OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Policies for Wireless Networks with Energy Harvesting Transmitters and Receivers: Effects of Decoding Costs
Venue: arXiv cs.IT 2015
Year: 2015

We consider the effects of decoding costs in energy harvesting communication systems. In our setting, receivers, in addition to transmitters, rely solely on energy harvested from nature, and need to spend some energy in order to decode their intended packets. We model the decoding energy as an increasing convex function of the rate of the incoming data. In this setting, in addition to the traditional energy causality constraints at the transmitters, we have the decoding causality constraints at the receivers, where energy spent by the receiver for decoding cannot exceed its harvested energy. We first consider the point-to-point single-user problem where the goal is to maximize the total throughput by a given deadline subject to both energy and decoding causality constraints. We show that decoding costs at the receiver can be represented as generalized data arrivals at the transmitter, and thereby moving all system constraints to the transmitter side. Then, we consider several multi-user settings. We start with a two-hop network where the relay and the destination have decoding costs, and show that separable policies, where the transmitter's throughput is maximized irrespective of the relay's transmission energy profile, are optimal. Next, we consider the multiple access channel (MAC) and the broadcast channel (BC) where the transmitters and the receivers harvest energy from nature, and characterize the maximum departure region. In all multi-user settings considered, we decompose our problems into inner and outer problems. We solve the inner problems by exploiting the structure of the particular model, and solve the outer problems by water-filling algorithms.
Growing Network Models Having Part Edges Removed/added Randomly
Venue: arXiv cs.SI 2015
Year: 2015

Since network motifs are an important property of networks and some networks have the behaviors of rewiring or reducing or adding edges between old vertices before new vertices entering the networks, we construct our non-randomized model N(t) and randomized model N'(t) that have the predicated fixed subgraphs like motifs and satisfy both properties of growth and preferential attachment by means of the recursive algorithm from the lower levels of the so-called bound growing network models. To show the scale-free property of the randomized model N'(t), we design a new method, called edge-cumulative distribution, and democrat two edge-cumulative distributions of N(t) and N'(t) are equivalent to each other.
Optimizing coverage of 3D Wireless Multimedia Sensor Networks by means of deploying redundant sensors
Venue: arXiv cs.NI 2015
Year: 2015

Coverage is one of the fundamental issues in wireless multimedia sensor networks (WMSNs). It reflects the ability of WMSNs to detect the fields. Motivated by the existing-enhancing algorithm of traditional 2D WMSNs, a new 3D WMSNs sensing model is established and a new coverage-enhancing algorithm based on this model is proposed. This algorithm defines the sensing model as trapezoidal pyramid, calculates the key parameters (tilt angle) then improves coverage ratio by optimizing it. However, there still exists redundant sensors in this optimized networks. Aiming at efficiently utilizing these redundant sensors and enhancing coverage ratio, the authors selects the redundant sensors by introducing the set cover model algorithm, further deploys them to the uncovered area following the greedy policy, so that the whole path coverage performance of WMSNs is enhanced.
Inter-Robot Interactions in Multi-Robot Systems Using Braids
Venue: arXiv cs.MA 2015
Year: 2015

This paper describes a framework for multi-robot coordination and motion planning with emphasis on inter-agent interactions. We focus on the characterization of inter-agent interactions with sufficient level of abstraction so as to allow for the enforcement of desired interaction patterns in a provably safe (i.e., collision-free) manner, e.g., for achieving rich movement patterns in a shared space, or to exchange sensor information. We propose to specify interaction patterns through elements of the so-called braid group. This allows us to not focus on a particular pattern per se, but rather on the problem of being able to execute a whole class of patterns. The result from such a construction is a hybrid system driven by symbolic inputs that must be mapped onto actual paths that both realize the desired interaction levels and remain safe in the sense that collisions are avoided.
On the discovery of social roles in large scale social systems
Venue: arXiv cs.SI 2015
Year: 2015

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems.
Study of Monetization as a Way of Motivating Freemium Service Users
Venue: arXiv cs.CY 2015
Year: 2015

The paper describes user behavior as a result of introducing monetization in the freemium educational online platform. Monetization resulted in alternative system growth mechanisms causing viral increase in the number of users. System metrics in terms of the $K$-factor was utilized as an indicator of the system user base growth. The weekly $K$-factor doubled as a result of monetization introduction.
Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networks
Venue: arXiv cs.LG 2015
Year: 2015

Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of "if" but a matter of "when" in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.
Online compressed sensing
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we explore the possibilities and limitations of recovering sparse signals in an online fashion. Employing a mean field approximation to the Bayes recursion formula yields an online signal recovery algorithm that can be performed with a computational cost that is linearly proportional to the signal length per update. Analysis of the resulting algorithm indicates that the online algorithm asymptotically saturates the optimal performance limit achieved by the offline method in the presence of Gaussian measurement noise, while differences in the allowable computational costs may result in fundamental gaps of the achievable performance in the absence of noise.
Models and Representations for Fractal Social Organizations
Venue: arXiv cs.MA 2015
Year: 2015

Our subject is oriented towards investigation of potential ways of societal organization, that allow for collective intelligent organization and management of resources. The main objective of such organizations is the exploration of the social energy from the existing societies. We conjecture that an organizational model that fulfills the mentioned requirements is the Fractal Social Organization (FSO). Our goal is to prove and verify the effectiveness of this model by performing various simulations using the NetLogo environment, a tool that allows agent-based rapid prototyping. We begin by simulating trivial real life activities that demonstrate the main properties of the core unit of the FSO, namely the SoC. Further, more complex scenarios involving various nested SoCs are simulated. Two main simulation models are presented, allowing us to obtain preliminary results using the FSO concepts as a potential solution. In the first simulation model we demonstrate that by the use of FSO properties the individuals may benefit by receiving more qualitative healthcare services, while in the second simulation model we show how it might be possible to improve the fall detection systems by the use of FSO mechanism.
Identifying Biochemical Reaction Networks From Heterogeneous Datasets
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.
Taming the ReLU with Parallel Dither in a Deep Neural Network
Venue: arXiv cs.LG 2015
Year: 2015

Rectified Linear Units (ReLU) seem to have displaced traditional 'smooth' nonlinearities as activation-function-du-jour in many - but not all - deep neural network (DNN) applications. However, nobody seems to know why. In this article, we argue that ReLU are useful because they are ideal demodulators - this helps them perform fast abstract learning. However, this fast learning comes at the expense of serious nonlinear distortion products - decoy features. We show that Parallel Dither acts to suppress the decoy features, preventing overfitting and leaving the true features cleanly demodulated for rapid, reliable learning.
Accelerated Distance Computation with Encoding Tree for High Dimensional Data
Venue: arXiv cs.CV 2015
Year: 2015

We propose a novel distance to calculate distance between high dimensional vector pairs, utilizing vector quantization generated encodings. Vector quantization based methods are successful in handling large scale high dimensional data. These methods compress vectors into short encodings, and allow efficient distance computation between an uncompressed vector and compressed dataset without decompressing explicitly. However for large datasets, these distance computing methods perform excessive computations. We avoid excessive computations by storing the encodings on an Encoding Tree(E-Tree), interestingly the memory consumption is also lowered. We also propose Encoding Forest(E-Forest) to further lower the computation cost. E-Tree and E-Forest is compatible with various existing quantization-based methods. We show by experiments our methods speed-up distance computing for high dimensional data drastically, and various existing algorithms can benefit from our methods.
Periods and borders of random words
Venue: arXiv cs.FL 2015
Year: 2015

We investigate the behavior of the periods and border lengths of random words over a fixed alphabet. We show that the asymptotic probability that a random word has a given maximal border length $k$ is a constant, depending only on $k$ and the alphabet size $\ell$. We give a recurrence that allows us to determine these constants with any required precision. This also allows us to evaluate the expected period of a random word. For the binary case, the expected period is asymptotically about $n-1.641$. We also give explicit formulas for the probability that a random word is unbordered or has maximum border length one.
Recurrent Spatial Transformer Networks
Venue: arXiv cs.CV 2015
Year: 2015

We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNN-SPN to classify digits in cluttered MNIST sequences. The proposed model achieves a single digit error of 1.5% compared to 2.9% for a convolutional networks and 2.0% for convolutional networks with SPN layers. The SPN outputs a zoomed, rotated and skewed version of the input image. We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance. The down-sampling in RNN-SPN can be thought of as adaptive down-sampling that minimizes the information loss in the regions of interest. We attribute the superior performance of the RNN-SPN to the fact that it can attend to a sequence of regions of interest.
Algorithmic statistics, prediction and machine learning
Venue: arXiv cs.LG 2015
Year: 2015

