January 2019 arXiv papers — page 14

Francesco Di Stasio, Inigo Ramiro, Yu Bi, Sotirios Christodoulou

Perovskite nanocrystal light-emitting diodes (LEDs) employing architecture comprising a ZnO nanoparticles electron-transport layer and a conjugated polymer hole-transport layer have been fabricated. The obtained LEDs demonstrate a maximum external-quantum-efficiency of 6.04%, luminance of 12998 Cd/m2 and stable electroluminescence at 519 nm. Importantly, suc

Sotirios Christodoulou, Francesco Di Stasio, Santanu Pradhan, Alexandros Stavrinadis

Lead halide perovskite nanocrystals (NCs) are currently emerging as one of the most interesting solution processed semiconductors since they possess high photoluminescence quantum yield (PLQY), and colour tunability through anion exchange reactions or quantum confinement. Here, we show efficient solar cells based on mixed halide (CsPbBrI2) NCs obtained via a

Yi Liao, Xiao-Dong Ma

We showed in a previous publication that there are six independent dimension-seven operators violating both lepton and baryon numbers ($L=-B=1$) and twelve ones violating lepton but preserving baryon number ($L=2,~B=0$) in standard model effective field theory, and we calculated one-loop renormalization for the former six operators. In this work we continue

Yuri I. Manin, Matilde Marcolli

Recently, it was found that there is a remarkable intuitive similarity between studies in theoretical computer science dealing with large data sets on the one hand, and categorical methods of topology and geometry in pure mathematics, on the other. In this article, we treat the key notion of persistency from computer science in the algebraic geometric contex

Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the

David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou

Generative Adversarial Networks (GANs) have achieved impressive results for many real-world applications. As an active research topic, many GAN variants have emerged with improvements in sample quality and training stability. However, visualization and understanding of GANs is largely missing. How does a GAN represent our visual world internally? What causes

Fernando Lund, Bruno Scheihing Hitschfeld

A canonical quantization procedure is applied to elastic waves interacting with pinned dislocation segments via the Peach-Koehler force. The interaction Hamiltonian, derived from an action principle that classically generates the Peach-Koehler force, is a power series of creation and annihilation operators. The leading term is quadratic, and keeping only thi

G. M. Gusev, Z. D. Kvon, A. D. Levin, E. B. Olshanetsky

We have measured the differential resistance in a two-dimensional topological insulator (2DTI) in a HgTe quantum well, as a function of the applied dc current. The transport near the charge neutrality point is characterized by a pair of counter propagating gapless edge modes. In the presence of an electric field, the energy is transported by counter propagat

Yingying Han, Xiao-Qing Luo, Tie-Fu Li, Wenxian Zhang

Physical systems in the time domain may exhibit analogous phenomena in real space, such as time crystals, time-domain Fresnel lenses, and modulational interference in a qubit. Here we report the experimental realization of time-domain grating using a superconducting qutrit in periodically modulated probe and control fields via two schemes: Simultaneous modul

Mireille Boutin, Gregor Kemper

We show that one can reconstruct the shape of a room with planar walls from the first-order echoes received by four non-planar microphones placed on a drone with generic position and orientation. Both the cases where the source is located in the room and on the drone are considered. If the microphone positions are picked at random, then with probability one,

Sébastien Comerón, Heikki Salo, Johan H. Knapen, Reynier F. Peletier

Thick discs are nearly ubiquitous components of the discs of present-day galaxies. It has been proposed that a fraction of their stars has been accreted. Here, we aim to find whether accretion of satellites is the main thick disc formation mechanism. To do so, we observed a sample of eight nearby edge-on galaxies with the MUSE integral field unit at the VLT.

