A Japanese subjective well-being indicator based on Twitter data

This study presents for the first time the SWB-J index, a subjective well-being indicator for Japan based on Twitter data. The index is composed by eight dimensions of subjective well-being and is estimated relying on Twitter data by using human supervised sentiment analysis. The index is then compared with the analogous SWB-I index for Italy in order to verify possible analogies and cultural differences. Further, through structural equation models, we investigate the relationship between economic and health conditions of the country and the well-being latent variable and illustrate how this latent dimension affects the SWB-J and SWB-I indicators. It turns out that, as expected, economic and health welfare is only one aspect of the multidimensional well-being that is captured by the Twitter-based indicator

Uncovering hidden information and relations in time series data with wavelet analysis: three case studies in finance

This thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods

A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Small Group Learning

Curiosity is a vital metacognitive skill in educational contexts, leading to creativity, and a love of learning. And while many school systems increasingly undercut curiosity by teaching to the test, teachers are increasingly interested in how to evoke curiosity in their students to prepare them for a world in which lifelong learning and reskilling will be more and more important. One aspect of curiosity that has received little attention, however, is the role of peers in eliciting curiosity. We present what we believe to be the first theoretical framework that articulates an integrated socio-cognitive account of curiosity that ties observable behaviors in peers to underlying curiosity states. We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions. We validate the proposed framework by leveraging a longitudinal latent variable modeling approach. Findings confirm a positive predictive relationship between the latent variables of individual and interpersonal functions and curiosity, with the interpersonal functions exercising a comparatively stronger influence. Prominent behavioral realizations of these functions are also discovered in a data-driven manner. We instantiate the proposed theoretical framework in a set of strategies and tactics that can be incorporated into learning technologies to indicate, evoke, and scaffold curiosity. This work is a step towards designing learning technologies that can recognize and evoke moment-by-moment curiosity during learning in social contexts and towards a more complete multimodal learning analytics. The underlying rationale is applicable more generally for developing computer support for other metacognitive and socio-emotional skills.Comment: arXiv admin note: text overlap with arXiv:1704.0748

Sensory coding and the causal impact of mouse cortex in a visual decision.

Correlates of sensory stimuli and motor actions are found in multiple cortical areas, but such correlates do not indicate whether these areas are causally relevant to task performance. We trained mice to discriminate visual contrast and report their decision by steering a wheel. Widefield calcium imaging and Neuropixels recordings in cortex revealed stimulus-related activity in visual (VIS) and frontal (MOs) areas, and widespread movement-related activity across the whole dorsal cortex. Optogenetic inactivation biased choices only when targeted at VIS and MOs,proportionally to each site's encoding of the visual stimulus, and at times corresponding to peak stimulus decoding. A neurometric model based on summing and subtracting activity in VIS and MOs successfully described behavioral performance and predicted the effect of optogenetic inactivation. Thus, sensory signals localized in visual and frontal cortex play a causal role in task performance, while widespread dorsal cortical signals correlating with movement reflect processes that do not play a causal role

Quantitative microscopy workflows for the study of cellular receptor trafficking

The trafficking and signalling of cellular receptors are complex, intertwined processes with many feedback mechanisms. Confocal microscopy is a powerful tool to study the trafficking of receptors. The aim of this thesis was to report and develop workflows to quantify the spatio-temporal dynamics of receptor trafficking and colocalization using confocal microscopy. Importantly, the workflows should be reproducible and unbiased, as well as automated and accurate. A 4D level set approach is developed to enable accurate cellular segmentation. Temporal constraints are introduced to further improve segmentation accuracy. This novel approach is thoroughly validated, and statistically significant performance increase over equivalent 2D and 3D approaches is demonstrated. We present a confocal microscopy based RNAi depletion screen. Specifically, quantitative workflows to identify genes which perturb the trafficking of receptor are described. Finally, a critical review of current approaches to the quantification of colocalization between receptors and endosomes is presented. Improvements to existing techniques and complete workflows are provided for 4D data (3D time-lapse). Together the described protocols provide a complete microscopy based platform to identify and investigate regulators of receptor signalling and trafficking

Population analysis of neural data -- developments in statistical methods and related computational models

