March 2020 arXiv papers — page 129

Jaewoo Park, Yoon Gyo Jung, Andrew Beng Jin Teoh

In one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, thus differentiating the in-class from out-class by the reconstruction error. However, compact modeling in an improper way might collapse the latent rep

Miriam Backens, Hector Miller-Bakewell, Giovanni de Felice, Leo Lobski

Translations between the quantum circuit model and the measurement-based one-way model are useful for verification and optimisation of quantum computations. They make crucial use of a property known as gflow. While gflow is defined for one-way computations allowing measurements in three different planes of the Bloch sphere, most research so far has focused o

Kun Wang, Jingya Zhu

The semi-constrained NMSSM (scNMSSM), or NMSSM with non-universal Higgs masses, can naturally predict a light dark matter under current constraints including Higgs data, sparticle-mass bounds, dark matter searches, and muon g-2, etc. In this work, we take this scenario of scNMSSM as an example to study the funnel-annihilation mechanisms of light dark matter

Nenglun Chen, Lingjie Liu, Zhiming Cui, Runnan Chen

Learning structures of 3D shapes is a fundamental problem in the field of computer graphics and geometry processing. We present a simple yet interpretable unsupervised method for learning a new structural representation in the form of 3D structure points. The 3D structure points produced by our method encode the shape structure intrinsically and exhibit sema

René Zander

We discuss the singularity structure of Kahan discretizations of a class of quadratric vector fields and provide a classification of the parameter values such that the corresponding Kahan map is integrable, in particular, admits an invariant pencil of elliptic curves.

F. A. Chishtie, Zhuo-Ran Huang, M. Reimer, T. G. Steele

The Coleman-Weinberg (CW) renormalization scheme for renormalization-group improvement of the effective potential is particularly valuable for CW symmetry-breaking mechanisms (including the challenging case of models with multiple scalar fields). CW mechanism is typically studied using models with classical scale invariance which not only provide a possibili

Lennart Bours, Maria Teresa Mercaldo, Mario Cuoco, Elia Strambini

We demonstrate that superconducting aluminium nano-bridges can be driven into a state with complete suppression of the critical supercurrent via electrostatic gating. Probing both in- and out-of-plane magnetic field responses in the presence of electrostatic gating can unveil the mechanisms that primarily cause the superconducting electric field effects. Rem

Xinming Qin, Jie Liu, Wei Hu, Jinlong Yang

The high cost associated with the evaluation of Hartree-Fock exchange (HFX) makes hybrid functionals computationally challenging for large systems. In this work, we present an efficient way to accelerate HFX calculations with numerical atomic basis sets. Our approach is based on the recently proposed interpolative separable density fitting (ISDF) decompositi

Hadi Daneshmand, Jonas Kohler, Francis Bach, Thomas Hofmann

Randomly initialized neural networks are known to become harder to train with increasing depth, unless architectural enhancements like residual connections and batch normalization are used. We here investigate this phenomenon by revisiting the connection between random initialization in deep networks and spectral instabilities in products of random matrices.

Kaiki Taro Inoue, Satoki Matsushita, Kouichiro Nakanishi, Takeo Minezaki

We report our high-resolution (0.03 arcsec to 0.07 arcsec) ALMA imaging of the quadruply lensed radio-loud quasar MG\,J0414+0534 at redshift $z=2.639$ in the continuum and the broad CO(11-10) line at $\sim 340\,$GHz. With the help of strong lensing magnification and ALMA's high-resolution, we succeeded in resolving the jet/dust and CO gas in the quasar h

Daniel Yang, Tarik Tosun, Ben Eisner, Volkan Isler

We present a novel approach to robotic grasp planning using both a learned grasp proposal network and a learned 3D shape reconstruction network. Our system generates 6-DOF grasps from a single RGB-D image of the target object, which is provided as input to both networks. By using the geometric reconstruction to refine the the candidate grasp produced by the

Mohamad Ali-Dib, Andrew Cumming, Douglas N. C. Lin

Super-Earths are by far the most dominant type of exoplanet, yet their formation is still not well understood. In particular, planet formation models predict that many of them should have accreted enough gas to become gas giants. Here we examine the role of the protoplanetary disk in the cooling and contraction of the protoplanetary envelope. In particular,

