April 2019 arXiv papers — page 94

Leonid Polterovich, Daniel Rosen, Karina Samvelyan, Jun Zhang

The theory of persistence modules is an emerging field of algebraic topology which originated in topological data analysis. In these notes we provide a concise introduction into this field and give an account on some of its interactions with geometry and analysis. In particular, we present applications of persistence to symplectic topology, including the geo

Xiao-Gang He, Jusak Tandean, German Valencia

We consider lepton-flavor violation in strangeness changing ($|ΔS|=1$) semileptonic $τ$-lepton decays arising from new physics encoded in a standard model effective Lagrangian. Its invariance under the standard model gauge group entails the relevance of other processes which can serve as complementary probes of the new physics operators. We show in particula

L. Pattavina, J. W. Beeman, M. Clemenza, O. Cremonesi

Archeological Roman lead (Pb) is known to be a suitable material for shielding experimental apparata in rare event searches. In the past years the intrinsic radiopurity of this material was investigated using different technologies. In this work we applied the latest advancements in cryogenic techniques to study the bulk radiopurity of a 1 cm$^{3}$ sample of

D. S. Kaparulin, S. L. Lyakhovich

We study the general structure of field theories with the unfree gauge symmetry where the gauge parameters are restricted by differential equations. The examples of unfree gauge symmetries include volume preserving diffeomorphisms in the unimodular gravity and various higher spin field theories with transverse gauge symmetries. All the known examples of the

Davide Massari, Amina Helmi, Alessio Mucciarelli, Laura V. Sales

Aims. We present the first three-dimensional internal motions for individual stars in the Draco dwarf spheroidal galaxy. Methods. By combining first-epoch $Hubble$ $Space$ $Telescope$ observations and second-epoch $Gaia$ Data Release 2 positions, we measured the proper motions of $149$ sources in the direction of Draco. We determined the line-of-sight veloci

Jia Liu, Maoguo Gong, Haibo He

Artificial neural networks which are inspired from the learning mechanism of brain have achieved great successes in many problems, especially those with deep layers. In this paper, we propose a nucleus neural network (NNN) and corresponding connecting architecture learning method. In a nucleus, there are no regular layers, i.e., a neuron may connect to all t

Stamatis Outsios, Christos Karatsalos, Konstantinos Skianis, Michalis Vazirgiannis

Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is of critical importance. Most research efforts are focusing on English word embeddings. This paper addresses the problem of constructing and evaluating such models for the Greek language. We created a new word analogy corpus considering the original English

Leonid Chaichenets, Dirk Hundertmark, Peer Christian Kunstmann, Nikolaos Pattakos

We study the one dimensional nonlinear Schrödinger equation with power nonlinearity $|u|^{α- 1} u$ for $α\in [1,5]$ and initial data $u_0 \in L^2(\mathbb{R}) + H^1(\mathbb{T})$. We show via Strichartz estimates that the Cauchy problem is locally well-posed. In the case of the quadratic nonlinearity ($α= 2$) we obtain global well-posedness in the space $C(\ma

Yannis Flet-Berliac, Philippe Preux

In reinforcement learning, policy gradient algorithms optimize the policy directly and rely on sampling efficiently an environment. Nevertheless, while most sampling procedures are based on direct policy sampling, self-performance measures could be used to improve such sampling prior to each policy update. Following this line of thought, we introduce SAUNA,

Frank Nielsen

The Jensen-Shannon divergence is a renown bounded symmetrization of the unbounded Kullback-Leibler divergence which measures the total Kullback-Leibler divergence to the average mixture distribution. However the Jensen-Shannon divergence between Gaussian distributions is not available in closed-form. To bypass this problem, we present a generalization of the

Liguang Li, Jie Xiao

This paper presents a highly non-trivial two-fold study of the fractional differential couples - derivatives ($\nabla^{0<s<1}_+=(-Δ)^\frac{s}{2}$) and gradients ($\nabla^{0<s<1}_-=\nabla (-Δ)^\frac{s-1}{2}$) of basic importance in the theory of fractional advection-dispersion equations: one is to discover the sharp Hardy-Rellich ($sp<p<n$) $|$ Adams-Moser ($

