OpenRaiser/Intern-Atlas huggingface dataset:
Algorithm and Architecture for Hybrid Decoding of Polar Codes
Venue: arXiv cs.IT 2014
Year: 2014

Polar codes are the first provable capacity-achieving forward error correction (FEC) codes. In general polar codes can be decoded via either successive cancellation (SC) or belief propagation (BP) decoding algorithm. However, to date practical applications of polar codes have been hindered by the long decoding latency and limited error-correcting performance problems. In this paper, based on our recent proposed early stopping criteria for the BP algorithm, we propose a hybrid BP-SC decoding scheme to improve the decoding performance of polar codes with relatively short latency. Simulation results show that, for (1024, 512) polar codes the proposed approach leads to at least 0.2dB gain over the BP algorithm with the same maximum number of iterations for the entire SNR region, and also achieves 0.2dB decoding gain over the BP algorithm with the same worst-case latency in the high SNR region. Besides, compared to the SC algorithm, the proposed scheme leads to 0.2dB gain in the medium SNR region with much less average decoding latency. In addition, we also propose the low-complexity unified hardware architecture for the hybrid decoding scheme, which is able to implement SC and BP algorithms using same hardware.
Analysis of Memory Ballooning Technique for Dynamic Memory Management of Virtual Machines (VMs)
Venue: arXiv cs.DC 2014
Year: 2014

Memory ballooning is dynamic memory management technique for virtual machines (VMs). Ballooning is a part of memory reclamation technique operations used by a hypervisor to allow the physical host system to retrieve unused memory from certain guest virtual machines (VMs) and share it with others. Memory ballooning allows the total amount ofRAM required by guest VMs to exceed the amount ofphysical RAM available on the host. Memory overcommitment enables a higher consolidation ratio in a hypervisor. Using memory overcommitment, users can consolidate VMs on a physical machine such that physical resources are utilized in an optimal manner while delivering good performance. Hence memory reclamation is an integral component ofmemory overcommitment. In this paper, we address that the basic cause of memory that ballooning is memory overcommitment from using memory-intensive virtual machines. We compared to others reclamation technique and identify Cost Associate with Memory Ballooning in state of Memory Overcommitment. The objective of this paper is to analyse memory ballooning technique for dynamic memory management of VMs. For this analysis, VMware based virtualization software e.g ESXi Server, vCenter Server, vSphere Client are installed and configured on the Centre for Innovation and Technology (CIT) Lab, DIU; for monitor and analyze VM performance for memory ballooning technique. The performance ofmemory ballooning technique is evaluated with two different test cases. The purpose is to help users understand, how this technique impact the performance. Finally, we presents the throughput ofheavy workload with different memory limits when using ballooning or swapping; and analyse VM performance issue for this technique.
Efficient Support of Big Data Storage Systems on the Cloud
Venue: arXiv cs.DC 2014
Year: 2014

Due to its advantages over traditional data centers, there has been a rapid growth in the usage of cloud infrastructures. These include public clouds (e.g., Amazon EC2), or private clouds, such as clouds deployed using OpenStack. A common factor in many of the well known infrastructures, for example OpenStack and CloudStack, is that networked storage is used for storage of persistent data. However, traditional Big Data systems, including Hadoop, store data in commodity local storage for reasons of high performance and low cost. We present an architecture for supporting Hadoop on Openstack using local storage. Subsequently, we use benchmarks on Openstack and Amazon to show that for supporting Hadoop, local storage has better performance and lower cost. We conclude that cloud systems should support local storage for persistent data (in addition to networked storage) so as to provide efficient support for Hadoop and other Big Data systems
Constant-Envelope Precoding with Time-Variation Constraint on the Transmitted Phase Angles
Venue: arXiv cs.IT 2014
Year: 2014

We consider downlink precoding in a frequency-selective multi-user massive MIMO system with highly efficient but non-linear power amplifiers at the base station (BS). A low-complexity precoding algorithm is proposed, which generates constant-envelope (CE) transmit signals for each BS antenna. To avoid large variations in the phase angle transmitted from each antenna, the difference of the phase angles transmitted in consecutive channel uses is limited to $[-απ\,,\, απ]$ for a fixed $0 < α\leq 1$. To achieve a desired per-user information rate, the extra total transmit power required under the time variation constraint when compared to the special case of no time variation constraint (i.e., $α=1$), is small for many practical values of $α$. In a i.i.d. Rayleigh fading channel with $80$ BS antennas, $5$ single-antenna users and a desired per-user information rate of $1$ bit-per-channel-use, the extra total transmit power required is less than $2.0$ dB when $α= 1/2$.
Graph Sensitive Indices for Comparing Clusterings
Venue: arXiv cs.LG 2014
Year: 2014

