VictorJuiz/thesis huggingface dataset:
Modeling and Analysis of Oligonucleotide Microarray Data for Pathogen Detection

Microarrays have emerged during the past decade as a viable platform for detection of DNA from microorganisms in clinical and environmental samples. These microbial detection arrays occupy a middle ground between low cost, narrowly focused assays such as multiplex PCR and more expensive, broad-spectrum technologies like high-throughput sequencing. The Pathogen Bioinformatics Group at Lawrence Livermore National Laboratory is one of several teams that are actively working to develop arrays for clinical diagnostics, biologic product safety testing, environmental monitoring and biodefense applications./nStatistical algorithms that can analyze data from microbial detection arrays and provide easily interpretable results are absolutely required in order for these efforts to succeed. Several researchers have developed methods to determine what organisms are present in a microbial detection array sample. The algorithms developed so far operate mainly within a hypothesis testing framework, and are not motivated by a physical model of the process by which microbial DNA hybridizes to DNA probes on the array. Therefore, they only provide probabilities for the absolute presence or absence of an organism, and lack the ability to infer the abundances of the microbes in the sample. They also have limited capacity to handle samples containing complex mixtures of microorganisms./nThis dissertation describes an approach to developing a quantitative algorithm for microbial detection array data analysis, capable of both identifying the organisms present in a sample and inferring their concentrations. After reviewing the most promising array designs and analysis algorithms that have been developed to date, I present a physical model for predicting probe signals on an array given a set of target organisms present in a sample and their concentrations. I describe the experiments that were performed to fit the key parameters in this model. Finally, I present an approach to solving the inverse problem, in which the probe signals are observed and used to infer the targets present and their concentrations.

Advisor: Speed, Terence P
The Impact of Fetal Exposure to SIV on the Outcome of Neonatal Infection

HIV kills more than 2 million people worldwide, annually. Progressive HIV disease is marked by the loss of peripheral CD4+ T cells, leading to immune dysfunction and leaving the host susceptible to opportunistic infections. Immune activation is thought to play a major role in T cell depletion and the ability of the host to control immune activation during chronic infection may enhance survival. How may a host learn to control immune activation? Pregnancy appears to be a time in which immune activation is tightly regulated as the immune systems of mom and her genetically non-identical fetus learn to become tolerant of one another's tissues. The trafficking of cells from mom across the placenta and into the fetus during gestation, a concept known as "maternal microchimerism," exposes the immune system of the developing fetus to maternal alloantigens and has been shown to promote tolerance by the fetal immune system to maternal cells. We hypothesized that if the fetus were exposed to antigens from HIV in utero that it might become tolerant to HIV antigens in a similar fashion to maternal cells and that this tolerance of HIV antigens would reduce the damage caused by immune activation if this fetus were infected with HIV later in life. To test this hypothesis, rhesus macaques (Macaca mulatta) were studied to determine if maternal microchimerism exists in this species and if exposure to SIV in fetal macaques can induce tolerance to the virus and reduce activation-induced damage after infection postnatally. Our data indicate that maternal microchimerism is detectable in low frequencies in fetal tissues and peripheral blood but can be found in relatively high frequencies in postnatal tissues. Fetal exposure to the nonpathogenic SIV clone, SIVmac1A11, was associated with reduced viral loads (p<0.02) and altered immunologic parameters after pathogenic SIVmac239 challenge. While we did not find evidence of immune tolerance to SIV antigens in these animals our data indicate that prenatal exposure to the virus has a lasting impact on the immune system that may allow for control of viral replication after pathogenic challenge. These data also indicate that the rhesus macaque is a tractable system for studying immunologic development in the context of prenatal infections that may be directly relevant to humans.

Advisor: Coscoy, Laurent
McCune, Joseph
Techniques for Model Inference and Bug Finding

Security bugs in network-based applications allow an attacker to compromise a system from the network. Existing techniques to find bugs in network-based applications are limited because the rules by which a program interacts with its network environment (i.e., the protocol) are often unknown. Further, the complexity of stateful interactions in such applications makes bug-finding difficult./nIn this thesis, we explore two directions towards solving the problem of finding bugs in network-based applications. First, to overcome the problem of not knowing how an application interacts with its network environment, we propose new techniques to automatically infer the protocol model implemented by an application, and use the inferred model to guide the search for bugs. We apply our technique to infer the command-and-control protocol model of a major spam botnet, and analyze it to discover its weakest link and protocol logic flaws. The analysis leads to new ways to disable botnets and new ways to fight spam./nSecond, we propose new techniques to analyze programs to find bugs. Our techniques are based on a family of techniques called symbolic analysis, that are equivalent to testing with entire classes of test case inputs. We show that a synergistic combination of model inference and symbolic analysis is more effective than symbolic analysis alone. Our technique finds several new vulnerabilities in popular applications that have remained undetected for years./nSymbolic analysis has limited scalability on programs of significant sizes. We propose a compositional approach to symbolic analysis, that scales to programs in the order of hundreds of thousands of lines of code (100 KLOC). Our technique uses a novel combination of satisfying assignments and proofs of unsatisfiability in a solver to generalize preconditions that must lead to bugs. Using the preconditions with the data-flow of the program allows it to prune irrelevant code from analysis. Our technique outperforms existing tools, and finds multiple new vulnerabilities in critical Internet infrastructure software.

