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1,802.0316
Distributed Spanner Approximation
We address the fundamental network design problem of constructing approximate minimum spanners. Our contributions are for the distributed setting, providing both algorithmic and hardness results. Our main hardness result shows that an $\alpha$-approximation for the minimum directed $k$-spanner problem for $k \geq 5$ requires $\Omega(n /\sqrt{\alpha}\log{n})$ rounds using deterministic algorithms or $\Omega(\sqrt{n }/\sqrt{\alpha}\log{n})$ rounds using randomized ones, in the CONGEST model of distributed computing. Combined with the constant-round $O(n^{\epsilon})$-approximation algorithm in the LOCAL model of [Barenboim, Elkin and Gavoille, 2016], as well as a polylog-round $(1+\epsilon)$-approximation algorithm in the LOCAL model that we show here, our lower bounds for the CONGEST model imply a strict separation between the LOCAL and CONGEST models. Notably, to the best of our knowledge, this is the first separation between these models for a local approximation problem. Similarly, a separation between the directed and undirected cases is implied. We also prove a nearly-linear lower bound for the minimum weighted $k$-spanner problem for $k \geq 4$, and we show lower bounds for the weighted 2-spanner problem. On the algorithmic side, apart from the aforementioned $(1+\epsilon)$-approximation algorithm for minimum $k$-spanners, our main contribution is a new distributed construction of minimum 2-spanners that uses only polynomial local computations. Our algorithm has a guaranteed approximation ratio of $O(\log(m/n))$ for a graph with $n$ vertices and $m$ edges, which matches the best known ratio for polynomial time sequential algorithms [Kortsarz and Peleg, 1994], and is tight if we restrict ourselves to polynomial local computations. Our approach allows us to extend our algorithm to work also for the directed, weighted, and client-server variants of the problem.
cs.DS cs.DC
we address the fundamental network design problem of constructing approximate minimum spanners our contributions are for the distributed setting providing both algorithmic and hardness results our main hardness result shows that an alphaapproximation for the minimum directed kspanner problem for k geq 5 requires omegan sqrtalphalogn rounds using deterministic algorithms or omegasqrtn sqrtalphalogn rounds using randomized ones in the congest model of distributed computing combined with the constantround onepsilonapproximation algorithm in the local model of barenboim elkin and gavoille 2016 as well as a polyloground 1epsilonapproximation algorithm in the local model that we show here our lower bounds for the congest model imply a strict separation between the local and congest models notably to the best of our knowledge this is the first separation between these models for a local approximation problem similarly a separation between the directed and undirected cases is implied we also prove a nearlylinear lower bound for the minimum weighted kspanner problem for k geq 4 and we show lower bounds for the weighted 2spanner problem on the algorithmic side apart from the aforementioned 1epsilonapproximation algorithm for minimum kspanners our main contribution is a new distributed construction of minimum 2spanners that uses only polynomial local computations our algorithm has a guaranteed approximation ratio of ologmn for a graph with n vertices and m edges which matches the best known ratio for polynomial time sequential algorithms kortsarz and peleg 1994 and is tight if we restrict ourselves to polynomial local computations our approach allows us to extend our algorithm to work also for the directed weighted and clientserver variants of the problem
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1,802.03161
The affine property of quasi-free states on self-dual CAR algebras
We study conditions for which the correspondence between quasi-free states on self-dual CAR algebras and covariant operators is affine.
math.OA
we study conditions for which the correspondence between quasifree states on selfdual car algebras and covariant operators is affine
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1,802.03162
URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
Malicious URLs host unsolicited content and are used to perpetrate cybercrimes. It is imperative to detect them in a timely manner. Traditionally, this is done through the usage of blacklists, which cannot be exhaustive, and cannot detect newly generated malicious URLs. To address this, recent years have witnessed several efforts to perform Malicious URL Detection using Machine Learning. The most popular and scalable approaches use lexical properties of the URL string by extracting Bag-of-words like features, followed by applying machine learning models such as SVMs. There are also other features designed by experts to improve the prediction performance of the model. These approaches suffer from several limitations: (i) Inability to effectively capture semantic meaning and sequential patterns in URL strings; (ii) Requiring substantial manual feature engineering; and (iii) Inability to handle unseen features and generalize to test data. To address these challenges, we propose URLNet, an end-to-end deep learning framework to learn a nonlinear URL embedding for Malicious URL Detection directly from the URL. Specifically, we apply Convolutional Neural Networks to both characters and words of the URL String to learn the URL embedding in a jointly optimized framework. This approach allows the model to capture several types of semantic information, which was not possible by the existing models. We also propose advanced word-embeddings to solve the problem of too many rare words observed in this task. We conduct extensive experiments on a large-scale dataset and show a significant performance gain over existing methods. We also conduct ablation studies to evaluate the performance of various components of URLNet.
cs.CR cs.LG
malicious urls host unsolicited content and are used to perpetrate cybercrimes it is imperative to detect them in a timely manner traditionally this is done through the usage of blacklists which cannot be exhaustive and cannot detect newly generated malicious urls to address this recent years have witnessed several efforts to perform malicious url detection using machine learning the most popular and scalable approaches use lexical properties of the url string by extracting bagofwords like features followed by applying machine learning models such as svms there are also other features designed by experts to improve the prediction performance of the model these approaches suffer from several limitations i inability to effectively capture semantic meaning and sequential patterns in url strings ii requiring substantial manual feature engineering and iii inability to handle unseen features and generalize to test data to address these challenges we propose urlnet an endtoend deep learning framework to learn a nonlinear url embedding for malicious url detection directly from the url specifically we apply convolutional neural networks to both characters and words of the url string to learn the url embedding in a jointly optimized framework this approach allows the model to capture several types of semantic information which was not possible by the existing models we also propose advanced wordembeddings to solve the problem of too many rare words observed in this task we conduct extensive experiments on a largescale dataset and show a significant performance gain over existing methods we also conduct ablation studies to evaluate the performance of various components of urlnet
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1,802.03163
Topological algebras of bounded operators with locally solid Riesz spaces
Let $X$ be a vector lattice and $(E,\tau)$ be a locally solid vector lattice. An operator $T:X\to E$ is said to be $ob$-bounded if, for each order bounded set $B$ in $X$, $T(B)$ is topologically bounded in $E$. In this paper, we study on algebraic properties of $ob$-bounded operators with respect to the topology of uniform convergence and equicontinuous convergence.
math.FA
let x be a vector lattice and etau be a locally solid vector lattice an operator txto e is said to be obbounded if for each order bounded set b in x tb is topologically bounded in e in this paper we study on algebraic properties of obbounded operators with respect to the topology of uniform convergence and equicontinuous convergence
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1,802.03164
Global weak Besov solutions of the Navier-Stokes equations and applications
We introduce a notion of global weak solution to the Navier-Stokes equations in three dimensions with initial values in the critical homogeneous Besov spaces $\dot{B}^{-1+\frac{3}{p}}_{p,\infty}$, $p > 3$. These solutions satisfy a certain stability property with respect to the weak-$\ast$ convergence of initial conditions. To illustrate this property, we provide applications to blow-up criteria, minimal blow-up initial data, and forward self-similar solutions. Our proof relies on a new splitting result in homogeneous Besov spaces that may be of independent interest.
math.AP
we introduce a notion of global weak solution to the navierstokes equations in three dimensions with initial values in the critical homogeneous besov spaces dotb1frac3p_pinfty p 3 these solutions satisfy a certain stability property with respect to the weakast convergence of initial conditions to illustrate this property we provide applications to blowup criteria minimal blowup initial data and forward selfsimilar solutions our proof relies on a new splitting result in homogeneous besov spaces that may be of independent interest
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1,802.03165
Static strain tuning of quantum dots embedded in a photonic wire
We use strain to statically tune the semiconductor band gap of individual InAs quantum dots (QDs) embedded in a GaAs photonic wire featuring very efficient single photon collection efficiency. Thanks to the geometry of the structure, we are able to shift the QD excitonic transition by more than 20 meV by using nano-manipulators to apply the stress. Moreover, owing to the strong transverse strain gradient generated in the structure, we can relatively tune two QDs located in the wire waveguide and bring them in resonance, opening the way to the observation of collective effects such as superradiance.
cond-mat.mes-hall
we use strain to statically tune the semiconductor band gap of individual inas quantum dots qds embedded in a gaas photonic wire featuring very efficient single photon collection efficiency thanks to the geometry of the structure we are able to shift the qd excitonic transition by more than 20 mev by using nanomanipulators to apply the stress moreover owing to the strong transverse strain gradient generated in the structure we can relatively tune two qds located in the wire waveguide and bring them in resonance opening the way to the observation of collective effects such as superradiance
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1,802.03166
Photon absorption and electron scattering by endohedrals
We concentrate here on photon absorption as well as electron and positron scattering upon endohedrals that consist of a fullerenes shell and an inner atom A. The aim is to understand the effect of fullerene electron shell in formation of corresponding cross-section. We consider the problem substituting the action of a complex multiatomic fullerenes shell by a combination of static pseudopotential and dynamic polarization potential. The electron correlations in the atom A are taken into account in the frame of the random phase approximation with exchange (RPAE). We demonstrate that the fullerenes shell strongly affects the cross-sections, bringing in a number of peculiarities, such as confinement resonances and giant-endohedral resonances and partial wave Ramsauer-type minima. Numerical data are obtained for endohedrals A@C60 and A@C60@C240, where A are noble gas atoms He, Ar and Xe.
physics.atm-clus
we concentrate here on photon absorption as well as electron and positron scattering upon endohedrals that consist of a fullerenes shell and an inner atom a the aim is to understand the effect of fullerene electron shell in formation of corresponding crosssection we consider the problem substituting the action of a complex multiatomic fullerenes shell by a combination of static pseudopotential and dynamic polarization potential the electron correlations in the atom a are taken into account in the frame of the random phase approximation with exchange rpae we demonstrate that the fullerenes shell strongly affects the crosssections bringing in a number of peculiarities such as confinement resonances and giantendohedral resonances and partial wave ramsauertype minima numerical data are obtained for endohedrals ac60 and ac60c240 where a are noble gas atoms he ar and xe
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1,802.03167
Equivalence of One Loop Diagrams in Covariant and Light Front QED Revisited
We revisit the proof of equivalence of one loop expressions for fermion self-energy and vertex correction in light-front QED and Covariant QED at the Feynman diagram level and generalize, to all components, the proof of equivalence for the one loop vertex correction diagram which was presented earlier by us only for the $+$ component of $\Lambda^\mu$. We demonstrate, in the general case also, that the equivalence cannot be established without the third term in the three-term photon propagator in light cone gauge.
hep-th hep-ph
we revisit the proof of equivalence of one loop expressions for fermion selfenergy and vertex correction in lightfront qed and covariant qed at the feynman diagram level and generalize to all components the proof of equivalence for the one loop vertex correction diagram which was presented earlier by us only for the component of lambdamu we demonstrate in the general case also that the equivalence cannot be established without the third term in the threeterm photon propagator in light cone gauge
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1,802.03168
Hierarchical Cloth Simulation using Deep Neural Networks
Fast and reliable physically-based simulation techniques are essential for providing flexible visual effects for computer graphics content. In this paper, we propose a fast and reliable hierarchical cloth simulation method, which combines conventional physically-based simulation with deep neural networks (DNN). Simulations of the coarsest level of the hierarchical model are calculated using conventional physically-based simulations, and more detailed levels are generated by inference using DNN models. We demonstrate that our method generates reliable and fast cloth simulation results through experiments under various conditions.
cs.GR
fast and reliable physicallybased simulation techniques are essential for providing flexible visual effects for computer graphics content in this paper we propose a fast and reliable hierarchical cloth simulation method which combines conventional physicallybased simulation with deep neural networks dnn simulations of the coarsest level of the hierarchical model are calculated using conventional physicallybased simulations and more detailed levels are generated by inference using dnn models we demonstrate that our method generates reliable and fast cloth simulation results through experiments under various conditions
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1,802.03169
Thermal disorder in finite-length carbon nanowire
Chemisorption is one of the active research areas in carbon materials. The occurrence of the monoatomic carbon chain can be made by surrounding the double walled carbon nanotube and meanwhile worldwide efforts have been made to create the extraction technique for unlashing the carbon chains from the enclosure. Here we report an extensive study of the kink structure in the free standing carbon nanowires. Our Monte Carlo simulation considers the multi-monoatomic carbon chains laterally interacted by the Van der Waal force. Despite the linearity of the carbon nanowires is independent of chain length at low temperatures, the same situation does not hold at high temperatures. Disordered kink structure is observed in the short carbon chains especially above Peierls transition temperature. For instance, the average kink angle of 50-atoms carbon nanowire is as large as 35 degree at 800K. We have provided an important inspection that any physical property of the finite-length carbon chain predicted by ab-initio calculation should reconsider the atomic rearrangement due to the thermal instability. Apart from this, the kink structure in the nanowires likely increases the probability of attaching negatively charged atoms which is an encouragement to find the next generation materials for chemisorption.
cond-mat.mtrl-sci
chemisorption is one of the active research areas in carbon materials the occurrence of the monoatomic carbon chain can be made by surrounding the double walled carbon nanotube and meanwhile worldwide efforts have been made to create the extraction technique for unlashing the carbon chains from the enclosure here we report an extensive study of the kink structure in the free standing carbon nanowires our monte carlo simulation considers the multimonoatomic carbon chains laterally interacted by the van der waal force despite the linearity of the carbon nanowires is independent of chain length at low temperatures the same situation does not hold at high temperatures disordered kink structure is observed in the short carbon chains especially above peierls transition temperature for instance the average kink angle of 50atoms carbon nanowire is as large as 35 degree at 800k we have provided an important inspection that any physical property of the finitelength carbon chain predicted by abinitio calculation should reconsider the atomic rearrangement due to the thermal instability apart from this the kink structure in the nanowires likely increases the probability of attaching negatively charged atoms which is an encouragement to find the next generation materials for chemisorption
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1,802.0317
Adversarial Metric Learning
In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results when the test data is similar to the training data. However, the trained models often fail to produce reliable distances on the ambiguous test pairs due to the distribution bias between training set and test set. To address this problem, the Adversarial Metric Learning (AML) is proposed in this paper, which automatically generates adversarial pairs to remedy the distribution bias and facilitate robust metric learning. Specifically, AML consists of two adversarial stages, i.e. confusion and distinguishment. In confusion stage, the ambiguous but critical adversarial data pairs are adaptively generated to mislead the learned metric. In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs. Thanks to the challenges posed by the confusion stage in such competing process, the AML model is able to grasp plentiful difficult knowledge that has not been contained by the original training pairs, so the discriminability of AML can be significantly improved. The entire model is formulated into optimization framework, of which the global convergence is theoretically proved. The experimental results on toy data and practical datasets clearly demonstrate the superiority of AML to the representative state-of-the-art metric learning methodologies.
cs.LG stat.ML
in the past decades intensive efforts have been put to design various loss functions and metric forms for metric learning problem these improvements have shown promising results when the test data is similar to the training data however the trained models often fail to produce reliable distances on the ambiguous test pairs due to the distribution bias between training set and test set to address this problem the adversarial metric learning aml is proposed in this paper which automatically generates adversarial pairs to remedy the distribution bias and facilitate robust metric learning specifically aml consists of two adversarial stages ie confusion and distinguishment in confusion stage the ambiguous but critical adversarial data pairs are adaptively generated to mislead the learned metric in distinguishment stage a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs thanks to the challenges posed by the confusion stage in such competing process the aml model is able to grasp plentiful difficult knowledge that has not been contained by the original training pairs so the discriminability of aml can be significantly improved the entire model is formulated into optimization framework of which the global convergence is theoretically proved the experimental results on toy data and practical datasets clearly demonstrate the superiority of aml to the representative stateoftheart metric learning methodologies
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1,802.03171
A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
Recently, a new multi-step temporal learning algorithm, called $Q(\sigma)$, unifies $n$-step Tree-Backup (when $\sigma=0$) and $n$-step Sarsa (when $\sigma=1$) by introducing a sampling parameter $\sigma$. However, similar to other multi-step temporal-difference learning algorithms, $Q(\sigma)$ needs much memory consumption and computation time. Eligibility trace is an important mechanism to transform the off-line updates into efficient on-line ones which consume less memory and computation time. In this paper, we further develop the original $Q(\sigma)$, combine it with eligibility traces and propose a new algorithm, called $Q(\sigma ,\lambda)$, in which $\lambda$ is trace-decay parameter. This idea unifies Sarsa$(\lambda)$ (when $\sigma =1$) and $Q^{\pi}(\lambda)$ (when $\sigma =0$). Furthermore, we give an upper error bound of $Q(\sigma ,\lambda)$ policy evaluation algorithm. We prove that $Q(\sigma,\lambda)$ control algorithm can converge to the optimal value function exponentially. We also empirically compare it with conventional temporal-difference learning methods. Results show that, with an intermediate value of $\sigma$, $Q(\sigma ,\lambda)$ creates a mixture of the existing algorithms that can learn the optimal value significantly faster than the extreme end ($\sigma=0$, or $1$).
cs.AI cs.LG stat.ML
recently a new multistep temporal learning algorithm called qsigma unifies nstep treebackup when sigma0 and nstep sarsa when sigma1 by introducing a sampling parameter sigma however similar to other multistep temporaldifference learning algorithms qsigma needs much memory consumption and computation time eligibility trace is an important mechanism to transform the offline updates into efficient online ones which consume less memory and computation time in this paper we further develop the original qsigma combine it with eligibility traces and propose a new algorithm called qsigma lambda in which lambda is tracedecay parameter this idea unifies sarsalambda when sigma 1 and qpilambda when sigma 0 furthermore we give an upper error bound of qsigma lambda policy evaluation algorithm we prove that qsigmalambda control algorithm can converge to the optimal value function exponentially we also empirically compare it with conventional temporaldifference learning methods results show that with an intermediate value of sigma qsigma lambda creates a mixture of the existing algorithms that can learn the optimal value significantly faster than the extreme end sigma0 or 1
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1,802.03172
Probability distribution of intensity fluctuations of vortex laser beams in the turbulent atmosphere
Numerical simulation is used to analyze statistical characteristics of vortex beams propagating in the atmosphere. The cumulative distribution function and the probability density function of intensity fluctuations are compared for Gaussian beams and vortex beams. It is shown that for propagation conditions in the turbulent atmosphere corresponding to weak fluctuations (Rytov parameter much smaller than unity), intensity fluctuations at the axis of the Gaussian beam have the lognormal distribution, whereas the probability density distribution of the radiation intensity fluctuations at the axis of the vortex beams is well approximated by the exponential distribution characteristic of conditions of saturated fluctuations (Rytov parameter much larger than unity)
physics.optics
numerical simulation is used to analyze statistical characteristics of vortex beams propagating in the atmosphere the cumulative distribution function and the probability density function of intensity fluctuations are compared for gaussian beams and vortex beams it is shown that for propagation conditions in the turbulent atmosphere corresponding to weak fluctuations rytov parameter much smaller than unity intensity fluctuations at the axis of the gaussian beam have the lognormal distribution whereas the probability density distribution of the radiation intensity fluctuations at the axis of the vortex beams is well approximated by the exponential distribution characteristic of conditions of saturated fluctuations rytov parameter much larger than unity
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1,802.03173
Arithmetic and dynamical degrees of self-morphisms of semi-abelian varieties
We prove a conjecture by Kawaguchi-Silverman on arithmetic and dynamical degrees, for self-morphisms of semi-abelian varieties. Moreover, we determine the set of the arithmetic degrees of orbits and the (first) dynamical degrees of self-morphisms of semi-abelian varieties.
