Data-sharing vision as facilitated by GA4GH through its working groups (credit: GA4GH)
Sharing genetic information from millions of cancer patients around the world could revolutionize cancer prevention and care, according to a paper in Nature Medicine by the Cancer Task Team of the Global Alliance for Genomics and Health (GA4GH).
Hospitals, laboratories and research facilities around the world hold huge amounts of this data from cancer patients, but it’s currently held in isolated “silos” that don’t talk to each other, according to GA4GH, a partnership between scientists, clinicians, patients, and the IT and Life Sciences industry, involving more than 400 organizations in over 40 countries. GA4GH intends to provide a common framework for the responsible, voluntary and secure sharing of patients’ clinical and genomic data.
A searchable global cancer database
“Imagine if we could create a searchable cancer database that allowed doctors to match patients from different parts of the world with suitable clinical trials,” said GA4GH co-chair professor Mark Lawler, a leading cancer expert from Queen’s University Belfast. “This genetic matchmaking approach would allow us to develop personalized treatments for each individual’s cancer, precisely targeting rogue cells and improving outcomes for patients.
“This data sharing presents logistical, technical, and ethical challenges. Our paper highlights these challenges and proposes potential solutions to allow the sharing of data in a timely, responsible and effective manner. We hope this blueprint will be adopted by researchers around the world and enable a unified global approach to unlocking the value of data for enhanced patient care.”
GA4GH acknowledges that there are security issues, and has created a Security Working Group and a policy paper that documents the standards and implementation practices for protecting the privacy and security of shared genomic and clinical data.
Examples of current initiatives for clinico-genomic data-sharing include the U.S.-based Precision Medicine Initiative and the UK’s 100,000 Genomes Project, both of which have cancer as a major focus.
Professor Lawler is funded by the Medical Research Council and Cancer Research UK.
Abstract of Facilitating a culture of responsible and effective sharing of cancer genome data
Rapid and affordable tumor molecular profiling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostication and treatment of cancer. A critical point has now been reached at which the analysis and storage of annotated clinical and genomic information in unconnected silos will stall the advancement of precision cancer care. Information systems must be harmonized to overcome the multiple technical and logistical barriers to data sharing. Against this backdrop, the Global Alliance for Genomic Health (GA4GH) was established in 2013 to create a common framework that enables responsible, voluntary and secure sharing of clinical and genomic data. This Perspective from the GA4GH Clinical Working Group Cancer Task Team highlights the data-aggregation challenges faced by the field, suggests potential collaborative solutions and describes how GA4GH can catalyze a harmonized data-sharing culture.
For the first time, scientists at IBM Research have demonstrated reliably storing 3 bits of data per cell using a relatively new memory technology known as phase-change memory (PCM). In this photo, the experimental multi-bit PCM chip used by IBM scientists is connected to a standard integrated circuit board. The chip consists of a 2 × 2 Mcell array with a 4- bank interleaved architecture. The memory array size is 2 × 1000 μm × 800 μm. The PCM cells are based on doped-chalcogenide alloy and were integrated into the prototype chip, serving as a characterization vehicle in 90 nm CMOS baseline technology. (credit: IBM Research)
Scientists at IBM Research have demonstrated — for the first time (today, May 17), at the IEEE International Memory Workshop in Paris — reliably storing 3 bits of data per cell in a 64k-cell array in a memory chip*, using a relatively new memory technology known as phase-change memory (PCM). Previously, scientists at IBM and elsewhere successfully demonstrated the ability to store only 1 bit per cell in PCM.
The current memory landscape includes DRAM, hard disk drives, and flash. But in the last several years, PCM has attracted the industry’s attention as a potential universal memory technology based on its combination of read/write speed, endurance, non-volatility and density. For example, PCM doesn’t lose data when powered off, unlike DRAM, and the technology can endure at least 10 million write cycles, compared to an average flash USB stick, which tops out at 3,000 write cycles.
IBM suggests this research breakthrough provides fast and easy storage to capture the exponential growth of data from mobile devices and the Internet of Things.
Scientists have long been searching for a universal, non-volatile memory technology with performance far superior to flash — today’s most ubiquitous non-volatile memory technology. The benefits of such a memory technology would allow computers and servers to boot instantaneously and would significantly enhance the overall performance of IT systems. A promising contender is PCM, which can write and retrieve data 100 times faster than Flash and enable high storage capacities, and like flash, not lose data when the power is turned off. Unlike flash, PCM is also very durable and can endure at least 10 million write cycles, compared to current enterprise-class flash at 30,000 cycles or consumer-class flash at 3,000 cycles. While 3,000 cycles will outlive many consumer devices, 30,000 cycles are orders of magnitude too low to be suitable for enterprise applications. (credit: IBM Research)
IBM scientists envision standalone PCM as well as hybrid applications that combine PCM and flash storage, with PCM as an extremely fast cache. For example, a mobile phone’s operating system could be stored in PCM, enabling the phone to launch in a few seconds. In the enterprise space, entire databases could be stored in PCM for blazing fast query processing in time-critical online applications, such as financial transactions.
