156 research outputs found
Understanding and Optimizing Python-Based Applications - A Case Study on PYPY
Python is nowadays one of the most popular programming languages. It has been used extensively for rapid prototyping and developing real-world applications. Unfortunately, very few empirical studies were conducted on Python-based applications. There are various Python implementations (e.g., CPython, and PyPy). Among them, PyPy is generally the fastest due to PyPy's efficient tracing-based Just-in-Time (JIT) compiler. Understanding how PyPy has been evolved and the rationale behind its high performance would be very useful for Python application developers and researchers.
In the first part of the thesis, we conducted a replication study on mining the historical code changes' of PyPy and compared our findings against Python-based applications from five other application domains. In the second part, we conducted a detailed empirical study on the performance impact of the JIT configuration settings of PyPy. The findings and the techniques in this thesis will be useful for Python application developers and researchers
Leakage-resilient biometric-based remote user authentication with fuzzy extractors
National Research Foundation (NRF) Singapor
Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice
tru
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization
Bayesian Optimization (BO) is a common solution to search optimal
hyperparameters based on sample observations of a machine learning model.
Existing BO algorithms could converge slowly even collapse when the potential
observation noise misdirects the optimization. In this paper, we propose a
novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to
solve the problem. We first propose a neighbor-based regularization to smooth
each sample observation, which could reduce the observation noise efficiently
without any extra training cost. Since the neighbor regularization highly
depends on the sample density of a neighbor area, we further design a
density-based acquisition function to adjust the acquisition reward and obtain
more stable statistics. In addition, we design a adjustment mechanism to ensure
the framework maintains a reasonable regularization strength and density reward
conditioned on remaining computation resources. We conduct experiments on the
bayesmark benchmark and important computer vision benchmarks such as ImageNet
and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it
consistently outperforms other state-of-the-art methods.Comment: Accepted by BMVC 202
Visible-light photocatalytic H2 evolution over a series of transition metal substituted Keggin-structure heteropoly blues
A new construction for linkable secret handshake
National Research Foundation (NRF) Singapore; AXA Research Fun
PriBioAuth: Privacy-preserving biometric-based remote user authentication
National Research Foundation (NRF) Singapor
Text-to-3D with Classifier Score Distillation
Text-to-3D generation has made remarkable progress recently, particularly
with methods based on Score Distillation Sampling (SDS) that leverages
pre-trained 2D diffusion models. While the usage of classifier-free guidance is
well acknowledged to be crucial for successful optimization, it is considered
an auxiliary trick rather than the most essential component. In this paper, we
re-evaluate the role of classifier-free guidance in score distillation and
discover a surprising finding: the guidance alone is enough for effective
text-to-3D generation tasks. We name this method Classifier Score Distillation
(CSD), which can be interpreted as using an implicit classification model for
generation. This new perspective reveals new insights for understanding
existing techniques. We validate the effectiveness of CSD across a variety of
text-to-3D tasks including shape generation, texture synthesis, and shape
editing, achieving results superior to those of state-of-the-art methods. Our
project page is https://xinyu-andy.github.io/Classifier-Score-DistillationComment: Our project page is
https://xinyu-andy.github.io/Classifier-Score-Distillatio
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