Hundreds of researchers in a collaborative project called "It from Qubit" say space and time may spring up from the quantum entanglement of tiny bits of information.
Learn about quantum computation and quantum information in this advanced graduate level course from MIT.
About this course
Already know something about quantum mechanics, quantum bits and quantum logic gates, but want to design new quantum algorithms, and explore multi-party quantum protocols? This is the course for you!
In this advanced graduate physics course on quantum computation and quantum information, we will cover:
The formalism of quantum errors (density matrices, operator sum representations)
🔖 Human Evolution: Our Brains and Behavior by Robin Dunbar (Oxford University Press) marked as want to read.
Official release date: November 1, 2016
09/14/16: downloaded a review copy via NetGalley
Description
The story of human evolution has fascinated us like no other: we seem to have an insatiable curiosity about who we are and where we have come from. Yet studying the “stones and bones” skirts around what is perhaps the realest, and most relatable, story of human evolution – the social and cognitive changes that gave rise to modern humans.
In Human Evolution: Our Brains and Behavior, Robin Dunbar appeals to the human aspects of every reader, as subjects of mating, friendship, and community are discussed from an evolutionary psychology perspective. With a table of contents ranging from prehistoric times to modern days, Human Evolution focuses on an aspect of evolution that has typically been overshadowed by the archaeological record: the biological, neurological, and genetic changes that occurred with each “transition” in the evolutionary narrative. Dunbar’s interdisciplinary approach – inspired by his background as both an anthropologist and accomplished psychologist – brings the reader into all aspects of the evolutionary process, which he describes as the “jigsaw puzzle” of evolution that he and the reader will help solve. In doing so, the book carefully maps out each stage of the evolutionary process, from anatomical changes such as bipedalism and increase in brain size, to cognitive and behavioral changes, such as the ability to cook, laugh, and use language to form communities through religion and story-telling. Most importantly and interestingly, Dunbar hypothesizes the order in which these evolutionary changes occurred-conclusions that are reached with the “time budget model” theory that Dunbar himself coined. As definitive as the “stones and bones” are for the hard dates of archaeological evidence, this book explores far more complex psychological questions that require a degree of intellectual speculation: What does it really mean to be human (as opposed to being an ape), and how did we come to be that way?
Logical probability theory was developed as a quantitative measure based on Boole's logic of subsets. But information theory was developed into a mature theory by Claude Shannon with no such connection to logic. But a recent development in logic changes this situation. In category theory, the notion of a subset is dual to the notion of a quotient set or partition, and recently the logic of partitions has been developed in a parallel relationship to the Boolean logic of subsets (subset logic is usually mis-specified as the special case of propositional logic). What then is the quantitative measure based on partition logic in the same sense that logical probability theory is based on subset logic? It is a measure of information that is named "logical entropy" in view of that logical basis. This paper develops the notion of logical entropy and the basic notions of the resulting logical information theory. Then an extensive comparison is made with the corresponding notions based on Shannon entropy.
Ellerman is visiting at UC Riverside at the moment. Given the information theory and category theory overlap, I’m curious if he’s working with John Carlos Baez, or what Baez is aware of this.
Based on a cursory look of his website(s), I’m going to have to start following more of this work.
I’ve run across some of his work before, but I ran into some new material by Hector Zenil that will likely interest those following information theory, complexity, and computer science here. I hadn’t previously noticed that he refers to himself on his website as an “information theoretic biologist” — everyone should have that as a title, shouldn’t they? As a result, I’ve also added him to the growing list of ITBio Researchers.
If you’re not following him everywhere (?) yet, start with some of the sites below (or let me know if I’ve missed anything).
A common practice in the estimation of the complexity of objects, in particular of graphs, is to rely on graph- and information-theoretic measures. Here, using integer sequences with properties such as Borel normality, we explain how these measures are not independent of the way in which a single object, such a graph, can be described. From descriptions that can reconstruct the same graph and are therefore essentially translations of the same description, we will see that not only is it necessary to pre-select a feature of interest where there is one when applying a computable measure such as Shannon Entropy, and to make an arbitrary selection where there is not, but that more general properties, such as the causal likeliness of a graph as a measure (opposed to randomness), can be largely misrepresented by computable measures such as Entropy and Entropy rate. We introduce recursive and non-recursive (uncomputable) graphs and graph constructions based on integer sequences, whose different lossless descriptions have disparate Entropy values, thereby enabling the study and exploration of a measure’s range of applications and demonstrating the weaknesses of computable measures of complexity.
