Physical Sciences and Mathematics Commons^™

Comparison Of Simulations Of Updraft Mass Fluxes And Their Response To Increasing Aerosol Concentration Between A Bin Scheme And A Bulk Scheme In A Deep-Convective Cloud System, Seoung Soo Lee, Chang-Hoon Jung, Sen Chiao, Junshik Um, Yong-Sang Choi, Won Jun Choi

Faculty Publications, Meteorology and Climate Science

Key microphysical processes whose parameterizations have substantial impacts on the simulation of updraft mass fluxes and their response to aerosol are investigated in this study. For this investigation, comparisons of these parameterizations are made between a bin scheme and a bulk scheme. These comparisons show that the differences in the prediction of cloud droplet number concentration (CDNC) between the two schemes determine whether aerosol-induced invigoration of updrafts or convection occurs. While the CDNC prediction leads to aerosol-induced invigoration of updrafts and an associated 20% increase in the peak value of the updraft-mass-flux vertical profile in the bin scheme, it leads …

Asian Long-Range Transport In Relation To Atmospheric Rivers In Northern California, Catherine Liu, Sen Chiao, Ju-Mee Ryoo

Faculty Publications, Meteorology and Climate Science

The study investigates the effect of aerosol long-range transport on precipitation over Northern California during atmospheric river (AR) events in the 2017 cold season (January–April). ARs in 2017 were one of the strongest to date, and the intense precipitation associated with the ARs resulted in flooding, destruction of property, and contamination of water supplies. The Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) data shows Asian dust traveling across the Northern Pacific Ocean along with AR events. Aerosol measurements in California, provided by the Interagency Monitoring of Protected Visual Environments (IMPROVE), show that more Asian dust tends to …

Physical Factors Influencing Phytoplankton Abundance In Southern Monterey Bay, C. Ryan Manzer, Thomas Connolly, Erika Mcphee-Shaw, G. Smith

Faculty Research, Scholarly, and Creative Activity

As the base of almost all marine food webs, phytoplankton play a dominant role in determining the productivity of marine ecosystems. Recent studies have highlighted the dynamic variability of phytoplankton abundance in nearshore ecosystems over synoptic time scales. Therefore, a greater understanding of the physical mechanisms that contribute to this variability is required to assess impacts of current as well as future weather patterns on these ecosystems. In this study, chlorophyll fluorescence data from a nearshore location in southern Monterey Bay was used to identify the timing and duration of increases in phytoplankton concentrations. Regional physical parameters, including wind stress, …

Music Mood Classification Using Convolutional Neural Networks, Revanth Akella

Master's Projects

Grouping music into moods is useful as music is migrating from to online streaming services as it can help in recommendations. To establish the connection between music and mood we develop an end-to-end, open source approach for mood classification using lyrics. We develop a pipeline for tag extraction, lyric extraction, and establishing classification models for classifying music into moods. We investigate techniques to classify music into moods using lyrics and audio features. Using various natural language processing methods with machine learning and deep learning we perform a comparative study across different classification and mood models. The results infer that features …

Fast High Resolution Image Completion, Chinmay Mishra

Master's Projects

This paper presents a method for image completion, an active research area in the field of computer vision. The method described in the paper aims at achieving comparable results to other state of the art methods with approximately four and a half times reduction in training time. It is a two step procedure which involves image completion and enhancing the resolution of the completed image. We use the SSIM metric to evaluate the quality of the completed image and to also time our model against other image completion models.

An Industry Driven Genre Classification Application Using Natural Language Processing, Sharan Duggirala

Master's Projects

With the advent of digitized music, many online streaming companies such as Spotify have capitalized on a listener’s need for a common stream platform. An essential component of such a platform is the recommender systems that suggest to the constituent user base, related tracks, albums and artists. In order to sustain such a recommender system, labeling data to indicate which genre it belongs to is essential. Most recent academic publications that deal with music genre classification focus on the use of deep neural networks developed and applied within the music genre classification domain. This thesis attempts to use some of …

Learning For Free – Object Detectors Trained On Synthetic Data, Charles Thane Mackay

