92,703 research outputs found
Ultrashort Laser Pulse Phenomena
Ultrashort Laser Pulse Phenomena, 2e serves as an introduction to the phenomena of ultra short laser pulses and describes how this technology can be used to examine problems in areas such as electromagnetism, optics, and quantum mechanics. Ultrashort Laser Pulse Phenomena combines theoretical backgrounds and experimental techniques and will serve as a manual on designing and constructing femtosecond (""faster than electronics"") systems or experiments from scratch. Beyond the simple optical system, the various sources of ultrashort pulses are presented, again with emphasis on the basi
MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
Text matching is the core problem in many natural language processing (NLP)
tasks, such as information retrieval, question answering, and conversation.
Recently, deep leaning technology has been widely adopted for text matching,
making neural text matching a new and active research domain. With a large
number of neural matching models emerging rapidly, it becomes more and more
difficult for researchers, especially those newcomers, to learn and understand
these new models. Moreover, it is usually difficult to try these models due to
the tedious data pre-processing, complicated parameter configuration, and
massive optimization tricks, not to mention the unavailability of public codes
sometimes. Finally, for researchers who want to develop new models, it is also
not an easy task to implement a neural text matching model from scratch, and to
compare with a bunch of existing models. In this paper, therefore, we present a
novel system, namely MatchZoo, to facilitate the learning, practicing and
designing of neural text matching models. The system consists of a powerful
matching library and a user-friendly and interactive studio, which can help
researchers: 1) to learn state-of-the-art neural text matching models
systematically, 2) to train, test and apply these models with simple
configurable steps; and 3) to develop their own models with rich APIs and
assistance
Value Operation: Linking Value in New Business Model Creation Process
Enterprise engineering is a discipline concerning designing and modeling an enterprise system. On creating a new business model, we can start from scratch ideation, or conduct innovation, manipulation, etc. of the current, existing business model. New Business Model Creation Process is a framework to conduct business model manipulation to create a new business. However, one of the seemingly important aspects of business is lost: Value. This research attempts to create a framework of Value Operation and link it into New Business Model Creation Process, using the concept of e3value. This paper will explain the literature review related to this work, the methodology, the demonstration of this framework, discussion of the result and the conclusion of this research
Block-Based Development of Mobile Learning Experiences for the Internet of Things
The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund
Creating Space: Building Digital Games
Studies of games, rhetoric, and pedagogy are increasingly common in our field, and indeed seem to grow each year. Nonetheless, composing and designing digital games, either as a mode of scholarship or as a classroom assignment, has not seen an equal groundswell. This selection first provides a brief overview of the existing scholarship in gaming and pedagogy, much of which currently focuses either on games as texts to analyze or as pedagogical models. While these approaches are certainly valuable, I advocate for an increased focus on game design and creation as valuable act of composition. Such a focus engages students and scholars in a deeply multimodal practice that incorporates critical design and computational thinking. I close with suggestions on tools for new and intrepid designers
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
Designing the structure of neural networks is considered one of the most
challenging tasks in deep learning, especially when there is few prior
knowledge about the task domain. In this paper, we propose an
Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of
succession, extinction, mimicry, and gene duplication to search neural network
structure from scratch with poorly initialized simple network and few
constraints forced during the evolution, as we assume no prior knowledge about
the task domain. Specifically, we first use primary succession to rapidly
evolve a population of poorly initialized neural network structures into a more
diverse population, followed by a secondary succession stage for fine-grained
searching based on the networks from the primary succession. Extinction is
applied in both stages to reduce computational cost. Mimicry is employed during
the entire evolution process to help the inferior networks imitate the behavior
of a superior network and gene duplication is utilized to duplicate the learned
blocks of novel structures, both of which help to find better network
structures. Experimental results show that our proposed approach can achieve
similar or better performance compared to the existing genetic approaches with
dramatically reduced computation cost. For example, the network discovered by
our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU
hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201
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