OpenRaiser/Intern-Atlas huggingface dataset:
Capacity of electron-based communication over bacterial cables: the full-CSI case
Venue: arXiv cs.IT 2015
Year: 2015

Motivated by recent discoveries of microbial communities that transfer electrons across centimeter-length scales, this paper studies the information capacity of bacterial cables via electron transfer, which coexists with molecular communications, under the assumption of full causal channel state information (CSI). The bacterial cable is modeled as an electron queue that transfers electrons from the encoder at the electron donor source, which controls the desired input electron intensity, to the decoder at the electron acceptor sink. Clogging due to local ATP saturation along the cable is modeled. A discrete-time scheme is investigated, enabling the computation of an achievable rate. The regime of asymptotically small time-slot duration is analyzed, and the optimality of binary input distributions is proved, i.e., the encoder transmits at either maximum or minimum intensity, as dictated by the physical constraints of the cable. A dynamic programming formulation of the capacity is proposed, and the optimal binary signaling is determined via policy iteration. It is proved that the optimal signaling has smaller intensity than that given by the myopic policy, which greedily maximizes the instantaneous information rate but neglects its effect on the steady-state cable distribution. In contrast, the optimal scheme balances the tension between achieving high instantaneous information rate, and inducing a favorable steady-state distribution, such that those states characterized by high information rates are visited more frequently, thus revealing the importance of CSI. This work represents a first contribution towards the design of electron signaling schemes in complex microbial structures, e.g., bacterial cables and biofilms, where the tension between maximizing the transfer of information and guaranteeing the well-being of the overall bacterial community arises.
An Improvement of the Cipolla-Lehmer Type Algorithms
Venue: arXiv cs.CR 2015
Year: 2015

Let F_q be a finite field with q elements with prime power q and let r>1 be an integer with $q\equiv 1 \pmod{r}$. In this paper, we present a refinement of the Cipolla-Lehmer type algorithm given by H. C. Williams, and subsequently improved by K. S. Williams and K. Hardy. For a given r-th power residue c in F_q where r is an odd prime, the algorithm of H. C. Williams determines a solution of X^r=c in $O(r^3\log q)$ multiplications in F_q, and the algorithm of K. S. Williams and K. Hardy finds a solution in $O(r^4+r^2\log q)$ multiplications in F_q. Our refinement finds a solution in $O(r^3+r^2\log q)$ multiplications in F_q. Therefore our new method is better than the previously proposed algorithms independent of the size of r, and the implementation result via SAGE shows a substantial speed-up compared with the existing algorithms.
Designing Low Cost and Energy Efficient Access Network for the Developing World
Venue: arXiv cs.NI 2015
Year: 2015

Internet is growing rapidly in the developing world now. Our survey of four networks in India, all having at least one thousand users, suggest that both installation cost and recurring cost due to power consumption pose a challenge in its deployment in developing countries. In this paper, we first model the access design problem by dividing the users in two types 1) those that may access the network anytime and 2) those who need it only during office hours on working days. The problem is formulated as a binary integer linear program which turns out to be NP-hard. We then give a distributed heuristic for network design. We evaluate our model and heuristic using real data collected from IIT Kanpur LAN for more than 50 days. Results show that even in a tree topology -- which is a common characteristic of all networks who participated in our study, our design can reduce the energy consumption of the network by up to 11% in residential-cum-office environments and up to 22% in office-only environments in comparison with current methods without giving up on the performance. The extra cost incurred due to our design can be compensated in less than an year by saving in electricity bill of the network.
NLP Solutions as Asymptotic Values of ODE Trajectories
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, it is shown that the solutions of general differentiable constrained optimization problems can be viewed as asymptotic solutions to sets of Ordinary Differential Equations (ODEs). The construction of the ODE associated to the optimization problem is based on an exact penalty formulation in which the weighting parameter dynamics is coordinated with that of the decision variable so that there is no need to solve a sequence of optimization problems, instead, a single ODE has to be solved using available efficient methods. Examples are given in order to illustrate the results. This includes a novel systematic approach to solve combinatoric optimization problems as well as fast computation of a class of optimization problems using analogic circuits leading to fast, parallel and highly scalable solutions.
Power Control and Soft Topology Adaptations in Multihop Cellular Networks with Multi-Point Connectivity
Venue: arXiv cs.IT 2015
Year: 2015