Master's Projects

A picture is worth a thousand words, or if you want it labeled, it’s worth about four cents per bounding box. Data is the fuel that powers modern technologies run by artificial intelligence engines which is increasingly valuable in today’s industry. High quality labeled data is the most important factor in producing accurate machine learning models which can be used to make powerful predictions and identify patterns humans may not see. Acquiring high quality labeled data however, can be expensive and time consuming. For small companies, academic researchers, or machine learning hobbyists, gathering large datasets for a specific task that …

Designing Single Guide Rnas For Crispr/Cas9, Neha Atul Bhagwat

Master's Projects

Researchers have been working towards development of tools to facilitate regular use genome engineering techniques. In recent years, the focus of these efforts has been the Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)/CRISPR associated(Cas) systems. These systems, while found naturally in bacteria and archaea as an immunity mechanism, can be used for genome engineering in eukaryotes.

There are three major computational challenges associated with the use of CRISPR/Cas9 in genome engineering for mammals - identification of CRISPR arrays, single guide RNA design and minimizing off-target effects. This project attempts to solve the problem of single guide RNA design using a novel …

Detecting Crispr Arrays Using Long-Short Term Memory Network, Shantanu Deshmukh

Master's Projects

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) is a se- quence found in the DNA sequence of an organism. It provides provides immunity to the organism. Recently, it was found that the CRISPR-based immunity mechanism can be manipulated to perform genome editing. The problem is, it is hard to know the specificity of this system and in turn, making it highly specific is difficult. More re- search is required to improve this CRISPR-based genome editing. Detecting CRISPR arrays in the DNA sequence is the first step towards this research. In this work, a CRISPR array detection pipeline, CRISPRLstm, is proposed. …

Randition: Random Blockchain Partitioning For Write Throughput, David Nguyen

Master's Projects

This paper proposes to support dynamic runtime partitioning of Tendermint, which is an in-development state machine replication algorithm that uses the blockchain model to provide Byzantine-fault tolerance. We call this variation Randition. We incorporate recent research from blockchain consensus and replicated state machine partitioning to allow Randition users to partition their blockchain for improved write performance at the cost of some Byzantine fault tolerance. We conduct an experiment to compare the raw write throughput of Randition and Tendermint. Finally, we discuss the experiment results and discuss further improvements to Randition.

Context-Based Multi-Stage Offline Handwritten Mathematical Symbol Recognition Using Deep Learning, Sui Kun Guan

Master's Projects

We propose a multi-stage machine learning (ML) architecture to improve the accuracy of offline handwritten mathematical symbol recognition. In the first stage, we train and assemble multiple deep convolutional neural networks to classify isolated mathematical symbols. However, certain ambiguous symbols are hard to classify without the context information of the mathematical expressions where the symbols belong. In the second stage, we train a deep convolutional neural network that further classifies the ambiguous symbols based on the context information of the symbols. To further improve the classification accuracy, in the third stage, we develop a set of rules to classify the …

Machine Learning In Crop Classification Of Temporal Multispectral Satellite Image, Ravali Koppaka

Master's Projects

Recently, there has been a remarkable growth in Artificial Intelligence (AI) with

the development of efficient AI models and high-power computational resources for processing complex datasets. There has been a growing number of applications of machine learning in satellite remote sensing image data processing. In this work, machine learning methods were applied for crop classification of temporal multi- spectral satellite image to achieve better prediction of crop-wise area statistics. In India, agriculture has a huge impact on the national economy and most of the critical decisions are dependent on agricultural statistics. Sentinel-2 satellite image data for the Guntur district region …

Sql Injection Detection Using Machine Learning, Sonali Mishra

Master's Projects

Sharing information over the Internet over multiple platforms and web-applications has become a quite common phenomenon in the recent times. The web-based applications that accept critical information from users store this information in databases. These applications and the databases connected to them are susceptible to all kinds of information security threats due to being accessible through the Internet. The threats include attacks such as Cross Side Scripting (CSS), Denial of Service Attack (DoS0, and Structured Query Language (SQL) Injection attacks. SQL Injection attacks fall under the top ten vulnerabilities when we talk about web-based applications. Through this kind of attack, …