Advisor: Song, Dawn Xiaodong
Italian Female Epistemologies beyond 'The Scene of the Crime'

The crime genre, more familiarly referred to in Italian as the giallo, thrives in Italy and finds it origins in the late nineteenth-century popular novel or romanzo d'appendice. This dissertation groups together four twentieth and twenty-first century Italian novels written by women that elicit the crime genre only to then short-circuit it. Rather than placing them within the revisionist subgenre `Feminist crime fiction,' I suggest that these novels are radical in that they stage their own withdrawal from a gendered structure of knowledge inhabited and engendered by the crime novel: an epistemology that leaves them unthought. Indeed, these novels must leave the genre behind in order to pursue the construction of a female site of knowledge and the possibility of a female knower. In creating space for alternate, experimental discourses, these narratives rethink the relation between epistemology and gender, epistemology and narrative, epistemology and the body, and epistemology and the maternal by returning to, and rewriting, stories of origin so as to begin again and know differently.

Advisor: Spackman, Barbara
EARLY CHILDHOOD EDUCATORS' BELIEFS ABOUT INCLUSION AND PERCEIVED SUPPORTS

Young children with special needs have been educated in classrooms for over 40 years. While their role in included classrooms has been debated for decades, children with special needs are to be educated with their general education peers with appropriate aids and supports. (Individuals with Disabilities Education Act, 2004). While studies about the definition of inclusion indicate that it means different things to different people, researchers have determined that teachers in part develop their pedagogy based on their beliefs of how children learn which could affect their beliefs about inclusive practices. In addition we know that teachers need supports to successfully include children with special needs in general education classrooms./nAlthough researchers have looked at inclusion through many lenses, little work has been done to see if there is a connection between early childhood education teachers' beliefs about and supports for inclusion. This study was designed to illuminate the connection between preschool teachers' beliefs about including children with special needs in his or her classroom and the supports that he or she receives to help facilitate that inclusion. In total, 218 Head Start and private early childhood educators were surveyed on their beliefs about inclusion and the quality of inclusive practices. Through a quantitative correlational design, I used the My Thinking about Inclusion Scale (MTAI; Stoiber et al., 1998) to score the teachers' overall beliefs about inclusion and the Quality of Inclusive Experiences Measure (QIEM; Wolery, Pauca, Brashers, & Grant, 2000) to analyze the level of support for inclusion that the teachers received./nResults indicated that overall, early childhood educators are favorable to inclusion and that no differences exist between groups. Teachers who self-reported high quality levels of support for inclusion however reported more favorable beliefs for including students with special needs in their classroom. In addition, teachers who reported more in-service training for inclusion also indicated more favorable beliefs for including students with special needs. Overall significant results indicated that there was a relationship between beliefs and supports, and teachers who had more favorable beliefs about inclusion had more supports for inclusion.

Advisor: Fuller, Bruce
Hanson, Marci J
Transient Absorption Spectroscopy with Isolated Attosecond Pulses

Ultrashort pulses with durations less than one femtosecond advance understanding of chemistry and physics by facilitating time-resolved measurements on the timescale of electronic motion. A very pragmatic type of experiment incorporating isolated attosecond (10&minus18 s) pulses is transient absorption measurements that utilize the broad bandwidth of attosecond pulses. In these measurements the dispersed spectrum of the isolated attosecond pulse is recorded after the attosecond pulse and a time-delayed optical pulse pass through an experimental target of interest. This thesis focuses on the development of an attosecond transient absorption spectrometer with the ultimate goal of using the spectrometer to measure atomic and molecular dynamics. Two experimental studies are presented: one focusing on the behavior of helium atoms bathed in a strong near-infrared optical field, and one focusing on measurement of the lifetimes of autoionizing states in xenon./nMeasurement of the absorption spectrum of atomic helium bathed in a near-infrared (780 nm) field exhibits new features when the attosecond pulse and the optical pulse are overlapped in the gas target. The features near the 1s2p transition at 21.21 eV are identified as intermediates in two-photon transitions to reach nearby s and d helium excited states that are inaccessible via one photon transitions from the 1s2 ground state of He. Initial measurements identified three intermediate states, and further experiments investigated the behavior of the states as the near infrared field intensity is increased./nAttosecond time-resolved measurement of ultrashort lifetimes of autoionizing states is also investigated. The 5s5p66p and 5s5p67p autoionizing states of xenon are visible in the extreme-ultraviolet absorption spectrum of Xe as Fano resonances. The Fano resonances are suppressed by a time-delayed near-infrared pulse and recover with a time-constant that is related to the lifetime of the state. The studies here identified key parameters for time-resolved measurements of the autoionization lifetime, including spectral resolution of the attosecond transient absorption spectrometer.

