Physical Sciences and Mathematics Commons^™

The Belle Ii Physics Book, E. Kou, P. Urquijo, W. Altmannshofer, F. Beajean, G. Bell, M. Beneke, I. I. Bigi, F. Bishara, M. Blanke, C. Bobeth, M. Bona, N. Brambilla, V. M. Braun, J. Brod, A. J. Buras, H. Y. Cheng, C. W. Chiang, M. Ciuchini, G. Colangelo, Milind Purohit, Et. Al.

Faculty Publications

No abstract provided.

Measurement Of The Integrated Luminosity Of The Phase 2 Data Of The Belle Ii Experiment, F. Abudinén, I. Adachi, P. Ahlburg, H. Aihara, N. Akopov, A. Aloisio, F. Ameli, L. Andricek, N. Anh Ky, D. M. Asner, H. Atmacan, T. Aushev, V. Aushev, T. Aziz, K. Azmi, V. Babu, S. Baehr, S. Bahinipati, A. M. Bakich, Milind Purohit, Et. Al.

Faculty Publications

From April to July 2018, a data sample at the peak energy of the Υ(4S) resonance was collected with the Belle II detector at the SuperKEKB electron-positron collider. This is the first data sample of the Belle II experiment. Using Bhabha and digamma events, we measure the integrated luminosity of the data sample to be (469.3±0.3±3.0) pb^-1, where the first uncertainty is statistical and the second is systematic. This work provides a basis for future luminosity measurements at Belle II.

Computational Screening Of New Perovskite Materials Using Transfer Learning And Deep Learning, Xiang Li, Yabo Dan, Rongzhi Dong, Zhuo Cao, Chengcheng Niu, Yuqi Song, Shaobo Li, Jianjun Hu

Faculty Publications

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us …

Search For B− → Λp⊽ ⊽ With The Babar Experiment, J. P. Lees, V. Poireau, V. Tisserand, E. Grauges, A. Palano, G. Eigen, D. N. Brown, Yu G. Kolomensky, M. Fritsch, H. Koch, T. Schroeder, R. Cheaib, C. Hearty, T. S. Mattison, J. A. Mckenna, R. Y. So, V. E. Blinov, A. R. Buzykaev, V. P. Druzhinin, Milind Purohit, Et. Al.

Faculty Publications

A search for the rare flavor-changing neutral current process B− → Λp̅ νv̅ using data from the BABAR experiment has been performed. A total of 424 fb⁻¹ of e⁺e⁻ collision data collected at the center-of-mass energy of the ϒ(4S) resonance is used in this study, corresponding to a sample of (471 ± 3) × 10⁶ BB̅ pairs. Signal B− → Λp̅ νv̅ candidates are identified by first fully reconstructing a B⁺ decay in one of many possible exclusive decays to hadronic final states, then examining detector activity that is not associated with …

Importance Of Refractory Ligands And Their Photodegradation For Iron Oceanic Inventories And Cycling, Christel Hassler, Damien Cabanes, Sonia Blanco-Ameijeiras, Sylvia G. Sander, Ronald Benner

Faculty Publications

Iron is an essential micronutrient that limits primary production in up to 40% of the surface ocean and influences carbon dioxide uptake and climate change. Dissolved iron is mostly associated with loosely characterised organic molecules, called ligands, which define key aspects of the iron cycle such as its residence time, distribution and bioavailability to plankton. Models based on in situ ligand distributions and the behaviour of purified compounds include long-lived ligands in the deep ocean, bioreactive ligands in the surface ocean and photochemical processes as important components of the iron cycle. Herein, we further characterise biologically refractory ligands in dissolved …

Machine Learning To Quantitate Neutrophil Netosis, Laila Elsherif, Noah Sciaky, Carrington A. Metts, Md. Modasshir, Ioannis Rekleitis, Christine A. Burris, Joshua A. Walker, Nadeem Ramadan, Tina M. Leisner, Stephen P. Holly, Martis W. Cowles, Kenneth I. Ataga, Joshua N. Cooper, Leslie V. Parise

Faculty Publications

We introduce machine learning (ML) to perform classifcation and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in diferentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for …

Molecular Properties Are A Primary Control On The Microbial Utilization Of Dissolved Organic Matter In The Ocean, Yuan Shen, Ronald Benner

