OpenRaiser/Intern-Atlas huggingface dataset:
Using Filter Banks in Convolutional Neural Networks for Texture Classification
Venue: arXiv cs.CV 2016
Year: 2016

Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.
Open Mobile API: Accessing the UICC on Android Devices
Venue: arXiv cs.CR 2016
Year: 2016

This report gives an overview of secure element integration into Android devices. It focuses on the Open Mobile API as an open interface to access secure elements from Android applications. The overall architecture of the Open Mobile API is described and current Android devices are analyzed with regard to the availability of this API. Moreover, this report summarizes our efforts of reverse engineering the stock ROM of a Samsung Galaxy S3 in order to analyze the integration of the Open Mobile API and the interface that is used to perform APDU-based communication with the UICC (Universal Integrated Circuit Card). It further provides a detailed explanation on how to integrate this functionality into CyanogenMod (an after-market firmware for Android devices).
An Application of the Generalized Rectangular Fuzzy Model to Critical Thinking Assessment
Venue: arXiv cs.AI 2016
Year: 2016

The authors apply the Generalized Rectangular Model to assessing critical thinking skills and its relations with their language competency.
A Performance Comparison of Sort and Scan Libraries for GPUs
Venue: arXiv cs.DC 2016
Year: 2016

Sorting and scanning are two fundamental primitives for constructing highly parallel algorithms. A number of libraries now provide implementations of these primitives for GPUs, but there is relatively little information about the performance of these implementations. We benchmark seven libraries for 32-bit integer scan and sort, and sorting 32-bit values by 32-bit integer keys. We show that there is a large variation in performance between the libraries, and that no one library has both optimal performance and portability.
Taxim: A Toolchain for Automated and Configurable Simulation for Embedded Multiprocessor Design
Venue: arXiv cs.DC 2016
Year: 2016

Multicore embedded systems have been constantly researched to improve the efficiency by changing certain metrics, such as processor, memory, cache hierarchies and their cache configurations. Using Multi2Sim and McPAT simulators in combination allows the user to design various multiprocessing architectures and estimate performance, power, area and timing metrics. However, the design time required to simulate these systems is daunting and prone to human error. In this paper, we introduce Taxim, a toolchain that can automatically create requested multicore on-chip topologies along with minimizing the simulation time due to repetitive tasks between architectural power, energy and timing simulations. Taxim's decision-tree-based topology synthesis tool creates processor configuration files that can be highly erroneous when generated manually. The toolchain also automates the steps from design entry to output report extraction by running automation scripts, and listing the results. Our experiments show that multiprocessing architectures with 32 cores and irregular cache hierarchies are more than 1k lines of code in Multi2Sim's processor configuration format and Taxim can create such a file in less than 10 milliseconds. The source code is freely available at https://github.com/bouncaslab/TaXim/.
Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT
Venue: arXiv cs.CV 2016
Year: 2016

The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies. Segmentation is especially challenging in the presence of pathology such as vertebral compression fractures. In this paper, we propose a method to produce segmentations for osteoporotic compression fractured vertebrae by applying a multi-atlas joint label fusion technique for clinical CT images. A total of 170 thoracic and lumbar vertebrae were evaluated using atlases from five patients with varying degrees of spinal degeneration. In an osteoporotic cohort of bundled atlases, registration provided an average Dice coefficient and mean absolute surface distance of 2.7$\pm$4.5% and 0.32$\pm$0.13mm for osteoporotic vertebrae, respectively, and 90.9$\pm$3.0% and 0.36$\pm$0.11mm for compression fractured vertebrae.
Collective Circular Motion of Multi-Agent Systems in Synchronized and Balanced Formations With Second-Order Rotational Dynamics
Venue: arXiv cs.SY 2016
Year: 2016

This paper considers the collective circular motion of multi-agent systems in which all the agents are required to traverse different circles or a common circle at a prescribed angular velocity. It is required to achieve these collective motions with the heading angles of the agents synchronized or balanced. In synchronization, the agents and their centroid have a common velocity direction, while in balancing, the movement of agents causes the location of the centroid to become stationary. The agents considered are initially moving at unit speed around individual circles at different angular velocities. It is assumed that the agents are subjected to limited communication constraints, and exchange relative information according to a time-invariant undirected graph. We present suitable feedback control laws for each of these motion coordination tasks by considering a second-order rotational dynamics of the agent. Simulations are given to illustrate the theoretical findings.
Question Answering on Linked Data: Challenges and Future Directions
Venue: arXiv cs.IR 2016
Year: 2016

Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While recently, underlying datasets for QA systems have been promoted from unstructured datasets to structured datasets with highly semantic-enriched metadata, but still question answering systems involve serious challenges which cause to be far beyond desired expectations. In this paper, we raise the challenges for building a Question Answering (QA) system especially with the focus of employing structured data (i.e. knowledge graph). This paper provide an exhaustive insight of the known challenges, so far. Thus, it helps researchers to easily spot open rooms for the future research agenda.
Evaluation of the Partitioned Global Address Space (PGAS) model for an inviscid Euler solver
Venue: arXiv cs.DC 2016
Year: 2016

In this paper we evaluate the performance of Unified Parallel C (which implements the partitioned global address space programming model) using a numerical method that is widely used in fluid dynamics. In order to evaluate the incremental approach to parallelization (which is possible with UPC) and its performance characteristics, we implement different levels of optimization of the UPC code and compare it with an MPI parallelization on four different clusters of the Austrian HPC infrastructure (LEO3, LEO3E, VSC2, VSC3) and on an Intel Xeon Phi. We find that UPC is significantly easier to develop in compared to MPI and that the performance achieved is comparable to MPI in most situations. The obtained results show worse performance (on VSC2), competitive performance (on LEO3, LEO3E and VSC3), and superior performance (on the Intel Xeon Phi).
Optimal 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. We follow a recent line of work that applies the approximate message passing (AMP) framework to multi-processor (MP) computational systems, where each processor node stores and processes a subset of rows of the measurement matrix along with corresponding measurements. 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. In this setup, we derive the optimal per-iteration coding rates using dynamic programming. We analyze the excess mean squared error (EMSE) beyond the minimum mean squared error (MMSE), and prove that, in the limit of low EMSE, the optimal coding rates increase approximately linearly per iteration. Additionally, we obtain that the combined cost of computation and communication scales with the desired estimation quality according to $O(\log^2(1/\text{EMSE}))$. Finally, we study trade-offs between the physical costs of the estimation process including computation time, communication loads, and the estimation quality as a multi-objective optimization problem, and characterize the properties of the Pareto optimal surfaces.
Matrix Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

Traditional neural networks assume vectorial inputs as the network is arranged as layers of single line of computing units called neurons. This special structure requires the non-vectorial inputs such as matrices to be converted into vectors. This process can be problematic. Firstly, the spatial information among elements of the data may be lost during vectorisation. Secondly, the solution space becomes very large which demands very special treatments to the network parameters and high computational cost. To address these issues, we propose matrix neural networks (MatNet), which takes matrices directly as inputs. Each neuron senses summarised information through bilinear mapping from lower layer units in exactly the same way as the classic feed forward neural networks. Under this structure, back prorogation and gradient descent combination can be utilised to obtain network parameters efficiently. Furthermore, it can be conveniently extended for multimodal inputs. We apply MatNet to MNIST handwritten digits classification and image super resolution tasks to show its effectiveness. Without too much tweaking MatNet achieves comparable performance as the state-of-the-art methods in both tasks with considerably reduced complexity.
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
Venue: arXiv cs.LG 2016
Year: 2016

We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms. We propose a new algorithm called Relative Exponential-weight algorithm for Exploration and Exploitation (REX3) to handle the adversarial utility-based formulation of this problem. This algorithm is a non-trivial extension of the Exponential-weight algorithm for Exploration and Exploitation (EXP3) algorithm. We prove a finite time expected regret upper bound of order O(sqrt(K ln(K)T)) for this algorithm and a general lower bound of order omega(sqrt(KT)). At the end, we provide experimental results using real data from information retrieval applications.
Modeling and Analysis of Converged Network-Cloud Services
Venue: arXiv cs.NI 2016
Year: 2016

