俐媽真的有夠拚!
上禮拜和主任、編輯開了N個會,
回答眾多針對北/全模、開學英文學習的私訊,
還錄了3課網版!
魂都快空了😵😵😵~
其中一課是翰林遠東的B3L3 A Journey Through Time,
文中提到時空旅行的可能性及結果,
更提到愛因斯坦的相對論(Theory of Relativity),
及好萊塢電影Interstellar 「星際效應」
回家和師丈討論到Interstellar,
他馬上推薦這本書給我,
果然是科學主軸的書,很硬😂😂
俐媽每個中文字都看得懂,
但有些觀念合起來頗難參透⋯⋯
但俐媽仍然從這本「有字天書」中學到不少英文專業單字唷👍🏼
有興趣的孩子可以看看,
相信收穫頗豐!
—————————————————————
🌟 俐媽英文教室—星際效應篇:
🪐 force line (n.) 力線
🪐 neutron star (n.) 中子星
🪐 gravitational anomalies (n.) 重力異常
🪐 black hole (n.) 黑洞
🪐 wormhole (n.) 蟲洞
🪐 Aurora Borealis (n.) 北極光
🪐 event horizon (n.) 事件視界
🪐 space colony (n.) 太空殖民站
🪐 membrane/brand (n.) 膜
🪐 science fiction (n.) 科幻小說
🪐 inverse square law for gravity (n.) 重力平方反比定律
🪐 gravity gradiometer (n.) 重力梯度計
🪐 gravitational field (n.) 重力場
🪐 gravitational slingshots (n.) 重力彈弓效應
🪐 geometrodynamics (n.) 時空幾何動力論
🪐 law of time warps (n.) 時間翹曲定論
🪐 pathogen (n.) 病原體
🪐 dynamical friction (n.) 動力摩擦
🪐 fifth dimension (n.) 第五次元
🪐 tesseract (n.) 超立方體
🪐 quantum gravity (n.) 量子重力
🪐 quantum fluctuation (n.) 量子漲落
🪐 magnetic fields (n.) 磁場
🪐 accretion disk (n.) 吸積盤
🪐 hypothetical (a.) 假設性的
🪐 quasar (n.) 類星體
🪐 critical orbit (n.) 臨界軌道
🪐 tidal (a.) 潮汐的
🪐 bulk (n.) 體
🪐 Milky Way galaxy (n.) 銀河系
🪐 flight simulator (n.) 飛行模擬器
🪐 pulsar (n.) 脈衝星
🪐 singularity (n.) 奇異點
—————————————————————
俐媽推薦了一部很硬的書唷,
但裡面不少理論也應用到最近上映的電影Tenet 「天能」,
這也是一部精彩又燒腦的電影👍🏼👍🏼
.
#俐媽英文教室
#俐媽英文教室網版篇
#俐媽英文電影
#俐媽英文電影星際效應篇
#俐媽好書推薦
#俐媽好書推薦Interstellar
#台大明明網版超前錄製
#大家要認真收看👀👀
同時也有1部Youtube影片,追蹤數超過12萬的網紅一二三渡辺,也在其Youtube影片中提到,伝説のフルブーストマシーン V-MAX1200 V-MAX YAMAHA...
「dynamical」的推薦目錄:
- 關於dynamical 在 辣媽英文天后 林俐 Carol Facebook 的最佳解答
- 關於dynamical 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的最讚貼文
- 關於dynamical 在 破地獄 Scattered Purgatory Facebook 的最佳貼文
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- 關於dynamical 在 'dynamical' vs. 'dynamic' - English Language & Usage Stack ... 的評價
- 關於dynamical 在 Introduction · DynamicalSystems.jl - GitHub Pages 的評價
dynamical 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的最讚貼文
【演講】2019/11/19 (二) @工四816 (智易空間),邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan) 演講「Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management」
IBM中心特別邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan)前來為我們演講,歡迎有興趣的老師與同學報名參加!
演講標題:Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management
演 講 者:Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間:2019/11/19(二) 9:00 ~ 12:00
地 點:交大工程四館816 (智易空間)
活動報名網址:https://forms.gle/vUr3kYBDB2vvKtca6
報名方式:
費用:(費用含講義、午餐及茶水)
1.費用:(1) 校內學生免費,校外學生300元/人 (2) 業界人士與老師1500/人
2.人數:60人,依完成報名順序錄取(完成繳費者始完成報名程序)
※報名及繳費方式:
1.報名:請至報名網址填寫資料
2.繳費:
(1)親至交大工程四館813室完成繳費(前來繳費者請先致電)
(2)匯款資訊如下:
戶名: 曾紫玲(國泰世華銀行 竹科分行013)
帳號: 075506235774 (國泰世華銀行 竹科分行013)
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據
聯絡方式:曾紫玲 Tel:03-5712121分機54599 Email:tzuling@nctu.edu.tw
Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.
3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.
Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.
Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).
dynamical 在 破地獄 Scattered Purgatory Facebook 的最佳貼文
錢賡·暗八仙
2019.5.2 Mountain Brush
We are collaborating with the Beijing based calligraphy/performance artist on the coming European tour, presenting 鬥法 Dou Fa - The Wizardry Battle.
Opposite to stereotype calligraphy, Qian Geng is a dynamical performer, from tranquil to volcanic, he will be the visual center of the coming tour.
#calligraphy #experimentalmusic #experimentalart
dynamical 在 Introduction · DynamicalSystems.jl - GitHub Pages 的推薦與評價
jl is an award-winning Julia software library for dynamical systems, nonlinear dynamics, deterministic chaos, and nonlinear timeseries analysis. It is part of ... ... <看更多>
dynamical 在 'dynamical' vs. 'dynamic' - English Language & Usage Stack ... 的推薦與評價
dynamical : refers to specific systems that change over time or dimension A dynamical systems is a mathematical formalization for any fixed "rule ... ... <看更多>
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