Chinese New Zealanders in Aotearoa: Media consumption and political engagement

This article outlines work in progress on project concerning interactions between the Chinese community in New Zealand, ethnic Chinese media, mainstream English language media, particularly around the New Zealand 2020 general election. A wealth of past research has discussed ethnic Chinese language media in New Zealand, the Chinese diaspora, and general elections. This study will go beyond previous research to include mainstream English language media as part of the media resources available to Chinese New Zealanders considering participating as voters in general elections. For Chinese New Zealanders, understanding the diversity of media in New Zealand is likely to have a positive effect on their voting decisions, and encourage more thinking about government policies. &nbsp

Deep Lifelong Cross-modal Hashing

Hashing methods have made significant progress in cross-modal retrieval tasks with fast query speed and low storage cost. Among them, deep learning-based hashing achieves better performance on large-scale data due to its excellent extraction and representation ability for nonlinear heterogeneous features. However, there are still two main challenges in catastrophic forgetting when data with new categories arrive continuously, and time-consuming for non-continuous hashing retrieval to retrain for updating. To this end, we, in this paper, propose a novel deep lifelong cross-modal hashing to achieve lifelong hashing retrieval instead of re-training hash function repeatedly when new data arrive. Specifically, we design lifelong learning strategy to update hash functions by directly training the incremental data instead of retraining new hash functions using all the accumulated data, which significantly reduce training time. Then, we propose lifelong hashing loss to enable original hash codes participate in lifelong learning but remain invariant, and further preserve the similarity and dis-similarity among original and incremental hash codes to maintain performance. Additionally, considering distribution heterogeneity when new data arriving continuously, we introduce multi-label semantic similarity to supervise hash learning, and it has been proven that the similarity improves performance with detailed analysis. Experimental results on benchmark datasets show that the proposed methods achieves comparative performance comparing with recent state-of-the-art cross-modal hashing methods, and it yields substantial average increments over 20\% in retrieval accuracy and almost reduces over 80\% training time when new data arrives continuously

On sliding mode observers for non-infinitely observable descriptor systems

International audienceThis paper presents a sliding mode observer (SMO) design method to estimate the states and unknown inputs (UIs) in a class of non-infinitely observable (NIO) descriptor systems that contain UIs in both the state and output equations. Existing works on SMO design for NIO systems did not consider UIs in the output equation. In order to overcome the difficulty caused by UIs in output channels and the NIO condition, we reformulated the original system and introduced new UIs to replace the original UIs to obtain an equivalent infinitely observable descriptor system whose output does not contain any UI. Based on the developed equivalent system, a new SMO method is proposed to estimate both the states and the UIs. Subsequently, the necessary and sufficient conditions for the existence of the SMO are derived in terms of the original system matrices, which thus makes the conditions easy to be examined. Finally, an example is used to verify the effectiveness of the proposed method

Multi-objective planning of multi-type distributed generation considering timing characteristics and environmental benefits

This paper presents a novel approach to multi-type distributed generation (DG) planning based on the analysis of investment and income brought by grid-connected DG. Firstly, the timing characteristics of loads and DG outputs, as well as the environmental benefits of DG are analyzed. Then, on the basis of the classification of daily load sequences, the typical daily load sequence and the typical daily output sequence of DG per unit capacity can be computed. The proposed planning model takes the location, capacity and types of DG into account as optimization variables. An improved adaptive genetic algorithm is proposed to solve the model. Case studies have been carried out on the IEEE 14-node distribution system to verify the feasibility and effectiveness of the proposed method and model

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte

Visualization 2.mp4

Visualization 2 shows the pulse sequence evolution of the synchronised pulses when the cavity delay is tuned precisely. The 1.9 µm laser pulse signal (bottom panel in the visualization) is used to trigger the oscilloscope. Initially, the self-started mode-locking of the 1.55 µm laser (top panel in the visualization) and the 1.9 µm laser are achieved in different repetition rates, results in clear swinging of the 1.55 µm laser displayed on the oscilloscope. Then we precisely tuned the cavity delay, the synchronised pulse sequences are obtained at the point that the repetition rate difference between the 1.55 µm laser and the 1.9 µm laser are within the maximum frequency difference, showing stable pulses without swinging. Further tunning the delay, it can be seen that the two pulse sequences move simultaneously with slight repetition rate decrease (i.e., increased time spacing). When the increasing cavity length of the 1.55 µm laser is beyond the synchronisation frequency range (SFR), the dual-color solitons resume to independent operation

Visualization 3.mp4

Visualization 3 shows the evolution process of the synchronised solitons corresponding to Visualization 2 recorded by a RF spectrum analyser. The signals of the 1.55 µm laser and the 1.9 µm laser are combined to an external 1550/1950 nm wavelength division multiplexer to facilitate the measurements. In the beginning, the signal envelope of the 1.9 µm laser (left one) remains stationary as the signal envelope of the 1.55 µm laser (right one) approaching it by tunning the VODL. The two signal envelopes merge into one when they overlap, indicating two pulses are in the same repetition rate. The amplitude of the merged signal envelope is larger than each individual one. Further tunning the VODL, the merged signal envelope moves forward and detaches after the increased length in the cavity length is larger than the SFR. The signal envelope of the 1.9 µm laser returns to its original frequency position and the signal envelope of the 1.55 µm laser remains in the frequency where the separation occurs and could move forward if the delay is further increased