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# Fusion

17.01.2019
10:46 Arxiv.org Physics3-D Radiation Mapping in Real-Time with the Localization and Mapping Platform LAMP from Unmanned Aerial Systems and Man-Portable Configurations. (arXiv:1901.05038v1 [physics.app-ph])

Real-time, meter-resolution gamma-ray mapping is relevant in the detection and mapping of radiological materials, and for applications ranging from nuclear decommissioning, waste management, and environmental remediation to homeland security, emergency response, and international safeguards. We present the Localization and Mapping Platform (LAMP) as a modular, contextual and radiation detector sensor suite, which performs gamma-ray mapping in three dimensions (3-D) and in real time, onboard an unmanned aerial vehicle (UAV) or in a man-portable configuration. The deployment of an unmanned aerial system (UAS) for gamma-ray mapping can be advantageous, as the UAS provides a means of measuring large areas efficiently and improving accessibility to some environments, such as multi-story structures. In addition, it is possible to increase measurement robustness through autonomous navigation, and to

16.01.2019
21:57 ScienceDaily.comFiery sighting: A new physics of eruptions that damage fusion experiments

Sudden bursts of heat that can damage the inner walls of tokamak fusion experiments are a hurdle that operators of the facilities must overcome. Such bursts, called 'edge localized modes (ELMs),' occur in doughnut-shaped tokamak devices that house the hot, charged plasma that is used to replicate on Earth the power that drives the sun and other stars. Now researchers have directly observed a possible and previously unknown process that can trigger damaging ELMs.

20:31 Phys.orgFiery sighting: A new physics of eruptions that damage fusion experiments

Sudden bursts of heat that can damage the inner walls of tokamak fusion experiments are a hurdle that operators of the facilities must overcome. Such bursts, called "edge localized modes (ELMs)," occur in doughnut-shaped tokamak devices that house the hot, charged plasma that is used to replicate on Earth the power that drives the sun and other stars. Now researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have directly observed a possible and previously unknown process that can trigger damaging ELMs.

06:29 Arxiv.org PhysicsThermonuclear fusion rates for tritium + deuterium using Bayesian methods. (arXiv:1901.04857v1 [nucl-th])

The $^3$H(d,n)$^4$He reaction has a large low-energy cross section and will likely be utilized in future commercial fusion reactors. This reaction also takes place during big bang nucleosynthesis. Studies of both scenarios require accurate and precise fusion rates. To this end, we implement a one-level, two-channel R-matrix approximation into a Bayesian model. Our main goals are to predict reliable astrophysical S-factors and to estimate R-matrix parameters using the Bayesian approach. All relevant parameters are sampled in our study, including the channel radii, boundary condition parameters, and data set normalization factors. In addition, we take uncertainties in both measured bombarding energies and S-factors rigorously into account. Thermonuclear rates and reactivities of the $^3$H(d,n)$^4$He reaction are derived by numerically integrating the Bayesian S-factor samples. The present

15.01.2019
09:19 Arxiv.org PhysicsRecovery of parameters of fast nonlocal heat transport in magnetic fusion plasmas: testing a model of waves with high internal reflections. (arXiv:1901.03789v1 [physics.plasm-ph])

Our analysis of the model [J. Phys.: Conf. Ser. 941 (2017) 012008] elaborated for interpreting the initial stage of the fast nonlocal transport events, which exhibit immediate response, in the heat diffusion time scale, of the spatial profile of electron temperature to its local perturbation, shows that the nonlocal transport by electromagnetic (EM) waves needs too high reflectivity of vacuum vessel walls to describe the experimental data. Here we try another model, which assumes high internal reflections and is compatible with the "wild cable" transport of TEM waves along magnetically-bound skeletal nanostructures. An inverse problem for recovery of the source and sink of waves, and internal reflectivity, is formulated and solved. Preliminary results of analyzing the data from tokamaks JET and TFTR, and stellarator LHD are presented.

14.01.2019
14:19 FightAging.OrgPUM2 and MFF in the Dysregulation of Mitochondrial Fission in Aging

Mitochondria, the power plants of the cell, become dysfunctional over the course of aging. This is a general process in all mitochondria, and not the same thing as the severe mitochondrial DNA damage that occurs in only a few cells, but that has a widespread detrimental effect. In this more general mitochondrial malaise, there are changes in shape and important functions decline; energy-hungry tissues such as brain and muscle suffer as a consequence. Mitochondria are the descendants of ancient symbiotic bacteria, and thus act much like bacteria in carrying out fission and fusion, and passing component parts around between one another. In recent years, researchers have found that imbalances between fission and fusion appear in aging, this impairs the ability of autophagic processes to remove […]

12:50 NewYork TimesQ&A: Clean, Abundant Energy: Fusion Dreams Never End

In theory, hydrogen fusion may power the future. But there are substantial scientific hurdles yet to overcome.

12:49 International Herald TribuneQ&A: Clean, Abundant Energy: Fusion Dreams Never End

In theory, hydrogen fusion may power the future. But there are substantial scientific hurdles yet to overcome.

12.01.2019
20:06 Technology.orgAstronomers find signatures of a ‘messy’ star that made its companion go supernova

Many stars explode as luminous supernovae when, swollen with age, they run out of fuel for nuclear fusion. But some

02:52 NYT ScienceQ&A: Clean, Abundant Energy: Fusion Dreams Never End

In theory, hydrogen fusion may power the future. But there are substantial scientific hurdles yet to overcome.

11.01.2019
06:32 Arxiv.org StatisticsDeep Learning for Human Affect Recognition: Insights and New Developments. (arXiv:1901.02884v1 [cs.LG])

Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature

06:00 Arxiv.org Quantitative BiologyA Martini coarse-grained model of the calcein fluorescent dye. (arXiv:1901.03105v1 [physics.comp-ph])

Calcein leakage assays are a standard experimental set-up for probing the extent of damage induced by external agents on synthetic lipid vesicles. The fluorescence signal associated with calcein release from liposomes is the signature of vesicle disruption, transient pore formation or vesicle fusion. This type of assay is widely used to test the membrane disruptive effect of biological macromolecules, such as proteins, antimicrobial peptides and RNA and is also used on synthetic nanoparticles with a polymer, metal or oxide core. Little is known about the effect that calcein and other fluorescent dyes may have on the properties of lipid bilayers, potentially altering their structure and permeability. Here we develop a coarse-grained model of calcein that is compatible with the Martini force field for lipids. We validate the model by comparing its dimerization free energy, aggregation behavior

06:00 Arxiv.org PhysicsA Martini coarse-grained model of the calcein fluorescent dye. (arXiv:1901.03105v1 [physics.comp-ph])

Calcein leakage assays are a standard experimental set-up for probing the extent of damage induced by external agents on synthetic lipid vesicles. The fluorescence signal associated with calcein release from liposomes is the signature of vesicle disruption, transient pore formation or vesicle fusion. This type of assay is widely used to test the membrane disruptive effect of biological macromolecules, such as proteins, antimicrobial peptides and RNA and is also used on synthetic nanoparticles with a polymer, metal or oxide core. Little is known about the effect that calcein and other fluorescent dyes may have on the properties of lipid bilayers, potentially altering their structure and permeability. Here we develop a coarse-grained model of calcein that is compatible with the Martini force field for lipids. We validate the model by comparing its dimerization free energy, aggregation behavior

06:00 Arxiv.org CSDeep Learning for Human Affect Recognition: Insights and New Developments. (arXiv:1901.02884v1 [cs.LG])

Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature

00:06 Phys.orgAstronomers find signatures of a 'messy' star that made its companion go supernova

Many stars explode as luminous supernovae when, swollen with age, they run out of fuel for nuclear fusion. But some stars can go supernova simply because they have a close and pesky companion star that, one day, perturbs its partner so much that it explodes.

