OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Genotypic Diversity Measurements Exploited in Real-Coded Representation
Venue: arXiv cs.NE 2015
Year: 2015

Numerous genotypic diversity measures (GDMs) are available in the literature to assess the convergence status of an evolutionary algorithm (EA) or describe its search behavior. In a recent study, the authors of this paper drew attention to the need for a GDM validation framework. In response, this study proposes three requirements (monotonicity in individual varieties, twinning, and monotonicity in distance) that can clearly portray any GDMs. These diversity requirements are analysed by means of controlled population arrangements. In this paper four GDMs are evaluated with the proposed validation framework. The results confirm that properly evaluating population diversity is a rather difficult task, as none of the analysed GDMs complies with all the diversity requirements.
Wireless networks of bounded capacity
Venue: arXiv cs.IT 2015
Year: 2015

We consider a noncoherent wireless network, where the transmitters and receivers are cognizant of the statistics of the fading coefficients, but are ignorant of their realizations. We demonstrate that if the nodes do not cooperate, if they transmit symbols that all follow the same distribution, and if the variances of the fading coefficients decay exponentially or more slowly, then the channel capacity is bounded in the SNR. This confirms a similar result by Lozano, Heath, and Andrews.
Analysis of Activities and Operations in the Current E-Health Landscape in Tanzania: Focus on Interoperability and Collaboration
Venue: arXiv cs.CY 2015
Year: 2015

Although the basic application of Information and Communication Technologies (ICT) in the Tanzanian health care systems started years ago, still fragmentation of Information Systems (IS) and limited interoperability remain to be big challenges. In this paper, we present an analysis done on the present health care delivery service, HIS and on some of existing eHealth solutions focusing on interoperability and collaboration. Through interviews, questionnaires and analysis on e-health implementations in relation to interoperability and collaboration we have established that, the lack of standard procedures to guide the lifecycle of eHealth systems across the health sector and poor willingness to collaboration among health stakeholders are key issues which hinders the manifestation of the benefit of ICT use in the health sector of Tanzania. Based on the findings, we provide some recommendations with a view to improve interoperability and collaboration.
Modeling and Analysis of Content Caching in Wireless Small Cell Networks
Venue: arXiv cs.IT 2015
Year: 2015

Network densification with small cell base stations is a promising solution to satisfy future data traffic demands. However, increasing small cell base station density alone does not ensure better users quality-of-experience and incurs high operational expenditures. Therefore, content caching on different network elements has been proposed as a mean of offloading he backhaul by caching strategic contents at the network edge, thereby reducing latency. In this paper, we investigate cache-enabled small cells in which we model and characterize the outage probability, defined as the probability of not satisfying users requests over a given coverage area. We analytically derive a closed form expression of the outage probability as a function of signal-to-interference ratio, cache size, small cell base station density and threshold distance. By assuming the distribution of base stations as a Poisson point process, we derive the probability of finding a specific content within a threshold distance and the optimal small cell base station density that achieves a given target cache hit probability. Furthermore, simulation results are performed to validate the analytical model.
Proactive Dependability Framework for Smart Environment Applications
Venue: arXiv cs.SY 2015
Year: 2015

Smart environment applications demand novel solutions for managing quality of services, especially availability and reliability at run-time. The underlying systems are changing dynamically due to addition and removal of system components, changing execution environments, and resources depletion. Therefore, in such dynamic systems, the functionality and the performance of smart environment applications can be hampered by faults. In this paper, we follow a proactive approach to anticipate system state at runtime. We present a proactive dependability framework to prevent faults at runtime based on predictive analysis to increase availability and reliability of smart environment applications, and reduce manual user interventions.
Compensation of Amplifier Distortion for OFDM signals based on Iterative Hard Thresholding
Venue: arXiv cs.IT 2015
Year: 2015

