What are the Top Cultural Characteristics That Appear in High-Performing Organizations Across Multiple Industries?

Recently, researchers have investigated the existence of High Performing Organization (HPO) and its characteristics. Because researchers approach the topic of high performance from different backgrounds and angles and with different goals, it makes sense there is not yet a consistent definition of a HPO. We found a meaningful research paper that identifies common characteristics or common themes that seemed to be part of a HPO. This report will cover definitions and cultural characteristics of HPO based on that research paper contrasting and combining results from 91 different quality studies done over the last fifteen years

What are Current Best Approaches Companies are Using for Performance Management for Wage Employees?

Academic journals mainly focus on performance management for white-collar employees and lack resources on best practices for wage employees. In response, we have consulted with two renowned professors at the ILR School for advice and also interviewed an HR manager at GE Aviation to find out how leading firms manage performance of hourly-wage workers in practice by probing into three major components of how they 1) approach goal-setting, 2) manage the performance evaluation process, and 3) align performance results with other HR programs

What can we do to Attract and Retain Young People to our Company as we Find it Difficult to Attract Employees at all Levels?

Question: As the workforce ages we are finding it a challenge to recruit new employees at all levels. So our question involves what can we do to attract and retain young people to our company? We have some insight into how to attract employees but where we would like your help is how to design our work and career paths to maintain the employees

Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.Comment: 13 pages, EMNLP 202

SAIN: Self-Attentive Integration Network for Recommendation

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201

Accounting for Global Dispersion of Current Accounts.

We undertake a quantitative analysis of the dispersion of current accounts in an open economy version of incomplete insurance model, incorporating important market frictions in trade and financial flows. Calibrated with conventional parameter values, the stochastic stationary equilibrium of the model with limited borrowing can account for about two-thirds of the global dispersion of current accounts. The easing of financial frictions can explain nearly all changes in the current account dispersion in the past four decades whereas the easing of trade frictions has almost no impact on the current account dispersion.Distribution of Current Account, Incomplete Markets, Frictions.

Entanglement of three-qubit pure states in terms of teleportation capability

We define an entanglement measure, called the partial tangle, which represents the residual two-qubit entanglement of a three-qubit pure state. By its explicit calculations for three-qubit pure states, we show that the partial tangle is closely related to the faithfulness of a teleportation scheme over a three-qubit pure state.Comment: 4 pages, 1 figure, accepted for publication as a Brief Report in Physical Review