Alliance One Tobacco (Malawi)
Academic Research Repository at the Institute of Developing EconomiesNot a member yet
52305 research outputs found
Sort by
Exploring Axion physics in quantum materials via magnetoelectric coupling
No full textAxion, a hypothetical particle in high-energy physics, was originally proposed to solve both the strong CP problem in quantum chromodynamics and the dark matter problem. Due to its extremely weak interaction with ordinary matter, the Axion has remained elusive despite decades of searches. In electromagnetism, Axion physics introduces an additional E · B term to Maxwell’s equations, which couples electric and magnetic fields. In condensed matter physics, this framework finds an analog in the magnetoelectric coupling of topological materials. In vacuum or conventional materials, an electric field induces electrical polarization, while a magnetic field generates magnetization—typically without cross-coupling between the two. However, beyond this paradigm, certain novel materials could exhibit magnetoelectric coupling, where an electric field induces magnetization (M = αE) or a magnetic field induces electric polarization (P = αH). The magnetoelectric effect is commonly observed in a special class of wide-bandgap magnetic insulators, known as magnetoelectric or multiferroic insulators (e.g., Cr₂O₃ and BiFeO₃), where the effect arises from localized magnetic ions. More recently, theoretical advances have predicted fundamentally new types of magnetoelectric coupling in topological materials—systems characterized by nontrivial topological invariants and Berry curvature. Unlike conventional insulators, these materials can support robust dissipationless edge states and exhibit magnetoelectric phenomena rooted in their topological electronic structure. In particular, magnetic topological insulators provide a platform where magnetism and topology intertwine, giving rise to rich physics, including a quantized magnetoelectric coefficient in topological insulators, Axion quasiparticles in antiferromagnetic (AFM) topological insulators, and the chiral anomaly in magnetic Weyl semi-metals.
In this thesis, we present several experimental discoveries, uncovering the unique magnetoelectric effect in magnetic topological material. First, we investigate the optical magnetoelectric effect in the prototypical AFM topological insulator MnBi₂Te₄, which enables unique reflection circular dichroism in an antiferromagnet. Furthermore, we achieved the optical control of the antiferromanetic order by circularly polarized light for the first time. Both the optical detection and control could be understood within the framework of optical Axion electrodynamics. Next, we report the direct observation of the Axion quasiparticle in MnBi₂Te₄, which is a condensed matter analog of the dark matter Axion. Using ultrafast optical pump-probe techniques, we observe coherent oscillations of the magnetoelectric coefficient α in MnBi₂Te₄, which is the smoking-gun evidence for the Axion quasiparticles. Microscopically, the Axion quasiparticle is enabled by magnon-induced Berry curvature modulation. The observed Axion quasiparticle not only can serve as a simulator for the elusive dark matter Axion, but it could also serve as a potential Axion detector, providing a novel path for dark matter detection. Lastly, we shift our focus to explore the magnetoelectric coupling in a magnetic Weyl semimetal CeAlSi. Firstly, we uncover a broadband nonlinear optical diode effect (NODE) in CeAlSi, where the magnetization induces a pronounced directional asymmetry in the optical second-harmonic generation (SHG). DFT calculations also show that this broadband NODE effect originates from the Weyl fermions. By applying an electrical current, we further explore the magnetoelectric coupling in this magnetic Weyl semimetal, in which we discover the electric control over the magnetic domains.Chemistry and Chemical Biolog
Visual analytics at the atlas scale for multimodal and spatial single-cell data
No full textScientific measurements at the resolution of individual cells – single-cell experiments – are central to biology because the cell is the fundamental unit of life. In the past decade, advancements in sequencing and bioimaging technologies have enabled cellular measurements to be made at high-throughput, forging the field of single-cell biology. Single-cell experiments are now being applied in large scale, driven by concerted efforts from funding institutions and consortia worldwide. The resulting datasets are being compiled into single-cell atlas resources: collections of cellular- resolution maps intended to summarize and communicate single-cell data through hierarchical and spatial organization and inclusion of multiple biosamples, tissue types, organs, and/or organisms from one or more experimental conditions. Single-cell atlases are intended to facilitate downstream usage in biology and medicine via their establishment as gold-standard sets of measurements that can serve as common points of reference. Current information visualization systems are not equipped to adequately deal with the scale, complexity, and heterogeneity of single-cell atlas data, nor are they tailored to the needs of target user audiences.
