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Of Rice And Men: Nationalist Military Grain Procurement And Transport Policies In Wartime China (1937-1945)
No full textThis dissertation examines the Chinese Nationalist government’s military grain procurement and transportation policies during the Second Sino-Japanese War (1937–1945). It examines both the bureaucratic agencies mandated to manage grain, and the mobilization programs which delivered provisions to more than 5 million soldiers. The acquisition of grain decisively shaped both Chinese and Japanese strategies for waging protracted war. This became starkly clear between late 1938 and early 1944, when grain acquisition replaced battlefield engagements as the crux of Nationalist, Communist and Japanese survival. Particularly after 1941, all three fighting forces were locked in a drawn-out competition for a dwindling pool of sustenance. Committed to self-sufficiency, all had to live off the same ravaged land. The war was therefore as much a struggle for food security as it was a contest of conscription or firepower. In response, the Nationalists resorted to the systemized extraction of labor beyond military conscription at the most localized levels of society. The civilians carrying out the various stages of grain provisioning were just as vital as combatants–for the armies relied on multiple interlinked networks of civilian effort for their daily sustenance. The militarization of food, the humblest of material concerns, drew whole communities into the fold of conflict. The central government’s food provisioning program put on display a remarkable capacity for organizing resources throughout Free China. Yet, it also revealed a readiness to thrust civilian lives and livelihoods on the line. Effective administration did not guarantee citizen welfare; in fact, the Nationalists’ victory hinged on imposed civilian sacrifice. The Chinese experience is vital to the historiography on food supply in World War II. Its defining contingencies–protracted foreign occupation, geographical expanse, and agrarian economy–resulted in direct, sustained civilian participation in military provisioning processes of remarkable depth and scale. The dissertation also presents logistics as the methodological bridge between social and military history. A focus on food supply demonstrates that studies of strategy and everyday experience are inextricably intertwined
Towards Precision Measurements Of The Optical Depth To Reionization Using 21 Cm Data And Machine Learning
Get PDFThe Epoch of Reionization (EoR) was a phase transition from a neutral state to an ionized state where the first generation of luminous objects were able to heat and ionize the surrounding predominantly neutral hydrogen gas. Detection of brightnesstemperature fluctuations from the redshifted hyperfine 21 cm line of neutral hydrogen would provide a direct three-dimensional probe of astrophysics and cosmology during this period. Another important property of reionization is the redshift of its midpoint, when half the hydrogen in the intergalactic medium (IGM) was ionized. This quantity is often estimated by using the cosmic microwave background (CMB) optical depth, tau . Since the optical depth is obtained by integrating along the line of sight, it provides just one number to characterize reionization. As a result, this can be converted into a constraint for the midpoint under the assumption of a parametric form for the ionization history. This is also a probe of the EoR.
Upcoming measurements of the high-redshift 21cm signal from the EoR are a promising probe of the astrophysics of the first galaxies and of cosmological parameters. In particular, the optical depth tau to the last scattering surface of the CMB should be tightly constrained by direct measurements of the neutral hydrogen state at high redshift. A robust measurement of from 21cm data would help eliminate it as a nuisance parameter from CMB estimates of cosmological parameters. Previous proposals for extracting tau from future 21cm datasets have typically used the 21cm power spectra generated by semi-numerical models to reconstruct the reionization history. I present in this thesis a different approach which uses convolution neural networks (CNNs) trained on mock images of the 21cm EoR signal to extract tau. I constructed a CNN that improves upon on previously proposed architectures, and perform an automated hyperparameter optimization. I showed that well-trained CNNs are able to accurately predict tau, even when removing Fourier modes that are expected to be corrupted by bright foreground contamination of the 21cm signal.
I then began answering a slightly different question that involved raining three different Bayesian models using mock images of ionized fields of hydrogen to extract the ionization fraction of hydrogen by only looks at one redshift to infer the ionization fraction of each simulated image. I showed that for a simple fully Bayesian network it is possible to successfully produces predicted values that are closely aligned with the true values and the model was tuned to find the ``best\u27\u27 generalized model architecture for this particular problem
Improvements In Interferometric Data Modeling For The New Era Of Radio Cosmology
Get PDFThe redshifted 21 cm line promises to provide a wealth of information about the evolution of our universe but remains as yet undetected. The general theme of this thesis is developing increasingly realistic models of the raw data we collect from a radio telescope. This is important because at the end of the day extracting the cosmological signal from the data will be accomplished by achieving a level of understanding of all the possible alternative sources that might mimic the cosmological signal, to a degree that we can confidently reject those alternatives as causes of our detection. The work presented in this thesis has been done in the context of working on the HERA experiment which aims to make the first measurements of spatial fluctuations in the emission from neutral hydrogen.
