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Robots are unable to perform everyday manipulation tasks, such as grasping or rearranging objects, with the same dexterity as humans. But Brazilian scientists have moved this research a step further by developing a new system that uses deep learning algorithms to improve a robot’s ability to independently detect how to grasp an object, known as autonomous robotic grasp detection.

In a paper published Feb. 24 in Robotics and Autonomous Systems, a team of engineers from the University of São Paulo addressed existing problems with the visual perception phase that occurs when a robot grasps an object. They created a model using deep learning neural networks that decreased the time a robot needs to process visual data, perceive an object’s location and successfully grasp it.

Deep learning is a subset of machine learning, in which computer algorithms are trained how to learn with data and to improve automatically through experience. Inspired by the structure and function of the human brain, deep learning uses a multilayered structure of algorithms called neural networks, operating much like the human brain in identifying patterns and classifying different types of information. Deep learning models are often based on convolutional neural networks, which specialize in analyzing visual imagery.

Advanced system-on-chip designs are extremely complex in terms of transistor count and are hard to build using the latest fabrication processes. In a bid to make production of next-generation chips economically feasible, chip fabs need to ensure high yields early in their lifecycle by quickly finding and correcting defects.

But finding and fixing defects is not easy today, as traditional optical inspection tools don’t offer sufficiently detailed image resolution, while high-resolution e-beam and multibeam inspection tools are relatively slow. Looking to bridge the gap on inspection costs and time, Applied Materials has been developing a technology called ExtractAI technology, which uses a combination of the company’s latest Enlight optical inspection tool, SEMVision G7 e-beam review system, and deep learning (AI) to quickly find flaws. And surprisingly, this solution has been in use for about a year now.

“Applied’s new playbook for process control combines Big Data and AI to deliver an intelligent and adaptive solution that accelerates our customers’ time to maximum yield,” said Keith Wells, group vice president and general manager, Imaging and Process Control at Applied Materials. “By combining our best-in-class optical inspection and eBeam review technologies, we have created the industry’s only solution with the intelligence to not only detect and classify yield-critical defects but also learn and adapt to process changes in real-time. This unique capability enables chipmakers to ramp new process nodes faster and maintain high capture rates of yield-critical defects over the lifetime of the process.”

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect some of the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why they matter.

This week brings a few unusual applications of or developments in machine learning, as well as a particularly unusual rejection of the method for pandemic-related analysis.

Continue reading “Deep science: AI is in the air, water, soil and steel” »

In this work, we carry out KS-MD simulations for a range of elements, temperatures, and densities, allowing for a systematic comparison of three RPP models. While multiple RPP models can be selected, ^7–11 7. J. Vorberger and D. Gericke, “Effective ion–ion potentials in warm dense matter,” High Energy Density Phys. 9, 178 (2013). https://doi.org/10.1016/j.hedp.2012.12.009 8. Y. Hou, J. Dai, D. Kang, W. Ma, and J. Yuan, “Equations of state and transport properties of mixtures in the warm dense regime,” Phys. Plasmas 22, 022711 (2015). https://doi.org/10.1063/1.4913424 9. K. Wünsch, J. Vorberger, and D. Gericke, “Ion structure in warm dense matter: Benchmarking solutions of hypernetted-chain equations by first-principle simulations,” Phys. Rev. E 79, 010201 (2009). https://doi.org/10.1103/PhysRevE.79.010201 10. L. Stanton and M. Murillo, “Unified description of linear screening in dense plasmas,” Phys. Rev. E 91, 033104 (2015). https://doi.org/10.1103/PhysRevE.91.033104 11. W. Wilson, L. Haggmark, and J. Biersack, “Calculations of nuclear stopping, ranges, and straggling in the low-energy region,” Phys. Rev. B 15, 2458 (1977). https://doi.org/10.1103/PhysRevB.15.2458 we choose to compare the widely used Yukawa potential, which accounts for screening by linearly perturbing around a uniform density in the long-wavelength (Thomas–Fermi) limit, a potential constructed from a neutral pseudo-atom (NPA) approach, ^12–15 12. L. Harbour, M. Dharma-wardana, D. D. Klug, and L. J. Lewis, “Pair potentials for warm dense matter and their application to x-ray Thomson scattering in aluminum and beryllium,” Phys. Rev. E 94, 053211 (2016). https://doi.org/10.1103/PhysRevE.94.053211 13. M. Dharma-wardana, “Electron-ion and ion-ion potentials for modeling warm dense matter: Applications to laser-heated or shock-compressed Al and Si,” Phys. Rev. E 86, 036407 (2012). https://doi.org/10.1103/PhysRevE.86.036407 14. F. Perrot and M. Dharma-Wardana, “Equation of state and transport properties of an interacting multispecies plasma: Application to a multiply ionized al plasma,” Phys. Rev. E 52, 5352 (1995). https://doi.org/10.1103/PhysRevE.52.5352 15. L. Harbour, G. Förster, M. Dharma-wardana, and L. J. Lewis, “Ion-ion dynamic structure factor, acoustic modes, and equation of state of two-temperature warm dense aluminum,” Phys. Rev. E 97, 043210 (2018). https://doi.org/10.1103/PhysRevE.97.043210 and the optimal force-matched RPP that is constructed directly from KS-MD simulation data.