Networks connecting distributed cloud services through multiple data centers are called cloud networks. These types of networks play a crucial role in cloud computing and a holistic performance evaluation is essential before planning a converged network-cloud environment. We analyze a specific case where some resources can be centralized in one datacenter or distributed among multiple data centers. The economy of scale in centralizing resources in a sin- gle pool of resources can be overcome by an increase in communication costs. We propose an analytical model to evaluate tradeoffs in terms of application requirements, usage patterns, number of resources and communication costs. We numerically evaluate the proposed model in a case study inspired by the oil and gas industry, indicating how to cope with the tradeoff between statisti- cal multiplexing advantages of centralization and the corresponding increase in communication infrastructure costs.
An Elastic Middleware Platform for Concurrent and Distributed Cloud and MapReduce Simulations
Venue: arXiv cs.DC 2016
Year: 2016

Cloud Computing researches involve a tremendous amount of entities such as users, applications, and virtual machines. Due to the limited access and often variable availability of such resources, researchers have their prototypes tested against the simulation environments, opposed to the real cloud environments. Existing cloud simulation environments such as CloudSim and EmuSim are executed sequentially, where a more advanced cloud simulation tool could be created extending them, leveraging the latest technologies as well as the availability of multi-core computers and the clusters in the research laboratories. While computing has been evolving with multi-core programming, MapReduce paradigms, and middleware platforms, cloud and MapReduce simulations still fail to exploit these developments themselves. This research develops Cloud2Sim, which tries to fill the gap between the simulations and the actual technology that they are trying to simulate. First, Cloud2Sim provides a concurrent and distributed cloud simulator, by extending CloudSim cloud simulator, using Hazelcast in-memory key-value store. Then, it also provides a quick assessment to MapReduce implementations of Hazelcast and Infinispan, adaptively distributing the execution to a cluster, providing means of simulating MapReduce executions. The dynamic scaler solution scales out the cloud and MapReduce simulations to multiple nodes running Hazelcast and Infinispan, based on load. The distributed execution model and adaptive scaling solution could be leveraged as a general purpose auto scaler middleware for a multi-tenanted deployment.
GPLMT: A Lightweight Experimentation and Testbed Management Framework
Venue: arXiv cs.NI 2016
Year: 2016

Conducting experiments in federated, distributed, and heterogeneous testbeds is a challenging task for researchers. Researchers have to take care of the whole experiment life cycle, ensure the reproducibility of each run, and the comparability of the results. We present GPLMT, a flexible and lightweight framework for managing testbeds and the experiment life cycle. \gplmt provides an intuitive way to formalize experiments. The resulting experiment description is portable across varying experimentation platforms. GPLMT enables researchers to manage and control networked testbeds and resources, and conduct experiments on large-scale, heterogeneous, and distributed testbeds. We state the requirements and the design of GPLMT, describe the challenges of developing and using such a tool, and present selected user studies along with their experience of using GPLMT in varying scenarios.
Differential Privacy of Populations in Routing Games
Venue: arXiv cs.CR 2016
Year: 2016

As our ground transportation infrastructure modernizes, the large amount of data being measured, transmitted, and stored motivates an analysis of the privacy aspect of these emerging cyber-physical technologies. In this paper, we consider privacy in the routing game, where the origins and destinations of drivers are considered private. This is motivated by the fact that this spatiotemporal information can easily be used as the basis for inferences for a person's activities. More specifically, we consider the differential privacy of the mapping from the amount of flow for each origin-destination pair to the traffic flow measurements on each link of a traffic network. We use a stochastic online learning framework for the population dynamics, which is known to converge to the Nash equilibrium of the routing game. We analyze the sensitivity of this process and provide theoretical guarantees on the convergence rates as well as differential privacy values for these models. We confirm these with simulations on a small example.
Parallel and Distributed Methods for Nonconvex Optimization--Part II: Applications
Venue: arXiv cs.IT 2016
Year: 2016

In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex (smooth) objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the application of the framework to some resource allocation problems in communication networks. In particular, we consider two non-trivial case-study applications, namely: (generalizations of) i) the rate profile maximization in MIMO interference broadcast networks; and the ii) the max-min fair multicast multigroup beamforming problem in a multi-cell environment. We develop a new class of algorithms enjoying the following distinctive features: i) they are \emph{distributed} across the base stations (with limited signaling) and lead to subproblems whose solutions are computable in closed form; and ii) differently from current relaxation-based schemes (e.g., semidefinite relaxation), they are proved to always converge to d-stationary solutions of the aforementioned class of nonconvex problems. Numerical results show that the proposed (distributed) schemes achieve larger worst-case rates (resp. signal-to-noise interference ratios) than state-of-the-art centralized ones while having comparable computational complexity.
Reduced Complexity Sum-Product Algorithm for Decoding Network Codes and In-Network Function Computation
Venue: arXiv cs.IT 2016
Year: 2016

