In this thesis, the learning of probabilistic relationships between stimulus-action pairs is investigated under the probability learning paradigm. The effect of reward is investigated in the first three experiments. Additionally, the effect of context and uncertainty is investigated in the second and third experiments, respectively. The fourth experiment is the replication of the second experiment with a group of Parkinson patients where the effect of dopamine medication on probability learning is studied. In Experiment 1, we replicate the classical probability learning task by comparing monetary and non-monetary reward feedback. Probability learning behavior is observed in both monetary and non-monetary rewarding feedback conditions. However, no significant difference between the monetary and non-monetary feedback conditions is observed. In Experiment 2, a variation of the probability learning task which includes irrelevant contextual information is applied. Probability learning behavior is observed, and a significant effect is found between monetary and non-monetary feedback conditions. In Experiment 3 / a probability learning task similar to that in Experiment 2 is applied, however, in this experiment, stimulus included relevant contextual information. As expected, due to the utilization of the relevant contextual information from the start of the experiment, no significant effect is found for probability learning behavior. The effect of uncertainty observed in this experiment is a replication of the reports in literature. Experiment 4 is identical to Experiment 2 / except that the subject population is a group of dopamine medicated Parkinson patients and a group of age matched controls. This experiment is introduced to test the suggestions in the literature regarding the enhancement effect of dopamine medication in probability learning based on positive feedback conditions. In Experiment 4, probability learning behavior is observed in both groups, but the difference in learning performance between Parkinson patients and controls was not significant, probably due to the low number of subject recruited in the experiment. In addition to these investigations, learning mechanisms are also examined in Experiments 1 and 4. Our results indicate that subjects initially search for patterns which lead to probability learning. At the end of Experiments 1 and 4, upon learning the winning frequencies, subjects change their behavior and demonstrate maximization behavior, which makes them prefer continuously one option over the other.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [147]-158).

Thesis (M.C.S.)--Carleton University, 2001. / Includes bibliographical references (p. 111-113). Also available in electronic format on the Internet.

In this work, we will present several contributions towards automatic recognition of BSL signs from continuous signing video sequences. Specifically, we will address three main points: (i) automatic detection and tracking of the hands using a generative model of the image; (ii) automatic learning of signs from TV broadcasts using the supervisory information available from subtitles; and (iii) generalisation given sign examples from one signer to recognition of signs from different signers. Our source material consists of many hours of video with continuous signing and corresponding subtitles recorded from BBC digital television. This is very challenging material for a number of reasons, including self-occlusions of the signer, self-shadowing, blur due to the speed of motion, and in particular the changing background. Knowledge of the hand position and hand shape is a pre-requisite for automatic sign language recognition. We cast the problem of detecting and tracking the hands as inference in a generative model of the image, and propose a complete model which accounts for the positions and self-occlusions of the arms. Reasonable configurations are obtained by efficiently sampling from a pictorial structure proposal distribution. The results using our method exceed the state-of-the-art for the length and stability of continuous limb tracking. Previous research in sign language recognition has typically required manual training data to be generated for each sign, e.g. a signer performing each sign in controlled conditions - a time-consuming and expensive procedure. We show that for a given signer, a large number of BSL signs can be learned automatically from TV broadcasts using the supervisory information available from subtitles broadcast simultaneously with the signing. We achieve this by modelling the problem as one of multiple instance learning. In this way we are able to extract the sign of interest from hours of signing footage, despite the very weak and "noisy" supervision from the subtitles. Lastly, we show that automatic recognition of signs can be extended to multiple signers. Using automatically extracted examples from a single signer, we train discriminative classifiers and show that these can successfully classify and localise signs in new signers. This demonstrates that the descriptor we extract for each frame (i.e. hand position, hand shape, and hand orientation) generalises between different signers.

<p>Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this, firstly, data on antimicrobial peptides is extracted from UniProt, manually curated and stored into a centralized database called dragon antimicrobial peptide database (DAMPD). Secondly, based on the curated data, models to predict antimicrobial peptides are created using support vector machine with optimized hyperparameters. In particular, global optimization methods such as grid search, pattern search and derivative-free methods are utilised to optimize the SVM hyperparameters. These models are useful in characterizing unknown antimicrobial peptides. Finally, a webserver is created that will be used to predict antimicrobial peptides in haemotophagous insects such as Glossina morsitan and Anopheles gambiae.</p>

<p>Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this, firstly, data on antimicrobial peptides is extracted from UniProt, manually curated and stored into a centralized database called dragon antimicrobial peptide database (DAMPD). Secondly, based on the curated data, models to predict antimicrobial peptides are created using support vector machine with optimized hyperparameters. In particular, global optimization methods such as grid search, pattern search and derivative-free methods are utilised to optimize the SVM hyperparameters. These models are useful in characterizing unknown antimicrobial peptides. Finally, a webserver is created that will be used to predict antimicrobial peptides in haemotophagous insects such as Glossina morsitan and Anopheles gambiae.</p>

