Transient epileptic amnesia: an emerging late-onset epileptic syndrome.

Transient epileptic amnesia (TEA) is a distinct neurologic condition occurring in late-middle/old age and presenting with amnesic attacks of epileptic nature and interictal memory disturbances. For many years this condition has been associated with the nonepileptic condition of transient global amnesia (TGA) and still today is poorly recognized by clinicians. Despite the clinical and laboratory findings that distinguish TEA from TGA, differential diagnosis may be difficult in the individual patient. Every effort must be employed for an early diagnosis, since antiepileptic treatment may readily control both ictal episodes and memory disturbances

The role of the sustainability report in capitalistic firm

The aim and the central topic of this research is the understanding of the importance of Sustainable growth strategy approach as a driver to achieving top-line growth and bottom-line results. The main contributions of this line of research are to demonstrate the idea that the sustainability report is an instrument for social interaction and social cost/benefit analysis and to show that such an instrument can describe, comment on and sum up the firm\u2019s own behaviour aimed at sustainable growth. The work integrates the CSR management literature with a large body of research in accounting and finance. This study draws from theoretical research about the nature of the corporation, its role in society and contributions by recent research on corporate social, environmental, ethical responsibility and accountability. Our research demonstrates that the economic existence of the capitalistic firm as a producer of economic and financial values must be appreciated, in terms of the sustainability of the development path of the firm, and evaluated by a wide range of social performance measures of outcome or benefit. It also shows how the Sustainability report emphasizes the link between firm and territory, and affirms the concept of the firm as an entity that, by pursuing its own prevailing interests, contributes to improving the quality of life of the members of the society in which it operates. This paper contributes primarily to the academic debate by reviewing past attempts to theorise CSR and stakeholder dialogue, identifying gaps and weaknesses, and proposing the Sustainable Growth implementation processes for the creation of value. It also highlights the relationship between CSR activity and corporate image and performance. The research shed light on aspects of CSR activity for which little is known and much less is being understood; namely, the channels and the mechanisms through which the CSR impact is perceived and realized for creation of value. Carlotta Meo Colombo (3) considers the capitalistic firm as Business Value-Creating Organizations and Patrizia Gazzola (1-2;4-6) considers the Sustainable Growth implementation processes for the creation of value

A Semi-supervised Method to Identify Urban Anomalies through LTE PDCCH Fingerprinting

In this paper we advocate the use of mobile networks as sensing platforms to monitor metropolitan areas. In particular, we are interested in detecting urban anomalies (e.g., crowd gathering) by processing the control information exchanged among the base stations and the mobile users. For this, we design an anomaly detection framework based on semi-supervised learning, which enables the automatic identification of different types of anomalous events without any a-priori information. The proposed approach uses unsupervised learning techniques to gain confidence in real mobile traffic demand patterns from the city of Madrid in Spain and build an ad-hoc ground truth. A recurrent neural network is then trained to detect contextual anomalies and identify different types of urban events. Simulation results confirm the better performance of the semi-supervised method compared to pure unsupervised anomaly detection frameworks

A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network