This thesis investigates how visual analytics systems can be employed for interactive visualization of multimodal and spatial single-cell datasets, addressing challenges at the scale of individual experiments to whole atlases. The first chapter provides an overview of the landscape of single-cell data visualizations, including interactive systems. The second chapter builds on preliminary work on a framework for interactive data visualization for single-cell data, including for spatial, imaging, and multimodal data. The third chapter builds upon this framework to develop a system tailored to cross-experiment comparisons, for example between single-cell data from case and control groups, informed through interviews with individuals from its intended audience of biologists, pathologists, and clinicians. The fourth chapter explores how algorithms for identification of spatial domains with relevance to biological function can be integrated with interactive visualizations. The fifth chapter explores how to integrate chromatin accessibility measurements into the existing framework for single-cell data visualization.
By combining and extending concepts from bioinformatics, information visualization, human-computer interaction, and software engineering, this thesis pioneers approaches for exploring, understanding, and communicating foundational single-cell atlas resources.Medical Science
A mutation rate model at the basepair resolution and selection metric using deviation of multinomial site-frequency spectrum
No full textFinding mutations under negative selection is a problem that has important applications in evolutionary theory, population genetics, and rare disease research. An important concept in determining whether a mutation is under strong negative selection is mutation-selection balance, an equilibrium in the number of deleterious alleles in a population that is reached by the two opposing forces of introduction by mutations and elimination by selection. With the recent explosion of sequencing data, we can now approach the deacades-old concept with novel methods. In this Dissertation, I present two such methods. First, I describe Roulette, a genome- wide mutation rate model at basepair resolution that incorporates known determinants of local mutation rate. Roulette is shown to be more accurate than previous models. Roulette is used for various applications, such as refining the estimate of recent population growth and finding novel mutational mechanisms. Second, I introduce multiSFS, a new statistic for estimating genomic regions under negative selection. While traditional methods have focused on using numbers of segregating sites, multiSFS uses the deviation of the site frequency spectrum (SFS) to estimate genomic regions under negative selection. MultiSFS demonstrates enhanced power in simulated data and have increased enrichment of coding sequence regions and higher accuracy in predicting for pathogenic variants.Medical Science
Optimizing the Infidelity, Sensitivity, and Complexity of Feature Importance Explanations for Machine Learning Models
No full textInterpretable machine learning aims to bridge the gap between complex model predictions and human understanding. Given the open-ended nature of the field, there is an abundance of different methods for achieving interpretability and metrics for evaluating the quality of explanations. In this thesis, we survey the existing work in the field and focus on three main types of metrics, which we refer to as \textit{infidelity}, \textit{sensitivity}, and \textit{complexity}. We explore a novel framework for interpretability that balances these three objectives using a metric of explanation quality that incorporates all three objectives, ensuring that explanations accurately capture the model's behavior, remain stable across similar inputs, and avoid being needlessly hard to interpret. Specifically, we consider explanations that attempt to generate an \textit{importance score} for each feature of the input. We calculate the optimal explanation by minimizing the metric according to an algorithm we introduce based on coordinate descent and the Adam optimizer. We implement this algorithm and evaluate our approach on a neural network trained on the MNIST dataset.Computer Scienc
Parental Bereavement in Childhood and the Development of Adult Psychopathology: A Scoping Literature Review
No full textTraumatic experiences have significantly negative effects on the mental and physical health of children and adolescents. This thesis aims to explore the long-term impacts of childhood bereavement and parental death on mental health using a scoping review approach. The review discusses the lasting consequences of parental death, identifies important characteristics, reviews gaps in current research, and situates the findings within existing literature. The central research question guiding this review is: To what extent does the existing literature present a relation between childhood bereavement and certain adult mental disorders? What factors moderate this relationship? Twelve key articles were included to form the basis of this discussion. These articles investigate the lasting psychological consequences of childhood bereavement on adults and their correlation to specific adult mental disorders, such as depression, anxiety, unipolar disorders. The results indicate several factors influencing psychiatric outcomes, including the cause of death, the age at bereavement, and the gender of the deceased parent and child. External causes of death, especially suicide, were strongly related to the highest psychiatric risks compared to natural causes. The developmental stage at bereavement was also a crucial moderating factor, with early childhood bereavement leading to increased vulnerability. Furthermore, the review emphasizes the importance of further research to explore age- and gender-specific risk patterns and effective strategies to reduce the long-term psychological effects of bereavement. These findings indicate the significant and persistent impact of childhood parental loss.Extension Studie
The New function of Oblique
No full textOrthogonality in architecture has shaped our perception of the built environment. Floors, walls, ceilings, and structural grids are typically perpendicular to each other, which affects people’s behavior inside heavily constrained three-dimensional boxes. This thesis challenges the widespread notion of orthogonality in manmade structures and explores opportunities in oblique composition.
