lectures/ideas: 04/20/17

2017-04-20

Metals and Alloys by Harry Bhadeshia

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source: bhadeshia123 2011年4月23日
The development of improved metallic materials is a vital activity at the leading edge of science and technology. Metals offer unrivalled combinations of properties and reliability at a cost which is affordable. They are versatile because subtle changes in their microstructure can cause dramatic variations in their properties. For example, it is possible to buy commercial steel with a strength as low as 50 MPa or as high as 5500 MPa. They can be made with a microstructure which is finer than that of carbon nanotubes. An understanding of the development of microstructure in metals, rooted in thermodynamics, crystallography and kinetic phenomena is essential for the materials scientist. The majority of the 1.4 billion tonnes of metals produced annually are the result of developments within the last ten years
http://www.msm.cam.ac.uk/phase-trans/...

lecture 1, Atomic Diffusion 44:15
lecture 2, Atomic Diffusion 38:23
lecture 3, Solidification 40:14
lecture 4, Solidification 33:20
lecture 5, Solidification 28:11
lecture 6, Recovery and Recrystallisation 44:52
lecture 8, Precipitation 41:41
lecture 10, Alloys of Iron 32:31
lecture 11, Some Metallic Alloys 39:39
lecture 12, Alloys for Elevated Temperatures 43:55
Effects of neutron radiation on nickel-based alloys 17:51

UCL Lunch Hour Lectures (videos of March 2017)

source: UCL Lunch Hour Lectures

41:49 How can 21st century research on autism empower London’s teachers? - UCL Lunch Hour Lecture Speaker: Dr Joseph Mintz, UCL Institute of Education, Thursday 16th March 2017 #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre, ...

39:53 Swearing as a second language - "bad language" and language learners - UCL Lunch Hour Lecture Speaker: Dr Geraldine Horan, UCL Department of German, Thursday 9th March 2017 #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre, ...

36:31 Adventures in the 7th Dimension - UCL Lunch Hour Lecture Speaker: Dr Jason Lotay, UCL Dept of Mathematics - Thurs 2nd March 2017 #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre, 1.15 - ...

42:21 Heads you Win: Tails you Lose - The Leadership Gamble - UCL Lunch Hour Lecture Speaker: Prof Kathryn Riley, UCL Institute of Education, Tuesday 14th March 2017 #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre...

41:55 Women Against Pit Closures: Women in the miners’ strike of 1984-5 - UCL Lunch Hour Lectures Speaker: Dr Florence Sutcliffe-Braithwaite, UCL History, Tuesday 7th March 2017 #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre,...

39:58 Knowledge and law: "landscape" in the context of wind energy - UCL Lunch Hour Lecture Speaker: Prof Maria Lee, UCL Laws, Tuesday 28th February 2017 #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre, 1.15 - 1.55pm (te...

37:17 Cooling babies to protect the brain - UCL Lunch Hour Lecture Speaker: Professor Nicola Robertson, Institute for Women's Health, Thursday 23rd Feb 2017 #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lectur...

38:57 The Battle Within: mobilising our immune system to fight cancer - UCL Lunch Hour Lecture Speaker: Dr Sergio Quezada, UCL Cancer Institute, Tuesday 21st February 2017, #ucllhl
Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre, 1...

The Royal Society (videos of March 2017)

source: The Royal Society

1:25:40 Wiring up the brain: How axons navigate Ferrier Prize Lecture 2017 given by Professor Christine Holt FMedSci FRS
The brain is made up of billions of nerve cells (neurons) that are wired together by axons and dendrites. The precision of ...

6:42 The chemical biology of mitochondria Mitochondrial biology is now turning out to be important in many disparate areas of biomedical research. Dr Mike Murphy is at the forefront of this research, and organised a scientific discussion m...

1:59:38 Out in STEM 2017 The Royal Society celebrated LGBT History Month with a keynote address by Sir Dermot Turing. Starts 1.42.
The theme for 2017 is Citizenship, PSHE and Law as we mark the 50th anniversary of the par...

1:01:55 Computing for the future of the planet Bakerian Lecture 2017 by Professor Andy Hopper CBE FREng FRS
Digital technology is an indispensable and crucial component of our lives, society, and the physical environment.
A challenge is how ...

1:09:33 Climate change: catastrophe, hoax or just lukewarm? Lecture by Professor Tim Palmer FRS
Views about climate change can be very polarised. For some, it spells inevitable catastrophe. For others it is a massive hoax.
However, in between these extre...

