# Browsing by Subject "Information science"

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Item Open Access An Information-Theoretic Analysis of X-Ray Architectures for Anomaly Detection(2018) Coccarelli, David ScottX-ray scanning equipment currently establishes a first line of defense in the aviation security space. The efficacy of these scanners is crucial to preventing the harmful use of threatening objects and materials. In this dissertation, I introduce a principled approach to the analyses of these systems by exploring performance limits of system architectures and modalities. Moreover, I validate the use of simulation as a design tool with experimental data as well as extend the use of simulation to create high-fidelity realizations of a real-world system measurements.

Conventional performance analysis of detection systems confounds the effects of the system architecture (sources, detectors, system geometry, etc.) with the effects of the detection algorithm. We disentangle the performance of the system hardware and detection algorithm so as to focus on analyzing the performance of just the system hardware. To accomplish this, we introduce an information-theoretic approach to this problem. This approach is based on a metric derived from Cauchy-Schwarz mutual information and is analogous to the channel capacity concept from communications engineering. We develop and utilize a framework that can produce thousands of system simulations representative of a notional baggage ensembles. These simulations and the prior knowledge of the virtual baggage allow us to analyze the system as it relays information pertinent to a detection task.

In this dissertation, I discuss the application of this information-theoretic approach to study variations of X-ray transmission architectures as well as novel screening systems based on X-ray scatter and phase. The results show how effective use of this metric can impact design decisions for X-ray systems. Moreover, I introduce a database of experimentally acquired X-ray data both as a means to validate the simulation approach and to produce a database ripe for further reconstruction and classification investigations. Next, I show the implementation of improvements to the ensemble representation in the information-theoretic material model. Finally I extend the simulation tool toward high-fidelity representation of real-world deployed systems.

Item Open Access Design and Usability Testing of a Mobile Phone-Based Patient Management System for Women in Rural Kenya(2014) Karnik, AmoghEvery day, approximately 800 women die from pregnancy-related complications. Most of these deaths are avoidable. Care from a skilled provider before, during, and after delivery has been shown to prevent a majority of maternal and neonatal deaths. However, time delays in recognizing the need to seek care, accessing health care facilities, and receiving adequate care from a provider of make the delivery of effective maternal healthcare practices very challenging. These three delays disproportionately affect women living in rural and remote regions, where awareness of maternal health problems can be low and health facilities are few and far between. In Kenya, maternal health care in these regions falls upon community health volunteers, who are unpaid and overworked.

In recent years, mobile phones have grown in popularity for improving disease prevention and management, especially in the field of maternal and child health. The intent of this study was to design and pilot a mobile phone-based patient management system intended for use by community health volunteers. Using a human-centered design framework, a system was developed to fit into the CHVs' existing workflows in order to improve the delivery of maternal and child health care at the community level. Integrating both voice and text messaging interfaces, the system was designed to provide the CHVs with a fast and easy method of recording and reporting data, a streamlined approach for tracking patient referrals to a health facility, and a reliable and effective way to report and respond to obstetric emergencies. The system was found to be highly usable based on self-report data from users, who indicated that the system saved them time and helped them complete their responsibilities as CHVs. In all, results of this pilot suggest that such a system may be useful for CHVs in monitoring the health of pregnant women over time and helping to avoid the time delays associated with maternal mortality.

Item Open Access Designing Quantum Channels Induced by Diagonal Gates(2023) Hu, JingzhenThe challenge of quantum computing is to combine error resilience with universal computation. Diagonal gates such as the transversal T gate play an important role in implementing a universal set of quantum operations. We introduce a framework that describes the process of preparing a code state, applying a diagonal physical gate, measuring a code syndrome, and applying a Pauli correction that may depend on the measured syndrome (the average logical channel induced by an arbitrary diagonal gate). The framework describes the interaction of code states and physical gates in terms of generator coefficients determined by the induced logical operator. The interaction of code states and diagonal gates depends on the signs of Z-stabilizers in the CSS code, and the proposed generator coefficient framework explicitly includes this degree of freedom. We derive necessary and sufficient conditions for an arbitrary diagonal gate to preserve the code space of a stabilizer code, and provide an explicit expression of the induced logical operator. When the diagonal gate is a quadratic form diagonal gate, the conditions can be expressed in terms of divisibility of weights in the two classical codes that determine the CSS code. These codes find applications in magic state distillation and elsewhere. When all the signs are positive, we characterize all possible CSS codes, invariant under transversal Z-rotation through π/2^l, that are constructed from classical Reed-Muller codes by deriving the necessary and sufficient constraints on the level l. According to the divisibility conditions, we construct new families of CSS codes using cosets of the first order Reed-Muller code defined by quadratic forms. The generator coefficient framework extends to arbitrary stabilizer codes but the more general class of non-degenerate stabilizer codes does not bring advantages when designing the code parameters.