Gal Ness, Anastasiya Vainbaum, Constantine Shkedrov, Yanay Florshaim

Absorption imaging is the most common probing technique in experiments with ultracold atoms. The standard procedure involves the division of two frames acquired at successive exposures, one with the atomic absorption signal and one without. A well-known problem is the presence of residual structured noise in the final image, due to small differences between

Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar

A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent. We treat this workflow as an interpretable machine learning problem by leveraging the model that learned the low-dimensional represen

Tianyu Ma, Ajay Gupta, Mert R. Sabuncu

Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection. A major challenge for accurate anatomical landmark detection in volumetric images such as clinical CT scans is that large-scale data often constrain the capacity of the employed neural network architecture due to GPU memory limitations,

Shinibali Bhattacharyya, Kristofer Björnson, Karim Zantout, Daniel Steffensen

Deviations of low-energy electronic structure of iron-based superconductors from density functional theory predictions have been parametrized in terms of band- and orbital-dependent mass renormalizations and energy shifts. The former have typically been described in terms of a local self-energy within the framework of dynamical mean field theory, while the l

Pablo Shmerkin

We prove a version of Bourgain's projection theorem for parametrized families of $C^2$ maps, that refines the original statement even in the linear case. As one application, we show that if $A$ is a Borel set of Hausdorff dimension close to $1$ in $\mathbb{R}^2$ or close to $3/2$ in $\mathbb{R}^3$, then for $y\in A$ outside of a very sparse set, the pinned d

D. Puggioni, M. Horio, J. Chang, James M. Rondinelli

The antiferromagnetic Ruddlesden-Popper ruthenate Ca$_3$Ru$_2$O$_7$ is a model polar metal, combining inversion symmetry breaking with metallic conductivity; however, its low temperature ($T < 48$ K) crystal structure and Fermi surface topology remain ambiguous despite numerous measurements and theoretical studies. Here we perform both first principles calcu

Anna Paola Todino

In this paper, we investigate some geometric functionals for band limited Gaussian and isotropic spherical random fields in dimension 2. In particular, we focus on the area of excursion sets, providing its behavior in the high energy limit. Our result is based on Wiener chaos expansion for non linear transform of Gaussian fields and on an explicit derivation

Dariusz Kosz

We answer the recently posed questions regarding the problem of differentiation of integrals for the Rubio de Francia basis $\mathcal{R}$ in the infinite torus $\mathbb{T}^ω$. In particular, we prove that $\mathcal{R}$ does not differentiate $L^\infty(\mathbb{T}^ω)$. Some remarks about differentiation in the context of arbitrary bases are also included.

Gonzalo Marín, Pedro Casas, Germán Capdehourat

Robust network security systems are essential to prevent and mitigate the harming effects of the ever-growing occurrence of network attacks. In recent years, machine learning-based systems have gain popularity for network security applications, usually considering the application of shallow models, which rely on the careful engineering of expert, handcrafted

Suat Gumussoy, Hitay Ozbay

Weighted sensitivity minimization is studied within the framework of strongly stabilizing (stable) H-infinity controller design for a class of infinite dimensional systems. This problem has been solved by Ganesh and Pearson, [11], for finite dimensional plants using Nevanlinna-Pick interpolation. We extend their technique to a class of unstable time delay sy

Yvelin Giret, Pu Guo, Li-Feng Wang, Jun Cheng

Photocatalysis induced by sunlight is one of the most promising approach to environmental protection, solar energy conversion and sustainable production of fuels. The computational modeling of photocatalysis is a rapidly expending field which requires to adapt and further develop the available theoretical tools. The coupled transfer of proton and electron is

Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama

Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training large deep networks. However, these methods usually require large amounts of training data, which is often a big problem for real-world applications. One natural question to ask is wh

Antonio Díaz Ramos, Oihana Garaialde Ocaña, Nadia Mazza, Sejong Park

We prove the Cartan-Eilenberg stable elements theorem and construct a Lyndon-Hochschild-Serre type spectral sequence for pro-fusion systems. As an application, we determine the continuous mod-$p$ cohomology ring of $\text{GL}_2(\mathbb{Z}_p)$ for any odd prime $p$.