Christian Budde, Marjeta Kramar Fijavž

We study transport processes on infinite metric graphs with non-constant velocities and matrix boundary conditions in the $\\mathrm{L}^{\infty}$-setting. We apply the theory of bi-continuous operator semigroups to obtain well-posedness of the problem under different assumptions on the velocities and for general stochastic matrices appearing in the boundary c

Kazuki Uematsu, Hikaru Kawamura

We investigate the zero- and finite-temperature properties of the bond-random $s=1/2$ Heisenberg antiferromagnet on the pyrochlore lattice by the exact diagonalization and the Hams--de Raedt methods. We find that the randomness induces the gapless quantum spin liquid (QSL) state, the random-singlet state. Implications to recent experiments on the mixed-anion

Sinan Kahraman

Finding fast polarizing transforms is an important problem as polar codes suffer from slow finitelength performance. This paper considers non binary polar codes for transmission over the AWGN channel and designs polarizing transforms with better distance characteristics using a simple procedure for signal sets. The main idea of the paper is to define Equidis

Ämin Baumeler, Stefan Wolf

Starting from Landauer's slogan "information is physical," we revise and modify Landauer's principle stating that the erasure of information has a minimal price in the form of a certain quantity of free energy. We establish a direct link between the erasure cost and the work value of a piece of information, and show that the former is essentially the length

Nil-Jana Akpinar, Bernhard Kratzwald, Stefan Feuerriegel

Learning to predict solutions to real-valued combinatorial graph problems promises efficient approximations. As demonstrated based on the NP-hard edge clique cover number, recurrent neural networks (RNNs) are particularly suited for this task and can even outperform state-of-the-art heuristics. However, the theoretical framework for estimating real-valued RN

Elisabeth Gaar, Daniel Krenn, Susan Margulies, Angelika Wiegele

Vizing's conjecture (open since 1968) relates the sizes of dominating sets in two graphs to the size of a dominating set in their Cartesian product graph. In this paper, we formulate Vizing's conjecture itself as a Positivstellensatz existence question. In particular, we encode the conjecture as an ideal/polynomial pair such that the polynomial is nonnegativ

Nathalie Cauchi, Kurt Degiorgio, Alessandro Abate

StocHy is a software tool for the quantitative analysis of discrete-time stochastic hybrid systems (SHS). StocHy accepts a high-level description of stochastic models and constructs an equivalent SHS model. The tool allows to (i) simulate the SHS evolution over a given time horizon; and to automatically construct formal abstractions of the SHS. Abstractions

Sarah A. Obead, Hsuan-Yin Lin, Eirik Rosnes, Jörg Kliewer

We consider private polynomial computation (PPC) over noncolluding coded databases. In such a setting a user wishes to compute a multivariate polynomial of degree at most $g$ over $f$ variables (or messages) stored in multiple databases while revealing no information about the desired polynomial to the databases. We construct two novel PPC schemes, where the

Sebastian J. Szybka, Adam Cieślik

We propose a covariant definition of standing gravitational waves in general relativity.

Felix Matuschke, Kévin Ginsburger, Cyril Poupon, Katrin Amunts

3D-Polarized Light Imaging (3D-PLI) is a neuroimaging technique used to study the structural connectivity of the human brain at the meso- and microscale. In 3D-PLI, the complex nerve fiber architecture of the brain is characterized by 3D orientation vector fields that are derived from birefringence measurements of unstained histological brain sections by mea

L. J. Chen, H. B. Zhu, X. C. Ai, M. Fu

Purpose: CMOS pixel sensors have become extremely attractive for future high performance tracking devices. Initial R\&D work has been conducted for the vertex detector for the proposed Circular Electron Positron Collider that will allow precision Higgs measurements. It is critical to achieve low power consumption to minimize the material budget. This require

Christo V. Christov

In this contribution I summarize and discuss the results of the bulk thermodynamic characteristics, meson and nucleon structure in hot matter obtained in the framework of a chiral quark-meson theory. A hybrid NJL model is used in which a Dirac sea of quarks is combined with a Fermi sea of quarks or of nucleons. In the model mesons are described as collective