Relying on the generator coefficient framework, we introduce a method of synthesizing CSS codes that realizes a target logical diagonal gate at some level l in the Clifford hierarchy. The method combines three basic operations: concatenation, removal of Z-stabilizers, and addition of X-stabilizers. It explicitly tracks the logical gate induced by a diagonal physical gate that preserves a CSS code. The first step is concatenation, where the input is a CSS code and a physical diagonal gate at level l inducing a logical diagonal gate at the same level. The output is a new code for which a physical diagonal gate at level l+1 induces the original logical gate. The next step is judicious removal of Z-stabilizers to increase the level of the induced logical operator. We identify three ways of climbing the logical Clifford hierarchy from level l to level l+1, each built on a recursive relation on the Pauli coefficients of the induced logical operators. Removal of Z-stabilizers may reduce distance, and the purpose of the third basic operation, addition of X-stabilizers, is to compensate for such losses. Our approach to logical gate synthesis is demonstrated by two proofs of concept: the [[2^(l+1) − 2, 2, 2]] triorthogonal code family, and the [[2^m, (m choose r) , 2^(min{r, m-r})]] quantum Reed-Muller code family.

Item Open Access Essays on Information and Dynamic Incentives(2023) Kim, YonggyunThis dissertations consists of three essays on information and dynamic incentives.

In Chapter 1, I study the value of information in monotone decision problems where the action spaces are potentially multidimensional. As a criterion for comparing information structures, I develop a condition called monotone quasi-garbling meaning that an information structure is obtained by adding reversely monotone noise (more likely to return a higher signal in a lower state and a lower signal in a higher state) to another. It is shown that monotone quasi-garbling is a necessary and sufficient condition for decision makers to get a higher ex-ante expected payoff. Under the monotone likelihood ratio property, this new criterion is equivalent to the accuracy condition by Lehmann (1988) and refines the garbling condition by Blackwell (1951, 1953). To illustrate, I apply the result to problems in nonlinear monopoly pricing and optimal insurance.

In Chapter 2, I study a dynamic principal-agent problem where there are two routes of completing a project: directly attacking it or splitting it into two subprojects. When the project is split, the principal can better monitor the agent by verifying the completion of the first subproject. However, the inflexible nature of this approach may generate inefficiencies. To mitigate moral hazard, the principal needs to commit to a deadline, which also affects her choice of project management strategy. The optimal contract is determined by the interplay of these three factors: monitoring, efficiency, and an endogenous deadline.

Chapter 3, which is a joint work with Francisco Poggi, investigates firms' incentives to conceal intermediate research discoveries in innovation races. To study this, we introduce an innovation game where two racing firms dynamically allocate their resources between two distinct research and development (R&D) paths towards a final innovation: (i) developing it with the currently available but slower technology; (ii) conducting research to discover a faster new technology for developing it. We fully characterize the equilibrium behavior of the firms in the cases where their research progress is public and private information. Then, we extend the private information setting by allowing firms to conceal or license their intermediate discoveries. We show that when the reward of winning the race is high enough, firms would conceal their interim discoveries, which inefficiently retards the pace of innovation.

Item Open Access FUNDAMENTAL LIMITS FOR COMMUNITY DETECTION IN LABELLED NETWORKS(2020) Mayya, Vaishakhi SathishThe problem of detecting the community structure of networks as well as closely related problems involving low-rank matrix factorization arise in applications throughout science and engineering. This dissertation focuses on the the fundamental limits of detection and recovery associated with a broad class of probabilistic network models, that includes the stochastic block model with labeled-edges. The main theoretical results are formulas that describe the asymptotically exact limits of the mutual information and reconstruction error. The formulas are described in terms of low-dimensional estimation problems in additive Gaussian noise.

The analysis builds upon a number of recent theoretical advances at the interface of information theory, random matrix theory, and statistical physics, including concepts such as channel universality and interpolation methods. The theoretical formulas provide insight into the ability to recover the community structure in the network. The analysis is supported by numerical simulations. Numerical simulations for different network models show that the observed performance closely follows the performance predicted by the formulas.