Each of the models we chose impacts our physics understanding and has clear computational consequences. For example, success of the Yukawa model reveals the insensitivity to choices in the pseudopotential and screening function and allows for the largest-scale simulations. Large improvements are expected from the NPA model, which makes many fewer assumptions with a modest cost of pre-computing and tabulating forces. (See the Appendix for more details on the NPA model.) The force-matched RPP requires KS-MD data and is therefore the most expensive to produce, but it reveals the limitations of RPPs themselves since they are by definition the optimal RPP.

Using multiple metrics of comparison between RPP-MD and KS-MD including the relative force error, ion–ion equilibrium radial distribution function g (r), Einstein frequency, power spectrum, and the self-diffusion transport coefficient, the accuracy of each RPP model is analyzed. By simulating disparate elements, namely, an alkali metal, multiple transition metals, a halogen, a nonmetal, and a noble gas, we see that force-matched RPPs are valid for simulating dense plasmas at temperatures above fractions of an eV and beyond. We find that for all cases except for low temperature carbon, force-matched RPPs accurately describe the results obtained from KS-MD to within a few percent. By contrast, the Yukawa model appears to systematically fail at describing results from KS-MD at low temperatures for the conditions studied here validating the need for alternate models such as force-matching and NPA approaches at these conditions.

Many machine learning algorithms on quantum computers suffer from the dreaded “barren plateau” of unsolvability, where they run into dead ends on optimization problems. This challenge had been relatively unstudied—until now. Rigorous theoretical work has established theorems that guarantee whether a given machine learning algorithm will work as it scales up on larger computers.

“The work solves a key problem of useability for quantum machine learning. We rigorously proved the conditions under which certain architectures of variational quantum algorithms will or will not have barren plateaus as they are scaled up,” said Marco Cerezo, lead author on the paper published in Nature Communications today by a Los Alamos National Laboratory team. Cerezo is a post doc researching quantum information theory at Los Alamos. “With our theorems, you can guarantee that the architecture will be scalable to quantum computers with a large number of qubits.”

“Usually the approach has been to run an optimization and see if it works, and that was leading to fatigue among researchers in the field,” said Patrick Coles, a coauthor of the study. Establishing mathematical theorems and deriving first principles takes the guesswork out of developing algorithms.

Just because a mathematical formula works does not mean it reflects reality.

Graph representations can solve complex problems in natural science, as patterns of connectivity can give rise to a magnitude of emergent phenomena. Graph-based approaches are specifically important during quantum communication, alongside quantum search algorithms in highly branched quantum networks. In a new report now published on Science Advances, Max Ehrhardt and a team of scientists in physics, experimental physics and quantum science in Germany introduced a hitherto unidentified paradigm to directly realize excitation dynamics associated with three-dimensional networks. To accomplish this, they explored the hybrid action of space and polarization degrees of freedom of photon pairs inside complex waveguide circuits. The team experimentally explored multiparticle quantum walks on complex and highly connected graphs as testbeds to pave the way to explore the potential applications of fermionic dynamics in integrated photonics.

Complex networks

Complex networks can occur across diverse fields of science, ranging from biological signaling pathways and biochemical molecules to exhibit efficient energy transport to neuromorphic circuits across to social interactions across the internet. Such structures are typically modeled using graphs whose complexity relies on the number of nodes and linkage patterns between them. The physical representation of a graph is limited by their requirement for arrangement in three-dimensional (3D) space. The human brain is a marked example of scaling behavior that is unfavorable for physical simulation due to its staggering number of 80 billion neurons, dwarfed by 100 trillion synapses that allow the flow of signals between them. Despite the number of comparably miniscule volume of nodes, discrete quantum systems faced a number of challenges owing to complex network topologies, efficient multipartite quantum communications and search algorithms.

Managers of the ImageNet data set paved the way for advances in deep learning. Now they’ve taken a big step to protect people’s privacy.

When physicists need to understand the quantum mechanics that describe how atomic clocks work, how your magnet sticks to your refrigerator or how particles flow through a superconductor, they use quantum field theories.

When they work through problems in quantum field theories, they do so in “imaginary” time, then map those simulations into real quantities. But traditionally, these simulations nearly always include uncertainties or unknown factors that could cause equation results to be “off.” So, when physicists interpret their simulation results into real quantities, these uncertainties amplify exponentially, making it difficult to have confidence that their results are as accurate as necessary.

Now, a pair of University of Michigan physicists have discovered that a set of functions called the Nevanlinna functions can tighten the interpretation step, showing that physicists may be able to overcome one of the major limitations of modern quantum simulation. The work, published in Physical Review Letters, was led by U-M physics undergraduate student Jiani Fei.