April 2020 arXiv papers — page 10

Jaya Sreevalsan-Nair, Pragyan Mohapatra

LiDAR point clouds provide rich geometric information, which is particularly useful for the analysis of complex scenes of urban regions. Finding structural and semantic differences between two different three-dimensional point clouds, say, of the same region but acquired at different time instances is an important problem. A comparison of point clouds involv

Wei Du, Jia Kong, Jun Jia, Sheng Ming

We present experimental and theoretical results on a new interferometer topology that nests a SU(2) interferometer, e.g., a Mach-Zehnder or Michelson interferometer, inside a SU(1,1) interferometer, i.e., a Mach-Zehnder interferometer with parametric amplifiers in place of beam splitters. This SU(2)-in-SU(1,1) nested interferometer (SISNI) simultaneously ach

Epaminondas Kapetanios, Vijayan Sugumaran, Anastassia Angelopoulou

Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and bases (KB), as a key ingredient of intelligent applications, is a never-ending challenge, si

M. Solsona, H. Keizer, H. de Boer, Y. P. Klein

The deflection due to the Magnus force of magnetic particles with a diameter of 80 micrometer dropping through fluids and rotating in a magnetic field was measured. With Reynolds number for this experiment around 1, we found trajectory deflections of the order of 1 degree, in agreement within measurement error with theory. This method holds promise for the s

Federico Grasselli, Gláucia Murta, Hermann Kampermann, Dagmar Bruß

Multiparty quantum cryptography based on distributed entanglement will find its natural application in the upcoming quantum networks. The security of many multipartite device-independent (DI) protocols, such as DI conference key agreement, relies on bounding the von Neumann entropy of the parties' outcomes conditioned on the eavesdropper's information, given

Marta Magnani, Ilaria Baneschi, Mariasilvia Giamberini, Pietro Mosca

In high mountains, the effects of climate change are manifesting most rapidly. This is especially critical for the high-altitude carbon cycle, for which new feedbacks could be triggered. However, mountain carbon dynamics is only partially known. In particular, models of the processes driving carbon fluxes in high-altitude grasslands and Alpine tundra need to

L. K. Upreti, C. Evain, S. Randoux, P. Suret

We report new oscillations of wavepackets in quantum walks subjected to electric fields, that decorate the usual Bloch-Zener oscillations of insulators. The number of turning points (or sub-oscillations) within one Bloch period of these oscillations is found to be governed by the winding of the quasienergy spectrum. Thus, this provides a new physical manifes

Keith Levin, Carey E. Priebe, Vince Lyzinski

Vertex nomination is a lightly-supervised network information retrieval task in which vertices of interest in one graph are used to query a second graph to discover vertices of interest in the second graph. Similar to other information retrieval tasks, the output of a vertex nomination scheme is a ranked list of the vertices in the second graph, with the her

Clare Burrage, Johannes Dombrowski

We determine the conditions for which the constraints from lunar laser ranging on the time evolution of the local gravitational constant can be extrapolated to impose constraints on the time evolution of the cosmological gravitational constant in scalar-tensor theories of modified gravity. We allow for the possibility that the scalar-tensor theories are non-

Chayan Majumdar, Sudhanwa Patra, Prativa Pritimita, Supriya Senapati

We consider a gauged $U(1)_{L_\mu-L_\tau}$ extension of the left-right symmetric theory in order to simultaneously explain neutrino mass, mixing and the muon anomalous magnetic moment. We get sizeable contribution from the interaction of the new light gauge boson $Z_{\mu\tau}$ of the $U(1)_{L_\mu-L_\tau}$ symmetry with muons which can individually satisfy th

Dong Pu, Jingjing Su, Hongwei Xu

In this paper, we investigate the mean curvature flow of compact surfaces in $4$-dimensional space forms. We prove the convergence theorems for the mean curvature flow under certain pinching conditions involving the normal curvature, which generalise Baker-Nguyen's convergence theorem.

Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos

This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic

Thomas Fösel, Stefan Krastanov, Florian Marquardt, Liang Jiang

Microwave cavities coupled to superconducting qubits have been demonstrated to be a promising platform for quantum information processing. A major challenge in this setup is to realize universal control over the cavity. A promising approach are selective number-dependent arbitrary phase (SNAP) gates combined with cavity displacements. It has been proven that

Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto

Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them cost-prohibitive in practice. Our proposed approach, called PreTTR (Precomputing Transformer Term Representations), considerably reduces the query-time latency of deep transformer

Cheng Zhong, Kangenbei Liao, Wei Chen, Qianlong Liu

Motivation: Disease diagnosis oriented dialogue system models the interactive consultation procedure as Markov Decision Process and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. This strategy works well in the simple s

Lorenzo De Mattei, Michele Cafagna, Felice Dell'Orletta, Malvina Nissim

In the last few years, pre-trained neural architectures have provided impressive improvements across several NLP tasks. Still, generative language models are available mainly for English. We develop GePpeTto, the first generative language model for Italian, built using the GPT-2 architecture. We provide a thorough analysis of GePpeTto's quality by means of b

Cheng-Yang Chang, Yu-Chuan Chuang, An-Yeu Wu

Brain-inspired Hyperdimensional (HD) computing is an emerging technique for cognitive tasks in the field of low-power design. As a fast-learning and energy-efficient computational paradigm, HD computing has shown great success in many real-world applications. However, an HD model incrementally trained on multiple tasks suffers from the negative impacts of ca

Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Luc Van Gool

This work studies the problem of predicting the sequence of future actions for surround vehicles in real-world driving scenarios. To this aim, we make three main contributions. The first contribution is an automatic method to convert the trajectories recorded in real-world driving scenarios to action sequences with the help of HD maps. The method enables aut

Ramón J. Aliaga, Camille Noûs, Colin Petitjean, Antonín Procházka

We prove a general principle satisfied by weakly precompact sets of Lipschitz-free spaces. By this principle, certain infinite dimensional phenomena in Lipschitz-free spaces over general metric spaces may be reduced to the same phenomena in free spaces over their compact subsets. As easy consequences we derive several new and some known results. The main new

Evgeny M Mirkes

Recently, A.N. Gorban presented a rich family of universal Lyapunov functions for any linear or non-linear reaction network with detailed or complex balance. Two main elements of the construction algorithm are partial equilibria of reactions and convex envelopes of families of functions. These new functions aimed to resolve "the mystery" about the difference

Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio

Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching those of Backpropagation Through Time (BPTT), but with a learning rule local in space. Given an input $x$ and associated target $y$, EP proceeds in two phases: in the first phase neurons evolve freely towards a first st

Yuta Imaizumi, Takeshi Omori, Hiroki Kusudo, Carlos Bistafa

We have given theoretical expressions for the forces exerted on a quasi-2D flat and smooth solid plate immersed into a liquid pool of a simple liquid. All forces given by the theory, the local forces on the top, the contact line and the bottom of the plate as well as the total force, showed an excellent agreement with the MD simulation results. We also revea

Nandana Rajatheva, Italo Atzeni, Emil Bjornson, Andre Bourdoux

This white paper explores the road to implementing broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, to support broadband connectivity at railway speeds up to 1000 km/h. To achieve these goal

Bernd Aichner, Kristijan L. Mletschnig, Benedikt Müller, Max Karrer

The angular dependence of magnetic-field commensurability effects in thin films of the cuprate high-critical-temperature superconductor YBa$_{2}$Cu$_{3}$O$_{7-\delta}$ (YBCO) with an artificial pinning landscape is investigated. Columns of point defects are fabricated by two different methods of ion irradiation -- scanning the focused 30 keV ion beam in a he

Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto

The identification of relevance with little textual context is a primary challenge in passage retrieval. We address this problem with a representation-based ranking approach that: (1) explicitly models the importance of each term using a contextualized language model; (2) performs passage expansion by propagating the importance to similar terms; and (3) grou