The LTE standards account for the use of relays to enhance coverage near the cell edge. In a traditional topology, a mobile can either establish a direct link to the base station (BS) or a link to the relay, but not both. In this paper, we consider the benefit of multipoint connectivity in allowing User Equipment (UEs) to split their transmit power over simultaneous links to the BS and the relay, in effect transmitting two parallel flows. We model decisions by the UEs as to: (i) which point of access to attach to (either a relay or a relay and the BS or only the BS); and (ii) how to allocate transmit power over these links so as to maximize their total rate. We show that this flexibility in the selection of points of access leads to substantial network capacity increase against when nodes operate in a fixed network topology. Individual adaptations by UEs, in terms of both point of access and transmit power, are interdependent due to interference and to the possibility of over-loading of the backhaul links. We show that these decisions can converge without any explicit cooperation and derive a closed-form expression for the transmit power levels.
Ricci Curvature of the Internet Topology
Venue: arXiv cs.SI 2015
Year: 2015

Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's "thin triangle condition", which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier, Lin, etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.
Efficient Sensor Fault Detection Using Group Testing
Venue: arXiv cs.SY 2015
Year: 2015

When faulty sensors are rare in a network, diagnosing sensors individually is inefficient. This study introduces a novel use of concepts from group testing and Kalman filtering in detecting these rare faulty sensors with significantly fewer number of tests. By assigning sensors to groups and performing Kalman filter-based fault detection over these groups, we obtain binary detection outcomes, which can then be used to recover the fault state of all sensors. We first present this method using combinatorial group testing. We then present a novel adaptive group testing method based on Bayesian inference. This adaptive method further reduces the number of required tests and is suitable for noisy group test systems. Compared to non-group testing methods, our algorithm achieves similar detection accuracy with fewer tests and thus lower computational complexity. Compared to other adaptive group testing methods, the proposed method achieves higher accuracy when test results are noisy. We perform extensive numerical analysis using a set of real vibration data collected from the New Carquinez Bridge in California using an 18-sensor network mounted on the bridge. We also discuss how the features of the Kalman filter-based group test can be exploited in forming groups and further improving the detection accuracy.
Analysis of Massive MIMO With Hardware Impairments and Different Channel Models
Venue: arXiv cs.IT 2015
Year: 2015

Massive Multiple-Input Multiple-Output (MIMO) is foreseen to be one of the main technology components in next generation cellular communications (5G). In this paper, fundamental limits on the performance of downlink massive MIMO systems are investigated by means of simulations and analytical analysis. Signal-to-noise-and-interference ratio (SINR) and sum rate for a single-cell scenario multi-user MIMO are analyzed for different array sizes, channel models, and precoding schemes. The impact of hardware impairments on performance is also investigated. Simple approximations are derived that show explicitly how the number of antennas, number of served users, transmit power, and magnitude of hardware impairments affect performance.
Service Provisioning and Profit Maximization in Network-assisted Adaptive HTTP Streaming
Venue: arXiv cs.NI 2015
Year: 2015

Adaptive HTTP streaming with centralized consideration of multiple streams has gained increasing interest. It poses a special challenge that the interests of both content provider and network operator need to be deliberately balanced. More importantly, the adaptation strategy is required to be flexible enough to be ported to various systems that work under different network environments, QoE levels, and economic objectives. To address these challenges, we propose a Markov Decision Process (MDP) based network-assisted adaptation framework, wherein cost of buffering, significant playback variation, bandwidth management and income of playback are jointly investigated. We then demonstrate its promising service provisioning and maximal profit for a mobile network in which fair or differentiated service is required.
Achieving Arbitrary Locality and Availability in Binary Codes
Venue: arXiv cs.IT 2015
Year: 2015

The $i$th coordinate of an $(n,k)$ code is said to have locality $r$ and availability $t$ if there exist $t$ disjoint groups, each containing at most $r$ other coordinates that can together recover the value of the $i$th coordinate. This property is particularly useful for codes for distributed storage systems because it permits local repair and parallel accesses of hot data. In this paper, for any positive integers $r$ and $t$, we construct a binary linear code of length $\binom{r+t}{t}$ which has locality $r$ and availability $t$ for all coordinates. The information rate of this code attains $\frac{r}{r+t}$, which is always higher than that of the direct product code, the only known construction that can achieve arbitrary locality and availability.
Comment on "Clustering by fast search and find of density peaks"
Venue: arXiv cs.LG 2015
Year: 2015

