September 2019 arXiv papers — page 24

Oran Shayer, Michael Lindenbaum

Deep learning approaches to generic (non-semantic) segmentation have so far been indirect and relied on edge detection. This is in contrast to semantic segmentation, where DNNs are applied directly. We propose an alternative approach called Deep Generic Segmentation (DGS) and try to follow the path used for semantic segmentation. Our main contribution is a n

Xiucai Ding, Hau-Tieng Wu

We apply local laws of random matrices and free probability theory to study the spectral properties of two kernel-based sensor fusion algorithms, nonparametric canonical correlation analysis (NCCA) and alternating diffusion (AD), for two simultaneously recorded high dimensional datasets under the null hypothesis. The matrix of interest is the product of the

Nicholas S. Kern, Aaron R. Parsons, Joshua S. Dillon, Adam E. Lanman

We present a study of internal reflection and cross coupling systematics in Phase I of the Hydrogen Epoch of Reionization Array (HERA). In a companion paper, we outlined the mathematical formalism for such systematics and presented algorithms for modeling and removing them from the data. In this work, we apply these techniques to data from HERA's first obser

Nicholas S. Kern, Aaron R. Parsons, Joshua S. Dillon, Adam E. Lanman

We study the behavior of internal signal chain reflections and antenna cross coupling as systematics for 21 cm cosmological surveys. We outline the mathematics for how these systematics appear in interferometric visibilities and describe their phenomenology. We then describe techniques for modeling and removing these systematics without attenuating the 21 cm

Jeremy Bailin

We present an up-to-date catalog of intrinsic iron abundance spreads in the 55 Milky Way globular clusters for which sufficiently precise spectroscopic measurements are available. Our method combines multiple datasets when possible to improve the statistics, taking into account the fact that different methods and instruments can lead to systematically offset

Andrew Hundt, Benjamin Killeen, Nicholas Greene, Hongtao Wu

Current Reinforcement Learning (RL) algorithms struggle with long-horizon tasks where time can be wasted exploring dead ends and task progress may be easily reversed. We develop the SPOT framework, which explores within action safety zones, learns about unsafe regions without exploring them, and prioritizes experiences that reverse earlier progress to learn

László Németh

Several articles deal with tilings with squares and dominoes on 2-dimensional boards, but only a few on boards in 3-dimensional space. We examine a tiling problem with colored cubes and bricks of $(2\times2\times n)$-board in three dimensions. After a short introduction and the definition of breakability we show a way to get the number of the tilings of an $

B. Aharmim, S. N. Ahmed, A. E. Anthony, N. Barros

Neutrons produced in nuclear interactions initiated by cosmic-ray muons present an irreducible background to many rare-event searches, even in detectors located deep underground. Models for the production of these neutrons have been tested against previous experimental data, but the extrapolation to deeper sites is not well understood. Here we report results

Nishant Gurunath, Sai Krishna Rallabandi, Alan Black

In order to build language technologies for majority of the languages, it is important to leverage the resources available in public domain on the internet - commonly referred to as `Found Data'. However, such data is characterized by the presence of non-standard, non-trivial variations. For instance, speech resources found on the internet have non-speech co

Jan Vybíral

We show the following version of the Schur's product theorem. If $M=(M_{j,k})_{j,k=1}^n\in{\mathbb R}^{n\times n}$ is a positive semidefinite matrix with all entries on the diagonal equal to one, then the matrix $N=(N_{j,k})_{j,k=1}^n$ with the entries $N_{j,k}=M_{j,k}^2-\frac{1}{n}$ is positive semidefinite. As a corollary of this result, we prove the conje

Callum Muir, Jordan Cortez, Paolo Grigolini

We study seismic fluctuations in California and Hindu Kush using Diffusion Entropy Analysis (DEA), a technique designed to detect the action of crucial events in time series generated by complex dynamical systems. The time distance between two consecutive crucial events is described by an inverse power law distribution density with a power index $\mu$ close

Jonathan Beardsley, Maximilien Péroux

We show that there is an equivalence in any $n$-topos $\mathcal{X}$ between the pointed and $k$-connective objects of $\mathcal{X}$ and the $\mathbb{E}_k$-group objects of the $(n-k-1)$-truncation of $\mathcal{X}$. This recovers, up to equivalence of $\infty$-categories, some classical results regarding algebraic models for $k$-connective, $(n-1)$-coconnecti