Dmitry Gourevitch, Henrik P. A. Gustafsson, Axel Kleinschmidt, Daniel Persson

We study the question of Eulerianity (factorizability) for Fourier coefficients of automorphic forms, and we prove a general transfer theorem that allows one to deduce the Eulerianity of certain coefficients from that of another coefficient. We also establish a `hidden' invariance property of Fourier coefficients. We apply these results to minimal and next-t

Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra

Recent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model's predictions. Attention distributions can be considered a faithful explanation if a higher attention weight implies a greater impact on the model's prediction. They can be considered a plausible explanation if they provide a h

Shuai Liu, Yi-Ming Hu, Jian-dong Zhang, Jianwei Mei

We study the prospect of using TianQin to detect stellar-mass binary black holes (SBBHs). We estimate the expected detection number as well as the precision of parameter estimation on SBBH inspirals, using five different population models. We note TianQin can possibly detect a few SBBH inspirals with signal to noise ratios greater than 12; lowering the thres

A. Belhaj, L. Chakhchi, H. El Moumni, J. Khalloufi

We investigate the relations between the black hole shadow and charged AdS black hole critical behaviour in the extended phase space. Using the thermo-shadow formalism built-in [1], we reveal that the shadow radius can be considered as an efficient tool to study thermodynamical black hole systems. Based on such arguments, we build a thermal profile by varyin

Sascha Kurz

Constant dimension codes are e.g. used for error correction and detection in random linear network coding, so that constructions for these codes have achieved wide attention. Here, we improve over 150 lower bounds by describing better constructions for subspace distance 4.

Fernando Coutinho, Benedito Leandro, Hiuri F. S. Reis

The fluid ball conjecture states that a static perfect fluid space-time is spherically symmetric. In this paper we construct a Robinson's divergence formula for the static perfect fluid space-time. Inspired by this conjecture, a rigidity result for the spatial factor of a static perfect fluid satisfying some boundary conditions is proved, provided that an eq

Justin Dove, Jeffrey H. Shapiro

Phasor-field (P-field) imaging is a promising recent solution to the task of non-line-of-sight (NLoS) imaging, colloquially referred to as "seeing around corners". It consists of treating the oscillating envelope of amplitude-modulated, spatially-incoherent light as if it were itself an optical wave, akin to the oscillations of the underlying electromagnetic

Manfred Buchacher, Sophie Hofmanninger, Manuel Kauers

We provide some first experimental data about generating functions of restricted lattice walks with small steps in NN^4.

Christopher Small, Andrew J. MacDonald, Daniel Sousa

Zipfs Law states that rank-size distributions of city populations follow a power law with an exponent of -1. The assertion of a universal power law is controversial because the linearity and slope appear to vary over time and among countries. We compare census enumerations and night light luminance as proxies for population density and intensity of developme

Lucia Donatelli, Jonas Groschwitz, Alexander Koller, Matthias Lindemann

The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. These MRs exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. He

F. Pimenta, M. A. Alves

This work addresses the conjugate heat transfer of a simplified PTT fluid flowing past an unbounded sphere in the Stokes regime ($Re = 0.01$). The problem is numerically solved with the finite-volume method assuming axial symmetry, absence of natural convection and constant physical properties. The sphere generates heat at a constant and uniform rate, and th

Lyne Moser, Maru Sarazola, Paula Verdugo

We construct a model structure on the category $\mathrm{DblCat}$ of double categories and double functors. Unlike previous model structures for double categories, it recovers the homotopy theory of 2-categories through the horizontal embedding $\mathbb{H}\colon2\mathrm{Cat}\to\mathrm{DblCat}$, which is both left and right Quillen, and homotopically fully fai

C. F. Manara, A. Natta, G. P. Rosotti, J. M. Alcala

Determining the mechanisms that drive the evolution of protoplanetary disks is a necessary step to understand how planets form. Here we measured the mass accretion rate for young stellar objects at age >5 Myr, a critical test for the current models of disk evolution. We present the analysis of the spectra of 36 targets in the ~5-10 Myr old Upper Scorpius reg

