Visual Analysis of Maya Glyphs via Crowdsourcing and Deep Learning

In this dissertation, we study visual analysis methods for complex ancient Maya writings. The unit sign of a Maya text is called glyph, and may have either semantic or syllabic significance. There are over 800 identified glyph categories, and over 1400 variations across these categories. To enable fast manipulation of data by scholars in Humanities, it is desirable to have automatic visual analysis tools such as glyph categorization, localization, and visualization. Analysis and recognition of glyphs are challenging problems. The same patterns may be observed in different signs but with different compositions. The inter-class variance can thus be significantly low. On the opposite, the intra-class variance can be high, as the visual variants within the same semantic category may differ to a large extent except for some patterns specific to the category. Another related challenge of Maya writings is the lack of a large dataset to study the glyph patterns. Consequently, we study local shape representations, both knowledge-driven and data-driven, over a set of frequent syllabic glyphs as well as other binary shapes, i.e. sketches. This comparative study indicates that a large data corpus and a deep network architecture are needed to learn data-driven representations that can capture the complex compositions of local patterns. To build a large glyph dataset in a short period of time, we study a crowdsourcing approach as an alternative to time-consuming data preparation of experts. Specifically, we work on individual glyph segmentation out of glyph-blocks from the three remaining codices (i.e. folded bark pages painted with a brush). With gradual steps in our crowdsourcing approach, we observe that providing supervision and careful task design are key aspects for non-experts to generate high-quality annotations. This way, we obtain a large dataset (over 9000) of individual Maya glyphs. We analyze this crowdsourced glyph dataset with both knowledge-driven and data-driven visual representations. First, we evaluate two competitive knowledge-driven representations, namely Histogram of Oriented Shape Context and Histogram of Oriented Gradients. Secondly, thanks to the large size of the crowdsourced dataset, we study visual representation learning with deep Convolutional Neural Networks. We adopt three data-driven approaches: assess- ing representations from pretrained networks, fine-tuning the last convolutional block of a pretrained network, and training a network from scratch. Finally, we investigate different glyph visualization tasks based on the studied representations. First, we explore the visual structure of several glyph corpora by applying a non-linear dimensionality reduction method, namely t-distributed Stochastic Neighborhood Embedding, Secondly, we propose a way to inspect the discriminative parts of individual glyphs according to the trained deep networks. For this purpose, we use the Gradient-weighted Class Activation Mapping method and highlight the network activations as a heatmap visualization over an input image. We assess whether the highlighted parts correspond to distinguishing parts of glyphs in a perceptual crowdsourcing study. Overall, this thesis presents a promising crowdsourcing approach, competitive data-driven visual representations, and interpretable visualization methods that can be applied to explore various other Digital Humanities datasets

Uzaktan algılama görüntülerinin koşullu rasgele alanlarla bağlamsal modellenmesi.

Large within-class variance is a challenging problem for classification tasks in remote sensing. Contextual models are promising to address this problem. In this thesis, a contextual conditional random field model is proposed for target detection in satellite imagery. The proposed algorithm has three stages. First, contextual cues of the target that come from domain knowledge are identified by sparse auto-encoders and shown to be statistically consistent. The region represented by the most repetitive feature learned by sparse autoencoders is used as central node in the proposed model and called candidate region. Other nodes of the model are chosen as land-use land-cover classes in the surroundings of the candidate regions, since the spatial context of the target class is defined over expected and unexpected classes in its neighborhood. Secondly, regions that represent these classes are obtained by merging segments with the same label according to support vector machines. These regions are called meta-segments. In the last stage, the same features are extracted from the meta-segments and candidate region to be used as unary features in the conditional random fields model. Pairwise features in conditional random fields are essential for representing contextual relations and they are designed as class co-occurrence frequencies in three di erent neighborhoods of the candidate region. For each candidate region, a dynamic conditional random fields model is generated. The proposed method is robust in terms of being threshold-free and selecting contextual cues via sparse auto-encoders. Performance of the method is competitive to rule-based methods and segmentation-based classification methods.M.S. - Master of Scienc

ON TRANSCENDENTAL CONTINUED FRACTIONS IN FIELDS OF FORMAL POWER SERIES OVER FINITE FIELDS

In the field of formal power series over a finite field, we prove a result which enables us to construct explicit examples of U-m-numbers by using continued fraction expansions of algebraic formal power series of degree m > 1

Concentration Characteristics of a Complex Antimony Ore

Selection of a proper concentration method for a sustainable production of antimony metal from an ore deposit has its unique challenges and crucial of importance due to the growing use of antimony compounds and increasing strategic importance. Therefore, detailed laboratory scale beneficiation studies of a complex stibnite ore and modeling & simulation studies based on the experimental results were investigated within this study. Quantitative mineralogical characterization, chemical analyses, sieve tests and the heavy liquid tests were performed in the scope of ore characterization. Froth flotation, gravity concentration, electrostatic separation and ore sorting were conducted to introduce the best possible flowsheet for the individual industrial sample. It was concluded that heavy medium separation would be the only method can be used for subjected stibnite ore. Therefore, four conjectural beneficiation scenarios were tested by simulation studies for the cases proposedly allowing to produce concentrates having 10, 12, 14 and 16% Sb content. Within the simulation studies substantiating the real-life processing operation in terms of realistic performance figures, flowsheet design covered the processing of -10+0.5 mm fraction and relatively fine sizes separately via heavy medium cyclone and shaking tables, respectively. Following the itemized mass and water balances, the simulation results showed that when the grade of the concentrates were requested in between 10-16%, the total recovery of the concentrates changed between 46-49% in case of feeding 1.18% Sb with 20 tones per hour feed rate