The oblique system was often overlooked due to its unfamiliarity compared to its orthogonal counterpart. From the factory buildings of the industrial era to contemporary supertall office buildings, planar walls, floors, and perpendicular connections between them consist of the basic structure of architecture. Conventional spatialization has a certain advantage regarding efficiency in space use and construction cost. However, adapting obliqueness opens up the possibility of a more active and dynamic space, as well as offering novel ways to connect spaces with different functions.
Typical perpendicular compartmentalization hardly offers a physical and sensorial connection with each space. Oblique planes bring obscurity, intentionally blurring the defined boundary between spaces. Different inclinations and slanted walls define the space, which also imagines how people interact with this novel method of comprising the building. There is a discrepancy between what people expect (orthogonal system) and what people will engage in (oblique). Claude Parent and Paul Virilio’s experiment on oblique architecture opened the discourse for novel ways of defining space.
This thesis uses Packard Plant, a well-known dilapidated industrial building in Detroit, as a testing ground to examine the contrast between the highly orthogonal and extreme oblique building orders. By doing so, the thesis expects to develop and play between traditional perception and visionary opportunity.Department of Architectur
Essays on Institutions, Beliefs, and Asset Prices
No full textThis dissertation comprises three chapters that examine the effect of institutional characteristics and preferences and investor beliefs on asset prices. I show that mixed bond mutual funds transmitted Quantitative Easing to the corporate bond market, dissect the composition of investor disagreement, and propose a risk premium resolution of the FOMC bond premium puzzle.
In the first chapter, I study the transmission of Large-Scale Asset Purchase (LSAP) through financial investors’ balance sheets, illustrating the channel through US corporate bonds. Post-2008 LSAPs didn’t directly target corporate bonds, but cross-market investors might transmit the shock to corporate bonds through their portfolio adjustment behaviors. I show that mixed bond mutual funds, who I label as “switchers”, accommodated all three rounds of QE by selling Treasury securities and agency MBS. During the same period, “switchers” increased their corporate bond holdings by a total of 254.7 billion dollars. For two of the three rounds of QE, this switching behavior is associated with detectable price impacts on the corporate bonds that they held: for otherwise similar corporate bond issues, one percent higher ownership by “switcher” funds predicts 2.6 (1.0) basis points lower yield, eight quarters after the onset of QE2 (QE3). The effect is stronger at the firm level and concentrated in firms with speculative-grade ratings: for otherwise similar speculative-grade firms, one percent higher firm-wide bond ownership by “switcher” funds predicts 8.0 (8.4) basis points lower yield, eight quarters after the onset of QE2 (QE3). I show that these effects on risky asset prices affect firm borrowing decisions, as firms with higher “switcher” ownership had higher liability ratio and probability of issuing bond after QE2 and QE3. These effects on bond prices and firm borrowing decisions are not present in QE1 and placebo periods.
In the second chapter, which is joint work with Robin Greenwood and Sam Hanson, we ask an empirical question: when investors disagree about the prospects of a firm, is it because they disagree about the industry or because of different assessments of the winners and losers in that industry? We decompose analyst disagreement about future EPS, Sales and Long-Term-Growth (LTG) into an idiosyncratic and an industry component. We show that the majority of disagreement is driven by the idiosyncratic component, meaning that investors mostly agree about industry prospects but disagree about which firms will perform the best within each industry. We present a model in which even idiosyncratic disagreement can have industry and market valuation effects. Even if investors agree on the prospects of the industry, in the presence of short-sales constraints the industry may be overvalued when investors disagree about the prospects of individual firms. We find evidence in future returns consistent with this idea.
