OpenRaiser/Intern-Atlas huggingface dataset:
Global sensitivity analysis with 2d hydraulic codes: applied protocol and practical tool
Venue: arXiv cs.CE 2016
Year: 2016

Global Sensitivity Analysis (GSA) methods are useful tools to rank input parameters uncertainties regarding their impact on result variability. In practice, such type of approach is still at an exploratory level for studies relying on 2D Shallow Water Equations (SWE) codes as GSA requires specific tools and deals with important computational capacity. The aim of this paper is to provide both a protocol and a tool to carry out a GSA for 2D hydraulic modelling applications. A coupled tool between Prom{é}th{é}e (a parametric computation environment) and FullSWOF 2D (a code relying on 2D SWE) has been set up: Prom{é}th{é}e-FullSWOF 2D (P-FS). The main steps of our protocol are: i) to identify the 2D hydraulic code input parameters of interest and to assign them a probability density function, ii) to propagate uncertainties within the model, and iii) to rank the effects of each input parameter on the output of interest. For our study case, simulations of a river flood event were run with uncertainties introduced through three parameters using P-FS tool. Tests were performed on regular computational mesh, spatially discretizing an urban area, using up to 17.9 million of computational points. P-FS tool has been installed on a cluster for computation. Method and P-FS tool successfully allow the computation of Sobol indices maps. Keywords Uncertainty, flood hazard modelling, global sensitivity analysis, 2D shallow water equation, Sobol index. Analyse globale de sensibilit{é} en mod{é}lisation hydrauliqu{è} a surface libre 2D : application d'un protocole et d{é}veloppement d'outils op{é}rationnels -- Les m{é}thodes d'analyse de sensibilit{é} permettent de contr{ô}ler la robustesse des r{é}sultats de mod{é}lisation ainsi que d'identifier le degr{é} d'influence des param etres d' entr{é}e sur le r{é}sultat en sortie d'un mod ele. Le processus complet constitue une analyse globale de sensibilit{é} (GSA). Ce type d'approche pr{é}sente un grand int{é}r{ê}t pour analyser les incer-titudes de r{é}sultats de mod{é}lisation , mais est toujours a un stade exploratoire dans les etudes appliqu{é}es mettant en jeu des codes bas{é}s sur la r{é}solution bidimensionnelle des equations de Saint-Venant. En effet, l' impl{é}mentation d'une GSA est d{é}licate car elle
OptCon: An Adaptable SLA-Aware Consistency Tuning Framework for Quorum-based Stores
Venue: arXiv cs.DC 2016
Year: 2016

Users of distributed datastores that employ quorum-based replication are burdened with the choice of a suitable client-centric consistency setting for each storage operation. The above matching choice is difficult to reason about as it requires deliberating about the tradeoff between the latency and staleness, i.e., how stale (old) the result is. The latency and staleness for a given operation depend on the client-centric consistency setting applied, as well as dynamic parameters such as the current workload and network condition.We present OptCon, a novel machine learning-based predictive framework, that can automate the choice of client-centric consistency setting under user-specified latency and staleness thresholds given in the service level agreement (SLA). Under a given SLA, OptCon predicts a client-centric consistency setting that is matching, i.e., it is weak enough to satisfy the latency threshold, while being strong enough to satisfy the staleness threshold. While manually tuned consistency settings remain fixed unless explicitly reconfigured, OptCon tunes consistency settings on a per-operation basis with respect to changing workload and network state. Using decision tree learning, OptCon yields 0.14 cross validation error in predicting matching consistency settings under latency and staleness thresholds given in the SLA. We demonstrate experimentally that OptCon is at least as effective as any manually chosen consistency settings in adapting to the SLA thresholds for different use cases. We also demonstrate that OptCon adapts to variations in workload, whereas a given manually chosen fixed consistency setting satisfies the SLA only for a characteristic workload.
An Empirical Study of Online Packet Scheduling Algorithms
Venue: arXiv cs.DS 2016
Year: 2016

This work studies online scheduling algorithms for buffer management, develops new algorithms, and analyzes their performances. Packets arrive at a release time r, with a non-negative weight w and an integer deadline d. At each time step, at most one packet is scheduled. The modified greedy (MG) algorithm is 1.618-competitive for the objective of maximizing the sum of weights of packets sent, assuming agreeable deadlines. We analyze the empirical behavior of MG in a situation with arbitrary deadlines and demonstrate that it is at a disadvantage when frequently preferring maximum weight packets over early deadline ones. We develop the MLP algorithm, which remedies this problem whilst mimicking the behavior of the offline algorithm. Our comparative analysis shows that, although the competitive ratio of MLP is not as good as that of MG, it performs better in practice. We validate this by simulating the behavior of both algorithms under a spectrum of simulated parameter settings. Finally, we propose the design of three additional algorithms, which may help in improving performance in practice.
Object Recognition Based on Amounts of Unlabeled Data
Venue: arXiv cs.CV 2016
Year: 2016

