Digital Commons Network^™

Finding The Shortest Path Using Dijkstra’S Algorithm, Orit D. Gruber, Deborah Sturm

Open Educational Resources

This lab experiment explores an algorithm which is used to find the shortest path between two or more locations. After completing the lab, you will be able to answer the following questions in the final lab report:

1. What is an Algorithm?
2. What is a Graph ?
3. What is the purpose and operation of Dijkstra’s Algorithm ?

Beyond Algorithmic Disclosure For Ai, Christopher S. Yoo

Articles

One of the most commonly recommended policy interventions with respect to algorithms in general and artificial intelligence ("AI") systems in particular is the need for greater transparency, often focusing on the disclosure of the variables employed by the algorithm and the weights given to those variables. This Essay argues that any meaningful transparency regime must provide information on other critical dimensions as well. For example, any transparency regime must also include key information about the data on which the algorithm was trained, including its source, scope, quality, and inner correlations, subject to constraints imposed by copyright, privacy, and cybersecurity law. …

Understanding Older People’S Voice Interactions With Smart Voice Assistants: A New Modified Rule-Based Natural Language Processing Model With Human Input, Zhengxu Yan, Victoria Dube, Judith Heselton, Kate Johnson, Changmin Yan, Valerie Jones, Julie Blaskewicz Boron, Marcia Shade

College of Journalism and Mass Communications: Faculty Publications

The COVID-19 pandemic has expedited the integration of Smart Voice Assistants (SVA) among older people. The qualitative data derived from user commands on SVA is pivotal for elucidating the engagement patterns of older individuals with such systems. However, the sheer volume of user-generated voice interaction data presents a formidable challenge for manual coding. Compounding this issue, age-related cognitive decline and alterations in speech patterns further complicate the interpretation of older users’ SVA voice interactions. Conventional dictionary-based textual analysis tools, which count word frequencies, are inadequate in capturing the evolving and communicative essence of these interactions that unfold over a series …

Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

The focus of this PhD thesis is on various distance and domination properties in graphs. In particular, we prove strong results about the interactions between asteroidal sets and dominating targets. Our results add to or extend a plethora of results on these properties within the literature. We define the class of strict dominating pair graphs and show structural and algorithmic properties of this class. Notably, we prove that such graphs have diameter 3, 4, or contain an asteroidal quadruple. Then, we design an algorithm to to efficiently recognize chordal hereditary dominating pair graphs. We provide new results that describe the …

Effectivity Analysis Of Recommendation Algorithm: A Comparative Study Of The Performance Of A Hybrid Model And An Individual Recommendation Algorithm, Saniah Safat

2024 Spring Honors Capstone Projects

This study explores the effectiveness of a hybrid recommendation system for e-commerce by integrating content-based, collaborative, and popularity-based models. Traditional individual algorithms have inherent limitations, such as handling new users or items data sparsity and ensuring relevance and diversity in suggestions. The hybrid model seeks to overcome these challenges by leveraging the strengths of all three methods, thus potentially offering more precise, personalized product suggestions. The performance of each model and its integration into a hybrid system are evaluated through logistic regression analysis. Initial results indicate that the hybrid system significantly outperforms the individual models in terms of accuracy and …

Randomized Feature Selection Based Semi-Supervised Latent Dirichlet Allocation For Microbiome Analysis., Namitha Pais, Nalini Ravishanker, Sanguthevar Rajasekaran, George M. Weinstock, Thi Dong Binh Tran

Faculty Research 2024

Health and disease are fundamentally influenced by microbial communities and their genes (the microbiome). An in-depth analysis of microbiome structure that enables the classification of individuals based on their health can be crucial in enhancing diagnostics and treatment strategies to improve the overall well-being of an individual. In this paper, we present a novel semi-supervised methodology known as Randomized Feature Selection based Latent Dirichlet Allocation (RFSLDA) to study the impact of the gut microbiome on a subject's health status. Since the data in our study consists of fuzzy health labels, which are self-reported, traditional supervised learning approaches may not be …