Oshin Olesegun, Ryan Noraas, Michael Giering, Nagendra Somanath

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource cap

A. M. Badalian, B. L. G. Bakker

The dynamical picture of a quark-antiquark interaction in light mesons, which provides linearity of radial and orbital Regge trajectories (RT), is studied with the use of the relativistic string Hamiltonian with flattened confining potential and taking into account the self-energy and string corrections. Due to the flattening effect both slopes, $\beta_n$ of

Dumitru Dan Smaranda, David J Miller

We examine orbifold theories of Grand Unification with Scherk-Schwarz twisting, performing a renormalisation group analysis and applying low energy experimental constraints. We rule out the minimal SU(5) models, and consider simple extensions including additional fields, such as an additional scalar field, or additional symmetries, such as $SU(5)\times U(1)$

Michael Hinze, Tran Nhan Tam Quyen

In this paper we investigate the problem of recovering the source term in an elliptic system from a measurement of the state on a part of the boundary. For the particular interest in reconstructing probably discontinuous sources, we use the standard least squares method with the total variation regularization. The finite element method is then applied to dis

Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila

We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent tec

Enci Wang, Simon J. Lilly, Gabriele Pezzulli, Jorryt Matthee

To understand star formation in galaxies, we investigate the star formation rate (SFR) surface density ($\Sigma_{\rm SFR}$) profiles for galaxies, based on a well-defined sample of 976 star-forming MaNGA galaxies. We find that the typical $\Sigma_{\rm SFR}$ profiles within 1.5Re of normal SF galaxies can be well described by an exponential function for diffe

M. Zaghoo, T. R. Boehly, J. R. Rygg, P. M. Celliers

We are reporting the observation of the breakdown of electrons degeneracy and emergence of classical statistics in the simplest element: metallic deuterium. We have studied the optical reflectance, shock velocity and temperature of dynamically compressed liquid deuterium up to its Fermi temperature, TF. Above the insulator-metal transition, the optical refle

Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela

Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC) algorithm whose privacy-guarantees are

Alexander D. Gilbert, Ivan G. Graham, Robert Scheichl, Ian H. Sloan

A key quantity that occurs in the error analysis of several numerical methods for eigenvalue problems is the distance between the eigenvalue of interest and the next nearest eigenvalue. When we are interested in the smallest or fundamental eigenvalue, we call this the spectral or fundamental gap. In a recent manuscript [Gilbert et al., arXiv:1808.02639], the

Amjad Yousef Majid, Michel Jansen, Guillermo Ortas Delgado, Kasım Sinan Yıldırım

We characterize the performance of a backscatter tag-to-tag (T2T) multi-hop network. For this, we developed a discrete component-based backscatter T2T transceiver and a communication protocol suite. The protocol composed of a novel (i) flooding-based link control tailored towards backscatter transmission, and (ii) low-power listening MAC. The MAC design is b

D. Copandean, O. Vaduvescu, D. Gorgan

Near Earth Asteroids (NEAs) are discovered daily, mainly by few major surveys, nevertheless many of them remain unobserved for years, even decades. Even so, there is room for new discoveries, including those submitted by smaller projects and amateur astronomers. Besides the well-known surveys that have their own automated system of asteroid detection, there

F. T. Brandt, J. Frenkel, D. G. C. McKeon, G. S. S. Sakoda

The problem of eliminating divergences arising in quantum gravity is generally addressed by modifying the classical Einstein-Hilbert action. These modifications might involve the introduction of local supersymmetry, the addition of terms that are higher-order in the curvature to the action, or invoking compactification of superstring theory from ten to four

Alessandro Renzaglia, Jilles Dibangoye, Vincent Le Doze, Olivier Simonin

In this paper we study the problem of steering a team of Unmanned Aerial Vehicles (UAVs) toward a static configuration which maximizes the visibility of a 3D environment. The UAVs are assumed to be equipped with visual sensors constrained by a maximum sensing range and the prior knowledge on the environment is considered to be very sparse. To solve this prob

