November 2019 arXiv papers — page 68

Feng Lin, Haohang Xu, Houqiang Li, Hongkai Xiong

Self-supervised learning by predicting transformations has demonstrated outstanding performances in both unsupervised and (semi-)supervised tasks. Among the state-of-the-art methods is the AutoEncoding Transformations (AET) by decoding transformations from the learned representations of original and transformed images. Both deterministic and probabilistic AE

Minghao Guo, Min Du, Luis C. Ho, Victor P. Debattista

Short (inner) bars of sub-kiloparsec radius have been hypothesized to be an important mechanism for driving gas inflows to small scales, thus feeding central black holes. Recent numerical simulations have shown that the growth of central black holes in galaxies can destroy short bars, when the black hole reaches a mass of $\sim 0.1\%$ of the total stellar ma

Berry Weinstein, Shai Fine, Yacov Hel-Or

We present a selective sampling method designed to accelerate the training of deep neural networks. To this end, we introduce a novel measurement, the minimal margin score (MMS), which measures the minimal amount of displacement an input should take until its predicted classification is switched. For multi-class linear classification, the MMS measure is a na

O. V. Pountougnigni, r. Yamapi, G. Filatrella, C. Tchawoua

This paper deal the effects of uncorrelated white noise, in a serie of Josephson Junctions coupled to a linear $RLC$ resonator. The junction are hysteretic, and hence can be considered birhythmic, that is capable to oscillate at different frequencies for the same set of parameters. Both Josephson Junctions with identical and disordered parameters are conside

Xiaoxia Yu, Hezhu Shao, Xueyun Wang, Yingcai Zhu

High performance thermoelectric devices requires materials with low lattice thermal conductivities. Many strategies, such as phonon engineering, have been made to reduce lattice thermal conductivity without simultaneously decrease of the charge transport performance. It is a simple and effective approach to use materials with heavy element to reduce the latt

Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama

Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown that augmentation strategies found by search algorithms outperform hand-made strategies. Such methods employ black-box search algorithms over image transformations with continuous

Akhlesh Lakhtakia. Jason T. Moyer

Axially excited chiral sculptured thin films (STFs) are shown to exhibit the circular Bragg phenomenon in the pre-resonant (long-wavelength) regime but not in some parts of the post-resonant (short-wavelength) regime. Chiral STFs act as very good polarization-independent reflectors in the vicinity of material resonances in the latter regime.

M. A. Belyaev, A. A. Velichko

We implement a capacitorless model of a VO2 oscillator by introducing into the circuit of a field-effect transistor and a VO2 thermal sensor, which provide negative current feedback with a time delay. We compare the dynamics of current and voltage oscillations on a switch in a circuit with a capacitor and without a capacitor. The oscillation period in the ca

Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang

Nowadays, massive urban human mobility data are being generated from mobile phones, car navigation systems, and traffic sensors. Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can b

Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu

Many applications such as scientific simulation, sensing, and power grid monitoring tend to generate massive amounts of data, which should be compressed first prior to storage and transmission. These data, mostly comprised of floating-point values, are known to be difficult to compress using lossless compression. A few compression methods based on lossy comp

Hai-Peng Ren, Zi-Xuan Zhou, Celso Grebogi

Silicon crystal puller (SCP) is a key equipment in silicon wafer manufacture, which is, in turn, the base material for the most currently used integrated circuit (IC) chips. With the development of the techniques, the demand for longer mono-silicon crystal rod with larger diameter is continuously increasing in order to reduce the manufacture time and the pri

Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen, Ashish Tawari

Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human actions, but they lack semantic understanding of image contents. This makes them brittle and potentially unsafe in situations that do not match training data. Here, we propose to ad

S. Garg, B. Maheshwari, D. Choudhury, A. K. Jain

In the present paper, we explore the idea of isospin conservation in new situations and contexts based on the directions provided by our earlier works. We present the results of our calculations for the relative yields of neutron-rich fission fragments emitted in fast neutron-induced fission, 238U (n, fission) reaction by using the concept of the conservatio

Prashant Raj, Mishu Paul, Mythreyi R., Balanarayan Pananghat

The electronic structure of benzene in the presence of a high-intensity high-frequency circularly polarized laser supports a middle-of-the-ring electron localization. Here, the laser polarization coincides with the ring plane of benzene. The high-frequency oscillating electric field creates circular currents centered at each atom with a circle radius equal t