A key goal of neuroscience is to understand how the remarkable computational abilities of our brain emerge as a result of interconnected neuronal populations. Recently, advances in technologies for recording neural activity have increased the number of simultaneously recorded neurons by orders of magnitude, and these technologies are becoming more widely adopted. At the same time, massive increases in computational power and improved algorithms have enabled advanced statistical analyses of neural population activity and promoted our understanding of population coding. Nevertheless, there are many unanswered emerging questions, when it comes to analyzing and interpreting neural recordings. There are two major parts to this study. First, we consider an issue of increasing importance: that many in vivo recordings are now made by calcium-dependent fluorescent imaging, which only indirectly reports neural activity. We compare measurements of extracellular single units with fluorescence changes extracted from single neurons (often used as a proxy for spike rates), both recorded from cortical neural populations of behaving mice. We perform identical analyses at the single cell level and population level, and compare the results, uncovering a number of differences, or biases. We propose a phenomenological model to transform spike trains into synthetic imaging data and test whether the transformation explains the biases found. We discover that the slow temporal dynamics of calcium imaging obscure rapid changes in neuronal selectivity and disperse dynamic features in time. As a result, spike rate modulation that is locked to temporally localized events can appear as a more sequence-like pattern of activity in the imaging data. In addition, calcium imaging is more sensitive to increases rather than decreases in spike rate, leading to biased estimates of neural selectivity. These biases need to be considered when interpreting calcium imaging data. The second part of this work embarks on a challenging yet fruitful study of latent variable analysis of simultaneously recorded neural activity in a decision-making task. To connect the neural dynamics in different stages of a decision-making task, we developed a time-varying latent dynamics system model that uncovers neural dynamics shared by neurons in a local decision-making circuit. The shared neural activity supports the dynamics of choice generation and memory in a fashion akin to drift diffusion models, and robustly maintains a decision signal in the post-decision period. Importantly, we find that error trials follow similar dynamics to those of correct trials, but their dynamics are separated in shared neural activity space, proving a more correct early decoding estimation of an animal's success or failure at a given trial. Overall, the shared neural activity dynamics can predict multiple measures of behavioral variability including performance, reaction time, and trial correctness, and therefore are a useful summary of the neural representation. Such an approach can be readily applied to study complex dynamics in other neural systems. In summary, this dissertation represents an important step towards developing model-based analysis of neuronal dynamics and understanding population codes in large-scale neural data