A Webrtc Video Chat Implementation Within The Yioop Search Engine, Yangcha Ho

Master's Projects

Web real-time communication (abbreviated as WebRTC) is one of the latest Web application technologies that allows voice, video, and data to work collectively in a browser without a need for third-party plugins or proprietary software installation. When two browsers from different locations communicate with each other, they must know how to locate each other,

bypass security and firewall protections, and transmit all multimedia communications in real time. This project not only illustrates how WebRTC technology works but also walks through a real example of video chat-style application. The application communicates between two remote users using WebSocket and the data encryption …

Learning To Play The Trading Game, Neeraj Kulkarni

Master's Projects

Can we train a stock trading bot that can take decisions in high-entropy envi- ronments like stock markets to generate profits based on some optimal policy? Can we further extend this learning for any general trading problem? Quantitative Al- gorithms are responsible for more than 75% of the stock trading around the world. Creating a stock market prediction model is comparatively easy. But creating a prof- itable prediction model is still considered as a challenging task in the field of machine learning and deep learning due to the unpredictability of the financial markets. Us- ing biologically inspired computing techniques of …

Intelligent Log Analysis For Anomaly Detection, Steven Yen

Master's Projects

Computer logs are a rich source of information that can be analyzed to detect various issues. The large volumes of logs limit the effectiveness of manual approaches to log analysis. The earliest automated log analysis tools take a rule-based approach, which can only detect known issues with existing rules. On the other hand, anomaly detection approaches can detect new or unknown issues. This is achieved by looking for unusual behavior different from the norm, often utilizing machine learning (ML) or deep learning (DL) models. In this project, we evaluated various ML and DL techniques used for log anomaly detection. We …

Using Computer Vision To Quantify Coral Reef Biodiversity, Niket Bhodia

Master's Projects

The preservation of the world’s oceans is crucial to human survival on this planet, yet we know too little to begin to understand anthropogenic impacts on marine life. This is especially true for coral reefs, which are the most diverse marine habitat per unit area (if not overall) as well as the most sensitive. To address this gap in knowledge, simple field devices called autonomous reef monitoring structures (ARMS) have been developed, which provide standardized samples of life from these complex ecosystems. ARMS have now become successful to the point that the amount of data collected through them has outstripped …

Benchmarking Optimization Algorithms For Capacitated Vehicle Routing Problems, Pratik Surana

Master's Projects

The Vehicle Routing Problem (VRP) originated in the 1950s when algorithms and mathematical approaches were applied to find solutions for routing vehicles. Since then, there has been extensive research in the field of VRPs to solve real-life problems. The process of generating an optimal routing schedule for a VRP is complex due to two reasons. First, VRP is considered to be an NP-Hard problem. Second, there are several constraints involved, such as the number of available vehicles, the vehicle capacities, time-windows for pickup or delivery etc.

The main goal for this project was to compare different optimization algorithms for solving …

Deep Learning On Graphs Using Graph Convolutional Networks, Saurabh Mithe

Master's Projects

Graphs are a powerful way to model network data with the objects as nodes and the relationship between the various objects as links. Such graphs contain a plethora of valuable information about the underlying data which can be extracted, analyzed, and visualized using Machine Learning (ML). The challenge to this task is that graphs are non-Euclidean structures which means that they cannot be directly used with ML techniques because ML techniques only work with Euclidean structures like grids or sequences. In order to overcome this challenge, the graph structure first needs to be encoded into an equivalent Euclidean representation in …

On Adversarial Attacks On Deep Learning Models, Nag Mani

Master's Projects

With recent advancements in the field of artificial intelligence, deep learning has created a niche in the technology space and is being actively used in autonomous and IoT systems globally. Unfortunately, these deep learning models have become susceptible to adversarial attacks which can severely impact their integrity. Research has shown that many state-of-the-art models are vulnerable to attacks by well-crafted adversarial examples. These adversarial examples are perturbed versions of clean data which have small amount of noise added to them. These adversarial samples are imperceptible to the human eye but can easily fool the targeted model. The exposed vulnerabilities of …

Breaking Audio Captcha Using Machine Learning/Deep Learning And Related Defense Mechanism, Heemany Shekhar

Master's Projects

CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots(attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. In this project, I analyzed the security of audio captchas from attacks that employ machine learning and deep learning models. Audio captchas of varying lengths (5, 7 and 10) and varying background noise (no noise, medium noise or high noise) were analyzed. I found that audio captchas with no background noise or medium background noise were easily attacked with 99% - 100% accuracy. …