Yusupjan Habibulla

The minimal dominating set for a digraph(directed graph)is a prototypical hard combinatorial optimization problem. In a previous paper, we studied this problem using the cavity method. Although we found a solution for a given graph that gives very good estimate of the minimal dominating size, we further developed the one step replica symmetry breaking theory

C. Domingo-Pardo, V. Babiano-Suarez, J. Balibrea-Correa, L. Caballero

The idea of slow-neutron capture nucleosynthesis formulated in 1957 triggered a tremendous experimental effort in different laboratories worldwide to measure the relevant nuclear physics input quantities, namely ($n,γ$) cross sections over the stellar temperature range (from few eV up to several hundred keV) for most of the isotopes involved from Fe up to Bi

Ya Gao, Yateng Zhang, Bo Zheng, Zhen-Hua Zhang

The $θ_K$ has been re-evaluated via mass relations and latest experimental results, meanwhile, the $M_{K_{1B}}$ also be obtained. Based on the singlet-octet mixing model and quark-flavor mixing model, the $θ_{h_1}$ has been recalculated with a modified formula, by inputting $M_{K_{1B}}$ instead of $θ_K$. The values are calculated to be $|{θ_K}| = {(42.6 \pm

George W. -S. Hou

The LHCb experiment has measured CP violation (CPV) across the Dalitz plot of charmless decays of B+ mesons to 3 charged tracks, namely in Kππ, KKK, πππ and πKK final states, with strikingly large CPV that vary strongly with Dalitz variables. Identifying these processes with b -> sqq(bar), sss(bar) and b -> dqq(bar), dss(bar), where q = u, d, then the "s

Hubert Jegierski, Stanisław Saganowski

A common problem of the real-world data sets is the class imbalance, which can significantly affect the classification abilities of classifiers. Numerous methods have been proposed to cope with this problem; however, even state-of-the-art methods offer a limited improvement (if any) for data sets with critically under-represented minority classes. For such p

Mina Lee, Tatsunori B. Hashimoto, Percy Liang

We study textual autocomplete---the task of predicting a full sentence from a partial sentence---as a human-machine communication game. Specifically, we consider three competing goals for effective communication: use as few tokens as possible (efficiency), transmit sentences faithfully (accuracy), and be learnable to humans (interpretability). We propose an

Jiaman Wu, Zhiqi Wang, Yang Yu, Chenye Wu

Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources. One promising solution is to introduce dynamic pricing to more consumers, which, if designed properly, could enable an active demand side. To further exploit flexibility, in this work, we seek to advice the

Naeemul Hassan, Amrit Poudel, Jason Hale, Claire Hubacek

Tracking sexual violence is a challenging task. In this paper, we present a supervised learning-based automated sexual violence report tracking model that is more scalable, and reliable than its crowdsource based counterparts. We define the sexual violence report tracking problem by considering victim, perpetrator contexts and the nature of the violence. We

Yayun Fu, Wenjun Cai, Yushun Wang

This paper aims to develop a linearly implicit structure-preserving numerical scheme for the space fractional sine-Gordon equation, which is based on the newly developed invariant energy quadratization method. First, we reformulate the equation as a canonical Hamiltonian system by virtue of the variational derivative of the functional with fractional Laplaci

Kanika Narang, Chaoqi Yang, Adit Krishnan, Junting Wang

This paper addresses the question of identifying the best candidate answer to a question on Community Question Answer (CQA) forums. The problem is important because Individuals often visit CQA forums to seek answers to nuanced questions. We develop a novel induced relational graph convolutional network (IR-GCN) framework to address the question. We make thre

Vishwanath Saragadam, Aswin Sankaranarayanan

We introduce and analyze the concept of space-spectrum uncertainty for certain commonly-used designs for spectrally programmable cameras. Our key finding states that, it is impossible to simultaneously capture high-resolution spatial images while programming the spectrum at high resolution. This phenomenon arises due to a Fourier relationship between the ape

