OpenRaiser/Intern-Atlas huggingface dataset:
Off-the-grid Two-Dimensional Line Spectral Estimation With Prior Information
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we provide a method to recover off-the-grid frequencies of a signal in two-dimensional (2-D) line spectral estimation. Most of the literature in this field focuses on the case in which the only information is spectral sparsity in a continuous domain and does not consider prior information. However, in many applications such as radar and sonar, one has extra information about the spectrum of the signal of interest. The common way of accommodating prior information is to use weighted atomic norm minimization. We present a new semidefinite program using the theory of positive trigonometric polynomials that incorporate this prior information into 2-D line spectral estimation. Specifically, we assume prior knowledge of 2-D frequency subbands in which signal frequency components are located. Our approach improves the recovery performance compared with the previous work that does not consider prior information. Through numerical experiments, we find out that the amount of this improvement depends on prior information we have about the locations of the true frequencies.
Analyzing Large-Scale Multiuser Molecular Communication via 3D Stochastic Geometry
Venue: arXiv cs.IT 2017
Year: 2017

Information delivery using chemical molecules is an integral part of biology at multiple distance scales and has attracted recent interest in bioengineering and communication theory. Potential applications include cooperative networks with a large number of simple devices that could be randomly located (e.g., due to mobility). This paper presents the first tractable analytical model for the collective signal strength due to randomly-placed transmitters in a three-dimensional (3D) large-scale molecular communication system, either with or without degradation in the propagation environment. Transmitter locations in an unbounded and homogeneous fluid are modelled as a homogeneous Poisson point process. By applying stochastic geometry, analytical expressions are derived for the expected number of molecules absorbed by a fully-absorbing receiver or observed by a passive receiver. The bit error probability is derived under ON/OFF keying and either a constant or adaptive decision threshold. Results reveal that the combined signal strength increases proportionately with the transmitter density, and the minimum bit error probability can be improved by introducing molecule degradation. Furthermore, the analysis of the system can be generalized to other receiver designs and other performance characteristics in large-scale molecular communication systems.
A Match in Time Saves Nine: Deterministic Online Matching With Delays
Venue: arXiv cs.DS 2017
Year: 2017

We consider the problem of online Min-cost Perfect Matching with Delays (MPMD) introduced by Emek et al. (STOC 2016). In this problem, an even number of requests appear in a metric space at different times and the goal of an online algorithm is to match them in pairs. In contrast to traditional online matching problems, in MPMD all requests appear online and an algorithm can match any pair of requests, but such decision may be delayed (e.g., to find a better match). The cost is the sum of matching distances and the introduced delays. We present the first deterministic online algorithm for this problem. Its competitive ratio is $O(m^{\log_2 5.5})$ $ = O(m^{2.46})$, where $2 m$ is the number of requests. This is polynomial in the number of metric space points if all requests are given at different points. In particular, the bound does not depend on other parameters of the metric, such as its aspect ratio. Unlike previous (randomized) solutions for the MPMD problem, our algorithm does not need to know the metric space in advance.
Stable Desynchronization for Wireless Sensor Networks: (I) Concepts and Algorithms
Venue: arXiv cs.NI 2017
Year: 2017

Desynchronization is one of the primitive services for complex networks because it arranges nodes to take turns accessing a shared resource. TDMA is a practical application of desynchronization because it allows node to share a common medium. In this paper, we propose a novel desynchronization algorithm using artificial force field called Multi-hop Desynchronization With an ARtificial Force field or M-DWARF and use it to solve TDMA problems in wireless sensor networks (WSNs). M-DWARF solves hidden terminal problems in multi-hop networks by using relative time relaying and improves resource utilization by employing force absorption. M-DWARF is suitable for use in WSNs because of its low complexity and autonomous operations. We implement M-DWARF using TinyOS and experiment it on both TelosB motes and TOSSIM. From our investigation, M-DWARF is the only desynchronization algorithm that achieves fast convergence with high stability, while maintaining channel utilization fairness. Moreover, we are the first to provide a stability analysis using dynamical systems and to prove that M-DWARF is stable at equilibrium. (This paper is the first part of the series Stable Desynchronization for Wireless Sensor Networks - (I) Concepts and Algorithms (II) Performance Evaluation (III) Stability Analysis)
Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges
Venue: arXiv cs.IT 2017
Year: 2017

