Co-evolution of content popularity and delivery in mobile P2P networks

Mobile P2P technology provides a scalable approach for content delivery to a large number of users on their mobile devices. In this work, we study the dissemination of a single item of content (e. g., an item of news, a song or a video clip) among a population of mobile nodes. Each node in the population is either a destination (interested in the content) or a potential relay (not yet interested in the content). There is an interest evolution process by which nodes not yet interested in the content (i.e., relays) can become interested (i.e., become destinations) on learning about the popularity of the content (i.e., the number of already interested nodes). In our work, the interest in the content evolves under the linear threshold model. The content is copied between nodes when they make random contact. For this we employ a controlled epidemic spread model. We model the joint evolution of the copying process and the interest evolution process, and derive joint fluid limit ordinary differential equations. We then study the selection of parameters under the content provider's control, for the optimization of various objective functions that aim at maximizing content popularity and efficient content delivery.

Mining sentiments from songs using latent dirichlet allocation

Song-selection and mood are interdependent. If we capture a song’s sentiment, we can determine the mood of the listener, which can serve as a basis for recommendation systems. Songs are generally classified according to genres, which don’t entirely reflect sentiments. Thus, we require an unsupervised scheme to mine them. Sentiments are classified into either two (positive/negative) or multiple (happy/angry/sad/...) classes, depending on the application. We are interested in analyzing the feelings invoked by a song, involving multi-class sentiments. To mine the hidden sentimental structure behind a song, in terms of “topics”, we consider its lyrics and use Latent Dirichlet Allocation (LDA). Each song is a mixture of moods. Topics mined by LDA can represent moods. Thus we get a scheme of collecting similar-mood songs. For validation, we use a dataset of songs containing 6 moods annotated by users of a particular website.

Fluorescent Tris-Imidazolium Sensors for Picric Acid Explosive

Two new anthracene-functionalized fluorescent tris-imidazolium salts have been synthesized, characterized, and proven to be selective sensors for picric acid, which is a common constituent of many powerful explosives. Theoretical studies revealed an unusual ground-state electron transfer from picrate anion to the sensor molecules.

Chromosomal translocations among the healthy human population: implications in oncogenesis

Chromosomal translocations are characteristic features of many cancers, especially lymphoma and leukemia. However, recent reports suggest that many chromosomal translocations can be found in healthy individuals, although the significance of this observation is still not clear. In this review, we summarize recent studies on chromosomal translocations in healthy individuals carried out in different geographical areas of the world and discuss the relevance of the observation with respect to oncogenesis.

Multispecies coexistence of trees in tropical forests: spatial signals of topographic niche differentiation increase with environmental heterogeneity

Neutral and niche theories give contrasting explanations for the maintenance of tropical tree species diversity. Both have some empirical support, but methods to disentangle their effects have not yet been developed. We applied a statistical measure of spatial structure to data from 14 large tropical forest plots to test a prediction of niche theory that is incompatible with neutral theory: that species in heterogeneous environments should separate out in space according to their niche preferences. We chose plots across a range of topographic heterogeneity, and tested whether pairwise spatial associations among species were more variable in more heterogeneous sites. We found strong support for this prediction, based on a strong positive relationship between variance in the spatial structure of species pairs and topographic heterogeneity across sites. We interpret this pattern as evidence of pervasive niche differentiation, which increases in importance with increasing environmental heterogeneity.

Modelling of metal-slag emulsion

Many industrial processes involve reaction between the two immiscible liquid systems. It is very important to increase the efficiency and productivity of such reactions. One of the important processes that involve such reactions is the metal-slag system. To increase the reaction rate or efficiency, one must increase the contact surface area of one of the phases. This is either done by emulsifying the slag into the metal phase or the metal into the slag phase. The latter is preferred from the stability viewpoint. Recently, we have proposed a simple and elegant mathematical model to describe metal emulsification in the presence of bottom gas bubbling. The same model is being extended here. The effect of slag and metal phase viscosity, density and metal droplet size on the metal droplet velocity in the slag phase is discussed for the above mentioned metal emulsification process. The models results have been compared with experimental data.

