OpenRaiser/Intern-Atlas huggingface dataset:
Licensed and Unlicensed Spectrum Allocation in Heterogeneous Networks
Venue: arXiv cs.NI 2016
Year: 2016

In future networks, an operator may employ a wide range of access points using diverse radio access technologies (RATs) over multiple licensed and unlicensed frequency bands. This paper studies centralized user association and spectrum allocation across many access points in such a heterogeneous network (HetNet). Such centralized control is on a relatively slow timescale to allow information exchange and joint optimization over multiple cells. This is in contrast and complementary to fast timescale distributed scheduling. A queueing model is introduced to capture the lower spectral efficiency, reliability, and additional delays of data transmission over the unlicensed bands due to contention and/or listen-before-talk requirements. Two optimization-based spectrum allocation schemes are proposed along with efficient algorithms for computing the allocations. The proposed solutions are fully aware of traffic loads, network topology, as well as external interference levels in the unlicensed bands. Packet-level simulation results show that the proposed schemes significantly outperform orthogonal and full-frequency-reuse allocations under all traffic conditions.
Analytic performance evaluation of M-QAM based decode-and-forward relay networks over enriched multipath fading channels
Venue: arXiv cs.IT 2016
Year: 2016

This work is devoted to the analysis of a regenerative multi-node dual-hop cooperative system over enriched multipath fading channels. Novel analytic expressions are derived for the symbol-error-rate for $M{-}$ary quadrature modulated signals in decode-and-forward relay systems over both independent and identically distributed as well as independent and non-identically distributed Nakagami${-}$q (Hoyt) fading channels. The derived expressions are based on the moment-generating-function approach and are given in closed-form in terms of the generalized Lauricella series. The offered results are validated extensively through comparisons with respective results from computer simulations and are useful in the analytic performance evaluation of regenerative cooperative relay communication systems. To this end, it is shown that the performance of the cooperative system is, as expected, affected by the number of employed relays as well as by the value of the fading parameter $q$, which accounts for \textit{pre-Rayleigh} fading conditions that are often encountered in mobile cellular radio systems.
Fast Robust PCA on Graphs
Venue: arXiv cs.CV 2016
Year: 2016

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associated with the nuclear norm minimization), non-convexity (for matrix factorization methods) and susceptibility to gross corruptions in the data. In this paper we propose a principal component analysis (PCA) based solution that overcomes these three issues and approximates a low-rank recovery method for high dimensional datasets. We target the low-rank recovery by enforcing two types of graph smoothness assumptions, one on the data samples and the other on the features by designing a convex optimization problem. The resulting algorithm is fast, efficient and scalable for huge datasets with O(nlog(n)) computational complexity in the number of data samples. It is also robust to gross corruptions in the dataset as well as to the model parameters. Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models. Our theoretical analysis proves that the proposed model is able to recover approximate low-rank representations with a bounded error for clusterable data.
Beamforming through regularized inverse problems in ultrasound medical imaging
Venue: arXiv cs.CV 2016
Year: 2016

Beamforming in ultrasound imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance method and its variants, which overcome the drawbacks of delay-and-sum, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform beamforming in ultrasound imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: i) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and ii) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right trade-off between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with \textit{in vivo} examples of carotid and thyroid. Compared to delay-and-sum, minimimum variance and two other recently published beamforming techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.
Explore Spatiotemporal and Demographic Characteristics of Human Mobility via Twitter: A Case Study of Chicago
Venue: arXiv cs.SI 2016
Year: 2016

