Patients with category-specific agnosia (CSA) of the biological type have a disproportionate deficit in recognizing objects from biological categories. Bukach et al. (in press) have shown that a similar pattern of category specificity (CS) arises in normal subjects due to the interaction of structural and conceptual knowledge in the episodic retrieval of object knowledge. The current set of studies extends these findings in two ways: The first series of 4 experiments uses the newly learned attribute recall developed by Bukach et al. to investigate CS in the verbal modality. When word reading is mediated by meaning, recall of newly learned attributes assessed in the verbal modality showed a CS pattern, just as it does in patients with CSA of the biological type.. The second serie.s of 3 experiments examines recognition of object form and the nature of structural similarity by using novel stimuli that vary in the number of structural dimensions that are required to uniquely identify an object. I demonstrate that structural similarity can be understood as the proximity of exemplars in a multidimensional space defined by the diagnostic structural features that have been integrated in the current task. Competition of retrieved episodes based on their structural similarity comes from 2 sources: When the values of diagnostic dimensions are poorly specified, errors reflect competition from exemplars that are close (dimensional proximity). When an insufficient number of diagnostic dimensions are integrated, errors reflect competition from exemplars that share values on diagnostic dimensions (dimensionuZpaucity). I also present preliminary evidence that conceptual relatedness modulates the structural integration process. These results are related to CSA of the biological type, and are discussed in terms of an episodic model of object recognition in which object information is retrieved and integrated from distributed episodic memories.

Le mélanome est le cancer de la peau le plus rare, mais celui qui cause le plus de mortalité. L’étapecritique de sa progression est l’angiogenèse, processus détourné par la tumeur pour s’oxygéner et senourrir. Les cellules tumorales peuvent alors former des métastases dans les ganglions lymphatiques,notamment. D’autre part, la tumeur inhibe les réponses immunitaires de l’hôte à son encontre. Tousces mécanismes font intervenir le monoxyde d’azote (NO ). Le but de ce projet a été d’approfondir lerôle du NO dans l’angiogenèse et le recrutement leucocytaire, mécanismes basés sur lareconnaissance cellulaire de l’endothélium.Nous avons montré que le NO est nécessaire à l’angiogenèse, mais que des doses plus fortes sontanti-angiogéniques. Cet effet inhibiteur peut s’expliquer par la diminution des interactions cellules-cellules,et l’inhibition de l’expression de PECAM-1/CD31, principalement.D’autre part, nous avons établi que l’adhésion des leucocytes est inhibée par le NO ce qui a été reliéà la modulation de l’expression des molécules capables de fixer des chimiokines : lesglycosaminoglycannes et les récepteurs de chimiokines, ainsi qu’à la sous-expression des moléculesd’adhésion CD34, ICAM-2/CD102 et VCAM-1/CD106.En conclusion, le NO est capable de réguler des mécanismes cellulaires majeurs de la progressiontumorale, par modulation de l’expression des molécules de surface de l’endothélium. Tout au long dece travail, nous avons observé que les différences de réponse sont dépendantes des doses de NO etdu type cellulaire, ce qui démontre le rôle pivot du NO dans la progression du cancer. / Melanoma is the rarest skin cancer, but one that causes the most of deaths. The critical step of itsprogression is angiogenesis, a physiological tumor-activated process which allows the delivery ofoxygen and nutrients. Tumor cells may then metastasize to the lymph nodes, in particular. Moreover,the tumor inhibits the host immune responses toward itself. All these mechanisms are regulated bynitric oxide (NO ). The aim of the project was to deepen the role of NO in angiogenesis andleukocyte recruitment, two mechanisms based on endothelial cell recognition.We have shown here that NO is necessary for angiogenesis, but that high concentrations are antiangiogenic.This inhibitory effect of NO can be attributed to the decrease of cell-cell interactions andthe inhibition of the PECAM-1/CD31 expression, mainly.Besides, we have demonstrated that leukocyte adhesion on endothelium is inhibited by NO . This canbe explained by the modulation of the expression of molecules able to bind to chemokines:glycosaminoglycans and chemokine receptors, as well as by the down-regulation of the adhesionmolecules CD34 and ICAM-2/CD102 VCAM-1/CD106.In conclusion, NO is able to regulate the main cellular mechanisms of tumor progression bymodulating the expression of surface molecules on endothelial cells. Throughout this work, we haveobserved that these modulations depend on NO concentrations and on the cell type, demonstratingthe pivotal role of NO in cancer progression.

