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# Fusion

28.04.2022
06:14 Simulating fusion reactions from Coulomb explosions within a transport approach. (arXiv:2204.12677v1 [nucl-th])

We have studied nuclear fusion reactions from the Coulomb explosion of deuterium clusters induced by high-intensity laser beams within a transport approach. By incorporating the D+D $\rightarrow$ n + He$^3$ channel as inelastic collisions based on the stochastic method, we have calibrated the neutron yield from the simulation in a box system with that from the reaction rate equation, and then have investigated the dynamics from Coulomb explosions of systems of different sizes and with different deuteron numbers in clusters. We have found that the kinetic energy spectrum of deuterons at final stages, which depends on the system size and deuteron number in clusters, is different from that when neutrons are abundantly produced, corresponding to significantly different reaction rates. We have also extrapolated the neutron yield result from small systems to large systems based on an intuitive parameterized form. The present framework can be extended by incorporating more channels, and

06:14 H-mode inhibition in negative triangularity tokamak reactor plasmas. (arXiv:2204.12625v1 [physics.plasm-ph])

Instability to high toroidal mode number ($n$) ballooning modes has been proposed as the primary gradient-limiting mechanism for tokamak equilibria with negative triangularity ($\delta$) shaping, preventing access to strong H-mode regimes when $\delta\ll0$. To understand how this mechanism extrapolates to reactor conditions, we model the infinite-$n$ ballooning stability as a function of internal profiles and equilibrium shape using a combination of the CHEASE and BALOO codes. While the critical $\delta$ required for avoiding $2^\mathrm{nd}$ stability to high-$n$ modes is observed to depend in a complicated way on various shaping parameters, including the equilibrium aspect ratio, elongation and squareness, equilibria with negative triangularity are robustly prohibited from accessing the $2^\mathrm{nd}$ stability region, offering the prediction that that negative triangularity reactors should maintain L-mode-like operation. In order to access high-$n$ $2^\mathrm{nd}$ stability, the

06:14 Multi stain graph fusion for multimodal integration in pathology. (arXiv:2204.12541v1 [eess.IV])

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between

27.04.2022
23:24 Machine learning, harnessed to extreme computing, aids fusion energy development

MIT research scientists Pablo Rodriguez-Fernandez and Nathan Howard have just completed one of the most demanding calculations in fusion science—predicting the temperature and density profiles of a magnetically confined plasma via first-principles simulation of plasma turbulence. Solving this problem by brute force is beyond the capabilities of even the most advanced supercomputers. Instead, the researchers used an optimization methodology developed for machine learning to dramatically reduce the CPU time required while maintaining the accuracy of the solution.

26.04.2022
11:32 Deep electric field predictions by drift-reduced Braginskii theory with plasma-neutral interactions based upon experimental images of boundary turbulence. (arXiv:2204.11689v1 [physics.plasm-ph])

We present 2-dimensional turbulent electric field calculations via physics-informed deep learning consistent with (i) drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field and (ii) experimental estimates of the fluctuating electron density and temperature obtained from analysis of gas puff imaging of a discharge on the Alcator C-Mod tokamak. The inclusion of effects from the locally puffed atomic helium on particle and energy sources within the reduced plasma turbulence model are found to strengthen correlations between the electric field and electron pressure. The neutrals are also directly associated with an observed broadening in the distribution of turbulent field amplitudes and increased ${\bf E \times B}$ shearing rates.

11:32 Open-domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion. (arXiv:2204.11239v1 [cs.CL])

Open-domain multi-turn conversations normally face the challenges of how to enrich and expand the content of the conversation. Recently, many approaches based on external knowledge are proposed to generate rich semantic and information conversation. Two types of knowledge have been studied for knowledge-aware open-domain dialogue generation: structured triples from knowledge graphs and unstructured texts from documents. To take both advantages of abundant unstructured latent knowledge in the documents and the information expansion capabilities of the structured knowledge graph, this paper presents a new dialogue generation model, Dynamic Multi-form Knowledge Fusion based Open-domain Chatt-ing Machine (DMKCM).In particular, DMKCM applies an indexed text (a virtual Knowledge Base) to locate relevant documents as 1st hop and then expands the content of the dialogue and its 1st hop using a commonsense knowledge graph to get apposite triples as 2nd hop. To merge these two forms of knowledge

11:32 RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoT. (arXiv:2204.11216v1 [cs.CV])

Depth Estimation and Object Detection Recognition play an important role in autonomous driving technology under the guidance of deep learning artificial intelligence. We propose a hybrid structure called RealNet: a co-design method combining the model-streamlined recognition algorithm, the depth estimation algorithm with information fusion, and deploying them on the Jetson-Nano for unmanned vehicles with monocular vision sensors. We use ROS for experiment. The method proposed in this paper is suitable for mobile platforms with high real-time request. Innovation of our method is using information fusion to compensate the problem of insufficient frame rate of output image, and improve the robustness of target detection and depth estimation under monocular vision.Object Detection is based on YOLO-v5. We have simplified the network structure of its DarkNet53 and realized a prediction speed up to 0.01s. Depth Estimation is based on the VNL Depth Estimation, which considers multiple

25.04.2022
07:12 Spinors in $\mathbb{K}$-Hilbert Spaces. (arXiv:2204.10808v1 [math-ph])

We consider a structure of the $\mathbb{K}$-Hilbert space, where $\mathbb{K}\simeq\mathbb{R}$ is a field of real numbers, $\mathbb{K}\simeq\mathbb{C}$ is a field of complex numbers, $\mathbb{K}\simeq\mathbb{H}$ is a quaternion algebra, within the framework of division rings of Clifford algebras. The $\mathbb{K}$-Hilbert space is generated by the Gelfand-Naimark-Segal construction, while the generating $C^\ast$-algebra consists of the energy operator $H$ and the generators of the group $SU(2,2)$ attached to $H$. The cyclic vectors of the $\mathbb{K}$-Hilbert space corresponding to the tensor products of quaternionic algebras define the pure separable states of the operator algebra. Depending on the division ring $\mathbb{K}$, all states of the operator algebra are divided into three classes: 1) charged states with $\mathbb{K}\simeq\mathbb{C}$; 2) neutral states with $\mathbb{K}\simeq\mathbb{H}$; 3) truly neutral states with $\mathbb{K}\simeq\mathbb{R}$. For pure separable states that

22.04.2022
07:52 Multimodal Hate Speech Detection from Bengali Memes and Texts. (arXiv:2204.10196v1 [cs.CL])

Numerous works have been proposed to employ machine learning (ML) and deep learning (DL) techniques to utilize textual data from social media for anti-social behavior analysis such as cyberbullying, fake news propagation, and hate speech mainly for highly resourced languages like English. However, despite having a lot of diversity and millions of native speakers, some languages such as Bengali are under-resourced, which is due to a lack of computational resources for natural language processing (NLP). Like English, Bengali social media content also includes images along with texts (e.g., multimodal contents are posted by embedding short texts into images on Facebook), only the textual data is not enough to judge them (e.g., to determine they are hate speech). In those cases, images might give extra context to properly judge. This paper is about hate speech detection from multimodal Bengali memes and texts. We prepared the only multimodal hate speech detection dataset1 for a kind of

07:52 Multi-Focus Image Fusion based on Gradient Transform. (arXiv:2204.09777v1 [cs.CV])

Multi-focus image fusion is a challenging field of study that aims to provide a completely focused image by integrating focused and un-focused pixels. Most existing methods suffer from shift variance, misregistered images, and data-dependent. In this study, we introduce a novel gradient information-based multi-focus image fusion method that is robust for the aforementioned problems. The proposed method first generates gradient images from original images by using Halftoning-Inverse Halftoning (H-IH) transform. Then, Energy of Gradient (EOG) and Standard Deviation functions are used as the focus measurement on the gradient images to form a fused image. Finally, in order to enhance the fused image a decision fusion approach is applied with the majority voting method. The proposed method is compared with 17 different novel and conventional techniques both visually and objectively. For objective evaluation, 6 different quantitative metrics are used. It is observed that the proposed method

