Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively

Preface

[No abstract available

Challenges associated with formal education in rural areas, Policy brief Rural NEET Youth Network

The youth demographic in rural areas continues to experience a global decline despite significant efforts from both national and international organisations to downturn this ne gative trend. Such efforts aim to create conditions for learning as well as opportunities that can enable young people to develop knowledge, skills, and competencies. Despite the economic recovery trends of recent years (before the COVID-19 pandemic), young people continue to be particularly vulnerable and especially during times of crisis. Youth disengagement from the labour market can lead to economic loss, demotivation, margina lisation, and be reflected in challenges such as a lack of qualifications, health issues, poverty, and other forms of social exclusion. To address such challenges, it is vital that a detailed understan ding of youth needs is developed. This work should be based on heterogeneous characteristics (personal vs institutional) that include (although not limited to) socio-economic, demographic, financial, technical, and institutional perspectives. This information should subsequently inform both future policy-making and decision-making processes

Classification d'expressions vocales passives versus actives

Six expressions sont généralement considérées pour caractériser les états émotifs humains : Sourire, Surprise, Colère, Tristesse, dégoût et Neutre. Différentes mesures peuvent être extraites à partir du signal de parole pour caractériser ces expressions, à savoir la fréquence fondamentale, l'énergie, le SPI (rapport des énergies des HF et des BF dans le signal) et le débit de parole. Une classification automatique des cinq expressions basées sur ces caractéristiques présente des conflits entre la Colère, la Surprise et le Sourire d'une part et le Neutre et la Tristesse d'autre part. Ce conflit entre classes d'expressions est également retrouvé chez le classifieur humain. Nous proposons donc de définir deux classes d'expressions: Active regroupant le Sourire, la Surprise et la Colère et Passive regroupant le Neutre et la Tristesse. Une telle classification est également plus réaliste et plus appropriée pour l'intégration d'information de parole dans un système de classification multimodale combinant la parole et la vidéo, ce qui est à long terme le but de notre travail. Dans ce papier, différentes méthodes de classification sont testées: un classifieur Bayésien, une Analyse Discriminante Linéaire (ADL), le classifieur au K plus proches vosins(KNN) et un classifieur à Machine à Vecteur de Support (SVM) avec une fonction de base gaussienne. Pour les deux classes considérées, les meilleurs taux de classification sont obtenus avec le classificateur SVM avec un taux de reconnaissance de 89.74% pour l'état Actif et de 86.54 % pour l'état Passif

Gene expression in acute Stanford type A dissection: a comparative microarray study

BACKGROUND: We compared gene expression profiles in acutely dissected aorta with those in normal control aorta. MATERIALS AND METHODS: Ascending aorta specimen from patients with an acute Stanford A-dissection were taken during surgery and compared with those from normal ascending aorta from multiorgan donors using the BD Atlas™ Human1.2 Array I, BD Atlas™ Human Cardiovascular Array and the Affymetrix HG-U133A GeneChip(®). For analysis only genes with strong signals of more than 70 percent of the mean signal of all spots on the array were accepted as being expressed. Quantitative real-time polymerase chain reaction (RT-PCR) was used to confirm regulation of expression of a subset of 24 genes known to be involved in aortic structure and function. RESULTS: According to our definition expression profiling of aorta tissue specimens revealed an expression of 19.1% to 23.5% of the genes listed on the arrays. Of those 15.7% to 28.9% were differently expressed in dissected and control aorta specimens. Several genes that encode for extracellular matrix components such as collagen IV α2 and -α5, collagen VI α3, collagen XIV α1, collagen XVIII α1 and elastin were down-regulated in aortic dissection, whereas levels of matrix metalloproteinases-11, -14 and -19 were increased. Some genes coding for cell to cell adhesion, cell to matrix signaling (e.g., polycystin1 and -2), cytoskeleton, as well as several myofibrillar genes (e.g., α-actinin, tropomyosin, gelsolin) were found to be down-regulated. Not surprisingly, some genes associated with chronic inflammation such as interleukin -2, -6 and -8, were up-regulated in dissection. CONCLUSION: Our results demonstrate the complexity of the dissecting process on a molecular level. Genes coding for the integrity and strength of the aortic wall were down-regulated whereas components of inflammatory response were up-regulated. Altered patterns of gene expression indicate a pre-existing structural failure, which is probably a consequence of insufficient remodeling of the aortic wall resulting in further aortic dissection

Convolutional neural network denoising of focused ion beam micrographs

Most research on deep learning algorithms for image denoising has focused on signal-independent additive noise. Focused ion beam (FIB) microscopy with direct secondary electron detection has an unusual Neyman Type A (compound Poisson) measurement model, and sample damage poses fundamental challenges in obtaining training data. Model-based estimation is difficult and ineffective because of the nonconvexity of the negative log likelihood. In this paper, we develop deep learning-based denoising methods for FIB micrographs using synthetic training data generated from natural images. To the best of our knowledge, this is the first attempt in the literature to solve this problem with deep learning. Our results show that the proposed methods slightly outperform a total variation-regularized model-based method that requires time-resolved measurements that are not conventionally available. Improvements over methods using conventional measurements and less accurate noise modeling are dramatic - around 10 dB in peak signal-to-noise ratio.Accepted manuscrip

Automatic Defect Segmentation of ‘Jonagold’ Apples on Multi-Spectral Images: A Comparative Study

In this work, several thresholding and classification-based techniques were employed for pixel-wise segmentation of surface defects of ‘Jonagold ’ apples. Observations showed that segmentation by supervised classifiers was more accurate than the rest. Also, average of class-specific recognition errors was more reliable than error measures based on defect size or global recognition. Segmentation accuracy im-proved when pixels were represented as a neighborhood. Effect of down-sampling on segmentation accuracy and computation times showed that multi-layer percep-trons were the best. Russet was the most difficult defect to segment, whereas flesh damage the least. The proposed method was much more precise on healthy fruit