Mushrooms are one of the protein rich foods, however they have a short pro-harvest life (2 to 3 days) compared to most vegetables. The aim of this study was to evaluate whether applying an anti-browning solution using vacuum impregnation and then electron beam irradiation can be used to extend the shelf life of fresh-cut mushrooms (Agaricus bisporus). Solutions made with (a) 2% ascorbic acid + 1% calcium lactate, (b) 2% citric acid + 1% calcium lactate, (c) 1% chitosan + 1% calcium lactate, and (d) 1% calcium lactate were used to impregnate mushroom slices at different vacuum pressures, vacuum pressure times, and atmospheric restoration times. Mushrooms were also irradiated at a dose of 1 kGy using a 1.35 MeV e-beam accelerator and their quality was evaluated in terms of color, texture, and microbial growth during 15 days storage at 4 degrees C. The best vacuum impregnation treatment was the 2% ascorbic acid and 1% calcium lactate solution using a vacuum pressure of 50 mmHg for 5 minutes and an atmospheric restoration time of 5 minutes. The control (not treated) and impregnated samples lost their structure (softening) during storage. The irradiated samples lost their firmness by day 4 of storage. The addition of calcium lactate to the samples during the treatment helped to keep the product’s texture during the 15 days storage time. Color of the mushrooms changed during storage for all the control and impregnated samples and only the irradiated samples showed an acceptable color by the end of day 15. Aerobics and psychrotrophics counts were significantly reduced by irradiation; while yeast and molds population increased by day 9 and were not completely inactivated with a dose of 1 kGy. Sensory panelists preferred the treated samples over the controls. The best treatment was the combination of vacuum impregnation with irradiation according to the consumer studies.

As zonas costeiras são regiões do planeta de elevada dinâmica geomorfológica, nas quais processos de erosão e deposição atuam em escalas temporais distintas. A abertura e fechamento sazonal do canal da Lagoa do Peixe - RS, têm influência direta sobre a biota aquática e terrestre bem como a agricultura e pecuária dos arredores deste corpo d’água. O objetivo geral desse trabalho foi analisar o processo de ligação da Lagoa do Peixe com o oceano Atlântico, utilizando imagens multitemporais de sensores orbitais visando identificar e quantificar as alterações espaciais e avaliar essa dinâmica com base na precipitação pluvial da região. Foram utilizadas imagens de satélite adquiridas entre 1985 a 2014 para gerar mapas temáticos da região do canal da Lagoa do Peixe. Os mapas foram feitos utilizando a técnica de fatiamento de imagens, gerando mapas temáticos binários com as classes água e não água. Verificou-se que existe relação direta entre a abertura natural do canal com a precipitação mensal acima da média, principalmente na estação da primavera. A precipitação pluvial mostrou influir diretamente na área alagada do canal, porém, a intervenção humana, por motivos econômicos, ainda é a principal causa das mudanças volumétricas e da abertura do canal. / The coastal zones are regions of great geomorphological dynamic where erosion and deposition processes operating on different time scales. The seasonal opening and closing of the Lagoa do Peixe channel (Brazil- RS), have a direct influence on aquatic and terrestrial biota as well as agriculture and livestock activities. The aim of this study were to analyze the connection process between Lagoa do Peixe and the Atlantic Ocean, using multi-temporal images of orbital sensors to identify and quantify the spatial changes and its dynamic based on the rainfall in the region. Satellite images acquired between 1985 to 2014 were used to generate thematic maps of Lagoa do Peixe channel. These maps were made using the slicing images technique, generating a binary thematic maps dataset with two classes, water and no water. It was found a direct relationship between the natural opening and monthly rainfall above the average, especially in springtime. Rainfall showed directly influence the flooded channel area, however, the human intervention, for economic reasons, it is still the leading cause of volumetric changes and channel opening.