Characterizing human mobility patterns is essential for understanding human behaviors and the interactions with socioeconomic and natural environment. With the continuing advancement of location and Web 2.0 technologies, location-based social media (LBSM) have been gaining widespread popularity in the past few years. With an access to locations of users, profiles and the contents of the social media posts, the LBSM data provided a novel modality of data source for human mobility study. By exploiting the explicit location footprints and mining the latent demographic information implied in the LBSM data, the purpose of this paper is to investigate the spatiotemporal characteristics of human mobility with a particular focus on the impact of demography. We first collect geo-tagged Twitter feeds posted in the conterminous United States area, and organize the collection of feeds using the concept of space-time trajectory corresponding to each Twitter user. Commonly human mobility measures, including detected home and activity centers, are derived for each user trajectory. We then select a subset of Twitter users that have detected home locations in the city of Chicago as a case study, and apply name analysis to the names provided in user profiles to learn the implicit demographic information of Twitter users, including race/ethnicity, gender and age. Finally we explore the spatiotemporal distribution and mobility characteristics of Chicago Twitter users, and investigate the demographic impact by comparing the differences across three demographic dimensions (race/ethnicity, gender and age). We found that, although the human mobility measures of different demographic groups generally follow the generic laws (e.g., power law distribution), the demographic information, particular the race/ethnicity group, significantly affects the urban human mobility patterns.
Perceptron like Algorithms for Online Learning to Rank
Venue: arXiv cs.LG 2016
Year: 2016

Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Average Precision (AP). A modern perspective on perceptron for classification is that it is simply an instance of online gradient descent (OGD), during mistake rounds, using the hinge loss function. Motivated by this interpretation, we propose a novel family of listwise, large margin ranking surrogates. Members of this family can be thought of as analogs of the hinge loss. Exploiting a certain self-bounding property of the proposed family, we provide a guarantee on the cumulative NDCG (or AP) induced loss incurred by our perceptron-like algorithm. We show that, if there exists a perfect oracle ranker which can correctly rank each instance in an online sequence of ranking data, with some margin, the cumulative loss of perceptron algorithm on that sequence is bounded by a constant, irrespective of the length of the sequence. This result is reminiscent of Novikoff's convergence theorem for the classification perceptron. Moreover, we prove a lower bound on the cumulative loss achievable by any deterministic algorithm, under the assumption of existence of perfect oracle ranker. The lower bound shows that our perceptron bound is not tight, and we propose another, \emph{purely online}, algorithm which achieves the lower bound. We provide empirical results on simulated and large commercial datasets to corroborate our theoretical results.
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers the classification of linear subspaces with mismatched classifiers. In particular, we assume a model where one observes signals in the presence of isotropic Gaussian noise and the distribution of the signals conditioned on a given class is Gaussian with a zero mean and a low-rank covariance matrix. We also assume that the classifier knows only a mismatched version of the parameters of input distribution in lieu of the true parameters. By constructing an asymptotic low-noise expansion of an upper bound to the error probability of such a mismatched classifier, we provide sufficient conditions for reliable classification in the low-noise regime that are able to sharply predict the absence of a classification error floor. Such conditions are a function of the geometry of the true signal distribution, the geometry of the mismatched signal distributions as well as the interplay between such geometries, namely, the principal angles and the overlap between the true and the mismatched signal subspaces. Numerical results demonstrate that our conditions for reliable classification can sharply predict the behavior of a mismatched classifier both with synthetic data and in a motion segmentation and a hand-written digit classification applications.
Allocation of Heterogeneous Resources of an IoT Device to Flexible Services
Venue: arXiv cs.NI 2016
Year: 2016

Internet of Things (IoT) devices can be equipped with multiple heterogeneous network interfaces. An overwhelmingly large amount of services may demand some or all of these interfaces' available resources. Herein, we present a precise mathematical formulation of assigning services to interfaces with heterogeneous resources in one or more rounds. For reasonable instance sizes, the presented formulation produces optimal solutions for this computationally hard problem. We prove the NP-Completeness of the problem and develop two algorithms to approximate the optimal solution for big instance sizes. The first algorithm allocates the most demanding service requirements first, considering the average cost of interfaces resources. The second one calculates the demanding resource shares and allocates the most demanding of them first by choosing randomly among equally demanding shares. Finally, we provide simulation results giving insight into services splitting over different interfaces for both cases.
Using Deep Learning for Detecting Spoofing Attacks on Speech Signals
Venue: arXiv cs.SD 2016
Year: 2016

It is well known that speaker verification systems are subject to spoofing attacks. The Automatic Speaker Verification Spoofing and Countermeasures Challenge -- ASVSpoof2015 -- provides a standard spoofing database, containing attacks based on synthetic speech, along with a protocol for experiments. This paper describes CPqD's systems submitted to the ASVSpoof2015 Challenge, based on deep neural networks, working both as a classifier and as a feature extraction module for a GMM and a SVM classifier. Results show the validity of this approach, achieving less than 0.5\% EER for known attacks.
An Automatic Machine Translation Evaluation Metric Based on Dependency Parsing Model
Venue: arXiv cs.CL 2016
Year: 2016