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The numerous surveillance videos recorded by a single stationary wide-angle-view camera persuade the use of a moving point as the representation of each small-size object in wide video scene. The sequence of the positions of each moving point can be used to generate a trajectory containing both spatial and temporal information of object's movement. In this study, we investigate how the relationship between two trajectories can be used to recognize multi-agent interactions. For this purpose, we present a simple set of qualitative atomic disjoint trajectory-segment relations which can be utilized to represent the relationships between two trajectories. Given a pair of adjacent concurrent trajectories, we segment the trajectory pair to get the ordered sequence of related trajectory-segments. Each pair of corresponding trajectory-segments then is assigned a token associated with the trajectory-segment relation, which leads to the generation of a string called a pairwise trajectory-segment relationship sequence. From a group of pairwise trajectory-segment relationship sequences, we utilize an unsupervised learning algorithm, particularly the k-medians clustering, to detect interesting patterns that can be used to classify lower-level multi-agent activities. We evaluate the effectiveness of the proposed approach by comparing the activity classes predicted by our method to the actual classes from the ground-truth set obtained using the crowdsourcing technique. The results show that the relationships between a pair of trajectories can signify the low-level multi-agent activities.

M.Com. (Informatics) / Please refer to full text to view abstract

M.Com. / Please refer to full text to view abstract

This dissertation deals with current political immigration debates, the conversations about the philosophical concept of recognition, and intercultural encounters in contemporary German literature. By reading contemporary literature in connection with philosophical, psychological, and theoretical works, new problem areas of the liberal promise of recognition become visible. Tied to assumptions of cultural essentialism, language use, and prejudice, one of the main findings of this work is how the recognition process is closely tied to narrative. Particularly within developmental psychology it is often argued that we learn and come to terms with ourselves through narrative. The chosen literary encounters written by Alev Tekinay, Emine Sevgi Özdamar, Maxim Biller, Rafael Seligmann, and Finn-Ole Heinrich magnify this particular human experience on an aesthetic level and dismantle “mechanisms of recognition,” particularly three aspects illustrating the recognition process: the role of the narrator and his or her description of the characters, the construction of family bonds within the texts, and the linguistic and cultural practice of naming with all of its connotations. Within the chosen texts there is no unified depiction of the recognition process, but rather the texts elucidate a multidimensionality of this concept, tying it closely to the political, social, and aesthetic sphere. In this context the analysis brings to light that the notion of “authenticity” crucially informs recognition as well as the circumstances of a power imbalance that dominates the process. My analysis shows that contrary to popular assumptions in philosophical and political debates, the concept of recognition turns out to be rather limiting instead of liberating.

Humans are well suited to the reading of textual information, but unfortunately it has not yet been possible to develop a machine to emulate this form of human behaviour. In the past, machines have been characterised by having static forms of specific knowledge necessary for character recognition. The resulting form of reading behaviour is most uncharacteristic of the way humans perceive textual information. The major problem with handprinted character recognition is the infinite variability in the character shapes and the ambiguities many of these shapes exhibit. Human perception of handprinted characters makes extensive use of "world knowledge" to remove such ambiguities. Humans are also continually modifying their world knowledge to further enhance their reading behaviour by acquiring new knowledge as they read. An information processing model for perception and learning of handprinted characters is proposed. The function of the model is to enable ambiguous character descriptions to converge to single character classifications. The accuracy of this convergence improves with reading experience on handprinted text. The model consists of three compon,ent parts. Firstly, a character classifier to recognise character patterns. These patterns may be both distorted anq noisy, where distortion is defined to be a consistent variability from known archetypical character descriptions and noise as a random inconsistent variability in character shape. Secondly, a perceptive mechanism that makes inferences from an incomplete linguistic world model of an author or of a specific domain of discourse from many authors. Finally, a incremental learning capability is integrated into the character classifier and perceptive mechanisms. This is to enable the internal world model to be continually adaptive to either changes in the domain of discourse or to different authors. A demonstrator is described, together with a summary of experimental results that clearly show the improvement in machine perception which results from continuous incremental learning.