As zonas costeiras são regiões do planeta de elevada dinâmica geomorfológica, nas quais processos de erosão e deposição atuam em escalas temporais distintas. A abertura e fechamento sazonal do canal da Lagoa do Peixe - RS, têm influência direta sobre a biota aquática e terrestre bem como a agricultura e pecuária dos arredores deste corpo d’água. O objetivo geral desse trabalho foi analisar o processo de ligação da Lagoa do Peixe com o oceano Atlântico, utilizando imagens multitemporais de sensores orbitais visando identificar e quantificar as alterações espaciais e avaliar essa dinâmica com base na precipitação pluvial da região. Foram utilizadas imagens de satélite adquiridas entre 1985 a 2014 para gerar mapas temáticos da região do canal da Lagoa do Peixe. Os mapas foram feitos utilizando a técnica de fatiamento de imagens, gerando mapas temáticos binários com as classes água e não água. Verificou-se que existe relação direta entre a abertura natural do canal com a precipitação mensal acima da média, principalmente na estação da primavera. A precipitação pluvial mostrou influir diretamente na área alagada do canal, porém, a intervenção humana, por motivos econômicos, ainda é a principal causa das mudanças volumétricas e da abertura do canal. / The coastal zones are regions of great geomorphological dynamic where erosion and deposition processes operating on different time scales. The seasonal opening and closing of the Lagoa do Peixe channel (Brazil- RS), have a direct influence on aquatic and terrestrial biota as well as agriculture and livestock activities. The aim of this study were to analyze the connection process between Lagoa do Peixe and the Atlantic Ocean, using multi-temporal images of orbital sensors to identify and quantify the spatial changes and its dynamic based on the rainfall in the region. Satellite images acquired between 1985 to 2014 were used to generate thematic maps of Lagoa do Peixe channel. These maps were made using the slicing images technique, generating a binary thematic maps dataset with two classes, water and no water. It was found a direct relationship between the natural opening and monthly rainfall above the average, especially in springtime. Rainfall showed directly influence the flooded channel area, however, the human intervention, for economic reasons, it is still the leading cause of volumetric changes and channel opening.

As zonas costeiras são regiões do planeta de elevada dinâmica geomorfológica, nas quais processos de erosão e deposição atuam em escalas temporais distintas. A abertura e fechamento sazonal do canal da Lagoa do Peixe - RS, têm influência direta sobre a biota aquática e terrestre bem como a agricultura e pecuária dos arredores deste corpo d’água. O objetivo geral desse trabalho foi analisar o processo de ligação da Lagoa do Peixe com o oceano Atlântico, utilizando imagens multitemporais de sensores orbitais visando identificar e quantificar as alterações espaciais e avaliar essa dinâmica com base na precipitação pluvial da região. Foram utilizadas imagens de satélite adquiridas entre 1985 a 2014 para gerar mapas temáticos da região do canal da Lagoa do Peixe. Os mapas foram feitos utilizando a técnica de fatiamento de imagens, gerando mapas temáticos binários com as classes água e não água. Verificou-se que existe relação direta entre a abertura natural do canal com a precipitação mensal acima da média, principalmente na estação da primavera. A precipitação pluvial mostrou influir diretamente na área alagada do canal, porém, a intervenção humana, por motivos econômicos, ainda é a principal causa das mudanças volumétricas e da abertura do canal. / The coastal zones are regions of great geomorphological dynamic where erosion and deposition processes operating on different time scales. The seasonal opening and closing of the Lagoa do Peixe channel (Brazil- RS), have a direct influence on aquatic and terrestrial biota as well as agriculture and livestock activities. The aim of this study were to analyze the connection process between Lagoa do Peixe and the Atlantic Ocean, using multi-temporal images of orbital sensors to identify and quantify the spatial changes and its dynamic based on the rainfall in the region. Satellite images acquired between 1985 to 2014 were used to generate thematic maps of Lagoa do Peixe channel. These maps were made using the slicing images technique, generating a binary thematic maps dataset with two classes, water and no water. It was found a direct relationship between the natural opening and monthly rainfall above the average, especially in springtime. Rainfall showed directly influence the flooded channel area, however, the human intervention, for economic reasons, it is still the leading cause of volumetric changes and channel opening.

