Non-locality effect of Majorana fermions via local entanglement entropy

In this paper, to characterize the effect of topological defects on the entanglement of topological states, we introduce the concept of local entanglement entropy. By using the spinless [Formula: see text] superconductor with quantized vortices, we numerically calculated the local entanglement entropy. In the topological superconducting (weak-pairing) phase, we found that the local entanglement entropy turns to 0.5 for a Majorana zero mode. For the system with two/four Majorana zero modes, by calculating local entanglement entropy, we found the non-locality effect of Majorana zero modes. In the future, we will use local entanglement entropy to characterize the defect effect on other many-body systems with long entanglement, for example, topological order and quantum spin liquid.

East, West, Home's Best: Do Local CEOs Behave Less Myopically?

ABSTRACT We test whether CEOs working near their childhood homes are less likely than nonlocal CEOs to make myopic decisions. Place attachment theories suggest that people develop mutual caretaking relationships with their birthplaces. Also, executive labor markets face less information asymmetry about local CEOs, resulting in lower pressure on local CEOs for quick profits. Consistent with the prediction, we find that local CEOs are less likely to cut R&D expenditures for beating analyst forecasts or avoiding earnings decreases. In their last year of office, local CEOs are significantly less likely to cut R&D than nonlocal CEOs. The CEO locality effect is stronger when more local business interests are embedded in the firm and when the residents of the CEO's birth state have stronger local social bonds. Local CEOs' longer horizons are consistently manifested in their other decisions, such as paying more state tax and being more socially responsible in business operation. JEL Classifications: G10; G23; M40.

Reservoir Computing for Healthcare Analytics

In this data age tools for sophisticated generation and handling of data are at epitome of usage. Data varying in both space and time poses a breed of challenges. Challenges they possess for forecasting can be well handled by Reservoir computing based neural networks. Challenges like class imbalance, missing values, locality effect are discussed here. Additionally, popular statistical techniques for forecasting such data are discussed. Results show how Reservoir Computing based technique outper-forms traditional neural networks.

Locality in German

Three experiments (self-paced reading, eyetracking and an ERP study) show that in relative clauses, increasing the distance between the relativized noun and the relative-clause verb makes it more difficult to process the relative-clause verb (the so-called locality effect). This result is consistent with the predictions of several theories (Gibson, 2000; Lewis and Vasishth, 2005), and contradicts the recent claim (Levy, 2008) that in relative-clause structures increasing argument-verb distance makes processing easier at the verb. Levy’s expectation-based account predicts that the expectation for a verb becomes sharper as distance is increased and therefore processing becomes easier at the verb. We argue that, in addition to expectation effects (which are seen in the eyetracking study in first-pass regression probability), processing load also increases with increasing distance. This contradicts Levy’s claim that heightened expectation leads to lower processing cost. Dependency- resolution cost and expectation-based facilitation are jointly responsible for determining processing cost.