The mitigation of nonlinear distortion caused by power amplifiers (PA) in Orthogonal Frequency Division Multiplexing (OFDM) systems is an essential issue to enable energy efficient operation. In this work we proposed a new algorithm for receiver-based clipping estimation in OFDM systems that combines the Iterative Hard Thresholding (IHT) method with a weighting corresponding to the estimated probability of clipping. Thereby a more general amplifier input-output characteristic is considered, which is assumed to be unknown at the receiver side. Further, we avoid the use of dedicated subcarriers and formulate the recovery problem solely on reliably detected sub-carriers. Through simulations, we show that the proposed technique achieves a better complexity-performance tradeoff compared to existing methods.
Bidding, Pricing, and User Subscription Dynamics in Asymmetric-valued Korean LTE Spectrum Auction: A Hierarchical Dynamic Game Approach
Venue: arXiv cs.NI 2015
Year: 2015

The tremendous increase in mobile data traffic coupled with fierce competition in wireless industry brings about spectrum scarcity and bandwidth fragmentation. This inevitably results in asymmetric-valued LTE spectrum allocation that stems from different timing for twice improvement in capacity between competing operators, given spectrum allocations today. This motivates us to study the economic effects of asymmetric-valued LTE spectrum allocation. In this paper, we formulate the interactions between operators and users as a hierarchical dynamic game framework, where two spiteful operators simultaneously make spectrum acquisition decisions in the upper-level first-price sealed-bid auction game, and dynamic pricing decisions in the lower-level differential game, taking into account user subscription dynamics. Using backward induction, we derive the equilibrium of the entire game under mild conditions. Through analytical and numerical results, we verify our studies by comparing the latest result of LTE spectrum auction in South Korea, which serves as the benchmark of asymmetric-valued LTE spectrum auction designs.
Bounded Refinement Types
Venue: arXiv cs.PL 2015
Year: 2015

We present a notion of bounded quantification for refinement types and show how it expands the expressiveness of refinement typing by using it to develop typed combinators for: (1) relational algebra and safe database access, (2) Floyd-Hoare logic within a state transformer monad equipped with combinators for branching and looping, and (3) using the above to implement a refined IO monad that tracks capabilities and resource usage. This leap in expressiveness comes via a translation to "ghost" functions, which lets us retain the automated and decidable SMT based checking and inference that makes refinement typing effective in practice.
Categorical Matrix Completion
Venue: arXiv cs.NA 2015
Year: 2015

We consider the problem of completing a matrix with categorical-valued entries from partial observations. This is achieved by extending the formulation and theory of one-bit matrix completion. We recover a low-rank matrix $X$ by maximizing the likelihood ratio with a constraint on the nuclear norm of $X$, and the observations are mapped from entries of $X$ through multiple link functions. We establish theoretical upper and lower bounds on the recovery error, which meet up to a constant factor $\mathcal{O}(K^{3/2})$ where $K$ is the fixed number of categories. The upper bound in our case depends on the number of categories implicitly through a maximization of terms that involve the smoothness of the link functions. In contrast to one-bit matrix completion, our bounds for categorical matrix completion are optimal up to a factor on the order of the square root of the number of categories, which is consistent with an intuition that the problem becomes harder when the number of categories increases. By comparing the performance of our method with the conventional matrix completion method on the MovieLens dataset, we demonstrate the advantage of our method.
Online Transfer Learning in Reinforcement Learning Domains
Venue: arXiv cs.AI 2015
Year: 2015

This paper proposes an online transfer framework to capture the interaction among agents and shows that current transfer learning in reinforcement learning is a special case of online transfer. Furthermore, this paper re-characterizes existing agents-teaching-agents methods as online transfer and analyze one such teaching method in three ways. First, the convergence of Q-learning and Sarsa with tabular representation with a finite budget is proven. Second, the convergence of Q-learning and Sarsa with linear function approximation is established. Third, the we show the asymptotic performance cannot be hurt through teaching. Additionally, all theoretical results are empirically validated.
Time Distortion Anonymization for the Publication of Mobility Data with High Utility
Venue: arXiv cs.CR 2015
Year: 2015

