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Articles 69031 - 69060 of 291657
Full-Text Articles in Physical Sciences and Mathematics
Mechanically Transduced Immunosorbent Assay To Measure Protein-Protein Interactions, Christopher J. Petell, Kathyrn Randene, Michael Pappas, Diego Sandoval, Brian Dstrahl, Joseph S. Harrison, Joshua P. Steimel
Mechanically Transduced Immunosorbent Assay To Measure Protein-Protein Interactions, Christopher J. Petell, Kathyrn Randene, Michael Pappas, Diego Sandoval, Brian Dstrahl, Joseph S. Harrison, Joshua P. Steimel
College of the Pacific Faculty Articles
Measuring protein-protein interaction (PPI) affinities is fundamental to biochemistry. Yet, conventional methods rely upon the law of mass action and cannot measure many PPIs due to a scarcity of reagents and limitations in the measurable affinity ranges. Here, we present a novel technique that leverages the fundamental concept of friction to produce a mechanical signal that correlates to binding potential. The mechanically transduced immunosorbent (METRIS) assay utilizes rolling magnetic probes to measure PPI interaction affinities. METRIS measures the translational displacement of protein-coated particles on a protein-functionalized substrate. The translational displacement scales with the effective friction induced by a PPI, thus …
Neutron Energy Tuning Assemblies For Nuclear Weapon Environment Applications At The National Ignition Facility, Nicholas J. Quartemont
Neutron Energy Tuning Assemblies For Nuclear Weapon Environment Applications At The National Ignition Facility, Nicholas J. Quartemont
Theses and Dissertations
An energy tuning assembly was developed to spectrally shape the National Ignition Facility deuterium-tritium fusion neutron source to a notional thermonuclear and prompt fission neutron spectrum to fulfill neutron source capability gaps. The experimental neutron environment was characterized with activation dosimetry, neutronics and covariance models, and unfolded to determine the as-fielded neutron spectrum. The first energy tuning assembly was demonstrated to create synthetic spectrally accurate post-detonation fission products, enhancing U.S. technical nuclear forensics capabilities. ATHENA, a second-generation energy tuning assembly, was also optimized to meet similar objectives, but the new platform neutron fluence efficiency was increased by a factor of …
Relationship Between Solar Energetic Particle He/H Abundance Ratios And Properties Of Flares And Cmes, Christopher R. Davidson
Relationship Between Solar Energetic Particle He/H Abundance Ratios And Properties Of Flares And Cmes, Christopher R. Davidson
Theses and Dissertations
Previous studies have investigated the He/H elemental abundance ratios of Solar Energetic Particle (SEP) Events of energies above 4 MeV. Also, studies have investigated the correlations between SEPs, Coronal Mass Ejections (CME), and Solar Flares. This work finds the correlations between the >4 MeV He/H abundance ratios and the solar parameters from the SEP, CME, and solar flare associated with the abundance increases. 43 SEP events located at solar west longitude are analyzed to find the correlation coefficients. Highly significant correlation was found between the He/H abundance ratios and the following parameters: solar flare flux, solar flare fluence, CME linear …
Development And Verification Of Extreme Space Weather Phenomena Models, Sophia G. Schwalbe
Development And Verification Of Extreme Space Weather Phenomena Models, Sophia G. Schwalbe
Theses and Dissertations
A range of 14 M-class flares from 1 June 2015 to 27 September 2016 were analyzed to find significant trends in electron frequency profile modeling using the GLobal airglOW (GLOW) model and radar parameters using a ray tracing algorithm developed by the Air Force Research Laboratory. GLOW was run for all the flares using three different solar spectrum schemes and an average of the three: the Hinteregger method, EUV flux model for aeronomic calculations (EUVAC), and a rebinned Flare Irradiance Spectrum Model (FISM) result. Comparing data for the E-layer where GLOW is most accurate, it was determined that GLOW using …
Transcontinental Retroarc Sediment Routing Controlled By Subduction Geometry And Climate Change (Central And Southern Andes, Argentina), Eduardo Garzanti, Tomas Capaldi, Giovanni Vezzoli, Mara Limonta, Numa Sosa
Transcontinental Retroarc Sediment Routing Controlled By Subduction Geometry And Climate Change (Central And Southern Andes, Argentina), Eduardo Garzanti, Tomas Capaldi, Giovanni Vezzoli, Mara Limonta, Numa Sosa
Geoscience Faculty Research
Central Argentina from the Pampean flat-slab segment to northern Patagonia (27°–41°S) represents a classic example of a broken retroarc basin with strong tectonic and climatic control on fluvial sediment transport. Combined with previous research focused on coastal sediments, this actualistic provenance study uses framework petrography and heavy-mineral data to trace multistep dispersal of volcaniclastic detritus first eastwards across central Argentina for up to ca. 1,500 km and next northwards for another 760 km along the Atlantic coast. Although detritus generated in the Andes is largely derived from mesosilicic volcanic rocks of the cordillera, its compositional signatures reflect different tectono-stratigraphic levels …
A Unified Approach To M Dwarf Ages, Rocio Kiman
A Unified Approach To M Dwarf Ages, Rocio Kiman
Dissertations, Theses, and Capstone Projects