math.AG math.NT
we prove a conjecture by kawaguchisilverman on arithmetic and dynamical degrees for selfmorphisms of semiabelian varieties moreover we determine the set of the arithmetic degrees of orbits and the first dynamical degrees of selfmorphisms of semiabelian varieties
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1,802.03174
Surrogate Models for Direct Dark Matter Detection
In this work we introduce RAPIDD, a surrogate model that speeds up the computation of the expected spectrum of dark matter particles in direct detection experiments. RAPIDD replaces the exact calculation of the dark matter differential rate (which in general involves up to three nested integrals) with a much faster parametrization in terms of ordinary polynomials of the dark matter mass and couplings, obtained in an initial training phase. In this article, we validate our surrogate model on the multi-dimensional parameter space resulting from the effective field theory description of dark matter interactions with nuclei, including also astrophysical uncertainties in the description of the dark matter halo. As a concrete example, we use this tool to study the complementarity of different targets to discriminate simplified dark matter models. We demonstrate that RAPIDD is fast and accurate, and particularly well-suited to explore a multi-dimensional parameter space, such as the one in effective field theory approach, and scans with a large number of evaluations.
hep-ph astro-ph.CO
in this work we introduce rapidd a surrogate model that speeds up the computation of the expected spectrum of dark matter particles in direct detection experiments rapidd replaces the exact calculation of the dark matter differential rate which in general involves up to three nested integrals with a much faster parametrization in terms of ordinary polynomials of the dark matter mass and couplings obtained in an initial training phase in this article we validate our surrogate model on the multidimensional parameter space resulting from the effective field theory description of dark matter interactions with nuclei including also astrophysical uncertainties in the description of the dark matter halo as a concrete example we use this tool to study the complementarity of different targets to discriminate simplified dark matter models we demonstrate that rapidd is fast and accurate and particularly wellsuited to explore a multidimensional parameter space such as the one in effective field theory approach and scans with a large number of evaluations
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1,802.03175
The modality of a Borel subgroup in a simple algebraic group of type $E_8$
Let $G$ be a simple algebraic group over an algebraically closed field $k$, where $\mathrm{char}\, k$ is either 0 or a good prime for $G$. We consider the modality $\mathrm{mod}(B : \mathfrak u)$ of the action of a Borel subgroup $B$ of $G$ on the Lie algebra $\mathfrak u$ of the unipotent radical of $B$, and report on computer calculations used to show that $\mathrm{mod}(B:\mathfrak u) = 20$, when $G$ is of type $\mathrm E_8$. This completes the determination of the values for $\mathrm{mod}(B:\mathfrak u)$ for $G$ of exceptional type.
math.GR
let g be a simple algebraic group over an algebraically closed field k where mathrmchar k is either 0 or a good prime for g we consider the modality mathrmmodb mathfrak u of the action of a borel subgroup b of g on the lie algebra mathfrak u of the unipotent radical of b and report on computer calculations used to show that mathrmmodbmathfrak u 20 when g is of type mathrm e_8 this completes the determination of the values for mathrmmodbmathfrak u for g of exceptional type
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1,802.03176
Monocrystalline free standing 3D yttrium iron garnet magnon nano resonators
Nano resonators in which mechanical vibrations and spin waves can be coupled are an intriguing concept that can be used in quantum information processing to transfer information between different states of excitation. Until now, the fabrication of free standing magnetic nanostructures which host long lived spin wave excitatons and may be suitable as mechanical resonators seemed elusive. We demonstrate the fabrication of free standing monocrystalline yttrium iron garnet (YIG) 3D nanoresonators with nearly ideal magnetic properties. The freestanding 3D structures are obtained using a complex lithography process including room temperature deposition and lift-off of amorphous YIG and subsequent crystallization by annealing. The crystallization nucleates from the substrate and propagates across the structure even around bends over distances of several micrometers to form e.g. monocrystalline resonators as shown by transmission electron microscopy. Spin wave excitations in individual nanostructures are imaged by time resolved scanning Kerr microscopy. The narrow linewidth of the magnetic excitations indicates a Gilbert damping constant of only $\alpha = 2.6 \times 10^{-4}$ rivalling the best values obtained for epitaxial YIG thin film material. The new fabrication process represents a leap forward in magnonics and magnon mechanics as it provides 3D YIG structures of unprecedented quality. At the same time it demonstrates a completely new route towards the fabrication of free standing crystalline nano structures which may be applicable also to other material systems.
cond-mat.mes-hall
nano resonators in which mechanical vibrations and spin waves can be coupled are an intriguing concept that can be used in quantum information processing to transfer information between different states of excitation until now the fabrication of free standing magnetic nanostructures which host long lived spin wave excitatons and may be suitable as mechanical resonators seemed elusive we demonstrate the fabrication of free standing monocrystalline yttrium iron garnet yig 3d nanoresonators with nearly ideal magnetic properties the freestanding 3d structures are obtained using a complex lithography process including room temperature deposition and liftoff of amorphous yig and subsequent crystallization by annealing the crystallization nucleates from the substrate and propagates across the structure even around bends over distances of several micrometers to form eg monocrystalline resonators as shown by transmission electron microscopy spin wave excitations in individual nanostructures are imaged by time resolved scanning kerr microscopy the narrow linewidth of the magnetic excitations indicates a gilbert damping constant of only alpha 26 times 104 rivalling the best values obtained for epitaxial yig thin film material the new fabrication process represents a leap forward in magnonics and magnon mechanics as it provides 3d yig structures of unprecedented quality at the same time it demonstrates a completely new route towards the fabrication of free standing crystalline nano structures which may be applicable also to other material systems
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1,802.03177
Multipartite entanglement, quantum coherence, and quantum criticality in triangular and Sierpi\'nski fractal lattices
We investigate the quantum phase transitions of the transverse-field quantum Ising model on the triangular lattice and Sierpi\'nski fractal lattices by employing multipartite entanglement and quantum coherence along with the quantum renormalization group method. It is shown that the quantum criticalities of these high-dimensional models closely relate to the behaviors of the multipartite entanglement and quantum coherence. As the thermodynamic limit is approached, the first derivatives of multipartite entanglement and quantum coherence exhibit singular behaviors and the consistent finite-size scaling behaviors for each lattice are also obtained from the first derivatives. The multipartite entanglement and quantum coherence are demonstrated to be good indicators for detecting the quantum phase transitions in the triangular lattice and Sierpi\'nski fractal lattices. Furthermore, the factors that determine the relations between the critical exponents and the correlation length exponents for these models are diverse. For the triangular lattice, the decisive factor is the spatial dimension, while for the Sierpi\'nski fractal lattices, it is the Hausdorff dimension.
quant-ph
we investigate the quantum phase transitions of the transversefield quantum ising model on the triangular lattice and sierpinski fractal lattices by employing multipartite entanglement and quantum coherence along with the quantum renormalization group method it is shown that the quantum criticalities of these highdimensional models closely relate to the behaviors of the multipartite entanglement and quantum coherence as the thermodynamic limit is approached the first derivatives of multipartite entanglement and quantum coherence exhibit singular behaviors and the consistent finitesize scaling behaviors for each lattice are also obtained from the first derivatives the multipartite entanglement and quantum coherence are demonstrated to be good indicators for detecting the quantum phase transitions in the triangular lattice and sierpinski fractal lattices furthermore the factors that determine the relations between the critical exponents and the correlation length exponents for these models are diverse for the triangular lattice the decisive factor is the spatial dimension while for the sierpinski fractal lattices it is the hausdorff dimension
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1,802.03178
Architectural Tactics for Big Data Cybersecurity Analytic Systems: A Review
Context: Big Data Cybersecurity Analytics is aimed at protecting networks, computers, and data from unauthorized access by analysing security event data using big data tools and technologies. Whilst a plethora of Big Data Cybersecurity Analytic Systems have been reported in the literature, there is a lack of a systematic and comprehensive review of the literature from an architectural perspective. Objective: This paper reports a systematic review aimed at identifying the most frequently reported quality attributes and architectural tactics for Big Data Cybersecurity Analytic Systems. Method: We used Systematic Literature Review (SLR) method for reviewing 74 primary studies selected using well-defined criteria. Results: Our findings are twofold: (i) identification of 12 most frequently reported quality attributes and the justification for their significance for Big Data Cybersecurity Analytic Systems; and (ii) identification and codification of 17 architectural tactics for addressing the quality attributes that are commonly associated with Big Data Cybersecurity Analytic systems. The identified tactics include six performance tactics, four accuracy tactics, two scalability tactics, three reliability tactics, and one security and usability tactic each. Conclusion: Our findings have revealed that (a) despite the significance of interoperability, modifiability, adaptability, generality, stealthiness, and privacy assurance, these quality attributes lack explicit architectural support in the literature (b) empirical investigation is required to evaluate the impact of codified architectural tactics (c) a good deal of research effort should be invested to explore the trade-offs and dependencies among the identified tactics and (d) there is a general lack of effective collaboration between academia and industry for supporting the field of Big Data Cybersecurity Analytic Systems.
cs.CR cs.SE
context big data cybersecurity analytics is aimed at protecting networks computers and data from unauthorized access by analysing security event data using big data tools and technologies whilst a plethora of big data cybersecurity analytic systems have been reported in the literature there is a lack of a systematic and comprehensive review of the literature from an architectural perspective objective this paper reports a systematic review aimed at identifying the most frequently reported quality attributes and architectural tactics for big data cybersecurity analytic systems method we used systematic literature review slr method for reviewing 74 primary studies selected using welldefined criteria results our findings are twofold i identification of 12 most frequently reported quality attributes and the justification for their significance for big data cybersecurity analytic systems and ii identification and codification of 17 architectural tactics for addressing the quality attributes that are commonly associated with big data cybersecurity analytic systems the identified tactics include six performance tactics four accuracy tactics two scalability tactics three reliability tactics and one security and usability tactic each conclusion our findings have revealed that a despite the significance of interoperability modifiability adaptability generality stealthiness and privacy assurance these quality attributes lack explicit architectural support in the literature b empirical investigation is required to evaluate the impact of codified architectural tactics c a good deal of research effort should be invested to explore the tradeoffs and dependencies among the identified tactics and d there is a general lack of effective collaboration between academia and industry for supporting the field of big data cybersecurity analytic systems
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1,802.03179
Time-reversal symmetry breaking Abelian chiral spin liquid in Mott phases of three-component fermions on the triangular lattice
We provide numerical evidence in favor of spontaneous chiral symmetry breaking and the concomitant appearance of an Abelian chiral spin liquid for three-component fermions on the triangular lattice described by an SU(3) symmetric Hubbard model with hopping amplitude $-t$ ($t>0$) and on-site interaction $U$. This chiral phase is stabilized in the Mott phase with one particle per site in the presence of a uniform $\pi$-flux per plaquette, and in the Mott phase with two particles per site without any flux. Our approach relies on effective spin models derived in the strong-coupling limit in powers of $t/U$ for general SU$(N)$ and arbitrary uniform charge flux per plaquette, which are subsequently studied using exact diagonalizations and variational Monte Carlo simulations for $N=3$, as well as exact diagonalizations of the SU($3$) Hubbard model on small clusters. Up to third order in $t/U$, and for the time-reversal symmetric cases (flux $0$ or $\pi$), the low-energy description is given by the $J$-$K$ model with Heisenberg coupling $J$ and real ring exchange $K$. The phase diagram in the full $J$-$K$ parameter range contains, apart from three already known, magnetically long-range ordered phases, two previously unreported phases: i) a lattice nematic phase breaking the lattice rotation symmetry and ii) a spontaneous time-reversal and parity symmetry breaking Abelian chiral spin liquid. For the Hubbard model, an investigation that includes higher-order itinerancy effects supports the presence of a phase transition inside the insulating region, occurring at $(t/U)_{\rm c}\approx 0.07$ [$(U/t)_{\rm c} \approx 13$] between the three-sublattice magnetically ordered phase at small $t/U$ and this Abelian chiral spin liquid.
cond-mat.str-el
we provide numerical evidence in favor of spontaneous chiral symmetry breaking and the concomitant appearance of an abelian chiral spin liquid for threecomponent fermions on the triangular lattice described by an su3 symmetric hubbard model with hopping amplitude t t0 and onsite interaction u this chiral phase is stabilized in the mott phase with one particle per site in the presence of a uniform piflux per plaquette and in the mott phase with two particles per site without any flux our approach relies on effective spin models derived in the strongcoupling limit in powers of tu for general sun and arbitrary uniform charge flux per plaquette which are subsequently studied using exact diagonalizations and variational monte carlo simulations for n3 as well as exact diagonalizations of the su3 hubbard model on small clusters up to third order in tu and for the timereversal symmetric cases flux 0 or pi the lowenergy description is given by the jk model with heisenberg coupling j and real ring exchange k the phase diagram in the full jk parameter range contains apart from three already known magnetically longrange ordered phases two previously unreported phases i a lattice nematic phase breaking the lattice rotation symmetry and ii a spontaneous timereversal and parity symmetry breaking abelian chiral spin liquid for the hubbard model an investigation that includes higherorder itinerancy effects supports the presence of a phase transition inside the insulating region occurring at tu_rm capprox 007 ut_rm c approx 13 between the threesublattice magnetically ordered phase at small tu and this abelian chiral spin liquid
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1,802.0318
In a One-Bit Rush: Low-Latency Wireless Spectrum Monitoring with Binary Sensor Arrays
Detecting the presence of a random wireless source with minimum latency utilizing an array of radio sensors is considered. The problem is studied under the constraint that the analog-to-digital conversion at each sensor is restricted to reading the sign of the analog received signal. We formulate the resulting digital signal processing task as a sequential hypothesis test in simple form. To circumvent the intractable probabilistic model of the multivariate binary array data, a reduced model representation within the exponential family in conjunction with a log-likelihood ratio approximation is employed. This approach allows us to design a likelihood-based sequential test and to analyze its analytic performance along Wald's classical arguments. In the context of wireless spectrum monitoring for satellite-based navigation and synchronization systems, we study the achievable processing latency, characterized by the average sample number, as a function of the binary sensors in use. The practical feasibility and potential of the discussed low-complexity sensing and decision-making technology is demonstrated via simulations.
eess.SP cs.IT math.IT
detecting the presence of a random wireless source with minimum latency utilizing an array of radio sensors is considered the problem is studied under the constraint that the analogtodigital conversion at each sensor is restricted to reading the sign of the analog received signal we formulate the resulting digital signal processing task as a sequential hypothesis test in simple form to circumvent the intractable probabilistic model of the multivariate binary array data a reduced model representation within the exponential family in conjunction with a loglikelihood ratio approximation is employed this approach allows us to design a likelihoodbased sequential test and to analyze its analytic performance along walds classical arguments in the context of wireless spectrum monitoring for satellitebased navigation and synchronization systems we study the achievable processing latency characterized by the average sample number as a function of the binary sensors in use the practical feasibility and potential of the discussed lowcomplexity sensing and decisionmaking technology is demonstrated via simulations
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1,802.03181
Storage of RF photons in minimal conditions
We investigate the minimal conditions to store coherently a RF pulse in a material medium. We choose a commercial quartz as memory support because it is a widely available component with a high Q-factor. Pulse storage is obtained by varying dynamically the light-matter coupling with an analog switch. This parametric driving of the quartz dynamics can be alternatively interpreted as a stopped light experiment. We obtain an efficiency of 26%, a storage time of 209$\mu$s and a time-to-bandwidth product of 98 by optimizing the pulse temporal shape. The coherent character of the storage is demonstrated. Our goal is to connect different types of memories in the RF and optical domain for quantum information processing. Our motivation is essentially fundamental.
physics.class-ph physics.atom-ph quant-ph
we investigate the minimal conditions to store coherently a rf pulse in a material medium we choose a commercial quartz as memory support because it is a widely available component with a high qfactor pulse storage is obtained by varying dynamically the lightmatter coupling with an analog switch this parametric driving of the quartz dynamics can be alternatively interpreted as a stopped light experiment we obtain an efficiency of 26 a storage time of 209mus and a timetobandwidth product of 98 by optimizing the pulse temporal shape the coherent character of the storage is demonstrated our goal is to connect different types of memories in the rf and optical domain for quantum information processing our motivation is essentially fundamental
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1,802.03182
Single-molecule force spectroscopy with photoluminescent semiconducting polymers: Harnessing entropy
We discuss implications of a recent experimental breakthrough which uses a fluorescence-doped flexible semiconducting polymer to construct a single-molecule sensor which can detect ultra-weak forces in the molecular environment, with a grey scale down to 300 femtonewtons.
cond-mat.soft cond-mat.mes-hall cond-mat.mtrl-sci physics.chem-ph
we discuss implications of a recent experimental breakthrough which uses a fluorescencedoped flexible semiconducting polymer to construct a singlemolecule sensor which can detect ultraweak forces in the molecular environment with a grey scale down to 300 femtonewtons
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1,802.03183
Optically transparent solid electrodes for precision Penning traps
We have conceived, built, and operated a cryogenic Penning trap with an electrically conducting yet optically transparent solid electrode. The trap, dedicated to spectroscopy and imaging of confined particles under large solid angles is of 'half-open' design with one open endcap and one closed endcap that mainly consists of a glass window coated with a highly transparent conductive layer. This arrangement allows for trapping of externally or internally produced particles, yields flexible access for optical excitation and efficient light collection from the trapping region. At the same time, it is electrically closed and ensures long-term ion confinement under well-defined conditions. With its superior surface quality and its high as well as homogeneous optical transmission, the window electrode is an excellent replacement for partially transmissive electrodes that use holes, slits, metallic meshes and the like.
physics.atom-ph
we have conceived built and operated a cryogenic penning trap with an electrically conducting yet optically transparent solid electrode the trap dedicated to spectroscopy and imaging of confined particles under large solid angles is of halfopen design with one open endcap and one closed endcap that mainly consists of a glass window coated with a highly transparent conductive layer this arrangement allows for trapping of externally or internally produced particles yields flexible access for optical excitation and efficient light collection from the trapping region at the same time it is electrically closed and ensures longterm ion confinement under welldefined conditions with its superior surface quality and its high as well as homogeneous optical transmission the window electrode is an excellent replacement for partially transmissive electrodes that use holes slits metallic meshes and the like
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1,802.03184
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Prediction suffix trees (PST) provide an effective tool for sequence modelling and prediction. Current prediction techniques for PSTs rely on exact matching between the suffix of the current sequence and the previously observed sequence. We present a provably correct algorithm for learning a PST with approximate suffix matching by relaxing the exact matching condition. We then present a self-bounded enhancement of our algorithm where the depth of suffix tree grows automatically in response to the model performance on a training sequence. Through experiments on synthetic datasets as well as three real-world datasets, we show that the approximate matching PST results in better predictive performance than the other variants of PST.
cs.LG stat.ML
prediction suffix trees pst provide an effective tool for sequence modelling and prediction current prediction techniques for psts rely on exact matching between the suffix of the current sequence and the previously observed sequence we present a provably correct algorithm for learning a pst with approximate suffix matching by relaxing the exact matching condition we then present a selfbounded enhancement of our algorithm where the depth of suffix tree grows automatically in response to the model performance on a training sequence through experiments on synthetic datasets as well as three realworld datasets we show that the approximate matching pst results in better predictive performance than the other variants of pst
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1,802.03185
On the detection of coronal dimmings and the extraction of their characteristic properties
Coronal dimmings are distinct phenomena associated to coronal mass ejections (CMEs). The study of coronal dimmings and the extraction of their characteristic parameters helps us to obtain additional information of CMEs, especially on the initiation and early evolution of Earth-directed CMEs. We present a new approach to detect coronal dimming regions based on a thresholding technique applied on logarithmic base-ratio images. Characteristic dimming parameters describing the dynamics, morphology, magnetic properties and the brightness of coronal dimming regions are extracted by cumulatively summing newly dimmed pixels over time. It is also demonstrated how core dimming regions are identified as a subset of the overall identified dimming region. We successfully apply our method to two well-observed coronal dimming events. For both events the core dimming regions are identified and the spatial evolution of the dimming area reveals the expansion of the dimming region around these footpoints. We also show that in the early impulsive phase of the dimming expansion the total unsigned magnetic flux involved in the dimming regions is balanced and that up to 30% of this flux results from the localized core dimming regions. Furthermore, the onset in the profile of the area growth rate is co-temporal with the start of the associated flares and in one case also with the fast rise of the CME, indicating a strong relationship of coronal dimmings with both flare and CMEs.