Li Yuan, Francis E. H. Tay, Guilin Li, Tao Wang

Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach weaker students in practice. In this wor

Tianshi Cao, Marc Law, Sanja Fidler

Few-shot classification is the task of predicting the category of an example from a set of few labeled examples. The number of labeled examples per category is called the number of shots (or shot number). Recent works tackle this task through meta-learning, where a meta-learner extracts information from observed tasks during meta-training to quickly adapt to

Wenxiang Cong, Hongming Shan, Xiaohua Zhang, Shaohua Liu

Cone-beam breast computed tomography (CT) provides true 3D breast images with isotropic resolution and high-contrast information, detecting calcifications as small as a few hundred microns and revealing subtle tissue differences. However, breast is highly sensitive to x-ray radiation. It is critically important for healthcare to reduce radiation dose. Few-vi

Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski

We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies. We show how ES can be applied to MAML

Yue Xing, Qifan Song, Guang Cheng

The over-parameterized models attract much attention in the era of data science and deep learning. It is empirically observed that although these models, e.g. deep neural networks, over-fit the training data, they can still achieve small testing error, and sometimes even {\em outperform} traditional algorithms which are designed to avoid over-fitting. The ma

Anastasiia Butko, George Michelogiannakis, Samuel Williams, Costin Iancu

Continuing the scaling of quantum computers hinges on building classical control hardware pipelines that are scalable, extensible, and provide real time response. The instruction set architecture (ISA) of the control processor provides functional abstractions that map high-level semantics of quantum programming languages to low-level pulse generation by hard

Angeliki Menegaki

We study a $1$-dimensional chain of $N$ weakly anharmonic classical oscillators coupled at its ends to heat baths at different temperatures. Each oscillator is subject to pinning potential and it also interacts with its nearest neighbors. In our set up both potentials are homogeneous and bounded (with $N$ dependent bounds) perturbations of the harmonic ones.

Denis Belomestny, Lukasz Szpruch, Shuren Tan

In this paper, we present a generic methodology for the efficient numerical approximation of the density function of the McKean-Vlasov SDEs. The weak error analysis for the projected process motivates us to combine the iterative Multilevel Monte Carlo method for McKean-Vlasov SDEs \cite{szpruch2019} with non-interacting kernels and projection estimation of p

Wen-Du Li, Wu-Sheng Dai

We show that there exists a duality family of self-interacting massive scalar fields. The scalar field in a duality family are related by a duality transformation. Such a duality of scalar fields is a field version of the Newton-Hooke duality in classical mechanics. The duality transformation preserves the type of the field equation: transforming a Klein-Gor

T. Ö. Çelik, A. Jamneshan, G. Montúfar, B. Sturmfels

We study the problem of minimizing the Wasserstein distance between a probability distribution and an algebraic variety. We consider the setting of finite state spaces and describe the solution depending on the choice of the ground metric and the given distribution. The Wasserstein distance between the distribution and the variety is the minimum of a linear

Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb

We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization. Further, we provide a theoretical analysis of how GraphMix improves the generalization boun

Gwendolyn Eadie, Thomas J. Loredo, Ashish A. Mahabal, Aneta Siemiginowska

This Astro2020 State of the Profession Consideration White Paper highlights the growth of astrostatistics and astroinformatics in astronomy, identifies key issues hampering the maturation of these new subfields, and makes recommendations for structural improvements at different levels that, if acted upon, will make significant positive impacts across astrono

Cristian Candia, Víctor Landaeta-Torres, César A. Hidalgo, Carlos Rodriguez-Sickert

Social networks are pivotal for learning. Yet, we still lack a full understanding of the mechanisms connecting networks with learning outcomes. Here, we present the results of a large scale study (946 elementary school children from 45 different classrooms) designed to understand the social strategies used by elementary school children. We mapped the social

Francesc Fité, Xavier Guitart

We introduce a tensor decomposition of the $\ell$-adic Tate module of an abelian variety $A_0$ defined over a number field which is geometrically isotypic. If $A_0$ is potentially of $\GL_2$-type and defined over a totally real number field, we use this decomposition to describe its Sato--Tate group and to prove the Sato--Tate conjecture in certain cases.

