, Alexander DeWitt, Abd AlRahman R. AlMomani, Paul J. Laurienti

The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. Key to this study is understanding structure of both the structural and functional connectivity between anatomical regions. In this paper we follow previous work in developing a simple dynamical model of the brain

Shang-Yu Su, Yung-Sung Chuang, Yun-Nung Chen

Natural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does the opposite. The prior work mainly focused on exploiting the duality in model training in order to obtain the models with better performance. However,

Huozhi Zhou, Jinglin Chen, Lav R. Varshney, Ashish Jagmohan

We consider reinforcement learning (RL) in episodic Markov decision processes (MDPs) with linear function approximation under drifting environment. Specifically, both the reward and state transition functions can evolve over time but their total variations do not exceed a $\textit{variation budget}$. We first develop $\texttt{LSVI-UCB-Restart}$ algorithm, an

Dong-han Yeom

We first revisit Hartle and Hawking&#39;s path integral derivation of Hawking radiation. In the first point of view, we interpret that a particle-antiparticle pair is created and the negative energy antiparticle falls into the black hole. On the other point of view, a particle inside the horizon, or beyond the Einstein-Rosen bridge, tunnels to outside the ho

Mohammad J. Salariseddigh, Uzi Pereg, Holger Boche, Christian Deppe

The identification capacity is developed without randomization at neither the encoder nor the decoder. In particular, full characterization is established for the deterministic identification (DI) capacity for the Gaussian channel and for the general discrete memoryless channel (DMC) with and without constraints. Originally, Ahlswede and Dueck established th

Scott Baldridge, Louis H. Kauffman, William Rushworth

We introduce a new equivalence relation on decorated ribbon graphs, and show that its equivalence classes directly correspond to virtual links. We demonstrate how this correspondence can be used to convert any invariant of virtual links into an invariant of ribbon graphs, and vice versa.

Christian J. Steinmetz, Joshua D. Reiss

By processing audio signals in the time-domain with randomly weighted temporal convolutional networks (TCNs), we uncover a wide range of novel, yet controllable overdrive effects. We discover that architectural aspects, such as the depth of the network, the kernel size, the number of channels, the activation function, as well as the weight initialization, al

Andrés Muñoz Medina, Jenny Gillenwater

We propose and analyze a general-purpose dataset-distance-based utility function family, Duff, for differential privacy&#39;s exponential mechanism. Given a particular dataset and a statistic (e.g., median, mode), this function family assigns utility to a possible output o based on the number of individuals whose data would have to be added to or removed fro

Lena Murchikova

In 2019, the Galactic center black hole Sgr A* produced an unusually high number of bright near-infrared flares, including the brightest-ever detected flare (Do et al 2019, Gravity Collaboration 2020). We propose that this activity was triggered by the near simultaneous infall of material shed by G1 and G2 objects due to their interaction with the background

M. Nouman Muteeb

We study the BPS counting functions (free energies) of the M-string configurations. We consider separated M5-branes along with M2-branes stretched between them, with M5-branes acting as domain walls interpolating different configurations of M2-branes. We find recursive structure in the free energies of these configurations. The M-string degrees of freedom on

Adriano Di Giacomo

In this paper we improve the existing order parameter for monopole condensation in gauge theory vacuum, making it gauge-invariant from scratch and free of the spurious infrared problems which plagued the old one. Computing the new parameter on the lattice will unambiguously detect weather dual superconductivity is the mechanism for color confinement. As a by

Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi

Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable, and require considerable tuning and domain expertise to apply successfully. In this work, we present a simple method fo

Ryosuke Sawata, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji

This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we take advantage of the frequency and time domain representation of audio signals. Next, we utilize the relationship among instruments by jointly considering them. We do this on the

A. Cloninger, H. N. Mhaskar

Many applications such as election forecasting, environmental monitoring, health policy, and graph based machine learning require taking expectation of functions defined on the vertices of a graph. We describe a construction of a sampling scheme analogous to the so called Leja points in complex potential theory that can be proved to give low discrepancy esti

