Continuous-Scale Kinetic Fluid Simulation

Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving incompressible Navier-Stokes equations in computational fluid dynamics. However, such approaches have not been widely adopted in graphics mainly due to the underlying inaccuracy, instability and inflexibility. In this paper, we try to tackle these problems in order to make kinetic approaches practical for graphical applications. To achieve more accurate and stable simulations, we propose to employ the non-orthogonal central-moment-relaxation model, where we develop a novel adaptive relaxation method to retain both stability and accuracy in turbulent flows. To achieve flexibility, we propose a novel continuous-scale formulation that enables samples at arbitrary resolutions to easily communicate with each other in a more continuous sense and with loose geometrical constraints, which allows efficient and adaptive sample construction to better match the physical scale. Such a capability directly leads to an automatic sample construction which generates static and dynamic scales at initialization and during simulation, respectively. This effectively makes our method suitable for simulating turbulent flows with arbitrary geometrical boundaries. Our simulation results with applications to smoke animations show the benefits of our method, with comparisons for justification and verification.Comment: 17 pages, 17 figures, accepted by IEEE Transactions on Visualization and Computer Graphic

Generalized derivations of 33-Lie algebras

Generalized derivations, quasiderivations and quasicentroid of $3$-algebras are introduced, and basic relations between them are studied. Structures of quasiderivations and quasicentroid of $3$-Lie algebras, which contains a maximal diagonalized tours, are systematically investigated. The main results are: for all $3$-Lie algebra $A$, 1) the generalized derivation algebra $GDer(A)$ is the sum of quasiderivation algebra $QDer(A)$ and quasicentroid $Q\Gamma(A)$; 2) quasiderivations of $A$ can be embedded as derivations in a larger algebra; 3) quasiderivation algebra $QDer(A)$ normalizer quasicentroid, that is, $[QDer(A), Q\Gamma(A)]\subseteq Q\Gamma(A)$; 4) if $A$ contains a maximal diagonalized tours $T$, then $QDer(A)$ and $Q\Gamma(A)$ are diagonalized $T$-modules, that is, as $T$-modules, $(T, T)$ semi-simplely acts on $QDer(A)$ and $Q\Gamma(A)$, respectively.Comment: 19 pages. arXiv admin note: text overlap with arXiv:0805.1202 by other author

Serial Concatenation of RS Codes with Kite Codes: Performance Analysis, Iterative Decoding and Design

In this paper, we propose a new ensemble of rateless forward error correction (FEC) codes. The proposed codes are serially concatenated codes with Reed-Solomon (RS) codes as outer codes and Kite codes as inner codes. The inner Kite codes are a special class of prefix rateless low-density parity-check (PRLDPC) codes, which can generate potentially infinite (or as many as required) random-like parity-check bits. The employment of RS codes as outer codes not only lowers down error-floors but also ensures (with high probability) the correctness of successfully decoded codewords. In addition to the conventional two-stage decoding, iterative decoding between the inner code and the outer code are also implemented to improve the performance further. The performance of the Kite codes under maximum likelihood (ML) decoding is analyzed by applying a refined Divsalar bound to the ensemble weight enumerating functions (WEF). We propose a simulation-based optimization method as well as density evolution (DE) using Gaussian approximations (GA) to design the Kite codes. Numerical results along with semi-analytic bounds show that the proposed codes can approach Shannon limits with extremely low error-floors. It is also shown by simulation that the proposed codes performs well within a wide range of signal-to-noise-ratios (SNRs).Comment: 34 pages, 15 figure

Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation

We study two mixed robust/average-case submodular partitioning problems that we collectively call Submodular Partitioning. These problems generalize both purely robust instances of the problem (namely max-min submodular fair allocation (SFA) and min-max submodular load balancing (SLB) and also generalize average-case instances (that is the submodular welfare problem (SWP) and submodular multiway partition (SMP). While the robust versions have been studied in the theory community, existing work has focused on tight approximation guarantees, and the resultant algorithms are not, in general, scalable to very large real-world applications. This is in contrast to the average case, where most of the algorithms are scalable. In the present paper, we bridge this gap, by proposing several new algorithms (including those based on greedy, majorization-minimization, minorization-maximization, and relaxation algorithms) that not only scale to large sizes but that also achieve theoretical approximation guarantees close to the state-of-the-art, and in some cases achieve new tight bounds. We also provide new scalable algorithms that apply to additive combinations of the robust and average-case extreme objectives. We show that these problems have many applications in machine learning (ML). This includes: 1) data partitioning and load balancing for distributed machine algorithms on parallel machines; 2) data clustering; and 3) multi-label image segmentation with (only) Boolean submodular functions via pixel partitioning. We empirically demonstrate the efficacy of our algorithms on real-world problems involving data partitioning for distributed optimization of standard machine learning objectives (including both convex and deep neural network objectives), and also on purely unsupervised (i.e., no supervised or semi-supervised learning, and no interactive segmentation) image segmentation

