20,156 research outputs found
Word-of-mouth in the Music Industry: An Empirical Analysis of Music Consumer Network Topology
No full text
First-principles study of sliding ferroelectricity in cellulose nanocrystals and other two-dimensional materials
No full textsliding ferroelectricity;symmetry;cellulose nanocrystals;density functional theoryList of Contents
Abstract i
List of contents ii
List of tables · iv
List of figures · v
Ⅰ. Introduction · 1
1.1 Ferroelectricity · 1
1.1.1 Two-dimensional ferroelectricity 2
1.1.2 Sliding ferroelectricity 2
1.1.3 Dipole locking 3
1.2 Cellulose nanocrystals 3
1.2.1 Structure of cellulose nanocrystals 4
Ⅱ. Theoretical framework · 6
2.1 Density functional theory · 6
2.1.1 Many-body Schrödinger equation · 6
2.1.2 Mean-field approximation · 8
2.1.3 Hartree-Fock approximation 10
2.1.4 Kohn-Sham equation 11
2.1.5 modern DFT algorithm 13
2.2 Nudged elastic band method 14
2.3 Modern theory of polarization 14
2.4 Symmetry operator 16
2.5 Computational detail · 20
Ⅲ. Result and Discussion: Symmetry · 24
3.1 Representation matrix of operator · 24
3.2 Symmetry in sliding ferroelectricity 29
3.2.1 − operator · 30
3.2.2 + operator · 37
Ⅳ. Result and Discussion: Examples of sliding ferroelectrics · 42
4.1 Hexagonal boron nitride 42
4.2 3R-TMDs materials 43
4.3 cellulose vdW bilayer 45
4.3.1 Cellulose vdW slab · 48
4.3.2 Cellulose CNC slab · 49
Ⅴ. Summary · 51
Reference · 54
Korean summary · 56MasterdCollectio
sj-pdf-1-mrj-10.1177_00222437211050351 - Supplemental material for Home-Tutoring Services Assisted with Technology: Investigating the Role of Artificial Intelligence Using a Randomized Field Experiment
No full textSupplemental material, sj-pdf-1-mrj-10.1177_00222437211050351 for Home-Tutoring Services Assisted
with Technology: Investigating
the Role of Artificial Intelligence
Using a Randomized Field Experiment by Jun Hyung Kim, Minki Kim, Do Won Kwak and Sol Lee in Journal of Marketing Research</p
SleepGuru: Personalized Sleep Planning System for Real-life Actionability and Negotiability
No full textWidely-accepted sleep guidelines advise regular bedtimes and sleep hygiene. An individual’s adherence is often viewed as a matter of self-regulation and anti-procrastination. We pose a question from a different perspective: What if it comes to a matter of one’s social or professional duty that mandates irregular daily life, making it incompatible with the premise of standard guidelines? We propose SleepGuru, an individually actionable sleep planning system featuring one’s real-life compatibility and extended forecast. Adopting theories on sleep physiology, SleepGuru builds a personalized predictor on the progression of the user’s sleep pressure over a course of upcoming schedules and past activities sourced from her online calendar and wearable fitness tracker. Then, SleepGuru service provides individually actionable multi-day sleep schedules which respect the user’s inevitable real-life irregularities while regulating her week-long sleep pressure. We elaborate on the underlying physiological principles and mathematical models, followed by a 3-stage study and deployment. We develop a mobile user interface providing individual predictions and adjustability backed by cloud-side optimization. We deploy SleepGuru in-the-wild to 20 users for 8 weeks, where we found positive effects of SleepGuru in sleep quality, compliance rate, sleep efficiency, alertness, long-term followability, and so on.1
Sparse Token Transformers with Attention Back Tracking
No full textDespite the success of Transformers in various applications from text, vision, and speech domains, they are yet to become standard architectures for mobile and edge device applications due to their heavy memory and computational requirements. While there exist many different approaches to reduce the complexities of the Transformers, such as the pruning of the weights/attentions/tokens, quantization, and distillation, we focus on token pruning, which reduces not only the complexity of the attention operations, but also the linear layers, which have non-negligible computational costs. However, previous token pruning approaches often remove tokens during the feed-forward stage without consideration of their impact on later layers' attentions, which has a potential risk of dropping out important tokens for the given task. To tackle this issue, we propose an attention back-tracking method that tracks the importance of each attention in a Transformer architecture from the outputs to the inputs, to preserve the tokens that have a large impact on the final predictions. We experimentally validate the effectiveness of the method on both NLP and CV benchmarks, using Transformer architectures for both domains, and the results show that the proposed attention back-tracking allows the model to better retain the full models' performance even at high sparsity rates, significantly outperforming all baselines. Qualitative analysis of the examples further shows that our method does preserve semantically meaningful tokens
Who Was Edmund Lee?
No full textLocal author Peggy Donoho discusses her pioneer ancestor, Edmund Lee, and her work to preserve their family cemetery
The Future of Canadian Climate Policy — with Marc Lee
No full textMarc Lee is a Senior Economist at the Canadian Centre for Policy Alternatives\u27 BC Office. In addition to tracking federal and provincial budgets and economic trends, Marc has published on a range of topics from poverty and inequality to globalization and international trade to public services and regulation. Marc is the Co-Director of the Climate Justice Project, a research partnership with UBC\u27s School of Community and Regional Planning that examines the links between climate change policies and social justice.Resources:Climate Justice Project: www.policyalternatives.ca/projects/cli…tice-projectMarc Lee\u27s Posts on Policy Note: www.policynote.ca/author/marclee/Canadian Centre for Policy Alternatives: www.policyalternatives.ca/Marc\u27s Twitter: twitter.com/MarcLeeCCPA International Panel on Climate Change, 2021 report: www.ipcc.ch/report/ar6/wg1
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