12,846 research outputs found
The temporality of rhetoric: the spatialization of time in modern criticism
Every conception of criticism conceals a notion of time which informs the manner in which the critic conceives of history, representation and criticism itself. This thesis reveals the philosophies of time inherent in certain key modern critical concepts: allegory, irony and the sublime. Each concept opens a breach in time, a disruption of chronology. In each case this gap or aporia is emphatically closed, elided or denied. Taking the philosophy of time elaborated by Giorgio Agamben as an introductory proposition, my argument turns in Chapter One to the allegorical temporality which Walter Benjamin sees as the time of photography. The second chapter examines the aesthetics of the sublime as melancholic or mournful untimeliness. In Chapter Three, Paul de Man's conception of irony provides an exemplary instance of the denial of this troubling temporal predicament. In opposition to the foreclosure of the disturbing temporalities of criticism, history and representation, the thesis proposes a fundamental rethinking of the philosophy of time as it relates to these categories of reflection. In a reading of an inaugural meditation on the nature of time, and in examining certain key contemporary philosophical and critical texts, I argue for a critical attendance to that which eludes those modes of thought that attempt to map time as a recognizable and essentially spatial field. The Confessions of Augustine provide, in the fourth chapter, a model for thinking through the problems set up earlier: Augustine affords us, precisely, a means of conceiving of the gap or the interim. In the final chapter, this concept is developed with reference to the criticism of Arnold and Eliot, the fiction of Virginia Woolf and the philosophy of cinema derived from Deleuze and Lyotard. In conclusion, the philosophical implications of the thesis are placed in relation to a conception of the untimeliness of death
Quantifying the Indirect Effect of Wolves on Aspen in Northern Yellowstone National Park: Evidence for a Trophic Cascade?
Yellowstone National Park is renowned for its incredible wildlife, and perhaps the most famous of these species is the gray wolf, which was reintroduced to the Park in the mid-1990s. After reintroduction, it was highly publicized by scientists, journalists, and environmentalists that the wolf both decreased elk density and changed elk behavior in a way that reduced elk effects on plants, a process known as a âtrophic cascade.â Aspen, which is eaten by elk in winter, is one species at the forefront of Yellowstone trophic cascade research because it has been in decline across the Park for over a century. However, due to the challenges of measuring trophic cascades, there is continued uncertainty regarding the effects of wolves on aspen in northern Yellowstone. Thus, the purpose of my dissertation was to provide a comprehensive test of a trophic cascade in this system. Specifically, I used 20 years of data on aspen, elk, and wolves in Yellowstone to: 1) clarify annual trends in browsing and height of young aspen (a proxy for regeneration) after wolf reintroduction, 2) assess the influence of wolves scaring elk on aspen (âtrait-mediated indirect effectsâ), and 3) evaluate the effect of wolves killing elk on aspen (âdensity-mediated indirect effectsâ).
My research suggests that wolves indirectly contributed to increased aspen over story recruitment following their reintroduction by helping to reduce the elk population size, but elk response to the risk of wolf predation did not reduce elk foraging in a way that measurably increased aspen recruitment. Additionally, hunter harvest of elk north of the park was twice as important as wolf predation in causing increased aspen recruitment. However, despite wolves and hunters limiting elk abundance, it is still uncommon for young aspen to grow past peak browsing height (120-cm), indicating that many stands remain vulnerable to elk herbivory nearly 30 years after wolf reintroduction. These results highlight that the strength and mechanism of predator effects on plant communities are context-specific. Thus, using predator reintroduction as a tool for ecosystem restoration without considering the many factors that shape trophic cascades may result in different management and conservation outcomes than intended
Socio-endocrinology revisited: New tools to tackle old questions
Animalsâ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)
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Mixture Models in Machine Learning
Modeling with mixtures is a powerful method in the statistical toolkit that can be used for representing the presence of sub-populations within an overall population. In many applications ranging from financial models to genetics, a mixture model is used to fit the data. The primary difficulty in learning mixture models is that the observed data set does not identify the sub-population to which an individual observation belongs. Despite being studied for more than a century, the theoretical guarantees of mixture models remain unknown for several important settings.
