104 research outputs found
OPTIMIZATION AND LEARNING UNDER UNCERTAINTY - A UNIFIED ROBUSTNESS PERSPECTIVE
Ph.DDOCTOR OF PHILOSOPH
Design and manufacturing of high precision roll-to-roll multi-layer printing machine : measurement and experiment
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 74-75).In 2008, a prototype machine demonstrating the application of roll-to-roll technology in micro-contact printing was developed. In this research, the prototype machine was upgraded by designing and machining a device that could fabricate a flat stamp with significantly less variance. The print roll wrapping system was reconfigured in order to capture the stamp with uniform force and good alignment. The motion control for the print roller and impression roller was also improved. In addition multi-layer printing with the updated machine was tested. This thesis focuses on the general design of the updated system and the measurement of key components of the systems as well as the print quality. Results demonstrate that the flat stamp can achieve the flatness of ±16 [micro]m with thickness of 1194 [micro]m; that the wrapping process can guarantee a print roller roundness error in the 20 [micro]m range; that the distortion of the print using the updated system is approximately 3.8. The multi-layer printing test did not achieve acceptable results owing to a lack of proper control of the machine. However, initial trials, achieved alignment errors of 1017 [micro]m along the printing direction and 113 [micro]m across the printing direction.by Wenzhuo Yang.M.Eng
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges
Artificial Intelligence for IT operations (AIOps) aims to combine the power
of AI with the big data generated by IT Operations processes, particularly in
cloud infrastructures, to provide actionable insights with the primary goal of
maximizing availability. There are a wide variety of problems to address, and
multiple use-cases, where AI capabilities can be leveraged to enhance
operational efficiency. Here we provide a review of the AIOps vision, trends
challenges and opportunities, specifically focusing on the underlying AI
techniques. We discuss in depth the key types of data emitted by IT Operations
activities, the scale and challenges in analyzing them, and where they can be
helpful. We categorize the key AIOps tasks as - incident detection, failure
prediction, root cause analysis and automated actions. We discuss the problem
formulation for each task, and then present a taxonomy of techniques to solve
these problems. We also identify relatively under explored topics, especially
those that could significantly benefit from advances in AI literature. We also
provide insights into the trends in this field, and what are the key investment
opportunities
Privacy-Preserving Aggregation in Federated Learning: A Survey
Over the recent years, with the increasing adoption of Federated Learning
(FL) algorithms and growing concerns over personal data privacy,
Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention
from both academia and industry. Practical PPFL typically allows multiple
participants to individually train their machine learning models, which are
then aggregated to construct a global model in a privacy-preserving manner. As
such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has
received substantial research interest. This survey aims to fill the gap
between a large number of studies on PPFL, where PPAgg is adopted to provide a
privacy guarantee, and the lack of a comprehensive survey on the PPAgg
protocols applied in FL systems. In this survey, we review the PPAgg protocols
proposed to address privacy and security issues in FL systems. The focus is
placed on the construction of PPAgg protocols with an extensive analysis of the
advantages and disadvantages of these selected PPAgg protocols and solutions.
Additionally, we discuss the open-source FL frameworks that support PPAgg.
Finally, we highlight important challenges and future research directions for
applying PPAgg to FL systems and the combination of PPAgg with other
technologies for further security improvement.Comment: 20 pages, 10 figure
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
We introduce the Salesforce CausalAI Library, an open-source library for
causal analysis using observational data. It supports causal discovery and
causal inference for tabular and time series data, of both discrete and
continuous types. This library includes algorithms that handle linear and
non-linear causal relationships between variables, and uses multi-processing
for speed-up. We also include a data generator capable of generating synthetic
data with specified structural equation model for both the aforementioned data
formats and types, that helps users control the ground-truth causal process
while investigating various algorithms. Finally, we provide a user interface
(UI) that allows users to perform causal analysis on data without coding. The
goal of this library is to provide a fast and flexible solution for a variety
of problems in the domain of causality. This technical report describes the
Salesforce CausalAI API along with its capabilities, the implementations of the
supported algorithms, and experiments demonstrating their performance and
speed. Our library is available at
\url{https://github.com/salesforce/causalai}
Deep Learning in Single-Cell Analysis
Single-cell technologies are revolutionizing the entire field of biology. The
large volumes of data generated by single-cell technologies are
high-dimensional, sparse, heterogeneous, and have complicated dependency
structures, making analyses using conventional machine learning approaches
challenging and impractical. In tackling these challenges, deep learning often
demonstrates superior performance compared to traditional machine learning
methods. In this work, we give a comprehensive survey on deep learning in
single-cell analysis. We first introduce background on single-cell technologies
and their development, as well as fundamental concepts of deep learning
including the most popular deep architectures. We present an overview of the
single-cell analytic pipeline pursued in research applications while noting
divergences due to data sources or specific applications. We then review seven
popular tasks spanning through different stages of the single-cell analysis
pipeline, including multimodal integration, imputation, clustering, spatial
domain identification, cell-type deconvolution, cell segmentation, and
cell-type annotation. Under each task, we describe the most recent developments
in classical and deep learning methods and discuss their advantages and
disadvantages. Deep learning tools and benchmark datasets are also summarized
for each task. Finally, we discuss the future directions and the most recent
challenges. This survey will serve as a reference for biologists and computer
scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi
Inflammatory factors and risk of meningiomas: a bidirectional mendelian-randomization study
BackgroundMeningiomas are one of the most common intracranial tumors, and the current understanding of meningioma pathology is still incomplete. Inflammatory factors play an important role in the pathophysiology of meningioma, but the causal relationship between inflammatory factors and meningioma is still unclear.MethodMendelian randomization (MR) is an effective statistical method for reducing bias based on whole genome sequencing data. It’s a simple but powerful framework, that uses genetics to study aspects of human biology. Modern methods of MR make the process more robust by exploiting the many genetic variants that may exist for a given hypothesis. In this paper, MR is applied to understand the causal relationship between exposure and disease outcome.ResultsThis research presents a comprehensive MR study to study the association of genetic inflammatory cytokines with meningioma. Based on the results of our MR analysis, which examines 41 cytokines in the largest GWAS datasets available, we were able to draw the relatively more reliable conclusion that elevated levels of circulating TNF-β, CXCL1, and lower levels of IL-9 were suggestive associated with a higher risk of meningioma. Moreover, Meningiomas could cause lower levels of interleukin-16 and higher levels of CXCL10 in the blood.ConclusionThese findings suggest that TNF-β, CXCL1, and IL-9 play an important role in the development of meningiomas. Meningiomas also affect the expression of cytokines such as IL-16 and CXCL10. Further studies are needed to determine whether these biomarkers can be used to prevent or treat meningiomas
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