June 2019 arXiv papers — page 30

David Durfee, Yu Gao, Gramoz Goranci, Richard Peng

We study \emph{dynamic} algorithms for maintaining spectral vertex sparsifiers of graphs with respect to a set of terminals $T$ of our choice. Such objects preserve pairwise resistances, solutions to systems of linear equations, and energy of electrical flows between the terminals in $T$. We give a data structure that supports insertions and deletions of edg

Sumin Choi, Viatcheslav N. Agafonov, Valery A. Davydov, L. F. Kulikova

We have characterized thermal susceptibilities of the spectral band at 740 nm of silicon-vacancy (SiV) centers in Si- and Si,P-doped nanodiamonds over a temperature range from 295 K to 350 K, which is of interest for thermometry in biological systems. Si-doped crystals reveal linear dependence of the SiV zero-phonon line position, width and relative amplitud

Jeremy Barnes, Roman Klinger

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast array of these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To

Behzad Boroujerdian, Hasan Genc, Srivatsan Krishnan, Bardienus Pieter Duisterhof

Autonomous-mobile cyber-physical machines are part of our future. Specifically, unmanned-aerial-vehicles have seen a resurgence in activity with use-cases such as package delivery. These systems face many challenges such as their low-endurance caused by limited onboard-energy, hence, improving the mission-time and energy are of importance. Such improvements

Reza Serajian, Said Asadi

In this paper, an exact analytical solution for free vibration analysis of thick laminated annular circular plates is presented.

Yunsheng Li, Nuno Vasconcelos

The problem of multi-domain learning of deep networks is considered. An adaptive layer is induced per target domain and a novel procedure, denoted covariance normalization (CovNorm), proposed to reduce its parameters. CovNorm is a data driven method of fairly simple implementation, requiring two principal component analyzes (PCA) and fine-tuning of a mini-ad

Klaus Schneider, Beichuan Zhang, Lotfi Benmohamed

The Internet can be made more efficient and robust with hop-by-hop multipath routing: Each router on the path can split packets between multiple nexthops in order to 1) avoid failed links and 2) reduce traffic on congested links. Before deciding how to split traffic, one first needs to decide which nexthops to allow at each step. In this paper, we investigat

Nashid Shahriar, Sepehr Taeb, Shihabur Rahman Chowdhury, Mubeen Zulfiqar

In 5G networks, slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of the so-called elastic optica

Muhammad Rehman Zafar, Naimul Mefraz Khan

Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically generates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g. linear classifier) around the prediction through g

Temitope Ajileye, Boris Motik, Ian Horrocks

Several centralised RDF systems support datalog reasoning by precomputing and storing all logically implied triples using the wellknown seminaive algorithm. Large RDF datasets often exceed the capacity of centralised RDF systems, and a common solution is to distribute the datasets in a cluster of shared-nothing servers. While numerous distributed query answe

Zachary Munro

The $\widetilde{A}_n$ Coxeter groups are known to not be systolic or cocompactly cubulated for $n\geq 3$. We prove that these groups act geometrically on weakly modular graphs, a weak notion of nonpositive curvature generalizing the 1-skeleta of $\mathrm{CAT}(0)$ cube complexes and systolic complexes. To prove weak modularity we describe the canonical emeddi

Nathan Clement

The strength or weakness of an algorithm is ultimately governed by the confidence of its result. When the domain of the problem is large (e.g. traversal of a high-dimensional space), a perfect solution cannot be obtained, so approximations must be made. These approximations often lead to a reported quantity of interest (QOI) which varies between runs, decrea

Jason T. Hinkle, Sylvain Veilleux, David S. N. Rupke

The various ionization mechanisms at play in active galactic nuclei (AGN) and quasars have been well studied, but relatively little has been done to separately investigate the contributions of these ionization mechanisms within the host galaxy and outflowing components. Using Gemini integral field spectroscopy (IFS) data, we study the ionization properties o