Machine learning algorithms using large datasets will also see a speed boost by reducing the latency overhead when reading the data between iterations.
How PCM Works: answering the grand challenge of combining properties of DRAM and flash
PCM materials exhibit two stable states: the amorphous (without a clearly defined structure) and crystalline (with structure) phases (low and high electrical conductivity, respectively).
To store a “0″ or a “1″ bit on a PCM cell, a high or medium electrical current is applied to the material. A “0″ can be programmed to be written in the amorphous phase and a “1″ in the crystalline phase, or vice versa. Then to read the bit back, a low voltage is applied. This is how re-writable (but slower) Blu-ray discs** store videos.
Phase-change memory (PCM) is one of the most promising candidates for next-generation non-volatile memory technology. The cross-sectional tunneling electron microscopy (TEM) image of a mushroom-type PCM cell is shown in this photo. The cell consists of a layer of phase-change material, such as germanium antimony telluride (GST), sandwiched between a bottom and a top electrode. In this architecture, the bottom electrode has a radius (denoted as rE ) of approx. 15 nm and is fabricated by sub-lithographic means. The top electrode has a radius in excess of 100 nm and the thickness of the phase change layer is approx. 100 nm. A transistor or a diode is typically employed as the access device. (credit: IBM Research — Zurich)
“Phase change memory is the first instantiation of a universal memory with properties of both DRAM and flash, thus answering one of the grand challenges of our industry,” said Haris Pozidis, PhD., an author of the workshop paper and the manager of non-volatile memory research at IBM Research–Zurich. “Reaching 3 bits per cell is a significant milestone because at this density, the cost of PCM will be significantly less than DRAM and closer to flash.”
To achieve multi-bit storage, IBM scientists have developed two innovative enabling technologies: a set of drift-immune cell-state metrics and drift-tolerant coding and detection schemes***. “Combined, these advancements address the key challenges of multi-bit PCM, including drift, variability, temperature sensitivity, and endurance cycling,” said IBM Fellow Evangelos Eleftheriou, PhD.
IBM scientists have also demonstrated, for the first time, phase-change memory attached to POWER8-based servers.
* At elevated temperatures and after 1 million endurance cycles.
*** More specifically, the new cell-state metrics measure a physical property of the PCM cell that remains stable over time, and are thus insensitive to drift, which affects the stability of the cell’s electrical conductivity with time. To provide additional robustness of the stored data in a cell over ambient temperature fluctuations, a novel coding and detection scheme is employed. This scheme adaptively modifies the level thresholds that are used to detect the cell’s stored data so that they follow variations due to temperature change. As a result, the cell state can be read reliably over long time periods after the memory is programmed, thus offering non-volatility.
The experimental multi-bit PCM chip used by IBM scientists is connected to a standard integrated circuit board. The chip consists of a 2 × 2 Mcell array with a four-bank interleaved architecture. The memory array size is 2 × 1000 μm × 800 μm. The PCM cells are based on doped-chalcogenide alloy and were integrated into the prototype chip serving as a characterization vehicle in 90 nm CMOS baseline technology.
IBM Research | IBM Scientists Achieve Storage Memory Breakthrough
Abstract of Multilevel-Cell Phase Change Memory: A Viable Technology
In order for any non-volatile memory (NVM) to be considered a viable technology, its reliability should be verified at the array level. In particular, properties such as high endurance and at least moderate data retention are considered essential. Phase-change memory (PCM) is one such NVM technology that possesses highly desirable features and has reached an advanced level of maturity through intensive research and development in the past decade. Multilevel-cell (MLC) capability, i.e., storage of two bits per cell or more, is not only desirable as it reduces the effective cost per storage capacity, but a necessary feature for the competitiveness of PCM against the incumbent technologies, namely DRAM and Flash memory. MLC storage in PCM, however, is seriously challenged by phenomena such as cell variability, intrinsic noise, and resistance drift. We present a collection of advanced circuit-level solutions to the above challenges, and demonstrate the viability of MLC PCM at the array level. Notably, we demonstrate reliable storage and moderate data retention of 2 bits/cell PCM, on a 64 k cell array, at elevated temperatures and after 1 million SET/RESET endurance cycles. Under similar operating conditions, we also show feasibility of 3 bits/cell PCM, for the first time ever.
A simulation of Brownian motion (random walk) of a dust particle (yellow) that collides with a large set of smaller particles (molecules of a gas) moving with different velocities in different random directions (credit: Lookang et al./CC)
Researchers at the Universities of Bristol and Western Australia have demonstrated a practical use of a “primitive” quantum computer, using an algorithm known as “quantum walk.” They showed that a two-qubit photonics quantum processor can outperform classical computers for this type of algorithm, without requiring more sophisticated quantum computers, such as IBM’s five-qubits cloud-based quantum processor (see IBM makes quantum computing available free on IBM Cloud).