Jakob Wasserthal, Peter Neher, Dusan Hirjak, Klaus H. Maier-Hein

While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce. In previous work we presented tract orientation mapping (TOM) as a novel concept for bundle-specific tractog

Carlo Collari, Paolo Lisca

Eisermann and Lamm introduced a notion of symmetric equivalence among symmetric union diagrams and studied it using a refined form of the Jones polynomial. We introduced invariants of symmetric equivalence via refined versions of topological spin models and provided a partial answer to a question left open by Eisermann and Lamm. In this paper we adopt a new

Michael C. H. Choi

Given a target function $U$ to minimize on a finite state space $\mathcal{X}$, a proposal chain with generator $Q$ and a cooling schedule $T(t)$ that depends on time $t$, in this paper we study two types of simulated annealing (SA) algorithms with generators $M_{1,t}(Q,U,T(t))$ and $M_{2,t}(Q,U,T(t))$ respectively. While $M_{1,t}$ is the classical SA algorit

Wei Cui, Zhen Xue, Khanh-Phuong Thai

Adaptive learning systems stand apart from traditional learning systems by offering a personalized learning experience to students according to their different knowledge states. Adaptive systems collect and analyse students' behavior data, update learner profiles, then accordingly provide timely individualized feedback to each student. Such interactions betw

Andreas Thom

We study the approximation of measurable functions on the hypercube by functions arising from affine neural networks. Our main achievement is an approximation of any measurable function $f \colon W_n \to [-1,1]$ up to a prescribed precision $\varepsilon>0$ by a bounded number of neurons, depending only on $\varepsilon$ and not on the function $f$ or $n \in \

Paul Egré, Lorenzo Rossi, Jan Sprenger

This paper explores trivalent truth conditions for indicative conditionals, examining the "defective" table put forward by de Finetti 1936, as well as Reichenbach 1944, first sketched in Reichenbach 1935. On their approach, a conditional takes the value of its consequent whenever its antecedent is True, and the value Indeterminate otherwise. Here we deal wit

Oskar Grocholski, Marcin Kalinowski, Maciej Kolanowski, Sébastien Renaux-Petel

We study the geometrical instability arising in multi-field models of inflation with negatively-curved field space. We analyse how the homogeneous background evolves in presence of geometrical destabilisation, and show that, in simple models, a kinematical backreaction effect takes place that shuts off the instability. We also follow the evolution of the uns

L. Elisa Celis, Vijay Keswani

Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes. This skew goes beyond that which already exists in the world - e.g., Kay et al. showed that although 28% of CEOs in US are women, only 10% of the top 100 results for CEO in Google Image Search are women. Most existing

Stephan Knapp

We investigate piecewise deterministic Markov processes (PDMP), where the deterministic dynamics follows a scalar conservation law and random jumps in the system are characterized by changes in the flux function. We show under which assumptions we can guarantee the existence of a PDMP and conclude bounded variation estimates for sample paths. Finally, we app

Cuong Xuan Chu, Simon Razniewski, Gerhard Weikum

Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity uni

Harrie Oosterhuis, Maarten de Rijke

Online Learning to Rank (OLTR) methods optimize ranking models by directly interacting with users, which allows them to be very efficient and responsive. All OLTR methods introduced during the past decade have extended on the original OLTR method: Dueling Bandit Gradient Descent (DBGD). Recently, a fundamentally different approach was introduced with the Pai

Krzysztof Szczygielski

We apply Wermuth's theorem on commuting operator exponentials to show that if $A, B \in B(X)$, $X$ being Banach space and $A$ of $2\pi i$-congruence free spectrum, then $e^A B = B e^A$ if and only if $AB=BA$. We employ this observation to provide alternative proof of similar result by Chaban and Mortad, applicable for $X$ being a Hilbert space.