The fifth-generation (5G) networks are expected to be able to satisfy users' different quality-of-service (QoS) requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network slicing based 5G systems. In this article, we introduce a logical architecture for network slicing based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network slicing based 5G networks are discussed, including network reconstruction, network slicing management and cooperation with other 5G technologies.
Anticipation of digital patterns
Venue: arXiv cs.ET 2017
Year: 2017

A memristive device is a novel passive device, which is essentially a resistor with memory. This device can be utilized for novel technical applications like neuromorphic computation. In this paper, we focus on anticipation - a capability of a system to decide how to react in an environment by predicting future states. Especially, we have designed an elementary memristive circuit for the anticipation of digital patterns, where this circuit is based on the capability of an amoeba to anticipate periodically occurring unipolar pulses. The resulting circuit has been verified by digital simulations and has been realized in hardware as well. For the practical realization, we have used an Ag-doped TiO2-x-based memristive device, which has been fabricated in planar capacitor structures on a silicon wafer. The functionality of the circuit is shown by simulations and measurements. Finally, the anticipation of information is demonstrated by using images, where the robustness of this anticipatory circuit against noise and faulty intermediate information is visualized.
Joint Spectrum Reservation and On-demand Request for Mobile Virtual Network Operators
Venue: arXiv cs.NI 2017
Year: 2017

With wireless network virtualization, Mobile Virtual Network Operators (MVNOs) can develop new services on a low-cost platform by leasing virtual resources from mobile network owners. In this paper, we investigate a two-stage spectrum leasing framework, where an MVNO acquires radio spectrum through both advance reservation and on-demand request. To maximize its surplus, the MVNO jointly optimizes the amount of spectrum to lease in the two stages by taking into account the traffic distribution, random user locations, wireless channel statistics, Quality of Service (QoS) requirements, and the prices differences. Meanwhile, the acquired spectrum resources are dynamically allocated to the MVNO's mobile subscribers (users) according to fast channel fadings in order to maximize the utilization of the resources. The MVNO's surplus maximization problem is naturally formulated as a tri-level nested optimization problem that consists of Dynamic Resource Allocation (DRA), on-demand request, and advance reservation subproblems. To solve the problem efficiently, we rigorously analyze the structure of the optimal solution in the DRA problem, and the optimal value is used to find the optimal leasing decisions in the two stages. In particular, we derive closed-form expressions of the optimal advance reservation and on-demand requests when the proportional fair utility function is adopted. We further extend the analysis to general utility functions and derive a Stochastic Gradient Decent (SGD) algorithm to find the optimal leasing decisions. Simulation results show that the two-stage spectrum leasing strategy can take advantage of both the price discount of advance reservation and the flexibility of on-demand request to deal with traffic variations.
Spatial random multiple access with multiple departure
Venue: arXiv cs.NI 2017
Year: 2017

We introduce a new model of spatial random multiple access systems with a non-standard departure policy: all arriving messages are distributed uniformly on a finite sphere in the space, and when a successful transmission of a single message occurs, the transmitted message leaves the system together with all its neighbours within a ball of a given radius centred at the message's location. We consider three classes of protocols: centralised protocols and decentralised protocols with either ternary or binary feedback; and analyse their stability. Further, we discuss some asymptotic properties of stable protocols.
FilteredWeb: A Framework for the Automated Search-Based Discovery of Blocked URLs
Venue: arXiv cs.CY 2017
Year: 2017

