OpenRaiser/Intern-Atlas huggingface dataset:
Information Theoretic Caching: The Multi-User Case
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider a cache aided network in which each user is assumed to have individual caches, while upon users' requests, an update message is sent though a common link to all users. First, we formulate a general information theoretic setting that represents the database as a discrete memoryless source, and the users' requests as side information that is available everywhere except at the cache encoder. The decoders' objective is to recover a function of the source and the side information. By viewing cache aided networks in terms of a general distributed source coding problem and through information theoretic arguments, we present inner and outer bounds on the fundamental tradeoff of cache memory size and update rate. Then, we specialize our general inner and outer bounds to a specific model of content delivery networks: File selection networks, in which the database is a collection of independent equal-size files and each user requests one of the files independently. For file selection networks, we provide an outer bound and two inner bounds (for centralized and decentralized caching strategies). For the case when the user request information is uniformly distributed, we characterize the rate vs. cache size tradeoff to within a multiplicative gap of 4. By further extending our arguments to the framework of Maddah-Ali and Niesen, we also establish a new outer bound and two new inner bounds in which it is shown to recover the centralized and decentralized strategies, previously established by Maddah-Ali and Niesen. Finally, in terms of rate vs. cache size tradeoff, we improve the previous multiplicative gap of 72 to 4.7 for the average case with uniform requests.
Ladder and Subdivision of Ladder Graphs with Pendant Edges are Odd Graceful
Venue: arXiv cs.IT 2016
Year: 2016

The ladder graph plays an important role in many applications as Electronics, Electrical and Wireless communication areas. The aim of this work is to present a new class of odd graceful labeling for the ladder graph. In particular, we show that the ladder graph Ln with m-pendant Ln + mk1 is odd graceful. We also show that the subdivision of ladder graph Ln with m-pendant S(Ln) + mk1 is odd graceful. Finally, we prove that all the subdivision of triangular snakes (delt-k snake) with pendant edges S(delt-k snake)+ mk are odd graceful.
$C^3$-index: Revisiting Authors' Performance Measure
Venue: arXiv cs.DL 2016
Year: 2016

Author performance indices (such as h-index and its variants) fail to resolve ties while ranking authors with low index values (majority in number) which includes the young researchers. In this work we leverage the citations as well as collaboration profile of an author in a novel way using a weighted multi-layered network and propose a variant of page-rank algorithm to obtain a new author performance measure, $C^3$-index. Experiments on a massive publication dataset reveal several interesting characteristics of our metric: (i) we observe that $C^3$-index is consistent over time, (ii) $C^3$-index has high potential to break ties among low rank authors, (iii) $C^3$-index can effectively be used to predict future achievers at the early stage of their career.
Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images
Venue: arXiv cs.CV 2016
Year: 2016

A method for segmenting water bodies in optical and synthetic aperture radar (SAR) satellite images is proposed. It makes use of the textural features of the different regions in the image for segmentation. The method consists in a multiscale analysis of the images, which allows us to study the images regularity both, locally and globally. As results of the analysis, coarse multifractal spectra of studied images and a group of images that associates each position (pixel) with its corresponding value of local regularity (or singularity) spectrum are obtained. Thresholds are then applied to the multifractal spectra of the images for the classification. These thresholds are selected after studying the characteristics of the spectra under the assumption that water bodies have larger local regularity than other soil types. Classifications obtained by the multifractal method are compared quantitatively with those obtained by neural networks trained to classify the pixels of the images in covered against uncovered by water. In optical images, the classifications are also compared with those derived using the so-called Normalized Differential Water Index (NDWI).
Throughput Maximization for Mobile Relaying Systems
Venue: arXiv cs.IT 2016
Year: 2016