Zhen-Yan Lu, Meng-Lin Du, Feng-Kun Guo, Ulf-G. Meißner

At low energies, the strong interaction is governed by the Goldstone bosons associated with the spontaneous chiral symmetry breaking, which can be systematically described by chiral perturbation theory. In this paper, we apply this theory to study the $θ$-vacuum energy density and hence the QCD axion potential up to next-to-leading order with $N$ non-degener

Hang Song, Bo Wei, Qun Yu, Xia Xiao

WiFi signal has become accessible everywhere, providing high-speed data transmission experience. Besides the communication service, channel state information (CSI) of the WiFi signals is widely employed for numerous Internet of Things (IoT) applications. Recently, most of these applications are based on analysis of the microwave reflections caused by physica

B. Olmos, G. Buonaiuto, P. Schneeweiss, I. Lesanovsky

We study theoretically a laser-driven one-dimensional chain of atoms interfaced with the guided optical modes of a nanophotonic waveguide. The period of the chain and the orientation of the laser field can be chosen such that emission occurs predominantly into a single guided mode. We find that the fluorescence excitation line shape changes as the number of

Stefan Buller, Per Helander

The turbulence-induced quasi-linear particle flux of a highly-charged, collisional impurity species is calculated from the electrostatic gyrokinetic equation including collisions with the bulk ions and the impurities themselves. The equation is solved by an expansion in powers of the impurity charge number $Z$. In this formalism, the collision operator only

Ira Wolfson, Azadeh Maleknejad, Eiichiro Komatsu

The key to the phenomenological success of inflation models with axion and SU(2) gauge fields is the isotropic background of the SU(2) field. Previous studies showed that this isotropic background is an attractor solution during inflation starting from anisotropic (Bianchi Type I) spacetime; however, not all possible initial anisotropic parameter space was e

Yongyang Cai

Integrated Assessment Models (IAMs) of the climate and economy aim to analyze the impact and efficacy of policies that aim to control climate change, such as carbon taxes and subsidies. A major characteristic of IAMs is that their geophysical sector determines the mean surface temperature increase over the preindustrial level, which in turn determines the da

Christopher Lane, Jian-Xin Zhu

We study the thickness dependence of the electronic, magnetic, and optical properties of a NiPS$_3$ thin film, an antiferromagnetic charge-transfer insulator. Utilizing state-of-the-art advanced density functionals, we find the antiferromagnetic Zig-Zag order, the band gap, and the main peaks in the dielectric tensor are all in good agreement with the corres

Donatella Darsena, Giacinto Gelli, Francesco Verde

This paper deals with the problem of jointly designing the source precoder, the relaying matrices, and the destination equalizer in a multiple-relay amplify-and-forward (AF) cooperative multiple-input multiple-output (MIMO) wireless network, when partial channel-state information (CSI) is available. Specifically, the considered approaches are based on the kn

Tuomas Hytönen, Stefanos Lappas

Let $T$ be a linear operator that, for some $p_1\in(1,\infty)$, is bounded on $L^{p_1}(\tilde w)$ for all $\tilde w\in A_{p_1}(\mathbb R^d)$ and in addition compact on $L^{p_1}(w_1)$ for some $w_1\in A_{p_1}(\mathbb R^d)$. Then $T$ is bounded and compact on $L^p(w)$ for all $p\in(1,\infty)$ and all $w\in A_p(\mathbb R^d)$. This &#34;compact version&#34; of R

Valentin Gebhart, Martin Bohmann

Machine-learning and neural-network approaches have gained huge attention in the context of quantum science and technology in recent years. One of the most essential tasks for the future development of quantum technologies is the verification of nonclassical resources. Here, we present an artificial neural network approach for the identification of nonclassi

Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu

To take full advantage of fast-growing unlabeled networked data, this paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself. Inspired by human social behavior, we assume that the global context of each node is composed of all nodes in the graph since two arbitrary ent

Mohammad Ardeshir, Erfan Khaniki, Mohsen Shahriari

We study Basic Arithmetic, BA introduced by W. Ruitenburg. BA is an arithmetical theory based on basic logic which is weaker than intuitionistic logic. We show that the class of the provably total recursive functions of BA is a proper sub-class of the primitive recursive functions. Three extensions of BA, called BA+U, BA_c and EBA are investigated with relat

