Pacemapping

Pacemapping (PM) is an electrophysiologic technique designed to help locating tachycardia sources by stimulating at different endocardial sites in order to reproduce the clinical tachycardia characteristics. A recorded electrocardiogram (ECG) during the clinical tachycardia has been conventionally used as reference. Yet, endocardial activation pattern during tachycardia may be utilized as well to guide the procedure. In focal tachycardia ablation, PM guide has consistently provided remarkable outcomes1, while outcomes in reentrant tachycardia ablation are less favourabl

Tratamento do pênfigo vulgar e pênfigo foliáceo: experiência com 71 pacientes no período de 20 anos

Quarenta e um casos de pênfigo vulgar e trinta casos de pênfigo foliáceo foram investigados no Hospital Universitário Clementino Fraga Filho, no período 1978-1999. Os pacientes foram divididos em dois grupos de tratamento: um recebendo até 100 mg/dia de prednisona e o outro grupo >;120 mg diariamente. Com o primeiro esquema, houve bom controle inicial dos pênfigos sem aumento da taxa de mortalidade associada às doenças. A dose acima de 120 mg induziu maior morbidade. Os resultados permitiram estabelecer um esquema de prednisona (1-2 mg/kg/dia) com dose máxima de 120 mg diários no tratamento dos pênfigos vulgar e foliáceo.Forty one cases of pemphigus vulgaris and thirty cases of pemphigus foliaceus were investigated at Hospital Universitário Clementino Fraga Filho from 1978 to 1999. They were divided into two treatment groups: one group received up to 100 mg of oral prednisone daily and the other group received >;120 mg daily. The dose up to 100 mg provided good initial control of pemphigus vulgaris and pemphigus foliaceus and did not increase the mortality rate associated to disease. The dose >;120 mg induced higher morbidity. These data allowed us to establish a regimen of oral prednisone (1-2 mg/kg/daily) with maximum of 120 mg daily in the treatment of pemphigus vulgaris and pemphigus foliaceus

Principal-agent dynamic interaction in the context of the lifecycle operation of infrastructure systems

A Public-private partnership (PPP) is contract framework designed to carry out public works in the hope that the more advanced knowledge and financial support of private agents may be advantageous to develop better infrastructure projects that serve public needs. This relationship, which is embodied by a principal (e.g., government) and an agent (e.g., independent contractor), is inherently conflictive. Three main factors give rise to such conflict: the interests of the public and private party do not generally coincide, there is information asymmetry between them and their interaction unfolds in environments under uncertainty. Traditionally, the regulations put forth to mitigate the cost overruns caused by moral hazard, low performance levels and litigations are determined by methods which neither take into account a formal mathematical description of the interaction among participants nor the deterioration of physical components and their susceptibility to natural hazards. In this paper we propose an alternative approach that addresses these issues. We describe an agent-based model which represents the infrastructure system as an entity that is affected by the operations of three players: principal, agent and nature. They perform operations on the infrastructure, based on their own strategies and perceived payoffs, but are bound by a contract that constraint their actions. The purpose of the model is to simulate the interaction history among players and compute the resulting outcome in the form of the utility that each player receives. The model can be used within an optimization routine to determine which contractual rules maximize the utility for both players simultaneously

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Skeleton-based Relational Reasoning for Group Activity Analysis

Research on group activity recognition mostly leans on the standard two-stream approach (RGB and Optical Flow) as their input features. Few have explored explicit pose information, with none using it directly to reason about the persons interactions. In this paper, we leverage the skeleton information to learn the interactions between the individuals straight from it. With our proposed method GIRN, multiple relationship types are inferred from independent modules, that describe the relations between the body joints pair-by-pair. Additionally to the joints relations, we also experiment with the previously unexplored relationship between individuals and relevant objects (e.g. volleyball). The individuals distinct relations are then merged through an attention mechanism, that gives more importance to those individuals more relevant for distinguishing the group activity. We evaluate our method in the Volleyball dataset, obtaining competitive results to the state-of-the-art. Our experiments demonstrate the potential of skeleton-based approaches for modeling multi-person interactions.Comment: 26 pages, 5 figures, accepted manuscript in Elsevier Pattern Recognition, minor writing revisions and new reference