Alfio Bonanno, Amir-Pouyan Khosravi, Frank Saueressig

Non-singular black hole geometries typically come with two spacetime horizons: an (outer) event horizon and an (inner) Cauchy horizon. This nurtures the speculation that they may be subject to a mass-inflation effect which renders the Cauchy horizon unstable. We analyze the dynamics associated with spherically symmetric, regular black holes taking the full b

Ryan Omidi, Ali Moghimi, Alireza Pourreza, Mohamed El-Hadedy

The large data size and dimensionality of hyperspectral data demands complex processing and data analysis. Multispectral data do not suffer the same limitations, but are normally restricted to blue, green, red, red edge, and near infrared bands. This study aimed to identify the optimal set of spectral bands for nitrogen detection in grape leaves using ensemb

Sokolov Artem, Andrey V. Savchenko

This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conduct experiments with finetuning on the gender-specific test subsets and. In general, we do not obtain essential WER reduction by finetuning techniques by this approach. We achieved

Muhammed O. Sayin, Francesca Parise, Asuman Ozdaglar

We present a novel variant of fictitious play dynamics combining classical fictitious play with Q-learning for stochastic games and analyze its convergence properties in two-player zero-sum stochastic games. Our dynamics involves players forming beliefs on the opponent strategy and their own continuation payoff (Q-function), and playing a greedy best respons

Felipe Gomez-Cuba, Tommaso Zugno, Junseok Kim, Michele Polese

This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this w

Asad Lodhia, Anna Maltsev

In this paper we analyze the covariance kernel of the Gaussian process that arises as the limit of fluctuations of linear spectral statistics for Wigner matrices with a few moments. More precisely, the process we study here corresponds to Hermitian matrices with independent entries that have $α$ moments for $2<α< 4$. We obtain a closed form $α$-dependent exp

Dipankar Chakrabarti, Chandan Mondal, Asmita Mukherjee, Sreeraj Nair

We obtain the gravitational form factors (GFFs) and investigate their applications for the description of the mechanical properties, i.e., the distributions of pressures, shear forces inside proton, and the mechanical radius, in a light-front quark-diquark model constructed by the soft-wall AdS/QCD. The GFFs, $A(Q^2)$ and $B(Q^2)$ are found to be consistent

Gopinath Chennupati, Nandakishore Santhi, Phill Romero, Stephan Eidenbenz

We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input, predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) in

Manuel Weber, Christoph Doblander, Peter Mandl

Information about room-level occupancy is crucial to many building-related tasks, such as building automation or energy performance simulation. Current occupancy detection literature focuses on data-driven methods, but is mostly based on small case studies with few rooms. The necessity to collect room-specific data for each room of interest impedes applicabi

Karim Halaseh, Tommi Muller, Elina Robeva

In this paper we study the problem of decomposing a given tensor into a tensor train such that the tensors at the vertices are orthogonally decomposable. When the tensor train has length two, and the orthogonally decomposable tensors at the two vertices are symmetric, we recover the decomposition by considering random linear combinations of slices. Furthermo

Andrey Ardentov, Gil Bor, Enrico Le Donne, Richard Montgomery

We relate the sub-Riemannian geometry on the group of rigid motions of the plane to `bicycling mathematics&#39;. We show that this geometry&#39;s geodesics correspond to bike paths whose front tracks are either non-inflectional Euler elasticae or straight lines, and that its infinite minimizing geodesics (or `metric lines&#39;) correspond to bike paths whose

Mehrana Raeisian Nejad, Amin Doostmohammadi, Julia Mary Yeomans

We use analytic arguments and numerical solutions of the continuum, active nematohydrodynamic equations to study how friction alters the behaviour of active nematics. Concentrating on the case where there is nematic ordering in the passive limit, we show that, as the friction is increased, memory effects become more prominent and $+1/2$ topological defects l