Stephan Knapp, Simone Göttlich

In this paper, we introduce a time-continuous production model that enables random machine failures, where the failure probability depends historically on the production itself. This bidirectional relationship between historical failure probabilities and production is mathematically modeled by the theory of piecewise deterministic Markov processes (PDMPs). O

Yue-Long Shen, Jing Gao, Cai-Dian Lü, Yan Miao

In this paper we investigate the power suppressed contributions from two-particle and three-particle twist-4 light-cone distribution amplitudes (LCDAs) of photon within the framework of light-cone sum rules. Compared with leading twist LCDA result, the contribution from three-particle twist-4 LCDAs is not suppressed in the expansion by $1/Q^2$, so that the p

Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif, Semeen Rehman

Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them require the output probability vector. These attacks can be mitigated by concealing the output probability vector. To address this limitation, decision-based attacks have been prop

Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier

Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output. We study the problem of learning such functions under conditions of context-dependence of preferences, which means that the preference in favor of a certain choice alternative may depend on what other options are also available. In spite of

Alireza Nejadettehad, Hamid Mahini, Behnam Bahrak

Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can be helpful in traffic management. Multipl

Earo Wang, Dianne Cook, Rob J Hyndman

Mining temporal data for information is often inhibited by a multitude of formats: irregular or multiple time intervals, point events that need aggregating, multiple observational units or repeated measurements on multiple individuals, and heterogeneous data types. On the other hand, the software supporting time series modeling and forecasting, makes strict

Xudong Wang, Weihua Deng, Yao Chen

Heterogeneous diffusion processes can be well described by an overdamped Langevin equation with space-dependent diffusivity $D(x)$. We investigate the ergodic and non-ergodic behavior of these processes in an arbitrary potential well $U(x)$ in terms of the observable---occupation time. Since our main concern is the large-$x$ behavior for long times, the diff

J. Ebad-Allah, J. Fernández Afonso, M. Krottenmüller, J. Hu

ZrSiS is a nodal-line semimetal, whose electronic band structure contains a diamond-shaped line of Dirac nodes. We carried out a comparative study on the optical conductivity of ZrSiS and related compounds ZrSiSe, ZrSiTe, ZrGeS, and ZrGeTe by reflectivity measurements over a broad frequency range combined with density functional theory calculations. The opti

Alexandre Araujo, Benjamin Negrevergne, Yann Chevaleyre, Jamal Atif

In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones. Besides making a theoretical analysis of their expressivity, we introduced principled techniques for training these models: we devise an initialization scheme and proposed a smart use of non-li

Xun Xu, Loong-Fah Cheong, Zhuwen Li

Multi-model fitting has been extensively studied from the random sampling and clustering perspectives. Most assume that only a single type/class of model is present and their generalizations to fitting multiple types of models/structures simultaneously are non-trivial. The inherent challenges include choice of types and numbers of models, sampling imbalance

Thies Gerken

We present a framework which enables the analysis of dynamic inverse problems for wave phenomena that are modeled through second-order hyperbolic PDEs. This includes well-posedness and regularity results for the forward operator in an abstract setting, where the operators in an evolution equation represent the unknowns. We also prove Fr\'echet-differentiabil

Ya-Hong Chen, Hua Wang, Xiao-Dong Zhang

Motivated from the study of eccentricity, center, and sum of eccentricities in graphs and trees, we introduce several new distance-based global and local functions based on the smallest distance from a vertex to some leaf (called the `uniformity' at that vertex). Some natural extremal problems on trees are considered. Then the middle parts of a tree is discu

Orr Krupnik, Igor Mordatch, Aviv Tamar

We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the same workspace. For non-trivial dynamical systems, MBRL typically suffers from accumulating errors. Several recent studies have addressed this problem by learning latent variable mo

Yandong Sun, Yanguang Zhou, Jian Han, Wei Liu

Dislocations can greatly enhance the figure of merit of thermoelectric materials by prominently reducing thermal conductivity. However, the evolution of phonon modes with different energies when they propagate through a single dislocation is unknown. Here we perform non-equilibrium molecular dynamics simulation to study phonon transport in PbTe crystal with

