MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images

This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. To this end, we employed the popular knowledge distillation (KD) method and identified two major shortcomings with its use: 1) a fine-grained grid search is needed for tuning the temperature hyperparameter and 2) to find the optimal size-accuracy balance, one needs to search for the final network size (or the compression rate). On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size. In addition, we hypothesized that translation invariance achieved using max-pooling layers would not be useful for the FER problem as the expressions are sensitive to small, pixel-wise changes around the eye and the mouth. However, we have found an intriguing improvement on generalization when max-pooling is used. We conducted experiments on two widely-used FER datasets, CK+ and Oulu-CASIA. Our smallest model (MicroExpNet), obtained using knowledge distillation, is less than 1MB in size and works at 1851 frames per second on an Intel i7 CPU. Despite being less accurate than the state-of-the-art, MicroExpNet still provides significant insights for designing a microarchitecture for the FER problem.Comment: International Conference on Image Processing Theory, Tools and Applications (IPTA) 2019 camera ready version. Codes are available at: https://github.com/cuguilke/microexpne

Automated learning rate search using batch-level cross-validation

Deep learning researchers and practitioners have accumulated a significant amount of experience on training a wide variety of architectures on various datasets. However, given a network architecture and a dataset, obtaining the best model (i.e. the model giving the smallest test set error) while keeping the training time complexity low is still a challenging task. Hyper-parameters of deep neural networks, especially the learning rate and its (decay) schedule, highly affect the network's final performance. The general approach is to search the best learning rate and learning rate decay parameters within a cross-validation framework, a process that usually requires a significant amount of experimentation with extensive time cost. In classical cross-validation (CV), a random part of the dataset is reserved for the evaluation of model performance on unseen data. This technique is usually run multiple times to decide learning rate settings with random validation sets. In this paper, we explore batch-level cross-validation as an alternative to the classical dataset-level, hence macro, CV. The advantage of batch-level or micro CV methods is that the gradient computed during training is re-used to evaluate several different learning rates. We propose an algorithm based on micro CV and stochastic gradient descent with momentum, which produces a learning rate schedule during training by selecting a learning rate per epoch, automatically. In our algorithm, a random half of the current batch (of examples) is used for training and the other half is used for validating several different step sizes or learning rates. We conducted comprehensive experiments on three datasets (CIFAR10, SVHN and Adience) using three different network architectures (a custom CNN, ResNet and VGG) to compare the performances of our micro-CV algorithm and the widely used stochastic gradient descent with momentum in a early-stopping macro-CV setup. The results show that, our micro-CV algorithm achieves comparable test accuracy to macro-CV with a much lower computational cost

Effect of slaughter age and muscle type on selected meat quality traits and fatty acid composition of Lindovskaya geese

The study was conducted in order to determine how the slaughter age (SA) and muscle type (MT) affect technological properties and fatty acid composition (FAC) of meat among Linda geese reared under the breeder conditions. A total of 16 male geese were slaughtered in order to determine the technological properties and FAC of meat. It was determined that the effect of SA on pH(15) and pH(24) (TM), water holding capacity (WHC), cooking loss (CL), and drip loss (DL) of the thigh muscle was all statistically significant (P < 0.05). The effect of SA on pH(24), WHC, CL, and the 168th-hour DL of pectoral muscle (PM), was statistically significant (P < 0.05). Moreover, the effect of MT on pH(15), pH(24), WHC, CL and DL in the 12th week and on pH(15), pH(24), WHC and DL in the 16th week was found to be statistically significant (P < 0.05). Additionally, the effect of SA on monounsaturated fatty acid (Sigma MUFA) of TM and polyunsaturated fatty acid (Sigma PUFA) of PM and Sigma omega-6 ratios was statistically significant (P < 0.05). Consequently, it was concluded that SA was better in the 12th week than the 16th week and PM was better than TM. The 16-week TM was better in terms of Sigma MUFA, the 12-week PM was better in terms of Sigma PUFA and Sigma omega-6, and the 12-week TM was better in terms of Sigma omega-3 amount and omega-6/omega-3 ratio. Therefore, it can be recommended that 12-week-old Linda geese be selected in terms of selected meat traits and FAC of meat