astro-ph.SR
coronal dimmings are distinct phenomena associated to coronal mass ejections cmes the study of coronal dimmings and the extraction of their characteristic parameters helps us to obtain additional information of cmes especially on the initiation and early evolution of earthdirected cmes we present a new approach to detect coronal dimming regions based on a thresholding technique applied on logarithmic baseratio images characteristic dimming parameters describing the dynamics morphology magnetic properties and the brightness of coronal dimming regions are extracted by cumulatively summing newly dimmed pixels over time it is also demonstrated how core dimming regions are identified as a subset of the overall identified dimming region we successfully apply our method to two wellobserved coronal dimming events for both events the core dimming regions are identified and the spatial evolution of the dimming area reveals the expansion of the dimming region around these footpoints we also show that in the early impulsive phase of the dimming expansion the total unsigned magnetic flux involved in the dimming regions is balanced and that up to 30 of this flux results from the localized core dimming regions furthermore the onset in the profile of the area growth rate is cotemporal with the start of the associated flares and in one case also with the fast rise of the cme indicating a strong relationship of coronal dimmings with both flare and cmes
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1,802.03186
A simplification problem in manifold theory
Two smooth manifolds M and N are called R-diffeomorphic if their product with the real line are diffeomorphic. We consider the following simplification problem: does R-diffeomorphism imply diffeomorphism or homeomorphism? For compact manifolds, analysis of this problem relies on some of the main achievements of the theory of manifolds, in particular the h- and s-cobordism theorems in high dimensions and the spectacular more recent classification results in dimensions 3 and 4. This paper presents what is currently known about the subject as well as some new results about classifications of R-diffeomorphisms.
math.GT
two smooth manifolds m and n are called rdiffeomorphic if their product with the real line are diffeomorphic we consider the following simplification problem does rdiffeomorphism imply diffeomorphism or homeomorphism for compact manifolds analysis of this problem relies on some of the main achievements of the theory of manifolds in particular the h and scobordism theorems in high dimensions and the spectacular more recent classification results in dimensions 3 and 4 this paper presents what is currently known about the subject as well as some new results about classifications of rdiffeomorphisms
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1,802.03187
Noise-Induced Limitations to the Scalability of Distributed Integral Control
We study performance limitations of distributed feedback control in large-scale networked dynamical systems. Specifically, we address the question of how the performance of distributed integral control is affected by measurement noise. We consider second-order consensus-like problems modeled over a toric lattice network, and study asymptotic scalings (in network size) of H2 performance metrics that quantify the variance of nodal state fluctuations. While previous studies have shown that distributed integral control fundamentally improves these performance scalings compared to distributed proportional feedback control, our results show that an explicit inclusion of measurement noise leads to the opposite conclusion. The noise's impact on performance is shown to decrease with an increased inter-nodal alignment of the local integral states. However, even though the controller can be tuned for acceptable performance for any given network size, performance will degrade as the network grows, limiting the scalability of any such controller tuning. In particular, the requirement for inter-nodal alignment increases with network size. We show that this in practice implies that large and sparse networks will require any integral control to be centralized, rather than distributed. In this case, the best-achievable performance scaling, which is shown to be that of proportional feedback control, is retrieved.
math.OC
we study performance limitations of distributed feedback control in largescale networked dynamical systems specifically we address the question of how the performance of distributed integral control is affected by measurement noise we consider secondorder consensuslike problems modeled over a toric lattice network and study asymptotic scalings in network size of h2 performance metrics that quantify the variance of nodal state fluctuations while previous studies have shown that distributed integral control fundamentally improves these performance scalings compared to distributed proportional feedback control our results show that an explicit inclusion of measurement noise leads to the opposite conclusion the noises impact on performance is shown to decrease with an increased internodal alignment of the local integral states however even though the controller can be tuned for acceptable performance for any given network size performance will degrade as the network grows limiting the scalability of any such controller tuning in particular the requirement for internodal alignment increases with network size we show that this in practice implies that large and sparse networks will require any integral control to be centralized rather than distributed in this case the bestachievable performance scaling which is shown to be that of proportional feedback control is retrieved
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1,802.03188
Quantum Relational Hoare Logic
We present a logic for reasoning about pairs of interactive quantum programs - quantum relational Hoare logic (qRHL). This logic follows the spirit of probabilistic relational Hoare logic (Barthe et al. 2009) and allows us to formulate how the outputs of two quantum programs relate given the relationship of their inputs. Probabilistic RHL was used extensively for computer-verified security proofs of classical cryptographic protocols. Since pRHL is not suitable for analyzing quantum cryptography, we present qRHL as a replacement, suitable for the security analysis of post-quantum cryptography and quantum protocols. The design of qRHL poses some challenges unique to the quantum setting, e.g., the definition of equality on quantum registers. Finally, we implemented a tool for verifying proofs in qRHL and developed several example security proofs in it.
quant-ph cs.LO
we present a logic for reasoning about pairs of interactive quantum programs quantum relational hoare logic qrhl this logic follows the spirit of probabilistic relational hoare logic barthe et al 2009 and allows us to formulate how the outputs of two quantum programs relate given the relationship of their inputs probabilistic rhl was used extensively for computerverified security proofs of classical cryptographic protocols since prhl is not suitable for analyzing quantum cryptography we present qrhl as a replacement suitable for the security analysis of postquantum cryptography and quantum protocols the design of qrhl poses some challenges unique to the quantum setting eg the definition of equality on quantum registers finally we implemented a tool for verifying proofs in qrhl and developed several example security proofs in it
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1,802.03189
Effects of Alfvenic Drift on Diffusive Shock Acceleration at Weak Cluster Shocks
Non-detection of $\gamma$-ray emission from galaxy clusters has challenged diffusive shock acceleration (DSA) of cosmic-ray (CR) protons at weak collisionless shocks that are expected to form in the intracluster medium. As an effort to address this problem, we here explore possible roles of Alfv\'en waves self-excited via resonant streaming instability during the CR acceleration at parallel shocks. The mean drift of Alfv\'en waves may either increase or decrease the scattering center compression ratio, depending on the postshock cross-helicity, leading to either flatter or steeper CR spectra. We first examine such effects at planar shocks, based on the transport of Alfv\'en waves in the small amplitude limit. For the shock parameters relevant to cluster shocks, Alfv\'enic drift flattens the CR spectrum slightly, resulting in a small increase of the CR acceleration efficiency, $\eta$. We then consider two additional, physically motivated cases: (1) postshock waves are isotropized via MHD and plasma processes across the shock transition and (2) postshock waves contain only forward waves propagating along with the flow due to a possible gradient of CR pressure behind the shock. In these cases, Alfv\'enic drift could reduce $\eta$ by as much as a factor of 5 for weak cluster shocks. For the canonical parameters adopted here, we suggest $\eta\sim10^{-4}-10^{-2}$ for shocks with sonic Mach number $M_{\rm s}\approx2-3$. The possible reduction of $\eta$ may help ease the tension between non-detection of $\gamma$-rays from galaxy clusters and DSA predictions.
astro-ph.HE
nondetection of gammaray emission from galaxy clusters has challenged diffusive shock acceleration dsa of cosmicray cr protons at weak collisionless shocks that are expected to form in the intracluster medium as an effort to address this problem we here explore possible roles of alfven waves selfexcited via resonant streaming instability during the cr acceleration at parallel shocks the mean drift of alfven waves may either increase or decrease the scattering center compression ratio depending on the postshock crosshelicity leading to either flatter or steeper cr spectra we first examine such effects at planar shocks based on the transport of alfven waves in the small amplitude limit for the shock parameters relevant to cluster shocks alfvenic drift flattens the cr spectrum slightly resulting in a small increase of the cr acceleration efficiency eta we then consider two additional physically motivated cases 1 postshock waves are isotropized via mhd and plasma processes across the shock transition and 2 postshock waves contain only forward waves propagating along with the flow due to a possible gradient of cr pressure behind the shock in these cases alfvenic drift could reduce eta by as much as a factor of 5 for weak cluster shocks for the canonical parameters adopted here we suggest etasim104102 for shocks with sonic mach number m_rm sapprox23 the possible reduction of eta may help ease the tension between nondetection of gammarays from galaxy clusters and dsa predictions
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1,802.0319
Non-Markovian quantum control as coherent stochastic trajectories
We develop a notion of stochastic quantum trajectories. First, we construct a basis set of trajectories, called elementary trajectories, and go on to show that any quantum dynamical process, including those that are non-Markovian, can be expressed as a linear combination of this set. We then show that the set of processes divide into two natural classes: those that can be expressed as convex mixture of elementary trajectories and those that cannot be. The former are shown to be entanglement breaking processes (in each step), while the latter are dubbed coherent processes. This division of processes is analogous to separable and entangled states. In the second half of the paper, we show, with an information theoretic game, that when a process is non-Markovian, coherent trajectories allow for decoupling from the environment while preserving arbitrary quantum information encoded into the system. We give explicit expressions for the temporal correlations (quantifying non-Markovianity) and show that, in general, there are more quantum correlations than classical ones. This shows that non-Markovian quantum processes are indeed fundamentally different from their classical counterparts. Furthermore, we demonstrate how coherent trajectories (with the aid of coherent control) could turn non-Markovianity into a resource. In the final section of the paper we explore this phenomenon in a geometric picture with a convenient set of basis trajectories.
quant-ph
we develop a notion of stochastic quantum trajectories first we construct a basis set of trajectories called elementary trajectories and go on to show that any quantum dynamical process including those that are nonmarkovian can be expressed as a linear combination of this set we then show that the set of processes divide into two natural classes those that can be expressed as convex mixture of elementary trajectories and those that cannot be the former are shown to be entanglement breaking processes in each step while the latter are dubbed coherent processes this division of processes is analogous to separable and entangled states in the second half of the paper we show with an information theoretic game that when a process is nonmarkovian coherent trajectories allow for decoupling from the environment while preserving arbitrary quantum information encoded into the system we give explicit expressions for the temporal correlations quantifying nonmarkovianity and show that in general there are more quantum correlations than classical ones this shows that nonmarkovian quantum processes are indeed fundamentally different from their classical counterparts furthermore we demonstrate how coherent trajectories with the aid of coherent control could turn nonmarkovianity into a resource in the final section of the paper we explore this phenomenon in a geometric picture with a convenient set of basis trajectories
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1,802.03191
An extended version of a Branch-Price-and-Cut Procedure for the Discrete Ordered Median Problem
The Discrete Ordered Median Problem (DOMP) is formulated as a set partitioning problem using an exponential number of variables. Each variable corresponds to a set of demand points allocated to the same facility with the information of the sorting position of their corresponding costs. We develop a column generation approach to solve the continuous relaxation of this model. Then, we apply a branch-price-and-cut algorithm to solve to optimality small to moderate size of DOMP in competitive computational time.
math.OC
the discrete ordered median problem domp is formulated as a set partitioning problem using an exponential number of variables each variable corresponds to a set of demand points allocated to the same facility with the information of the sorting position of their corresponding costs we develop a column generation approach to solve the continuous relaxation of this model then we apply a branchpriceandcut algorithm to solve to optimality small to moderate size of domp in competitive computational time
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1,802.03192
Tracking all members of a honey bee colony over their lifetime
Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a tracking system customized to track up to $4000$ bees over several weeks. In this contribution we present an in-depth description of the underlying multi-step algorithm which both produces the motion paths, and also improves the marker decoding accuracy significantly. We automatically tracked ${\sim}2000$ marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ${\sim}13\%$ to around $2\%$ post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ${\sim} 4$ million images. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.
cs.CV
computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions honey bees are a popular model for learning and memory previous experience has been shown to affect and modulate future social interactions so far no lifetime history observations have been reported for all bees of a colony in a previous work we introduced a tracking system customized to track up to 4000 bees over several weeks in this contribution we present an indepth description of the underlying multistep algorithm which both produces the motion paths and also improves the marker decoding accuracy significantly we automatically tracked sim2000 marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits we found that the proposed twostep tracking reduced incorrect id decodings from initially sim13 to around 2 posttracking alongside this paper we publish the first trajectory dataset for all bees in a colony extracted from sim 4 million images we invite researchers to join the collective scientific effort to investigate this intriguing animal system all components of our system are opensource
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1,802.03193
Young differential delay equations driven by H\"older continuous paths
In this paper we prove the existence and uniqueness of the solution of Young differential delay equations under weaker conditions than it is known in the literature. We also prove the continuity and differentiability of the solution with respect to the initial function and give an estimate for the growth of the solution. The proofs use techniques of stopping times, Shauder-Tychonoff fixed point theorem and a Gronwall-type lemma.
math.DS
in this paper we prove the existence and uniqueness of the solution of young differential delay equations under weaker conditions than it is known in the literature we also prove the continuity and differentiability of the solution with respect to the initial function and give an estimate for the growth of the solution the proofs use techniques of stopping times shaudertychonoff fixed point theorem and a gronwalltype lemma
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1,802.03194
Ambrosetti-Prodi problem with degenerate potential and Neumann boundary condition
We study the degenerate elliptic equation $-\mathop{\rm div}(|x|^\alpha\nabla u) =f(u)+t\phi(x)+h(x)$ in a bounded open set $\Omega$ with homogeneous Neumann boundary condition, where $\alpha\in(0,2)$ and $f$ has a linear growth. The main result establishes the existence of real numbers $t_*$ and $t^*$ such that the problem has at least two solutions if $t\leq t_*$, there is at least one solution if $t_*<t\leq t^*$, and no solution exists for all $t>t^*$. The proof combines a priori estimates with topological degree arguments.
math.AP
we study the degenerate elliptic equation mathoprm divxalphanabla u futphixhx in a bounded open set omega with homogeneous neumann boundary condition where alphain02 and f has a linear growth the main result establishes the existence of real numbers t_ and t such that the problem has at least two solutions if tleq t_ there is at least one solution if t_tleq t and no solution exists for all tt the proof combines a priori estimates with topological degree arguments
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1,802.03195
Limits on Sparse Data Acquisition: RIC Analysis of Finite Gaussian Matrices
One of the key issues in the acquisition of sparse data by means of compressed sensing (CS) is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distributions of the extreme eigenvalues for Wishart matrices. First, we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the RIC, and propose a probabilistic framework to study both the symmetric and asymmetric RICs. Then, we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness. The presented framework determines limits on various sparse recovery algorithms for finite size problems. In particular, it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability. Also, we derive simple approximations for the RICs based on the Tracy-Widom distribution.
cs.IT eess.SP math.IT math.ST stat.TH
one of the key issues in the acquisition of sparse data by means of compressed sensing cs is the design of the measurement matrix gaussian matrices have been proven to be informationtheoretically optimal in terms of minimizing the required number of measurements for sparse recovery in this paper we provide a new approach for the analysis of the restricted isometry constant ric of finite dimensional gaussian measurement matrices the proposed method relies on the exact distributions of the extreme eigenvalues for wishart matrices first we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the ric and propose a probabilistic framework to study both the symmetric and asymmetric rics then we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness the presented framework determines limits on various sparse recovery algorithms for finite size problems in particular it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability also we derive simple approximations for the rics based on the tracywidom distribution
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1,802.03196
The Classification Problem for 2-Forms in Four Variables
The notion of type of a differential 2-form in four variables is introduced and for 2-forms of type < 4, local normal models are given. If the type of a 2-form $\Omega$ is 4, then the equivalence under diffeomorphisms of $\Omega$ is reduced to the equivalence of a symplectic linear frame functorially attached to $\Omega$. As the equivalence problem for linear parallelisms is known, the present work solves generically the equivalence problem under diffeomorphisms of germs of 2-forms in 4 variables.
math.DG
the notion of type of a differential 2form in four variables is introduced and for 2forms of type 4 local normal models are given if the type of a 2form omega is 4 then the equivalence under diffeomorphisms of omega is reduced to the equivalence of a symplectic linear frame functorially attached to omega as the equivalence problem for linear parallelisms is known the present work solves generically the equivalence problem under diffeomorphisms of germs of 2forms in 4 variables
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1,802.03197
Overdetermined problems and constant mean curvature surfaces in cones
We consider a partially overdetermined problem in a sector-like domain $\Omega$ in a cone $\Sigma$ in $\mathbb{R}^N$, $N\geq 2$, and prove a rigidity result of Serrin type by showing that the existence of a solution implies that $\Omega$ is a spherical sector, under a convexity assumption on the cone. We also consider the related question of characterizing constant mean curvature compact surfaces $\Gamma$ with boundary which satisfy a "gluing" condition with respect to the cone $\Sigma$. We prove that if either the cone is convex or the surface is a radial graph then $\Gamma$ must be a spherical cap. Finally we show that, under the condition that the relative boundary of the domain or the surface intersects orthogonally the cone, no other assumptions are needed.
math.AP
we consider a partially overdetermined problem in a sectorlike domain omega in a cone sigma in mathbbrn ngeq 2 and prove a rigidity result of serrin type by showing that the existence of a solution implies that omega is a spherical sector under a convexity assumption on the cone we also consider the related question of characterizing constant mean curvature compact surfaces gamma with boundary which satisfy a gluing condition with respect to the cone sigma we prove that if either the cone is convex or the surface is a radial graph then gamma must be a spherical cap finally we show that under the condition that the relative boundary of the domain or the surface intersects orthogonally the cone no other assumptions are needed
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1,802.03198
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report
We have tried to reproduce the results of the paper "Natural Language Inference over Interaction Space" submitted to ICLR 2018 conference as part of the ICLR 2018 Reproducibility Challenge. Initially, we were not aware that the code was available, so we started to implement the network from scratch. We have evaluated our version of the model on Stanford NLI dataset and reached 86.38% accuracy on the test set, while the paper claims 88.0% accuracy. The main difference, as we understand it, comes from the optimizers and the way model selection is performed.
cs.CL
we have tried to reproduce the results of the paper natural language inference over interaction space submitted to iclr 2018 conference as part of the iclr 2018 reproducibility challenge initially we were not aware that the code was available so we started to implement the network from scratch we have evaluated our version of the model on stanford nli dataset and reached 8638 accuracy on the test set while the paper claims 880 accuracy the main difference as we understand it comes from the optimizers and the way model selection is performed
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1,802.03199
N\'{e}el Spin Orbit Torque driven antiferromagnetic resonance in Mn$_{2}$Au probed by time-domain THz spectroscopy
We observe the excitation of collective modes in the THz range driven by the recently discovered N\'{e}el spin-orbit torques (NSOT) in the metallic antiferromagnet Mn$_{2}$Au. Temperature dependent THz spectroscopy reveals a strong absorption mode centered near 1 THz, which upon heating from 4 K to 450 K softens and looses intensity. Comparison with the estimated eigenmode frequencies implies that the observed mode is an in-plane antiferromagnetic resonance (AFMR) mode. The AFMR absorption strength exceeds those found in antiferromagnetic insulators, driven by the magnetic field of the THz radiation, by three orders of magnitude. Based on this and the agreement with our theory modelling, we infer that the driving mechanism for the observed mode is the current induced NSOT. This electric manipulation of the Ne\'{e}l order parameter at high frequencies makes Mn$_{2}$Au a prime candidate for AFM ultrafast memory applications.