Qingchun Hou, Ning Zhang, Ershun Du, Miao Miao

The high penetration of photovoltaic (PV) is reshaping the electricity net-load curve and has a significant impact on power system operation and planning. The concept of duck curve is widely used to describe the timing imbalance between peak demand and PV generation. The traditional duck curve is deterministic and only shows a single extreme or typical scena

Xavier Laforgue, Stéphane Guérin, Xi Chen

We developed single-shot shaped pulses for ultra high fidelity (UH-fidelity) population transfer on a 3-level quantum system in lambda configuration. To ensure high fidelity, we use the Lewis-Riesenfeld (L-R) method to derive a family of solutions leading to an exact transfer, where the solutions follow a single dynamical mode of the L-R invariant. Among thi

Mohamed Amine Kerker

In this paper we give an explicit representation of the solutions of a characteristic Cauchy problem for a class of PDEs with singular coefficients. We give the explicit solutions in terms of the Gauss hypergeometric functions, which enable us to study the singularities and the analytic continuation. Our results are illustrated through some examples.

Alexander V Turbiner, Willard Miller, Adrian M Escobar-Ruiz

In this work we study 2- and 3-body oscillators with quadratic and sextic pairwise potentials which depend on relative distances, $|{\bf r}_i - {\bf r}_j |$, between particles. The two-body harmonic oscillator is two-parametric and can be reduced to a one-dimensional radial Jacobi oscillator, while in the 3-body case such a reduction is not possible in gener

Yunjie Yi, Guang Gong, Kalikinkar Mandal

With the rapid deployment of Internet of Things (IoT) devices in applications such as smarthomes, healthcare and industrial automation, security and privacy has become a major concern. Recently, National Institute of Standards and Technology (NIST) has initiated a lightweight cryptography (LWC) competition to standardize new cryptographic algorithm(s) for pr

Minjun Kim, Hiroki Sayama

Text classification is one of the most critical areas in machine learning and artificial intelligence research. It has been actively adopted in many business applications such as conversational intelligence systems, news articles categorizations, sentiment analysis, emotion detection systems, and many other recommendation systems in our daily life. One of th

Hang Huang, Michael Perlman, Claudia Polini, Claudiu Raicu

We prove the case t = 2 of a conjecture of Bruns-Conca-Varbaro, describing the minimal relations between the t x t minors of a generic matrix. Interpreting these relations as polynomial functors, and applying transpose duality as in the work of Sam-Snowden, this problem is equivalent to understanding the relations satisfied by t x t generalized permanents. O

Luka Stanisic, Klaus Reuter

This paper reports on the design and implementation of the HPC performance monitoring system deployed to continuously monitor performance metrics of all jobs on the HPC systems at the Max Planck Computing and Data Facility (MPCDF). Thereby it reveals important information to various stakeholders, in particular to users, application support, system administra

Gary T. Horowitz, Diandian Wang

Contrary to popular belief, asymptotically anti-de Sitter solutions of gravitational theories cannot be obtained by taking initial data (satisfying the constraints) on a spacelike surface, and choosing an arbitrary conformal metric on the timelike boundary at infinity. There are an infinite number of corner conditions that also must be satisfied where the in

Tyler R. Scott, Karl Ridgeway, Michael C. Mozer

Supervised deep-embedding methods project inputs of a domain to a representational space in which same-class instances lie near one another and different-class instances lie far apart. We propose a probabilistic method that treats embeddings as random variables. Extending a state-of-the-art deterministic method, Prototypical Networks (Snell et al., 2017), ou

Ujjal Debnath, Behnam Pourhassan, Izzet Sakalli

In this paper, we construct a new charged AdS black hole with modified cosmic Chaplygin gas (MCCG). In comparison to the previous model (modified Chaplygin AdS black hole) existed in the literature, we now have two new parameters: the cosmic parameter and the black hole's electric charge. We examine the asymptotically charged AdS black hole thermodynamics wi

Mariano Lemus, Mariana F. Ramos, Preeti Yadav, Nuno A. Silva

The oblivious transfer primitive is sufficient to implement secure multiparty computation. However, secure multiparty computation based only on classical cryptography is severely limited by the security and efficiency of the oblivious transfer implementation. We present a method to efficiently and securely generate and distribute oblivious keys by exchanging