Randall Reese

When the number of features exponentially outnumbers the number of samples, feature screening plays a pivotal role in reducing the dimension of the feature space and developing models based on such data. While most extant feature screening approaches are only applicable to data having univariate response, we propose a new method (GenCorr) that admits a multi

Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang

Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way. Such manually-defined nature eventually results in the high-cost and shared encoders for both style and content encoding, making style transfer systems cumbersome to be deployed in resource-constrai

Hao Cao, Michele K. Dougherty, Gregory J. Hunt, Gabrielle Provan

The Cassini mission entered the Grand Finale phase in April 2017 and executed 22.5 highly inclined, close-in orbits around Saturn before diving into the planet on September 15th 2017. Here we present our analysis of the Cassini Grand Finale magnetometer (MAG) dataset, focusing on Saturn's internal magnetic field. These measurements demonstrate that Satur

Zaoxing Liu, Samson Zhou, Ori Rottenstreich, Vladimir Braverman

Network performance problems are notoriously difficult to diagnose. Prior profiling systems collect performance statistics by keeping information about each network flow, but maintaining per-flow state is not scalable on resource-constrained NIC and switch hardware. Instead, we propose sketch-based performance monitoring using memory that is sublinear in the

Negin Zaraee, Fulya Ekiz kanik, Abdul Muyeed Bhuiya, Emily S. Gong

Bacterial infectious diseases are a major threat to human health. Timely and sensitive pathogenic bacteria detection is crucial in identifying the bacterial contaminations and preventing the spread of infectious diseases. Due to limitations of conventional bacteria detection techniques there have been concerted research efforts towards development of new bio

Hanpeng Hu, Dan Wang, Chuan Wu

Many emerging AI applications request distributed machine learning (ML) among edge systems (e.g., IoT devices and PCs at the edge of the Internet), where data cannot be uploaded to a central venue for model training, due to their large volumes and/or security/privacy concerns. Edge devices are intrinsically heterogeneous in computing capacity, posing signifi

Mantong Zhou, Minlie Huang, Xiaoyan Zhu

In spite of great advancements of machine reading comprehension (RC), existing RC models are still vulnerable and not robust to different types of adversarial examples. Neural models over-confidently predict wrong answers to semantic different adversarial examples, while over-sensitively predict wrong answers to semantic equivalent adversarial examples. Exis

Palak Bhushan, Claire Tomlin

Here we present the design of an insect-scale microrobot that generates lift by spinning its wings. This is in contrast to most other microrobot designs at this size scale which rely on flapping wings to produce lift. The robot has a wing span of 4 centimeters and weighs 133 milligrams. It spins its wings at 47 revolutions/second generating $>$ 138 milligram

Xi Fu, Dan Norback, Qianqian Yuan, Yanling Li

Indoor microbial diversity and composition are suggested to affect the prevalence and severity of asthma. In this study, we collected floor dust and environmental characteristics from 21 classrooms, and health data related to asthma symptoms from 309 students, in junior high schools in Johor Bahru, Malaysia. Bacterial and fungal composition was characterized

Ze-Pei Xing, Xiang-Dong Li

The neutron star low-mass X-ray binary 2A 1822$-$371 has an orbital period of 5.57 hr. Mass transfer in such short-period binaries is thought to be driven by magnetic braking with orbital shrinking. However, 2A 1822$-$371 shows a very rapid orbital expansion, implying that mass transfer occurs rapidly in this system. The accretion rate of the neutron star is

Ke Alexander Wang, Xinran Bian, Pan Liu, Donghui Yan

Divide-and-conquer is a general strategy to deal with large scale problems. It is typically applied to generate ensemble instances, which potentially limits the problem size it can handle. Additionally, the data are often divided by random sampling which may be suboptimal. To address these concerns, we propose the $DC^2$ algorithm. Instead of ensemble instan

Tao Cai

In this paper, we investigate the upward overshooting by three-dimensional numerical simulations. We find that the above convectively stable zone can be partitioned into three layers: the thermal adjustment layer (mixing both entropy and material), the turbulent dissipation layer (mixing material but not entropy), and the thermal dissipation layer (mixing ne