Yau Pui Yuk. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 147-152). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Speech Recognition --- p.2 / Chapter 1.2.1 --- How Speech Recognition Works --- p.3 / Chapter 1.2.2 --- Types of Speech Recognition Tasks --- p.4 / Chapter 1.2.3 --- Variabilities in Speech 一 a Challenge for Speech Recog- nition --- p.6 / Chapter 1.3 --- Performance Prediction of Speech Recognition Task --- p.7 / Chapter 1.4 --- Thesis Goals --- p.9 / Chapter 1.5 --- Thesis Organization --- p.10 / Chapter 2 --- Background --- p.11 / Chapter 2.1 --- The Acoustic-phonetic Approach --- p.12 / Chapter 2.1.1 --- Prediction based on the Degree of Mismatch --- p.12 / Chapter 2.1.2 --- Prediction based on Acoustic Similarity --- p.13 / Chapter 2.1.3 --- Prediction based on Between-Word Distance --- p.14 / Chapter 2.2 --- The Lexical Approach --- p.16 / Chapter 2.2.1 --- Perplexity --- p.16 / Chapter 2.2.2 --- SMR-perplexity --- p.17 / Chapter 2.3 --- The Combined Acoustic-phonetic and Lexical Approach --- p.18 / Chapter 2.3.1 --- Speech Decoder Entropy (SDE) --- p.19 / Chapter 2.3.2 --- Ideal Speech Decoding Difficulty (ISDD) --- p.20 / Chapter 2.4 --- Chapter Summary --- p.23 / Chapter 3 --- Components for Predicting the Performance of Speech Recog- nition Task --- p.24 / Chapter 3.1 --- Components of Speech Recognizer --- p.25 / Chapter 3.2 --- Word Similarity Measure --- p.27 / Chapter 3.2.1 --- Universal Phoneme Symbol (UPS) --- p.30 / Chapter 3.2.2 --- Definition of Phonetic Distance --- p.31 / Chapter 3.2.3 --- Definition of Word Pair Phonetic Distance --- p.45 / Chapter 3.2.4 --- Definition of Word Similarity Measure --- p.47 / Chapter 3.3 --- Word Occurrence Measure --- p.62 / Chapter 3.4 --- Chapter Summary --- p.64 / Chapter 4 --- Formulation of Recognition Error Predictive Index (REPI) --- p.65 / Chapter 4.1 --- Formulation of Recognition Error Predictive Index (REPI) --- p.66 / Chapter 4.2 --- Characteristics of Recognition Error Predictive Index (REPI) --- p.74 / Chapter 4.2.1 --- Weakness of Ideal Speech Decoding Difficulty (ISDD) --- p.75 / Chapter 4.2.2 --- Advantages of Recognition Error Predictive Index (REPI) --- p.79 / Chapter 4.3 --- Chapter Summary --- p.82 / Chapter 5 --- Experimental Design and Setup --- p.83 / Chapter 5.1 --- Objectives --- p.83 / Chapter 5.2 --- Experiments Preparation --- p.84 / Chapter 5.2.1 --- Speech Corpus and Speech Recognizers --- p.85 / Chapter 5.2.2 --- Speech Recognition Tasks --- p.93 / Chapter 5.2.3 --- Evaluation Criterion --- p.98 / Chapter 5.3 --- Experiment Categories and their Setup --- p.99 / Chapter 5.3.1 --- Experiment Category 1 一 Investigating and comparing the overall prediction performance of the two predictive indices --- p.102 / Chapter 5.3.2 --- Experiment Category 2 一 Comparing the applicability of the word similarity measures of the two predictive indices on predicting the recognition performance --- p.104 / Chapter 5.3.3 --- Experiment Category 3 - Comparing the applicability of the formulation method of the two predictive indices on predicting the recognition performance --- p.107 / Chapter 5.3.4 --- Experiment Category 4 一 Comparing the performance of different phonetic distance definitions --- p.109 / Chapter 5.4 --- Chapter Summary --- p.111 / Chapter 6 --- Experimental Results and Analysis --- p.112 / Chapter 6.1 --- Experimental Results and Analysis --- p.113 / Chapter 6.1.1 --- Experiment Category 1 - Investigating and comparing the overall prediction performance of the two predictive indices --- p.113 / Chapter 6.1.2 --- Experiment Category 2- Comparing the applicability of the word similarity measures of the two predictive indices on predicting the recognition performance --- p.117 / Chapter 6.1.3 --- Experiment Category 3 一 Comparing the applicability of the formulation method of the two predictive indices on predicting the recognition performance --- p.124 / Chapter 6.1.4 --- Experiment Category 4 - Comparing the performance of different phonetic distance definitions --- p.131 / Chapter 6.2 --- Experimental Summary --- p.137 / Chapter 6.3 --- Chapter Summary --- p.141 / Chapter 7 --- Conclusions --- p.142 / Chapter 7.1 --- Contributions --- p.144 / Chapter 7.2 --- Future Directions --- p.145 / Bibliography --- p.147 / Chapter A --- Table of Universal Phoneme Symbol --- p.153 / Chapter B --- Vocabulary Lists --- p.157 / Chapter C --- Experimental Results of Two-words Speech Recognition Tasks --- p.171 / Chapter D --- Experimental Results of Three-words Speech Recognition Tasks --- p.180 / Chapter E --- Significance Testing --- p.190 / Chapter E.1 --- Procedures of Significance Testing --- p.190 / Chapter E.2 --- Results of the Significance Testing --- p.191 / Chapter E.2.1 --- Experiment Category 1 --- p.191 / Chapter E.2.2 --- Experiment Category 2 --- p.192 / Chapter E.2.3 --- Experiment Category 3 --- p.194 / Chapter E.2.4 --- Experiment Category 4 --- p.196 / Chapter F --- Linear Regression Models --- p.197