Faculty Publications

The global ocean sequesters a large amount of reduced carbon in dissolved organic molecules that can persist for centuries to millennia. The persistence of dissolved organic carbon (DOC) in the deep ocean has been attributed to inherently refractory molecules and to low concentrations of molecules, but the relative roles of molecular properties and molecular concentrations remain uncertain. We investigate both of these possibilities using bioassay experiments with unfiltered seawater collected from five depths (50–1500 m) at the Bermuda Atlantic Time-Series Study site. The microbial utilization of compositionally distinct forms of seawater DOC at in situ and elevated concentrations was determined. …

Lorentz- And C P T -Violating Standard Model Extension In Chiral Perturbation Theory, Brett Altschul, Matthias R. Schindler

Faculty Publications

Lorentz and CPT violation in hadronic physics must be tied to symmetry violations at the underlying quark and gluon level. Chiral perturbation theory provides a method for translating novel operators that may appear in the Lagrange density for color-charged parton fields into equivalent forms for effective theories at the meson and baryon levels. We extend the application of this technique to the study of Lorentzviolating and potentially CPT-violating operators from the minimal standard model extension. For dimension-4 operators, there are nontrivial relations between the coefficients of baryon-level operators related to underlying quark and gluon operators with the same Lorentz structures. …

Observation Of Τ−→Π−ΝΤE+E− And Search For Τ−→Π−Ντμ+Μ−, Y. Jin, H. Aihara, D. Epifanov, I. Adachi, S. Ai. Said, D. M. Asner, V. Aulchenko, T. Aulchenko, T. Aushev, R. Ayad, V. Babu, I. Badhrees, S. Bahinipati, V. Bansal, P. Behera, M. Berger, V. Bhardwaj, T. Bilka, J. Biswal, Milind Purohit, Et. Al.

Faculty Publications

We present the first measurements of branching fractions of rare tau-lepton decays, τ−→π−ντℓ+ℓ− (ℓ=e or μ), using a data sample corresponding to 562 fb⁻¹ collected at a center-of-mass energy of 10.58 GeV with the Belle detector at the KEKB asymmetric-energy e⁺e⁻collider. The τ−→π⁻ν_τe⁺e⁻ decay is observed for the first time with 7.0σ significance. The partial branching fraction determined by the structure-dependent mechanisms mediated by either a vector or an axial-vector current for the mass region M_πee > 1.05 GeV=c² is measured to be B(τ⁻ → π …

First Measurement Of Neutrino Oscillation Parameters Using Neutrinos And Antineutrinos By Nova, M. A. Acero, P. Adamson, L. Aliaga, T. Alion, V. Allakhverdian, S. Altakarli, N. Anfimov, A. Antoshkin, A. Aurisano, A. Back, C. Backhouse, M. Baird, N. Balashov, P. Baldi, B. A. Bambah, S. Bashar, K. Bays, S. Bending, R. Bernstein, V. Bhatnagar, Roberto Petti, Et. Al.

Faculty Publications

The NOvA experiment has seen a 4.4σ signal of ⊽ _e appearance in a 2 GeV⊽_μ beam at a distance of 810 km. Using 12.33×10²⁰ protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 ⊽_μ→⊽_e candidates with a background of 10.3 and 102⊽_μ→⊽_μ candidates. This new antineutrino data are combined with neutrino data to measure the parameters |Δm ² ₃₂ | = 2.48 ^+0.11 _-0.06 x 10 ^-3 eV² / c⁴ and sin² θ₂₃ in the ranges …

Frontiers In Fast Voltammetry: Novel Analytes And Applications, Jordan Holmes

Theses and Dissertations

Electrochemical sensors are beneficial towards the development and advancement of monitoring devices. As this type of technology progresses, so does our ability to create state-of-the-art sensing strategies to probe environmental and biological systems at the source. In the environment, it is essential to monitor particularly harmful contaminants like trace metals in order to better mitigate risk. Additionally, biological molecules are often times challenging to measure because matrices are complex and difficult to probe; Recent advancements in chemical ex vivo and in vivo sensing platforms have offered insight into physiological processes. The brain in particular requires a sophisticated, implantable sensor as …

Improving Person-Independent Facial Expression Recognition Using Deep Learning, Jie Cai

Theses and Dissertations

Over the past few years, deep learning, e.g., Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have shown promise on facial expression recog- nition. However, the performance degrades dramatically especially in close-to-real-world settings due to high intra-class variations and high inter-class similarities introduced by subtle facial appearance changes, head pose variations, illumination changes, occlusions, and identity-related attributes, e.g., age, race, and gender. In this work, we developed two novel CNN frameworks and one novel GAN approach to learn discriminative features for facial expression recognition.