An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published after the application of simple anonymization techniques (e.g., putting an identifier instead of the users' names), which might lead to severe threats to the privacy of the participating users. Literature contains more sophisticated anonymization techniques, often based on adding noise to the spatial data. However, these techniques either compromise the privacy if the added noise is too little or the utility of the data if the added noise is too strong. We investigate in this paper an alternative solution, which builds on time distortion instead of spatial distortion. Specifically, our contribution lies in (1) the introduction of the concept of time distortion to anonymize mobility datasets (2) Promesse, a protection mechanism implementing this concept (3) a practical study of Promesse compared to two representative spatial distortion mechanisms, namely Wait For Me, which enforces k-anonymity, and Geo-Indistinguishability, which enforces differential privacy. We evaluate our mechanism practically using three real-life datasets. Our results show that time distortion reduces the number of points of interest that can be retrieved by an adversary to under 3 %, while the introduced spatial error is almost null and the distortion introduced on the results of range queries is kept under 13 % on average.
Cross Modal Distillation for Supervision Transfer
Venue: arXiv cs.CV 2015
Year: 2015

In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as a supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations for unlabeled modalities and can be used as a pre-training procedure for new modalities with limited labeled data. We show experimental results where we transfer supervision from labeled RGB images to unlabeled depth and optical flow images and demonstrate large improvements for both these cross modal supervision transfers. Code, data and pre-trained models are available at https://github.com/s-gupta/fast-rcnn/tree/distillation
Business Models for e-Health: Evidence from Ten Case Studies
Venue: arXiv cs.CY 2015
Year: 2015

An increasingly aging population and spiraling healthcare costs have made the search for financially viable healthcare models an imperative of this century. The careful and creative application of information technology can play a significant role in meeting that challenge. Valuable lessons can be learned from an analysis of ten innovative telemedicine and e-health initiatives. Having proven their effectiveness in addressing a variety of medical needs, they have progressed beyond small-scale implementations to become an established part of healthcare delivery systems around the world.
Simple, Fast Semantic Parsing with a Tensor Kernel
Venue: arXiv cs.CL 2015
Year: 2015

We describe a simple approach to semantic parsing based on a tensor product kernel. We extract two feature vectors: one for the query and one for each candidate logical form. We then train a classifier using the tensor product of the two vectors. Using very simple features for both, our system achieves an average F1 score of 40.1% on the WebQuestions dataset. This is comparable to more complex systems but is simpler to implement and runs faster.
Compressed Manifold Modes: Fast Calculation and Natural Ordering
Venue: arXiv cs.CG 2015
Year: 2015

Compressed manifold modes are locally supported analogues of eigenfunctions of the Laplace-Beltrami operator of a manifold. In this paper we describe an algorithm for the calculation of modes for discrete manifolds that, in experiments, requires on average 47% fewer iterations and 44% less time than the previous algorithm. We show how to naturally order the modes in an analogous way to eigenfunctions, that is we define a compressed eigenvalue. Furthermore, in contrast to the previous algorithm we permit unlumped mass matrices for the operator and we show, unlike the case of eigenfunctions, that modes can, in general, be oriented.
On the Approximability of Digraph Ordering
Venue: arXiv cs.DS 2015
Year: 2015

Given an n-vertex digraph D = (V, A) the Max-k-Ordering problem is to compute a labeling $\ell : V \to [k]$ maximizing the number of forward edges, i.e. edges (u,v) such that $\ell$(u) < $\ell$(v). For different values of k, this reduces to Maximum Acyclic Subgraph (k=n), and Max-Dicut (k=2). This work studies the approximability of Max-k-Ordering and its generalizations, motivated by their applications to job scheduling with soft precedence constraints. We give an LP rounding based 2-approximation algorithm for Max-k-Ordering for any k={2,..., n}, improving on the known 2k/(k-1)-approximation obtained via random assignment. The tightness of this rounding is shown by proving that for any k={2,..., n} and constant $\varepsilon > 0$, Max-k-Ordering has an LP integrality gap of 2 - $\varepsilon$ for $n^{Ω\left(1/\log\log k\right)}$ rounds of the Sherali-Adams hierarchy. A further generalization of Max-k-Ordering is the restricted maximum acyclic subgraph problem or RMAS, where each vertex v has a finite set of allowable labels $S_v \subseteq \mathbb{Z}^+$. We prove an LP rounding based $4\sqrt{2}/(\sqrt{2}+1) \approx 2.344$ approximation for it, improving on the $2\sqrt{2} \approx 2.828$ approximation recently given by Grandoni et al. (Information Processing Letters, Vol. 115(2), Pages 182-185, 2015). In fact, our approximation algorithm also works for a general version where the objective counts the edges which go forward by at least a positive offset specific to each edge. The minimization formulation of digraph ordering is DAG edge deletion or DED(k), which requires deleting the minimum number of edges from an n-vertex directed acyclic graph (DAG) to remove all paths of length k. We show that both, the LP relaxation and a local ratio approach for DED(k) yield k-approximation for any $k\in [n]$.
Truthful Online Scheduling with Commitments
Venue: arXiv cs.DS 2015
Year: 2015