本文以維度縮減(dimension reduction)為主題，介紹其定義以及四種目前較被廣為討論的處理方式。文中首先針對Li (1991)所使用的維度縮減定義型式y = g(x,ε) = g1(βx,ε)，與Cook (1994)所採用的定義型式「條件密度函數f(y | x)=f(y |βx)」作探討，並就Cook (1994)對最小維度縮減子空間的相關討論作介紹。此外文中也試圖提出另一種適用於pHd的可能定義(E(y | x)=E(y |βx)，亦即縮減前後y的條件期望值不變)，並發現在此一新定義下所衍生而成的子空間會包含於Cook (1994)所定義的子空間。 有關現有四種維度縮減方法(SIR、SAVE、SIR-II、pHd)的理論架構，則重新予以說明並作必要的補充證明，並以兩個機率模式(y = bx +ε及y = |z| +ε)為例，分別測試四種方法能否縮減出正確的方向。文中同時也分別找出對應於這四種方法的等價條件，並利用這些等價條件相互比較，得到彼此間的關係。我們發現當解釋變數x為多維常態情形下，四種方法理論上都不會保留可以被縮減的方向，而該保留住的方向卻不一定能夠被保留住，但是使用SAVE所可以保留住的方向會比單獨使用其他三者之一來的多(或至少一樣多)，而如果SIR與SIR-II同時使用則恰好等同於使用SAVE。另外使用pHd似乎時並不需要「E(y│x)二次可微分」這個先決條件。 / The focus of the study is on the dimension reduction and the over-view of the four methods frequently cited in the literature, i.e. SIR, SAVE, SIR-II, and pHd. The definitions of dimension reduction proposed by Li (1991)(y = g( x,ε) = g1(βx,ε)), and by Cook (1994)(f(y | x)=f(y|βx)) are briefly reviewed. Issues on minimum dimension reduction subspace (Cook (1994)) are also discussed. In addition, we propose a possible definition (E(y | x)=E(y |βx)), i.e. the conditional expectation of y remains the same both in the original subspace and the reduced subspace), which seems more appropriate when pHd is concerned. We also found that the subspace induced by this definition would be contained in the subspace generated based on Cook (1994). We then take a closer look at basic ideas behind the four methods, and supplement some more explanations and proofs, if necessary. Equivalent conditions related to the four methods that can be used to locate "right" directions are presented. Two models (y = bx +ε and y = |z| +ε) are used to demonstrate the methods and to see how good they can be. In order to further understand the possible relationships among the four methods, some comparisons are made. We learn that when x is normally distributed, directions that are redundant will not be preserved by any of the four methods. Directions that contribute significantly, however, may be mistakenly removed. Overall, SAVE has the best performance in terms of saving the "right" directions, and applying SIR along with SIR-II performs just as well. We also found that the prerequisite, 「E(y | x) is twice differentiable」, does not seem to be necessary when pHd is applied.

Variable selection methods play important roles in modeling high-dimensional data and are key to data-driven scientific discoveries. In this thesis, we consider the problem of variable selection with interaction detection. Instead of building a predictive model of the response given combinations of predictors, we start by modeling the conditional distribution of predictors given partitions based on responses. We use this inverse modeling perspective as motivation to propose a stepwise procedure for effectively detecting interaction with few assumptions on parametric form. The proposed procedure is able to detect pairwise interactions among p predictors with a computational time of \(O(p)\) instead of \(O(p^2)\) under moderate conditions. We establish consistency of the proposed procedure in variable selection under a diverging number of predictors and sample size. We demonstrate its excellent empirical performance in comparison with some existing methods through simulation studies as well as real data examples. Next, we combine the forward and inverse modeling perspectives under the Bayesian framework to detect pleiotropic and epistatic effects in effects in expression quantitative loci (eQTLs) studies. We augment the Bayesian partition model proposed by Zhang et al. (2010) to capture complex dependence structure among gene expression and genetic markers. In particular, we propose a sequential partition prior to model the asymmetric roles played by the response and the predictors, and we develop an efficient dynamic programming algorithm for sampling latent individual partitions. The augmented partition model significantly improves the power in detecting eQTLs compared to previous methods in both simulations and real data examples pertaining to yeast. Finally, we study the application of Bayesian partition models in the unsupervised learning of transcription factor (TF) families based on protein binding microarray (PBM). The problem of TF subclass identification can be viewed as the clustering of TFs with variable selection on their binding DNA sequences. Our model provides simultaneous identification of TF families and their shared sequence preferences, as well as DNA sequences bound preferentially by individual members of TF families. Our analysis may aid in deciphering cis regulatory codes and determinants of protein-DNA binding specificity. / Statistics