First, a novel island loss is proposed to enhance the discriminative power of learned deep …

Designing, Constructing, And Employing Two-Dimensional Ultrafast Spectroscopy To Resolve Complex Kinetics, Jason R. Darvin

Theses and Dissertations

Multiple population-period transient spectroscopy (MUPPETS) is a two dimensional ultrafast time resolved spectroscopy technique. MUPPETS uses three pairs of optical pulses over two time periods to separate homogeneous and heterogeneous causes of rate dispersion. This dissertation details improvements to the MUPPETS optical assembly enabling measurements on systems that previously fell outside of the MUPPETS 2-ns time window. In addition, the theoretical groundwork is laid to unveil the hidden coordinate controlling rate exchange using 2D and 3D correlation functions.

The first project details improvements made to the MUPPETS assembly. A detailed method was developed to eliminate astigmatism, coma, and spherical aberration …

Study Of Transition State Stabilization Using Molecular Rotors, Erik Carl Vik

Theses and Dissertations

Molecular devices that function as rotors and measurement devices are the main topic of this dissertation. Each study contains a device based on an N-phenylimide framework, which has restricted rotation about the N-C (imide-phenyl) single bond due to a steric clash from the imide carbonyl and the phenyl rings ortho substituent. In general, two ground states are observed by 1H NMR, which are separated by a single transition state (TS). Incorporation of non-covalent interactions into the TS led to measurable changes in the rate for rotation. While the molecules in this dissertation revealed details that could not have been predicted, …

Studies On The Mechanism And Application Of Steam Thermography, Raymond Gerard Belliveau Iii

Theses and Dissertations

The detection of blood on fabrics for forensic purposes is a widely studied topic in forensic science, and to that end, effort in this laboratory has been devoted to developing a thermal imaging method called steam thermography. Steam thermography is a method used to enhance chemical contrast in thermographic images by exposing a surface to water vapor during imaging. The exposure of water vapor to the surface generates heat, and can differentially increase the thermographically measured apparent temperature of imaged surfaces. This can result in thermographic contrast between surfaces with different chemical properties. Previously reported proposed mechanisms to describe the …

Properties, Learning Algorithms, And Applications Of Chain Graphs And Bayesian Hypergraphs, Mohammad Ali Javidian

Theses and Dissertations

Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represent possible dependencies among the variables of a multivariate probability distri- bution. PGMs, such as Bayesian networks and Markov networks, are now widely accepted as a powerful and mature framework for reasoning and decision making under uncertainty in knowledge-based systems. With the increase of their popularity, the range of graphical models being investigated and used has also expanded. Several types of graphs with dif- ferent conditional independence interpretations - also known as Markov properties - have been proposed and used in graphical models.

The graphical structure of a …

Investigation Of Electrostatic Interactions Towards Controlling Silylation-Based Kinetic Resolutions And Exploring The Effect Of The Polymer Backbone On Silylation-Based Kinetic Resolutions Employing A Polymer-Supported Triphenylsilyl Chloride, Tian Zhang

Theses and Dissertations

This dissertation focuses on studies of silylation-based kinetic resolution methodology developed by the Wiskur group, which is a powerful method for the separation of a single enantiomer from a mixture of racemic secondary alcohols. Chapter 1 introduces the background of our silylation-based kinetic resolution..

Chapter 2 involves mechanistic studies of electrostatic interactions in controlling enantioselectivities of our silylation-based kinetic resolution. Electrostatic interactions between a silylated isothiourea intermediate and an ester π system is determined via linear free energy relationship study. To be specific, how variations in sterics and electronics affect the selectivity of a silylation-based kinetic resolution.