Item Open Access Integrating Computer Architecture and Coding Theory to Advance Emerging Memory Technologies(2020) Mappouras, GeorgiosNew memory technologies constantly emerge promising higher density, bandwidth, latency, and power efficiency comparing to traditional solutions. However, these technologies often suffer from substantial drawbacks like limited lifetime or low fault tolerance. These drawbacks prevent the integration of these technologies in modern computer systems and increase their cost of implementation. In this work, we utilize solutions both from the disciplines of computer architecture and coding theory to address the drawbacks of emerging memory technologies. By integrating computer architecture and coding theory we can design more optimized solutions, paving the way for emerging memory technologies to become viable and reliable options for modern computer systems.

More specifically we design MinWear codes to increase the lifetime of Flash memory, providing larger lifetime gains for smaller capacity costs comparing to prior work. We also design GreenFlag codes to address shift errors in 3D racetrack memory, providing double shift error detection and correction. Additionally, we enhance the fault tolerance of 3D-stacked DRAM with a two-level coding technique called Jenga. Jenga provides fault tolerance in all the granularity levels of the 3D-stacked DRAM with minimal performance overheads while outperforming prior solutions.

Item Open Access Locally Adaptive Protocols for Quantum State Discrimination(2021) Brandsen, SarahThis dissertation makes contributions to two rapidly developing fields: quantum information theory and machine learning. It has recently been demonstrated that reinforcement learning is an effective tool for a wide variety of tasks in quantum information theory, ranging from quantum error correction to quantum control to preparation of entangled states. In this work, we demonstrate that reinforcement learning is additionally highly effective for the task of multiple quantum hypothesis testing.

Quantum hypothesis testing consists of finding the quantum measurement which allows one to discriminate with minimal error between $m$ possible states $\{\rho_{k}\}|_{k=1}^{m}$ of a quantum system with corresponding prior probabilities $p_{k} = \text{Pr}[\rho = \rho_{k}]$. In the general case, although semi-definite programming offers a way to numerically approximate the optimal solution~\cite{Eldar_Semidefinite2}, a closed-form analytical solution for the optimal measurement is not known.

Additionally, when the quantum system is large and consists of many subsystems, the optimal measurement may be experimentally difficult to implement. In this work, we provide a comprehensive study of locally adaptive approaches to quantum hypothesis testing where only a single subsystem is measured at a time and the order and types of measurements implemented may depend on previous measurement results. Thus, these locally adaptive protocols present an experimentally feasible approach to quantum state discrimination.

We begin with the case of binary hypothesis testing (where $m=2$), and generalize previous work by Acin et al. (Phys. Rev. A 71, 032338) to show that a simple Bayesian-updating scheme can optimally distinguish between any pair of arbitrary pure, tensor product quantum states. We then demonstrate that this same Bayesian-updating scheme has poor asymptotic behaviour when the candidate states are not pure, and based on this we introduce a modified scheme with strictly better performance. Finally, a dynamic programming (DP) approach is used to find the optimal local protocol for binary state discrimination and numerical simulations are run for both qubit and qutrit subsystems.

Based on these results, we then turn to the more general case of multiple hypothesis testing where there may be several candidate states. Given that the dynamic-programming approach has a high complexity when there are a large number of subsystems, we turn to reinforcement learning methods to learn adaptive protocols for even larger systems. Our numerical results support the claim that reinforcement learning with neural networks (RLNN) is able to successfully find the optimal locally adaptive approach for up to 20 subsystems. We additionally find the optimal collective measurement through semidefinite programming techniques, and demonstrate that the RLNN approach meets or comes close to the optimal collective measurement in every random trial.

Next, we focus on quantum information theory and provide an operational interpretation for the entropy of a channel. This task is motivated by the central role of entropy across several areas of physics and science. We use games of chance as a more systematic and unifying approach to define entropy, as a system's performance in any game of chance depends solely on the uncertainty of the output. We construct families of games which result in a pre-order on channels and provide an operational interpretation for all pre-orders (corresponding to majorization, conditional majorization, and channel majorization respectively), and this defines the unique asymptotically continuous entropy function for classical channels.

