OpenRaiser/Intern-Atlas huggingface dataset:
Simple Baseline for Visual Question Answering
Venue: arXiv cs.CV 2015
Year: 2015

We describe a very simple bag-of-words baseline for visual question answering. This baseline concatenates the word features from the question and CNN features from the image to predict the answer. When evaluated on the challenging VQA dataset [2], it shows comparable performance to many recent approaches using recurrent neural networks. To explore the strength and weakness of the trained model, we also provide an interactive web demo and open-source code. .
Minimizing the Continuous Diameter when Augmenting Paths and Cycles with Shortcuts
Venue: arXiv cs.CG 2015
Year: 2015

We seek to augment a geometric network in the Euclidean plane with shortcuts to minimize its continuous diameter, i.e., the largest network distance between any two points on the augmented network. Unlike in the discrete setting where a shortcut connects two vertices and the diameter is measured between vertices, we take all points along the edges of the network into account when placing a shortcut and when measuring distances in the augmented network. We study this network augmentation problem for paths and cycles. For paths, we determine an optimal shortcut in linear time. For cycles, we show that a single shortcut never decreases the continuous diameter and that two shortcuts always suffice to reduce the continuous diameter. Furthermore, we characterize optimal pairs of shortcuts for convex and non-convex cycles. Finally, we develop a linear time algorithm that produces an optimal pair of shortcuts for convex cycles. Apart from the algorithms, our results extend to rectifiable curves. Our work reveals some of the underlying challenges that must be overcome when addressing the discrete version of this network augmentation problem, where we minimize the discrete diameter of a network with shortcuts that connect only vertices.
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Venue: arXiv cs.LG 2015
Year: 2015

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processing or human action recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method is based on deep Taylor decomposition and efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.
Distributed Adaptive LMF Algorithm for Sparse Parameter Estimation in Gaussian Mixture Noise
Venue: arXiv cs.IT 2015
Year: 2015

A distributed adaptive algorithm for estimation of sparse unknown parameters in the presence of nonGaussian noise is proposed in this paper based on normalized least mean fourth (NLMF) criterion. At the first step, local adaptive NLMF algorithm is modified by zero norm in order to speed up the convergence rate and also to reduce the steady state error power in sparse conditions. Then, the proposed algorithm is extended for distributed scenario in which more improvement in estimation performance is achieved due to cooperation of local adaptive filters. Simulation results show the superiority of the proposed algorithm in comparison with conventional NLMF algorithms.
Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC)
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we study a tracking control problem for linear time-invariant systems, with model parametric uncertainties, under input and states constraints. We apply the idea of modular design introduced in Benosman et al. 2014, to solve this problem in the model predictive control (MPC) framework. We propose to design an MPC with input-to-state stability (ISS) guarantee, and complement it with an extremum seeking (ES) algorithm to iteratively learn the model uncertainties. The obtained MPC algorithms can be classified as iterative learning control (ILC)-MPC.
SimRank Computation on Uncertain Graphs
Venue: arXiv cs.DB 2015
Year: 2015

SimRank is a similarity measure between vertices in a graph, which has become a fundamental technique in graph analytics. Recently, many algorithms have been proposed for efficient evaluation of SimRank similarities. However, the existing SimRank computation algorithms either overlook uncertainty in graph structures or is based on an unreasonable assumption (Du et al). In this paper, we study SimRank similarities on uncertain graphs based on the possible world model of uncertain graphs. Following the random-walk-based formulation of SimRank on deterministic graphs and the possible worlds model of uncertain graphs, we define random walks on uncertain graphs for the first time and show that our definition of random walks satisfies Markov's property. We formulate the SimRank measure based on random walks on uncertain graphs. We discover a critical difference between random walks on uncertain graphs and random walks on deterministic graphs, which makes all existing SimRank computation algorithms on deterministic graphs inapplicable to uncertain graphs. To efficiently compute SimRank similarities, we propose three algorithms, namely the baseline algorithm with high accuracy, the sampling algorithm with high efficiency, and the two-phase algorithm with comparable efficiency as the sampling algorithm and about an order of magnitude smaller relative error than the sampling algorithm. The extensive experiments and case studies verify the effectiveness of our SimRank measure and the efficiency of our SimRank computation algorithms.
Robustness enhancement of cloud computing network based on coupled networks model
Venue: arXiv cs.CR 2015
Year: 2015

