146 research outputs found
Multiferroic CoFe2O4-BiFeO3 core-shell nanofibers and their nanoscale magnetoelectric coupling
No full textMultiferroic CoFe2O4-BiFeO3 (CFO-BFO) core-shell nanofibers were synthesized by coaxial electrospinning. The spinel structure of CFO and perovskite structure of BFO were confirmed by x-ray diffraction and high-resolution transmission electron microscopy. The core-shell configuration of nanofibers was verified by scanning electron microscopy and transmission electron microscopy images. The macroscopic ferromagnetic property of core-shell nanofibers was demonstrated by magnetic hysteresis loop. The local magnetoelectric (ME) coupling was confirmed by using dual frequency piezoresponse force microscopy (PFM) under an external magnetic field, showing magnetically induced evolution of piezoresponse and domain structure. The ferroelectric characteristics are demonstrated by the switching spectroscopy PFM. From PFM hysteresis and butterfly loops, it is observed that the piezoresponse amplitude is reduced while coercive voltage increased under external in-plane magnetic field, induced through the mechanical interactions between magnetostrictive CFO and piezoelectric BFO, from which the lateral ME coupling can be estimated quantitatively. The nanofibers thus can find a variety of applications as a one-dimensional multiferroic material
Predictive Representation Learning for Language Modeling
No full textLanguage Modeling (LM) is often formulated as a next-word prediction problem over a large vocabulary, which makes it challenging. To effectively perform the task of next-word prediction, Long Short Term Memory networks (LSTMs) must keep track of many types of information. Some information is directly related to the next word’s identity, but some is more secondary (e.g. discourse-level features or features of downstream words). Correlates of secondary information appear in LSTM representations, even though they are not part of an explicitly supervised prediction task. In contrast, Reinforcement Learning (RL) has found success in techniques that explicitly supervise representations to predict secondary information. Inspired by that success, we propose Predictive Representation Learning (PRL), which explicitly constrains LSTMs to encode specific predictions, like those that might need to be learned implicitly. By dividing the complex next-word prediction task into many simpler prediction tasks of secondary information, we show that PRL 1) significantly improves two strong language modeling methods, 2) converges more quickly, and 3) performs better when data is limited. Our fusion of RL with LSTMs shows that explicitly encoding a simple predictive task facilitates the search for a more effective language model
“JUSTICE” In Architecture
No full textThe Degree Book is a comprehensive account of the author’s academic journey in pursuing the Master of Science in Advanced Architectural Design (M.S. AAD) - Architecture and Representation at Cornell University. The book encompasses various aspects of the author’s academic work, including design projects, research, elective courses, and architectural theory.During the author’s studies, a significant realization was the importance of designing architecture with the objective of helping residents, communities, and the environment. This realization led to a deeper understanding of the concept of “justice” in architecture. The author identifies sustainable design as a prime example of “environmental justice” and acknowledges that architecture has the potential to create causal relationships between space or technology and various forms of “justice”. The book discusses the author’s design projects, and how they are inspired by the concept of “justice” in architecture. The author revisits the projects to evaluate how the author created a causal relationship between space, technology, and “justice”. The author also examines how “justice” has affected the author’s design thinking. The Degree Book also includes a discussion of emerging technologies, digital and generative design, new drawing and media space, and their impact on the field of architecture. The author acknowledges the importance of understanding these emerging technologies to better express and display research results. In conclusion, the author posits that architecture in the new era should focus on realizing various social, cultural, racial, and environmental design concepts of “justice”. The author recognizes that architects have a responsibility to design for the betterment of society and that architecture can play a crucial role in creating a just and equitable world. The Degree Book is an insightful and inspiring account of the author’s academic journey
pRPL: an open-source general-purpose parallel Raster Processing programming Library
No full textpRPL is an open-source general-purpose programming library developed by the author to parallelize almost any raster-processing algorithm with any arbitrary neighborhood configuration, and support any data type. This paper introduces the advanced features of pRPL, compares it with other similar programming libraries, and demonstrates the performance of a parallel geographic Cellular Automata (CA) model developed using pRPL with real-world datasets. In conclusion, pRPL effectively reduces the development complexity of parallel programming, and efficiently reduces the computing time
Grouping miRNAs of similar functions via weighted information content of gene ontology
No full text© 2016 The Author(s). Background: Regulation mechanisms between miRNAs and genes are complicated. To accomplish a biological function, a miRNA may regulate multiple target genes, and similarly a target gene may be regulated by multiple miRNAs. Wet-lab knowledge of co-regulating miRNAs is limited. This work introduces a computational method to group miRNAs of similar functions to identify co-regulating miRNAsfrom a similarity matrix of miRNAs. Results: We define a novel information content of gene ontology (GO) to measure similarity between two sets of GO graphs corresponding to the two sets of target genes of two miRNAs. This between-graph similarity is then transferred as a functional similarity between the two miRNAs. Our definition of the information content is based on the size of a GO term's descendants, but adjusted by a weight derived from its depth level and the GO relationships at its path to the root node or to the most informative common ancestor (MICA). Further, a self-tuning technique and the eigenvalues of the normalized Laplacian matrix are applied to determine the optimal parameters for the spectral clustering of the similarity matrix of the miRNAs. Conclusions: Experimental results demonstrate that our method has better clustering performance than the existing edge-based, node-based or hybrid methods. Our method has also demonstrated a novel usefulness for the function annotation of new miRNAs, as reported in the detailed case studies
A zero-density estimate for -functions associated with Hecke--Maass cusp forms
No full textIn this paper, we establish an asymptotic formula for the twisted second moment of -functions associated with Hecke--Maass cusp forms for , and further deduce a weighted zero-density estimate for these -functions in the spectral aspect which may have important applications in other problems.33 pages. Comments welcome! arXiv admin note: text overlap with arXiv:1704.00314 by other author
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