Scale-dependent relationships between tree species richness and ecosystem function in forests

1. The relationship between species richness and ecosystem function, as measured by productivity or biomass, is of long-standing theoretical and practical interest in ecology. This is especially true for forests, which represent a majority of global biomass, productivity and biodiversity. 2. Here, we conduct an analysis of relationships between tree species richness, biomass and productivity in 25 forest plots of area 8-50ha from across the world. The data were collected using standardized protocols, obviating the need to correct for methodological differences that plague many studies on this topic. 3. We found that at very small spatial grains (0.04ha) species richness was generally positively related to productivity and biomass within plots, with a doubling of species richness corresponding to an average 48% increase in productivity and 53% increase in biomass. At larger spatial grains (0.25ha, 1ha), results were mixed, with negative relationships becoming more common. The results were qualitatively similar but much weaker when we controlled for stem density: at the 0.04ha spatial grain, a doubling of species richness corresponded to a 5% increase in productivity and 7% increase in biomass. Productivity and biomass were themselves almost always positively related at all spatial grains. 4. Synthesis. This is the first cross-site study of the effect of tree species richness on forest biomass and productivity that systematically varies spatial grain within a controlled methodology. The scale-dependent results are consistent with theoretical models in which sampling effects and niche complementarity dominate at small scales, while environmental gradients drive patterns at large scales. Our study shows that the relationship of tree species richness with biomass and productivity changes qualitatively when moving from scales typical of forest surveys (0.04ha) to slightly larger scales (0.25 and 1ha). This needs to be recognized in forest conservation policy and management.

Call intensity is a repeatable and dominant acoustic feature determining male call attractiveness in a field cricket

Acoustic signal variation and female preference for different signal components constitute the prerequisite framework to study the mechanisms of sexual selection that shape acoustic communication. Despite several studies of acoustic communication in crickets, information on both male calling song variation in the field and female preference in the same system is lacking for most species. Previous studies on acoustic signal variation either were carried out on populations maintained in the laboratory or did not investigate signal repeatability. We therefore used repeatability analysis to quantify variation in the spectral, temporal and amplitudinal characteristics of the male calling song of the field cricket Plebeiogryllus guttiventris in a wild population, at two temporal scales, within and across nights. Carrier frequency (CF) was the most repeatable character across nights, whereas chirp period (CP) had low repeatability across nights. We investigated whether female preferences were more likely to be based on features with high (CF) or low (CP) repeatability. Females showed no consistent preferences for CF but were significantly more attracted towards signals with short CPs. The attractiveness of lower CP calls disappeared, however, when traded off with sound pressure level (SPL). SPL was the only acoustic feature that was significantly positively correlated with male body size. Since relative SPL affects female phonotaxis strongly and can vary unpredictably based on male spacing, our results suggest that even strong female preferences for acoustic features may not necessarily translate into greater advantage for males possessing these features in the field. (C) 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Quantifying Vocal Mimicry in the Greater Racket-Tailed Drongo: A Comparison of Automated Methods and Human Assessment

Objective identification and description of mimicked calls is a primary component of any study on avian vocal mimicry but few studies have adopted a quantitative approach. We used spectral feature representations commonly used in human speech analysis in combination with various distance metrics to distinguish between mimicked and non-mimicked calls of the greater racket-tailed drongo, Dicrurus paradiseus and cross-validated the results with human assessment of spectral similarity. We found that the automated method and human subjects performed similarly in terms of the overall number of correct matches of mimicked calls to putative model calls. However, the two methods also misclassified different subsets of calls and we achieved a maximum accuracy of ninety five per cent only when we combined the results of both the methods. This study is the first to use Mel-frequency Cepstral Coefficients and Relative Spectral Amplitude - filtered Linear Predictive Coding coefficients to quantify vocal mimicry. Our findings also suggest that in spite of several advances in automated methods of song analysis, corresponding cross-validation by humans remains essential.

Explosives Sensing by Using Electron-Rich Supramolecular Polymers: Role of Intermolecular Hydrogen Bonding in Significant Enhancement of Sensitivity

