Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Internet of Musical Things: Vision and Challenges

Abstract: The Internet of Musical Things (IoMusT) is an emerging research field positioned at the intersection of Internet of Things, new interfaces for musical expression, ubiquitous music, human–computer interaction, artificial intelligence, and participatory art. From a computer science perspective, IoMusT refers to the networks of computing devices embedded in physical objects (musical things) dedicated to the production and/or reception of musical content. Musical things, such as smart musical instruments or wearables, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. We present a vision in which the IoMusT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel musical applications and services. The ecosystems associated with the IoMusT include interoperable devices and services that connect musicians and audiences to support musician–musician, audience–musicians, and audience–audience interactions. In this paper, we first propose a vision for the IoMusT and its motivations. We then discuss five scenarios illustrating how the IoMusT could support: 1) augmented and immersive concert experiences; 2) audience participation; 3) remote rehearsals; 4) music e-learning; and 5) smart studio production. We identify key capabilities missing from today’s systems and discuss the research needed to develop these capabilities across a set of interdisciplinary challenges. These encompass network communication (e.g., ultra-low latency and security), music information research (e.g., artificial intelligence for real-time audio content description and multimodal sensing), music interaction (e.g., distributed performance and music e-learning), as well as legal and responsible innovation aspects to ensure that future IoMusT services are socially desirable and undertaken in the public interest.

Estimation of biomass and carbon stocks: the case of the Atlantic Forest

Abstract: The main objective of this paper is to present and discuss the best methods to estimate live above ground biomass in the Atlantic Forest. The methods presented and conclusions are the products of a workshop entitled "Estimation of Biomass and Carbon Stocks: the Case of Atlantic Rain Forest". Aboveground biomass (AGB) in tropical forests is mainly contained in trees. Tree biomass is a function of wood volume, obtained from the diameter and height, architecture and wood density (dry weight per unit volume of fresh wood). It can be quantified by the direct (destructive) or indirect method where the biomass quantification is estimated using mathematical models. The allometric model can be site specific when elaborated to a particular ecosystem or general that can be used in different sites. For the Atlantic Forest, despite the importance of it, there are only two direct measurements of tree biomass, resulting in allometric models specific for this ecosystem. To select one or other of the available models in the literature to estimate AGB it is necessary take into account what is the main question to be answered and the ease with which it is possible to measure the independent variables in the model. Models that present more accurate estimates should be preferred. However, more simple models (those with one independent variable, usually DBH) can be used when the focus is monitoring the variation in carbon storage through the time. Our observations in the Atlantic Forest suggest that pan-tropical relations proposed by Chave et al. (2005) can be confidently used to estimated tree biomass across biomes as long as tree diameter (DBH), height, and wood density are accounted for in the model. In Atlantic Forest, we recommend the quantification of biomass of lianas, bamboo, palms, tree ferns and epiphytes, which are an important component in this ecosystem. This paper is an outcome of the workshop entitled "Estimation of Biomass and Carbon Stocks: the Case of Atlantic Rain Forest", that was conducted at Ubatuba, Sao Paulo, Brazil, between 4 and 8 December 2006 as part of the Brazilian project "Ombrophylus Dense Forest floristic composition, structure and function at the Nucleos Picinguaba and Santa Virginia of the Serra do Mar State Park", BIOTA Gradiente.

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

Abstract: We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.