Quantum walk is the quantum-mechanical analog of “random-walk” models such as Brownian motion (for example, the random motion of a dust particle in air). The researchers implemented “continuous-time quantum walk” computations on circulant graphs* in a proof-of-principle experiment.
The probability distribution of quantum walk on an example circulant graph. Sampling this probability distribution is generally hard for a classical computer, but simple on a primitive quantum computer. (credit: University of Bristol)
Jonathan Matthews, PhD., EPSRC Early Career Fellow and Lecturer in the School of Physics and the Centre for Quantum Photonics, explained in an open-access paper in Nature Communications: “An exciting outcome of our work is that we may have found a new example of quantum walk physics that we can observe with a primitive quantum computer, that otherwise a classical computer could not see. These otherwise hidden properties have practical use, perhaps in helping to design more sophisticated quantum computers.”
Microsoft | Quantum Computing 101
* A circulant graph is a graph where every vertex is connected to the same set of relative vertices, as explained in an open-access paper by Salisbury University student Shealyn Tucker, including a practical example of the use of a circulant graph:
Example of a circulent graph depicting how products should be optimally collocated based on which products customers buy at a grocery store (credit: Shealyn Tucker/Salisbury University)
Abstract of Efficient quantum walk on a quantum processor
The random walk formalism is used across a wide range of applications, from modelling share prices to predicting population genetics. Likewise, quantum walks have shown much potential as a framework for developing new quantum algorithms. Here we present explicit efficient quantum circuits for implementing continuous-time quantum walks on the circulant class of graphs. These circuits allow us to sample from the output probability distributions of quantum walks on circulant graphs efficiently. We also show that solving the same sampling problem for arbitrary circulant quantum circuits is intractable for a classical computer, assuming conjectures from computational complexity theory. This is a new link between continuous-time quantum walks and computational complexity theory and it indicates a family of tasks that could ultimately demonstrate quantum supremacy over classical computers. As a proof of principle, we experimentally implement the proposed quantum circuit on an example circulant graph using a two-qubit photonics quantum processor.
Smartphones and other digital technology may be causing ADHD-like symptoms, according to an open-access study published in the proceedings of ACM CHI ’16, the Human-Computer Interaction conference of the Association for Computing Machinery, ongoing in San Jose.
In a two-week experimental study, University of Virginia and University of British Columbia researchers showed that when students kept their phones on ring or vibrate and with notification alerts on, they reported more symptoms of inattention and hyperactivity than when they kept their phones on silent.
The results suggest that even people who have not been diagnosed with ADHD may experience some of the disorder’s symptoms, including distraction, fidgeting, having trouble sitting still, difficulty doing quiet tasks and activities, restlessness, and difficulty focusing and getting bored easily when trying to focus, the researchers said.
“We found the first experimental evidence that smartphone interruptions can cause greater inattention and hyperactivity — symptoms of attention deficit hyperactivity disorder — even in people drawn from a nonclinical population,”said Kostadin Kushlev, a psychology research scientist at the University of Virginia who led the study with colleagues at the University of British Columbia.
In the study, 221 students at the University of British Columbia drawn from the general student population were assigned for one week to maximize phone interruptions by keeping notification alerts on, and their phones within easy reach.
Indirect effects of manipulating smartphone interruptions on psychological well-being via inattention symptoms. Numbers are unstandardized regression coefficients. (credit: Kostadin Kushlev et al./CHI 2016)
During another week participants were assigned to minimize phone interruptions by keeping alerts off and their phones away.
At the end of each week, participants completed questionnaires assessing inattention and hyperactivity. Unsurprisingly, the results showed that the participants experienced significantly higher levels of inattention and hyperactivity when alerts were turned on.
Digital mobile users focus more on concrete details than the big picture
Using digital platforms such as tablets and laptops for reading may also make you more inclined to focus on concrete details rather than interpreting information more contemplatively or abstractly (seeing the big picture), according to another open-access study published in ACM CHI ’16 proceedings.
Researchers at Dartmouth’s Tiltfactor lab and the Human-Computer Interaction Institute at Carnegie Mellon University conducted four studies with a total of 300 participants. Participants were tested by reading a short story and a table of information about fictitious Japanese car models.
The studies revealed that individuals who completed the same information processing task on a digital mobile device (a tablet or laptop computer) versus a non-digital platform (a physical printout) exhibited a lower level of “construal” (abstract) thinking. However, the researchers also found that engaging the subjects in a more abstract mindset prior to an information processing task on a digital platform appeared to help facilitate a better performance on tasks that require abstract thinking.
Coping with digital overload
Given the widespread acceptance of digital devices, as evidenced by millions of apps, ubiquitous smartphones, and the distribution of iPads in schools, surprisingly few studies exist about how digital tools affect us, the researchers noted.
