A Study on Human Face Expressions using Convolutional Neural Networks and Generative Adversarial Networks

Human beings express themselves via words, signs, gestures, and facial emotions. Previous research using pre-trained convolutional models had been done by freezing the entire network and running the models without the use of any image processing techniques. In this research, we attempt to enhance the accuracy of many deep CNN architectures like ResNet and Senet, using a variety of different image processing techniques like Image Data Generator, Histogram Equalization, and UnSharpMask. We used FER 2013, which is a dataset containing multiple classes of images. While working on these models, we decided to take things to the next level, and we attempted to make changes to the models themselves to improve their accuracy. While working on this research, we were introduced to another concept in Deep Learning known as Generative Adversarial Networks, which are also known as GANs. They are generative deep learning models which are based on deep CNN models, and they comprise two CNN models - a Generator and a Discriminator. The primary task of the former is to generate random noises in the form of images and passes them to the latter. The Discriminator compares the noise with the input image and accepts/rejects it, based on the similarity. Over the years, there have been various distinguished architectures of GANs namely CycleGAN, StyleGAN, etc. which have allowed us to create sophisticated architectures to not only generate the same image as the original input but also to make changes to them and generate different images. For example, CycleGAN allows us to change the season of scenery from Summer to Winter or change the emotion in the face of a person from happy to sad. Though these sophisticated models are good, we are working with an architecture that has two deep neural networks, which essentially creates problems with hyperparameter tuning and overfitting

Islet-Like Cell Aggregates Generated from Human Adipose Tissue Derived Stem Cells Ameliorate Experimental Diabetes in Mice

BACKGROUND: Type 1 Diabetes Mellitus is caused by auto immune destruction of insulin producing beta cells in the pancreas. Currently available treatments include transplantation of isolated islets from donor pancreas to the patient. However, this method is limited by inadequate means of immuno-suppression to prevent islet rejection and importantly, limited supply of islets for transplantation. Autologous adult stem cells are now considered for cell replacement therapy in diabetes as it has the potential to generate neo-islets which are genetically part of the treated individual. Adopting methods of islet encapsulation in immuno-isolatory devices would eliminate the need for immuno-suppressants. METHODOLOGY/PRINCIPAL FINDINGS: In the present study we explore the potential of human adipose tissue derived adult stem cells (h-ASCs) to differentiate into functional islet like cell aggregates (ICAs). Our stage specific differentiation protocol permit the conversion of mesodermic h-ASCs to definitive endoderm (Hnf3β, TCF2 and Sox17) and to PDX1, Ngn3, NeuroD, Pax4 positive pancreatic endoderm which further matures in vitro to secrete insulin. These ICAs are shown to produce human C-peptide in a glucose dependent manner exhibiting in-vitro functionality. Transplantation of mature ICAs, packed in immuno-isolatory biocompatible capsules to STZ induced diabetic mice restored near normoglycemia within 3-4 weeks. The detection of human C-peptide, 1155±165 pM in blood serum of experimental mice demonstrate the efficacy of our differentiation approach. CONCLUSIONS: h-ASC is an ideal population of personal stem cells for cell replacement therapy, given that they are abundant, easily available and autologous in origin. Our findings present evidence that h-ASCs could be induced to differentiate into physiologically competent functional islet like cell aggregates, which may provide as a source of alternative islets for cell replacement therapy in type 1 diabetes

Improving encoding efficiency in test compression using sequential linear decompressors with retained free variables

textThis thesis proposes an approach to improve test compression using sequential linear decompressors by using retained free variables. Sequential linear decompressors are inherently efficient and attractive for encoding test vectors with high percentages of don't cares (i.e., test cubes). The encoding of these test cubes is done by solving a system of linear equations. In streaming decompression, a fixed number of free variables are used to encode each test cube. The non-pivot free variables used in Gaussian Elimination are wasted when the decompressor is reset before encoding the next test cube which is conventionally done to keep computational complexity manageable. In this thesis, a technique for retaining the non-pivot free variables when encoding one test cube and using them in encoding the subsequent test cubes is explored. This approach retains most of the non-pivot free variables with a minimal increase in runtime for solving the equations. Also, no additional control information is needed. Experimental results are presented showing that the encoding efficiency and hence compression, can be significantly boosted.Electrical and Computer Engineerin