21.04.2022
10:32 Existence of weakly quasisymmetric magnetic fields in asymmetric toroidal domains with non-tangential quasisymmetry. (arXiv:2204.09241v1 [physics.plasm-ph])

A quasisymmetry is a special symmetry that enhances the ability of a magnetic field to trap charged particles. Quasisymmetric magnetic fields may allow the realization of next generation fusion reactors (stellarators) with superior performance when compared with classical (tokamak) designs. Nevertheless, the existence of such magnetic configurations lacks mathematical proof due to the complexity of the governing equations. Here, we prove the existence of weakly quasisymmetric magnetic fields by constructing explicit examples. This result is achieved by a tailored parametrization of both magnetic field and hosting toroidal domain, which are optimized to fulfill quasisymmetry. The obtained solutions hold in a toroidal volume, are smooth, possess nested flux surfaces, are not invariant under continuous Euclidean isometries, have a non-vanishing current, exhibit a direction of quasisymmetry that is not tangential to the toroidal boundary, and fit within the framework of anisotropic

10:32 Laser pulse shape designer for direct-drive inertial confinement fusion. (arXiv:2204.09203v1 [physics.plasm-ph])

A pulse shape designer for direct drive inertial confinement fusion has been developed, it aims at high compression of the fusion fuel while keeping hydrodynamics instability within tolerable level. Fast linear analysis on implosion instability enables the designer to fully scan the vast pulse configuration space at a practical computational cost, machine learning helps to summarize pulse performance into an implicit scaling metric that promotes the pulse shape evolution. The designer improves its credibility by incorporating various datasets including extra high-precision simulations or experiments. When tested on the double-cone ignition scheme [J. Zhang et al, Phil. Trans. R. Soc. A. 378.2184 (2020)], optimized pulses reach the assembly requirements, show significant imprint mitigation and adiabatic shaping capability, and have the potential to achieve better implosion performance in real experiments. This designer serves as an efficient alternative to traditional empirical pulse

19.04.2022
08:42 Two-dimensional shaping of Solov'ev equilibrium with vacuum using external coils. (arXiv:2204.07984v1 [physics.plasm-ph])

In this work, we demonstrate a method for constructing the Solov'ev equilibrium with any given 2D shape surrounded by a vacuum region using external poloidal field coils. The computational domain consists of two parts: the plasma region, where the solution is the same as the Solov'ev solution, and the vacuum region, where the magnetic field generated by external coils as well as plasma current is determined using the Green function method through a matching condition near the separatrix. However, the method is not limited to the Solov'ev equilibrium in particular. The accuracy, efficiency, and robustness of such a scheme suggest that this method may be applied to the 2D shaping of tokamak plasma with vacuum region using external coils in general.

08:42 Quasi-static magnetic compression of field-reversed configuration plasma: Amended scalings and limits from two-dimensional MHD equilibrium. (arXiv:2204.07978v1 [physics.plasm-ph])

In this work, several key scaling laws of the quasi-static magnetic compression of field reversed configuration (FRC) plasma [Spencer, Tuszewski, and Linford, 1983] are amended from a series of 2D FRC MHD equilibriums numerically obtained using the Grad-Shafranov equation solver NIMEQ. Based on the new scaling for the elongation and the magnetic fields at the separatrix and the wall, the empirically stable limits for the compression ratio, the fusion gain, and the neutron yield are evaluated, which may serve as a more accurate estimate for the upper ceiling of performance from the magnetic compression of FRC plasma as a potential fusion energy as well as neutron source devices.

08:42 Two-dimensional plasma density evolution local to the inversion layer during sawtooth crash events using Beam Emission Spectroscopy. (arXiv:2204.07700v1 [physics.plasm-ph])

We present methods for analyzing Beam Emission Spectroscopy (BES) data to obtain the plasma density evolution associated with rapid sawtooth crash events at the DIII-D tokamak. BES allows coverage over a 2-D spatial plane, inherently local measurements, with fast time responses, and therefore provides a valuable new channel for data during sawtooth events. A method is developed to remove sawtooth-induced edge-light pulses contained in the BES data. The edge light pulses appear to be from the $\rm{D}_{\alpha}$ emission produced by edge recycling during sawtooth events, and are large enough that traditional spectroscopic filtering and data analysis techniques are insufficient to deduce physically meaningful quantities. A cross-calibration of 64 BES channels is performed using a novel method to ensure accurate measurements. For the large-amplitude density oscillations observed, we discuss and use the non-linear relationship between BES signal $\delta I/I_{0}$ and plasma density variation

08:42 Design of an arrangement of cubic magnets for a quasi-axisymmetric stellarator experiment. (arXiv:2204.07648v1 [physics.plasm-ph])

The usage of permanent magnets to shape the confining magnetic field of a stellarator has the potential to reduce or eliminate the need for non-planar coils. As a proof-of-concept for this idea, we have developed a procedure for designing an array of cubic permanent magnets that works in tandem with a set of toroidal-field coils to confine a stellarator plasma. All of the magnets in the design are constrained to have identical geometry and one of three polarization types in order to simplify fabrication while still producing sufficient field accuracy. We present some of the key steps leading to the design, including the geometric arrangement of the magnets around the device, the procedure for optimizing the polarizations according to the three allowable magnet types, and the choice of magnet types to be used. We apply these methods to design an array of rare-Earth permanent magnets that can be paired with a set of planar toroidal-field coils to confine a quasi-axisymmetric plasma with

08:42 AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation. (arXiv:2202.13687v2 [cs.CV] UPDATED)

Automatic and accurate lesion segmentation is critical for clinically estimating the lesion statuses of stroke diseases and developing appropriate diagnostic systems. Although existing methods have achieved remarkable results, further adoption of the models is hindered by: (1) inter-class indistinction, the normal brain tissue resembles the lesion in appearance. (2) intra-class inconsistency, large variability exists between different areas of the lesion. To solve these challenges in stroke segmentation, we propose a novel method, namely Attention Guided Multiscale Recovery framework (AGMR-Net) in this paper. Firstly, a coarse-grained patch attention module in the encoding is adopted to get a patch-based coarse-grained attention map in a multi-stage explicitly supervised way, enabling target spatial context saliency representation with a patch-based weighting technique that eliminates the effect of intra-class inconsistency. Secondly, to obtain a more detailed boundary partitioning to

08:42 IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G. (arXiv:2204.07854v1 [cs.NI])

In this article, we present our vision of preamble detection in a physical random access channel for next-generation (Next-G) networks using machine learning techniques. Preamble detection is performed to maintain communication and synchronization between devices of the Internet of Everything (IoE) and next-generation nodes. Considering the scalability and traffic density, Next-G networks have to deal with preambles corrupted by noise due to channel characteristics or environmental constraints. We show that when injecting 15% random noise, the detection performance degrades to 48%. We propose an informative instance-based fusion network (IIFNet) to cope with random noise and to improve detection performance, simultaneously. A novel sampling strategy for selecting informative instances from feature spaces has also been explored to improve detection performance. The proposed IIFNet is tested on a real dataset for preamble detection that was collected with the help of a reputable company

08:42 A Distributed and Elastic Aggregation Service for Scalable Federated Learning Systems. (arXiv:2204.07767v1 [cs.LG])

Federated Learning has promised a new approach to resolve the challenges in machine learning by bringing computation to the data. The popularity of the approach has led to rapid progress in the algorithmic aspects and the emergence of systems capable of simulating Federated Learning. State of art systems in Federated Learning support a single node aggregator that is insufficient to train a large corpus of devices or train larger-sized models. As the model size or the number of devices increase the single node aggregator incurs memory and computation burden while performing fusion tasks. It also faces communication bottlenecks when a large number of model updates are sent to a single node. We classify the workload for the aggregator into categories and propose a new aggregation service for handling each load. Our aggregation service is based on a holistic approach that chooses the best solution depending on the model update size and the number of clients. Our system provides a

15.04.2022
18:12 A new understanding of how COVID infects humans

The Australian Nuclear Science and Technology Organisation's National Deuteration Facility has provided deuterated cholesterol for international research to gain a better understanding of how the Spike protein of the COVID virus, SARS-Co-V-2, infects human cells through a membrane fusion mechanism.