10.01.2019
20:55 WhatReallyHappened.comFLASHBACK - The Coldest Case

Over the next 11 years, the question of who killed Mallove would lead Curtis down a path he never expected. Mallove, the detective discovered, was one of the world’s most outspoken advocates for cold fusion. “It’s science well above my intellect,” Curtis says. Yet cold fusion isn’t just a complicated form of nuclear energy. It’s also highly controversial. Supporters see it as energy’s holy grail, the key to saving the Earth from environmental destruction. Critics maintain it might not even be possible—and that any claims that it’s already been achieved are total fringe-science lunacy. Understanding Mallove, and what his death meant, required delving into a world of knowledge and intrigue where the scientist had once battled and thrived.
Last November, on a rainy, gray afternoon, Curtis, a 48-year-old with graying hair and bright blue eyes, drives to the spot in Norwich where his

08:44 Arxiv.org CSDeCoILFNet: Depth Concatenation and Inter-Layer Fusion based ConvNet Accelerator. (arXiv:1901.02774v1 [cs.DC])

Convolutional Neural Networks (CNNs) are rapidly gaining popularity in varied fields. Due to their increasingly deep and computationally heavy structures, it is difficult to deploy them on energy constrained mobile applications. Hardware accelerators such as FPGAs have come up as an attractive alternative. However, with the limited on-chip memory and computation resources of FPGA, meeting the high memory throughput requirement and exploiting the parallelism of CNNs is a major challenge. We propose a high-performance FPGA based architecture - Depth Concatenation and Inter-Layer Fusion based ConvNet Accelerator - DeCoILFNet which exploits the intra-layer parallelism of CNNs by flattening across depth and combines it with a highly pipelined data flow across the layers enabling inter-layer fusion. This architecture significantly reduces off-chip memory accesses and maximizes the throughput.

09.01.2019
18:03 Nature.ComThreatened UK nuclear-fusion lab secures short-term extension

11:24 Arxiv.org PhysicsKinetic fluid moments closure for a magnetized plasma with collisions. (arXiv:1901.02429v1 [physics.plasm-ph])

A novel method aimed at a kinetic moments closure for a magnetized plasma with arbitrary collisionality is proposed. The intended first application is to a tokamak edge and scrape-off-layer plasma. The velocity distribution function for each species is expanded in 8 Gaussian Radial Basis Functions (GRBFs) which are essentially shifted Maxwellians at eight representative 3D-velocity points of drift. The vector of 8 fluid moments (for particle density, 3 particle fluxes, total energy density, and 3 energy fluxes) has an 8x8 analytic linear matrix relation to the vector of 8 GRBF density weights in 3D real space. The 8 fluid moments with sources for each species are advanced in time while the 8 GRBF weighs are determined from the 8x8 inverse matrix. The two closure moments (for the stress tensor and the energy weighted stress tensor) are linearly determined from the GRBF weights. Most

10:30 Phys.orgScientists discover a process that stabilizes fusion plasmas

Scientists seeking to bring the fusion reaction that powers the sun and stars to Earth must keep the superhot plasma free from disruptions. Now researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have discovered a process that can help to control the disruptions thought to be most dangerous.

07.01.2019
09:34 Arxiv.org CSMachine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding. (arXiv:1901.00949v1 [cs.AI])

The design of reward functions in reinforcement learning is a human skill that comes with experience. Unfortunately, there is not any methodology in the literature that could guide a human to design the reward function or to allow a human to transfer the skills developed in designing reward functions to another human and in a systematic manner. In this paper, we use Systematic Instructional Design, an approach in human education, to engineer a machine education methodology to design reward functions for reinforcement learning. We demonstrate the methodology in designing a hierarchical genetic reinforcement learner that adopts a neural network representation to evolve a swarm controller for an agent shepherding a boids-based swarm. The results reveal that the methodology is able to guide the design of hierarchical reinforcement learners, with each model in the hierarchy learning incrementally

04.01.2019
10:28 Arxiv.org PhysicsHardening and Strain Localisation in Helium-Ion-Implanted Tungsten. (arXiv:1901.00745v1 [cond-mat.mtrl-sci])

Tungsten is the main candidate material for plasma-facing armour components in future fusion reactors. In service, bombardment with energetic neutrons will create collision cascades that leave behind lattice defects. Helium, injected from the plasma and produced by transmutation, strongly interacts with these defects, modifying their behaviour and retention. Helium-ion-implantation provides an effective tool for examining helium-defect interactions and their effect on the properties of tungsten armour. We use nano-indentation to probe the mechanical properties of the shallow helium-ion-implanted layer. Comparison of spherical indents in unimplanted and helium-implanted regions of the same single crystal shows a large increase in hardness and substantial pile-up in the implanted material. The complex lattice distortions beneath indents are probed non-destructively using 3D-resolved synchrotron

03.01.2019
23:08 WhatReallyHappened.comScott Adams: Romney, Trump’s Press Conference, Arctic Ice and Nuclear Power

A+ Good risk/reward play by Romney President Trump’s comments on walls that exist and work Anti-Trump forced to deal with Obama and Popes walls “Sand and Death”, President Trump’s branding is excellent Visual persuasion examples and the essence of issues AOC in group picture of new House members AOC once again, makes the right choice when others don’t Focusing on ice to determine if climate change is real Is ice increasing or decreasing in net total? How do we not know the clear answer to that question? Steve Goddard and Tony Heller are the same guy One powerful skeptic voice…not two, pen name Rebuttal charts for temperature data on climate change The most basic, easily verifiable facts aren’t agreed on? Are nuclear and fusion power cost effective or not? Really smart people aren’t in agreement

31.12.2018
07:13 Arxiv.org PhysicsCoulomb cluster explosion boosted by an electrical pulse -- neutron source, diagnostic tool, and test of nuclear fusion efficiency. (arXiv:1812.10547v1 [physics.atm-clus])

To greatly enhance output of fusion-produced neutrons in a laser-initiated Coulomb explosion of $D$ clusters, we propose to accelerate $D^+$ ions by an electrical pulse to the energies where the $D^+ + D$ collision cross-section is the highest. With $D^+$ ions bombarding a $D$-rich cathode, this solves a few problems simultaneously by (a) removing electron cloud hindering the Coulomb explosion, (b) utilizing up to $100 \%$ of the ions to hit the high-density packed nuclei, and (c) reaching highly increased cross-section of neutron production in a single $D^+ + D$ collision, by using a multi-layered target. We show that neutron output can reach up to $10^{15}$ per shot, which provides for a powerful and compact neutron source. We also consider the use of $E$-pulse acceleration for diagnostic purposes.

