Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Queuing theoretic and information theoretic capacity of energy harvesting sensor nodes

Energy harvesting sensor networks provide near perpetual operation and reduce carbon emissions thereby supporting `green communication'. We study such a sensor node powered with an energy harvesting source. We obtain energy management policies that are throughput optimal. We also obtain delay-optimal policies. Next we obtain the Shannon capacity of such a system. Further we combine the information theoretic and queuing theoretic approaches to obtain the Shannon capacity of an energy harvesting sensor node with a data queue. Then we generalize these results to models with fading and energy consumption in activities other than transmission.

A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Tuning the parameters to establish Quenching and enhancement regimes in hybrid Gold nanoparticles and CdSe Quantum dots monolayer

The multi-component nanomaterials combine the individual properties and give rise to emergent phenomenon. Optical excitations in such hybrid nonmaterial's ( for example Exciton in semiconductor quantum dots and Plasmon in Metal nanomaterials) undergo strong weak electromagnetic coupling. Such exciton-plasmon interactions allow design of absorption and emission properties, control of nanoscale energy-transfer processes, and creation of new excitations in the strong coupling regime.This Exciton plasmon interaction in hybrid nanomaterial can lead to both enhancement in the emission as well as quenching. In this work we prepared close-packed hybrid monolayer of thiol capped CdSe and gold nanoparticles. They exhibit both the Quenching and enhancements the in PL emission.The systematic variance of PL from such hybrid nanomaterials monolayer is studied by tuning the Number ratio of Gold per Quantum dots, the surface density of QDs and the spectral overlap of emission spectrum of QD and absorption spectrum of Gold nanoparticles. Role of Localized surface Plasmon which not only leads to quenching but strong enhancements as well, is explored.

Analysis of composite single lap joints using numerical and experimental approach

Adhesives are widely used to execute the assembly of aerospace and automotive structures due to their ability to join dissimilar materials, reduced stress concentration, and improved fatigue resistance. The mechanical behavior of adhesive joints can be studied either using analytical models or by conducting mechanical tests. However, the complexity owing to multiple interfaces, layers with different properties, material and geometric nonlinearity and its three-dimensional nature combine to increase the difficulty in obtaining an overall system of governing equations to predict the joint behavior. On the other hand, experiments are often time consuming and expensive due to a number of parameters involved. Finite element analysis (FEA) is profoundly used in recent years to overcome these limitations. The work presented in this paper involves the finite element modeling and analysis of a composite single lap joint where the adhesive-adherend interface region was modeled using connector elements. The computed stresses were compared with the experimental stresses obtained using digital image correlation technique. The results showed an agreement. Further, the failure load predicted using FEA was found to be closer to the actual failure load obtained by mechanical tests.

Evaluating competing theories of street entrepreneurship

Conventionally, street entrepreneurs were either seen as a residue from a pre-modern era that is gradually disappearing (modernisation theory), or an endeavour into which marginalised populations are driven out of necessity in the absence of alternative ways of securing a livelihood (structuralist theory). In recent years, however, participa-tioninstreetentrepreneurshiphas beenre-read eitherasa rationaleconomicchoice(neo-liberal theory) or as conducted for cultural reasons (post-modern theory). The aim of this paper is to evaluate critically these competing explanations for participation in street entrepreneurship. To do this, face-to-face interviews were conducted with 871 street entrepreneurs in the Indian city of Bangalore during 2010 concerning their reasons for participation in street entrepreneurship. The finding is that no one explanation suffices. Some 12 % explain their participation in street entrepreneurship as necessity-driven, 15 % as traditional ancestral activity, 56 % as a rational economic choice and 17 % as pursued for social or lifestyle reasons. The outcome is a call to combine these previously rival explanations in order to develop a richer and more nuanced theorisation of the multifarious motives for street entrepreneurship in emerging market economies.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

Rainbow Emission from an Atomic Transition in Doped Quantum Dots

Although semiconductor quantum dots are promising materials for displays and lighting due to their tunable emissions, these materials also suffer from the serious disadvantage of self-absorption of emitted light. The reabsorption of emitted light is a serious loss mechanism in practical situations because most phosphors exhibit subunity quantum yields. Manganese-based phosphors that also exhibit high stability and quantum efficiency do not suffer from this problem but in turn lack emission tunability, seriously affecting their practical utility. Here, we present a class of manganese-doped quantum dot materials, where strain is used to tune the wavelength of the dopant emission, extending the otherwise limited emission tunability over the yellow-orange range for manganese ions to almost the entire visible spectrum covering all colors from blue to red. These new materials thus combine the advantages of both quantum dots and conventional doped phosphors, thereby opening new possibilities for a wide range of applications in the future.

