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20.05.2019
11:16 AzoRobotics.comResearchers Use Machine Learning to Create Model for Rapid Control of Plasma During Fusion Reactions

Machine learning (ML) is a kind of artificial intelligence that understands language, recognizes faces, and navigates self-driving cars. It could be helpful in bringing the clean fusion energy that...

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08:31 Arxiv.org PhysicsHigh frequency mode generation by toroidal Alfven eigenmodes. (arXiv:1905.07250v1 [physics.plasm-ph])

Nonlinear generation of high frequency mode (HFM) by toroidal Alfven eigenmode (TAE) observed in HL-2A tokamak is analyzed using nonlinear gyrokinetic theory. It is found that, the HFM can be dominated by $|nq-m|=1$ perturbations with predominantly ideal magnetohydrodynamic if the two primary TAEs are co-propagating; while the HFM can be characterized by $nq-m=0$ electrostatic perturbations if the two primary TAEs are counter-propagating. Here, $n$ and $m$ are respectively the toroidal and poloidal mode numbers, and $q$ is the safety factor. The nonlinear process is sensitive to the equilibrium magnetic geometry of the device.

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17.05.2019
22:15 ScienceDaily.comMachine learning speeds modeling of experiments aimed at capturing fusion energy on Earth

Machine learning can help bring to Earth the clean fusion energy that lights the sun and stars. Researchers are using this form of artificial intelligence to create a model for rapid control of plasma -- the state of matter composed of free electrons and atomic nuclei, or ions -- that fuels fusion reactions.

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22:08 Phys.orgMachine learning speeds modeling of experiments aimed at capturing fusion energy on Earth

Machine learning (ML), a form of artificial intelligence that recognizes faces, understands language and navigates self-driving cars, can help bring to Earth the clean fusion energy that lights the sun and stars. Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) are using ML to create a model for rapid control of plasma—the state of matter composed of free electrons and atomic nuclei, or ions—that fuels fusion reactions.

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08:46 Arxiv.org CSX2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks. (arXiv:1905.06902v1 [eess.IV])

Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too. Traditional CT reconstruction methods require hundreds of X-ray projections through a full rotational scan of the body, which cannot be performed on a typical X-ray machine. In this work, we propose to reconstruct CT from two orthogonal X-rays using the generative adversarial network (GAN) framework. A specially designed generator network is exploited to increase data dimension from 2D (X-rays) to 3D (CT), which is not addressed in previous research of GAN. A novel feature fusion method is proposed to combine information from two X-rays.The mean squared error (MSE) loss and adversarial loss are combined to train the generator, resulting in a high-quality CT

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16.05.2019
05:35 Arxiv.org PhysicsBuSCOPE : Fusing Individual & Aggregated Mobility Behavior for "Live" Smart City Services. (arXiv:1905.06116v1 [physics.soc-ph])

While analysis of urban commuting data has a long and demonstrated history of providing useful insights into human mobility behavior, such analysis has been performed largely in offline fashion and to aid medium-to-long term urban planning. In this work, we demonstrate the power of applying predictive analytics on real-time mobility data, specifically the smart-card generated trip data of millions of public bus commuters in Singapore, to create two novel and "live" smart city services. The key analytical novelty in our work lies in combining two aspects of urban mobility: (a) conformity: which reflects the predictability in the aggregated flow of commuters along bus routes, and (b) regularity: which captures the repeated trip patterns of each individual commuter. We demonstrate that the fusion of these two measures of behavior can be performed at city-scale using our BuScope platform, and can

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04:35 Arxiv.org StatisticsCluster, Classify, Regress: A General Method For Learning Discountinous Functions. (arXiv:1905.06220v1 [cs.LG])

This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the corresponding label is the output; and finally (iii) perform one separate regression for each class, where the training data corresponds to the subset of the original input-output pairs which have that label according to the classifier. It has not yet been proposed to combine these 3 fundamental building blocks of machine learning in this simple and powerful fashion. This can be viewed as a form of deep learning, where any of the intermediate layers can itself be deep. The utility and robustness of the methodology is illustrated on some toy problems, including one example problem arising

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04:23 Arxiv.org CSCluster, Classify, Regress: A General Method For Learning Discountinous Functions. (arXiv:1905.06220v1 [cs.LG])

This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the corresponding label is the output; and finally (iii) perform one separate regression for each class, where the training data corresponds to the subset of the original input-output pairs which have that label according to the classifier. It has not yet been proposed to combine these 3 fundamental building blocks of machine learning in this simple and powerful fashion. This can be viewed as a form of deep learning, where any of the intermediate layers can itself be deep. The utility and robustness of the methodology is illustrated on some toy problems, including one example problem arising

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15.05.2019
08:07 Arxiv.org PhysicsVerification of the global gyrokinetic stellarator code XGC-S for linear ion temperature gradient driven modes. (arXiv:1905.05653v1 [physics.plasm-ph])

XGC (X-point Gyrokinetic Code) is a whole-volume, total-f gyrokinetic particle-in-cell code developed for modelling tokamaks. In recent work, XGC has been extended to model more general 3D toroidal magnetic configurations, such as stellarators. These improvements have resulted in the XGC-S version. In this paper, XGC-S is benchmarked for linear electrostatic ion temperature gradient-driven microinstabilities, which can underlie turbulent transport in stellarators. An initial benchmark of XGC-S in tokamak geometry shows good agreement with the XGC1, ORB5, and global GENE codes. A benchmark between XGC-S and the EUTERPE global gyrokinetic code for stellarators has also been performed, this time in geometry of the optimised stellarator Wendelstein 7-X. Good agreement has been found for the mode number spectrum, mode structure, and growth rate.