Claudius Gros

Given a certain complexity level, humanized agents may select from a wide range of possible tasks, with each activity corresponding to a transient goal. In general there will be no overarching credit assignment scheme allowing to compare available options with respect to expected utilities. For this situation we propose a task selection framework that is bas

Shupeng Gui, Xiangliang Zhang, Pan Zhong, Shuang Qiu

Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approaches specify (either explicitly or implici

Andrew Rosenberg, Yu Zhang, Bhuvana Ramabhadran, Ye Jia

Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human speech that is used to train speech recognizers. The multi-speaker speech synthesis architecture can learn latent

Johann Gan, M. E. Pantalon, F. Robicheaux

The results of simulations of cooling based on Sawtooth-Wave Adiabatic Passage (SWAP) are presented including the possibility of population leaking to states outside of the cycling transition. The amount of population leaking can be substantially suppressed compared to Doppler cooling, which could be useful for systems that are difficult to repump back to th

A. Pacheco, E. Gorbeña, T. A. Plomaritis, E. Garel

This paper presents the results of a pilot experiment with an existing tidal energy converter (TEC), Evopod 1 kW floatable prototype, in a real test case scenario (Faro Channel, Ria Formosa, Portugal). A baseline marine geophysical, hydrodynamic and ecological study based on the experience collected on the test site is presented. The collected data was used

Stefan Wager

Cross-validation is a popular non-parametric method for evaluating the accuracy of a predictive rule. The usefulness of cross-validation depends on the task we want to employ it for. In this note, I discuss a simple non-parametric setting, and find that cross-validation is asymptotically uninformative about the expected test error of any given predictive rul

Zeyu Zhou, Hsing-Ta Chen, Abraham Nitzan, Joseph Eli Subotnik

We investigate two well-known approaches for extending the fewest switches surface hopping (FSSH) algorithm to periodic time-dependent couplings. The first formalism acts as if the instantaneous adiabatic electronic states were standard adiabatic states, which just happen to evolve in time. The second formalism replaces the role of the usual adiabatic states

Sinan G. Aksoy, Paul Bruillard, Stephen J. Young, Mark Raugas

Graph eigenvalues play a fundamental role in controlling structural properties, such as bisection bandwidth, diameter, and fault tolerance, which are critical considerations in the design of supercomputing interconnection networks. This motivates considering graphs with optimal spectral expansion, called Ramanujan graphs, as potential candidates for intercon

Elizabeth Newman, Misha E. Kilmer

In recent work (Soltani, Kilmer, Hansen, BIT 2016), an algorithm for non-negative tensor patch dictionary learning in the context of X-ray CT imaging and based on a tensor-tensor product called the $t$-product (Kilmer and Martin, 2011) was presented. Building on that work, in this paper, we use of non-negative tensor patch-based dictionaries trained on other

Pablo Barceló, Nelson Higuera, Jorge Pérez, Bernardo Subercaseaux

We study the expressive power of the LARA language -- a recently proposed unified model for expressing relational and linear algebra operations -- both in terms of traditional database query languages and some analytic tasks often performed in machine learning pipelines. We start by showing LARA to be expressive complete with respect to first-order logic wit

Y. Zhu, S. H. Yue, Z. W. Ge, Y. W. Zhu

Scintillating NaI(Tl) crystals are widely used in a large variety of experimental applications. However, for the use as Dark Matter (DM) detectors, such crystals demand a high level of radio-purity, not achievable by means of standard industrial techniques. One of the main difficulties comes from the presence of potassium that always accompanies sodium in al

Friedemann Queisser, Sebastian Schreiber, Peter Kratzer, Ralf Schützhold

Motivated by the recent interest in non-equilibrium phenomena in quantum many-body systems, we study strongly interacting fermions on a lattice by deriving and numerically solving quantum Boltzmann equations that describe their relaxation to thermodynamic equilibrium.The derivation is carried out by inspecting the hierarchy of correlations within the framewo

Geoffrey Ryan, Hendrik van Eerten, Luigi Piro, Eleonora Troja

Gamma-ray bursts (GRBs) associated with gravitational wave events are, and will likely continue to be, viewed at a larger inclination than GRBs without gravitational wave detections. As demonstrated by the afterglow of GW170817, this requires an extension of the common GRB afterglow models which typically assume emission from an on-axis top hat jet. We prese