Ye-Hong Chen, Wei Qin, Franco Nori

We explore an intriguing alternative for a fast and high-fidelity generation of steady-state entanglement. By exponentially enhancing the atom-cavity interaction, we obtain an exponentially-enhanced effective cooperativity of the system, which results in a high fidelity of the state generation. Meanwhile, we modulate the amplitudes of the driving fields to a

Olivier Faugeras, James Maclaurin, Etienne Tanre

We study the asymptotic behaviour for asymmetric neuronal dynamics in a network of Hopfield neurons. The randomness in the network is modelled by random couplings which are centered Gaussian correlated random variables. We prove that the annealed law of the empirical measure satisfies a large deviation principle without any condition on time. We prove that t

Lê Thành Dũng Nguyên

This paper establishes a bridge between linear logic and mainstream graph theory, building on previous work by Retor\'e (2003). We show that the problem of correctness for MLL+Mix proof nets is equivalent to the problem of uniqueness of a perfect matching. By applying matching theory, we obtain new results for MLL+Mix proof nets: a linear-time correctness cr

F. Ait Benkhali, W. Hofmann, F. M. Rieger, N. Chakraborty

The detection of periodicities in light curves of active galacticnuclei (AGN) could have profound consequences for our understanding of the nature and radiation physics of these objects. At high energies (HE; E>100 MeV) 5 blazars (PG 1553+113,PKS 2155-304, 0426-380, 0537-441, 0301-243) have been reported to show year-like quasi-periodic variations (QPVs) wit

Jun Zhao, Ge Wang, Carys Dally, Petr Slovak

Children under 11 are often regarded as too young to comprehend the implications of online privacy. Perhaps as a result, little research has focused on younger kids' risk recognition and coping. Such knowledge is, however, critical for designing efficient safeguarding mechanisms for this age group. Through 12 focus group studies with 29 children aged 6-10 fr

James R. Clough, Ilkay Oksuz, Nicholas Byrne, Julia A. Schnabel

We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our method uses the concept of persistent homology, a tool from topological data analysis, to capture high-level topological characteristics of segmentation results in a way which is diffe

Lieven Le Bruyn

Right adjoints for the forgetful functors on $\lambda$-rings and bi-rings are applied to motivic measures and their zeta functions on the Grothendieck ring of $\mathbb{F}_1$-varieties in the sense of Lorscheid and Lopez-Pena (torified schemes). This leads us to a specific subring of $\mathbb{W}(\mathbb{Z})$, properly containing Almkvist's ring $\mathbb{W}_0(

Sarah-Alexa Hauschild, Nicole Marheineke, Volker Mehrmann

Port-based network modeling of multi-physics problems leads naturally to a formulation as port-Hamiltonian differential-algebraic system. In this way, the physical properties are directly encoded in the structure of the model. Since the state space dimension of such systems may be very large, in particular when the model is a space-discretized partial differ

Flavio Calvo, Luca Belluzzi, Oskar Steiner

Context. The spectrum of the hydrogen atom was explained by Bohr more than one century ago. We revisit here some of the aspects of the underlying quantum structure, with a modern formalism, focusing on the limit of the Balmer series. Aims. We investigate the behaviour of the absorption coefficient of the isolated hydrogen atom in the neighbourhood of the Bal

M. Huzaifah, L. Wyse

Style transfer is a technique for combining two images based on the activations and feature statistics in a deep learning neural network architecture. This paper studies the analogous task in the audio domain and takes a critical look at the problems that arise when adapting the original vision-based framework to handle spectrogram representations. We conclu

Georgios Giasemidis, Nikolaos Kaplis, Ioannis Agrafiotis, Jason R. C. Nurse

Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely

Prem Singh, Himanshu B. Mishra, Aditya K. Jagannatham, K. Vasudevan

This paper analyses the performance of filter bank multicarrier (FBMC) signaling in conjunction with offset quadrature amplitude modulation (OQAM) in multi-user (MU) massive multiple-input multiple-output (MIMO) systems. Initially, closed form expressions are derived for tight lower bounds corresponding to the achievable uplink sum-rates for FBMC-based singl

Adam Zsolt Wagner

We resolve two conjectures of Black-Drellich-Tymoczko about the numbers of valid plane trees for given primary sequences.