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14.05.2019
19:35 NewYork TimesThe Fusion Reactor Next Door

Entrepreneurs are taking up the search for a near limitless energy source and seeking investors willing to put money behind a long-shot bet against climate change.

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19:33 International Herald TribuneThe Fusion Reactor Next Door

Entrepreneurs are taking up the search for a near limitless energy source and seeking investors willing to put money behind a long-shot bet against climate change.

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10:48 Arxiv.org PhysicsEliminating poor statistics in Monte-Carlo simulations of fast-ion losses to plasma-facing components and detectors. (arXiv:1905.04952v1 [physics.plasm-ph])

With Wendelstein 7-X now up and running, and the construction of ITER proceeding, predicting fast-ion losses to sensitive plasma-facing components and detectors is gaining significant interest. A common recipe to perform such studies is to push a large population of marker particles along their equations of motion, the trajectories randomized with Monte Carlo operators accounting for Coulomb collisions, and to record possible intersections of the marker trajectories with synthetic detectors or areas of interest in the first wall. While straightforward to implement and easy to parallelize, this Forward Monte Carlo (FMC) approach tends to suffer from poor statistics and error estimation as the detector domain is often small: it is difficult to guess how to set up the initial weights and locations of the markers for them to remain representative of the source distribution, yet record enough hits

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10:48 Arxiv.org PhysicsA stable semi-implicit algorithm. (arXiv:1905.04520v1 [physics.plasm-ph])

When the singular values of the evolution operator are all smaller or all greater than one, stable integration algorithms are obtained either by explicit or implicit methods. When the singular spectrum mixes greater and smaller than one values, neither explicit nor implicit methods insure stabilty. The problem is solved by using a splitting of the evolution operator and a semi-implicit scheme. The method is illustrated in the study of a two-field model of the tokamak scrape-off layer.

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09:35 Arxiv.org CSSensor Defense In-Software (SDI):Practical Software Based Detection of Spoofing Attacks on Position Sensor. (arXiv:1905.04691v1 [cs.CR])

Position sensors, such as the gyroscope, the magnetometer and the accelerometer, are found in a staggering variety of devices, from smartphones and UAVs to autonomous robots. Several works have shown how adversaries can mount spoofing attacks to remotely corrupt or even completely control the outputs of these sensors. With more and more critical applications relying on sensor readings to make important decisions, defending sensors from these attacks is of prime importance.
In this work we present practical software based defenses against attacks on two common types of position sensors, specifically the gyroscope and the magnetometer. We first characterize the sensitivity of these sensors to acoustic and magnetic adversaries. Next, we present two software-only defenses: a machine learning based single sensor defense, and a sensor fusion defense which makes use of the mathematical

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09:35 Arxiv.org CSA stable semi-implicit algorithm. (arXiv:1905.04520v1 [physics.plasm-ph])

When the singular values of the evolution operator are all smaller or all greater than one, stable integration algorithms are obtained either by explicit or implicit methods. When the singular spectrum mixes greater and smaller than one values, neither explicit nor implicit methods insure stabilty. The problem is solved by using a splitting of the evolution operator and a semi-implicit scheme. The method is illustrated in the study of a two-field model of the tokamak scrape-off layer.

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10.05.2019
06:56 Arxiv.org PhysicsInvestigation of light ion fusion reactions with plasma discharges. (arXiv:1905.03400v1 [physics.plasm-ph])

The scaling of reaction yields in light ion fusion to low reaction energies is important for our understanding of stellar fuel chains and the development of future energy technologies. Experiments become progressively more challenging at lower reaction energies due to the exponential drop of fusion cross sections below the Coulomb barrier. We report on experiments where deuterium-deuterium (D-D) fusion reactions are studied in a pulsed plasma in the glow discharge regime using a benchtop apparatus. We model plasma conditions using particle-in-cell codes. Advantages of this approach are relatively high peak ion currents and current densities (0.1 to several A/cm^2) that can be applied to metal wire cathodes for several days. We detect neutrons from D-D reactions with scintillator-based detectors. For palladium targets, we find neutron yields as a function of cathode voltage that are over 100

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06:09 Arxiv.org StatisticsHuman Activity Recognition Using Visual Object Detection. (arXiv:1905.03707v1 [cs.CV])

Visual Human Activity Recognition (HAR) and data fusion with other sensors can help us at tracking the behavior and activity of underground miners with little obstruction. Existing models, such as Single Shot Detector (SSD), trained on the Common Objects in Context (COCO) dataset is used in this paper to detect the current state of a miner, such as an injured miner vs a non-injured miner. Tensorflow is used for the abstraction layer of implementing machine learning algorithms, and although it uses Python to deal with nodes and tensors, the actual algorithms run on C++ libraries, providing a good balance between performance and speed of development. The paper further discusses evaluation methods for determining the accuracy of the machine-learning and an approach to increase the accuracy of the detected activity/state of people in a mining environment, by means of data fusion.