Subjects: Information Theory (cs.IT); Computational Complexity (cs.CC); Combinatorics (math.CO)
Cite as: arXiv:1608.05972 [cs.IT] (or arXiv:1608.05972v4 [cs.IT]
YouTube
Yesterday he also posted two new introductory videos to his YouTube channel. There’s nothing overly technical here, but they’re nice short productions that introduce some of his work. (I wish more scientists did communication like this.) I’m hoping he’ll post them to his blog and write a bit more there in the future as well.
Cross-boundary Behavioural Reprogrammability Reveals Evidence of Pervasive Turing Universality by Jürgen Riedel, Hector Zenil
Preprint available at http://arxiv.org/abs/1510.01671
Ed.: 9/7/16: Updated videos with links to relevant literature
The book is a collection of papers written by a selection of eminent authors from around the world in honour of Gregory Chaitin s 60th birthday. This is a unique volume including technical contributions, philosophical papers and essays.
Hardcover: 468 pages;
Publisher: World Scientific Publishing Company (October 18, 2007);
ISBN: 9789812770820
Pubmed Commons provides a forum, independent of a journal, where comments on articles in that journal can be posted. Why not air your displeasure there? The article is easily found (see PMID: 27467019) and, so far, there are no comments.
I’m hoping that one day (in the very near future) that scientific journals and other science communications on the web will support the W3C’s Webmention candidate specification so that when commentators [like Lior, in this case, above] post something about an article on their site, that the full comment is sent to the original article to appear there automatically. This means that one needn’t go to the site directly to comment (and if the comment isn’t approved, then at least it still lives somewhere searchable on the web).
Some journals already count tweets, and blog mentions (generally for PR reasons) but typically don’t allow access to finding them on the web to see if they indicate positive or negative sentiment or to further the scientific conversation.
I’ve also run into cases in which scientific journals who are “moderating” comments, won’t approve reasoned thought, but will simultaneously allow (pre-approved?) accounts to flame every comment that is approved [example on Sciencemag.org: http://boffosocko.com/2016/04/29/some-thoughts-on-academic-publishing/ — see also comments there], so having the original comment live elsewhere may be useful and/or necessary depending on whether the publisher is a good or bad actor, or potentially just lazy.
I’ve also seen people use commenting layers like hypothes.is or genius.com to add commentary directly on journals, but these layers are often hidden to most. The community certainly needs a more robust commenting interface. I would hope that a decentralized version using web standards like Webmentions might be a worthwhile and robust solution.
Analysis of phase 1 and 2 trials testing the safety of spinal cord transplantation of human stem cells in patients with amyotrophic lateral sclerosis (ALS) with escalating doses and expansion of the trial to multiple clinical centers.
I built the microinjectors used in these experiments for injecting stem cells into the first human patients.
Transplantation of spinal cord–derived neural stem cells for ALS
Analysis of phase 1 and 2 trials
Authors: Jonathan D. Glass, MD; Vicki S. Hertzberg, PhD; Nicholas M. Boulis, MD; Jonathan Riley, MD; Thais Federici, PhD; Meraida Polak, RN; Jane Bordeau, RN; Christina Fournier, MD; Karl Johe, PhD; Tom Hazel, PhD; Merit Cudkowicz, MD; Nazem Atassi, MD; Lawrence F. Borges, MD; Seward B. Rutkove, MD; Jayna Duell, RN; Parag G. Patil, MD; Stephen A. Goutman, MD; Eva L. Feldman, MD, PhD
ABSTRACT
Objective: To test the safety of spinal cord transplantation of human stem cells in patients with amyotrophic lateral sclerosis (ALS) with escalating doses and expansion of the trial to multiple clinical centers.
Methods: This open-label trial included 15 participants at 3 academic centers divided into 5 treatment groups receiving increasing doses of stem cells by increasing numbers of cells/injection and increasing numbers of injections. All participants received bilateral injections into the cervical spinal cord (C3-C5). The final group received injections into both the lumbar (L2-L4) and cervical cord through 2 separate surgical procedures. Participants were assessed for adverse events and progression of disease, as measured by the ALS Functional Rating Scale–Revised, forced vital capacity, and quantitative measures of strength. Statistical analysis focused on the slopes of decline of these phase 2 trial participants alone or in combination with the phase 1 participants (previously reported), comparing these groups to 3 separate historical control groups.