Algorithmic statistics considers the following problem: given a binary string $x$ (e.g., some experimental data), find a "good" explanation of this data. It uses algorithmic information theory to define formally what is a good explanation. In this paper we extend this framework in two directions. First, the explanations are not only interesting in themselves but also used for prediction: we want to know what kind of data we may reasonably expect in similar situations (repeating the same experiment). We show that some kind of hierarchy can be constructed both in terms of algorithmic statistics and using the notion of a priori probability, and these two approaches turn out to be equivalent. Second, a more realistic approach that goes back to machine learning theory, assumes that we have not a single data string $x$ but some set of "positive examples" $x_1,\ldots,x_l$ that all belong to some unknown set $A$, a property that we want to learn. We want this set $A$ to contain all positive examples and to be as small and simple as possible. We show how algorithmic statistic can be extended to cover this situation.
Streaming Verification in Data Analysis
Venue: arXiv cs.DS 2015
Year: 2015

Streaming interactive proofs (SIPs) are a framework to reason about outsourced computation, where a data owner (the verifier) outsources a computation to the cloud (the prover), but wishes to verify the correctness of the solution provided by the cloud service. In this paper we present streaming interactive proofs for problems in data analysis. We present protocols for clustering and shape fitting problems, as well as an improved protocol for rectangular matrix multiplication. The latter can in turn be used to verify $k$ eigenvectors of a (streamed) $n \times n$ matrix. In general our solutions use polylogarithmic rounds of communication and polylogarithmic total communication and verifier space. For special cases (when optimality certificates can be verified easily), we present constant round protocols with similar costs. For rectangular matrix multiplication and eigenvector verification, our protocols work in the more restricted annotated data streaming model, and use sublinear (but not polylogarithmic) communication.
Adaptive DRX Scheme to Improve Energy Efficiency in LTE Networks with Bounded Delay
Venue: arXiv cs.NI 2015
Year: 2015

The Discontinuous Reception (DRX) mechanism is commonly employed in current LTE networks to improve energy efficiency of user equipment (UE). DRX allows UEs to monitor the physical downlink control channel (PDCCH) discontinuously when there is no downlink traffic for them, thus reducing their energy consumption. However, DRX power savings are achieved at the expense of some increase in packet delay since downlink traffic transmission must be deferred until the UEs resume listening to the PDCCH. In this paper, we present a promising mechanism that reduces energy consumption of UEs using DRX while simultaneously maintaining average packet delay around a desired target. Furthermore, our proposal is able to achieve significant power savings without either increasing signaling overhead or requiring any changes to deployed wireless protocols.
Visual Generalized Coordinates
Venue: arXiv cs.RO 2015
Year: 2015

An open problem in robotics is that of using vision to identify a robot's own body and the world around it. Many models attempt to recover the traditional C-space parameters. Instead, we propose an alternative C-space by deriving generalized coordinates from $n$ images of the robot. We show that the space of such images is bijective to the motion space, so these images lie on a manifold $\mathcal{V}$ homeomorphic to the canonical C-space. We now approximate this manifold as a set of $n$ neighbourhood tangent spaces that result in a graph, which we call the Visual Roadmap (VRM). Given a new robot image, we perform inverse kinematics visually by interpolating between nearby images in the image space. Obstacles are projected onto the VRM in $O(n)$ time by superimposition of images, leading to the identification of collision poses. The edges joining the free nodes can now be checked with a visual local planner, and free-space motions computed in $O(nlogn)$ time. This enables us to plan paths in the image space for a robot manipulator with unknown link geometries, DOF, kinematics, obstacles, and camera pose. We sketch the proofs for the main theoretical ideas, identify the assumptions, and demonstrate the approach for both articulated and mobile robots. We also investigate the feasibility of the process by investigating various metrics and image sampling densities, and demonstrate it on simulated and real robots.
On the Capacity of Point-to-Point and Multiple-Access Molecular Communications with Ligand-Receptors
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider the bacterial point-to-point and multiple-access molecular communications with ligand-receptors. For the point-to-point communication, we investigate common signaling methods, namely the Level Scenario (LS), which uses one type of a molecule with different concentration levels, and the Type Scenario (TS), which employs multiple types of molecules with a single concentration level. We investigate the trade-offs between the two scenarios from the capacity point of view. We derive an upper bound on the capacity using a Binomial Channel (BIC) model and the symmetrized Kullback-Leibler (KL) divergence. A lower bound is also derived when the environment noise is negligible. For the TS, we also consider the effect of blocking of a receptor by a different molecule type. Then, we consider multiple-access communications, for which we investigate three scenarios based on molecule and receptor types, i.e., same types of molecules with Different Labeling and Same types of Receptors (DLSR), Different types of Molecules and Receptors (DMDR), and Same types of Molecules and Receptors (SMSR). We investigate the trade-offs among the three scenarios from the total capacity point of view. We derive some inner bounds on the capacity region of these scenarios when the environment noise is negligible.
CLAMShell: Speeding up Crowds for Low-latency Data Labeling
Venue: arXiv cs.DB 2015
Year: 2015

