Deep Learning for Plant Identification in Natural Environment

Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry

The Relationship Between Professional Quality Life, Coping Mechanisms, and Mental Fortitude

The health and mental fortitude of individuals enforcing policy and law is important to communities, agencies, and families. This helping profession is plagued by high suicide rates, maladaptive coping, and other negative health-related ailments. The present study used the DRS 15-R, ProQOL, RS-14, and BriefCOPE scales across 315 participants in order to investigate coping mechanisms and professional quality of life facets among individuals enforcing policy and law. It revealed multiple statistically significant relationships using multiple linear regression, hierarchical linear regression, and binary logistic regression. Emotion-focused coping techniques and compassion satisfaction both possessed statistically significant direct relationships with resilience and hardiness. Less productive coping techniques and burnout both exhibited statistically significant indirect relationships with hardiness. Burnout and less productive coping practices showed statistically significant indirect relationships with resilience. Compassion satisfaction exhibited a statistically significant direct relationship with rigid control, and burnout showed a statistically significant indirect relationship. Secondary traumatic stress symptoms were found to have a statistically significant indirect relationship with rigid control. The results may be used by law enforcement to manage stress in healthier ways which can benefit families, as well as decrease sick time, maladaptive patterns escalating into self-harm, and the intangible and tangible costs of workforce turnover rates

CHARACTERIZING THE SPATIAL AND TEMPORAL ASPECTS OF SUBSTRATES, CHANNEL MORPHOLOGY, AND LARGE WOOD IN FORESTED STREAMS IN THE WESTERN UPPER PENINSULA, MICHIGAN.

The relationships between wood and streambed substrates, among other abiotic components, are complex and an important part of the arrangement and dynamics of habitat in forested stream ecosystems. The objective of this research was to expand how we approach the study of the physical components of forested streams by considering the methods used to quantify these features, particularly substrates. Additionally, we assessed the temporal change over 14 years for streambed substrates, channel morphology, and large wood in a selectively-logged watershed. Our final objective was to understand if a relationship exists between the complexity of streambed morphology derived from variograms, and volume of instream large wood in forested streams. Our results suggest that Structure from Motion photogrammetry is a suitable complement or alternative to pebble counts for quantifying submerged streambed substrate composition as well as temporal changes in streambed morphology at small spatial scales (chapter 2). We determined the volume and abundance of large wood decreased within streams located in selectively logged catchments over the 14 years, but that the stability in streambed substrates and channel morphology did not appear relate to the amount of wood present (chapter 3). Finally, we found that in these tributaries of the Otter River, channel complexity metrics developed from variograms were not related to the volume of large wood present in stream channels (chapter 4). We hypothesize that may be due to the relatively low volume of wood compared to western US streams in addition to wood being too small relative to the local channel and larger landscape features, and that other underlying factors may be driving morphological complexity in these stream channels. Together, this research demonstrates that the association between large wood and channel complexity may not apply to all forested streams, and highlights some of the complexity in understanding the spatial and temporal relationships in forested streams, as well as presents an innovative approach to quantifying and monitoring streambed substrates and morphology

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

Essays on the nonlinear and nonstochastic nature of stock market data

The nature and structure of stock-market price dynamics is an area of ongoing and rigourous scientific debate. For almost three decades, most emphasis has been given on upholding the concepts of Market Efficiency and rational investment behaviour. Such an approach has favoured the development of numerous linear and nonlinear models mainly of stochastic foundations. Advances in mathematics have shown that nonlinear deterministic processes i.e. "chaos" can produce sequences that appear random to linear statistical techniques. Till recently, investment finance has been a science based on linearity and stochasticity. Hence it is important that studies of Market Efficiency include investigations of chaotic determinism and power laws. As far as chaos is concerned, there are rather mixed or inconclusive research results, prone with controversy. This inconclusiveness is attributed to two things: the nature of stock market time series, which are highly volatile and contaminated with a substantial amount of noise of largely unknown structure, and the lack of appropriate robust statistical testing procedures. In order to overcome such difficulties, within this thesis it is shown empirically and for the first time how one can combine novel techniques from recent chaotic and signal analysis literature, under a univariate time series analysis framework. Three basic methodologies are investigated: Recurrence analysis, Surrogate Data and Wavelet transforms. Recurrence Analysis is used to reveal qualitative and quantitative evidence of nonlinearity and nonstochasticity for a number of stock markets. It is then demonstrated how Surrogate Data, under a statistical hypothesis testing framework, can be simulated to provide similar evidence. Finally, it is shown how wavelet transforms can be applied in order to reveal various salient features of the market data and provide a platform for nonparametric regression and denoising. The results indicate that without the invocation of any parametric model-based assumptions, one can easily deduce that there is more to linearity and stochastic randomness in the data. Moreover, substantial evidence of recurrent patterns and aperiodicities is discovered which can be attributed to chaotic dynamics. These results are therefore very consistent with existing research indicating some types of nonlinear dependence in financial data. Concluding, the value of this thesis lies in its contribution to the overall evidence on Market Efficiency and chaotic determinism in financial markets. The main implication here is that the theory of equilibrium pricing in financial markets may need reconsideration in order to accommodate for the structures revealed

