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# Fusion

19.03.2019
22:25 ScienceDaily.comSpeeding the development of fusion power to create unlimited energy on Earth

A detailed examination of the challenges and tradeoffs in the development of a compact fusion facility with high-temperature superconducting magnets.

19:42 Phys.orgSpeeding the development of fusion power to create unlimited energy on Earth

Can tokamak fusion facilities, the most widely used devices for harvesting on Earth the fusion reactions that power the sun and stars, be developed more quickly to produce safe, clean, and virtually limitless energy for generating electricity? Physicist Jon Menard of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) has examined that question in a detailed look at the concept of a compact tokamak equipped with high temperature superconducting (HTS) magnets. Such magnets can produce higher magnetic fields—necessary to produce and sustain fusion reactions—than would otherwise be possible in a compact facility.

17:20 Nature.ComUK pledges to fully fund EU nuclear-fusion facility

04:59 Arxiv.org PhysicsSymplectic integration with non-canonical quadrature for guiding-center orbits in magnetic confinement devices. (arXiv:1903.06885v1 [physics.comp-ph])

We study symplectic numerical integration of mechanical systems with a Hamiltonian specified in non-canonical coordinates and its application to guiding-center motion of charged plasma particles in magnetic confinement devices. The technique combines time-stepping in canonical coordinates with quadrature in non-canonical coordinates and is applicable in systems where a global transformation to canonical coordinates is known but its inverse is not. A fully implicit class of symplectic Runge-Kutta schemes has recently been introduced and applied to guiding-center motion by [Zhang et al., Phys. Plasmas 21, 32504 (2014); doi:10.1063/1.4867669]. Here a generalization of this approach with emphasis on semi-implicit partitioned schemes is described together with methods to enhance their performance. For application in toroidal plasma confinement configurations with nested magnetic flux surfaces a

15.03.2019
08:50 Arxiv.org Physics[Plasma 2020 Decadal] Disentangling the Spatiotemporal Structure of Turbulence Using Multi-Spacecraft Data. (arXiv:1903.05710v1 [physics.space-ph])

This white paper submitted for 2020 Decadal Assessment of Plasma Science concerns the importance of multi-spacecraft missions to address fundamental questions concerning plasma turbulence. Plasma turbulence is ubiquitous in the universe, and it is responsible for the transport of mass, momentum, and energy in such diverse systems as the solar corona and wind, accretion discs, planet formation, and laboratory fusion devices. Turbulence is an inherently multi-scale and multi-process phenomenon, coupling the largest scales of a system to sub-electron scales via a cascade of energy, while simultaneously generating reconnecting current layers, shocks, and a myriad of instabilities and waves. The solar wind is humankind's best resource for studying the naturally occurring turbulent plasmas that permeate the universe. Since launching our first major scientific spacecraft mission, Explorer 1, in

14.03.2019
04:28 Arxiv.org PhysicsMachine learning characterisation of Alfv\'{e}nic and sub-Alfv\'{e}nic chirping and correlation with fast ion loss at NSTX. (arXiv:1903.05213v1 [physics.comp-ph])

Abrupt large events in the Alfv\'{e}nic and sub-Alfv\'{e}nic frequency bands in tokamaks are typically correlated with increased fast ion loss. Here, machine learning is used to speed up the laborious process of characterizing the behaviour of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans. Analysis allows for comparison between different mode character (such as quiescent, fixed-frequency, chirping, avalanching) and plasma parameters obtained from the TRANSP code (such as $v_{\textrm{inj.}}/v_{\textrm{A}}$, $q$-profile, $\beta_{\textrm{inj.}}/\beta_{\textrm{A}}$). In agreement with previous work by Fredrickson \emph{et al.} [Nucl. Fusion 2014, 54 093007], we find correlation between $\beta_{\textrm{inj.}}$ and mode character. In addition, previously unknown correlations are found between moments of the spectrograms and mode

13.03.2019
16:44 ScienceDaily.comInvestigation of the origin of heavy elements

Atomic physicists working on nuclear fusion research succeeded in computing the world's highest accuracy atomic data of neodymium ions which is used in analysis of the light from a binary neutron star merger. This research accelerates studies of a long-standing mystery about the cosmic origins of heavy elements.

06:26 Arxiv.org CSThe Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection. (arXiv:1903.04484v1 [cs.CL])

We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks, feeding that to a Support Vector Machine to test deceit or truth prediction. We then try out a method to incorporate utterance-based fusion of visual and lexical analysis, using string based matching.