R*-Tree Index In Cassandra For Geospatial Processing, Avinashilingam Nanjappan

Master's Projects

Geospatial data has garnered enough attention in recent times that it is being used everywhere right from simple applications such as booking a taxi ride to complex applications such as autonomous driving. Though the attention towards geospatial processing is something new, substantial research has been going on for years. With the evolution of NoSQL databases in recent times, geospatial processing has attained a new dimension concerning its applications and capability. The most popular NoSQL database to be used for geospatial processing is the MongoDB followed by Cassandra. It is the indexing process that is important concerning the data at hand …

Schema Migration From Relational Databases To Nosql Databases With Graph Transformation And Selective Denormalization, Krishna Chaitanya Mullapudi

Master's Projects

We witnessed a dramatic increase in the volume, variety and velocity of data leading to the era of big data. The structure of data has become highly flexible leading to the development of many storage systems that are different from the traditional structured relational databases where data is stored in “tables,” with columns representing the lowest granularity of data. Although relational databases are still predominant in the industry, there has been a major drift towards alternative database systems that support unstructured data with better scalability leading to the popularity of “Not Only SQL.”

Migration from relational databases to NoSQL databases …

Improving Steering Ability Of An Autopilot In A Fully Autonomous Car, Shivanku Mahna

Master's Projects

The world we live in is developing at a really rapid pace and along with it is developing the technology that we use. We have clearly come a long way from calling a car modern because it had a touch screen infotainment system to calling it modern because it drives on its own. The progress has been so rapid that it demands for us to analyze this and try to improvise a small part of this journey. With the same thought in mind, this project focuses on improvising the steering ability of an autonomous car. In order to make more …

Deep Learning Based Real Time Devanagari Character Recognition, Aseem Chhabra

Master's Projects

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are …

Predicting Off-Target Potential Of Crispr-Cas9 Single Guide Rna, Ishita Mathur

Master's Projects

With advancements in the field of genome engineering, researchers have come up with potential ways for site-specific gene editing. One of the methods uses the Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR-Cas technology. It consists of a Cas9 nuclease and a single guide RNA (sgRNA) that cleaves the DNA at the intended target site. However, the target genome could contain multiple potential off-target sites and cleaving an off-target site can have deleterious effects in case of gene editing in humans.

Lab based assays have been developed to test the off-target effects of guide RNAs. However, it is not feasible …

Robust Lightweight Object Detection, Siddharth Kumar

Master's Projects

Object detection is a very challenging problem in computer vision and has been a prominent subject of research for nearly three decades. There has been a promising in- crease in the accuracy and performance of object detectors ever since deep convolutional networks (CNN) were introduced. CNNs can be trained on large datasets made of high resolution images without flattening them, thereby using the spatial information. Their superior learning ability also makes them ideal for image classification and object de- tection tasks. Unfortunately, this power comes at the big cost of compute and memory. For instance, the Faster R-CNN detector required …

Network Alignment In Heterogeneous Social Networks, Priyanka Kasbekar

Master's Projects

Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social networks at the same time. Identifying a similar user from one social network on another social network will give in- formation about a user’s behavior on different platforms. It further helps in community detection and link prediction tasks. The process of identifying or aligning users in multiple networks is called Network Alignment. More the information we have about the nodes / users better the results of Network Alignment. Unlike other related work in this field that …

Glovenor - Global Vectors For Node Representations, Shishir Kulkarni

Master's Projects

A graph is a very powerful abstract data type that can be used to model entities (nodes) and relationships (edges). Many real world networks like biological, computer and friendship networks can be represented as graphs. Graphs can be mined to extract interesting patterns and interactions between the participating entities. Recently, various Artificial Intelligence (AI) and Machine Learning (ML) techniques are used for this purpose. In order to do that, the nodes of a graph have to be represented as low dimensional feature vectors. Node embedding is the process of generating a �-dimensional feature vector corresponding to each node of a …

Graph Classification Using Machine Learning Algorithms, Monica Golahalli Seenappa

Master's Projects

In the Graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efficient classification approaches.

For this project, we will be focusing on modifying an existing kernel graph …