Vorticity and the birth of optical vortices
We exploit the vorticity, familiar from fluid mechanics and the theory of superfluids, as a tool to track the birth and subsequent development of optical vortices at a spiral phase plate.
physics.optics↗
arXiv subjects
Yu Wang
Publications and source records attributed to Yu Wang.
Contribution of Radio Halos to the Foreground for SKA EoR Experiments
The overwhelming foreground contamination is one of the primary impediments to probing the Epoch of Reionization (EoR) through measuring the redshifted 21 cm signal. Among various foreground components, radio halos are less studied and their impacts on the EoR observations are still poorly understood. In this work, we employ the Press-Schechter formalism, merger-induced turbulent re-acceleration model, and the latest SKA1-Low layout configuration to simulate the SKA "observed" images of radio halos. We calculate the one-dimensional power spectra from simulated images and find that radio halos can be about $10^4$, $10^3$ and $10^{2.5}$ times more luminous than the EoR signal on scales of $0.1\,\text{Mpc}^{-1} < k < 2\,\text{Mpc}^{-1}$ in the 120-128, 154-162, and 192-200 MHz bands, respectively. By examining the two-dimensional power spectra inside properly defined EoR windows, we find that the power leaked by radio halos can still be significant, as the power ratios of radio halos to the EoR signal on scales of $0.5\,\text{Mpc}^{-1} \lesssim k \lesssim 1\,\text{Mpc}^{-1}$ can be up to about 230-800%, 18-95%, and 7-40% in the three bands, when the 68% uncertainties caused by the variation of the number density of bright radio halos are considered. Furthermore, we find that radio halos located inside the far side-lobes of the station beam can also impose strong contamination within the EoR window. In conclusion, we argue that radio halos are severe foreground sources and need serious treatments in future EoR experiments.
astro-ph.CO↗
Security-Aware Synthesis Using Delayed-Action Games
Stochastic multiplayer games (SMGs) have gained attention in the field of strategy synthesis for multi-agent reactive systems. However, standard SMGs are limited to modeling systems where all agents have full knowledge of the state of the game. In this paper, we introduce delayed-action games (DAGs) formalism that simulates hidden-information games (HIGs) as SMGs, where hidden information is captured by delaying a player's actions. The elimination of private variables enables the usage of SMG off-the-shelf model checkers to implement HIGs. Furthermore, we demonstrate how a DAG can be decomposed into subgames that can be independently explored, utilizing parallel computation to reduce the model checking time, while alleviating the state space explosion problem that SMGs are notorious for. In addition, we propose a DAG-based framework for strategy synthesis and analysis. Finally, we demonstrate applicability of the DAG-based synthesis framework on a case study of a human-on-the-loop unmanned-aerial vehicle system under stealthy attacks, where the proposed framework is used to formally model, analyze and synthesize security-aware strategies for the system.
cs.GT↗
The equations of motion for consistency of a post-Newtonian Lagrangian formulation
Equations of motion for a general relativistic post-Newtonian Lagrangian approach mainly refer to acceleration equations, i.e. differential equations of velocities. They are directly from the Euler-Lagrangian equations, and usually have higher-order terms truncated when they remain at the same post-Newtonian order of the Lagrangian. In this sense, they are incoherent equations of the Lagrangian and approximately conserve constants of motion in this system. In this paper, we show that the Euler-Lagrangian equations can also yield the equations of motion for consistency of the Lagrangian in the general case. The coherent equations are the \emph{differential} equations of generalized momenta rather than those of the velocities, and have no terms truncated. The velocities are not integration variables, but they can be solved from the \emph{algebraic} equations of the generalized momenta with an iterative method. Taking weak relativistic fields in the Solar System and strong relativistic fields of compact objects as examples, we numerically evaluate the accuracies of the constants of motion in the two sets of equations of motion. It is confirmed that these accuracies well satisfy the theoretical need if the chosen integrator can provide a high enough precision. The differences in the dynamical behavior of order and chaos between the two sets of equations are also compared. Unlike the incoherent post-Newtonian Lagrangian equations of motion, the coherent ones can theoretically, strictly conserve all integrals in some post-Newtonian Lagrangian problems, and therefore are worth recommending.
