OpenRaiser/Intern-Atlas huggingface dataset:
Nonlinear-Programming-Based Model of Power System Marginal States: Theoretical Substantiation
Venue: arXiv cs.SY 2015
Year: 2015

In order to maintain the security of power system at an appropriate level and at low cost, it is essential to accurately assess the steady-state stability limits and power flow feasibility boundaries, i.e., the power system marginal states (MS). This paper is devoted to creation and theoretical substantiation of the MS model based on nonlinear programming (NLP-MS model), its research to reveal MS properties which promote better MS understanding, to evolution of the theory of power systems and MS, to elaboration of more effective algorithms of MS problem solution. The proposed NLP-MS model is universal and allows to determine and to take into account various MS, including the MS in a given direction of power change, the closest MS, a moving the power system state into a power flow feasibility region, etc.
Tight Approximations for the Two Dimensional Gaussian $Q-$function
Venue: arXiv cs.IT 2015
Year: 2015

The aim of this work is the derivation of two approximated expressions for the two dimensional Gaussian Q-function, $Q(x,y;ρ)$. These expressions are highly accurate and are expressed in closed-form. Furthermore, their algebraic representation is relatively simple and therefore, convenient to handle both analytically and numerically. This feature is particularly useful for two reasons: firstly because it renders the derived expressions useful mathematical tools that can be utilized in numerous analytic performance evaluation studies in digital communications under fading; secondly because the two dimensional Gaussian Q-function is neither tabulated nor a built-in function in popular mathematical software packages such as $Maple$, $Mathematica$ and $Matlab$.
Automatic Facial Expression Recognition Using Features of Salient Facial Patches
Venue: arXiv cs.CV 2015
Year: 2015

Extraction of discriminative features from salient facial patches plays a vital role in effective facial expression recognition. The accurate detection of facial landmarks improves the localization of the salient patches on face images. This paper proposes a novel framework for expression recognition by using appearance features of selected facial patches. A few prominent facial patches, depending on the position of facial landmarks, are extracted which are active during emotion elicitation. These active patches are further processed to obtain the salient patches which contain discriminative features for classification of each pair of expressions, thereby selecting different facial patches as salient for different pair of expression classes. One-against-one classification method is adopted using these features. In addition, an automated learning-free facial landmark detection technique has been proposed, which achieves similar performances as that of other state-of-art landmark detection methods, yet requires significantly less execution time. The proposed method is found to perform well consistently in different resolutions, hence, providing a solution for expression recognition in low resolution images. Experiments on CK+ and JAFFE facial expression databases show the effectiveness of the proposed system.
Repeated Games for Inter-operator Spectrum Sharing
Venue: arXiv cs.NI 2015
Year: 2015

As wireless communication becomes an ever-more evolving and pervasive part of the existing world, system capacity and Quality of Service (QoS) provisioning are becoming more critically evident. In order to improve system capacity and QoS, it is mandatory that we pay closer attention to operational bandwidth efficiency issues. We address this issue for two operators' spectrum sharing in the same geographical area. We model and analyze interactions between the competitive operators coexisting in the same frequency band as a strategic noncooperative game, where the operators simultaneously share the spectrum dynamically as per their relative requirement. If resources are allocated in a conventional way (static orthogonal allocation), spectrum utilization becomes inefficient when there is load asymmetry between the operators and low inter-operator interference. Theoretically, operators can share resources in a cooperative manner, but pragmatically they are reluctant to reveal their network information to competitors. By using game theory, we design a distributed implementation, in which self-interested operators play strategies and contend for the spectrum resources in a noncooperative manner. We have proposed two game theoretic approaches in the thesis, one using a virtual carrier price; and the other based on a mutual history of favors. The former approach takes into account a penalty proportional to spectrum usage in its utility function, whereas in the latter, operators play strategies based on their history of interactions, i.e., how well the other behaved in the past. Finally, based on the simulations, we assess the performance of the proposed game theoretic approaches in comparison to existing conventional allocations.
A Video Database of Human Faces under Near Infra-Red Illumination for Human Computer Interaction Aplications
Venue: arXiv cs.CV 2015
Year: 2015