Semra Gurtas Dogan, Ganim Gecim, Yusuf Sucu

In the present paper, we solve the Dirac equation in the 2+1 dimensional exponentially expanding magnetized by uniform magnetic field and non-magnetized universes, separately. Asymptotic behaviors of the solutions are determined. Using these results we discuss the current of a Dirac particle to discuss the polarization densities and the magnetization density

Anton Galajinsky

N=2 supersymmetric extensions of both the periodic and non-periodic relativistic Toda lattice are built within the framework of the Hamiltonian formalism. A geodesic description in terms of a non-metric connection is discussed.

Liguang Liu, Jie Xiao

This paper explores the intrinsic nature of the celebrated Stein-Weiss $H^1$-inequality $$ \|I_s u\|_{L^\frac{n}{n-s}}\lesssim \|u\|_{L^1}+\|\vec{R}u\|_{L^{1}}=\|u\|_{H^1} $$ through the tracing and duality laws based on Riesz&#39;s singular integral operator $I_s$. We discover that $f\in I_s\big([\mathring{H}^{s,1}_{-}]^\ast\big)$ if and only if $\exists\ \

Daniel Meljanac, Stjepan Meljanac, Zoran Škoda, Rina Štrajn

We propose an explicit generalization of the Jordanian twist proposed in $r$-symmetrized form by Giaquinto and Zhang. It is proved that this generalization satisfies the 2-cocycle condition. We present explicit formulas for the corresponding star product and twisted coproduct. Finally, we show that our generalization coincides with the twist obtained from th

Khun Sang Phukon, Anuradha Gupta, Sukanta Bose, Pankaj Jain

We study precession dynamics of generic binary black holes in eccentric orbits using an effective potential based formalism derived in [M. Kesden et al., PRL {\bf 114}, 081103 (2015)]. This effective potential is used to classify binary black holes into three mutually exclusive spin morphologies. During the inspiral phase, binaries make transitions from one

Marcel Eichelmann, Jan Wiersig

The excitation of semiconductor quantum dots often involves an attached wetting layer with delocalized single-particle energy eigenstates. These wetting-layer states are usually approximated by (orthogonalized) plane waves. We show that this approach is too crude. Even for a simple model based on the effective-mass approximation and containing one or a few l

Eyal Castiel, Sem Borst, Laurent Miclo, Florian Simatos

We examine a queue-based random-access algorithm where activation and deactivation rates are adapted as functions of queue lengths. We establish its heavy traffic behavior on a complete interference graph, which turns out to be highly nonstandard in two respects: (1) the scaling depends on some parameter of the algorithm and is not the $N/N^2$ scaling usuall

Yanmin Wang, Wei Feng, Ming Xiao, Cheng-Xiang Wang

Satellite and terrestrial cellular networks can be integrated together for extended broadband coverage in e.g., maritime communication scenarios, in the upcoming sixth-generation (6G) era. To counter spectrum scarcity, collaborative spectrum sharing is considered for a hybrid satellite-terrestrial network (HSTN) in this paper. With only slowly-varying large-

Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi, Paavo Alku

Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to mel-spectrogram acoustic features, which are convenient for modeling, but present additional challenges for vocoding (i.e., waveform generation from the acoustic features). High-qua

A. Jorissen, H. M. J. Boffin, D. Karinkuzhi, S. Van Eck

This paper provides long-period and revised orbits for barium and S stars adding to previously published ones. The sample of barium stars with strong anomalies comprise all such stars present in the Lu et al. catalogue. We find orbital motion for all barium and extrinsic S stars monitored. We obtain the longest period known so far for a spectroscopic binary