Oussama A. Dambri, Soumaya Cherkaoui

Inter-Symbol Interference (ISI) is the main challenge of bio-inspired diffusion-based molecular communication. In real biological systems, the degradation of the remaining molecules from a previous transmission is used to mitigate ISI. While most prior works have proposed the use of enzymes to catalyze the molecule degradation, enzymes also degrade the molec

Clinton DeW. Van Siclen

A probabilistic method is derived for solution of ohmic circuit problems. It is compared to the standard approach, which is construction and solution of a set of coupled, linear equations manifesting Kirchhoff's laws. An example is made of an electrical circuit that has the complicated connectivity of a bond-and-node Sierpinski triangle, which would be t

Ramya Srinivasan, Ajay Chander, Pouya Pezeshkpour

Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI decisions to various stakeholders. State-

Michaela Meyns, Sebastian Primpke, Gunnar Gerdts

AFM is a technique widely applied in the nanoscale characterisation of polymers and their surface properties. With nano-FTIR and IR-sSNOM imaging an optical dimension is added to this technique that allows for straightforward high resolution characterisation and spectroscopy of polymers. As the volume sampled by these near-field techniques depends mostly on

Luyun Gan, Brosnan Yuen, Tao Lu

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multi gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance - partial least squares discriminant analysis when signal-to-

T. J. Sullivan

The recent article "A Bayesian conjugate gradient method" by Cockayne, Oates, Ipsen, and Girolami proposes an approximately Bayesian iterative procedure for the solution of a system of linear equations, based on the conjugate gradient method, that gives a sequence of Gaussian/normal estimates for the exact solution. The purpose of the probabilistic e

Jan S. Rellermeyer, Maher Amer, Richard Smutzer, Karthick Rajamani

While containers efficiently implement the idea of operating-system-level application virtualization, they are often insufficient to increase the server utilization to a desirable level. The reason is that in practice many containerized applications experience a limited amount of load while there are few containers with a high load. In such a scenario, the v

Bin Zhao

Due to Android's open source feature and low barriers to entry for developers, millions of developers and third-party organizations have been attracted into the Android ecosystem. However, over 90 percent of mobile malware are found targeted on Android. Though Android provides multiple security features and layers to protect user data and system resource

Sebastien Picard, Xiangwen Zhang

We study the long-time existence and convergence of general parabolic complex Monge-Ampere type equations whose second order operator is not necessarily convex or concave in the Hessian matrix of the unknown solution.

Santiago Jimenez, Guillermo Tenorio-Tagle, Sergiy Silich

Supernova explosions and their remnants (SNRs) drive important feedback mechanisms that impact considerably the galaxies that host them. Then, the knowledge of the SNRs evolution is of paramount importance in the understanding of the structure of the interstellar medium (ISM) and the formation and evolution of galaxies. Here we study the evolution of SNRs in

Benjamin Aymard, Fabien Campillo, Romain Veltz

In this work, we propose a nonlinear stochastic model of a network of stochastic spiking neurons. We heuristically derive the mean-field limit of this system. We then design a Monte Carlo method for the simulation of the microscopic system, and a finite volume method (based on an upwind implicit scheme) for the mean-field model. The finite volume method resp

Ron Bitton, Kobi Boymgold, Rami Puzis, Asaf Shabtai

Information security awareness (ISA) is a practice focused on the set of skills, which help a user successfully mitigate a social engineering attack. Previous studies have presented various methods for evaluating the ISA of both PC and mobile users. These methods rely primarily on subjective data sources such as interviews, surveys, and questionnaires that a

Gregory M. Stephen, Christopher Lane, Gianina Buda, David Graf

The spin-filter material CrVTiAl is a promising candidate for producing highly spin-polarized currents at room temperature in a nonmagnetic architecture. Thin films of compensated-ferrimagnetic CrVTiAl have been grown and their electrical and magnetic properties have been studied. The resistivity shows two-channel semiconducting behavior with one disordered

