A large number of sequential decision-making problems in uncertain environments
can be modeled as Markov Decision Processes (MDPs). In such settings, an agent
can observe at each time step the state of the environment and then executes an
action, causing a stochastic transition to a new state of the environment...
Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
This thesis examines the mixing times for one-dimensional interacting particle systems. We use the coupling method to study the mixing rates for particle systems on the circle which move according to specific permutations e.g., transpositions and 3-cycles.
Coordinating multiple robots to achieve a complex task requires solving two distinct control problems: the high-level control problem of ensuring that each robot aims to perform a useful task (e.g., coordination) and the low-level control problem of ensuring that each robot actually performs the correct actions to achieve its task...
This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....
Given a video, we would like to recognize group activities, localize video parts where these activities occur, and detect actors involved in them. To this and, we propose a novel, mid-level feature, called control point, for representing group activities. The control points are aimed at summarizing visual cues, lifting from...
This thesis studies cooperative techniques that rely on femtocell user diversity to improve the downlink communication quality of macrocell users. We analytically analyze and evaluate the achievable performance of these techniques in the downlink of Rayleigh fading channels. We provide an approximation of both the bit-error rate (BER) and the...
A fundamental problem in computer vision is to partition an image into meaningful segments. While image segmentation is required by many applications, the thesis focuses on segmentation of computed tomography (CT) images for analysis and quality control of composite materials. The key research contribution of this thesis is a novel...
The study of physical activity is important in improving people’s health as it can help people understand the relationship between physical activity and health. Accelerometers, due to its small size, low cost, convenience and its ability to provide objective information about the frequency, intensity, and duration of physical activity, has...
Traditionally, networking protocol designs have placed much emphasis on point-to-point reliability and efficiency. With the recent rise of mobile and multimedia applications, other considerations such as power consumption and/or Quality of Service (QoS) are becoming increasingly important factors in designing network protocols. As such, we present a new flexible framework...
Image classification is a difficult problem, often requiring large training sets to get satisfactory results. However this is a task that humans perform very well, and incorporating user feedback into these learning algorithms could help reduce the dependency on large amounts of labeled training data. This process has already been...
We consider the problem of wireless spectrum management in cognitive wireless networks that maximizes the revenue for a spectrum operator. Specifically, we study the problem on how a wireless spectrum operator can optimally allocate its limited spectrum to various classes users/devices who pay differently for their spectrum per unit time....
This thesis presents an interactive software tool for tracking a moving object in a video. In particular, we focus on the problem of tracking a player in American football videos. Object tracking is one of the fundamental problems in computer vision. It is one of the most important components in...
This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to detect nematocysts in Scanning Electron Microscope (SEM) images. This biological domain presents challenges for existing...
Pardoxes in voting has been an interest of voting theorists since the 1800's when Condorcet demonstrated the key example of a voting paradox: voters with individually transitive rankings produce an election outcome which is not transitive. With Arrow's Impossibility Theorem, the hope of finding a fair voting method which accurately...
Constructing a panorama from a set of videos is a long-standing problem in computer vision. A panorama represents an enhanced still-image representation of an entire scene captured in a set of videos, where each video shows only a part of the scene. Importantly, a panorama shows only the scene background,...
In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements and despite targeted efforts to improve the operation and control of the electric grid, the power systems industry has seen relatively small advances in this regard. For instance, today's power system is...
Counting problems are rich in opportunities for students to make meaningful mathematical connections and develop non-algorithmic thinking; their accessible nature and applications to computer science make counting problems a valuable part of mathematics curricula. However, students struggle in various ways with counting, and while previous studies have indicated that listing...
This thesis addresses the problem of temporal action segmentation in videos, where the goal is to label every video frame with the appropriate action class present. We focus on the domain of NFL football videos, where action classes represent common football play types. For action segmentation, we use a temporal...
Data variations are prevalent in real-world applications. For example, software vendors have to handle numerous variations in the business requirements, conventions, and environmental settings of a software product. In database-backed software, the database of each version may have a different schema and content. As another example, data scientists often need...
In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
Deep neural networks currently comprise the backbone of many applications where safety is a critical concern, for example: autonomous driving and medical diagnostics. Unfortunately these systems currently fail to detect out-of-distribution (OOD) inputs and can be prone to making dangerous errors when exposed to them. In addition, these same systems...
Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
Significance: Movement intent decoding algorithms can interpret human bioelectrical signals to control prosthetic limbs with many degrees of freedom (DOFs). This work involves decoding volitional movement intent from surface electromyogram (sEMG) signals to control prosthetic arms. To train these algorithms, patients flex their muscles to “follow” a movement prompt, and...
Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
Correctness and efficiency are important properties of programs. However, to support maintenance and debugging, the programs should also be understandable. Program explanations also play a vital role in educational settings, enhancing the understanding of programs among students.
Proof trees provide a sound basis for generating dynamic explanations of programs. But...
Multi-relation aggregation queries process the join operator before computing the aggregation function. This join is arguably the most costly operation since traditional join algorithms spend majority of their time trying to join the parts of the relations that do not generate any output tuples. This causes slow response times with...
Relational binary operators, such as join, are arguably the most costly and frequently used operations in relational data systems. In many join algorithms, the majority of the process time is spent on scanning and attempting to join the parts of the relations that do not satisfy the join condition and...
As bipedal robots move ever closer to being integrated into all manner of real world envi-ronments there is a necessity to push their dynamic capabilities to meet or exceed those of humans and animals. Advancements must be made to address ordinary challenges that arise everyday in the same environments that...
Machine common sense remains a broad, potentially unbounded problem in AI. Our focus is to move toward AI systems that can develop common-sense reasoning similar to humans to detect anomalies. In particular, we study the problem of detecting the violation of expectations when object appearance or motion dynamics change from...
We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
Alignment of genomic sequences from different species is becoming an increasingly powerful method in biology, and is being used for many purposes. The result of sequence alignments is a list of pairs of matched locations between the pattern string and the text string. However, without any proper visualization tools to...