Title: Statistical Modeling and analysis of allele-specific DNA methylation at the haplotype level
Abstract: Epigenetics is the branch of biology concerned with the study of phenotypical changes due to alterations of DNA, maintained during cell division, excluding modifications of the sequence itself. Epigenetic information includes DNA methylation, histone modifications, and higher order chromatin structure among others. DNA methylation is a stable epigenetic mechanism that chemically marks the DNA by adding methyl groups at individual cytosines immediately adjacent to guanines (CpG sites). Methylation marks are used to identify cell-type specific aspects of gene regulation, since marks located within a gene promoter or enhancer typically act to repress gene transcription, whereas promoter or enhancer demethylation is associated with gene activation. Notably, patterns of methylation marks are highly polymorphic and stochastic, containing information about a broad range of normal and aberrant biological processes, such as development and differentiation, aging, and carcinogenesis.
The epigenetic information content of two homologous chromosomal regions need not be the same. For example, it is well established that the ability of a cell to methylate the promoter region of a specific copy of a gene (an allele), is crucial for proper development. In fact, many known phenotypical traits stem from allele-specific epigenetic marks. Moreover, some allele-specific epigenetic differences have been found to be associated with local genetic differences between copies of a chromosome. Thus, developing a framework for studying such epigenetic differences in diploid organisms is our main goal. More specifically, our objective is to develop a statistical method that can be used to detect regions in the genome, with genetic differences between homologous chromosomes, in which there are biologically relevant differences in DNA methylation between alleles.
State of the art methods for allele-specific methylation modeling and analysis have critical shortcomings rendering them unsuitable for this type of analysis. We present a statistical physics inspired model for allele-specific methylation analysis that contains a sensible number of parameters, considering the limited sample size in whole genome bisulfite sequencing data, which is rich enough to capture the complexity in the data. We demonstrate the appropriateness of this model for allele-specific methylation analysis using simulation data as well as real data. Using our model, we compute mean methylation level differences between alleles, as well as information-theoretic quantities, such as the entropy of the methylation state in each allele and the mutual information between the methylation state and the allele of origin, and assess the statistical significance of each quantity by learning the null distribution from the data. This complementary set of statistics allows for an unparalleled level of insight in subsequent biological analysis. As a result, the developed framework provides an unprecedented descriptive power to characterize (i) the circumstances under which allele-specific methylation events arise, and (ii) the cis-effect, or lack of thereof, that genetic mutations have on DNA methylation.
Title: Exploring scalable coating of inorganic semiconductor inks: the surface structure-property-performance correlations
Abstract: Inorganic semiconductor inks – such as colloidal quantum dots (CQDs) and transition metal oxides (MOs) – can potentially enable low-cost flexible and transparent electronics via ‘roll-to-roll’ printing. Surfaces of these nanometer-sized CQDs and MO ultra-thin films lead to surface phenomenon with implications on film formation during coating, crystallinity and charge transport. In this talk, I will describe my recent efforts aimed at understanding the crucial role of surface structure in these materials using photoemission spectroscopy and X-ray scattering. Time-resolved X-ray scattering helps reveal the various stages during CQD ink-to-film transformation during blade-coating. Interesting insights include evidence of an early onset of CQD nucleation toward self-assembly and superlattice formation. I will close by discussing fresh results which suggest that nanoscale morphology significantly impacts charge transport in MO ultra-thin (≈5 nm) films. Control over crystallographic texture and film densification allows us to achieve high-performing (electron mobility ≈40 cm2V-1s-1), blade-coated MO thin-film transistors.
Bio: Dr. Ahmad R. Kirmani is a Guest Researcher in the Materials Science and Engineering Division, National Institute of Standards and Technology (NIST) in the group of Dr. Dean M. DeLongchamp and Dr. Lee J. Richter. He is exploring scalable coating of inorganic semiconductor inks using X-ray scattering. He received his PhD in Materials Science and Engineering from the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Aram Amassian in 2017 for probing the surface structure-property relationship in colloidal quantum dot photovoltaics. He has published 30 articles in high-impact journals such Advanced Materials, ACS Energy Letters and the Nature family, and is also a volunteer science writer for the Materials Research Society (MRS) since the last couple of years and has contributed 10 news articles, opinions and perspectives.