Advisor: Leone, Stephen R
Neumark, Daniel M
From the Real Vehicle to the Virtual Vehicle

We focus on systems with task arrivals in time and space. A good model for the single-customer case is the Dynamic Traveling Repairman Problem (DTRP) [1]. The DTRP literature has focused on optimizing the expected value of system time, defined as the elapsed time between the arrival and the completion of each task. We focus on the stability and distribution of system time, including its variance. This dissertation establishes a partially policy independent necessary and sufficient condition for stability in the DTRP. The policy class includes some of the policies proven to be optimal for system time expectation under light and heavy loads in the literature. We propose a new policy named PART-n-TSP and compute a good approximation for its system time distribution. PART-n-TSP has lower system time variance than PART-TSP [2] and Nearest Neighbor [1] when the load is neither too small or too large. We prove that PART-n-TSP is also optimal for system time expectation under light and heavy loads./nIn the multi-customer case, the scheduling policies of the DTRP and other vehicle routing problems do not create performance isolation [3] between customers. We explore performance isolation between customers by borrowing the virtual machine abstraction from cloud computing. Since our servers are moving vehicles, we propose a new equivalent of the virtual machine called the virtual vehicle enabling what we call cloud computing in space. The customer operates virtual vehicles that are in reality hosted by fewer, shared provider-operated real vehicles. We show that cloud computing in space can do better than conventional cloud computing in the sense of realizing high performance isolation (e.g. 98%) while requiring significantly fewer real vehicles (e.g. approximately 1-for-5).

Advisor: Sengupta, Raja
Reconstruction algorithms for x-ray nanocrystallography via solution of the twinning problem

X-ray nanocrystallography is an emerging technique for imaging nanoscale objects that alleviates the large crystallization requirement of conventional crystallography by collecting diffraction patterns from a large ensemble of smaller and easier to build nanocrystals, which are typically delivered to the x-ray beam via a liquid jet. In order to determine the structure of an imaged object, several parameters must first be determined, including the crystal sizes, incident photon flux densities, and crystal orientations. Autoindexing techniques, which have been used extensively to orient conventional crystals, only determine the orientation of the nanocrystals up to symmetry of the crystal lattice, which is often greater than the symmetry of the diffraction information, resulting in what is known as the twinning problem. In addition, the image data is corrupted by large degrees of shot noise due to low collected signal, background signal due to the liquid jet and detector electronics, as well as other sources of noise. Furthermore, diffraction only measures the magnitudes of the Fourier transform of the object and, thus, one must recover phase information in order to invert the data and recover a three-dimensional reconstruction of the constituent molecular structure. Previous approaches for handling the twinning problem have mainly relied on having a known similar structure available, which may not be present for fundamentally new structures. We present a series of techniques to determine the crystal sizes, incident photon flux densities, and crystal orientations in the presence of large amounts of noise common in experiments. Additionally, by using a new sampling strategy, we demonstrate that phase information can be computed from nanocrystallographic diffraction images using only Fourier magnitude information, via a compressive phase retrieval algorithm. We demonstrate the feasibility of this new approach by testing it on simulated data with parameters and noise levels common in current experiments.