Codes With Local Regeneration and Erasure Correction

Regenerating codes and codes with locality are two coding schemes that have recently been proposed, which in addition to ensuring data collection and reliability, also enable efficient node repair. In a situation where one is attempting to repair a failed node, regenerating codes seek to minimize the amount of data downloaded for node repair, while codes with locality attempt to minimize the number of helper nodes accessed. This paper presents results in two directions. In one, this paper extends the notion of codes with locality so as to permit local recovery of an erased code symbol even in the presence of multiple erasures, by employing local codes having minimum distance >2. An upper bound on the minimum distance of such codes is presented and codes that are optimal with respect to this bound are constructed. The second direction seeks to build codes that combine the advantages of both codes with locality as well as regenerating codes. These codes, termed here as codes with local regeneration, are codes with locality over a vector alphabet, in which the local codes themselves are regenerating codes. We derive an upper bound on the minimum distance of vector-alphabet codes with locality for the case when their constituent local codes have a certain uniform rank accumulation property. This property is possessed by both minimum storage regeneration (MSR) and minimum bandwidth regeneration (MBR) codes. We provide several constructions of codes with local regeneration which achieve this bound, where the local codes are either MSR or MBR codes. Also included in this paper, is an upper bound on the minimum distance of a general vector code with locality as well as the performance comparison of various code constructions of fixed block length and minimum distance.

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

Inferring biochemical routes from biochemical networks

Metabolism is a defining feature of life, and its study is important to understand how a cell works, alterations that lead to disease and for applications in drug discovery. From a systems perspective, metabolism can be represented as a network that captures all the metabolites as nodes and the inter-conversions among pairs of them as edges. Such an abstraction enables the networks to be studied by applying graph theory, particularly, to infer the flow of chemical information in the networks by identifying relevant metabolic pathways. In this study, different weighting schemes are used to illustrate that appropriately weighted networks can capture the quantitative cellular dynamics quite accurately. Thus, the networks now combine the elegance and simplicity of representation of the system and ease of analysing metabolic graphs. Metabolic routes or paths determined by this therefore are likely to be more biologically meaningful. The usefulness of the approach is demonstrated with two examples, first for understanding bacterial stress response and second for studying metabolic alterations that occurs in cancer cells.

Re-analysis of cryoEM data on HCV IRES bound to 40S subunit of human ribosome integrated with recent structural information suggests new contact regions between ribosomal proteins and HCV RNA

In this study, we combine available high resolution structural information on eukaryotic ribosomes with low resolution cryo-EM data on the Hepatitis C Viral RNA (IRES) human ribosome complex. Aided further by the prediction of RNA-protein interactions and restrained docking studies, we gain insights on their interaction at the residue level. We identified the components involved at the major and minor contact regions, and propose that there are energetically favorable local interactions between 40S ribosomal proteins and IRES domains. Domain II of the IRES interacts with ribosomal proteins S5 and S25 while the pseudoknot and the downstream domain IV region bind to ribosomal proteins S26, S28 and S5. We also provide support using UV cross-linking studies to validate our proposition of interaction between the S5 and IRES domains II and IV. We found that domain IIIe makes contact with the ribosomal protein S3a (S1e). Our model also suggests that the ribosomal protein S27 interacts with domain IIIc while S7 has a weak contact with a single base RNA bulge between junction IIIabc and IIId. The interacting residues are highly conserved among mammalian homologs while IRES RNA bases involved in contact do not show strict conservation. IRES RNA binding sites for S25 and S3a show the best conservation among related viral IRESs. The new contacts identified between ribosomal proteins and RNA are consistent with previous independent studies on RNA-binding properties of ribosomal proteins reported in literature, though information at the residue level is not available in previous studies.

MAXIMUM A-POSTERIORI ESTIMATION OF MISSING SAMPLES WITH CONTINUITY CONSTRAINT IN ELECTROMAGNETIC ARTICULOGRAPHY DATA

Electromagnetic Articulography (EMA) technique is used to record the kinematics of different articulators while one speaks. EMA data often contains missing segments due to sensor failure. In this work, we propose a maximum a-posteriori (MAP) estimation with continuity constraint to recover the missing samples in the articulatory trajectories recorded using EMA. In this approach, we combine the benefits of statistical MAP estimation as well as the temporal continuity of the articulatory trajectories. Experiments on articulatory corpus using different missing segment durations show that the proposed continuity constraint results in a 30% reduction in average root mean squared error in estimation over statistical estimation of missing segments without any continuity constraint.

Organic photovoltaics: key photophysical, device and design aspects

Key aspects of Organic Photovoltaics (OPVs) have been reviewed in this tutorial. Issues pertaining to the choice of materials, fabrication processes, photophysical mechanisms, device characterization, morphology of active layers and manufacturing are discussed. Special emphasis has been given to recent developments in large-area modules. Current strategies in enhancing the performance using external optical engineering approaches have also been highlighted. OPVs as a technology combine low weight, flexibility, low cost, good form factor and high-throughput processing; making them a promising PV technology for the future.

Limiting two-Higgs-doublet models

We update the constraints on two-Higgs-doublet models (2HDMs) focusing on the parameter space relevant to explain the present muon g - 2 anomaly, Delta alpha(mu), in four different types of models, type I, II, ``lepton specific'' (or X) and ``flipped'' (or Y). We show that the strong constraints provided by the electroweak precision data on the mass of the pseudoscalar Higgs, whose contribution may account for Delta alpha(mu), are evaded in regions where the charged scalar is degenerate with the heavy neutral one and the mixing angles alpha and beta satisfy the Standard Model limit beta - alpha approximate to pi/2. We combine theoretical constraints from vacuum stability and perturbativity with direct and indirect bounds arising from collider and B physics. Possible future constraints from the electron g - 2 are also considered. If the 126 GeV resonance discovered at the LHC is interpreted as the light CP-even Higgs boson of the 2HDM, we find that only models of type X can satisfy all the considered theoretical and experimental constraints.