Yifan Chen, Thomas Y. Hou

There is an intimate connection between numerical upscaling of multiscale PDEs and scattered data approximation of heterogeneous functions: the coarse variables selected for deriving an upscaled equation (in the former) correspond to the sampled information used for approximation (in the latter). As such, both problems can be thought of as recovering a targe

Charles C. Onu, Jacob E. Miller, Doina Precup

Recurrent neural networks (RNNs) are powerful tools for sequential modeling, but typically require significant overparameterization and regularization to achieve optimal performance. This leads to difficulties in the deployment of large RNNs in resource-limited settings, while also introducing complications in hyperparameter selection and training. To addres

Ryan Plestid

Solar neutrinos upscattering inside the Earth can source unstable particles that can decay inside terrestrial detectors. Contrary to naive expectations we show that when the decay length is much shorter than the radius of the \emph{Earth} (rather than the detector), the event rate is independent of the decay length. In this paper we study a transition dipole

S. Ettori, L. Lovisari, M. Sereno

As the end products of the hierarchical process of cosmic structure formation, galaxy clusters present some predictable properties, like those mostly driven by gravity, and some others, more affected by astrophysical dissipative processes, that can be recovered from observations and that show remarkable &#34;universal&#34; behaviour once rescaled by halo mas

Abhishek Singh

Companies provide annual reports to their shareholders at the end of the financial year that describes their operations and financial conditions. The average length of these reports is 80, and it may extend up to 250 pages long. In this paper, we propose our methodology PoinT-5 (the combination of Pointer Network and T-5 (Test-to-text transfer Transformer) a

Ilia L. Rasskazov, Vadim I. Zakomirnyi, Anton D. Utyushev, P. Scott Carney

The modified long-wavelength approximation (MLWA), a next order approximation beyond the Rayleigh limit, has been applied usually only to the dipole $\ell=1$ contribution and for the range of size parameters $x$ not exceeding $x\lesssim 1$ to estimate far- and near-field electromagnetic properties of plasmonic nanoparticles. Provided that the MLWA functional

Elizabeth Denne, John Carr Haden, Troy Larsen, Emily Meehan

We study Kauffman's model of folded ribbon knots: knots made of a thin strip of paper folded flat in the plane. The folded ribbonlength is the length to width ratio of such a ribbon knot. We give upper bounds on the folded ribbonlength of 2-bridge, $(2,q)$ torus, twist, and pretzel knots, and these upper bounds turn out to be linear in the crossing number. W

Troy J. Raen, Héctor Martínez-Rodríguez, Travis J. Hurst, Andrew R. Zentner

Most of the dark matter (DM) search over the last few decades has focused on WIMPs, but the viable parameter space is quickly shrinking. Asymmetric Dark Matter (ADM) is a WIMP-like DM candidate with slightly smaller masses and no present day annihilation, meaning that stars can capture and build up large quantities. The captured ADM can transport energy thro

Dong Yeap Kang, Daniela Kühn, Abhishek Methuku, Deryk Osthus

Let $H$ be a $k$-uniform $D$-regular simple hypergraph on $N$ vertices. Based on an analysis of the R\"odl nibble, Alon, Kim and Spencer (1997) proved that if $k \ge 3$, then $H$ contains a matching covering all but at most $ND^{-1/(k-1)+o(1)}$ vertices, and asked whether this bound is tight. In this paper we improve their bound by showing that for all $k >

Sihan Yuan, Boryana Hadzhiyska, Sownak Bose, Daniel J. Eisenstein

Building accurate and flexible galaxy-halo connection models is crucial in modeling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and that the local environment of the halo can be an excellent indicator of galaxy assembly bias. In this paper, we propose

Evan McDonough, Alan H. Guth, David I. Kaiser

We study the multifield dynamics of axion models nonminimally coupled to gravity. As usual, we consider a canonical $U(1)$ symmetry-breaking model in which the axion is the phase of a complex scalar field. If the complex scalar field has a nonminimal coupling to gravity, then the (oft-forgotten) radial component can drive a phase of inflation prior to an inf