Hamdy F. F. Mahmoud

Three types of regression models researchers need to be familiar with and know the requirements of each: parametric, semiparametric and nonparametric regression models. The type of modeling used is based on how much information are available about the form of the relationship between response variable and explanatory variables, and the random error distribut

Ravi Sankar Vaddi, Yifei Guan, Zhi Yan Chen, Alexander Mamishev

Electrohydrodynamic (EHD) flow induced by planar corona discharge in the wall boundary layer region is investigated experimentally and via a multiphysics computational model. The EHD phenomena has many potential engineering applications, its optimization requires a mechanistic understanding of the ion and flow transport. The corona EHD actuator consisting of

William Zygmunt, Erin G. Teich, Greg van Anders, Sharon C. Glotzer

The existence of topological order is frequently associated with strongly coupled quantum matter. Here, we demonstrate the existence of topological phases in classical systems of densely packed, hard, anisotropic polyhedrally shaped colloidal particles. We show that previously reported transitions in dense packings lead to the existence of topologically orde

Madison S. Krieger, Marcelo A. Dias

Instabilities in thin elastic sheets, such as wrinkles, are of broad interest both from a fundamental viewpoint and also because of their potential for engineering applications. Nematic liquid crystal elastomers offer a new form of control of these instabilities through direct coupling between microscopic degrees of freedom, resulting from orientational orde

Gustavo Aguilar, Viktor Rozgić, Weiran Wang, Chao Wang

Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings where both modalities are available in training and evaluation. However, in practice, this is not always the case; getting ASR output may represent a bottleneck in a deployment pipe

Md Abu Sayed, Theodore Stanoev

The anisotropy of an equivalent medium resulting from the Backus (1962) average is induced by the vertical inhomogeneity among its constituent layers. The velocity field of the constituent isotropic layers increases linearly with depth, which is assumed to be a good seismological description of sedimentary layers Slotnick (1959). We derive an analytical rela

Jin Gao, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong

This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the proble

Juan Ferrera, Javier Gómez Gil

The Takagi function is a classical example of a continuous nowhere differentiable function. It has empty subdifferential except in a countable set where its subdifferential is $\mathbb{R}$. In this paper we characterize its superdifferential.

Pegah Karimi, Mary Lou Maher, Nicholas Davis, Kazjon Grace

This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching canvas. The AI agent responds to the huma

Junyu Zhang, Lin Xiao

We consider the problem of minimizing the composition of a smooth (nonconvex) function and a smooth vector mapping, where the inner mapping is in the form of an expectation over some random variable or a finite sum. We propose a stochastic composite gradient method that employs an incremental variance-reduced estimator for both the inner vector mapping and i

Karl Friston

This monograph attempts a theory of every 'thing' that can be distinguished from other things in a statistical sense. The ensuing statistical independencies, mediated by Markov blankets, speak to a recursive composition of ensembles (of things) at increasingly higher spatiotemporal scales. This decomposition provides a description of small things; e.

Yading Yuan, Ren-Dih Sheu, Luke Fu, Yeh-Chi Lo

Post-implant dosimetry (PID) is an essential step of prostate brachytherapy that utilizes CT to image the prostate and allow the location and dose distribution of the radioactive seeds to be directly related to the actual prostate. However, it it a very challenging task to identify these seeds in CT images due to the severe metal artifacts and high-overlappe

Meenakshi Sarkar, Prabhu Pradhan, Debasish Ghose

In this paper, we introduce a novel framework that can learn to make visual predictions about the motion of a robotic agent from raw video frames. Our proposed motion prediction network (PROM-Net) can learn in a completely unsupervised manner and efficiently predict up to 10 frames in the future. Moreover, unlike any other motion prediction models, it is lig

Deepak Mishra, Håkan Johansson

Usage of low-cost hardware in large antenna arrays and low-power wireless devices in Internet-of-Things (IoT) has led to the degradation of practical beamforming gains due to the underlying hardware impairments like in-phase-and-quadrature-phase imbalance (IQI). To address this timely concern, we present a new nontrivial closed-form expression for the global