Estimating ages of M dwarfs, the lowest mass stars in the Galaxy, is a current unresolved problem in Astrophysics. This dissertation focuses on developing a method to estimate ages for M dwarfs. This method consists of combining different age indicators in a Bayesian framework to achieve a precise age measurement. The age indicators I use are: 1) position in the color-magnitude diagram which is a proxy for effective temperature and luminosity, 2) movement of the stars in the Galaxy, or its 3D kinematics, 3) magnetic activity, measured by the Halpha emission line of the spectrum. In this dissertation I analyze …
Galaxy And Mass Assembly (Gama), U. Sureshkumar, A. Durkalec, A. Pollo, M. Bilicki, J. Loveday, D. J. Farrow, Benne Holwerda, A. M. Hopkins, J. Liske, K. A. Pimbblet, E. N. Taylor, A. H. Wright
Galaxy And Mass Assembly (Gama), U. Sureshkumar, A. Durkalec, A. Pollo, M. Bilicki, J. Loveday, D. J. Farrow, Benne Holwerda, A. M. Hopkins, J. Liske, K. A. Pimbblet, E. N. Taylor, A. H. Wright
Faculty and Staff Scholarship
Context. Galaxies are biased tracers of the underlying network of dark matter. The strength of this bias depends on various galaxy properties and on redshift. One of the methods used to study these dependences of the bias is measurement of galaxy clustering. Such studies are made using galaxy samples from various catalogues, which frequently bear their own problems related to sample selection methods. It is therefore crucial to understand how sample choice influences clustering measurements and which galaxy property is the most direct tracer of the galaxy environment.
Aims. We investigate how different galaxy properties, such as luminosities in the …
Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger
Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger
Research Collection School Of Computing and Information Systems
We propose a Recursive Logit (STD-RL) model for routing policy choice in a stochastic time-dependent (STD) network, where a routing policy is a mapping from states to actions on which link to take next, and a state is defined by node, time and information. A routing policy encapsulates travelers’ adaptation to revealed traffic conditions when making route choices. The STD-RL model circumvents choice set generation, a procedure with known issues related to estimation and prediction. In a given state, travelers make their link choice maximizing the sum of the utility of the outgoing link and the expected maximum utility until …
Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele
Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele
Research Collection School Of Computing and Information Systems
Pre-trained language models have been widely adopted as backbones in various natural language processing tasks. However, existing pre-trained language models ignore the descriptive meta-information in the text such as the distinction between the title and the mainbody, leading to over-weighted attention to insignificant text. In this paper, we propose a hypernetwork-based architecture to model the descriptive meta-information and integrate it into pre-trained language models. Evaluations on three natural language processing tasks show that our method notably improves the performance of pre-trained language models and achieves the state-of-the-art results on keyphrase extraction.
Semi-Supervised Semantic Visualization For Networked Documents, Delvin Ce Zhang, Hady W. Lauw
Semi-Supervised Semantic Visualization For Networked Documents, Delvin Ce Zhang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Semantic interpretability and visual expressivity are important objectives in exploratory analysis of text. On the one hand, while some documents may have explicit categories, we could develop a better understanding of a corpus by studying its finer-grained structures, which may be latent. By inferring latent topics and discovering keywords associated with each topic, one obtains a semantic interpretation of the corpus. One the other hand, by visualizing documents, latent topics, and category labels on the same plot, one gains a bird’s eye view of the relationships among documents, topics, and various categories. Semantic visualization is a class of methods that …
Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen
Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen
Research Collection School Of Computing and Information Systems
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers (or invariant checkers). In this work, we …
Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber
Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber
Research Collection School Of Computing and Information Systems
Although established marketing techniques have been applied to design more effective health campaigns, more often than not, the same message is broadcasted to large populations, irrespective of unique characteristics. As individual digital device use has increased, so have individual digital footprints, creating potential opportunities for targeted digital health interventions. We propose a novel precision public health campaign framework to structure and standardize the process of designing and delivering tailored health messages to target particular population segments using social media–targeted advertising tools. Our framework consists of five stages: defining a campaign goal, priority audience, and evaluation metrics; splitting the target audience …
Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process, Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi
Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process, Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi
Research Collection School Of Computing and Information Systems
Graph embedding, aiming to learn low-dimensional representations of nodes while preserving valuable structure information, has played a key role in graph analysis and inference. However, most existing methods deal with static homogeneous topologies, while graphs in real-world scenarios are gradually generated with different-typed temporal events, containing abundant semantics and dynamics. Limited work has been done for embedding dynamic heterogeneous graphs since it is very challenging to model the complete formation process of heterogeneous events. In this paper, we propose a novel Heterogeneous Hawkes Process based dynamic Graph Embedding (HPGE) to handle this problem. HPGE effectively integrates the Hawkes process into …
Quantum Computing For Supply Chain Finance, Paul R. Griffin, Ritesh Sampat
Quantum Computing For Supply Chain Finance, Paul R. Griffin, Ritesh Sampat
Research Collection School Of Computing and Information Systems
Applying quantum computing to real world applications to assess the potential efficacy is a daunting task for non-quantum specialists. This paper shows an implementation of two quantum optimization algorithms applied to portfolios of trade finance portfolios and compares the selections to those chosen by experienced underwriters and a classical optimizer. The method used is to map the financial risk and returns for a trade finance portfolio to an optimization function of a quantum algorithm developed in a Qiskit tutorial. The results show that whilst there is no advantage seen by using the quantum algorithms, the performance of the quantum algorithms …
Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft
Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft
Research Collection School Of Computing and Information Systems
We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As …
Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang
Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose …
Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo
Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo
Research Collection School Of Computing and Information Systems
Although Sentiment Analysis (SA) is widely applied in many domains, existing research has revealed that the unfairness in SA systems can be harmful to the welfare of less privileged people. Several works propose pre-processing and in-processing methods to eliminate bias in SA systems, but little attention is paid to utilizing post-processing methods to heal bias. Postprocessing methods are particularly important for systems that use third-party SA services. Systems that use such services have no access to the SA engine or its training data and thus cannot apply pre-processing nor in-processing methods. Therefore, this paper proposes a black-box post-processing method to …
Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang
Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang
Research Collection School Of Computing and Information Systems
Understanding idioms is important in NLP. In this paper, we study to what extent pre-trained BERT model can encode the meaning of a potentially idiomatic expression (PIE) in a certain context. We make use of a few existing datasets and perform two probing tasks: PIE usage classification and idiom paraphrase identification. Our experiment results suggest that BERT indeed can separate the literal and idiomatic usages of a PIE with high accuracy. It is also able to encode the idiomatic meaning of a PIE to some extent.
Secure And Verifiable Outsourced Data Dimension Reduction On Dynamic Data, Zhenzhu Chen, Anmin Fu, Robert H. Deng, Ximeng Liu, Yang Yang, Yinghui Zhang
Secure And Verifiable Outsourced Data Dimension Reduction On Dynamic Data, Zhenzhu Chen, Anmin Fu, Robert H. Deng, Ximeng Liu, Yang Yang, Yinghui Zhang
Research Collection School Of Computing and Information Systems
Dimensionality reduction aims at reducing redundant information in big data and hence making data analysis more efficient. Resource-constrained enterprises or individuals often outsource this time-consuming job to the cloud for saving storage and computing resources. However, due to inadequate supervision, the privacy and security of outsourced data have been a serious concern to data owners. In this paper, we propose a privacypreserving and verifiable outsourcing scheme for data dimension reduction, based on incremental Non-negative Matrix Factorization (NMF) method. We emphasize the importance of incremental data processing, exploiting the properties of NMF to enable data dynamics in consideration of data updating …
Publication-Driven Research Experience For Undergraduates (Reu) Program In Optics And Photonics In The Philippines Using Circuit Analogue-Based Research Experiments, Benjamin Dingel, Clint Dominic Bennett
Publication-Driven Research Experience For Undergraduates (Reu) Program In Optics And Photonics In The Philippines Using Circuit Analogue-Based Research Experiments, Benjamin Dingel, Clint Dominic Bennett
Physics Faculty Publications
We summarize a unique publication-driven Research Experience for Undergraduates program in optics/photonics that uses electronic circuit analogue-based research to strengthen students’ engagement in research. At present, we have published 4 journal and conference papers.