“The ever-increasing demands of multitasking, divided attention, and information overload that individuals encounter in their use of digital technologies may cause them to ‘retreat’ to the less cognitively demanding lower end of the concrete-abstract continuum,” according to the authors. They also say the new research suggests that “this tendency may be so well-ingrained that it generalizes to contexts in which those resource demands are not immediately present.”
Their recommendation for human-computer interaction designers and researchers: “Consider strategies for encouraging users to see the ‘forest’ as well as the ‘trees’ when interacting with digital platforms.”
Jony Ive, are you listening?
Abstract of “Silence your phones”: Smartphone notifications increase inattention and hyperactivity symptoms
As smartphones increasingly pervade our daily lives, people are ever more interrupted by alerts and notifications. Using both correlational and experimental methods, we explored whether such interruptions might be causing inattention and hyperactivity-symptoms associated with Attention Deficit Hyperactivity Disorder (ADHD) even in people not clinically diagnosed with ADHD. We recruited a sample of 221 participants from the general population. For one week, participants were assigned to maximize phone interruptions by keeping notification alerts on and their phones within their reach/sight. During another week, participants were assigned to minimize phone interruptions by keeping alerts off and their phones away. Participants reported higher levels of inattention and hyperactivity when alerts were on than when alerts were off. Higher levels of inattention in turn predicted lower productivity and psychological well-being. These findings highlight some of the costs of ubiquitous connectivity and suggest how people can reduce these costs simply by adjusting existing phone settings.
Abstract of High-Low Split: Divergent Cognitive Construal Levels Triggered by Digital and Non-digital Platforms
The present research investigated whether digital and non-digital platforms activate differing default levels of cognitive construal. Two initial randomized experiments revealed that individuals who completed the same information processing task on a digital mobile device (a tablet or laptop computer) versus a non-digital platform (a physical print-out) exhibited a lower level of construal, one prioritizing immediate, concrete details over abstract, decontextualized interpretations. This pattern emerged both in digital platform participants’ greater preference for concrete versus abstract descriptions of behaviors as well as superior performance on detail-focused items (and inferior performance on inference-focused items) on a reading comprehension assessment. A pair of final studies found that the likelihood of correctly solving a problem-solving task requiring higher-level “gist” processing was: (1) higher for participants who processed the information for task on a non-digital versus digital platform and (2) heightened for digital platform participants who had first completed an activity activating an abstract mindset, compared to (equivalent) performance levels exhibited by participants who had either completed no prior activity or completed an activity activating a concrete mindset.
Layout of IBM’s five superconducting quantum bit device. In 2015, IBM scientists demonstrated critical breakthroughs to detect quantum errors by combining superconducting qubits in latticed arrangements, and whose quantum circuit design is the only physical architecture that can scale to larger dimensions. Now, IBM scientists have achieved a further advance by combining five qubits in the lattice architecture, which demonstrates a key operation known as a parity measurement — the basis of many quantum error correction protocols. (credit: IBM Research)
IBM Research has announced that effective Wednesday May 4, it is making quantum computing available free to members of the public, who can access and run experiments on IBM’s quantum processor, via the IBM Cloud, from any desktop or mobile device.
IBM believes quantum computing is the future of computing and has the potential to solve certain problems that are impossible to solve on today’s supercomputers.
The cloud-enabled quantum computing platform, called IBM Quantum Experience, will allow users to run algorithms and experiments on IBM’s quantum processor, work with the individual quantum bits (qubits), and explore tutorials and simulations around what might be possible with quantum computing.
The quantum processor is composed of five superconducting qubits and is housed at the IBM T.J. Watson Research Center in New York. IBM’s quantum architecture can scale to larger quantum systems. It is aimed at building a universal quantum computer that can be programmed to perform any computing task and will be exponentially faster than classical computers for a number of important applications for science and business, IBM says.
IBM | Explore our 360 Video of the IBM Research Quantum Lab
IBM envisions medium-sized quantum processors of 50–100 qubits to be possible in the next decade. With a quantum computer built of just 50 qubits, none of today’s TOP500 supercomputers could successfully emulate it, reflecting the tremendous potential of this technology.
“Quantum computing is becoming a reality and it will extend computation far beyond what is imaginable with today’s computers,” said Arvind Krishna, senior vice president and director, IBM Research. “This moment represents the birth of quantum cloud computing. By giving hands-on access to IBM’s experimental quantum systems, the IBM Quantum Experience will make it easier for researchers and the scientific community to accelerate innovations in the quantum field, and help discover new applications for this technology.”
This leap forward in computing could lead to the discovery of new pharmaceutical drugs and completely safeguard cloud computing systems, IBM believes. It could also unlock new facets of artificial intelligence (which could lead to future, more powerful Watson technologies), develop new materials science to transform industries, and search large volumes of big data.
The IBM Quantum Experience
IBM | Running an experiment in the IBM Quantum Experience
Coupled with software expertise from the IBM Research ecosystem, the team has built a dynamic user interface on the IBM Cloud platform that allows users to easily connect to the quantum hardware via the cloud.