1:01:50 Why we write - the history of science While it might seem that science is firmly in the telling camp and literature in the showing camp, scientific diaries and autobiographies seem to be spaces in which scientists have decided to show ...

Steels (2016) by Harry Bhadeshia (University of Cambridge)

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source: bhadeshia123 2016年11月7日
The characteristics of martensite in steels, part 1.
Associated teaching materials can be found on:
http://www.msm.cam.ac.uk/phase-trans/...
http://www.msm.cam.ac.uk/phase-trans/2002/martensite.html

Steels: characteristics of martensite, lecture 1 (2016) 50:32
Steels: characteristics of martensite, lecture 2 (2016) 37:54
Steels: mechanism of bainite, lecture 3 (2016) 47:28
Steels: design of bainitic steel, lecture 4 (2016) 31:22
Steels: Widmanstätten ferrite, lecture 5 (2016) 35:29
Steels: Ferrite, lecture 6 (2016) 35:58
Steels: Ferrite in multicomponent alloy, lecture 7 (2016) 43:30
Steels: Pearlite, lecture 8 (2016) 25:52
Steels: Overall transformation kinetics, lecture 9 (2016) 52:32
Steels: transformation-induced plasticity, lecture 10 (2016) 44:08
Steels: TRIP, TWIP & residual stress, lecture 11 (2016) 39:48
Steels: First bulk nanostructured steel, lecture 12 (2016) 34:02

Modern Steel Products (2014-15) by Bruno de Cooman

Modern Steel Products (2013) by Bruno De Cooman

Mechanical Properties of Steels by Bruno de Cooman

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source: bhadeshia123 2013年10月7日
The first in a series of lectures given by Professor Bruno de Cooman of the Graduate Institute of Ferrous Technology, POSTECH, South Korea. This particular lecture introduces the general concepts which will be explored in detail in subsequent lectures.
http://www.msm.cam.ac.uk/phase-trans

1. introduction 53:23
2. Elastic deformation 1:18:55
3. Plastic deformation 1:11:06
4: stress-strain relations 1:03:40
5: plasticity 1:07:38
6: yield criteria for plasticity 1:16:43
7: evolution of plastic strain 1:08:45
8: formability 1:14:54
9: dislocations 1:05:27
10: dislocations & faults 1:13:00
11: texture, dislocations, defects 1:03:05
12: dislocations and other defects 1:01:56
13: defects, irradiation 36:41
14: theoretical strength 1:04:07
15: single to polycrystal 1:16:05
16: solid solution strengthening 1:16:14
17: solid solution strengthening 1:06:31
18: strain hardening 1:13:44
19 - strain hardening 1:07:01
20: grain size strengthening 1:00:34
21: grain size strengthening 49:53
22 - precipitation hardening 1:17:03
23 - precipitation hardening 1:03:52
24: polycrystal strain hardening 1:12:19
Fatigue testing of wire 0:14
Prince Rupert's drops 0:32
High strain rate testing for white matter in bearing steels 0:58

Simons Institute (videos of March 2017)

source: Simons Institute

20:15 Spotlght Talk: Performance Guarantees for Transferring Representations Daniel McNamara, Australian National University
Foundations of Machine Learning
https://simons.berkeley.edu/talks/daniel-mcnamara-2017-03-31

47:18 Word Representation Learning without unk Assumptions Chris Dyer, Carnegie Mellon University
Foundations of Machine Learning
https://simons.berkeley.edu/talks/chris-dyer-2017-3-31

42:18 Resilient Representation and Provable Generalization Dawn Song, UC Berkeley
Foundations of Machine Learning
https://simons.berkeley.edu/talks/dawn-song-2017-03-31

1:06:38 Learning Representations for Active Vision Bruno Olshausen, UC Berkeley
Foundations of Machine Learning
https://simons.berkeley.edu/talks/bruno-olshausen-2017-3-31

43:17 Formation and Association of Symbolic Memories in the Brain Christos Papadimitriou, UC Berkeley
Foundations of Machine Learning
https://simons.berkeley.edu/talks/christos-papadimitriou-...