M. Keunecke, C. Möller, D. Schmitt, H. Nolte

Recent progress in laser-based high-repetition rate extreme ultraviolet (EUV) lightsources and multidimensional photoelectron spectroscopy enable the build-up of a new generation of time-resolved photoemission experiments. Here, we present a setup for time-resolved momentum microscopy driven by a 1 MHz femtosecond EUV table-top light source optimized for the

Chris Parker, Jason Semeraro

For a prime $p$, we describe a protocol for handling a specific type of fusion system on a $p$-group by computer. These fusion systems contain all saturated fusion systems. This framework allows us to computationally determine whether or not two subgroups are conjugate in the fusion system for example. We describe a generation procedure for automizers of eve

Oleksandr Vlasiuk

It is shown that the supports of measures minimizing weakly repulsive energies on Riemannian manifolds with sectional curvature bounded below do not have concentration points. This extends the results of Björck and Carrillo, Figalli, and Patacchini for such energies on the Euclidean space, and complements the results about the discreteness of minimizers of t

Viktor Novičenko

In the paper https://doi.org/10.1016/j.physleta.2011.08.072 authors propose a modification of the conventional delayed feedback control algorithm, where time-delay is varied continuously to minimize the power of control force. Minimization is realized via gradient-descent method. However, the derivation of the gradient with respect to time-delay is not accur

Luca Calderoni, Luciano Margara, Moreno Marzolla

We investigate the computational complexity of the graph primality testing problem with respect to the direct product (also known as Kronecker, cardinal or tensor product). In [1] Imrich proves that both primality testing and a unique prime factorization can be determined in polynomial time for (finite) connected and nonbipartite graphs. The author states as

Peter Feller, Marco Golla

We discuss differences between genera of smooth and locally-flat non-orientable surfaces in the 4-ball with boundary a given torus knot or 2-bridge knot. In particular, we establish that a result by Batson on the smooth non-orientable 4-genus of torus knots does not hold in the locally-flat category. We further show that certain families of torus knots are n

Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric. Our pipeline&#39;s modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embeddi

Wei Hao Khoong

In recent years, convolutional neural networks (CNNs) have revolutionized medical image analysis. One of the most well-known CNN architectures in semantic segmentation is the U-net, which has achieved much success in several medical image segmentation applications. Also more recently, with the rise of autoML ad advancements in neural architecture search (NAS

Ulli Wolff

We show the equivalence of the 2D Ising model to standard free Euclidean lattice fermions of the Wilson Majorana type. The equality of the loop representations for the partition functions of both systems is established exactly for finite lattices with well-defined boundary conditions. The honeycomb lattice is particularly simple in this context and therefore

Lifeng Liu, Fengda Zhang, Jun Xiao, Chao Wu

Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy. Howev

Laura Colmenarejo, Joscha Diehl, Miruna-Stefana Sorea

Motivated by a polynomial identity of certain iterated integrals, first observed in [CGM20] in the setting of lattice paths, we prove an intriguing combinatorial identity in the shuffle algebra. It has a close connection to de Bruijn&#39;s formula when interpreted in the framework of signatures of paths.

B. Ruta, S. Hechler, N. Neuber, D. Orsi

We present a detailed investigation of the wave vector dependence of collective atomic motion in Au49Cu26.9Si16.3Ag5.5Pd2.3 and Pd42.5Cu27Ni9.5P21 supercooled liquids close to the glass transition temperature. Using x-ray photon correlation spectroscopy in a precedent uncovered spatial range of only few interatomic distances, we show that the microscopic str

Stylianos I. Mimilakis, Konstantinos Drossos, Gerald Schuller

In this work, we present a method for learning interpretable music signal representations directly from waveform signals. Our method can be trained using unsupervised objectives and relies on the denoising auto-encoder model that uses a simple sinusoidal model as decoding functions to reconstruct the singing voice. To demonstrate the benefits of our method,

Brandon Hanson

We give $L^1$-norm estimates for exponential sums of a finite sets $A$ consisting of integers or lattice points. Under the assumption that $A$ possesses sufficient multidimensional structure, our estimates are stronger than those of McGehee-Pigno-Smith and Konyagin. These theorems improve upon past work of Petridis.