In the future, users will have the opportunity to contribute and review their results in the community hosted on the IBM Quantum Experience and IBM scientists will be directly engaged to offer more research and insights on new advances. IBM plans to add more qubits and different processor arrangements to the IBM Quantum Experience over time, so users can expand their experiments and help uncover new applications for the technology.
IBM employs superconducting qubits that are made with superconducting metals on a silicon chip and can be designed and manufactured using standard silicon fabrication techniques. Last year, IBM scientists demonstrated critical breakthroughs to detect quantum errors by combining superconducting qubits in latticed arrangements, and whose quantum circuit design is the only physical architecture that can scale to larger dimensions.
IBM | IBM Brings Quantum Computing to the Cloud
Now, IBM scientists have achieved a further advance by combining five qubits in the lattice architecture, which demonstrates a key operation known as a parity measurement — the basis of many quantum error correction protocols.
By giving users access to IBM’s experimental quantum systems, IBM believes it will help businesses and organizations begin to understand the technology’s potential, for universities to grow their teaching programs in quantum computing and related subjects, and for students (IBM’s potential future customers) to become aware of promising new career paths. And of course, to raise IBM’s marketing profile in this emerging field.
Lawrence Livermore’s new supercomputer system uses 16 IBM TrueNorth chips developed by IBM Research (credit: IBM Research)
Lawrence Livermore National Laboratory (LLNL) has purchased IBM Research’s supercomputing platform for deep-learning inference, based on 16 IBM TrueNorth neurosynaptic computer chips, to explore deep learning algorithms.
IBM says the scalable platform processing power is the equivalent of 16 million artificial “neurons” and 4 billion “synapses.” The brain-like neural-network design of the IBM Neuromorphic System can process complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips, says IBM.
The technology represents a fundamental departure from computer design that has been prevalent for the past 70 years and could be incorporated in next-generation supercomputers able to perform at exascale speeds — 50 times faster than today’s most advanced petaflop (quadrillion floating point operations per second) systems.
Ultra-low-energy TrueNorth processor
IBM TrueNorth neuromorphic supercomputing processor chip (credit: IBM Research)
The TrueNorth processor chip was introduced in 2014 (see IBM launches functioning brain-inspired chip). It consists of 5.4 billion transistors wired together to create an array of 1 million digital “neurons” that communicate with one another via 256 million electrical “synapses.”
Like the human brain, neurosynaptic systems require significantly less electrical power and volume. The 16 TrueNorth chips consume the energy equivalent of only a tablet computer — 2.5 watts of power. At 0.8 volts, each chip consumes 70 milliwatts of power running in real time and delivers 46 giga synaptic operations per second.
“The delivery of this advanced computing platform represents a major milestone as we enter the next era of cognitive computing,” said Dharmendra S. Modha, IBM Fellow, chief scientist, brain-inspired computing, IBM Research – Almaden. “Prior to design and fabrication, we simulated the IBM TrueNorth processor using LLNL’s Sequoia supercomputer. This collaboration will push the boundaries of brain-inspired computing to enable future systems that deliver unprecedented capability and throughput, while helping to minimize the capital, operating, and programming costs.”
Protecting the US nuclear stockpile
The new system will be used to explore new computing capabilities important to the National Nuclear Security Administration’s (NNSA) missions in cyber security — stewardship of the nation’s nuclear deterrent and non-proliferation.
NNSA’s Advanced Simulation and Computing (ASC) program — a cornerstone of NNSA’s Stockpile Stewardship Program — will evaluate machine learning applications, deep learning algorithms, and architectures, and conduct general computing feasibility studies.
FingerIO lets you interact with mobile devices by writing or gesturing on any nearby surface, turning a smartphone or smartwatch into an active sonar device (credit: Dennis Wise, University of Washington)
A new sonar technology called FingerIO will make it easier to interact with screens on smartwatches and smartphones by simply writing or gesturing on any nearby surface. It’s is an active sonar system using the device’s own microphones and speakers to track fine-grained finger movements (to within 8mm).
Because sound waves travel through fabric and do not require line of sight, users can even interact with these devices (including writing text) inside a front pocket or a smartwatch hidden under a sweater sleeve.
University of Washington Computer Science & Engineering | FingerIO
Developed by University of Washington computer scientists and electrical engineers, FingerIO uses the device’s own speaker to emit an inaudible ultrasonic wave. That signal bounces off the finger, and those “echoes” are recorded by the device’s microphones and used to calculate the finger’s location in space.
Using sound waves to track finger motion offers several advantages over cameras — which don’t work without line-of-sight or when the device is hidden by fabric or another obstructions — and other technologies like radar that require both custom sensor hardware and greater computing power, said senior author and UW assistant professor of computer science and engineering Shyam Gollakota.
