OpenRaiser/Intern-Atlas huggingface dataset:
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Venue: arXiv cs.CV 2016
Year: 2016

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member auto-encoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33\% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 . Comparison with other major algorithms substantiates the high efficacy of our model.
Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching
Venue: arXiv cs.AI 2016
Year: 2016

The Gaussian mixture model is a classic technique for clustering and data modeling that is used in numerous applications. With the rise of big data, there is a need for parameter estimation techniques that can handle streaming data and distribute the computation over several processors. While online variants of the Expectation Maximization (EM) algorithm exist, their data efficiency is reduced by a stochastic approximation of the E-step and it is not clear how to distribute the computation over multiple processors. We propose a Bayesian learning technique that lends itself naturally to online and distributed computation. Since the Bayesian posterior is not tractable, we project it onto a family of tractable distributions after each observation by matching a set of sufficient moments. This Bayesian moment matching technique compares favorably to online EM in terms of time and accuracy on a set of data modeling benchmarks.
Window Parity Games: An Alternative Approach Toward Parity Games with Time Bounds (Full Version)
Venue: arXiv cs.LO 2016
Year: 2016

Classical objectives in two-player zero-sum games played on graphs often deal with limit behaviors of infinite plays: e.g., mean-payoff and total-payoff in the quantitative setting, or parity in the qualitative one (a canonical way to encode omega-regular properties). Those objectives offer powerful abstraction mechanisms and often yield nice properties such as memoryless determinacy. However, their very nature provides no guarantee on time bounds within which something good can be witnessed. In this work, we consider two approaches toward inclusion of time bounds in parity games. The first one, parity-response games, is based on the notion of finitary parity games [CHH09] and parity games with costs [FZ14,WZ16]. The second one, window parity games, is inspired by window mean-payoff games [CDRR15]. We compare the two approaches and show that while they prove to be equivalent in some contexts, window parity games offer a more tractable alternative when the time bound is given as a parameter (P-c. vs. PSPACE-C.). In particular, it provides a conservative approximation of parity games computable in polynomial time. Furthermore, we extend both approaches to the multi-dimension setting. We give the full picture for both types of games with regard to complexity and memory bounds. [CHH09] K. Chatterjee, T.A. Henzinger, F. Horn (2009): Finitary winning in omega-regular games. ACM Trans. Comput. Log. 11(1). [FZ14] N. Fijalkow, M. Zimmermann (2014): Parity and Streett Games with Costs. LMCS 10(2). [WZ16] A. Weinert, M. Zimmermann (2016): Easy to Win, Hard to Master: Optimal Strategies in Parity Games with Costs. Proc. of CSL, LIPIcs 62, pp. 31:1-31:17, Schloss Dagstuhl - LZI. [CDRR15] K. Chatterjee, L. Doyen, M. Randour, J.-F. Raskin (2015): Looking at mean-payoff and total-payoff through windows. Information and Computation 242, pp. 25-52.
A Multiband OFDMA Heterogeneous Network for Millimeter Wave 5G Wireless Applications
Venue: arXiv cs.IT 2016
Year: 2016

Emerging fifth generation (5G) wireless networks require massive bandwidth in higher frequency bands, extreme network densities, and flexibility of supporting multiple wireless technologies in order to provide higher data rates and seamless coverage. It is expected that utilization of the large bandwidth in the millimeter-wave (mmWave) band and deployment of heterogeneous networks (HetNets) will help address the data rate requirements of 5G networks. However, high pathloss and shadowing in the mmWave frequency band, strong interference in the HetNets due to massive network densification, and coor- dination of various air interfaces are challenges that must be addressed. In this paper, we consider a relay-based multiband orthogonal frequency division multiple access (OFDMA) HetNet in which mmWave small cells are deployed within the service area of macro cells. Specifically, we attempt to exploit the distinct propagation characteristics of mmWave bands (i.e., 60 GHz- the V-band - and 70-80 GHz -the E-band-) and the Long Term Evolution (LTE) band to maximize overall data rate of the network via efficient resource allocation. The problem is solved using a modified dual decomposition approach and then a low complexity greedy solution based on iterative activity selection algorithm is presented. Simulation results show that the proposed approach outperforms conventional schemes.
Levelized Cost of Energy for PV and Grid Scale Energy Storage Systems
Venue: arXiv cs.SY 2016
Year: 2016