Amin Rassekh, Jean-Michel Sallese, Farzan Jazaeri, Morteza Fathipour

In this paper, an analytical predictive model of the negative capacitance (NC) effect in symmetric long channel double-gate junctionless transistor is proposed based on a charge-based model. In particular, we have investigated the effect of the thickness of the ferroelectric on the I-V characteristics. Importantly, for the first time, our model predicts that

A. de Clercq, F. Luca, L. Martirosyan, M. Matthis

Given a sequence of distinct positive integers $w_0 , w_1, w_2, \ldots$ and any positive integer $n$, we define the discriminator function $\mathcal{D}_{\bf w}(n)$ to be the smallest positive integer $m$ such that $w_0,\ldots, w_{n-1}$ are pairwise incongruent modulo $m$. In this paper, we classify all binary recurrent sequences $\{w_n\}_{n\geq 0}$ consistin

G. Molchan

For n + 1 particles moving independently on a straight line, we study the question of how long the leading position of one of them can last. Our focus is the asymptotics of the probability p(T,n) that the leader time will exceed T when n and T are large. It is assumed that the dynamics of particles are described by independent, either stationary or self-simi

Alireza Akbari, Peter Thalmeier

The underscreened Kondo lattice consisting of a single twofold degenerate conduction band and a CEF split 4f-electron quasi-quartet has non-conventional quasiparticle dispersions obtained from the constrained mean-field theory. An additional genuinely heavy band is found in the main hybridization band gap of the upper and lower hybridzed bands whose heavy ef

Donatella Darsena, Giacinto Gelli, Ivan Iudice, Francesco Verde

While the combination of multi-antenna and relaying techniques has been extensively studied for Long Term Evolution Advanced (LTE-A) and Internet of Things (IoT) applications, it is expected to still play an important role in 5th Generation (5G) networks. However, the expected benefits of these technologies cannot be achieved without a proper system design.

Donatella Darsena, Giacinto Gelli, Ivan Iudice, Francesco Verde

In multiple antenna systems employing time-division duplexing, spatial precoder design at the base station (BS) leverages channel state information acquired through uplink pilot transmission, under the assumption of channel reciprocity. Malicious eavesdroppers can start pilot spoofing attacks to alter such design, so as to improve their eavesdropping perform

M. Kohler, A. Krzyzak, B. Walter

Image classifiers based on convolutional neural networks are defined, and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Under suitable assumptions on the smoothness and structure of the aposteriori probability a rate of convergence is shown which is independent of the dimension

Polina Zilberman, Rami Puzis, Sunders Bruskin, Shai Shwarz

Threat emulators are tools or sets of scripts that emulate cyber attacks or malicious behavior. They can be used to create and launch single procedure attacks and multi-step attacks; the resulting attacks may be known or unknown cyber attacks. The motivation for using threat emulators varies and includes the need to perform automated security audits in organ

Kai Behrens

We show that most classes of K3 surfaces have only finitely many Enriques quotients. For supersingular K3 surfaces over fields of characteristic $p \geq 3$, we give a formula which generically yields the number of their Enriques quotients. We reprove via a lattice theoretic argument that supersingular K3 surfaces always have an Enriques quotient over fields

Zicheng Qiu, Wei Jiang, Turghunjan Mamut

Uyghur is a minority language, and its resources for Automatic Speech Recognition (ASR) research are always insufficient. THUYG-20 is currently the only open-sourced dataset of Uyghur speeches. State-of-the-art results of its clean and noiseless speech test task haven&#39;t been updated since the first release, which shows a big gap in the development of ASR

Robert Haslhofer, Or Hershkovits, Brian White

In this paper, we introduce a version of the moving plane method that applies to potentially quite singular hypersurfaces, generalizing the classical moving plane method for smooth hypersurfaces. Loosely speaking, our version for varifolds shows that smoothness and symmetry at infinity (respectively at the boundary) can be promoted to smoothness and symmetry