gr-qc↗
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this problem,namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation.More specifically, the encoder adopts a ResNet as backbone network, where two new operations, channel split and shuffle, are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. Our model has less than 1M parameters,and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The comprehensive experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off on CityScapes dataset.
cs.CV↗
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
In this paper, we introduce a new idea for unsupervised domain adaptation via a remold of Prototypical Networks, which learn an embedding space and perform classification via a remold of the distances to the prototype of each class. Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar. Technically, TPN initially matches each target example to the nearest prototype in the source domain and assigns an example a "pseudo" label. The prototype of each class could then be computed on source-only, target-only and source-target data, respectively. The optimization of TPN is end-to-end trained by jointly minimizing the distance across the prototypes on three types of data and KL-divergence of score distributions output by each pair of the prototypes. Extensive experiments are conducted on the transfers across MNIST, USPS and SVHN datasets, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain an accuracy of 80.4% of single model on VisDA 2017 dataset.
cs.CV↗
Self-similarity and power-laws in GRB 190114C
Following Fermi and NOT observations, Ruffini et al. (2019b) soon identified GRB 190114C as BdHN I at z=0.424, it has been observed since, with unprecedented accuracy, [...] all the way to the successful optical observation of our predicted supernova (SN). This GRB is a twin of GRB 130427A. Here we take advantage of the GBM data and identify in it three different Episodes. Episode 1 represents the precursor which includes the SN breakout and the creation of the new neutron star ($ν$NS), the hypercritical accretion of the SN ejecta onto the NS binary companion, exceeding the NS critical mass at $t_{rf}=1.9$s. Episode 2 starting at $t_{rf}=1.9$s includes three major events: the formation of the BH, the onset of the GeV emission and the onset of the ultra-relativistic prompt emission (UPE), which extends all the way up to $t_{rf}=3.99$s. Episode 3 which occurs at times following $t_{rf}=3.99$s reveals the presence of a cavity carved out in the SN ejecta by the BH formation. We perform an in depth time-resolved spectral analysis on the entire UPE with the corresponding determination of the spectra best fit by a cut-off power-law and a black body (CPL+BB) model, and then we repeat the spectral analysis in 5 successive time iterations in increasingly shorter time bins: we find a similarity in the spectra in each stage of the iteration revealing clearly a self-similar structure. We find a power-law dependence of the BB temperature with index $-1.56\pm0.38$, a dependence with index $-1.20\pm0.26$ for the gamma-ray luminosity confirming a similar dependence with index $-1.20\pm0.36$ which we find as well in the GeV luminosity, both expressed in the rest-frame. We thus discover in the realm of relativistic astrophysics the existence of a self-similar physical process and power-law dependencies, extensively described in the micro-physical world by the classical works of Heisenberg-Landau-Wilson.
astro-ph.HE↗
Recognition of Pyralidae Insects Using Intelligent Monitoring Autonomous Robot Vehicle in Natural Farm Scene
The Pyralidae pests, such as corn borer and rice leaf roller, are main pests in economic crops. The timely detection and identification of Pyralidae pests is a critical task for agriculturists and farmers. However, the traditional identification of pests by humans is labor intensive and inefficient. To tackle the challenges, a pest monitoring autonomous robot vehicle and a method to recognize Pyralidae pests are presented in this paper. Firstly, the robot on autonomous vehicle collects images by performing camera sensing in natural farm scene. Secondly, the total probability image can be obtained by using inverse histogram mapping, and then the object contour of Pyralidae pests can be extracted quickly and accurately with the constrained Otsu method. Finally, by employing Hu moment and the perimeter and area characteristics, the correct contours of objects can be drawn, and the recognition results can be obtained by comparing them with the reference templates of Pyralidae pests. Additionally, the moving speed of the mechanical arms on the vehicle can be adjusted adaptively by interacting with the recognition algorithm. The experimental results demonstrate that the robot vehicle can automatically capture pest images, and can achieve 94.3$\%$ recognition accuracy in natural farm planting scene.