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06:09 Arxiv.org CSHuman Activity Recognition Using Visual Object Detection. (arXiv:1905.03707v1 [cs.CV])

Visual Human Activity Recognition (HAR) and data fusion with other sensors can help us at tracking the behavior and activity of underground miners with little obstruction. Existing models, such as Single Shot Detector (SSD), trained on the Common Objects in Context (COCO) dataset is used in this paper to detect the current state of a miner, such as an injured miner vs a non-injured miner. Tensorflow is used for the abstraction layer of implementing machine learning algorithms, and although it uses Python to deal with nodes and tensors, the actual algorithms run on C++ libraries, providing a good balance between performance and speed of development. The paper further discusses evaluation methods for determining the accuracy of the machine-learning and an approach to increase the accuracy of the detected activity/state of people in a mining environment, by means of data fusion.

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07.05.2019
08:15 Arxiv.org PhysicsNumerical study of transition between even and odd toroidal Alfv\'en eigenmodes on EAST. (arXiv:1905.01801v1 [physics.plasm-ph])

Linear simulations of toriodal Alfv\'en eigenmodes (TAEs) driven by energetic particles (EPs) on EAST (Experimental Advanced Superconducting Tokamak) are performed using the hybrid-kinetic MHD (HK-MHD) model implemented in NIMROD code. The EAST equilibrium is reconstructed using the EFIT code based on experimental measurement. The "slowing down" distribution is adopted for modeling the equilibrium distribution of the energetic ions from the deuterium neutral beam injection on EAST. The frequency, the dominant poloidal mode number, the radial location and the detailed 2D mode structure of the TAE/RSAE/EPM modes are consistent between the eigenvalue analysis and the NIMROD simulation. As the $\beta$ fraction of EP increases, a transition between even and odd TAEs occurs, along with that between the ballooning and anti-ballooning mode structures. When the $\beta$ fraction of EP is close to the

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07:15 Arxiv.org CSWhen Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing. (arXiv:1905.01430v1 [cs.NI])

Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified sensing data. However, existing studies usually make network or attack assumptions biased towards the defense (e.g., assuming the prior knowledge of attacks is known). In practice, attackers can adopt any arbitrary behavior and avoid any pre-assumed pattern or assumption used by defense strategies. In this paper, we revisit this traditional security problem and propose a novel learning-empowered framework named Learn-Evaluate-Beat (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing process, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. Then, we propose a generic algorithm to create malicious

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02.05.2019
19:41 Phys.orgMachine set to see if lithium can help bring fusion to Earth

Lithium, the light silvery metal used in everything from pharmaceutical applications to batteries that power your smart phone or electric car, could also help harness on Earth the fusion energy that lights the sun and stars. Lithium can maintain the heat and protect the walls inside doughnut-shaped tokamaks that house fusion reactions, and will be used to produce tritium, the hydrogen isotope that will combine with its cousin deuterium to fuel fusion in future reactors.

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01.05.2019
09:06 Arxiv.org CSFacial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation. (arXiv:1904.13154v1 [cs.CV])

Although much progress has been made in the facial expression analysis field, facial occlusions are still challenging. The main innovation brought by this contribution consists in exploiting the specificities of facial movement propagation for recognizing expressions in presence of important occlusions. The movement induced by an expression extends beyond the movement epicenter. Thus, the movement occurring in an occluded region propagates towards neighboring visible regions. In presence of occlusions, per expression, we compute the importance of each unoccluded facial region and we construct adapted facial frameworks that boost the performance of per expression binary classifier. The output of each expression-dependant binary classifier is then aggregated and fed into a fusion process that aims constructing, per occlusion, a unique model that recognizes all the facial expressions considered.

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01:13 WhatReallyHappened.comFusion power start-ups go small in effort to bring commercial reactors to life

For decades, the quest for nuclear fusion energy has been driven by giant government-led projects — with giant price tags to match.
Just as spaceflight companies such as SpaceX and Blue Origin have built upon NASA technology, a handful of fusion startups are building on government-funded fusion research, with the goal of firing up the first commercial fusion power plant as early as the 2020s.
"Fusion is poised for a “SpaceX moment,” says Christofer Mowry, CEO of General Fusion, a British Columbia-based firm that’s among the companies staking a claim in the fusion field. “We’re in a position today to combine new, enabling technologies to make something that was possible but not practical into something that’s commercially viable.”

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29.04.2019
08:10 Arxiv.org PhysicsRotational stabilisation of the Rayleigh-Taylor instability. (arXiv:1904.11584v1 [physics.flu-dyn])

A number of applications utilise the energy focussing potential of imploding shells to dynamically compress matter or magnetic fields, including magnetised target fusion schemes. This paper examines the effect of fluid rotation on the Rayleigh-Taylor (RT) driven growth of perturbations at the inner surface of an imploding cylindrical liquid shell which compresses a gas-filled cavity. The shell was formed by rotating water in solid body rotation prior to the piston-driven implosion, which was propelled by a modest external gas pressure. The fast rise in pressure in the gas-filled cavity at the point of maximum convergence results in an RT unstable configuration where the cavity surface accelerates in the direction of the density gradient at the interface. The experimental arrangement allowed for visualization of the cavity surface during the implosion using high-speed videography, while

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28.04.2019
10:59 Phys.orgChina's quest for clean, limitless energy heats up

A ground-breaking fusion reactor built by Chinese scientists is underscoring Beijing's determination to be at the core of clean energy technology, as it eyes a fully-functioning plant by 2050.