cond-mat.str-el cond-mat.mtrl-sci
we observe the excitation of collective modes in the thz range driven by the recently discovered neel spinorbit torques nsot in the metallic antiferromagnet mn_2au temperature dependent thz spectroscopy reveals a strong absorption mode centered near 1 thz which upon heating from 4 k to 450 k softens and looses intensity comparison with the estimated eigenmode frequencies implies that the observed mode is an inplane antiferromagnetic resonance afmr mode the afmr absorption strength exceeds those found in antiferromagnetic insulators driven by the magnetic field of the thz radiation by three orders of magnitude based on this and the agreement with our theory modelling we infer that the driving mechanism for the observed mode is the current induced nsot this electric manipulation of the neel order parameter at high frequencies makes mn_2au a prime candidate for afm ultrafast memory applications
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1,802.032
Reducing cavity-pulling shift in Ramsey-operated compact clocks
We describe a method to stabilize the amplitude of the interrogating microwave field in compact atomic clocks working in a Ramsey approach. In this technique, we take advantage of the pulsed regime to use the atoms themselves as microwave amplitude discriminators. Specifically, in addition to the dependence on the microwave detuning, the atomic signal after the Ramsey interrogation acquires a dependence on the microwave pulse area (amplitude times duration) that can be exploited to implement an active stabilization of the microwave field amplitude, in a similar way in which the Ramsey clock signal is used to lock the local oscillator frequency to the atomic reference. This stabilization results in a reduced sensitivity of the clock frequency to microwave amplitude fluctuations that are transferred to the atoms through the cavity-pulling effect. The proposed technique is then effective to improve the clock stability and drift on medium and long term. We demonstrate the method for a vapor-cell clock working with a hot sample of atoms but it can be extended to cold-atom compact clocks.
physics.atom-ph
we describe a method to stabilize the amplitude of the interrogating microwave field in compact atomic clocks working in a ramsey approach in this technique we take advantage of the pulsed regime to use the atoms themselves as microwave amplitude discriminators specifically in addition to the dependence on the microwave detuning the atomic signal after the ramsey interrogation acquires a dependence on the microwave pulse area amplitude times duration that can be exploited to implement an active stabilization of the microwave field amplitude in a similar way in which the ramsey clock signal is used to lock the local oscillator frequency to the atomic reference this stabilization results in a reduced sensitivity of the clock frequency to microwave amplitude fluctuations that are transferred to the atoms through the cavitypulling effect the proposed technique is then effective to improve the clock stability and drift on medium and long term we demonstrate the method for a vaporcell clock working with a hot sample of atoms but it can be extended to coldatom compact clocks
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1,802.03201
Freestyle, a randomized version of ChaCha for resisting offline brute-force and dictionary attacks
This paper introduces Freestyle, a randomized and variable round version of the ChaCha cipher. Freestyle uses the concept of hash based halting condition where a decryption attempt with an incorrect key is likely to take longer time to halt. This makes Freestyle resistant to key-guessing attacks i.e. brute-force and dictionary based attacks. Freestyle demonstrates a novel approach for ciphertext randomization by using random number of rounds for each block, where the exact number of rounds are unknown to the receiver in advance. Freestyle provides the possibility of generating $2^{128}$ different ciphertexts for a given key, nonce, and message; thus resisting key and nonce reuse attacks. Due to its inherent random behavior, Freestyle makes cryptanalysis through known-plaintext, chosen-plaintext, and chosen-ciphertext attacks difficult in practice. On the other hand, Freestyle has costlier cipher initialization process, typically generates 3.125% larger ciphertext, and was found to be 1.6 to 3.2 times slower than ChaCha20. Freestyle is suitable for applications that favor ciphertext randomization and resistance to key-guessing and key reuse attacks over performance and ciphertext size. Freestyle is ideal for applications where ciphertext can be assumed to be in full control of an adversary, and an offline key-guessing attack can be carried out.
cs.CR
this paper introduces freestyle a randomized and variable round version of the chacha cipher freestyle uses the concept of hash based halting condition where a decryption attempt with an incorrect key is likely to take longer time to halt this makes freestyle resistant to keyguessing attacks ie bruteforce and dictionary based attacks freestyle demonstrates a novel approach for ciphertext randomization by using random number of rounds for each block where the exact number of rounds are unknown to the receiver in advance freestyle provides the possibility of generating 2128 different ciphertexts for a given key nonce and message thus resisting key and nonce reuse attacks due to its inherent random behavior freestyle makes cryptanalysis through knownplaintext chosenplaintext and chosenciphertext attacks difficult in practice on the other hand freestyle has costlier cipher initialization process typically generates 3125 larger ciphertext and was found to be 16 to 32 times slower than chacha20 freestyle is suitable for applications that favor ciphertext randomization and resistance to keyguessing and key reuse attacks over performance and ciphertext size freestyle is ideal for applications where ciphertext can be assumed to be in full control of an adversary and an offline keyguessing attack can be carried out
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1,802.03202
On the completeness and orthonormality of the Volkov states and the Volkov propagator in configuration space
Volkov states and Volkov propagator are the basic analytical tools to investigate QED processes occurring in the presence of an intense plane-wave electromagnetic field. In the present paper we provide alternative and relatively simple proofs of the completeness and of the orthonormality at a fixed time of the Volkov states. Concerning the completeness, we exploit some known properties of the Green's function of the Dirac operator in a plane wave, whereas the orthonormality of the Volkov states is proved, relying only on a geometric argument based on the Gauss theorem in four dimensions. In relation with the completeness of the Volkov states, we also study some analytical properties of the Green's function of the Dirac operator in a plane wave, which we explicitly prove to coincide with the Volkov propagator in configuration space. In particular, a closed-form expression in terms of modified Bessel functions and Hankel functions is derived by means of the operator technique in a plane wave and different asymptotic forms are determined. Finally, the transformation properties of the Volkov propagator under general gauge transformations and a general gauge-invariant expression of the so-called dressed mass in configuration space are presented.
hep-ph
volkov states and volkov propagator are the basic analytical tools to investigate qed processes occurring in the presence of an intense planewave electromagnetic field in the present paper we provide alternative and relatively simple proofs of the completeness and of the orthonormality at a fixed time of the volkov states concerning the completeness we exploit some known properties of the greens function of the dirac operator in a plane wave whereas the orthonormality of the volkov states is proved relying only on a geometric argument based on the gauss theorem in four dimensions in relation with the completeness of the volkov states we also study some analytical properties of the greens function of the dirac operator in a plane wave which we explicitly prove to coincide with the volkov propagator in configuration space in particular a closedform expression in terms of modified bessel functions and hankel functions is derived by means of the operator technique in a plane wave and different asymptotic forms are determined finally the transformation properties of the volkov propagator under general gauge transformations and a general gaugeinvariant expression of the socalled dressed mass in configuration space are presented
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1,802.03203
Curve Registered Coupled Low Rank Factorization
We propose an extension of the canonical polyadic (CP) tensor model where one of the latent factors is allowed to vary through data slices in a constrained way. The components of the latent factors, which we want to retrieve from data, can vary from one slice to another up to a diffeomorphism. We suppose that the diffeomorphisms are also unknown, thus merging curve registration and tensor decomposition in one model, which we call registered CP. We present an algorithm to retrieve both the latent factors and the diffeomorphism, which is assumed to be in a parametrized form. At the end of the paper, we show simulation results comparing registered CP with other models from the literature.
stat.ML cs.LG
we propose an extension of the canonical polyadic cp tensor model where one of the latent factors is allowed to vary through data slices in a constrained way the components of the latent factors which we want to retrieve from data can vary from one slice to another up to a diffeomorphism we suppose that the diffeomorphisms are also unknown thus merging curve registration and tensor decomposition in one model which we call registered cp we present an algorithm to retrieve both the latent factors and the diffeomorphism which is assumed to be in a parametrized form at the end of the paper we show simulation results comparing registered cp with other models from the literature
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1,802.03204
The Betti map associated to a section of an abelian scheme (with an appendix by Z. Gao)
Given a point $\xi$ on a complex abelian variety $A$, its abelian logarithm can be expressed as a linear combination of the periods of $A$ with real coefficients, the Betti coordinates of $\xi$. When $(A, \xi)$ varies in an algebraic family, these coordinates define a system of multivalued real-analytic functions. Computing its rank (in the sense of differential geometry) becomes important when one is interested about how often $\xi$ takes a torsion value (for instance, Manin's theorem of the kernel implies that this coordinate system is constant in a family without fixed part only when $\xi$ is a torsion section). We compute this rank in terms of the rank of a certain contracted form of the Kodaira-Spencer map associated to $(A, \xi)$ (assuming $A$ without fixed part, and $\mathbb{Z} \xi$ Zariski-dense in $A$), and deduce some explicit lower bounds in special situations. For instance, we determine this rank in relative dimension $\leq 3$, and study in detail the case of jacobians of families of hyperelliptic curves. Our main application, obtained in collaboration with Z. Gao, states that if $A\to S$ is a principally polarized abelian scheme of relative dimension $g$ which has no non-trivial endomorphism (on any finite covering), and if the image of $S$ in the moduli space $\mathcal{A}_g$ has dimension at least $g$, then the Betti map of any non-torsion section $\xi$ is generically a submersion, so that $\xi^{-1}A_{tors}$ is dense in $S(\mathbb{C})$.
math.AG math.NT
given a point xi on a complex abelian variety a its abelian logarithm can be expressed as a linear combination of the periods of a with real coefficients the betti coordinates of xi when a xi varies in an algebraic family these coordinates define a system of multivalued realanalytic functions computing its rank in the sense of differential geometry becomes important when one is interested about how often xi takes a torsion value for instance manins theorem of the kernel implies that this coordinate system is constant in a family without fixed part only when xi is a torsion section we compute this rank in terms of the rank of a certain contracted form of the kodairaspencer map associated to a xi assuming a without fixed part and mathbbz xi zariskidense in a and deduce some explicit lower bounds in special situations for instance we determine this rank in relative dimension leq 3 and study in detail the case of jacobians of families of hyperelliptic curves our main application obtained in collaboration with z gao states that if ato s is a principally polarized abelian scheme of relative dimension g which has no nontrivial endomorphism on any finite covering and if the image of s in the moduli space mathcala_g has dimension at least g then the betti map of any nontorsion section xi is generically a submersion so that xi1a_tors is dense in smathbbc
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1,802.03205
Transport of hydrogen isotopes through interlayer spacing in van der Waals crystals
Atoms start behaving as waves rather than classical particles if confined in spaces commensurate with their de Broglie wavelength. At room temperature this length is only about one angstrom even for the lightest atom, hydrogen. This restricts quantum-confinement phenomena for atomic species to the realm of very low temperatures. Here we show that van der Waals gaps between atomic planes of layered crystals provide angstrom-size channels that make quantum confinement of protons apparent even at room temperature. Our transport measurements show that thermal protons experience a notably higher barrier than deuterons when entering van der Waals gaps in hexagonal boron nitride and molybdenum disulfide. This is attributed to the difference in de Broglie wavelength of the isotopes. Once inside the crystals, transport of both isotopes can be described by classical diffusion, albeit with unexpectedly fast rates, comparable to that of protons in water. The demonstrated angstrom-size channels can be exploited for further studies of atomistic quantum confinement and, if the technology can be scaled up, for sieving hydrogen isotopes.
cond-mat.mes-hall
atoms start behaving as waves rather than classical particles if confined in spaces commensurate with their de broglie wavelength at room temperature this length is only about one angstrom even for the lightest atom hydrogen this restricts quantumconfinement phenomena for atomic species to the realm of very low temperatures here we show that van der waals gaps between atomic planes of layered crystals provide angstromsize channels that make quantum confinement of protons apparent even at room temperature our transport measurements show that thermal protons experience a notably higher barrier than deuterons when entering van der waals gaps in hexagonal boron nitride and molybdenum disulfide this is attributed to the difference in de broglie wavelength of the isotopes once inside the crystals transport of both isotopes can be described by classical diffusion albeit with unexpectedly fast rates comparable to that of protons in water the demonstrated angstromsize channels can be exploited for further studies of atomistic quantum confinement and if the technology can be scaled up for sieving hydrogen isotopes
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1,802.03206
Perturbative corrections to technicolor
The full solution of technicolor (TC) Schwinger-Dyson equations should include radiative corrections induced by extended technicolor (ETC) (or other) interactions. We verify that when TC is embedded into a larger theory including also QCD, these radiative corrections couple the different strongly interacting Schwinger-Dyson equations, providing a tiny mass to technifermions and changing the ultraviolet behavior of the gap equation solution. We argue about the origin of the different quark masses without appealing for different ETC boson masses, in one scenario where most of the new physics will appear in interactions with the third fermion generation and with a TC scalar boson possibly lighter than the TC characteristic scale ($\Lambda_{\tt{TC}}$)
hep-ph hep-lat hep-th
the full solution of technicolor tc schwingerdyson equations should include radiative corrections induced by extended technicolor etc or other interactions we verify that when tc is embedded into a larger theory including also qcd these radiative corrections couple the different strongly interacting schwingerdyson equations providing a tiny mass to technifermions and changing the ultraviolet behavior of the gap equation solution we argue about the origin of the different quark masses without appealing for different etc boson masses in one scenario where most of the new physics will appear in interactions with the third fermion generation and with a tc scalar boson possibly lighter than the tc characteristic scale lambda_tttc
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1,802.03207
Preliminary investigation of a new state estimation technique using device-independent maximum likelihood estimation
In quantum state tomography, the estimated frequencies do not correspond directly to a physical quantum state, due to statistical fluctuations. Thus, one resorts to point estimators that return the state that matches observations the best, and a variety of estimators have been proposed -- linear inversion, least squares, maximum likelihood (ML) -- each making different trade-offs. In this short note, we investigate an alternative approach inspired by device-independent quantum information protocols. We embed a tomographic complete set of measurement operators within the framework of a Bell scenario, and first regularize the estimated frequencies using a device-independent maximum likelihood algorithm. We then run the standard maximum likelihood algorithm to estimate the underlying quantum state. In this preliminary investigation, we do not observe significant differences in the reconstructed state when using the new algorithm.
quant-ph
in quantum state tomography the estimated frequencies do not correspond directly to a physical quantum state due to statistical fluctuations thus one resorts to point estimators that return the state that matches observations the best and a variety of estimators have been proposed linear inversion least squares maximum likelihood ml each making different tradeoffs in this short note we investigate an alternative approach inspired by deviceindependent quantum information protocols we embed a tomographic complete set of measurement operators within the framework of a bell scenario and first regularize the estimated frequencies using a deviceindependent maximum likelihood algorithm we then run the standard maximum likelihood algorithm to estimate the underlying quantum state in this preliminary investigation we do not observe significant differences in the reconstructed state when using the new algorithm
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1,802.03208
Rotational spectroscopy of cold, trapped molecular ions in the Lamb-Dicke regime
Sympathetic cooling of trapped ions has been established as a powerful technique for manipulation of non-laser-coolable ions (Raizen1992,Waki1992,Bowe1999,Barrett2003). For molecular ions, it promises vastly enhanced spectroscopic resolution and accuracy. However, this potential remains untapped so far, with the best resolution achieved being not better than $5\times10^{-8}$ fractionally, due to residual Doppler broadening being present in ion clusters even at the lowest achievable translational temperatures (Bressel2012). Here we introduce a general and accessible approach that enables Doppler-free rotational spectroscopy. It makes use of the strong radial spatial confinement of molecular ions when trapped and crystallized in a linear quadrupole trap, providing the Lamb-Dicke regime for rotational transitions. We achieve a line width of $1\times10^{-9}$ fractionally and $1.3~\textrm{kHz}$ absolute, an improvement by $50$ and nearly $3\times10^{3}$, respectively, over other methods. The systematic uncertainty is $2.5\times10^{-10}$. As an application, we demonstrate the most precise test of $\textit{ab initio}$ molecular theory and the most precise ($1.3~\textrm{PPB}$) spectroscopic determination of the proton mass. The results represent the long overdue extension of Doppler-free microwave spectroscopy of laser-cooled atomic ion clusters (Berkeland1998) to higher spectroscopy frequencies and to molecules. This approach enables a vast range of high-precision measurements on molecules, both on rotational and, as we project, vibrational transitions.
quant-ph physics.atom-ph
sympathetic cooling of trapped ions has been established as a powerful technique for manipulation of nonlasercoolable ions raizen1992waki1992bowe1999barrett2003 for molecular ions it promises vastly enhanced spectroscopic resolution and accuracy however this potential remains untapped so far with the best resolution achieved being not better than 5times108 fractionally due to residual doppler broadening being present in ion clusters even at the lowest achievable translational temperatures bressel2012 here we introduce a general and accessible approach that enables dopplerfree rotational spectroscopy it makes use of the strong radial spatial confinement of molecular ions when trapped and crystallized in a linear quadrupole trap providing the lambdicke regime for rotational transitions we achieve a line width of 1times109 fractionally and 13textrmkhz absolute an improvement by 50 and nearly 3times103 respectively over other methods the systematic uncertainty is 25times1010 as an application we demonstrate the most precise test of textitab initio molecular theory and the most precise 13textrmppb spectroscopic determination of the proton mass the results represent the long overdue extension of dopplerfree microwave spectroscopy of lasercooled atomic ion clusters berkeland1998 to higher spectroscopy frequencies and to molecules this approach enables a vast range of highprecision measurements on molecules both on rotational and as we project vibrational transitions
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1,802.03209
Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule
This paper explores the use of the standard approach for proving runtime bounds in discrete domains---often referred to as drift analysis---in the context of optimization on a continuous domain. Using this framework we analyze the (1+1) Evolution Strategy with one-fifth success rule on the sphere function. To deal with potential functions that are not lower-bounded, we formulate novel drift theorems. We then use the theorems to prove bounds on the expected hitting time to reach a certain target fitness in finite dimension $d$. The bounds are akin to linear convergence. We then study the dependency of the different terms on $d$ proving a convergence rate dependency of $\Theta(1/d)$. Our results constitute the first non-asymptotic analysis for the algorithm considered as well as the first explicit application of drift analysis to a randomized search heuristic with continuous domain.
cs.NE
this paper explores the use of the standard approach for proving runtime bounds in discrete domainsoften referred to as drift analysisin the context of optimization on a continuous domain using this framework we analyze the 11 evolution strategy with onefifth success rule on the sphere function to deal with potential functions that are not lowerbounded we formulate novel drift theorems we then use the theorems to prove bounds on the expected hitting time to reach a certain target fitness in finite dimension d the bounds are akin to linear convergence we then study the dependency of the different terms on d proving a convergence rate dependency of theta1d our results constitute the first nonasymptotic analysis for the algorithm considered as well as the first explicit application of drift analysis to a randomized search heuristic with continuous domain
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1,802.0321
Quantitative aspects of acyclicity
We study several aspects of the $k$-th Cheeger constant of a complex X, a parameter that quantifies the distance of $X$ from a complex $Y$ with nontrivial $k$-th cohomology over $\mathbb{Z}_2$. Our results include general methods for bounding the cosystolic norm of a cochain and for bounding the Cheeger constant of a complex, a discussion of expansion of pseudomanifolds and geometric lattices, probabilistic upper bounds on Cheeger constants, and application of non-Abelian expansion to random complexes.
math.CO
we study several aspects of the kth cheeger constant of a complex x a parameter that quantifies the distance of x from a complex y with nontrivial kth cohomology over mathbbz_2 our results include general methods for bounding the cosystolic norm of a cochain and for bounding the cheeger constant of a complex a discussion of expansion of pseudomanifolds and geometric lattices probabilistic upper bounds on cheeger constants and application of nonabelian expansion to random complexes
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1,802.03211
Towards realistic HPC models of the neuromuscular system
Realistic simulations of detailed, biophysics-based, multi-scale models require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are designed to allow for a high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version towards a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment, which is based on the MegaMol environment capable of handling large amounts of simulated data. It offers a platform for fast visualization prototyping, distributed rendering, and advanced visualization techniques. We present results of a variety of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieved good scalability on up to 768 cores, where the previous implementation used only 4 cores.