Avinash, Chandan Jana, Arnab Rudra

We compute Passarino-Veltman (PV) reduction for tensor loop integrals, that appear in open field theories. We apply these results to open-Yukawa theory and compute the self-energy correction of the fields. We found that non-local divergences show up in the one loop correction to the fermionic self-energy. These non-local divergences do not disappear even if

Lisa Schlosser, Torsten Hothorn, Achim Zeileis

A core step of every algorithm for learning regression trees is the selection of the best splitting variable from the available covariates and the corresponding split point. Early tree algorithms (e.g., AID, CART) employed greedy search strategies, directly comparing all possible split points in all available covariates. However, subsequent research showed t

Merry Mani, Hemant Kumar Aggarwal, Vincent Magnotta, Mathews Jacob

Purpose: To introduce a fast and improved direct reconstruction method for multi-shot diffusion weighted (msDW) scans for high-resolution studies. Methods:Multi-shot EPI methods can enable higher spatial resolution for diffusion MRI studies. Traditionally, such acquisitions required specialized reconstructions involving phase compensation to correct for inte

John Ellis, Dimitri V. Nanopoulos, Keith A. Olive, Sarunas Verner

We have presented previously a general treatment of Starobinsky-like inflation in no-scale supergravity where the tensor-to-scalar ratio $r = 3(1 - n_s)^2$, and $n_s$ is the tilt of the scalar perturbations. In particular, we have shown how this scenario can be unified with modulus fixing, supersymmetry breaking and a small cosmological constant. In this pap

Maria Alberich-Carramiñana, Antoni Ferragut, Jaume Llibre

In this paper we show how we can transform quadratic systems into new quadratic systems after some kind of birational transformations, the quadratic plane Cremona maps. We afterwards apply these transformations to the families of quadratic differential systems having an algebraic limit cycle. As a consequence, we provide a new family of quadratic systems hav

Aiying Zhao, Qiang Gu, Richard A. Klemm

From the Dirac equation of an electron in an anisotropic conduction band, the anisotropy of its motion dramatically affects its interaction with applied electric and magnetic fields. The quantum spin Hall effect (QSHE) is observable in two-dimensional metals without spin-orbit coupling. The dimensionality of the Zeeman interaction plays an important role in

Qian Li, Zhe He, Yi Guo, Hansi Zhang

Existing trials had not taken enough consideration of their population representativeness, which can lower the effectiveness when the treatment is applied in real-world clinical practice. We analyzed the eligibility criteria of Bevacizumab colorectal cancer treatment trials, assessed their a priori generalizability, and examined how it affects patient outcom

Keita Nii

We study the low-energy dynamics in three-dimensional $\mathcal{N}=2$ exceptional gauge theories with matters in a fundamental representation, especially focusing on confinement phases and on a quantum structure of the Coulomb branch in the moduli space of vacua. We argue that the confinement phases of these exceptional gauge theories have a single Coulomb b

Wlodzimierz Jaskolski

We demonstrate that single layer graphene exhibits the electronic structure of a bilayer when it is connected to two gated bilayers. The energy gap characteristic for gated bilayer is induced in the single layer and it persists for distances separating the bilayers up to 20 nm. Most importantly, we report on the appearance of topologically protected gapless

Anastasiia A. Pervishko, Jacob Biamonte

This $Physics$ viewpoint considers recent work by Tilloy and Cirac [Phys. Rev. X 9, 021040 (2019), arXiv:1808.00976]; those authors overcame several past limitations in the generalization of tensor networks to the continuum and proposed a new class of continuous tensor network states (cMPS) which apply to spatial dimensions of two and higher.