Data labeling is a necessary but often slow process that impedes the development of interactive systems for modern data analysis. Despite rising demand for manual data labeling, there is a surprising lack of work addressing its high and unpredictable latency. In this paper, we introduce CLAMShell, a system that speeds up crowds in order to achieve consistently low-latency data labeling. We offer a taxonomy of the sources of labeling latency and study several large crowd-sourced labeling deployments to understand their empirical latency profiles. Driven by these insights, we comprehensively tackle each source of latency, both by developing novel techniques such as straggler mitigation and pool maintenance and by optimizing existing methods such as crowd retainer pools and active learning. We evaluate CLAMShell in simulation and on live workers on Amazon's Mechanical Turk, demonstrating that our techniques can provide an order of magnitude speedup and variance reduction over existing crowdsourced labeling strategies.
Deep Convolutional Features for Image Based Retrieval and Scene Categorization
Venue: arXiv cs.CV 2015
Year: 2015

Several recent approaches showed how the representations learned by Convolutional Neural Networks can be repurposed for novel tasks. Most commonly it has been shown that the activation features of the last fully connected layers (fc7 or fc6) of the network, followed by a linear classifier outperform the state-of-the-art on several recognition challenge datasets. Instead of recognition, this paper focuses on the image retrieval problem and proposes a examines alternative pooling strategies derived for CNN features. The presented scheme uses the features maps from an earlier layer 5 of the CNN architecture, which has been shown to preserve coarse spatial information and is semantically meaningful. We examine several pooling strategies and demonstrate superior performance on the image retrieval task (INRIA Holidays) at the fraction of the computational cost, while using a relatively small memory requirements. In addition to retrieval, we see similar efficiency gains on the SUN397 scene categorization dataset, demonstrating wide applicability of this simple strategy. We also introduce and evaluate a novel GeoPlaces5K dataset from different geographical locations in the world for image retrieval that stresses more dramatic changes in appearance and viewpoint.
Dependable Structural Helath Monitoring Using Wireless Sensor Networks
Venue: arXiv cs.DC 2015
Year: 2015

As an alternative to current wired-based networks, wireless sensor networks (WSNs) are becoming an increasingly compelling platform for engineering structural health monitoring (SHM) due to relatively low-cost, easy installation, and so forth. However, there is still an unaddressed challenge: the application-specific dependability in terms of sensor fault detection and tolerance. The dependability is also affected by a reduction on the quality of monitoring when mitigating WSN constrains (e.g., limited energy, narrow bandwidth). We address these by designing a dependable distributed WSN framework for SHM (called DependSHM) and then examining its ability to cope with sensor faults and constraints. We find evidence that faulty sensors can corrupt results of a health event (e.g., damage) in a structural system without being detected. More specifically, we bring attention to an undiscovered yet interesting fact, i.e., the real measured signals introduced by one or more faulty sensors may cause an undamaged location to be identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis. This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift, noise, and so forth. In DependSHM, we present a distributed automated algorithm to detect such types of faults, and we offer an online signal reconstruction algorithm to recover from the wrong diagnosis. Through comprehensive simulations and a WSN prototype system implementation, we evaluate the effectiveness of DependSHM.