OpenRaiser/Intern-Atlas huggingface dataset:
On Energy Efficiency of the Nearest-Neighbor Cooperative Communication in Heterogeneous Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we consider a two-dimensional heterogeneous cellular network scenario consisting of one base station (BS) and some mobile stations (MSs) whose locations follow a Poisson point process (PPP). The MSs are equipped with multiple radio access interfaces including a cellular access interface and at least one short-range communication interface. We propose a nearest-neighbor cooperation communication (NNCC) scheme by exploiting the short-range communication between a MS and its nearest neighbor to collaborate on their uplink transmissions. In the proposed cooperation scheme, a MS and its nearest neighbor first exchange data by the short-range communication. Upon successful decoding of the data from each other, they proceed to send their own data, as well as the data received from the other to the BS respectively in orthogonal time slots. The energy efficiency analysis for the proposed scheme is presented based on the characteristics of the PPP and the Rayleigh fading channel. Numerical results show that the NNCC scheme significantly improves the energy efficiency compared to the conventional non-cooperative uplink transmissions.
Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks
Venue: arXiv cs.IR 2016
Year: 2016

People often use a web search engine to find information about events of interest, for example, sport competitions, political elections, festivals and entertainment news. In this paper, we study a problem of detecting event-related queries, which is the first step before selecting a suitable time-aware retrieval model. In general, event-related information needs can be observed in query streams through various temporal patterns of user search behavior, e.g., spiky peaks for popular events, and periodicities for repetitive events. However, it is also common that users search for non-popular events, which may not exhibit temporal variations in query streams, e.g., past events recently occurred, historical events triggered by anniversaries or similar events, and future events anticipated to happen. To address the challenge of detecting dynamic classes of events, we propose a novel deep learning model to classify a given query into a predetermined set of multiple event types. Our proposed model, a Stacked Multilayer Perceptron (S-MLP) network, consists of multilayer perceptron used as a basic learning unit. We assemble stacked units to further learn complex relationships between neutrons in successive layers. To evaluate our proposed model, we conduct experiments using real-world queries and a set of manually created ground truth. Preliminary results have shown that our proposed deep learning model outperforms the state-of-the-art classification models significantly.
Parallel Scheduling Algorithm based on Complex Coloring for Input-Queued Switches
Venue: arXiv cs.NI 2016
Year: 2016

This paper explores the application of a new algebraic method of edge coloring, called complex coloring, to the scheduling problems of input queued switches. The proposed distributed parallel scheduling algorithm possesses two important features: optimality and rearrangeability. Optimality ensures that the algorithm always returns a proper coloring with the minimum number of required colors, and rearrangeability allows partially re-coloring the existing connection patterns if the underlying graph only changes slightly. The running time of the proposed scheduling algorithm is on the order of $O(\log^2 N)$ per frame, and the amortized time complexity, the time to compute a matching per timeslot, is only $O(\log N)$. The scheduling algorithm is highly robust in the face of traffic fluctuations. Since the higher the variable density, the higher the efficiency of the variable elimination process, complex coloring provides a natural adaptive solution to non-uniform input traffic patterns. The proposed scheduling algorithm for packet switching can achieve nearly 100% throughput.
Joint User Association and Downlink Beamforming for Green Cloud-RANs with Limited Fronthaul
Venue: arXiv cs.IT 2016
Year: 2016

With the explosive growth of smart devices and mobile data traffic, limited fronthaul capacity has become a notable bottleneck of green communication access networks, such as cloud radio access networks(C-RANs). In this paper, we proposed a joint user association and downlink beamforming scheme for green C-RANs to minimize the network power consumption with the limited fronthaul links. We first formulate the design problem as a mixed integer nonlinear programming (MINLP), and then transformed the MINLP problem into a mixed integer second-order cone program (MI-SOCP) which is a convex program when the integer variables are fixed. By relaxing the integer variables to continuous ones, an inflation algorithm, which can be finished within polynomial time, was proposed to solved the problem. The simulation results are presented to validate the effectiveness of our proposed algorithm compared with the the scheme adopted by LTE-A.
On the Theoretical Capacity of Evolution Strategies to Statistically Learn the Landscape Hessian
Venue: arXiv cs.NE 2016
Year: 2016