But standard sonar echoes are weak and typically not accurate enough to track finger motion at high resolution. Errors of a few centimeters would make it impossible to differentiate between writing individual letters or subtle hand gestures.
So the UW researchers used “Orthogonal Frequency Division Multiplexing” (used in cellular telecommunications and WiFi), allowing for tracking phase changes in the echoes and correcting for any errors in the finger location.
Applications of fingerIO. a) Transform any surface into a writing interface; b) provide a new interface for smartwatch form factor devices; c) enable gesture interaction with a phone in a pocket; d) work even when the watch is occluded. (credit: R. Nandakumar et al.)
Two microphones are needed to track finger motion in two dimensions, and three for three dimensions. So this system may work (when available commercially*) with some smartphones (it was tested with a Samsung Galaxy S4), but today’s smartwatches typically only have one microphone.
Next steps for the research team include demonstrating how FingerIO can be used to track multiple fingers moving at the same time, and extending its tracking abilities into three dimensions by adding additional microphones to the devices.
The research was funded by the National Science Foundation and Google and will be described in a paper to be presented in May at the Association for Computing Machinery’s CHI 2016 conference in San Jose, California.
* Hint: Microsoft Research principal researcher Desney Tan is a co-author.
editor’s comments: This tech will be great for students and journalists taking notes and for controlling music and videos. It could also help prevent robberies. How would you use it?
Abstract of FingerIO: Using Active Sonar for Fine-Grained Finger Tracking
We present fingerIO, a novel fine-grained finger tracking solution for around-device interaction. FingerIO does not require instrumenting the finger with sensors and works even in the presence of occlusions between the finger and the device. We achieve this by transforming the device into an active sonar system that transmits inaudible sound signals and tracks the echoes of the finger at its microphones. To achieve subcentimeter level tracking accuracies, we present an innovative approach that use a modulation technique commonly used in wireless communication called Orthogonal Frequency Division Multiplexing (OFDM). Our evaluation shows that fingerIO can achieve 2-D finger tracking with an average accuracy of 8 mm using the in-built microphones and speaker of a Samsung Galaxy S4. It also tracks subtle finger motion around the device, even when the phone is inside a pocket. Finally, we prototype a smart watch form-factor fingerIO device and show that it can extend the interaction space to a 0.5×0.25 m2 region on either side of the device and work even when it is fully occluded from the finger.
The novel approach to making systems forget data is called “machine unlearning” by the two researchers who are pioneering the concept. Instead of making a model directly depend on each training data sample (left), they convert the learning algorithm into a summation form (right) – a process that is much easier and faster than retraining the system from scratch. (credit: Yinzhi Cao and Junfeng Yang)
Machine learning systems are becoming ubiquitous, but what about false or damaging information about you (and others) that these systems have learned? Is it even possible for that information to be ever corrected? There are some heavy security and privacy questions here. Ever Google yourself?
Some background: machine-learning software programs calculate predictive relationships from massive amounts of data. The systems identify these predictive relationships using advanced algorithms — a set of rules for solving math problems — and “training data.” This data is then used to construct the models and features that enable a system to predict things, like the probability of rain next week or when the Zika virus will arrive in your town.
This intricate process means that a piece of raw data often goes through a series of computations in a system. The computations and information derived by the system from that data together form a complex propagation network called the data’s “lineage” (a term coined by Yinzhi Cao, a Lehigh University assistant professor of computer science and engineering, and his colleague, Junfeng Yang of Columbia University).
“Effective forgetting systems must be able to let users specify the data to forget with different levels of granularity,” said Cao. “These systems must remove the data and undo its effects so that all future operations run as if the data never existed.”
Widely used learning systems such as Google Search are, for the most part, only able to forget a user’s raw data upon request — not the data’s lineage (what the user’s data connects to). However, in October 2014, Google removed more than 170,000 links to comply with the ruling, which affirmed users’ right to control what appears when their names are searched. In July 2015, Google said it had received more than a quarter-million such requests.
How “machine unlearning” works
Now the two researchers say they have developed a way to forget faster and more effectively. Their concept, called “machine unlearning,” led to a four-year, $1.2 million National Science Foundation grant to develop the approach.
Building on work that was presented at a 2015 IEEE Symposium and then published, Cao and Yang’s “machine unlearning” method is based on the assumption that most learning systems can be converted into a form that can be updated incrementally without costly retraining from scratch.
Their approach introduces a layer of a small number of summations between the learning algorithm and the training data to eliminate dependency on each other. That means the learning algorithms depend only on the summations and not on individual data.
Using this method, unlearning a piece of data and its lineage no longer requires rebuilding the models and features that predict relationships between pieces of data. Simply recomputing a small number of summations would remove the data and its lineage completely — and much faster than through retraining the system from scratch, the researchers claim.
Verification?
Cao and Yang tested their unlearning approach on four diverse, real-world systems: LensKit, an open-source recommendation system; Zozzle, a closed-source JavaScript malware detector; an open-source OSN spam filter; and PJScan, an open-source PDF malware detector.