Stefan Bluecher, Lukas Kades, Jan M. Pawlowski, Nils Strodthoff

Machine learning has the potential to aid our understanding of phase structures in lattice quantum field theories through the statistical analysis of Monte Carlo samples. Available algorithms, in particular those based on deep learning, often demonstrate remarkable performance in the search for previously unidentified features, but tend to lack transparency

Irene Heinrich, Till Heller, Eva Schmidt, Manuel Streicher

If a biconnected graph stays connected after the removal of an arbitrary vertex and an arbitrary edge, then it is called 2.5-connected. We prove that every biconnected graph has a canonical decomposition into 2.5-connected components. These components are arranged in a tree-structure. We also discuss the connection between 2.5-connected components and tricon

Peng Gang, Lu Zezao, Chen Bocheng, Chen Shanliang

The visual SLAM method is widely used for self-localization and mapping in complex environments. Visual-inertia SLAM, which combines a camera with IMU, can significantly improve the robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the sc

Deren Luo, Panyue Zhou

In this note, we show that the long $n$-exact sequence theorem holds in any $n$-abelian category. As an application, the $n$-abelian version of Wakamatsu&#39;s Lemma is given.

Witold Wydmański, Szymon Szott

The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number of stations, yet it remains the default method of channel access for 802.11ax single-user transmissions. Ther

Jonathan Sterling, Carlo Angiuli, Daniel Gratzer

We present XTT, a version of Cartesian cubical type theory specialized for Bishop sets \`a la Coquand, in which every type enjoys a definitional version of the uniqueness of identity proofs. Using cubical notions, XTT reconstructs many of the ideas underlying Observational Type Theory, a version of intensional type theory that supports function extensionalit

Quentin Changeat, Billy Edwards, Ahmed F. Al-Refaie, Angelos Tsiaras

Recent analysis of the planet K2-18b has shown the presence of water vapour in its atmosphere. While the H2O detection is significant, the Hubble Space Telescope (HST) WFC3 spectrum suggests three possible solutions of very different nature which can equally match the data. The three solutions are a primary cloudy atmosphere with traces of water vapour (clou

P. Cabanelas, D. González, H. Alvarez-Pol, J. M. Boillos

CALIFA is the high efficiency and energy resolution calorimeter for the R3B experiment at FAIR, intended for detecting high energy light charged particles and gamma rays in scattering experiments, and is being commissioned during the Phase-0 experiments at FAIR, between 2018 and 2020. It surrounds the reaction target in a segmented configuration with 2432 de

Ramón Flores, Delaram Kahrobaei, Thomas Koberda

We characterize $k$--colorability of a simplicial graph via the intrinsic algebraic structure of the associated right-angled Artin group. As a consequence, we show that a certain problem about the existence of homomorphisms from right-angled Artin groups to products of free groups is NP--complete.

Long Xu, Ilia Tutunnikov, Erez Gershnabel, Yehiam Prior

Control of molecular rotations by laser fields is an active area of research focusing on the alignment/orientation of otherwise isotropic molecular samples. Oriented molecules are useful in many applications, such as molecular orbital tomography, femtosecond imaging of molecular structure, dynamics and chemical reaction control. In this work, we present a no

Jingye Li, Meishan Zhang, Donghong Ji, Yijiang Liu

Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effective speaker-sensitive utterance representation is one major challenge for CER. In this paper, we exploit speaker identification (SI) as an auxiliary task to enhance the utterance

János Barát, Géza Tóth

The crossing number of a graph $G$ is the minimum number of edge crossings over all drawings of $G$ in the plane. A graph $G$ is $k$-crossing-critical if its crossing number is at least $k$, but if we remove any edge of $G$, its crossing number drops below $k$. There are examples of $k$-crossing-critical graphs that do not have drawings with exactly $k$ cros

Adria Ruiz, Jakob Verbeek

Inference in deep neural networks can be computationally expensive, and networks capable of anytime inference are important in mscenarios where the amount of compute or quantity of input data varies over time. In such networks the inference process can interrupted to provide a result faster, or continued to obtain a more accurate result. We propose Hierarchi