Sen He, Wentong Liao, Hamed R. Tavakoli, Michael Yang

Automatic captioning of images is a task that combines the challenges of image analysis and text generation. One important aspect in captioning is the notion of attention: How to decide what to describe and in which order. Inspired by the successes in text analysis and translation, previous work have proposed the \textit{transformer} architecture for image c

Evgeny M. Mirkes, Jeza Allohibi, Alexander N. Gorban

The curse of dimensionality causes the well-known and widely discussed problems for machine learning methods. There is a hypothesis that using of the Manhattan distance and even fractional quasinorms lp (for p less than 1) can help to overcome the curse of dimensionality in classification problems. In this study, we systematically test this hypothesis. We co

Günther Of, Raphael Watschinger

We consider a fast, data-sparse directional method to realize matrix-vector products related to point evaluations of the Helmholtz kernel. The method is based on a hierarchical partitioning of the point sets and the matrix. The considered directional multi-level approximation of the Helmholtz kernel can be applied even on high-frequency levels efficiently. W

Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu

An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains difficult due to the availability of limited resources and the expense and significant effort required to collect mixed-language data. We therefore propose a new learning method,

Sanyou Wu, Xingdong Feng, Fan Zhou

Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed input sets. Although these methods may achieve positive results, they ignore the relationship information between

Roderich Tumulka

The thermal equilibrium distribution over quantum-mechanical wave functions is a so-called Gaussian adjusted projected (GAP) measure, $GAP(\rho_\beta)$, for a thermal density operator $\rho_\beta$ at inverse temperature $\beta$. More generally, $GAP(\rho)$ is a probability measure on the unit sphere in Hilbert space for any density operator $\rho$ (i.e., a p

Lukas Fischer, Andreas M. Menzel

Soft elastic composite materials can serve as actuators when they transform changes in external fields into mechanical deformation. Here, we address the corresponding deformational behavior of magnetic gels and elastomers, consisting of magnetizable colloidal particles in a soft polymeric matrix and exposed to external magnetic fields. Since many practical r

Tao Shen, Yi Mao, Pengcheng He, Guodong Long

In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulf

Yu. G. Ignat'ev

The complete theory of cosmological evolution's macroscopic description is constructed with a help of self-consistent field. The theory includes a subsystem of linear equations of perturbations' evolution and nonlinear macroscopic Einstein equations and equations of a scalar field. We provide examples of system's solution which show the principal difference

Johannes Holler, Sebastian Meier, Michael Kempf, Philipp Nagler

We investigate WSe$_2$-MoSe$_2$ heterobilayers with different twist angles $\theta \pm \delta$ between the two layers, by low-frequency Raman scattering. In sufficiently aligned samples with $\theta=0^\circ$, or $\theta=60^\circ$, and $\delta \lesssim 3^\circ$, we observe an interlayer shear mode (ISM), which is a clear sign of a commensurate bilayer structu

Sibylle Schroll, Hipolito Treffinger

In this paper we study the behaviour of modules over finite dimensional algebras whose endomorphism algebra is a division ring. We show that there are finitely many such modules in the module category of an algebra if and only if the length of all such modules is bounded.

Andrea Gagna

We introduce a notion of normalised oplax $3$-functor suitable for the elementary homotopy theory of strict $3$-categories, following the combinatorics of orientals. We show that any such morphism induces a morphism of simplicial sets between the Street nerves and we characterise those morphisms of simplicial sets coming from normalised oplax $3$-functors. T

Marc G. Cuxart, Miguel Angel Valbuena, Roberto Robles, César Moreno

Controlling interfacial interactions in magnetic/topological insulator heterostructures is a major challenge for the emergence of novel spin-dependent electronic phenomena. As for any rational design of heterostructures that rely on proximity effects, one should ideally retain the overall properties of each component while tuning interactions at the interfac

Zihan Liu, Genta Indra Winata, Andrea Madotto, Pascale Fung

Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results. However, the fine-tuning process inevitably changes the parameters of the pre-trained model and weakens its cross-lingual ability, which leads to sub-optimal performance. To alleviate this problem, we leverage continual l