We study the theoretical capacity to statistically learn local landscape information by Evolution Strategies (ESs). Specifically, we investigate the covariance matrix when constructed by ESs operating with the selection operator alone. We model continuous generation of candidate solutions about quadratic basins of attraction, with deterministic selection of the decision vectors that minimize the objective function values. Our goal is to rigorously show that accumulation of winning individuals carries the potential to reveal valuable information about the search landscape, e.g., as already practically utilized by derandomized ES variants. We first show that the statistically-constructed covariance matrix over such winning decision vectors shares the same eigenvectors with the Hessian matrix about the optimum. We then provide an analytic approximation of this covariance matrix for a non-elitist multi-child $(1,λ)$-strategy, which holds for a large population size $λ$. Finally, we also numerically corroborate our results.
Identifying individual facial expressions by deconstructing a neural network
Venue: arXiv cs.CV 2016
Year: 2016

This paper focuses on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks. Since psychological attribute datasets typically suffer from small sample sizes, we apply transfer learning with two base models to avoid overfitting. These models were trained on an age and gender prediction task, respectively. Using a novel explanation method we extract heatmaps that highlight the parts of the image most responsible for the prediction. We further observe that the explanation method provides important insights into the nature of features of the base model, which allow one to assess the aptitude of the base model for a given transfer learning task. Finally, we observe that the multiclass model is more feature rich than its binary counterpart. The experimental evaluation is performed on the 2222 images from the 10k US faces dataset containing psychological attribute labels as well as on a subset of KDEF images.
Fault-Tolerant Adaptive Parallel and Distributed Simulation
Venue: arXiv cs.DC 2016
Year: 2016

Discrete Event Simulation is a widely used technique that is used to model and analyze complex systems in many fields of science and engineering. The increasingly large size of simulation models poses a serious computational challenge, since the time needed to run a simulation can be prohibitively large. For this reason, Parallel and Distributes Simulation techniques have been proposed to take advantage of multiple execution units which are found in multicore processors, cluster of workstations or HPC systems. The current generation of HPC systems includes hundreds of thousands of computing nodes and a vast amount of ancillary components. Despite improvements in manufacturing processes, failures of some components are frequent, and the situation will get worse as larger systems are built. In this paper we describe FT-GAIA, a software-based fault-tolerant extension of the GAIA/ARTÌS parallel simulation middleware. FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes; furthermore, FT-GAIA offers some protection against byzantine failures since synchronization messages are replicated as well, so that the receiving entity can identify and discard corrupted messages. We provide an experimental evaluation of FT-GAIA on a running prototype. Results show that a high degree of fault tolerance can be achieved, at the cost of a moderate increase in the computational load of the execution units.
Dynamical optical flow of saliency maps for predicting visual attention
Venue: arXiv cs.CV 2016
Year: 2016

Saliency maps are used to understand human attention and visual fixation. However, while very well established for static images, there is no general agreement on how to compute a saliency map of dynamic scenes. In this paper we propose a mathematically rigorous approach to this prob- lem, including static saliency maps of each video frame for the calculation of the optical flow. Taking into account static saliency maps for calculating the optical flow allows for overcoming the aperture problem. Our ap- proach is able to explain human fixation behavior in situations which pose challenges to standard approaches, such as when a fixated object disappears behind an occlusion and reappears after several frames. In addition, we quantitatively compare our model against alternative solutions using a large eye tracking data set. Together, our results suggest that assessing optical flow information across a series of saliency maps gives a highly accurate and useful account of human overt attention in dynamic scenes.
A Relevant Content Filtering Based Framework For Data Stream Summarization
Venue: arXiv cs.IR 2016
Year: 2016

Social media platforms are a rich source of information these days, however, of all the available information, only a small fraction is of users' interest. To help users catch up with the latest topics of their interests from the large amount of information available in social media, we present a relevant content filtering based framework for data stream summarization. More specifically, given the topic or event of interest, this framework can dynamically discover and filter out relevant information from irrelevant information in the stream of text provided by social media platforms. It then further captures the most representative and up-to-date information to generate a sequential summary or event story line along with the evolution of the topic or event. Our framework does not depend on any labeled data, it instead uses the weak supervision provided by the user, which matches the real scenarios of users searching for information about an ongoing event. We experimented on two real events traced by a Twitter dataset from TREC 2011. The results verified the effectiveness of relevant content filtering and sequential summary generation of the proposed framework. It also shows its robustness of using the most easy-to-obtain weak supervision, i.e., trending topic or hashtag. Thus, this framework can be easily integrated into social media platforms such as Twitter to generate sequential summaries for the events of interest. We also make the manually generated gold-standard sequential summaries of the two test events publicly available for future use in the community.
Network Infusion to Infer Information Sources in Networks
Venue: arXiv cs.SI 2016
Year: 2016