14.04.2022
08:12 Core localized alpha-channeling via low frequency Alfven mode generation in reversed shear scenarios. (arXiv:2204.06169v1 [physics.plasm-ph])

A novel channel for fuel ions heating in tokamak core plasma is proposed and analyzed using nonlinear gyrokinetic theory. The channel is achieved via spontaneous decay of reversed shear Alfven eigenmode (RSAE) into low frequency Alfven modes (LFAM), which then heat fuel ions via collisionless ion Landau damping. The conditions for RSAE spontaneous decay are investigated, and the saturation level and the consequent fuel ion heating rate are also derived. The channel is expected to be crucial for future reactors operating under reversed shear configurations, where fusion alpha particles are generated in the tokamak core where the magnetic shear is typically reversed, and there is a dense RSAE spectrum due to the small alpha particle characteristic dimensionless orbits.

08:12 Center-of-Mass Corrections in Associated Particle Imaging. (arXiv:2204.06124v1 [physics.ins-det])

Associated Particle Imaging utilizes the inelastic scattering of neutrons produced in deuterium-tritium fusion reactions to obtain 3D isotopic distributions within an object. The locations of the inelastic scattering centers are calculated by measuring the arrival time and the position of the associated particle, the alpha particle in the fusion reaction, and the arrival time of the prompt gamma created in the scattering event. While the neutron and its associated particle move in opposite directions in the center-of-mass (COM) system, in the laboratory system the angle is slightly less than 180 degrees, and the COM movement must be taken into account in the reconstruction of the scattering location. Furthermore, due to energy loss of ions in the target, the fusion reactions are produced by ions of different energies, and thus the COM velocity varies, resulting in an uncertainty in the reconstructed positions. In this paper, we analyze the COM corrections to this reconstruction by

13.04.2022
21:52 Inside the Largest Nuclear Fusion Reactor (Video)

Nuclear fusion reactors are still in experimental development stages. However, there is one facility of this kind already

18:22 Validating models for next-generation fusion facilities

The flagship fusion facility of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) could serve as the model for an economically attractive next-generation fusion pilot plant, according to recent simulations and analysis. The pilot plant could become the next U.S. step for harvesting on Earth the fusion power that drives the sun and stars as a safe and clean source of power for generating electricity.

17:50 Validating models for next-generation fusion facilities

The National Spherical Torus Experiment-Upgrade (NSTX-U) could serve as the model for a fusion energy pilot plant.

10:52 New insights on divertor parallel flows, ExB drifts, and fluctuations from in situ, two-dimensional probe measurement in the Tokamak \a Configuration Variable. (arXiv:2204.05756v1 [physics.plasm-ph])

In-situ, two-dimensional (2D) Langmuir probe measurements across a large part of the TCV divertor are reported in L-mode discharges with and without divertor baffles. This provides detailed insights into time averaged profiles, particle fluxes, and fluctuations behavior in different divertor regimes. The presence of the baffles is shown to substantially increase the divertor neutral pressure for a given upstream density and to facilitate the access to detachment, an effect that increases with plasma current. The detailed, 2D probe measurements allow for a divertor particle balance, including ion flux contributions from parallel flows and ExB drifts. The poloidal flux contribution from the latter is often comparable or even larger than the former, such that the divertor parallel flow direction reverses in some conditions, pointing away from the target. In most conditions, the integrated particle flux at the outer target can be predominantly ascribed to ionization along the outer

10:52 Global gyrokinetic simulations of ASDEX Upgrade up to the transport time-scale with GENE-Tango. (arXiv:2204.05651v1 [physics.plasm-ph])

An accurate description of turbulence up to the transport time scale is essential for predicting core plasma profiles and enabling reliable calculations for designing advanced scenarios and future devices. Here, we exploit the gap separation between turbulence and transport time scales and couple the global gyrokinetic code GENE to the transport-solver Tango, including kinetic electrons, collisions, realistic geometries, toroidal rotation and electromagnetic effects for the first time. This approach overcomes gyrokinetic codes' limitations and enables high-fidelity profile calculations in experimentally relevant plasma conditions, significantly reducing the computational cost. We present numerical results of GENE-Tango for two ASDEX Upgrade discharges, one of which exhibits a pronounced peaking of the ion temperature profile not reproduced by TGLF-ASTRA. We show that GENE-Tango can correctly capture the ion temperature peaking observed in the experiment. By retaining different

10:52 DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection. (arXiv:2204.05575v1 [cs.CV])

Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities. It has been widely agreed that vehicle-infrastructure cooperation is required to achieve Level 5 autonomy. However, there is still NO dataset from real scenarios available for computer vision researchers to work on vehicle-infrastructure cooperation-related problems. To accelerate computer vision research and innovation for Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first large-scale, multi-modality, multi-view dataset from real scenarios for VICAD. DAIR-V2X comprises 71254 LiDAR frames and 71254 Camera frames, and all frames are captured from real scenes with 3D annotations. The Vehicle-Infrastructure Cooperative 3D Object Detection problem (VIC3D) is introduced, formulating the problem of collaboratively locating and identifying 3D objects using sensory inputs from both vehicle and

10:52 Glass Segmentation with RGB-Thermal Image Pairs. (arXiv:2204.05453v1 [cs.CV])

This paper proposes a new glass segmentation method utilizing paired RGB and thermal images. Due to the large difference between the transmission property of visible light and that of the thermal energy through the glass where most glass is transparent to the visible light but opaque to thermal energy, glass regions of a scene are made more distinguishable with a pair of RGB and thermal images than solely with an RGB image. To exploit such a unique property, we propose a neural network architecture that effectively combines an RGB-thermal image pair with a new multi-modal fusion module based on attention, and integrate CNN and transformer to extract local features and long-range dependencies, respectively. As well, we have collected a new dataset containing 5551 RGB-thermal image pairs with ground-truth segmentation annotations. The qualitative and quantitative evaluations demonstrate the effectiveness of the proposed approach on fusing RGB and thermal data for glass segmentation. Our

12.04.2022
06:32 Multimodal Machine Learning in Precision Health. (arXiv:2204.04777v1 [cs.LG])

As machine learning and artificial intelligence are more frequently being leveraged to tackle problems in the health sector, there has been increased interest in utilizing them in clinical decision-support. This has historically been the case in single modal data such as electronic health record data. Attempts to improve prediction and resemble the multimodal nature of clinical expert decision-making this has been met in the computational field of machine learning by a fusion of disparate data. This review was conducted to summarize this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for Scoping Reviews to characterize multi-modal data fusion in health. We used a combination of content analysis and literature searches to establish search strings and databases of PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 125 articles

08.04.2022
16:12 Tiger to make late start in astonishing Masters comeback

The 15-time major champion battled through pain to walk the hilly 7,510-yard Augusta National layout, firing a one-under par 71 to stand four strokes behind South Korean leader Im Sung-jae after 18 holes. "I'm right where I need to be," Woods said. "I'm going to be sore. That's just the way it is. "But the training cycles we've had make sure I have the stamina to keep going. We've got three more to go. There's a long way to go and a lot of shots to be played. "This golf course is going to change and it's going to get a lot more difficult." Woods made an astonishing return to competition 14 months after a car crash in Southern California caused him severe right leg injuries. Rods, plates and pins help hold together his leg after he was hospitalized for weeks and unable to walk for months. Rehabilitation work has allowed the 46-year-old medical marvel to make an epic comeback at an iconic setting. "I'm as sore as I expected to feel, but it was amazing," Woods said after his first round.