Ehsan Montahaei, Danial Alihosseini, Mahdieh Soleymani Baghshah

Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, r

Dipankar Das, Ipsita Saha

The LHC Higgs data is showing a gradual inclination towards the SM result and realization of an SM-like limit becomes essential for BSM scenarios to survive. Considering the accuracy that can be achieved in future colliders, BSMs that acquire the alignment limit with an SM-like Higgs boson coupling can surpass others in the long run. Using a convenient param

Mareike Hartmann, Tallulah Jansen, Isabelle Augenstein, Anders Søgaard

In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in ne

Moncef Ghazel

We here define a cell structure for real, complex and quaternionic flag manifolds in a unified way. Our method is geometric in nature and is inspired from a method due to Milnor and Stasheff, which they used to define a cell structure for real Grassmann manifolds.

Budhaditya Chatterjee, Camille Lévêque, Jörg Schmiedmayer, Axel U. J. Lode

We explore the groundstates of a few dipolar bosons in optical lattices with incommensurate filling. The competition of kinetic, potential, and interaction energies leads to the emergence of a variety of crystal state orders with characteristic one- and two-body densities. We probe the transitions between these orders and construct the emergent state diagram

Yang He, Ping Liu, Linchao Zhu, Yi Yang

Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addi

Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl

Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present the generalized SIPA (sampling, intervention, prediction, aggregation) framework of work stages for model-agnos

C. M. Ko, D. Breitschwerdt, D. O. Chernyshov, H. Cheng

Capture and tidal disruption of stars by the supermassive black hole in the Galactic center (GC) should occur regularly. The energy released and dissipated by this processes will affect both the ambient environment of the GC and the Galactic halo. A single star of super-Eddington eruption generates a subsonic out ow with an energy release of more than $10^{5

S. Danial Forghani, S. Habib Mazharimousavi, Mustafa Halilsoy

By using the formalism of thin-shells, we construct a geometrical model of a particle in third-order Lovelock gravity. This particular theory which is valid at least in 7 dimensions, provides enough degrees of freedom and grounds towards such a construction. The particle consists of a flat interior and a non-black hole exterior spacetimes whose mass, charge,

Chen Wang, Jianfei Yang, Lihua Xie, Junsong Yuan

Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve this problem, a new operation, kervolution

Jean-Claude Cuenin

We extend a result of Davies and Nath on the location of eigenvalues of Schrödinger operators with slowly decaying complex-valued potentials to higher dimensions. In this context, we also discuss various examples related to the Laptev--Safronov conjecture.

Xiaohui Bei, Shiteng Chen, Ji Guan, Youming Qiao

In the 1970&#39;s, Lovász built a bridge between graphs and alternating matrix spaces, in the context of perfect matchings (FCT 1979). A similar connection between bipartite graphs and matrix spaces plays a key role in the recent resolutions of the non-commutative rank problem (Garg-Gurvits-Oliveira-Wigderson, FOCS 2016; Ivanyos-Qiao-Subrahmanyam, ITCS 2017)

LHCb collaboration, R. Aaij, C. Abellán Beteta, B. Adeva

A narrow pentaquark state, $P_c(4312)^+$, decaying to $J/ψp$ is discovered with a statistical significance of $7.3σ$ in a data sample of ${Λ_b^0\to J/ψp K^-}$ decays which is an order of magnitude larger than that previously analyzed by the LHCb collaboration. The $P_c(4450)^+$ pentaquark structure formerly reported by LHCb is confirmed and observed to consi

F. Francisco, O. Bertolami

We propose using the temperature gradients between the Moon&#39;s surface and the soil at a certain depth to power an Organic Rankine Cycle that could supply a permanent installation, particularly at night, when solar power is not available. Our theoretical and engineering considerations show that, with existing working fluids and quite feasible technical re

Bjoern Haefner, Zhenzhang Ye, Maolin Gao, Tao Wu

Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable. To eliminate such restrictions, we propose an efficient principled variational approach to uncalibrated PS under general illumination. To this end, the Lambertian reflectance model is approximated through a spher

Wenhui Chen, Michael Reissig

In this note we try to understand the blow-up of solutions to Nakao&#39;s problem by using nonlinear ordinary differential inequalities.