With the increasing penetration of renewable energy sources and energy storage devices in the power system, it is important to evaluate the cost of the system by using Levelized Cost of Energy (LCOE). In this paper a new metric, Levelized Cost of Delivery (LCOD) is proposed to calculate the LCOE for the energy storage. The recent definitions in LCOE for renewable energy system has been reviewed. From fundamental principles, it is demonstrated that there is a need to introduce a new method to evaluate LCOE of the system as the conventional LCOE is not applicable for renewable energy storage systems. Three years of solar irradiance data in Africa collected from Johannesburg and the national load data from Kenya are obtained for case studies. The proposed cost calculation methods are evaluated for two types of storage technologies (Vanadium Redox Battery (VRB) and Lithium-ion) with real-life data. It shows that the marginal LCOE and LCOD indices can be used to assist policymakers to consider the discount rate and the type of storage technology for a cost effective renewable storage energy system.
Deep CTR Prediction in Display Advertising
Venue: arXiv cs.CV 2016
Year: 2016

Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction model. However, LR model lacks the ability of extracting complex and intrinsic nonlinear features from handcrafted high-dimensional image features, which limits its effectiveness. To solve this issue, in this paper, we introduce a novel deep neural network (DNN) based model that directly predicts the CTR of an image ad based on raw image pixels and other basic features in one step. The DNN model employs convolution layers to automatically extract representative visual features from images, and nonlinear CTR features are then learned from visual features and other contextual features by using fully-connected layers. Empirical evaluations on a real world dataset with over 50 million records demonstrate the effectiveness and efficiency of this method.
Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities
Venue: arXiv cs.CV 2016
Year: 2016

The human activity recognition in the IoT environment plays the central role in the ambient assisted living, where the human activities can be represented as a concatenated event stream generated from various smart objects. From the concatenated event stream, each activity should be distinguished separately for the human activity recognition to provide services that users may need. In this regard, accurately segmenting the entire stream at the precise boundary of each activity is indispensable high priority task to realize the activity recognition. Multiple human activities in an IoT environment generate varying event stream patterns, and the unpredictability of these patterns makes them include redundant or missing events. In dealing with this complex segmentation problem, we figured out that the dynamic and confusing patterns cause major problems due to: inclusive event stream, redundant events, and shared events. To address these problems, we exploited the contextual relationships associated with the activity status about either ongoing or terminated/started. To discover the intrinsic relationships between the events in a stream, we utilized the LSTM model by rendering it for the activity segmentation. Then, the inferred boundaries were revised by our validation algorithm for a bit shifted boundaries. Our experiments show the surprising result of high accuracy above 95%, on our own testbed with various smart objects. This is superior to the prior works that even do not assume the environment with multi-user activities, where their accuracies are slightly above 80% in their test environment. It proves that our work is feasible enough to be applied in the IoT environment.
Power-Delay Tradeoff in Multi-User Mobile-Edge Computing Systems
Venue: arXiv cs.IT 2016
Year: 2016

Mobile-edge computing (MEC) has recently emerged as a promising paradigm to liberate mobile devices from increasingly intensive computation workloads, as well as to improve the quality of computation experience. In this paper, we investigate the tradeoff between two critical but conflicting objectives in multi-user MEC systems, namely, the power consumption of mobile devices and the execution delay of computation tasks. A power consumption minimization problem with task buffer stability constraints is formulated to investigate the tradeoff, and an online algorithm that decides the local execution and computation offloading policy is developed based on Lyapunov optimization. Specifically, at each time slot, the optimal frequencies of the local CPUs are obtained in closed forms, while the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method. Performance analysis is conducted for the proposed algorithm, which indicates that the power consumption and execution delay obeys an [O (1/V); O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters to the system performance.
Markov Random Field Model-Based Salt and Pepper Noise Removal
Venue: arXiv cs.CV 2016
Year: 2016