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26.04.2019
09:21 Arxiv.org PhysicsTargeted evolution of pinning landscapes for large superconducting critical currents. (arXiv:1904.11120v1 [cond-mat.supr-con])

The ability of type-II superconductors to carry large amounts of current at high magnetic fields is a key requirement for future design innovations in high-field magnets for accelerators and compact fusion reactors and largely depends on the vortex pinning landscape comprised of material defects. The complex interaction of vortices with defects that can be grown chemically, e.g., self-assembled nanoparticles and nanorods, or introduced by post-synthesis particle irradiation precludes a priori prediction of the critical current and can result in highly non-trivial effects on the critical current. Here, we borrow concepts from biological evolution to create a genetic algorithm evolving pinning landscapes to accommodate vortex pinning and determine the best possible configuration of inclusions for two different scenarios: an evolution process starting from a pristine system and one with

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08:22 Arxiv.org CSDeep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers. (arXiv:1904.11151v1 [cs.CV])

Many machine learning problems concern with discovering or associating common patterns in data of multiple views or modalities. Multi-view learning is of the methods to achieve such goals. Recent methods propose deep multi-view networks via adaptation of generic Deep Neural Networks (DNNs), which concatenate features of individual views at intermediate network layers (i.e., fusion layers). In this work, we study the problem of multi-view learning in such end-to-end networks. We take a regularization approach via multi-view learning criteria, and propose a novel, effective, and efficient neuron-wise correlation-maximizing regularizer. We implement our proposed regularizers collectively as a correlation-regularized network layer (CorrReg). CorrReg can be applied to either fully-connected or convolutional fusion layers, simply by replacing them with their CorrReg counterparts. By partitioning

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23.04.2019
14:27 AzoRobotics.comDeep Learning AI Code for Successful Prediction of Disruptions that Occur in Fusion Reactors

For many years, researchers have been making efforts to generate clean, unlimited energy by re-creating on Earth the conditions found at the center of the sun. However, for nuclear fusion to be...

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00:03 WhatReallyHappened.comAir Force Deploys Stealth Fighters To Middle East For First Time 

Amid the threats of war with Iran, the U.S. Air Force has forward deployed Lockheed Martin F-35 Lightning II stealth fighter jets to the Middle East, reported Air Force Times.
Air Force Central Command (AFCENT) announced last week that F-35s from the 388th and 419th Fighter Wings at Hill Air Force Base, Utah, have arrived at Al Dhafra Air Base, United Arab Emirates to continue air superiority missions across the region.
It's the first time Air Force F-35s have been sent to the Middle East.
"We are adding a cutting-edge weapons system to our arsenal that significantly enhances the capability of the coalition," Lt. Gen. Joseph T. Guastella, commander of AFCENT, said in the release. "The sensor fusion and survivability this aircraft provides to the joint force will enhance security and stability across the theater and deter aggressors."

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22.04.2019
06:04 Arxiv.org PhysicsGlobal turbulence simulations of the tokamak edge region with GRILLIX. (arXiv:1904.09230v1 [physics.plasm-ph])

Turbulent dynamics in the scrape-off layer (SOL) of magnetic fusion devices is intermittent with large fluctuations in density and pressure. Therefore, a model is required that allows perturbations of similar or even larger magnitude to the time-averaged background value. The fluid-turbulence code GRILLIX is extended to such a global model, which consistently accounts for large variation in plasma parameters. Derived from the drift reduced Braginskii equations, the new GRILLIX model includes electromagnetic and electron-thermal dynamics, retains global parametric dependencies and the Boussinesq approximation is not applied. The penalisation technique is combined with the flux-coordinate independent (FCI) approach [F. Hariri and M. Ottaviani, Comput.Phys.Commun. 184:2419, (2013); A. Stegmeir et al., Comput.Phys.Commun. 198:139, (2016)], which allows to study realistic diverted geometries with

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05:07 Arxiv.org StatisticsEmbraceNet: A robust deep learning architecture for multimodal classification. (arXiv:1904.09078v1 [cs.LG])

Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper, we propose a novel deep learning-based multimodal fusion architecture for classification tasks, which guarantees compatibility with any kind of learning models, deals with cross-modal information carefully, and prevents performance degradation due to partial absence of data. We employ two datasets for multimodal classification tasks, build models based on our architecture and other state-of-the-art models, and analyze their performance on various situations. The results show that our architecture outperforms the other multimodal fusion architectures when some parts of data are not available.