cs.CE physics.comp-ph
realistic simulations of detailed biophysicsbased multiscale models require very high resolution and thus largescale compute facilities existing simulation environments especially for biomedical applications are designed to allow for a high flexibility and generality in model development flexibility and model development however are often a limiting factor for largescale simulations therefore new models are typically tested and run on smallscale compute facilities by using a detailed biophysicsbased chemoelectromechanical skeletal muscle model and the international opensource software library opencmiss as an example we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version towards a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes for this purpose we investigate different modeling algorithmic and implementational aspects we present improvements addressing both numerical and parallel scalability in addition our approach includes a novel visualization environment which is based on the megamol environment capable of handling large amounts of simulated data it offers a platform for fast visualization prototyping distributed rendering and advanced visualization techniques we present results of a variety of scaling studies at the tier1 supercomputer hazelhen at the high performance computing center stuttgart hlrs we improve the overall runtime by a factor of up to 26 and achieved good scalability on up to 768 cores where the previous implementation used only 4 cores
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1,802.03212
Deep clustering of longitudinal data
Deep neural networks are a family of computational models that have led to a dramatical improvement of the state of the art in several domains such as image, voice or text analysis. These methods provide a framework to model complex, non-linear interactions in large datasets, and are naturally suited to the analysis of hierarchical data such as, for instance, longitudinal data with the use of recurrent neural networks. In the other hand, cohort studies have become a tool of importance in the research field of epidemiology. In such studies, variables are measured repeatedly over time, to allow the practitioner to study their temporal evolution as trajectories, and, as such, as longitudinal data. This paper investigates the application of the advanced modelling techniques provided by the deep learning framework in the analysis of the longitudinal data provided by cohort studies. Methods: A method for visualizing and clustering longitudinal dataset is proposed, and compared to other widely used approaches to the problem on both real and simulated datasets. Results: The proposed method is shown to be coherent with the preexisting procedures on simple tasks, and to outperform them on more complex tasks such as the partitioning of longitudinal datasets into non-spherical clusters. Conclusion: Deep artificial neural networks can be used to visualize longitudinal data in a low dimensional manifold that is much simpler to interpret than traditional longitudinal plots are. Consequently, practitioners should start considering the use of deep artificial neural networks for the analysis of their longitudinal data in studies to come.
stat.ML cs.LG
deep neural networks are a family of computational models that have led to a dramatical improvement of the state of the art in several domains such as image voice or text analysis these methods provide a framework to model complex nonlinear interactions in large datasets and are naturally suited to the analysis of hierarchical data such as for instance longitudinal data with the use of recurrent neural networks in the other hand cohort studies have become a tool of importance in the research field of epidemiology in such studies variables are measured repeatedly over time to allow the practitioner to study their temporal evolution as trajectories and as such as longitudinal data this paper investigates the application of the advanced modelling techniques provided by the deep learning framework in the analysis of the longitudinal data provided by cohort studies methods a method for visualizing and clustering longitudinal dataset is proposed and compared to other widely used approaches to the problem on both real and simulated datasets results the proposed method is shown to be coherent with the preexisting procedures on simple tasks and to outperform them on more complex tasks such as the partitioning of longitudinal datasets into nonspherical clusters conclusion deep artificial neural networks can be used to visualize longitudinal data in a low dimensional manifold that is much simpler to interpret than traditional longitudinal plots are consequently practitioners should start considering the use of deep artificial neural networks for the analysis of their longitudinal data in studies to come
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1,802.03213
MULTIGRAIN: A smoothed particle hydrodynamics algorithm for multiple small dust grains and gas
We present a new algorithm, MULTIGRAIN, for modelling the dynamics of an entire population of small dust grains immersed in gas, typical of conditions that are found in molecular clouds and protoplanetary discs. The MULTIGRAIN method is more accurate than single-phase simulations because the gas experiences a backreaction from each dust phase and communicates this change to the other phases, thereby indirectly coupling the dust phases together. The MULTIGRAIN method is fast, explicit and low storage, requiring only an array of dust fractions and their derivatives defined for each resolution element.
astro-ph.IM astro-ph.EP
we present a new algorithm multigrain for modelling the dynamics of an entire population of small dust grains immersed in gas typical of conditions that are found in molecular clouds and protoplanetary discs the multigrain method is more accurate than singlephase simulations because the gas experiences a backreaction from each dust phase and communicates this change to the other phases thereby indirectly coupling the dust phases together the multigrain method is fast explicit and low storage requiring only an array of dust fractions and their derivatives defined for each resolution element
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1,802.03214
Vector-Interaction-Enhanced Bag Model
A commonly applied quark matter model in astrophysics is the thermodynamic bag model (tdBAG). The original MIT bag model approximates the effect of quark confinement, but does not explicitly account for the breaking of chiral symmetry, an important property of Quantum Chromodynamics (QCD). It further ignores vector repulsion. The vector-interaction-enhanced bag model (vBag) improves the tdBAG approach by accounting for both dynamical chiral symmetry breaking and repulsive vector interactions. The latter is of particular importance to studies of dense matter in beta-equilibriumto explain the two solar mass maximum mass constraint for neutron stars. The model is motivated by analyses of QCD based Dyson-Schwinger equations (DSE), assuming a simple quark-quark contact interaction. Here, we focus on the study of hybrid neutron star properties resulting from the application of vBag and will discuss possible extensions.
nucl-th astro-ph.HE hep-ph
a commonly applied quark matter model in astrophysics is the thermodynamic bag model tdbag the original mit bag model approximates the effect of quark confinement but does not explicitly account for the breaking of chiral symmetry an important property of quantum chromodynamics qcd it further ignores vector repulsion the vectorinteractionenhanced bag model vbag improves the tdbag approach by accounting for both dynamical chiral symmetry breaking and repulsive vector interactions the latter is of particular importance to studies of dense matter in betaequilibriumto explain the two solar mass maximum mass constraint for neutron stars the model is motivated by analyses of qcd based dysonschwinger equations dse assuming a simple quarkquark contact interaction here we focus on the study of hybrid neutron star properties resulting from the application of vbag and will discuss possible extensions
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1,802.03215
Passive tracer in non-Markovian, Gaussian velocity field
We consider the trajectory of a tracer that is the solution of an ordinary differential equation $\dot\bbX(t)=\bbV(t, \bbX(t)),\ X(0)=0$, with the right hand side, that is a stationary, zero-mean, Gaussian vector field with incompressible realizations. It is known, see [K-F;C-X;K-L-O], that $\bbX(t)/\sqrt{t}$ converges in law, as $t\to+\infty$, to a normal, zero mean vector, provided that the field $V(t,x)$ is Markovian and has the spectral gap property. We wish to extend this result to the case when the field is not Markovian and its covariance matrix is given by a completely monotone Bernstein function.
math.PR
we consider the trajectory of a tracer that is the solution of an ordinary differential equation dotbbxtbbvt bbxt x00 with the right hand side that is a stationary zeromean gaussian vector field with incompressible realizations it is known see kfcxklo that bbxtsqrtt converges in law as ttoinfty to a normal zero mean vector provided that the field vtx is markovian and has the spectral gap property we wish to extend this result to the case when the field is not markovian and its covariance matrix is given by a completely monotone bernstein function
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1,802.03216
Balancing Two-Player Stochastic Games with Soft Q-Learning
Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers. Current frameworks for stochastic games and reinforcement learning prohibit tuneable strategies as they seek optimal performance. In this paper, we enable such tuneable behaviour by generalising soft Q-learning to stochastic games, where more than one agent interact strategically. We contribute both theoretically and empirically. On the theory side, we show that games with soft Q-learning exhibit a unique value and generalise team games and zero-sum games far beyond these two extremes to cover a continuous spectrum of gaming behaviour. Experimentally, we show how tuning agents' constraints affect performance and demonstrate, through a neural network architecture, how to reliably balance games with high-dimensional representations.
cs.AI
within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations where humans face tough adversarial decision makers current frameworks for stochastic games and reinforcement learning prohibit tuneable strategies as they seek optimal performance in this paper we enable such tuneable behaviour by generalising soft qlearning to stochastic games where more than one agent interact strategically we contribute both theoretically and empirically on the theory side we show that games with soft qlearning exhibit a unique value and generalise team games and zerosum games far beyond these two extremes to cover a continuous spectrum of gaming behaviour experimentally we show how tuning agents constraints affect performance and demonstrate through a neural network architecture how to reliably balance games with highdimensional representations
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1,802.03217
Diffusion of Elements in the Interstellar Medium in Early-Type Galaxies
We consider the role of diffusion in the redistribution of elements in the hot interstellar medium (ISM) of early-type galaxies. It is well known that gravitational sedimentation can affect significantly the abundances of helium and heavy elements in the intracluster gas of massive galaxy clusters. The self-similarity of the temperature profiles and tight mass--temperature relation of relaxed cool-core clusters suggest that the maximum effect of sedimentation take place in the most massive virialized objects in the Universe. However, Chandra and XMM-Newton observations demonstrate more complex scaling relations between the masses of early-type galaxies and other parameters, such as the ISM temperature and gas mass fraction. An important fact is that early-type galaxies can show both decreasing and increasing radial temperature profiles. We have calculated the diffusion based on the observed gas density and temperature distributions for 13 early-type galaxies that belonging to the different environments and cover a wide range of X-ray luminosities. To estimate the maximum effect of sedimentation and thermal diffusion, we have solved the full set of Burgers' equations for a non-magnetized ISM plasma. The results obtained demonstrate a considerable increase of the He/H ratio within one effective radius for all galaxies of our sample. For galaxies with a flat or declining radial temperature profile the average increase of the helium abundance is 60\% in one billion years of diffusion. The revealed effect can introduce a significant bias in the metal abundance measurements based on X-ray spectroscopy and can affect the evolution of stars that could be formed from a gas with a high helium abundance.
astro-ph.HE astro-ph.GA
we consider the role of diffusion in the redistribution of elements in the hot interstellar medium ism of earlytype galaxies it is well known that gravitational sedimentation can affect significantly the abundances of helium and heavy elements in the intracluster gas of massive galaxy clusters the selfsimilarity of the temperature profiles and tight masstemperature relation of relaxed coolcore clusters suggest that the maximum effect of sedimentation take place in the most massive virialized objects in the universe however chandra and xmmnewton observations demonstrate more complex scaling relations between the masses of earlytype galaxies and other parameters such as the ism temperature and gas mass fraction an important fact is that earlytype galaxies can show both decreasing and increasing radial temperature profiles we have calculated the diffusion based on the observed gas density and temperature distributions for 13 earlytype galaxies that belonging to the different environments and cover a wide range of xray luminosities to estimate the maximum effect of sedimentation and thermal diffusion we have solved the full set of burgers equations for a nonmagnetized ism plasma the results obtained demonstrate a considerable increase of the heh ratio within one effective radius for all galaxies of our sample for galaxies with a flat or declining radial temperature profile the average increase of the helium abundance is 60 in one billion years of diffusion the revealed effect can introduce a significant bias in the metal abundance measurements based on xray spectroscopy and can affect the evolution of stars that could be formed from a gas with a high helium abundance
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1,802.03218
Concentration and energy invariance for a class of fully nonlinear elliptic equations
We study the asymptotic behaviour of positive solutions of fully nonlinear elliptic equations in a ball, as the exponent of the power nonlinearity approaches a critical value. We show that solutions concentrate and blow up at the center of the ball, while a suitable associated energy remains invariant.
math.AP
we study the asymptotic behaviour of positive solutions of fully nonlinear elliptic equations in a ball as the exponent of the power nonlinearity approaches a critical value we show that solutions concentrate and blow up at the center of the ball while a suitable associated energy remains invariant
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1,802.03219
An LTE effective temperature scale for red supergiants in the Magellanic clouds
We present a self-consistent study of cool supergiants (CSGs) belonging to the Magellanic clouds. We calculated stellar atmospheric parameters using LTE KURUCZ and MARCS atmospheric models for more than 400 individual targets by fitting a careful selection of weak metallic lines. We explore the existence of a T eff scale and its implications in two different metallicity environments (each Magellanic cloud). Critical and in-depth tests have been performed to assess the reliability of our stellar parameters (i.e. internal error budget, NLTE systematics). In addition, several Montercarlo tests have been carried out to infer the significance of the Teff scale found. Our findingspoint towards a unique Teff scale that seems to be independent of the environment.
astro-ph.SR
we present a selfconsistent study of cool supergiants csgs belonging to the magellanic clouds we calculated stellar atmospheric parameters using lte kurucz and marcs atmospheric models for more than 400 individual targets by fitting a careful selection of weak metallic lines we explore the existence of a t eff scale and its implications in two different metallicity environments each magellanic cloud critical and indepth tests have been performed to assess the reliability of our stellar parameters ie internal error budget nlte systematics in addition several montercarlo tests have been carried out to infer the significance of the teff scale found our findingspoint towards a unique teff scale that seems to be independent of the environment
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1,802.0322
Stabilization of the arrival time of a relativistic electron beam to the 50 fs level
We report the results of a low-latency beam phase feed-forward system built to stabilize the arrival time of a relativistic electron beam. The system was operated at the Compact Linear Collider (CLIC) Test Facility (CTF3) at CERN where the beam arrival time was stabilized to approximately 50 fs. The system latency was 350 ns and the correction bandwidth >23 MHz. The system meets the requirements for CLIC.
physics.acc-ph
we report the results of a lowlatency beam phase feedforward system built to stabilize the arrival time of a relativistic electron beam the system was operated at the compact linear collider clic test facility ctf3 at cern where the beam arrival time was stabilized to approximately 50 fs the system latency was 350 ns and the correction bandwidth 23 mhz the system meets the requirements for clic
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1,802.03221
Research and Implementation of Global Path Planning for Unmanned Surface Vehicle Based on Electronic Chart
Unmanned Surface Vehicle (USV) is a new type of intelligent surface craft, and global path planning is the key technology of USV research, which can reflect the intelligent level of USV. In order to solve the problem of global path planning of USV, this paper proposes an improved A* algorithm for sailing cost optimization based on electronic charts. This paper uses the S-57 electronic chart to realize the establishment of the octree grid environment model, and proposes an improved A* algorithm based on sailing safety weight, pilot quantity and path curve smoothing to ensure the safety of the route, reduce the planning time, and improve path smoothness. The simulation results show that the environmental model construction method and the improved A* algorithm can generate safe and reasonable global path.
cs.RO
unmanned surface vehicle usv is a new type of intelligent surface craft and global path planning is the key technology of usv research which can reflect the intelligent level of usv in order to solve the problem of global path planning of usv this paper proposes an improved a algorithm for sailing cost optimization based on electronic charts this paper uses the s57 electronic chart to realize the establishment of the octree grid environment model and proposes an improved a algorithm based on sailing safety weight pilot quantity and path curve smoothing to ensure the safety of the route reduce the planning time and improve path smoothness the simulation results show that the environmental model construction method and the improved a algorithm can generate safe and reasonable global path
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1,802.03222
Unitarity violation in non-integer dimensional Gross-Neveu-Yukawa model
We construct an explicit example of unitarity violation in fermionic quantum field theories in non-integer dimensions. We study the two-point correlation function of four-fermion operators. We compute the one-loop anomalous dimensions of these operators in the Gross-Neveu-Yukawa Model. We find that at one loop order, the four-fermion operators split into three classes with one class having negative norms. This implies that the theory violates unitarity following the definition in arXiv:1512.00013 [hep-th].
hep-th
we construct an explicit example of unitarity violation in fermionic quantum field theories in noninteger dimensions we study the twopoint correlation function of fourfermion operators we compute the oneloop anomalous dimensions of these operators in the grossneveuyukawa model we find that at one loop order the fourfermion operators split into three classes with one class having negative norms this implies that the theory violates unitarity following the definition in arxiv151200013 hepth
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1,802.03223
Maximum Lyapunov exponent revisited: Long-term attractor divergence of gait dynamics is highly sensitive to the noise structure of stride intervals
The local dynamic stability method (maximum Lyapunov exponent) can assess gait stability. Two variants of the method exist: the short-term divergence exponent (DE), and the long-term DE. Only the short-term DE can predict fall risk. The significance of long-term DE has been unclear so far. Some studies have suggested that the complex, fractal-like structure of fluctuations among consecutive strides correlates with long-term DE. The aim, therefore, was to assess whether the long-term DE is a gait complexity index. The study reanalyzed a dataset of trunk accelerations from 100 healthy adults walking at preferred speed on a treadmill for 10 minutes. By interpolation, the stride intervals were modified within the acceleration signals for the purpose of conserving the original shape of the signal, while imposing a known stride-to-stride fluctuation structure. 4 types of hybrid signals with different noise structures were built: constant, anti-correlated, random, and correlated (fractal). Short- and long-term DEs were then computed. The results show that long-term DEs, but not short-term DEs, are sensitive to the noise structure of stride intervals. It was that observed that random hybrid signals exhibited significantly lower long-term DEs than hybrid correlated signals did (0.100 vs 0.144, i.e. a 44% difference). Long-term DEs from constant hybrid signals were close to zero (0.006). Short-term DEs of anti-correlated, random, and correlated hybrid signals were closely grouped (2.49, 2.50, and 2.51). The short- and long-term DEs, although they are both computed from divergence curves, should not be interpreted in a similar way. The long-term DE is very likely an index of gait complexity, which may be associated with gait automaticity or cautiousness. To better differentiate between short- and long-term DEs, the use of the term attractor complexity index (ACI) is proposed for the latter.
q-bio.QM
the local dynamic stability method maximum lyapunov exponent can assess gait stability two variants of the method exist the shortterm divergence exponent de and the longterm de only the shortterm de can predict fall risk the significance of longterm de has been unclear so far some studies have suggested that the complex fractallike structure of fluctuations among consecutive strides correlates with longterm de the aim therefore was to assess whether the longterm de is a gait complexity index the study reanalyzed a dataset of trunk accelerations from 100 healthy adults walking at preferred speed on a treadmill for 10 minutes by interpolation the stride intervals were modified within the acceleration signals for the purpose of conserving the original shape of the signal while imposing a known stridetostride fluctuation structure 4 types of hybrid signals with different noise structures were built constant anticorrelated random and correlated fractal short and longterm des were then computed the results show that longterm des but not shortterm des are sensitive to the noise structure of stride intervals it was that observed that random hybrid signals exhibited significantly lower longterm des than hybrid correlated signals did 0100 vs 0144 ie a 44 difference longterm des from constant hybrid signals were close to zero 0006 shortterm des of anticorrelated random and correlated hybrid signals were closely grouped 249 250 and 251 the short and longterm des although they are both computed from divergence curves should not be interpreted in a similar way the longterm de is very likely an index of gait complexity which may be associated with gait automaticity or cautiousness to better differentiate between short and longterm des the use of the term attractor complexity index aci is proposed for the latter
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1,802.03224
Unification nets: canonical proof net quantifiers
Proof nets for MLL (unit-free Multiplicative Linear Logic) are concise graphical representations of proofs which are canonical in the sense that they abstract away syntactic redundancy such as the order of non-interacting rules. We argue that Girard's extension to MLL1 (first-order MLL) fails to be canonical because of redundant existential witnesses, and present canonical MLL1 proof nets called unification nets without them. For example, while there are infinitely many cut-free Girard nets $\forall x Px \vdash \exists xPx$, one per arbitrary choice of witness for $\exists x$, there is a unique cut-free unification net, with no specified witness. Redundant existential witnesses cause Girard's MLL1 nets to suffer from severe complexity issues: (1) cut elimination is non-local and exponential-time (and -space), and (2) some sequents require exponentially large cut-free Girard nets. Unification nets solve both problems: (1) cut elimination is local and linear-time, and (2) cut-free unification nets grow linearly with the size of the sequent. Since some unification nets are exponentially smaller than corresponding Girard nets and sequent proofs, technical delicacy is required to ensure correctness is polynomial-time (quadratic). These results extend beyond MLL1 via a broader methodological insight: for canonical quantifiers, the standard parallel/sequential dichotomy of proof nets fails; an implicit/explicit witness dichotomy is also needed. Work in progress extends unification nets to additives and uses them to extend combinatorial proofs [Proofs without syntax, Annals of Mathematics, 2006] to classical first-order logic.
