OpenRaiser/Intern-Atlas huggingface dataset:
The Practical Challenges of Interference Alignment
Venue: arXiv cs.IT 2012
Year: 2012

Interference alignment (IA) is a revolutionary wireless transmission strategy that reduces the impact of interference. The idea of interference alignment is to coordinate multiple transmitters so that their mutual interference aligns at the receivers, facilitating simple interference cancellation techniques. Since IA's inception, researchers have investigated its performance and proposed improvements, verifying IA's ability to achieve the maximum degrees of freedom (an approximation of sum capacity) in a variety of settings, developing algorithms for determining alignment solutions, and generalizing transmission strategies that relax the need for perfect alignment but yield better performance. This article provides an overview of the concept of interference alignment as well as an assessment of practical issues including performance in realistic propagation environments, the role of channel state information at the transmitter, and the practicality of interference alignment in large networks.
Dynamic Domain Classification for Fractal Image Compression
Venue: arXiv cs.CV 2012
Year: 2012

Fractal image compression is attractive except for its high encoding time requirements. The image is encoded as a set of contractive affine transformations. The image is partitioned into non-overlapping range blocks, and a best matching domain block larger than the range block is identified. There are many attempts on improving the encoding time by reducing the size of search pool for range-domain matching. But these methods are attempting to prepare a static domain pool that remains unchanged throughout the encoding process. This paper proposes dynamic preparation of separate domain pool for each range block. This will result in significant reduction in the encoding time. The domain pool for a particular range block can be selected based upon a parametric value. Here we use classification based on local fractal dimension.
Completeness for the coalgebraic cover modality
Venue: arXiv cs.LO 2012
Year: 2012

We study the finitary version of the coalgebraic logic introduced by L. Moss. The syntax of this logic, which is introduced uniformly with respect to a coalgebraic type functor, required to preserve weak pullbacks, extends that of classical propositional logic with a so-called coalgebraic cover modality depending on the type functor. Its semantics is defined in terms of a categorically defined relation lifting operation. As the main contributions of our paper we introduce a derivation system, and prove that it provides a sound and complete axiomatization for the collection of coalgebraically valid inequalities. Our soundness and completeness proof is algebraic, and we employ Pattinson's stratification method, showing that our derivation system can be stratified in countably many layers, corresponding to the modal depth of the formulas involved. In the proof of our main result we identify some new concepts and obtain some auxiliary results of independent interest. We survey properties of the notion of relation lifting, induced by an arbitrary but fixed set functor. We introduce a category of Boolean algebra presentations, and establish an adjunction between it and the category of Boolean algebras. Given the fact that our derivation system involves only formulas of depth one, it can be encoded as a endo-functor on Boolean algebras. We show that this functor is finitary and preserves embeddings, and we prove that the Lindenbaum-Tarski algebra of our logic can be identified with the initial algebra for this functor.
Convergence of the Continuous Time Trajectories of Isotropic Evolution Strategies on Monotonic C^2-composite Functions
Venue: arXiv cs.NE 2012
Year: 2012

The Information-Geometric Optimization (IGO) has been introduced as a unified framework for stochastic search algorithms. Given a parametrized family of probability distributions on the search space, the IGO turns an arbitrary optimization problem on the search space into an optimization problem on the parameter space of the probability distribution family and defines a natural gradient ascent on this space. From the natural gradients defined over the entire parameter space we obtain continuous time trajectories which are the solutions of an ordinary differential equation (ODE). Via discretization, the IGO naturally defines an iterated gradient ascent algorithm. Depending on the chosen distribution family, the IGO recovers several known algorithms such as the pure rank-μupdate CMA-ES. Consequently, the continuous time IGO-trajectory can be viewed as an idealization of the original algorithm. In this paper we study the continuous time trajectories of the IGO given the family of isotropic Gaussian distributions. These trajectories are a deterministic continuous time model of the underlying evolution strategy in the limit for population size to infinity and change rates to zero. On functions that are the composite of a monotone and a convex-quadratic function, we prove the global convergence of the solution of the ODE towards the global optimum. We extend this result to composites of monotone and twice continuously differentiable functions and prove local convergence towards local optima.
Automatic Test Generation for Space
Venue: arXiv cs.SE 2012
Year: 2012