Takaaki Nomura, Hiroshi Okada

We propose a model based on modular $A_4$ symmetry containing a dark matter candidate, realizing radiatively induced neutrino mass at one-loop level. One finds that stability of dark matter candidate can be assured by nonzero value of modular weight and heavy neutral fermion mass hierarchies, which include dark matter under the $A_4$ triplet, are uniquely de

Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry, Rémi Flamary

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstein distance. Using this distance allows t

Sergey V. Tikhonov

The genus $gen(D)$ of a finite-dimensional central division algebra $D$ over a field $F$ is defined as the collection of classes $[D&#39;]\in Br(F)$, where $D&#39;$ is a central division $F$-algebra having the same maximal subfields as $D$. We show that the fact that quaternion division algebras $D$ and $D&#39;$ have the same maximal subfields does not imply

Lei Yu, Vincent Y. F. Tan

We leverage proof techniques Fourier analysis and an existing result in coding theory to derive new bounds for the problem of non-interactive simulation of binary random variables. Previous bounds in the literature were derived by applying data processing inequalities concerning maximal correlation or hypercontractivity. We show that our bounds are sharp in

Laura Sánchez-Menguiano, Jorge Sánchez Almeida, Casiana Muñoz-Tuñón, Sebastián F. Sánchez

The role of gas accretion in galaxy evolution is still a matter of debate. The presence of inflows of metal-poor gas that trigger star formation bursts of low metallicity has been proposed as an explanation for the local anti-correlation between star formation rate (SFR) and gas-phase metallicity ($\rm Z_g$) found in the literature. In the present study, we

Jakub Rydzewski

Recent developments in enhanced sampling methods showed that it is possible to reconstruct ligand unbinding pathways with spatial and temporal resolution inaccessible to experiments. Ideally, such techniques should provide an atomistic definition of possibly many reaction pathways, because crude estimates may lead either to overestimating energy barriers, or

Rafayel Petrosyan

In single-molecule force spectroscopy experiments, the dependence of the mean unfolding force on the loading rate is used for obtaining information about the energetic and dynamic properties of the system under study. However, it is crucial to understand that different dynamic force spectroscopy (DFS) models are applicable in different regimes, and that diff

P. Syntelis, V. Archontis, K. Tsinganos

We present results of three-dimensional MHD simulations of recurrent eruptions in emerging flux regions. The initial numerical setup is the same with the work by Syntelis et al. 2017 (hereafter, Paper I). Here, we perform a parametric study on the magnetic field strength ($B_0$) of the emerging field. The kinetic energy of the produced ejective eruptions in

Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan

Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization propert

Krzysztof R. Apt, Ernst-Ruediger Olderog

We present a history of Hoare&#39;s logic.

Ping Kwan Man Ellgan

We analyze BI model in a complete form and compare the predictions with that of Starobinsky model. Under the parameter constraints in Planck 2018, we find that the dynamics of the whole inflation process described by BI and Starobinsky models are nearly the same, even though there are some differences in the regions out of inflation. We also find the scales

Bence Agg, Andrea Csaszar, Mate Szalay-Beko, Daniel V. Veres

Motivation: Network visualizations of complex biological datasets usually result in &#39;hairball&#39; images, which do not discriminate network modules. Results: We present the EntOptLayout Cytoscape plug-in based on a recently developed network representation theory. The plug-in provides an efficient visualization of network modules, which represent major

Benjamin Hellouin de Menibus, Hugo Maturana Cornejo

We consider a set of necessary conditions which are efficient heuristics for deciding when a set of Wang tiles cannot tile a group. Piantadosi gave a necessary and sufficient condition for the existence of a valid tiling of any free group. This condition is actually necessary for the existence of a valid tiling for an arbitrary finitely generated group. We t

Imran Khan

In particle physics, study of the symmetry plays very important role in order to get useful information about the nature. The classification and arrangements of subatomic particles is also necessary to study particle physics. Particles which are building blocks of nature are quarks, gluons and leptons. Baryons and Mesons composed of quarks were arranged by G