Results: Adverse events were mostly related to transient pain associated with surgery and to side effects of immunosuppressant medications. There was one incident of acute postoperative deterioration in neurologic function and another incident of a central pain syndrome. We could not discern differences in surgical outcomes between surgeons. Comparisons of the slopes of decline with the 3 separate historical control groups showed no differences in mean rates of progression.
Conclusions: Intraspinal transplantation of human spinal cord–derived neural stem cells can be safely accomplished at high doses, including successive lumbar and cervical procedures. The procedure can be expanded safely to multiple surgical centers.
Classification of evidence: This study provides Class IV evidence that for patients with ALS, spinal cord transplantation of human stem cells can be safely accomplished and does not accelerate the progression of the disease. This study lacks the precision to exclude important benefit or safety issues.
This tutorial will review the basics of theory in the field of evolutionary quantitative genetics and its connections to evolution observed at various time scales. Quantitative genetics deals with the inheritance of measurements of traits that are affected by many genes. Quantitative genetic theory for natural populations was developed considerably in the period from 1970 to 1990 and up to the present, and it has been applied to a wide range of phenomena including the evolution of differences between the sexes, sexual preferences, life history traits, plasticity of traits, as well as the evolution of body size and other morphological measurements. Textbooks have not kept pace with these developments, and currently few universities offer courses in this subject aimed at evolutionary biologists. There is a need for evolutionary biologists to understand this field because of the ability to collect large amounts of data by computer, the development of statistical methods for changes of traits on evolutionary trees and for changes in a single species through time, and the realization that quantitative characters will not soon be fully explained by genomics. This tutorial aims to fill this need by reviewing basic aspects of theory and illustrating how that theory can be tested with data, both from single species and with multiple-species phylogenies. Participants will learn to use R, an open-source statistical programming language, to build and test evolutionary models. The intended participants for this tutorial are graduate students, postdocs, and junior faculty members in evolutionary biology.
I ran across a link to this textbook by way of a standing Google alert, and was excited to check it out. I was immediately disappointed to think that I would have to wait another month and change for the physical textbook to be released, but made my pre-order directly. Then with a bit of digging around, I realized that individual chapters are available immediately to quench my thirst until the physical text is printed next month.
The power of network science, the beauty of network visualization.
Network Science, a textbook for network science, is freely available under the Creative Commons licence. Follow its development on Facebook, Twitter or by signing up to our mailing list, so that we can notify you of new chapters and developments.
The book is the result of a collaboration between a number of individuals, shaping everything, from content (Albert-László Barabási), to visualizations and interactive tools (Gabriele Musella, Mauro Martino, Nicole Samay, Kim Albrecht), simulations and data analysis (Márton Pósfai). The printed version of the book will be published by Cambridge University Press in 2016. In the coming months the website will be expanded with an interactive version of the text, datasets, and slides to teach the material.
Book Contents
Personal Introduction
1. Introduction
2. Graph Theory
3. Random Networks
4. The Scale-Free Property
5. The Barabási-Albert Model
6. Evolving Networks
7. Degree Correlations
8. Network Robustness
9. Communities
10. Spreading Phenomena
Usage & Acknowledgements
About
Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network science.
Author Andrew Solomon introduces his new book, "Far and Away."
A great interview with Andrew Solomon relating to his new book Far and Away: Reporting from the Brink of Change, travel, and the world in which we live. Though it’s not discussed directly, there’s a feel of Big History philosophy in the discussion.
During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.
Download a pre-publication version of the book which will be published by Cambridge University Press.
The book arises from notes of courses taught at the second year graduate level at the University of Minnesota and is suitable to accompany study at that level.
“Why should we want to know about representations over rings that are not fields of characteristic zero? It is because they arise in many parts of mathematics. Group representations appear any time we have a group of symmetries where there is some linear structure present, over some commutative ring. That ring need not be a field of characteristic zero.
Here are some examples.
[…]
In the theory of error-correcting codes many important codes have a non-trivial symmetry group and are vector spaces over a finite field, thereby providing a representation of the group over that field.”
Weaverbot (Twitter)
Clara Moskowitz (Scientific American)Isaac Chuang Professor of Electrical Engineering and Computer Science, and Professor of Physics MIT
David Ellerman (128.84.21.199)
Anand Sarwate (Twitter)
Lior Pachter (& Comments by Donald Forsdyke) (Bits of DNA)
Albert-László Barabási (Cambridge University Press)
Charlie Rose from Charlie Rose.com
César A. Hidalgo (Applied Network Science | SpringerLink)
Peter Webb (math.umn.edu)