Guangwei Hu, Qingdong Ou, Guangyuan Si, Yingjie Wu

Twisted two-dimensional bi-layers offer exquisite control on the electronic bandstructure through the interlayer rotation and coupling, enabling magic-angle flat-band superconductivity and moir\'e excitons. Here, we demonstrate how analogous principles, combined with large anisotropy, enable extreme control and manipulation of the photonic dispersion of phon

Anatoliy Sozutov

The proper subgroup $B$ of the group $G$ is called {\it strongly embedded}, if $2\in\pi(B)$ and $2\notin\pi(B \cap B^g)$ for any element $g \in G \setminus B $ and, therefore, $ N_G(X) \leq B$ for any 2-subgroup $ X \leq B $. An element $a$ of a group $G$ is called {\it finite} if for all $ g\in G $ the subgroups $ \langle a, a^g \rangle $ are finite. In the

Paresh Pradhan, Michael Wojcik, Xianrong Huang, Elina Kasman

Wavefront-preserving x-ray diamond crystal optics are essential for numerous applications in x-ray science. Perfect crystals with flat Bragg planes are a prerequisite for wavefront preservation in Bragg diffraction. However, this condition is difficult to realize in practice because of inevitable crystal imperfections. Here we use x-ray rocking curve imaging

Eyyüb Sari, Vahid Partovi Nia

Implementation of quantized neural networks on computing hardware leads to considerable speed up and memory saving. However, quantized deep networks are difficult to train and batch~normalization (BatchNorm) layer plays an important role in training full-precision and quantized networks. Most studies on BatchNorm are focused on full-precision networks, and t

Guo-Qiang Zhang, Zhen Chen, J. Q. You

We study the quantum phase transition (QPT) in a non-Hermitian Tavis-Cummings (TC) model of experimentally accessible parameters, which is engineered with two drive fields applied to an ensemble of two-level systems (TLSs) and a cavity, respectively. When the two drive fields satisfy a given parameter-matching condition, the coupled cavity-TLS ensemble syste

Iker de las Heras

A countably based profinite group can be naturally seen as a metric space with respect to a given filtration, and thus, it has a well defined Hausdorff dimension function. Barnea and Shalev found a group theoretical expression for the Hausdorff dimension of the closed subgroups of a profinite group $G$, opening, in this way, a bunch of possibilities to explo

J. A. Gracey

We renormalize a six dimensional cubic theory to four loops in the MSbar scheme where the scalar is in a bi-adjoint representation. The underlying model was originally derived in a problem relating to gravity being a double copy of Yang-Mills theory. As a field theory in its own right we find that it has a curious property in that while unexpectedly there is

Diego Klabjan, Xiaofeng Zhu

We propose incremental (re)training of a neural network model to cope with a continuous flow of new data in inference during model serving. As such, this is a life-long learning process. We address two challenges of life-long retraining: catastrophic forgetting and efficient retraining. If we combine all past and new data it can easily become intractable to

Sophia Helmrich, Alexander W. Achtstein, Hery Ahmad, Matthias Kunz

We analyze the lineshape of the quasiparticle photoluminescence of monolayer and bilayer molybdenum ditelluride in temperature- and excitation intensity-dependent experiments. We confirm the existence of a negatively charged trion in the bilayer based on its emission characteristics and find hints for a coexistence of intra- and interlayer trions with a few

Claude-Alexandre Simonetti

The understanding of probability can be difficult for a few young scientists. Consequently, this new mathematical symbol, related to binomial coefficients and simplicial polytopic numbers, could be helpful to science education. Moreover, one can obtain kinds of remarkable identities and generalize them to a sort of "Newton's binomial theorem". Finally, this

Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong

We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The fo

Sufeng Duan, Hai Zhao, Dongdong Zhang, Rui Wang

Data augmentation is an effective performance enhancement in neural machine translation (NMT) by generating additional bilingual data. In this paper, we propose a novel data augmentation enhancement strategy for neural machine translation. Different from existing data augmentation methods which simply choose words with the same probability across different s