In [1], a clustering algorithm was given to find the centers of clusters quickly. However, the accuracy of this algorithm heavily depend on the threshold value of d-c. Furthermore, [1] has not provided any efficient way to select the threshold value of d-c, that is, one can have to estimate the value of d_c depend on one's subjective experience. In this paper, based on the data field [2], we propose a new way to automatically extract the threshold value of d_c from the original data set by using the potential entropy of data field. For any data set to be clustered, the most reasonable value of d_c can be objectively calculated from the data set by using our proposed method. The same experiments in [1] are redone with our proposed method on the same experimental data set used in [1], the results of which shows that the problem to calculate the threshold value of d_c in [1] has been solved by using our method.
Image classification by visual bag-of-words refinement and reduction
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a new framework for visual bag-of-words (BOW) refinement and reduction to overcome the drawbacks associated with the visual BOW model which has been widely used for image classification. Although very influential in the literature, the traditional visual BOW model has two distinct drawbacks. Firstly, for efficiency purposes, the visual vocabulary is commonly constructed by directly clustering the low-level visual feature vectors extracted from local keypoints, without considering the high-level semantics of images. That is, the visual BOW model still suffers from the semantic gap, and thus may lead to significant performance degradation in more challenging tasks (e.g. social image classification). Secondly, typically thousands of visual words are generated to obtain better performance on a relatively large image dataset. Due to such large vocabulary size, the subsequent image classification may take sheer amount of time. To overcome the first drawback, we develop a graph-based method for visual BOW refinement by exploiting the tags (easy to access although noisy) of social images. More notably, for efficient image classification, we further reduce the refined visual BOW model to a much smaller size through semantic spectral clustering. Extensive experimental results show the promising performance of the proposed framework for visual BOW refinement and reduction.
On Channel Estimation for 802.11p in Highly Time-Varying Vehicular Channels
Venue: arXiv cs.IT 2015
Year: 2015

Vehicular wireless channels are highly time-varying and the pilot pattern in the 802.11p orthogonal frequency-division multiplexing frame has been shown to be ill suited for long data packets. The high frame error rate in off-the-shelf chipsets with noniterative receiver configurations is mostly due to the use of outdated channel estimates for equalization. This paper deals with improving the channel estimation in 802.11p systems using a cross layered approach, where known data bits are inserted in the higher layers and a modified receiver makes use of these bits as training data for improved channel estimation. We also describe a noniterative receiver configuration for utilizing the additional training bits and show through simulations that frame error rates close to the case with perfect channel knowledge can be achieved.
Semi-Active Control of the Sway Dynamics for Elevator Ropes
Venue: arXiv cs.SY 2015
Year: 2015

In this work we study the problem of rope sway dynamics control for elevator systems. We choose to actuate the system with a semi-active damper mounted on the top of the elevator car. We propose nonlinear controllers based on Lyapunov theory, to actuate the semi-active damper and stabilize the rope sway dynamics. We study the stability of the proposed controllers, and test their performances on a numerical example.
Does Learning Imply a Decrease in the Entropy of Behavior?
Venue: arXiv cs.RO 2015
Year: 2015

Shannon's information entropy measures of the uncertainty of an event's outcome. If learning about a system reflects a decrease in uncertainty, then a plausible intuition is that learning should be accompanied by a decrease in the entropy of the organism's actions and/or perceptual states. To address whether this intuition is valid, I examined an artificial organism -- a simple robot -- that learned to navigate in an arena and analyzed the entropy of the outcome variables action, state, and reward. Entropy did indeed decrease in the initial stages of learning, but two factors complicated the scenario: (1) the introduction of new options discovered during the learning process and (2) the shifting patterns of perceptual and environmental states resulting from changes to the robot's learned movement strategies. These factors lead to a subsequent increase in entropy as the agent learned. I end with a discussion of the utility of information-based characterizations of learning.
Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
Venue: arXiv cs.AI 2015
Year: 2015