Chapter 3 is …

Person Identification With Convolutional Neural Networks, Kang Zheng

Theses and Dissertations

Person identification aims at matching persons across images or videos captured by different cameras, without requiring the presence of persons’ faces. It is an important problem in computer vision community and has many important real-world applica- tions, such as person search, security surveillance, and no-checkout stores. However, this problem is very challenging due to various factors, such as illumination varia- tion, view changes, human pose deformation, and occlusion. Traditional approaches generally focus on hand-crafting features and/or learning distance metrics for match- ing to tackle these challenges. With Convolutional Neural Networks (CNNs), feature extraction and metric learning can be combined in …

Cdse Quantum Dot Surface Chemistry Thermodynamics Via Isothermal Titration Calorimetry: An Emphasis On The Fundamentals, Megan Y. Gee

Theses and Dissertations

For several decades, the study and development of colloidal semiconductor nanocrystals, or quantum dots (QD), has become a rich field heralding improved integration into applications ranging from photovoltaics and photocatalysis to biomedical imaging and drug delivery. CdxSey is the most extensively studied QD system, however numerous compositional details still confound the nanocrystal field. Although CdSe QDs with native ligand coatings can show high fluorescence quantum yield and may be suitable for some applications, often times these original ligand layers are comprised of long aliphatic chains that preclude incorporation into biological matrices or severely impede charge transfer – depending on the …

Studies Of Ch Activation In Unsaturated Amides And Esters By Trinuclear Metal Carbonyl Clusters Of Osmium, Morteza Maleki

Theses and Dissertations

The chemistry of the reaction of Os3(CO)10(NCCH3)2 with representatives of unsaturated amides and esters, RCOCHCH2 (R=(CH3)2N, CH3O) has been investigated. In these reactions, it has been observed that a CH bond on the β-carbon atom is readily activated by triosmium carbonyl clusters. The activation of β-carbon C-H bond in unsaturated amides and esters provides a robust platform for studying multicenter C-H bond transformations and for C-C bond formation via hydrogen shift and CO insertion processes. In this work, proposed mechanistic approaches have been taken in order to better understand and study the relationship between the characterized species. In addition, a …

Habitability Of The Oceanic Alkaline Serpentinite Subsurface: A Case Study Of The Lost City Hydrothermal Field, Susan Q. Lang, William J. Brazelton

Faculty Publications

The Lost City hydrothermal field is a dramatic example of the biological potential of serpentinization. Microbial life is prevalent throughout the Lost City chimneys, powered by the hydrogen gas and organic molecules produced by serpentinization and its associated geochemical reactions. Microbial life in the serpentinite subsurface below the Lost City chimneys, however, is unlikely to be as dense or active. The marine serpentinite subsurface poses serious challenges for microbial activity, including low porosities, the combination of stressors of elevated temperature, high pH and a lack of bioavailable ∑CO2. A better understanding of the biological opportunities and challenges in serpentinizing systems …

Green Forest Businesses As A Method To Improve Communities In Unesco’S East Usambara Biosphere Reserve In Tanzania, Myoung Su Ko

Theses and Dissertations

A number of developing countries, especially those in Africa which have experienced former colonization, are still struggling with exploitation of their natural resources. Throughout the development of environmental management, the strategy of natural resource management has evolved from mistreating the environment for economic and social development, to separating human activities from the environment for extreme environmental protection, to ensuring the interaction between human life and environment for sustainable development. Although an abundance of natural resources, and particularly forests, exist in the protected areas, the residents in communities surrounding protected areas are usually economically and socially poor.

With this situation, the …

Semantic Segmentation Considering Image Degradation, Global Context, And Data Balancing, Dazhou Guo

Theses and Dissertations

Recently, semantic segmentation – assigning a categorical label to each pixel in an im- age – plays an important role in image understanding applications, e.g., autonomous driving, human-machine interaction and medical imaging. Semantic segmentation has made progress by using the deep convolutional neural networks, which are sur- passing the traditional methods by a large margin. Despite the success of the deep convolutional neural networks (CNNs), there remain three major challenges.

The first challenge is how to segment the degraded images semantically, i.e., de- graded image semantic segmentation. In general, image degradations increase the difficulty of semantic segmentation, usually leading to …

Moving Off Collections And Their Applications, In Particular To Function Spaces, Aaron Fowlkes

Theses and Dissertations

The main focus of this paper is the concept of a moving off collection of sets. We will be looking at how this relatively lesser known idea connects and interacts with other more widely used topological properties. In particular we will examine how moving off collections act with the function spaces Cp(X), C0(X), and CK (X). We conclude with a chapter on the Cantor tree and its moving off connections.

