FIJICLIM description and users guide

The FIJICLIM prototype is based on PACCLIM which was developed by the International Global Change Institute (IGCI) as part of the Pacific Islands Climate Change Assistance Programme (PICCAP) executed by the South Pacific Regional Environment Programme (SPREP). Both FIJICLIM and PACCLIM build directly on a comparable model development for New Zealand, known as the CLIMPACTS system (Kenny et al., 1995, 1999; Warrick et al., 1996, 1999). The development of CLIMPACTS has been funded by the Foundation for Research Science and Technology since 1993. Its core components, which include a graphic user interface (GUI), a customised geographic information system (GIS), and data compression routines, have provided the basis for the development of FIJICLIM. The development of FIJICLIM is complementary to similar developments that have evolved from CLIMPACTS, for Bangladesh (BDCLIM), Australia (OZCLIM), and for training in climate change V&A assessment (VANDACLIM)

Hotspots: Modelling capacity for vector-borne disease risk analysis in New Zealand: A case study of Ochlerotatus camptorhynchus incursions in New Zealand

This Hotspots case study of Oc. camptorhynchus in New Zealand forms part of the wider aims and objectives of the Hotspots project. The overall aims of the case study were: 1. To evaluate the performance of the Hotspots model as a risk analysis tool for Oc. camptorhynchus; 2. To use and learn from the experience of the various incursions of Oc. camptorhynchus in order to critically assess and improve the model; 3. To gain experience in using the model for risk analysis for Oc. camptorhynchus in particular, and in so doing, also develop experience applicable to risk analysis for other vectors of concern (Table 1); and, 4. To develop an experience and knowledge base as well as guidelines for future use of the model in its various applications related to biosecurity, surveillance and risk assessment and management

Hotspots: Exotic mosquito risk profiles for New Zealand

This document reports the main findings of the first systematic, spatial analyses of risks to New Zealand associated with exotic mosquitoes of current public health concern

A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. Typical CIL methods tend to save representative exemplars from former classes to resist forgetting, while recent works find that storing models from history can substantially boost the performance. However, the stored models are not counted into the memory budget, which implicitly results in unfair comparisons. We find that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work, especially for the case with limited memory budgets. As a result, we need to holistically evaluate different CIL methods at different memory scales and simultaneously consider accuracy and memory size for measurement. On the other hand, we dive deeply into the construction of the memory buffer for memory efficiency. By analyzing the effect of different layers in the network, we find that shallow and deep layers have different characteristics in CIL. Motivated by this, we propose a simple yet effective baseline, denoted as MEMO for Memory-efficient Expandable MOdel. MEMO extends specialized layers based on the shared generalized representations, efficiently extracting diverse representations with modest cost and maintaining representative exemplars. Extensive experiments on benchmark datasets validate MEMO's competitive performance. Code is available at: https://github.com/wangkiw/ICLR23-MEMOComment: Accepted to ICLR 2023 as a Spotlight Presentation. Code is available at: https://github.com/wangkiw/ICLR23-MEM

Accurate simulation of ice and snow runoff for the mountainous terrain of the Kunlun Mountains, China

While mountain runoff provides great potential for the development and life quality of downstream populations, it also frequently causes seasonal disasters. The accurate modeling of hydrological processes in mountainous areas, as well as the amount of meltwater from ice and snow, is of great significance for the local sustainable development, hydropower regulations, and disaster prevention. In this study, an improved model, the Soil Water Assessment Tool with added ice-melt module (SWATAI) was developed based on the Soil Water Assessment Tool (SWAT), a semi-distributed hydrological model, to simulate ice and snow runoff. A temperature condition used to determine precipitation types has been added in the SWATAI model, along with an elevation threshold and an accumulative daily temperature threshold for ice melt, making it more consistent with the runoff process of ice and snow. As a supplementary reference, the comparison between the normalized difference vegetation index (NDVI) and the quantity of meltwater were conducted to verify the simulation results and assess the impact of meltwater on the ecology. Through these modifications, the accuracy of the daily flow simulation results has been considerably improved, and the contribution rate of ice and snow melt to the river discharge calculated by the model increased by 18.73%. The simulation comparison of the flooding process revealed that the accuracy of the simulated peak flood value by the SWATAI was 77.65% higher than that of the SWAT, and the temporal accuracy was 82.93% higher. The correlation between the meltwater calculated by the SWATAI and the NDVI has also improved significantly. This improved model could simulate the flooding processes with high temporal resolution in alpine regions. The simulation results could provide technical support for economic benefits and reasonable reference for flood prevention

