Making computer animation more agile, acrobatic — and realistic

Graduate student Xue Bin “Jason” Peng (advisors Pieter Abbeel and Sergey Levine) has made a major advance in realistic computer animation using deep reinforcement learning to recreate natural motions, even for acrobatic feats like break dancing and martial arts. The simulated characters can also respond naturally to changes in the environment, such as recovering from tripping or being pelted by projectiles.  “We developed more capable agents that behave in a natural manner,” Peng said. “If you compare our results to motion-capture recorded from humans, we are getting to the point where it is pretty difficult to distinguish the two, to tell what is simulation and what is real. We’re moving toward a virtual stuntman.”  Peng will present his paper at the 2018 SIGGRAPH conference in August.

Atomically thin light emitting device opens the possibility for ‘invisible’ displays

Prof. Ali Javey,  postdoc Der-Hsien Lien, and graduate students Matin Amani and Sujay Desai have built a bright-light emitting device that is millimeters wide and fully transparent when turned off.  The light emitting material in this device is a monolayer semiconductor, which is just three atoms thick.  It opens the door to invisible displays on walls and windows – displays that would be bright when turned on but see-through when turned off — or in futuristic applications such as light-emitting tattoos.  “The materials are so thin and flexible that the device can be made transparent and can conform to curved surfaces,” said  Lien. Their research was published in the journal Nature Communications on March 26.

A step forward in Stephen Derenzo's search for dark matter

Prof. Stephen Derenzo is quoted in an article for Australia’s Particle about a new material for a proposed detector of weakly interactive massive particles (WIMPs).  Derenzo is the lead author of a study published March 20 in the Journal of Applied Physics about a crystal called gallium arsenide (GaAs) which features added concentrations, or “dopants,” of silicon and boron.  This material possesses a scintillation property--it lights up in particle interactions that knock away electrons. According to Derenzo, who is a senior physicist in the Molecular Biophysics and Integrated Bioimaging Division at Berkeley Lab, the new ultrasensitive detector technology could scan for dark matter signals at energies thousands of times lower than those measurable by more conventional WIMP detectors. “It’s a privilege to be working on such an important problem in physics, but the celebration will have to wait until clear signals are seen,” he says. “It’s possible that dark matter particles are even lighter than what we can see with GaAs, and their discovery will have to wait for even more sensitive experiments.”

John Kubiatowicz and Group's (Circa 2000) Paper Named Most Influential at ASPLOS 2018

At the ASPLOS conference in late March, John Kubitowicz and his group from 2000 were celebrated for their paper, "OceanStore: an architecture for global-scale persistent storage." The paper was named Most Influential Paper 2018, and the authors receiving the award included David Bindel, Yan Chen, Steven Czerwinski, Patrick Eaton, Dennis Geels, Ramakrishna Gummadi, Sean Rhea, Hakim Weatherspoon, Chris Wells, and Ben Zhao, as well as Kubi, a long-time Berkeley CS faculty member. The paper was originally published in the Proceedings of the ninth international conference on Architectural support for programming languages and operating systems (ASPLOS IX). 

Carlini (photo: Kore Chan/Daily Cal)

AI training may leak secrets to canny thieves

A paper released on arXiv last week by a team of researchers including Prof. Dawn Song and Ph.D. student Nicholas Carlini (B.A. CS/Math '13), reveals just how vulnerable deep learning is to information leakage.  The researchers labelled the problem “unintended memorization” and explained it happens if miscreants can access to the model’s code and apply a variety of search algorithms. That's not an unrealistic scenario considering the code for many models are available online, and it means that text messages, location histories, emails or medical data can be leaked.  The team doesn't “really know why neural networks memorize these secrets right now, ” Carlini says.  “At least in part, it is a direct response to the fact that we train neural networks by repeatedly showing them the same training inputs over and over and asking them to remember these facts."   The best way to avoid all problems is to never feed secrets as training data. But if it’s unavoidable then developers will have to apply differentially private learning mechanisms, to bolster security, Carlini concluded.