cs.RO↗
GRB 190114C: An Upgraded Legend
Gamma-ray burst (GRB) 190114C first resembles the legendary GRB 130427A: Both are strong sources of GeV emission, exhibiting consistent GeV spectral evolution, and almost identical in detail for the morphology of light-curves in X-ray, gamma-ray and GeV bands, inferring a standard system with different scales. GRB 190114C is richer than GRB 130427A: a large percentage of $\sim 30\%$ energy is thermal presenting in the gamma-ray prompt emission, making it as one of the most thermal-prominent GRBs; Moreover, GRB 190114C extends the horizon of GRB research, that for the first time the ultra-high energy TeV emission ($> 300$~GeV) is detected in a GRB as reported by the MAGIC team. Furthermore, GRB 190114C urges us to revisit the traditional theoretical framework, since most of the GRB's energy may emit in the GeV and TeV range, not in the conventional MeV range. Since GRB 190114C confidently supports that MeV and GeV emissions have the same origin, it helps to establish a new comprehensive acceleration and radiation mechanism. Overall, GRB 190114C refreshes our GRB knowledge and challenges our current GRB theoretical interpretation.
astro-ph.HE↗
Two Predictions of supernova: GRB 130427A / SN 2013cq and GRB 180728A / SN 2018fip
On 2018 July 28, GRB 180728A triggered \textit{Swift} satellites and, soon after the determination of the redshift, we identified this source as a type II binary-driven hypernova (BdHN II) in our model. Consequently, we predicted the appearance time of its associated supernova (SN), which was later confirmed as SN 2018fip. A BdHN II originates in a binary composed of a carbon-oxygen core (CO$_{\rm core}$) undergoing SN, and the SN ejecta hypercritically accrete onto a companion neutron star (NS). From the time of the SN shock breakout to the time when the hypercritical accretion starts, we infer the binary separation $\simeq 3 \times 10^{10}$ cm. The accretion explains the prompt emission of isotropic energy $\simeq 3 \times 10^{51}$ erg, lasting $\sim 10$ s, and the accompanying observed blackbody emission from a thermal convective instability bubble. The new neutron star ($ν$NS) originating from the SN powers the late afterglow from which a $ν$NS initial spin of $2.5$ ms is inferred. We compare GRB 180728A with GRB 130427A, a type I binary-driven hypernova (BdHN I) with isotropic energy $> 10^{54}$ erg. For GRB 130427A we have inferred an initially closer binary separation of $\simeq 10^{10}$ cm, implying a higher accretion rate leading to the collapse of the NS companion with consequent black hole formation, and a faster, $1$ ms spinning $ν$NS. In both cases, the optical spectra of the SNe are similar, and not correlated to the energy of the gamma-ray burst. We present three-dimensional smoothed-particle-hydrodynamic simulations and visualisations of the BdHNe I and II.
astro-ph.HE↗
Distribution-Sensitive Bounds on Relative Approximations of Geometric Ranges
A family $\mathcal{R}$ of ranges and a set $X$ of points together define a range space $(X, \mathcal{R}|_X)$, where $\mathcal{R}|_X = \{X \cap h \mid h \in \mathcal{R}\}$. We want to find a structure to estimate the quantity $|X \cap h|/|X|$ for any range $h \in \mathcal{R}$ with the $(ρ, ε)$-guarantee: (i) if $|X \cap h|/|X| > ρ$, the estimate must have a relative error $ε$; (ii) otherwise, the estimate must have an absolute error $ρε$. The objective is to minimize the size of the structure. Currently, the dominant solution is to compute a relative $(ρ, ε)$-approximation, which is a subset of $X$ with $\tilde{O}(λ/(ρε^2))$ points, where $λ$ is the VC-dimension of $(X, \mathcal{R}|_X)$, and $\tilde{O}$ hides polylog factors. This paper shows a more general bound sensitive to the content of $X$. We give a structure that stores $O(\log (1/ρ))$ integers plus $\tilde{O}(θ\cdot (λ/ε^2))$ points of $X$, where $θ$ - called the disagreement coefficient - measures how much the ranges differ from each other in their intersections with $X$. The value of $θ$ is between 1 and $1/ρ$, such that our space bound is never worse than that of relative $(ρ, ε)$-approximations, but we improve the latter's $1/ρ$ term whenever $θ= o(\frac{1}{ρ\log (1/ρ)})$. We also prove that, in the worst case, summaries with the $(ρ, 1/2)$-guarantee must consume $Ω(θ)$ words even for $d = 2$ and $λ\le 3$. We then constrain $\mathcal{R}$ to be the set of halfspaces in $\mathbb{R}^d$ for a constant $d$, and prove the existence of structures with $o(1/(ρε^2))$ size offering $(ρ,ε)$-guarantees, when $X$ is generated from various stochastic distributions. This is the first formal justification on why the term $1/ρ$ is not compulsory for "realistic" inputs.