Effect of climate factors on wood veneers exposed to outdoor conditions in black sea region

In this study, 2×100×200 mm wood veneers obtained from Scots pine (Pinus sylvestris L.), European black pine (Pinus nigra Arn.) and beech (Fagus orientalis L.) wood were exposed to outdoor climate conditions at three different cities (Trabzon, Artvin, and Kastamonu) of Black Sea region in Turkey for totally 4 months from May to August, 2012. The aim of this study was to investigate the effect of climate factors on the changes occurred on different types of wood veneers that were subjected to outdoor weathering conditions. Weight losses, surface roughness, and color changes occurred on veneers were determined during natural weathering. Additionally, the weathering map of wood in three cities studied was calculated using Scheffer Climate Index (SCI) in order to characterize the potential decay risk of wood materials. Accordingly, at the end of the 4 months, the highest weight losses for Scots pine veneers were obtained from the weathering conditions in Artvin while the lowest weight losses were obtained in Kastamonu. For European black pine and beech veneers, the highest weight losses were obtained from Trabzon, and the lowest weight losses were obtained from Kastamonu. The highest color change value (?E*) obtained from Trabzon and Artvin, the lowest ?E* obtained from Kastamonu for Scots pine veneers. For European black pine veneers, the highest ?E* obtained from Trabzon while the lowest ?E* was obtained from Artvin and Kastamonu. For beech veneers, the highest ?E* was obtained from Artvin, the lowest ?E* obtained from Kastamonu. For all veneers, the lowest surface roughness was determined in Trabzon. The highest surface roughness was obtained in Artvin for Scots pine and it was obtained in Kastamonu for European black pine and beech. Based on the result of SCI analysis, the most risky city for decay potential of fungi was Trabzon, then Artvin and Kastamonu, respectively

Dört bacaklı robotlar için önizlemeli kontrol ile sıfır moment noktası tabanlı yürüme yörüngesi sentezi

Bacakları üzerinde hareket eden robotların engel aşma konusunda önemli avantajları söz konusudur. Özellikle dört bacaklı robotların değişken arazi yapıları üzerinde birçok uygulamaları düşünülmektedir. Bu çalışmada, dört bacaklı bir robotun düz zemin üzerinde hızlı yol almasına yönelik tırıs türü ilerleme üzerinde durulmaktadır. Sıfır Moment Noktası (SMN) karalılık kriterine ve Doğrusal Ters Sarkaç Modeli’ne (DTSM) dayalı bir yürüme referansı sentez yöntemi sunulmaktadır. Tırıs ilerleme için bir SMN referans yörüngesi önerilmiş, bu yörüngeden, önizlemeli kontrol yaklaşımı ile Robot Ağırlık Merkezi (RAM) için bir referans yörünge elde edilmiştir. Oluşturulan ağırlık merkezi yörüngesi ters kinematik yöntemi ile bacak eklemlerinin konum referanslarının hesaplanmasında kullanılmıştır. Önerilen referans sentezi yöntemi, 16 serbestlik dereceli bir robot modeli ile üç boyutlu ve tam dinamikli bir simülasyon ortamında denenmiştir. Simülasyon sonuçları sunulan yaklaşımın başarılı olduğunu göstermektedir

