A tale of two students: Building a culture of engagement in the community college

Enrolling in a community college may be as intimidating for those students who eventually succeed as it is for those who don't. Why, then, do some persevere while others leave before they meet their goals? Institutional practice can tip the balance.     

体力工作者：不是运动员的运动员

工矿企业的劳动者 ,因工作所需 ,在岗位上要从事不同强度、定时定量的体力工作。他们是另一种意义上的运动员。每个劳动者从事体力工作的能力与其公司的生产力和竞争力密切相关 ,就如同运动员的个人能力与全队的成功密不可分一样。某些艰苦的工作环境使工人产生身体上和心理上的巨大压力 ,比起运动员在世界级大赛中的压力 ,也有过之而无不及。在这里专家们将讨论体力工作者会面临哪些身体和精神上的压力 ,并进而就职业生理学进行深入探讨。1　请介绍一下工作中人们可能遇到的各种环境问题 ,以便使我们的读者了解掌握工作压力的类型和工伤发…     

When off-line seeks information online: The effect of modality switching and time on attributional confidence and social attraction

This study’s goal was to learn how adding the extractive information-seeking behavior of Facebook profile viewing following face-to-face information-seeking behavior in initial interactions affected attributional confidence (AC) and social attraction (SA). Additionally, the present study was a quasi-replication of research exploring how changes in time spent on information seeking impacted AC and SA testing two uncertainty reduction theory axioms. Two modalities, face-to-face (FtF) only versus FtF and profile viewing, across three time conditions of 4, 6, and 8 minutes yielded six experimental conditions. AC for the two modalities were similar when time was held constant. Comparing the two modalities for 8 minutes resulted in higher social attraction for the FtF-only condition. Also, the data report a significant interaction with modality and time. Time impacts AC and SA even with relatively small changes.     

Neural information retrieval: at the end of the early years

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.