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Work33

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Dissertation32
Report1

Creator

Besler, Emre1
Broad, Alexander Sherman1
Bruckner, Eric Paul1
Cambo, Scott Allen1
Camille Galles1
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Keyword

Machine Learning[remove]33
Deep Learning5
Breast Cancer2
Electronic Health Records2
Natural Language Processing2
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Subject

Computer science8
Materials Science5
Bioinformatics4
Applied mathematics3
Artificial intelligence2
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en25

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Computational Electron Microscopy for the Characterization of Soft, Hard, and Hybrid Materials

Description:

Connecting structure and function in nanoscale engineered materials and devices relies on the analysis of the fundamental arrangement of matter, frequently under dynamic conditions. The demand to image structures at fundamental length scales has touched inorganic materials, biology, and frequently hybrid hard/soft materials with unique phenomena driven by heterogeneous components....

Keyword:

Imaging, Materials Imaging, Soft Materials, Crystallography, Machine Learning, and Electron Microscopy

Subject:

Nanoscience, Materials Science, and Computer science

Creator:

Hujsak, Karl

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/12/2020

Date Created:

2018-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:14515 and etdadmin_upload_638887
Nonequilibrium Dynamics of Soft Materials

Description:

Soft materials such as colloids and polymers often exhibit a variety of mesoscopic structures that are governed merely by weak physical interactions. Due to these intermediate structures, they can be easily taken out of thermal equilibrium by introducing external stimuli such as a shear flow and electromagnetic fields. This thesis...

Keyword:

Phase transition, Nonequilibrium dynamics, Coarse-grained simulations, Self-assembly, Soft matter, and Machine Learning

Subject:

Materials Science, Computational physics, and Statistical physics

Creator:

Han, Ming

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/12/2020

Date Created:

2018-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:14411 and etdadmin_upload_618073
Integrating Machine Learning and Symbolic Reasoning: Learning to Generate Symbolic Representations from Weak Supervision

Description:

Machine learning and symbolic reasoning have been two main approaches to build intelligent systems. Symbolic reasoning has been used in many applications by making use of expressive symbolic representations to encode prior knowledge, conduct complex reasoning and provide explanations. Recently, machine learning has enabled various successful applications by learning from...

Keyword:

Structural Alignment, Structured Prediction, Semantic Parsing, Machine Learning, Reinforcement Learning, and Symbolic Reasoning

Subject:

Computer Science

Creator:

Chen Liang

Owner:

Scholarly Digital Publishing

Date Uploaded:

10/28/2019

Date Modified:

10/28/2019

Date Created:

2018-01-01

Resource Type:

Dissertation
Methods for Images, Time Series, and Activation Functions with Deep Neural Networks

Description:

In this work, we explore the utility of the three main types of neural networks: feed forward, convolutional, and recurrent. While using these networks, we develop a new way to model multiagent trajectory data, explore the use of multiple activation functions for neurons at each layer of a neural network,...

Keyword:

Stock Prediction, Deep Learning, Basketball Trajectory, Activation Functions, and Machine Learning

Subject:

Engineering Sciences and Applied Mathematics

Creator:

Mark Harmon

Owner:

Scholarly Digital Publishing

Date Uploaded:

10/14/2019

Date Modified:

10/14/2019

Date Created:

2018-01-01

Resource Type:

Dissertation
Machine learning approaches towards understanding movement planning in naturalistic settings

Description:

A central question in neuroscience is how the brain plans movements. Here, I apply neural data analysis and machine learning methods to better understand both eye and arm movement planning, in particular focusing on naturalistic settings. First, I built encoding models to investigate the factors that led to neural activity...

Keyword:

Frontal Eye Field, Generalized Linear Model, Saccade, Neuroscience, Premotor Cortex, and Machine Learning

Subject:

Interdepartmental Neuroscience Program (NUIN)

Creator:

Joshua I Glaser

Owner:

Scholarly Digital Publishing

Date Uploaded:

02/22/2019

Date Modified:

02/22/2019

Date Created:

2018-01-01

Resource Type:

Dissertation
Modeling Bacterial Infection Risk for Data-Driven Antibiotic De-Escalation in Critically Ill Adults

Description:

Bacterial infections (BI) are a frequent, expensive, and life-threatening condition for critically ill patients. For patients with serious BI, minimizing the time between admission to the intensive care unit (ICU) and administration of appropriate antibiotic therapy is crucial to improve prognosis. However, the current gold-standard for identifying the appropriate antimicrobials...

Keyword:

MIMIC, Antibiotic Stewardship, Clinical Prediction, Critical Care, Machine Learning, and Electronic Health Records

Subject:

Icelandic & Scandinavian literature and Bioinformatics

Creator:

Eickelberg, Garrett

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

08/23/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_999752 and http://dissertations.umi.com/northwestern:16645
Design of Tungsten-free, L12-strengthened Cobalt-based Superalloys

Description:

Recent developments have enabled L12-strengthened Co-based superalloys, which have thepotential to surpass Ni-based superalloys as the material of choice for the hottest sections of turbine blades due to cobalt’s 40 ºC higher melting point. The most-studied branch of Co-based superalloys are based on the L12 phase Co3(Al,W); however, there is...

Keyword:

Superalloys, Cobalt, Microstructure, Machine Learning, Creep, and High Entropy Alloys

Subject:

Materials Science

Creator:

Ohl, Brandon

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_971014 and http://dissertations.umi.com/northwestern:16450
Learning from Limited and Imperfect Data in Cyber-Physical System

Description:

Machine learning is seeping into every fabric in various practical domains such as autonomous driving, wearable computing, and smart buildings. However, in the actual development and integration, especially when the learning-based components are frequently included as components of large complex systems where the physical instances can be included as interactable...

Keyword:

Transfer Learning, Reinforcement Learning, Computer Vision, Building HVAC Control, Image Classification, and Machine Learning

Subject:

Computer engineering and Computer science

Creator:

Xu, Shichao

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_983783 and http://dissertations.umi.com/northwestern:16519
Digitization for Innovative Platform Design and Advertising

Description:

Technology that processes text, audio and video, as well as location data, has revolutionized many industries by enabling innovative operations for customer retention. To retain transactions for a platform and viewers for advertisers, this dissertation leverages novel digital tools to analyze consumer behavior, proposes original economic frameworks to guide platform...

Keyword:

Ad Avoidance, Multimedia Data, Platform, Geolocation, Machine Learning, and Disintermediation

Subject:

Marketing, Economics, and Information technology

Creator:

Xie, Yingkang

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:16589 and etdadmin_upload_986414
Toward a Digital Twin of Metal Additive Manufacturing: Process Optimization and Control Enabled by Physics-based and Data-driven Models

Description:

In recent decades, metal additive manufacturing has seen rapid advancements, offering promising applications across various industries. However, addressing existing challenges in metal AM, such as process stability, defect avoidance, and quality control, is essential for fully exploiting its potential in fabricating parts with a desired geometry, as well as tailored...

Keyword:

Physics-based Model, Digital Twin, Data-driven Model, Machine Learning, and Additive Manufacturing

Subject:

Mechanical engineering

Creator:

Liao, Shuheng

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:16572 and etdadmin_upload_985942

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