Cette thèse propose trois extensions de la Régression linéaire par tranches (Sliced Inverse Regression, SIR), notamment Collaborative SIR, Student SIR et Knockoff SIR.Une des faiblesses de la méthode SIR est l’impossibilité de vérifier si la Linearity Design Condition (LDC) est respectée. Il est établi que, si x suit une distribution elliptique, la condition est vraie ; dans le cas d’une composition de distributions elliptiques il n y a aucune garantie que la condition soit vérifiée globalement, pourtant, elle est respectée localement.On va donc proposer une extension sur la base de cette considération. Étant donné une variable explicative x, Collaborative SIR réalise d’abord un clustering. Pour chaque cluster, la méthode SIR est appliquée de manière indépendante.Le résultat de chaque composant contribue à créer la solution finale.Le deuxième papier, Student SIR, dérive de la nécessité de robustifier la méthode SIR.Vu que cette dernière repose sur l’estimation de la covariance et contient une étape APC, alors elle est sensible au bruit.Afin d’étendre la méthode SIR on a utilisé une stratégie fondée sur une formulation inverse du SIR, proposée par R.D. Cook.Finalement, Knockoff SIR est une extension de la méthode SIR pour la sélection des variables et la recherche d’une solution sparse, ayant son fondement dans le papier publié par R.F. Barber et E.J. Candès qui met l’accent sur le false discovery rate dans le cadre de la régression. L’idée sous-jacente à notre papier est de créer des copies de variables d’origine ayant certaines proprietés.On va montrer que la méthode SIR est robuste par rapport aux copies et on va proposer une stratégie pour utiliser les résultats dans la sélection des variables et pour générer des solutions sparse / This thesis proposes three extensions of Sliced Inverse Regression namely: Collaborative SIR, Student SIR and Knockoff SIR.One of the weak points of SIR is the impossibility to check if the Linearity Design Condition (LDC) holds. It is known that if X follows an elliptic distribution thecondition holds true, in case of a mixture of elliptic distributions there are no guaranties that the condition is satisfied globally, but locally holds. Starting from this consideration an extension is proposed. Given the predictor variable X, Collaborative SIR performs initially a clustering. In each cluster, SIR is applied independently. The result from each component collaborates to give the final solution.Our second contribution, Student SIR, comes from the need to robustify SIR. Since SIR is based on the estimation of the covariance, and contains a PCA step, it is indeed sensitive to noise. To extend SIR, an approach based on a inverse formulation of SIR proposed by R.D. Cook has been used.Finally Knockoff SIR is an extension of SIR to perform variable selection and give sparse solution that has its foundations in a recently published paper by R. F. Barber and E. J. Candès that focuses on the false discovery rate in the regression framework. The underlying idea of this paper is to construct copies of the original variables that have some properties. It is shown that SIR is robust to this copies and a strategy is proposed to use this result for variable selection and to generate sparse solutions.

In sufficient dimension reduction (Li, 1991; Cook, 1998b), original predictors are replaced by their low-dimensional linear combinations while preserving all of the conditional information of the response given the predictors. Sliced inverse regression [SIR; Li, 1991] and principal Hessian directions [PHD; Li, 1992] are two popular sufficient dimension reduction methods, and both SIR and PHD estimators involve all of the original predictor variables. To deal with the cases when the linear combinations involve only a subset of the original predictors, we propose a Bayesian model averaging (Raftery et al., 1997) approach to achieve sparse sufficient dimension reduction. We extend both SIR and PHD under the Bayesian framework. The superior performance of the proposed methods is demonstrated through extensive numerical studies as well as a real data analysis. / Statistics

Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) approach proposed by Li (1991). We show that missing responses generally affect the validity of the inverse regressions under the mechanism of missing at random. We then propose a simple and effective adjustment with inverse probability weighting that guarantees the validity of SIR. Furthermore, a marginal coordinate test is introduced for this adjusted estimator. The proposed method share the simplicity of SIR and requires the linear conditional mean assumption. The second part of the dissertation proposes two new estimating equation procedures: the complete case estimating equation approach and the inverse probability weighted estimating equation approach. The two approaches are applied to a family of dimension reduction methods, which includes ordinary least squares, principal Hessian directions, and SIR. By solving the estimating equations, the two approaches are able to avoid the common assumptions in the SDR literature, the linear conditional mean assumption, and the constant conditional variance assumption. For all the aforementioned methods, the asymptotic properties are established, and their superb finite sample performances are demonstrated through extensive numerical studies as well as a real data analysis. In addition, existing estimators of the central mean space have uneven performances across different types of link functions. To address this limitation, a new hybrid SDR estimator is proposed that successfully recovers the central mean space for a wide range of link functions. Based on the new hybrid estimator, we further study the order determination procedure and the marginal coordinate test. The superior performance of the hybrid estimator over existing methods is demonstrated in simulation studies. Note that the proposed procedures dealing with the missing response at random can be simply adapted to this hybrid method. / Statistics

Experimental design is a powerful tool for gathering highly informative observations using a small number of experiments. The demand for smart data collection strategies is increasing due to the need to save time and budget, especially in online experiments and machine learning. However, the traditional experimental design method falls short in systematically assessing changing variables' effects. Specifically within Artificial Intelligence (AI), the challenge lies in assessing the impacts of model structures and training strategies on task performances with a limited number of trials. This shortfall underscores the necessity for the development of novel approaches. On the other side, the optimal design criterion has typically been model-based in classic design literature, which leads to restricting the flexibility of experimental design strategies. However, machine learning's inherent flexibility can empower the estimation of metrics efficiently using nonparametric and optimization techniques, thereby broadening the horizons of experimental design possibilities. In this dissertation, the aim is to develop a set of novel methods to bridge the merits between these two domains: 1) applying ideas from statistical experimental design to enhance data efficiency in machine learning, and 2) leveraging powerful deep neural networks to optimize experimental design strategies. This dissertation consists of 5 chapters. Chapter 1 provides a general introduction to mutual information, fractional factorial design, hyper-parameter tuning, multi-modality, etc. In Chapter 2, I propose a new mutual information estimator FLO by integrating techniques from variational inference (VAE), contrastive learning, and convex optimization. I apply FLO to broad data science applications, such as efficient data collection, transfer learning, fair learning, etc. Chapter 3 introduces a new design strategy called multi-layer sliced design (MLSD) with the application of AI assurance. It focuses on exploring the effects of hyper-parameters under different models and optimization strategies. Chapter 4 investigates classic vision challenges via multimodal large language models by implicitly optimizing mutual information and thoroughly exploring training strategies. Chapter 5 concludes this proposal and discusses several future research topics. / Doctor of Philosophy / In the digital age, artificial intelligence (AI) is reshaping our interactions with technology through advanced machine learning models. These models are complex, often opaque mechanisms that present challenges in understanding their inner workings. This complexity necessitates numerous experiments with different settings to optimize performance, which can be costly. Consequently, it is crucial to strategically evaluate the effects of various strategies on task performance using a limited number of trials. The Design of Experiments (DoE) offers invaluable techniques for investigating and understanding these complex systems efficiently. Moreover, integrating machine learning models can further enhance the DoE. Traditionally, experimental designs pre-specify a model and focus on finding the best strategies for experimentation. This assumption can restrict the adaptability and applicability of experimental designs. However, the inherent flexibility of machine learning models can enhance the capabilities of DoE, unlocking new possibilities for efficiently optimizing experimental strategies through an information-centric approach. Moreover, the information-based method can also be beneficial in other AI applications, including self-supervised learning, fair learning, transfer learning, etc. The research presented in this dissertation aims to bridge machine learning and experimental design, offering new insights and methodologies that benefit both AI techniques and DoE.