In the third chapter, I study the FOMC announcement premium for long-duration bonds, which is the fact that the average price return on 10-year nominal US Treasury securities during an FOMC announcement window (day before, day of, and day after an FOMC announcement) is 39 to 68 times that of a normal trading day (Hillenbrand [2023]). I present a model where risk aversion from a segment of the market (nervous sellers) drives the FOMC bond premium. Consistent with model predictions, bond returns are lower (not statistically significant) 2 days to 10 days before FOMC announcements. In addition, consistent with the model, the FOMC bond premium is higher when VIX is higher, realized variance of past bond returns is higher, and analyst forecast dispersion is higher. Using market-making data in the cash bond market from a large primary dealer, I examine investor flows around FOMC announcements. I find weak evidence that a subset of investors are persistent sellers of bonds before FOMC announcements, but the magnitude of nervous selling is too small to account for the FOMC bond premium.Business Economic
Long-Term Single-Cell Imaging of Live Microbes by Correlative Fluorescence and Raman Microscopy & Time-Resolved Stark Effect Spectroscopy of Protein Crystals
No full textUnderstanding the behavior of a microbe requires not only an understanding of molecular mechanisms, e.g. DNA replication, transcription, and translation, but also knowledge of the cell’s chemical composition over time. There are powerful tools for probing the mechanisms of core cell-biological processes. However, the existing tools to measure cell composition have significant limitations. Information-rich methods, such as mass spectrometry, are lethal, while fluorescence methods provide good time resolution and work on live cells, but are limited in what they can measure. Prior work has demonstrated that spontaneous Raman spectroscopy can be a powerful tool to measure cellular composition. However, low throughput has limited its potential for discovery. Here, I describe my contributions to correlated epifluorescence and laser scanning Raman microscopy to enable the collection of single-cell spectra from many single cells in long-term time-lapse experiments, with imaging every 15 minutes. I then establish that this single-cell Raman imaging (scRaman) system can be used without substantial phototoxic effects to image the yeast Saccharomyces cerevisiae. To benchmark the ability to track biologically important changes in cellular composition, I study S. cerevisiae under conditions of nitrogen starvation and repletion. I demonstrate the ability of this system to resolve the dynamics of compositional changes at a single-cell level, mapping the observed dynamics to known biological mechanisms. I describe the analysis pipeline required for this work and the advances in combined Raman and microscope control software to achieve these experiments. Finally, I describe a separate project in which I developed an analytical framework for interpreting time-resolved Stark effect spectroscopy data obtained from single protein crystals.Engineering and Applied Sciences - Applied Physic
Communicating Common Goal Knowledge Improves Trust-Calibration in Human-AI Collaboration
No full textIn Human-AI collaboration, human agents often have a clear goal in mind and the AI Assistant tries to help users achieve their goals more efficiently. However, inferring users’ goals is non-trivial based on noisy user behavior and there is often mismatch in agent’s belief about each other’s knowledge of the ground-truth goal, leading to coordination failure. In this study, we propose that building common goal knowledge through communication improves human user’s mental model of the AI Assistant and leads to more efficient and effective human-AI collaboration. To test this hypothesis, we design an experiment where an AI assistant helps a human user shop for recipes on a grocery platform. We compare the user behavior and team performance under three experimental conditions: AI providing no information over its knowledge over human goals, AI expressing its belief over human’s goal through verbal communication(“Show”) and AI indicating its confidence in its belief over human’s goal (“Tell”). We find that communicating goal knowledge (in “Show” and “Tell”) increases user’s tendency to use AI when AI is indeed correct and improves user’s subjective ratings of the AI assistant.Proo
Distributed encoding of natural and drug-induced physiological states in the insular cortex
No full textInteroception—the sensing of internal bodily signals—is crucial for maintaining homeostasis and plays a significant role in pathological states including drug addiction. Both the rewarding aspects of drug consumption and the aversive effects of withdrawal are experienced as salient body states. The insular cortex (InsCtx) is a key interoceptive region that integrates external sensory, visceral, and limbic information, and has been implicated in the maintenance of nicotine dependence. Here, we combine chronic two-photon imaging of hundreds of InsCtx neurons with physiological recordings of heart rate, pupil area, and body temperature during repeated nicotine exposure. We find that InsCtx neurons exhibit stable, distributed encoding of natural physiological states, enabling accurate predictions of arousal and cardiovascular variables across days. Nicotine administration triggers unique, centrally mediated physiological changes, which are reflected in distinct patterns of InsCtx activity. Longitudinal nicotine administration resulted in physiological tolerance, which contrasts with nicotine-evoked InsCtx neural responses that did not adapt across days. This study highlights the InsCtx’s role in tracking and distinguishing between natural and drug-induced states, and offers insight into the neural basis of tolerance -- a core feature of addiction.Neuroscienc