cs.CG↗
Algorithm and Architecture for Path Metric Aided Bit-Flipping Decoding of Polar Codes
Polar codes attract more and more attention of researchers in recent years, since its capacity achieving property. However, their error-correction performance under successive cancellation (SC) decoding is inferior to other modern channel codes at short or moderate blocklengths. SC-Flip (SCF) decoding algorithm shows higher performance than SC decoding by identifying possibly erroneous decisions made in initial SC decoding and flipping them in the sequential decoding attempts. However, it performs not well when there are more than one erroneous decisions in a codeword. In this paper, we propose a path metric aided bit-flipping decoding algorithm to identify and correct more errors efficiently. In this algorithm, the bit-flipping list is generated based on both log likelihood ratio (LLR) based path metric and bit-flipping metric. The path metric is used to verify the effectiveness of bit-flipping. In order to reduce the decoding latency and computational complexity, its corresponding pipeline architecture is designed. By applying these decoding algorithms and pipeline architecture, an improvement on error-correction performance can be got up to 0.25dB compared with SCF decoding at the frame error rate of $10^{-4}$, with low average decoding latency.
eess.SP↗
IoT Virtualization: A Survey of Software Definition & Function Virtualization Techniques for Internet of Things
Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for next generation Internet. The deployment and applications of IoT ranges from smart cities to urban computing, and from ubiquitous healthcare to tactile Internet. For this reason the physical infrastructure of heterogeneous network systems has become more complicated, and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks (SDN) and Network Function Virtualization (NFV) has been extensively researched for IoT in recent past. In this article we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software defined networks designed for IoT, function virtualization for IoT networks, and software defined IoT networks. These categories are further divided into works which present architectural, security, and management solutions. In addition, the paper highlights a number of short term and long term research challenges and open issues related to adoption of software defined Internet of things.
cs.NI↗
Unique Sharp Local Minimum in $\ell_1$-minimization Complete Dictionary Learning
We study the problem of globally recovering a dictionary from a set of signals via $\ell_1$-minimization. We assume that the signals are generated as i.i.d. random linear combinations of the $K$ atoms from a complete reference dictionary $D^*\in \mathbb R^{K\times K}$, where the linear combination coefficients are from either a Bernoulli type model or exact sparse model. First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the expected $\ell_1$ objective function. Our result substantially extends that of Wu and Yu (2015) and allows the combination coefficient to be non-negative. Secondly, we obtain an explicit bound on the region within which the objective value of the reference dictionary is minimal. Thirdly, we show that the reference dictionary is the unique sharp local minimum, thus establishing the first known global property of $\ell_1$-minimization dictionary learning. Motivated by the theoretical results, we introduce a perturbation-based test to determine whether a dictionary is a sharp local minimum of the objective function. In addition, we also propose a new dictionary learning algorithm based on Block Coordinate Descent, called DL-BCD, which is guaranteed to have monotonic convergence. Simulation studies show that DL-BCD has competitive performance in terms of recovery rate compared to many state-of-the-art dictionary learning algorithms.
