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Voting, contagion and the trade-off between public health and political rights: Quasi-experimental evidence from the Italian 2020 polls
No full textFISH FEEDING INTENSITY ASSESSMENT IN AQUACULTURE: A NEW AUDIO DATASET AFFIA3K AND A DEEP LEARNING ALGORITHM
Get PDFAssessment of suspended growth biological process for treatment and reuse of mixed wastewater for irrigation of edible crops under hydroponic conditions
Get PDFDue to the increasing freshwater deterioration and demand for irrigation, there is pressing need to reclaim and reuse wastewater for agricultural operations. While this practice is gaining significant traction in developed world, it is quite rare in most developing countries with inadequate or no functional sewerage facilities and treatment systems at both municipal and industrial levels occasioned by high investment and operational costs. Consequently, wastewaters generated are in complex heterogenous mix of industrial, domestic, municipal and agricultural runoff wastewater. Biological technologies which utilize the expertise of microorganisms are considered robust, efficient and economically attractive for treatment of wide range of wastewaters and they have high suitability in developing countries. This work therefore assessed the potential of suspended growth biological process (SGBP) for reclamation and reuse of mixed wastewater composed a mixture of domestic effluent, pharmaceutical, textile, petroleum discharges and agricultural runoff for irrigation of edible crops (lettuce and beets) with plants phenological parameters as measuring indicators. The germination and phenological characteristics of crops were studied in a hydroponic unit under four irrigation regimes: tap water as control, mixed wastewater, SGBP treated wastewater, and tap water mixed with nutrient solution as upper control, for a duration of 45-d. The results proved that the SGBP treated wastewater had no negative impact on germination responses of the seed crops. However, residual recalcitrant compounds caused early stunted growth in plant root systems with resultant limited access to nutrients. Consequently, plant vegetative growth and phenological development as well as chlorophyll production were reduced. In comparison to nutrients supplemented solution, nutrients deficiency and imbalance in treated wastewater contributed to the poor development in irrigated plants. The outcomes of seed germination and plant growth experiments show a positive indication for reuse of mixed wastewater in agriculture. However, there is need for further research to explore the long-term benefits and limitations of reusing such treated wastewater
Ensemble-based method for the Inverse Frobenius-Perron Operator Problem: Data Driven Global Analysis from Spatiotemporal "Movie" data
Get PDFGiven a sequence of empirical distribution data (e.g. a movie of a spatiotemporal process such as a fluid flow), this work develops an ensemble data assimilation method to estimate the transition probability that represents a finite approximation of the Frobenius-Perron operator. This allows a dynamical systems knowledge to be incorporated into a prior ensemble, which provides sensible estimates in instances of limited observation. We demonstrate improved estimates over a constrained optimization approach (based on a quadratic programming problem) which does not impose a prior on the solution except for Markov properties. The estimated transition probability then enables several probabilistic analysis of dynamical systems. We focus only on the identification of coherent patterns from the estimated Markov transition to demonstrate its application as a proof-of-concept. To the best of our knowledge, there have not been many works on data-driven methods to identify coherent patterns from this type of data. While here the results are presented only in the context of dynamical systems applications, this work we present here has the potential to make a contribution in wider application areas that require the estimation of transition probabilities from a time-ordered spatio-temporal distribution data
SP-GAN: Self-growing and Pruning Generative Adversarial Networks
Get PDFThis paper presents a new Self-growing and Pruning Generative Adversarial Network (SP-GAN) for realistic image generation. In contrast to traditional GAN models, our SPGAN is able to dynamically adjust the size and architecture of a network in the training stage, by using the proposed selfgrowing and pruning mechanisms. To be more specific, we first train two seed networks as the generator and discriminator, each only contains a small number of convolution kernels. Such small-scale networks are much easier and faster to train than large-capacity networks. Second, in the self-growing step,we replicate the convolution kernels of each seed network to augment the scale of the network, followed by fine-tuning the augmented/expanded network. More importantly, to prevent the excessive growth of each seed network in the self-growing stage, we propose a pruning strategy that reduces the redundancy of an augmented network, yielding the optimal scale of the network. Last, we design a new adaptive loss function that is treated as a variable loss computational process for the training of the proposed SP-GAN model. By design, the hyperparameters of the loss function can dynamically adapt to different training stages. Experimental results obtained on a set of datasets demonstrate the merits of the proposed method, especially in terms of the stability and efficiency of network training. The source code of the proposed SP-GAN method is publicly available at https://github.com/Lambert-chen/SPGAN.git