As a novel technology, cloud computing attracts more and more people including technology enthusiasts and malicious users. Different from the classical network architecture, cloud environment has many its own features which make the traditional defense mechanism invalid. To make the network more robust against a malicious attack, we introduce a new method to mitigate this risk efficiently and systematically. In this paper, we first propose a coupled networks model which adequately considers the interactions between physical layer and virtual layer in a practical cloud computing environment. Based on this new model and our systematical method, we show that with the addition of protection of some specific nodes in the network structure, the robustness of cloud computing's network can be significantly improved whereas their functionality remains unchanged. Our results demonstrate that our new method can effectively settle the hard problems which cloud computing now is facing without much cost.
Sensor Fusion of Camera, GPS and IMU using Fuzzy Adaptive Multiple Motion Models
Venue: arXiv cs.RO 2015
Year: 2015

A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay; occurring because of the delay in estimating the motion of the user and displaying images based on this estimate. This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models (FAMM) was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS-IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context.
On the Uniform Random Generation of Non Deterministic Automata Up to Isomorphism
Venue: arXiv cs.FL 2015
Year: 2015

In this paper we address the problem of the uniform random generation of non deterministic automata (NFA) up to isomorphism. First, we show how to use a Monte-Carlo approach to uniformly sample a NFA. Secondly, we show how to use the Metropolis-Hastings Algorithm to uniformly generate NFAs up to isomorphism. Using labeling techniques, we show that in practice it is possible to move into the modified Markov Chain efficiently, allowing the random generation of NFAs up to isomorphism with dozens of states. This general approach is also applied to several interesting subclasses of NFAs (up to isomorphism), such as NFAs having a unique initial states and a bounded output degree. Finally, we prove that for these interesting subclasses of NFAs, moving into the Metropolis Markov chain can be done in polynomial time. Promising experimental results constitute a practical contribution.
Web application for size and topology optimization of trusses and gusset plates
Venue: arXiv cs.OH 2015
Year: 2015

With its ever growing popularity, providing Internet based applications tuned towards practical applications is on the rise. Advantages such as no external plugins and additional software, ease of use, updating and maintenance have increased the popularity of web applications. In this work, a web-based application has been developed which can perform size optimization of truss structure as a whole as well as topology optimization of individual gusset plate of each joint based on specified joint displacements and load conditions. This application is developed using cutting-edge web technologies such as Three.js and HTML5. The client side boasts of an intuitive interface which in addition to its modeling capabilities also recommends configurations based on user input, provides analysis options and finally displays the results. The server side, using a combination of Scilab and DAKOTA, computes solution and also provides the user with comparisons of the optimal design with that conforming to Indian Standard (IS 800-2007). It is a freely available one-stop web-based application to perform optimal and/or code based design of trusses.
On Variants of k-means Clustering
Venue: arXiv cs.CG 2015
Year: 2015

\textit{Clustering problems} often arise in the fields like data mining, machine learning etc. to group a collection of objects into similar groups with respect to a similarity (or dissimilarity) measure. Among the clustering problems, specifically \textit{$k$-means} clustering has got much attention from the researchers. Despite the fact that $k$-means is a very well studied problem its status in the plane is still an open problem. In particular, it is unknown whether it admits a PTAS in the plane. The best known approximation bound in polynomial time is $9+\eps$. In this paper, we consider the following variant of $k$-means. Given a set $C$ of points in $\mathcal{R}^d$ and a real $f > 0$, find a finite set $F$ of points in $\mathcal{R}^d$ that minimizes the quantity $f*|F|+\sum_{p\in C} \min_{q \in F} {||p-q||}^2$. For any fixed dimension $d$, we design a local search PTAS for this problem. We also give a "bi-criterion" local search algorithm for $k$-means which uses $(1+\eps)k$ centers and yields a solution whose cost is at most $(1+\eps)$ times the cost of an optimal $k$-means solution. The algorithm runs in polynomial time for any fixed dimension. The contribution of this paper is two fold. On the one hand, we are being able to handle the square of distances in an elegant manner, which yields near optimal approximation bound. This leads us towards a better understanding of the $k$-means problem. On the other hand, our analysis of local search might also be useful for other geometric problems. This is important considering that very little is known about the local search method for geometric approximation.
Scheduling in Instantaneous-Interference-Limited CR Networks with Delay Guarantees
Venue: arXiv cs.IT 2015
Year: 2015