29.12.2018
16:35 Gizmag Global wake up call headlines the year in science

Another year has come to an end, so it's time to look back at the scientific breakthroughs and inventions that excited us this year. From innovative new materials that open the door to more advanced tools and products, to discoveries that continue to push the borders of human knowledge, 2018 didn't fail to deliver. The inspirational scientific stories were offset somewhat by the flood of bad news regarding the climate, but maybe science can be a source of hope there too.
.. Continue Reading Global wake up call headlines the year in science Category: Science Tags: Best of 2018 Climate Change Energy Fossils Fusion Materials Physics Solar Power Stephen Hawking

24.12.2018
05:06 Arxiv.org PhysicsTransport of collisional impurities with flux-surface density variation in stellarator plasmas. (arXiv:1812.09194v1 [physics.plasm-ph])

Avoiding impurity accumulation is a requirement for steady-state stellarator operation. The accumulation of impurities can be heavily affected by variations in their density on the flux-surface. Using recently derived semi-analytic expressions for the transport of a collisional impurity species with high-$Z$ and flux-surface density-variation in the presence of a low-collisionality bulk ion species, we numerically optimize the impurity density-variation on the flux-surface to minimize the radial peaking-factor of the impurities. These optimized density-variations can notably reduce the peaking-factor in the Large Helical Device (LHD) case considered here, but have only a minor effect on the peaking-factor in a Wendelstein 7-X (W7-X) standard configuration case, where the peaking-factor already is negative in the core plasma. On the other hand, when the same procedure is used to find

21.12.2018
18:14 Phys.orgOn the right path to fusion energy

A new report on the development of fusion as an energy source, written at the request of the U.S. Secretary of Energy, proposes adoption of a national fusion strategy that closely aligns with the course charted in recent years by MIT's Plasma Science and Fusion Center (PSFC) and privately funded Commonwealth Fusion Systems (CFS), a recent MIT spinout.

02:39 WhatReallyHappened.comFLASHBACK - IRmep wins release of Pentagon report detailingaspects of Israel's nuclear weapons production facilities

The report revealed that in 1987 the Israelis were "developing the kind of codes which will enable them to make hydrogen bombs. That is, codes which detail fission and fusion processes on a microscopic and macroscopic level." Such research was taking place in Israeli facilities similar to the major US nuclear weapons development sites. "The SOREQ and the Dimona/Beer Shiva facilities are the equivalent of our Los Alamos, Lawrence Livermore and Oak Ridge National Laboratories. The SOREQ center runs the full nuclear gamut of activities from engineering, administration and non-destructive testing to electro-optics, pulsed power, process engineering and chemistry and nuclear research and safety. This is the technology base required for nuclear weapons design and fabrication."
Israel’s facilities at the time were stunningly advanced. "The capability of SOREQ to support SDIO and nuclear

20.12.2018
17:18 ScienceDaily.comThe coming of age of plasma physics

The story of the generation of physicists involved in the development of a sustainable energy source, controlled fusion, using a method called magnetic confinement.

09:12 Arxiv.org MathAn arbitrary order time-stepping algorithm for tracking particles in inhomogeneous magnetic fields. (arXiv:1812.08117v1 [math.NA])

The Lorentz equations describe the motion of electrically charged particles in electric and magnetic fields and are used widely in plasma physics. The most popular numerical algorithm for solving them is the Boris method, a variant of the St\"ormer-Verlet algorithm. Boris' method is phase space volume conserving and simulated particles typically remain near the correct trajectory. However, it is only second order accurate. Therefore, in scenarios where it is not enough to know that a particle stays on the right trajectory but one needs to know where on the trajectory the particle is at a given time, Boris method requires very small time steps to deliver accurate phase information, making it computationally expensive. We derive an improved version of the high-order Boris spectral deferred correction algorithm (Boris-SDC) by adopting a convergence acceleration strategy for second order problems

09:12 Arxiv.org CSAn arbitrary order time-stepping algorithm for tracking particles in inhomogeneous magnetic fields. (arXiv:1812.08117v1 [math.NA])

The Lorentz equations describe the motion of electrically charged particles in electric and magnetic fields and are used widely in plasma physics. The most popular numerical algorithm for solving them is the Boris method, a variant of the St\"ormer-Verlet algorithm. Boris' method is phase space volume conserving and simulated particles typically remain near the correct trajectory. However, it is only second order accurate. Therefore, in scenarios where it is not enough to know that a particle stays on the right trajectory but one needs to know where on the trajectory the particle is at a given time, Boris method requires very small time steps to deliver accurate phase information, making it computationally expensive. We derive an improved version of the high-order Boris spectral deferred correction algorithm (Boris-SDC) by adopting a convergence acceleration strategy for second order problems

19.12.2018
21:24 Phys.orgThe coming of age of plasma physics

Once upon a time, people thought that electrons and ions always stuck together, living happily ever after. However, under low density of matter or high temperatures, the components of matter are no longer bound together. Instead, they form plasma, a state of matter naturally occurring in our universe, which has since been harnessed for everyday applications such as TV screens, chip etching and torches, but also propulsion and even sustained energy production via controlled fusion.

04:59 Arxiv.org PhysicsCode O-SUKI: Simulation of Direct-Drive Fuel Target Implosion in Heavy Ion Inertial Fusion. (arXiv:1812.07128v1 [physics.plasm-ph])

The Code O-SUKI is an integrated 2-dimensional (2D) simulation program system for a fuel implosion, ignition and burning of a direct-drive nuclear-fusion pellet in heavy ion beam (HIB) inertial confinement fusion (HIF). The Code O-SUKI consists of the four programs of the HIB illumination and energy deposition program of OK3 (Comput. Phys. Commun. 181, 1332 (2010)), a Lagrangian fluid implosion program, a data conversion program, and an Euler fluid implosion, ignition and burning program. The OK3 computes the multi-HIBs irradiation onto a spherical fuel target. One HIB is divided into many beamlets in OK3. Each heavy ion beamlet deposits its energy along the trajectory in a deposition layer depending on the particle energy. The OK3 also has a function of a wobbling motion of the HIB axis oscillation, and the HIBs energy deposition spatial detail profile is obtained inside the energy absorber

18.12.2018
07:37 Arxiv.org PhysicsNuclear probes of an out-of-equilibrium plasma at the highest compression. (arXiv:1812.06868v1 [physics.plasm-ph])

We report the highest compression reached in laboratory plasmas using eight laser beams, E$_{laser}$$\approx12 kJ, \tau_{laser}=2 ns in third harmonic on a CD_2 target at the ShenGuang-II Upgrade (SGII-Up) facility in Shanghai, China. We estimate the deuterium density \rho_D= 2.0 \pm 0.9 kg/cm^{3}, and the average kinetic energy of the plasma ions less than 1 keV. The highest reached areal density \Lambda \rho_{D}=4.8 \pm 1.5 g/cm^{2} was obtained from the measured ratio of the sequential ternary fusion reactions (dd\rightarrowt+p and t+d\rightarrow$$\alpha$+n) and the two body reaction fusions (dd$\rightarrow$$^3$He+n). At such high densities, sequential ternary and also quaternary nuclear reactions become important as well (i.e. n(14.1 MeV) + $^{12}$C $\rightarrow$ n'+$^{12}$C* etc.) resulting in a shift of the neutron (and proton) kinetic energies from their birth

07:37 Arxiv.org CSMachine learning approaches to understand the influence of urban environments on human's physiological response. (arXiv:1812.06128v1 [cs.HC])

This research proposes a framework for signal processing and information fusion of spatial-temporal multi-sensor data pertaining to understanding patterns of humans physiological changes in an urban environment. The framework includes signal frequency unification, signal pairing, signal filtering, signal quantification, and data labeling. Furthermore, this paper contributes to human-environment interaction research, where a field study to understand the influence of environmental features such as varying sound level, illuminance, field-of-view, or environmental conditions on humans' perception was proposed. In the study, participants of various demographic backgrounds walked through an urban environment in Zurich, Switzerland while wearing physiological and environmental sensors. Apart from signal processing, four machine learning techniques, classification, fuzzy rule-based inference,

17.12.2018
19:05 Phys.orgTeam wins major supercomputer time to study the edge of fusion plasmas

The U.S. Department of Energy (DOE) has awarded major computer hours on three leading supercomputers, including the world's fastest, to a team led by C.S. Chang of the DOE's Princeton Plasma Physics Laboratory (PPPL). The team is addressing issues that must be resolved for successful operation of ITER, the international experiment under construction in France to demonstrate the feasibility of producing fusion energy—the power that drives the sun and stars—in a magnetically controlled fusion facility called a "tokamak."