Various methods have been proposed for creating and maintaining lists of potentially filtered URLs to allow for measurement of ongoing internet censorship around the world. Whilst testing a known resource for evidence of filtering can be relatively simple, given appropriate vantage points, discovering previously unknown filtered web resources remains an open challenge. We present a new framework for automating the process of discovering filtered resources through the use of adaptive queries to well-known search engines. Our system applies information retrieval algorithms to isolate characteristic linguistic patterns in known filtered web pages; these are then used as the basis for web search queries. The results of these queries are then checked for evidence of filtering, and newly discovered filtered resources are fed back into the system to detect further filtered content. Our implementation of this framework, applied to China as a case study, shows that this approach is demonstrably effective at detecting significant numbers of previously unknown filtered web pages, making a significant contribution to the ongoing detection of internet filtering as it develops. Our tool is currently deployed and has been used to discover 1355 domains that are poisoned within China as of Feb 2017 - 30 times more than are contained in the most widely-used public filter list. Of these, 759 are outside of the Alexa Top 1000 domains list, demonstrating the capability of this framework to find more obscure filtered content. Further, our initial analysis of filtered URLs, and the search terms that were used to discover them, gives further insight into the nature of the content currently being blocked in China.
Formal Analysis of V2X Revocation Protocols
Venue: arXiv cs.CR 2017
Year: 2017

Research on vehicular networking (V2X) security has produced a range of security mechanisms and protocols tailored for this domain, addressing both security and privacy. Typically, the security analysis of these proposals has largely been informal. However, formal analysis can be used to expose flaws and ultimately provide a higher level of assurance in the protocols. This paper focusses on the formal analysis of a particular element of security mechanisms for V2X found in many proposals: the revocation of malicious or misbehaving vehicles from the V2X system by invalidating their credentials. This revocation needs to be performed in an unlinkable way for vehicle privacy even in the context of vehicles regularly changing their pseudonyms. The REWIRE scheme by Forster et al. and its subschemes BASIC and RTOKEN aim to solve this challenge by means of cryptographic solutions and trusted hardware. Formal analysis using the TAMARIN prover identifies two flaws with some of the functional correctness and authentication properties in these schemes. We then propose Obscure Token (OTOKEN), an extension of REWIRE to enable revocation in a privacy preserving manner. Our approach addresses the functional and authentication properties by introducing an additional key-pair, which offers a stronger and verifiable guarantee of successful revocation of vehicles without resolving the long-term identity. Moreover OTOKEN is the first V2X revocation protocol to be co-designed with a formal model.
Recognizing Union-Find trees built up using union-by-rank strategy is NP-complete
Venue: arXiv cs.DS 2017
Year: 2017

Disjoint-Set forests, consisting of Union-Find trees, are data structures having a widespread practical application due to their efficiency. Despite them being well-known, no exact structural characterization of these trees is known (such a characterization exists for Union trees which are constructed without using path compression) for the case assuming union-by-rank strategy for merging. In this paper we provide such a characterization by means of a simple push operation and show that the decision problem whether a given tree (along with the rank info of its nodes) is a Union-Find tree is NP-complete, complementing our earlier similar result for the union-by-size strategy.
A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Segmenting blood vessels in fundus imaging plays an important role in medical diagnosis. Many algorithms have been proposed. While deep Neural Networks have been attracting enormous attention from computer vision community recent years and several novel works have been done in terms of its application in retinal blood vessel segmentation, most of them are based on supervised learning which requires amount of labeled data, which is both scarce and expensive to obtain. We leverage the power of Deep Convolutional Neural Networks (DCNN) in feature learning, in this work, to achieve this ultimate goal. The highly efficient feature learning of DCNN inspires our novel approach that trains the networks with automatically-generated samples to achieve desirable performance on real-world fundus images. For this, we design a set of rules abstracted from the domain-specific prior knowledge to generate these samples. We argue that, with the high efficiency of DCNN in feature learning, one can achieve this goal by constructing the training dataset with prior knowledge, no manual labeling is needed. This approach allows us to take advantages of supervised learning without labeling. We also build a naive DCNN model to test it. The results on standard benchmarks of fundus imaging show it is competitive to the state-of-the-art methods which implies a potential way to leverage the power of DCNN in feature learning.
Sharing deep generative representation for perceived image reconstruction from human brain activity
Venue: arXiv cs.AI 2017
Year: 2017