10:12 Classification of events from $\alpha$-induced reactions in the MUSIC detector via statistical and ML methods. (arXiv:2204.03137v1 [physics.data-an])

The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other applications. From the MUSIC data produced in one experiment scientists carefully extract an order of $10^3$ events of interest from about $10^{9}$ total events, where each event can be represented by an 18-dimensional vector. However, the standard data classification process is based on expert-driven, manually intensive data analysis techniques that require several months to identify patterns and classify the relevant events from the collected data. To address this issue, we present a method for the classification of events originating from specific $\alpha$-induced reactions by combining statistical and machine learning methods that require significantly less input from the domain scientist, relative to the standard technique. We applied the new method to two experimental data sets and compared our

09:52 Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks. (arXiv:2204.03027v1 [cs.NI])

NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault diagnostics, user equipment identification, and authentication. A network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center (such as in cooperative spectrum sensing). In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification. In distributed federated learning, each sensor broadcasts its trained model to its neighbors, collects the DNN models

07.04.2022
05:52 First-principles density limit scaling in tokamaks based on edge turbulent transport and implications for ITER. (arXiv:2204.02911v1 [physics.plasm-ph])

A first-principles scaling law, based on turbulent transport considerations, and a multi-machine database of density limit discharges from the ASDEX Upgrade, JET and TCV tokamaks, show that the increase of the boundary turbulent transport with the plasma collisionality sets the maximum density achievable in tokamaks. This scaling law shows a strong dependence on the heating power, therefore predicting for ITER a significantly larger safety margin than the Greenwald empirical scaling (Greenwald et al, Nucl. Fusion, 28(12), 1988) in case of unintentional H-L transition.

06.04.2022
08:32 First Identification of New X-Ray Spectra of Mo39+, Mo40+, W43+, W44+ and W45+ on EAST. (arXiv:2204.02051v1 [physics.plasm-ph])

New high-resolution x-ray spectra of Mo39+, Mo40+, W43+, W44+ and W45+ have been carefully confirmed for the first time by use of the x-ray imaging crystal spectrometer (XCS) in Experimental Advanced Superconducting Tokamak (EAST) under various combined auxiliary heating plasmas conditions. Wavelength of these new x-ray spectra is ranged from 3.895 {\AA} to 3.986 {\AA}. When core electron temperature (Te0) reaches 6.0 keV, Mo39+ and Mo40+ lines of 3.9727, 3.9294 and 3.9480 {\AA} can be effectively detected on XCS for EAST; meanwhile, line-integrated brightness of these spectral lines of Mo39+ and Mo40+ is very considerable when electron temperature reaches 12.9 keV. Multi-components spectral lines for W43+, W44+ and W45+ have also been identified when Te0 reaches 6 keV. Parts of spectral lines, such as Zn-1, Cu-2, Cu-4a, Cu-4d and Cu-5 lines of tungsten, are first observed experimentally. When electron temperature reaches 12.9 keV, line-integrated intensity for part of these spectral

08:02 An efficient real-time target tracking algorithm using adaptive feature fusion. (arXiv:2204.02054v1 [cs.CV])

Visual-based target tracking is easily influenced by multiple factors, such as background clutter, targets fast-moving, illumination variation, object shape change, occlusion, etc. These factors influence the tracking accuracy of a target tracking task. To address this issue, an efficient real-time target tracking method based on a low-dimension adaptive feature fusion is proposed to allow us the simultaneous implementation of the high-accuracy and real-time target tracking. First, the adaptive fusion of a histogram of oriented gradient (HOG) feature and color feature is utilized to improve the tracking accuracy. Second, a convolution dimension reduction method applies to the fusion between the HOG feature and color feature to reduce the over-fitting caused by their high-dimension fusions. Third, an average correlation energy estimation method is used to extract the relative confidence adaptive coefficients to ensure tracking accuracy. We experimentally confirm the proposed method on

05.04.2022
11:12 Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System. (arXiv:2204.01289v1 [physics.plasm-ph])

This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 Nature publication. In particular, the predictor now features not only the disruption score, as an indicator of the probability of an imminent disruption, but also a sensitivity score in real-time to indicate the underlying reasons for the imminent disruption. This adds valuable physics-interpretability for the deep-learning model and can provide helpful guidance for control actuators now that it is fully implemented into a modern Plasma Control System (PCS). The advance is a significant step forward in moving from modern deep-learning disruption prediction to real-time control and brings novel AI-enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII-D vetted in the earlier NATURE publication. In addition to when a shot

11:12 Mechanical and electrical properties of MWCNT/PP films and structural health monitoring of GF/PP joints. (arXiv:2204.00909v1 [physics.app-ph])

While welding of thermoplastic composites (TPCs) is a promising rivetless method to reduce weight, higher confidence in joints' structural integrity and failure prediction must be achieved for widespread use in industry. In this work, we present an innovative study on damage detection for ultrasonically welded TPC joints with multiwalled carbon nanotubes (MWCNTs) and embedded buckypaper films. MWCNTs show promise for structural health monitoring (SHM) of composite joints, assembled by adhesive bonding or fusion bonding, through electrical resistance changes. This study focuses on investigating multifunctional films and their suitability for ultrasonic welding (USW) of TPCs, using two approaches: 1) MWCNT/filled polypropylene (PP) nanocomposites prepared via solvent dispersion, and 2) high conductivity MWCNT buckypaper embedded between PP films by hot pressing. Nanocomposite formulations containing 5 wt and 10 wt MWCNTs were synthesized using solvent dispersion method, followed by

10:52 Modern Views of Machine Learning for Precision Psychiatry. (arXiv:2204.01607v1 [cs.LG])

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further

10:52 Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification. (arXiv:2204.00973v1 [cs.CV])

To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the spectral features, local binary pattern (LBP) to extract spatial features, and feature superposition to obtain the fused features of hyperspectral images. A new swarm intelligence optimization method with high convergence and strong global search capability, the Sparrow Search Algorithm (SSA), is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). In summary, a multiscale fusion feature hyperspectral image classification method (MLS-KELM) is proposed in this paper. The Indian Pines, Pavia University and Houston 2013 datasets were selected to validate the classification performance of MLS-KELM, and the method was applied to ZY1-02D hyperspectral data. The experimental results show that MLS-KELM has better classification performance and

10:31 Oxford start-up claims major nuclear fusion breakthrough

04.04.2022
06:52 Comments to "A non-thermal laser-driven mixed fuel nuclear fusion reactor concept" by H. Ruhl and G. Korn (Marvel Fusion, Munich). (arXiv:2204.00269v1 [physics.plasm-ph])

The declared aim of Marvel Fusion is the realization of a reactor based on the aneutronic fusion of proton and boron-11 nuclei in the near future, making use of latest-day laser technology and nano-structured materials. The aim of the preprint quoted above is to demonstrate the feasibility of fusion energy gain from the irradiation of an initially uncompressed target consisting of an array of nano-wires, by a femto-second laser. This proposal is in apparent contrast to text-book wisdom, which postulates - even for the fast-ignitor concept, and a DT fuel mix - a target density about 1000 times that of solid state. The novel, optimistic predictions of Ruhl and Korn are based, however, not on rigorous estimates, but only on parametric dependencies, extrapolated far beyond their conventional limits of validity. The authors invoke the effects of self-arising magnetic fields to result in additional magnetic confinement, but we show their model to contain intrinsic contradictions. The

06:52 The DESC Stellarator Code Suite Part III: Quasi-symmetry optimization. (arXiv:2204.00078v1 [physics.plasm-ph])

The DESC stellarator optimization code takes advantage of advanced numerical methods to search the full parameter space much faster than conventional tools. Only a single equilibrium solution is needed at each optimization step thanks to automatic differentiation, which efficiently provides exact derivative information. A Gauss-Newton trust-region optimization method uses second-order derivative information to take large steps in parameter space and converges rapidly. With just-in-time compilation and GPU portability, high-dimensional stellarator optimization runs take orders of magnitude less computation time with DESC compared to other approaches. This paper presents the theory of the DESC fixed-boundary local optimization algorithm along with demonstrations of how to easily implement it in the code. Example quasi-symmetry optimizations are shown and compared to results from conventional tools. Three different forms of quasi-symmetry objectives are available in DESC, and their