Mattia Zorzi

We address the problem to estimate a Kronecker graphical model corresponding to an autoregressive Gaussian stochastic process. The latter is completely described by the power spectral density function whose inverse has support which admits a Kronecker product decomposition. We propose a Bayesian approach to estimate such a model. We test the effectiveness of

Yao-Hung Hubert Tsai, Martin Q. Ma, Muqiao Yang, Ruslan Salakhutdinov

The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we

Mikhail Khovanov, Nitu Kitchloo

We consider a deformation of the Robert-Wagner foam evaluation formula, with an eye toward a relation to formal groups. Integrality of the deformed evaluation is established, giving rise to state spaces for planar GL(N) MOY graphs (Murakami-Ohtsuki-Yamada graphs). Skein relations for the deformation are worked out in details in the GL(2) case. These skein re

Murarka Utsav Anil, Kinjal Banerjee, Tuhin Malik, Constança Providência

Constructing the outer crust of the neutron stars requires the knowledge of the Binding Energy (BE) of the atomic nuclei. Although the BE of a lot of the nuclei is experimentally determined and can be obtained from the AME data table, for the others we need to depend on theoretical models. There exist a lot of physical theories to predict the BE, each with i

Valery Isaev

In this short note, we construct a class of models of an extension of homotopy type theory, which we call homotopy type theory with an interval type.

Kieran Kalair, Colm Connaughton, Pierfrancesco Alaimo Di Loro

A self-exciting spatio-temporal point process is fitted to incident data from the UK National Traffic Information Service to model the rates of primary and secondary accidents on the M25 motorway in a 12-month period during 2017-18. This process uses a background component to represent primary accidents, and a self-exciting component to represent secondary a

Anton Rodomanov, Yurii Nesterov

We present a new theoretical analysis of local superlinear convergence of classical quasi-Newton methods from the convex Broyden class. As a result, we obtain a significant improvement in the currently known estimates of the convergence rates for these methods. In particular, we show that the corresponding rate of the Broyden-Fletcher-Goldfarb-Shanno method

Massimo Frittelli, Pierluigi Toma

In addressing the problem of commodity production out of feedstock imports, an eco-environmentally rational agent aims at minimizing the cost of feedstock imports and their transportation, but also the water footprint of the feedstock production process and the water scarcity in the exporting countries. The problem is formulated as a nonlinear program. This

Edward Witten

There are hints of a novel object ("Planet 9") with a mass $5-10$ $M_\oplus$ in the outer Solar System, at a distance of order 500 AU. If it is a relatively conventional planet, it can be found in telescopic searches. Alternatively, it has been suggested that this body might be a primordial black hole (PBH). In that case, conventional searches will fail. A p

M. Ammar Ben Khadra, Dominik Stoffel, Wolfgang Kunz

Code coverage analysis plays an important role in the software testing process. More recently, the remarkable effectiveness of coverage feedback has triggered a broad interest in feedback-guided fuzzing. In this work, we introduce bcov, a tool for binary-level coverage analysis. Our tool statically instruments x86-64 binaries in the ELF format without compil

Piyush J. Shah, Derek A. Bas, Ivan Lisenkov, Alexei Matyushov

Nonreciprocity, the defining characteristic of isolators, circulators and a wealth of other applications in radio/microwave communications technologies, is in general difficult to achieve as most physical systems incorporate symmetries that prevent the effect. In particular, acoustic waves are an important medium for information transport, but they are inher

E. J. Chun, Arnab Dasgupta, Sin Kyu Kang

We propose a novel mechanism to realize leptogenesis through the Breit-Wigner resonance of a dark $U(1)_D$ gauge boson $Z_D$, which mediates lepton number violating annihilations of dark matter (DM) in the context of the scotogenic model with a $U(1)_D$. The processes occur out of equilibrium and the DM freezes out lately giving rise to the observed abundanc