Decoding human brain activities via functional magnetic resonance imaging (fMRI) has gained increasing attention in recent years. While encouraging results have been reported in brain states classification tasks, reconstructing the details of human visual experience still remains difficult. Two main challenges that hinder the development of effective models are the perplexing fMRI measurement noise and the high dimensionality of limited data instances. Existing methods generally suffer from one or both of these issues and yield dissatisfactory results. In this paper, we tackle this problem by casting the reconstruction of visual stimulus as the Bayesian inference of missing view in a multiview latent variable model. Sharing a common latent representation, our joint generative model of external stimulus and brain response is not only "deep" in extracting nonlinear features from visual images, but also powerful in capturing correlations among voxel activities of fMRI recordings. The nonlinearity and deep structure endow our model with strong representation ability, while the correlations of voxel activities are critical for suppressing noise and improving prediction. We devise an efficient variational Bayesian method to infer the latent variables and the model parameters. To further improve the reconstruction accuracy, the latent representations of testing instances are enforced to be close to that of their neighbours from the training set via posterior regularization. Experiments on three fMRI recording datasets demonstrate that our approach can more accurately reconstruct visual stimuli.
A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends
Venue: arXiv cs.IT 2017
Year: 2017

Multiple antennas have been exploited for spatial multiplexing and diversity transmission in a wide range of communication applications. However, most of the advances in the design of high speed wireless multiple-input multiple output (MIMO) systems are based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. Although the Gaussian signal is capacity-achieving, signals conforming to discrete constellations are transmitted in practical communication systems. As a result, this paper is motivated to provide a comprehensive overview on MIMO transmission design with discrete input signals. We first summarize the existing fundamental results for MIMO systems with discrete input signals. Then, focusing on the basic point-to-point MIMO systems, we examine transmission schemes based on three most important criteria for communication systems: the mutual information driven designs, the mean square error driven designs, and the diversity driven designs. Particularly, a unified framework which designs low complexity transmission schemes applicable to massive MIMO systems in upcoming 5G wireless networks is provided in the first time. Moreover, adaptive transmission designs which switch among these criteria based on the channel conditions to formulate the best transmission strategy are discussed. Then, we provide a survey of the transmission designs with discrete input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, we discuss the transmission designs with discrete input signals for other systems using MIMO technology. Finally, technical challenges which remain unresolved at the time of writing are summarized and the future trends of transmission designs with discrete input signals are addressed.
Graph Sampling for Covariance Estimation
Venue: arXiv cs.IT 2017
Year: 2017

In this paper the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power spectrum forms an important component of stationary graph signal processing and related inference tasks such as Wiener prediction or inpainting on graphs. The central result of this paper is that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the second-order statistics of the graph signal from the subsampled observations, and more importantly, without any spectral priors. To this end, both a nonparametric approach as well as parametric approaches including moving average and autoregressive models for the graph power spectrum are considered. The results specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers. A near-optimal greedy algorithm is developed to design the subsampling scheme for the non-parametric and the moving average models, whereas a particular subsampling scheme that allows linear estimation for the autoregressive model is proposed. Numerical experiments on synthetic as well as real datasets related to climatology and processing handwritten digits are provided to demonstrate the developed theory.
Automatic Anomaly Detection in the Cloud Via Statistical Learning
Venue: arXiv cs.LG 2017
Year: 2017

Performance and high availability have become increasingly important drivers, amongst other drivers, for user retention in the context of web services such as social networks, and web search. Exogenic and/or endogenic factors often give rise to anomalies, making it very challenging to maintain high availability, while also delivering high performance. Given that service-oriented architectures (SOA) typically have a large number of services, with each service having a large set of metrics, automatic detection of anomalies is non-trivial. Although there exists a large body of prior research in anomaly detection, existing techniques are not applicable in the context of social network data, owing to the inherent seasonal and trend components in the time series data. To this end, we developed two novel statistical techniques for automatically detecting anomalies in cloud infrastructure data. Specifically, the techniques employ statistical learning to detect anomalies in both application, and system metrics. Seasonal decomposition is employed to filter the trend and seasonal components of the time series, followed by the use of robust statistical metrics -- median and median absolute deviation (MAD) -- to accurately detect anomalies, even in the presence of seasonal spikes. We demonstrate the efficacy of the proposed techniques from three different perspectives, viz., capacity planning, user behavior, and supervised learning. In particular, we used production data for evaluation, and we report Precision, Recall, and F-measure in each case.