12.03.2019
18:34 Phys.orgTied in knots: New insights into plasma behavior focus on twists and turns

Whether zipping through a star or a fusion device on Earth, the electrically charged particles that make up the fourth state of matter better known as plasma are bound to magnetic field lines like beads on a string. Unfortunately for plasma physicists who study this phenomenon, the magnetic field lines often lack simple shapes that equations can easily model. Often they twist and knot like pretzels. Sometimes, when the lines become particularly twisted, they snap apart and join back together, ejecting blobs of plasma and tremendous amounts of energy.

14:37 Phys.orgCollaboration enables investigation of the origin of heavy elements

A team of experts in atomic physics, nuclear fusion, and astronomy has computed high-accuracy atomic data for analyzing light from a kilonova, a birth place of heavy elements. They found that their new data set could predict kilonovae brightness with much better accuracy than before. This aids our understanding of the cosmic origins of heavy elements.

09:20 Arxiv.org MathSAT-based Compressive Sensing. (arXiv:1903.03650v1 [cs.IT])

We propose to reduce the original problem of compressive sensing to the weighted-MAX-SAT. Compressive sensing is a novel randomized data acquisition method that outperforms the traditional signal processing approaches (namely, the Nyquist-Shannon technique) in acquiring and reconstructing sparse or compressible signals. The original problem of compressive sensing in sparse recovery has a combinatorial nature so that one needs to apply severe constraints on the design matrix to handle it by its convex or nonconvex relaxations. In practice, such constraints are not only intractable to be verified but also invalid in broad applications. This paper bridges the gap between employing the modern SAT solvers and a vast variety of compressive sensing based real-world applications -ranging from imaging, video processing, remote sensing, communication systems, electronics and VLSI to machine learning,

09:20 Arxiv.org CSSAT-based Compressive Sensing. (arXiv:1903.03650v1 [cs.IT])

We propose to reduce the original problem of compressive sensing to the weighted-MAX-SAT. Compressive sensing is a novel randomized data acquisition method that outperforms the traditional signal processing approaches (namely, the Nyquist-Shannon technique) in acquiring and reconstructing sparse or compressible signals. The original problem of compressive sensing in sparse recovery has a combinatorial nature so that one needs to apply severe constraints on the design matrix to handle it by its convex or nonconvex relaxations. In practice, such constraints are not only intractable to be verified but also invalid in broad applications. This paper bridges the gap between employing the modern SAT solvers and a vast variety of compressive sensing based real-world applications -ranging from imaging, video processing, remote sensing, communication systems, electronics and VLSI to machine learning,

11.03.2019
22:20 ScienceDaily.comResearchers turn liquid metal into a plasma

Researchers have found a way to turn a liquid metal into a plasma and to observe the temperature where a liquid under high-density conditions crosses over to a plasma state. Their observations have implications for better understanding stars and planets and could aid in the realization of controlled nuclear fusion -- a promising alternative energy source whose realization has eluded scientists for decades.

08.03.2019
06:31 Arxiv.org PhysicsDirect Gyrokinetic Comparison of Pedestal Transport in JET with Carbon and ITER-Like Walls. (arXiv:1903.02627v1 [physics.plasm-ph])

This paper compares the gyrokinetic instabilities and transport in two representative JET pedestals, one (pulse 78697) from the JET configuration with a carbon wall (C) and another (pulse 92432) from after the installation of JET's ITER-like Wall (ILW). The discharges were selected for a comparison of JET-ILW and JET-C discharges with good confinement at high current (3 MA, corresponding also to low $\rho_*$) and retain the distinguishing features of JET-C and JET-ILW, notably, decreased pedestal top temperature for JET-ILW. A comparison of the profiles and heating power reveals a stark qualitative difference between the discharges: the JET-ILW pulse (92432) requires twice the heating power, at a gas rate of $1.9 \times 10^{22}e/s$, to sustain roughly half the temperature gradient of the JET-C pulse (78697), operated at zero gas rate. This points to heat transport as a central component of

06:09 Arxiv.org CSDeep Learning in Medical Image Registration: A Survey. (arXiv:1903.02026v1 [q-bio.QM] CROSS LISTED)

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and

07.03.2019
09:07 Arxiv.org PhysicsMicro-Faraday cup matrix detector for ion beam measurements in fusion plasmas. (arXiv:1903.02239v1 [physics.plasm-ph])

Atomic Beam Probe (ABP) is an extension of the routinely used Beam Emission Spectroscopy (BES) diagnostic for plasma edge current fluctuation measurement at magnetically confined plasmas. Beam atoms ionized by the plasma are directed to a curved trajectory by the magnetic field and may be detected close to the wall of the device. The arrival location and current distribution of the ions carry information about the plasma current distribution, the density profile and the electric potential in the plasma edge. This paper describes a micro-Faraday cup matrix detector for the measurement of the few microampere ion current distribution close to the plasma edge. The device implements a shallow Faraday cup matrix, produced by printed-circuit board technology. Secondary electrons induced by the plasma radiation and the ion bombardment are basically confined into the cups by the tokamak magnetic