Title: Electrets (Dielectrics with quasi-permanent Charges or Dipoles) – A long history and a bright future
Abstract: The history of electrets can be traced back to Thales of Miletus (approx. 624-546 B.C.E.) who reported that pieces of amber (“electron”) attract or repel each other. The science of fundamental electrical phenomena is closely intertwined with the development of electrets which came under such terms as “electrics”, “electrophores”, “charged/poled dielectrics”, etc. until about one century ago. Modern electret research started with Oliver Heaviside (1850-1925), who defined the concept of a “permanently electrized body” and proposed the name “electret” in 1885, and Mototarô Eguchi, who experimentally investigated carnauba wax electrets at the Higher Naval College in Tokyo around 1920. Today, we see a wide range of electret types, electret materials, and electret applications, which are being investigated and developed all over the world in a truly global endeavour. A classification of electrets will be followed by a few examples of useful electret effects and exciting device applications – mainly in the area of electro-mechanical and electro-acoustical transduction which started with the invention of the electret microphone by Sessler and West in the early 1960s. Furthermore, possible synergies between electret research and ultra-high-voltage DC electrical insulation will be mentioned.
Bio: Reimund Gerhard is a Professor of Physics and Astronomy at the University of Potsdam and the current President of the IEEE Dielectrics and Electrical Insulation Society (DEIS). He graduated from the Technical University of Darmstadt as Diplom-Physiker in 1978 and earned his PhD (Doktor-Ingenieur) in Communications Engineering from TU Darmstadt in 1984. From 1985 to 1994, Gerhard was a Research Scientist and Project Manager at the Heinrich-Hertz Institute for Communications Technology (now the Fraunhofer Institute) in Berlin, Germany. He was appointed as a Professor at the University of Potsdam in 1994. From 2004 to 2012, Gerhard served as the Chairman of the Joint Board for the Master-of-Science Program in Polymer Science of FU Berlin, HU Berlin, TU Berlin, and the University of Potsdam. He also served as the Dean of the Faculty of Science at the University of Potsdam from 2008 to 2012, eventually serving as a Senator of the University of Potsdam from 2014 to 2016.
Prof. Gerhard has received many awards and honors over his long career, including an Award (ITG-Preis) from the Information Technology Society (ITG) in the VDE, a silver medal from the Foundation Werner-von-Siemens-Ring, a First Prize Technology Transfer Award Brandenburg, Whitehead Memorial Lecturer of the IEEE CEIDP, and the Award of the EuroEAP Society “for his fundamental scientific contributions in the field of transducers based on dielectric polymers.” He is a Fellow of the American Physical Society (APS) and the Institute of Electrical and Electronics Engineers (IEEE). His research interests include polymer electrets with quasi-permanent space charge, ferro- or piezoelectrets (polymer films with electrically charged cavities), ferroelectric polymers with piezo- and pyroelectric properties, polymer composites with novel property combinations, physical mechanisms of dipole orientation and charge storage, electrically deformable dielectric elastomers (sometimes also called “electro-electrets”), as well as the physics of musical instruments.
Note: There will be a reception after the lecture.
Title: A Theory and Practice of the Lifelong Learnable Forest
Abstract: Since Vapnik’s and Valiant’s seminal papers on learnability, various lines of research have generalized his concept of learning and learners. In this paper, we formally define what it means to be a lifelong learner. Given this definition, we propose the first lifelong learning algorithm with theoretical guarantees that it can perform forward transfer and reverse transfer, while not experiencing catastrophic forgetting. Our algorithm, dubbed Lifelong Learning Forests, outperforms the current state-of-the-art deep lifelong learning algorithm on the CIFAR 10-by-10 challenge problem, despite its simplicity and mathematical tractability. Our approach immediately lends to further algorithmic developments that promise to exceed current performance limits of existing approaches.
Title: A Practical and Efficient Multi-Stream Framework for End-to-End Speech Recognition
Abstract: The multi-stream paradigm in Automatic Speech Recognition (ASR) considers scenarios where parallel streams carry diverse or complementary task-related knowledge. In these cases, an appropriate strategy to fuse streams or select the most informative source is necessary. In recent years, with the increasing use of Deep Neural Networks (DNNs) in ASR, End-to-End (E2E) approaches, which directly transcribe human speech into text, have received greater attention. In this proposal, a multi-stream framework is present based on joint CTC/Attention E2E model, where parallel streams are represented by separate encoders aiming to capture diverse information. On top of the regular attention networks, a secondary stream-fusion network is introduced to steer the decoder toward the most informative encoders.
Two representative framework have been proposed, which are MultiEncoder Multi-Resolution (MEM-Res) and Multi-Encoder Multi-Array (MEM-Array), respectively. Moreover, with an increasing number of streams (encoders) requiring substantial memory and massive amounts of parallel data, a practical two-stage training scheme is further proposed in this work. Experiments are conducted on various corpora including Wall Street Journal (WSJ), CHiME-4, DIRHA and AMI. Compared with the best single-stream performance, the proposed framework has achieved substantial improvement, which also outperforms various conventional fusion strategies.
