119 research outputs found
Retinal blood vessel segmentation: methods and implementations
Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation.
The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation.
The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel.
The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition.
Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation
Retinal blood vessel segmentation: methods and implementations
Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation.
The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation.
The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel.
The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition.
Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation
Retinal blood vessel segmentation: methods and implementations
Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation.
The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation.
The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel.
The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition.
Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation
Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs
Binary code analysis allows analyzing binary code without having access to
the corresponding source code. A binary, after disassembly, is expressed in an
assembly language. This inspires us to approach binary analysis by leveraging
ideas and techniques from Natural Language Processing (NLP), a rich area
focused on processing text of various natural languages. We notice that binary
code analysis and NLP share a lot of analogical topics, such as semantics
extraction, summarization, and classification. This work utilizes these ideas
to address two important code similarity comparison problems. (I) Given a pair
of basic blocks for different instruction set architectures (ISAs), determining
whether their semantics is similar or not; and (II) given a piece of code of
interest, determining if it is contained in another piece of assembly code for
a different ISA. The solutions to these two problems have many applications,
such as cross-architecture vulnerability discovery and code plagiarism
detection. We implement a prototype system INNEREYE and perform a comprehensive
evaluation. A comparison between our approach and existing approaches to
Problem I shows that our system outperforms them in terms of accuracy,
efficiency and scalability. And the case studies utilizing the system
demonstrate that our solution to Problem II is effective. Moreover, this
research showcases how to apply ideas and techniques from NLP to large-scale
binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium
201
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Large pre-trained language models achieve impressive results across many
tasks. However, recent works point out that pre-trained language models may
memorize a considerable fraction of their training data, leading to the privacy
risk of information leakage. In this paper, we propose a method named Ethicist
for targeted training data extraction through loss smoothed soft prompting and
calibrated confidence estimation, investigating how to recover the suffix in
the training data when given a prefix. To elicit memorization in the attacked
model, we tune soft prompt embeddings while keeping the model fixed. We further
propose a smoothing loss that smooths the loss distribution of the suffix
tokens to make it easier to sample the correct suffix. In order to select the
most probable suffix from a collection of sampled suffixes and estimate the
prediction confidence, we propose a calibrated confidence estimation method,
which normalizes the confidence of the generated suffixes with a local
estimation. We show that Ethicist significantly improves the extraction
performance on a recently proposed public benchmark. We also investigate
several factors influencing the data extraction performance, including decoding
strategy, model scale, prefix length, and suffix length. Our code is available
at https://github.com/thu-coai/Targeted-Data-Extraction.Comment: ACL 2023 Long Paper (Main Conference
Essays on Labour and Regional Economics
This thesis consists of three chapters in empirical labour and regional economics. They generally analyze how local labour market performance varies across different times and spaces.
The first chapter provides a comprehensive analysis of labour market evolutions in rural areas in four most populous European countries since 1970. We document large differences in employment growth and changes in the industry structure are fast. Furthermore, industry turnover is positively associated with employment growth. Finally, our evidence indicates that successful rural areas experience stronger employment growth in manufacturing of food and beverages.
In the second chapter, I investigate the employment consequences of deindustrialization between 2010 and 2020 for cities in seven Chinese provinces, which could be viewed as China's Rust Belt, and explore the role of local multipliers. Cities within this Rust Belt reacted very differently to the aggregate decreasing trend of manufacturing employment. I document a high level of spatial heterogeneity across the local labour markets. I then study the role of local multiplier effects exploiting a shift-share approach. My estimates indicate that for every job created (lost) in the tradable sector in a given city, between 1.6 and 1.9 additional jobs are created (lost) in the non-tradable sector in the same city.
The third chapter presents direct evidence on the extent to which firms’ innovation is affected by access to knowledgeable labor through co-worker network connections. Displacements of inventors because of plant closures generate labor supply shocks to firms that employ their previous co-workers. We estimate (a) event-study models where the treatment is the displacement of a connected inventor and (b) IV specifications where we use such a displacement as an instrument for the hire of a connected inventor. Estimates indicate that firms take advantage of displacements to recruit connected inventors and that the improved capacity increases innovation
Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization
Large Language Models (LLMs) continue to advance in their capabilities, yet
this progress is accompanied by a growing array of safety risks. While
significant attention has been dedicated to exploiting weaknesses in LLMs
through jailbreaking attacks, there remains a paucity of exploration into
defending against these attacks. We point out a pivotal factor contributing to
the success of jailbreaks: the inherent conflict between the goals of being
helpful and ensuring safety. To counter jailbreaking attacks, we propose to
integrate goal prioritization at both training and inference stages.
Implementing goal prioritization during inference substantially diminishes the
Attack Success Rate (ASR) of jailbreaking attacks, reducing it from 66.4% to
2.0% for ChatGPT and from 68.2% to 19.4% for Vicuna-33B, without compromising
general performance. Furthermore, integrating the concept of goal
prioritization into the training phase reduces the ASR from 71.0% to 6.6% for
LLama2-13B. Remarkably, even in scenarios where no jailbreaking samples are
included during training, our approach slashes the ASR by half, decreasing it
from 71.0% to 34.0%. Additionally, our findings reveal that while stronger LLMs
face greater safety risks, they also possess a greater capacity to be steered
towards defending against such attacks. We hope our work could contribute to
the comprehension of jailbreaking attacks and defenses, and shed light on the
relationship between LLMs' capability and safety. Our code will be available at
\url{https://github.com/thu-coai/JailbreakDefense_GoalPriority}.Comment: 14 page
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