stat.ML↗
A Comprehensive Survey of Interface Protocols for Software Defined Networks
Software Defined Networks has seen tremendous growth and deployment in different types of networks. Compared to traditional networks it decouples the control logic from network layer devices, and centralizes it for efficient traffic forwarding and flow management across the domain. This multi-layered architecture has data forwarding devices at the bottom in data plane, which are programmed by controllers in the control plane. The high level application or management plane interacts with control layer to program the whole network and enforce different policies. The interaction among these layers is done through interfaces which work as communication/programming protocols. In this survey, we present a comprehensive study of such interfaces available for southbound, northbound, and east/westbound communication. We have classified each type into different categories based on their properties and capabilities. Virtualization of networks devices is a common practice in Software Defined Networks. This paper also analyzes interface solution which work with different virtualization schemes. In addition, the paper highlights a number of short term and long term research challenges and open issues related to SDN interfaces.
cs.NI↗
Helium-hydrogen immiscibility at high pressures
Hydrogen and helium are the most abundant elements in the universe and they constitute the interiors of gas giant planets. Thus, their equations of states, phase, chemical state, and chemical reactivity at extreme conditions are of great interest. Applying Raman spectroscopy, visual observation, and synchrotron X-ray diffraction in diamond anvil cells (DAC), we performed experiments on H2-He 1:1 and D2-He 1:10 compressed gas mixture up to 100 GPa at 300 K. By comparing with the available data on pure bulk materials, we find no sign of miscibility, chemical reactivity, and new compound formation.
cond-mat.mtrl-sci↗
Revealing A Head-on Major Merger in the Nearby NGC 6338 Group with Chandra and VLA observations
By analyzing the Chandra archival data of the nearby NGC 6338 galaxy group, we identify two X-ray bright clumps (N-clump and S-clump) within the central 100 kpc, and detect an arc-like X-ray brightness discontinuity at the south boundary of the N-clump, which is defined as a cold front with a gas flow Mach number of M<0.8. Furthermore, at the north-east boundary of the S-clump (dominated by galaxy NGC 6338) another X-ray edge is detected that corresponds to a weaker cold front. Therefore, the two clumps are approaching each other approximately from opposite directions, and the group is undergoing a head-on collision that is in a stage of pre-core passage. This merger scenario is also supported by the study of the line-of-sight velocity distribution of the group member galaxies. The merger mass ratio is about 1:1.8 as estimated from the central gas temperature of the two clumps, which suggests the merger is most likely to be a major merger. We also analyze the VLA 1.4 and 4.9 GHz radio data, but we do not detect any extended radio emission that is associated with the merger.
astro-ph.GA↗
Ultrafast quasiparticle dynamics in correlated semimetal Ca$_3$Ru$_2$O$_7$
The correlated polar semimetal Ca$_3$Ru$_2$O$_7$ exhibits a rich phase diagram including two magnetic transitions ($T_N$=56 K and $T_C$=48 K) with the appearance of an insulating-like pseudogap (at $T_C$). In addition, there is a crossover back to metallic behavior at $T^*$=30 K, the origin of which is still under debate. We utilized ultrafast optical pump optical probe spectroscopy to investigate quasiparticle dynamics as a function of temperature in this enigmatic quantum material. We identify two dynamical processes, both of which are influenced by the onset of the pseudogap. This includes electron-phonon relaxation and, below $T_C$, the onset of a phonon bottleneck hindering the relaxation of quasiparticles across the pseudogap. We introduce a gap-modified two-temperature model to describe the temperature dependence of electron-phonon thermalization, and use the Rothwarf-Taylor to model the phonon bottleneck. In conjunction with density functional theory, our experimental results synergistically reveal the origin of the $T$-dependent pseudogap. Further, our data and analysis indicate that $T^*$ emerges as a natural consequence of $T$-dependent gapping out of carriers, and does not correspond to a separate electronic transition. Our results highlight the value of low fluence ultrafast optics as a sensitive probe of low energy electronic structure, thermodynamic parameters, and transport properties of Ruddlesden-Popper ruthenates.
cond-mat.str-el↗