Algorithmic Approaches For Object Tracking And Facial Detection Using Drones, Kareem Shahatta, Peter Savarese, Gina Egitto, Jongwook Kim

Computer Science Student Work

Drones are unmanned aerial vehicles that have a variety of uses in many fields such as package delivery and search operations. Tello is a small, programmable drone designed for educational purposes. We developed algorithms using DJI Tello Py, an open-source Application Programming Interface, to command the movements of Tello for tracking a target object (i.e., human). Our algorithms utilize digital image processing techniques on Tello's live video stream to optimize the number of movements Tello needs to reach its target. Our poster presentation will explain our approaches to implement object-tracking and facial detection for Tello, discuss lessons we learned, and …

Capn2 Correlates With Insulin Resistance States In Pcos As Evidenced By Multi-Dataset Analysis, Xi Luo, Yunhua Dong, Haishan Zheng, Xiaoting Zhou, Lujuan Rong, Xiaoping Liu, Yun Bai, Yunxiu Li, Ze Wu

Journal Articles

OBJECTIVE: IR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies.

METHODS: We retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR …

Improving Prenatal Diagnosis Through Standards And Aggregation., Michael H Duyzend, Pilar Cacheiro, Julius O B Jacobsen, Jessica Giordano, Harrison Brand, Ronald J Wapner, Michael E Talkowski, Peter N Robinson, Damian Smedley

Faculty Research 2024

Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and …

Automated Seizure Detection Based On State-Space Model Identification, Zhuo Wang, Michael Sperling, Dale Wyeth, Allon Guez

Department of Neuroscience Faculty Papers

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and …

Algorithmic Design And Computational Modeling Using Dynamic Spectrum Allocation Techniques To Optimize Bandwidth Management In Wireless Communication Systems, Ankit Walishetti

Distinguished Student Work

This study aims to address the pressing need for efficient spectrum management methodologies in wireless communication systems by developing innovative sorting and allocation algorithms. Leveraging Dynamic Spectrum Allocation (DSA) techniques, this research devises strategies to optimize the utilization of bandwidth within existing spectrum space, ultimately reducing the need for network infrastructure expansion.

Ensuring thorough coverage of DSA techniques, 5 distinct transmitter sorting algorithms were programmed and tested across 8 performance metrics designed to measure specific capabilities. For consistency, a single bandwidth allocation program was designed to ‘pack’ transmitters starting from the left endpoint of the spectrum space. Progressively varying the …

The Impact Of Artificial Intelligence And Machine Learning On Organizations Cybersecurity, Mustafa Abdulhussein

Doctoral Dissertations and Projects

As internet technology proliferate in volume and complexity, the ever-evolving landscape of malicious cyberattacks presents unprecedented security risks in cyberspace. Cybersecurity challenges have been further exacerbated by the continuous growth in the prevalence and sophistication of cyber-attacks. These threats have the capacity to disrupt business operations, erase critical data, and inflict reputational damage, constituting an existential threat to businesses, critical services, and infrastructure. The escalating threat is further compounded by the malicious use of artificial intelligence (AI) and machine learning (ML), which have increasingly become tools in the cybercriminal arsenal. In this dynamic landscape, the emergence of offensive AI introduces …

Small Polymorphisms Are A Source Of Ancestral Bias In Structural Variant Breakpoint Placement., Peter A Audano, Christine R Beck

Faculty Research 2024

High-quality genome assemblies and sophisticated algorithms have increased sensitivity for a wide range of variant types, and breakpoint accuracy for structural variants (SVs, ≥50 bp) has improved to near base pair precision. Despite these advances, many SV breakpoint locations are subject to systematic bias affecting variant representation. To understand why SV breakpoints are inconsistent across samples, we reanalyzed 64 phased haplotypes constructed from long-read assemblies released by the Human Genome Structural Variation Consortium (HGSVC). We identify 882 SV insertions and 180 SV deletions with variable breakpoints not anchored in tandem repeats (TRs) or segmental duplications (SDs). SVs called from aligned …