M. Schreck

The current article reviews results on vacuum Cherenkov radiation obtained for modified fermions. Two classes of processes can occur that have completely distinct characteristics. The first one does not include a spin flip of the radiating fermion, whereas the second one does. A résumé will be given of the decay rates for these processes and their properties

V. V. Bobylev, A. T. Bajkova

We have studied a sample containing about 6000 OB stars with proper motions and trigonometric parallaxes from the Gaia DR2 catalogue. The following parameters of the angular velocity of Galactic rotation have been found: $Ω_0=29.70\pm0.11$ km s$^{-1}$ kpc$^{-1}$, $Ω'_0=-4.035\pm0.031$ km s$^{-1}$ kpc$^{-2}$, and $Ω''_0= 0.620\pm0.014$ km s$^{-1}$

Hiroki Nagakura, Kohsuke Sumiyoshi, Shoichi Yamada

We present a new method for neutrino-matter coupling in multi-dimensional radiation-hydrodynamic simulations of core-collapse supernova (CCSN) with the full Boltzmann neutrino transport. The development is motivated by the fact that the accurate conservation of momentum is required for reliable numerical modelings of CCSN dynamics including a recoil of proto

Berthold Jäck, Yonglong Xie, Jian Li, Sangjun Jeon

Superconducting proximity pairing in helical edge modes, such as those of topological insulators (TI), is predicted to provide a unique platform for realizing Majorana zero modes (MZMs). We use scanning tunneling microscopy measurements to probe the influence of proximity induced superconductivity and magnetism on the helical hinge states of Bi(111) films, g

Mattia C. Sormani, Robin G. Tress, Simon C. O. Glover, Ralf S. Klessen

Observations of molecular gas near the Galactic centre ($| l | < 10^\circ$, $| b | < 1^\circ$) reveal the presence of a distinct population of enigmatic compact clouds which are characterised by extreme velocity dispersions ($Δv > 100\, \rm km/s$). These Extended Velocity Features (EVFs) are very prominent in the datacubes and dominate the kinematics of mole

Osama Idais

Optimal designs for generalized linear models require a prior knowledge of the regression parameters. At certain values of the parameters we propose particular assumptions which allow to derive a locally optimal design for a model without intercept from a locally optimal design for the corresponding model with intercept and vice versa. Applications to Poisso

Brendan Juba

We consider the problem of learning rules from a data set that support a proof of a given query, under Valiant&#39;s PAC-Semantics. We show how any backward proof search algorithm that is sufficiently oblivious to the contents of its knowledge base can be modified to learn such rules while it searches for a proof using those rules. We note that this gives su

Lore Goetschalckx, Alex Andonian, Aude Oliva, Phillip Isola

We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability, aesthetics, and emotional valence. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more or less memorable? GANs allow us to generate a manif

Pratik Bhaskar, Yashasvi Maurya, Rajesh Kumar, R. Ravinder

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of their atomic structure remain unclear. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline

Erick Nagel, Jerome Bouvier

Aims: We present an analysis of simultaneously observed CoRoT and Spitzer lightcurves for $4$ systems in the stellar forming region NGC 2264: Mon-660, Mon-811, Mon-1140 and Mon-1308. These objects share in common a high resemblance between the optical and infrared lightcurves, such that the mechanism responsible to produce them is the same. The aim of this p

Alexander Tyurin

In this work we consider a possibility to use the conception of $(δ, L)$-model of a function for optimization tasks, whereby solving a primal problem there is a necessity to recover a solution of a dual problem. The conception of $(δ, L)$-model is based on the conception of $(δ, L)$-oracle which was proposed by Devolder-Glineur-Nesterov, herewith the authors

Vaishak Belle, Brendan Juba

We consider the problem of answering queries about formulas of first-order logic based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed probability distribution. PAC semantics, introduced by Valiant, is one rigorous, general proposal for learning to reason in fo

Mohammad Vahid Jamali, Mahdi Soleymani, Hessam Mahdavifar

We consider the problem of coded distributed computing where a large linear computational job, such as a matrix multiplication, is divided into $k$ smaller tasks, encoded using an $(n,k)$ linear code, and performed over $n$ distributed nodes. The goal is to reduce the average execution time of the computational job. We provide a connection between the proble