Several significant models have been developed that enable the study of diffusion of signals across biological, social and engineered networks. Within these established frameworks, the inverse problem of identifying the source of the propagated signal is challenging, owing to the numerous alternative possibilities for signal progression through the network. In real world networks, the challenge of determining sources is compounded as the true propagation dynamics are typically unknown, and when they have been directly measured, they rarely conform to the assumptions of any of the well-studied models. In this paper we introduce a method called Network Infusion (NI) that has been designed to circumvent these issues, making source inference practical for large, complex real world networks. The key idea is that to infer the source node in the network, full characterization of diffusion dynamics, in many cases, may not be necessary. This objective is achieved by creating a diffusion kernel that well-approximates standard diffusion models, but lends itself to inversion, by design, via likelihood maximization or error minimization. We apply NI for both single-source and multi-source diffusion, for both single-snapshot and multi-snapshot observations, and for both homogeneous and heterogeneous diffusion setups. We prove the mean-field optimality of NI for different scenarios, and demonstrate its effectiveness over several synthetic networks. Moreover, we apply NI to a real-data application, identifying news sources in the Digg social network, and demonstrate the effectiveness of NI compared to existing methods. Finally, we propose an integrative source inference framework that combines NI with a distance centrality-based method, which leads to a robust performance in cases where the underlying dynamics are unknown.
jInv -- a flexible Julia package for PDE parameter estimation
Venue: arXiv cs.MS 2016
Year: 2016

Estimating parameters of Partial Differential Equations (PDEs) from noisy and indirect measurements often requires solving ill-posed inverse problems. These so called parameter estimation or inverse medium problems arise in a variety of applications such as geophysical, medical imaging, and nondestructive testing. Their solution is computationally intense since the underlying PDEs need to be solved numerous times until the reconstruction of the parameters is sufficiently accurate. Typically, the computational demand grows significantly when more measurements are available, which poses severe challenges to inversion algorithms as measurement devices become more powerful. In this paper we present jInv, a flexible framework and open source software that provides parallel algorithms for solving parameter estimation problems with many measurements. Being written in the expressive programming language Julia, jInv is portable, easy to understand and extend, cross-platform tested, and well-documented. It provides novel parallelization schemes that exploit the inherent structure of many parameter estimation problems and can be used to solve multiphysics inversion problems as is demonstrated using numerical experiments motivated by geophysical imaging.
Is a Picture Worth Ten Thousand Words in a Review Dataset?
Venue: arXiv cs.CV 2016
Year: 2016

While textual reviews have become prominent in many recommendation-based systems, automated frameworks to provide relevant visual cues against text reviews where pictures are not available is a new form of task confronted by data mining and machine learning researchers. Suggestions of pictures that are relevant to the content of a review could significantly benefit the users by increasing the effectiveness of a review. We propose a deep learning-based framework to automatically: (1) tag the images available in a review dataset, (2) generate a caption for each image that does not have one, and (3) enhance each review by recommending relevant images that might not be uploaded by the corresponding reviewer. We evaluate the proposed framework using the Yelp Challenge Dataset. While a subset of the images in this particular dataset are correctly captioned, the majority of the pictures do not have any associated text. Moreover, there is no mapping between reviews and images. Each image has a corresponding business-tag where the picture was taken, though. The overall data setting and unavailability of crucial pieces required for a mapping make the problem of recommending images for reviews a major challenge. Qualitative and quantitative evaluations indicate that our proposed framework provides high quality enhancements through automatic captioning, tagging, and recommendation for mapping reviews and images.
Wireless Sensor Networks Localization Methods: Multidimensional Scaling vs. Semidefinite Programming Approach
Venue: arXiv cs.DC 2016
Year: 2016

With the recent development of technology, wireless sensor networks are becoming an important part of many applications such as health and medical applications, military applications, agriculture monitoring, home and office applications, environmental monitoring, etc. Knowing the location of a sensor is important, but GPS receivers and ophisticated sensors are too expensive and require processing power. Therefore, the localization wireless sensor network problem is a growing field of interest. The aim of this paper is to give a comparison of wireless sensor network localization methods, and therefore, multidimensional scaling and semidefinite programming are chosen for this research. Multidimensional scaling is a simple mathematical technique widely-discussed that solves the wireless sensor networks localization problem. In contrast, semidefinite programming is a relatively new field of optimization with a growing use, although being more complex. In this paper, using extensive simulations, a detailed overview of these two approaches is given, regarding different network topologies, various network parameters and performance issues. The performances of both techniques are highly satisfactory and estimation errors are minimal
Epistemic Protocols for Distributed Gossiping
Venue: arXiv cs.AI 2016
Year: 2016