This paper proposes a novel semi-supervised method on object recognition. First, based on Boost Picking, a universal algorithm, Boost Picking Teaching (BPT), is proposed to train an effective binary-classifier just using a few labeled data and amounts of unlabeled data. Then, an ensemble strategy is detailed to synthesize multiple BPT-trained binary-classifiers to be a high-performance multi-classifier. The rationality of the strategy is also analyzed in theory. Finally, the proposed method is tested on two databases, CIFAR-10 and CIFAR-100. Using 2% labeled data and 98% unlabeled data, the accuracies of the proposed method on the two data sets are 78.39% and 50.77% respectively.
Multimedia Satellite Networks and TCP/IP Traffic Transport
Venue: arXiv cs.NI 2016
Year: 2016

To meet an increasing demand for multimedia services and electronic connectivity across the world, satellite networks will play an indispensable role in the deployment of global networks. The new services gaining momentum include mobile services, private intranets and high data rate internet access carried over integrated satellite-fiber networks. Several performance issues need to be addressed before a transport layer protocol, like TCP can satisfactorily work over satellite ATM for large delay-bandwidth networks. In this paper, we review the proposed satellite systems and discuss challenges such as, traffic management and QoS requirements for broadband satellite ATM networks. The performance results of TCP enhancements for Unspecified Bit Rate over ATM (ATM-UBR+) for large bandwidth-delay environments with various end system policies and drop policies for several buffer sizes are presented.
Energy Sensing and Monitoring Framework with an Integrated Communication Backbone in the Energy Efficient Intelligent Buildings
Venue: arXiv cs.NI 2016
Year: 2016

Building environments are significant sources of global energy consumption. To create energy efficient buildings, the first step is to sense and monitor all the energy-consuming appliances in the buildings and record all the energy consumption information. After that, appropriate energy saving policies can be decided and the instructions can be sent to the control devices to apply the energy saving adjustments. To do that, in-building two-way communication networks are needed to connect all the sensors to collect information as well as to send control instructions. However, most of the current devices are provided by separate manufacturers and with separate network infrastructures and so there is not much integration and interaction among different subsystems. In this paper, we envision a new energy sensing and monitoring framework with integrated communication backbone in the intelligent building environments. Specifically, through comprehensive comparisons and investigations, we study different candidate communicating media and protocols like wireline, wireless, and power-line communications technologies that potentially can be used in the intelligent buildings to realize the goals of coordination, integration, and energy efficiency. Also, we propose an extension "smart box" for integration of the devices before the maturity of the standardization process. Cloud computing and smart phone technologies are also introduced to realize the goals of improving energy efficiency and promote global sustainability.
On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem
Venue: arXiv cs.LG 2016
Year: 2016

This paper studies the trade-off between two different kinds of pure exploration: breadth versus depth. The most biased coin problem asks how many total coin flips are required to identify a "heavy" coin from an infinite bag containing both "heavy" coins with mean $θ_1 \in (0,1)$, and "light" coins with mean $θ_0 \in (0,θ_1)$, where heavy coins are drawn from the bag with probability $α\in (0,1/2)$. The key difficulty of this problem lies in distinguishing whether the two kinds of coins have very similar means, or whether heavy coins are just extremely rare. This problem has applications in crowdsourcing, anomaly detection, and radio spectrum search. Chandrasekaran et. al. (2014) recently introduced a solution to this problem but it required perfect knowledge of $θ_0,θ_1,α$. In contrast, we derive algorithms that are adaptive to partial or absent knowledge of the problem parameters. Moreover, our techniques generalize beyond coins to more general instances of infinitely many armed bandit problems. We also prove lower bounds that show our algorithm's upper bounds are tight up to $\log$ factors, and on the way characterize the sample complexity of differentiating between a single parametric distribution and a mixture of two such distributions. As a result, these bounds have surprising implications both for solutions to the most biased coin problem and for anomaly detection when only partial information about the parameters is known.
Optimal Traffic Aggregation in Multi-RAT Heterogeneous Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

Traffic load balancing and radio resource management is key to harness the dense and increasingly heterogeneous deployment of next generation "$5$G" wireless infrastructure. Strategies for aggregating user traffic from across multiple radio access technologies (RATs) and/or access points (APs) would be crucial in such heterogeneous networks (HetNets), but are not well investigated. In this paper, we develop a low complexity solution for maximizing an $α$-optimal network utility leveraging the multi-link aggregation (simultaneous connectivity to multiple RATs/APs) capability of users in the network. The network utility maximization formulation has maximization of sum rate ($α=0$), maximization of minimum rate ($α\to \infty$), and proportional fair ($α=1$) as its special cases. A closed form is also developed for the special case where a user aggregates traffic from at most two APs/RATs, and hence can be applied to practical scenarios like LTE-WLAN aggregation (LWA) and LTE dual-connectivity solutions. It is shown that the required objective may also be realized through a decentralized implementation requiring a series of message exchanges between the users and network. Using comprehensive system level simulations, it is shown that optimal leveraging of multi-link aggregation leads to substantial throughput gains over single RAT/AP selection techniques.
On the Performance of Millimeter Wave-based RF-FSO Links with HARQ Feedback
Venue: arXiv cs.IT 2016
Year: 2016