The European Space Agency (ESA) uses an engine to perform tests in the Ground Segment infrastructure, specially the Operational Simulator. This engine uses many different tools to ensure the development of regression testing infrastructure and these tests perform black-box testing to the C++ simulator implementation. VST (VisionSpace Technologies) is one of the companies that provides these services to ESA and they need a tool to infer automatically tests from the existing C++ code, instead of writing manually scripts to perform tests. With this motivation in mind, this paper explores automatic testing approaches and tools in order to propose a system that satisfies VST needs.
Multi-robot Cooperative Box-pushing problem using Multi-objective Particle Swarm Optimization Technique
Venue: arXiv cs.RO 2012
Year: 2012

The present work provides a new approach to solve the well-known multi-robot co-operative box pushing problem as a multi objective optimization problem using modified Multi-objective Particle Swarm Optimization. The method proposed here allows both turning and translation of the box, during shift to a desired goal position. We have employed local planning scheme to determine the magnitude of the forces applied by the two mobile robots perpendicularly at specific locations on the box to align and translate it in each distinct step of motion of the box, for minimization of both time and energy. Finally the results are compared with the results obtained by solving the same problem using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed scheme is found to give better results compared to NSGA-II.
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams
Venue: arXiv cs.AI 2012
Year: 2012

Remote sensors are becoming the standard for observing and recording ecological data in the field. Such sensors can record data at fine temporal resolutions, and they can operate under extreme conditions prohibitive to human access. Unfortunately, sensor data streams exhibit many kinds of errors ranging from corrupt communications to partial or total sensor failures. This means that the raw data stream must be cleaned before it can be used by domain scientists. In our application environment|the H.J. Andrews Experimental Forest|this data cleaning is performed manually. This paper introduces a Dynamic Bayesian Network model for analyzing sensor observations and distinguishing sensor failures from valid data for the case of air temperature measured at 15 minute time resolution. The model combines an accurate distribution of long-term and short-term temperature variations with a single generalized fault model. Experiments with historical data show that the precision and recall of the method is comparable to that of the domain expert. The system is currently being deployed to perform real-time automated data cleaning.
Template Based Inference in Symmetric Relational Markov Random Fields
Venue: arXiv cs.AI 2012
Year: 2012

Relational Markov Random Fields are a general and flexible framework for reasoning about the joint distribution over attributes of a large number of interacting entities. The main computational difficulty in learning such models is inference. Even when dealing with complete data, where one can summarize a large domain by sufficient statistics, learning requires one to compute the expectation of the sufficient statistics given different parameter choices. The typical solution to this problem is to resort to approximate inference procedures, such as loopy belief propagation. Although these procedures are quite efficient, they still require computation that is on the order of the number of interactions (or features) in the model. When learning a large relational model over a complex domain, even such approximations require unrealistic running time. In this paper we show that for a particular class of relational MRFs, which have inherent symmetry, we can perform the inference needed for learning procedures using a template-level belief propagation. This procedure's running time is proportional to the size of the relational model rather than the size of the domain. Moreover, we show that this computational procedure is equivalent to sychronous loopy belief propagation. This enables a dramatic speedup in inference and learning time. We use this procedure to learn relational MRFs for capturing the joint distribution of large protein-protein interaction networks.
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders
Venue: arXiv cs.LG 2012
Year: 2012

We present a new algorithm for Independent Component Analysis (ICA) which has provable performance guarantees. In particular, suppose we are given samples of the form $y = Ax + η$ where $A$ is an unknown $n \times n$ matrix and $x$ is a random variable whose components are independent and have a fourth moment strictly less than that of a standard Gaussian random variable and $η$ is an $n$-dimensional Gaussian random variable with unknown covariance $Σ$: We give an algorithm that provable recovers $A$ and $Σ$ up to an additive $ε$ and whose running time and sample complexity are polynomial in $n$ and $1 / ε$. To accomplish this, we introduce a novel "quasi-whitening" step that may be useful in other contexts in which the covariance of Gaussian noise is not known in advance. We also give a general framework for finding all local optima of a function (given an oracle for approximately finding just one) and this is a crucial step in our algorithm, one that has been overlooked in previous attempts, and allows us to control the accumulation of error when we find the columns of $A$ one by one via local search.
It takes two to tango. A Review of the Empirical Literature on Information Technology Outsourcing Relationship Satisfaction
Venue: arXiv cs.OH 2012
Year: 2012