Gossip protocols aim at arriving, by means of point-to-point or group communications, at a situation in which all the agents know each other's secrets. We consider distributed gossip protocols which are expressed by means of epistemic logic. We provide an operational semantics of such protocols and set up an appropriate framework to argue about their correctness. Then we analyze specific protocols for complete graphs and for directed rings.
Coordination Games on Directed Graphs
Venue: arXiv cs.GT 2016
Year: 2016

We study natural strategic games on directed graphs, which capture the idea of coordination in the absence of globally common strategies. We show that these games do not need to have a pure Nash equilibrium and that the problem of determining their existence is NP-complete. The same holds for strong equilibria. We also exhibit some classes of games for which strong equilibria exist and prove that a strong equilibrium can then be found in linear time.
Ceteris paribus logic in counterfactual reasoning
Venue: arXiv cs.LO 2016
Year: 2016

The semantics for counterfactuals due to David Lewis has been challenged on the basis of unlikely, or impossible, events. Such events may skew a given similarity order in favour of those possible worlds which exhibit them. By updating the relational structure of a model according to a ceteris paribus clause one forces out, in a natural manner, those possible worlds which do not satisfy the requirements of the clause. We develop a ceteris paribus logic for counterfactual reasoning capable of performing such actions, and offer several alternative (relaxed) interpretations of ceteris paribus. We apply this framework in a way which allows us to reason counterfactually without having our similarity order skewed by unlikely events. This continues the investigation of formal ceteris paribus reasoning, which has previously been applied to preferences, logics of game forms, and questions in decision-making, among other areas.
An Axiomatic Approach to Routing
Venue: arXiv cs.GT 2016
Year: 2016

Information delivery in a network of agents is a key issue for large, complex systems that need to do so in a predictable, efficient manner. The delivery of information in such multi-agent systems is typically implemented through routing protocols that determine how information flows through the network. Different routing protocols exist each with its own benefits, but it is generally unclear which properties can be successfully combined within a given algorithm. We approach this problem from the axiomatic point of view, i.e., we try to establish what are the properties we would seek to see in such a system, and examine the different properties which uniquely define common routing algorithms used today. We examine several desirable properties, such as robustness, which ensures adding nodes and edges does not change the routing in a radical, unpredictable ways; and properties that depend on the operating environment, such as an "economic model", where nodes choose their paths based on the cost they are charged to pass information to the next node. We proceed to fully characterize minimal spanning tree, shortest path, and weakest link routing algorithms, showing a tight set of axioms for each.
Parameterized Complexity Results for a Model of Theory of Mind Based on Dynamic Epistemic Logic
Venue: arXiv cs.LO 2016
Year: 2016

In this paper we introduce a computational-level model of theory of mind (ToM) based on dynamic epistemic logic (DEL), and we analyze its computational complexity. The model is a special case of DEL model checking. We provide a parameterized complexity analysis, considering several aspects of DEL (e.g., number of agents, size of preconditions, etc.) as parameters. We show that model checking for DEL is PSPACE-hard, also when restricted to single-pointed models and S5 relations, thereby solving an open problem in the literature. Our approach is aimed at formalizing current intractability claims in the cognitive science literature regarding computational models of ToM.
Adaptability of Neural Networks on Varying Granularity IR Tasks
Venue: arXiv cs.IR 2016
Year: 2016

Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks. Deep neural networks (DNN) are capable of learning ideal representations of data during the training process, removing the need for independently extracting features. However, the structures of these DNNs are often tailored to perform on specific datasets. In addition, IR tasks deal with text at varying levels of granularity from single factoids to documents containing thousands of words. In this paper, we examine the role of the granularity on the performance of common state of the art DNN structures in IR.
Representing Extended Finite State Machines for SDL by A Novel Control Model of Discrete Event Systems
Venue: arXiv cs.FL 2016
Year: 2016

This paper discusses EFSM for SDL and transforms EFSM into a novel control model of discrete event systems. We firstly propose a control model of discrete event systems, where the event set is made up of several conflicting pairs and control is implemented to select one event of the pair. Then we transform EFSM for SDL to the control model to clarify the control mechanism functioning in SDL flow graphs. This work views the EFSM for SDL in the perspective of supervisory control theory, and this contributes to the field of software cybernetics, which explores the theoretically justified interplay of software and the control.