Esophageal squamous papilloma

Özofagus skuamoz papillomu, genellikle alt özofagusta soliter bir lezyon olarak görülen, iyi huylu skuamoz epitelyal polipoid bir tümördür. Prevalans oranları çok düşüktür ve 1959’dan bu yana literatürde yaklaşık 250 vaka bidirilmiştir. Kronik mukozal irritasyon ve human papilloma virus enfeksiyonu suçlanan etyolojik nedenlerdir. Tipik özofagus skuamöz papillomunun hiçbir özgün semptomu yoktur. Bizim vakamız kronik dispeptik şikayetleri olan ve endoskopik olarak reflü özofajiti saptanan 51 yaşındaki erkek hasta idi. Endoskopik incelemede özofagus 25. cm’de 3-4 mm’lik polipoid oluşum izlendi. Buradan aldığımız biyopsi örneğinin patolojik inceleme sonucu özofagus skuamoz papillomu olarak geldi.Esophageal squamous papilloma is a benign squamous epithelial polypoid tumor and is usually seen as a solitary lesion of the lower esophagus. They have a low prevalence, and about 250 cases have been reported in the literature since 1959. Chronic mucosal irritation and infection with human papilloma virus are proposed etiologies. There are no pathognomonic symptoms for the typical esophageal squamous papilloma. Our patient was a 51-yearold man who had chronic dyspeptic complaints and endoscopically detected reflux esophagitis. A 3-4 mm sized polypoid formation was seen at the 25’th cm of his esophagus during the endoscopic examination. Pathologic inspection of the biopsied specimen taken from this part of his esophagus indicated esophageal squamous papilloma

Dört bacaklı robotlar için önizleme kontrolü ve sıfır moment noktası esaslı yürüyüş yörüngesi üretimi

Robota verilen görevde engel aşımı gerektiğinde bacaklı robotların geri kalan mobil robotlara göre önemli avantajları bulunmaktadır. Bu makalede dört bacaklı robotların düz bir yüzeyde yürüyüşü için bir ölçümleme üretimi yöntemi sunuldu. Bu yaklaşım sıfır moment noktası (SMN) temelli kararlılık ve doğrusal ters sarkaç modeli (DTSM) üzerinedir. Yürüyüş için SMN referans gezingeleri ileri sürülüp oradan önizleme kontorü vasıtasıyla robotun ağırlık merkezi (RAM) referansı için referans gezingeleri elde edildi. Bacak eklemlerinin pozisyonları RAM referans gezingeleri üzerine ters kinematik uygulanarak hesaplandı. Öne sürülen referans gezinge üretimi sentezi, tamamen dinamik 3 boyutlu benzetimle test edildi. Benzetimde 16 serbestlik derecesine (SD) sahip dört bacaklı robot modeli kullanıldı. Benzetim sonuçları, yürüyüş için yapılan referans üretim tekniğinin başarıya ulaştığını gösteriyor

Derin Sinir Ağı Tabanlı Nesne Algılama Yöntemlerine Bağlam Modeli Entegre Edilmesi

Nesne algılama (İng. object detection) problemini kısaca, girdi olarak verilen bir görüntüde yine girdi olarak verilen bir nesne sınıfına ait örnekler var ise bunların her birinin bir sınırlayıcı kutu (İng. bounding box) aracılığıyla işaretlenmesi olarak tanımlayabiliriz. Nesne algılamada bağlam (İng. context) kullanımı çokça çalışılmış olmasına rağmen (bknz. Literatür Özeti), bugün en iyi en güncel (İng. state of the art) nesne algılama yöntemleri (örneğin, Girshick, 2015; Ren vd., 2015; Lin vd. 2017), nesne örneklerini ararken sadece yerel görüntü özniteliklerinden yararlanmakta ve genel olarak bağlamı kullanmamaktadır. Bunda, nesne algılama için bağlam kullanımının bazı nesne sınıfları için başarımı arttırması, bazı sınıflar için azaltması ve ortalamada çok az bir artış getiriyor olması gibi “karışık” sonuçların (Divvala vd., 2009) etkisi olduğu düşünülmektedir. Fakat bağlam kullanımı için iki temel, çok güçlü motivasyon vardır. Bunlardan ilki, yeteri kadar görsel kanıt vermeyen ve bu yüzden algılanması zor küçük nesneler için bağlamın kritik önemde olmasıdır (bknz. Şekil 1). İkinci motivasyon ise nesne algılama problemini çok yüksek başarımla çözen biyolojik görü sistemlerinde bağlamdan yanlış pozitifleri (İng. false positives) azaltan ve arama uzayını (İng. search space) daraltan bir şekilde yararlanılmasıdır (bknz. Şekil 2). Bu projede amaç, derin sinir ağı tabanlı yeni nesil nesne algılama yöntemlerine bir bağlam modeli entegre etmektir. Bu amaç doğrultusunda belirled