We demonstrate here that supramolecular interactions enhance the sensitivity towards detection of electron-deficient nitro-aromatic compounds (NACs) over discrete analogues. NACs are the most commonly used explosive ingredients and are common constituents of many unexploded landmines used during World WarII. In this study, we have synthesised a series of pyrene-based polycarboxylic acids along with their corresponding discrete esters. Due to the electron richness and the fluorescent behaviour of the pyrene moiety, all the compounds act as sensors for electron-deficient NACs through a fluorescence quenching mechanism. A Stern-Volmer quenching constant determination revealed that the carboxylic acids are more sensitive than the corresponding esters towards NACs in solution. The high sensitivity of the acids was attributed to supramolecular polymer formation through hydrogen bonding in the case of the acids, and the enhancement mechanism is based on an exciton energy migration upon excitation along the hydrogen-bond backbone. The presence of intermolecular hydrogen bonding in the acids in solution was established by solvent-dependent fluorescence studies and dynamic light scattering (DLS) experiments. In addition, the importance of intermolecular hydrogen bonds in solid-state sensing was further explored by scanning tunnelling microscopy (STM) experiments at the liquid-solid interface, in which structures of self-assembled monolayer of the acids and the corresponding esters were compared. The sensitivity tests revealed that these supramolecular sensors can even detect picric acid and trinitrotoluene in solution at levels as low as parts per trillion (ppt), which is much below the recommended permissible level of these constituents in drinking water.

On entanglement entropy functionals in higher-derivative gravity theories

In arXiv:1310.5713 1] and arXiv:1310.6659 2] a formula was proposed as the entanglement entropy functional for a general higher-derivative theory of gravity, whose lagrangian consists of terms containing contractions of the Riemann tensor. In this paper, we carry out some tests of this proposal. First, we find the surface equation of motion for general four-derivative gravity theory by minimizing the holographic entanglement entropy functional resulting from this proposed formula. Then we calculate the surface equation for the same theory using the generalized gravitational entropy method of arXiv:1304.4926 3]. We find that the two do not match in their entirety. We also construct the holographic entropy functional for quasi-topological gravity, which is a six-derivative gravity theory. We find that this functional gives the correct universal terms. However, as in the R-2 case, the generalized gravitational entropy method applied to this theory does not give exactly the surface equation of motion coming from minimizing the entropy functional.

Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.

Dynamics of Cricket Sound Production

The clever designs of natural transducers are a great source of inspiration for man-made systems. At small length scales, there are many transducers in nature that we are now beginning to understand and learn from. Here, we present an example of such a transducer that is used by field crickets to produce their characteristic song. This transducer uses two distinct components-a file of discrete teeth and a plectrum that engages intermittently to produce a series of impulses forming the loading, and an approximately triangular membrane, called the harp, that acts as a resonator and vibrates in response to the impulse-train loading. The file-and-plectrum act as a frequency multiplier taking the low wing beat frequency as the input and converting it into an impulse-train of sufficiently high frequency close to the resonant frequency of the harp. The forced vibration response results in beats producing the characteristic sound of the cricket song. With careful measurements of the harp geometry and experimental measurements of its mechanical properties (Young's modulus determined from nanoindentation tests), we construct a finite element (FE) model of the harp and carry out modal analysis to determine its natural frequency. We fine tune the model with appropriate elastic boundary conditions to match the natural frequency of the harp of a particular species-Gryllus bimaculatus. We model impulsive loading based on a loading scheme reported in literature and predict the transient response of the harp. We show that the harp indeed produces beats and its frequency content matches closely that of the recorded song. Subsequently, we use our FE model to show that the natural design is quite robust to perturbations in the file. The characteristic song frequency produced is unaffected by variations in the spacing of file-teeth and even by larger gaps. Based on the understanding of how this natural transducer works, one can design and fabricate efficient microscale acoustic devices such as microelectromechanical systems (MEMS) loudspeakers.

Activity Recognition in Egocentric Life-Logging Videos

With the increasing availability of wearable cameras, research on first-person view videos (egocentric videos) has received much attention recently. While some effort has been devoted to collecting various egocentric video datasets, there has not been a focused effort in assembling one that could capture the diversity and complexity of activities related to life-logging, which is expected to be an important application for egocentric videos. In this work, we first conduct a comprehensive survey of existing egocentric video datasets. We observe that existing datasets do not emphasize activities relevant to the life-logging scenario. We build an egocentric video dataset dubbed LENA (Life-logging EgoceNtric Activities) (http://people.sutd.edu.sg/similar to 1000892/dataset) which includes egocentric videos of 13 fine-grained activity categories, recorded under diverse situations and environments using the Google Glass. Activities in LENA can also be grouped into 5 top-level categories to meet various needs and multiple demands for activities analysis research. We evaluate state-of-the-art activity recognition using LENA in detail and also analyze the performance of popular descriptors in egocentric activity recognition.