We study an uplink multi secondary user (SU) cognitive radio system having average delay constraints as well as an instantaneous interference constraint to the primary user (PU). If the interference channels from the SUs to the PU have independent but not identically distributed fading coefficients, then the SUs will experience heterogeneous delay performances. This is because SUs causing low interference to the PU will be scheduled more frequently, and/or allocated more transmission power than those causing high interference. We propose a dynamic scheduling-and-power-control algorithm that can provide the required average delay guarantees to all SUs as well as protecting the PU from interference. Using the Lyapunov technique, we show that our algorithm is asymptotically delay optimal while satisfying the delay and interference constraints. We support our findings by extensive system simulations and show the robustness of the proposed algorithm against channel estimation errors.
A token-passing net implementation of optimal reduction with embedded read-back
Venue: arXiv cs.LO 2015
Year: 2015

In this paper, we introduce a new interaction net implementation of optimal reduction for pure untyped lambda calculus. Unlike others, our implementation allows to reach normal form regardless of interaction net reduction strategy using the approach of so-called token-passing nets. Another new feature is the read-back mechanism also implemented without leaving the formalism of interaction nets.
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.
Minimally Supervised Feature Selection for Classification (Master's Thesis, University Politehnica of Bucharest)
Venue: arXiv cs.CV 2015
Year: 2015

In the context of the highly increasing number of features that are available nowadays we design a robust and fast method for feature selection. The method tries to select the most representative features that are independent from each other, but are strong together. We propose an algorithm that requires very limited labeled data (as few as one labeled frame per class) and can accommodate as many unlabeled samples. We also present here the supervised approach from which we started. We compare our two formulations with established methods like AdaBoost, SVM, Lasso, Elastic Net and FoBa and show that our method is much faster and it has constant training time. Moreover, the unsupervised approach outperforms all the methods with which we compared and the difference might be quite prominent. The supervised approach is in most cases better than the other methods, especially when the number of training shots is very limited. All that the algorithm needs is to choose from a pool of positively correlated features. The methods are evaluated on the Youtube-Objects dataset of videos and on MNIST digits dataset, while at training time we also used features obtained on CIFAR10 dataset and others pre-trained on ImageNet dataset. Thereby, we also proved that transfer learning is useful, even though the datasets differ very much: from low-resolution centered images from 10 classes, to high-resolution images with objects from 1000 classes occurring in different regions of the images or to very difficult videos with very high intraclass variance. 7
Assessing Google Correlate Queries for Influenza H1N1 Surveillance in Asian Developing Countries
Venue: arXiv cs.CY 2015
Year: 2015

So far, Google Trend data have been used for influenza surveillance in many European and American countries; however, there are few attempts to apply the low-cost surveillance method in Asian developing countries. To investigate the correlation between the search trends and the influenza activity in Asia, we examined the Google query data of four Asian developing countries.
Enhanced image feature coverage: Key-point selection using genetic algorithms
Venue: arXiv cs.CV 2015
Year: 2015