We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior of any feature. We theoretically prove good properties of our estimators and empirically show that our estimators considerably outperform the estimators from the previous state-of-the-art methods.
Optimal Power Allocation for Secure Communications in Large-Scale MIMO Relaying Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we address the problem of optimal power allocation at the relay in two-hop secure communications. In order to solve the challenging issue of short-distance interception in secure communications, the benefit of large-scale MIMO (LS-MIMO) relaying techniques is exploited to improve the secrecy performance significantly, even in the case without eavesdropper channel state information (CSI). The focus of this paper is on the analysis and design of optimal power allocation for the relay, so as to maximize the secrecy outage capacity. We reveal the condition that the secrecy outage capacity is positive, prove that there is one and only one optimal power, and present an optimal power allocation scheme. Moreover, the asymptotic characteristics of the secrecy outage capacity is carried out to provide some clear insights for secrecy performance optimization. Finally, simulation results validate the effectiveness of the proposed scheme.
Improving the use of equational constraints in cylindrical algebraic decomposition
Venue: arXiv cs.SC 2015
Year: 2015

When building a cylindrical algebraic decomposition (CAD) savings can be made in the presence of an equational constraint (EC): an equation logically implied by a formula. The present paper is concerned with how to use multiple ECs, propagating those in the input throughout the projection set. We improve on the approach of McCallum in ISSAC 2001 by using the reduced projection theory to make savings in the lifting phase (both to the polynomials we lift with and the cells lifted over). We demonstrate the benefits with worked examples and a complexity analysis.
Information Leakage of Heterogeneous Encoded Correlated Sequences over Eavesdropped Channel
Venue: arXiv cs.IT 2015
Year: 2015

Correlated sources are present in communication systems where protocols ensure that there is some predetermined information for sources. Here correlated sources across an eavesdropped channel that incorporate a heterogeneous encoding scheme and their effect on the information leakage when some channel information and a source have been wiretapped is investigated. The information leakage bounds for the Slepian-Wolf scenario are provided. Thereafter, the Shannon cipher system approach is presented. Further, an implementation method using a matrix partition approach is described.
Transferring Rich Feature Hierarchies for Robust Visual Tracking
Venue: arXiv cs.CV 2015
Year: 2015

Convolutional neural network (CNN) models have demonstrated great success in various computer vision tasks including image classification and object detection. However, some equally important tasks such as visual tracking remain relatively unexplored. We believe that a major hurdle that hinders the application of CNN to visual tracking is the lack of properly labeled training data. While existing applications that liberate the power of CNN often need an enormous amount of training data in the order of millions, visual tracking applications typically have only one labeled example in the first frame of each video. We address this research issue here by pre-training a CNN offline and then transferring the rich feature hierarchies learned to online tracking. The CNN is also fine-tuned during online tracking to adapt to the appearance of the tracked target specified in the first video frame. To fit the characteristics of object tracking, we first pre-train the CNN to recognize what is an object, and then propose to generate a probability map instead of producing a simple class label. Using two challenging open benchmarks for performance evaluation, our proposed tracker has demonstrated substantial improvement over other state-of-the-art trackers.
Tracing the boundaries of materials in transparent vessels using computer vision
Venue: arXiv cs.CV 2015
Year: 2015

Visual recognition of material boundaries in transparent vessels is valuable for numerous applications. Such recognition is essential for estimation of fill-level, volume and phase-boundaries as well as for tracking of such chemical processes as precipitation, crystallization, condensation, evaporation and phase-separation. The problem of material boundary recognition in images is particularly complex for materials with non-flat surfaces, i.e., solids, powders and viscous fluids, in which the material interfaces have unpredictable shapes. This work demonstrates a general method for finding the boundaries of materials inside transparent containers in images. The method uses an image of the transparent vessel containing the material and the boundary of the vessel in this image. The recognition is based on the assumption that the material boundary appears in the image in the form of a curve (with various constraints) whose endpoints are both positioned on the vessel contour. The probability that a curve matches the material boundary in the image is evaluated using a cost function based on some image properties along this curve. Several image properties were examined as indicators for the material boundary. The optimal boundary curve was found using Dijkstra's algorithm. The method was successfully examined for recognition of various types of phase-boundaries, including liquid-air, solid-air and solid-liquid interfaces, as well as for various types of glassware containers from everyday life and the chemistry laboratory (i.e., bottles, beakers, flasks, jars, columns, vials and separation-funnels). In addition, the method can be easily extended to materials carried on top of carrier vessels (i.e., plates, spoons, spatulas).