Tuning the detection wavelength of quantum-well infrared photodetectors by single high-energy implantation

Single high-energy (0.9 MeV) proton implantation and rapid thermal annealing was used to tune the spectral response of the quantum-well infrared photodetectors (QWIPs). In addition to the large redshift of the QWIPs’ response wavelength after implantation, either narrowed or broadened spectrum was obtained at different interdiffusion extent. In general, the overall device performance for the low-dose implantation was not significantly degraded. In comparison with the other implantation schemes, this single high-energy implantation is the most effective and simple technique in tuning the wavelength of QWIPs, thus, to achieve the fabrication of multicolor detectors.Partial financial support from Australian Research Council, Hong Kong Research Grants Council, and the Australian Agency for International Development ~AusAID! through IDP Education Australia under Australia–China Institutional Links Program (ACILP) is acknowledged

Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration

Motor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans

Ensemble Pruning via Individual Contribution Ordering

An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend to construct unnecessarily large ensembles, which increases the memory consumption and computational cost. Ensemble pruning tackles this problem by selecting a subset of ensemble members to form subensembles that are subject to less resource consumption and response time with accuracy that is similar to or better than the original ensemble. In this paper, we analyze the accuracy/diversity trade-off and prove that classifiers that are more accurate and make more predictions in the minority group are more important for subensemble construction. Based on the gained insights, a heuristic metric that considers both accuracy and diversity is proposed to explicitly evaluate each individual classifier’s contribution to the whole ensemble. By incorporating ensemble members in decreasing order of their contributions, subensembles are formed such that users can select the top p percent of ensemble members, depending on their resource availability and tolerable waiting time, for predictions. Experimental results on 26 UCI data sets show that subensembles formed by the proposed EPIC (Ensemble Pruning via Individual Contribution ordering) algorithm outperform the original ensemble and a state-ofthe-art ensemble pruning method, Orientation Ordering (OO) [16]

A unified framework for semantics and feature based relevance feedback in image retrieval systems

The relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few years. Various ad hoe parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform optimization on multi-level image content model have been formulated. However, these methods only perform relevance feedback on the low-level image features and fail to address the images ' semantic content. In this paper, we propose a relevance feedback technique, iFind, to take advantage of the semantic contents of the images in addition to the low-level features. By forming a semantic network on top of the keyword association on the images, we are able to accurately deduce and utilize the images ' semantic contents for retrieval purposes. The accuracy and effectiveness of our method is demonstrated with experimental results on real-world image collections

A Robust Distributed Power Control Algorithm for Minimum Interference to Primary Users in Underlay Cognitive Radio Networks

A robust distributed optimal power control (RDPC) scheme under worst case condition is proposed to make primary users (PUs) receive minimum interference generated from all secondary users (SUs) in underlay cognitive radio networks (CRNs). The strategy considers the transmit power of each SU below the maximum allowable power of the devices and interference plus noise ratio (SINR) of each SU under the minimum threshold. Simulation illustrate thatthe RDPC can lead SUs to reduce the interference to PUs, and simultaneously the better meet quality of service (QoS) requirement of SUs in comparison with the distributed power control algorithm (DPC) and the traditional iterative water filling algorithm (IWFA) in time-varying channel environment

What predicts large vessel occlusion in mild stroke patients?

Abstract Background and purpose Mild acute ischemic stroke (AIS) patients with large vessel occlusion (LVO) may benefit from thrombolysis or thrombectomy therapy. However, the predictors for LVO in mild AIS patients have not been extensively explored. We aimed to investigate the predictors for LVO in mild AIS patients. Methods We collected the data of consecutive AIS patients with a National Institutes of Health Stroke Scale (NIHSS) score ≤ 5 from The Third China National Stroke Registry - a prospective nationwide registry of AIS or transient ischemic attack (TIA) patients in China from August 2015 to March 2018. Patients were divided into LVO and non-LVO group based on the vascular imaging during the hospitalization. Multivariable regression analyses involving clinical characteristics and NIHSS subitems was performed to detect the predictors for LVO. Result A total of 7653 mild AIS patients from The Third China National Stroke Registry were included in this study. Among them, 620 patients (8.1%) had LVO. The level of consciousness (adjusted odds ratio, 1.87; 95% confidence interval, 1.08 to 3.23), visual field (adjusted odds ratio, 2.10; 95% confidence interval, 1.43 to 3.06) and sensory (adjusted odds ratio, 0.75; 95% confidence interval, 0.60 to 0.94) were predictors for mild AIS patients with LVO. Conclusions Impaired LOC, visual field and sensory were independently predictors for LVO in mild stroke patients. Further studies are warranted to test these predictors in prehospital setting and in other population

Association of sex and age with in‐hospital mortality and complications of patients with intracerebral hemorrhage: A study from the Chinese Stroke Center Alliance

Abstract Background and purpose The impact of sex and age on prognosis in patients with intracerebral hemorrhage (ICH) in the Chinese population remains unclear. Our study aimed to investigate the relationship between sex and age of Chinese ICH patients and adverse prognosis. Methods We used the Chinese Stroke Center Alliance database with in‐hospital mortality as the primary outcome and hospital complications as the secondary outcome. Patients were divided into four groups by sex and age. Logistic regression analyses were performed to assess the association between sex and age and the prognosis of ICH patients. Results We enrolled 60,911 ICH patients, including 22,284 young and middle‐aged males, 15,651 older males, 11,948 young and middle‐aged females, and 11,028 older females. After adjusting for variables, older male patients had a higher mortality rate (OR = 1.21, 95% CI 1.01–1.45), combined with more frequent hematoma expansion (OR = 1.14, 95% CI 1.03–1.26), pneumonia (OR = 1.91, 95% CI 1.81–2.03), and hydrocephalus (OR = 1.28, 95% CI 1.04–1.59). Young and middle‐aged female patients had a lower mortality rate (OR = 0.74, 95% CI 0.58–0.95) and less frequent combined pneumonia (OR = 0.81, 95% CI 0.75–0.87). In‐hospital mortality was not significantly different in older females compared with young and middle‐aged males, but the odds of deep vein thrombosis, swallowing disorders, urinary tract infections, and gastrointestinal bleeding were significantly higher. Conclusion Among young and middle‐aged patients, females are related to a lower in‐hospital mortality rate from ICH. Older patients are at an increased risk of ICH complications, with higher in‐hospital mortality in older men