Dyskeratosis congenita associated with three malignancies

kavak, ayse/0000-0002-4679-1181WOS: 000182018900020PubMed: 12705757Dyskeratosis congenita is a rare inheritable disorder characterized by abnormalities of the skin, nails and oral mucosa. Aplastic anaemia resulting from bone marrow hypoplasia is a frequent cause of death. Squamous cell carcinoma developing from leukoplakia and visceral malignancies are other complications of the disease. We report here a case of dyskeratosis congenita in a man who developed three neoplasias of different systems over a period of many years. Squamous cell carcinoma and gastric adenocarcinoma manifested 17 years after the man was diagnosed with Hodgkin's disease

Edge aware segmentation in satellite imagery A case study of shoreline detection

Shoreline extraction algorithms from multispectral imagery depend on threshold selection over spectral values and segmentation in general. Although this method gives high performance values for water delineation, error is accumulated on pixels near shoreline and complicates detection of nearby ships, docks etc. Water-shadow spectral mixing and spectral difference in water regions are two of the reasons for such untrustworthy shoreline results. With only four bands available, improvement in water detection depending only on pixel values is not very promising. Therefore, segmentation gains importance. By an edge-aware segmentation method, we aim to improve overall water and shoreline detection performances. In this study, a robust three-stage shoreline extraction algorithm is proposed. In the first stage, segmentation is applied over spectral values and then, some segments are combined according to edge information. In the second stage of the algorithm, pixel-based water information is combined with segmentation. The last step consists of enhancement of water regions based on local optimization by merging regions near shore boundary. Additionally, two new boundary-sensitive performance metrics are introduced for measuring the accuracy of the detected boundaries

Evaluation of diabetes risk and eating habits of university students and personnel

Amaç: Bu çalışmanın amacı İstanbul Medeniyet Üniversitesi’nin Göztepe ve Maltepe kampüslerinde öğrenim gören üniversite öğrencileri ve çalışanlarının beslenme alışkanlıkları, obezite prevalansı ve diyabet risklerini değerlendirmektir. Materyal ve metot: Çalışmaya 13 – 14 Kasım 2013 tarihlerinde 200’ü üniversite öğrencisi ve 157’si üniversite çalışanı olmak üzere toplam 357 kişi katılmıştır. Çalışma verileri katılımcılarla yüz yüze uygulanan anketler ve ölçümlerle elde edilmiştir. Bulgular: Çalışma sonunda katılımcıların %30,8’inin kilolu veya obez olduğu ve düzenli egzersiz yapma oranlarının ise %54,3 düzeyinde olduğu belirlenmiştir. Üniversite öğrencilerinin %14,0’ü ve üniversite çalışanlarının %31,8’i sigara içmektedir. Diyabet risk değerlendirmesi sonucunda öğrencilerin %37,5’inde, çalışanların ise %61’inde diyabet riski olduğu belirlenmiştir. Tüketilen gıda türü ile diyabet riski arasında anlamlı ilişki saptanmamıştır. Üniversite öğrencilerinin%36,5’i haftada birden fazla fast-food tüketirken, öğrencilerin çok büyük bir kısmı (%82,5) haftada en az bir defa gazlı içecek tüketmektedir. Her gün gazlı içecek tüketen öğrencilerin oranı ise %62’dir. Sonuç: Genç popülasyonda önemli oranda diyabet riski mevcuttur. Bu gruba yönelik yapılacak hayat tarzı değişikliği ve eğitimin bu riski azaltabileceği düşünmekteyiz.Aim: The aim of this study was to assess the eating habits, pre- valence of obesity and diabetes risk of university students and employees in Istanbul Medeniyet University Goztepe and Maltepe campuses. Material and method: Three hundred and fifty seven people comprising 200 students and 157 employees of university were included to the study between 13 and 14 November 2013. Study data were obtained via face to face applied questionnaires and measurements. Results: As a result, 30.8% of participants were overweight or obese and only 54.3% were regularly doing exercise. Fourteen percent of students and 31.8% of employees were smoking. The diabetes risk assessment showed that 37.5% of students and 61.1% of employees had diabetes risk. There was no relationship between consumed food products and diabete srisk. Fast-food consuming more than once a week were 36.5% in students and most of the students (82.5%) were consuming at least one fizzy drink a week. The rate of students having every day fizzy drinks was 62%. Conclusion: Diabetes risk is considerable in young population. We think that changing life style and education for this group can reduce that risk