Silvio Capobianco

We prove an analogue of Fekete&#39;s subadditivity lemma for functions of several real variables which are subadditive in each variable taken singularly. This extends both the classical case for subadditive functions of one real variable, and a result in a previous paper by the author. While doing so, we prove that the functions with the property mentioned a

Simone Landi, Luca Franci, Emanuele Papini, Andrea Verdini

By means of three dimensional high-resolution hybrid simulations we study the properties of the magnetic field spectral anisotropies near and beyond ion kinetic scales. By using both a Fourier analysis and a local analysis based on multi-point 2nd-order structure function techniques, we show that the anisotropy observed is less than what expected by standard

Lan Luo, Zhi Ma, Dongdai Lin

In this paper, we present two new ways of quantum synchronization coding based on the $(\bm{u}+\bm{v}|\bm{u}-\bm{v})$ construction and the product construction respectively, and greatly enrich the varieties of available quantum synchronizable codes. The circumstances where the maximum synchronization error tolerance can be reached are explained for both cons

V. P. Mineev

The contribution of pair fluctuations to the spin current in liquid 3He in aerogel near the critical temperature of transition to the superfluid state is calculated.

Fengda Zhu, Linchao Zhu, Yi Yang

There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite labour intensive to obtain sufficient real environment data for training robots while

Thierry Cazenave, Yvan Martel, Lifeng Zhao

We prove that any sufficiently differentiable space-like hypersurface of ${\mathbb R}^{1+N} $ coincides locally around any of its points with the blow-up surface of a finite-energy solution of the focusing nonlinear wave equation $\partial_{tt} u - Δu=|u|^{p-1} u$ on ${\mathbb R} \times {\mathbb R} ^N$, for any $1\leq N\leq 4$ and $1 < p \le \frac {N+2} {N-2

Taibou Birgui Sekou, Moncef Hidane, Julien Olivier, Hubert Cardot

Deep learning based models, generally, require a large number of samples for appropriate training, a requirement that is difficult to satisfy in the medical field. This issue can usually be avoided with a proper initialization of the weights. On the task of medical image segmentation in general, two techniques are oftentimes employed to tackle the training o

Hugo Gimbert, Claire Mathieu, Simon Mauras

Stable matching in a community consisting of men and women is a classical combinatorial problem that has been the subject of intense theoretical and empirical study since its introduction in 1962 in a seminal paper by Gale and Shapley, who designed the celebrated ``deferred acceptance&#39;&#39; algorithm for the problem. In the input, each participant ranks

Prabal Adhikari, Jens O. Andersen, Patrick Kneschke

In this paper, we calculate the equation of state of two-flavor finite isospin chiral perturbation theory at next-to-leading order in the pion-condensed phase at zero temperature. We show that the transition from the vacuum phase to a Bose-condensed phase is of second order. While the tree-level result has been known for some time, surprisingly quantum effec

Daigo Ohki, Yukiko Omori, Akito Kobayashi

Motivated by the results of recent transport and optical conductivity studies, we propose a semi-infinite two-dimensional lattice model for interacting massive Dirac electrons in the pressurized organic conductor $α$-(BEDT-TTF)$_2$I$_3$, and address the problem of domain wall conductivity in a charge-ordered insulating phase under realistic experimental cond

Taiju Amano, Daisuke Jido

Speculating that the $ud$ diquark with spin 0 has a similar mass to the constituent $s$ quark, we introduce a symmetry between the $s$ quark and the $\overline{ud}$ diquark. Constructing an algebra for this symmetry, we regard a triplet of the $s$ quarks with spin up and down and the $\overline{ud}$ diquark with spin 0 as a fundamental representation of this

Yongjie Shi, Chengjie Yu

In this paper, we first introduce higher order Dirichlet-to-Neumann maps on graphs which can be viewed as a discrete analogue of the corresponding Dirichlet-to-Neumann maps on compact Riemannian manifolds with boundary and a higher order generalization of the Dirichlet-to-Neumann map on graphs introduced by Hua-Huang-Wang\cite{HHW} and Hassannezhad-Miclo \ci