Impact of open innovation on industries and firms – A dynamic complex systems view
Get PDFThis paper develops novel behavioural models of open innovation (OI) for competitive markets and uses them to compare the impact of two types of OI frameworks – open source (OS) and patent-licensing (PL). The dynamic consequences of OI, for both OS and PL, are studied using a complex adaptive systems approach. We examine how profits, technology levels, R&D investment, technology adoption and market structure evolve under each and are impacted by underlying market characteristics. While both OS and PL are found to be equivalent in technology outcomes, OS comes with additional advantages to participating firms. Firms in the OS framework earn higher profit and are more efficient with their R&D investments. The industry is less concentrated under OS than under PL, except when market size is very large. In both frameworks, consumer preference for new product adoption has a significant impact. When consumers adopt newly introduced products relatively quickly, market concentration is the higher and overall rate of technological progress slower. These results contribute towards a deeper theoretical understanding of OI, opening new avenues for future research
Essays on the impact of technology and trade on the economy
Get PDFThis thesis is composed of two chapters each studying different aspects of the impact oftechnology and international trade on the economy.Chapter 1 analyses the effects of trade and technology shocks on the internal migrationof workers in the United States between 1990 and 2007. It finds that routine workersexhibit a lower migration response to rising Chinese import competition than non-routineworkers. This is the case despite the China trade shock triggering disproportionate falls inroutine wages and employment in high-trade exposed regions. It also finds that the sharesof routine in-migrants decline as the automation of routine-tasks displaces routine workersin technologically advanced regions. Using a spatial equilibrium model, the chapters explainsthe regional adjustment process emerging from the disjoint geography of trade andtechnology shocks from theoretical standpoint. The migration of routine workers is hamperedby lower equilibrium wages. Less routine workers migrate across regions, and otherlabour market outcomes such as unemployment worsen in response to the shock.Chapter 2 analyses optimal fiscal policy in an economy with two types of capital: physicalcapital, and automation capital (“robots”), that is substitutable with labour. If the taxsystem is complete, the optimal robot and capital taxes should be set in such a way, so thatthe pre-tax returns on automation and physical capital are equalized along the transitionto the zero-distortion steady state. I examine the implications of this result in an economywith a declining price of automation that matches U.S. data.<br
Improved capsule routing for weakly labeled sound event detection
Get PDFPolyphonic sound event detection aims to detect the types of sound events that occur in given audio clips, and their onset and offset times, in which multiple sound events may occur simultaneously. Deep learning-based methods such as convolutional neural networks (CNN) achieved state-of-the-art results in polyphonic sound event detection. However, two open challenges still remain: overlap between events and prone to overfitting problem. To solve the above two problems, we proposed a capsule network-based method for polyphonic sound event detection. With so-called dynamic routing, capsule networks have the advantage of handling overlapping objects and the generalization ability to reduce overfitting. However, dynamic routing also greatly slows down the training process. In order to speed up the training process, we propose a weakly labeled polyphonic sound event detection model based on the improved capsule routing. Our proposed method is evaluated on task 4 of the DCASE 2017 challenge and compared with several baselines, demonstrating competitive results in terms of F-score and computational efficiency
Assessing variability in vascular response to cocoa with personal devices: a series of double-blind randomized cross-over n-of-1 trials
Get PDFThe Dynamics of Organizational Autonomy: Oscillations at Automobili Lamborghini
Get PDFThrough a 21-year longitudinal study of the relationship between Italian supercar manufacturer Automobili Lamborghini and its parent, German carmaker Audi AG, we examine how a unit's degree of organizational autonomy is renegotiated over long periods of time. Using detailed empirical data, we develop a process model of the dynamics of organizational autonomy in a unit-parent relationship. This process model shows an ongoing dialectical tension between parent managers' autonomy-reduction efforts and unit managers' autonomy-extension efforts, and it reveals oscillations in the unit managers' discretion over resource-orchestration decisions. Driving this dialectic are parent managers' appraisal respect for the unit, their search for firm-wide strategic integration, and unit managers' organizational identity and concern for distinctiveness. Our process model captures concurrent feedback loops that endogenously produce these oscillations between lower and higher autonomy. We then conceptualize a harmonic domain in the unit-parent relationship, in which these oscillations persist without deviating toward amalgamation or separation. Finally, we develop a theory of change in autonomy by identifying a theoretical link between resource orchestration and specific dimensions of organizational identity. Our study highlights the dialectical, dynamic, and ongoing nature of organizational autonomy
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