Xinghao Chen, Yan Li, Xiaobin Zhang

We computed a grid of theoretical models to fit the $δ$ Scuti frequencies of CoRoT 100866999 detected earlier from the CoRoT timeserials. The pulsating primary star is determined to be a main sequence star with a rotation period of $4.1^{+0.6}_{-0.5}$ days, rotating slower than the orbital motion. The fundamental parameters of the primary star are determined

Jia-Chen Gu, Zhen-Hua Ling, Quan Liu

This paper proposes an utterance-to-utterance interactive matching network (U2U-IMN) for multi-turn response selection in retrieval-based chatbots. Different from previous methods following context-to-response matching or utterance-to-response matching frameworks, this model treats both contexts and responses as sequences of utterances when calculating the m

Alan A. Kaptanoglu, Kyle D. Morgan, Chris J. Hansen, Steven L. Brunton

Accurate and efficient plasma models are essential to understand and control experimental devices. Existing magnetohydrodynamic or kinetic models are nonlinear, computationally intensive, and can be difficult to interpret, while often only approximating the true dynamics. In this work, data-driven techniques recently developed in the field of fluid dynamics

Natalia Martinez, Martin Bertran, Guillermo Sapiro

Common fairness definitions in machine learning focus on balancing notions of disparity and utility. In this work, we study fairness in the context of risk disparity among sub-populations. We are interested in learning models that minimize performance discrepancies across sensitive groups without causing unnecessary harm. This is relevant to high-stakes doma

Hanyue Li, Ju Hee Yeo, Ashly L. Bornsheuer, Thomas J. Overbye

Synthetic power systems that imitate functional and statistical characteristics of the actual grid have been developed to promote researchers' access to public system models. Developing time series to represent different operating conditions of these synthetic systems will expand the potential of synthetic power systems applications. This paper proposes

Praneet Dutta, Bruce Power, Adam Halpert, Carlos Ezequiel

We propose GAN-based image enhancement models for frequency enhancement of 2D and 3D seismic images. Seismic imagery is used to understand and characterize the Earth's subsurface for energy exploration. Because these images often suffer from resolution limitations and noise contamination, our proposed method performs large-scale seismic volume frequency

Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet

We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories. This issue of missing trajectory data or information occurs in many situations, e.g., GPS signals from vehicles moving on a road network are inte

Shaokang Li, Gang Li, Wei Wu, Qing Fan

We present two long-working-distance objective lenses with numerical apertures (NA) of 0.29 and 0.4 for single-atom experiments. The objective lenses are assembled entirely by the commercial on-catalog $Φ$1'' singlets. Both the objectives are capable to correct the spherical aberrations due to the standard flat vacuum glass windows with various thick

Diego Catalano Ferraioli, Tarcísio Castro Silva, Keti Tenenblat

Second order partial differential equations which describe spherical surfaces (ss) or pseudospherical surfaces (pss) are considered. These equations are equivalent to the structure equations of a metric with Gaussian curvature $K = 1$ or $K = -1$, respectively, and they can be seen as the compatibility condition of an associated su(2)-valued or sl(2, R)-valu

Joseph L. Natale, H. George E. Hentschel, Ilya Nemenman

Self-sustained, elevated neuronal activity persisting on time scales of ten seconds or longer is thought to be vital for aspects of working memory, including brain representations of real space. Continuous-attractor neural networks, one of the most well-known modeling frameworks for persistent activity, have been able to model crucial aspects of such spatial

Frank E. Curtis, Rui Shi

A stochastic second-order trust region method is proposed, which can be viewed as a second-order extension of the trust-region-ish (TRish) algorithm proposed by Curtis et al. (INFORMS J. Optim. 1(3) 200-220, 2019). In each iteration, a search direction is computed by (approximately) solving a trust region subproblem defined by stochastic gradient and Hessian

Fajri Koto, Jey Han Lau, Timothy Baldwin

Despite the success of attention-based neural models for natural language generation and classification tasks, they are unable to capture the discourse structure of larger documents. We hypothesize that explicit discourse representations have utility for NLP tasks over longer documents or document sequences, which sequence-to-sequence models are unable to ca

Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Zening Fu

As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics express the disorder is crucial for early diagnosis. The traditional approach to this problem via training classifiers either proceeds from handcrafted features or requires large dat