08:44 Arxiv.org Quantitative BiologyDeep Learning in Medical Image Registration: A Survey. (arXiv:1903.02026v1 [q-bio.QM])

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and

06.03.2019
18:44 WhatReallyHappened.comChina intends to complete artificial sun device this year: Official

China plans to complete the construction of the artificial sun this year, achieving an ion temperature of 100 million degrees Celsius, an official has said. The HL-2M Tokamak device is designed to replicate the nuclear fusion process that occurs naturally in the sun and stars to provide almost infinite clean energy through controlled nuclear fusion, which is often dubbed as the "artificial sun."

17:02 CNBC technologyWhy Bezos and Microsoft are betting on this $10 trillion energy fix for the planet Jeff Bezos and others have sunk more than$127 million into General Fusion, a start-up trying to commercialize fusion energy. Microsoft is partnering with the company. The goal: to provide energy to 1 billion people that don't have electricity.

17:02 CNBC top newsWhy Bezos and Microsoft are betting on this $10 trillion energy fix for the planet Jeff Bezos and others have sunk more than$127 million into General Fusion, a start-up trying to commercialize fusion energy. Microsoft is partnering with the company. The goal: to provide energy to 1 billion people that don't have electricity.

11:30 Arxiv.org CSVisual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments. (arXiv:1903.01656v1 [cs.RO])

With an ever-widening domain of aerial robotic applications, including many mission critical tasks such as disaster response operations, search and rescue missions and infrastructure inspections taking place in GPS-denied environments, the need for reliable autonomous operation of aerial robots has become crucial. Operating in GPS-denied areas aerial robots rely on a multitude of sensors to localize and navigate. Visible spectrum cameras are the most commonly used sensors due to their low cost and weight. However, in environments that are visually-degraded such as in conditions of poor illumination, low texture, or presence of obscurants including fog, smoke and dust, the reliability of visible light cameras deteriorates significantly. Nevertheless, maintaining reliable robot navigation in such conditions is essential. In contrast to visible light cameras, thermal cameras offer visibility in

00:16 ScienceDaily.comNew reactor-liner alloy material offers strength, resilience

A new tungsten-based alloy can withstand unprecedented amounts of radiation without damage. Essential for extreme irradiation environments such as the interiors of magnetic fusion reactors, previously explored materials have thus far been hobbled by weakness against fracture, but this new alloy seems to defeat that problem.

05.03.2019
23:13 Phys.orgNew reactor-liner alloy material offers strength, resilience

A new tungsten-based alloy developed at Los Alamos National Laboratory can withstand unprecedented amounts of radiation without damage. Essential for extreme irradiation environments such as the interiors of magnetic fusion reactors, previously explored materials have thus far been hobbled by weakness against fracture, but this new alloy seems to defeat that problem.

06:56 Arxiv.org CSX-Section: Cross-section Prediction for Enhanced RGBD Fusion. (arXiv:1903.00987v1 [cs.CV])

Detailed 3D reconstruction is an important challenge with application to robotics, augmented and virtual reality, which has seen impressive progress throughout the past years. Advancements were driven by the availability of depth cameras (RGB-D), as well as increased compute power, e.g.\ in the form of GPUs -- but also thanks to inclusion of machine learning in the process. Here, we propose X-Section, an RGB-D 3D reconstruction approach that leverages deep learning to make object-level predictions about thicknesses that can be readily integrated into a volumetric multi-view fusion process, where we propose an extension to the popular KinectFusion approach. In essence, our method allows to complete shape in general indoor scenes behind what is sensed by the RGB-D camera, which may be crucial e.g.\ for robotic manipulation tasks or efficient scene exploration. Predicting object thicknesses

01.03.2019
11:12 Arxiv.org PhysicsDetermination of the quark-gluon string parameters from the data on pp, pA and AA collisions at wide energy range using Bayesian Gaussian Process Optimization. (arXiv:1902.11082v1 [physics.data-an])