This report discusses two new indices for comparing clusterings of a set of points. The motivation for looking at new ways for comparing clusterings stems from the fact that the existing clustering indices are based on set cardinality alone and do not consider the positions of data points. The new indices, namely, the Random Walk index (RWI) and Variation of Information with Neighbors (VIN), are both inspired by the clustering metric Variation of Information (VI). VI possesses some interesting theoretical properties which are also desirable in a metric for comparing clusterings. We define our indices and discuss some of their explored properties which appear relevant for a clustering index. We also include the results of these indices on clusterings of some example data sets.
Modulated Unit-Norm Tight Frames for Compressed Sensing
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we propose a compressed sensing (CS) framework that consists of three parts: a unit-norm tight frame (UTF), a random diagonal matrix and a column-wise orthonormal matrix. We prove that this structure satisfies the restricted isometry property (RIP) with high probability if the number of measurements $m = O(s \log^2s \log^2n)$ for $s$-sparse signals of length $n$ and if the column-wise orthonormal matrix is bounded. Some existing structured sensing models can be studied under this framework, which then gives tighter bounds on the required number of measurements to satisfy the RIP. More importantly, we propose several structured sensing models by appealing to this unified framework, such as a general sensing model with arbitrary/determinisic subsamplers, a fast and efficient block compressed sensing scheme, and structured sensing matrices with deterministic phase modulations, all of which can lead to improvements on practical applications. In particular, one of the constructions is applied to simplify the transceiver design of CS-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems.
A statistical reduced-reference method for color image quality assessment
Venue: arXiv cs.CV 2014
Year: 2014

Although color is a fundamental feature of human visual perception, it has been largely unexplored in the reduced-reference (RR) image quality assessment (IQA) schemes. In this paper, we propose a natural scene statistic (NSS) method, which efficiently uses this information. It is based on the statistical deviation between the steerable pyramid coefficients of the reference color image and the degraded one. We propose and analyze the multivariate generalized Gaussian distribution (MGGD) to model the underlying statistics. In order to quantify the degradation, we develop and evaluate two measures based respectively on the Geodesic distance between two MGGDs and on the closed-form of the Kullback Leibler divergence. We performed an extensive evaluation of both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID 2008 benchmark and the FRTV Phase I validation process. Experimental results demonstrate the effectiveness of the proposed framework to achieve a good consistency with human visual perception. Furthermore, the best configuration is obtained with CIELAB color space associated to KLD deviation measure.
A High Availability Clusters Model Combined with Load Balancing and Shared Storage Technologies for Web Servers
Venue: arXiv cs.DC 2014
Year: 2014

This paper designs and implements a high availability clusters and incorporated with load balance infrastructure of web servers. The paper described system can provide full facilities to the website hosting provider and large business organizations. This system can provide continuous service though any system components fail uncertainly with the help of Linux Virtual Server (LVS) loadbalancing cluster technology and combined with virtualization as well as shared storage technology to achieve the three-tier architecture of Web server clusters. This technology not only improves availability, but also affects the security and performance of the application services being requested. Benefits of the system include node failover overcome; network failover overcome; storage limitation overcome and load distribution.
On Low Discrepancy Samplings in Product Spaces of Motion Groups
Venue: arXiv cs.CG 2014
Year: 2014

Deterministically generating near-uniform point samplings of the motion groups like SO(3), SE(3) and their n-wise products SO(3)^n, SE(3)^n is fundamental to numerous applications in computational and data sciences. The natural measure of sampling quality is discrepancy. In this work, our main goal is construct low discrepancy deterministic samplings in product spaces of the motion groups. To this end, we develop a novel strategy (using a two-step discrepancy construction) that leads to an almost exponential improvement in size (from the trivial direct product). To the best of our knowledge, this is the first nontrivial construction for SO(3)^n, SE(3)^n and the hypertorus T^n. We also construct new low discrepancy samplings of S^2 and SO(3). The central component in our construction for SO(3) is an explicit construction of N points in S^2 with discrepancy \tildeØ(1/\sqrt{N}) with respect to convex sets, matching the bound achieved for the special case of spherical caps in \cite{ABD_12}. We also generalize the discrepancy of Cartesian product sets \cite{Chazelle04thediscrepancy} to the discrepancy of local Cartesian product sets. The tools we develop should be useful in generating low discrepancy samplings of other complicated geometric spaces.
Two Gaussian Approaches to Black-Box Optomization
Venue: arXiv cs.NE 2014
Year: 2014