Development of carbon nanostructures from non-conventional resources

Carbon nanostructures (CNSs) perpetuate the scientific interest over decades due to their remarkable properties and emerging technological applications. The development of sustainable technologies for the synthesis of CNSs from natural resources grabbed immense research attention aiming to implement these high-end materials in wide range of nano electronic devices through safe and environmentally friendly routes. Even though a number of top down and bottom up approaches have been developed for the production of CNSs, most of them either aided by catalysts or involved solvent assisted multi-step process that considerably increase the cost of production and hinders the realization of low cost CNSs based commercial devices. In addition, vast majority of these techniques use high pure petroleum derived hydrocarbon gas precursors that are non-renewable and expensive. Hence, it is imperative to develop scalable techniques that can derive high quality CNSs directly on arbitrary substrates from naturally derived carbon feed stocks. This work aims to develop an environmentally benign plasma enhanced chemical vapor deposition technique for fabricating CNSs from Citrus sinensis essential oil, a bio renewable precursor, and explored the potential of these nanostructures for gas sensing application. C. sinensis essential oil, obtained through cold extraction of orange peels is a rich source of non-synthetic hydrocarbon compounds principally limonene. Inherently volatile in nature, C. sinensis essential oil can serve as an ideal candidate material compatible to plasma enhanced chemical vapor deposition. This thesis investigated the fabrication of vertically-oriented graphene nanostructures from C.sinensis essential oil through radio frequency plasma enhanced chemical vapor deposition process, the fundamental properties, extend to which the process parameters influenced the structure and morphological features, and the suitability of the developed vertical graphene arrays for gas sensing applications. Special attention is paid to probe deep into the morphological evolution with the help of a set of advanced analytical characterization methods and multi-parameter model simulations. In the first phase, C.sinensis vapors were subjected to low RF power glow discharge that resulted in the formation of plasma polymer thin films, and the material properties were studied as a function of input RF energy. The fundamental properties of plasma polymer thin films fabricated at different RF power level (10−75 W) were characterized with variable angle spectroscopic ellipsometry, UV-visible spectroscopy, Fourier transform infrared spectroscopy X-ray photoelectron spectroscopy and atomic force microscopy. Optical characterization showed that independent of deposition power films exhibited good transparency (~90 %) in the visible region and a refractive index of 1.55 at 500nm. The optical band gap measured around 3.60 eV and falls within the insulating region. The atomic force microscopic (AFM) images revealed that the surface is pinhole-free and smooth at nanoscale, with average surface roughness dependent on the deposition power. Film hardness increased from 0.50 GPa to 0.78 GPa as applied power increased from 10 to 75 W. In the second phase, experiments were modified by redesigning the experimental set up in order to eliminate hydrogen from the deposits leaving only crystalline carbon. The RF power deliberately kept high, substrate temperature was raised and hydrogen gas fed into the reactor in controlled manner. A sequence of experiments were carried out by systematically changing the process parameters such as in put RF power (300-500W), hydrogen flow rate (10-50 sccm) and deposition duration (2-8 min) and analysed the structural and morphological evolution of the resulted vertical graphene nanostructure. The structure-property correlation of vertical graphene arrays with the plasma process parameters was performed. The Raman spectra ascertained the formation of less defected multilayered graphene nanostructures and scanning electron microscopic images provided the primary evidences of morphological evolution. The potential of the novel analytical techniques such as Hough transformations, fractal dimension distributions and Minkowski connectivity for the analysis of graphene array morphology was then successfully demonstrated. Worth noting that, these advanced techniques displayed significant changes and revealed the complex morphological transformation of C. sinensis derived vertical graphene subjected to change in process conditions. Precisely, vertical graphene nanowalls obtained at 300 and 500W presented a narrow height distribution profile but much wider array formed at 400 W. Fourier and Hough transformation spectra showed a prominent change with an increase in power, thus highlighted change in the morphology with the density of nanoflakes. Similarly, 2D FFT transform spectra of vertical graphene samples also presented notable changes with increasing hydrogen flux. The most narrow height distributions, well-shaped Hough transformation spectra and distribution of fractal dimensions obtained for structures formed at 20 and 50 sccm of hydrogen flow rate. In addition to this, the principal characteristics of thus fabricated vertical graphene such as flake length (Lvg) and flake half width (Wvg) are theoretically modelled by an ad hoc model based on a large number of interaction elemental processes and correlated with the experimental results. The combination of the experimental and simulation results ensured important insights and deeper understanding of the processes that govern formation of the vertical graphene morphology.Vertical graphene nanostructures having superior structural and morphological properties were successfully fabricated at an input RF energy of 500W, hydrogen flow rate of 30 sccm and deposition duration of 6 minutes. The third phase presented an in-depth study of the properties of C.sinensis oil derived graphene over a set of conducting (copper and nickel) and insulating substrates (silicon and quartz). The SEM images of thus fabricated graphene patterns showed the unique feature of vertically interconnected and non-agglomerated carbon nanowall structures having maze-like and petal-like networks. Moreover, the normalized height distribution function and 2-D FFT spectra analysis ascertained that vertical graphene formed on silicon substrates displayed the most uniform distribution. X-ray photoelectron spectroscopy spotted only the presence of carbon and the transmission electron microscopic studies revealed the formation of unique onion-like closed loops. The 3-D nanoporous structure of C.sinensis oil derived graphene showed high hydrophobicity and measured a water contact angle of 129°. The surface energy studies were performed using Neumann model, Owens-Wendt-Kaelble approach and van Oss- Chaudhury-Good relation and estimated within the range 35‒41 mJ/m². Finally, plasma reformed vertical graphene from C. sinensis was integrated into a sensor device prototype to evaluate the performance in gas sensing. The chemiresistive type sensor exhibited sensing activity towards acetone. In summary, this thesis has identified a viable renewable resource and successfully developed a process that transform them into vertical graphene nanostructures. Furthermore, the fabricated graphene was integrated to real world devices and evaluated the performance. The outcomes of this investigation add knowledge base to the state-of-the-art of green chemistry approach for the synthesis of vertical graphene carbon nanostructures