Public Platform With 39,472 Exome Control Samples Enables Association Studies Without Genotype Sharing, Mykyta Artomov, Alexander A Loboda, Maxim N Artyomov, Mark J Daly

2020-Current year OA Pubs

Acquiring a sufficiently powered cohort of control samples matched to a case sample can be time-consuming or, in some cases, impossible. Accordingly, an ability to leverage genetic data from control samples that were already collected elsewhere could dramatically improve power in genetic association studies. Sharing of control samples can pose significant challenges, since most human genetic data are subject to strict sharing regulations. Here, using the properties of singular value decomposition and subsampling algorithm, we developed a method allowing selection of the best-matching controls in an external pool of samples compliant with personal data protection and eliminating the need for …

The Human Phenotype Ontology In 2024: Phenotypes Around The World., Michael Gargano, Nicolas Matentzoglu, Ben D Coleman, Eunice B Addo-Lartey, Anna V Anagnostopoulos, Joel Anderton, Paul Avillach, Anita M Bagley, Eduard Bakštein, James P Balhoff, Gareth Baynam, Susan M Bello, Michael Berk, Holli Bertram, Somer Bishop, Hannah Blau, David F Bodenstein, Pablo Botas, Kaan Boztug, Jolana Čady, Tiffany J Callahan, Rhiannon Cameron, Seth J Carbon, Francisco Castellanos, J Harry Caufield, Lauren E Chan, Christopher G Chute, Jaime Cruz-Rojo, Noémi Dahan-Oliel, Jon R Davids, Maud De Dieuleveult, Vinicius De Souza, Bert B A De Vries, Esther De Vries, J Raymond Depaulo, Beata Derfalvi, Ferdinand Dhombres, Claudia Diaz-Byrd, Alexander J M Dingemans, Bruno Donadille, Michael Duyzend, Reem Elfeky, Shahim Essaid, Carolina Fabrizzi, Giovanna Fico, Helen V Firth, Yun Freudenberg-Hua, Janice M Fullerton, Davera L Gabriel, Kimberly Gilmour, Jessica Giordano, Fernando S Goes, Rachel Gore Moses, Ian Green, Matthias Griese, Tudor Groza, Weihong Gu, Julia Guthrie, Benjamin Gyori, Ada Hamosh, Marc Hanauer, Kateřina Hanušová, Yongqun Oliver He, Harshad Hegde, Ingo Helbig, Kateřina Holasová, Charles Tapley Hoyt, Shangzhi Huang, Eric Hurwitz, Julius O B Jacobsen, Xiaofeng Jiang, Lisa Joseph, Kamyar Keramatian, Bryan King, Katrin Knoflach, David A Koolen, Megan L Kraus, Carlo Kroll, Maaike Kusters, Markus S Ladewig, David Lagorce, Meng-Chuan Lai, Pablo Lapunzina, Bryan Laraway, David Lewis-Smith, Xiarong Li, Caterina Lucano, Marzieh Majd, Mary L Marazita, Victor Martinez-Glez, Toby H Mchenry, Melvin G Mcinnis, Julie A Mcmurry, Michaela Mihulová, Caitlin E Millett, Philip B Mitchell, Veronika Moslerová, Kenji Narutomi, Shahrzad Nematollahi, Julian Nevado, Andrew A Nierenberg, Nikola Novák Čajbiková, John I Nurnberger, Soichi Ogishima, Daniel Olson, Abigail Ortiz, Harry Pachajoa, Guiomar Perez De Nanclares, Amy Peters, Tim Putman, Christina K Rapp, Ana Rath, Justin Reese, Lauren Rekerle, Angharad M Roberts, Suzy Roy, Stephan J Sanders, Catharina Schuetz, Eva C Schulte, Thomas G Schulze, Martin Schwarz, Katie Scott, Dominik Seelow, Berthold Seitz, Yiping Shen, Morgan N Similuk, Eric S Simon, Balwinder Singh, Damian Smedley, Cynthia Smith, Jake T Smolinsky, Sarah Sperry, Elizabeth Stafford, Ray Stefancsik, Robin Steinhaus, Rebecca Strawbridge, Jagadish Chandrabose Sundaramurthi, Polina Talapova, Jair A Tenorio Castano, Pavel Tesner, Rhys H Thomas, Audrey Thurm, Marek Turnovec, Marielle E Van Gijn, Nicole A Vasilevsky, Markéta Vlčková, Anita Walden, Kai Wang, Ron Wapner, James S Ware, Addo A Wiafe, Samuel A Wiafe, Lisa D Wiggins, Andrew E Williams, Chen Wu, Margot J Wyrwoll, Hui Xiong, Nefize Yalin, Yasunori Yamamoto, Lakshmi N Yatham, Anastasia K Yocum, Allan H Young, Zafer Yüksel, Peter P Zandi, Andreas Zankl, Ignacio Zarante, Miroslav Zvolský, Sabrina Toro, Leigh Carmody, Nomi L Harris, Monica C Munoz-Torres, Daniel Danis, Christopher J Mungall, Sebastian Köhler, Melissa A Haendel, Peter N Robinson