Hai-Duong Nguyen, Soo-Hyung Kim

Bone age assessment gives us evidence to analyze the children growth status and the rejuvenation involved chronological and biological ages. All the previous works consider left-hand X-ray image of a child in their works. In this paper, we carry out a study on estimating human age using whole-body bone CT images and a novel convolutional neural network. Our

Xuesong Cai, Wei Fan

Millimeter wave (mm-wave) communication with large-scale antenna array configuration is seen as the key enabler of the next generation communication systems. Accurate knowledge of the mm-wave propagation channels is fundamental and essential. In this contribution, a novel complexity-efficient high resolution parameter estimation (HRPE) algorithm is proposed

Sebastian Engelnkemper, Uwe Thiele

Employing a long-wave mesoscopic hydrodynamic model for the film height evolution we study ensembles of pinned and sliding drops of a volatile liquid that continuously condense onto a chemically heterogeneous inclined substrate. Our analysis combines, on the one hand, path continuation techniques to determine bifurcation diagrams for the depinning of single

Guoji Fu, Bo Yuan, Qiqi Duan, Xin Yao

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackle this issue, this paper proposes a new

Denis Volkhonskiy, Ekaterina Muravleva, Oleg Sudakov, Denis Orlov

In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper, we propose a novel deep learning architecture for three-dimensional porous media reconstru

Simone Scardapane, Elena Nieddu, Donatella Firmani, Paolo Merialdo

The design of activation functions is a growing research area in the field of neural networks. In particular, instead of using fixed point-wise functions (e.g., the rectified linear unit), several authors have proposed ways of learning these functions directly from the data in a non-parametric fashion. In this paper we focus on the kernel activation function

GuruRaj Awate

There is a need for automatic diagnosis of certain diseases from medical images that could help medical practitioners for further assessment towards treating the illness. Alzheimers disease is a good example of a disease that is often misdiagnosed. Alzheimers disease (Hear after referred to as AD), is caused by atrophy of certain brain regions and by brain c

Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen

We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show that any block-switch invariant function h

Javid Naikoo, Ashutosh Kumar Alok, Subhashish Banerjee, S. Uma Sankar

The present work is devoted to the characterization of the Leggett-Garg inequality for three-flavoured neutrino oscillations in presence of both matter and Charge-Conjugation and Parity violating (CP) effects. This study complements and completes the recent one put forward in [17] by relaxing the stationary condition. At variance with the latter case, the LG

Vladimir L. Popov

It is shown that the main result of N. R. Wallach, Principal orbit type theorems for reductive algebraic group actions and the Kempf--Ness Theorem, arXiv:1811.07195v1 (17 Nov 2018) is a special case of a more general statement, which can be deduced, using a short argument, from the classical Richardson and Luna theorems.

E. A. A. Pogna, A. I. Chumakov, C. Ferrante, M. A. Ramos

Fossil amber offers the unique opportunity of investigating an amorphous material which has been exploring its energy landscape for more than 110 Myears of natural aging. By applying different x-ray scattering methods to amber before and after annealing the sample to erase its thermal history, we identify a link between the potential energy landscape and the

Junming Duan, Huazhong Tang

This paper proposes an efficient ADER (Arbitrary DERivatives in space and time) discontinuous Galerkin (DG) scheme to directly solve the Hamilton-Jacobi equation. Unlike multi-stage Runge-Kutta methods used in the Runge-Kutta DG (RKDG) schemes, the ADER scheme is one-stage in time discretization, which is desirable in many applications. The ADER scheme used