Cao and Yang are now adapting the technique to other systems and creating verifiable machine unlearning to statistically test whether unlearning has indeed repaired a system or completely wiped out unwanted data.
“We foresee easy adoption of forgetting systems because they benefit both users and service providers,” they said. “With the flexibility to request that systems forget data, users have more control over their data, so they are more willing to share data with the systems.”
The researchers envision “forgetting systems playing a crucial role in emerging data markets where users trade data for money, services, or other data, because the mechanism of forgetting enables a user to cleanly cancel a data transaction or rent out the use rights of her data without giving up the ownership.”
editor’s comments: I’d like to see case studies and critical reviews of this software by independent security and privacy experts. Yes, I’m paranoid but… etc. Your suggestions? To be continued…
Abstract of Towards Making Systems Forget with Machine Unlearning
Today’s systems produce a rapidly exploding amount of data, and the data further derives more data, forming a complex data propagation network that we call the data’s lineage. There are many reasons that users want systems to forget certain data including its lineage. From a privacy perspective, users who become concerned with new privacy risks of a system often want the system to forget their data and lineage. From a security perspective, if an attacker pollutes an anomaly detector by injecting manually crafted data into the training data set, the detector must forget the injected data to regain security. From a usability perspective, a user can remove noise and incorrect entries so that a recommendation engine gives useful recommendations. Therefore, we envision forgetting systems, capable of forgetting certain data and their lineages, completely and quickly. This paper focuses on making learning systems forget, the process of which we call machine unlearning, or simply unlearning. We present a general, efficient unlearning approach by transforming learning algorithms used by a system into a summation form. To forget a training data sample, our approach simply updates a small number of summations — asymptotically faster than retraining from scratch. Our approach is general, because the summation form is from the statistical query learning in which many machine learning algorithms can be implemented. Our approach also applies to all stages of machine learning, including feature selection and modeling. Our evaluation, on four diverse learning systems and real-world workloads, shows that our approach is general, effective, fast, and easy to use.
Experimental system for maze solving (credit: Yipeng Yu/PLoS ONE)
What would happen if we combined synthetic and biological systems, creating an intelligent cyborg rat? How would it perform?
Researchers in China decided to find out by comparing the problem-solving abilities of rats, computers, and rat-computer “cyborgs,” as they reported in an open-access PLOS ONE paper.
Rats: Six rats were trained for a week to run a series of unique mazes. After training, the researchers tested the rats on 14 new mazes, monitoring their paths, strategies and time spent solving the mazes.
Maze-solving computer algorithm: Implementing left-hand and right-hand wall-following rules, the algorithm completed the same 14 mazes run by the rats.
Electrode implant in a laboratory rat used to deliver electrical stimulation to the brain (credit: Vdegroot at Dutch Wikipedia/Creative Commons)
Rat cyborgs: The rats were implanted with a wireless microstimulator mounted on the back of the rat to deliver electric stimuli via microelectrodes into their somatosensory cortex and medial forebrain bundle, which releases dopamine to the nucleus accumbens and is a key node of the brain’s reward system. The computer tracked the rats, analyzed the explored maze information, and decided when and how to intervene when the rats needed help in traversing unique paths and avoiding dead ends and loops (by stimulating the rats’ left and right somatosensory cortex to prompt them to move left or right).
Rat cyborg in maze (credit: Yipeng Yu/PLoS ONE)
Intelligent cyborgs beat both rats and computer
Performance of the rats, computer and rat-cyborgs were compared by evaluating how many times they visited the same location (steps), how many locations they visited, and total time spent to reach the target. Although the cyborgs and computers took roughly the same number of steps, the cyborgs took fewer than the rats, a sign of more efficient problem solving. The cyborgs also visited fewer locations than computers or rats, and took less time than the rats to solve the mazes.*
The researchers suggest that the experiment shows that optimal intelligence may reside in the integration of animals and computers.
In future work, the researchers plan to introduce more tasks and the complexity of tasks will be quantified. “To avoid excessive intervention with the rats, the strength of the computer’s assistance will be graded,” the authors say in the paper. “In addition, more practical rat cyborgs will be investigated: the web camera will be replaced by sensors mounted on rats, such as tiny camera, ultrasonic sensors, infrared sensors, electric compass, and so on, to perceive the real unknown environment in real time; and the computer-aided algorithms can be housed on a wireless backpack stimulator instead of in the computer.”
* The computer aided the rats under three rules: (1) if there was a path to the unique road, the computer would find the shortest path, then Left and Right commands would be sent to navigate the rat to the unique road; (2) if the rat was going to enter a dead cell, Left or Right commands would be sent to prevent such a move; (3) if the rat was in a loop, the computer would find the shortest path to the current destination, then Left and Right commands would be sent to navigate the rat to follow the path.
