Effective fair pricing of international mutual funds

We propose a new methodology to provide fair prices of international mutual funds by adjusting prices at the individual security level using a comprehensive and economically relevant information set. Stepwise regressions are used to endogenously determine the stock-specific optimal set of factors. Using 16 synthetic funds whose characteristics are extracted from 16 corresponding actual US-based Japanese mutual funds, we demonstrate that our method estimates fund prices significantly more accurately than existing methods. Although existing fair-pricing methods provide an improvement over the current practice of simply using Japanese market closing prices, they are still highly vulnerable to exploitation by market-timers. By contrast, our method is the most successful in preventing such strategic exploitation since no competing method can profit from our stated prices. © 2008 Elsevier B.V. All rights reserved.preprin

Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

© 2017 IEEE. Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy

Response of dispersed droplets to shock waves in supersonic mixing layers

The response of dispersed droplets to oblique shock waves in the supersonic mixing layer was investigated using the large eddy simulation coupled with the particle Lagrangian tracking model. The generated disturbances based on the most-unstable wave model were imposed to excite the development of supersonic shear layer. The oblique shock wave was numerically introduced in the flow field. Small- and medium-sized droplets remained their preferential distribution in the vortices after crossing the shock wave, while large-sized droplet became more dispersed. The influence of shock waves on the momentum and heat transfers from surrounding gas to droplets was analyzed by tracking droplets’ motion paths. Small-sized droplets responded easily to the shock wave. Compared with the aerodynamic response, the thermal response of droplets was slower, especially under the impaction of the shock wave. The present research conclusions are conductive to analyze the mixing of air and fuel droplets and of important academic value for further understanding the two-phase dynamics in combustors of scramjet

Sustained attention driving task analysis based on recurrent residual neural network using EEG data

© 2018 IEEE. This paper proposes applying recurrent residual network (RRN) for analyzing electroencephalogram (EEG) data captured during a simulated sustained attention driving task. We first address the suitableness of utilizing residual structure as well as adopting recurrent structure for EEG signal processing. Then based on these descriptions a recurrent residual network is tailored and depicted in detail. Thirdly we use an EEG dataset obtained from a sustained-attention experiment for our model justification. By applying the RRN model to the experimental data and via the competitive result achieved, we demonstrate the elegance of the proposed model. At last, we discuss the characteristics of the learned filters and their interpretations from EEG frequency band perspectives

Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression

© 2016 IEEE. There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI

Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%

Adaptive subspace sampling for class imbalance processing

© 2016 IEEE. This paper presents a novel oversampling technique that addresses highly imbalanced data distribution. At present, the imbalanced data that have anomalous class distribution and underrepresented data are difficult to deal with through a variety of conventional machine learning technologies. In order to balance class distributions, an adaptive subspace self-organizing map (ASSOM) that combines the local mapping scheme and globally competitive rule is proposed to artificially generate synthetic samples focusing on minority class samples. The ASSOM is conformed with feature-invariant characteristics, including translation, scaling and rotation, and it retains the independence of basis vectors in each module. Specifically, basis vectors generated via each ASSOM module can avoid generating repeated representative features that offer nothing but heavy computational load. Several experimental results demonstrate that the proposed ASSOM method with supervised learning manner is superior to other existing oversampling techniques

Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)

© 1993-2012 IEEE. One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS

A survey to understand the feelings towards and impact of COVID-19 on the households of juvenile dermato myositis patients from a parent or carer perspective

Objectives: This aim of this study was to gain a better understanding of how parents and carers feel about the effects and impact of the coronavirus disease 2019 (COVID-19) pandemic lockdown and how this impacted upon their child/young person with JDM. / Method: We approached 139 participants from the JDM Cohort Biomarker Study (JDCBS), with specific consent to approach electronically for research studies. A secure electronic questionnaire with study introduction was sent to participants for their parents and carers around the UK to complete. It consisted of 20 questions about the impact of the pandemic on their child or young person’s clinical care. Data were analysed quantitatively and qualitatively. / Results: There were 76 (55%) responses to the survey. More than 50% of participants were actively being treated for their JDM at the point of survey completion as recorded by their parent or carer. More than 40% attested to disrupted treatment owing to COVID-19. The biggest impact upon clinical care was cancellation of appointments, initiating virtual appointments and extension of time between blood tests. Parents and carers expressed their own feelings of worry, concern and anxiety, but also those of their child or young person. / Conclusion: Families who have a child or young person with JDM have been affected by COVID-19. Qualitative comments highlight that it has been a very difficult time. Further investigation is required into this area and could be compared with research on the effects of COVID-19 on other patient groups with chronic disease

A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines

A joint moment projection method and maximum entropy approach for treating the soot population balance equations is developed and presented in this work. The moment projection method is used to solve the population balance equations and generate moments that are supplied to the maximum entropy approach as a post-processing technique to reconstruct the soot particle size distribution. The particle size range required by the maximum entropy for particle size distribution reconstruction is determined based on the weighted particles generated in the moment projection method. The performance of the joint approach is first evaluated by solving a set of simplified population balance equations in Matlab, then it is implemented into a Stochastic Reactor Model engine code to simulate the formation and oxidation of soot particles in a single-cylinder direct injection diesel engine. Results suggest that the joint approach has the advantages of ease of implementation, high accuracy and low computational cost. It enables a detailed analysis on the soot formation and oxidation processes in diesel engines. Complete information on the soot particle size distribution can be provided with little CPU cost induced