Fourier Transform Infrared (FTIR) microscopy is a sensitive method for detecting difference in the morphology of biological cells. In this study FTIR spectra were obtained for uninfected cells, and cells infected with two different viruses. The spectra obtained are difficult to discriminate visually. Here we apply advanced statistical methods to the analysis of the spectra, to test if such spectra are useful for diagnosing viral infections in cells. Logistic Regression (LR) and Partial Least Squares Regression (PLSR) were used to build models which allow us to diagnose if spectral differences are related to infection state of the cells. A three-fold, balanced cross-validation method was applied to estimate the shrinkages of the area under the receiving operator characteristic curve (AUC), and specificities at sensitivities of 95%, 90% and 80%. AUC, sensitivity and specificity were used to gauge the goodness of the discrimination methods. Our statistical results shows that the spectra associated with different cellular states are very effectively discriminated. We also find that the overall performance of PLSR is better than that of LR, especially for new data validation. Our analysis supports the idea that FTIR microscopy is a useful tool for detection of viral infections in biological cells.

Fourier transform infrared (FTIR) spectroscopy, one method of electromagnetic radiation for detecting specific cellular molecular structure, can be used to discriminate different types of cells. The objective is to find the minimum time (choice among 2 hour, 4 hour and 6 hour) to record FTIR readings such that different viruses can be discriminated. A new method is adopted for the datasets. Briefly, inner differences are created as the control group, and Wilcoxon Signed Rank Test is used as the first selecting variable procedure in order to prepare the next stage of discrimination. In the second stage we propose either partial least squares (PLS) method or simply taking significant differences as the discriminator. Finally, k-fold cross-validation method is used to estimate the shrinkages of the goodness measures, such as sensitivity, specificity and area under the ROC curve (AUC). There is no doubt in our mind 6 hour is enough for discriminating mock from Hsv1, and Coxsackie viruses. Adeno virus is an exception.

Cette étude montre que le domaine para-autochtone est, en fait, un ensemble structuralement hétérogène, une juxtaposition d'unités septentrionales (zone axiale) et d'unités méridionales (nappes du versant sud). En ce qui concerne la couche minéralisée de la mine de Salsigne, l'hypothèse d'une minéralisation stratiforme volcano-sédimentaire exhalative est mise en doute, elle s'intègrerait plutôt a l'ensemble des minéralisations filoniennes du district de Salsigne.

Ensembles are today routinely applied to estimate uncertainty in numerical predictions of complex systems such as the weather. Instead of initializing a single numerical forecast, using only the best guess of the present state as initial conditions, a collection (an ensemble) of forecasts whose members start from slightly different initial conditions is calculated. By varying the initial conditions within their error bars, the sensitivity of the resulting forecasts to these measurement errors can be accounted for. The ensemble approach can also be applied to estimate forecast errors that are due to insufficiently known model parameters by varying these parameters between ensemble members. An important (and difficult) question in ensemble weather forecasting is how well does an ensemble of forecasts reproduce the actual forecast uncertainty. A widely used criterion to assess the quality of forecast ensembles is statistical consistency which demands that the ensemble members and the corresponding measurement (the ``verification\'\') behave like random independent draws from the same underlying probability distribution. Since this forecast distribution is generally unknown, such an analysis is nontrivial. An established criterion to assess statistical consistency of a historical archive of scalar ensembles and verifications is uniformity of the verification rank: If the verification falls between the (k-1)-st and k-th largest ensemble member it is said to have rank k. Statistical consistency implies that the average frequency of occurrence should be the same for each rank. A central result of the present thesis is that, in a statistically consistent K-member ensemble, the (K+1)-dimensional vector of rank probabilities is a random vector that is uniformly distributed on the K-dimensional probability simplex. This behavior is universal for all possible forecast distributions. It thus provides a way to describe forecast ensembles in a nonparametric way, without making any assumptions about the statistical behavior of the ensemble data. The physical details of the forecast model are eliminated, and the notion of statistical consistency is captured in an elementary way. Two applications of this result to ensemble analysis are presented. Ensemble stratification, the partitioning of an archive of ensemble forecasts into subsets using a discriminating criterion, is considered in the light of the above result. It is shown that certain stratification criteria can make the individual subsets of ensembles appear statistically inconsistent, even though the unstratified ensemble is statistically consistent. This effect is explained by considering statistical fluctuations of rank probabilities. A new hypothesis test is developed to assess statistical consistency of stratified ensembles while taking these potentially misleading stratification effects into account. The distribution of rank probabilities is further used to study the predictability of outliers, which are defined as events where the verification falls outside the range of the ensemble, being either smaller than the smallest, or larger than the largest ensemble member. It is shown that these events are better predictable than by a naive benchmark prediction, which unconditionally issues the average outlier frequency of 2/(K+1) as a forecast. Predictability of outlier events, quantified in terms of probabilistic skill scores and receiver operating characteristics (ROC), is shown to be universal in a hypothetical forecast ensemble. An empirical study shows that in an operational temperature forecast ensemble, outliers are likewise predictable, and that the corresponding predictability measures agree with the analytically calculated ones.