Faculty Research 2024

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition …

Accuracy Of True-Net In Comparison To Established White Matter Hyperintensity Segmentation Methods: An Independent Validation Study, Jeremy F Strain, Maryam Rahmani, Donna Dierker, Christopher Owen, Hussain Jafri, Andrei G Vlassenko, Kyle Womack, Jurgen Fripp, Duygu Tosun, Tammie L S Benzinger, Michael Weiner, Colin Masters, Jin-Moo Lee, John C Morris, Manu S Goyal, Adopic And Adni Investigators

2020-Current year OA Pubs

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the …

Pectoral Muscle Removal In Mammogram Images: A Novel Approach For Improved Accuracy And Efficiency, Simin Chen, Debbie L Bennett, Graham A Colditz, Shu Jiang

2020-Current year OA Pubs

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra).

METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast …

A Formalism For Extracting Track Functions From Jet Measurements, Kyle Lee, Ian Moult, Felix Ringer, Wouter J. Waalewijn

Physics Faculty Publications

The continued success of the jet substructure program will require widespread use of tracking information to enable increasingly precise measurements of a broader class of observables. The recent reformulation of jet substructure in terms of energy correlators has simplified the incorporation of universal non-perturbative matrix elements, so called “track functions”, in jet substructure calculations. These advances make it timely to understand how these universal non-perturbative functions can be extracted from hadron collider data, which is complicated by the use jet algorithms. In this paper we introduce a new class of jet functions, which we call (semi-inclusive) track jet functions, which …

Persistent Relative Homology For Topological Data Analysis, Christian J. Lentz

Mathematics, Statistics, and Computer Science Honors Projects

A central problem in data-driven scientific inquiry is how to interpret structure in noisy, high-dimensional data. Topological data analysis (TDA) provides a solution via the language of persistent homology, which encodes features of interest as holes within a filtration of the data. The recently presented U-Match Decomposition places the standard persistence computation in a flexible form, allowing for straight-forward extensions of the algorithm to variations of persistent homology. We describe U-Match Decomposition in the context of persistent homology, and extend it to an algorithm for persistent relative homology, providing proofs for the correctness and stability of the presented algorithm.