Teppo Niinimäki, Mikko Heikkilä, Antti Honkela, Samuel Kaski

Motivation: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymisation strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help by guaranteeing that the published result

V. Balakrishnan, E. Abad, T. Abil, J. J. Kozak

We consider a mortal random walker on a family of hierarchical graphs in the presence of some trap sites. The configuration comprising the graph, the starting point of the walk, and the locations of the trap sites is taken to be exactly self-similar as one goes from one generation of the family to the next. Under these circumstances, the total probability th

Sally Paganin, Amy H. Herring, Andrew F. Olshan, David B. Dunson

There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations in terms of Exchangeable Partition Probability Functions (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and P

Jian Zhang, Zhaofeng Ding, Kevin Huang, Cheng Tan

The superconducting state of the filled skutterudite alloy series Pr$_{1-x}$La$_{x}$Pt$_{4}$Ge$_{12}$ has been systematically studied by specific heat, zero-field muon spin relaxation ($\mu$SR), and superconducting critical field measurements. An additional inhomogeneous local magnetic field, indicative of broken time-reversal symmetry (TRS), is observed in

M. Salz, P. C. Schneider, L. Fossati, S. Czesla

Close-in gas planets are subject to continuous photoevaporation that can erode their volatile envelopes. Today, ongoing mass loss has been confirmed in a few individual systems via transit observations in the ultraviolet spectral range. We demonstrate that the Ultraviolet/Optical Telescope (UVOT) onboard the Neil Gehrels Swift Observatory enables photometry

Jonas Deré

Every Lie algebra over a field $E$ gives rise to new Lie algebras over any subfield $F \subseteq E$ by restricting the scalar multiplication. This paper studies the structure of these underlying Lie algebra in relation to the structure of the original Lie algebra, in particular the question how much of the original Lie algebra can be recovered from its under

Angelo Massimo Perillo, Giuseppe Persiano, Alberto Trombetta

Performing searches over encrypted data is a very current and active area. Several efficient solutions have been provided for the single-writer scenario in which all sensitive data originates with one party (the Data Owner) that encrypts it and uploads it to a public repository. Subsequently the Data Owner (or authorized clients, the Query Sources) accesses

Emery D. Berger, Celeste Hollenbeck, Petr Maj, Olga Vitek

This paper is a reproduction of work by Ray et al. which claimed to have uncovered a statistically significant association between eleven programming languages and software defects in projects hosted on GitHub. First we conduct an experimental repetition, repetition is only partially successful, but it does validate one of the key claims of the original work

Ming-Siang Huang, Po-Ting Lai, Richard Tzong-Han Tsai, Wen-Lian Hsu

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which pl

Hua-Dong Li, Cai-Dian Lu, Lian-You Shan

We study the sensitivity of constraining the model independent Higgs-Z-Z coupling under effective theory up to dimension-6 operators at the future Higgs factory. Utilizing the current conceptual design parameters of the Circular Electron Positron Collider, we give the experimental limits for the model independent operators by the total Higgsstrahlung cross s

Gino B. Kpogbezan, Mark A. van de Wiel, Wessel N. van Wieringen, Aad W. van der Vaart

We introduce a sparse high-dimensional regression approach that can incorporate prior information on the regression parameters and can borrow information across a set of similar datasets. Prior information may for instance come from previous studies or genomic databases, and information borrowed across a set of genes or genomic networks. The approach is base

Frank M. Rieger

The last few years have seen gamma-ray astronomy maturing and advancing in the field of time-domain astronomy, utilizing source variability on timescales over many orders of magnitudes, from a decade down to a few minutes and shorter, depending on the source. This review focuses on some of the key science issues and conceptual developments concerning the tim

Shibing Chen, Jiakun Liu, Xu-Jia Wang

In this paper, we establish a global regularity result for the optimal transport problem with the quadratic cost, where the domains may not be convex. This result is obtained by a perturbation argument, using a recent global regularity of optimal transportation in convex domains by the authors.