Nonlinear plasmonics at high temperatures

We solve the Maxwell and heat equations self-consistently for metal nanoparticles under intense continuous wave (CW) illumination. Unlike previous studies, we rely on {\em experimentally}-measured data for the metal permittivity for increasing temperature and for the visible spectral range. We show that the thermal nonlinearity of the metal can lead to substantial deviations from the predictions of the linear model for the temperature and field distribution, and thus, can explain qualitatively the strong nonlinear scattering from such configurations observed experimentally. We also show that the incompleteness of existing data of the temperature dependence of the thermal properties of the system prevents reaching a quantitative agreement between the measured and calculated scattering data. This modelling approach is essential for the identification of the underlying physical mechanism responsible for the thermo-optical nonlinearity of the metal and should be adopted in all applications of high temperature nonlinear plasmonics, especially for refractory metals, both for CW and pulsed illumination

Assessment Of Quality Standards Of Supported Employment By In-House Job Coaches And Employees With Disabilities

Quality standards of supported employment are established for individuals with disabilities in many developed countries. In Malaysia, however, there is still no assessment of quality standards since the beginning of supported employment 10 years ago. The purpose of this study is to assess the quality standards of supported employment by in-house job coaches and employees with disabilities based on a developed model of quality supported employment in Malaysia. The objectives are to assess quality standards from three key dimensions which are organization, job coach service and outcomes. Furthermore, the predictors of outcomes are identified. The correlations in the assessment between job coach and employee with disabilities are identified. A mixed method research design which includes primarily quantitative research methods as well as qualitative research methods was utilized to collect data from 16 companies which provides in-house job coaching services and employment to employees with disabilities. The sample of the study includes 173 in-house job coaches and 173 employees with disabilities. A Quality Standards Framework for Supported Employment in Malaysia (QSFM) was operationalized into the Quality Supported Employment (QSE) Instrument for the purpose of the assessment. The results of the study indicated that the organizations surveyed were of the view that the quality standards of supported employment were good. Findings also included predictors of quality outcomes of supported employment which were the management and leadership of the organization, the intensive support at the workplace and follow-up services

Skin Wound Healing Revealed by Multimodal Optical Microscopies

Skin is the largest organ of our body serving as the first line defense against pathogens and toxicity. The skin can heal itself if any damage in it occur. Wounds, if not taken care properly, can become chronic and can even cause death. In the field of cosmetics and plastic reconstructive surgery, wounds, are major cause of trauma and costs, which demand proper diagnosis that can help in appropriate treatment. In conventional medicine, wound diagnosis mostly relied on the expertise and experience of physicians on the basis of non-quantitative observation of clinical signs, or invasive histochemical assessment of biopsies

Communication-Efficient Distributed Deep Learning: A Comprehensive Survey

Distributed deep learning becomes very common to reduce the overall training time by exploiting multiple computing devices (e.g., GPUs/TPUs) as the size of deep models and data sets increases. However, data communication between computing devices could be a potential bottleneck to limit the system scalability. How to address the communication problem in distributed deep learning is becoming a hot research topic recently. In this paper, we provide a comprehensive survey of the communication-efficient distributed training algorithms in both system-level and algorithmic-level optimizations. In the system-level, we demystify the system design and implementation to reduce the communication cost. In algorithmic-level, we compare different algorithms with theoretical convergence bounds and communication complexity. Specifically, we first propose the taxonomy of data-parallel distributed training algorithms, which contains four main dimensions: communication synchronization, system architectures, compression techniques, and parallelism of communication and computing. Then we discuss the studies in addressing the problems of the four dimensions to compare the communication cost. We further compare the convergence rates of different algorithms, which enable us to know how fast the algorithms can converge to the solution in terms of iterations. According to the system-level communication cost analysis and theoretical convergence speed comparison, we provide the readers to understand what algorithms are more efficient under specific distributed environments and extrapolate potential directions for further optimizations