10:18 Arxiv.org CSTransfer learning to model inertial confinement fusion experiments. (arXiv:1812.06055v1 [cs.LG])

Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality, and therefore must be calibrated to accurately predict experimental observations. In this work, we propose a novel nonlinear technique for calibrating from simulations to experiments, or from low fidelity simulations to high fidelity simulations, via "transfer learning". Transfer learning is a commonly used technique in the machine learning community, in which models trained on one task are partially retrained to solve a separate, but related task, for which there is a limited quantity of data. We introduce the idea of hierarchical transfer learning, in which neural networks trained on low fidelity models are calibrated to high fidelity models, then to experimental data. This technique essentially bootstraps the calibration process, enabling the creation of models which

10:18 Arxiv.org CSSpatial Fusion GAN for Image Synthesis. (arXiv:1812.05840v1 [cs.CV])

Recent advances in generative adversarial networks (GANs) have shown great potentials in realistic image synthesis whereas most existing works address synthesis realism in either appearance space or geometry space but few in both. This paper presents an innovative Spatial Fusion GAN (SF-GAN) that combines a geometry synthesizer and an appearance synthesizer to achieve synthesis realism in both geometry and appearance spaces. The geometry synthesizer learns contextual geometries of background images and transforms and places foreground objects into the background images unanimously. The appearance synthesizer adjusts the color, brightness and styles of the foreground objects and embeds them into background images harmoniously, where a guided filter is introduced for detail preserving. The two synthesizers are inter-connected as mutual references which can be trained end-to-end without

10:18 Arxiv.org CSAction Machine: Rethinking Action Recognition in Trimmed Videos. (arXiv:1812.05770v1 [cs.CV])

Existing methods in video action recognition mostly do not distinguish human body from the environment and easily overfit the scenes and objects. In this work, we present a conceptually simple, general and high-performance framework for action recognition in trimmed videos, aiming at person-centric modeling. The method, called Action Machine, takes as inputs the videos cropped by person bounding boxes. It extends the Inflated 3D ConvNet (I3D) by adding a branch for human pose estimation and a 2D CNN for pose-based action recognition, being fast to train and test. Action Machine can benefit from the multi-task training of action recognition and pose estimation, the fusion of predictions from RGB images and poses. On NTU RGB-D, Action Machine achieves the state-of-the-art performance with top-1 accuracies of 97.2% and 94.3% on cross-view and cross-subject respectively. Action Machine also

05:02 Arxiv.org StatisticsTransfer learning to model inertial confinement fusion experiments. (arXiv:1812.06055v1 [cs.LG])

Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality, and therefore must be calibrated to accurately predict experimental observations. In this work, we propose a novel nonlinear technique for calibrating from simulations to experiments, or from low fidelity simulations to high fidelity simulations, via "transfer learning". Transfer learning is a commonly used technique in the machine learning community, in which models trained on one task are partially retrained to solve a separate, but related task, for which there is a limited quantity of data. We introduce the idea of hierarchical transfer learning, in which neural networks trained on low fidelity models are calibrated to high fidelity models, then to experimental data. This technique essentially bootstraps the calibration process, enabling the creation of models which

14.12.2018
21:06 ScientificAmerican.ComExperts Urge U.S. to Continue Support for Nuclear Fusion Research

An international fusion project could help the nation eventually develop its own, smaller reactor -- Read more on ScientificAmerican.com

14:32 Phys.orgBetter superconductors from ceramic copper oxides

Medical magnetic resonance imaging, high-power microwave generators, superconducting magnetic energy storage units, and the solenoids in nuclear fusion reactors are very different technologies which all critically rely on the ability of superconducting materials to carry and store large electric currents in a compact space without overheating or dissipating large amounts of energy.

04:55 Nanowerk.comTangled magnetic fields power cosmic particle accelerators

Scientists find a new way to explain how a black hole's plasma jets boost particles to the highest energies observed in the universe. The results could also prove useful for fusion and accelerator research on Earth.

01:02 Nature.ComUS science academies urge expansion of fusion-energy research

13.12.2018
11:35 TechInvestorNews.comChoetech 19-Watt/dual-port solar smartphone charger (ZDNet Latest News)

ZDNet Latest NewsChoetech 19-Watt/dual-port solar smartphone charger - There may be times when were nowhere near a power outlet, so that might be a good time to turn to the huge ball of nuclear fusion that regularly makes an appearance in the sky. Who doesnt love free power? ...

07:31 Arxiv.org PhysicsA multi-dimensional, moment-accelerated deterministic particle method for time-dependent, multi-frequency thermal radiative transfer problems. (arXiv:1812.04736v1 [physics.comp-ph])

Thermal Radiation Transport (TRT) is the dominant energy transfer mechanism in high-energy density physics with applications in fusion plasma physics and astrophysics. The stiff interactions between the material and radiation fields make TRT problems challenging to model. In this study, we propose a multi-dimensional extension of the deterministic particle (DP) method. The DP method combines aspects from both particle and deterministic methods. If the reemission source is known apriori, and no physical scattering is present, the intensity of a particle can be integrated analytically. This results in no statistical noise compared to Monte-Carlo methods, while maintaining the flexibility of particle methods. The method is closely related to the popular method of long characteristics. The combination of the DP-method with a discretely-consistent, nonlinear, gray low-order system enables an

12.12.2018
14:26 Technology.orgTaming turbulence: Seeking to make complex simulations a breeze

For scientists wrestling with problems as diverse as containing superhot plasma in a fusion reactor, improving the accuracy

11.12.2018
23:54 Phys.orgTaming turbulence: Seeking to make complex simulations a breeze

For scientists wrestling with problems as diverse as containing superhot plasma in a fusion reactor, improving the accuracy of weather forecasts, or probing the unexplained dynamics of a distant galaxy, turbulence-spawning shear flow is a serious complicating factor.

10.12.2018
09:44 Arxiv.org StatisticsAutomatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction. (arXiv:1812.02852v1 [cs.LG])

Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. Methods: This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on

08:36 Arxiv.org CSAn Attempt towards Interpretable Audio-Visual Video Captioning. (arXiv:1812.02872v1 [cs.CV])

Automatically generating a natural language sentence to describe the content of an input video is a very challenging problem. It is an essential multimodal task in which auditory and visual contents are equally important. Although audio information has been exploited to improve video captioning in previous works, it is usually regarded as an additional feature fed into a black box fusion machine. How are the words in the generated sentences associated with the auditory and visual modalities? The problem is still not investigated. In this paper, we make the first attempt to design an interpretable audio-visual video captioning network to discover the association between words in sentences and audio-visual sequences. To achieve this, we propose a multimodal convolutional neural network-based audio-visual video captioning framework and introduce a modality-aware module for exploring modality

08:36 Arxiv.org CSAutomatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction. (arXiv:1812.02852v1 [cs.LG])

Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. Methods: This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on

05.12.2018
04:37 ScienceDaily.comA step closer to fusion energy

Harnessing nuclear fusion is a step closer after researchers showed that using two types of imaging can help them assess the safety and reliability of parts used in a fusion energy device.

04.12.2018
22:41 Phys.orgA step closer to fusion energy: Imaging allows better testing of components for devices

Harnessing nuclear fusion, which powers the sun and stars, to help meet earth's energy needs, is a step closer after researchers showed that using two types of imaging can help them assess the safety and reliability of parts used in a fusion energy device.

