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

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

Inferring User Knowledge Level from Eye Movement Patterns

The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n=40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n=409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session

Learning a Complete Image Indexing Pipeline

To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, the former continues to be based on unsupervised clustering in the literature. In this work, we propose a first system that learns both components within a unifying neural framework of structured binary encoding