The Economic Pay-Offs To On-The-Job Training In Routine Service Work

This study examines the relationship between on-the-job training and job performance among 3,408 telephone operators in a large unionized telecommunications company. We utilize individual data on monthly training hours and job performance over a five-month period as provided by the company’s electronic monitoring system. Results indicate that the receipt of on-the-job training is associated with significantly higher productivity over time, when unobserved individual heterogeneity is taken into account. Moreover, workers with lower pre-training proficiency show greater improvements over time than those with higher pre-training proficiency. Finally, whether the training is provided by a supervisor or a peer also matters. Workers with lower proficiency achieve greater productivity gains through supervisor training, while workers with higher proficiency achieve greater productivity gains through peer training

What Determines Employment of Part-Time Faculty in Higher Education Institutions?

This study uses a cross-section national sample of four-year colleges and universities in the United States to examine the variation of part-time faculty employment. Results of this study suggest that higher educational institutions actively design and adopt contingent work arrangements to save on labor costs and to manage their resource dependence with constituencies. Institutions that pay high salaries to their full-time faculty members, have limited resource slack, and are located in major urban areas tend to employ a high proportion of part-time faculty. Furthermore, institutions that have small student enrollment and large proportion of part-time students are found to rely more heavily on part-time faculty employment. Private institutions, on average, have higher levels of part-time faculty than their public counterparts; however, this result does not hold for doctoral and research institutions. Finally, institutions that rely more on tuition and fees revenue tend to employ more part-time faculty. Such a relationship is significantly moderated by institutional quality, suggesting that different institutions may adopt different strategies to attract students and secure their tuition revenues

The Economic Pay-Offs to Informal Training: Evidence From Routine Service Work

This study examines the relationship between informal training and job performance among 2,803 telephone operators in a large unionized U.S. telecommunications company. The authors analyze individual-level data on monthly training hours and job performance over a five-month period in 2001 as provided by the company\u27s electronic monitoring system. The results indicate that the receipt of informal training was associated with higher productivity over time, when unobserved individual heterogeneity is taken into account. Workers with lower pre-training proficiency showed greater improvements over time than did those with higher pre-training proficiency. Finally, whether the trainer was a supervisor or a peer also mattered: workers with below-average pre-training proficiency achieved greater productivity gains through supervisor training, while workers with average pre-training proficiency achieved greater productivity gains through peer training

Adaptive Guidance: Effects On Self-Regulated Learning In Technology-Based Training

Guidance provides trainees with the information necessary to make effective use of the learner control inherent in technology-based training, but also allows them to retain a sense of control over their learning (Bell & Kozlowski, 2002). One challenge, however, is determining how much learner control, or autonomy, to build into the guidance strategy. We examined the effects of alternative forms of guidance (autonomy supportive vs. controlling) on trainees’ learning and performance, and examined trainees’ cognitive ability and motivation to learn as potential moderators of these effects. Consistent with our hypotheses, trainees receiving adaptive guidance had higher levels of knowledge and performance than trainees in a learner control guidance. Controlling guidance had the most consistent positive impact on the learning outcomes, while autonomy supportive guidance demonstrated utility for more strategic outcomes. In addition, guidance was generally more effective for trainees with higher levels of cognitive ability and autonomy guidance served to enhance the positive effects of motivation to learn on the training outcomes

Human Cytomegalovirus Encoded miR-US25-1-5p Attenuates CD147/EMMPRIN-Mediated Early Antiviral Response.