Relaying is an effective technique to achieve reliable wireless connectivity in harsh communication environment. However, most of the existing relaying schemes are based on relays with fixed locations, or \emph{static relaying}. In this paper, we consider a novel \emph{mobile relaying} technique, where the relay nodes are assumed to be capable of moving at high speed. Compared to static relaying, mobile relaying offers a new degree of freedom for performance enhancement via careful relay trajectory design. We study the throughput maximization problem in mobile relaying systems by optimizing the source/relay transmit power along with the relay trajectory, subject to practical mobility constraints (on the relay speed and initial/final relay locations), as well as the \emph{information-causality constraint} at the relay owing to its decode-store-and-forward (DSF) strategy. It is shown that for fixed relay trajectory, the throughput-optimal source/relay power allocations over time follow a "staircase" water filling (WF) structure, with \emph{non-increasing} and \emph{non-decreasing} water levels at the source and relay, respectively. On the other hand, with given power allocations, the throughput can be further improved by optimizing the relay trajectory via successive convex optimization. An iterative algorithm is thus proposed to optimize the power allocations and relay trajectory alternately. Furthermore, for the special case with free initial and final relay locations, the jointly optimal power allocation and relay trajectory are derived. Numerical results show that by optimizing the trajectory of the relay and power allocations adaptive to its induced channel variation, mobile relaying is able to achieve significant throughput gains over the conventional static relaying.
A Novel Efficient Task-Assign Route Planning Method for AUV Guidance in a Dynamic Cluttered Environment
Venue: arXiv cs.RO 2016
Year: 2016

Promoting the levels of autonomy facilitates the vehicle in performing long-range operations with minimum supervision. The capability of Autonomous Underwater Vehicles (AUVs) to fulfill the mission objectives is directly influenced by route planning and task assignment system performance. The system fives the error of "Bad character(s) in field Abstract" for no reason. Please refer to manuscript for the full abstract
A Case for Data Commons: Towards Data Science as a Service
Venue: arXiv cs.CY 2016
Year: 2016

As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including long-term data storage, data exploration and discovery services, and compute capabilities to support data analysis and re-analysis, as new data are added and as scientific pipelines are refined. We describe our experience developing data commons-- interoperable infrastructure that co-locates data, storage, and compute with common analysis tools--and present several cases studies. Across these case studies, several common requirements emerge, including the need for persistent digital identifier and metadata services, APIs, data portability, pay for compute capabilities, and data peering agreements between data commons. Though many challenges, including sustainability and developing appropriate standards remain, interoperable data commons bring us one step closer to effective Data Science as Service for the scientific research community.
Network Topology Identification from Spectral Templates
Venue: arXiv cs.SI 2016
Year: 2016

Network topology inference is a cornerstone problem in statistical analyses of complex systems. In this context, the fresh look advocated here permeates benefits from convex optimization and graph signal processing, to identify the so-termed graph shift operator (encoding the network topology) given only the eigenvectors of the shift. These spectral templates can be obtained, for example, from principal component analysis of a set of graph signals defined on the particular network. The novel idea is to find a graph shift that while being consistent with the provided spectral information, it endows the network structure with certain desired properties such as sparsity. The focus is on developing efficient recovery algorithms along with identifiability conditions for two particular shifts, the adjacency matrix and the normalized graph Laplacian. Application domains include network topology identification from steady-state signals generated by a diffusion process, and design of a graph filter that facilitates the distributed implementation of a prescribed linear network operator. Numerical tests showcase the effectiveness of the proposed algorithms in recovering synthetic and structural brain networks.
Modeling and Simulation of Passenger Traffic in a National Airport
Venue: arXiv cs.MA 2016
Year: 2016

Optimal operation of a country's air transport infrastructure plays a major role in the economic development of nations. Due to the increasing use of air transportation in today's world, flights' boarding times have become a concern for both airlines and airports, thus the importance of knowing beforehand how changes in flights demand parameters and physical airport layout will affect passengers flow and boarding times. This paper presents a pedestrian modeling study in which a national airport passenger flow was analyzed. The study was conducted at Vanguardia National Airport in Villavicencio, Meta, Colombia. Different effects of structural changes are shown and provide judging elements for decision makers regarding passenger traffic in airport design.
Soccer Field Localization from a Single Image
Venue: arXiv cs.CV 2016
Year: 2016

In this work, we propose a novel way of efficiently localizing a soccer field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. In contrast, we formulate this problem as a branch and bound inference in a Markov random field where an energy function is defined in terms of field cues such as grass, lines and circles. Moreover, our approach is fully automatic and depends only on single images from the broadcast video of the game. We demonstrate the effectiveness of our method by applying it to various games and obtain promising results. Finally, we posit that our approach can be applied easily to other sports such as hockey and basketball.
Performance Trade-Offs in Multi-Processor Approximate Message Passing
Venue: arXiv cs.IT 2016
Year: 2016

