Bar-Ilan University

About: Bar-Ilan University is a education organization based out in Ramat Gan, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 12835 authors who have published 34964 publications receiving 995648 citations. The organization is also known as: Bar Ilan University & BIU.

Motivation to Lead, Motivation to Follow: The Role of the Self-Regulatory Focus in Leadership Processes

Abstract: In this paper we integrate recent theories of motivation and leadership. Drawing on the self-regulatory focus theory and on self-concept based theories of leadership, we develop a conceptual framework proposing that leaders' chronic self-regulatory focus (promotion versus prevention), in conjunction with their values, influences their motivation to lead and, subsequently, their leadership behavior. We further suggest that leaders may influence the motivational self-regulatory foci of their followers, which will mediate different follower outcomes at the individual and group level.

Universal resilience patterns in complex networks

Abstract: An analytical framework is proposed for a complex network to accurately predict its dynamic resilience and unveil the network characteristics that can enhance or diminish resilience. Failing nodes in a complex network, for example, stations in a power grid that are are switched off, can lead to a breakdown of the whole system. The ability of the network to adjust so that it still functions despite the errors is called its resilience. Although — at first glance — the points at which different networks lose their resilience seem to have little in common, Jainxi Gao and colleagues show here that, in fact, resilience has underlying universal features. They develop a universal resilience function that depends on a system's dynamics and topology, and show that this analytical framework readily describes ecological networks, power grids, and gene regulatory networks. Their framework may contribute to understanding the vulnerability of many additional natural and man-made systems. Resilience, a system’s ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems1. Despite widespread consequences for human health2, the economy3 and the environment4, events leading to loss of resilience—from cascading failures in technological systems5 to mass extinctions in ecological networks6—are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components7, and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system’s resilience. The proposed analytical framework allows us systematically to separate the roles of the system’s dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes.

Linguistic Regularities in Sparse and Explicit Word Representations

Abstract: Recent work has shown that neuralembedded word representations capture many relational similarities, which can be recovered by means of vector arithmetic in the embedded space. We show that Mikolov et al.’s method of first adding and subtracting word vectors, and then searching for a word similar to the result, is equivalent to searching for a word that maximizes a linear combination of three pairwise word similarities. Based on this observation, we suggest an improved method of recovering relational similarities, improving the state-of-the-art results on two recent word-analogy datasets. Moreover, we demonstrate that analogy recovery is not restricted to neural word embeddings, and that a similar amount of relational similarities can be recovered from traditional distributional word representations.

A Proof of Security of Yao’s Protocol for Two-Party Computation

Abstract: In the mid 1980s, Yao presented a constant-round protocol for securely computing any two-party functionality in the presence of semi-honest adversaries (FOCS 1986). In this paper, we provide a complete description of Yao’s protocol, along with a rigorous proof of security. Despite the importance of Yao’s protocol to the theory of cryptography and in particular to the field of secure computation, to the best of our knowledge, this is the first time that an explicit proof of security has been published.

Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

Abstract: We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.