The future plan aims to improve robustness of the proposed multistream framework. Measuring performance of an ASR system without ground-truth could be beneficial in multi-stream scenarios to emphasize on more informative streams than corrupted ones. In this proposal, four different Performance Monitoring (PM) techniques are investigated. The preliminary results suggest that PM measures on attention distributions and decoder posteriors are well-correlated with true performances. Integration of PM measures and more sophisticated fusion mechanism in multi-stream framework will be the focus for future exploration.
Title: Automated Spore Analysis Using Bright-Field Imaging and Raman Microscopy
Abstract: In 2015, it was determined that the United States Department of Defense had been shipping samples of B. anthracis spores which had undergone gamma irradiation but were not fully inactivated. In the aftermath of this event alternative and orthogonal methods were investigated to analyze spores determine their viability. In this thesis we demonstrate a novel analysis technique that combines bright-field microscopy images with Raman chemical microscopy.
We first developed an image segmentation routine based on the watershed method to locate individual spores within bright-field images. This routine was able to effectively demarcate 97.4% of the Bacillus spores within the bright-field images with minimal over-segmentation. Size and shape measurements, to include major and minor axis and area, were then extracted for 4048 viable spores which showed very good agreement with previously published values. When similar measurements were taken on 3627 gamma-irradiated spores, a statistically significant difference was noted for the minor axis length, ratio of major to minor axis, and total area when compared to the non-irradiated spores. Classification results show the ability to correctly classify 67% of viable spores with an 18% misclassification rate using the bright-field image by thresholding the minimum classification length.
Raman chemical imaging microscopy (RCIM) was then used to measure populations of viable, gamma irradiated, and autoclaved spores of B. anthracis Sterne, B. atrophaeus. B. megaterium, and B. thuringensis kurstaki. Significant spectral differences were observed between viable and inactivated spores due to the disappearance of features associated with calcium dipicolinate after irradiation. Principal component analysis was used which showed the ability to distinguish viable spores of B. anthracis Sterne and B. atrophaeus from each other and the other two Bacillus species.
Finally, Raman microscopy was used to classify mixtures of viable and gamma inactivated spores. A technique was developed that fuses the size and shape characteristics obtained from the bright-field image to preferentially target viable spores. Simulating a scenario of a practical demonstration of the technique was performed on a field of view containing approximately 7,000 total spores of which are only 12 were viable to simulate a sample that was not fully irradiated. Ten of these spores are properly classified while interrogating just 25% of the total spores.
Title: Robust Adaptive Strategies for Myographic Prosthesis Movement Decoding
Abstract: Improving the condition-tolerance, stability, response time, and dexterity of neural prosthesis control strategies are major clinical goals to aid amputees in achieving natural restorative upper-limb function. Currently, the dominant noninvasive neural source for prosthesis motor control is the skin-surface recorded electromyographic (EMG) signal. Decoding movement intentions from EMG is a challenging problem because this signal type is subject to a high degree of interference from noise and conditional influences. As a consequence, much of the movement intention information contained within the EMG signal has remained significantly under-utilized for the purposes of controlling robotic prostheses. We sought to overcome this information deficit through the use of adaptive strategies for machine learning, sparse representations, and signal processing to significantly improve myographic prosthesis control. This body of research represents the current state-of-the-art in condition-tolerant EMG movement classification (Chapter 3), stable and responsive EMG sequence decoding during movement transitions (Chapter 4), and positional regression to reliably control 7 wrist and finger degrees-of-freedom (Chapter 5). To our knowledge, the methods we describe in Chapter 5 elicit the most dexterous, biomimetic, and natural prosthesis control performance ever obtained from the surface EMG signal.
Title: “Honey I shrank the microscope!” And Other Adventures in Functional Imaging
Abstract: Imaging the brain in action, in awake freely behaving animals without the confounding effect of anesthetics poses unique design and experimental challenges. Moreover, imaging the evolution of disease models in the preclinical setting over their entire lifetime is also difficult with conventional imaging techniques. This lecture will describe the development and applications of a miniaturized microscope that circumvents these hurdles. This lecture will also describe how image acquisition, data visualization and engineering tools can be leveraged to answer fundamental questions in cancer, neuroscience and tissue engineering applications.