南海海域以往僅不過是來往商船的航路與漁船作業的漁場，在1960 年代末期，該區域被認定蘊藏豐富的資源後，南海周邊國家開始爭相佔領附近島礁。再加上南海是某些國家戰略物資之必經航路，南海頓時轉變為重要的戰略要域，因多方國家經濟利益交錯複雜的牽扯下，地區亦時有摩擦事件發生，南海從此成為亞太地區的熱點。 外交是內政的延續，外交政策應在維護或爭取國家利益的先決條件下制定，並主導國家整體對外的作為，謀求國家在國際地位中的生存與發展。南海問題涉及國家事務，亦屬外交政策項下之一環，所以中國大陸南海政策，亦受其外交政策指導。中國大陸因為經濟力的發展，帶動了軍事力的茁壯，而使其遠在南海海域的諸島主權得以有軍事力量予以維護。也因其在南海地區的軍力強化，造成南海聲索國 普遍的不安。相對之下，各聲索國亦增購軍備，加強南海防務。再加上美國與東協組織的介入，使南海地區各方較勁的意味濃厚。 當南海各聲索國增購軍備，加強維護其南海所佔島礁主權時，同是南海聲索國的中華民國，雖對於軍事武力的建置從未懈怠，只不過所增強的軍事武力均建置於台澎金馬區域，而對於東沙群島的東沙島及南沙群島的太平島而言，中華民國是不是應當有完善的南海軍事戰略，足以維護其安全。 / The area of the South China Sea was route of the merchant ships coming and going and fishing ground for the fishing boats in the past. In the late 1960s, after this area was found with abundant resources, the surrounding countries of the South China Sea began to occupy the nearby islands and reefs. In addition, the South China Sea is the passage of some countries’ strategic materials; hence, the South China Sea tends to be an important strategic point. Regional frictions occur under the conflict of interests involving a number of surrounding countries. This is why the South China Sea becomes the flash point of Asian-Pacific area. Diplomacy is the continuity of the internal affairs. The foreign policy should be made under the preconditions of maintaining or striving for the interests of the State as well as the guideline for the State，s foreign affairs , seeking the State，s survival and development in the international arena. The issue of the South China Sea involves the national affairs, also affected under the foreign policy, so the policy of the South China Sea should be guided by their individual foreign policies. Mainland China，s military power is supported by its economic growth and cause an uneasy atmosphere for claimant in the area. As each claimant purchases arms and equipments along with the involvement of the U.S. and Association of South-east Asian Nations, the South China Sea becomes the hub of tension. When every claimant of the South China Sea purchases the arms, strengthening to safeguard the islands and reefs sovereign right in the South China Sea, Republic of China , one of the claimants of the South China Sea, although the building of the military power has never been stopped, but the focus has been placed only in Taiwan、Penghu、Kinmen and Matsu area. For Dongsha islands and Taiping island, whether the government of Republic of China should build a complete strategy of the South China Sea to maintain its security remains debatable.

Abstract Malignancy Associated Changes are subtle alterations in the morphology and nuclear texture of cells in the vicinity of a malignant lesion. The phenomenon was first described in 1959, and has been the subject of considerable research in the four intervening decades, due to its potential utility to cancer screening programs. In this thesis the history of research into malignancy associated changes is reviewed, and the major findings of previous workers summarized. Original work aimed at improving the accuracy of classification of Pap smear slides is described in detail. A novel algorithm, which incorporates a genetic algorithm for feature selection and training of a neural network, is described. The algorithm was tested upon a large artificial dataset consisting of points from nested spheres in multiple dimensions. It was able to select the most discriminatory features and classify data with 99% accuracy on 80% of runs for two dimensional data, and on 90% of runs for three-dimensional data. The algorithm was also tested on two real data sets from the UCI Machine Learning Repository, the “sonar” data and the “ionosphere” data. On both of these datasets the algorithm produced a classifier using a subset of features which performed as well as previously reported classifiers using the full feature set. This algorithm was then tested on a large dataset of cell images, and its performance compared with that of the standard stepwise linear discriminant analysis approach. Both of these approaches produced similar results, which are comparable to those of previous workers in this field. Interestingly, runs of the genetic algorithm with different random number seeds tended to select different feature subsets, which produced approximately equivalent performance. This finding indicates that amongst the features used, which were selected from those previously identified in the literature as useful for MACs detection, many subsets exist which are equally discriminatory.