Bayesian Gaussian Process Optimization can be considered as a method of the determination of the model parameters, based on the experimental data. In the range of soft QCD physics, the processes of hadron and nuclear interactions require using phenomenological models containing many parameters. In order to minimize the computation time, the model predictions can be parameterized using Gaussian Process regression, and then provide the input to the Bayesian Optimization. In this paper, the Bayesian Gaussian Process Optimization has been applied to the Monte Carlo model with string fusion. The parameters of the model are determined using experimental data on multiplicity and cross section of pp, pA and AA collisions at wide energy range. The results provide important constraints on the transverse radius of the quark-gluon string ($r_{str}$) and the mean multiplicity per rapidity from one

11:12 Arxiv.org PhysicsFirst heat flux estimation in the lower divertor of WEST with embedded thermal measurements. (arXiv:1902.10969v1 [physics.plasm-ph])

The present paper deals with the surface heat flux estimation with thermocouples (TC) and fiber Bragg grating (FBG) embedded in the plasma facing components (PFC) of the WEST tokamak. A 2D heat transfer model combined with the conjugate gradient method (CGM) and the adjoint state is used to estimate the plasma heat flux deposited on the PFC. The plasma heat flux is characterized by the time evolution of its amplitude and spatial shape on the target (heat flux decay length $\lambda^t_q$, power spreading in the private flux region $S^t$ and the strike point location $x_0$). As a first step, five ohmic pulses have been investigated with different magnetic configuration and divertor X-point height varying from 44 to 68 mm from the surface. Despite an outboard shift, the relative displacements of the outer strike point as well as the heat flux decay length derived from the TC/FBG systems are

28.02.2019
06:52 Arxiv.org PhysicsA fully implicit, scalable, nonlinear, conservative, relativistic Fokker-Planck solver for runaway electrons. (arXiv:1902.10241v1 [physics.comp-ph])

Upon application of a sufficiently strong electric field, electrons break away from thermal equilibrium and approach relativistic speeds. These highly energetic 'runaway' electrons (~MeV) play a crucial role in understanding tokamak disruption events, and therefore their accurate understanding is essential to develop reliable mitigation strategies. For this purpose, we have developed a fully implicit, scalable relativistic Fokker-Planck kinetic electron solver. Energy and momentum conservation are ensured for the electron-electron relativistic collisional interactions. Electron-ion interactions are modeled using the Lorentz operator, and synchrotron damping using the Abraham-Lorentz-Dirac reaction term. We use a positivity-preserving finite-difference scheme for both advection and tensor-diffusion terms. The proposed numerical treatment allows us to investigate accurately phenomena spanning a

27.02.2019
15:11 TechnologyReview.comThe new, safer nuclear reactors that might help stop climate change

From sodium-cooled fission to advanced fusion, a fresh generation of projects hopes to rekindle trust in nuclear energy.

09:26 News-Medical.NetFusion of mitochondria enables cells to more efficiently use oxygen for energy

Mitochondria are the powerhouses of the cell. And for mitochondria, much like for double-header engines stacked together in a steam train, working in multiples has its benefits.

01:39 ScienceDaily.comBetter together: Mitochondrial fusion supports cell division

New research shows that when cells divide rapidly, their mitochondria are fused together. In this configuration, the cell is able to more efficiently use oxygen for energy. This work illuminates the inner workings of dividing cells and shows how mitochondria combine to help cells to multiply in unexpected ways.

26.02.2019
12:44 Arxiv.org PhysicsData-driven Material Models for Atomistic Simulation. (arXiv:1902.09395v1 [physics.comp-ph])

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parameterizations for potentials using traditional approaches. Machine-learning has emerged as an effective alternative approach to developing accurate and robust interatomic potentials. Starting with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time- and length-scales where the two methods can be directly compared. In this work, we use the Spectral Neighbor Analysis Potential (SNAP) and show how a fit can

24.02.2019
16:55 LiveScience.comA 12-Year-Old Built a Fusion Reactor in His Playroom

A 12-year-old kid from Tennessee created a nuclear reaction in his family's playroom. Here's how he did it.