by Cheng Yoik. / Thesis submitted in: October 1998. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 91-95). / Text in English; abstract also in Chinese. / by Cheng Yoik. / Chapter 1 --- Introduction --- p.13 / Chapter 1.1 --- What is Speaker Verification --- p.13 / Chapter 1.2 --- Review on Recent Speaker Verification Research --- p.15 / Chapter 1.2.1 --- Hidden Markov Modeling --- p.15 / Chapter 1.2.2 --- Cohort Normalization Scoring --- p.16 / Chapter 1.3 --- Objective of Thesis --- p.17 / Chapter 1.3.1 --- Text-prompted Speaker Verification System --- p.18 / Chapter 1.3.2 --- Fundamental Frequency (FO) Information --- p.18 / Chapter 1.3.3 --- Cohort Normalization on Cantonese --- p.19 / Chapter 1.4 --- Chapter Outline --- p.19 / Chapter 2 --- System Description --- p.21 / Chapter 2.1 --- System Overview --- p.21 / Chapter 2.2 --- Speech Signal Representations --- p.23 / Chapter 2.2.1 --- LPC Cesptral Coefficients --- p.24 / Chapter 2.2.2 --- Prosodic Features --- p.27 / Chapter 2.3 --- HMM Modeling Technique --- p.30 / Chapter 2.4 --- Speaker Classification --- p.34 / Chapter 2.4.1 --- Likelihood Scoring --- p.34 / Chapter 2.4.2 --- Verification Process --- p.36 / Chapter 2.4.2.1 --- General Approach --- p.36 / Chapter 2.4.2.2 --- Normalization Approach --- p.37 / Chapter 2.4.3 --- Cohort Sets --- p.39 / Chapter 2.5 --- Summary --- p.41 / Chapter 3 --- Experimental Setup --- p.42 / Chapter 3.1 --- Introduction --- p.42 / Chapter 3.2 --- Databases --- p.42 / Chapter 3.2.1 --- Cantonese Database --- p.43 / Chapter 3.2.2 --- YOHO Corpus --- p.45 / Chapter 3.3 --- Feature Analysis --- p.46 / Chapter 3.4 --- Speaker Models --- p.47 / Chapter 3.5 --- Experiments --- p.48 / Chapter 3.5.1 --- Evaluation --- p.48 / Chapter 3.5.2 --- FO Experiments --- p.50 / Chapter 3.5.2.1 --- FO Value --- p.51 / Chapter 3.5.2.2 --- Log FO Value --- p.51 / Chapter 3.5.2.3 --- Normalized FO --- p.52 / Chapter 3.5.2.4 --- Normalized Log FO --- p.53 / Chapter 3.5.3 --- Cohort Normalization Experiments --- p.53 / Chapter 3.5.3.1 --- Preliminary Study --- p.55 / Chapter 3.5.3.2 --- Cohort Normalization on Cantonese --- p.57 / Chapter 3.5.3.3 --- Cohort Normalization with Pitch Information on Cantonese --- p.58 / Chapter 4 --- Results and Analysis --- p.59 / Chapter 4.1 --- Introduction --- p.59 / Chapter 4.2 --- FO Experiments --- p.60 / Chapter 4.2.1 --- Results of Various Representation of FO Value on Cantonese --- p.60 / Chapter 4.2.2 --- Performance Comparison between Cantonese and English --- p.63 / Chapter 4.3 --- Cohort Normalization Experiments --- p.67 / Chapter 4.3.1 --- Performance Comparison on Our Results to Other Researches --- p.67 / Chapter 4.3.2 --- Results of Applying Cohort Normalization on Cantonese --- p.71 / Chapter 4.3.3 --- Results of Applying Cohort Normalization with Pitch Information on Cantonese --- p.74 / Chapter 4.4 --- Summary --- p.79 / Chapter 5 --- Conclusions and Future Work --- p.81 / Chapter 5.1 --- Conclusions --- p.81 / Chapter 5.2 --- Future Work --- p.82 / Chapter 5.2.1 --- Refinements --- p.82 / Chapter 5.2.2 --- Formant --- p.84 / Chapter 5.2.3 --- Independent Cohort Models --- p.85 / Chapter 6 --- Application --- p.86 / Chapter 6.1 --- Overview --- p.86 / Chapter 6.2 --- Telephony Interface --- p.87 / Chapter 6.3 --- Verification --- p.88 / Chapter 6.4 --- Discussion --- p.89