Yu Liu, Milinda Abeykoon, Eli Stavitski, Klaus Attenkofer

We present a comprehensive investigation on anisotropic magnetic and magnetocaloric properties of the quasi-two-dimensional weak itinerant ferromagnet trigonal Cr$_{0.62}$Te single crystals. Magnetic-anisotropy-induced satellite transition $T^*$ is observed at low fields applied parallel to the $ab$ plane below $T_c$. The $T^*$ is featured by an anomalous ma

Shuhao Gu, Yang Feng, Qun Liu

In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model the two kinds of information in the encoder-decoder framework so as to exploit out-of-domain data in in-domain training. In our method, we m

Haitao Tang, Yuan Yu, Xinliang Zhang

This manuscript was retracted after discussions among the authors.

Lucas Ondel, Hari Krishna Vydana, Lukáš Burget, Jan Černocký

This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages. Our approach may be described by the following two steps procedure: first the model learns the notion of acoustic units from the labelled data and then the model uses its knowledge to fin

Yu Liu, Jun Li, Jing Tao, Yimei Zhu

We present a comprehensive study on anisotropic magnetocaloric porperties of the van der Waals weak-itinerant ferromagnet Fe$_{3-x}$GeTe$_2$ that features gate-tunable room-temperature ferromagnetism in few-layer device. Intrinsic magnetocrystalline anisotropy is observed to be temperature-dependent and most likely favors the long-range magnetic order in thi

Xi Chen, Simon P. Ellingsen, Ying Mei

That the laws of physics are the same at all times and places throughout the Universe is one of the basic assumptions of physics. Astronomical observations provide the only means to test this basic assumption on cosmological time and distance scales. The possibility of variations in the dimensionless physical constant μ - the proton-to-electron mass ratio, c

Satoshi Okuzumi, Ryo Tazaki

Recent (sub)millimeter polarimetric observations toward the young star HL Tau have successfully detected polarization emission from its circumstellar disk. The polarization pattern observed at 0.87 mm is uniform and parallel to the disk&#39;s minor axis, consistent with the self-scattering of thermal emission by dust particles whose maximum radius is $\appro

Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the com

Giacomo Albi, Giacomo Dimarco, Lorenzo Pareschi

We consider the development of high order space and time numerical methods based on Implicit-Explicit (IMEX) multistep time integrators for hyperbolic systems with relaxation. More specifically, we consider hyperbolic balance laws in which the convection and the source term may have very different time and space scales. As a consequence the nature of the asy

Kishora Nayak, Shusu Shi, Nu Xu, Zi-Wei Lin

The rapidity-odd component of directed flow ($v_{1}$) of identified hardons ($π^{\pm}$, $K^{\pm}$, $K^{0}_{S}$, $p$, $\bar{p}$, $ϕ$, $Ξ$, $\barΞ$, $Λ$, $\barΛ$) and partons ($u$, $\bar{u}$, $d$, $\bar{d}$, $s$, $\bar{s}$) in Au+Au collisions at various beam energies ($\sqrt{s_{\rm NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4, 200 GeV) using a multi-phas

Thomas Gotfredsen, David Kyed

We construct a general cohomological induction isomorphism from a uniform measure equivalence of locally compact, second countable, unimodular groups which, as a special case, yields that the graded cohomology rings of quasi-isometric, connected, simply connected, nilpotent Lie groups are isomorphic. This unifies results of Shalom and Sauer and also provides

A. W. Eggels, D. T. Crommelin

We consider the estimation of sensitivity indices based on divergence measures such as Hellinger distance. For sensitivity analysis of complex models, these divergence-based indices can be estimated by Monte-Carlo sampling (MCS) in combination with kernel density estimation (KDE). In a direct approach, the complex model must be evaluated at every input point