22.02.2019
19:45 WhatReallyHappened.com'Youngest person' to ever build a nuclear reactor: Boy wonder, 12, made a working atomic fusion experiment in his parent's spare room using $10,000 worth of parts from eBay (3 Pics) 21.02.2019 11:41 Arxiv.org PhysicsA Thin Foil-foil Proton Recoil Spectrometer for DT neutrons using annular silicon detectors. (arXiv:1902.07633v1 [physics.plasm-ph]) The use of Thin-foil proton recoil (TPR) spectrometers to measure neutrons from Deuterium-Tritium (DT) fusion plasma has been studied previously and is a well established technique for neutron spectrometry. The study presented here focuses on the optimisation of the TPR spectrometer configurations consisting of dE and E silicon detectors. In addition an investigation of the spectrometer's ability to determine fuel ion temperature and fuel ion density ratio in ITER like DT plasmas has been performed. A Python code was developed for the purpose of calculating detection efficiency and energy resolution as a function of several spectrometer geometrical parameters. An optimisation of detection efficiency for selected values of resolution was performed regarding the geometrical spectrometer parameters foil thickness, distance from a foil to the first detector and distance between the two detectors 11:41 Arxiv.org CSDNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators. (arXiv:1902.07463v1 [cs.DC]) The convolutional neural network (CNN) has become a state-of-the-art method for several artificial intelligence domains in recent years. The increasingly complex CNN models are both computation-bound and I/O-bound. FPGA-based accelerators driven by custom instruction set architecture (ISA) achieve a balance between generality and efficiency, but there is much on them left to be optimized. We propose the full-stack compiler DNNVM, which is an integration of optimizers for graphs, loops and data layouts, and an assembler, a runtime supporter and a validation environment. The DNNVM works in the context of deep learning frameworks and transforms CNN models into the directed acyclic graph: XGraph. Based on XGraph, we transform the optimization challenges for both the data layout and pipeline into graph-level problems. DNNVM enumerates all potentially profitable fusion opportunities by a heuristic 20.02.2019 07:05 Arxiv.org CSSPINBIS: Spintronics based Bayesian Inference System with Stochastic Computing. (arXiv:1902.06886v1 [cs.ET]) Bayesian inference is an effective approach for solving statistical learning problems, especially with uncertainty and incompleteness. However, Bayesian inference is a computing-intensive task whose efficiency is physically limited by the bottlenecks of conventional computing platforms. In this work, a spintronics based stochastic computing approach is proposed for efficient Bayesian inference. The inherent stochastic switching behaviors of spintronic devices are exploited to build stochastic bitstream generator (SBG) for stochastic computing with hybrid CMOS/MTJ circuits design. Aiming to improve the inference efficiency, an SBG sharing strategy is leveraged to reduce the required SBG array scale by integrating a switch network between SBG array and stochastic computing logic. A device-to-architecture level framework is proposed to evaluate the performance of spintronics based Bayesian 18.02.2019 05:13 Arxiv.org PhysicsBurn regimes in the hydrodynamic scaling of perturbed inertial confinement fusion hotspots. (arXiv:1902.05861v1 [physics.plasm-ph]) We present simulations of ignition and burn based on the Highfoot and High-Density Carbon indirect drive designs of the National Ignition Facility for three regimes of alpha-heating - self-heating, robust ignition and propagating burn - exploring hotspot power balance, perturbations and hydrodynamic scaling. A Monte-Carlo Particle-in-Cell charged particle transport package for the radiation-magnetohydrodynamics code Chimera was developed for this work. Hotspot power balance between alpha-heating, electron thermal conduction and radiation was studied in 1D for each regime, and the impact of perturbations on this power balance explored in 3D using a single Rayleigh-Taylor spike. Heat flow into the spike from thermal conduction and alpha-heating increases by$\sim2-3\times$, due to sharper temperature gradients and increased proximity of the cold, dense material to the main fusion regions 05:13 Arxiv.org PhysicsMicrostructure and thermal properties of unalloyed tungsten deposited by Wire + Arc Additive Manufacturing. (arXiv:1902.05816v1 [physics.app-ph]) Tungsten is considered as one of the most promising materials for nuclear fusion reactor chamber applications. Wire + Arc Additive Manufacturing has already demonstrated the ability to deposit defect-free large-scale tungsten structures, with considerable deposition rates. In this study, the microstructure of the as-deposited and heat-treated material has been characterised; it featured mainly large elongated grains for both conditions. The heat treatment at 1273 K for 6 hours had a negligible effect on microstructure and on thermal diffusivity. Furthermore, the linear coefficient of thermal expansion was in the range of 4.5x10-6 micron m-1 K-1 to 6.8x10-6 micron m-1 K-1; the density of the deposit was as high as 99.4% of the theoretical tungsten density; the thermal diffusivity and the thermal conductivity were measured and calculated, respectively, and seen to decrease considerably in the 05:13 Arxiv.org PhysicsIrradiation Damage Calculation with Angular Distribution. (arXiv:1902.05620v1 [physics.ins-det]) The operating lifetime of a reactor is determined by the irradiation damage which is quantitatively accounted by the