Liang Si, Wen Xiao, Josef Kaufmann, Jan M. Tomczak

Superconducting nickelates appear to be difficult to synthesize. Since the chemical reduction of ABO3 (A: rare earth; B transition metal) with CaH2 may result in both, ABO2 and ABO2H, we calculate the topotactic H binding energy by density functional theory (DFT). We find intercalating H is energetically favorable for LaNiO2 but not for Sr-doped NdNiO2. This

Kamer A. Yuksel, Jann Goschenhofer, Hridya V. Varma, Urban Fietzek

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide and affects around 1% of the (60+ years old) elderly population in industrial nations. More than 80% of PD patients suffer from motor symptoms, which could be well addressed if a personalized medication schedule and dosage could be administered to them. However, such p

Bjorn Engquist, Kui Ren, Yunan Yang

This work characterizes, analytically and numerically, two major effects of the quadratic Wasserstein ($W_2$) distance as the measure of data discrepancy in computational solutions of inverse problems. First, we show, in the infinite-dimensional setup, that the $W_2$ distance has a smoothing effect on the inversion process, making it robust against high-freq

Bo Peng, Renqiang Min, Xia Ning

Knowledge graph learning plays a critical role in integrating domain specific knowledge bases when deploying machine learning and data mining models in practice. Existing methods on knowledge graph learning primarily focus on modeling the relations among entities as translations among the relations and entities, and many of these methods are not able to hand

Dai Shi, Junbin Gao, Xia Hong, S. T. Boris Choy

Optimal transport (OT) is a powerful tool for measuring the distance between two defined probability distributions. In this paper, we develop a new manifold named the coupling matrix manifold (CMM), where each point on CMM can be regarded as the transportation plan of the OT problem. We firstly explore the Riemannian geometry of CMM with the metric expressed

Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost

Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems. In particular, there has been a growing interest in adapting structure-aware neural methods to work with the underlying graph representations of logical expressions. While more effe

Urun Dogan, Aniket Anand Deshmukh, Marcin Machura, Christian Igel

Curriculum learning can improve neural network training by guiding the optimization to desirable optima. We propose a novel curriculum learning approach for image classification that adapts the loss function by changing the label representation. The idea is to use a probability distribution over classes as target label, where the class probabilities reflect

German A. Gomez-Vargas, Daniel E. Lopez-Fogliani, Carlos Munoz, Andres D. Perez

In $R$-parity violating supersymmetry, the gravitino as the lightest supersymmetric particle (LSP) is a good candidate for dark matter, with the interesting characteristic to be detectable through $γ$-ray telescopes. We extend this analysis considering an axino next-to-LSP (NLSP) as a coexisting dark matter particle contributing with a detectable signal in t

Marcelo Fiore, Andrew M. Pitts, S. C. Steenkamp

This paper introduces an expressive class of quotient-inductive types, called QW-types. We show that in dependent type theory with uniqueness of identity proofs, even the infinitary case of QW-types can be encoded using the combination of inductive-inductive definitions involving strictly positive occurrences of Hofmann-style quotient types, and Abel's size

Mingsong Jiang, Ziyi Zhou, Nicholas G. Gravish

One of the many secrets to the success and prevalence of insects is their versatile, robust, and complex exoskeleton morphology. A fundamental challenge in insect-inspired robotics has been the fabrication of robotic exoskeletons that can match the complexity of exoskeleton structural mechanics. Hybrid robots composed of rigid and soft elements have previous

Daniel Llewellyn, Yunhong Ding, Imad I. Faruque, Stefano Paesani

Exploiting semiconductor fabrication techniques, natural carriers of quantum information such as atoms, electrons, and photons can be embedded in scalable integrated devices. Integrated optics provides a versatile platform for large-scale quantum information processing and transceiving with photons. Scaling up the integrated devices for quantum applications

Upasana Sridhar, Mark Blanco, Rahul Mayuranath, Daniele G. Spampinato

GraphBLAS is an interface for implementing graph algorithms. Algorithms implemented using the GraphBLAS interface are cast in terms of linear algebra-like operations. However, many graph algorithms are canonically described in terms of operations on vertices and/or edges. Despite the known duality between these two representations, the differences in the way