18:08 Phys.orgNew discovery complicates efforts to measure universe's expansion

A study led by Texas Tech University shows that supersoft X-ray emissions can come from accretion as well as nuclear fusion.

12:02 Arxiv.org StatisticsTowards Gaussian Bayesian Network Fusion. (arXiv:1812.00262v1 [cs.LG])

Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are leading to the proposal of new methods and paradigms, in order to be able to deal with what is nowadays referred to as Big Data. In this paper we propose a method for the aggregation of different Bayesian network structures that have been learned from separate data sets, as a first step towards mining data sets that need to be partitioned in an horizontal way, i.e. with respect to the instances, in order to be processed. Considerations that should be taken into account when dealing with this situation are discussed. Scalable learning of Bayesian networks is slowly emerging, and our method constitutes one of the first insights into Gaussian Bayesian network aggregation from different sources. Tested on synthetic data

10:42 Arxiv.org PhysicsGPU-acceleration with OpenACC for an 3D Tokamak MHD code (CLT). (arXiv:1812.00525v1 [physics.comp-ph])

The OpenACC programming model has been applied in the 3D tokamak magnetohydrodynamics (MHD) code (CLT) successfully. Great speedup has been achieved on single TITAN Xp and TITAN V GPUs with very few modifications done on the source code. And the combination of OpenACC with MPI makes the multiple GPUs parallel program feasible. The validity of the double precision operations on above two graphics cards has been checked strictly with the simulations of m/n=2/1 resistive tearing mode instability in tokamak. The implementation of OpenACC in CLT code, its performance test and benchmark will be introduced in detail.

10:42 Arxiv.org PhysicsResistive evolution of toroidal field configurations and their relation to magnetic clouds. (arXiv:1812.00005v1 [physics.plasm-ph])

We study the resistive evolution of a localized self-organizing magnetohydrodynamic equilibrium. In this configuration the magnetic forces are balanced by a pressure force caused by a toroidal depression in the pressure. Equilibrium is attained when this low pressure region prevents further expansion into the higher-pressure external plasma. We find that, for the parameters investigated, the resistive evolution of the structures follows a universal pattern when rescaled to resistive time. The finite resistivity causes both a decrease in the magnetic field strength and a finite slip of the plasma fluid against the static equilibrium. This slip is caused by a Pfirsch-Schl\"uter type diffusion, similar to what is seen in tokamak equilibria. The net effect is that the configuration remains in Magnetostatic equilibrium whilst it slowly grows in size. The rotational transform of the structure

10:42 Arxiv.org CSTowards Gaussian Bayesian Network Fusion. (arXiv:1812.00262v1 [cs.LG])

Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are leading to the proposal of new methods and paradigms, in order to be able to deal with what is nowadays referred to as Big Data. In this paper we propose a method for the aggregation of different Bayesian network structures that have been learned from separate data sets, as a first step towards mining data sets that need to be partitioned in an horizontal way, i.e. with respect to the instances, in order to be processed. Considerations that should be taken into account when dealing with this situation are discussed. Scalable learning of Bayesian networks is slowly emerging, and our method constitutes one of the first insights into Gaussian Bayesian network aggregation from different sources. Tested on synthetic data

30.11.2018
07:29 Arxiv.org Quantitative BiologyUnimolecular FRET Sensors: Simple Linker Designs and Properties. (arXiv:1811.12305v1 [q-bio.BM])

Protein activation and deactivation is central to a variety of biological mechanisms, including cellular signaling and transport. Unimolecular fluorescent resonance energy transfer (FRET) probes are a class of fusion protein sensors that allow biologists to visualize using an optical microscope whether specific proteins are activated due to the presence nearby of small drug-like signaling molecules, ligands or analytes. Often such probes comprise a donor fluorescent protein attached to a ligand binding domain, a sensor or reporter domain attached to the acceptor fluorescent protein, with these ligand binding and sensor domains connected by a protein linker. Various choices of linker type are possible ranging from highly flexible proteins to hinge-like proteins. It is also possible to select donor and acceptor pairs according to their corresponding F\"oster radius, or even to mutate binding

07:08 Arxiv.org PhysicsPreliminary computation of the gap eigenmode of shear Alfv\'{e}n waves on LAPD. (arXiv:1811.12096v1 [physics.plasm-ph])

Characterizing the gap eigenmode of shear Alfv\'{e}n waves (SAW) and its interaction with energetic ions is important to the success of magnetically confined fusion. Previous studies have reported an experimental observation of the spectral gap of SAW on LAPD (Zhang et al 2008 Phys. Plasmas 15 012103), a linear large plasma device (Gekelman et al 1991 Rev. Sci. Instrum. 62 2875) possessing easier diagnostic access and lower cost compared with traditional fusion devices, and analytical theory and numerical gap eigenmode using ideal conditions (Chang 2014 PhD Thesis at Australian National University). To guide experimental implementation, the present work models the gap eigenmode of SAW using exact LAPD parameters. A full picture of the wave field for previous experiment reveals that the previously observed spectral gap is not global but an axially local result. To form a global spectral gap,

07:08 Arxiv.org PhysicsAlfv\'{e}nic gap eigenmode in a linear plasma with ending magnetic throats. (arXiv:1811.12066v1 [physics.plasm-ph])

To guide the experimental design of a linear plasma device for studying the interaction between energetic ions and Alfv\'{e}nic gap eigenmode (AGE), this work computes AGE referring to fusion conditions in a ultra-long large plasma cylinder ended with strong magnetic throats for axial confinement of charged particles. It is shown that: (i) for uniform equilibrium field between the ending throats, the dispersion relation of computed wave field agrees well with a simple analytical model for shear Alfv\'{e}nic mode; (ii) for periodic equilibrium field with local defect, clear AGE is formed inside spectral gap for both low and high depths of magnetic throats, although lower depth yields easier observation. The strongest AGE can be in order of $3.1\times10^{-4}$ to equilibrium field, making it conveniently measurable in experiment. The AGE is a standing wave localized around the defect which is

Visual question answering (VQA) demands simultaneous comprehension of both the image visual content and natural language questions. In some cases, the reasoning needs the help of common sense or general knowledge which usually appear in the form of text. Current methods jointly embed both the visual information and the textual feature into the same space. However, how to model the complex interactions between the two different modalities is not an easy task. In contrast to struggling on multimodal feature fusion, in this paper, we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem. With this transformation, our method not only can tackle VQA datasets that focus on observation based questions, but can also be naturally extended to handle knowledge-based VQA which requires to explore large-scale external knowledge

07:08 Arxiv.org CSNon-Volume Preserving-based Feature Fusion Approach to Group-Level Expression Recognition on Crowd Videos. (arXiv:1811.11849v1 [cs.CV])

Group-level emotion recognition (ER) is a growing research area as the demands for assessing crowds of all sizes is becoming an interest in both the security arena and social media. This work investigates group-level expression recognition on crowd videos where information is not only aggregated across a variable length sequence of frames but also over the set of faces within each frame to produce aggregated recognition results. In this paper, we propose an effective deep feature level fusion mechanism to model the spatial-temporal information in the crowd videos. Furthermore, we extend our proposed NVP fusion mechanism to temporal NVP fussion appoarch to learn the temporal information between frames. In order to demonstrate the robustness and effectiveness of each component in the proposed approach, three experiments were conducted: (i) evaluation on the AffectNet database to benchmark the

28.11.2018
05:13 Arxiv.org PhysicsDielectronic recombination of lanthanide and low ionization state tungsten ions: W$^{13+}$ - W$^{1+}$. (arXiv:1811.10726v1 [physics.atom-ph])