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05:07 Arxiv.org CSLATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking. (arXiv:1904.09085v1 [cs.CV])

LiDAR (Light Detection And Ranging) is an essential and widely adopted sensor for autonomous vehicles, particularly for those vehicles operating at higher levels (L4-L5) of autonomy. Recent work has demonstrated the promise of deep-learning approaches for LiDAR-based detection. However, deep-learning algorithms are extremely data hungry, requiring large amounts of labeled point-cloud data for training and evaluation. Annotating LiDAR point cloud data is challenging due to the following issues: 1) A LiDAR point cloud is usually sparse and has low resolution, making it difficult for human annotators to recognize objects. 2) Compared to annotation on 2D images, the operation of drawing 3D bounding boxes or even point-wise labels on LiDAR point clouds is more complex and time-consuming. 3) LiDAR data are usually collected in sequences, so consecutive frames are highly correlated, leading to

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05:07 Arxiv.org CSEmbraceNet: A robust deep learning architecture for multimodal classification. (arXiv:1904.09078v1 [cs.LG])

Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper, we propose a novel deep learning-based multimodal fusion architecture for classification tasks, which guarantees compatibility with any kind of learning models, deals with cross-modal information carefully, and prevents performance degradation due to partial absence of data. We employ two datasets for multimodal classification tasks, build models based on our architecture and other state-of-the-art models, and analyze their performance on various situations. The results show that our architecture outperforms the other multimodal fusion architectures when some parts of data are not available.

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19.04.2019
11:21 Technology.orgArtificial intelligence accelerates efforts to develop clean, virtually limitless fusion energy

Artificial intelligence (AI), a branch of computer science that is transforming scientific inquiry and industry, could now speed

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06:44 Arxiv.org PhysicsInitial operation of the recoil mass spectrometer EMMA at the ISAC-II facility of TRIUMF. (arXiv:1904.08409v1 [physics.ins-det])

The Electromagnetic Mass Analyser (EMMA) is a new vacuum-mode recoil mass spectrometer currently undergoing the final stages of commissioning at the ISAC-II facility of TRIUMF. EMMA employs a symmetric configuration of electrostatic and magnetic deflectors to separate the products of nuclear reactions from the beam, focus them in both energy and angle, and disperse them in a focal plane according to their mass/charge (m/q) ratios. The spectrometer was designed to accommodate the gamma-ray detector array TIGRESS around the target position in order to provide spectroscopic information from electromagnetic transitions. EMMA is intended to be used in the measurement of fusion evaporation, radiative capture, and transfer reactions for the study of nuclear structure and astrophysics. Its complement of focal plane detectors facilitates the identification of recoiling nuclei and subsequent recoil

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00:06 ScienceDaily.comArtificial intelligence speeds efforts to develop clean, virtually limitless fusion energy

Scientists are applying deep learning -- a powerful new version of the machine learning form of artificial intelligence -- to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks that house the reactions.

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18.04.2019
17:05 Phys.orgCapturing energy flow in a plasma by measuring scattered light

Whether studying the core of our sun or the inside of a fusion reactor, scientists need to determine how energy flows in plasma. Scientists use simulations to calculate the flow. The simulations rely on the classical thermal transport model. Despite over 50 years of research, an ad hoc multiplier is often required. Without it, the simulation doesn't match real-world observations. Now, a team devised a way to measure energy flow and determined why the models need the multiplier. Further, the team's new approach lets them quantitatively test simulations.

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17.04.2019
23:59 Phys.orgArtificial intelligence speeds efforts to develop clean, virtually limitless fusion energy

Artificial intelligence (AI), a branch of computer science that is transforming scientific inquiry and industry, could now speed the development of safe, clean and virtually limitless fusion energy for generating electricity. A major step in this direction is under way at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University, where a team of scientists working with a Harvard graduate student is for the first time applying deep learning—a powerful new version of the machine learning form of AI—to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks that house the reactions.

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05:06 Arxiv.org PhysicsNumerical study of tearing mode seeding in tokamak X-point plasma. (arXiv:1904.07542v1 [physics.plasm-ph])

A detailed understanding of island seeding is crucial to avoid (N)TMs and their negative consequences like confinement degradation and disruptions. In the present work, we investigate the growth of 2/1 islands in response to magnetic perturbations. Although we use externally applied perturbations produced by resonant magnetic perturbation (RMP) coils for this study, results are directly transferable to island seeding by other MHD instabilities creating a resonant magnetic field component at the rational surface. Experimental results for 2/1 island penetration from ASDEX Upgrade are presented extending previous studies. Simulations are based on an ASDEX Upgrade L-mode discharge with low collisionality and active RMP coils. Our numerical studies are performed with the 3D, two fluid, non-linear MHD code JOREK. All three phases of mode seeding observed in the experiment are also seen in the

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16.04.2019
16:28 ScienceDaily.comPhysicists improve understanding of heat and particle flow in the edge of a fusion device

Physicists have discovered valuable information about how plasma flows at the edge inside doughnut-shaped fusion devices. The findings mark an encouraging sign for the development of machines to produce fusion energy for generating electricity without creating long-term hazardous waste.