Broadband enhanced transmission through the stacked metallic multi-layers perforated with coaxial annular apertures

This paper theoretically and experimentally presents a first report on broadband enhanced transmission through stacked metallic multi-layers perforated with coaxial annular apertures (CAAs). Different from previous studies on extraordinary transmission that occurs at a single frequency, the enhanced transmission of our system with two or three metallic layers can span a wide frequency range with a bandwidth about 60% of the central frequency. The phenomena arise from the excitation and hybridization of guided resonance modes in CAAs among different layers. Measured transmission spectra are in good agreement with calculations semi-analytically resolved by modal expansion method.Comment: 9 pages,4 figure

Few-Shot Learning with a Strong Teacher

Few-shot learning (FSL) aims to train a strong classifier using limited labeled examples. Many existing works take the meta-learning approach, sampling few-shot tasks in turn and optimizing the few-shot learner's performance on classifying the query examples. In this paper, we point out two potential weaknesses of this approach. First, the sampled query examples may not provide sufficient supervision for the few-shot learner. Second, the effectiveness of meta-learning diminishes sharply with increasing shots (i.e., the number of training examples per class). To resolve these issues, we propose a novel objective to directly train the few-shot learner to perform like a strong classifier. Concretely, we associate each sampled few-shot task with a strong classifier, which is learned with ample labeled examples. The strong classifier has a better generalization ability and we use it to supervise the few-shot learner. We present an efficient way to construct the strong classifier, making our proposed objective an easily plug-and-play term to existing meta-learning based FSL methods. We validate our approach in combinations with many representative meta-learning methods. On several benchmark datasets including miniImageNet and tiredImageNet, our approach leads to a notable improvement across a variety of tasks. More importantly, with our approach, meta-learning based FSL methods can consistently outperform non-meta-learning based ones, even in a many-shot setting, greatly strengthening their applicability

Contextualizing Multiple Tasks via Learning to Decompose

One single instance could possess multiple portraits and reveal diverse relationships with others according to different contexts. Those ambiguities increase the difficulty of learning a generalizable model when there exists one concept or mixed concepts in a task. We propose a general approach Learning to Decompose Network (LeadNet) for both two cases, which contextualizes a model through meta-learning multiple maps for concepts discovery -- the representations of instances are decomposed and adapted conditioned on the contexts. Through taking a holistic view over multiple latent components over instances in a sampled pseudo task, LeadNet learns to automatically select the right concept via incorporating those rich semantics inside and between objects. LeadNet demonstrates its superiority in various applications, including exploring multiple views of confusing tasks, out-of-distribution recognition, and few-shot image classification

Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

We investigate learning a ConvNet classifier with class-imbalanced data. We found that a ConvNet significantly over-fits the minor classes that do not have sufficient training instances, which is quite opposite to a traditional machine learning model like logistic regression that often under-fits minor classes. We conduct a series of analysis and argue that feature deviation between the training and test instances serves as the main cause. We propose to incorporate class-dependent temperatures (CDT) in learning a ConvNet: CDT forces the minor-class instances to have larger decision values in the training phase, so as to compensate for the effect of feature deviation in the test data. We validate our approach on several benchmark datasets and achieve promising performance. We hope that our insights can inspire new ways of thinking in resolving class-imbalanced deep learning

PILOT: A Pre-Trained Model-Based Continual Learning Toolbox

While traditional machine learning can effectively tackle a wide range of problems, it primarily operates within a closed-world setting, which presents limitations when dealing with streaming data. As a solution, incremental learning emerges to address real-world scenarios involving new data's arrival. Recently, pre-training has made significant advancements and garnered the attention of numerous researchers. The strong performance of these pre-trained models (PTMs) presents a promising avenue for developing continual learning algorithms that can effectively adapt to real-world scenarios. Consequently, exploring the utilization of PTMs in incremental learning has become essential. This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT also fits typical class-incremental learning algorithms (e.g., DER, FOSTER, and MEMO) within the context of pre-trained models to evaluate their effectiveness.Comment: Code is available at https://github.com/sun-hailong/LAMDA-PILO