Zugl.: Bern, Univ., Diss., 2005

中輟，一直是近年來被廣為討論的議題，特別是在台灣屬於少數民族的原住民，其就學的學生常因為許多不利的因素，造成其學業成就低落。因而原民學生中輟率，和漢人比較起來，比率較偏高。在台灣，礙於我國對義務教育的定義，對於原住民大專學生中輟的研究更是有限，並且多以如何促使原住民學生融入學校體制中的學習問題為主。類似的研究總是試圖引導原住民學生回歸教育主軸，因此筆者欲尋部落中與學習相關的生長過程，企圖追尋學生在部落中的學習價值、動機、內涵與教育體制所認定的學習兩者間落差為何。 / 部落學習的環境中，包括家庭教育、同儕學習、部落環境、社會環境。然而部落傳統學習方式下，學生學習習慣的養成過程及偏好，卻在一般研究其教育環節中隱而未現。筆者欲藉此論文看見以家庭教育、部落教育為主的中輟生生活面貌，企圖了解部落學習的樣貌。而這樣的學習是無法用簡單的因果導向為論文內容，因此本文切入角度十分多元由部落經濟、家庭教育、同儕關係、傳統文化學習、宗教信仰等面向述及，欲意探究碧候村原住民中輟生的輟學因素。本研究以歸因理論為架構，以質性研究為取向，採用半結構性訪談、深度訪談等研究方法，探討碧候部落原住民中輟生對自己輟學的歸因歷程。 / 並認為傳統學習與學校學習的落差，來自教科書內容，而其中隱而未現的是主流價值觀替代部落中實用導向的學習價值，教材中並早已決定何未有價值的知識，這與原住民從小生活價值學習取向相異甚鉅。以及學校同儕間與部落生長環境不同的人際互動，部落孩子的直言，與戲謔遊戲卻常引發學校體系師生對立情況，部落所慣於展示的群體力量，被看成是擾亂學校風紀挑戰師長權威的矩動。部落裡，以實用性為主的學習學習動機不同於漢人社會教科書所教導的取向，並且從小對自然理解、對生活需求的認知都是來自部落智慧而非書本式的課外讀物，自然而然對閱讀並不感到興趣。而這一切都是在做中學，並非像教科書先給予我們預防性的知識，這都是與部落學習差異處。 / 部落與補習班的距離，以及家中所可以提供的資源性，甚至部落的師資再再都展現部落學生在面對城鄉差距間學生的弱勢出外就讀後，人際相處模式或課業教與學的不同，所需獨立面對、承擔，甚至改變的各種習慣都是需要長時間調適的，並不若外界想像的容易，而這些弱勢還層遞著原住民世世代代對gaga生活規範的價值觀，何謂人生的價值，因此才造就中輟。 / 沒有祭典的部落，gaga的概念由傳統價值觀濡化基督宗教價值觀，例如，男女關係的保守，以及努力遵守gaga的人可以通過彩虹橋（今為基督教天堂），使得他們不想汲汲營營於書中的智慧，反而以遵守傳統規範為生活要點。這是源於過去歷史故事的集體記憶。而過去對人生的價值觀是人雖勤勞工作，但究竟不是主宰，一切耕耘的成果猶待神的賜予，所以在盡本份後，認為人事盡矣，其他的就只有期待神的裁判了。對部落社會和宇宙觀的典範都需遵守，尤其是兩性間更有嚴格的行為準則，對這些典範和準則如有違犯，也就是迫害整個群體的制序，違害全體安全，因此遵守人與人之間的一切準則，是其重要價值觀念所在。 / 這樣種種的價值觀，都是與社會大眾價值觀不同的。因此如何看見部落價值與主流價值的不同，進而幫助原住民學生可以肯定自己生活環境中，所賦予的價值觀，並在主流社會中自信的成長，是本論文的研究目的。筆者並企圖理清原住民之所以是原住民，不單單是因為血緣，更重要的是代代相傳的價值觀，這樣的價值觀來自家庭、同儕及部落。本篇論文所寄望的是幫助學校師長，連結原住民中輟原因的起源，進而看見多元文化的世界觀。 / 本研究已中輟學生訪談為主，也嘗試經由訪談部落學生的老師、家長和同學，以及部落中的耆老，企圖聯結受訪者對孩子的成敗歸因與中輟生的自我歸因之間的關係，以大致推論碧候部落原住民學生中途輟學的原因。研究的結果將可供關心原住民學童教育之家長、學校老師及教育單位參考。 / Over the past years, a lot of discussions have specially targeted on aboriginal dropouts. Because of many unfavorable factors, aboriginal students have poor academic performances. Compared with Han students, they are much more likely to drop out of school. However, few papers focus on aboriginal college dropouts. Such essays simply argue how to resolve their learning problems and call them back to the mainstream education system. I think otherwise. In their tribes, aboriginal students are cultivated and affected by its surroundings and its norms. They are influential but invisible in the learning process. What matters for aboriginal students is not only the genealogy but also the values passed down from generation to generation. In working on my paper, I aim to find out the relationship between what aboriginal students learn from their tribes and learning models of the current education system. My thesis takes dropouts of Pi-hou Tribe as an example and investigates such factors as tribal economy, family education, peer relationships, religious beliefs, and traditions learning. My study builds upon attribution theory, orients toward qualitative research, and adopts semi structure and in-depth interviews so as to explore Pi-hou-Tribe case. Finally, this paper can serve as a reference for those who are concerned with aboriginal dropouts in view of schooling education and as a starting point for further studies on related issues.

This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.