In this thesis I emphasized aspects of the visibility function that are important for efficient and realistic visibility simulations including full account of polarization effects, in particular using a harmonic parameterization of the integrand. I assessed the effect of potential ionospheric attenuation on the suppression of polarization contamination in 21 cm power spectrum measurements using visibility simulations based on historical ionospheric plasma density data. I showed how we can use closed-form calculations of the cross-frequency angular power spectrum on the sky to generate simple mock cosmological signal simulations that are useful for validating data analysis methods. I showed how the window functions associated with a 21 cm power spectrum estimate can be approximated by simple forms that are much cheaper to evaluate than the general definition. Finally, I produced a new Southern Sky Model that combines the best available diffuse radio emission surveys that cover HERA\u27s field of view and observing bandwidth with a point source catalog without double counting flux
Extracting Generalizable Hierarchical Patterns Of Functional Connectivity In The Brain
Get PDFThe study of the functional organization of the human brain using resting-state functional MRI (rsfMRI) has been of significant interest in cognitive neuroscience for over two decades. The functional organization is characterized by patterns that are believed to be hierarchical in nature. From a clinical context, studying these patterns has become important for understanding various disorders such as Major Depressive Disorder, Autism, Schizophrenia, etc. However, extraction of these interpretable patterns might face challenges in multi-site rsfMRI studies due to variability introduced due to confounding variability introduced by different sites and scanners. This can reduce the predictive power and reproducibility of the patterns, affecting the confidence in using these patterns as biomarkers for assessing and predicting disease. In this thesis, we focus on the problem of robustly extracting hierarchical patterns that can be used as biomarkers for diseases.
We propose a matrix factorization based method to extract interpretable hierarchical decomposition of the rsfRMI data. We couple the method with adversarial learning to improve inter-site robustness in multi-site studies, removing non-biological variability that can result in less interpretable and discriminative biomarkers. Finally, a generative-discriminative model is built on top of the proposed framework to extract robust patterns/biomarkers characterizing Major Depressive Disorder.
Results on large multi-site rsfMRI studies show the effectiveness of our method in uncovering reproducible connectivity patterns across individuals with high predictive power while maintaining clinical interpretability. Our framework robustly identifies brain patterns characterizing MDD and provides an understanding of the manifestation of the disorder from a functional networks perspective which can be crucial for effective diagnosis, treatment and prevention. The results demonstrate the method\u27s utility and facilitate a broader understanding of the human brain from a functional perspective
Kinegami: Algorithmic Design of Compliant Kinematic Chains From Tubular Origami
Get PDFOrigami processes can generate both rigid and compliant structures from the same homogeneous sheet material. In this article, we advance the origami robotics literature by showing that it is possible to construct an arbitrary rigid kinematic chain with prescribed joint compliance from a single tubular sheet. Our “Kinegami” algorithm converts a Denavit–Hartenberg specification into a single-sheet crease pattern for an equivalent serial robot mechanism by composing origami modules from a catalogue. The algorithm arises from the key observation that tubular origami linkage design reduces to a Dubins path planning problem. The automatically generated structural connections and movable joints that realize the specified design can also be endowed with independent user-specified compliance. We apply the Kinegami algorithm to a number of common robot mechanisms and hand-fold their algorithmically generated single-sheet crease patterns into functioning kinematic chains. We believe this is the first completely automated end-to-end system for converting an abstract manipulator specification into a physically realizable origami design that requires no additional human input
Tracking the Summary Statistics in Long-Term Memory
No full textDecades of research have demonstrated humans’ extraordinary ability to extract summary statistics across individual experiences. Less is known about how exactly the items contribute to the summary statistics and how the relationship between memory for the items and memory for the summary statistics evolves and changes over time. I propose that memory of summary statistics that are initially extracted from individual instances starts to guide memory for individual items over time, and not all items contribute to the summary statistics equally. Sources of item distinctiveness influence the summary statistics extraction in terms of the contribution of each item and the accuracy of summary statistics. The three empirical chapters enlighten our understanding of summary statistics extraction in long-term memory by bridging fields ranging from perception and memory to emotion and motivation