Qiaolin Xia, Haoyang Huang, Nan Duan, Dongdong Zhang

While many BERT-based cross-modal pre-trained models produce excellent results on downstream understanding tasks like image-text retrieval and VQA, they cannot be applied to generation tasks directly. In this paper, we propose XGPT, a new method of Cross-modal Generative Pre-Training for Image Captioning that is designed to pre-train text-to-image caption ge

Hadrien Titeux, Rachid Riad, Xuan-Nga Cao, Nicolas Hamilakis

We introduce Seshat, a new, simple and open-source software to efficiently manage annotations of speech corpora. The Seshat software allows users to easily customise and manage annotations of large audio corpora while ensuring compliance with the formatting and naming conventions of the annotated output files. In addition, it includes procedures for checking

Mir Alimuddin, Tamal Guha, Preeti Parashar

In this article, in addition to the characterization of geometrical state spaces for the passive states, an operational approach has been introduced to distinguish them on their charging capabilities of a quantum battery. Unlike the thermal states, the structural instability of passive states assures the existence of a natural number $n$, for which $n+1$-cop

Rima Khouja, Houssam Khalil, Bernard Mourrain

The Symmetric Tensor Approximation problem (STA) consists of approximating a symmetric tensor or a homogeneous polynomial by a linear combination of symmetric rank-1 tensors or powers of linear forms of low symmetric rank. We present two new Riemannian Newton-type methods for low rank approximation of symmetric tensor with complex coefficients.The first meth

Xing Cheng, Zihua Guo, Satoshi Masaki

In this paper we consider the real-valued mass-critical nonlinear Klein-Gordon equations in three and higher dimensions. We prove the dichotomy between scattering and blow-up below the ground state energy in the focusing case, and the energy scattering in the defocusing case. We use the concentration-compactness/rigidity method as R. Killip, B. Stovall, and

Carlo Tiseo, Wolfgang Merkt, Keyhan Kouhkiloui Babarahmati, Wouter Wolfslag

The ability of animals to interact with complex dynamics is unmatched in robots. Especially important to the interaction performances is the online adaptation of body dynamics, which can be modeled as an impedance behaviour. However, the variable impedance controller still possesses a challenge in the current control frameworks due to the difficulties of ret

Huilin Zhou, Tao Ouyang, Yadan Li, Jian Liu

Electromagnetic inverse scattering problems (ISPs) aim to retrieve permittivities of dielectric scatterers from the scattering measurement. It is often highly nonlinear, caus-ing the problem to be very difficult to solve. To alleviate the issue, this letter exploits a linear model-based network (LMN) learning strategy, which benefits from both model complexi

Keyhan Kouhkiloui Babarahmati, Carlo Tiseo, Quentin Rouxel, Zhibin Li

Robotic teleoperation will allow us to perform complex manipulation tasks in dangerous or remote environments, such as needed for planetary exploration or nuclear decommissioning. This work proposes a novel telemanipulation architecture using a passive Fractal Impedance Controller (FIC), which does not depend upon an active viscous component for stability gu

Jiandong Shi, Dehui Luo, Hong Weng, Xian-Tao Zeng

When reporting the results of clinical studies, some researchers may choose the five-number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation, particularly for skewed data. For these studies, when included in a meta-analysis, it is often desired to conve

Vincent Le Guen, Nicolas Thome

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods. Since physics is too restrictive for describing the full visual content of generic videos, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown compleme

Vladimir Dzhunushaliev, Vladimir Folomeev

We consider modified Weyl gravity where a Dirac spinor field is nonminimally coupled to gravity. It is assumed that such modified gravity is some approximation for the description of quantum gravitational effects related to the gravitating spinor field. It is shown that such a theory contains solutions for a class of metrics which are conformally equivalent

Julian Chibane, Thiemo Alldieck, Gerard Pons-Moll

While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the ou

Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona

Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem. ZSL trains a model once, and generalizes to new tasks whose classes are not present in the training dataset. We propose the first end-to-en

P. M. W. Kalberla, U. Haud

Context. The interstellar medium is affected by turbulence and observed H i structures in channel maps are shaped by turbulent motions. It is taken for granted by a few theoreticians that observed H i structures do not represent real density enhancement but velocity caustics, caused by velocity crowding. This interpretation was questioned and objections by C

Adelina Mânzăţeanu, Rachel Newton, Ekin Ozman, Nicole Sutherland

Let $L$ be a finite extension of $\mathbb{F}_q(t)$. We calculate the proportion of polynomials of degree $d$ in $\mathbb{F}_q[t]$ that are everywhere locally norms from $L/\mathbb{F}_q(t)$ which fail to be global norms from $L/\mathbb{F}_q(t)$.