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Dew: A Wavelet Approach Of Rare Sound Event Detection., Sania Gul, Muhammad Salman Khan, Ata Ur-Rehman

Journal Articles

This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the …

Professionalism In Artificial Intelligence: The Link Between Technology And Ethics, Anton Klarin, Hossein Ali Abadi, Rifat Sharmelly

Research outputs 2022 to 2026

Ethical conduct of artificial intelligence (AI) is undoubtedly becoming an ever more pressing issue considering the inevitable integration of these technologies into our lives. The literature so far discussed the responsibility domains of AI; this study asks the question of how to instil ethicality into AI technologies. Through a three-step review of the AI ethics literature, we find that (i) the literature is weak in identifying solutions in ensuring ethical conduct of AI, (ii) the role of professional conduct is underexplored, and (iii) based on the values extracted from studies about AI ethical breaches, we thus propose a conceptual framework …

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

Vodka2: A Fast And Accurate Method To Detect Non-Standard Viral Genomes From Large Rna-Seq Data Sets, Emna Achouri, Sébastien A Felt, Matthew Hackbart, Nicole S Rivera-Espinal, Carolina B López

2020-Current year OA Pubs

During viral replication, viruses carrying an RNA genome produce non-standard viral genomes (nsVGs), including copy-back viral genomes (cbVGs) and deletion viral genomes (delVGs), that play a crucial role in regulating viral replication and pathogenesis. Because of their critical roles in determining the outcome of RNA virus infections, the study of nsVGs has flourished in recent years, exposing a need for bioinformatic tools that can accurately identify them within next-generation sequencing data obtained from infected samples. Here, we present our data analysis pipeline, Viral Opensource DVG Key Algorithm 2 (VODKA2), that is optimized to run on a parallel computing environment for …

Term-Blast-Like Alignment Tool For Concept Recognition In Noisy Clinical Texts., Tudor Groza, Honghan Wu, Marcel E Dinger, Daniel Danis, Coleman Hilton, Anita Bagley, Jon R Davids, Ling Luo, Zhiyong Lu, Peter N Robinson

Faculty Research 2023

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts.

RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical …

Predicting Multiple Sclerosis Severity With Multimodal Deep Neural Networks, Kai Zhang, John A Lincoln, Xiaoqian Jiang, Elmer V Bernstam, Shayan Shams

Journal Articles

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning …

Predicting Multiple Sclerosis Severity With Multimodal Deep Neural Networks, Kai Zhang, John A Lincoln, Xiaoqian Jiang, Elmer V Bernstam, Shayan Shams

Journal Articles

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning …

Hiking Trail Generation In Infinite Landscapes, Matthew Jensen

MS in Computer Science Project Reports

This project procedurally generates an infinite wilderness populated with deterministic hiking trails. Our approach recognizes that hiking trails depend on contextual information beyond the location of the path itself. To address this, we implemented a layered procedural system that orchestrates the generation process. This helps ensure the availability of contextual data at each stage. The first layer handles terrain generation, establishing the foundational landscape upon which trails will traverse. Subsequent layers handle point of interest identification and selection, trail network optimization through proximity graphs, and efficient pathfinding across the terrain. A notable feature of our approach is the deterministic nature …

An Open Guide To Data Structures And Algorithms, Paul W. Bible, Lucas Moser

Computer Science Faculty publications

This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of …

Maxsim: Multi-Angle-Crossing Structured Illumination Microscopy With Height-Controlled Mirror For 3d Topological Mapping Of Live Cells, Pedro Felipe Gardeazabal Rodriguez, Yigal Lilach, Abhijit Ambegaonkar, Teresa Vitali, Haani Jafri, Hae Won Sohn, Matthew B. Dalva, Susan Pierce, Inhee Chung

Department of Neuroscience Faculty Papers

Mapping 3D plasma membrane topology in live cells can bring unprecedented insights into cell biology. Widefield-based super-resolution methods such as 3D-structured illumination microscopy (3D-SIM) can achieve twice the axial ( ~ 300 nm) and lateral ( ~ 100 nm) resolution of widefield microscopy in real time in live cells. However, twice-resolution enhancement cannot sufficiently visualize nanoscale fine structures of the plasma membrane. Axial interferometry methods including fluorescence light interference contrast microscopy and its derivatives (e.g., scanning angle interference microscopy) can determine nanoscale axial locations of proteins on and near the plasma membrane. Thus, by combining super-resolution lateral imaging of 2D-SIM …