Alexander S. Wein, Ahmed El Alaoui, Cristopher Moore

For the tensor PCA (principal component analysis) problem, we propose a new hierarchy of increasingly powerful algorithms with increasing runtime. Our hierarchy is analogous to the sum-of-squares (SOS) hierarchy but is instead inspired by statistical physics and related algorithms such as belief propagation and AMP (approximate message passing). Our level-$\

Ahmad Mohamadnejad

We study a classically accidental scale-invariant extension of the Standard Model (SM) containing three additional fields, a vector leptoquark ($ V_μ $), a real scalar ($ ϕ$), and a neutral Majorana fermion ($ χ$) as a dark matter (DM) candidate. The scalar $ ϕ$ (scalon) and Majorana fermion $ χ$ are both singlets under the SM gauge group, while $ V_μ $ has

Jørgen Nordmoen, Tønnes F. Nygaard, Kai Olav Ellefsen, Kyrre Glette

Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization using simulations is a promising way to automatically design such control systems, however, if the disparity between simulation and the real world becomes too large, the optimizat

Chao Huang, Haojie Liu, Tong Chen, Qiu Shen

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the &#34;priors&#34; at different resolution scale to improve the compression efficiency, and also employs the generative adversarial network(GAN) with multiscale discriminators to perform t

Keisuke Yanagi, Natsumi Nagata, Koichi Hamaguchi

Recent observations have found several candidates for old warm neutron stars whose surface temperatures are above the prediction of the standard neutron star cooling scenario, and thus require some heating mechanism. Motivated by these observations, we study the non-equilibrium beta process in the minimal cooling scenario of neutron stars, which inevitably o

Yiran Zhong, Pan Ji, Jianyuan Wang, Yuchao Dai

Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothness constraint to train the networks. Their performance degrades at regions where repetitive textures or occlusions occur. In this paper, we propose Deep Epipolar Flow, an unsupervi

Guangrun Wang, Guangcong Wang, Xujie Zhang, Jianhuang Lai

Person re-identification (Re-ID) benefits greatly from the accurate annotations of existing datasets (e.g., CUHK03 [1] and Market-1501 [2]), which are quite expensive because each image in these datasets has to be assigned with a proper label. In this work, we ease the annotation of Re-ID by replacing the accurate annotation with inaccurate annotation, i.e.,

Ahmed Hareedy, Rohith Kuditipudi, Robert Calderbank

In order to meet the demands of data-hungry applications, data storage devices are required to be increasingly denser. Various sources of error appear with this increase in density. Multi-dimensional (MD) graph-based codes are capable of mitigating error sources like interference and channel non-uniformity in dense storage devices. Recently, a technique was

Plamen Stefanov, Gunther Uhlmann, Andras Vasy

We study the isotropic elastic wave equation in a bounded domain with boundary with coefficients having jumps at a nested set of interfaces satisfying the natural transmission conditions there. We analyze in detail the microlocal behavior of such solution like reflection, transmission and mode conversion of S and P waves, evanescent modes, Rayleigh and Stone

Heinrich Dinkel, Kai Yu

Task 4 of the DCASE2018 challenge demonstrated that substantially more research is needed for a real-world application of sound event detection. Analyzing the challenge results it can be seen that most successful models are biased towards predicting long (e.g., over 5s) clips. This work aims to investigate the performance impact of fixed-sized window median

Felipe Oyarce, Miguel Orszag

We propose, in a Ramsey interferometer, to cool the cavity field to its ground state, starting from a thermal distribution by a dispersive atom-field coupling followed by an atomic postselection. We also analyze the effect of the cavity and atomic losses. The proposed experiment can be realized with realistic parameters with high fidelity.