Christopher Hojny, Marc E. Pfetsch, Matthias Walter

Polynomial optimization problems over binary variables can be expressed as integer programs using a linearization with extra monomials in addition to those arising in the given polynomial. We characterize when such a linearization yields an integral relaxation polytope, generalizing work by Del Pia and Khajavirad (SIAM Journal on Optimization, 2018) and Buch

Veronica Piccialli, Antonio M. Sudoso

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classi

R. Aros, F. Bugini, D. E. Diaz

The aim of this note is to unveil a striking equivalence between the one-loop divergences in 7D Einstein and 6D Conformal Gravities. The particular combination of 6D pointwise Weyl invariants of the 6D Conformal Gravity corresponds to that of Branson's Q-curvature and can be written solely in terms of the Ricci tensor and its covariant derivatives. The q

Mikhail Pletyukhov, Dante M. Kennes, Jelena Klinovaja, Daniel Loss

In the absence of any symmetry constraints we address universal properties of the boundary charge $Q_B$ for a wide class of nearest-neighbor tight-binding models in one dimension with one orbital per site but generic modulations of on-site potentials and hoppings. We provide a precise formulation of the bulk-boundary correspondence relating the boundary char

George Leckie, William Browne, Harvey Goldstein, Juan Merlo

A first step when fitting multilevel models to continuous responses is to explore the degree of clustering in the data. Researchers fit variance-component models and then report the proportion of variation in the response that is due to systematic differences between clusters. Equally they report the response correlation between units within a cluster. These

Mikhail Pletyukhov, Dante M. Kennes, Jelena Klinovaja, Daniel Loss

For a wide class of noninteracting tight-binding models in one dimension we present an analytical solution for all scattering and edge states on a half-infinite system. Without assuming any symmetry constraints we consider models with nearest-neighbor hoppings and one orbital per site but arbitrary size of the unit cell and generic modulations of on-site pot

Chiara Lastoria

The Deep Underground Neutrino Experiment (DUNE) is a leading-edge experiment designed to perform neutrino science and proton decay searches. In particular, the far detector will consist of four 10-kton Liquid Argon (LAr) Time Projection Chambers using both single and dual-phase technologies. The latter provides charge amplification in the gaseous phase. In o

Victor Balcer, Albert Cheu

Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users' hands. We present a protocol in this model that estimates histograms with error independent of the domain size. This implies an arbitrarily large gap in sample complexity between the shuffled and loc

Wenhao Ding, Liang He

The goal of acoustic (or sound) events detection (AED or SED) is to predict the temporal position of target events in given audio segments. This task plays a significant role in safety monitoring, acoustic early warning and other scenarios. However, the deficiency of data and diversity of acoustic event sources make the AED task a tough issue, especially for

Krishna Dasaratha

We study a model of innovation with a large number of firms that create new technologies by combining several discrete ideas. These ideas are created via private investment and spread between firms. Firms face a choice between secrecy, which protects existing intellectual property, and openness, which facilitates learning from others. Their decisions determi

Frank Osterbrink, Dirk Pauly

We prove that the time-harmonic solutions to Maxwell's equations in a 3D exterior domain converge to a certain static solution as the frequency tends to zero. We work in weighted Sobolev spaces and construct new compactly supported replacements for Dirichlet-Neumann fields. Moreover, we even show convergence in operator norm.

Somnath Bhadra, Kaustav Chakraborty, Srijan Sengupta, Soumendra Lahiri

This paper studies the matched network inference problem, where the goal is to determine if two networks, defined on a common set of nodes, exhibit a specific form of stochastic similarity. Two notions of similarity are considered: (i) equality, i.e., testing whether the networks arise from the same random graph model, and (ii) scaling, i.e., testing whether

Antonios Tsokaros, Milton Ruiz, Stuart L. Shapiro, Lunan Sun

Can one distinguish a binary black hole undergoing a merger from a binary neutron star if the individual compact companions have masses that fall inside the so-called mass gap of $3-5\ M_\odot$? For neutron stars, achieving such masses typically requires extreme compactness and in this work we present initial data and evolutions of binary neutron stars initi

Klaus Hulek, Carsten Liese

In this paper we study the Mori fan of the Dolgachev-Nikulin-Voisin family in degree $2$ as well as the associated secondary fan. The main result is an enumeration of all maximal dimensional cones of the two fans.

