Gas Dynamics in the Barred Seyfert Galaxy NGC4151 - II. High Resolution HI Study

We present sensitive, high angular resolution (6" x 5") 21-cm observations of the neutral hydrogen in the nearby barred Seyfert galaxy, NGC4151. These HI observations, obtained using the VLA in B-configuration, are the highest resolution to date of this galaxy, and reveal hitherto unprecedented detail in the distribution and kinematics of the HI on sub-kiloparsec scales. A complete analysis and discussion of the HI data are presented and the global properties of the galaxy are related to the bar dynamics presented in Paper I.Comment: 13 pages including 9 figures and 3 tables; accepted for publication in MNRA

Botulinum neurotoxin serotype F is a zinc endopeptidase specific for VAMP/synaptobrevin

Botulinum neurotoxin serotype F contains the zinc binding motif of zinc endopeptidases. Atomic adsorption analysis of highly purified toxin preparation revealed the presence of one atom of zinc per molecule of toxin, which could be removed with EDTA or o-phenanthroline. The light chain of the neurotoxin was shown to have a zinc-dependent protease activity specific for VAMP/synaptobrevin, an integral membrane protein of synaptic vesicles. Both isoforms of rat VAMP were cleaved at the same site corresponding to the single Gln-Lys peptide bond present in their sequences. This proteolytic activity was inhibited by EDTA, o-phenanthroline, and captopril as well as by VAMP peptides spanning the cleavage site

Digital Memories Based Mobile User Authentication for IoT

The increasing number of devices within the IoT is raising concerns over the efficiency and exploitability of existing authentication methods. The weaknesses of such methods, in particular passwords, are well documented. Although alternative methods have been proposed, they often rely on users being able to accurately recall complex and often unmemorable information. With the profusion of separate online accounts, this can often be a difficult task. The emerging digital memories concept involves the creation of a repository of memories specific to individuals. We believe this abundance of personal data can be utilised as a form of authentication. In this paper, we propose our digital memories based two-factor authentication mechanism, and also present our promising evaluation results. Keywords—Digital memories, authentication, IoT, securit

The Open Access Advantage Revisited

This paper is a revision of one that appeared in 2008, incorporating the many developments and changes that have happened since then.published_or_final_versio

The Open Access Advantage

Hong Kong Open Access Committeepublished_or_final_versio

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018