Item Open Access Minimax Fairness in Machine Learning(2022) Martinez Gil, Natalia LucienneThe notion of fairness in machine learning has gained significant popularity in the last decades, in part due to the large number of decision-making models that are being deployed on real-world applications, which have presented unwanted behavior. In this work, we analyze fairness in machine learning from a multi-objective optimization perspective, where the goal is to learn a model that achieves a good performance across different groups or demographics. In particular, we analyze how to achieve models that are efficient in the Pareto sense, providing the best performance for the worst group (i.e., minimax solutions). We study how to achieve minimax Pareto fair solutions when sensitive groups are available at training time, and also when the demographics are completely unknown. We provide experimental results showing how the discussed techniques to achieve minimax Pareto fair solutions perform on classification tasks, and how they can be adapted to work on other applications such as backward compatibility and federated learning. Finally, we analyze the problem of achieving minimax solutions asymptotically when we optimize models that can perfectly fit their training data, such as deep neural networks trained with stochastic gradient descent.

Item Open Access MITIGATING COHERENT NOISE(2023) Liang, QingzhongStochastic errors in quantum systems occur randomly but coherent errors may be more damaging since they can accumulate in a particular direction. We develop a framework for designing decoherence free subspaces (DFS), that are unperturbed by coherent noise. We consider a particular form of coherent $Z$-errors and construct stabilizer codes that form DFS for such noise (``Z-DFS''). More precisely, we develop conditions for transversal $\exp(\imath \theta \sigma_Z)$ to preserve a stabilizer code subspace for all $\theta$. If the code is error-detecting, then this implies a trivial action on the logical qubits. These conditions require the existence of a large number of weight-$2$ $Z$-stabilizers, and together, these weight-$2$ $Z$-stabilizers generate a direct product of single-parity-check codes.

By adjusting the size of these components, we are able to construct a constant rate family of CSS Z-DFS codes. Invariance under transversal $\exp(\frac{\imath \pi}{2^l} \sigma_Z)$ translates to a trigonometric equation satisfied by $\tan\frac{2\pi}{2^l}$, and for every non-zero $X$-component of a stabilizer, there is a trigonometric equation that must be satisfied. The $Z$-stabilizers supported on this non-zero $X$-component form a classical binary code C, and the trigonometric constraint connects signs of $Z$-stabilizers to divisibility of weights in $C^{\perp}$. This construction may be of independent interest to classical coding theorists who have long been interested in codes $C$ with the property that all weights are divisible by some integer $d$. If we require that transversal $\exp(\frac{\imath \pi}{2^l} \sigma_Z)$ preserves the code space only up to some finite level $l$ in the Clifford hierarchy, then we can construct higher level gates necessary for universal quantum computation. The aforesaid code $C$ contains a self-dual code and the classical Gleason's theorem constrains its weight enumerator.

The trigonometric conditions corresponding to higher values of $l$ lead to generalizations of Gleason's theorem that may be of independent interest to classical coding theorists. The $[[16, 4, 2]]$ Reed-Muller code and the family of $[[4L^2, 1, 2L]]$ Shor codes are included in our general framework.

Item Open Access Theory and Application of SBS-based Group Velocity Manipulation in Optical Fibers(2013) Zhu, YunhuiAll-optical devices have attracted many research interests due to their ultimately low heat dissipation compared to conventional devices based on electric-optical conversion. With recent advances in nonlinear optics, it is now possible to design the optical properties of a medium via all-optical nonlinear effects in a table-top device or even on a chip.

In this thesis, I realize all-optical control of the optical group velocity using the nonlinear process of stimulated Brillouin scattering (SBS) in optical fibers. The SBS-based techniques generally require very low pump power and offer a wide transparent window and a large tunable range. Moreover, my invention of the arbitrary SBS resonance tailoring technique enables engineering of the optical properties to optimize desired function performance,

which has made the SBS techniques particularly widely adapted for

various applications.

I demonstrate theoretically and experimentally how the all-optical

control of group velocity is achieved using SBS in optical fibers.

Particularly, I demonstrate that the frequency dependence of the

wavevector experienced by the signal beam can be tailored using

multi-line and broadband pump beams in the SBS process. Based on the theoretical framework, I engineer the spectral profile

to achieve two different application goals: a uniform low group velocity (slow light) within a broadband spectrum, and a group velocity with a linear dependence on the frequency detuning (group velocity dispersion or GVD).