Henk Hoekstra, Arun Kannawadi, Thomas D. Kitching

Weak lensing by large-scale structure is a powerful probe of cosmology if the apparent alignments in the shapes of distant galaxies can be accurately measured. Most studies have therefore focused on improving the fidelity of the shape measurements themselves, but the preceding step of object detection has been largely ignored. In this paper we study the impa

Andrés N. Salcedo, Ying Zu, Youcai Zhang, Huiyuan Wang

We investigate the level of galaxy assembly bias in the Sloan Digital Sky Survey (SDSS) main galaxy sample using ELUCID, a state-of-the-art constrained simulation that accurately reconstructed the initial density perturbations within the SDSS volume. On top of the ELUCID haloes, we develop an extended HOD model that includes the assembly bias of central and

Tony Metger, Yfke Dulek, Andrea Coladangelo, Rotem Arnon-Friedman

In device-independent quantum key distribution (DIQKD), an adversary prepares a device consisting of two components, distributed to Alice and Bob, who use the device to generate a secure key. The security of existing DIQKD schemes holds under the assumption that the two components of the device cannot communicate with one another during the protocol executio

Ken Osato, Masahiro Takada

The thermal Sunyaev-Zel&#39;dovich (tSZ) effect is a powerful probe of cosmology. The statistical errors in the tSZ power spectrum measurements are dominated by the presence of massive clusters in a survey volume that are easy to identify on individual cluster basis. First, we study the impact of super sample covariance (SSC) on the tSZ power spectrum measur

Lewis Wright, Fergus Barratt, James Dborin, George H. Booth

Tasks such as classification of data and determining the groundstate of a Hamiltonian cannot be carried out through purely unitary quantum evolution. Instead, the inherent non-unitarity of the measurement process must be harnessed. Post-selection and its extensions provide a way to do this. However they make inefficient use of time resources -- a typical com

Daniel R. Mayerson, Masaki Shigemori

We quantize the D1-D5-P microstate geometries known as superstrata directly in supergravity. We use Rychkov&#39;s consistency condition [hep-th/0512053] which was derived for the D1-D5 system; for superstrata, this condition turns out to be strong enough to fix the symplectic form uniquely. For the $(1,0,n)$ superstrata, we further confirm this quantization

Jesus M. Salas, Smadar Naoz, Mark R. Morris

The gas dynamics in the inner few kiloparsecs of barred galaxies often results in configurations that give rise to nuclear gas rings. However, the generic dynamical description of the formation of galactic nuclear rings does not take into account the effects of thermal pressure and turbulence. Here we perform 3D hydrodynamic simulations of gas in a galactic

Mihir Kulkarni, Eli Visbal, Greg L. Bryan

A critical dark matter halo mass ($M_{\rm crit}$) for Population III (Pop III) stars can be defined as the typical minimum halo mass that hosts sufficient cold dense gas required for the formation of the first stars. The presence of Lyman-Werner (UV) radiation, which can dissociate molecular hydrogen, and the baryon-dark matter streaming velocity both delay

Stefano Pirandola

The study of free-space quantum communications requires tools from quantum information theory, optics and turbulence theory. Here we combine these tools to bound the ultimate rates for key and entanglement distribution through a free-space link, where the propagation of quantum systems is generally affected by diffraction, atmospheric extinction, turbulence,

Morgan Bennett, Jo Bovy

The vertical distribution of stars in the solar neighbourhood is not in equilibrium but contains a wave signature in both density and velocity space originating from a perturbation. With the discovery of the phase-space spiral in Gaia data release 2, determining the origin of this perturbation has become even more urgent. We develop and test a fast method fo

Arun Kannawadi, Erik Rosenberg, Henk Hoekstra

Metacalibration is a state-of-the-art technique for measuring weak gravitational lensing shear from well-sampled galaxy images. We investigate the accuracy of shear measured with metacalibration from fitting elliptical Gaussians to undersampled galaxy images. In this case, metacalibration introduces aliasing effects leading to an ensemble multiplicative shea

