Resource-constrained Real-time Network Traffic Classification using One-Dimensional Convolutional Neural Networks

Real-time network traffic classification is vital for networks to implement Quality of Service (QoS) traffic engineering. Deep learning techniques have proven to be effective for classification tasks, even when the traffic is encrypted. The pursuit for higher accuracy has incentivized implementations of deep learning models that are larger and slower, and require higher computational resources. This poses a problem for real-time online classification, particularly in low resource environments. This paper considers the trade-off between prediction speed and accuracy for the packet-based network traffic classification tasks when computing resources are limited. We build and compare 1D Convolutional Neural Network (1D-CNN) and the Multilayer Perceptron (MLP) models of various sizes with varying packet payload lengths used as in- put. These deep learning models are further compared to Support Vector Machine (SVM) models across the same metrics. The models are evaluated on six different sets of hardware constraints that are likely to be found in low-resource community networks. The study finds a clear trade-off between prediction rate and attainable accuracy. Our results suggest that MLP can achieve sufficiently fast prediction in community networks with middle-range CPUs, and for the most powerful of CPUs, a 1D-CNN should be the preferred model

Profiling interactions of vaborbactam with metallo-β-lactamases

β-Lactams are the most successful antibacterials, yet their use is threatened by resistance, importantly as caused by β-lactamases. β-Lactamases fall into two mechanistic groups: the serine β-lactamases that utilise a covalent acyl-enzyme mechanism and the metallo β-lactamases that utilise a zinc-bound water nucleophile. Achieving simultaneous inhibition of both β-lactamase classes remains a challenge in the field. Vaborbactam is a boronate-based inhibitor that reacts with serine-β-lactamases to form covalent complexes that mimic tetrahedral intermediates in catalysis. Vaborbactam has recently been approved for clinical use in combination with the carbapenem meropenem. Here we show that vaborbactam moderately inhibits metallo-β-lactamases from all 3 subclasses (B1, B2 and B3), with a potency of around 20–100 fold below that by which it inhibits its current clinical targets, the Class A serine β-lactamases. This result contrasts with recent investigations of bicyclic boronate inhibitors, which potently inhibit subclass B1 MBLs but which presently lack activity against B2 and B3 enzymes. These findings indicate that cyclic boronate scaffolds have the potential to inhibit the full range of β-lactamases and justify further work on the development of boronates as broad-spectrum β-lactamase inhibitors

Using high-resolution LiDAR data to quantify the three-dimensional structure of vegetation in urban green space

The spatial arrangement and vertical structure of vegetation in urban green spaces are key factors in determining the types of benefits that urban parks provide to people. This includes opportunities for recreation, spiritual fulfilment and biodiversity conservation. However, there has been little consideration of how the fine-scale spatial and vertical structure of vegetation is distributed in urban parks, primarily due to limitations in methods for doing so. We addressed this gap by developing a method using Light Detection and Ranging (LiDAR) data to map, at a fine resolution, tree cover, vegetation spatial arrangement, and vegetation vertical structure. We then applied this method to urban parks in Brisbane, Australia. We found that parks varied mainly in their amount of tree cover and its spatial arrangement, but also in vegetation vertical structure. Interestingly, the vertical structure of vegetation was largely independent of its cover and spatial arrangement. This suggests that vertical structure may be being managed independently to tree cover to provide different benefits across urban parks with different levels of tree cover. Finally, we were able to classify parks into three distinct classes that explicitly account for both the spatial and vertical structure of tree cover. Our approach for mapping the three-dimensional vegetation structure of urban green space provides a much more nuanced and functional description of urban parks than has previously been possible. Future research is now needed to quantify the relationships between vegetation structure and the actual benefits people derive from urban green space.</p