Inverse Raman Scattering in Femtosecond Broadband Transient Absorption Experiments

This chapter reports on one of the nonlinear spectral features, the inverse Raman scattering (IRS), observed upon the interaction of ultrafast‐pulsed lasers in a Raman‐active medium. Hereby, a comprehensive theoretical description of the IRS is exposed. Furthermore, the investigation carried out on synthetic eumelanin dispersions is addressed to show how the transient absorption measurements can be influenced by the IRS, if probing at energies close to Stokes and anti‐Stokes vibrational modes of the medium. A thorough analysis demonstrates that the IRS affects the sign of dynamics but not relaxation times. A specific kind of spectroscopy based on the IRS effect (ultrafast Raman loss spectroscopy) is eventually illustrated as valuable tool to characterize the structure of molecules and to investigate their dynamics during chemical reactions, even occurring at ultrafast timescales

DOES CORPORATE SOCIAL RESPONSIBILITY INFLUENCE JOB STRESS AND TURNOVER OF EMPLOYEE IN PRIVATE COLLEGES OF PESHAWAR, KP-PAKISTAN?

The main aim of this research is to fill the gap by accomplish a realistic study in Private colleges of Peshawar-Pakistan, by knowing the impact of Corporate Social Responsibility on job stress and turnover of employee. Adopted questionnaires were used and Data was collected from existing literature through extensive study. Data analysis was perform using SPSS. Corporate Social Responsibility, job stress and turnover have negative correlation. This study will give a base for planning out strategy for establishing corporate social Responsibility in Private Colleges of Peshawar for maintainable developments besides decreasing job stress level and employee turnover rate. There is less research done on corporate social Responsibility, job stress and turnover relation in educational sector particularly in private colleges of Peshawar, Pakistan

DOES CORPORATE SOCIAL RESPONSIBILITY INFLUENCE JOB STRESS AND TURNOVER OF EMPLOYEE IN PRIVATE COLLEGES OF PESHAWAR, KP-PAKISTAN?

The main aim of this research is to fill the gap by accomplish a realistic study in Private colleges of Peshawar-Pakistan, by knowing the impact of Corporate Social Responsibility on job stress and turnover of employee. Adopted questionnaires were used and Data was collected from existing literature through extensive study. Data analysis was perform using SPSS. Corporate Social Responsibility, job stress and turnover have negative correlation. This study will give a base for planning out strategy for establishing corporate social Responsibility in Private Colleges of Peshawar for maintainable developments besides decreasing job stress level and employee turnover rate. There is less research done on corporate social Responsibility, job stress and turnover relation in educational sector particularly in private colleges of Peshawar, Pakistan

EFFECT OF ISLAMIC WORK ETHICS ON JOB PERFORMANCE: MEDIATING ROLE OF INTRINSIC MOTIVATION

The determination of this research is to examine the effect of Islamic work ethics on job performance and intrinsic motivation. This study also try to investigate the mediating role of intrinsic motivation on the association between Islamic Work Ethics and job performance. Nonprobability sampling, method was used. Data were collected through adopted questionnaires from 310 teachers of different Universities situated in Malakand Division, KP-Pakistan. SPSS and AMOS were used for Statistical tests. Empirical results indicate that there is a significant positive association between Islamic work ethics, Job performance and intrinsic motivation. The study determined that the idea of Islamic work ethics works as a therapy for the emergent ethical crisis of education sector of Pakistan which should be pervaded in organizational culture for sustainable job performance and growth. Further, study explain that intrinsic motivation mediates the association between Islamic Work Ethics and Job Performance. Implications and upcomingstudy recommendations are discuss in the conclusion