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07:38 Arxiv.org PhysicsA gyrokinetic model for the plasma periphery of tokamak devices. (arXiv:1904.06863v1 [physics.plasm-ph])

A gyrokinetic model is presented that can properly describe strong flows, large and small amplitude electromagnetic fluctuations occurring on scale lengths ranging from the electron Larmor radius to the equilibrium perpendicular pressure gradient scale length, and large deviations from thermal equilibrium. The formulation of the gyrokinetic model is based on a second order description of the single charged particle dynamics, derived from Lie perturbation theory, where the fast particle gyromotion is decoupled from the slow drifts, assuming that the ratio of the ion sound Larmor radius to the perpendicular equilibrium pressure scale length is small. The collective behavior of the plasma is obtained by a gyrokinetic Boltzmann equation that describes the evolution of the gyroaveraged distribution function and includes a non-linear gyrokinetic Dougherty collision operator. The gyrokinetic model

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07:38 Arxiv.org PhysicsAn adjoint method for neoclassical stellarator optimization. (arXiv:1904.06430v1 [physics.plasm-ph])

Stellarators are a promising route to steady-state fusion power. However, to achieve the required confinement, the magnetic geometry must be highly optimized. This optimization requires navigating high-dimensional ($N \sim 10^2$ parameters) spaces, often necessitating the use of gradient-based methods. The gradient of the neoclassical fluxes is expensive to compute with classical methods, requiring $O(N)$ flux computations. Hence, simplified neoclassical figures of merit are often used, such as a low-collisionality bootstrap current model or the effective ripple. To reduce the cost of the gradient computation, we present an adjoint method for computing the derivatives of moments of the neoclassical distribution function for stellarator optimization. The linear adjoint method allows derivatives of quantities which depend on solutions of a linear system (such as moments of the distribution

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07:05 Arxiv.org MathEnergy Efficient Node Deployment in Wireless Ad-hoc Sensor Networks. (arXiv:1904.06380v1 [cs.IT])

We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.

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07:05 Arxiv.org CSEnergy Efficient Node Deployment in Wireless Ad-hoc Sensor Networks. (arXiv:1904.06380v1 [cs.IT])

We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.

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00:01 Phys.orgPhysicists improve understanding of heat and particle flow in the edge of a fusion device

Physicists at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have discovered valuable information about how electrically charged gas known as "plasma" flows at the edge inside doughnut-shaped fusion devices called "tokamaks." The findings mark an encouraging sign for the development of machines to produce fusion energy for generating electricity without creating long-term hazardous waste.

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15.04.2019
09:44 Arxiv.org CSSTC Speaker Recognition Systems for the VOiCES From a Distance Challenge. (arXiv:1904.06093v1 [cs.SD])

This paper presents the Speech Technology Center (STC) speaker recognition (SR) systems submitted to the VOiCES From a Distance challenge 2019. The challenge's SR task is focused on the problem of speaker recognition in single channel distant/far-field audio under noisy conditions. In this work we investigate different deep neural networks architectures for speaker embedding extraction to solve the task. We show that deep networks with residual frame level connections outperform more shallow architectures. Simple energy based speech activity detector (SAD) and automatic speech recognition (ASR) based SAD are investigated in this work. We also address the problem of data preparation for robust embedding extractors training. The reverberation for the data augmentation was performed using automatic room impulse response generator. In our systems we used discriminatively trained cosine similarity

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11.04.2019
15:06 Phys.orgNew device in Z machine measures power for nuclear fusion

If you're chasing the elusive goal of nuclear fusion and think you need a bigger reactor to do the job, you first might want to know precisely how much input energy emerging from the wall plug is making it to the heart of your machine.

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08:22 Technology.orgNew device in Z machine measures power for nuclear fusion

If you’re chasing the elusive goal of nuclear fusion and think you need a bigger reactor to do

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06:49 Arxiv.org PhysicsOn the interplay between turbulent forces and neoclassical particle losses in Zonal Flow dynamics. (arXiv:1904.04864v1 [physics.plasm-ph])

This study presents the investigation of the connection between radial electric field, gradient of Reynolds stress and Long Range Correlation (LRC), as a proxy for Zonal Flows (ZF), in different plasma scenarios in the TJ-II stellarator. Monte Carlo simulations were made showing that radial electric fields in the range of those experimentally measured have an effect on the neoclassical orbit losses. The results indicate that, despite the order of magnitude of turbulent acceleration is comparable to the neoclassical damping of perpendicular flows, its dependence with radial electric field is not correlated with the evolution of LRC amplitude, indicating that turbulent acceleration alone cannot explain the dynamics of Zonal Flows. These results are in line with the expectation that the interplay between turbulent and neoclassical mechanisms is a crucial ingredient of the dynamics of edge Zonal

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06:38 GizmagNuclear fusion breakthrough breathes life into the overlooked Z-pinch approach


Nuclear fusion holds untold potential as a source of power, but to recreate the colliding atomic nuclei taking place inside the Sun and generate inexhaustible amounts of clean energy scientists will need to achieve remarkable things. Tokamak reactors and fusion stellarators are a couple of the experimental devices used in pursuit of these lofty goals, but scientists at the University of Washington (UW) are taking a far less-frequented route known as a Z-pinch, with the early signs pointing to a cheaper and more efficient path forward.
.. Continue Reading Nuclear fusion breakthrough breathes life into the overlooked Z-pinch approach Category: Energy Tags: Fusion Nuclear University of Washington

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10.04.2019
19:33 Phys.orgReady, set, go: Scientists evaluate novel technique for firing up fusion-reaction fuel

To capture and control on Earth the fusion reactions that drive the sun and stars, researchers must first turn room-temperature gas into the hot, charged plasma that fuels the reactions. At the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL), scientists have conducted an analysis that confirms the effectiveness of a novel, non-standard way for starting up plasma in future compact fusion facilities.

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08:43 Arxiv.org CSGiving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection. (arXiv:1904.04388v1 [cs.CL])

Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model.