06:22 The DESC Stellarator Code Suite Part III: Quasi-symmetry optimization. (arXiv:2204.00078v1 [physics.plasm-ph])

The DESC stellarator optimization code takes advantage of advanced numerical methods to search the full parameter space much faster than conventional tools. Only a single equilibrium solution is needed at each optimization step thanks to automatic differentiation, which efficiently provides exact derivative information. A Gauss-Newton trust-region optimization method uses second-order derivative information to take large steps in parameter space and converges rapidly. With just-in-time compilation and GPU portability, high-dimensional stellarator optimization runs take orders of magnitude less computation time with DESC compared to other approaches. This paper presents the theory of the DESC fixed-boundary local optimization algorithm along with demonstrations of how to easily implement it in the code. Example quasi-symmetry optimizations are shown and compared to results from conventional tools. Three different forms of quasi-symmetry objectives are available in DESC, and their

06:22 Fusing Interpretable Knowledge of Neural Network Learning Agents For Swarm-Guidance. (arXiv:2204.00272v1 [cs.MA])

Neural-based learning agents make decisions using internal artificial neural networks. In certain situations, it becomes pertinent that this knowledge is re-interpreted in a friendly form to both the human and the machine. These situations include: when agents are required to communicate the knowledge they learn to each other in a transparent way in the presence of an external human observer, in human-machine teaming settings where humans and machines need to collaborate on a task, or where there is a requirement to verify the knowledge exchanged between the agents. We propose an interpretable knowledge fusion framework suited for neural-based learning agents, and propose a Priority on Weak State Areas (PoWSA) retraining technique. We first test the proposed framework on a synthetic binary classification task before evaluating it on a shepherding-based multi-agent swarm guidance task. Results demonstrate that the proposed framework increases the success rate on the swarm-guidance

01.04.2022
08:02 The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations. (arXiv:2203.17173v1 [physics.plasm-ph])

3D equilibrium codes are vital for stellarator design and operation, and high-accuracy equilibria are also necessary for stability studies. This paper details comparisons of two 3D equilibrium codes, VMEC, which uses a steepest-descent algorithm to reach a minimum-energy plasma state, and DESC, which minimizes the MHD force error in real space directly. Accuracy as measured by final plasma energy and satisfaction of MHD force balance, as well as other metrics, will be presented for each code, along with the computation time. It is shown that DESC is able to achieve more accurate solutions, especially near-axis. DESC's global Fourier-Zernike basis also yields the solution everywhere in the plasma volume, not just on discrete flux surfaces. Further, DESC can compute the same accuracy solution as VMEC in an order of magnitude less time.

07:12 Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems. (arXiv:2203.16730v1 [cs.CR])

As a promising candidate to complement traditional biometric modalities, brain biometrics using electroencephalography (EEG) data has received a widespread attention in recent years. However, compared with existing biometrics such as fingerprints and face recognition, research on EEG biometrics is still in its infant stage. Most of the studies focus on either designing signal elicitation protocols from the perspective of neuroscience or developing feature extraction and classification algorithms from the viewpoint of machine learning. These studies have laid the ground for the feasibility of using EEG as a biometric authentication modality, but they have also raised security and privacy concerns as EEG data contains sensitive information. Existing research has used hash functions and cryptographic schemes to protect EEG data, but they do not provide functions for revoking compromised templates as in cancellable template design. This paper proposes the first cancellable EEG template

07:12 Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard. (arXiv:2203.16706v1 [eess.SP])

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolutions over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-to-thousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in 'beamforming', where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for

00:32 Innovation of the Year: 5 technology breakthroughs named finalists in GeekWire Awards (Todd Bishop/GeekWire)

Todd Bishop / GeekWireInnovation of the Year: 5 technology breakthroughs named finalists in GeekWire Awards - From fuel cells to fusion energy, theres a common theme among many of the finalists for Innovation of the Year in the GeekWire Awards this year: alternative sources of power and new forms of transportation. And while were waiting for breakthroughs like those to improve the climate, weve got some ...

31.03.2022
10:42 The DESC Stellarator Code Suite Part II: Perturbation and continuation methods. (arXiv:2203.15927v1 [physics.plasm-ph])

A new perturbation and continuation method is presented for computing and analyzing stellarator equilibria. The method is formally derived from a series expansion about the equilibrium condition $F \equiv J \times B - \nabla p = 0$, and an efficient algorithm for computing solutions to 2nd and 3rd order perturbations is developed. The method has been implemented in the DESC stellarator equilibrium code, using automatic differentiation to compute the required derivatives. Examples are shown demonstrating its use for computing complicated equilibria, perturbing a tokamak into a stellarator, and performing parameter scans in pressure, rotational transform and boundary shape in a fraction of the time required for a full solution.

10:42 Characteristics of grassy ELMs and its impact on the divertor heat flux width. (arXiv:2203.15909v1 [physics.plasm-ph])

BOUT++ turbulence simulations are conducted for a 60s steady-state long pulse high \{beta}p EAST grassy ELM discharge. BOUT++ linear simulations show that the unstable mode spectrum covers a range of toroidal mode numbers from low-n (n=10~15) peeling-ballooning modes (P-B) to high-n (n=40~80) drift-Alfv\'en instabilities. Nonlinear simulations show that the ELM crash is trigged by low-n peeling modes and fluctuation is generated at the peak pressure gradient position and radially spread outward into the Scrape-Off-Layer (SOL), even though the drift-Alfv\'en instabilities dominate the linear growth phase. However, drift-Alfv\'en turbulence delays the onset of the grassy ELM and enhances the energy loss with the fluctuation extending to pedestal top region. Simulations further show that if the peeling drive is removed, the fluctuation amplitude drops by an order of magnitude and the ELM crashes disappear. The divertor heat flux width is ~2 times larger than the estimates based on the HD

09:52 The DESC Stellarator Code Suite Part II: Perturbation and continuation methods. (arXiv:2203.15927v1 [physics.plasm-ph])

A new perturbation and continuation method is presented for computing and analyzing stellarator equilibria. The method is formally derived from a series expansion about the equilibrium condition $F \equiv J \times B - \nabla p = 0$, and an efficient algorithm for computing solutions to 2nd and 3rd order perturbations is developed. The method has been implemented in the DESC stellarator equilibrium code, using automatic differentiation to compute the required derivatives. Examples are shown demonstrating its use for computing complicated equilibria, perturbing a tokamak into a stellarator, and performing parameter scans in pressure, rotational transform and boundary shape in a fraction of the time required for a full solution.

09:52 The DESC Stellarator Code Suite Part II: Perturbation and continuation methods. (arXiv:2203.15927v1 [physics.plasm-ph])

A new perturbation and continuation method is presented for computing and analyzing stellarator equilibria. The method is formally derived from a series expansion about the equilibrium condition $F \equiv J \times B - \nabla p = 0$, and an efficient algorithm for computing solutions to 2nd and 3rd order perturbations is developed. The method has been implemented in the DESC stellarator equilibrium code, using automatic differentiation to compute the required derivatives. Examples are shown demonstrating its use for computing complicated equilibria, perturbing a tokamak into a stellarator, and performing parameter scans in pressure, rotational transform and boundary shape in a fraction of the time required for a full solution.