There is growing recognition that the overall client-vendor relationship, and not only the contract, plays a critical role in Information Technology Outsourcing (ITO) success. However, our understanding of how ITO relationships function is limited. This paper contributes to this understanding by reviewing empirical literature on ITO success in terms of relationship satisfaction. A key finding is that the majority of reviewed studies concentrates on client satisfaction, thus neglecting the vendor perspective. We argue that this raises questions about the construct validity of these studies. Consequently, concerns exist about the validity and reliability of their empirical findings. Some scholars have acknowledged the problem and use a dyadic perspective. However, a review of these studies reveals that the authors have underestimated their contributions and do not explain why there is a problem. Therefore, the purpose of this paper is to highlight their contributions by comparing the findings of the dyadic perspective studies with those of the "client perspective" research. In doing so, we assess whether the dyadic studies produce better explanations for ITO success than the client-oriented studies. We argue that this is indeed the case, by producing a better view on how underlying mechanisms of ITO relationships work.
Speeding up the construction of slow adaptive walks
Venue: arXiv cs.NE 2012
Year: 2012

An algorithm (bliss) is proposed to speed up the construction of slow adaptive walks. Slow adaptive walks are adaptive walks biased towards closer points or smaller move steps. They were previously introduced to explore a search space, e.g. to detect potential local optima or to assess the ruggedness of a fitness landscape. To avoid the quadratic cost of computing Hamming distance (HD) for all-pairs of strings in a set in order to find the set of closest strings for each string, strings are sorted and clustered by bliss such that similar strings are more likely to get paired off for HD computation. To efficiently arrange the strings by similarity, bliss employs the idea of shared non-overlapping position specific subsequences between strings which is inspired by an alignment-free protein sequence comparison algorithm. Tests are performed to evaluate the quality of b-walks, i.e. slow adaptive walks constructed from the output of bliss, on enumerated search spaces. Finally, b-walks are applied to explore larger search spaces with the help of Wang-Landau sampling.
Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model
Venue: arXiv cs.AI 2012
Year: 2012

Assistive systems for persons with cognitive disabilities (e.g. dementia) are difficult to build due to the wide range of different approaches people can take to accomplishing the same task, and the significant uncertainties that arise from both the unpredictability of client's behaviours and from noise in sensor readings. Partially observable Markov decision process (POMDP) models have been used successfully as the reasoning engine behind such assistive systems for small multi-step tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modelling assistance that can deal with uncertainty and utility. Unfortunately, POMDPs usually require a very labour intensive, manual procedure for their definition and construction. Our previous work has described a knowledge driven method for automatically generating POMDP activity recognition and context sensitive prompting systems for complex tasks. We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The spreadsheet-like result of the analysis does not correspond to the POMDP model directly and the translation to a formal POMDP representation is required. To date, this translation had to be performed manually by a trained POMDP expert. In this paper, we formalise and automate this translation process using a probabilistic relational model (PRM) encoded in a relational database. We demonstrate the method by eliciting three assistance tasks from non-experts. We validate the resulting POMDP models using case-based simulations to show that they are reasonable for the domains. We also show a complete case study of a designer specifying one database, including an evaluation in a real-life experiment with a human actor.
On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation
Venue: arXiv cs.DS 2012
Year: 2012

We study classic streaming and sparse recovery problems using deterministic linear sketches, including l1/l1 and linf/l1 sparse recovery problems (the latter also being known as l1-heavy hitters), norm estimation, and approximate inner product. We focus on devising a fixed matrix A in R^{m x n} and a deterministic recovery/estimation procedure which work for all possible input vectors simultaneously. Our results improve upon existing work, the following being our main contributions: * A proof that linf/l1 sparse recovery and inner product estimation are equivalent, and that incoherent matrices can be used to solve both problems. Our upper bound for the number of measurements is m=O(eps^{-2}*min{log n, (log n / log(1/eps))^2}). We can also obtain fast sketching and recovery algorithms by making use of the Fast Johnson-Lindenstrauss transform. Both our running times and number of measurements improve upon previous work. We can also obtain better error guarantees than previous work in terms of a smaller tail of the input vector. * A new lower bound for the number of linear measurements required to solve l1/l1 sparse recovery. We show Omega(k/eps^2 + klog(n/k)/eps) measurements are required to recover an x' with |x - x'|_1 <= (1+eps)|x_{tail(k)}|_1, where x_{tail(k)} is x projected onto all but its largest k coordinates in magnitude. * A tight bound of m = Theta(eps^{-2}log(eps^2 n)) on the number of measurements required to solve deterministic norm estimation, i.e., to recover |x|_2 +/- eps|x|_1. For all the problems we study, tight bounds are already known for the randomized complexity from previous work, except in the case of l1/l1 sparse recovery, where a nearly tight bound is known. Our work thus aims to study the deterministic complexities of these problems.
A meta-analysis of state-of-the-art electoral prediction from Twitter data
Venue: arXiv cs.SI 2012
Year: 2012