Problem of impulse noise reduction is a very well studied problem in image processing community and many different approaches have been proposed to tackle this problem. In the current work, the problem of fixed value impulse noise (salt and pepper) removal from images is investigated by use of a Markov Random Field (MRF) models with smoothness priors. After the formulation of the problem as an inpainting problem, graph cuts with $α$-expansion moves are considered for minimization of the energy functional. As for comparisons, several other minimization techniques that are widely used for MRF models' optimization are considered and the results are compared using Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) as metrics. The investigations show the superiority of graph cuts with $α$-expansion moves over the other techniques both in terms of PSNR and also computational times.
Asymptotically Optimal Regenerating Codes Over Any Field
Venue: arXiv cs.IT 2016
Year: 2016

The study of regenerating codes has advanced tremendously in recent years. However, most known constructions require large field size, and hence may be hard to implement in practice. By using notions from the theory of extension fields, we obtain two explicit constructions of regenerating codes. These codes approach the cut-set bound as the reconstruction degree increases, and may be realized over any given field if the file size is large enough. Since distributed storage systems are the main purpose of regenerating codes, this file size restriction is trivially satisfied in most conceivable scenarios. The first construction attains the cut-set bound at the MBR point asymptotically for all parameters, whereas the second one attains the cut-set bound at the MSR point asymptotically for low-rate parameters.
Towards a global scientific brain: Indicators of researcher mobility using co-affiliation data
Venue: arXiv cs.DL 2016
Year: 2016

This paper analyses the potential use of bibliometric data for mapping and applying network analysis to mobility flows. We show case mobility networks at three different levels of aggregation: at the country level, at the city level and at the institutional level. We reflect on the potential uses of bibliometric data to inform research policies with regard to scientific mobility.
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Venue: arXiv cs.CR 2016
Year: 2016

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
Venue: arXiv cs.CL 2016
Year: 2016

We compare policy differences across institutions by embedding representations of the entire legal corpus of each institution and the vocabulary shared across all corpora into a continuous vector space. We apply our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and official summaries of Congressional bills. The model discerns meaningful differences between government branches. We also learn representations for more fine-grained word sources: individual Presidents and (2-year) Congresses. The similarities between learned representations of Congresses over time and sitting Presidents are negatively correlated with the bill veto rate, and the temporal ordering of Presidents and Congresses was implicitly learned from only text. With the resulting vectors we answer questions such as: how does Obama and the 113th House differ in addressing climate change and how does this vary from environmental or economic perspectives? Our work illustrates vector-arithmetic-based investigations of complex relationships between word sources based on their texts. We are extending this to create a more comprehensive legal semantic map.
The Cascading Haar Wavelet algorithm for computing the Walsh-Hadamard Transform
Venue: arXiv cs.DS 2016
Year: 2016

We propose a novel algorithm for computing the Walsh-Hadamard Transform (WHT) which consists entirely of Haar wavelet transforms. We prove that the algorithm, which we call the Cascading Haar Wavelet (CHW) algorithm, shares precisely the same serial complexity as the popular divide-and-conquer algorithm for the WHT. We also propose a natural way of parallelizing the algorithm which has a number of attractive features.
Consistency Maintenance of State of Management Data in P2P-based Autonomic Network Management
Venue: arXiv cs.NI 2016
Year: 2016