In the broadband SBS slow light experiment, I develop a novel noise current modulation method that arbitrarily tailors the spectrum of a diode laser. Applying this method, I obtain a 5-GHz broadband SBS gain with optimized flat-topped profile, in comparison to the ~40 MHz natural linewidth of the SBS resonance. Based on the broadband SBS resonance, I build a 5-GHz optical buffer and use this optical buffer to delay a return-to-zero data sequence of rate 2.5 GHz (pulse width 200 ps). The fast noise modulation method significantly stabilizes the SBS gain and improves the signal fidelity. I obtain a tunable delay up to one pulse-width with a peak signal-to-noise ratio of 7. I also find that SBS slow light performance can be improved by avoiding competing nonlinear effects. A gain-bandwidth product of 344 dB.GHz is obtained in our system with a highly-nonlinear optical fiber.

Besides the slow light applications, I realize that group velocity dispersion is also optically controlled via the SBS process. In the very recent GVD experiment, I use a dual-line SBS resonance and obtain a tunable GVD parameter of 7.5 ns$^2$/m, which is 10$^9$ times larger than the value found in a single-mode fiber. The large GVD system is used to disperse an optical pulse with a pulse width of 28 ns, which is beyond the capability for current dispersion techniques working in the picosecond and sub picosecond region. The SBS-based all-optical control of GVD is also widely tunable and can

be applied to any wavelength within the transparent window of the

optical fiber. I expect many future extensions following this work

on the SBS-based all-optical GVD control using the readily developed SBS tailoring techniques.

Finally, I extend the basic theory of backwards SBS to describe the forward SBS observed in a highly nonlinear fiber, where asymmetric forward SBS resonances are observed at the gigahertz range. An especially large gain coefficient of 34.7 W$^{-1}$ is observed at the resonance frequency of 933.8 MHz. This is due to good overlap between the optical wave and the high order guided radial acoustic wave. The interplay from the competing process known as the Kerr effect is also accounted for in the theory.

Item Open Access Using Knowledge-Based Models to Teach Complex Lung IMRT Planning(2019) Mistro, MatthewKnowledge-based treatment planning models are commonly built from straightforward principles and utilize experience that it is able to learn from previous high-quality plans. This knowledge can be harnessed by having an e-learning system incorporating knowledge-based treatment planning models to serve as informative, efficient bases to train individuals to develop IMRT plans for a particular site while building confidence in utilizing these models in a clinical setting.

A previously developed beam angle selection model and a previously developed DVH prediction model for lung/mediastinum IMRT planning are used as the information centers within a directed e-learning system guided by scoring criteria and communicated with the trainees via a user interface ran from the treatment planning system (Eclipse). The scoring system serves both to illustrate relative quality of plans and to serve as a guide to facilitate directed changes within the plan. One patient serves as a benchmark to show skill development from the e-learning system and is completed without intervention. Five additional lung/mediastinum patients follow in the subsequent training pipeline where the models, graphical user interface (GUI) and trainer work with trainee’s directives and guide meaningful beam selection and tradeoffs within IMRT optimization. Five trainees with minimal treatment planning background were evaluated by both the scoring criteria and a physician to look for improved planning quality and relative effectiveness against the clinically delivered plan.

Trainees scored an average of 22.7% of the total points within the scoring criteria for their benchmark yet improved to an average of 51.9% compared to the clinically delivered plan which achieved 54.1% of the total potential points. Two of the five trainee final plans were rated as comparable to the clinically delivered by a physician and all five were noticeably improved by the physicians standards. For plans within the system, trainees performed on average 24.5% better than the clinically delivered plan with respect to the scoring criteria.

This first attempt at creating a dynamic interface communicating prior experience built in models to an end-user was approximately 10 hours to rapidly improve planning quality. It brings unexperienced planners to a level comparable of experienced dosimetrists for a specific treatment site and when used to inform decisions, the knowledge-based models aided in producing high quality plans.

Item Open Access VizMaps: A Bayesian Topic Modeling Based PubMed Search Interface(2015) Kamboj, KirtiA common challenge that users of academic databases face is making sense of their query outputs for knowledge discovery. This is exacerbated by the size and growth of modern databases. PubMed, a central index of biomedical literature, contains over 25 million citations, and can output search results containing hundreds of thousands of citations. Under these conditions, efficient knowledge discovery requires a different data structure than a chronological list of articles. It requires a method of conveying what the important ideas are, where they are located, and how they are connected; a method of allowing users to see the underlying topical structure of their search. This paper presents VizMaps, a PubMed search interface that addresses some of these problems. Given search terms, our main backend pipeline extracts relevant words from the title and abstract, and clusters them into discovered topics using Bayesian topic models, in particular the Latent Dirichlet Allocation (LDA). It then outputs a visual, navigable map of the query results.