number of Displacement per Atom (DPA). The DPA rate is conventionally computed with DPA cross sections in reactor applications. However, the Gauss-Legendre Quadrature (GLQ) method used in current processing codes such as NJOY is shown unable to ensure the convergence of DPA cross sections due to the discontinuity of the damage energy versus the emission angle. The GLQ-based Piecewise Integration (GLQPI) is proposed to ensure the numerical convergence. The integration based on the GLQPI is shown suitable to compute DPA. On the other hand, even if high-order Legendre polynomials are important to describe the anisotropic angular distribution, the DPA cross section is not sensitive to the high-order Legendre polynomials because the former is an angle-integrated quantity. Numerical 14.02.2019 08:56 Arxiv.org StatisticsWireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification. (arXiv:1902.04763v1 [cs.LG]) The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale 08:56 Arxiv.org PhysicsI-mode investigation on the Experimental Advanced Superconducting Tokamak. (arXiv:1902.04750v1 [physics.plasm-ph]) By analyzing large quantities of discharges in the unfavorable ion$ \vec B\times \nabla B $drift direction, the I-mode operation has been confirmed in EAST tokamak. During the L-mode to I-mode transition, the energy confinement has a prominent improvement by the formation of a high-temperature edge pedestal, while the particle confinement remains almost identical to that in the L-mode. Simultaneously, a weak coherent mode (WCM) with the frequency range of 40-150 kHz is observed at the edge plasma by the eight-channel Doppler backscattering system (DBS8) in EAST, and this type of WCM can be observed in both density fluctuation and radial electric field ($ E_r $) fluctuation. In addition, a low-frequency oscillation with a frequency range of 5-10 kHz in both density and$ E_r $fluctuations is always observed with the WCM. The$ E_r $profiles are obtained by the DBS8 system, and a deeper 08:56 Arxiv.org CSWireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification. (arXiv:1902.04763v1 [cs.LG]) The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale 09.02.2019 22:25 WhatReallyHappened.comScott Adams: The News is Becoming Friendlier to President Trump Lately Border budget negotiators report they might approve 2Billion Tell us what the EXPERTS say we need, and what it costs Is what you’re approving…what the experts say is needed? Nobody cares about “percentages of crime”, when they’re a victim I’ve been robbed but it’s okay. I’m in a low crime area Annexing Mexico into America, should we? The cartels are running the country to a large degree “Turnaround Groups” for stabilization of failing countries? UN controlled, a country applies to them for assistance A neutral organization of turnaround specialists goes in Limited time, they do a government reorganization NO outside military presence imposing new government Mike Cernovich and the New Green Deal Mike says the New Green Deal is brilliant They’re big ideas that we have no idea how to accomplish The fact that it’s impossible is what focuses energy Republicans SHOULD embrace the New Green Deal 08.02.2019 10:50 Arxiv.org PhysicsEffects of Plasmoid Formation on Sawtooth Process in a Tokamak. (arXiv:1902.02683v1 [physics.plasm-ph]) For realistic values of Lundquist number in tokamak plasmas, the 1/1 magnetic island leads to the formation of secondary thin current sheet, which breaks up into a chain of small magnetic islands, called plasmoids. The role of plasmoid dynamics during the sawtooth reconnection process in fusion plasmas remains an unresolved issue. In this study, systematic simulations are performed to investigate the resistive internal kink mode using the full resistive MHD equations implemented in the NIMROD code in a simplified tokamak geometry. For Lundquist number$S\ge1.6\times10^7$, secondary current sheet is found to be unstable to plasmoids during the nonlinear resistive kink mode evolution with a critical aspect ratio of the current sheet of ~70. The merging of small plasmoids leads to the formation of a monster plasmoid that can significantly affect the primary island evolution. This may provide an 10:50 Arxiv.org PhysicsSnare machinery is optimized for ultrafast fusion. (arXiv:1902.02548v1 [physics.bio-ph]) SNARE proteins zipper to form SNAREpins that power vesicle fusion with target membranes in a variety of biological processes. A single SNAREpin takes about 1 second to fuse two bilayers, yet a handful can ensure release of neurotransmitters from synaptic vesicles much faster, in a 10th of a millisecond. We propose that, similar to the case of muscle myosins, the ultrafast fusion results from cooperative action of many SNAREpins. The coupling originates from mechanical interactions induced by confining scaffolds. Each SNAREpin is known to have enough energy to overcome the fusion barrier of 25-35 kB T, however, the fusion barrier only becomes relevant when the SNAREpins are nearly completely zippered and from this state each SNAREpin can deliver only a small fraction of this energy as mechanical work. Therefore they have to act cooperatively and we show that at least 3 of them are needed to 06.02.2019 16:15 Nature.ComNeutrino hunt resumes, ITER’s new confidence and Elsevier’s woes 09:08 Arxiv.org PhysicsOptimized quasisymmetric stellarators are consistent with the Garren-Boozer construction. (arXiv:1902.01672v1 [physics.plasm-ph]) Most quasisymmetric stellarators to date have been designed by numerically optimizing the plasma boundary shape to minimize symmetry-breaking Fourier modes of the magnetic field strength$B$. At high aspect ratio, a faster approach is to directly construct the plasma shape from the equations of quasisymmetry near the magnetic axis derived by Garren and Boozer [Phys Fluids B 3, 2805 (1991)]. Here we show that the core shape and rotational transform of many optimization-based configurations can be accurately described by this direct-construction approach. This consistency supports use of the near-axis construction as an accurate analytical model for modern stellarator configurations. 