29.03.2022
09:04 First-Principles Theory of the Rate of Magnetic Reconnection in Magnetospheric and Solar Plasmas. (arXiv:2203.14268v1 [physics.plasm-ph])

The rate of magnetic reconnection is of the utmost importance in a variety of processes because it controls, for example, the rate energy is released in solar flares, the speed of the Dungey convection cycle in Earth's magnetosphere, and the energy release rate in harmful geomagnetic substorms. It is known from numerical simulations and satellite observations that the rate is approximately 0.1 in normalized units, but despite years of effort, a full theoretical prediction has not been obtained. Here, we present a first-principles theory for the reconnection rate in non-relativistic electron-ion collisionless plasmas, and show that the same prediction explains why Sweet-Parker reconnection is considerably slower. The key consideration of this analysis is the pressure at the reconnection site (i.e., the x-line). We show that the Hall electromagnetic fields in antiparallel reconnection cause an energy void, equivalently a pressure depletion, at the x-line, so the reconnection exhaust

25.03.2022
05:41 Design and simulation of an Imaging Neutral Particle Analyzer for the ASDEX Upgrade tokamak. (arXiv:2203.12995v1 [physics.plasm-ph])

An Imaging Neutral Particle Analyser (INPA) diagnostic has been designed for the ASDEX Upgrade (AUG) tokamak. The AUG INPA diagnostic will measure fast neutrals escaping the plasma after CX reactions. The neutrals will be ionised by a 20 nm carbon foil and deflected towards a scintillator by the local magnetic field. The use of a neutral beam injector (NBI) as active source of neutrals will provide radially resolved measurements while the use of a scintillator as active component will allow us to cover the whole plasma along the NBI line with unprecedented phase-space resolution ($05:41 Optimizing beam-ion confinement in ITER by adjusting the toroidal phase of the 3-D magnetic fields applied for ELM control. (arXiv:2203.12986v1 [physics.plasm-ph]) The confinement of Neutral Beam Injection (NBI) particles in the presence of n=3 Resonant Magnetic Perturbations (RMPs) in 15 MA ITER DT plasmas has been studied using full orbit ASCOT simulations. Realistic NBI distribution functions, and 3D wall and equilibria, including the plasma response to the externally applied 3D fields calculated with MARS-F, have been employed. The observed total fast-ion losses depend on the poloidal spectra of the applied n=3 RMP as well as on the absolute toroidal phase of the applied perturbation with respect to the NBI birth distribution. The absolute toroidal phase of the RMP perturbation does not affect the ELM control capabilities, which makes it a key parameter in the confinement optimization. The physics mechanisms underlying the observed fast-ion losses induced by the applied 3D fields have been studied in terms of the variation of the particle canonical angular momentum ($\delta P_{\phi}) induced by the applied 3D fields. The presented 24.03.2022 08:27 Electronic pair alignment and roton feature in the warm dense electron gas. (arXiv:2203.12288v1 [cond-mat.quant-gas]) The study of matter under extreme densities and temperatures as they occur e.g. in astrophysical objects and nuclear fusion applications has emerged as one of the most active frontiers in physics, material science, and related disciplines. In this context, a key quantity is given by the dynamic structure factorS(\mathbf{q},\omega)$, which is probed in scattering experiments -- the most widely used method of diagnostics at these extreme conditions. In addition to its crucial importance for the study of warm dense matter, the modelling of such dynamic properties of correlated quantum many-body systems constitutes one of the most fundamental theoretical challenges of our time. Here we report a hitherto unexplained \emph{roton feature} in$S(\mathbf{q},\omega)of the warm dense electron gas, and introduce a microscopic explanation in terms of a new \emph{electronic pair alignment} model. This new paradigm will be highly important for the understanding of warm dense matter, and has a 08:27 A cylindrical implosion platform for the study of highly magnetized plasmas at LMJ. (arXiv:2203.12099v1 [physics.plasm-ph]) The potential benefits of the use of magnetic fields in Inertial Confinement Fusion (ICF) experiments have been investigated for several years, and exploring them has given rise to new experimental platforms such as the Magnetized Liner Inertial Fusion (MagLIF) approach at the Z-machine, or its laser-driven equivalent at the OMEGA 60 laser. Implementing these experimental platforms at MJ-scale laser facilities such as the National Ignition Facility (NIF) or the Laser MegaJoule (LMJ) enlarges the range of plasma parameters that can be explored, in particular the level of magnetization that can be achieved through a more efficient compression. In addition, these facilities are crucial to reaching self-sustained nuclear fusion, including laser-based magneto-inertial approach. In this paper, we present a complete design of an experimental platform for magnetized implosions using cylindrical targets at LMJ. In this case, the initial magnetic field is generated along the axis of the cylinder 08:27 Gyrokinetic particle-in-cell simulations of electromagnetic turbulence in the presence of fast particles and global modes. (arXiv:2203.11983v1 [physics.plasm-ph]) Global simulations of electromagnetic turbulence, collisionless tearing modes, and Alfven Eigenmodes in the presence of fast particles are carried out using the gyrokinetic particle-in-cell codes ORB5 (E. Lanti et al, Comp. Phys. Comm.,{\bf 251}$, 107072 (2020)) and EUTERPE (V. Kornilov et al, Phys. Plasmas,${\bf 11}$, 3196 (2004)) in tokamak and stellarator geometries. Computational feasibility of simulating such complex coupled systems is demonstrated. 07:23 Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions. (arXiv:2203.12616v1 [cs.LG]) Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been fully explored for clinical data analysis. Even though an immense amount of Electronic Health Record (EHR) data is recorded, data and labels can be scarce if the data is collected in small hospitals or deals with rare diseases. In such scenarios, pre-training on a larger set of EHR data could improve the model performance. In this paper, we apply unsupervised pre-training to heterogeneous, multi-modal EHR data for patient outcome prediction. To model this data, we leverage graph deep learning over population graphs. We first design a network architecture based on graph transformer designed to handle various input feature types occurring in EHR data, like continuous, discrete, and time-series features, allowing better multi-modal data fusion. Further, we design pre-training methods based on masked imputation 22.03.2022 06:43 X-point and divertor filament dynamics from Gas Puff Imaging on TCV. (arXiv:2203.10907v1 [physics.plasm-ph]) A new Gas Puff Imaging (GPI) diagnostic has been installed on the TCV tokamak, providing two-dimensional insights into Scrape-Off-Layer (SOL) turbulence dynamics above, at and below the magnetic X-point. A detailed study in L-mode, attached, lower single-null discharges shows that statistical properties have little poloidal variations, while vast differences are present in the 2D behaviour of intermittent filaments. Strongly elongated filaments, just above the X-point and in the divertor far-SOL, show a good consistency in shape and dynamics with field-line tracing from filaments at the outboard midplane, highlighting their connection. In the near-SOL of the outer divertor leg, shortlived, high frequency and more circular (diameter$\sim$15 sound Larmour radii) filaments are observed. These divertor-localised filaments appear born radially at the position of maximum density and display a radially outward motion with velocity$\approx$400\,m/s that is comparable to radial velocities of 06:43 A-posteriori optimization for increasing manufacturing tolerances in stellarator coil design. (arXiv:2203.10164v1 [physics.plasm-ph]) It was recently shown in [Wechsung et. al., Proc. Natl. Acad. Sci. USA, 2022, to appear] that there exist electromagnetic coils that generate magnetic fields which are excellent approximations to quasi-symmetric fields and have very good particle confinement properties. Using a Gaussian process based model for coil perturbations, we investigate the impact of manufacturing errors on the performance of these coils. We show that even fairly small errors result in noticeable performance degradation and that stochastic optimization is not able to mitigate these errors significantly. We then formulate a new optimization problem for computing optimal adjustments of the coil positions and currents without changing the shapes of the coil. These a-posteriori adjustments are able to reduce the impact of coil errors by an order of magnitude, providing a new perspective for dealing with manufacturing uncertainties in stellarator design. 05:25 A-posteriori optimization for increasing manufacturing tolerances in stellarator coil design. (arXiv:2203.10164v1 [physics.plasm-ph]) It was recently shown in [Wechsung et. al., Proc. Natl. Acad. Sci. USA, 2022, to appear] that there exist electromagnetic coils that generate magnetic fields which are excellent approximations to quasi-symmetric fields and have very good particle confinement properties. Using a Gaussian process based model for coil perturbations, we investigate the impact of manufacturing errors on the performance of these coils. We show that even fairly small errors result in noticeable performance degradation and that stochastic optimization is not able to mitigate these errors significantly. We then formulate a new optimization problem for computing optimal adjustments of the coil positions and currents without changing the shapes of the coil. These a-posteriori adjustments are able to reduce the impact of coil errors by an order of magnitude, providing a new perspective for dealing with manufacturing uncertainties in stellarator design. 21.03.2022 23:10 Quantum technology could make charging electric cars as fast as pumping gas Whether it's photovoltaics or fusion, sooner or later, human civilization must turn to renewable energies. This is deemed inevitable, considering the ever-growing energy demands of humanity and the finite nature of fossil fuels. Much research has been pursued in order to develop alternative sources of energy, most of which use electricity as the main energy carrier. The extensive R&D in renewables has been accompanied by gradual societal changes as the world adopted new products and devices running on renewables. The most striking change has been the rapid adoption of electric vehicles. While they were rarely seen on the roads even 10 years ago, now, millions of electric cars are being sold annually. The electric car market is one of the most rapidly growing sectors. 