This paper studies the performance of hybrid radio-frequency (RF) and free-space optical (FSO) links in the cases with and without hybrid automatic repeat request (HARQ). Considering millimeter wave (mmwave) characteristics in the RF link and pointing errors in the FSO link, we derive closed-form expressions for the message decoding probabilities as well as the throughput and the outage probability of the RF-FSO setups. We also evaluate the effect of various parameters such as power amplifiers efficiency, different transmission techniques in the FSO link, pointing errors in the FSO link as well as different coherence times/symbol rates of the RF and the FSO links on the throughput and outage probability. The results show the efficiency of the RF-FSO links in different conditions. Moreover, the HARQ can effectively improve the outage probability/energy efficiency, and compensate the effect of hardware impairments in RF-FSO links.
Measuring Book Impact Based on the Multi-granularity Online Review Mining
Venue: arXiv cs.DL 2016
Year: 2016

As with articles and journals, the customary methods for measuring books' academic impact mainly involve citations, which is easy but limited to interrogating traditional citation databases and scholarly book reviews, Researchers have attempted to use other metrics, such as Google Books, libcitation, and publisher prestige. However, these approaches lack content-level information and cannot determine the citation intentions of users. Meanwhile, the abundant online review resources concerning academic books can be used to mine deeper information and content utilizing altmetric perspectives. In this study, we measure the impacts of academic books by multi-granularity mining online reviews, and we identify factors that affect a book's impact. First, online reviews of a sample of academic books on Amazon.cn are crawled and processed. Then, multi-granularity review mining is conducted to identify review sentiment polarities and aspects' sentiment values. Lastly, the numbers of positive reviews and negative reviews, aspect sentiment values, star values, and information regarding helpfulness are integrated via the entropy method, and lead to the calculation of the final book impact scores. The results of a correlation analysis of book impact scores obtained via our method versus traditional book citations show that, although there are substantial differences between subject areas, online book reviews tend to reflect the academic impact. Thus, we infer that online reviews represent a promising source for mining book impact within the altmetric perspective and at the multi-granularity content level. Moreover, our proposed method might also be a means by which to measure other books besides academic publications.
Blind signal separation and identification of mixtures of images
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a fresh procedure to handle image mixtures by means of blind signal separation relying on a combination of second order and higher order statistics techniques are introduced. The problem of blind signal separation is reassigned to the wavelet domain. The key idea behind this method is that the image mixture can be decomposed into the sum of uncorrelated and/or independent sub-bands using wavelet transform. Initially, the observed image is pre-whitened in the space domain. Afterwards, an initial separation matrix is estimated from the second order statistics de-correlation model in the wavelet domain. Later, this matrix will be used as an initial separation matrix for the higher order statistics stage in order to find the best separation matrix. The suggested algorithm was tested using natural images.Experiments have confirmed that the use of the proposed process provides promising outcomes in identifying an image from noisy mixtures of images.
GENMR: Generalized Query Processing through Map Reduce In Cloud Database Management System
Venue: arXiv cs.DC 2016
Year: 2016

Big Data, Cloud computing, Cloud Database Management techniques, Data Science and many more are the fantasizing words which are the future of IT industry. For all the new techniques one common thing is that they deal with Data, not just Data but the Big Data. Users store their various kinds of data on cloud repositories. Cloud Database Management System deals with such large sets of data. For processing such gigantic amount of data, traditional approaches are not suitable because these approaches are not able to handle such size of data. To handle these, various solutions have been developed such as Hadoop, Map Reduce Programming codes, HIVE, PIG etc. Map Reduce codes provides both scalability and reliability. But till date, users are habitual of SQL, Oracle kind of codes for dealing with data and they are not aware of Map Reduce codes. In this paper, a generalized model GENMR has been implemented, which takes queries written in various RDBMS forms like SQL, ORACLE, DB2, MYSQL and convert into Map Reduce codes. A comparison has been done to evaluate the performance of GENMR with latest techniques like HIVE and PIG and it has been concluded that GENMR shows much better performance as compare to both the techniques. We also introduce an optimization technique for mapper placement problems to enhance the effect of parallelism which improves the performance of such Amalgam approach.
Support Driven Wavelet Frame-based Image Deblurring
Venue: arXiv cs.CV 2016
Year: 2016