Outline of several strategies for using Gaussian processes as surrogate models for the covariance matrix adaptation evolution strategy (CMA-ES).
Erratum to "Lattice constellation and codes from quadratic number fields" [IEEE Trans. Inform. Theory, vol. 47, No. 4, May. 2001]
Venue: arXiv cs.IT 2014
Year: 2014

We correct a partial mistake for a metric presented in the article "Lattice constellation and codes from quadratic number fields" [IEEE Trans. Inform. Theory, vol. 47, No. 4, May. 2001]. We show that the metric defined in the article is not true, therefore, this brings about to destroy the encoding and decoding procedures. Also, we define a proper metric for some codes defined in the article and show that there exist some $1-$error correcting perfect codes with respect to this new metric.
Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic Models and Encyclopedic Knowledge
Venue: arXiv cs.CL 2014
Year: 2014

We present a method for coarse-grained cross-lingual alignment of comparable texts: segments consisting of contiguous paragraphs that discuss the same theme (e.g. history, economy) are aligned based on induced multilingual topics. The method combines three ideas: a two-level LDA model that filters out words that do not convey themes, an HMM that models the ordering of themes in the collection of documents, and language-independent concept annotations to serve as a cross-language bridge and to strengthen the connection between paragraphs in the same segment through concept relations. The method is evaluated on English and French data previously used for monolingual alignment. The results show state-of-the-art performance in both monolingual and cross-lingual settings.
On Rendering Synthetic Images for Training an Object Detector
Venue: arXiv cs.CV 2014
Year: 2014

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available.
Alignment of Polarized Sets
Venue: arXiv cs.IT 2014
Year: 2014

Arıkan's polar coding technique is based on the idea of synthesizing $n$ channels from the $n$ instances of the physical channel by a simple linear encoding transformation. Each synthesized channel corresponds to a particular input to the encoder. For large $n$, the synthesized channels become either essentially noiseless or almost perfectly noisy, but in total carry as much information as the original $n$ channels. Capacity can therefore be achieved by transmitting messages over the essentially noiseless synthesized channels. Unfortunately, the set of inputs corresponding to reliable synthesized channels is poorly understood, in particular how the set depends on the underlying physical channel. In this work, we present two analytic conditions sufficient to determine if the reliable inputs corresponding to different discrete memoryless channels are aligned or not, i.e. if one set is contained in the other. Understanding the alignment of the polarized sets is important as it is directly related to universality properties of the induced polar codes, which are essential in particular for network coding problems. We demonstrate the performance of our conditions on a few examples for wiretap and broadcast channels. Finally we show that these conditions imply that the simple quantum polar coding scheme of Renes et al. [Phys. Rev. Lett. 109, 050504 (2012)] requires entanglement assistance for general channels, but also show such assistance to be unnecessary in many cases of interest.
Understanding Deep Image Representations by Inverting Them
Venue: arXiv cs.CV 2014
Year: 2014

Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited. In this paper we conduct a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself? To answer this question we contribute a general framework to invert representations. We show that this method can invert representations such as HOG and SIFT more accurately than recent alternatives while being applicable to CNNs too. We then use this technique to study the inverse of recent state-of-the-art CNN image representations for the first time. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.
Pedestrian Detection aided by Deep Learning Semantic Tasks
Venue: arXiv cs.CV 2014
Year: 2014

Deep learning methods have achieved great success in pedestrian detection, owing to its ability to learn features from raw pixels. However, they mainly capture middle-level representations, such as pose of pedestrian, but confuse positive with hard negative samples, which have large ambiguity, e.g. the shape and appearance of `tree trunk' or `wire pole' are similar to pedestrian in certain viewpoint. This ambiguity can be distinguished by high-level representation. To this end, this work jointly optimizes pedestrian detection with semantic tasks, including pedestrian attributes (e.g. `carrying backpack') and scene attributes (e.g. `road', `tree', and `horizontal'). Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources. Since distinct tasks have distinct convergence rates and data from different datasets have different distributions, a multi-task objective function is carefully designed to coordinate tasks and reduce discrepancies among datasets. The importance coefficients of tasks and network parameters in this objective function can be iteratively estimated. Extensive evaluations show that the proposed approach outperforms the state-of-the-art on the challenging Caltech and ETH datasets, where it reduces the miss rates of previous deep models by 17 and 5.5 percent, respectively.
Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images
Venue: arXiv cs.CV 2014
Year: 2014