Görsel tanımlayıcı topluluklarıyla otomatik görüntü açıklama.

Automatic image annotation is the process of automatically producing words to de- scribe the content for a given image. It provides us with a natural means of semantic indexing for content based image retrieval. In this thesis, two novel automatic image annotation systems targeting dierent types of annotated data are proposed. The rst system, called Supervised Ensemble of Visual Descriptors (SEVD), is trained on a set of annotated images with predened class labels. Then, the system auto- matically annotates an unknown sample depending on the classication results. The second system, called Unsupervised Ensemble of Visual Descriptors (UEVD), assumes no class labels. Therefore, the annotation of an unknown sample is accomplished by unsupervised learning based on the visual similarity of images. The available auto- matic annotation systems in the literature mostly use a single set of features to train a single learning architecture. On the other hand, the proposed annotation systems utilize a novel model of image representation in which an image is represented with a variety of feature sets, spanning an almost complete visual information comprising color, shape, and texture characteristics. In both systems, a separate learning entity is trained for each feature set and these entities are gathered under an ensemble learning approach. Empirical results show that both SEVD and UEVD outperform some of the state-of-the-art automatic image annotation systems in equivalent experimental setups.M.S. - Master of Scienc

Synhesis Of Alkyl Sulfate Via Acidolysis Of Boron Esters With Sulfuric Acid

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2010Bu tezde, kantitatife yakın bir verimle alkil sülfat elde edilmesine imkan veren yeni, güvenli ve ucuz bir sülfatlama metodu geliştirilmiştir. Trialkil boratların sülfürik asit ile asidolizine dayanan bu yeni metotda basit bir şekilde trialkil boratların üzerine damla damla sülfürik asit ilave edilmesiyle alkil sülfatlar sentezlenmektedir. Renk değişiminin gözlenmediği asidoliz reaksiyonu oda sıcaklığında 1–2 saat içinde tamamlanmaktadır. Elde edilen alkil sülfatlar 1H-NMR, 13C-NMR ve FT-IR ile karakterize edilmiştir. Yüksek reaksiyon verimi, ürünlerin saflığı ve işlemin kükürt trioksit veya klorsülfonik asit gibi tehlikeli kimyasalların kullanılmasını gerektirmeden yapılabilmesi bu çalışmada ortaya konan yöntemin genel sülfatlama metodu olarak büyük ölçekli alkil sülfatların üretiminde kullanılmaya uygun olduğunu işaret etmektedir.In this thesis, we describe a novel, safe and cheap sulfation process yielding alkyl sulfates in near quantitative yields. This process is based on acidolysis of trialkyl borates with sulfuric acid and simply achieved by drop wise addition of sulfuric acid to trialkyl borate. The acidolysis reaction is completed within 1-2 h at room temperature. No coloration is observed during the course of reaction. The resulting alkyl sulfates were characterized by 1H-NMR, 13C-NMR and FT-IR techniques. High reaction yields, purity of products and avoidance the use of dangerous chemicals such as sulfur trioxide or chlorosulfonic acid make the procedure superior to common sulfation methods. Considering purities of the products and simplicity of the process, this procedure seems to be very suitable also for large-scale production of alkyl sulfates.Yüksek LisansM.Sc