39:44 Learning Paraphrastic Representations of Natural Language Sentences Kevin Gimpel, TTI Chicago
Foundations of Machine Learning
https://simons.berkeley.edu/talks/kevin-gimpel-2017-3-31

41:32 Provably Learning of Noisy-or Networks Rong Ge, Duke University
Foundations of Machine Learning
https://simons.berkeley.edu/talks/rong-ge-2017-3-30

1:09:16 Unsupervised Representation Learning Yann LeCun, New York University
Foundations of Machine Learning
https://simons.berkeley.edu/talks/yann-lecun-2017-3-30

43:28 Generalization and Equilibrium in Generative Adversarial Nets (GANs) Sanjeev Arora, Princeton University
Foundations of Machine Learning
https://simons.berkeley.edu/talks/sanjeev-arora-2017-3-30

42:44 Representation Learning of Grounded Language and Knowledge: with and without End-to-End Learning Yejin Choi, University of Washington
Foundations of Machine Learning
https://simons.berkeley.edu/talks/yejin-choi-2017-3-30

41:54 Re-Thinking Representational Learning in Robotics and Music Sham Kakade, University of Washington
Foundations of Machine Learning
https://simons.berkeley.edu/talks/sham-kakade-2017-3-30

16:19 Spotlight Talk: How to Escape Saddle Points Efficiently Praneeth Netrapalli, Microsoft Research India
Foundations of Machine Learning
https://simons.berkeley.edu/talks/praneeth-netrapalli-201...

40:09 Tractable Learning in Structured Probability Spaces Adnan Darwiche, UCLA
Foundations of Machine Learning
https://simons.berkeley.edu/talks/adnan-darwiche-2017-3-29

22:47 Spotlight Talk: Convolutional Dictionary Learning through Tensor Factorization Furong Huang, UC Irvine
Foundations of Machine Learning
https://simons.berkeley.edu/talks-furong-huang-2017-03-29

45:01 Representation Learning for Reading Comprehension Russ Salakhutdinov, Carnegie Mellon University
Foundations of Machine Learning
https://simons.berkeley.edu/talks/russ-salakhutdinov-2017...

1:04:15 Evaluating Neural Network Representations Against Human Cognition Tom Griffiths, UC Berkeley
Foundations of Machine Learning
https://simons.berkeley.edu/talks/tom-griffiths-2017-3-29

46:57 Adversarial Perceptual Representation Learning Across Diverse Modalities and Domains Trevor Darrell, UC Berkeley
Foundations of Machine Learning
https://simons.berkeley.edu/talks/trevor-darrell-2017-3-29

38:00 Continuous State Machines and Grammars for Linguistic Structure Prediction Noah Smith, University of Washington
Foundations of Machine Learning
https://simons.berkeley.edu/talks/noah-smith-201

45:44 Failures of Deep Learning Shai Shalev-Shwartz, Hebrew University of Jerusalem
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

40:42 Supersizing Self-Supervision: Learning Perception and Action without Human Supervision Abhinav Gupta, Carnegie Mellon University
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

1:11:34 Deep Reinforcement Learning Pieter Abbeel, UC Berkeley
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

44:54 Learning from Unlabeled Video Kristen Grauman, University of Texas, Austin
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

41:44 Unsupervised Discovery Through Adversarial Self-Play Rob Fergus, New York University
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

18:30 Spotlight Talk: Semi-Random Units for Learning Neural Networks with Guarantees Bo Xie, Georgia Institute of Technology
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

17:44 Spotlight Talk: A Formalization of Representation Learning Andrej Risteski, Princeton University
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

42:54 Representations of Relationships in Images and Text Hal Daume, University of Maryland at College Park
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

59:31 The Missing Signal Leon Bottou, Facebook AI Research
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

1:14:06 Representations for Language: From Word Embeddings to Sentence Meanings Christopher Manning, Stanford University
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

36:01 A Non-generative Framework and Convex Relaxations for Unsupervised Learning Elad Hazan, Princeton University
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

49:19 Geometry, Optimization and Generalization in Multilayer Networks Nathan Srebro, TTI Chicago
Foundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2

1:06:03 Understanding Silicon Valley John Markoff, Simons Institute Journalist in Residence
https://simons.berkeley.edu/events/understanding-silicon-...
John Markoff reported on the emergence of Silicon Valley and has covered tech...