We consider large-scale linear inverse problems in Bayesian settings. Our general approach follows a recent line of work that applies the approximate message passing (AMP) framework in multi-processor (MP) computational systems by storing and processing a subset of rows of the measurement matrix along with corresponding measurements at each MP node. In each MP-AMP iteration, nodes of the MP system and its fusion center exchange lossily compressed messages pertaining to their estimates of the input. There is a trade-off between the physical costs of the reconstruction process including computation time, communication loads, and the reconstruction quality, and it is impossible to simultaneously minimize all the costs. We pose this minimization as a multi-objective optimization problem (MOP), and study the properties of the best trade-offs (Pareto optimality) in this MOP. We prove that the achievable region of this MOP is convex, and conjecture how the combined cost of computation and communication scales with the desired mean squared error. These properties are verified numerically.
Decomposing generalized bent and hyperbent functions
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we introduce generalized hyperbent functions from $F_{2^n}$ to $Z_{2^k}$, and investigate decompositions of generalized (hyper)bent functions. We show that generalized (hyper)bent functions from $F_{2^n}$ to $Z_{2^k}$ consist of components which are generalized (hyper)bent functions from $F_{2^n}$ to $Z_{2^{k^\prime}}$ for some $k^\prime < k$. For odd $n$, we show that the Boolean functions associated to a generalized bent function form an affine space of semibent functions. This complements a recent result for even $n$, where the associated Boolean functions are bent.
SymNet: scalable symbolic execution for modern networks
Venue: arXiv cs.NI 2016
Year: 2016

We present SymNet, a network static analysis tool based on symbolic execution. SymNet quickly analyzes networks by injecting symbolic packets and tracing their path through the network. Our key novelty is SEFL, a language we designed for network processing that is symbolic-execution friendly. SymNet is easy to use: we have developed parsers that automatically generate SEFL models from router and switch tables, firewall configurations and arbitrary Click modular router configurations. Most of our models are exact and have optimal branching factor. Finally, we built a testing tool that checks SEFL models conform to the real implementation. SymNet can check networks containing routers with hundreds of thousands of prefixes and NATs in seconds, while ensuring packet header memory-safety and capturing network functionality such as dynamic tunneling, stateful processing and encryption. We used SymNet to debug middlebox interactions documented in the literature, to check our department's network and the Stanford backbone network. Results show that symbolic execution is fast and more accurate than existing static analysis tools.
Solving the Team Orienteering Problem with Cutting Planes
Venue: arXiv cs.RO 2016
Year: 2016

The Team Orienteering Problem (TOP) is an attractive variant of the Vehicle Routing Problem (VRP). The aim is to select customers and at the same time organize the visits for a vehicle fleet so as to maximize the collected profits and subject to a travel time restriction on each vehicle. In this paper, we investigate the effective use of a linear formulation with polynomial number of variables to solve TOP. Cutting planes are the core components of our solving algorithm. It is first used to solve smaller and intermediate models of the original problem by considering fewer vehicles. Useful information are then retrieved to solve larger models, and eventually reaching the original problem. Relatively new and dedicated methods for TOP, such as identification of irrelevant arcs and mandatory customers, clique and independent-set cuts based on the incompatibilities, and profit/customer restriction on subsets of vehicles, are introduced. We evaluated our algorithm on the standard benchmark of TOP. The results show that the algorithm is competitive and is able to prove the optimality for 12 instances previously unsolved.
Unified Stochastic Geometry Model for MIMO Cellular Networks with Retransmissions
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents a unified mathematical paradigm, based on stochastic geometry, for downlink cellular networks with multiple-input-multiple-output (MIMO) base stations (BSs). The developed paradigm accounts for signal retransmission upon decoding errors, in which the temporal correlation among the signal-to-interference plus-noise-ratio (SINR) of the original and retransmitted signals is captured. In addition to modeling the effect of retransmission on the network performance, the developed mathematical model presents twofold analysis unification for MIMO cellular networks literature. First, it integrates the tangible decoding error probability and the abstracted (i.e., modulation scheme and receiver type agnostic) outage probability analysis, which are largely disjoint in the literature. Second, it unifies the analysis for different MIMO configurations. The unified MIMO analysis is achieved by abstracting unnecessary information conveyed within the interfering signals by Gaussian signaling approximation along with an equivalent SISO representation for the per-data stream SINR in MIMO cellular networks. We show that the proposed unification simplifies the analysis without sacrificing the model accuracy. To this end, we discuss the diversity-multiplexing tradeoff imposed by different MIMO schemes and shed light on the diversity loss due to the temporal correlation among the SINRs of the original and retransmitted signals. Finally, several design insights are highlighted.
Wisdom of the Crowd or Wisdom of a Few? An Analysis of Users' Content Generation
Venue: arXiv cs.CY 2016
Year: 2016

