Format results

Talk


Attosecond Quantum Spectroscopy Measurement
David Villeneuve National Research Council Canada (NRC)

Efficient Preparation of Nontrivial Quantum States
Timothy Hsieh Perimeter Institute for Theoretical Physics

Time And Gravity Measurement
Pierre Dube National Research Council Canada (NRC)


Canadian Astronomy Data Center: Tools and Analytics for Large Data Sets
Sebastien Fabbro National Research Council Canada (NRC)


SI Unit Fundamental Measurements

Angela Gamouras National Research Council Canada (NRC)

Barry Wood National Research Council Canada (NRC)
PIRSA:18050045 


Talk


Data Mists, Blockchain Republics, and the Moon Shot
Simon DeDeo Indiana University

Like penguins on an ice floe: The scary business of adopting open science practices
Benedikt Fecher Alexander von HumboldtStiftung

Collaborative Knowledge Ratchets and Fermat's Library

Jess Riedel NTT Research

Luis Batalha Fermat's Library
PIRSA:18030101 


What’s not to like? Open science will fail unless it takes the costs seriously
Rosie Redfield University of British Columbia




Talk

PSI 2016/2017  Quantum Field Theory III  Lecture 15
Jaume Gomis Perimeter Institute for Theoretical Physics

PSI 2016/2017  Quantum Field Theory III  Lecture 14
Jaume Gomis Perimeter Institute for Theoretical Physics

PSI 2016/2017  Quantum Field Theory III  Lecture 13
Jaume Gomis Perimeter Institute for Theoretical Physics
PIRSA:17020089 
PSI 2016/2017  Quantum Field Theory III  Lecture 12
Jaume Gomis Perimeter Institute for Theoretical Physics

PSI 2016/2017  Quantum Field Theory III  Lecture 11
Jaume Gomis Perimeter Institute for Theoretical Physics

PSI 2016/2017  Quantum Field Theory III  Lecture 10
Jaume Gomis Perimeter Institute for Theoretical Physics

PSI 2016/2017  Quantum Field Theory III  Lecture 9
Jaume Gomis Perimeter Institute for Theoretical Physics

PSI 2016/2017  Quantum Field Theory III  Lecture 8
Jaume Gomis Perimeter Institute for Theoretical Physics


Talk

Talk

PSI 2016/2017  Mathematica  Lecture 4
Erik Schnetter Perimeter Institute for Theoretical Physics
PIRSA:16090040 
PSI 2016/2017  Mathematica  Lecture 3
Erik Schnetter Perimeter Institute for Theoretical Physics
PIRSA:16090039 

PSI 2016/2017  Mathematica  Lecture 1
Erik Schnetter Perimeter Institute for Theoretical Physics


Talk







PSI 2016/2017  Functions, "Functions", etc.  Lecture 1
Dan Wohns Perimeter Institute for Theoretical Physics


Talk




PSI 2016/2017  Complex Analysis  Lecture 1
Tibra Ali Perimeter Institute for Theoretical Physics


Talk







PSI 2016/2017  Classical Mechanics  Lecture 1
David Kubiznak Charles University


Classical Black Hole Scattering from a WorldLine Quantum Field Theory  VIRTUAL
Jan Plefka Humboldt University of Berlin

Machine Learning Renormalization Group (VIRTUAL)
YiZhuang You University of California, San Diego

Deeptech Commercialization through Entrepreneurial Capabilities
Elicia Maine Simon Fraser University (SFU)


Open Research: Rethinking Scientific Collaboration
Scientific inquiry in the 21st century is beset with inefficiencies: a flood of papers not read theories not tested and experiments not repeated; a narrow research agenda driven by a handful of highimpact journals; a publishing industry that turns public funding into private profit; the exclusion of many scientists particularly in developing countries from cuttingedge research; and countless projects that are not completed for lack of skilled collaborators. These are all symptoms of a major communication bottleneck within the scientific community; the channels we rely on to share our ideas and findings especially peerreviewed journal articles and conference proceedings are inadequate to the scale and scope of modern science. The practice of open research doing science on a public platform that facilitates collaboration feedback and the spread of ideas addresses these concerns. Opensource science lowers barriers to entry catalyzing new discoveries. It fosters the realtime sharing of ideas across the globe favoring cooperative endeavor and complementarity of thought rather than wasteful competition. It reduces the influence of publishing monopolies enabling a new credit attribution model based on contributions made rather than references accrued. Overall it democratizes science while creating a new standard of prestige: quality of work instead of quantity of output. This workshop will bring together a diverse group of researchers from fields as diverse as physics biology computer science and sociology committed to opensource science. Together we will review the lessons learnt from various pioneering initiatives such as the Polymath project and Data for Democracy. We will discuss the opportunity to build a new tool similar to the software development platform GitHub to enable online collaborative science. We will consider the challenges associated with the adoption of such a tool by our peers and discuss ways to overcome them. Finally we will sketch a roadmap for the actual development of that tool.