54:29 The Contextual Bandits Problem Robert Schapire, Microsoft Research
Simons Institute Open Lecture Series
https://simons.berkeley.edu/events/openlectures2017-spring-2

50:20 A Reduction from Efficient Non-Malleable Extractors to Low-Error Two-Source Extractors Dean Doron, Tel Aviv University
https://simons.berkeley.edu/talks/dean-doron-2017-03-10
Proving and Using Pseudorandomness

1:03:53 Randomness Intuition Jacob Fox, Stanford University
https://simons.berkeley.edu/talks/jacob-fox-2017-03-10
Proving and Using Pseudorandomness

49:14 Fast Permutation Property Testing and Metrics of Permutations Fan Wei, Stanford University
https://simons.berkeley.edu/talks/fan-wei-2017-03-10
Proving and Using Pseudorandomness

28:40 Query-to-Communication Lifting for BPP Mika Göös, University of Toronto
https://simons.berkeley.edu/talks/mika-goos-2017-03-09
Proving and Using Pseudorandomness

34:35 Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas Rocco Servedio, Columbia University
https://simons.berkeley.edu/talks/rocco-servedio-2017-03-09
Proving and Using Pseudorandomness

33:34 PRGs for Small Space via Fourier Analysis Thomas Steinke, IBM Almaden
https://simons.berkeley.edu/talks/thomas-steinke-2017-03-09
Proving and Using Pseudorandomness

32:57 Deterministic Isolation for Bipartite Matching and Matroid Intersection Rohit Gurjar, Tel Aviv University
https://simons.berkeley.edu/talks/rohit-gurjar-2017-03-09
Proving and Using Pseudorandomness

59:44 Derandomizing "Algebraic RL" Michael Forbes, Princeton University
https://simons.berkeley.edu/talks/michael-forbes-2017-03-09
Proving and Using Pseudorandomness

59:55 Linear-Algebraic Pseudorandomness: Subspace Designs and Dimension Expanders Venkatesan Guruswami, Carnegie Mellon University
https://simons.berkeley.edu/talks/venkatesan-guruswami-20...
Proving and Using Pseudorandomness

30:54 Algorithmic Regularity Lemmas and Applications László Miklós Lovász, MIT
https://simons.berkeley.edu/talks/laszlo-miklos-lovasz-20...
Proving and Using Pseudorandomness

49:46 Mixing Implies Lower Bounds for Space Bounded Learning Dana Moshkovitz, University of Texas, Austin
https://simons.berkeley.edu/talks/dana-moshkovitz-2017-03-08
Proving and Using Pseudorandomness

1:00:26 Preserving Randomness for Adaptive Adversaries Adam Klivans, University of Texas, Austin
https://simons.berkeley.edu/talks/adam-klivans-2017-03-08
Proving and Using Pseudorandomness

1:04:49 Explicit, Almost Optimal, Epsilon-Balanced Codes Amnon Ta-Shma, Tel Aviv University
https://simons.berkeley.edu/talks/amnon-ta-shma-2017-03-07
Proving and Using Pseudorandomness

1:01:25 Forcing Quasirandomness with Triangles Mathias Schacht, University of Hamburg
https://simons.berkeley.edu/talks/Mathias-Schacht-2017-03-07
Proving and Using Pseudorandomness

57:05 Removal Lemmas with Polynomial Bounds Asaf Shapira, Tel-Aviv University
https://simons.berkeley.edu/talks/asaf-shapira-2017-03-07
Proving and Using Pseudorandomness

36:27 The Number of B_h-sets of a Given Cardinality Yoshiharu Kohayakawa, University of São Paulo
https://simons.berkeley.edu/talks/yoshiharu-kohayakawa-20...
Proving and Using Pseudorandomness

31:43 Regularity Inheritance in Pseudorandom Graphs Maya Stein, University of Chile
https://simons.berkeley.edu/talks/maya-stein-2017-03-06
Proving and Using Pseudorandomness

1:00:35 Packing Degenerate Graphs Using Pseudorandomness Julia Böttcher, London School of Economics
https://simons.berkeley.edu/talks/julia-bottcher-2017-03-06
Proving and Using Pseudorandomness

1:00:42 Pseudorandomness in Data Structures Omer Reingold, Stanford University
https://simons.berkeley.edu/talks/omer-reingold-2017-03-06
Proving and Using Pseudorandomness

30:39 Approximately Counting Solutions to Systems of Quadratic Equations Ryan Williams, MIT
https://simons.berkeley.edu/talks/ryan-williams-2017-03-06
Proving and Using Pseudorandomness

34:00 The Uncanny Usefulness of Constructive Proofs of Pseudorandomness Valentine Kabanets, Simon Fraser University
https://simons.berkeley.edu/talks/valentine-kabanets-2017...
Proving and Using Pseudorandomness

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2017-04-20