Weilian Song, Tawfiq Salem, Hunter Blanton, Nathan Jacobs

We propose an automated method to estimate a road segment&#39;s free-flow speed from overhead imagery and road metadata. The free-flow speed of a road segment is the average observed vehicle speed in ideal conditions, without congestion or adverse weather. Standard practice for estimating free-flow speeds depends on several road attributes, including grade,

Dong Wang, Yitong Li, Wei Cao, Liqun Chen

We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs. The motion is modeled by a newly proposed component, motion guider, which plays the role of both learner and teacher. Specifically, it {\em learns} the temporal features from real data and {\em guides} the generator

Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy

Many realistic robotics tasks are best solved compositionally, through control architectures that sequentially invoke primitives and achieve error correction through the use of loops and conditionals taking the system back to alternative earlier states. Recent end-to-end approaches to task learning attempt to directly learn a single controller that solves an

Xiaoting Wu, Eric Granger, Xiaoyi Feng

Visual kinship verification entails confirming whether or not two individuals in a given pair of images or videos share a hypothesized kin relation. As a generalized face verification task, visual kinship verification is particularly difficult with low-quality found Internet data. Due to uncontrolled variations in background, pose, facial expression, blur, i

Hanyuan Hang, Yuchao Cai, Hanfang Yang

Single-level density-based approach has long been widely acknowledged to be a conceptually and mathematically convincing clustering method. In this paper, we propose an algorithm called &#34;best-scored clustering forest&#34; that can obtain the optimal level and determine corresponding clusters. The terminology &#34;best-scored&#34; means to select one rand

G. G. Amosov, A. S. Mokeev

We continue the study of non-commutative operator graphs generated by resolutions of identity covariant with respect to unitary actions of the circle group and the Heisenber-Weyl group as well. It is shown that the graphs generated by the circle group has the system of unitary generators fulfilling permutations of basis vectors. For the graph generated by th

Nick Devereux

Spectroscopic observations of the Seyfert 1/Liner nucleus of M81, obtained recently with the Space Telescope Imaging Spectrograph (STIS) aboard the Hubble Space Telescope (HST), have revealed a UV--visible spectrum rich with emission lines of a variety of widths, ionization potentials, and critical densities, including several in the UV that have not previou

Gohin Shaikh Samad, Wayne Polyzou

Relativistic invariance in Euclidean formulations of quantum mechanics is discussed. Relativistic treatments of quantum theory are needed to study hadronic systems at sub-hadronic distance scales. Euclidean formulations of relativistic quantum mechanics have some computational advantages. In the Euclidean representation the physical Hilbert space inner produ

Weitao Duan, Qian Wang, Rogier Verhulst, Ya Xu

With the advancement in technology, raw event data generated by the digital world have grown tremendously. However, such data tend to be insufficient and noisy when it comes to measuring user intention or satisfaction. One effective way to measure user experience directly is through surveys. In particular, with the popularity of online surveys, extensive wor

Jie Ren, Kai Wu, Dong Li

Emerging non-volatile memory (NVM) is promising for building future HPC. Leveraging the non-volatility of NVM as main memory, we can restart the application using data objects remaining on NVM when the application crashes. This paper explores this solution to handle HPC under failures, based on the observation that many HPC applications have good enough intr

Thomas Lehéricy

We consider large random planar maps and study the first-passage percolation distance obtained by assigning independent identically distributed lengths to the edges. We consider the cases of quadrangulations and of general planar maps. In both cases, the first-passage percolation distance is shown to behave in large scales like a constant times the usual gra

Josephine Lamp, Simone Silvetti, Marc Breton, Laura Nenzi

Type I Diabetes (T1D) is a chronic disease in which the body&#39;s ability to synthesize insulin is destroyed. It can be difficult for patients to manage their T1D, as they must control a variety of behavioral factors that affect glycemic control outcomes. In this paper, we explore T1D patient behaviors using a Signal Temporal Logic (STL) based learning appr