Abstract of Intelligence-Augmented Rat Cyborgs in Maze Solving
Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.
Documents captured in nanostructured glass, expected to last billions of years (credit: University of Southampton)
Scientists at the University of Southampton Optoelectronics Research Centre (ORC) have developed the first digital data storage system capable of creating archives that can survive for billions of years.
Using nanostructured glass, the system has 360 TB per disc capacity, thermal stability up to 1,000°C, and virtually unlimited lifetime at room temperature (or 13.8 billion years at 190°C ).
As a “highly stable and safe form of portable memory,” the technology opens up a new era of “eternal” data archiving that could be essential to cope with the accelerating amount of information currently being created and stored, the scientists says.* The system could be especially useful for organizations with big archives, such as national archives, museums, and libraries, according to the scientists.
Superman memory crystal
5D optical storage writing setup. FSL: femtosecond laser; SLM: spatial light modulator; FL1 and FL2: Fourier lens; HPM: half-wave plate matrix; AP: aperture; WIO: water immersion objective. Inset: Linearly polarized light (white arrows) with different intensity levels propagate simultaneously through each half-wave plate segment with different slow-axis orientation (black arrows). The colors of the rectangle indicate four different intensity levels. (credit: University of Southampton)
The recording system uses an ultrafast laser to produce extremely short (femtosecond) and intense pulses of light. The file is written in three up to 18 layers of nanostructured dots separated by five micrometers (one millionth of a meter) in fuzed quartz (coined as a “Superman memory crystal” (as in “memory crystals” used in the Superman films).” The self-assembled nanostructures change the way light travels through glass, modifying the polarization of light, which can then be read by a combination optical microscope and polarizer, similar to that found in Polaroid sunglasses.
The recording method is described as “5D” because the information encoding is in five dimensions — three-dimensional position plus size and orientation.
So far, the researchers have saved major documents from human history, such as the Universal Declaration of Human Rights (UDHR), Newton’s Opticks, Magna Carta, and Kings James Bible as digital copies. A copy of the UDHR encoded to 5D data storage was recently presented to UNESCO by the ORC at the International Year of Light (IYL) closing ceremony in Mexico.
The team is now looking for industry partners to further develop and commercialize this technology.
The researchers will present their research at the photonics industry’s SPIE (the International Society for Optical Engineering Conference) in San Francisco on Wednesday Feb. 17.
* In 2008, the International Data Corporation [found] that total capacity of data stored is increasing by around 60% each year. As a result, more than 39,000 exabytes of data will be generated by 2020. This amount of data will cause a series of problems and one of the main will be power consumption. 1.5% of the total U.S. electricity consumption in 2010 was given to the data centers in the U.S. According to a report by the Natural Resources Defence Council, the power consumption of all data centers in the U.S. will reach roughly 140 billion kilowatt-hours per each year by 2020. This amount of electricity is equivalent to that generated by roughly thirteen Heysham 2 nuclear power stations (one of the biggest stations in UK, net 1240 MWe).
Most of these data centers are built based on hard-disk drive (HDD), with only a few designed on optical discs. HDD is the most popular solution for digital data storage according to the International Data Corporation. However, HDD is not an energy-efficient option for data archiving; the loading energy consumption is around 0.04 W/GB. In addition, HDD is an unsatisfactory candidate for long-term storage due to the short lifetime of the hardware and requires transferring data every two years to avoid any loss.
— Jingyu Zhang et al. Eternal 5D data storage by ultrafast laser writing in glass. Proceedings of the SPIE OPTO 2016
Abstract of Eternal 5D data storage by ultrafast laser writing in glass
Femtosecond laser writing in transparent materials has attracted considerable interest due to new science and a wide range of applications from laser surgery, 3D integrated optics and optofluidics to geometrical phase optics and ultra-stable optical data storage. A decade ago it has been discovered that under certain irradiation conditions self-organized subwavelength structures with record small features of 20 nm, could be created in the volume of silica glass. On the macroscopic scale the self-assembled nanostructure behaves as a uniaxial optical crystal with negative birefringence. The optical anisotropy, which results from the alignment of nano-platelets, referred to as form birefringence, is of the same order of magnitude as positive birefringence in crystalline quartz. The two independent parameters describing birefringence, the slow axis orientation (4th dimension) and the strength of retardance (5th dimension), are explored for the optical encoding of information in addition to three spatial coordinates. The slow axis orientation and the retardance are independently manipulated by the polarization and intensity of the femtosecond laser beam. The data optically encoded into five dimensions is successfully retrieved by quantitative birefringence measurements. The storage allows unprecedented parameters including hundreds of terabytes per disc data capacity and thermal stability up to 1000°. Even at elevated temperatures of 160oC, the extrapolated decay time of nanogratings is comparable with the age of the Universe – 13.8 billion years. The demonstrated recording of the digital documents, which will survive the human race, including the eternal copies of Kings James Bible and Magna Carta, is a vital step towards an eternal archive.