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04.04.2019
21:51 WhatReallyHappened.comCities are exploring nuclear fusion as a potential power source

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10:45 Arxiv.org StatisticsGenerating Labels for Regression of Subjective Constructs using Triplet Embeddings. (arXiv:1904.01643v1 [stat.ML])

Human annotations serve an important role in computational models where the target constructs under study are hidden, such as dimensions of affect. This is especially relevant in machine learning, where subjective labels derived from related observable signals (e.g., audio, video, text) are needed to support model training and testing. Current research trends focus on correcting artifacts and biases introduced by annotators during the annotation process while fusing them into a single annotation. In this work, we propose a novel annotation approach using triplet embeddings. By lifting the absolute annotation process to relative annotations where the annotator compares individual target constructs in triplets, we leverage the accuracy of comparisons over absolute ratings by human annotators. We then build a 1-dimensional embedding in Euclidean space that is indexed in time and serves as a

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10:45 Arxiv.org CSGenerating Labels for Regression of Subjective Constructs using Triplet Embeddings. (arXiv:1904.01643v1 [stat.ML])

Human annotations serve an important role in computational models where the target constructs under study are hidden, such as dimensions of affect. This is especially relevant in machine learning, where subjective labels derived from related observable signals (e.g., audio, video, text) are needed to support model training and testing. Current research trends focus on correcting artifacts and biases introduced by annotators during the annotation process while fusing them into a single annotation. In this work, we propose a novel annotation approach using triplet embeddings. By lifting the absolute annotation process to relative annotations where the annotator compares individual target constructs in triplets, we leverage the accuracy of comparisons over absolute ratings by human annotators. We then build a 1-dimensional embedding in Euclidean space that is indexed in time and serves as a

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02.04.2019
09:33 Arxiv.org PhysicsInfluence of massive material injection on avalanche runaway generation during tokamak disruptions. (arXiv:1904.00602v1 [physics.plasm-ph])

In high-current tokamak devices such as ITER, a runaway avalanche can cause a large amplification of a seed electron population. We show that disruption mitigation by impurity injection may significantly increase the runaway avalanche growth rate in such devices. This effect originates from the increased number of target electrons available for the avalanche process in weakly ionized plasmas, which is only partially compensated by the increased friction force on fast electrons. We derive an expression for the avalanche growth rate in partially ionized plasmas and investigate the effects of impurity injection on the avalanche multiplication factor and on the final runaway current for ITER-like parameters. For impurity densities relevant for disruption mitigation, the maximum amplification of a runaway seed can be increased by tens of orders of magnitude compared to previous predictions. This

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01.04.2019
04:56 Arxiv.org PhysicsThe importance of the classical channel in the impurity transport of optimized stellarators. (arXiv:1903.12511v1 [physics.plasm-ph])

In toroidal magnetic confinement devices, such as tokamaks and stellarators, neoclassical transport is usually an order of magnitude larger than its classical counterpart. However, when a high-collisionality species is present in a stellarator optimized for low Pfirsch-Schl\"uter current, its classical transport can be comparable to the neoclassical transport. In this letter, we compare neoclassical and classical fluxes and transport coefficients calculated for Wendelstein 7-X (W7-X) and Large Helical Device (LHD) cases. In W7-X, we find that the classical transport of a collisional impurity is comparable to the neoclassical transport for all radii, while it is small in the LHD cases.

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29.03.2019
22:54 ScienceMag.orgLast-minute deal grants European money to U.K.-based fusion reactor

Joint European Torus fusion reactor in the United Kingdom gets €100 million to continue to operate as a European facility

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28.03.2019
08:57 Arxiv.org PhysicsSMITER: A field-line tracing environment for ITER. (arXiv:1903.11547v1 [physics.plasm-ph])

Built around the SMARDDA modules for magnetic field-line tracing [IEEE Tr. Plasma Sc. 42 (2014) 1932], the SMITER code package (SMARDDA for ITER) is a new graphical user interface (GUI) framework for power deposition mapping on tokamak plasma-facing components (PFC) in the full 3-D CAD geometry of the machine, taking as input a user-defined specification for parallel heat flux in the scrape-off layer (SOL) and a description of the equilibrium magnetic flux. The software package provides CAD model import and integration with the ITER Integrated Modelling and Analysis Suite (IMAS), parametric CAD components catalogue and modelling, CAD de-featuring for PFC surface extraction, meshing, visualization (using an integrated ParaView module), Python scripting and batch processing, storage in hierarchical data files, with several simulation cases in one study running in parallel and using message

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27.03.2019
04:19 Arxiv.org PhysicsRadiation dominated implosion with flat target. (arXiv:1903.10896v1 [physics.plasm-ph])

Inertial Confinement Fusion is a promising option to provide massive, clean, and affordable energy for humanity in the future. The present status of research and development is hindered by hydrodynamic instabilities occurring at the intense compression of the target fuel by energetic laser beams. A recent proposal Csernai et al. (2018) combines advances in two fields: detonations in relativistic fluid dynamics and radiative energy deposition by plasmonic nano-shells. The initial compression of the target pellet can be eliminated or decreased, not to reach instabilities. A final and more energetic laser pulse can achieve rapid volume ignition, which should be as short as the penetration time of the light across the target. In the present study, we discuss a flat fuel target irradiated from both sides simultaneously. Here we propose an ignition energy with smaller compression, largely increased

