OpenRaiser/Intern-Atlas huggingface dataset:
Application of Volterra Equations to Solve Unit Commitment Problem of Optimised Energy Storage and Generation
Venue: arXiv cs.SY 2016
Year: 2016

Development of reliable methods for optimised energy storage and generation is one of the most imminent challenges in moder power systems. In this paper an adaptive approach to load leveling problem using novel dynamic models based on the Volterra integral equations of the first kind with piecewise continuous kernels. These integral equations efficiently solve such inverse problem taking into account both the time dependent efficiencies and the availability of generation/storage of each energy storage technology. In this analysis a direct numerical method is employed to find the least-cost dispatch of available storages. The proposed collocation type numerical method has second order accuracy and enjoys self-regularization properties, which is associated with confidence levels of system demand. This adaptive approach is suitable for energy storage optimisation in real time. The efficiency of the proposed methodology is demonstrated on the Single Electricity Market of Republic of Ireland and Sakhalin island in the Russian Far East.
A Source-level Energy Optimization Framework for Mobile Applications
Venue: arXiv cs.SE 2016
Year: 2016

Energy efficiency can have a significant influence on user experience of mobile devices such as smartphones and tablets. Although energy is consumed by hardware, software optimization plays an important role in saving energy, and thus software developers have to participate in the optimization process. The source code is the interface between the developer and hardware resources. In this paper, we propose an energy-optimization framework guided by a source code energy model that allows developers to be aware of energy usage induced by the code and to apply very targeted source-level refactoring strategies. The framework also lays a foundation for the code optimization by automatic tools. To the best of our knowledge, our work is the first that achieves this for a high-level language such as Java. In a case study, the experimental evaluation shows that our approach is able to save from 6.4% to 50.2% of the CPU energy consumption in various application scenarios.
Design and Implementation of Probabilistic Programming Language Anglican
Venue: arXiv cs.PL 2016
Year: 2016

Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We introduce the programming language Anglican, outline our design choices, and discuss in depth the implementation of the Anglican language and runtime, including macro-based compilation, extended CPS-based evaluation model, and functional representations for probabilistic paradigms, such as a distribution, a random process, and an inference algorithm. We show that a probabilistic functional language can be implemented efficiently and integrated tightly with a conventional functional language with only moderate computational overhead. We also demonstrate how advanced probabilistic modeling concepts are mapped naturally to the functional foundation.
How Much Training is Needed in One-Bit Massive MIMO Systems at Low SNR?
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power.
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking
Venue: arXiv cs.NI 2016
Year: 2016

This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows. Since traffic data is large-scale time-structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Candecomp/PARAFAC decomposition exploiting the recursive least squares (RLS) algorithm. We estimate abnormal flows as outlier sparse flows via sparsity maximization in the underlying under-constrained linear-inverse problem. A major advantage is that our algorithm estimates normal flows by low-dimensional matrices with time-directional features as well as the spatial correlation of multiple links without using the past observed measurements and the past model parameters. Extensive numerical evaluations show that the proposed algorithm achieves faster convergence per iteration of model approximation, and better volume anomaly detection performance compared to state-of-the-art algorithms.
Towards Reversible Computation in Erlang
Venue: arXiv cs.PL 2016
Year: 2016

In a reversible language, any forward computation can be undone by a finite sequence of backward steps. Reversible computing has been studied in the context of different programming languages and formalisms, where it has been used for debugging and for enforcing fault-tolerance, among others. In this paper, we consider a subset of Erlang, a concurrent language based on the actor model. We formally introduce a reversible semantics for this language. To the best of our knowledge, this is the first attempt to define a reversible semantics for Erlang.
A Survey on Routing in Anonymous Communication Protocols
Venue: arXiv cs.CR 2016
Year: 2016

The Internet has undergone dramatic changes in the past 15 years, and now forms a global communication platform that billions of users rely on for their daily activities. While this transformation has brought tremendous benefits to society, it has also created new threats to online privacy, ranging from profiling of users for monetizing personal information to nearly omnipotent governmental surveillance. As a result, public interest in systems for anonymous communication has drastically increased. Several such systems have been proposed in the literature, each of which offers anonymity guarantees in different scenarios and under different assumptions, reflecting the plurality of approaches for how messages can be anonymously routed to their destination. Understanding this space of competing approaches with their different guarantees and assumptions is vital for users to understand the consequences of different design options. In this work, we survey previous research on designing, developing, and deploying systems for anonymous communication. To this end, we provide a taxonomy for clustering all prevalently considered approaches (including Mixnets, DC-nets, onion routing, and DHT-based protocols) with respect to their unique routing characteristics, deployability, and performance. This, in particular, encompasses the topological structure of the underlying network; the routing information that has to be made available to the initiator of the conversation; the underlying communication model; and performance-related indicators such as latency and communication layer. Our taxonomy and comparative assessment provide important insights about the differences between the existing classes of anonymous communication protocols, and it also helps to clarify the relationship between the routing characteristics of these protocols, and their performance and scalability.
Computing with Dynamical Systems Based on Insulator-Metal-Transition Oscillators
Venue: arXiv cs.ET 2016
Year: 2016

