Calendar

Dec
12
Thu
Dissertation Defense: Joseph Betthauser @ Shaffer 202
Dec 12 @ 10:00 am – 12:00 pm
Dissertation Defense: Joseph Betthauser @ Shaffer 202

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.

ECE Special Seminar: Arvind Pathak @ Hodson Hall 316
Dec 12 @ 3:00 pm – 4:15 pm
ECE Special Seminar: Arvind Pathak @ Hodson Hall 316

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.

Dec
20
Fri
Dissertation Defense: Akshay Rangamani @ Hackerman Hall 320
Dec 20 @ 10:00 am – 12:00 pm
Dissertation Defense: Akshay Rangamani @ Hackerman Hall 320

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.

Jan
30
Thu
Thesis Proposal: Pramuditha Perera @ Hackerman Hall B-17
Jan 30 @ 3:00 pm – 4:00 pm
Thesis Proposal: Pramuditha Perera @ Hackerman Hall B-17

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.

Feb
18
Tue
Dissertation Defense: Nagaraj Mahajan @ Hackerman Hall B-17
Feb 18 @ 3:00 pm – 5:00 pm
Dissertation Defense: Nagaraj Mahajan @ Hackerman Hall B-17

Title: Neural Circuit Mechanisms of Stimulus Selection Underlying Spatial Attention

Thesis Committee: Shreesh P. Mysore, Hynek Hermansky, Mounya Elhilali, Ralph Etienne-Cummings

Abstract: Humans and animals routinely encounter competing pieces of information in their environments, and must continually select the most salient in order to survive and behave adaptively. Here, using computational modeling, extracellular neural recordings, and focal, reversible silencing of neurons in the midbrain of barn owls, we uncovered how two essential computations underlying competitive selection are implemented in the brain: a) the ability to select the most salient stimulus among all pairs of stimulus locations, and b) the ability to signal the most salient stimulus categorically.

We first discovered that a key inhibitory nucleus in the midbrain attention network, called isthmi pars magnocellularis (Imc), encodes visual space with receptive fields that have multiple excitatory hotspots (‘‘lobes’’). Such (previously unknown) multilobed encoding of visual space is necessitated for selection at all location-pairs in the face of scarcity of Imc neurons. Although distributed seemingly randomly, the RF lobe-locations are optimized across the high-firing Imc neurons, allowing them to combinatorially solve selection across space. This combinatorially optimized inhibition strategy minimizes metabolic and wiring costs.

Next, we discovered that a ‘donut-like’ inhibitory mechanism in which each competing option suppresses all options except itself is highly effective at generating categorical responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization used commonly in decision-making models. We demonstrated experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is required for categorical signaling by it. Moreover, the pattern of inhibition in the midbrain forms an exquisitely structured ‘multi-holed’ donut consistent with this network’s combinatorial inhibitory function (computation 1).

Our work demonstrates that the vertebrate midbrain uses seemingly carefully optimized structural and functional strategies to solve challenging computational problems underlying stimulus selection and spatial attention at all location pairs. The neural motifs discovered here represent circuit-based solutions that are generalizable to other brain areas, other forms of behavior (such as decision-making, action selection) as well as for the design of artificial systems (such as robotics, self-driving cars) that rely on the selection of one among many options.

 

Feb
27
Thu
Thesis Proposal: Raghavendra Pappagari @ Hackerman Hall B-17
Feb 27 @ 3:00 pm
Thesis Proposal: Raghavendra Pappagari @ Hackerman Hall B-17

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.

Mar
5
Thu
Thesis Proposal: Matthew Maciejewski @ Hackerman Hall B-17
Mar 5 @ 3:00 pm
Thesis Proposal: Matthew Maciejewski @ Hackerman Hall B-17

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.

Mar
12
Thu
Dissertation Defense: Pramuditha Perera @ Malone Hall G33/35
Mar 12 @ 3:00 pm
Dissertation Defense: Pramuditha Perera @ Malone Hall G33/35

University policy at this present time: Students and faculty CAN attend dissertation defenses as long as there are fewer than 25 people.

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.

In this thesis, deep learning-based approaches to solve novelty detection is studied under four different settings. In the first two settings, the availability of out-of-distributional data (OOD) is assumed. With this assumption, novelty detection can be studied for cases where there are multiple known classes and a single known class separately. These two problem settings are referred to as Multi-class novelty detection with OOD data and one-class novelty detection with OOD data in the literature, respectively. It is also possible to study this problem in a more constrained setting where only the data from known classes are considered for training. When there exist multiple classes in this setting novelty detection problem is known as Multiple-class novelty detection or Open-set recognition. On the other hand, when only a single class exists it is known as one-class novelty detection.

Finally, we study a practical application of novelty detection in mobile Active Authentication (AA).   For a  practical AA-based novelty detector, latency and efficiency are as important as the detection accuracy. Solutions are presented for the problem of quickly detecting intrusions with lower false detection rates in mobile AA systems with higher resource efficiency. Bayesian and Minimax versions of the Quickest Change Detection (QCD) algorithms are introduced to quickly detect intrusions in mobile AA systems. These algorithms are extended with an update rule to facilitate low-frequency sensing which leads to low utilization of resources.

