When to be vaccinated? What to consider? Modelling decision-making and time preference for COVID-19 vaccine through a conjoint experiment.

How do citizens choose COVID-19 vaccines, and when do they wish to be vaccinated? A choice-based conjoint experiment was conducted in Hong Kong to examine factors that shape citizens' preference toward COVID-19 vaccines and their time preference to be vaccinated, which is overlooked in extant literature. Results suggest people are most concerned about vaccines' efficacy and severe side-effects, and that cash incentives are not useful in enhancing vaccine appeal. The majority of respondents show low intention for immediate vaccination, and many of them want to delay their vaccination. Further analysis shows that their time preference is shaped more by respondent characteristics than vaccine attributes. In particular, confidence in the vaccine, trust in government, and working in high-risk professions are associated with earlier timing for vaccine uptake. Meanwhile, forced COVID testing would delay vaccination. The findings offer a novel view in understanding how people decide whether and when to receive new vaccines, which have pivotal implications for a head start of any mass vaccination programs.

Transplantation and diabetes (Transdiab): a pilot randomised controlled trial of metformin in impaired glucose tolerance after kidney transplantation.

BACKGROUND: Post transplantation diabetes mellitus (PTDM) is a common and serious complication after renal transplantation with significant morbidity and mortality. Metformin has proven benefits in the general population and might be advantageous in the prevention and management of PTDM. METHODS: Transplantation and Diabetes (Transdiab) is a single-centre, unblinded, pilot randomised controlled trial assessing the feasibility, tolerability and efficacy of metformin after renal transplantation in patients with impaired glucose tolerance (IGT). Participants had an oral glucose tolerance test (OGTT) in the 4-12 weeks post-transplantation; those with IGT were randomised to standard care or standard care and metformin 500 mg twice daily and followed up for 12 months. RESULTS: Seventy eight patients had an OGTT over 24 months, 25 of them had IGT, of those, 19 patients were randomised, giving a feasibility of recruitment of 24.4%. Ten patients were randomised to metformin and 9 patients to standard care. Tolerability and efficacy was similar between the 2 groups with no serious adverse events. There was no difference in secondary outcomes relating to the metabolic profile. CONCLUSIONS: The use of metformin post renal transplantation appeared feasible and safe. Larger randomised controlled trials (RCTs) are needed to establish and confirm the efficacy and safety of metformin post renal transplantation. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12614001171606 . Date of registration 7/11/2014.

Causal Inference on Multidimensional Data Using Free Probability Theory.

In this paper, we deal with the problem of inferring causal relations for multidimensional data. Based on the postulate that the distribution of the cause and the conditional distribution of the effect given cause are generated independently, we show that the covariance matrix of the mean embedding of the cause in reproducing kernel Hilbert space (RKHS) is free independent with the covariance matrix of the conditional embedding of the effect given cause. This, called freeness condition, induces a cause-effect asymmetry that a designed measurement is 0 in the causal direction but smaller than 0 in the anticausal direction, and it uncovers the causal direction. One important novel aspect of this paper is that we interpret the independence as a freeness condition between covariance matrices of RKHS distribution embeddings, and it has a wide applicability. We show that our freeness condition-based inference method succeeds in scenarios like additive noise cases, where other methods fail, by theoretical analysis and experimental results.

A rare case of anti-N-methyl-D-aspartate receptor encephalitis during pregnancy.

Anti-N-methyl-D-aspartate (NMDA) receptor encephalitis was first described as a severe form of encephalitis by Dalmau et al in 2006. This is an autoimmune disorder usually associated with paraneoplastic mechanism that manifests as neuropsychiatric disorder affecting mainly women of child-bearing age. Nevertheless anti- NMDA receptor encephalitis is a rare condition during pregnancy. To -date, there have been only four reported cases during pregnancy. We report a case of a 23-year-old primigravida in first trimester pregnancy with altered mental status and multiple neurological symptoms related to anti-NMDA receptor encephalitis. To the best of our knowledge, this is the first reported anti-NMDA receptor encephalitis during pregnancy in Australasia.

Dual extended Kalman filtering in recurrent neural networks(1).

In the classical deterministic Elman model, the estimation of parameters must be very accurate. Otherwise, the system performance is very poor. To improve the system performance, we can use a Kalman filtering algorithm to guide the operation of a trained recurrent neural network (RNN). In this case, during training, we need to estimate the state of hidden layer, as well as the weights of the RNN. This paper discusses how to use the dual extended Kalman filtering (DEKF) for this dual estimation and how to use our proposing DEKF for removing some unimportant weights from a trained RNN. In our approach, one Kalman algorithm is used for estimating the state of the hidden layer, and one recursive least square (RLS) algorithm is used for estimating the weights. After training, we use the error covariance matrix of the RLS algorithm to remove unimportant weights. Simulation showed that our approach is an effective joint-learning-pruning method for RNNs under the online operation.

Design of trellis coded vector quantizers using Kohonen maps.

Although the performance of trellis coded vector quantizers (TCVQs) is much better than that of conventional memoryless vector quantizers, designing the trellis in TCVQs has a certain amount of computational overhead. In this paper, we apply Kohonen maps to simplify the design process of the TCVQs by exploiting the ordering property of Kohonen maps. This new implementation of the TCVQs is named as trellis coded Kohonen maps (TCKMs). Simulation results show that, with such a simple design process, the performance of the TCKMs is still comparable to that of the conventional TCVQs.