Localizing Seizure Onset with Diffusion Models
No full textDiffusion models are models that describe the spread of anything -- atoms, ideas, people, seizures. They have developed independently across fields, from economics, computer science, and physics, to biology and medicine. They have a wide variety of applications including modeling the spread of pathogens, information, and ideas. In this dissertation, diffusion models are applied to modeling the spread of seizures. Our ability to predict how seizures spread -- its timing, speed, extent of activity, where seizures start and where seizures go -- can help us solve a critical problem in the effective treatment of refractory epilepsy: localization of seizure onset for its eventual resection, ablation, or neuromodulation. This dissertation encompasses a multidisciplinary approach (from analyses of signals and networks to newer methods in deep learning) across many brain states (from interictal, preictal, ictal to postictal) and with multimodal data (from structure to function, MRI to EEG) in different outcomes of epilepsy patients (from good to poor). New hypotheses about epilepsy pathophysiology are presented in the original research section of this dissertation, a new framework on the conceptualization of brain atlas is presented in Chapter 5, a taxonomy of seizure spread patterns is presented in Chapter 6, the investigation of white matter EEG recordings is presented in Chapter 7 -- this dissertation contains work more than about diffusion models applied to epilepsy; however the research and ideas presented throughout this work show promise for using diffusion models, or other models of epilepsy, to solve a clinical problem in epilepsy and hopefully improve our patients\u27 quality of life
Probing The Dark Universe From Galactic To Cosmological Scales
Get PDFAstronomical observations strongly suggest that the universe is mostly dark. Its two dominant components, dark energy and dark matter, remain among the most mysterious concepts in cosmology today. The effects of these two substances are imprinted in the remaining few percent of the universe that consists of normal (baryonic) matter. Dark energy is responsible for the accelerating expansion of the universe and the existence of dark matter is deduced from the orbital properties of stars in galaxies. This thesis probes the observable effects of both these phenomena. The first part is about Baryon Acoustic Oscillations (BAO) by which we can measure the expansion rate of the universe and constrain dark energy. The second part focuses on ways to probe the nature of dark matter by studying the dynamics of galaxies and the orbital properties of their stars. The third and final part of this thesis discusses Optimal Transport (OT) theory, which unites the BAO and the Galactic Dynamics parts. The results of this thesis would develop novel ways to place stronger constraints on cosmology and dark energy; while also revealing the distribution of dark matter in galaxies, thus constraining dark matter\u27s properties
Effects of Government Intervention in Agricultural Sustainability and Profits: Study of Dutch And American Agricultural Policy
Get PDFThis conceptual paper raises questions about the influence policy plays in forming culture, a consumer\u27s willingness to pay, and ultimately profits of a farmer. I look at the Netherlands and compare it to the U.S., as the top two agricultural exporters
How Parental Beliefs About School Can Potentially Influence Student Engagement
Get PDFDecades of research have demonstrated that beliefs matter, driving people’s emotional responses and, in turn, their behaviors. The recent work of Clifton and colleagues (2019) has significantly advanced the understanding of world beliefs through the development of the primal world belief’s (primals) scale. Primals are highly correlated with personality and well-being variables. Evidence suggests they serve as a schematic lens influencing how people view their experiences of the world. Building on this research, this capstone examines the hidden biases influencing judgment when it comes to the messages parents share with their children about school. Taking a metacognitive approach, the potential for a parent’s beliefs about school to influence their children’s beliefs and, in turn, their children’s mastery are examined, and are considered in the context of mattering. It is possible that parent beliefs could create positive and negative spirals, influencing both student and community outcomes. For this reason, the primals scale was modified to measure (1) student beliefs about school (2) student perceptions of their parent’s beliefs about school and (3) student engagement. Data will be gathered and analyzed over this next year. A positive psychology intervention (PPI) was also created using the modified primals scale to gain a better understanding of the possible underlying mechanisms associated with beliefs and to potentially identify elements of causation. It was also developed to guide parents—alongside their children—to regularly savor the Good in schools. Intended to alter hidden biases and framing beliefs, it is expected to help parents and their children develop a broader base of resources and strategies for support. The intervention is targeted to improve beliefs about school, increase PERMA, and increase mattering, agency, and hope. This analysis suggests there may be opportunities for expanding the role of positive psychology in schools