Ramiro Cayuso, Finnian Gray, David Kubiznak, Aoibheann Margalit

Many black hole spacetimes with a 3-form field exhibit a hidden symmetry encoded in a torsion generalization of the principal Killing--Yano tensor. This tensor determines basic properties of such black holes while also underlying the separability of the Hamilton--Jacobi, Klein--Gordon, and (torsion-modified) Dirac field equations in their background. As a sp

Maximilian Spliethöver, Jonas Klaff, Hendrik Heuer

Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new State-of-the-Art results. A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing, though. With this paper, we report a comparative evaluation of attention layers in combination with a bidirec

Esin Demir, Y. Ilker Yaman, Askin Kocabas

Many animals in their natural habitat exhibit collective motion and form complex patterns to tackle environmental difficulties. Several physical and biological factors, such as animal motility, population densities, and chemical cues, play significant roles in this process. However, very little is known about how sensory information interplays with all these

Yuchen Li, Frank Rudzicz, Jekaterina Novikova

We seek to improve the data efficiency of neural networks and present novel implementations of parameterized piece-wise polynomial activation functions. The parameters are the y-coordinates of n+1 Chebyshev nodes per hidden unit and Lagrangian interpolation between the nodes produces the polynomial on [-1, 1]. We show results for different methods of handlin

Graham Cox, Scott Scott MacLachlan, Luke Steeves

Inequalities between the Dirichlet and Neumann eigenvalues of the Laplacian have received much attention in the literature, but open problems abound. Here, we study the number of Neumann eigenvalues no greater than the first Dirichlet eigenvalue. Based on a combination of analytical and numerical results, we conjecture that this number is controlled by the i

Riccardo Nagar

We present an efficient numerical solution of the DGLAP equations for single and double parton distribution functions (PDFs and DPDs), based on the Chebyshev interpolation of these functions. For PDF evolution, our method allows for a higher numerical accuracy using a considerably smaller number of grid points compared to other methods. The DPD evolution is

Abhishek Tripathi, Stefan Zaefferer

We measured the physical lateral resolution of the electron backscatter diffraction (EBSD) technique for the case of pure magnesium and tungsten. Spatial resolution, among other parameters, depends significantly on the accelerating voltage and the atomic number of the material. For the case of lighter metals, it is supposed to be lower than in the case of he

Jean-Philippe Lansberg, Hua-Sheng Shao, Nodoka Yamanaka, Yu-Jie Zhang

Prompt double-$J/ψ$ production at high-energy hadron colliders can be considered as a golden channel to probe double parton scatterings (DPS) --in particular to study gluon-gluon correlations inside the proton-- and, at the same time, to measure the distribution of linearly-polarised gluons inside the proton. Such studies however require a good control of bo

Rudrasis Chakraborty, Jiayun Wang, Stella X. Yu

We develop a novel deep learning architecture for naturally complex-valued data, which is often subject to complex scaling ambiguity. We treat each sample as a field in the space of complex numbers. With the polar form of a complex-valued number, the general group that acts in this space is the product of planar rotation and non-zero scaling. This perspectiv

Benjamin Allen, Arshad Kudrolli

We measure the drag encountered by a vertically oriented rod moving across a sedimented granular bed immersed in a fluid under steady-state conditions. At low rod speeds, the presence of the fluid leads to a lower drag because of buoyancy, whereas a significantly higher drag is observed with increasing speeds. The drag as a function of depth is observed to d

Jie, Zhang, Jonathan P. Newman, Xiao Wang

We demonstrated a CMOS imaging system that adapts each pixel&#39;s exposure and sampling rate to capture high dynamic range (HDR) videos. The system consist of a custom designed image sensor with pixel-wise exposure configurability and a real-time pixel exposure controller. These parts operate in a closed-loop to sample, detect and optimize each pixel&#39;s

Joon Son Chung, Bong-Jin Lee, Icksang Han

The goal of this work is to determine &#39;who spoke when&#39; in real-world meetings. The method takes surround-view video and single or multi-channel audio as inputs, and generates robust diarisation outputs. To achieve this, we propose a novel iterative approach that first enrolls speaker models using audio-visual correspondence, then uses the enrolled mo