Bio: Dr. Pathak is an ideator, educator and mentor focused on transforming lives through the power of imaging. He received the BS in Electronics Engineering from the University of Poona, India. He received his PhD from the joint program in Functional Imaging between the Medical College of Wisconsin and Marquette University. During his PhD he was a Whitaker Foundation Fellow. He completed his postdoctoral fellowship at the Johns Hopkins University School of Medicine in Molecular Imaging. He is currently Associate Professor of Radiology, Oncology and Biomedical Engineering at Johns Hopkins University (JHU). His research is focused on developing new imaging methods, computational models and visualization tools to ‘make visible’ critical aspects of cancer, neurobiology and tissue engineering. His work has been recognized by multiple journal covers and awards including the Bill Negendank Award from the International Society for Magnetic Resonance in Medicine (ISMRM) given to “outstanding young investigators in cancer MRI” and the Career Catalyst Award from the Susan Komen Breast Cancer Foundation. He serves on review panels for national and international funding agencies, and the editorial boards of imaging journals. He is dedicated to mentoring the next generation of imagers and innovators. He has mentored over sixty students, was the recipient of the ISMRM’s Outstanding Teacher Award in 2014, a 125 Hopkins Hero in 2018 for outstanding dedication to the core values of JHU, and a Career Champion Nominee in 2018 for student career guidance and support.
Title: Loss Landscapes of Neural Networks and their Generalization: Theory and Applications
Abstract: In the last decade or so, deep learning has revolutionized entire domains of machine learning. Neural networks have helped achieve significant improvements in computer vision, machine translation, speech recognition, etc. These powerful empirical demonstrations leave a wide gap between our current theoretical understanding of neural networks and their practical performance. The theoretical questions in deep learning can be put under three broad but inter-related themes: 1) Architecture/Representation, 2) Optimization, and 3) Generalization. In this dissertation, we study the landscapes of different deep learning problems to answer questions in the above themes.
First, in order to understand what representations can be learned by neural networks, we study simple Autoencoder networks with one hidden layer of rectified linear units. We connect autoencoders to the well-known problem in signal processing of Sparse Coding. We show that the squared reconstruction error loss function has a critical point at the ground truth dictionary under an appropriate generative model.
Next, we turn our attention to a problem at the intersection of optimization and generalization. Training deep networks through empirical risk minimization is a non-convex problem with many local minima in the loss landscape. A number of empirical studies have observed that “flat minima” for neural networks tend to generalize better than sharper minima. However, quantifying the flatness or sharpness of minima has been an issue due to possible rescaling in neural networks with positively homogenous activations. We use ideas from Riemannian geometry to define a new measure of flatness that is invariant to rescaling. We test the hypothesis that flatter minima generalize better through a number of different experiments on deep networks.
Finally, we apply deep networks to computer vision problems with compressed measurements of natural images and videos. We conduct experiments to characterize the situations in which these networks fail, and those in which they succeed. We train deep networks to perform object detection and classification directly on these compressive measurements of images, without trying to reconstruct the scene first. These experiments are conducted on public datasets as well as datasets specific to a sponsor of our research.
Title: Deep Learning-based Novelty Detection
Abstract: In recent years, intelligent systems powered by artificial intelligence and computer vision that perform visual recognition have gained much attention. These systems observe instances and labels of known object classes during training and learn association patterns that can be
used during inference. A practical visual recognition system should first determine whether an observed instance is from a known class. If it is from a known class, then the identity of the instance is queried through classification. The former process is commonly known as novelty detection (or novel class detection) in the literature. Given a set of image instances from known classes, the goal of novelty detection is to determine whether an observed image during inference belongs to one of the known classes.
We consider one-class novelty detection, where all training data are assumed to belong to a single class without any finer-annotations available. We identify limitations of conventional approaches in one-class novelty detection and present a Generative Adversarial Network(GAN) based solution. Our solution is based on learning latent representations of in-class examples using a denoising auto-encoder network. The key contribution of our work is our proposal to explicitly constrain the latent space to exclusively represent the given class. In order to accomplish this goal, firstly, we force the latent space to have bounded support by introducing a tanh activation in the encoder’s output layer. Secondly, using a discriminator in the latent space that is trained adversarially, we ensure that encoded representations of in-class examples resemble uniform random samples drawn from the same bounded space. Thirdly, using a second adversarial discriminator in the input space, we ensure all randomly drawn latent samples generate examples that look real.
Finally, we introduce a gradient-descent based sampling technique that explores points in the latent space that generate potential out-of-class examples, which are fed back to the network to further train it to generate in-class examples from those points. The effectiveness of the proposed method is measured across four publicly available datasets using two one-class novelty detection protocols where we achieve state-of-the-art results.