The wavelet frame systems have been playing an active role in image restoration and many other image processing fields over the past decades, owing to the good capability of sparsely approximating piece-wise smooth functions such as images. In this paper, we propose a novel wavelet frame based sparse recovery model called \textit{Support Driven Sparse Regularization} (SDSR) for image deblurring, where the partial support information of frame coefficients is attained via a self-learning strategy and exploited via the proposed truncated $\ell_0$ regularization. Moreover, the state-of-the-art image restoration methods can be naturally incorporated into our proposed wavelet frame based sparse recovery framework. In particular, in order to achieve reliable support estimation of the frame coefficients, we make use of the state-of-the-art image restoration result such as that from the IDD-BM3D method as the initial reference image for support estimation. Our extensive experimental results have shown convincing improvements over existing state-of-the-art deblurring methods.
Video Interpolation using Optical Flow and Laplacian Smoothness
Venue: arXiv cs.CV 2016
Year: 2016

Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the local smoothness. Similar to Li et al., our approach adopts a mesh to the image with a resolution up to one vertex per pixel and uses angle constraints to ensure sensible local deformations between image pairs. The Laplacian Mesh constraints are expressed wholly inside the optical flow optimization, and can be applied in a straightforward manner to a wide range of image tracking and registration problems. We evaluate our approach by testing on several benchmark datasets, including the Middlebury and Garg et al. datasets. In addition, we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data.
Robust transport over networks
Venue: arXiv cs.SY 2016
Year: 2016

We consider transport over a strongly connected, directed graph. The scheduling amounts to selecting transition probabilities for a discrete-time Markov evolution which is designed to be consistent with certain initial and final marginals. The random evolution is selected to be closest to a prior measure on paths in the relative entropy sense, i.e., a Schroedinger bridge between the two marginals. This is an atypical stochastic control problem where the control consists in suitably modifying the transition mechanism. The prior can incorporate cost of traversing edges or allocate equal probability to all paths of equal length connecting any two given nodes, i.e., a uniform measure on paths. This latter choice relies on the so-called Ruelle-Bowen random walk and gives rise to a scheduling that tends to utilize all paths as uniformly as the topology allows. Thus, when the Ruelle-Bowen law is taken as prior, the transportation plan tends to lessen congestion and ensure a level of robustness. We show that the Ruelle-Bowen law is itself a Schroedinger bridge albeit with a prior that is not a probability measure. The paradigm of Schroedinger bridges as a mechanism for scheduling transport on networks can be adapted to graphs that are not strongly connected as well as to weighted graphs. The latter leads to transportation plans that effect a compromise between robustness and transportation cost.
Optimal Joint Power and Subcarrier Allocation for MC-NOMA Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate the resource allocation algorithm design for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The proposed algorithm is obtained from the solution of a non-convex optimization problem for the maximization of the weighted system throughput. We employ monotonic optimization to develop the optimal joint power and subcarrier allocation policy. The optimal resource allocation policy serves as a performance benchmark due to its high complexity. Furthermore, to strike a balance between computational complexity and optimality, a suboptimal scheme with low computational complexity is proposed. Our simulation results reveal that the suboptimal algorithm achieves a close-to-optimal performance and MC-NOMA employing the proposed resource allocation algorithm provides a substantial system throughput improvement compared to conventional multicarrier orthogonal multiple access (MC-OMA).
A Draft Memory Model on Spiking Neural Assemblies
Venue: arXiv cs.NE 2016
Year: 2016

A draft memory model (DM) for neural networks with spike propagation delay (SNNwD) is described. Novelty in this approach are that the DM learns immediately, with stimuli presented once, without synaptic weight changes, and without external learning algorithm. Basal on this model is to trap spikes within neural loops. In order to construct the DM we developed two functional blocks, also described herein. The decoder block receives input from a single spikes source and connect it to one among many outputs. The selector block operates in the opposite direction, receiving many spikes sources and connecting one of them to a single output. We realized conceptual proofs by testing the DM in the prime numbers classifying task. This activation-based memory can be used as immediate and short-term memory.
Evolution of active categorical image classification via saccadic eye movement
Venue: arXiv cs.CV 2016
Year: 2016

Pattern recognition and classification is a central concern for modern information processing systems. In particular, one key challenge to image and video classification has been that the computational cost of image processing scales linearly with the number of pixels in the image or video. Here we present an intelligent machine (the "active categorical classifier," or ACC) that is inspired by the saccadic movements of the eye, and is capable of classifying images by selectively scanning only a portion of the image. We harness evolutionary computation to optimize the ACC on the MNIST hand-written digit classification task, and provide a proof-of-concept that the ACC works on noisy multi-class data. We further analyze the ACC and demonstrate its ability to classify images after viewing only a fraction of the pixels, and provide insight on future research paths to further improve upon the ACC presented here.