math.LO cs.LO
proof nets for mll unitfree multiplicative linear logic are concise graphical representations of proofs which are canonical in the sense that they abstract away syntactic redundancy such as the order of noninteracting rules we argue that girards extension to mll1 firstorder mll fails to be canonical because of redundant existential witnesses and present canonical mll1 proof nets called unification nets without them for example while there are infinitely many cutfree girard nets forall x px vdash exists xpx one per arbitrary choice of witness for exists x there is a unique cutfree unification net with no specified witness redundant existential witnesses cause girards mll1 nets to suffer from severe complexity issues 1 cut elimination is nonlocal and exponentialtime and space and 2 some sequents require exponentially large cutfree girard nets unification nets solve both problems 1 cut elimination is local and lineartime and 2 cutfree unification nets grow linearly with the size of the sequent since some unification nets are exponentially smaller than corresponding girard nets and sequent proofs technical delicacy is required to ensure correctness is polynomialtime quadratic these results extend beyond mll1 via a broader methodological insight for canonical quantifiers the standard parallelsequential dichotomy of proof nets fails an implicitexplicit witness dichotomy is also needed work in progress extends unification nets to additives and uses them to extend combinatorial proofs proofs without syntax annals of mathematics 2006 to classical firstorder logic
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1,802.03225
Extreme-ultraviolet-initiated High-harmonic Generation in Ar$^{+}$
We employ the R-matrix with time-dependence method to investigate extreme-ultraviolet-initiated high-harmonic generation (XIHHG) in Ar$^{+}$. Using a combination of extreme-ultraviolet (XUV, $92\textrm{ nm}$, $3\times 10^{12}\,\textrm{Wcm}^{-2}$) and time-delayed, infrared (IR, $800\textrm{ nm}$, $3\times 10^{14}\,\textrm{Wcm}^{-2}$) laser pulses, we demonstrate that control over both the mechanism, and timing, of ionization can afford significant enhancements in the yield of plateau, and sub-threshold, harmonics alike. The presence of the XUV pulse is also shown to alter the relative contribution of different electron emission pathways. Manifestation of the Ar$^{+}$ electronic structure is found in the appearance of a pronounced Cooper minimum. Interferences amongst the outer-valence $3p$, and inner-valence $3s$, electrons are found to incur only a minor suppression of the harmonic intensities, at least for the present combination of XUV and IR laser light. Additionally, the dependence of the XIHHG efficiency on time delay is discussed, and rationalized with the aid of classical trajectory simulations.
physics.atom-ph
we employ the rmatrix with timedependence method to investigate extremeultravioletinitiated highharmonic generation xihhg in ar using a combination of extremeultraviolet xuv 92textrm nm 3times 1012textrmwcm2 and timedelayed infrared ir 800textrm nm 3times 1014textrmwcm2 laser pulses we demonstrate that control over both the mechanism and timing of ionization can afford significant enhancements in the yield of plateau and subthreshold harmonics alike the presence of the xuv pulse is also shown to alter the relative contribution of different electron emission pathways manifestation of the ar electronic structure is found in the appearance of a pronounced cooper minimum interferences amongst the outervalence 3p and innervalence 3s electrons are found to incur only a minor suppression of the harmonic intensities at least for the present combination of xuv and ir laser light additionally the dependence of the xihhg efficiency on time delay is discussed and rationalized with the aid of classical trajectory simulations
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1,802.03226
Sub-arcsecond imaging of Arp\,299-A at 150 MHz with LOFAR: Evidence for a starburst-driven outflow
We report on the first sub-arcsecond (0.44 $\times$ 0.41 arcsec$\rm ^2$) angular resolution image at 150 MHz of the A-nucleus in the Luminous Infrared Galaxy Arp$\,$299, from International Low Frequency Array (LOFAR) Telescope observations. The most remarkable finding is that of an intriguing two-sided, filamentary structure emanating from A-nucleus, which we interpret as an outflow that extends up to at least 14 arcseconds from the A-nucleus in the N-S direction ($\approx$ 5 kpc deprojected size) and accounts for almost 40% of the extended emission of the entire galaxy system. We also discuss HST/NICMOS [FeII] 1.64 $\rm \mu m$ and H$\rm_2$ 2.12 $\rm \mu m$ images of Arp$\,$299-A, which show similar features to those unveiled by our 150 MHz LOFAR observations, thus giving string morphological support for the outflow scenario. Finally, we discuss unpublished NaI D spectra that confirm the outflow nature of this structure. From energetic arguments, we rule out the low-luminosity active galactic nucleus in Arp$\,$299-A as a driver for the outflow. On the contrary, the powerful, compact starburst in the central regions of Arp$\,$299-A provides plenty of mechanical energy to sustain an outflow, and we conclude that the intense supernova (SN) activity in the nuclear region of Arp299-A is driving the observed outflow. We estimate that the starburst wind can support a mass-outflow rate in the range (11-63) $\rm M_{\odot} yr^{-1}$ at speeds of up to (370 - 890) $\rm km \, s^{-1}$, and is relatively young, with an estimated kinematic age of (3 - 7) Myr. Those results open an avenue to the use of low-frequency (150 MHz), sub-arcsecond imaging with LOFAR to detect outflows in the central regions of local luminous infrared galaxies.
astro-ph.GA
we report on the first subarcsecond 044 times 041 arcsecrm 2 angular resolution image at 150 mhz of the anucleus in the luminous infrared galaxy arp299 from international low frequency array lofar telescope observations the most remarkable finding is that of an intriguing twosided filamentary structure emanating from anucleus which we interpret as an outflow that extends up to at least 14 arcseconds from the anucleus in the ns direction approx 5 kpc deprojected size and accounts for almost 40 of the extended emission of the entire galaxy system we also discuss hstnicmos feii 164 rm mu m and hrm_2 212 rm mu m images of arp299a which show similar features to those unveiled by our 150 mhz lofar observations thus giving string morphological support for the outflow scenario finally we discuss unpublished nai d spectra that confirm the outflow nature of this structure from energetic arguments we rule out the lowluminosity active galactic nucleus in arp299a as a driver for the outflow on the contrary the powerful compact starburst in the central regions of arp299a provides plenty of mechanical energy to sustain an outflow and we conclude that the intense supernova sn activity in the nuclear region of arp299a is driving the observed outflow we estimate that the starburst wind can support a massoutflow rate in the range 1163 rm m_odot yr1 at speeds of up to 370 890 rm km s1 and is relatively young with an estimated kinematic age of 3 7 myr those results open an avenue to the use of lowfrequency 150 mhz subarcsecond imaging with lofar to detect outflows in the central regions of local luminous infrared galaxies
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1,802.03227
Thermodynamic perturbation theory for non-interacting quantum particles with application to spin-spin interactions in solids
The determination of the Landau free energy (the grand thermodynamic potential) by a perturbation theory is advanced to arbitrary order for the specific case of non-interacting fermionic systems perturbed by a one-particle potential. Peculiar features of the formalism are highlighted, and its applicability for bosons is indicated. The results are employed to develop a more explicit approach describing exchange interactions between spins of Anderson's magnetic impurities in metals, semiconductors, and insulators. Within the fourth order our theory provides on the equal footing formulae for the Ruderman-Kittel-Kasuya-Yosida, Bloembergen-Rowland, superexchange, and two-electron exchange integrals at non-zero temperature.
cond-mat.stat-mech cond-mat.mtrl-sci
the determination of the landau free energy the grand thermodynamic potential by a perturbation theory is advanced to arbitrary order for the specific case of noninteracting fermionic systems perturbed by a oneparticle potential peculiar features of the formalism are highlighted and its applicability for bosons is indicated the results are employed to develop a more explicit approach describing exchange interactions between spins of andersons magnetic impurities in metals semiconductors and insulators within the fourth order our theory provides on the equal footing formulae for the rudermankittelkasuyayosida bloembergenrowland superexchange and twoelectron exchange integrals at nonzero temperature
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1,802.03228
Electroweak interaction beyond the Standard Model and Dark Matter in the Tangent Bundle Quantum Field Theory
A generalized theory of electroweak interaction is developed based on the underlying geometrical structure of the tangent bundle with symmetries arising from transformations of tangent vectors along the fiber axis at a fixed space-time point, leaving the scalar product invariant. Transformations with this property are given by the $SO(3,1)$ group with the little groups $SU(2),E^{c}(2)$ and $SU(1,1)$ where the group $E^{c}(2)$ is the central extended group of the Euclidian group $E(2).$ Electroweak interaction beyond the standard model (SM) is described by the transformation group $SU(2)\otimes E^{c}\mathbf{(}2)$ without a priori introduction of a phenomenologically determined gauge group. The Laplacian on this group yields the known internal quantum numbers of isospin and hypercharge, but in addition the extra $E^{c}$-charge $\varkappa $ and the family quantum number $n$ which explains the existence of families in the SM. The connection coefficients deliver the SM gauge potentials but also hypothetical gauge bosons and other hypothetical particles as well as candidate Dark Matter particles are predicted. It is shown that the interpretation of the $SO(3,1)$ connection coefficients as elctroweak gauge potentials is compatible with teleparallel gauge gravity theory based on the translational group.
hep-ph hep-th
a generalized theory of electroweak interaction is developed based on the underlying geometrical structure of the tangent bundle with symmetries arising from transformations of tangent vectors along the fiber axis at a fixed spacetime point leaving the scalar product invariant transformations with this property are given by the so31 group with the little groups su2ec2 and su11 where the group ec2 is the central extended group of the euclidian group e2 electroweak interaction beyond the standard model sm is described by the transformation group su2otimes ecmathbf2 without a priori introduction of a phenomenologically determined gauge group the laplacian on this group yields the known internal quantum numbers of isospin and hypercharge but in addition the extra eccharge varkappa and the family quantum number n which explains the existence of families in the sm the connection coefficients deliver the sm gauge potentials but also hypothetical gauge bosons and other hypothetical particles as well as candidate dark matter particles are predicted it is shown that the interpretation of the so31 connection coefficients as elctroweak gauge potentials is compatible with teleparallel gauge gravity theory based on the translational group
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1,802.03229
Plasma acceleration limitations due to betatron radiation
High energy spread caused by the longitudinal size of the beam is well known in wake-field acceleration. Usually this issue can be solved with beam loading effect that allows to keep accelerating field nearly constant, along the whole duration of the beam. In this work, however, we would like to address another source of energy spread that arises at high energy, due to betatron radiation.
physics.acc-ph
high energy spread caused by the longitudinal size of the beam is well known in wakefield acceleration usually this issue can be solved with beam loading effect that allows to keep accelerating field nearly constant along the whole duration of the beam in this work however we would like to address another source of energy spread that arises at high energy due to betatron radiation
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1,802.0323
Iterative Coupling of Mixed and Discontinuous Galerkin Methods for Poroelasticity
We analyze an iterative coupling of mixed and discontinuous Galerkin methods for numerical modelling of coupled flow and mechanical deformation in porous media. The iteration is based on an optimized fixed-stress split along with a discontinuous variational time discretization. For the spatial discretization of the subproblem of flow mixed finite element techniques are applied. The discretization of the subproblem of mechanical deformation uses discontinuous Galerkin methods. They have shown their ability to eliminate locking that sometimes arises in numerical algorithms for poroelasticity and causes nonphysical pressure oscillations.
math.NA
we analyze an iterative coupling of mixed and discontinuous galerkin methods for numerical modelling of coupled flow and mechanical deformation in porous media the iteration is based on an optimized fixedstress split along with a discontinuous variational time discretization for the spatial discretization of the subproblem of flow mixed finite element techniques are applied the discretization of the subproblem of mechanical deformation uses discontinuous galerkin methods they have shown their ability to eliminate locking that sometimes arises in numerical algorithms for poroelasticity and causes nonphysical pressure oscillations
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1,802.03231
Self-stabilizing processes based on random signs
A self-stabilizing processes $\{Z(t), t\in [t_0,t_1)\}$ is a random process which when localized, that is scaled to a fine limit near a given $t\in [t_0,t_1)$, has the distribution of an $\alpha(Z(t))$-stable process, where $\alpha: \mathbb{R}\to (0,2)$ is a given continuous function. Thus the stability index near $t$ depends on the value of the process at $t$. In an earlier paper we constructed self-stabilizing processes using sums over plane Poisson point processes in the case of $\alpha: \mathbb{R}\to (0,1)$ which depended on the almost sure absolute convergence of the sums. Here we construct pure jump self-stabilizing processes when $\alpha$ may take values greater than 1 when convergence may no longer be absolute. We do this in two stages, firstly by setting up a process based on a fixed point set but taking random signs of the summands, and then randomizing the point set to get a process with the desired local properties.
math.PR
a selfstabilizing processes zt tin t_0t_1 is a random process which when localized that is scaled to a fine limit near a given tin t_0t_1 has the distribution of an alphaztstable process where alpha mathbbrto 02 is a given continuous function thus the stability index near t depends on the value of the process at t in an earlier paper we constructed selfstabilizing processes using sums over plane poisson point processes in the case of alpha mathbbrto 01 which depended on the almost sure absolute convergence of the sums here we construct pure jump selfstabilizing processes when alpha may take values greater than 1 when convergence may no longer be absolute we do this in two stages firstly by setting up a process based on a fixed point set but taking random signs of the summands and then randomizing the point set to get a process with the desired local properties
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1,802.03232
Twisted Flato-Fronsdal Theorem for Higher-Spin Algebras
We explore the relation between the singleton and adjoint modules of higher-spin algebras via so(2,d) characters. In order to relate the tensor product of the singleton and its dual to the adjoint module, we consider a heuristic formula involving symmetrization over the variables of the character. We show that our formula reproduces correctly the adjoint-module character for type-A (and its high-order extensions) and type-B higher-spin gravity theories in any dimension. Implications and subtleties of this symmetrization prescription in other models are discussed.
hep-th
we explore the relation between the singleton and adjoint modules of higherspin algebras via so2d characters in order to relate the tensor product of the singleton and its dual to the adjoint module we consider a heuristic formula involving symmetrization over the variables of the character we show that our formula reproduces correctly the adjointmodule character for typea and its highorder extensions and typeb higherspin gravity theories in any dimension implications and subtleties of this symmetrization prescription in other models are discussed
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1,802.03233
Periods of $t$-modules as special values
In this article we show that all periods of uniformizable $t$-modules (resp. their coordinates) can be obtained via specializing a rigid analytic trivialization of a related dual $t$-motive at $t=\theta$. The proof is even constructive. The central object in the construction is a subset $H$ of the Tate algebra points of $E$ which turns out to be isomorphic to the period lattice of $E$ via kind of generating series in one direction and residues in the other. This isomorphism even holds for arbitrary $t$-modules $E$, even non-abelian ones.
math.NT math.AC
in this article we show that all periods of uniformizable tmodules resp their coordinates can be obtained via specializing a rigid analytic trivialization of a related dual tmotive at ttheta the proof is even constructive the central object in the construction is a subset h of the tate algebra points of e which turns out to be isomorphic to the period lattice of e via kind of generating series in one direction and residues in the other this isomorphism even holds for arbitrary tmodules e even nonabelian ones
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1,802.03234
A revised distance to IRAS 16293$-$2422 from VLBA astrometry of associated water masers
IRAS 16293-2422 is a very well studied young stellar system seen in projection towards the L1689N cloud in the Ophiuchus complex. However, its distance is still uncertain with a range of values from 120 pc to 180 pc. Our goal is to measure the trigonometric parallax of this young star by means of H$_2$O maser emission. We use archival data from 15 epochs of VLBA observations of the 22.2 GHz water maser line. By modeling the displacement on the sky of the H$_2$O maser spots, we derived a trigonometric parallax of $7.1\pm1.3$ mas, corresponding to a distance of $141_{-21}^{+30}$ pc. This new distance is in good agreement with recent values obtained for other magnetically active young stars in the L1689 cloud. We relate the kinematics of these masers with the outflows and the recent ejections powered by source A in the system.
astro-ph.SR astro-ph.GA
iras 162932422 is a very well studied young stellar system seen in projection towards the l1689n cloud in the ophiuchus complex however its distance is still uncertain with a range of values from 120 pc to 180 pc our goal is to measure the trigonometric parallax of this young star by means of h_2o maser emission we use archival data from 15 epochs of vlba observations of the 222 ghz water maser line by modeling the displacement on the sky of the h_2o maser spots we derived a trigonometric parallax of 71pm13 mas corresponding to a distance of 141_2130 pc this new distance is in good agreement with recent values obtained for other magnetically active young stars in the l1689 cloud we relate the kinematics of these masers with the outflows and the recent ejections powered by source a in the system
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1,802.03235
The $b$-bibranching Problem: TDI System, Packing, and Discrete Convexity
In this paper, we introduce the $b$-bibranching problem in digraphs, which is a common generalization of the bibranching and $b$-branching problems. The bibranching problem, introduced by Schrijver (1982), is a common generalization of the branching and bipartite edge cover problems. Previous results on bibranchings include polynomial algorithms, a linear programming formulation with total dual integrality, a packing theorem, and an M-convex submodular flow formulation. The $b$-branching problem, recently introduced by Kakimura, Kamiyama, and Takazawa (2018), is a generalization of the branching problem admitting higher indegree, i.e., each vertex $v$ can have indegree at most $b(v)$. For $b$-branchings, a combinatorial algorithm, a linear programming formulation with total dual integrality, and a packing theorem for branchings are extended. A main contribution of this paper is to extend those previous results on bibranchings and $b$-branchings to $b$-bibranchings. That is, we present a linear programming formulation with total dual integrality, a packing theorem, and an M-convex submodular flow formulation for $b$-bibranchings. In particular, the linear program and M-convex submodular flow formulations respectively imply polynomial algorithms for finding a shortest $b$-bibranching.
cs.DM cs.DS math.CO
in this paper we introduce the bbibranching problem in digraphs which is a common generalization of the bibranching and bbranching problems the bibranching problem introduced by schrijver 1982 is a common generalization of the branching and bipartite edge cover problems previous results on bibranchings include polynomial algorithms a linear programming formulation with total dual integrality a packing theorem and an mconvex submodular flow formulation the bbranching problem recently introduced by kakimura kamiyama and takazawa 2018 is a generalization of the branching problem admitting higher indegree ie each vertex v can have indegree at most bv for bbranchings a combinatorial algorithm a linear programming formulation with total dual integrality and a packing theorem for branchings are extended a main contribution of this paper is to extend those previous results on bibranchings and bbranchings to bbibranchings that is we present a linear programming formulation with total dual integrality a packing theorem and an mconvex submodular flow formulation for bbibranchings in particular the linear program and mconvex submodular flow formulations respectively imply polynomial algorithms for finding a shortest bbibranching
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1,802.03236
Learning Robust Options
Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose parameters have strong uncertainty. Existing work uses value-based methods and the usual primitive action setting. In this paper, we propose robust methods for learning temporally abstract actions, in the framework of options. We present a Robust Options Policy Iteration (ROPI) algorithm with convergence guarantees, which learns options that are robust to model uncertainty. We utilize ROPI to learn robust options with the Robust Options Deep Q Network (RO-DQN) that solves multiple tasks and mitigates model misspecification due to model uncertainty. We present experimental results which suggest that policy iteration with linear features may have an inherent form of robustness when using coarse feature representations. In addition, we present experimental results which demonstrate that robustness helps policy iteration implemented on top of deep neural networks to generalize over a much broader range of dynamics than non-robust policy iteration.
