Recommender Systems with Characterized Social Regularization

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social network. Existing social recommendation methods are based on the fact that users preference or decision is influenced by their social friends' behaviors. However, they assume that the influences of social relation are always the same, which violates the fact that users are likely to share preference on diverse products with different friends. In this paper, we present a novel CSR (short for Characterized Social Regularization) model by designing a universal regularization term for modeling variable social influence. Our proposed model can be applied to both explicit and implicit iteration. Extensive experiments on a real-world dataset demonstrate that CSR significantly outperforms state-of-the-art social recommendation methods.Comment: to appear in CIKM 201

Drop Test and Finite Element Analysis of Test Board

AbstractMost electronic components have high precision and expensive features and people used the drop test to determine the reliability of these electronic components effectively. This study focused on the drop test of FR-4 test board according to the JEDEC standard, to understand the test board's basic mechanical properties and variations of stress and strain on test board. This study used finite element analysis software ANSYS/LS-DYNA to perform a drop test simulation of test board under JESD22-B111 standard, with 0.5ms pulse duration time and 1500G peak acceleration as test conditions. The support excitation method was used to predict the test board response during impact. The results between full model and quarter model were compared to verify the accuracy and efficiency of finite element analysis

Why Differentiation Strategy Fails?

Differentiation strategy has been considered critical for securing a competitive advantage. However, not all firms can create competitive advantages through differentiation. In this paper, we draw on a Taiwanese hotel, restaurant, and TV program provider to show why differentiation strategy fails. On the basis of these three cases, three failed differentiation strategies are proposed and a framework for implementing a differentiation strategy is provided. Finally, we present the discussion and conclusions for the theory and practice of differentiation strategy

Train 'n Trade: Foundations of Parameter Markets

Organizations typically train large models individually. This is costly and time-consuming, particularly for large-scale foundation models. Such vertical production is known to be suboptimal. Inspired by this economic insight, we ask whether it is possible to leverage others' expertise by trading the constituent parts in models, i.e., sets of weights, as if they were market commodities. While recent advances in aligning and interpolating models suggest that doing so may be possible, a number of fundamental questions must be answered to create viable parameter markets. In this work, we address these basic questions, propose a framework containing the infrastructure necessary for market operations to take place, study strategies for exchanging parameters, and offer means for agents to monetize parameters. Excitingly, compared to agents who train siloed models from scratch, we show that it is possible to mutually gain by using the market, even in competitive settings. This suggests that the notion of parameter markets may be a useful paradigm for improving large-scale model training in the future.Comment: accepted at NeurIPS 202

Academic libraries and research data management: a case study of Dataverse global adoption

Purpose: The purpose of this study is to examine the development of Dataverse, a global research data management consortium. The authors examine specifically the institutional characteristics, the utilization of the associated data sets and the relevant research data management services at its participating university libraries. This evidence-based approach is essential for understanding the current state of research data management practices in the global context. Design/methodology/approach: The data was collected from 67 participants’ data portals between December 1, 2020, and January 31, 2021. Findings: Over 80% of its current participants joined the group in the past five years, 2016–2020. Thirty-three Dataverse portals have had less than 10,000 total downloads since their inception. Twenty-nine participating universities are included in three major global university ranking systems, and 18 of those university libraries offer research data services. Originality/value: This project is an explorative study on Dataverse, an international research data management consortium. The findings contribute to the understanding of the current development of the Dataverse project as well as the practices at the participating institutions. Moreover, they offer insights to other global higher education institutions and research organizations regarding research data management. While this study is practical, its findings and observations could be of use to future researchers interested in developing a framework for data work in academic libraries

DISTRIBUTION OF GRIP PRESSURE THROUGHOUT THE PHASES OF PUTTING IN ELITE GOLF COLLEGE PLAYERS

The purpose of this study is to investigate the distribution of grip pressure, force and the peak pressure of different phases during the putting stroke. Five elite college players with handicaps of 2-8 participated in the study. The Novel Pliance-x System and 150Hz 8- camera Motion Analysis Corporation System were used to collect grip pressure and identify each phase of the putting stroke. At each phase of the putting stroke, average grip pressure, peak pressure and grip force were investigated. Results indicated that lowest grip pressure occurred at address up to the top of backswing (2.41±1.36 Kpa). Grip pressure started to increase during the downswing and reached its peak, 0.02±0.05s, before impact (4.70±1.97 Kpa). The pressure reduced again after impact (4.36±2.06 Kpa). Results indicate that grip pressure does not remain the same throughout the stroke