Human Computer Interaction (HCI) is an evolving area of research for coherent communication between computers and human beings. Some of the important applications of HCI as reported in literature are face detection, face pose estimation, face tracking and eye gaze estimation. Development of algorithms for these applications is an active field of research. However, availability of standard database to validate such algorithms is insufficient. This paper discusses the creation of such a database created under Near Infra-Red (NIR) illumination. NIR illumination has gained its popularity for night mode applications since prolonged exposure to Infra-Red (IR) lighting may lead to many health issues. The database contains NIR videos of 60 subjects in different head orientations and with different facial expressions, facial occlusions and illumination variation. This new database can be a very valuable resource for development and evaluation of algorithms on face detection, eye detection, head tracking, eye gaze tracking etc. in NIR lighting.
Discovering Attribute Shades of Meaning with the Crowd
Venue: arXiv cs.CV 2015
Year: 2015

To learn semantic attributes, existing methods typically train one discriminative model for each word in a vocabulary of nameable properties. However, this "one model per word" assumption is problematic: while a word might have a precise linguistic definition, it need not have a precise visual definition. We propose to discover shades of attribute meaning. Given an attribute name, we use crowdsourced image labels to discover the latent factors underlying how different annotators perceive the named concept. We show that structure in those latent factors helps reveal shades, that is, interpretations for the attribute shared by some group of annotators. Using these shades, we train classifiers to capture the primary (often subtle) variants of the attribute. The resulting models are both semantic and visually precise. By catering to users' interpretations, they improve attribute prediction accuracy on novel images. Shades also enable more successful attribute-based image search, by providing robust personalized models for retrieving multi-attribute query results. They are widely applicable to tasks that involve describing visual content, such as zero-shot category learning and organization of photo collections.
Interactive Scalar Quantization for Distributed Resource Allocation
Venue: arXiv cs.IT 2015
Year: 2015

In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremum of the distributed users' utilities. The overhead rate necessary to enable the controller to reproduce the users' local state can be prohibitively high. An approach to reduce this overhead is interactive communication wherein rate savings are achieved by tolerating an increase in delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstrate that tolerating a small increase in delay can yield significant rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the rate-delay trade-offs. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, a novel approach for optimizing and managing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows to jointly exploit both the wireless and social context of wireless users for optimizing the overall allocation of resources and improving traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel, selforganizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows to offload a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on the wireless links and on the backhaul.
Reversible monadic computing
Venue: arXiv cs.LO 2015
Year: 2015

We extend categorical semantics of monadic programming to reversible computing, by considering monoidal closed dagger categories: the dagger gives reversibility, whereas closure gives higher-order expressivity. We demonstrate that Frobenius monads model the appropriate notion of coherence between the dagger and closure by reinforcing Cayley's theorem; by proving that effectful computations (Kleisli morphisms) are reversible precisely when the monad is Frobenius; by characterizing the largest reversible subcategory of Eilenberg-Moore algebras; and by identifying the latter algebras as measurements in our leading example of quantum computing. Strong Frobenius monads are characterized internally by Frobenius monoids.
Shrinkage degree in $L_2$-re-scale boosting for regression
Venue: arXiv cs.LG 2015
Year: 2015

Re-scale boosting (RBoosting) is a variant of boosting which can essentially improve the generalization performance of boosting learning. The key feature of RBoosting lies in introducing a shrinkage degree to re-scale the ensemble estimate in each gradient-descent step. Thus, the shrinkage degree determines the performance of RBoosting. The aim of this paper is to develop a concrete analysis concerning how to determine the shrinkage degree in $L_2$-RBoosting. We propose two feasible ways to select the shrinkage degree. The first one is to parameterize the shrinkage degree and the other one is to develope a data-driven approach of it. After rigorously analyzing the importance of the shrinkage degree in $L_2$-RBoosting learning, we compare the pros and cons of the proposed methods. We find that although these approaches can reach the same learning rates, the structure of the final estimate of the parameterized approach is better, which sometimes yields a better generalization capability when the number of sample is finite. With this, we recommend to parameterize the shrinkage degree of $L_2$-RBoosting. To this end, we present an adaptive parameter-selection strategy for shrinkage degree and verify its feasibility through both theoretical analysis and numerical verification. The obtained results enhance the understanding of RBoosting and further give guidance on how to use $L_2$-RBoosting for regression tasks.
Elastic Routing in Wireless Ad Hoc Networks With Directional Antennas
Venue: arXiv cs.IT 2015
Year: 2015