Deep Learning Based Robot for Automatically Picking up Garbage on the Grass

This paper presents a novel garbage pickup robot which operates on the grass. The robot is able to detect the garbage accurately and autonomously by using a deep neural network for garbage recognition. In addition, with the ground segmentation using a deep neural network, a novel navigation strategy is proposed to guide the robot to move around. With the garbage recognition and automatic navigation functions, the robot can clean garbage on the ground in places like parks or schools efficiently and autonomously. Experimental results show that the garbage recognition accuracy can reach as high as 95%, and even without path planning, the navigation strategy can reach almost the same cleaning efficiency with traditional methods. Thus, the proposed robot can serve as a good assistance to relieve dustman's physical labor on garbage cleaning tasks.Comment: 8 pages, 13 figures,TCE accepte

Smart Guiding Glasses for Visually Impaired People in Indoor Environment

To overcome the travelling difficulty for the visually impaired group, this paper presents a novel ETA (Electronic Travel Aids)-smart guiding device in the shape of a pair of eyeglasses for giving these people guidance efficiently and safely. Different from existing works, a novel multi sensor fusion based obstacle avoiding algorithm is proposed, which utilizes both the depth sensor and ultrasonic sensor to solve the problems of detecting small obstacles, and transparent obstacles, e.g. the French door. For totally blind people, three kinds of auditory cues were developed to inform the direction where they can go ahead. Whereas for weak sighted people, visual enhancement which leverages the AR (Augment Reality) technique and integrates the traversable direction is adopted. The prototype consisting of a pair of display glasses and several low cost sensors is developed, and its efficiency and accuracy were tested by a number of users. The experimental results show that the smart guiding glasses can effectively improve the user's travelling experience in complicated indoor environment. Thus it serves as a consumer device for helping the visually impaired people to travel safely.Comment: 9 pages,15 figures, IEEE transaction on consumer electronics receive

Learning Robust, Transferable Sentence Representations for Text Classification

Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available. To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch. In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning. Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.Comment: arXiv admin note: substantial text overlap with arXiv:1804.0791

RepNet: Cutting Tail Latency in Data Center Networks with Flow Replication

Data center networks need to provide low latency, especially at the tail, as demanded by many interactive applications. To improve tail latency, existing approaches require modifications to switch hardware and/or end-host operating systems, making them difficult to be deployed. We present the design, implementation, and evaluation of RepNet, an application layer transport that can be deployed today. RepNet exploits the fact that only a few paths among many are congested at any moment in the network, and applies simple flow replication to mice flows to opportunistically use the less congested path. RepNet has two designs for flow replication: (1) RepSYN, which only replicates SYN packets and uses the first connection that finishes TCP handshaking for data transmission, and (2) RepFlow which replicates the entire mice flow. We implement RepNet on {\tt node.js}, one of the most commonly used platforms for networked interactive applications. {\tt node}'s single threaded event-loop and non-blocking I/O make flow replication highly efficient. Performance evaluation on a real network testbed and in Mininet reveals that RepNet is able to reduce the tail latency of mice flows, as well as application completion times, by more than 50\%

Reddening of the BLR and NLR in AGN From a Systematic Analysis of Balmer Decrement

We selected an active galactic nuclei (AGN) sample ($0 < z \le 0.35$) from Sloan Digital Sky Survey Data Release 7, and measured the broad- (${\rm H\alpha^{b}/H\beta^{b}}$) and narrow-line Balmer decrements (${\rm H\alpha^{n}/H\beta^{n}}$) of 554 selected AGNs. We found that the distributions of Balmer decrements can be fitted by a Gaussian function and give the best estimates of ${\rm H\alpha^{b}/H\beta^{b}} = 3.16$ with a standard deviation 0.07 dex, and ${\rm H\alpha^{n}/H\beta^{n}} = 4.37$ with a standard deviation 0.10 dex. We inspected the distributions of ${\rm H\alpha^{b}/H\beta^{b}}$ and ${\rm H\alpha^{n}/H\beta^{n}}$ in the Baldwin$-$Phillips$-$Terlevich (BPT) diagram and found that only narrow-line Balmer decrements depend on the physical conditions of the narrow-line region (NLR). We tested the relationship between ${\rm H\alpha^{b}/H\beta^{b}}$ and ${\rm H\alpha^{n}/H\beta^{n}}$, and found that ${\rm H\alpha^{b}/H\beta^{b}}$ does not correlate with ${\rm H\alpha^{n}/H\beta^{n}}$. We investigated the relationship between Balmer decrements and Seyfert sub-type, and found that only broad-line Balmer decrements correlate with Seyfert sub-type, We also examined the dependency of Balmer decrements on AGN properties, and found that Balmer decrements have no correlation with optical luminosity, but show some dependence on accretion rate. These results indicate that the NLR is subject to more reddening by dust than the broad-line region (BLR).Comment: 10 pages, 9 figures, 1 table, accepted for publication in MNRA

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.Comment: Accepted by CVPR201