In this thesis, we look at three groups of problems. The first part is aimed at estimating the parameters of a mixture of simple distributions. We ask the following question: How many samples are necessary and sufficient to learn the latent parameters? We propose several approaches for this problem that include complex analytic tools to connect statistical distances between pairs of mixtures with the characteristic function. We show sufficient sample complexity guarantees for mixtures of popular distributions (including Gaussian, Poisson and Geometric). For many distributions, our results provide the first sample complexity guarantees for parameter estimation in the corresponding mixture. Using these techniques, we also provide improved lower bounds on the Total Variation distance between Gaussian mixtures with two components and demonstrate new results in some sequence reconstruction problems.
In the second part, we study Mixtures of Sparse Linear Regressions where the goal is to learn the best set of linear relationships between the scalar responses (i.e., labels) and the explanatory variables (i.e., features). We focus on a scenario where a learner is able to choose the features to get the labels. To tackle the high dimensionality of data, we further assume that the linear maps are also sparse , i.e., have only few prominent features among many. For this setting, we devise algorithms with sub-linear (as a function of the dimension) sample complexity guarantees that are also robust to noise.
In the final part, we study Mixtures of Sparse Linear Classifiers in the same setting as above. Given a set of features and the binary labels, the objective of this task is to find a set of hyperplanes in the space of features such that for any (feature, label) pair, there exists a hyperplane in the set that justifies the mapping. We devise efficient algorithms with sub-linear sample complexity guarantees for learning the unknown hyperplanes under similar sparsity assumptions as above. To that end, we propose several novel techniques that include tensor decomposition methods and combinatorial designs
SYSTEMS METHODS FOR ANALYSIS OF HETEROGENEOUS GLIOBLASTOMA DATASETS TOWARDS ELUCIDATION OF INTER-TUMOURAL RESISTANCE PATHWAYS AND NEW THERAPEUTIC TARGETS
In this PhD thesis is described an endeavour to compile litterature about Glioblastoma key molecular mechanisms into a directed network followin Disease Maps standards, analyse its topology and compare results with quantitative analysis of multi-omics datasets in order to investigate Glioblastoma resistance mechanisms. The work also integrated implementation of Data Management good practices and procedures
A comprehensive review on laser powder bed fusion of steels : processing, microstructure, defects and control methods, mechanical properties, current challenges and future trends
Laser Powder Bed Fusion process is regarded as the most versatile metal additive manufacturing process, which has been proven to manufacture near net shape up to 99.9% relative density, with geometrically complex and high-performance metallic parts at reduced time. Steels and iron-based alloys are the most predominant engi-neering materials used for structural and sub-structural applications. Availability of steels in more than 3500 grades with their wide range of properties including high strength, corrosion resistance, good ductility, low cost, recyclability etc., have put them in forefront of other metallic materials. However, LPBF process of steels and iron-based alloys have not been completely established in industrial applications due to: (i) limited insight available in regards to the processing conditions, (ii) lack of specific materials standards, and (iii) inadequate knowledge to correlate the process parameters and other technical obstacles such as dimensional accuracy from a design model to actual component, part variability, limited feedstock materials, manual post-processing and etc. Continued efforts have been made to address these issues. This review aims to provide an overview of steels and iron-based alloys used in LPBF process by summarizing their key process parameters, describing thermophysical phenomena that is strongly linked to the phase transformation and microstructure evolution during solidifica-tion, highlighting metallurgical defects and their potential control methods, along with the impact of various post-process treatments; all of this have a direct impact on the mechanical performance. Finally, a summary of LPBF processed steels and iron-based alloys with functional properties and their application perspectives are presented. This review can provide a foundation of knowledge on LPBF process of steels by identifying missing information from the existing literature
A Synergistic Compilation Workflow for Tackling Crosstalk in Quantum Machines
Near-term quantum systems tend to be noisy. Crosstalk noise has been
recognized as one of several major types of noises in superconducting Noisy
Intermediate-Scale Quantum (NISQ) devices. Crosstalk arises from the concurrent
execution of two-qubit gates on nearby qubits, such as \texttt{CX}. It might
significantly raise the error rate of gates in comparison to running them
individually. Crosstalk can be mitigated through scheduling or hardware machine
tuning. Prior scientific studies, however, manage crosstalk at a really late
phase in the compilation process, usually after hardware mapping is done. It
may miss great opportunities of optimizing algorithm logic, routing, and
crosstalk at the same time. In this paper, we push the envelope by considering
all these factors simultaneously at the very early compilation stage. We
propose a crosstalk-aware quantum program compilation framework called CQC that
can enhance crosstalk mitigation while achieving satisfactory circuit depth.