cs.AI cs.LG stat.ML
robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environmentstransition models whose parameters have strong uncertainty existing work uses valuebased methods and the usual primitive action setting in this paper we propose robust methods for learning temporally abstract actions in the framework of options we present a robust options policy iteration ropi algorithm with convergence guarantees which learns options that are robust to model uncertainty we utilize ropi to learn robust options with the robust options deep q network rodqn that solves multiple tasks and mitigates model misspecification due to model uncertainty we present experimental results which suggest that policy iteration with linear features may have an inherent form of robustness when using coarse feature representations in addition we present experimental results which demonstrate that robustness helps policy iteration implemented on top of deep neural networks to generalize over a much broader range of dynamics than nonrobust policy iteration
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1,802.03237
Full-Frame Scene Coordinate Regression for Image-Based Localization
Image-based localization, or camera relocalization, is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. Recent state-of-the-art approaches use learning based methods, such as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress for each pixel in the image its corresponding position in the scene's world coordinate frame, and solve the final pose via a RANSAC-based optimization scheme using the predicted correspondences. In this paper, instead of in a patch-based manner, we propose to perform the scene coordinate regression in a full-frame manner to make the computation efficient at test time and, more importantly, to add more global context to the regression process to improve the robustness. To do so, we adopt a fully convolutional encoder-decoder neural network architecture which accepts a whole image as input and produces scene coordinate predictions for all pixels in the image. However, using more global context is prone to overfitting. To alleviate this issue, we propose to use data augmentation to generate more data for training. In addition to the data augmentation in 2D image space, we also augment the data in 3D space. We evaluate our approach on the publicly available 7-Scenes dataset, and experiments show that it has better scene coordinate predictions and achieves state-of-the-art results in localization with improved robustness on the hardest frames (e.g., frames with repeated structures).
cs.CV
imagebased localization or camera relocalization is a fundamental problem in computer vision and robotics and it refers to estimating camera pose from an image recent stateoftheart approaches use learning based methods such as random forests rfs and convolutional neural networks cnns to regress for each pixel in the image its corresponding position in the scenes world coordinate frame and solve the final pose via a ransacbased optimization scheme using the predicted correspondences in this paper instead of in a patchbased manner we propose to perform the scene coordinate regression in a fullframe manner to make the computation efficient at test time and more importantly to add more global context to the regression process to improve the robustness to do so we adopt a fully convolutional encoderdecoder neural network architecture which accepts a whole image as input and produces scene coordinate predictions for all pixels in the image however using more global context is prone to overfitting to alleviate this issue we propose to use data augmentation to generate more data for training in addition to the data augmentation in 2d image space we also augment the data in 3d space we evaluate our approach on the publicly available 7scenes dataset and experiments show that it has better scene coordinate predictions and achieves stateoftheart results in localization with improved robustness on the hardest frames eg frames with repeated structures
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1,802.03238
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
Sequence-to-sequence (Seq2seq) models have played an important role in the recent success of various natural language processing methods, such as machine translation, text summarization, and speech recognition. However, current Seq2seq models have trouble preserving global latent information from a long sequence of words. Variational autoencoder (VAE) alleviates this problem by learning a continuous semantic space of the input sentence. However, it does not solve the problem completely. In this paper, we propose a new recurrent neural network (RNN)-based Seq2seq model, RNN semantic variational autoencoder (RNN--SVAE), to better capture the global latent information of a sequence of words. To reflect the meaning of words in a sentence properly, without regard to its position within the sentence, we construct a document information vector using the attention information between the final state of the encoder and every prior hidden state. Then, the mean and standard deviation of the continuous semantic space are learned by using this vector to take advantage of the variational method. By using the document information vector to find the semantic space of the sentence, it becomes possible to better capture the global latent feature of the sentence. Experimental results of three natural language tasks (i.e., language modeling, missing word imputation, paraphrase identification) confirm that the proposed RNN--SVAE yields higher performance than two benchmark models.
cs.CL
sequencetosequence seq2seq models have played an important role in the recent success of various natural language processing methods such as machine translation text summarization and speech recognition however current seq2seq models have trouble preserving global latent information from a long sequence of words variational autoencoder vae alleviates this problem by learning a continuous semantic space of the input sentence however it does not solve the problem completely in this paper we propose a new recurrent neural network rnnbased seq2seq model rnn semantic variational autoencoder rnnsvae to better capture the global latent information of a sequence of words to reflect the meaning of words in a sentence properly without regard to its position within the sentence we construct a document information vector using the attention information between the final state of the encoder and every prior hidden state then the mean and standard deviation of the continuous semantic space are learned by using this vector to take advantage of the variational method by using the document information vector to find the semantic space of the sentence it becomes possible to better capture the global latent feature of the sentence experimental results of three natural language tasks ie language modeling missing word imputation paraphrase identification confirm that the proposed rnnsvae yields higher performance than two benchmark models
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1,802.03239
Using Discretization for Extending the Set of Predictive Features
To date, attribute discretization is typically performed by replacing the original set of continuous features with a transposed set of discrete ones. This paper provides support for a new idea that discretized features should often be used in addition to existing features and as such, datasets should be extended, and not replaced, by discretization. We also claim that discretization algorithms should be developed with the explicit purpose of enriching a non-discretized dataset with discretized values. We present such an algorithm, D-MIAT, a supervised algorithm that discretizes data based on Minority Interesting Attribute Thresholds. D-MIAT only generates new features when strong indications exist for one of the target values needing to be learned and thus is intended to be used in addition to the original data. We present extensive empirical results demonstrating the success of using D-MIAT on $ 28 $ benchmark datasets. We also demonstrate that $ 10 $ other discretization algorithms can also be used to generate features that yield improved performance when used in combination with the original non-discretized data. Our results show that the best predictive performance is attained using a combination of the original dataset with added features from a "standard" supervised discretization algorithm and D-MIAT.
cs.LG cs.AI stat.ML
to date attribute discretization is typically performed by replacing the original set of continuous features with a transposed set of discrete ones this paper provides support for a new idea that discretized features should often be used in addition to existing features and as such datasets should be extended and not replaced by discretization we also claim that discretization algorithms should be developed with the explicit purpose of enriching a nondiscretized dataset with discretized values we present such an algorithm dmiat a supervised algorithm that discretizes data based on minority interesting attribute thresholds dmiat only generates new features when strong indications exist for one of the target values needing to be learned and thus is intended to be used in addition to the original data we present extensive empirical results demonstrating the success of using dmiat on 28 benchmark datasets we also demonstrate that 10 other discretization algorithms can also be used to generate features that yield improved performance when used in combination with the original nondiscretized data our results show that the best predictive performance is attained using a combination of the original dataset with added features from a standard supervised discretization algorithm and dmiat
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1,802.0324
Compressibility effects on quasistationary vortex and transient hole patterns through vortex merger
The effect of compressibility in hydrodynamic vortex merging has been discussed. In the past, in incompressible limit it has been observed that the merging of a collection of intense point-like vortices arranged uniformly outside a circular vortex, can lead to quasistationary vortex patch and transient hole pattern inside the patch via nonlinear merger process. These patterns are akin to \textquoteleft vortex crystals\textquoteright. Compressibility can introduce a natural acoustic scale to the problem. We find that the natural mode is independent of the number of point-like vortices and the amplitude scales linearly with compressibility. Further it has been identified that after merging, the system exhibits oscillation at a natural frequency together with its harmonics and beats with its own harmonics. The power of the frequency is found to scale as $M^{-2}$, where $M$ is the Mach number. Also the vortex crystals formed out of the merging process are found to melt faster as compressibility is increased.
physics.flu-dyn nlin.PS
the effect of compressibility in hydrodynamic vortex merging has been discussed in the past in incompressible limit it has been observed that the merging of a collection of intense pointlike vortices arranged uniformly outside a circular vortex can lead to quasistationary vortex patch and transient hole pattern inside the patch via nonlinear merger process these patterns are akin to textquoteleft vortex crystalstextquoteright compressibility can introduce a natural acoustic scale to the problem we find that the natural mode is independent of the number of pointlike vortices and the amplitude scales linearly with compressibility further it has been identified that after merging the system exhibits oscillation at a natural frequency together with its harmonics and beats with its own harmonics the power of the frequency is found to scale as m2 where m is the mach number also the vortex crystals formed out of the merging process are found to melt faster as compressibility is increased
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1,802.03241
Extraordinary supernova iPTF14hls: An attempt at interpretation
It is shown that the H$\alpha$ luminosity and the Thomson optical depth of the iPTF14hls on day 600 after the detection provide us with the estimate of the envelope age which turns to be about 1000 days. I propose a model that suggests an explosion of a massive star with the radius of $\sim 2\times10^{13}$ cm at 450 days prior to the discovery. For the optimal model the ejected mass is $30\,M_{\odot}$, and the kinetic energy is $8\times10^{51}$ erg. The energy source at the dominant luminosity stage is presumably related to the relativistic bipolar outflow originated from a disk accretion onto the black hole. The [O\,I] 6300, 6364 \AA\ doublet in the spectrum on day 600 is shown to be the result of the emission of at least $1-3\,M_{\odot}$ of oxygen in the ejecta inner zone. The oxygen distribution is non-spherical and can be represented either by two components with blue and red shifts (in the optically thin case), or by one blue shifted component, in the case of optically thick lines for the filling factor of $\sim 2\times10^{-3}$.
astro-ph.HE
it is shown that the halpha luminosity and the thomson optical depth of the iptf14hls on day 600 after the detection provide us with the estimate of the envelope age which turns to be about 1000 days i propose a model that suggests an explosion of a massive star with the radius of sim 2times1013 cm at 450 days prior to the discovery for the optimal model the ejected mass is 30m_odot and the kinetic energy is 8times1051 erg the energy source at the dominant luminosity stage is presumably related to the relativistic bipolar outflow originated from a disk accretion onto the black hole the oi 6300 6364 aa doublet in the spectrum on day 600 is shown to be the result of the emission of at least 13m_odot of oxygen in the ejecta inner zone the oxygen distribution is nonspherical and can be represented either by two components with blue and red shifts in the optically thin case or by one blue shifted component in the case of optically thick lines for the filling factor of sim 2times103
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1,802.03242
Projecting UK Mortality using Bayesian Generalised Additive Models
Forecasts of mortality provide vital information about future populations, with implications for pension and health-care policy as well as for decisions made by private companies about life insurance and annuity pricing. Stochastic mortality forecasts allow the uncertainty in mortality predictions to be taken into consideration when making policy decisions and setting product prices. Longer lifespans imply that forecasts of mortality at ages 90 and above will become more important in such calculations. This paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a Generalised Additive Model (GAM) for mortality for the majority of the age-range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age-specific improvement rates, together with a non-smoothed period effect. Forecasts for the United Kingdom are produced using data from the Human Mortality Database spanning the period 1961-2013. A metric that approximates predictive accuracy under Leave-One-Out cross-validation is used to estimate weights for the `stacking' of forecasts with different points of transition between the GAM and parametric elements. Mortality for males and females are estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes, and furthermore provides for forecasts accounting for correlations in period innovations. The joint and single sex model forecasts estimated using data from 1961-2003 are compared against observed data from 2004-2013 to facilitate model assessment.
stat.AP
forecasts of mortality provide vital information about future populations with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing stochastic mortality forecasts allow the uncertainty in mortality predictions to be taken into consideration when making policy decisions and setting product prices longer lifespans imply that forecasts of mortality at ages 90 and above will become more important in such calculations this paper presents a bayesian approach to the forecasting of mortality that jointly estimates a generalised additive model gam for mortality for the majority of the agerange and a parametric model for older ages where the data are sparser the gam allows smooth components to be estimated for age cohort and agespecific improvement rates together with a nonsmoothed period effect forecasts for the united kingdom are produced using data from the human mortality database spanning the period 19612013 a metric that approximates predictive accuracy under leaveoneout crossvalidation is used to estimate weights for the stacking of forecasts with different points of transition between the gam and parametric elements mortality for males and females are estimated separately at first but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations the joint and single sex model forecasts estimated using data from 19612003 are compared against observed data from 20042013 to facilitate model assessment
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1,802.03243
RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations
Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, however, challenging to preoperatively predict surgery duration since it varies significantly depending on the patient condition, surgeon skills, and intraoperative situation. In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos. Previous state-of-the-art approaches for RSD prediction are dependent on manual annotation, whose generation requires expensive expert knowledge and is time-consuming, especially considering the numerous types of surgeries performed in a hospital and the large number of laparoscopic videos available. A crucial feature of RSDNet is that it does not depend on any manual annotation during training, making it easily scalable to many kinds of surgeries. The generalizability of our approach is demonstrated by testing the pipeline on two large datasets containing different types of surgeries: 120 cholecystectomy and 170 gastric bypass videos. The experimental results also show that the proposed network significantly outperforms a traditional method of estimating RSD without utilizing manual annotation. Further, this work provides a deeper insight into the deep learning network through visualization and interpretation of the features that are automatically learned.
cs.CV
accurate surgery duration estimation is necessary for optimal or planning which plays an important role in patient comfort and safety as well as resource optimization it is however challenging to preoperatively predict surgery duration since it varies significantly depending on the patient condition surgeon skills and intraoperative situation in this paper we propose a deep learning pipeline referred to as rsdnet which automatically estimates the remaining surgery duration rsd intraoperatively by using only visual information from laparoscopic videos previous stateoftheart approaches for rsd prediction are dependent on manual annotation whose generation requires expensive expert knowledge and is timeconsuming especially considering the numerous types of surgeries performed in a hospital and the large number of laparoscopic videos available a crucial feature of rsdnet is that it does not depend on any manual annotation during training making it easily scalable to many kinds of surgeries the generalizability of our approach is demonstrated by testing the pipeline on two large datasets containing different types of surgeries 120 cholecystectomy and 170 gastric bypass videos the experimental results also show that the proposed network significantly outperforms a traditional method of estimating rsd without utilizing manual annotation further this work provides a deeper insight into the deep learning network through visualization and interpretation of the features that are automatically learned
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1,802.03244
Production of $W^+ W^-$ pairs via $\gamma^*\gamma^* \to W^+ W^-$ subprocess with photon transverse momenta
We discuss production of $W^+ W^-$ pairs in proton-proton collisions induced by two-photon fusion including, for a first time, transverse momenta of incoming photons. The unintegrated inelastic fluxes (related to proton dissociation) of photons are calculated based on modern parametrizations of deep inelastic structure functions in a broad range of their arguments ($x$ and $Q^2$). In our approach we can get separate contributions of different $W$ helicities states. Several one- and two-dimensional differential distributions are shown and discussed. The present results are compared to the results of previous calculations within collinear factorization approach. Similar results are found except of some observables such as e.g. transverse momentum of the pair of $W^+$ and $W^-$. We find large contributions to the cross section from the region of large photon virtualities. We show decomposition of the total cross section as well as invariant mass distribution into polarisation states of both W bosons. The role of the longitudinal $F_L$ structure function is quantified. Its inclusion leads to a 4-5 % decrease of the cross section, almost independent of $M_{WW}$.
hep-ph
we discuss production of w w pairs in protonproton collisions induced by twophoton fusion including for a first time transverse momenta of incoming photons the unintegrated inelastic fluxes related to proton dissociation of photons are calculated based on modern parametrizations of deep inelastic structure functions in a broad range of their arguments x and q2 in our approach we can get separate contributions of different w helicities states several one and twodimensional differential distributions are shown and discussed the present results are compared to the results of previous calculations within collinear factorization approach similar results are found except of some observables such as eg transverse momentum of the pair of w and w we find large contributions to the cross section from the region of large photon virtualities we show decomposition of the total cross section as well as invariant mass distribution into polarisation states of both w bosons the role of the longitudinal f_l structure function is quantified its inclusion leads to a 45 decrease of the cross section almost independent of m_ww
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1,802.03245
Ordered topographically patterned silicon by insect-inspired capillary submicron stamping
Insect-inspired capillary submicron stamping and subsequent surface-limited metal-assisted chemical etching (MACE) with ammonium bifluoride as HF source is employed for high-throughput production of ordered topographically patterned silicon (tpSi). Insect feet often possess hairy contact elements through which adhesive secretion is deployed. Thus, arrays of adhesive secretion drops remain as footprints on contact surfaces. Stamps for insect-inspired capillary submicron stamping having surfaces topographically patterned with contact elements mimic the functional principles of such insect feet. They contain spongy continuous nanopore networks penetrating the entire stamps. Any ink (organic, aqueous) may be supplied from the backside of the nanoporous stamps to the contact elements. We generated ordered arrays of AgNO3 dots extending mm^2 on Si by manual stamping with cycle times of a few seconds under ambient conditions; at higher load ordered holey AgNO3 films were obtained. Surface-limited MACE correspondingly yielded either macroporous tpSi or Si pillar arrays. Inkjet printing of polymer solutions onto tpSi yielded patterns of polymer blots conformally covering the tpSi. Such blot patterns could potentially represent a starting point for the development of persistent and scratch-resistant identity labels or QR codes on silicon surfaces.
physics.app-ph
insectinspired capillary submicron stamping and subsequent surfacelimited metalassisted chemical etching mace with ammonium bifluoride as hf source is employed for highthroughput production of ordered topographically patterned silicon tpsi insect feet often possess hairy contact elements through which adhesive secretion is deployed thus arrays of adhesive secretion drops remain as footprints on contact surfaces stamps for insectinspired capillary submicron stamping having surfaces topographically patterned with contact elements mimic the functional principles of such insect feet they contain spongy continuous nanopore networks penetrating the entire stamps any ink organic aqueous may be supplied from the backside of the nanoporous stamps to the contact elements we generated ordered arrays of agno3 dots extending mm2 on si by manual stamping with cycle times of a few seconds under ambient conditions at higher load ordered holey agno3 films were obtained surfacelimited mace correspondingly yielded either macroporous tpsi or si pillar arrays inkjet printing of polymer solutions onto tpsi yielded patterns of polymer blots conformally covering the tpsi such blot patterns could potentially represent a starting point for the development of persistent and scratchresistant identity labels or qr codes on silicon surfaces
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1,802.03246
RF assisted switching in magnetic Josephson junctions
We test the effect of an external RF field on the switching processes of magnetic Josephson junctions (MJJs) suitable for the realization of fast, scalable cryogenic memories compatible with Single Flux Quantum logic. We show that the combined application of microwaves and magnetic field pulses can improve the performances of the device, increasing the separation between the critical current levels corresponding to logical '0' and '1'. The enhancement of the current level separation can be as high as 80% using an optimal set of parameters. We demonstrate that external RF fields can be used as an additional tool to manipulate the memory states, and we expect that this approach may lead to the development of new methods of selecting MJJs and manipulating their states in memory arrays for various applications.
cond-mat.supr-con
we test the effect of an external rf field on the switching processes of magnetic josephson junctions mjjs suitable for the realization of fast scalable cryogenic memories compatible with single flux quantum logic we show that the combined application of microwaves and magnetic field pulses can improve the performances of the device increasing the separation between the critical current levels corresponding to logical 0 and 1 the enhancement of the current level separation can be as high as 80 using an optimal set of parameters we demonstrate that external rf fields can be used as an additional tool to manipulate the memory states and we expect that this approach may lead to the development of new methods of selecting mjjs and manipulating their states in memory arrays for various applications
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1,802.03247
Structural and electronic properties of V2O5 and their tuning by doping with 3d elements - Modelling with DFT+U method and dispersion correction
New electrode materials for alkaline-ion batteries are a timely topic. Among many promising candidates, V2O5 is one of the most interesting cathode materials. While having very high theoretical capacity, in practice, its performance is hindered by low stability and poor conductivity. As regards theoretical descriptions of V2O5, common DFT-GGA calculations fail to reproduce both the electronic and crystal structure. While the band gap is underestimated, the interlayer spacing is overestimated as weak dispersion interactions are not properly described within GGA. Here we show that the combination of the DFT+U method and semi-empirical D2 correction can compensate for the drawbacks of the GGA approximation when it comes to the modelling of V2O5. When compared to common PBE calculations, with a modest increase of the computational cost, PBE+U+D2 fully reproduced the experimental band gap of V2O5, while the errors in the lattice parameters are only a few percent. Using the proposed PBE+U+D2 methodology we studied V2O5 doped with 3d elements (from Sc to Zn). We show that both the structural and electronic parameters are affected by doping. Most importantly, a significant increase of conductivity is expected upon doping, which is of great importance for the application of V2O5 in metal-ion batteries.