Many of the discussions of important theorems in the literature are expressed in terms that do not suggest the concept …

Stacked Modelling Framework, Kareem Abdelfatah

Theses and Dissertations

The thesis develops a predictive modeling framework based on stacked Gaussian processes and applies it to two main applications in environmental and chemical en- gineering. First, a network of independently trained Gaussian processes (StackedGP) is introduced to obtain analytical predictions of quantities of interest (model out- puts) with quantified uncertainties. StackedGP framework supports component- based modeling in different fields such as environmental and chemical science, en- hances predictions of quantities of interest through a cascade of intermediate predic- tions usually addressed by cokriging, and propagates uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first and …

Cybersecurity Issues In The Context Of Cryptographic Shuffling Algorithms And Concept Drift: Challenges And Solutions, Hatim Alsuwat

Theses and Dissertations

In this dissertation, we investigate and address two kinds of data integrity threats. We first study the limitations of secure cryptographic shuffling algorithms regarding preservation of data dependencies. We then study the limitations of machine learning models regarding concept drift detection. We propose solutions to address these threats.

Shuffling Algorithms have been used to protect the confidentiality of sensitive data. However, these algorithms may not preserve data dependencies, such as functional de- pendencies and data-driven associations. We present two solutions for addressing these shortcomings: (1) Functional dependencies preserving shuffle, and (2) Data-driven asso- ciations preserving shuffle. For preserving functional dependencies, …

Ultra-Fast Sensors To Study The Fundamental Neurochemistry Underlying Disease, Alyssa West

Theses and Dissertations

Serotonin is a vital neurotransmitter whose exact roles are ill-defined. Dysfunctions in serotonin signaling are thought to underlie a myriad of neurological problems including depression and autism spectrum disorder (ASD). Prior to investigating serotonin’s role in disease states, one must understand more about the functionality of serotonin in healthy in vivo models. To study serotonin in vivo, fast-scan cyclic voltammetry (FSCV) and fast- scan controlled adsorption voltammetry (FSCAV) are employed to measure evoked and basal serotonin concentration, respectively. These methods, combined with mathematical modeling, provide information regarding regulatory mechanisms of serotonin neurotransmission. FSCV and FSCAV are first applied to …

Synthesis And Characterization Of Hyaluronic Acid Based Biomaterials For Drug Delivery And Virus Modification, Amanda Wagner

Theses and Dissertations

In this thesis, hyaluronic acid based biomaterials are studied. The properties and potential applications of these polymers are characterized and discussed.

In Chapter 1, biomaterials are introduced as well as hydrogels and nanogels. Hyaluronic acid is highlighted as a remarkable bio-polymer. The overall objectives of my research are also discussed.

Chapter 2 is focused around trials for utilizing graphene oxide as a nanomaterial for controlling the drug release of an aromatic compound. Hyaluronic acid based hydrogels were synthesized as a delivery vessel. The results of the drug release profiles are examined and discussed.

A different direction is taken in Chapter …

Numerical Methods For A Class Of Reaction-Diffusion Equations With Free Boundaries, Shuang Liu

Theses and Dissertations

The spreading behavior of new or invasive species is a central topic in ecology. The modelings of free boundary problems are widely studied to better understand the nature of spreading behavior of new species. From mathematical modeling point of view, it is a challenge to perform numerical simulations of free boundary problems, due to the moving boundary, the stiffness of the system and topological changes.

In this work, we design numerical methods to investigate the spreading behavior of new species for a diffusive logistic model with a free boundary and a diffusive competition system with free boundaries. We develop a …

Machine Learning Based Ultra High Carbon Steel Image Segmentation, Sumith Kuttiyil Suresh

Theses and Dissertations

Mechanical and structural properties of ultra-high carbon steel are determined by their microstructures composed of constituents such as pearlite and spheroidites. Locating micro constituents and quantitatively measuring its presence is key for material researchers to study the physical properties of the carbon steel materials. This micrograph analysis is currently done manually and subjectively by material scientists, which is tedious and time-consuming. Here we propose to apply the image segmentation algorithm called U-Net to achieve automated labeling of steel microstructures on a subset of ultra- high carbon steel image dataset containing pearlite and spheroidite as the primary micro constituents. Our work …