Chandini Dash, Snigdha Mishra, Sudhanwa Patra, Purushottam Sahu

We consider a non-supersymmetric $E_6$ Grand Unified Theory (GUT) with intermediate trinification symmetry $SU(3)_C \times SU(3)_L \times SU(3)_R \times D$ (D denoted as D-parity for discrete left-right symmetry) and study the effect of one-loop threshold corrections arising due to every class of superheavy particles (scalars, fermions and vectors). It is ob

Daniele Alpago, Mattia Zorzi, Augusto Ferrante

The positive link prediction (PLP) problem is formulated in a system identification framework: we consider dynamic graphical models for auto-regressive moving-average (ARMA) Gaussian random processes. For the identification of the parameters, we model our network on two different time scales: a quicker one, over which we assume that the process representing

Hanpeng Gao, Zhaoyong Huang

Let $\Lambda,\Gamma$ be rings and $R=\left(\begin{array}{cc}\Lambda & 0 \\ M & \Gamma\end{array}\right)$ the triangular matrix ring with $M$ a $(\Gamma,\Lambda)$-bimodule. Let $X$ be a right $\Lambda$-module and $Y$ a right $\Gamma$-module. We prove that $(X, 0)$$\oplus$$(Y\otimes_\Gamma M, Y)$ is a silting right $R$-module if and only if both $X_{\Lambda}$

Simon Van Eyndhoven, Patrick Dupont, Simon Tousseyn, Nico Vervliet

EEG-correlated fMRI analysis is widely used to detect regional blood oxygen level dependent fluctuations that are significantly synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, such an asymmetrical, mass-univariate approach cannot capture the inherent, higher order structure in the EEG

Mattia Zorzi

The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian process. We propose a kernel based maximum entropy spectral estimator. The latter searches the optimal spectrum over a class of high order autoregressive models while the penalty term induced by the kernel matrix promotes regularity and exponential decay to z

Daniele Alpago, Mattia Zorzi, Augusto Ferrante

We consider the problem of link prediction in networks whose edge structure may vary (sufficiently slowly) over time. This problem, with applications in many important areas including social networks, has two main variants: the first, known as positive link prediction or PLP consists in estimating the appearance of a link in the network. The second, known as

L. S. Ricco, J. E. Sanches, Y. Marques, M. de Souza

We consider transport properties of a hybrid device composed by a quantum dot placed between normal and superconducting reservoirs, and coupled to a Majorana nanowire: a topological superconducting segment hosting Majorana zero-modes at the opposite ends. It is demonstrated that if topologically protected (nonoverlapping) Majorana zero-modes are formed in th

Walker H. Stern, Lóránt Szegedy

In this article, we establish a connection between two models for $r$-spin structures on surfaces: the marked PLCW decompositions of Novak and Runkel-Szegedy, and the structured graphs of Dyckerhoff-Kapranov. We use these models to describe $r$-spin structures on open-closed bordisms, leading to a generators-and-relations characterization of the 2-dimensiona

Congliang Chen, Li Shen, Haozhi Huang, Wei Liu

In this paper, we present a distributed variant of adaptive stochastic gradient method for training deep neural networks in the parameter-server model. To reduce the communication cost among the workers and server, we incorporate two types of quantization schemes, i.e., gradient quantization and weight quantization, into the proposed distributed Adam. Beside

M. J. T. Costa, J. Fernández-Rossier, N. M. R. Peres, A. T. Costa

Non reciprocal spin waves have a chiral asymmetry so that their energy is different for two opposite wave vectors. They are found in atomically thin ferromagnetic overlayers with in plane magnetization and are linked to the anti-symmetric Dzyaloshinskii-Moriya surface exchange. We use an itinerant fermion theory based on first principles calculations to pred

Rahul U, Ragul M, Raja Vignesh K, Tejeswinee K

Deeplearning has been used to solve complex problems in various domains. As it advances, it also creates applications which become a major threat to our privacy, security and even to our Democracy. Such an application which is being developed recently is the "Deepfake". Deepfake models can create fake images and videos that humans cannot differentiate them f

Jorge Littin

In this article, we provide different representations for a time-fractional birth and death process $N_{\alpha}(t)$, whose transition probabilities are governed by a time-fractional system of differential equations. More specifically, we present two equivalent characterizations for its trajectories: the first one as a time-changed classic birth and death pro