Vikas Jadhav Y, Arunima Banerjee

Superthin galaxies are low surface brightness (LSB) bulgeless disc galaxies having stellar discs with unusually high planar-to-vertical axes ratio $b/a$ $>$ 10 - 20, the formation and evolution of which is not well-understood. We calculate the specific angular momenta of a sample of six superthins and nine other bulgeless LSBs using stellar photometry, atomi

Stefano Corda, Gagandeep Singh, Ahsan Javed Awan, Roel Jordans

Near-memory Computing (NMC) promises improved performance for the applications that can exploit the features of emerging memory technologies such as 3D-stacked memory. However, it is not trivial to find such applications and specialized tools are needed to identify them. In this paper, we present PISA-NMC, which extends a state-of-the-art hardware agnostic p

Frank Schlawin, Dieter Jaksch

We investigate long-range pairing interactions between ultracold fermionic atoms confined in an optical lattice which are mediated by the coupling to a cavity. In the absence of other perturbations, we find three degenerate pairing symmetries for a two-dimensional square lattice. By tuning a weak local atomic interaction via a Feshbach resonance or by tuning

K. T. Schütt, M. Gastegger, A. Tkatchenko, K. -R. Müller

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for r

D. I. Valdivia, M. Geiß, M. Hellmuth, M. Hernandez Rosales

Cographs are exactly hereditarily well-colored graphs, i.e., the graphs for which a greedy coloring of every induced subgraph uses only the minimally necessary number of colors $χ(G)$. In recent work on reciprocal best match graphs so-called hierarchically coloring play an important role. Here we show that greedy colorings are a special case of hierarchical

Yan Yang

Market definition is an important component in the premerger investigation, but the models used in the market definition have not developed much in the past three decades since the Critical Loss Analysis (CLA) was proposed in 1989. The CLA helps the Hypothetical Monopolist Test to determine whether the hypothetical monopolist is going to profit from the smal

Adam Rutkowski, Michał Studziński

In this paper we show an explicit construction of multipartite class of entangled states with the PPT (Positive Partial Transposition) property in every cut. We investigate properties of this class of states focusing on the trace distance from the set of separable states. We provide an explicit sub-class of the multipartite entangled PPT states which are arb

Ismael Moumen, Carmelle Robert, Daniel Devost, René Pierre Martin

We present the first optical identification and confirmation of a sample of supernova remnants (SNRs) in the nearby galaxy NGC3344. Using high spectral and spatial resolution data, obtained with the CFHT imaging Fourier transform spectrograph SITELLE, we identified about 2200 emission line regions, many of which are HII regions, diffuse ionized gas regions,

Sandy Engelhardt, Lalith Sharan, Matthias Karck, Raffaele De Simone

Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are

Tianshu Cong, Aihua Xia, Fuxi Zhang

One of the main differences between the central limit theorem and the Poisson law of small numbers is that the former possesses the large sample property (LSP), i.e., the error of normal approximation to the sum of $n$ independent identically distributed (i.i.d.) random variables is a decreasing function of $n$. Since 1980&#39;s, considerable effort has been

Daniel Loureiro, Alipio Jorge

Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focus

Nidhi Kaihnsa, Yue Ren, Mohab Safey El Din, Johannes W. R. Martini

We consider a measure of cooperativity based on the minimal absolute interaction required to generate an observed titration behavior. We describe the corresponding algebraic optimization problem and show how it can be solved using the nonlinear algebra tool \texttt{SCIP}. Moreover, we compute the minimal absolute interactions for various binding polynomials

Akash Hossain, Francois Laroussinie

QCTL extends the temporal logic CTL with quantifications over atomic propositions. This extension is known to be very expressive: QCTL allows us to express complex properties over Kripke structures (it is as expressive as MSO). Several semantics exist for the quantifications: here, we work with the structure semantics, where the extra propositions label the