Coverage of image features play an important role in many vision algorithms since their distribution affect the estimated homography. This paper presents a Genetic Algorithm (GA) in order to select the optimal set of features yielding maximum coverage of the image which is measured by a robust method based on spatial statistics. It is shown with statistical tests on two datasets that the metric yields better coverage and this is also confirmed by an accuracy test on the computed homography for the original set and the newly selected set of features. Results have demonstrated that the new set has similar performance in terms of the accuracy of the computed homography with the original one with an extra benefit of using fewer number of features ultimately reducing the time required for descriptor calculation and matching.
Consensus of Hybrid Multi-agent Systems
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we consider the consensus problem of hybrid multi-agent system. First, the hybrid multi-agent system is proposed which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for hybrid multi-agent system. The analysis tool developed in this paper is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of hybrid multi-agent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.
Parameterized Tractability of the Maximum-Duo Preservation String Mapping Problem
Venue: arXiv cs.DS 2015
Year: 2015

In this paper we investigate the parameterized complexity of the Maximum-Duo Preservation String Mapping Problem, the complementary of the Minimum Common String Partition Problem. We show that this problem is fixed-parameter tractable when parameterized by the number k of conserved duos, by first giving a parameterized algorithm based on the color-coding technique and then presenting a reduction to a kernel of size O(k^6 ).
A Discrete Time-Switching Protocol for Wireless-Powered Communications with Energy Accumulation
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates a wireless-powered communication network (WPCN) setup with one multi-antenna access point (AP) and one single-antenna source. It is assumed that the AP is connected to an external power supply, while the source does not have an embedded energy supply. But the source could harvest energy from radio frequency (RF) signals sent by the AP and store it for future information transmission. We develop a discrete time-switching (DTS) protocol for the considered WPCN. In the proposed protocol, either energy harvesting (EH) or information transmission (IT) operation is performed during each transmission block. Specifically, based on the channel state information (CSI) between source and AP, the source can determine the minimum energy required for an outage-free IT operation. If the residual energy of the source is sufficient, the source will start the IT phase. Otherwise, EH phase is invoked and the source accumulates the harvested energy. To characterize the performance of the proposed protocol, we adopt a discrete Markov chain (MC) to model the energy accumulation process at the source battery. A closed-form expression for the average throughput of the DTS protocol is derived. Numerical results validate our theoretical analysis and show that the proposed DTS protocol considerably outperforms the existing harvest-then-transmit protocol when the battery capacity at the source is large.
Distributionally Robust Game Theory
Venue: arXiv cs.GT 2015
Year: 2015

The classical, complete-information two-player games assume that the problem data (in particular the payoff matrix) is known exactly by both players. In a now famous result, Nash has shown that any such game has an equilibrium in mixed strategies. This result was later extended to a class of incomplete-information two-player games by Harsanyi, who assumed that the payoff matrix is not known exactly but rather represents a random variable that is governed by a probability distribution known to both players. In 2006, Bertsimas and Aghassi proposed a new class of distribution-free two-player games where the payoff matrix is only known to belong to a given uncertainty set. This model relaxes the distributional assumptions of Harsanyi's Bayesian games, and it gives rise to an alternative distribution-free equilibrium concept. In this thesis we present a new model of incomplete information games without private information in which the players use a distributionally robust optimization approach to cope with the payoff uncertainty. With some specific restrictions, we show that our "Distributionally Robust Game" constitutes a true generalization of the three aforementioned finite games (Nash games, Bayesian Games and Robust Games). Subsequently, we prove that the set of equilibria of an arbitrary distributionally robust game with specified ambiguity set can be computed as the component-wise projection of the solution set of a multi-linear system of equations and inequalities. Finally, we demonstrate the applicability of our new model of games and highlight its importance.
Mobile Robots Adaptive Control Using Neural Networks
Venue: arXiv cs.RO 2015
Year: 2015

The paper proposes a feed-forward control strategy for mobile robot control that accounts for a non-linear model of the vehicle with interaction between inputs and outputs. It is possible to include specific model uncertainties in the dynamic model of the mobile robot in order to see how the control problem should be addressed taking into consideration the complete dynamic mobile robot model. By means of a neural network feed-forward controller a real non-linear mathematical model of the vehicle can be taken into consideration. The classical velocity control strategy can be extended using artificial neural networks in order to compensate for the modelling uncertainties. It is possible to develop an intelligent strategy for mobile robot control.