Complex Dynamic Networks can be exploited in solving problems where traditional solutions may not be sufficient. The increasing complexity of computer networks imposes problems to the current network management solutions. In this context, network management is an example of a research area that could benefit from the use of CDNs. However, the consistency of state of management data among the elements that build management CDNs (management nodes) is an important challenge. Traditional mechanisms to maintain consistency of these states are supported by some centralization which wastes some desirable properties of CDNs (e.g., robustness). In contrast to these mechanisms, we propose a distributed, scalable and robust mechanism to maintain the consistency of state of management data in management CDNs. Our mechanism introduces multi-agent truth maintenance features and communication strategies based on dynamic process to provide consistency maintenance of state of management data. We developed a model of a management CDN on Peersim simulator to perform experiments. Besides, 2 case studies are presented. The result obtained supports our scalability and robustness claims.
Improving and extending the testing of distributions for shape-restricted properties
Venue: arXiv cs.CC 2016
Year: 2016

Distribution testing deals with what information can be deduced about an unknown distribution over $\{1,\ldots,n\}$, where the algorithm is only allowed to obtain a relatively small number of independent samples from the distribution. In the extended conditional sampling model, the algorithm is also allowed to obtain samples from the restriction of the original distribution on subsets of $\{1,\ldots,n\}$. In 2015, Canonne, Diakonikolas, Gouleakis and Rubinfeld unified several previous results, and showed that for any property of distributions satisfying a "decomposability" criterion, there exists an algorithm (in the basic model) that can distinguish with high probability distributions satisfying the property from distributions that are far from it in the variation distance. We present here a more efficient yet simpler algorithm for the basic model, as well as very efficient algorithms for the conditional model, which until now was not investigated under the umbrella of decomposable properties. Additionally, we provide an algorithm for the conditional model that handles a much larger class of properties. Our core mechanism is a way of efficiently producing an interval-partition of $\{1,\ldots,n\}$ that satisfies a "fine-grain" quality. We show that with such a partition at hand we can directly move forward with testing individual intervals, instead of first searching for the "correct" partition of $\{1,\ldots,n\}$.
Adaptive Control Strategy for Constant Optical Flow Divergence Landing
Venue: arXiv cs.RO 2016
Year: 2016

Bio-inspired methods can provide efficient solutions to perform autonomous landing for Micro Air Vehicles (MAVs). Flying insects such as honeybees perform vertical landings by keeping flow divergence constant. This leads to an exponential decay of both height and vertical velocity, and allows for smooth and safe landings. However, the presence of noise and delay in obtaining flow divergence estimates will cause instability of the landing when the control gains are not adapted to the height. In this paper, we propose a strategy that deals with this fundamental problem of optical flow control. The key to the strategy lies in the use of a recent theory that allows the MAV to see distance by means of its control instability. At the start of a landing, the MAV detects the height by means of an oscillating movement and sets the control gains accordingly. Then, during descent, the gains are reduced exponentially, with mechanisms in place to reduce or increase the gains if the actual trajectory deviates too much from an ideal constant divergence landing. Real-world experiments demonstrate stable landings of the MAV in both indoor and windy outdoor environments.
An Analysis of Technical Debt Management Through Resources Allocation Policies in Software Maintenance Process
Venue: arXiv cs.SE 2016
Year: 2016

This paper presents an analysis of technical debt management through resources allocation policies in software maintenance process during its operation to demonstrate how different strategies leads to the emergence of different behaviors along the evolution path. To achieve this objective, this work used the System Dynamic approach for building a computational simulation model based on extensive literature review and secondary data. Most of the works that applied the System Dynamics on software projects research, focused on initial phases of its life cycle, leaving a gap to be explored regarding the long-term behaviors of the operation and maintenance phases. The results demonstrated that the excessive focus on the perfective maintenance activities could be more costly than performing regular preventive maintenance to reduce the technical debt incurred, ending up with fewer functionalities deployed, higher backlog, lower productivity, lower maintainability and higher technical principal.
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a theoretical analysis and practical evaluation of the main bottlenecks towards a scalable distributed solution for the training of Deep Neuronal Networks (DNNs). The presented results show, that the current state of the art approach, using data-parallelized Stochastic Gradient Descent (SGD), is quickly turning into a vastly communication bound problem. In addition, we present simple but fixed theoretic constraints, preventing effective scaling of DNN training beyond only a few dozen nodes. This leads to poor scalability of DNN training in most practical scenarios.