The experimental thermonuclear reactor, ITER, is currently being constructed in Cadarache, France. The reactor vessel will be constructred with a beryllium coated wall, and a tungsten coated divertor. As a plasma-facing component, the divertor will be under conditions of extreme temperature, resulting in the sputtering of tungsten impurities into the main body plasma. Modelling and understanding the potential cooling effects of these impurities requires detailed collisional-radiative modelling. These models require a wealth of atomic data for the various atomic species in the plasma. In particular, partial, final-state resolved dielectronic/radiative recombination (DR/RR) rate coefficients for tungsten are required. In this manuscript, we present our calculations of detailed DR/RR rate coefficients for the lanthanide-like, and low ionization stages of tungsten, spanning charge states

27.11.2018
05:15 Gizmag Wendelstein 7-X fusion reactor keeps its cool en route to record-breaking results

Scientists toiling away on the cutting edge Wendelstein 7-X nuclear fusion reactor in Germany have pulled together results from their latest round of testing, with a few records to be found amongst them. Following a series of upgrades, the team is reporting the experimental device has achieved its highest energy density and the longest plasma discharge times for device of this type, marking another step forward in the quest for clean fusion power.
.. Continue Reading Wendelstein 7-X fusion reactor keeps its cool en route to record-breaking results Category: Energy Tags: Fusion Max Planck Institute Nuclear Stellarator Wendelstein 7-X

01:09 Phys.orgSuccessful second round of experiments with Wendelstein 7-X

During the course of the step-by-step upgrading of Wendelstein 7-X, the plasma vessel was fitted with inner cladding since September of last year. Graphite tiles are now protecting the vessel walls. In addition, the so-called "divertor" is used to regulate the purity and density of the plasma. In ten broad strips on the wall of the plasma vessel, the divertor tiles follow the contour of the plasma edge. Specifically, they cover the wall areas on which the particles from the edge of the plasma are diverted in a targeted way. After three months of experiments with the new equipment, the next round of upgrades began at the end of 2017; among other things, new measuring devices and heating systems were installed. The experiments were resumed from July 2018 onwards.

26.11.2018
10:48 Arxiv.org PhysicsThe Vlasov equation correct application based on the higher orders kinematic values for the dissipative systems description. (arXiv:1811.09424v1 [physics.comp-ph])

This paper revises traditional phenomenological approaches to the application of Vlasov equation to describe the dissipative systems. The original equation by A.A. Vlasov obtained differs from the classical Vlasov equation used in the scientific literature. The classical Vlasov equation cannot be used to describe the dissipative systems. The original Vlasov equation contains a non-zero right-hand side, derived from the first principles. It is shown that the original Vlasov equation describes the dissipative systems by the non-phenomenological way. The numerical modeling of the dissipative systems using the motion equations solution is performed. The numerical results show the good agreement with the exact solutions of the original Vlasov equation for the dissipative systems. In this way, a wide range of the statistical physics problems, the plasma physics, the astrophysics, the high-energy

22.11.2018
07:14 Arxiv.org PhysicsPosition Sensitive Alpha Detector for an Associate Particle Imaging System. (arXiv:1811.08591v1 [physics.ins-det])

Associated Particle Imaging (API) is a nuclear technique that allows for the nondestructive determination of 3D isotopic distributions. The technique is based on the detection of the alpha particles associated with the neutron emitted in the deuterium-tritium (DT) fusion reaction, which provides information regarding the direction and time of the emitted 14 MeV neutron. Inelastic neutron scattering leads to characteristic gamma-ray emission from certain isotopes, for example C-12, that can be correlated with the neutron interaction location. An API system consisting of a sealed-type neutron generator, gamma detectors, and a position-sensitive alpha detector is under development for the nondestructive quantification of carbon distribution in soils. This paper describes the design of the alpha detector, detector response simulations, and first experimental results. The alpha detector consists

07:14 Arxiv.org CSBoosting in Image Quality Assessment. (arXiv:1811.08429v1 [eess.IV])

In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. Existing multi-method studies focus on proposing a single quality estimator. On the contrary, we investigate the generalizability of multi-method fusion as a framework. In addition to support vector machines that are commonly used in the multi-method fusion, we propose using neural networks in the boosting. To span different types of image quality assessment algorithms, we use quality estimators based on fidelity, perceptually-extended fidelity, structural similarity, spectral similarity, color, and learning. In the experiments, we perform k-fold cross validation using the LIVE, the multiply distorted LIVE, and the TID 2013 databases and the performance of image quality assessment algorithms are measured via accuracy-, linearity-, and ranking-based metrics. Based on the experiments, we

20.11.2018
12:46 Arxiv.org PhysicsMagnetothermodynamics: measuring the equations of state of a compressible magnetized plasma. (arXiv:1811.07008v1 [physics.plasm-ph])

Magnetothermodynamics (MTD) is the study of compression and expansion of magnetized plasma with an eye towards identifying equations of state for magneto-inertial fusion experiments. We present recent results from SSX experiments on the thermodynamics of compressed magnetized plasmas. In these experiments, we generate twisted flux ropes of magnetized, relaxed plasma accelerated from one end of a $1.5~m$ long copper flux conserver, and observe their compression in a closed conducting boundary installed at the other end. Plasma parameters are measured during compression. The instances of ion heating during compression are identified by constructing a PV diagram using measured density, temperature, and volume of the magnetized plasma. The theoretically predicted MHD and double adiabatic (CGL) equations of state are compared to experimental measurements to estimate the adiabatic nature of the

19.11.2018
08:20 Arxiv.org PhysicsEn-route to the fission-fusion reaction mechanism: a status update on laser-driven heavy ion acceleration. (arXiv:1811.06720v1 [physics.plasm-ph])

The fission-fusion reaction mechanism was proposed in order to generate extremely neutron-rich nuclei close to the waiting point N = 126 of the rapid neutron capture nucleosynthesis process (r-process). The production of such isotopes and the measurement of their nuclear properties would fundamentally help to increase the understanding of the nucleosynthesis of the heaviest elements in the universe. Major prerequisite for the realization of this new reaction scheme is the development of laser-based acceleration of ultra-dense heavy ion bunches in the mass range of A = 200 and above. In this paper, we review the status of laser-driven heavy ion acceleration in the light of the fission-fusion reaction mechanism. We present results from our latest experiment on heavy ion acceleration, including a new milestone with laser-accelerated heavy ion energies exceeding 5 MeV/u.

16.11.2018
09:33 Arxiv.org PhysicsEffects of Flow Collisionality on ELM Replication in Plasma Guns. (arXiv:1811.06010v1 [physics.plasm-ph])

Degradation of first wall materials due to plasma disturbances severely limit both the lifetime and longevity of fusion reactors. Among the various kinds of disturbances, type I edge localized modes (ELMs) in particular present significant design challenges due to their expected heat loading and relative frequency in next step fusion reactors. Plasma gun devices have been used extensively to replicate ELM conditions in the laboratory, however feature higher density, lower temperatures, and thus higher flow collisionality than those expected in fusion conditions. This work presents experimental visualizations that indicate strong shocks form in gun devices over spatial and temporal scales that precede ablation dynamics. These measurements are used to validate detailed magnetohydrodynamic simulations that capture the production of plasma jets and the shielding effect collisionality plays in

05:10 BBCHow 'miniature suns' could provide cheap, clean energy

Is much-heralded nuclear fusion finally ready to fulfil its promise of abundant energy for all?