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04:19 Arxiv.org PhysicsFirst principles gyrokinetic analysis of electromagnetic plasma instabilities. (arXiv:1903.10812v1 [physics.plasm-ph])

A two-fold analysis of electromagnetic core tokamak instabilities in the framework of the gyrokinetic theory is presented. First principle theoretical foundations of the gyrokinetic theory are used to explain and justify the numerical results obtained with the global electromagnetic particle-in-cell code Orb5 whose model is derived from the Lagrangian formalism. The energy conservation law corresponding to the Orb5 model is derived from the Noether theorem and implemented in the code as a diagnostics for energy balance and conservation verification. An additional Noether theorem based diagnostics is implemented in order to analyse destabilising mechanisms for the electrostatic and the electromagnetic Ion Temperature Gradient (ITG) instabilities in the core region of the tokamak. The transition towards the Kinetic Ballooning Modes (KBM) at high electromagnetic $\beta$ is also

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26.03.2019
06:35 Arxiv.org CSKnowledge Aware Conversation Generation with Reasoning on Augmented Graph. (arXiv:1903.10245v1 [cs.AI])

Two types of knowledge, factoid knowledge from graphs and non-factoid knowledge from unstructured documents, have been studied for knowledge aware open-domain conversation generation, in which edge information in graphs can help generalization of knowledge selectors, and text sentences of non-factoid knowledge can provide rich information for response generation. Fusion of knowledge triples and sentences might yield mutually reinforcing advantages for conversation generation, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, augmented knowledge graph containing both factoid and non-factoid knowledge, knowledge selector, and response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning that is more flexible in comparison with previous one-hop knowledge selection

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25.03.2019
09:37 Arxiv.org PhysicsNeutron diagnostics for the physics of a high-field, compact, $Q\geq1$ tokamak. (arXiv:1903.09479v1 [physics.plasm-ph])

Advancements in high temperature superconducting technology have opened a path toward high-field, compact fusion devices. This new parameter space introduces both opportunities and challenges for diagnosis of the plasma. This paper presents a physics review of a neutron diagnostic suite for a SPARC-like tokamak [Greenwald et al 2018 doi:10.7910/DVN/OYYBNU]. A notional neutronics model was constructed using plasma parameters from a conceptual device, called the MQ1 (Mission $Q \geq 1$) tokamak. The suite includes time-resolved micro-fission chamber (MFC) neutron flux monitors, energy-resolved radial and tangential magnetic proton recoil (MPR) neutron spectrometers, and a neutron camera system (radial and off-vertical) for spatially-resolved measurements of neutron emissivity. Geometries of the tokamak, neutron source, and diagnostics were modeled in the Monte Carlo N-Particle transport code

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22.03.2019
10:34 Arxiv.org StatisticsClassification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network. (arXiv:1903.08858v1 [cs.LG])

We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of

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10:34 Arxiv.org Quantitative BiologyClassification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network. (arXiv:1903.08858v1 [cs.LG])

We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of

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10:34 Arxiv.org CSClassification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network. (arXiv:1903.08858v1 [cs.LG])

We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of

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21.03.2019
06:33 Arxiv.org PhysicsNovel inferences of ionisation & recombination for particle/power balance during detached discharges using deuterium Balmer line spectroscopy. (arXiv:1903.08157v1 [physics.plasm-ph])

The process of divertor detachment, whereby particle fluxes to divertor surfaces are strongly reduced, is required to reduce heat loading and erosion in a magnetic fusion reactor. The ions reaching the target during divertor detachment are primarily generated in the divertor (ion source), which costs energy. In addition, there can be additional ion sinks such as volumetric recombination. Experimental power/particle balance investigations are thus crucial for understanding detachment and were not possible previously.
For that purpose, we have developed a new analysis technique for the Balmer line series to extract information on power and particle balance. First we employ line ratios to quantitatively separate excitation/recombination emission from the Balmer line series. This enabled us to analyse each of those two components individually, ultimately providing ionisation/recombination and

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06:00 Arxiv.org Quantitative BiologyEarly Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence. (arXiv:1903.08511v1 [q-bio.NC])

While different neuroimaging modalities have been proposed to detect mental stress, each modality experiences certain limitations. This study proposed novel approaches to detect stress based on fusion of EEG and fNIRS signals in the feature-level using joint independent component analysis (jICA) and canonical correlation analysis method (CCA) and predected the level of stress using machine-learning approach. The jICA and CCA were then developed to combine the features to detect mental stress. The jICA fusion scheme discovers relationships between modalities by utilizing ICA to identify sources from each modality that modulate in the same way across subjects. The CCA fuse information from two sets of features to discover the associations across modalities and to ultimately estimate the sources responsible for these associations. The study further explored the functional connectivity (FC) and

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20.03.2019
18:51 Nature.ComImpenetrable ice, Mars rumbles and nuclear-fusion lab

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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.

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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.

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

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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

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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

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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

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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.

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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.

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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.

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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.

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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,

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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,

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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.

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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

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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

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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

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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

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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."

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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.

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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.

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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

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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.

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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.

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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

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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

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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

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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

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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.

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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.

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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.

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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

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