Throughput scaling laws of an ad hoc network equipping directional antennas at each node are analyzed. More specifically, this paper considers a general framework in which the beam width of each node can scale at an arbitrary rate relative to the number of nodes. We introduce an elastic routing protocol, which enables to increase per-hop distance elastically according to the beam width, while maintaining an average signal-to-interference-and-noise ratio at each receiver as a constant. We then identify fundamental operating regimes characterized according to the beam width scaling and analyze throughput scaling laws for each of the regimes. The elastic routing is shown to achieve a much better throughput scaling law than that of the conventional nearest-neighbor multihop for all operating regimes. The gain comes from the fact that more source--destination pairs can be simultaneously activated as the beam width becomes narrower, which eventually leads to a linear throughput scaling law. In addition, our framework is applied to a hybrid network consisting of both wireless ad hoc nodes and infrastructure nodes. As a result, in the hybrid network, we analyze a further improved throughput scaling law and identify the operating regime where the use of directional antennas is beneficial.
Rethinking Network Connectivity in Rural Communities in Cameroon
Venue: arXiv cs.CY 2015
Year: 2015

To bridge the digital divide between the urban and rural regions, the government of Cameroon has launched the Multipurpose Community Telecentres (MCT). But this project does not seems to sustain the local development. The aim of this study is threefold: to determine the ICT penetration in rural Cameroon and Internet adoption; to evaluate the impact of MCTs in rural region and to provide some recommendations for both the network planning and the development of suitable services. The study considers two rural communities in Cameroon. The results show that despite low incomes, and MCTs that are missing their goal, there is some readiness of local populations to welcome ICT projects in order to improve their daily life and activities. To design sustainable ICT projects for those regions, we provide some recommendations from network design to business strategy.
On the Existence of an MVU Estimator for Target Localization with Censored, Noise Free Binary Detectors
Venue: arXiv cs.IT 2015
Year: 2015

The problem of target localization with censored noise free binary detectors is considered. In this setting only the detecting sensors report their locations to the fusion center. It is proven that if the radius of detection is not known to the fusion center, a minimum variance unbiased (MVU) estimator does not exist. Also it is shown that when the radius is known the center of mass of the possible target region is the MVU estimator. In addition, a sub-optimum estimator is introduced whose performance is close to the MVU estimator but is preferred computationally. Furthermore, minimal sufficient statistics have been provided, both when the detection radius is known and when it is not. Simulations confirmed that the derived MVU estimator outperforms several heuristic location estimators.
Providing Current and Future Cellular Services to High Speed Trains
Venue: arXiv cs.NI 2015
Year: 2015

The demand for a broadband wireless connection is nowadays no longer limited to stationary situations, but also required while traveling. Therefore, there exist combined efforts to provide wireless access also on High Speed Trains (HSTs), in order to add to the attractiveness of this means for transportation. Installing an additional relay on the train, to facilitate the communication, is an approach that has already been extensively discussed in literature. The possibility of a direct communication between the base station and the passenger has been neglected until now, despite it having numerous advantages. Therefore, a comparison between these two opposing approaches is presented in this paper, accompanied by a detailed discussion of the related aspects. The focus is set on the feasibility of the direct link approach, including simulation results. Further technical issues are also presented, especially regarding the interdependencies of the different aspects and providing a view of mobile- and train-operators on the topic.
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning
Venue: arXiv cs.DC 2015
Year: 2015