15.11.2018
22:13 ExtremeTech.comChinese Fusion Reactor Gets 6 Times Hotter Than the Sun

The team operating the Experimental Advanced Superconducting Tokamak (EAST) managed to heat the reactor's internal plasma to 100 million degrees Celsius (212 million Fahrenheit).
The post Chinese Fusion Reactor Gets 6 Times Hotter Than the Sun appeared first on ExtremeTech.

15:24 Phys.orgChinese fusion tool pushes past 100 million degrees

The Experimental Advanced Superconducting Tokamak (EAST), nicknamed the "Chinese artificial sun," achieved an electron temperature of over 100 million degrees in its core plasma during a four-month experiment this year. That's about seven times greater than the interior of the sun, which is about 15 million degrees C.

07:30 Gizmag Fusion breakthrough as China's "artificial sun" reaches 100 million degrees

The day of clean, limitless energy from nuclear fusion has taken another step closer after China's Experimental Advanced Superconducting Tokamak (EAST) reached a core plasma temperature of over 100 million degrees Celsius (180 million degrees Fahrenheit). During a four-month experiment, the "Chinese artificial sun" achieved a temperature over six times greater than the interior of the Sun for around 10 seconds.
.. Continue Reading Fusion breakthrough as China's "artificial sun" reaches 100 million degrees Category: Energy Tags: China Fusion Nuclear

03:01 WhatReallyHappened.comReaching for the stars: China creates nuke-powered fake sun that burns hotter than the real deal

Chinese researchers pushing to find a major clean energy source have created an incredible artificial sun that can reach temperatures of 100 million degrees Celsius – a heat so intense it makes the real sun seem merely lukewarm.
The earth-based solar simulator has reached mind-bending temperatures of 100 million degrees Celsius, the research team announced Tuesday. Now, that’s hot. For comparison, the real sun’s core is about 15 million degrees Celsius.
The Institute of Plasma Physics, affiliated with the Chinese Academy of Sciences, said it has been testing an “artificial sun,” known as the Experimental Advanced Superconducting Tokamak (EAST). The sci-fi-sounding contraption has been designed to replicate the way in which the star at the center of our solar system generates its colossal energy.

14.11.2018
08:18 Arxiv.org StatisticsNeuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks. (arXiv:1811.05105v1 [cs.LG])

Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and PET, but a comprehensive and balanced comparison of these modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

07:56 Arxiv.org Quantitative BiologyNeuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks. (arXiv:1811.05105v1 [cs.LG])

Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and PET, but a comprehensive and balanced comparison of these modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

07:56 Arxiv.org CSFusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs. (arXiv:1811.05213v1 [cs.DC])

In recent years, there is a surge on machine learning applications in industry. Many of them are based on popular AI frameworks like Tensorflow, Torch, Caffe, or MxNet, etc, and are enpowered by accelerator platforms such as GPUs. One important challenge of running Tensorflow computations on GPUs is the fine granularity problem, namely, FLOPS of individual ops are far from enough to fully exploit the computing power of underlying accelerators. The XLA framework provides a solid foundation to explore this problem further. In this paper, we propose FusionStitching, a novel, comprehensive Op fusion and code generation system to stitch computations into large GPU kernels. Experimental results on four public models and two of our large inhouse applications show another 55% (geometric mean) reduction of GPU kernel launches, compared to the XLA fusion baseline. This increases the E2E performance of

07:56 Arxiv.org CSNeuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks. (arXiv:1811.05105v1 [cs.LG])

Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and PET, but a comprehensive and balanced comparison of these modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

13.11.2018
10:35 Arxiv.org PhysicsVacuum Solution for Solov'ev's Equilibrium Configuration in Tokamaks. (arXiv:1811.04443v1 [physics.plasm-ph])

In this work, we have revisited the Solov'ev analytical solution for a tokamak equilibrium. We point out that the vacuum solution in the Solov'ev formulation is inapplicable, since a distributed current density is assumed to fill the vacuum. The realistic vacuum should be current-free. To amend this vacuum solution problem, we use Green's function method to compute the plasma current contribution, together with a homogeneous solution to the Grad-Shafranov equation, to construct the full solution. Matching with the Solov'ev solution on the last closed flux surface is performed to determine the homogeneous solution. The total solution is then extended into the vacuum region to get a realistic vacuum solution. We find that the actual vacuum solution is different from the Solov'ev solution in the vacuum region, especially the X-point structure. The X-point obtained at the last closed flux surface

12.11.2018
06:21 Arxiv.org CSGender Effect on Face Recognition for a Large Longitudinal Database. (arXiv:1811.03680v1 [cs.CV])

Aging or gender variation can affect the face recognition performance dramatically. While most of the face recognition studies are focused on the variation of pose, illumination and expression, it is important to consider the influence of gender effect and how to design an effective matching framework. In this paper, we address these problems on a very large longitudinal database MORPH-II which contains 55,134 face images of 13,617 individuals. First, we consider four comprehensive experiments with different combination of gender distribution and subset size, including: 1) equal gender distribution; 2) a large highly unbalanced gender distribution; 3) consider different gender combinations, such as male only, female only, or mixed gender; and 4) the effect of subset size in terms of number of individuals. Second, we consider eight nearest neighbor distance metrics and also Support Vector

09.11.2018
07:40 Arxiv.org CSAttention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support. (arXiv:1811.03169v1 [cs.CL])

Customer support is a central objective at Square as it helps us build and maintain great relationships with our sellers. In order to provide the best experience, we strive to deliver the most accurate and quasi-instantaneous responses to questions regarding our products.
In this work, we introduce the Attention Fusion Network model which combines signals extracted from seller interactions on the Square product ecosystem, along with submitted email questions, to predict the most relevant solution to a seller's inquiry. We show that the innovative combination of two very different data sources that are rarely used together, using state-of-the-art deep learning systems outperforms, candidate models that are trained only on a single source.

07.11.2018
18:52 ExtremeTech.comMIT Plans New Fusion Reactor That Could Actually Generate Power

MIT says it has the tools to make true fusion power happen, and it may be producing energy in a few years.
The post MIT Plans New Fusion Reactor That Could Actually Generate Power appeared first on ExtremeTech.

06.11.2018
08:49 Arxiv.org PhysicsFirst Commissioning Results of the Multicusp Ion Source at MIT (MIST-1) for H$_2^+$. (arXiv:1811.01868v1 [physics.acc-ph])

IsoDAR is an experiment under development to search for sterile neutrinos using the isotope Decay-At-Rest (DAR) production mechanism, where protons impinging on $^9$Be create neutrons which capture on $^7$Li which then beta-decays producing $\bar{\nu}_e$. As this will be an isotropic source of $\bar{\nu}_e$, the primary driver current must be large (10 mA cw) for IsoDAR to have sufficient statistics to be conclusive within 5 years of running. H$_2^+$ was chosen as primary ion to overcome some of the space-charge limitations during low energy beam transport and injection into a compact cyclotron. The H$_2^+$ will be stripped into protons before the target. At MIT, a multicusp ion source (MIST-1) was designed and built to produce a high intensity beam with a high H$_2^+$ fraction. MIST-1 is now operational at the Plasma Science and Fusion Center (PSFC) at MIT and under

05.11.2018
19:56 ScienceDaily.comA faster, cheaper path to fusion energy

Scientists are working to dramatically speed up the development of fusion energy in an effort to deliver power to the electric grid soon enough to help mitigate impacts of climate change. The arrival of a breakthrough technology -- high-temperature superconductors, which can be used to build magnets that produce stronger magnetic fields than previously possible -- could help them achieve this goal. Researchers plan to use this technology to build magnets at the scale required for fusion.