05.02.2019 12:21 Technology.orgWith data science, Rochester’s laser lab moves closer to controlled nuclear fusion Scientists have been working for decades to develop controlled nuclear fusion. Controlled nuclear fusion would improve the ability 11:47 Arxiv.org MathTaylor States in Stellarators: A Fast High-order Boundary Integral Solver. (arXiv:1902.01205v1 [math.NA]) We present a boundary integral equation solver for computing Taylor relaxed states in non-axisymmetric solid and shell-like toroidal geometries. The computation of Taylor states in these geometries is a key element for the calculation of stepped pressure stellarator equilibria. The integral representation of the magnetic field in this work is based on the generalized Debye source formulation, and results in a well-conditioned second-kind boundary integral equation. The integral equation solver is based on a spectral discretization of the geometry and unknowns, and the computation of the associated weakly-singular integrals is performed with high-order quadrature based on a partition of unity. The resulting scheme for applying the integral operator is then coupled with an iterative solver and suitable preconditioners. Several numerical examples are provided to demonstrate the accuracy and 11:47 Arxiv.org PhysicsTaylor States in Stellarators: A Fast High-order Boundary Integral Solver. (arXiv:1902.01205v1 [math.NA]) We present a boundary integral equation solver for computing Taylor relaxed states in non-axisymmetric solid and shell-like toroidal geometries. The computation of Taylor states in these geometries is a key element for the calculation of stepped pressure stellarator equilibria. The integral representation of the magnetic field in this work is based on the generalized Debye source formulation, and results in a well-conditioned second-kind boundary integral equation. The integral equation solver is based on a spectral discretization of the geometry and unknowns, and the computation of the associated weakly-singular integrals is performed with high-order quadrature based on a partition of unity. The resulting scheme for applying the integral operator is then coupled with an iterative solver and suitable preconditioners. Several numerical examples are provided to demonstrate the accuracy and 01.02.2019 08:35 Arxiv.org CSCapturing Object Detection Uncertainty in Multi-Layer Grid Maps. (arXiv:1901.11284v1 [cs.RO]) We propose a deep convolutional object detector for automated driving applications that also estimates classification, pose and shape uncertainty of each detected object. The input consists of a multi-layer grid map which is well-suited for sensor fusion, free-space estimation and machine learning. Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks. We train our models based on the KITTI object detection data set. In a quantitative and qualitative evaluation some models show a similar performance and superior robustness compared to previously developed object detectors. However, our evaluation also points to undesired data set properties which should be addressed when training data-driven models or creating new data sets. 30.01.2019 21:17 Nature.ComExperimentally trained statistical models boost nuclear-fusion performance 29.01.2019 09:48 Arxiv.org PhysicsMeasuring national capability over big sciences multidisciplinarity: A case study of nuclear fusion research. (arXiv:1901.09099v1 [cs.DL]) In the era of big science, countries allocate big research and development budgets to large scientific facilities that boost collaboration and research capability. A nuclear fusion device called the "tokamak" is a source of great interest for many countries because it ideally generates sustainable energy expected to solve the energy crisis in the future. Here, to explore the scientific effects of tokamaks, we map a country's research capability in nuclear fusion research with normalized revealed comparative advantage on five topical clusters -- material, plasma, device, diagnostics, and simulation -- detected through a dynamic topic model. Our approach captures not only the growth of China, India, and the Republic of Korea but also the decline of Canada, Japan, Sweden, and the Netherlands. Time points of their rise and fall are related to tokamak operation, highlighting the importance of 09:48 Arxiv.org CSMeasuring national capability over big sciences multidisciplinarity: A case study of nuclear fusion research. (arXiv:1901.09099v1 [cs.DL]) In the era of big science, countries allocate big research and development budgets to large scientific facilities that boost collaboration and research capability. A nuclear fusion device called the "tokamak" is a source of great interest for many countries because it ideally generates sustainable energy expected to solve the energy crisis in the future. Here, to explore the scientific effects of tokamaks, we map a country's research capability in nuclear fusion research with normalized revealed comparative advantage on five topical clusters -- material, plasma, device, diagnostics, and simulation -- detected through a dynamic topic model. Our approach captures not only the growth of China, India, and the Republic of Korea but also the decline of Canada, Japan, Sweden, and the Netherlands. Time points of their rise and fall are related to tokamak operation, highlighting the importance of 28.01.2019 05:23 Arxiv.org CSMulti-stream Network With Temporal Attention For Environmental Sound Classification. (arXiv:1901.08608v1 [cs.SD]) Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral features and utilizes a temporal attention function computed from energy changes over time. Training and classification utilizes decision fusion and data augmentation techniques that incorporate uncertainty. We evaluate this network on three commonly used data sets for environmental sound and audio scene classification and achieve new state-of-the-art performance without any changes in network architecture or front-end preprocessing, thus demonstrating better generalizability. 