More Like this

1. corpus and behavioral analysis of the quality of young children’s STEM books.

   Miller, H.E. (January 2020, Proceedings of the 42nd Annual Conference of the Cognitive Science Society)

   Promoting early STEM knowledge helps to prepare children for formal schooling. Shared book reading may promote early STEM knowledge. This research examined the quality of available STEM books in children’s environments and investigated how such books influenced children’s learning in shared book reading contexts. In Study 1, we used both meaning-based human coding and computerized latent semantic analysis to categorize books based on the extent to which they provided support for encoding and demand for active recall. We found similarity in the ratings using the two approaches. Most books fell into categories characterized by low Support and Demand. In Study 2, we found that 4- to 5-year-olds learned more STEM facts when books were high in Support and/or Demand, although few books fell into those categories. This research highlights the importance that textual features of books play in promoting early STEM knowledge during shared book reading. 

   more » « less
2. Deep Learning for Natural Language Processing: A Gentle Introduction

   Surdeanu, Mihai; Valenzuela-Escárcega, Marco (February 2024, Cambridge University Press)

   Upon encountering this publication, one might ask the obvious question, "Why do we need another deep learning and natural language processing book?" Several excellent ones have been published, covering both theoretical and practical aspects of deep learning and its application to language processing. However, from our experience teaching courses on natural language processing, we argue that, despite their excellent quality, most of these books do not target their most likely readers. The intended reader of this book is one who is skilled in a domain other than machine learning and natural language processing and whose work relies, at least partially, on the automated analysis of large amounts of data, especially textual data. Such experts may include social scientists, political scientists, biomedical scientists, and even computer scientists and computational linguists with limited exposure to machine learning. Existing deep learning and natural language processing books generally fall into two camps. The first camp focuses on the theoretical foundations of deep learning. This is certainly useful to the aforementioned readers, as one should understand the theoretical aspects of a tool before using it. However, these books tend to assume the typical background of a machine learning researcher and, as a consequence, I have often seen students who do not have this background rapidly get lost in such material. To mitigate this issue, the second type of book that exists today focuses on the machine learning practitioner; that is, on how to use deep learning software, with minimal attention paid to the theoretical aspects. We argue that focusing on practical aspects is similarly necessary but not sufficient. Considering that deep learning frameworks and libraries have gotten fairly complex, the chance of misusing them due to theoretical misunderstandings is high. We have commonly seen this problem in our courses, too. This book, therefore, aims to bridge the theoretical and practical aspects of deep learning for natural language processing. We cover the necessary theoretical background and assume minimal machine learning background from the reader. Our aim is that anyone who took introductory linear algebra and calculus courses will be able to follow the theoretical material. To address practical aspects, this book includes pseudo code for the simpler algorithms discussed and actual Python code for the more complicated architectures. The code should be understandable by anyone who has taken a Python programming course. After reading this book, we expect that the reader will have the necessary foundation to immediately begin building real-world, practical natural language processing systems, and to expand their knowledge by reading research publications on these topics. https://doi.org/10.1017/9781009026222 

   more » « less
3. The Montevideo Interpretation: How the Inclusion of a Quantum Gravitational Notion of Time Solves the Measurement Problem

   https://doi.org/10.3390/universe6120236  

   Gambini, Rodolfo; Pullin, Jorge (December 2020, Universe)

   null (Ed.)

   We review the Montevideo Interpretation of quantum mechanics, which is based on the use of real clocks to describe physics, using the framework that was recently introduced by Höhn, Smith, and Lock to treat the problem of time in generally covariant systems. These new methods, which solve several problems in the introduction of a notion of time in such systems, do not change the main results of the Montevideo Interpretation. The use of the new formalism makes the construction more general and valid for any system in a quantum generally covariant theory. We find that, as in the original formulation, a fundamental mechanism of decoherence emerges that allows for supplementing ordinary environmental decoherence and avoiding its criticisms. The recent results on quantum complexity provide additional support to the type of global protocols that are used to prove that within ordinary—unitary—quantum mechanics, no definite event—an outcome to which a probability can be associated—occurs. In lieu of this, states that start in a coherent superposition of possible outcomes always remain as a superposition. We show that, if one takes into account fundamental inescapable uncertainties in measuring length and time intervals due to general relativity and quantum mechanics, the previously mentioned global protocols no longer allow for distinguishing whether the state is in a superposition or not. One is left with a formulation of quantum mechanics purely defined in quantum mechanical terms without any reference to the classical world and with an intrinsic operational definition of quantum events that does not need external observers. 

   more » « less
4. Using a comic book to engage students in a cryptology and cybersecurity curriculum

   https://doi.org/10.1080/15391523.2022.2150726  

   Wusylko, Christine; Xu, Zhen; Dawson, Kara M.; Antonenko, Pavlo D.; Koh, Do Hyong; Lee, Minyoung; Benedict, Amber E.; Bhunia, Swarup (March 2023, Journal of Research on Technology in Education)

   This article describes the use of a comic book to anchor a cryptology and cybersecurity curriculum for upper elementary students. Perceptions about the comic book from 138 students across 11 afterschool programs were examined using survey, classroom observation, and interviews. Data analysis revealed that the comic book created a “macrocontext” to situate learners in an exciting adventure about cryptology and cybersecurity. Students found the characters relatable, and their perceptions were tightly tied to other components of the curriculum. Reading the word bubbles was chal- lenging at first, but got easier over time. This study illustrates how comic books can anchor unfamiliar STeM content for younger learners. 

   more » « less
5. Optimized Quantum F-Divergences

   https://doi.org/10.1109/ISIT.2018.8437925  

   Wilde, Mark M. (June 2018, Proceedings of the 2018 IEEE International Symposium on Information Theory)

   null (Ed.)

   The quantum relative entropy is a measure of the distinguishability of two quantum states, and it is a unifying concept in quantum information theory: many information measures such as entropy, conditional entropy, mutual information, and entanglement measures can be realized from it. As such, there has been broad interest in generalizing the notion to further understand its most basic properties, one of which is the data processing inequality. The quantum f-divergence of Petz is one generalization of the quantum relative entropy, and it also leads to other relative entropies, such as the Petz--Renyi relative entropies. In this contribution, I introduce the optimized quantum f-divergence as a related generalization of quantum relative entropy. I prove that it satisfies the data processing inequality, and the method of proof relies upon the operator Jensen inequality, similar to Petz's original approach. Interestingly, the sandwiched Renyi relative entropies are particular examples of the optimized f-divergence. Thus, one benefit of this approach is that there is now a single, unified approach for establishing the data processing inequality for both the Petz--Renyi and sandwiched Renyi relative entropies, for the full range of parameters for which it is known to hold. 

   more » « less