Yu Li, Hu Wang, Wenquan Shuai, Honghao Zhang

With the improvement of the pattern recognition and feature extraction of Deep Neural Networks (DPNNs), image-based design and optimization have been widely used in multidisciplinary researches. Recently, a Reconstructive Neural Network (ReConNN) has been proposed to obtain an image-based model from an analysis-based model [1, 2], and a steady-state heat tra

Guanyang Wang

Uniform sampling of binary matrix with fixed margins is an important and difficult problem in statistics, computer science, ecology and so on. The well-known swap algorithm would be inefficient when the size of the matrix becomes large or when the matrix is too sparse/dense. Here we propose the Rectangle Loop algorithm, a Markov chain Monte Carlo algorithm t

Wanpeng Tan

Baryon asymmetry of the universe (BAU) is naturally explained with $K^0-K^{0&#39;}$ oscillations of a newly developed mirror-matter model and new understanding of quantum chromodynamics (QCD) phase transitions. A consistent picture for the origin of both BAU and dark matter is presented with the aid of $n-n&#39;$ oscillations of the new model. The global sym

Alexander Greaves-Tunnell, Zaid Harchaoui

Representation and learning of long-range dependencies is a central challenge confronted in modern applications of machine learning to sequence data. Yet despite the prominence of this issue, the basic problem of measuring long-range dependence, either in a given data source or as represented in a trained deep model, remains largely limited to heuristic tool

Siddique Latif, Rajib Rana, Sara Khalifa, Raja Jurdak

Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not always guaranteed to be the best in a statistical modelling framework where the end goal is for example emoti

Jingke Meng, Sheng Wu, Wei-Shi Zheng

In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual; this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming. To overcome this difficulty, we consider weakly supervised person re-id modeling. The weak settin

Simone Calamai, Fangyu Zou

We propose a flow to study the Chern-Yamabe problem and discuss the long time existence of the flow. In the balanced case we show that the Chern-Yamabe problem is the Euler-Lagrange equation of some functional. The monotonicity of the functional along the flow is derived. We also show that the functional is not bounded from below.

Siavash Mirarab

ASTRAL is a method for reconstructing species trees after inferring a set of gene trees and is increasingly used in phylogenomic analyses. It is statistically consistent under the multi-species coalescent model, is scalable, and has shown high accuracy in simulated and empirical studies. This chapter discusses practical considerations in using ASTRAL, starti

Motohiko Ezawa

We analyze the non-Hermitian Haldane model where the spin-orbit interaction is made non-Hermitian. The Dirac mass becomes complex. We propose to realize it by an $LC$ circuit with operational amplifiers. A topological phase transition is found to occur at a critical point where the real part of the bulk spectrum is closed. The Chern number changes its value

Heinrich Dinkel, Mengyue Wu, Kai Yu

Previous text-based depression detection is commonly based on large user-generated data. Sparse scenarios like clinical conversations are less investigated. This work proposes a text-based multi-task BGRU network with pretrained word embeddings to model patients&#39; responses during clinical interviews. Our main approach uses a novel multi-task loss functio

Inseok Oh, Seungeun Rho, Sangbin Moon, Seongho Son

Reinforcement learning combined with deep neural networks has performed remarkably well in many genres of games recently. It has surpassed human-level performance in fixed game environments and turn-based two player board games. However, to the best of our knowledge, current research has yet to produce a result that has surpassed human-level performance in m

Ruoyu Wang, Shiheng Wang, Songyu Du, Erdong Xiao

Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3

David V. Gealy, Stephen McKinley, Brent Yi, Philipp Wu

Robots must cost less and be force-controlled to enable widespread, safe deployment in unconstrained human environments. We propose Quasi-Direct Drive actuation as a capable paradigm for robotic force-controlled manipulation in human environments at low-cost. Our prototype - Blue - is a human scale 7 Degree of Freedom arm with 2kg payload. Blue can cost less

Seungwoo Choi, Seokjun Seo, Beomjun Shin, Hyeongmin Byun

Keyword spotting (KWS) plays a critical role in enabling speech-based user interactions on smart devices. Recent developments in the field of deep learning have led to wide adoption of convolutional neural networks (CNNs) in KWS systems due to their exceptional accuracy and robustness. The main challenge faced by KWS systems is the trade-off between high acc