09:22 Runaway electron generation during tokamak start-up. (arXiv:2203.09900v1 [physics.plasm-ph]) Tokamak start-up is characterized by low electron densities and strong electric fields, in order to quickly raise the plasma current and temperature, allowing the plasma to fully ionize and magnetic flux surfaces to form. Such conditions are ideal for the formation of superthermal electrons, which may reduce the efficiency of ohmic heating and prevent the formation of a healthy thermal fusion plasma. This is of particular concern in ITER where engineering limitations put restrictions on the allowable electric fields and limit the prefill densities during start-up. In this study, we present a new 0D burn-through simulation tool called STREAM (STart-up Runaway Electron Analysis Model), which self-consistently evolves the plasma density, temperature and electric field, while accounting for the generation and loss of relativistic runaway electrons. After verifying the burn-through model, we investigate conditions under which runaway electrons can form during tokamak start-up as well as 07:59 A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT. (arXiv:2203.09631v1 [eess.SP]) In wireless Internet of things (IoT), the sensors usually have limited bandwidth and power resources. Therefore, in a distributed setup, each sensor should compress and quantize the sensed observations before transmitting them to a fusion center (FC) where a global decision is inferred. Most of the existing compression techniques and entropy quantizers consider only the reconstruction fidelity as a metric, which means they decouple the compression from the sensing goal. In this work, we argue that data compression mechanisms and entropy quantizers should be co-designed with the sensing goal, specifically for machine-consumed data. To this end, we propose a novel deep learning-based framework for compressing and quantizing the observations of correlated sensors. Instead of maximizing the reconstruction fidelity, our objective is to compress the sensor observations in a way that maximizes the accuracy of the inferred decision (i.e., sensing goal) at the FC. Unlike prior work, we do not 07:59 Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation. (arXiv:2203.09811v1 [cs.CV]) Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph. Existing SGG approaches generally not only neglect the insufficient modality fusion between vision and language, but also fail to provide informative predicates due to the biased relationship predictions, leading SGG far from practical. Towards this end, in this paper, we first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction, to serve as the encoder. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder. Particularly, based upon the observation that the recognition capability of one classifier is limited towards an extremely unbalanced dataset, we first deploy a group of classifiers that are expert in distinguishing different subsets of classes, and then cooperatively 07:59 A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT. (arXiv:2203.09631v1 [eess.SP]) In wireless Internet of things (IoT), the sensors usually have limited bandwidth and power resources. Therefore, in a distributed setup, each sensor should compress and quantize the sensed observations before transmitting them to a fusion center (FC) where a global decision is inferred. Most of the existing compression techniques and entropy quantizers consider only the reconstruction fidelity as a metric, which means they decouple the compression from the sensing goal. In this work, we argue that data compression mechanisms and entropy quantizers should be co-designed with the sensing goal, specifically for machine-consumed data. To this end, we propose a novel deep learning-based framework for compressing and quantizing the observations of correlated sensors. Instead of maximizing the reconstruction fidelity, our objective is to compress the sensor observations in a way that maximizes the accuracy of the inferred decision (i.e., sensing goal) at the FC. Unlike prior work, we do not 18.03.2022 20:09 To Help Tackle Climate Crisis, White House Touts Nuclear Fusion Gina McCarthy and other administration officials showcased fusion energy in a bid to accelerate vast amounts of carbon-free power -- Read more on ScientificAmerican.com 10:16 Confinement of passing and trapped runaway electrons in the simulation of an ITER current quench. (arXiv:2203.09344v1 [physics.plasm-ph]) Runaway electrons (REs) present a high-priority issue for ITER but little is known about the extent to which RE generation is affected by the stochastic field intrinsic to disrupting plasmas. RE generation can be modelled with reduced kinetic models and there has been recent progress in involving losses due to field stochasticity, either via a loss-time parameter or radial transport coefficients which can be estimated by tracing test electrons in 3D fields. We evaluate these terms in ITER using a recent JOREK 3D MHD simulation of plasma disruption to provide the stochastic magnetic fields where RE markers are traced with the built-in particle tracing module. While the MHD simulation modelled only the current quench phase, the case is MHD unstable and exhibits similar relaxation as would be expected during the thermal quench. Therefore, the RE simulations can be considered beginning right after the thermal quench but before the MHD relaxation is complete. The plasma is found to become 10:16 Simulations of scrape-off layer power width for EAST H-mode plasma and ITER 15MA baseline scenario by 2D electrostatic turbulence code. (arXiv:2203.09039v1 [physics.plasm-ph]) The scrape-off layer power width (\lambda_q) is an important parameter for characterizing the divertor heat loads. Many experimental, theoretical, and numerical studies have been performed in recent years. In this paper, a 2D electrostatic turbulence code, BOUT-HESEL, has been upgraded to simulate H-mode plasmas for the first time. The code is validated against the previous implementation and the experiments. The simulated \lambda_q is found to agree quite well with the Eich scaling for the EAST H-mode discharge. The code is utilized to simulate the ITER 15MA baseline scenario. The ITER simulation reveals that the radial particle/heat transports are dominated by blobby transports, and predicts \lambda_{q,ITER} = 9.6 mm, which is much larger than the prediction by the Eich scaling. Based on the EAST modified cases, an estimated HESEL H-mode scaling, \lambda_q=0.51R_c^1.1B_t^(-0.3)q_95^1.1 is proposed. This scaling predicts \lambda_{q,ITER} = 9.3 mm, which agrees surprisingly well with 17.03.2022 05:41 On the drift wave eigenmode crossing zero frequency in Tokamak. (arXiv:2203.08346v1 [physics.plasm-ph]) The conventional ion temperature gradient or \eta_i mode is known to propagate in the ion diamagnetic direction. Investigation of a generic drift fluid model with warm ions and adiabatic electrons, reveals that as \eta_i decreases, the propagation characteristics of the unstable mode may change drastically, the mode frequency first decreases in magnitude, and reaches zero for a critical \eta_i. But as \eta_i goes down further, the mode begins to propagate in the electron diamagnetic direction. The lower toroidal mode number perturbations are more prone to reversal in propagation direction. Even for \eta_i=0, the mode remains unstable, drawing free energy form the density gradients. Since finite ion temperature appears to be essential for propagation in the electron direction, it is appropriate to introduce new terminology and call this wave the warm ion electron drift (WIED) mode. The model drift wave system is explored within the framework of the two dimensional (2D) weakly asymmetric 05:41 Role of self-generated magnetic fields in the inertial fusion ignition threshold. (arXiv:2203.08258v1 [physics.plasm-ph]) Magnetic fields spontaneously grow at unstable interfaces around hot-spot asymmetries during inertial confinement fusion implosions. Although difficult to measure, theoretical considerations and numerical simulations predict field strengths exceeding 5kT in current national ignition facility experiments. Magnetic confinement of electrons then reduces the hot-spot heat loss by >5%. We demonstrate this via magnetic post-processing of two-dimensional xRAGE hydrodynamic simulation data at bang time. We then derive a model for the self-magnetization, finding that it varies with the square of the stagnation temperature and inversely with the areal density. The self-magnetized Lawson analysis then gives a slightly reduced ignition threshold. Time dependent hot-spot energy balance models corroborate this finding, with the magnetic field quadrupling the fusion yield for near threshold parameters. The inclusion of magnetized multi-dimensional fluid instabilities could further alter the ignition 04:22 Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry. (arXiv:2010.02033v2 [physics.plasm-ph] UPDATED) We present a new coil design paradigm for magnetic confinement in stellarators. Our approach directly optimizes coil shapes and coil currents to produce a vacuum quasi-symmetric magnetic field with a target rotational transform on the magnetic axis. This approach differs from the traditional two-stage approach in which first a magnetic configuration with desirable physics properties is found, and then coils to approximately realize this magnetic configuration are designed. The proposed single-stage approach allows us to find a compromise between confinement and engineering requirements, i.e., find easy-to-build coils with good confinement properties. Using forward and adjoint sensitivities, we derive derivatives of the physical quantities in the objective, which is constrained by a nonlinear periodic differential equation. In two numerical examples, we compare different gradient-based descent algorithms and find that incorporating approximate second-order derivative information through 16.03.2022 21:40 German start-up aims to generate unlimited clean fusion energy with lasers Marvel Fusion, a German start-up that has raised more than$65 million, aims to commercialize fusion using laser technology.