Committee Members: Vishal Patel, Trac Tran, Najim Dehak

Mar
18
Wed
Dissertation Defense: Yan Cheng @ Malone Hall G33/35
Mar 18 @ 2:00 pm
Dissertation Defense: Yan Cheng @ Malone Hall G33/35

Taking place remotely. Email Belinda Blinkoff for more information.

Title: Engineering Earth-Abundant Colloidal Plasmonic and Semiconductor Nanomaterials for Solar Energy Harvesting and Detection Applications

Abstract: Colloidal nanomaterials have shown intriguing optical and electronic properties, making them important building blocks for a variety of applications, including photocatalysis, photovoltaics, and photodetectors. Their morphology and composition are effective tuning knobs for achieving desirable spectral characteristics for specific applications. In addition, they can be synthesized using solution-processed methods which possess the advantages of low cost, facile fabrication, and compatibility with building flexible devices. There is an ongoing quest for better colloidal materials with superior properties and high natural abundance for commercial viability. This thesis focuses on three such materials classes and applications: 1) studying the photophysical properties of earth-abundant plasmonic alumionum nanoparticles, 2) tailoring the optical profiles of semiconductor quantum dot solar cells with near-infrared sensitivity, and 3) using one-dimensional nanostructures for photodetector applications. A variety of analytical techniques and simulations are employed for characterization of both the morphology and optical properties of the nanostructures and for evaluating the performance of nanomaterial-based optoelectronic devices.

The first experimental section of this thesis consists of a systematic study of electron relaxation dynamics in solution-processed large aluminum nanocrystals. Transient absorption measurement are used to obtain the important characteristic relaxation timescales for each thermalization process. We show that several of the relevant timescales in aluminum differ from those in analogous noble metal nanoparticles and proposed that surface modification could be a useful tool for tuning heat transfer rates between the nanostructures and solvent. Further systematic studies on the relaxation dynamics in aluminum nanoparticles with tunable sizes show size-dependent phonon vibrational and damping characteristics that are influenced by size polydispersity, surface oxidation, and the presence of organic capping layers on the particles. These studies are significant first steps in demonstrating the feasibility of using aluminum nanomaterials for efficient photocatalysis.

The next section summarizes studies on the design and fabrication of multicolored PbS-based quantum dot solar cells. Specifically, thin film interference effects and multi-objective optimization methods are used to generate cell designs with controlled reflection and transmission spectra resulting in programmable device colors or visible transparency. Detailed investigations into the trade-off between the attainable color or transparency and photocurrent are discussed. The results of this study could be used to enable solar cell window-coatings and other controlled-color optoelectronic devices.

The last experimental section of thesis describes work on using 1D antimony selenide nanowires for flexible photodetector applications. A one-pot solution-based synthetic method is developed for producing a molecular ink which allows fabrication of devices on flexible substrates. Thorough characterization of the nanowire composition and morphology are performed. Flexible, broadband antimony selenide nanowire photodetectors are fabricated and show fast response and good mechanical stability. With further tuning of the nanowire size, spectral selectivity should be achievable. The excellent performance of the nanowire photodetectors is promising for the broad implementation of semiconductor inks in flexible photodetectors and photoelectronic switches.

Committee Members: Susanna Thon, Amy Foster, Jin Kang

Mar
26
Thu
Seminar: David Harwath, Massachusetts Institute of Technology
Mar 26 @ 3:00 pm
Seminar: David Harwath, Massachusetts Institute of Technology

This presentation happened remotely. Follow this link to view it. Please note that the presentation doesn’t start until 30 minutes into the video.

Title: Learning Spoken Language Through Vision

Abstract: Humans learn spoken language and visual perception at an early age by being immersed in the world around them. Why can’t computers do the same? In this talk, I will describe our work to develop methodologies for grounding continuous speech signals at the raw waveform level to natural image scenes. I will first present self-supervised models capable of jointly discovering spoken words and the visual objects to which they refer, all without conventional annotations in either modality. Next, I will show how the representations learned by these models implicitly capture meaningful linguistic structure directly from the speech signal. Finally, I will demonstrate that these models can be applied across multiple languages, and that the visual domain can function as an “interlingua,” enabling the discovery of word-level semantic translations at the waveform level.

Bio: David Harwath is a research scientist in the Spoken Language Systems group at the MIT Computer Science and Artificial Intelligence Lab (CSAIL). His research focuses on multi-modal learning algorithms for speech, audio, vision, and text. His work has been published at venues such as NeurIPS, ACL, ICASSP, ECCV, and CVPR. Under the supervision of James Glass, his doctoral thesis introduced models for the joint perception of speech and vision. This work was awarded the 2018 George M. Sprowls Award for the best Ph.D. thesis in computer science at MIT.

He holds a Ph.D. in computer science from MIT (2018), a S.M. in computer science from MIT (2013), and a B.S. in electrical engineering from UIUC (2010).

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