CEO Turnover, Equity-Based Compensation And Firms Investment Decisions

This paper examines the impact of the newly appointed CEOs on firms future investment decisions and whether the relation is affected by the equity-based compensation, corporate governance provisions and other CEO characteristics. Using CEO turnover data from 1992-2004, the results show that new CEOs with high options-based compensation, following forced turnover and with shorter organization tenure, are associated with high R&D and advertisement investments. These results are consistent with the managerial incentive effect and the dismissal effect

Non-Euclidean cloaking for light waves

Non-Euclidean geometry combined with transformation optics has recently led to the proposal of an invisibility cloak that avoids optical singularities and therefore can work, in principle, in a broad band of the spectrum [U. Leonhardt and T. Tyc, Science 323, 110 (2009)]. Such a cloak is perfect in the limit of geometrical optics, but not in wave optics. Here we analyze, both analytically and numerically, full wave propagation in non-Euclidean cloaking. We show that the cloaking device performs remarkably well even in a regime beyond geometrical optics where the device is comparable in size with the wavelength. In particular, the cloak is nearly perfect for a spectrum of frequencies that are related to spherical harmonics. We also show that for increasing wavenumber the device works increasingly better, approaching perfect behavior in the limit of geometrical optics

Anticipating Daily Intention using On-Wrist Motion Triggered Sensing

Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average

Training Database Automation System

The purpose of this paper is to propose a system that will improve record management of the training records in companies. PHILIPS LUMILEDS LIGHTING COMPANY is chosen as a background of research and studies which have shown lack of proper maintenance and management of their training records system. Consequently the problems affect the accuracy and completeness of training records for the purpose of auditing and other related operations. The proposed Training Database Automation System enables the storage of all training records into a single database system which helps in reducing human error. With the higher accuracy and completeness of data, Training Database Automation System has also equipped with flexible criteria searching modules which aid users for report retrieval for various criteria and requirement. Moreover, the system facilitate user in enrolling participant by generating the suggestion list for training for each of employees. Training Database Automation System delivers latest and accurate data management and efficient records retrieval and support flexible reports production for auditing purpose and evidently has shown significant time reduction for training record retrieval

Fall Reduction with Nursing Interventions

The aim of the project is to reduce the patient’s fall rate in the medical surgical unit at an acute care hospital through improving the fall precaution process. The unit consists of 37 bed with a population of stroke, medical, and surgical patients. The focus of the nursing staff education using handouts, brochures, and poster will be based on the results from pre and post intervention audits. The goal of this fall reduction project is to have the nursing staff learn, review, and apply fall prevention interventions for fall risk patients in order to help reduce fall rate by 50 percent within a six month period, from October 2015 through March 2016. The CALNOC (Collaborative Alliance for Nursing Outcomes) and NDNQI (The National Database of Nursing Quality Indicators) data on fall rate will be compared before and after the intervention. Results indicate that as staff knowledge and implementation of fall prevention interventions increase, the patient fall rate will decrease

Continuous Monitoring of Distributed Data Streams over a Time-based Sliding Window

The past decade has witnessed many interesting algorithms for maintaining statistics over a data stream. This paper initiates a theoretical study of algorithms for monitoring distributed data streams over a time-based sliding window (which contains a variable number of items and possibly out-of-order items). The concern is how to minimize the communication between individual streams and the root, while allowing the root, at any time, to be able to report the global statistics of all streams within a given error bound. This paper presents communication-efficient algorithms for three classical statistics, namely, basic counting, frequent items and quantiles. The worst-case communication cost over a window is $O(\frac{k} {\epsilon} \log \frac{\epsilon N}{k})$ bits for basic counting and $O(\frac{k}{\epsilon} \log \frac{N}{k})$ words for the remainings, where $k$ is the number of distributed data streams, $N$ is the total number of items in the streams that arrive or expire in the window, and $\epsilon < 1$ is the desired error bound. Matching and nearly matching lower bounds are also obtained.Comment: 12 pages, to appear in the 27th International Symposium on Theoretical Aspects of Computer Science (STACS), 201

The Streptococcus pneumoniae pezAT toxin-antitoxin system reduces β-Lactam resistance and genetic competence

13 p.-6 fig.Chromosomally encoded Type II Toxin-Antitoxin operons are ubiquitous in bacteria and archaea. Antitoxins neutralize the toxic effect of cognate Toxins by protein-protein interactions and sequestering the active residues of the Toxin. Toxins target essential bacterial processes, mostly translation and replication. However, one class apart is constituted by the PezAT pair because the PezT toxin target cell wall biosynthesis. Here, we have examined the role of the pezAT toxin-antitoxin genes in its natural host, the pathogenic bacterium Streptococcus pneumoniae. The pezAT operon on Pneumococcal Pathogenicity Island 1 was deleted from strain R6 and its phenotypic traits were compared with those of the wild type. The mutant cells formed shorter chains during exponential phase, leading to increased colony-forming units. At stationary phase, the mutant was more resilient to lysis. Importantly, the mutant exhibited higher resistance to antibiotics targeting cell walls (β-lactams), but not to antibiotics acting at other levels. In addition, the mutants also showed enhanced genetic competence. We suggest that PezAT participates in a subtle equilibrium between loss of functions (resistance to β-lactams and genetic competence) and gain of other traits (virulence).Work supported by Grants CSD2008/00013 and BIO2015-69085-REDC from the Spanish Ministry of Economy and Competitiveness.Peer reviewe