PSI 2016/2017  Quantum Field Theory III (Gomis)
PSI 2016/2017  Quantum Field Theory III (Gomis) 
PSI 2016/2017  Condensed Matter (Dalidovich)
PSI 2016/2017  Condensed Matter (Dalidovich) 
PSI 2016/2017  Mathematica (Schnetter)
PSI 2016/2017  Mathematica (Schnetter) 
PSI 2016/2017  Functions, "Functions", etc. (Wohns)
PSI 2016/2017  Functions, "Functions", etc. (Wohns) 
PSI 2016/2017  Complex Analysis (Ali)
PSI 2016/2017  Complex Analysis (Ali) 
PSI 2016/2017  Classical Mechanics (Kubiznak)
PSI 2016/2017  Classical Mechanics (Kubiznak) 
Classical Black Hole Scattering from a WorldLine Quantum Field Theory  VIRTUAL
Jan Plefka Humboldt University of Berlin
Predicting the outcome of scattering processes of elementary particles in colliders is the central achievement of relativistic quantum field theory applied to the fundamental (nongravitational) interactions of nature. While the gravitational interactions are too minuscule to be observed in the microcosm, they dominate the interactions at large scales. As such the inspiral and merger of black holes and neutron stars in our universe are now routinely observed by gravitational wave detectors. The need for high precision theory predictions of the emitted gravitational waveforms has opened a new window for the application of perturbative quantum field theory techniques to the domain of gravity. In this talk I will show how observables in the classical scattering of black holes and neutron stars can be efficiently computed in a perturbative expansion using a worldline quantum field theory; thereby combining stateoftheart Feynman integration technology with perturbative quantum gravity. Here, the black holes or neutron stars are modelled as point particles in an effective field theory sense. Fascinatingly, the intrinsic spin of the black holes may be captured by a supersymmetric extension of the worldline theory, enabling the computation of the far field waveform including spin and tidal effects to highest precision. I will review our most recent results at the fifth order in the postMinkowskian expansion amounting to the computations of hundreds of thousands of four loop Feynman integrals.


Machine Learning Renormalization Group (VIRTUAL)
YiZhuang You University of California, San Diego
We develop a MachineLearning Renormalization Group (MLRG) algorithm to explore and analyze manybody lattice models in statistical physics. Using the representation learning capability of generative modeling, MLRG automatically learns the optimal renormalization group (RG) transformations from selfgenerated spin configurations and formulates RG equations without human supervision. The algorithm does not focus on simulating any particular lattice model but broadly explores all possible models compatible with the internal and lattice symmetries given the onsite symmetry representation. It can uncover the RG monotone that governs the RG flow, assuming a strong form of the $c$theorem. This enables several downstream tasks, including unsupervised classification of phases, automatic location of phase transitions or critical points, controlled estimation of critical exponents, and operator scaling dimensions. We demonstrate the MLRG method in twodimensional lattice models with Ising symmetry and show that the algorithm correctly identifies and characterizes the Ising criticality.


Deeptech Commercialization through Entrepreneurial Capabilities
Elicia Maine Simon Fraser University (SFU)
Presented in collaboration with Navigating Quantum and AI Career Trajectories: A Beginner’s MiniCourse on Computational Methods and their Applications

Deeptech or sciencebased innovations often spend more than a decade percolating within academic and government labs before their value is recognized (Park et al., 2022). This development lag time prior to venture formation is only partly due to technological development hurdles. Because sciencebased inventions are often generic in nature (Maine & Garnsey, 2006), meaning that they have broad applicability across many different markets, the problem of identifying a first application requires the confluence of deep technical understanding with expert knowledge of the practice of commercialization. This process of technologymarket matching is a critical aspect of the translation of sciencebased research out of the lab (Pokrajak 2021, Gruber and Tal, 2017; Thomas et al, 2020, Maine et al, 2015) and is often delayed by a lack of capacity to identify, prioritize and protect market opportunities. Typically, deeptech innovations can take 1015 years of development, and tens (or even hundreds) of millions of dollars of investment to derisk before a first commercial application (Maine & Seegopaul, 2016). Academics seeking to commercialize such inventions face the daunting challenge of competing for investment dollars in markets that are ill suited to the uncertainty and timescales of deep tech development. The timemoney uncertainty challenge faced by sciencebased innovators is compounded by the fact that most of the scientists and engineers with the worldleading technical skills required to develop sciencebased inventions, lack innovation skills training, and so cannot navigate the complexities of early and precommercialization development critical to venture success. Some researchers, having developed a mix of technical and business expertise, have demonstrated a longterm ability to serially spin out successful ventures (Thomas et al., 2020). Entrepreneurial capabilities, which can be learned, enable scientistentrepreneurs to play formative roles in commercialising labbased scientific inventions through the formation of wellendowed university spinoffs. (Park et al, 2022; 2024). Commercialization postdocs, when supported by well designed training, stipends, and derisking supports, can lead the mobilization of fundamental research along multiple commercialization pathways. Recommendations are provided for scholars, practitioners, and policymakers to more effectively commercialise deeptech inventions.