We study online mechanisms for preemptive scheduling with deadlines, with the goal of maximizing the total value of completed jobs. This problem is fundamental to deadline-aware cloud scheduling, but there are strong lower bounds even for the algorithmic problem without incentive constraints. However, these lower bounds can be circumvented under the natural assumption of deadline slackness, i.e., that there is a guaranteed lower bound $s > 1$ on the ratio between a job's size and the time window in which it can be executed. In this paper, we construct a truthful scheduling mechanism with a constant competitive ratio, given slackness $s > 1$. Furthermore, we show that if $s$ is large enough then we can construct a mechanism that also satisfies a commitment property: it can be determined whether or not a job will finish, and the requisite payment if so, well in advance of each job's deadline. This is notable because, in practice, users with strict deadlines may find it unacceptable to discover only very close to their deadline that their job has been rejected.
On the Privacy Practices of Just Plain Sites
Venue: arXiv cs.CY 2015
Year: 2015

In addition to visiting high profile sites such as Facebook and Google, web users often visit more modest sites, such as those operated by bloggers, or by local organizations such as schools. Such sites, which we call "Just Plain Sites" (JPSs) are likely to inadvertently represent greater privacy risks than high profile sites by virtue of being unable to afford privacy expertise. To assess the prevalence of the privacy risks to which JPSs may inadvertently be exposing their visitors, we analyzed a number of easily observed privacy practices of such sites. We found that many JPSs collect a great deal of information from their visitors, share a great deal of information about their visitors with third parties, permit a great deal of tracking of their visitors, and use deprecated or unsafe security practices. Our goal in this work is not to scold JPS operators, but to raise awareness of these facts among both JPS operators and visitors, possibly encouraging the operators of such sites to take greater care in their implementations, and visitors to take greater care in how, when, and what they share.
D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM
Venue: arXiv cs.LG 2015
Year: 2015

Bayesian models provide a framework for probabilistic modelling of complex datasets. However, many of such models are computationally demanding especially in the presence of large datasets. On the other hand, in sensor network applications, statistical (Bayesian) parameter estimation usually needs distributed algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a general framework for distributed Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed Matrix Factorization (MF) and distributed affine structure from motion (SfM).
Best bang for your buck: GPU nodes for GROMACS biomolecular simulations
Venue: arXiv cs.DC 2015
Year: 2015

The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well exploited with a combination of SIMD, multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as accelerators to compute interactions offloaded from the CPU. Here we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Though hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Although memory issues in consumer-class GPUs could pass unnoticed since these cards do not support ECC memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime.
LogDet Rank Minimization with Application to Subspace Clustering
Venue: arXiv cs.CV 2015
Year: 2015

Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose to use a log-determinant (LogDet) function as a smooth and closer, though non-convex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based non-convex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.
Coherent 100G Nonlinear Compensation with Single-Step Digital Backpropagation
Venue: arXiv cs.OH 2015
Year: 2015

Enhanced-SSFM digital backpropagation (DBP) is experimentally demonstrated and compared to conventional DBP. A 112 Gb/s PM-QPSK signal is transmitted over a 3200 km dispersion-unmanaged link. The intradyne coherent receiver includes single-step digital backpropagation based on the enhanced-SSFM algorithm. In comparison, conventional DBP requires twenty steps to achieve the same performance. An analysis of the computational complexity and structure of the two algorithms reveals that the overall complexity and power consumption of DBP are reduced by a factor of 16 with respect to a conventional implementation, while the computation time is reduced by a factor of 20. As a result, the proposed algorithm enables a practical and effective implementation of DBP in real-time optical receivers, with only a moderate increase of the computational complexity, power consumption, and latency with respect to a simple feed-forward equalizer for dispersion compensation.