Sarath P, Soorya M, Shaik Abdul Rahman A, S Suresh Kumar

Picture based vehicle protection handling is a significant region with enormous degree for mechanization. In this paper we consider the issue of vehicle harm characterization, where a portion of the classifications can be fine-granular. We investigate profound learning based procedures for this reason. At first, we attempt legitimately preparing a CNN. In an

Kuikui Li, Meixia Tao, Jingjing Zhang, Osvaldo Simeone

Consider a multi-cell mobile edge computing network, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix. This is done through task offloading by means of input uploading, distributed computing at edge nodes (ENs), and output downloading. Task offloading may suffer long delay since servers at some ENs

Chris Halcrow

We consider the quantum multisoliton scattering problem. For BPS theories one truncates the full field theory to the moduli space, a finite dimensional manifold of energy minimising field configurations, and studies the quantum mechanical problem on this. Non-BPS theories -- the generic case -- have no such obvious truncation. We define a quantum soliton sca

Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas

In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner. Recently, there have been some attempts towards a similar go

Kamoliddin Haydarov, Javlon Rayimbaev, Ahmadjon Abdujabbarov, Satimbay Palvanov

In this paper, we have presented the studies of the motion of magnetized particles and energetic processes around Schwarzschild black holes in modified gravity (MOG). The study of circular stable orbits shows that orbits of magnetized particles can not be stable for the values of magnetic coupling parameter $\beta \geq 1$. It was also shown that the range of

Ana-Isabel Gómez, Domingo Gómez-Pérez, Friedrich Pillichshammer

The star-discrepancy is a quantitative measure for the irregularity of distribution of a point set in the unit cube that is intimately linked to the integration error of quasi-Monte Carlo algorithms. These popular integration rules are nowadays also applied to very high-dimensional integration problems. Hence multi-dimensional point sets of reasonable size w

Paolo Piazza, Boris Vertman

Let M be a compact smoothly stratified pseudomanifold with boundary, satisfying the Witt assumption. In this paper we introduce the de Rham signature and the Hodge signature of M, and prove their equality. Next, building also on recent work of Albin and Gell-Redman, we extend the Atiyah-Patodi-Singer index theory established in our previous work under the hy

Shatskiy Alexander

The equations of motion of a test particle are integrated for the field of a rotating Kerr black hole (BH). Due to the lack of analytical transformations for the Carter-Penrose diagrams (CPDs) for the Kerr metric, the topology of the Kerr BH is studied by analytical investigation of the equations of motion. Transformations for the CPDs for the Reisner-Nordst

Muhammad Ahmad

Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications. HSI classification (HSIC) is a challenging task due to high inter-class similarity, high intra-class variability, overlapping, and nested regions. A 2D Convolutional Neural Network (CNN) is a viable approach whereby HSIC highly depends on both Spectral-Spatial

Manoranjan Swain, Amit Rai, M. Karthick Selvan, Prasanta K. Panigrahi

We study the generation of single photon perfect W-state. An important aspect of this perfect W-state is that, it can be used for perfect teleportation and superdense coding, which are not achievable with maximally entangled W-state. Our scheme for generation involves entanglement between various path degrees of freedom of a single photon in a compact and we

Frederik Geth, Carleton Coffrin, David M Fobes

This paper proposes a simple and flexible storage model for use in a variety of multi-period optimal power flow problems. The proposed model is designed for research use in a broad assortment of contexts enabled by the following key features: (i) the model can represent the dynamics of an energy buffer at a wide range of scales, from residential battery stor

Jens Fohlmeister, Ny Riavo G. Voarintsoa, Franziska A. Lechleitner, Meighan Boyd

The climatic controls on the stable carbon isotopic composition (d13C) of speleothem carbonate are less often discussed in the scientific literature in contrast to the frequently used stable oxygen isotopes. Various local processes influence speleothem d13C values and confident and detailed interpretations of this proxy are often complex. A better understand