Most of the syntax-based metrics obtain the similarity by comparing the sub-structures extracted from the trees of hypothesis and reference. These sub-structures are defined by human and can't express all the information in the trees because of the limited length of sub-structures. In addition, the overlapped parts between these sub-structures are computed repeatedly. To avoid these problems, we propose a novel automatic evaluation metric based on dependency parsing model, with no need to define sub-structures by human. First, we train a dependency parsing model by the reference dependency tree. Then we generate the hypothesis dependency tree and the corresponding probability by the dependency parsing model. The quality of the hypothesis can be judged by this probability. In order to obtain the lexicon similarity, we also introduce the unigram F-score to the new metric. Experiment results show that the new metric gets the state-of-the-art performance on system level, and is comparable with METEOR on sentence level.
Performance Analysis of Self-Interference Cancellation in Full-Duplex Large-Scale MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents the performance analysis of the self-interference cancellation (SIC) methods in full-duplex largescale multiple-input multiple-output systems. To mitigate selfinterference (SI), we assume that the full duplex-base station (BS) uses SI-subtraction or spatial suppression. Analytical and numerical results confirm that the SI-subtraction outperforms the spatial suppression for SIC in a perfect channel estimation case. It is also concluded that the uplink and overall ergodic rates performance of the spatial suppression is respectively better than those of the SI-subtraction in a imperfect channel estimation case under a given system constraint such as uplink/downlink sum rates and the total transmit power at the BS.
Multi-Resolution Compressed Sensing via Approximate Message Passing
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider the problem of multi-resolution compressed sensing (MR-CS) reconstruction, which has received little attention in the literature. Instead of always reconstructing the signal at the original high resolution (HR), we enable the reconstruction of a low-resolution (LR) signal when there are not enough CS samples to recover a HR signal. We propose an approximate message passing (AMP)-based framework dubbed MR-AMP, and derive its state evolution, phase transition, and noise sensitivity, which show that in addition to reduced complexity, our method can recover a LR signal with bounded noise sensitivity even when the noise sensitivity of the conventional HR reconstruction is unbounded. We then apply the MR-AMP to image reconstruction using either soft-thresholding or total variation denoiser, and develop three pairs of up-/down-sampling operators in transform or spatial domain. The performance of the proposed scheme is demonstrated by both 1D synthetic data and 2D images.
The SP theory of intelligence: distinctive features and advantages
Venue: arXiv cs.AI 2016
Year: 2016

This paper highlights distinctive features of the "SP theory of intelligence" and its apparent advantages compared with some AI-related alternatives. Distinctive features and advantages are: simplification and integration of observations and concepts; simplification and integration of structures and processes in computing systems; the theory is itself a theory of computing; it can be the basis for new architectures for computers; information compression via the matching and unification of patterns and, more specifically, via multiple alignment, is fundamental; transparency in the representation and processing of knowledge; the discovery of 'natural' structures via information compression (DONSVIC); interpretations of mathematics; interpretations in human perception and cognition; and realisation of abstract concepts in terms of neurons and their inter-connections ("SP-neural"). These things relate to AI-related alternatives: minimum length encoding and related concepts; deep learning in neural networks; unified theories of cognition and related research; universal search; Bayesian networks and more; pattern recognition and vision; the analysis, production, and translation of natural language; Unsupervised learning of natural language; exact and inexact forms of reasoning; representation and processing of diverse forms of knowledge; IBM's Watson; software engineering; solving problems associated with big data, and in the development of intelligence in autonomous robots. In conclusion, the SP system can provide a firm foundation for the long-term development of AI, with many potential benefits and applications. It may also deliver useful results on relatively short timescales. A high-parallel, open-source version of the SP machine, derived from the SP computer model, would be a means for researchers everywhere to explore what can be done with the system, and to create new versions of it.
A Comprehensive Perspective on Pilot-Job Systems
Venue: arXiv cs.DC 2016
Year: 2016