David Brander, Farid Tari

Wave maps (or Lorentzian-harmonic maps) from a $1+1$-dimensional Lorentz space into the $2$-sphere are associated to constant negative Gaussian curvature surfaces in Euclidean 3-space via the Gauss map, which is harmonic with respect to the metric induced by the second fundamental form. We give a method for constructing germs of Lorentzian-harmonic maps from

Pei Zeng, You Zhou, Zhenhuan Liu

To guarantee the normal functioning of quantum devices in different scenarios, appropriate benchmarking tool kits are quite significant. Inspired by the recent progress on quantum state verification, here we establish a general framework of verifying a target unitary gate. In both the non-adversarial and adversarial scenarios, we provide efficient methods to

Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue

We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications. Given the increasing interest in deploying learning-based methods, there has been a flurry of recent proposals for OPE method, leading to a need for standardized empirical analys

Ricardo A. Podestá, Denis E. Videla

We use known characterizations of generalized Paley graphs which are cartesian decomposable to explicitly compute the spectra of the corresponding associated irreducible cyclic codes. As applications, we give reduction formulas for the number of rational points in Artin-Schreier curves defined over extension fields and to the computation of Gaussian periods.

Ernesto Garcia, Pablo Lessa

We consider the control problem where, given an orthonormal tangent frame in the hyperbolic plane or three dimensional hyperbolic space, one is allowed to transport the frame a fixed distance $r > 0$ along the geodesic in direction of the first vector, or rotate it in place a right angle. We characterize the values of $r > 0$ for which the set of orthonormal

Nicholas Wogan, David Catling

Chemical disequilibrium in exoplanetary atmospheres (detectable with remote spectroscopy) can indicate life. The modern Earth's atmosphere-ocean system has a much larger chemical disequilibrium than other solar system planets with atmospheres because of oxygenic photosynthesis. However, no analysis exists comparing disequilibrium on lifeless, prebiotic p

Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe

Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e.g. by translating images with ground-truth labels from the source domain to th

Hongpeng Zhou, Chahine Ibrahim, Wei Pan

Nonlinear system identification is important with a wide range of applications. The typical approaches for nonlinear system identification include Volterra series models, nonlinear autoregressive with exogenous inputs models, block-structured models, state-space models and neural network models. Among them, neural networks (NN) is an important black-box meth

Pulak Talukdar, Vanamali C. Shastry, Udit Raha, Fred Myhrer

We use heavy baryon chiral perturbation theory to evaluate the two-photon exchange corrections to the low-energy elastic lepton-proton scattering at next-to-leading order accuracy, i.e., ${\mathcal O}(α, M^{-1})$, including a non-zero lepton mass. We consider the elastic proton intermediate state in the two-photon exchange together in the soft photon approxi

Shaoru Chen, Han Wang, Manfred Morari, Victor M. Preciado

In this paper, we consider the robust closed-loop model predictive control (MPC) of a linear time-variant (LTV) system with norm bounded disturbances and LTV model uncertainty, wherein a series of constrained optimal control problems (OCPs) are solved. Guaranteeing robust feasibility of these OCPs is challenging due to disturbances perturbing the predicted s

Pedro Cal, Duff Neill, Felix Ringer, Wouter J. Waalewijn

We study the angle between i) the standard jet axis, ii) the axis of a jet which has been groomed using soft drop, with reduced sensitivity to soft radiation, iii) the jet axis obtained with the winner-take-all recombination scheme, which is insensitive to soft radiation at leading power. We calculate the distributions for these angles at next-to-leading log

John T. Stefan, Alexander G. Kosovichev

Sunquakes are one of the more distinct secondary phenomena related to solar flares, where energy deposition in the lower layers of the Sun's atmosphere excites acoustic waves easily visible in Helioseismic and Magnetic Imager (HMI) dopplergrams. We explore two possible sources of sunquakes in the context of the electron beam hypothesis: an instantaneous

M. Bergevin, N. Bowden, H. P. Mumm, M. Verstraeten

Proceedings for the 14th installment of Applied Antineutrino Physics (AAP) workshop series.