Advisor: Sethian, James A
Uncertainties in the Value of Bill Savings from Behind-the-Meter, Residential Photovoltaic Systems: The Roles of Electricity Market Conditions, Retail Rate Design, and Net Metering

Net metering has become a widespread policy mechanism in the U.S. for supporting customer adoption of distributed photovoltaics (PV), allowing customers with PV systems to reduce their electric bills by offsetting their consumption with PV generation, independent of the timing of the generation relative to consumption. Although net metering is one of the principal drivers for the residential PV market in the U.S., the academic literature on this policy has been sparse and this dissertation contributes to this emerging body of literature./nThis dissertation explores the linkages between the availability of net metering, wholesale electricity market conditions, retail rates, and the residential bill savings from behind-the-meter PV systems. First, I examine the value of the bill savings that customers receive under net metering and alternatives to net metering, and the associated role of retail rate design, based on current rates and a sample of approximately two hundred residential customers of California's two largest electric utilities. I find that the bill savings per kWh of PV electricity generated varies greatly, largely attributable to the increasing block structure of the California utilities' residential retail rates. I also find that net metering provides significantly greater bill savings than alternative compensation mechanisms based on avoided costs. However, retail electricity rates may shift as wholesale electricity market conditions change./nI then investigate a potential change in market conditions - increased solar PV penetrations - on wholesale prices in the short-term based on the merit-order effect. This demonstrates the potential price effects of changes in market conditions, but also points to a number of methodological shortcomings of this method, motivating my usage of a long-term capacity investment and economic dispatch model to examine wholesale price effects of various wholesale market scenarios in the subsequent analysis. By developing three types of retail rates (a flat rate, a time-of-use rate, and real-time pricing) from these wholesale price profiles, I examine bill savings from PV generation for the ten wholesale market scenarios under net metering and an alternative to net metering where hourly excess PV generation is compensated at the wholesale price. Most generally, I challenge the common assertion that PV compensation is likely to stay constant (or rise) due to constant (or rising) retail rates, and find that future electricity market scenarios can drive substantial changes in residential retail rates and that these changes, in concert with variations in retail rate structures and PV compensation mechanisms, interact to place substantial uncertainty on the future value of bill savings from residential PV.

Advisor: Borenstein, Severin
The Subsumptive Constraints Account of why explaining "why?" helps learning

Constructing explanations in response to why-questions plays an important role in learning and generalization - in young children, students, adults and scientists - but much remains to be understood about the underlying mechanisms and their implications for practical learning and teaching. The first chapter of this dissertation presents theSubsumptive Constraints account, which proposes that explaining why a fact or observation is true drives learners to understand how it can be subsumed as an instance of a pattern or generalization. Chapters two through four present nine experiments which test whether explaining exerts this selective effect, rather than a general boost for learning, examining contexts ranging from learning artificial categories to learning to predict other people's behavior. In these nine experiments, three specific predictions of the Subsumptive Constraints account were supported. The second chapter presents the first three experiments, which provide evidence that explaining category membership promoted the discovery of broad underlying patterns that supported generalization. Four experiments reported in the third chapter further revealed that seeking explanations increased learners' consultation of existing knowledge that could be used to discover patterns in their observations. Explaining also interacted with existing knowledge to influence which patterns learners generalized to novel contexts. The final two experiments in chapter four confirmed the Subsumptive Constraints account's counterintuitive prediction that seeking explanations can impair learning, by revealing that learning was driven by an interaction between explanation and the reliability of underlying patterns. Explaining drove people to ignore specific observations that were exceptions to patterns, so that explaining fostered overgeneralization at the expense of accurate learning. The fifth chapter concludes this work. By drawing together theory and methodology from cognitive psychology, education, and philosophy, this dissertation provides novel theoretical, empirical, and practical insight into how generating explanations influences and improves learning. Explaining does not simply promote an 'all-purpose' boost to engagement and metacognition - and is therefore not always practically beneficial or optimal. By characterizing the particular nature of the processing constraints promoted by explaining - as selectively constraining the search for and evaluation of patterns that underlie specific observations - this novel Subsumptive Constraints account explains why explaining especially impacts learners' use of existing knowledge in transfer and generalization, and provides insight into when to expect and how to maximize explanation's practical benefits.

Advisor: Lombrozo, Tania
Covariance matrix estimation and variable selection in high dimension

First part of the thesis focuses on sparse covariance matrices estimation under the scenario of large dimension p and small sample size n. In particular, we consider a class of covariance matrices which are approximately block diagonal under unknown permutations. We propose a block recovery estimator and show it achieves minimax optimal convergence rate for the class, which is the same as if the permutation were known. The problem is also related to sparse PCA and k-densest subgraphs, where the spike model is a special case of their intersection. Simulations of the spike model and multiple block model, together with a real world application, confirm that the proposed estimator is both statistically and computationally efficient./nSecond part of the thesis focuses on variable selection in linear regression, also under the high dimensional scenario of large p and small n. We propose a general framework to search variables based on their covariance structures, with a specific variable selection algorithm called kForward which iteratively fits local/small linear models among relatively highly correlated variables. For simulation experiments and a real world data set, we compare kForward to other popular methods including the Lasso, Elastic Net, SCAD, MC+, FoBa for both variable selection and prediction.

Advisor: Bickel, Peter J