Hydraulic traits and drought mortality risk of tree species

Increased drought frequency and severity associated with global climate changehas contributed to large scale forest dieback on all vegetated continents. Forest dieback may alter community composition, leading to cascading negative impacts on ecosystem function and service, and creating a positive feedback loop between biosphere and atmosphere. Traits-based approaches have emerged as a promising way to accurately predict the impacts of climate change on vegetation dynamics. Yet predicting the forest mortality pattern resulting from drought stress remains challenging, largely because of a lack of knowledge of the plant traits determining the risk and modulating the process of drought-induced mortality, and how these traits vary across and within species. Hydraulic traits define species distributions along local or regional gradients of water availability, and recent advances in modelling forest dynamics highlight the critical role of hydraulic traits in improving model predictive power with respect to mortality events. Using various ecologically and economically important tree species from New South Wales, Australia, my PhD thesis was designed to examine inter-specific variation of various hydraulic traits across a wide range of species native to five different vegetation types: Rainforest (Acmena smithii), Wet sclerophyll forest (Eucalyptus grandis, E. viminalis), Dry sclerophyll forest (Angophora costata, Corymbia gummifera, E. sideroxylon), Grassy woodland (E. blakelyi, E. macrorhyncha, E. melliodora) and Semi-arid woodland (Acacia aneura, E. largiflorens, E. populnea). In addition, intra-specific variation of key hydraulic traits was examined for Banksia serrata. The primary objective of my work was to provide trait values that will help to predict the dynamics of tree species upon climate change with vegetation models. Furthermore, the correlative relationships among hydraulic traits and between traits and climate presented in this study broaden our understanding of plant hydraulic strategies and plant adaptation to low-rainfall environments

A Coordination Language for Databases

We present a coordination language for the modeling of distributed database applications. The language, baptized Klaim-DB, borrows the concepts of localities and nets of the coordination language Klaim but re-incarnates the tuple spaces of Klaim as databases. It provides high-level abstractions and primitives for the access and manipulation of structured data, with integrity and atomicity considerations. We present the formal semantics of Klaim-DB and develop a type system that avoids potential runtime errors such as certain evaluation errors and mismatches of data format in tables, which are monitored in the semantics. The use of the language is illustrated in a scenario where the sales from different branches of a chain of department stores are aggregated from their local databases. Raising the abstraction level and encapsulating integrity checks in the language primitives have benefited the modeling task considerably

PeF: Poisson's Equation Based Large-Scale Fixed-Outline Floorplanning

Floorplanning is the first stage of VLSI physical design. An effective floorplanning engine definitely has positive impact on chip design speed, quality and performance. In this paper, we present a novel mathematical model to characterize non-overlapping of modules, and propose a flat fixed-outline floorplanning algorithm based on the VLSI global placement approach using Poisson's equation. The algorithm consists of global floorplanning and legalization phases. In global floorplanning, we redefine the potential energy of each module based on the novel mathematical model for characterizing non-overlapping of modules and an analytical solution of Poisson's equation. In this scheme, the widths of soft modules appear as variables in the energy function and can be optimized. Moreover, we design a fast approximate computation scheme for partial derivatives of the potential energy. In legalization, based on the defined horizontal and vertical constraint graphs, we eliminate overlaps between modules remained after global floorplanning, by modifying relative positions of modules. Experiments on the MCNC, GSRC, HB+ and ami49\_x benchmarks show that, our algorithm improves the average wirelength by at least 2\% and 5\% on small and large scale benchmarks with certain whitespace, respectively, compared to state-of-the-art floorplanners

Case-Aware Adversarial Training

The neural network (NN) becomes one of the most heated type of models in various signal processing applications. However, NNs are extremely vulnerable to adversarial examples (AEs). To defend AEs, adversarial training (AT) is believed to be the most effective method while due to the intensive computation, AT is limited to be applied in most applications. In this paper, to resolve the problem, we design a generic and efficient AT improvement scheme, namely case-aware adversarial training (CAT). Specifically, the intuition stems from the fact that a very limited part of informative samples can contribute to most of model performance. Alternatively, if only the most informative AEs are used in AT, we can lower the computation complexity of AT significantly as maintaining the defense effect. To achieve this, CAT achieves two breakthroughs. First, a method to estimate the information degree of adversarial examples is proposed for AE filtering. Second, to further enrich the information that the NN can obtain from AEs, CAT involves a weight estimation and class-level balancing based sampling strategy to increase the diversity of AT at each iteration. Extensive experiments show that CAT is faster than vanilla AT by up to 3x while achieving competitive defense effect

SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasing popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and finetuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy. Code is available at https://github.com/Vision.Comment: This paper has been accepted for presentation at the AAAI202

PriBioAuth: Privacy-preserving biometric-based remote user authentication

National Research Foundation (NRF) Singapor

Multi-authority attribute-based keyword search over encrypted cloud data

National Research Foundation (NRF) Singapore; AXA Research Fun

Lightweight sharable and traceable secure mobile health system

National Research Foundation (NRF) Singapor