Differential Effects Of Polyphenols On Insulin Proteolysis By The Insulin-Degrading Enzyme, Qiuchen Zheng, Micheal T. Kebede, Bethany Lee, Claire A. Krasinski, Saadman Islam, Liliana A. Wurfl, Merc M. Kemeh, Valerie A. Ivancic, Charles E. Jakobsche, Donald E. Spratt, Noel Lazo
Differential Effects Of Polyphenols On Insulin Proteolysis By The Insulin-Degrading Enzyme, Qiuchen Zheng, Micheal T. Kebede, Bethany Lee, Claire A. Krasinski, Saadman Islam, Liliana A. Wurfl, Merc M. Kemeh, Valerie A. Ivancic, Charles E. Jakobsche, Donald E. Spratt, Noel Lazo
Chemistry
The insulin-degrading enzyme (IDE) possesses a strong ability to degrade insulin and Aβ42 that has been linked to the neurodegeneration in Alzheimer’s disease (AD). Given this, an attractive IDE-centric strategy for the development of therapeutics for AD is to boost IDE’s activity for the clearance of Aβ42 without offsetting insulin proteostasis. Recently, we showed that resveratrol enhances IDE’s activity toward Aβ42. In this work, we used a combination of chromatographic and spectroscopic techniques to investigate the effects of resveratrol on IDE’s activity toward insulin. For comparison, we also studied epigallocatechin-3-gallate (EGCG). Our results show that the two polyphenols affect the …
Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.
Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.
Silver Bow Creek/Butte Area Superfund Site
No abstract provided.
Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.
Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.
Silver Bow Creek/Butte Area Superfund Site
No abstract provided.
Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.
Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.
Silver Bow Creek/Butte Area Superfund Site
No abstract provided.
Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang
Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang
Dissertations, Theses, and Capstone Projects
Nature usually divides complex systems into smaller building blocks specializing in a few tasks since one entity cannot achieve everything. Therefore, self-assembly is a robust tool exploited by Nature to build hierarchical systems that accomplish unique functions. The cell membrane distinguishes itself as an example of Nature’s self-assembly, defining and protecting the cell. By mimicking Nature’s designs using synthetically designed self-assemblies, researchers with advanced nanotechnological comprehension can manipulate these synthetic self-assemblies to improve many aspects of modern medicine and materials science. Understanding the competing underlying molecular interactions in self-assembly is always of interest to the academic scientific community and industry. …
Differentiability Of The Liouville Map Via Geodesic Currents, Xinlong Dong
Differentiability Of The Liouville Map Via Geodesic Currents, Xinlong Dong
Dissertations, Theses, and Capstone Projects
For a conformally hyperbolic Riemann surface, the Teichmüller space is the space of quasiconformal maps factored by an equivalence relation, and it is a complex Banach manifold. The space of geodesic currents endowed with the uniform weak* topology is a subset of a Fréchet space of Hölder distributions. We introduce an appropriate topology on the space of Hölder distributions and this new topology coincides with the uniform weak* topology on the space of geodesic currents. The Liouville map of the Teichmüller space becomes differentiable in the Fréchet sense. In particular, the derivative of Liouville currents exists and belongs to the …
Making Space For Unquantifiable Data: Hand-Drawn Data Visualization, Eva Sibinga
Making Space For Unquantifiable Data: Hand-Drawn Data Visualization, Eva Sibinga
Dissertations, Theses, and Capstone Projects
This project makes space for personal “data” around labor and care, prompting users to consider the concrete and abstract (quantifiable and unquantifiable) forms labor and care take in their lives. The interactive, subjective data visualization uses hand-drawn visual elements to foreground that data about care and human interaction will always be ambiguous and complex, that they may never be satisfactorily or universally quantified, and that they will always be out of reach of perfect categorization.
The project provides an alternative to prescriptive truth-telling with data. Instead of using a dataset to provide data-driven answers and insights to users, the interactive …
Analytical Properties For The Fifth Order Camassa-Holm (Foch) Model, Mingxuan Zhu, Lu Cao, Zaihong Jiang, Zhijun Qiao
Analytical Properties For The Fifth Order Camassa-Holm (Foch) Model, Mingxuan Zhu, Lu Cao, Zaihong Jiang, Zhijun Qiao
School of Mathematical & Statistical Sciences Faculty Publications
This paper devotes to present analysiswork on the fifth order Camassa-Holm (FOCH) modelwhich recently proposed by Liu and Qiao. Firstly, we establish the local and global existence of the solution to the FOCH model. Secondly, we study the property of the infinite propagation speed. Finally, we discuss the long time behavior of the support of momentum density with a compactly supported initial data.
2021 September - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University
2021 September - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University
Tennessee Climate Office Monthly Reports
No abstract provided.
Clifford Harmonics, Samuel L. Hosmer
Clifford Harmonics, Samuel L. Hosmer
Dissertations, Theses, and Capstone Projects
In 1980 Michelsohn defined a differential operator on sections of the complex Clifford bundle over a compact Kähler manifold M. This operator is a differential and its Laplacian agrees with the Laplacian of the Dolbeault operator on forms through a natural identification of differential forms with sections of the Clifford bundle. Relaxing the condition that M be Kähler, we introduce two differential operators on sections of the complex Clifford bundle over a compact almost Hermitian manifold which naturally generalize the one introduced by Michelsohn. We show surprising Kähler- like symmetries of the kernel of the Laplacians of these operators in …