Lukas Prader

Let $R$ be an affine domain of characteristic zero with finite quotients. We prove that a polynomial map over $R$ is surjective if and only if it is surjective over $\hat{R_{\mathfrak{m}}}$, the completion of $R$ with respect to $\mathfrak{m}$, for every maximal ideal $\mathfrak{m} \subseteq R$. In fact, the completions $\hat{R_{\mathfrak{m}}}$ may be replac

Kaitlin Lovering, Srikanth Nayak, Wei Bu, Ahmet Uysal

The selective transport of trivalent rare earth metals from aqueous to organic environments with the help of amphiphilic "extractants" is an industrially important process. When the amphiphilic extractant is positively charged or neutral, the coextracted background anions are not only necessary for charge balance but also have a large impact on extraction ef

T. Prokscha, K. H. Chow, Z. Salman, E. Stilp

We demonstrate that, by using low-energy positive muon ($\mu^+$) spin spectroscopy as a local probe technique, the profiles of free charge carriers can be directly determined in the accumulation/depletion surface regions of p- or n-type Ge wafers. The detection of free holes is accomplished by measuring the effect of the interaction of the free carriers with

Sanqiang Zhao, Raghav Gupta, Yang Song, Denny Zhou

Pretrained language models like BERT have achieved good results on NLP tasks, but are impractical on resource-limited devices due to memory footprint. A large fraction of this footprint comes from the input embeddings with large input vocabulary and embedding dimensions. Existing knowledge distillation methods used for model compression cannot be directly ap

T. Faber, J. Jones-Pérez, Y. Liu, W. Porod

We study natural supersymmetric scenarios with light right-handed neutrino superfields, and consider the possibility of having either a neutrino or a sneutrino as a dark matter candidate. For the former, we evaluate the possibility of having SUSY corrections on the $\nu_4\to\nu_\ell\gamma$ decay rate, such that the NuStar bounds are relaxed. We find that cor

Kiwoon Choi, Chang Sub Shin, Seokhoon Yun

We study the axion field range and low energy couplings in models with St\"uckelberg mixing between axions and $U(1)$ gauge bosons. It is noted that the gauge-invariant axion combination $\xi$ in the model is periodic $modulo$ an appropriate shift of gauge-variant axions eaten by the massive $U(1)$ gauge bosons, which in some cases makes the connection betwe

Grzegorz Szamel

We propose a generalization of stochastic thermodynamics to systems of active particles, which move under the combined influence of stochastic internal self-propulsions (activity) and a heat bath. The main idea is to consider joint trajectories of particles' positions and self-propulsions. It is then possible to exploit formal similarity of an active system

Joseph Bramante, Andrew Buchanan, Alan Goodman, Eesha Lodhi

If dark matter is efficiently captured by a planet, energy released in its annihilation can exceed that planet's total heat output. Building on prior work, we treat Earth's composition and dark matter capture in detail and present improved limits on dark matter-nucleon scattering cross sections for dark matter masses ranging from 0.1 to $10^{10}$ GeV. We als

D. R. Madison, D. Agarwal, K. Aggarwal, O. Young

Some short gamma-ray bursts (SGRBs) are thought to be caused by the mergers of binary neutron stars which may sometimes produce massive neutron star remnants capable of producing extragalactic fast radio bursts (FRBs). We conducted a deep search for FRBs from the sites of six low-redshift SGRBs. We collected high time- and frequency-resolution data from each

J. Xavier Prochaska, Jean-Pierre Macquart, Matthew McQuinn, Sunil Simha

Present-day galaxies are surrounded by cool and enriched halo gas extending to hundreds of kiloparsecs. This halo gas is thought to be the dominant reservoir of material available to fuel future star formation, but direct constraints on its mass and physical properties have been difficult to obtain. We report the detection of a fast radio burst (FRB 181112)

Nicha Leethochawalit, Evan N. Kirby, Richard S. Ellis, Sean M. Moran

We present the stellar mass-[Fe/H] and mass-[Mg/H] relation of quiescent galaxies in two galaxy clusters at $z\sim0.39$ and $z\sim0.54$. We derive the age, [Fe/H], and [Mg/Fe] for each individual galaxy using a full-spectrum fitting technique. By comparing with the relations for $z\sim0$ SDSS galaxies, we confirm our previous finding that the mass-[Fe/H] rel