Fine-tuned CLIP Models are Efficient Video Learners

Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain. In this pursuit, new parametric modules are added to learn temporal information and inter-frame relationships which require meticulous design efforts. Furthermore, when the resulting models are learned on videos, they tend to overfit on the given task distribution and lack in generalization aspect. This begs the following question: How to effectively transfer image-level CLIP representations to videos? In this work, we show that a simple Video Fine-tuned CLIP (ViFi-CLIP) baseline is generally sufficient to bridge the domain gap from images to videos. Our qualitative analysis illustrates that the frame-level processing from CLIP image-encoder followed by feature pooling and similarity matching with corresponding text embeddings helps in implicitly modeling the temporal cues within ViFi-CLIP. Such fine-tuning helps the model to focus on scene dynamics, moving objects and inter-object relationships. For low-data regimes where full fine-tuning is not viable, we propose a `bridge and prompt' approach that first uses fine-tuning to bridge the domain gap and then learns prompts on language and vision side to adapt CLIP representations. We extensively evaluate this simple yet strong baseline on zero-shot, base-to-novel generalization, few-shot and fully supervised settings across five video benchmarks. Our code is available at https://github.com/muzairkhattak/ViFi-CLIP.Comment: Accepted at CVPR 202

MaPLe: Multi-modal Prompt Learning

Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text prompts and require careful selection of prompt templates to perform well. Inspired by the Natural Language Processing (NLP) literature, recent CLIP adaptation approaches learn prompts as the textual inputs to fine-tune CLIP for downstream tasks. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. In this work, we propose Multi-modal Prompt Learning (MaPLe) for both vision and language branches to improve alignment between the vision and language representations. Our design promotes strong coupling between the vision-language prompts to ensure mutual synergy and discourages learning independent uni-modal solutions. Further, we learn separate prompts across different early stages to progressively model the stage-wise feature relationships to allow rich context learning. We evaluate the effectiveness of our approach on three representative tasks of generalization to novel classes, new target datasets and unseen domain shifts. Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable performance and achieves an absolute gain of 3.45% on novel classes and 2.72% on overall harmonic-mean, averaged over 11 diverse image recognition datasets. Our code and pre-trained models are available at https://github.com/muzairkhattak/multimodal-prompt-learning.Comment: Accepted at CVPR202

Primary care placements in the post-COVID era: A qualitative evaluation of a final year undergraduate clerkship.

INTRODUCTION: In March 2020, UK primary care changed dramatically due to the COVID-19 pandemic. It now has a much greater reliance on triaging, e-consultations, remote consultations, online meetings and less home visits. Re-evaluating the nature and value of learning medicine in primary care has therefore become a priority. METHOD: 70 final-year medical students placed in 38 GP practices (primary care centres) across the East of England undertook a 5-week clerkship during November 2020. A sample of 10 students and 11 supervising general practitioners from 16 different GP practices were interviewed following the placement. Qualitative analysis was conducted to determine their perceptions regarding the nature and value of learning medicine in primary care now compared with prior to the pandemic. RESULTS: A variety of models of implementing supervised student consultations were identified. Although contact with patients was felt to be less than pre-pandemic placements, triaging systems appeared to have increased the educational value of each individual student-patient contact. Remote consultations were essential to achieving adequate case-mix and they conferred specific educational benefits. However, depending on how they were supervised, they could have the potential to decrease students' level of responsibility for patient care. CONCLUSIONS: Undergraduate primary care placements in the post-COVID era can still possess the educationally valuable attributes documented in the pre-pandemic literature. However, this is dependent on specific factors regarding their delivery

SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications

Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolution limits its use in real-time applications, especially for deployment on resource-constrained mobile devices. Although hybrid approaches have been proposed to combine the advantages of convolutions and self-attention for a better speed-accuracy trade-off, the expensive matrix multiplication operations in self-attention remain a bottleneck. In this work, we introduce a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations with linear element-wise multiplications. Our design shows that the key-value interaction can be replaced with a linear layer without sacrificing any accuracy. Unlike previous state-of-the-art methods, our efficient formulation of self-attention enables its usage at all stages of the network. Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Our small variant achieves 78.5% top-1 ImageNet-1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2x faster compared to MobileViT-v2. Code: https://github.com/Amshaker/SwiftFormerComment: Technical repor