While the capacity, feasibility and methods to obtain codes for network coding problems are well studied, the decoding procedure and complexity have not garnered much attention. In this work, we pose the decoding problem at a sink node in a network as a marginalize a product function (MPF) problem over a Boolean semiring and use the sum-product (SP) algorithm on a suitably constructed factor graph to perform iterative decoding. We use \textit{traceback} to reduce the number of operations required for SP decoding at sink node with general demands and obtain the number of operations required for decoding using SP algorithm with and without traceback. For sinks demanding all messages, we define \textit{fast decodability} of a network code and identify a sufficient condition for the same. Next, we consider the in-network function computation problem wherein the sink nodes do not demand the source messages, but are only interested in computing a function of the messages. We present an MPF formulation for function computation at the sink nodes in this setting and use the SP algorithm to obtain the value of the demanded function. The proposed method can be used for both linear and nonlinear as well as scalar and vector codes for both decoding of messages in a network coding problem and computing linear and nonlinear functions in an in-network function computation problem.
Determining Tournament Payout Structures for Daily Fantasy Sports
Venue: arXiv cs.DS 2016
Year: 2016

With an exploding global market and the recent introduction of online cash prize tournaments, fantasy sports contests are quickly becoming a central part of the social gaming and sports industries. For sports fans and online media companies, fantasy sports contests are an opportunity for large financial gains. However, they present a host of technical challenges that arise from the complexities involved in running a web-scale, prize driven fantasy sports platform. We initiate the study of these challenges by examining one concrete problem in particular: how to algorithmically generate contest payout structures that are 1) economically motivating and appealing to contestants and 2) reasonably structured and succinctly representable. We formalize this problem and present a general two-staged approach for producing satisfying payout structures given constraints on contest size, entry fee, prize bucketing, etc. We then propose and evaluate several potential algorithms for solving the payout problem efficiently, including methods based on dynamic programming, integer programming, and heuristic techniques. Experimental results show that a carefully designed heuristic scales very well, even to contests with over 100,000 prize winners. Our approach extends beyond fantasy sports -- it is suitable for generating engaging payout structures for any contest with a large number of entrants and a large number of prize winners, including other massive online games, poker tournaments, and real-life sports tournaments.
Partial-Match Queries with Random Wildcards: In Tries and Distributed Hash Tables
Venue: arXiv cs.DS 2016
Year: 2016

Consider an $m$-bit query $q$ to a bitwise trie $T$. A wildcard $*$ is an unspecified bit in $q$ for which the query asks the membership for both cases $*=0$ and $*=1$. It is common that such partial-match queries with wildcards are issued in tries. With uniformly random occurrences of $w$ wildcards in $q$ assumed, the obvious upper bound on the average number of traversal steps in $T$ is $2^w m$. We show that the average does not exceed \[ \frac{m+1}{w+1} \left( 2^{w+2} - 2 w - 4 \right) + m = O \left( \frac{2^w m}{w} \right), \] and equals the value exactly when $T$ includes all the $m$-bit keys as the worst case. Here the query $q$ performs with the naive backtracking algorithm in $T$. It is similarly shown that the average is $O \left( \frac{k^w m}{w} \right)$ in a general trie of maximum out-degree $k$. Our analysis for tries is extended to a distributed hash table (DHT), which is among the most frequently used decentralized data structures in networking. We show, under a natural probabilistic assumption for the largest class of DHTs, that the average number of hops required by an $m$-bit query $q$ to a DHT $D$ with random $w$ wildcards meets the same asymptotic bound. As a result, $q$ is answered with average $O \left( \frac{2^w m}{w} \right)$ hops rather than $Θ\left( 2^w m \right)$ in the four major DHTs Chord, Pastry, Tapestry and Kademlia. In addition, with a uniform key distribution for sufficiently many entries, we prove that a lookup request to the DHT Chord is answered correctly with $O(m)$ hops and probability $1 - 2^{-Ω(m)}$. To the author's knowledge, the probability $1 - 2^{-Ω(m)}$ of correct lookup in Chord has not been identified so far.
BOOST: Base station on-off switching strategy for energy efficient massive MIMO HetNets
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.