Erin Boettcher, John S. Gallagher, Ellen G. Zweibel

The structure and kinematics of gaseous, disk-halo interfaces are imprinted with the processes that transfer mass, metals, and energy between galactic disks and their environments. We study the extraplanar diffuse ionized gas (eDIG) layer in the interacting, star-forming galaxy NGC 5775 to better understand the consequences of star-formation feedback on the

Nathan Musoke, Shaun Hotchkiss, Richard Easther

In simple inflationary cosmological scenarios the near-exponential growth can be followed by a long period in which the Universe is dominated by the oscillating inflaton condensate. The condensate is initially almost homogeneous, but perturbations grow gravitationally, eventually fragmenting the condensate if it is not disrupted more quickly by resonance or

Bartosz Regula, Kaifeng Bu, Ryuji Takagi, Zi-Wen Liu

We study the one-shot distillation of general quantum resources, providing a unified quantitative description of the maximal fidelity achievable in this task, and revealing similarities shared by broad classes of resources. We establish fundamental quantitative and qualitative limitations on resource distillation applicable to all convex resource theories. W

Ying-Hsuan Lin, David Meltzer, Shu-Heng Shao, Andreas Stergiou

How many charged degrees of freedom are necessary to accommodate a certain amount of 't Hooft anomaly? Using the conformal bootstrap for the four-point function of flavor current multiplets, we show that in all (3+1)d superconformal field theories the 't Hooft anomaly of a continuous flavor symmetry is bounded from above by the 3/2 power of the current two-p

Theodore Erler, Carlo Maccaferri

Generalizing previous work, we give a new analytic solution in Witten's open bosonic string field theory which can describe any open string background. The central idea is to use Riemann surface degenerations as a mechanism for taming OPE singularities. This requires leaving the familiar subalgebra of wedge states with insertions, but the payoff is that the

Enrico Rossi, Christopher Triola

In this article we review recent work on van der Waals (vdW) systems in which at least one of the components has strong spin-orbit coupling. We focus on a selection of vdW heterostructures to exemplify the type of interesting electronic properties that can arise in these systems. We first present a general effective model to describe the low energy electroni

S. E. Clark, Brandon S. Hensley

Recent analyses of 21-cm neutral hydrogen (HI) emission have demonstrated that HI gas is organized into linear filamentary structures that are preferentially aligned with the local magnetic field, and that the coherence of these structures in velocity space traces line-of-sight magnetic field tangling. On this basis, we introduce a paradigm for modeling the

João Calhau, David Sobral, Sérgio Santos, Jorryt Matthee

Despite recent progress in understanding Lyman-alpha (Lya) emitters (LAEs), relatively little is known regarding their typical black hole activity across cosmic time. Here, we study the X-ray and radio properties of ~4000 LAEs at 2.2<z<6 from the SC4K survey in the COSMOS field. We detect 254 (6.8+-0.4%) of the LAEs individually in the X-rays (S/N>3) and fin

Jinsung Yoon, Sercan O. Arik, Tomas Pfister

Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning. To adaptively learn data values jointly with the target task predictor model, we propose a meta learning fra

Brian Batell, Joshua Berger, Ahmed Ismail

The Fermilab Short-Baseline Neutrino (SBN) experiments, MicroBooNE, ICARUS, and SBND, are expected to have significant sensitivity to light weakly coupled hidden sector particles. Here we study the capability of the SBN experiments to probe dark scalars interacting through the Higgs portal. We investigate production of dark scalars using both the Fermilab Bo

Yolanda Lozano, Niall T. Macpherson, Carlos Nunez, Anayeli Ramirez

We establish a map between AdS$_3 \times$S$^2$ and AdS$_7$ solutions to massive IIA supergravity that allows one to interpret the former as holographic duals to D2-D4 defects inside 6d (1,0) CFTs. This relation singles out in a particular manner the AdS$_3\times$S$^2$ solution constructed from AdS$_3\times$S$^3\times $CY$_2$ through non-Abelian T-duality, wi

Thomas C. Bachlechner, Matthew Kleban

The phase of the wave function of charged matter is sensitive to the value of the electric potential, even when the matter never enters any region with non-vanishing electromagnetic fields. Despite its fundamental character, this archetypal electric Aharonov-Bohm effect has evidently never been observed. We propose an experiment to detect the electric potent

