The Measure of Land Consumption Caused by Urban Planning

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How to Select Knowledge Management Systems: A Framework to Support Managers

The purpose of this paper is to provide a methodological framework which could support managers in the selection of Knowledge Management Systems. The framework is based on the Analytic Hierarchy Process approach. Several aspects should draw the attention of an organization’s upper level of management seeking to implement a Knowledge Management System and many specific issues have to be considered. As such, the framework has been built by making use of an ad‐hoc hierarchical structure, where each singular specificity is described and compared, second‐order criteria are studied and analysed, and optional decisions are highlighted and evaluated. This methodological framework offers a good applicability to different business contexts, since its hierarchical arrangement suits most of the needs of numerous organizations. Consequently, it can be regarded as a holistic approach able to assist decision makers in their Knowledge Management System selection process

Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding

Modeling virtual agents with behavior style is one factor for personalizing human agent interaction. We propose an efficient yet effective machine learning approach to synthesize gestures driven by prosodic features and text in the style of different speakers including those unseen during training. Our model performs zero shot multimodal style transfer driven by multimodal data from the PATS database containing videos of various speakers. We view style as being pervasive while speaking, it colors the communicative behaviors expressivity while speech content is carried by multimodal signals and text. This disentanglement scheme of content and style allows us to directly infer the style embedding even of speaker whose data are not part of the training phase, without requiring any further training or fine tuning. The first goal of our model is to generate the gestures of a source speaker based on the content of two audio and text modalities. The second goal is to condition the source speaker predicted gestures on the multimodal behavior style embedding of a target speaker. The third goal is to allow zero shot style transfer of speakers unseen during training without retraining the model. Our system consists of: (1) a speaker style encoder network that learns to generate a fixed dimensional speaker embedding style from a target speaker multimodal data and (2) a sequence to sequence synthesis network that synthesizes gestures based on the content of the input modalities of a source speaker and conditioned on the speaker style embedding. We evaluate that our model can synthesize gestures of a source speaker and transfer the knowledge of target speaker style variability to the gesture generation task in a zero shot setup. We convert the 2D gestures to 3D poses and produce 3D animations. We conduct objective and subjective evaluations to validate our approach and compare it with a baseline

Experimental Characterization of a Multiple Spark Ignition System

Abstract The paper reports on the experimental analysis of a multiple spark ignition system, carried out with conventional and optical non intrusive methods. The system features a plug-top ignition coil with integrated electronics which delivers high ignition energy and high voltage compared to conventional ignition coils, and is capable of multiple discharges with reduced dwell time. The ignition system is characterized in terms of electrical parameters to evaluate the spark power and energy as a function of different hardware configurations and operating conditions. A high speed camera is used to visualize, at different ambient pressures, the time evolution of the electric arc discharge in order to highlight its position variability, which could have an impact on combustion kernel development and deflagration front stability in engines

Large left ventricular aneurysm

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Multisystemic Manifestations in Rare Diseases: The Experience of Dyskeratosis Congenita

Dyskeratosis congenital (DC) is the first genetic syndrome described among telomeropathies. Its classical phenotype is characterized by the mucocutaneous triad of reticulated pigmentation of skin lace, nail dystrophy and oral leukoplakia. The clinical presentation, however, is heterogeneous and serious clinical complications include bone marrow failure, hematological and solid tumors. It may also involve immunodeficiencies, dental, pulmonary and liver disorders, and other minor complication. Dyskeratosis congenita shows marked genetic heterogeneity, as at least 14 genes are responsible for the shortening of telomeres characteristic of this disease. This review discusses clinical characteristics, molecular genetics, disease evolution, available therapeutic options and differential diagnosis of dyskeratosis congenita to provide an interdisciplinary and personalized medical assessment that includes family genetic counseling