Distributed computing excels at processing large scale data, but the communication cost for synchronizing the shared parameters may slow down the overall performance. Fortunately, the interactions between parameter and data in many problems are sparse, which admits efficient partition in order to reduce the communication overhead. In this paper, we formulate data placement as a graph partitioning problem. We propose a distributed partitioning algorithm. We give both theoretical guarantees and a highly efficient implementation. We also provide a highly efficient implementation of the algorithm and demonstrate its promising results on both text datasets and social networks. We show that the proposed algorithm leads to 1.6x speedup of a state-of-the-start distributed machine learning system by eliminating 90\% of the network communication.
A Relay Can Increase Degrees of Freedom in Bursty Interference Networks
Venue: arXiv cs.IT 2015
Year: 2015

We investigate the benefits of relays in multi-user wireless networks with bursty user traffic, where intermittent data traffic restricts the users to bursty transmissions. To this end, we study a two-user bursty MIMO Gaussian interference channel with a relay, where two Bernoulli random states govern the bursty user traffic. We show that an in-band relay can provide a degrees of freedom (DoF) gain in this bursty channel. This beneficial role of in-band relays in the bursty channel is in direct contrast to their role in the non-bursty channel which is not as significant to provide a DoF gain. More importantly, we demonstrate that for certain antenna configurations, an in-band relay can help achieve interference-free performances with increased DoF. We find the benefits particularly substantial with low data traffic, as the DoF gain can grow linearly with the number of antennas at the relay. In this work, we first derive an outer bound from which we obtain a necessary condition for interference-free DoF performances. Then, we develop a novel scheme that exploits information of the bursty traffic states to achieve them.
A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation
Venue: arXiv cs.NA 2015
Year: 2015

This paper constructs an ensemble-based sampling smoother for four-dimensional data assimilation using a Hybrid/Hamiltonian Monte-Carlo approach. The smoother samples efficiently from the posterior probability density of the solution at the initial time. Unlike the well-known ensemble Kalman smoother, which is optimal only in the linear Gaussian case, the proposed methodology naturally accommodates non-Gaussian errors and non-linear model dynamics and observation operators. Unlike the four-dimensional variational met\-hod, which only finds a mode of the posterior distribution, the smoother provides an estimate of the posterior uncertainty. One can use the ensemble mean as the minimum variance estimate of the state, or can use the ensemble in conjunction with the variational approach to estimate the background errors for subsequent assimilation windows. Numerical results demonstrate the advantages of the proposed method compared to the traditional variational and ensemble-based smoothing methods.
Advances in Bioinformatics and Computational Biology: Don't take them too seriously anyway
Venue: arXiv cs.CE 2015
Year: 2015

In the last few decades or so, we witness a paradigm shift in our nature studies - from a data-processing based computational approach to an information-processing based cognitive approach. The process is restricted and often misguided by the lack of a clear understanding about what information is and how it should be treated in research applications (in general) and in biological studies (in particular). The paper intend to provide some remedies for this bizarre situation.
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Venue: arXiv cs.CL 2015
Year: 2015

Word embedding, which refers to low-dimensional dense vector representations of natural words, has demonstrated its power in many natural language processing tasks. However, it may suffer from the inaccurate and incomplete information contained in the free text corpus as training data. To tackle this challenge, there have been quite a few works that leverage knowledge graphs as an additional information source to improve the quality of word embedding. Although these works have achieved certain success, they have neglected some important facts about knowledge graphs: (i) many relationships in knowledge graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many}, rather than simply \emph{one-to-one}; (ii) most head entities and tail entities in knowledge graphs come from very different semantic spaces. To address these issues, in this paper, we propose a new algorithm named ProjectNet. ProjecNet models the relationships between head and tail entities after transforming them with different low-rank projection matrices. The low-rank projection can allow non \emph{one-to-one} relationships between entities, while different projection matrices for head and tail entities allow them to originate in different semantic spaces. The experimental results demonstrate that ProjectNet yields more accurate word embedding than previous works, thus leads to clear improvements in various natural language processing tasks.