José Ortuño-Macías, Krzysztof Nalewajko

We present the results of 2D particle-in-cell (PIC) simulations of relativistic magnetic reconnection (RMR) in electron-positron plasma, including the dynamical influence of the synchrotron radiation process, and integrating the observable emission signatures. The simulations are initiated with a single Harris current layer with a central gap that triggers t

Kaloian D. Lozanov, Mustafa A. Amin

We present a new numerical algorithm and code, ${\sf GFiRe}$, for solving the non-linear evolution of Abelian gauge fields coupled to complex scalar fields in homogeneous and isotropic spacetimes. We adopt a hybrid approach to solving the system: the spatial derivatives are discretized using standard Lattice Gauge Field Theory techniques, whereas the time ev

F. Fragkoudi, R. J. J. Grand, R. Pakmor, G. Blázquez-Calero

We explore the chemodynamical properties of a sample of barred galaxies in the Auriga magneto-hydrodynamical cosmological zoom-in simulations, which form boxy/peanut (b/p) bulges, and compare these to the Milky Way (MW). We show that the Auriga galaxies which best reproduce the chemodynamical properties of stellar populations in the MW bulge have quiescent m

Donghyeon J. Khim, Sukyoung K. Yi, Yohan Dubois, Julia J. Bryant

Recent integral field spectroscopy observations have found that about 11% of galaxies show star-gas misalignment. The misalignment possibly results from external effects such as gas accretion, interaction with other objects, and other environmental effects, hence providing clues to these effects. We explore the properties of misaligned galaxies using Horizon

S. Centelles Chuliá, W. Rodejohann, U. J. Saldaña-Salazar

Two Higgs-doublet models usually consider an ad-hoc $\mathbb{Z}_2$ discrete symmetry to avoid flavor changing neutral currents. We consider a new class of two Higgs-doublet models where $\mathbb{Z}_2$ is enlarged to the symmetry group ${\cal{F}}\rtimes \mathbb{Z}_2$, i.e. an inner semi-direct product of a discrete symmetry group ${\cal{F}}$ and $\mathbb{Z}_2

Holmfridur Hannesdottir, Matthew D. Schwartz

The traditional $S$-matrix does not exist for theories with massless particles, such as quantum electrodynamics. The difficulty in isolating asymptotic states manifests itself as infrared divergences at each order in perturbation theory. Building on insights from the literature on coherent states and factorization, we construct an $S$-matrix that is free of

O. Del Pozo-Zamudio, A. Genco, S. Schwarz, F. Withers

Vertical stacking of atomically thin layered materials opens new possibilities for the fabrication of heterostructures with favorable optoelectronic properties. The combination of graphene, hexagonal boron nitride and semiconducting transition metal dichalcogenides allows fabrication of electroluminescence (EL) devices, compatible with a wide range of substr

K. Immer, J. Li, L. H. Quiroga-Nuñez, M. J. Reid

We present trigonometric parallax and proper motion measurements toward 22 GHz water and 6.7 GHz methanol masers in 16 high-mass star-forming regions. These sources are all located in the Scutum spiral arm of the Milky Way. The observations were conducted as part of the Bar and Spiral Structure Legacy (BeSSeL) survey. A combination of 14 sources from a forth

Fernando Peñaranda, Ramón Aguado, Pablo San-Jose, Elsa Prada

Full-shell nanowires (semiconducting nanowires fully coated with a superconducting shell) have been recently presented as a novel means to create Majorana zero modes. In contrast to partially coated nanowires, it has been argued that full-shell nanowires do not require high magnetic fields and low densities to reach a putative topological regime. Here we pre

Michael Harrison

A fibration of $\mathbb{R}^3$ by oriented lines is given by a unit vector field $V : \mathbb{R}^3 \to S^2$, for which all of the integral curves are oriented lines. A line fibration is called skew if no two fibers are parallel. Skew fibrations have been the focus of recent study, in part due to their close relationships with great circle fibrations of $S^3$

Lukas Schneiderbauer, Watse Sybesma, Lárus Thorlacius

We obtain the holographic complexity of an evaporating black hole in the semi-classical RST model of two-dimensional dilaton gravity, using a volume prescription that takes into account the higher-dimensional origin of the model. For classical black holes, we recover the expected late time behaviour of the complexity, but new features arise at the semi-class