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.
Predictive Approaches For Gaussian Process Classifier Model Selection
Venue: arXiv cs.LG 2012
Year: 2012

In this paper we consider the problem of Gaussian process classifier (GPC) model selection with different Leave-One-Out (LOO) Cross Validation (CV) based optimization criteria and provide a practical algorithm using LOO predictive distributions with such criteria to select hyperparameters. Apart from the standard average negative logarithm of predictive probability (NLP), we also consider smoothed versions of criteria such as F-measure and Weighted Error Rate (WER), which are useful for handling imbalanced data. Unlike the regression case, LOO predictive distributions for the classifier case are intractable. We use approximate LOO predictive distributions arrived from Expectation Propagation (EP) approximation. We conduct experiments on several real world benchmark datasets. When the NLP criterion is used for optimizing the hyperparameters, the predictive approaches show better or comparable NLP generalization performance with existing GPC approaches. On the other hand, when the F-measure criterion is used, the F-measure generalization performance improves significantly on several datasets. Overall, the EP-based predictive algorithm comes out as an excellent choice for GP classifier model selection with different optimization criteria.
Predicting the behavior of interacting humans by fusing data from multiple sources
Venue: arXiv cs.AI 2012
Year: 2012

Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but high-fidelity simulations to produce an accurate model of a system of interest at minimal cost. They have proven useful in modeling physical systems and have been applied to engineering problems such as wing-design optimization. During human-in-the-loop experimentation, it has become increasingly common to use online platforms, like Mechanical Turk, to run low-fidelity experiments to gather human performance data in an efficient manner. One concern with these experiments is that the results obtained from the online environment generalize poorly to the actual domain of interest. To address this limitation, we extend traditional multi-fidelity approaches to allow us to combine fewer data points from high-fidelity human-in-the-loop experiments with plentiful but less accurate data from low-fidelity experiments to produce accurate models of how humans interact. We present both model-based and model-free methods, and summarize the predictive performance of each method under different conditions.
Planar Lombardi Drawings for Subcubic Graphs
Venue: arXiv cs.CG 2012
Year: 2012

We prove that every planar graph with maximum degree three has a planar drawing in which the edges are drawn as circular arcs that meet at equal angles around every vertex. Our construction is based on the Koebe-Thurston-Andreev circle packing theorem, and uses a novel type of Voronoi diagram for circle packings that is invariant under Moebius transformations, defined using three-dimensional hyperbolic geometry. We also use circle packing to construct planar Lombardi drawings of a special class of 4-regular planar graphs, the medial graphs of polyhedral graphs, and we show that not every 4-regular planar graph has a Lombardi drawing. We have implemented our algorithm for 3-connected planar cubic graphs.
Performance Evaluation of A Proposed Variant of Frequency Count (VFC) List Accessing Algorithm
Venue: arXiv cs.DS 2012
Year: 2012

Frequency Count (FC) algorithm is considered as the static optimal algorithm for the list accessing problem. In this paper, we have made a study of FC algorithm and explore its limitation. Using the concept of weak look ahead, we have proposed a novel Variant of Frequency Count (VFC) list accessing algorithm. We have evaluated the performance of FC and our proposed VFC algorithm experimentally using input data set from Calgary Corpus. Our experiments show that for all request sequences and list generated from the above data set VFC performs better than FC.
The Necessity for Hardware QoS Support for Server Consolidation and Cloud Computing
Venue: arXiv cs.AR 2012
Year: 2012

Chip multiprocessors (CMPs) are ubiquitous in most of today's computing fields. Although they provide noticeable benefits in terms of performance, cost and power efficiency, they also introduce some new issues. In this paper we analyze how the interference from Virtual Private Servers running in other cores is a significant component of performance unpredictability and can threaten the attainment of cloud computing. Even if virtualization is used, the sharing of the on-chip section of the memory hierarchy by different cores makes performance isolation strongly dependent on what is running elsewhere in the system. We will show in three actual computing systems, based on Sun UltraSparc T1, Sun UltraSparc T2 and Intel Xeon processors, how state-of-the-art virtualization techniques are unable to guarantee performance isolation in a representative workload such as SPECweb2005. In an especially conceived near worst-case scenario, it is possible to reduce the performance achieved by a Solaris Zones consolidated server for this suite of benchmarks in a Sun Fire T1000 and a Sun Enterprise T5120 by up to 80%. The performance drop observed by a Xen consolidated server running in a HP Proliant DL160 G5 is almost 45%. For all systems under study, off-chip bandwidth is shown to be the most critical resource.