In this paper we analyze how user generated content (UGC) is created, challenging the well known {\it wisdom of crowds} concept. Although it is known that user activity in most settings follow a power law, that is, few people do a lot, while most do nothing, there are few studies that characterize well this activity. In our analysis of datasets from two different social networks, Facebook and Twitter, we find that a small percentage of active users and much less of all users represent 50\% of the UGC. We also analyze the dynamic behavior of the generation of this content to find that the set of most active users is quite stable in time. Moreover, we study the social graph, finding that those active users are highly connected among them. This implies that most of the wisdom comes from a few users, challenging the independence assumption needed to have a wisdom of crowds. We also address the content that is never seen by any people, which we call digital desert, that challenges the assumption that the content of every person should be taken in account in a collective decision. We also compare our results with Wikipedia data and we address the quality of UGC content using an Amazon dataset. At the end our results are not surprising, as the Web is a reflection of our own society, where economical or political power also is in the hands of minorities.
Binarized Neural Networks on the ImageNet Classification Task
Venue: arXiv cs.CV 2016
Year: 2016

We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance. With a moderate size network of 13 layers, we obtained top-5 classification accuracy rate of 84.1 % on validation set through network distillation, much better than previous published results of 73.2% on XNOR network and 69.1% on binarized GoogleNET. We expect networks of better performance can be obtained by following our current strategies. We provide a detailed discussion and preliminary analysis on strategies used in the network training.
Hardware-oriented Approximation of Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption. One of the most important steps in accelerator development is hardware-oriented model approximation. In this paper we present Ristretto, a model approximation framework that analyzes a given CNN with respect to numerical resolution used in representing weights and outputs of convolutional and fully connected layers. Ristretto can condense models by using fixed point arithmetic and representation instead of floating point. Moreover, Ristretto fine-tunes the resulting fixed point network. Given a maximum error tolerance of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.
Distributed Index Coding
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the capacity region of the general distributed index coding. In contrast to the traditional centralized index coding where a single server contains all $n$ messages requested by the receivers, in the distributed index coding there are $2^n-1$ servers, each containing a unique non-empty subset $J$ of the messages and each is connected to all receivers via a noiseless independent broadcast link with an arbitrary capacity $C_J \ge 0$. First, we generalize the existing polymatroidal outer bound on the capacity region of the centralized problem to the distributed case. Next, building upon the existing centralized composite coding scheme, we propose three distributed composite coding schemes and derive the corresponding inner bounds on the capacity region. We present a number of interesting numerical examples, which highlight the subtleties and challenges of dealing with the distributed index coding, even for very small problem sizes of $n=3$ and $n=4$.
Design of Three-Phase Hybrid Active Power Filter for Compensating the Harmonic Currents of Three-Phase System
Venue: arXiv cs.SY 2016
Year: 2016

Power quality standards (IEEE-519) require to limit the total harmonic distortion within satisfactory range caused by power electronic based devices. Our work deals with the design of hybrid active filter to reduce current perturbations produced by power electronics based devices. The Instantaneous Active and Reactive Power Method (pq) is used to perform the identification of disturbing currents. The pq algorithm creates a reference current, whereas, this reference current is tracked by the current of the voltage source converter. The currents of the voltage source converter are controlled by hysteresis controller. Simulation results showed that the hybrid active filter can compensate the harmonic currents effectively and improve power quality.
Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines
Venue: arXiv cs.CV 2016
Year: 2016

Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.