Christopher E. O&#39;Connor, Bin Liu, Dong Lai

We investigate the possible origin of the transiting giant planet WD1856+534b, the first strong exoplanet candidate orbiting a white dwarf, through high-eccentricity migration (HEM) driven by the Lidov-Kozai (LK) effect. The host system&#39;s overall architecture is an hierarchical quadruple in the &#39;2+2&#39; configuration, owing to the presence of a tert

Doğa Veske, Zsuzsa Márka, Imre Bartos, Szabolcs Márka

Quantification of the significance of a candidate multi-messenger detection of cosmic events is an emerging need in the astrophysics and astronomy communities. In this paper we show that a model-independent optimal search does not exist, and we present a general Bayesian method for the optimal model-dependent search, which is scalable to any number and any k

J. K. Jochum, A. Hecht, O. Soltwedel, C. Fuchs

The generation of high frequency oscillatory magnetic fields represents a fundamental component underlying the successful implementation of neutron resonant spin-echo spectrometers, a class of instrumentation critical for the high-resolution extraction of dynamical excitations (structural and magnetic) in materials. In this paper, the setup of the resonant c

José Nicasio, James P. Edwards, Christian Schubert, Naser Ahmadiniaz

We study the transformation of the dressed electron propagator and the general $N$-point functions under a change in the covariant gauge of internal photon propagators. We re-establish the well known Landau-Khalatnikov-Fradkin transformation for the propagator and generalise it to arbitrary correlation functions in configuration space, finding that it coinci

Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li

DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Defor

Karsten Jedamzik, Levon Pogosian, Gong-Bo Zhao

The mismatch between the locally measured expansion rate of the universe and the one inferred from the cosmic microwave background measurements by Planck in the context of the standard $Λ$CDM, known as the Hubble tension, has become one of the most pressing problems in cosmology. A large number of amendments to the $Λ$CDM model have been proposed in order to

Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau

In this work we revisit two classic high-dimensional online learning problems, namely linear regression and contextual bandits, from the perspective of adversarial robustness. Existing works in algorithmic robust statistics make strong distributional assumptions that ensure that the input data is evenly spread out or comes from a nice generative model. Is it

Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, Francois Lanusse

Automated inference of binary microlensing events with traditional sampling-based algorithms such as MCMC has been hampered by the slowness of the physical forward model and the pathological likelihood surface. Current analysis of such events requires both expert knowledge and large-scale grid searches to locate the approximate solution as a prerequisite to

Fabrizio Renzi, Natalie B. Hogg, Matteo Martinelli, Savvas Nesseris

The observation of strongly lensed Type Ia supernovae enables both the luminosity and angular diameter distance to a source to be measured simultaneously using a single observation. This feature can be used to measure the distance duality parameter $η(z)$ without relying on multiple datasets and cosmological assumptions to reconstruct the relation between an

Joshua L. Barrow, Steven Gardiner, Saori Pastore, Minerba Betancourt

The usage of Monte Carlo neutrino event generators (MC$ν$EGs) is a norm within the high-energy $ν$ scattering community. The relevance of quasielastic (QE) energy regimes to $ν$ oscillation experiments implies that accurate calculations of $νA$ cross sections in this regime will be a key contributor to reducing the systematic uncertainties affecting the extr

Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch

Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation has sparked interest in optimization-guided autonomous discovery and closed-loop experimentation. Experiment planning strategies based on off

Zackery A. Benson, Anton Peshkov, Derek C. Richardson, Wolfgang Losert

We perform experimental and numerical studies of a granular system under cyclic-compression to investigate reversibility and memory effects. We focus on the quasi-static forcing of dense systems, which is most relevant to a wide range of geophysical, industrial, and astrophysical problems. We find that soft-sphere simulations with proper stiffness and fricti