The basic problem of speech is a complete separation of the natural noise which arise from correct articulation of voiced and unvoiced consonants from noise and disturbance environment. Objective of this master’s thesis is to find an effective method that could identify the pauses without speech activity, which can identify the properties of noise and disturbance. Once the noise is correctly identified, it is already possible to use different methods for its removal. The master’s thesis describes two methods of pauses identification. These methods are programmed in Matlab and tested on nine speech recordings. Methods analysis of the results was performed using the ROC (Receiver Operating Characteristic) curves. In the end are summarized results analysis of created methods.

Ensembles are today routinely applied to estimate uncertainty in numerical predictions of complex systems such as the weather. Instead of initializing a single numerical forecast, using only the best guess of the present state as initial conditions, a collection (an ensemble) of forecasts whose members start from slightly different initial conditions is calculated. By varying the initial conditions within their error bars, the sensitivity of the resulting forecasts to these measurement errors can be accounted for. The ensemble approach can also be applied to estimate forecast errors that are due to insufficiently known model parameters by varying these parameters between ensemble members. An important (and difficult) question in ensemble weather forecasting is how well does an ensemble of forecasts reproduce the actual forecast uncertainty. A widely used criterion to assess the quality of forecast ensembles is statistical consistency which demands that the ensemble members and the corresponding measurement (the ``verification\'\') behave like random independent draws from the same underlying probability distribution. Since this forecast distribution is generally unknown, such an analysis is nontrivial. An established criterion to assess statistical consistency of a historical archive of scalar ensembles and verifications is uniformity of the verification rank: If the verification falls between the (k-1)-st and k-th largest ensemble member it is said to have rank k. Statistical consistency implies that the average frequency of occurrence should be the same for each rank. A central result of the present thesis is that, in a statistically consistent K-member ensemble, the (K+1)-dimensional vector of rank probabilities is a random vector that is uniformly distributed on the K-dimensional probability simplex. This behavior is universal for all possible forecast distributions. It thus provides a way to describe forecast ensembles in a nonparametric way, without making any assumptions about the statistical behavior of the ensemble data. The physical details of the forecast model are eliminated, and the notion of statistical consistency is captured in an elementary way. Two applications of this result to ensemble analysis are presented. Ensemble stratification, the partitioning of an archive of ensemble forecasts into subsets using a discriminating criterion, is considered in the light of the above result. It is shown that certain stratification criteria can make the individual subsets of ensembles appear statistically inconsistent, even though the unstratified ensemble is statistically consistent. This effect is explained by considering statistical fluctuations of rank probabilities. A new hypothesis test is developed to assess statistical consistency of stratified ensembles while taking these potentially misleading stratification effects into account. The distribution of rank probabilities is further used to study the predictability of outliers, which are defined as events where the verification falls outside the range of the ensemble, being either smaller than the smallest, or larger than the largest ensemble member. It is shown that these events are better predictable than by a naive benchmark prediction, which unconditionally issues the average outlier frequency of 2/(K+1) as a forecast. Predictability of outlier events, quantified in terms of probabilistic skill scores and receiver operating characteristics (ROC), is shown to be universal in a hypothetical forecast ensemble. An empirical study shows that in an operational temperature forecast ensemble, outliers are likewise predictable, and that the corresponding predictability measures agree with the analytically calculated ones.

A child dies of pneumonia every 39 seconds, and the process of preventing deaths caused by pneumonia has been considerably slower compared to other infectious diseases. Meanwhile, the traditional method of manually diagnosing patients has reached its ceiling on performance. With the support of a machine learning classification algorithm to help with the screening of pneumonia from x-ray images combined with the expertise of a physician, the identification and diagnosis of pediatric pneumonia should be both quicker and more accurate. In this study, four different types of supervised machine learning algorithms have been trained, tested, and evaluated to see which model could predict most accurately whether a patient in an x-ray image has pneumonia or not. The four models included in this study have been trained by four different supervised machine learning algorithms: logistic regression, k-nearest-neighbor, support vector machine, and neural network. The results show that KNN has the highest sensitivity, NN adapts to new data the best by not being under- or overfit. SVM had the highest balanced accuracy on both train and test data but a proportionally high difference between the in- and out-sample error. In conclusion, relatively high performance can be achieved when classifying x-ray images of pneumonia even with limited resources.

This thesis aims to investigate different machine learning (ML) models and their performance to find the best performing model to predict credit risk at a specific company. Since granting credit to corporate customers is a part of this company's core business, managing the credit risk is of high importance. The company has of today only one credit risk measurement, which is obtained through an external company, and the goal is to find a model that outperforms this measurement.     The study consists of two ML models, Logistic Regression (LR) and eXtreme Gradient Boosting. This thesis proves that both methods perform better than the external risk measurement and the LR method achieves the overall best performance. One of the most important analyses done in this thesis was handling the dataset and finding the best-suited combination of features that the ML models should use.