State-of-the-art visual recognition and detection systems increasingly rely on large amounts of training data and complex classifiers. Therefore it becomes increasingly expensive both to manually annotate datasets and to keep running times at levels acceptable for practical applications. In this paper, we propose two solutions to address these issues. First, we introduce a weakly supervised, segmentation-based approach to learn accurate detectors and image classifiers from weak supervisory signals that provide only approximate constraints on target localization. We illustrate our system on the problem of action detection in static images (Pascal VOC Actions 2012), using human visual search patterns as our training signal. Second, inspired from the saccade-and-fixate operating principle of the human visual system, we use reinforcement learning techniques to train efficient search models for detection. Our sequential method is weakly supervised and general (it does not require eye movements), finds optimal search strategies for any given detection confidence function and achieves performance similar to exhaustive sliding window search at a fraction of its computational cost.
Outage Performance of Uplink Two-tier Networks Under Backhaul Constraints
Venue: arXiv cs.IT 2014
Year: 2014

Multi-tier cellular communication networks constitute a promising approach to expand the coverage of cellular networks and enable them to offer higher data rates. In this paper, an uplink two-tier communication network is studied, in which macro users, femto users and femto access points are geometrically located inside the coverage area of a macro base station according to Poisson point processes. Each femtocell is assumed to have a fixed backhaul constraint that puts a limit on the maximum number of femto and macro users it can service. Under this backhaul constraint, the network adopts a special open access policy, in which each macro user is either assigned to its closest femto access point or to the macro base station, depending on the ratio between its distances from those two. Under this model, upper and lower bounds on the outage probabilities experienced by users serviced by femto access points are derived as functions of the distance between the macro base station and the femto access point serving them. Similarly, upper and lower bounds on the outage probabilities of the users serviced by the macro base station are obtained. The bounds in both cases are confirmed via simulation results.
Multi-scale Population and Mobility Estimation with Geo-tagged Tweets
Venue: arXiv cs.SI 2014
Year: 2014

Recent outbreaks of Ebola and Dengue viruses have again elevated the significance of the capability to quickly predict disease spread in an emergent situation. However, existing approaches usually rely heavily on the time-consuming census processes, or the privacy-sensitive call logs, leading to their unresponsive nature when facing the abruptly changing dynamics in the event of an outbreak. In this paper we study the feasibility of using large-scale Twitter data as a proxy of human mobility to model and predict disease spread. We report that for Australia, Twitter users' distribution correlates well the census-based population distribution, and that the Twitter users' travel patterns appear to loosely follow the gravity law at multiple scales of geographic distances, i.e. national level, state level and metropolitan level. The radiation model is also evaluated on this dataset though it has shown inferior fitness as a result of Australia's sparse population and large landmass. The outcomes of the study form the cornerstones for future work towards a model-based, responsive prediction method from Twitter data for disease spread.
$\ell_p$ Row Sampling by Lewis Weights
Venue: arXiv cs.DS 2014
Year: 2014

We give a simple algorithm to efficiently sample the rows of a matrix while preserving the p-norms of its product with vectors. Given an $n$-by-$d$ matrix $\boldsymbol{\mathit{A}}$, we find with high probability and in input sparsity time an $\boldsymbol{\mathit{A}}'$ consisting of about $d \log{d}$ rescaled rows of $\boldsymbol{\mathit{A}}$ such that $\| \boldsymbol{\mathit{A}} \boldsymbol{\mathit{x}} \|_1$ is close to $\| \boldsymbol{\mathit{A}}' \boldsymbol{\mathit{x}} \|_1$ for all vectors $\boldsymbol{\mathit{x}}$. We also show similar results for all $\ell_p$ that give nearly optimal sample bounds in input sparsity time. Our results are based on sampling by "Lewis weights", which can be viewed as statistical leverage scores of a reweighted matrix. We also give an elementary proof of the guarantees of this sampling process for $\ell_1$.