Predicting Stock Volatility Using After-Hours Information

We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four NASDAQ stocks (MSFT, AMGN, CSCO, and YHOO) we find that the inclusion of the preopen variance can improve the out-of-sample forecastability of the next day conditional day volatility. Additionally, we find that the postclose variance and the overnight squared return do not provide any predictive power for the next day conditional volatility. Our findings support the results of prior studies that traders trade for non-information reasons in the postclose period and trade for information reasons in the preopen period.

Plane-projection multi-photon microscopy for high-frame-rate Live Tissue Imaging

We present a wide-field multi-photon microscopy that provides optical sectioning at high frame rate under biocompatible laser dosage. Axial resolution comparable to confocal microscopy and 5-frame-per-second live tissue imaging are demonstrated

Complementary Therapy with Traditional Chinese Medicine for Treating Atherosclerosis-Related Diseases

Atherosclerosis-related diseases are the leading cause of morbidity or mortality in the world. They result in serious outcomes such as sudden cardiac death, unstable angina pectoris, acute myocardial infarction, stroke, or intermittent claudication due to vessel obliteration or plaque rupture with subsequent thrombosis. There are some limitations with standard treatments such as antiplatelet drugs, angiotensin-converting enzyme inhibitors, beta-blockers, coronary artery bypass surgery, and percutaneous transluminal coronary angioplasty. Therefore, complementary and alternative medicine is necessary for medication. Traditional Chinese medicine is the main complementary therapy used in the Chinese community. This article aims to explore complementary therapy with traditional Chinese medication for atherosclerosis-related diseases. There is some scientific evidence to support that traditional Chinese medicine could treat atherosclerosis and its associated conditions. Acupuncture through needling on ST36, ST40, PC6, or BL15 could alleviate atherosclerosis-related cardiovascular diseases. Tai chi and meditation have beneficial effects for mental and physical health. In addition, extracts or compounds of single Chinese herbs such as Salvia miltiorrhiza, Panax notoginseng, Ginkgo biloba, Curcuma longa, Crataegus pinnatifida, Paeonia lactiflora, Prunella vulgaris, Polygonum multiflorum, Coptis chinensis, and red yeast rice also could treat atherosclerosis-related diseases through their endothelial protective, antioxidative, anti-inflammatory, inhibiting of smooth muscle cells proliferation, and lipid-lowering effects. In accordance with evidence-based medicine, well-designed and conducted clinical studies such as randomized control clinical trials will be necessary in the future

Complementary Therapy with Traditional Chinese Medicine for Neonatal Hypoxic Ischemic Encephalopathy

Hypoxic ischemic encephalopathy (HIE) is one of the most significant causes of morbidity, mortality, and lifelong disability in newborns. The diagnosis of neonatal HIE is based on the dysfunction of neurogenic signs and classification according to the Sarnat staging system, which evaluates conscious level, neuromuscular control, complex reflexes, autonomic function, seizures, electroencephalogram readings, and duration of neurologic sign. There is no standard treatment for neonatal HIE, but it is widely accepted that hypothermia therapy is a safe and effective method for treating neonates with HIE. Traditional Chinese medicine (TCM) has recently been used to treat cases of neonatal HIE, especially herbal medicine prescriptions. Acupuncture is a common method used in TCM and is another promising therapy for neonatal HIE due to its demonstrated effective treatment of the disease in animal models. While there is a lack of direct evidence in clinical practice, we have observed acupuncture to be useful in adult HIE and in animal studies; therefore, we believe a clinical trial designed to evaluate the effectiveness of acupuncture in neonatal HIE treatment is worthwhile. Taken together, TCM is a promising technique that can be integrated into the conventional therapies for neonatal HIE

Complementary Therapy with Traditional Chinese Medicine for Childhood Asthma

Asthma is a heterogeneous disease that is typically characterized by chronic airway inflammation and obstruction of airflow; it frequently presents in early childhood and is the leading chronic disease in children in the western world. This review presents a brief description of the pathophysiology of asthma and summarizes recent research results on the mechanisms of action of anti-asthma Chinese herbal medicine commonly used in clinical practice. Other interventions of traditional Chinese medicine (TCM), such as acupuncture, tai chi, and meditation are also briefly discussed. We believe that this contribution is theoretically and practically relevant because the prevalence of asthma is increasing and, in addition to standard treatment, the use of complementary therapy is increasing and there is increasing scientific evidence demonstrating that TCM has potential for the treatment of childhood asthma

High Improvement in Conversion Efficiency of μc-SiGe Thin-Film Solar Cells with Field-Enhancement Layers

The improved performance for hydrogenated microcrystalline silicon-germanium (μc-Si1−xGex:H, x~0.1) p-i-n single solar cells with hydrogenated microcrystalline silicon (μc-Si:H) field-enhancement layers (FELs) is demonstrated for the first time. The fill factor (FF) and conversion efficiency (η) increase by about 19% and 28% when the thickness of the μc-Si FEL is increased from 0 to 200 nm, it is attributed to the longer hole life-time and enhanced electric field in the μc-Si0.9Ge0.1:H layer. Therefore, we can successfully manufacture high-performance μc-SiGe:H solar cells with the thickness of absorbers smaller than 1 μm by conducting FELs. Moreover, the simulation tool is used to simulate the current-voltage (J-V) curve, thus we can investigate the carrier transport in the absorber of μc-Si0.9Ge0.1:H solar cells with different EFLs

Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits

Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit trade-off in linear bandits problems as well as generalized linear bandits problems. We develop novel index policies that we prove achieve order-optimality, and show that they achieve empirical performance competitive with the state-of-the-art benchmark methods in extensive experiments. The new policies achieve this with low computation time per pull for linear bandits, and thereby resulting in both favorable regret as well as computational efficiency

Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs

We consider the infinite-horizon linear Markov Decision Processes (MDPs), where the transition probabilities of the dynamic model can be linearly parameterized with the help of a predefined low-dimensional feature mapping. While the existing regression-based approaches have been theoretically shown to achieve nearly-optimal regret, they are computationally rather inefficient due to the need for a large number of optimization runs in each time step, especially when the state and action spaces are large. To address this issue, we propose to solve linear MDPs through the lens of Value-Biased Maximum Likelihood Estimation (VBMLE), which is a classic model-based exploration principle in the adaptive control literature for resolving the well-known closed-loop identification problem of Maximum Likelihood Estimation. We formally show that (i) VBMLE enjoys $\widetilde{O}(d\sqrt{T})$ regret, where $T$ is the time horizon and $d$ is the dimension of the model parameter, and (ii) VBMLE is computationally more efficient as it only requires solving one optimization problem in each time step. In our regret analysis, we offer a generic convergence result of MLE in linear MDPs through a novel supermartingale construct and uncover an interesting connection between linear MDPs and online learning, which could be of independent interest. Finally, the simulation results show that VBMLE significantly outperforms the benchmark method in terms of both empirical regret and computation time