Ben Recht wins NIPS Test of Time Award

Prof. Ben Recht has won the Neural Information Processing System (NIPS) 2017 Test of Time Award for a paper he co-wrote with Ali Rahimi in 2007 titled "Random Features for Large-Scale Kernel Machines."   Deep learning, which involves stacking many neural networks on top of one another to learn the features of giant databases and develop clever algorithms, is being used to carry out more and more tasks in an expanding number of areas.  In their acceptance speech at the NIPS conference, Recht and Rahimi posited that more theory is needed to understand the state-of-the-art empirical performance of deep learning, and called for simple theorems and simple, easily reproducible experiments.  "We are building systems that govern healthcare and mediate our civic dialogue, we influence elections," said Rahimi. "I would like to live in a society where systems are built on top of verifiable, rigorous thorough knowledge and not alchemy."

Arcak and Coogan

Murat Arcak and Sam Coogan win the 2017 IEEE Transactions on Control of Network Systems Outstanding Paper Award

Prof. Murat Arcak, alumnus Samuel Coogan (M.S. '12/Ph.D. '15), and their co-authors on the paper titled “Traffic network control from temporal logic specifications,” have won the 2017 IEEE Transactions on Control of Network Systems Outstanding Paper Award.  The award is presented annually by the IEEE Control Systems Society to recognize an outstanding paper published in the IEEE Transactions on Control Systems Technology.  Judging is based on originality, potential impact on the foundations of network systems, importance and practical significance in applications, and clarity.  Coogan, who is now an assistant professor at UCLA, received the EECS Eli Jury Award in 2016 for "outstanding achievement in the area of systems, communications, control, or signal processing," and the 2014 Leon O. Chua Award for "outstanding achievement in an area of nonlinear science."

C. L. Hoang publishes "Rain Falling on Tamarind Trees"

A  new book, Rain Falling on Tamarind Trees, by EE alumnus C. L. Hoang (M.S. '82), is set to be released by Willow Stream Publishing tomorrow.  Hoang was born and raised in Vietnam during the war and came to the US in the 1970s.  He wrote this travelogue about his experiences returning to his ancestral homeland for the first time, in 2016, after a decades-long absence.   He still makes his living as an engineer--he holds 11 patents--and says that his engineering training helped him hone his organizational skills and develop an analytical eye for details as well as a love for research.  His first book, Once upon a Mulberry Field, is a historical novel set in Vietnam during the height of the war.

Schematic of a magnetic memory array

EECS-affiliated team develops new, ultrafast method for electrically controlling magnetism in certain metals

A UC Berkeley/UC Riverside research group that includes Prof. Jeffrey Bokor, Prof. Sayeef Salahuddin, postdoc Charles-Henri Lambert, postdoctoral fellow Jon Gorchon, and EE graduate student Akshay Pattabi have developed a new, ultrafast method for electrically controlling magnetism in certain metals, a breakthrough that could lead to greatly increased performance and more energy-efficient computer memory and processing technologies.  Their findings are published in both Science Advances (Vol. 3, No. 49, Nov. 3, 2017) under the title Ultrafast magnetization reversal by picosecond electrical pulses and Applied Physics Letters (Vol. III, No. 4, July 24, 2017) under the title Single shot ultrafast all optical magnetization switching of ferromagnetic Co/Pt multilayers.  “The development of a non-volatile memory that is as fast as charge-based random-access memories could dramatically improve performance and energy efficiency of computing devices,” says Bokor. “That motivated us to look for new ways to control magnetism in materials at much higher speeds than in today’s MRAM.”

Pramod Subramanyan and Rohit Sinha

"A Formal Foundation for Secure Remote Execution of Enclaves" wins Best Paper Award at ACM CCS 2017

A paper co-authored by postdoc Pramod Subramanyan, grad student Rohit Sinha, alumnus Ilia Lebedev (B.S. '10), alumnus and MIT Prof. Srinivas Devadas (M.S. '86/Ph.D. '88), and EECS Prof. Sanjit A. Seshia has won Best Paper Award at the 2017 ACM Conference on Computer and Communications Security (CCS).  The paper, A Formal Foundation for Secure Remote Execution of Enclaves, introduces a formal modeling and verification methodology for secure remote execution based on the notion of a trusted abstract platform.  CCS is the flagship annual conference of the Special Interest Group on Security, Audit and Control (SIGSAC) of the Association for Computing Machinery (ACM).