19:56 ScienceDaily.comInside job: A new technique to cool a fusion reactor

Fusion offers the potential of near limitless energy by heating a gas trapped in a magnetic field to incredibly high temperatures where atoms are so energetic that they fuse together when they collide. But if that hot gas, called a plasma, breaks free from the magnetic field, it must be safely put back in place to avoid damaging the fusion device -- this problem has been one of the great challenges of magnetically confined fusion.

19:56 ScienceDaily.comTaming plasmas: Improving fusion using microwaves

We all know microwaves are good for cooking popcorn, but scientists have recently shown they can also prevent dangerous waves in plasmas and help produce clean, nearly limitless energy with fusion. Fusion takes place when fast moving atomic particles slam into each other and stick together. The particles need to be so hot that atoms break down, leaving a gas of charged particles called a plasma.

19:56 ScienceDaily.comPeak performance: New stellarator experiments show promising results

Imagine building a machine so advanced and precise you need a supercomputer to help design it. That's exactly what scientists and engineers in Germany did when building the Wendelstein 7-X experiment. The device is a type of fusion device called a stellarator.

18:41 Phys.orgInside job: A new technique to cool a fusion reactor

Fusion offers the potential of near limitless energy by heating a gas trapped in a magnetic field to incredibly high temperatures where atoms are so energetic that they fuse together when they collide. But if that hot gas, called a plasma, breaks free from the magnetic field, it must be safely put back in place to avoid damaging the fusion device—this problem has been one of the great challenges of magnetically confined fusion.

18:41 Phys.orgTaming plasmas: Improving fusion using microwaves

We all know microwaves are good for cooking popcorn, but scientists have recently shown they can also prevent dangerous waves in plasmas and help produce clean, nearly limitless energy with fusion. Fusion takes place when fast moving atomic particles slam into each other and stick together. The particles need to be so hot that atoms break down, leaving a gas of charged particles called a plasma. The energy given off when plasma particles fuse can be harnessed to make electricity.

18:29 Phys.orgA faster, cheaper path to fusion energy

Scientists are working to dramatically speed up the development of fusion energy in an effort to deliver power to the electric grid soon enough to help mitigate impacts of climate change. The arrival of a breakthrough technology—high-temperature superconductors, which can be used to build magnets that produce stronger magnetic fields than previously possible—could help them achieve this goal. Researchers plan to use this technology to build magnets at the scale required for fusion, followed by construction of what would be the world's first fusion experiment to yield a net energy gain.

18:29 Phys.orgPeak performance: new stellarator experiments show promising results

Imagine building a machine so advanced and precise you need a supercomputer to help design it. That's exactly what scientists and engineers in Germany did when building the Wendelstein 7-X experiment. The device, funded by the German federal and state governments and the European Union, is a type of fusion device called a stellarator. The new experiment's goal is to contain a super-heated gas, called plasma, in a donut-shaped vessel using magnets that twist their way around the donut.

11:37 Arxiv.org StatisticsIndependent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning. (arXiv:1811.00628v1 [stat.ML])

Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery. A main ingredient required for machine learning is a training dataset consisting of molecular features\textemdash for example fingerprint bits, chemical descriptors, etc. that adequately characterize the corresponding molecules. However, choosing features for any application is highly non-trivial. No "universal" method for feature selection exists. In this work, we propose a data fusion framework that uses Independent Vector Analysis to exploit underlying complementary information contained in different molecular featurization methods, bringing us a step closer to automated feature generation. Our approach takes an arbitrary number of

11:26 Arxiv.org CSIndependent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning. (arXiv:1811.00628v1 [stat.ML])

Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery. A main ingredient required for machine learning is a training dataset consisting of molecular features\textemdash for example fingerprint bits, chemical descriptors, etc. that adequately characterize the corresponding molecules. However, choosing features for any application is highly non-trivial. No "universal" method for feature selection exists. In this work, we propose a data fusion framework that uses Independent Vector Analysis to exploit underlying complementary information contained in different molecular featurization methods, bringing us a step closer to automated feature generation. Our approach takes an arbitrary number of

02.11.2018
05:43 Arxiv.org PhysicsApplications of Deep Learning to Nuclear Fusion Research. (arXiv:1811.00333v1 [physics.plasm-ph])

Nuclear fusion is the process that powers the sun, and it is one of the best hopes to achieve a virtually unlimited energy source for the future of humanity. However, reproducing sustainable nuclear fusion reactions here on Earth is a tremendous scientific and technical challenge. Special devices -- called tokamaks -- have been built around the world, with JET (Joint European Torus, in the UK) being the largest tokamak currently in operation. Such devices confine matter and heat it up to extremely high temperatures, creating a plasma where fusion reactions begin to occur. JET has over one hundred diagnostic systems to monitor what happens inside the plasma, and each 30-second experiment (or pulse) generates about 50 GB of data. In this work, we show how convolutional neural networks (CNNs) can be used to reconstruct the 2D plasma profile inside the device based on data coming from those

05:43 Arxiv.org CSApplications of Deep Learning to Nuclear Fusion Research. (arXiv:1811.00333v1 [physics.plasm-ph])

Nuclear fusion is the process that powers the sun, and it is one of the best hopes to achieve a virtually unlimited energy source for the future of humanity. However, reproducing sustainable nuclear fusion reactions here on Earth is a tremendous scientific and technical challenge. Special devices -- called tokamaks -- have been built around the world, with JET (Joint European Torus, in the UK) being the largest tokamak currently in operation. Such devices confine matter and heat it up to extremely high temperatures, creating a plasma where fusion reactions begin to occur. JET has over one hundred diagnostic systems to monitor what happens inside the plasma, and each 30-second experiment (or pulse) generates about 50 GB of data. In this work, we show how convolutional neural networks (CNNs) can be used to reconstruct the 2D plasma profile inside the device based on data coming from those

05:43 Arxiv.org CSHybrid Self-Attention Network for Machine Translation. (arXiv:1811.00253v1 [cs.CL])

The encoder-decoder is the typical framework for Neural Machine Translation (NMT), and different structures have been developed for improving the translation performance. Transformer is one of the most promising structures, which can leverage the self-attention mechanism to capture the semantic dependency from global view. However, it cannot distinguish the relative position of different tokens very well, such as the tokens located at the left or right of the current token, and cannot focus on the local information around the current token either. To alleviate these problems, we propose a novel attention mechanism named Hybrid Self-Attention Network (HySAN) which accommodates some specific-designed masks for self-attention network to extract various semantic, such as the global/local information, the left/right part context. Finally, a squeeze gate is introduced to combine different kinds of

31.10.2018
04:14 Arxiv.org PhysicsThree Dimensional Pseudo-Spectral Compressible Magnetohydrodynamic GPU Code for Astrophysical Plasma Simulation. (arXiv:1810.12707v1 [physics.comp-ph])

This paper presents the benchmarking and scaling studies of a GPU accelerated three dimensional compressible magnetohydrodynamic code. The code is developed keeping an eye to explain the large and intermediate scale magnetic field generation is cosmos as well as in nuclear fusion reactors in the light of the theory given by Eugene Newman Parker. The spatial derivatives of the code are pseudo-spectral method based and the time solvers are explicit. GPU acceleration is achieved with minimal code changes through OpenACC parallelization and use of NVIDIA CUDA Fast Fourier Transform library (cuFFT). NVIDIAs unified memory is leveraged to enable over-subscription of the GPU device memory for seamless out-of-core processing of large grids. Our experimental results indicate that the GPU accelerated code is able to achieve upto two orders of magnitude speedup over a corresponding OpenMP parallel, FFTW

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