Sean D. Johnson, John S. Mulchaey, Hsiao-Wen Chen, Nastasha A. Wijers

The relationship between galaxies and the state/chemical enrichment of the warm-hot intergalactic medium (WHIM) expected to dominate the baryon budget at low-z provides sensitive constraints on structure formation and galaxy evolution models. We present a deep redshift survey in the field of 1ES1553+113, a blazar with a unique combination of UV+X-ray spectra

Lakshya Bhardwaj, Patrick Jefferson, Hee-Cheol Kim, Houri-Christina Tarazi

We study 6d superconformal field theories (SCFTs) compactified on a circle with arbitrary twists. The theories obtained after compactification, often referred to as 5d Kaluza-Klein (KK) theories, can be viewed as starting points for RG flows to 5d SCFTs. According to a conjecture, all 5d SCFTs can be obtained in this fashion. We compute the Coulomb branch pr

Asimina Arvanitaki, Savas Dimopoulos, Marios Galanis, Luis Lehner

Axions are some of the best motivated particles beyond the Standard Model. We show how the attractive self-interactions of dark matter (DM) axions over a broad range of masses, from $10^{-22}$ eV to $10^7$ GeV, can lead to nongravitational growth of density fluctuations and the formation of bound objects. This structure formation enhancement is driven by par

Thayne Currie, Eugene Pluzhnik, Ruslan Belikov, Olivier Guyon

Imaging rocky planets in reflected light, a key focus of future NASA missions and ELTs, requires advanced wavefront control to maintain a deep, temporally correlated null of stellar halo at just several diffraction beam widths. We discuss development of Linear Dark Field Control (LDFC) to achieve this aim. We describe efforts to test spatial LDFC in a labora

Bin Yue, Andrea Ferrara

We investigate the [CII] line intensity mapping (IM) signal from galaxies in the Epoch of Reionization (EoR) to assess its detectability, the possibility to constrain the $L_{\rm CII}-{\rm SFR}$ relation, and to recover the [CII] luminosity function (LF) from future experiments. By empirically assuming that ${\rm log}L_{\rm CII}={\rm log}A+\gamma {\rm SFR}\p

AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik

Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced

Troy D. Loeffler, Tarak K. Patra, Henry Chan, Mathew Cherukara

ANNs are currently trained by generating large quantities (On the order of $10^{4}$ or greater) of structural data in hopes that the ANN has adequately sampled the energy landscape both near and far-from-equilibrium. This can, however, be a bit prohibitive when it comes to more accurate levels of quantum theory. As such it is desirable to train a model using

Jeremy Epps, Eric Feron, Mark Mote

The field of bio-inspired robotics seeks to create mechanical systems that mimic the designs and concepts used by biological systems. One of the more challenging biological concepts to imitate in mechanical systems is the ability to create an internal environment that can foster homeostasis through the use of a membrane similar to skin. A robot with this abi

Anusha Nagabandi, Kurt Konoglie, Sergey Levine, Vikash Kumar

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object manipulation, executing finger gaits to move objects, and exhibiting precise fine motor skills such as writing, all require

Manuel Pérez-Carrasco, Guillermo Cabrera-Vives, Pavlos Protopapas, Nicolás Astorga

In this work we address the problem of transferring knowledge obtained from a vast annotated source domain to a low labeled target domain. We propose Adversarial Variational Domain Adaptation (AVDA), a semi-supervised domain adaptation method based on deep variational embedded representations. We use approximate inference and domain adversarial methods to ma

David Byrd

This paper is intended to explain, in simple terms, some of the mechanisms and agents common to multiagent financial market simulations. We first discuss the necessity to include an exogenous price time series ("the fundamental value") for each asset and three methods for generating that series. We then illustrate one process by which a Bayesian agent may re

Yang Hu, Vladyslav Turlo, Irene J. Beyerlein, Subhash Mahajan

While deformation twinning in hexagonal close-packed metals has been widely studied due to its substantial impact on mechanical properties, an understanding of the detailed atomic processes associated with twin embryo growth is still lacking. Conducting molecular dynamics simulations on Mg, we show that the propagation of twinning disconnections emitted by b