In this paper we review recent work on novel computing paradigms using coupled oscillatory dynamical systems. We explore systems of relaxation oscillators based on linear state transitioning devices, which switch between two discrete states with hysteresis. By harnessing the dynamics of complex, connected systems we embrace the philosophy of "let physics do the computing" and demonstrate how complex phase and frequency dynamics of such systems can be controlled, programmed and observed to solve computationally hard problems. Although our discussion in this paper is limited to Insulator-to-Metallic (IMT) state transition devices, the general philosophy of such computing paradigms can be translated to other mediums including optical systems. We present the necessary mathematical treatments necessary to understand the time evolution of these systems and demonstrate through recent experimental results the potential of such computational primitives.
Fast estimation of approximate matrix ranks using spectral densities
Venue: arXiv cs.NA 2016
Year: 2016

In many machine learning and data related applications, it is required to have the knowledge of approximate ranks of large data matrices at hand. In this paper, we present two computationally inexpensive techniques to estimate the approximate ranks of such large matrices. These techniques exploit approximate spectral densities, popular in physics, which are probability density distributions that measure the likelihood of finding eigenvalues of the matrix at a given point on the real line. Integrating the spectral density over an interval gives the eigenvalue count of the matrix in that interval. Therefore the rank can be approximated by integrating the spectral density over a carefully selected interval. Two different approaches are discussed to estimate the approximate rank, one based on Chebyshev polynomials and the other based on the Lanczos algorithm. In order to obtain the appropriate interval, it is necessary to locate a gap between the eigenvalues that correspond to noise and the relevant eigenvalues that contribute to the matrix rank. A method for locating this gap and selecting the interval of integration is proposed based on the plot of the spectral density. Numerical experiments illustrate the performance of these techniques on matrices from typical applications.
Inference in Probabilistic Logic Programs using Lifted Explanations
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we consider the problem of lifted inference in the context of Prism-like probabilistic logic programming languages. Traditional inference in such languages involves the construction of an explanation graph for the query and computing probabilities over this graph. When evaluating queries over probabilistic logic programs with a large number of instances of random variables, traditional methods treat each instance separately. For many programs and queries, we observe that explanations can be summarized into substantially more compact structures, which we call lifted explanation graphs. In this paper, we define lifted explanation graphs and operations over them. In contrast to existing lifted inference techniques, our method for constructing lifted explanations naturally generalizes existing methods for constructing explanation graphs. To compute probability of query answers, we solve recurrences generated from the lifted graphs. We show examples where the use of our technique reduces the asymptotic complexity of inference.
Chance constraint based multi agent navigation under uncertainty
Venue: arXiv cs.RO 2016
Year: 2016

We present Probabilistic Reciprocal Velocity Obstacle or PRVO as a general algorithm for navigating multiple robots under perception and motion uncertainty. PRVO is defined as the space of velocities that ensures dynamic collision avoidance between a pair of robots with a specified probability. Our approach is based on defining chance constraints over the inequalities defined by the deterministic Reciprocal Velocity Obstacle (RVO). The computational complexity of the proposed probabilistic RVO is comparable to the deterministic counterpart. This is achieved by a series of reformulations where we first substitute the computationally intractable chance constraints with a family of surrogate constraints and then adopt a time scaling based solution methodology to efficiently characterize their solution space. Further, we also show that the solution space of each member of the family of surrogate constraints can be mapped in closed form to the probability with which the original chance constraints are satisfied and thus consequently to probability of collision avoidance. We validate our formulations through numerical simulations where we highlight the importance of incorporating the effect of motion uncertainty and the advantages of PRVO over existing formulations which handles the effect of uncertainty by using conservative bounding volumes.
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
Venue: arXiv cs.CV 2016
Year: 2016

Recently, convolutional networks (convnets) have proven useful for predicting optical flow. Much of this success is predicated on the availability of large datasets that require expensive and involved data acquisition and laborious la- beling. To bypass these challenges, we propose an unsuper- vised approach (i.e., without leveraging groundtruth flow) to train a convnet end-to-end for predicting optical flow be- tween two images. We use a loss function that combines a data term that measures photometric constancy over time with a spatial term that models the expected variation of flow across the image. Together these losses form a proxy measure for losses based on the groundtruth flow. Empiri- cally, we show that a strong convnet baseline trained with the proposed unsupervised approach outperforms the same network trained with supervision on the KITTI dataset.
Online Feature Selection with Group Structure Analysis
Venue: arXiv cs.CV 2016
Year: 2016

Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture and other visual information. Simply breaking the group structure in feature selection may degrade performance. Motivated by this fact, we formulate the problem as an online group feature selection. The problem assumes that features are generated individually but there are group structure in the feature stream. To the best of our knowledge, this is the first time that the correlation among feature stream has been considered in the online feature selection process. To solve this problem, we develop a novel online group feature selection method named OGFS. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we design a criterion based on spectral analysis to select discriminative features in each group. In the inter-group selection, we utilize a linear regression model to select an optimal subset. This two-stage procedure continues until there are no more features arriving or some predefined stopping conditions are met. %Our method has been applied Finally, we apply our method to multiple tasks including image classification %, face verification and face verification. Extensive empirical studies performed on real-world and benchmark data sets demonstrate that our method outperforms other state-of-the-art online feature selection %method methods.
Convergence of Even Simpler Robots without Location Information
Venue: arXiv cs.DC 2016
Year: 2016

The design of distributed gathering and convergence algorithms for tiny robots has recently received much attention. In particular, it has been shown that convergence problems can even be solved for very weak, \emph{oblivious} robots: robots which cannot maintain state from one round to the next. The oblivious robot model is hence attractive from a self-stabilization perspective, where state is subject to adversarial manipulation. However, to the best of our knowledge, all existing robot convergence protocols rely on the assumption that robots, despite being "weak", can measure distances. We in this paper initiate the study of convergence protocols for even simpler robots, called \emph{monoculus robots}: robots which cannot measure distances. In particular, we introduce two natural models which relax the assumptions on the robots' cognitive capabilities: (1) a Locality Detection ($\mathcal{LD}$) model in which a robot can only detect whether another robot is closer than a given constant distance or not, (2) an Orthogonal Line Agreement ($\mathcal{OLA}$) model in which robots only agree on a pair of orthogonal lines (say North-South and West-East, but without knowing which is which). The problem turns out to be non-trivial, and simple median and angle bisection strategies can easily increase the distances among robots (e.g., the area of the enclosing convex hull) over time. Our main contribution are deterministic self-stabilizing convergence algorithms for these two models, together with a complexity analysis. We also show that in some sense, the assumptions made in our models are minimal: by relaxing the assumptions on the \textit{monoculus robots} further, we run into impossibility results.
Feedback-Controlled Sequential Lasso Screening
Venue: arXiv cs.LG 2016
Year: 2016

One way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features. Prior research has shown that sequential screening methods offer the greatest promise in this endeavor. Most existing work on sequential screening targets the context of tuning parameter selection, where one screens and solves a sequence of $N$ lasso problems with a fixed grid of geometrically spaced regularization parameters. In contrast, we focus on the scenario where a target regularization parameter has already been chosen via cross-validated model selection, and we then need to solve many lasso instances using this fixed value. In this context, we propose and explore a feedback controlled sequential screening scheme. Feedback is used at each iteration to select the next problem to be solved. This allows the sequence of problems to be adapted to the instance presented and the number of intermediate problems to be automatically selected. We demonstrate our feedback scheme using several datasets including a dictionary of approximate size 100,000 by 300,000.
Scaling Laws for Ergodic Spectral Efficiency in MIMO Poisson Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is that of the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device (D2D) networks. The primary finding is that, with knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both 1) the minimum of the number of transmit and receive antennas, 2) the density of nodes and 3) the path-loss exponent. This linear gain is achieved by using the transmit antennas to send multiple data streams (e.g. through an open-loop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when using a successive interference cancellation method. A major implication of the derived scaling laws is that spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in multiple antenna random wireless networks. Simulation results support this analysis.
SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events
Venue: arXiv cs.CY 2016
Year: 2016

We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient.
Reconstructing Neural Parameters and Synapses of arbitrary interconnected Neurons from their Simulated Spiking Activity
Venue: arXiv cs.NE 2016
Year: 2016

To understand the behavior of a neural circuit it is a presupposition that we have a model of the dynamical system describing this circuit. This model is determined by several parameters, including not only the synaptic weights, but also the parameters of each neuron. Existing works mainly concentrate on either the synaptic weights or the neural parameters. In this paper we present an algorithm to reconstruct all parameters including the synaptic weights of a spiking neuron model. The model based on works of Eugene M. Izhikevich (Izhikevich 2007) consists of two differential equations and covers different types of cortical neurons. It combines the dynamical properties of Hodgkin-Huxley-type dynamics with a high computational efficiency. The presented algorithm uses the recordings of the corresponding membrane potentials of the model for the reconstruction and consists of two main components. The first component is a rank based Genetic Algorithm (GA) which is used to find the neural parameters of the model. The second one is a Least Mean Squares approach which computes the synaptic weights of all interconnected neurons by minimizing the squared error between the calculated and the measured membrane potentials for each time step. In preparation for the reconstruction of the neural parameters and of the synaptic weights from real measured membrane potentials, promising results based on simulated data generated with a randomly parametrized Izhikevich model are presented. The reconstruction does not only converge to a global minimum of neural parameters, but also approximates the synaptic weights with high precision.