Pilot-Job systems play an important role in supporting distributed scientific computing. They are used to consume more than 700 million CPU hours a year by the Open Science Grid communities, and by processing up to 1 million jobs a day for the ATLAS experiment on the Worldwide LHC Computing Grid. With the increasing importance of task-level parallelism in high-performance computing, Pilot-Job systems are also witnessing an adoption beyond traditional domains. Notwithstanding the growing impact on scientific research, there is no agreement upon a definition of Pilot-Job system and no clear understanding of the underlying abstraction and paradigm. Pilot-Job implementations have proliferated with no shared best practices or open interfaces and little interoperability. Ultimately, this is hindering the realization of the full impact of Pilot-Jobs by limiting their robustness, portability, and maintainability. This paper offers a comprehensive analysis of Pilot-Job systems critically assessing their motivations, evolution, properties, and implementation. The three main contributions of this paper are: (i) an analysis of the motivations and evolution of Pilot-Job systems; (ii) an outline of the Pilot abstraction, its distinguishing logical components and functionalities, its terminology, and its architecture pattern; and (iii) the description of core and auxiliary properties of Pilot-Jobs systems and the analysis of seven exemplar Pilot-Job implementations. Together, these contributions illustrate the Pilot paradigm, its generality, and how it helps to address some challenges in distributed scientific computing.
A Software-only Mechanism for Device Passthrough and Sharing
Venue: arXiv cs.OS 2016
Year: 2016

Network processing elements in virtual machines, also known as Network Function Virtualization (NFV) often face CPU bottlenecks at the virtualization interface. Even highly optimized paravirtual device interfaces fall short of the throughput requirements of modern devices. Passthrough devices, together with SR-IOV support for multiple device virtual functions (VF) and IOMMU support, mitigate this problem somewhat, by allowing a VM to directly control a device partition bypassing the virtualization stack. However, device passthrough requires high-end (expensive and power-hungry) hardware, places scalability limits on consolidation ratios, and does not support efficient switching between multiple VMs on the same host. We present a paravirtual interface that securely exposes an I/O device directly to the guest OS running inside the VM, and yet allows that device to be securely shared among multiple VMs and the host. Compared to the best-known paravirtualization interfaces, our paravirtual interface supports up to 2x higher throughput, and is closer in performance to device passthrough. Unlike device passthrough however, we do not require SR-IOV or IOMMU support, and allow fine-grained dynamic resource allocation, significantly higher consolidation ratios, and seamless VM migration. Our security mechanism is based on a novel approach called dynamic binary opcode subtraction.
A survey on formal specification and verification of separation kernels
Venue: arXiv cs.SE 2016
Year: 2016

Separation kernels are fundamental software of safety and security-critical systems, which provide to their hosted applications spatial and temporal separation as well as controlled information flows among partitions. The application of separation kernels in critical domain demands the correctness of the kernel by formal verification. To the best of our knowledge, there is no survey paper on this topic. This paper presents an overview of formal specification and verification of separation kernels. We first present the background including the concept of separation kernel and the comparisons among different kernels. Then, we survey the state of the art on this topic since 2000. Finally, we summarize research work by detailed comparison and discussion.
Subdifferential-based implicit return-mapping operators in Mohr-Coulomb plasticity
Venue: arXiv cs.CE 2016
Year: 2016

The paper is devoted to a constitutive solution, limit load analysis and Newton-like methods in elastoplastic problems containing the Mohr-Coulomb yield criterion. Within the constitutive problem, we introduce a self-contained derivation of the implicit return-mapping solution scheme using a recent subdifferential-based treatment. Unlike conventional techniques based on Koiter's rules, the presented scheme a priori detects a position of the unknown stress tensor on the yield surface even if the constitutive solution cannot be found in closed form. This fact eliminates blind guesswork from the scheme, enables to analyze properties of the constitutive operator, and simplifies construction of the consistent tangent operator which is important for the semismooth Newton method applied on the incremental boundary value elastoplastic problem. The incremental problem in Mohr-Coulomb plasticity is combined with the limit load analysis. Beside a conventional direct method of the incremental limit analysis, a recent indirect one is introduced and its advantages are described. The paper contains 2D and 3D numerical experiments on slope stability with publicly available Matlab implementations.