Moreover, we identify opportunities for translation from intermediate
representation to the circuit for application-specific crosstalk mitigation,
for instance, the \texttt{CX} ladder construction in variational quantum
eigensolvers (VQE). Evaluations through simulation and on real IBM-Q devices
show that our framework can significantly reduce the error rate by up to
6, with only 60\% circuit depth compared to state-of-the-art gate
scheduling approaches. In particular, for VQE, we demonstrate 49\% circuit
depth reduction with 9.6\% fidelity improvement over prior art on the H4
molecule using IBMQ Guadalupe. Our CQC framework will be released on GitHub
Associations of Discrimination With Drinking Behavior in Multiracial College Students: Protective Role of Racial Socialization
Despite the rapid growth of the Multiracial population in the United States, less is known about correlates of their health behaviors. Nascent findings demonstrate elevated rates of drinking behavior among Multiracial college students compared to their monoracial counterparts. Theoretical models posit that racial socialization by primary caregivers may change the magnitude of the relationship of discrimination with drinking behavior among Multiracial individuals. The role of racial socialization, however, has not been tested specifically among Multiracial college students. In this cross-sectional survey study, 193 undergraduate students (Mage = 20 years [SD = 1.33]; 30% male; 33% Greek affiliated) reporting lifetime alcohol use completed an online questionnaire on drinking behaviors, experiences of racial socialization, and experiences of general as well as Multiracial discrimination. Results from path models indicated that the relationship between general or multiracial discrimination with drinking behaviors was not weaker among those reporting higher levels of racial socialization. The current finding adds to the limited and underrepresented alcohol use literature of Multiracial college students by demonstrating that primary caregiver racial socialization may not be protective against discrimination experiences and drinking behavior among Multiracial college students. The implications of these findings may be used to inform further research, clinical programming, as well as policy development
Flexographic printed nanogranular LBZA derived ZnO gas sensors: Synthesis, printing and processing
Within this document, investigations of the processes towards the production of a flexographic printed ZnO gas sensor for breath H2 analysis are presented. Initially, a hexamethylenetetramine (HMTA) based, microwave assisted, synthesis method of layered basic zinc acetate (LBZA) nanomaterials was investigated. Using the synthesised LBZA, a dropcast nanogranular ZnO gas sensor was produced. The testing of the sensor showed high sensitivity towards hydrogen with response (Resistanceair/ Resistancegas) to 200 ppm H2 at 328 °C of 7.27. The sensor is highly competitive with non-catalyst surface decorated sensors and sensitive enough to measure current H2 guideline thresholds for carbohydrate malabsorption (Positive test threshold: 20 ppm H2, Predicted response: 1.34). Secondly, a novel LBZA synthesis method was developed, replacing the HMTA by NaOH. This resulted in a large yield improvement, from a [OH-] conversion of 4.08 at% to 71.2 at%. The effects of [OH-]/[Zn2+] ratio, microwave exposure and transport to nucleation rate ratio on purity, length, aspect ratio and polydispersity were investigated in detail. Using classical nucleation theory, analysis of the basal layer charge symmetries, and oriented attachment theory, a dipole-oriented attachment reaction mechanism is presented. The mechanism is the first theory in literature capable of describing all observed morphological features along length scales. The importance of transport to nucleation rate ratio as the defining property that controls purity and polydispersity is then shown. Using the NaOH derived LBZA, a flexographic printing ink was developed, and proof-of-concept sensors printed. Gas sensing results showed a high response to 200 ppm H2 at 300 °C of 60.2. Through IV measurements and SEM analysis this was shown to be a result of transfer of silver between the electrode and the sensing layer during the printing process. Finally, Investigations into the intense pulsed light treatment of LBZA were conducted. The results show that dehydration at 150 °C prior to exposure is a requirement for successful calcination, producing ZnO quantum dots (QDs) in the process. SEM measurements show mean radii of 1.77-2.02 nm. The QDs show size confinement effects with the exciton blue shifting by 0.105 eV, and exceptionally low defect emission in photoluminescence spectra, indicative of high crystalline quality, and high conductivity. Due to the high crystalline quality and amenity to printing, the IPL ZnO QDs have numerous potential uses ranging from sensing to opto-electronic devices
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