arXiv subjects

Keith Mills

Publications and source records attributed to Keith Mills.

4 recordsLinked to original sources

arXiv2023

The Structure of the Spin^h Bordism Spectrum

Spin$^h$ manifolds are the quaternionic analogue to Spin$^c$ manifolds. We compute the spin$^h$ bordism groups at the prime 2 by proving a structure theorem for the cohomology of the spin$^h$ bordism spectrum $\mathrm{MSpin}^h$ as a module over the mod 2 Steenrod algebra. This provides a 2-local splitting of $\mathrm{MSpin}^h$ as a wedge sum of familiar spectra. We also compute the decomposition of $H^*(\mathrm{MSpin}^h;\mathbb{Z}/2\mathbb{Z})$ explicitly in degrees up through 30 via a counting process.

math.AT↗

arXiv2022

Almost Complex Structures on Homotopy Complex Projective Spaces

We show that all homotopy $\mathbb{C}P^n$s, smooth closed manifolds with the oriented homotopy type of $\mathbb{C}P^n$, admit almost complex structures for $3 \leq n \leq 6$, and classify these structures by their Chern classes. Our methods provide a new proof of a result of Libgober and Wood on the classification of almost complex structures on homotopy $\mathbb{C}P^4$s.

math.GT↗

arXiv2021

Generative Adversarial Neural Architecture Search

Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess. In this paper, we propose Generative Adversarial NAS (GA-NAS) with theoretically provable convergence guarantees, promoting stability and reproducibility in neural architecture search. Inspired by importance sampling, GA-NAS iteratively fits a generator to previously discovered top architectures, thus increasingly focusing on important parts of a large search space. Furthermore, we propose an efficient adversarial learning approach, where the generator is trained by reinforcement learning based on rewards provided by a discriminator, thus being able to explore the search space without evaluating a large number of architectures. Extensive experiments show that GA-NAS beats the best published results under several cases on three public NAS benchmarks. In the meantime, GA-NAS can handle ad-hoc search constraints and search spaces. We show that GA-NAS can be used to improve already optimized baselines found by other NAS methods, including EfficientNet and ProxylessNAS, in terms of ImageNet accuracy or the number of parameters, in their original search space.

cs.LG↗

arXiv2018

Android Malware Detection based on Factorization Machine

As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users face, the detection of malware on Android devices has become an increasingly important issue in cyber security. Traditional methods like signature-based routines are unable to protect users from the ever-increasing sophistication and rapid behavior changes in new types of Android malware. Therefore, a great deal of effort has been made recently to use machine learning models and methods to characterize and generalize the malicious behavior patterns of mobile apps for malware detection. In this paper, we propose a novel and highly reliable classifier for Android Malware detection based on a Factorization Machine architecture and the extraction of Android app features from manifest files and source code. Our results indicate that the numerical feature representation of an app typically results in a long and highly sparse vector and that the interactions among different features are critical to revealing malicious behavior patterns. After performing an extensive performance evaluation, our proposed method achieved a test result of 100.00% precision score on the DREBIN dataset and 99.22% precision score with only 1.10% false positive rate on the AMD dataset. These metrics match the performance of state-of-the-art machine-learning-based Android malware detection methods and several commercial antivirus engines with the benefit of training up to 50 times faster.

cs.CR↗