Title: Soroban: A Mixed-Signal Neuromorphic Processing in Memory Architecture
Abstract: To meet the scientific demand for future data-intensive processing for every day mundane tasks such as searching via images to the uttermost serious health care disease diagnosis in personalized medicine, we urgently need a new cloud computing paradigm and energy efficient i.e. “green” technologies. We believe that a brain-inspired approach that employs unconventional processing offers an alternative paradigm for BIGDATA computing.
My research aims to go beyond the state of the art processor in memory architectures. In the realm of un-conventional processors, charge based computing has been an attractive solution since it’s introduction with charged-coupled device (CCD) imagers in the seventies. Such architectures have been modified to compute-in-memory arrays that have been used for signal processing, neural networks and pattern recognition using the same underlying physics. Other work has utilized the same concept in the charge-injection devices (CIDs), which have also been used for similar pattern recognition tasks. However, these computing elements have not been integrated with the support infrastructure for high speed input/output commensurate with BIGDATA processing streaming applications. In this work, the CID concept is taken to a smaller CMOS 55nm node and has shown promising preliminary results as a multilevel input computing element for hardware inference applications. A mixed signal charge-based vector-vector multiplier (VMM) is explored which computes directly on a common readout line of a dynamic random-access memory (DRAM). Low power consumption and high area density is achieved by storing local parameters in a DRAM computing crossbar.
Title: Advanced Image Reconstruction and Analysis for Fluorescence Molecular Tomography (FMT) and Positron Emission Tomography (PET)
Abstract: Molecular imaging provides efficient ways to monitor different biological processes noninvasively, and high-quality imaging is necessary in order to fully explore the value of molecular imaging. To this end, advanced image generation algorithms are able to significantly improve image quality and quantitative performance. In this research proposal, we focus on two imaging modalities, fluorescence molecular tomography (FMT) and positron emission tomography (PET), that fall in the category of molecular imaging. Specifically, we studied the following two problems: i) reconstruction problem in FMT and ii) partial volume correction in brain PET imaging.
Reconstruction in FMT: FMT is an optical imaging modality that uses diffuse light for imaging. Reconstruction problem for FMT is highly ill-posed due to photon scattering in biological tissue, and thus, regularization techniques tend to be used to alleviate the ill-posed nature of the problem. Conventional reconstruction algorithms cause oversmoothing which reduces resolution of the reconstructed images. Moreover, a Gaussian model is commonly chosen as the noise model although most FMT systems based on charged-couple device (CCD) or photon multiplier tube (PMT) are contaminated by Poisson noise. In our work, we propose a reconstruction algorithm for FMT using sparsity-initialized maximum-likelihood expectation maximization (MLEM). The algorithm preserves edges by exploiting sparsity, as well as taking Poisson noise into consideration. Through simulation experiments, we compare the proposed method with pure sparse reconstruction method and MLEM with uniform initialization. We show the proposed method holds several advantages compared to the other two methods.
Partial volume correction of brain PET imaging: The so-called partial volume effect (PVE) is caused by the limited resolution of PET systems, reducing quantitative accuracy of PET imaging. Based on the stage of implementation, partial volume correction (PVC) algorithms could be categorized into reconstruction-based and post-reconstruction methods.Post reconstruction PVC methods can be directly implemented on reconstructed PET images and do not require access to raw data or reconstruction algorithms of PET scanners. Many of these methods use anatomical information from MRI to further improve their performance. However, conventional MR guided post-reconstruction PVC methods require segmentation of MR images and assume uniform activity distribution within each segmented region. In this proposal, we develop post-reconstruction PVC method based on deconvolution via parallel level set regularization. The method is implemented with non-smooth optimization based on the split Bregman method. The proposed method incorporates MRI information without requiring segmentation or making any assumption on activity distribution. Simulation experiments are conducted to compare the proposed method with several other segmentationfree method, as well as conventional segmentation-based PVC method. The results show the proposed method outperforms other segmentation-free method and shows stronger resistance to MR information mismatch compared to conventional segmentation-based PVC method.
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: 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: “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: 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.
Title: Towards a better understanding of spoken conversations: Assessment of sentiment and emotion
Abstract: In this talk, we present our work on understanding the emotional aspects of spoken conversations. Emotions play a vital role in our daily life as they help us convey information impossible to express verbally to other parties.
While humans can easily perceive emotions, these are notoriously difficult to define and recognize by machines. However, automatically detecting the emotion of a spoken conversation can be useful for a diverse range of applications such as human-machine interaction and conversation analysis. In this work, we considered emotion recognition in two particular scenarios. The first scenario is predicting customer sentiment/satisfaction (CSAT) in a call center conversation, and the second consists of emotion prediction in short utterances.
CSAT is defined as the overall sentiment (positive vs. negative) of the customer about his/her interaction with the agent. In this work, we perform a comprehensive search for adequate acoustic and lexical representations.
For acoustic representation, we propose to use the x-vector model, which is known for its state-of-the-art performance in the speaker recognition task. The motivation behind using x-vectors for CSAT is we observed that emotion information encoded in x-vectors affected speaker recognition performance. For lexical, we introduce a novel method, CSAT Tracker, which computes the overall prediction based on individual segment outcomes. Both methods rely on transfer learning to obtain the best performance. We classified using convolutional neural networks combining the acoustic and lexical features. We evaluated our systems on US English telephone speech from call center data. We found that lexical models perform better than acoustic models and fusion of them provided significant gains. The analysis of errors uncovers that the calls where customers accomplished their goal but were still dissatisfied are the most difficult to predict correctly. Also, we found that the customer’s speech is more emotional compared to the agent’s speech.
For the second scenario of predicting emotion, we present a novel approach based on x-vectors. We show that adapting the x-vector model for emotion recognition provides the best-published results on three public datasets.
Title: Single-Channel Speech Separation in Noisy and Reverberant Conditions
Abstract: An inevitable property of multi-party conversations is that more than one speaker will end up speaking simultaneously for portions of time. Many speech technologies, such as automatic speech recognition and speaker identification, are not designed to function on overlapping speech and suffer severe performance degradation under such conditions. Speech separation techniques aim to solve this problem by producing a separate waveform for each speaker in an audio recording with multiple talkers speaking simultaneously. The advent of deep neural networks has resulted in strong performance gains on the speech separation task. However, training and evaluation has been nearly ubiquitously restricted to a single dataset of clean, near-field read speech, not representative of many multi-person conversational settings which are frequently recorded on room microphones, introducing noise and reverberation. Due to the degradation of other speech technologies in these sorts of conditions, speech separation systems are expected to suffer a decrease in performance as well.
The primary goal of this proposal is to develop novel techniques to improve speech separation in noisy and reverberant recording conditions. One core component of this work is the creation of additional synthetic overlap corpora spanning a range of more realistic and challenging conditions. The lack of appropriate data necessitates a first step of creating appropriate conditions with which to benchmark the performance of state-of-the-art methods in these more challenging conditions. Another proposed line of investigation is the integration of speech separation techniques with speech enhancement, the task of enhancing a speech signal through the removal of noise or reverberation. This is a natural combination due to similarities in problem formulation and general approach. Finally, we propose an investigation into the effectiveness of speech separation as a pre-processing step to speech technologies, such as automatic speech recognition, that struggle with overlapping speech, as well as tighter integration of speech separation with these “downstream” systems.