RECOGNITION OF FACES FROM SINGLE AND MULTI-VIEW VIDEOS

Face recognition has been an active research field for decades. In recent years, with videos playing an increasingly important role in our everyday life, video-based face recognition has begun to attract considerable research interest. This leads to a wide range of potential application areas, including TV/movies search and parsing, video surveillance, access control etc. Preliminary research results in this field have suggested that by exploiting the abundant spatial-temporal information contained in videos, we can greatly improve the accuracy and robustness of a visual recognition system. On the other hand, as this research area is still in its infancy, developing an end-to-end face processing pipeline that can robustly detect, track and recognize faces remains a challenging task. The goal of this dissertation is to study some of the related problems under different settings. We address the video-based face association problem, in which one attempts to extract face tracks of multiple subjects while maintaining label consistency. Traditional tracking algorithms have difficulty in handling this task, especially when challenging nuisance factors like motion blur, low resolution or significant camera motions are present. We demonstrate that contextual features, in addition to face appearance itself, play an important role in this case. We propose principled methods to combine multiple features using Conditional Random Fields and Max-Margin Markov networks to infer labels for the detected faces. Different from many existing approaches, our algorithms work in online mode and hence have a wider range of applications. We address issues such as parameter learning, inference and handling false positves/negatives that arise in the proposed approach. Finally, we evaluate our approach on several public databases. We next propose a novel video-based face recognition framework. We address the problem from two different aspects: To handle pose variations, we learn a Structural-SVM based detector which can simultaneously localize the face fiducial points and estimate the face pose. By adopting a different optimization criterion from existing algorithms, we are able to improve localization accuracy. To model other face variations, we use intra-personal/extra-personal dictionaries. The intra-personal/extra-personal modeling of human faces has been shown to work successfully in the Bayesian face recognition framework. It has additional advantages in scalability and generalization, which are of critical importance to real-world applications. Combining intra-personal/extra-personal models with dictionary learning enables us to achieve state-of-arts performance on unconstrained video data, even when the training data come from a different database. Finally, we present an approach for video-based face recognition using camera networks. The focus is on handling pose variations by applying the strength of the multi-view camera network. However, rather than taking the typical approach of modeling these variations, which eventually requires explicit knowledge about pose parameters, we rely on a pose-robust feature that eliminates the needs for pose estimation. The pose-robust feature is developed using the Spherical Harmonic (SH) representation theory. It is extracted using the surface texture map of a spherical model which approximates the subject's head. Feature vectors extracted from a video are modeled as an ensemble of instances of a probability distribution in the Reduced Kernel Hilbert Space (RKHS). The ensemble similarity measure in RKHS improves both robustness and accuracy of the recognition system. The proposed approach outperforms traditional algorithms on a multi-view video database collected using a camera network

Detection of loci associated with water-soluble carbohydrate accumulation and environmental adaptation in white clover (Trifolium repens L.) : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Plant Biology at Massey University, Palmerston North, New Zealand

White clover (Trifolium repens L.) is an economically important forage legume in New Zealand/Aotearoa (NZ). It provides quality forage and a source of bioavailable nitrogen fixed through symbiosis with soil Rhizobium bacteria. This thesis investigated the genetic basis of two traits of significant agronomic interest in white clover. These were foliar water-soluble carbohydrate (WSC) accumulation and soil moisture deficit (SMD) tolerance. Previously generated divergent WSC lines of white clover were characterised for foliar WSC and leaf size. Significant (p < 0.05) divergence in foliar WSC content was observed between five breeding pools. Little correlation was observed between WSC and leaf size, indicating that breeding for increased WSC content could be achieved in large and small leaf size classes of white clover in as few as 2 – 3 generations. Genotyping by sequencing (GBS) data were obtained for 1,113 white clover individuals (approximately 47 individuals from each of 24 populations). Population structure was assessed using discriminant analysis of principal components (DAPC) and individuals were assigned to 11 genetic clusters. Divergent selection created a structure that differentiated high and low WSC populations. Outlier detection methodologies using PCAdapt, BayeScan and KGD-FST applied to the GBS data identified 33 SNPs in diverse gene families that discriminated high and low WSC populations. One SNP associated with the starch biosynthesis gene, glgC was identified in a genome-wide association study (GWAS) of 605 white clover individuals. Transcriptome and proteome analyses also provided evidence to suggest that high WSC levels in different breeding pools were achieved through sorting of allelic variants of carbohydrate metabolism pathway genes. Transcriptome and proteome analyses suggested 14 gene models from seven carbohydrate gene families (glgC, WAXY, glgA, glgB, BAM, AMY and ISA3) had responded to artificial selection. Patterns of SNP variation in the AMY, glgC and WAXY gene families separated low and high WSC individuals. Allelic variants in these gene families represent potential targets for assisted breeding of high WSC levels. Overall, multiple lines of evidence corroborate the importance of glgC for increasing foliar WSC accumulation in white clover. Soil moisture deficit (SMD) tolerance was investigated in naturalised populations of white clover collected from 17 sites representing contrasting SMD across the South Island/Te Waipounamu of NZ. Weak genetic differentiation of populations was detected in analyses of GBS data, with three genetic clusters identified by ADMIXTURE. Outlier detection and environmental association analyses identified 64 SNPs significantly (p < 0.05) associated with environmental variation. Mapping of these SNPs to the white clover reference genome, together with gene ontology analyses, suggested some SNPs were associated with genes involved in carbohydrate metabolism and root morphology. A common set of allelic variants in a subset of the populations from high SMD environments may also identify targets for selective breeding, but this variation needs further investigation