A. Mazel, I. Stuhl, Y. Suhov

We study the Gibbs statistics of high-density hard-core configurations on a unit square lattice $\mathbb{Z}^2$, for a general Euclidean exclusion distance $D$. As a by-product, we solve the disk-packing problem on $\mathbb{Z}^2$ for disks of diameter $D$. The key point is an analysis of solutions to norm equations in $\mathbb{Z}[{\sqrt[6]{-1}}]$. We describe

Clara C. Wanjura, Matteo Brunelli, Andreas Nunnenkamp

Directional amplification, in which signals are selectively amplified depending on their propagation direction, has attracted much attention as key resource for applications, including quantum information processing. Recently, several, physically very different, directional amplifiers have been proposed and realized in the lab. In this work, we present a uni

Ryan P. Day, Berend Zwartsenberg, Ilya S. Elfimov, Andrea Damascelli

We have developed the numerical software package $chinook$, designed for the simulation of photoemission matrix elements. This quantity encodes a depth of information regarding the orbital structure of the underlying wavefunctions from which photoemission occurs. Extraction of this information is often nontrivial, owing to the influence of the experimental g

Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman, Aidan Clark

Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human spe

T. Valla, I. Pletikosić, I. K. Drozdov, G. D. Gu

The effects of structural supermodulation with the period $\lambda \approx26$ \AA \ along the $b$-axis of Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ have been observed in photoemission studies from the early days as the presence of diffraction replicas of the intrinsic electronic structure. Although predicted to affect the electronic structure of the Cu-O plane, the

Reza Fotohi, Mehdi Effatparvar, Fateme Sarkohaki, Shahram Behzad

Multicore is an integrated circuit chip that uses two or more computational engines (cores) places in a single processor. This new approach is used to split the computational work of a threaded application and spread it over multiple execution cores, so that the computer system can benefits from a better performance and better responsiveness of the system. A

Xinyue Zhang, Yu Liu, Qingsong Huang

Two-dimensional (2D) materials have wide applications towards electronic devices, energy storages, and catalysis, et al. So far, most of the pure element 2D materials are composed of group IIIA,IVA, and VA elements. Beyond the scope, the orbit hybrid configuration becomes a key fact to influence 2D structure stably. Here we show a sp2d3 hybridization in the

D. Belitz, T. R. Kirkpatrick

We consider clean Dirac metals where the linear band crossing is caused by a strong spin-orbit interaction, and study the quantum phase transitions from the paramagnetic phase to various magnetic phases, including homogeneous ferromagnets, ferrimagnets, canted ferromagnets, and magnetic nematics. We show that in all of these cases the coupling of fermionic s

Dimitri A. Schreiber, Florian Richter, Andrew Bilan, Peter V. Gavrilov

This paper presents the design and performance of a screw-propelled redundant serpentine robot. This robot comprises serially linked, identical modules, each incorporating an Archimedes' screw for propulsion and a universal joint (U-Joint) for orientation control. When serially chained, these modules form a versatile snake robot platform which enables the ro

Lucy L. Gao, Daniela Witten, Jacob Bien

In this paper, we consider data consisting of multiple networks, each comprised of a different edge set on a common set of nodes. Many models have been proposed for the analysis of such multi-view network data under the assumption that the data views are closely related. In this paper, we provide tools for evaluating this assumption. In particular, we ask: g

Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte

ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D'Kitty is a four-legged robot that

Julián Fresán-Figueroa, Diego González-Moreno, Mika Olsen

The \emph{packing chromatic number $\chi_\rho (G)$} of a graph $G$ is the smallest integer $k$ for which there exists a vertex coloring $\Gamma: V(G)\rightarrow \{1,2,\dots , k\}$ such that any two vertices of color $i$ are at distance at least $i + 1$. For $g\in \{6,8,12\}$, $(q+1,g)$-Moore graphs are $(q+1)$-regular graphs with girth $g$ which are the inci

Jakub Kopřiva

We study the problem of existence of pushouts in the category of algebraic sets over an infinite field. This problem can be reduced to asking whether the property of being a finitely generated algebra over a field, or a Noetherian ring in general, is preserved under taking pullbacks. We show that this problem can be fully solved in terms of preservation of t

Konstantin Herbst, John Lee Grenfell, Miriam Sinnhuber, Heike Rauer

The first opportunity to detect indications for life outside the Solar System may be provided already within the next decade with upcoming missions such as the James Webb Space Telescope (JWST), the European Extremely Large Telescope (E-ELT) and/or the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) mission, searching for atmospheric biosi