cond-mat.mtrl-sci
new electrode materials for alkalineion batteries are a timely topic among many promising candidates v2o5 is one of the most interesting cathode materials while having very high theoretical capacity in practice its performance is hindered by low stability and poor conductivity as regards theoretical descriptions of v2o5 common dftgga calculations fail to reproduce both the electronic and crystal structure while the band gap is underestimated the interlayer spacing is overestimated as weak dispersion interactions are not properly described within gga here we show that the combination of the dftu method and semiempirical d2 correction can compensate for the drawbacks of the gga approximation when it comes to the modelling of v2o5 when compared to common pbe calculations with a modest increase of the computational cost pbeud2 fully reproduced the experimental band gap of v2o5 while the errors in the lattice parameters are only a few percent using the proposed pbeud2 methodology we studied v2o5 doped with 3d elements from sc to zn we show that both the structural and electronic parameters are affected by doping most importantly a significant increase of conductivity is expected upon doping which is of great importance for the application of v2o5 in metalion batteries
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1,802.03248
Piecewise Flat Embedding for Image Segmentation
We introduce a new multi-dimensional nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for image segmentation. Based on the theory of sparse signal recovery, piecewise flat embedding with diverse channels attempts to recover a piecewise constant image representation with sparse region boundaries and sparse cluster value scattering. The resultant piecewise flat embedding exhibits interesting properties such as suppressing slowly varying signals, and offers an image representation with higher region identifiability which is desirable for image segmentation or high-level semantic analysis tasks. We formulate our embedding as a variant of the Laplacian Eigenmap embedding with an $L_{1,p} (0<p\leq1)$ regularization term to promote sparse solutions. First, we devise a two-stage numerical algorithm based on Bregman iterations to compute $L_{1,1}$-regularized piecewise flat embeddings. We further generalize this algorithm through iterative reweighting to solve the general $L_{1,p}$-regularized problem. To demonstrate its efficacy, we integrate PFE into two existing image segmentation frameworks, segmentation based on clustering and hierarchical segmentation based on contour detection. Experiments on four major benchmark datasets, BSDS500, MSRC, Stanford Background Dataset, and PASCAL Context, show that segmentation algorithms incorporating our embedding achieve significantly improved results.
cs.CV eess.IV stat.ML
we introduce a new multidimensional nonlinear embedding piecewise flat embedding pfe for image segmentation based on the theory of sparse signal recovery piecewise flat embedding with diverse channels attempts to recover a piecewise constant image representation with sparse region boundaries and sparse cluster value scattering the resultant piecewise flat embedding exhibits interesting properties such as suppressing slowly varying signals and offers an image representation with higher region identifiability which is desirable for image segmentation or highlevel semantic analysis tasks we formulate our embedding as a variant of the laplacian eigenmap embedding with an l_1p 0pleq1 regularization term to promote sparse solutions first we devise a twostage numerical algorithm based on bregman iterations to compute l_11regularized piecewise flat embeddings we further generalize this algorithm through iterative reweighting to solve the general l_1pregularized problem to demonstrate its efficacy we integrate pfe into two existing image segmentation frameworks segmentation based on clustering and hierarchical segmentation based on contour detection experiments on four major benchmark datasets bsds500 msrc stanford background dataset and pascal context show that segmentation algorithms incorporating our embedding achieve significantly improved results
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1,802.03249
Analysis of the Robustness of Conventional and Topologically Protected Edge States in Phononic Crystal Plates
In this work we theoretically study the interface acoustic states of resonators on a thin plate with topologically protected and conventional designs. Topologically protected interface state is first analyzed by employing the conception of breaking inversion symmetry within the unit cell of a honeycomb lattice for cylindrical and spherical resonators; we further demonstrate the robustness of the wave propagation along a zig-zag path containing sharp corners, defect and disorder. The wave propagation ceases to be preserved if we increase the degree of disorder along the zig-zag path. In parallel, the conventional interface state is also designed and compared to the same situations. We found that the conventional interface state suffers back scattering in the zig-zag path while it can show a more confined wave transport in some cases. The presence of a defect along the propagation path scatters the conventional interface wave and in particular can prohibit a full propagation in presence of a localized state at the defect. If the zig-zag path is made disordered, the propagation of the conventional interface mode can be conserved at given frequencies for a low random degree and disappears for higher random degree as the interface bands become flat in dispersion and turn to localized states. Finally, we show that the immunity of the topologically protected design needs the interface to be surrounded by at least two hexagons of the phononic crystals on both sides, especially at the sharp corners in the zig-zag path, while the conventional design only needs one hexagon bulk media with the advantage of compact wave transport. This work puts a step forward for the interface states in micro-/nano-scale characterization and figures out the behaviors for both topologically protected and conventional interface states.
cond-mat.mtrl-sci
in this work we theoretically study the interface acoustic states of resonators on a thin plate with topologically protected and conventional designs topologically protected interface state is first analyzed by employing the conception of breaking inversion symmetry within the unit cell of a honeycomb lattice for cylindrical and spherical resonators we further demonstrate the robustness of the wave propagation along a zigzag path containing sharp corners defect and disorder the wave propagation ceases to be preserved if we increase the degree of disorder along the zigzag path in parallel the conventional interface state is also designed and compared to the same situations we found that the conventional interface state suffers back scattering in the zigzag path while it can show a more confined wave transport in some cases the presence of a defect along the propagation path scatters the conventional interface wave and in particular can prohibit a full propagation in presence of a localized state at the defect if the zigzag path is made disordered the propagation of the conventional interface mode can be conserved at given frequencies for a low random degree and disappears for higher random degree as the interface bands become flat in dispersion and turn to localized states finally we show that the immunity of the topologically protected design needs the interface to be surrounded by at least two hexagons of the phononic crystals on both sides especially at the sharp corners in the zigzag path while the conventional design only needs one hexagon bulk media with the advantage of compact wave transport this work puts a step forward for the interface states in micronanoscale characterization and figures out the behaviors for both topologically protected and conventional interface states
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1,802.0325
Exponential mixing for a class of dissipative PDEs with bounded degenerate noise
We study a class of discrete-time random dynamical systems with compact phase space. Assuming that the deterministic counterpart of the system in question possesses a dissipation property, its linearisation is approximately controllable, and the driving noise is bounded and has a decomposable structure, we prove that the corresponding family of Markov processes has a unique stationary measure, which is exponentially mixing in the dual-Lipschitz metric. The abstract result is applicable to nonlinear dissipative PDEs perturbed by a bounded random force which affects only a few Fourier modes. We assume that the nonlinear PDE in question is well posed, its nonlinearity is non-degenerate in the sense of the control theory, and the random force is a regular and bounded function of time which satisfies some decomposability and observability hypotheses. This class of forces includes random Haar series, where the coefficients for high Haar modes decay sufficiently fast. In particular, the result applies to the 2D Navier-Stokes system and the nonlinear complex Ginzburg-Landau equations. The proof of the abstract theorem uses the coupling method, enhanced by the Newton-Kantorovich-Kolmogorov fast convergence.
math.AP math-ph math.MP math.OC math.PR
we study a class of discretetime random dynamical systems with compact phase space assuming that the deterministic counterpart of the system in question possesses a dissipation property its linearisation is approximately controllable and the driving noise is bounded and has a decomposable structure we prove that the corresponding family of markov processes has a unique stationary measure which is exponentially mixing in the duallipschitz metric the abstract result is applicable to nonlinear dissipative pdes perturbed by a bounded random force which affects only a few fourier modes we assume that the nonlinear pde in question is well posed its nonlinearity is nondegenerate in the sense of the control theory and the random force is a regular and bounded function of time which satisfies some decomposability and observability hypotheses this class of forces includes random haar series where the coefficients for high haar modes decay sufficiently fast in particular the result applies to the 2d navierstokes system and the nonlinear complex ginzburglandau equations the proof of the abstract theorem uses the coupling method enhanced by the newtonkantorovichkolmogorov fast convergence
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1,802.03251
Projective lines in the affine flag manifold with given tangent root vector
We first describe the tangent space to the affine flag manifold associated to a simple algebraic group over $\mathbb{C}$ at the distinguished point starting from standard definitions. We then construct projective lines in the affine flag manifold tangent to given root vectors associated to imaginary roots of the corresponding affine Kac-Moody algebra and describe in which Schubert varieties they lie.
math.AG math.RT
we first describe the tangent space to the affine flag manifold associated to a simple algebraic group over mathbbc at the distinguished point starting from standard definitions we then construct projective lines in the affine flag manifold tangent to given root vectors associated to imaginary roots of the corresponding affine kacmoody algebra and describe in which schubert varieties they lie
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1,802.03252
Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly
Designing a robust affinity model is the key issue in multiple target tracking (MTT). This paper proposes a novel affinity model by learning feature representation and distance metric jointly in a unified deep architecture. Specifically, we design a CNN network to obtain appearance cue tailored towards person Re-ID, and an LSTM network for motion cue to predict target position, respectively. Both cues are combined with a triplet loss function, which performs end-to-end learning of the fused features in a desired embedding space. Experiments in the challenging MOT benchmark demonstrate, that even by a simple Linear Assignment strategy fed with affinity scores of our method, very competitive results are achieved when compared with the most recent state-of-theart approaches.
cs.CV
designing a robust affinity model is the key issue in multiple target tracking mtt this paper proposes a novel affinity model by learning feature representation and distance metric jointly in a unified deep architecture specifically we design a cnn network to obtain appearance cue tailored towards person reid and an lstm network for motion cue to predict target position respectively both cues are combined with a triplet loss function which performs endtoend learning of the fused features in a desired embedding space experiments in the challenging mot benchmark demonstrate that even by a simple linear assignment strategy fed with affinity scores of our method very competitive results are achieved when compared with the most recent stateoftheart approaches
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1,802.03253
Recent studies on single-shot diagnostics for plasma accelerators at SPARC_LAB
Plasma wakefield acceleration is the most promising acceleration technique for compact and cheap accelerators, thanks to the high accelerating gradients achievable. Nevertheless, this approach still suffers of shot-to-shot instabilities, mostly related to experimental parameters fluctuations. Therefore, the use of single shot diagnostics is needed to properly understand the acceleration mechanism. In this work, we present two diagnostics to probe electron beams from laser-plasma interactions, one relying on Electro Optical Sampling (EOS) for laser-solid matter interactions, the other one based on Optical Transition Radiation (OTR) for single shot measurements of the transverse emittance of plasma accelerated electron beams, both developed at the SPARC_LAB Test Facility.
physics.plasm-ph physics.acc-ph
plasma wakefield acceleration is the most promising acceleration technique for compact and cheap accelerators thanks to the high accelerating gradients achievable nevertheless this approach still suffers of shottoshot instabilities mostly related to experimental parameters fluctuations therefore the use of single shot diagnostics is needed to properly understand the acceleration mechanism in this work we present two diagnostics to probe electron beams from laserplasma interactions one relying on electro optical sampling eos for lasersolid matter interactions the other one based on optical transition radiation otr for single shot measurements of the transverse emittance of plasma accelerated electron beams both developed at the sparc_lab test facility
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1,802.03254
Triplet-based Deep Similarity Learning for Person Re-Identification
In recent years, person re-identification (re-id) catches great attention in both computer vision community and industry. In this paper, we propose a new framework for person re-identification with a triplet-based deep similarity learning using convolutional neural networks (CNNs). The network is trained with triplet input: two of them have the same class labels and the other one is different. It aims to learn the deep feature representation, with which the distance within the same class is decreased, while the distance between the different classes is increased as much as possible. Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one. However, the enormous number of possible triplet data among the large number of training samples makes the training impossible. To address this challenge, a double-sampling scheme is proposed to generate triplets of images as effective as possible. The proposed framework is evaluated on several benchmark datasets. The experimental results show that, our method is effective for the task of person re-identification and it is comparable or even outperforms the state-of-the-art methods.
cs.CV
in recent years person reidentification reid catches great attention in both computer vision community and industry in this paper we propose a new framework for person reidentification with a tripletbased deep similarity learning using convolutional neural networks cnns the network is trained with triplet input two of them have the same class labels and the other one is different it aims to learn the deep feature representation with which the distance within the same class is decreased while the distance between the different classes is increased as much as possible moreover we trained the model jointly on six different datasets which differs from common practice one model is just trained on one dataset and tested also on the same one however the enormous number of possible triplet data among the large number of training samples makes the training impossible to address this challenge a doublesampling scheme is proposed to generate triplets of images as effective as possible the proposed framework is evaluated on several benchmark datasets the experimental results show that our method is effective for the task of person reidentification and it is comparable or even outperforms the stateoftheart methods
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1,802.03255
A universal-algebraic proof of the complexity dichotomy for Monotone Monadic SNP
The logic MMSNP is a restricted fragment of existential second-order logic which allows to express many interesting queries in graph theory and finite model theory. The logic was introduced by Feder and Vardi who showed that every MMSNP sentence is computationally equivalent to a finite-domain constraint satisfaction problem (CSP); the involved probabilistic reductions were derandomized by Kun using explicit constructions of expander structures. We present a new proof of the reduction to finite-domain CSPs which does not rely on the results of Kun. This new proof allows us to obtain a stronger statement and to verify the more general Bodirsky-Pinsker dichotomy conjecture for CSPs in MMSNP. Our approach uses the fact that every MMSNP sentence describes a finite union of CSPs for countably infinite $\omega$-categorical structures; moreover, by a recent result of Hubi\v{c}ka and Ne\v{s}et\v{r}il, these structures can be expanded to homogeneous structures with finite relational signature and the Ramsey property. This allows us to use the universal-algebraic approach to study the computational complexity of MMSNP.
cs.CC cs.LO math.LO
the logic mmsnp is a restricted fragment of existential secondorder logic which allows to express many interesting queries in graph theory and finite model theory the logic was introduced by feder and vardi who showed that every mmsnp sentence is computationally equivalent to a finitedomain constraint satisfaction problem csp the involved probabilistic reductions were derandomized by kun using explicit constructions of expander structures we present a new proof of the reduction to finitedomain csps which does not rely on the results of kun this new proof allows us to obtain a stronger statement and to verify the more general bodirskypinsker dichotomy conjecture for csps in mmsnp our approach uses the fact that every mmsnp sentence describes a finite union of csps for countably infinite omegacategorical structures moreover by a recent result of hubivcka and nevsetvril these structures can be expanded to homogeneous structures with finite relational signature and the ramsey property this allows us to use the universalalgebraic approach to study the computational complexity of mmsnp
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1,802.03256
Antiferromagnetic ground state in the MnGa$_4$ intermetallic compound
Magnetism of the binary intermetallic compound MnGa$_4$ is re-investigated. Band-structure calculations predict antiferromagnetic behavior in contrast to Pauli paramagnetism reported previously. Magnetic susceptibility measurements on single crystals indeed reveal an antiferromagnetic transition at $T_N=393$ K. Neutron powder diffraction and $^{69,71}$Ga nuclear quadrupole resonance spectroscopy show collinear antiferromagnetic order with magnetic moments alligned along the [111] direction of the cubic unit cell. The magnetic moment of 0.80(3)$\mu_B$ at 1.5 K extracted from the neutron data is in good agreement with the band-structure results.
cond-mat.mtrl-sci
magnetism of the binary intermetallic compound mnga_4 is reinvestigated bandstructure calculations predict antiferromagnetic behavior in contrast to pauli paramagnetism reported previously magnetic susceptibility measurements on single crystals indeed reveal an antiferromagnetic transition at t_n393 k neutron powder diffraction and 6971ga nuclear quadrupole resonance spectroscopy show collinear antiferromagnetic order with magnetic moments alligned along the 111 direction of the cubic unit cell the magnetic moment of 0803mu_b at 15 k extracted from the neutron data is in good agreement with the bandstructure results
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1,802.03257
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models
In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos. In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions. Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities. This learning process is unsupervised. Given a training video sequence, low-level visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions. The HDP model automatically decide the number of clusters, i.e. the categories of atomic activities and interactions. Based on the learned atomic activities and interactions, a training dataset is generated to train the Gaussian Process (GP) classifier. Then the trained GP models work in newly captured video to classify interactions and detect abnormal events in real time. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification in newly captured videos. Our framework couples the benefits of the generative model (HDP) with the discriminant model (GP). We provide detailed experiments showing that our framework enjoys favorable performance in video event classification in real-time in a crowded traffic scene.
cs.CV
in this paper we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos in our framework three levels of video events are connected by hierarchical dirichlet process hdp model lowlevel visual features simple atomic activities and multiagent interactions atomic activities are represented as distribution of lowlevel features while complicated interactions are represented as distribution of atomic activities this learning process is unsupervised given a training video sequence lowlevel visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions the hdp model automatically decide the number of clusters ie the categories of atomic activities and interactions based on the learned atomic activities and interactions a training dataset is generated to train the gaussian process gp classifier then the trained gp models work in newly captured video to classify interactions and detect abnormal events in real time furthermore the temporal dependencies between video events learned by hdphidden markov models hmm are effectively integrated into gp classifier to enhance the accuracy of the classification in newly captured videos our framework couples the benefits of the generative model hdp with the discriminant model gp we provide detailed experiments showing that our framework enjoys favorable performance in video event classification in realtime in a crowded traffic scene
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1,802.03258
On the accuracy of mass measurement for microlensing black holes as seen by Gaia and OGLE
We investigate the impact of combining Gaia astrometry from space with precise, high cadence OGLE photometry from the ground. For the archival event OGLE3-ULENS-PAR-02, which is likely a black hole, we simulate a realistic astrometric time-series of Gaia measurements and combine it with the real photometric data collected by the OGLE project. We predict that at the end of the nominal 5 years of the Gaia mission, for the events brighter than $G\approx15.5$ mag at the baseline, caused by objects heavier than 10 $M_{\odot}$, it will be possible to unambiguously derive masses of the lenses, with accuracy between a few to 15 per cent. We find that fainter events ($G<17.5$) can still have their lens masses determined, provided that they are heavier than 30 $M_{\odot}$. We estimate that the rate of astrometric microlensing events caused by the stellar-origin black holes is $\approx 4 \times 10^{-7} \, \rm yr^{-1}$, which implies, that after 5 years of Gaia operation and $\approx 5 \times 10^6$ bright sources in Gaia, it will be possible to identify few such events in the Gaia final catalogues.
astro-ph.SR astro-ph.GA
we investigate the impact of combining gaia astrometry from space with precise high cadence ogle photometry from the ground for the archival event ogle3ulenspar02 which is likely a black hole we simulate a realistic astrometric timeseries of gaia measurements and combine it with the real photometric data collected by the ogle project we predict that at the end of the nominal 5 years of the gaia mission for the events brighter than gapprox155 mag at the baseline caused by objects heavier than 10 m_odot it will be possible to unambiguously derive masses of the lenses with accuracy between a few to 15 per cent we find that fainter events g175 can still have their lens masses determined provided that they are heavier than 30 m_odot we estimate that the rate of astrometric microlensing events caused by the stellarorigin black holes is approx 4 times 107 rm yr1 which implies that after 5 years of gaia operation and approx 5 times 106 bright sources in gaia it will be possible to identify few such events in the gaia final catalogues
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1,802.03259
Optimal data fitting: a moment approach
We propose a moment relaxation for two problems, the separation and covering problem with semi-algebraic sets generated by a polynomial of degree d. We show that (a) the optimal value of the relaxation finitely converges to the optimal value of the original problem, when the moment order r increases and (b) after performing some small perturbation of the original problem, convergence can be achieved with r=d. We further provide a practical iterative algorithm that is computationally tractable for large datasets and present encouraging computational results.
math.OC
we propose a moment relaxation for two problems the separation and covering problem with semialgebraic sets generated by a polynomial of degree d we show that a the optimal value of the relaxation finitely converges to the optimal value of the original problem when the moment order r increases and b after performing some small perturbation of the original problem convergence can be achieved with rd we further provide a practical iterative algorithm that is computationally tractable for large datasets and present encouraging computational results
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