Improving encoding efficiency in test compression based on linear techniques

textSequential linear decompressors are widely used to implement test compression. Bits stored on the tester (called free variables) are assigned values to encode the test vectors such that when the tester data is decompressed, it reproduces the care bits in the test cube losslessly. In order to do this, the free variable dependence of the scan cells is obtained by symbolic simulation and a system of linear equations, one equation per care bit in a test cube, is solved to obtain the tester data. Existing techniques reset the decompressor after every test cube to avoid accumulating too many free variables, to keep the computation for encoding manageable. This leads to wastage of unused free variables and reduces the efficiency in encoding. Moreover, existing techniques preload the decompressor with free variables before scan shifting, which increases test time to help encode the early scan cells. This dissertation presents new approaches that improve the efficiency of the decompression process, achieving greater test compression and reducing test costs. The contributions of this dissertation include a low cost method to retain unused free variables while encoding a test cube and reuse them while encoding other test cubes with a minor increase in computational complexity. In addition, a test scheduling mechanism is described for system on chip (SoC) architectures that implements retaining unused free variables for SoCs without any hardware overhead and with little additional control. For testing 3D-ICs, a novel daisy-chain architecture for the sequential linear decompressor is proposed for sharing unused free variables across layers with a reduced number of TSVs (through silicon via) needed to transport test data (also called test elevators) to non-bottom layers. A scan feedforward technique is proposed which improves the free variable dependence of the scan cells, thereby increasing the probability of encoding of test cubes, especially when the early scan cells have a lot of specified bits, thereby avoiding the need for preloading the decompressor. Lastly, a feedforward/feedback mechanism in the scan chains for combinational linear decompressors is proposed which improves encoding flexibility and reduces tester data without pipelining the decompressor like the conventional methods, thereby reducing the test time.Electrical and Computer Engineerin

Human Placenta-Derived Mesenchymal Stem Cells and Islet-Like Cell Clusters Generated From These Cells as a Novel Source for Stem Cell Therapy in Diabetes

Placental tissue holds great promise as a source of cells for regenerative medicine due to its plasticity, and easy availability. Human placenta-derived mesenchymal stem cells (hPDMSCs) have the potential to differentiate into insulin-producing cells. Upon transplantation, they can reverse experimental diabetes in mice. However, it is not known whether culture-expanded undifferentiated hPDMSCs are capable of restoring normoglycemia upon transplantation in streptozotocin (STZ)-induced diabetic mice. Hence we prepared long-term cultures of hPDMSCs from the chorionic villi of full-term human placenta. Flow cytometry analyses and immunocytochemistry study revealed bonafide mesenchymal nature of the isolated hPDMSCs. These cultures could differentiate into adipogenic, oesteogenic, chondrogenic, and neuronal lineages on exposure to lineage-specific cocktails. Furthermore, we showed that hPDMSCs can form islet-like cell clusters (ILCs) on stepwise exposure to serum-free defined media containing specific growth factors and differentiating agents. qRT-PCR showed the expression of insulin, glucagon, and somatostatin in undifferentiated hPDMSCs and in ILCs. Differentiated ILCs were found to express human insulin, glucagon, and somatostatin by immunocytochemistry. Additionally, ILCs also showed abundance of pancreatic transcription factors ngn3 and isl1. Both undifferentiated hPDMSCs and ILCs exihibited insulin secretion in response to glucose. Transplantation of hPDMSCs or ILCs derived from hPDMSCs in STZ-induced diabetic mice led to restoration of normoglycemia. Our results demonstrate, for the first time, reversal of hyperglycemia by undifferentiated hPDMSCs and ILCs derived from hPDMSCs. These results suggest human placenta-derived MSCs as an alternative source for cell replacement therapy in diabetes