Katia Lupinetti, Andrea Ranieri, Franca Giannini, Marina Monti

Defining methods for the automatic understanding of gestures is of paramount importance in many application contexts and in Virtual Reality applications for creating more natural and easy-to-use human-computer interaction methods. In this paper, we present a method for the recognition of a set of non-static gestures acquired through the Leap Motion sensor. T

Elvise Berchio, Matteo Bonforte, Debdip Ganguly, Gabriele Grillo

We consider the nonlinear degenerate parabolic equation of porous medium type, whose diffusion is driven by the (spectral) fractional Laplacian on the hyperbolic space. We provide existence results for solutions, in an appropriate weak sense, for data belonging either to the usual $L^p$ spaces or to larger (weighted) spaces determined either in terms of a gr

Keith Horne, G. De Rosa, B. M. Peterson, A. J. Barth

We report velocity-delay maps for prominent broad emission lines, Ly_alpha, CIV, HeII and H_beta, in the spectrum of NGC5548. The emission-line responses inhabit the interior of a virial envelope. The velocity-delay maps reveal stratified ionization structure. The HeII response inside 5-10 light-days has a broad single-peaked velocity profile. The Ly_alpha,

Cornelia Hille, Daniel Rohe, Carsten Honerkamp, Sabine Andergassen

Using the recently introduced multiloop extension of the functional renormalization group, we compute the frequency- and momentum-dependent self-energy of the two-dimensional Hubbard model at half filling and weak coupling. We show that, in the truncated-unity approach for the vertex, it is essential to adopt the Schwinger-Dyson form of the self-energy flow

Chongwei Liu, Zhihui Wang, Shijie Wang, Tao Tang

To boost the object grabbing capability of underwater robots for open-sea farming, we propose a new dataset (UDD) consisting of three categories (seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our knowledge, it is the first 4K HD dataset collected in a real open-sea farm. We also propose a novel Poisson-blending Generative Adversarial

Ashwin K Seshadri

Cumulative emissions accounting for carbon-dioxide (CO2) is founded on recognition that global warming in Earth System Models (ESMs) is roughly proportional to cumulative CO2 emissions, regardless of emissions pathway. However, cumulative emissions accounting only requires the graph between global warming and cumulative emissions to be approximately independ

Chunyu Zhang, Qing Gao, Yungui Gong, Bin Wang

The discovery of gravitational waves, which are ripples of space-time itself, opened a new window to test general relativity, because it predicts that there are only plus and cross polarizations for gravitational waves. For alternative theories of gravity, there may be up to six polarizations. The measurement of the polarization is one of the major scientifi

Traiko Dinev, Songyan Xin, Wolfgang Merkt, Vladimir Ivan

In this paper, we study a wheeled robot with a prismatic extension joint. This allows the robot to build up momentum to perform jumps over obstacles and to swing up to the upright position after the loss of balance. We propose a template model for the class of such two-wheeled jumping robots. This model can be considered as the simplest wheeled-legged system

Arindam Banerjee, Kriti Goel, J. K. Verma

Let $R$ be the face ring of a simplicial complex of dimension $d-1$ and ${\mathcal R}(\mathfrak{n})$ be the Rees algebra of the maximal homogeneous ideal $\mathfrak{n}$ of $R.$ We show that the generalized Hilbert-Kunz function $HK(s)=\ell({\mathcal R}(\mathfrak n)/(\mathfrak n, \mathfrak n t)^{[s]})$ is given by a polynomial for all large $s.$ We calculate

Shanchao Yang, Jing Liu, Kai Wu, Mingming Li

Deep learning based approaches have been utilized to model and generate graphs subjected to different distributions recently. However, they are typically unsupervised learning based and unconditioned generative models or simply conditioned on the graph-level contexts, which are not associated with rich semantic node-level contexts. Differently, in this paper