Cellular receptor-mediated signaling pathways play critical roles during the initial immune response to Human Cytomegalovirus (HCMV) infection. However, the involvement of type-I transmembrane glycoprotein CD147/EMMPRIN (extracellular matrix metalloproteinase inducer) in the antiviral response to HCMV infection is still unknown. Here, we demonstrated the specific knockdown of CD147 significantly decreased HCMV-induced activation of NF-κB and Interferon-beta (IFN-β), which contribute to the cellular antiviral responses. Next, we confirmed that HCMV-encoded miR-US25-1-5p could target the 3 UTR (Untranslated Region) of CD147 mRNA, and thus facilitate HCMV lytic propagation at a low multiplicity of infection (MOI). The expression and secretion of Cyclophilin A (sCyPA), as a ligand for CD147 and a proinflammatory cytokine, were up-regulated in response to HCMV stimuli. Finally, we confirmed that CD147 mediated HCMV-triggered antiviral signaling via the sCyPA-CD147-ERK (extracellular regulated protein kinases)/NF-κB axis signaling pathway. These findings reveal an important HCMV mechanism for evading antiviral innate immunity through its encoded microRNA by targeting transmembrane glycoprotein CD147, and a potential cause of HCMV inflammatory disorders due to the secretion of proinflammatory cytokine CyPA

Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach

International audienceThis paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV) path planning with particle swarm optimization (PSO) technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samples -test is made and an index called straight line rate (SLR) is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques

Examining Users’ Knowledge Change in the Task Completion Process

This paper examines the changes of information searchers’ topic knowledge levels in the process of completing information tasks. Multi-session tasks were used in the study, which enables the convenience of eliciting users’ topic knowledge during their process of completing the whole tasks. The study was a 3-session laboratory experiment with 24 participants, each time working on one subtask in an assigned 3-session general task. The general task was either parallel or dependently structured. Questionnaires were administered before and after each session to elicit users’ perceptions of their knowledge levels, task attributes, and other task features, for both the overall task and the sub-tasks. Our results support the assumption that users’ knowledge generally increases after each search session, but there were exceptions in which a “ceiling” effect was shown. We also found that knowledge was correlated with users’ perceptions of task attributes and accomplishment. In addition, task type was found to affect several aspects of knowledge levels and knowledge change. These findings further our understanding of users’ knowledge in information tasks and are thus helpful for information retrieval research and system design

Doubly resonant photonic crystal cavity using merged bound states in the continuum

In this work, a doubly resonant photonic crystal (PhC) cavity using the merged bound states in the continuum (BICs) is proposed to obtain a higher second harmonic generation (SHG) efficiency. Firstly by scanning geometry parameters the accidental BICs and a band-edge mode outside the light cone can be obtained. Then as the lattice constant or the thickness of the slab is adjusted the accidental BICs will merge. A supercell with large and small holes is constructed and the band-edge mode outside the light cone can be mode-matched with the merged BICs mode. Finally the heterostructure PhC cavity is designed. The merged BICs show a high quality factor for the photonic crystal with finite size. Consequently, the SHG efficiency of the lattice constant near merged BICs of ~6000% W-1 is higher than the one of the isolated BIC

LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch

Structured pruning is a commonly used convolutional neural network (CNN) compression approach. Pruning rate setting is a fundamental problem in structured pruning. Most existing works introduce too many additional learnable parameters to assign different pruning rates across different layers in CNN or cannot control the compression rate explicitly. Since too narrow network blocks information flow for training, automatic pruning rate setting cannot explore a high pruning rate for a specific layer. To overcome these limitations, we propose a novel framework named Layer Adaptive Progressive Pruning (LAPP), which gradually compresses the network during initial training of a few epochs from scratch. In particular, LAPP designs an effective and efficient pruning strategy that introduces a learnable threshold for each layer and FLOPs constraints for network. Guided by both task loss and FLOPs constraints, the learnable thresholds are dynamically and gradually updated to accommodate changes of importance scores during training. Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer. What's more, in order to maintain the expressive power of the pruned layer, before training starts, we introduce an additional lightweight bypass for each convolutional layer to be pruned, which only adds relatively few additional burdens. Our method demonstrates superior performance gains over previous compression methods on various datasets and backbone architectures. For example, on CIFAR-10, our method compresses ResNet-20 to 40.3% without accuracy drop. 55.6% of FLOPs of ResNet-18 are reduced with 0.21% top-1 accuracy increase and 0.40% top-5 accuracy increase on ImageNet.Comment: 12 pages, 8 tables, 3 figure