25.01.2019 10:58 Phys.orgFast action: Novel device may rapidly control plasma disruptions in a fusion facility Scientists seeking to capture and control on Earth fusion energy, the process that powers the sun and stars, face the risk of disruptions—sudden events that can halt fusion reactions and damage facilities called tokamaks that house them. Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL), and the University of Washington have developed a novel prototype for rapidly controlling disruptions before they can take full effect. 24.01.2019 17:46 Phys.orgScientists predict reaction data for fusion research, insight into universe's origins Using simulations and calculations, Lawrence Livermore National Laboratory (LLNL) nuclear scientists for the first time have accurately predicted the properties of polarized thermonuclear fusion. Analogous calculations could be used to answer some of the most fundamental questions about the origins of the universe and the evolution of stars. 06:02 Arxiv.org PhysicsDiscovery of an Electron Gyroradius Scale Current Layer Its Relevance to Magnetic Fusion Energy, Earth Magnetosphere and Sunspots. (arXiv:1901.08041v1 [physics.plasm-ph]) In the Earth magnetosphere, sunspots and magnetic cusp fusion devices, the boundary between the plasma and the magnetic field is marked by a diamagnetic current layer with a rapid change in plasma pressure and magnetic field strength. First principles numerical simulations were conducted to investigate this boundary layer with a spatial resolution beyond electron gyroradius while incorporating a global equilibrium structure. The boundary layer thickness is discovered to be on the order of electron gyroradius scale due to a self-consistent electric field suppressing ion gyromotion at the boundary. Formed at the scale of the electron gyroradius, the electric field plays a critical role in determining equilibrium structure and plasma transport. The discovery highlights the necessity to incorporate electron gyroradius scale physics in studies aimed at advancing our understanding of fusion 23.01.2019 12:16 Technology.orgScientists predict reaction data for fusion research, insight into universe’s origins Using simulations and calculations, Lawrence Livermore National Laboratory (LLNL) nuclear scientists for the first time have accurately predicted 08:38 Arxiv.org MathUsing Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks. (arXiv:1901.06742v1 [cs.IT]) We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing clustering methods like Minimum Energy Routing, 08:38 Arxiv.org CSUsing Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks. (arXiv:1901.06742v1 [cs.IT]) We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing clustering methods like Minimum Energy Routing, 18.01.2019 08:03 Arxiv.org CSVisual Feature Fusion and its Application to Support Unsupervised Clustering Tasks. (arXiv:1901.05556v1 [cs.CV]) On visual analytics applications, the concept of putting the user on the loop refers to the ability to replace heuristics by user knowledge on machine learning and data mining tasks. On supervised tasks, the user engagement occurs via the manipulation of the training data. However, on unsupervised tasks, the user involvement is limited to changes in the algorithm parametrization or the input data representation, also known as features. Depending on the application domain, different types of features can be extracted from the raw data. Therefore, the result of unsupervised algorithms heavily depends on the type of employed feature. Since there is no perfect feature extractor, combining different features have been explored in a process called feature fusion. The feature fusion is straightforward when the machine learning or data mining task has a cost function. However, when such a function 17.01.2019 14:21 Phys.orgFound: A precise method for determining how waves and particles affect fusion reactions Like surfers catching ocean waves, particles within the hot, electrically charged state of matter known as plasma can ride waves that oscillate through the plasma during experiments to investigate the production of fusion energy. The oscillations can displace the particles so far that they escape from the doughnut-shaped tokamak that houses the experiments, cooling the plasma and making fusion reactions less efficient. Now a team of physicists led by the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) has devised a faster method to determine how much this interaction between particles and waves contributes to the efficiency loss in tokamaks. 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 Comments at: https://twitter.com/ScottAdamsSays/status/1080842402166845441 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-$Zand 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

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