19:34 German start-up aims to generate unlimited clean fusion energy with lasers

Marvel Fusion, a German start-up which has raised more than $65 million, aims to commercialize fusion using laser technology. 08:18 Low-pressure THGEM-based operation with Ne+H2 Penning mixtures. (arXiv:2203.07874v1 [physics.ins-det]) Operation of Thick Gas Electron Multipliers (THGEMs) in Time Projection Chambers (TPCs) using various gas mixtures find applications in various nuclear physics and particle detection experiments. Of particular interest are use of neon-based gas mixtures at low pressures in TPCs as active targets for radioactive beams. Here, we report on the low-pressure operation of THGEMs in Ne+H2 based gas mixtures, where neon in this mixture is an important target for fusion studies with TPCs. We show that since the Ne+H2 forms a Penning pair, higher gains are achievable compared to pure neon gas. Moreover, H2 as a quench-gas produces minimal background in fusion reactions compared to carbon-based neon gas mixtures. Detailed electron transport and amplification simulations have been performed and they qualitatively agree with the increased gain H2 provides in the Ne:H2 mixture. This allows for sufficiently high gains for the use of Ne gas with THGEMs that will enable the measurement of heavy-ion 06:52 Energy-efficient Dense DNN Acceleration with Signed Bit-slice Architecture. (arXiv:2203.07679v1 [cs.AR]) As the number of deep neural networks (DNNs) to be executed on a mobile system-on-chip (SoC) increases, the mobile SoC suffers from the real-time DNN acceleration within its limited hardware resources and power budget. Although the previous mobile neural processing units (NPUs) take advantage of low-bit computing and exploitation of the sparsity, it is incapable of accelerating high-precision and dense DNNs. This paper proposes energy-efficient signed bit-slice architecture which accelerates both high-precision and dense DNNs by exploiting a large number of zero values of signed bit-slices. Proposed signed bit-slice representation (SBR) changes signed$1111_{2}$bit-slice to$0000_{2}$by borrowing a$1$value from its lower order of bit-slice. As a result, it generates a large number of zero bit-slices even in dense DNNs. Moreover, it balances the positive and negative values of 2's complement data, allowing bit-slice based output speculation which pre-computes high order of 06:52 Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation. (arXiv:2203.07627v1 [cs.CL]) Multilingual neural machine translation models are trained to maximize the likelihood of a mix of examples drawn from multiple language pairs. The dominant inductive bias applied to these models is a shared vocabulary and a shared set of parameters across languages; the inputs and labels corresponding to examples drawn from different language pairs might still reside in distinct sub-spaces. In this paper, we introduce multilingual crossover encoder-decoder (mXEncDec) to fuse language pairs at an instance level. Our approach interpolates instances from different language pairs into joint crossover examples' in order to encourage sharing input and output spaces across languages. To ensure better fusion of examples in multilingual settings, we propose several techniques to improve example interpolation across dissimilar languages under heavy data imbalance. Experiments on a large-scale WMT multilingual dataset demonstrate that our approach significantly improves quality on 15.03.2022 11:02 Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features. (arXiv:2203.06314v1 [cs.CV]) Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment response assessment. Different image discretization parameters (e.g. bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels can be used to generate radiomics features and ultimately signatures. Commonly, only one set of parameters is used; resulting in only one value or flavour for a given RF. We propose tensor radiomics (TR) where tensors of features calculated with multiple combinations of parameters (i.e. flavours) are utilized to optimize the construction of radiomics signatures. We present examples of TR as applied to PET/CT, MRI, and CT imaging invoking machine learning or deep learning solutions, and reproducibility analyses: (1) TR via varying bin sizes on CT images of lung cancer and PET-CT images of head & neck cancer (HNC) for overall survival prediction. A 09:31 Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features. (arXiv:2203.06314v1 [cs.CV]) Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment response assessment. Different image discretization parameters (e.g. bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels can be used to generate radiomics features and ultimately signatures. Commonly, only one set of parameters is used; resulting in only one value or flavour for a given RF. We propose tensor radiomics (TR) where tensors of features calculated with multiple combinations of parameters (i.e. flavours) are utilized to optimize the construction of radiomics signatures. We present examples of TR as applied to PET/CT, MRI, and CT imaging invoking machine learning or deep learning solutions, and reproducibility analyses: (1) TR via varying bin sizes on CT images of lung cancer and PET-CT images of head & neck cancer (HNC) for overall survival prediction. A 14.03.2022 06:41 A Deep Learning Model with Radiomics Analysis Integration for Glioblastoma Post-Resection Survival Prediction. (arXiv:2203.05891v1 [physics.med-ph]) Purpose: To develop a novel deep-learning model that integrates radiomics analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM) post-resection survival prediction. Methods: A cohort of 235 GBM patients with complete surgical resection was divided into short-term/long-term survival groups with 1-yr survival time threshold. Each patient received a pre-surgery multi-parametric MRI exam, and three tumor subregions were segmented by neuroradiologists. The developed model comprises three data source branches: in the 1st radiomics branch, 456 radiomics features (RF) were from each patient; in the 2nd deep learning branch, an encoding neural network architecture was trained for survival group prediction using each single MR modality, and high-dimensional parameters of the last two network layers were extracted as deep features (DF). The extracted radiomics features and deep features were processed by a feature selection procedure to reduce dimension size of each 04:59 ActiveMLP: An MLP-like Architecture with Active Token Mixer. (arXiv:2203.06108v1 [cs.CV]) This paper presents ActiveMLP, a general MLP-like backbone for computer vision. The three existing dominant network families, i.e., CNNs, Transformers and MLPs, differ from each other mainly in the ways to fuse contextual information into a given token, leaving the design of more effective token-mixing mechanisms at the core of backbone architecture development. In ActiveMLP, we propose an innovative token-mixer, dubbed Active Token Mixer (ATM), to actively incorporate contextual information from other tokens in the global scope into the given one. This fundamental operator actively predicts where to capture useful contexts and learns how to fuse the captured contexts with the original information of the given token at channel levels. In this way, the spatial range of token-mixing is expanded and the way of token-mixing is reformed. With this design, ActiveMLP is endowed with the merits of global receptive fields and more flexible content-adaptive information fusion. Extensive 11.03.2022 05:48 Mathematical modelling of transport phenomena in compressible multicomponent flows. (arXiv:2203.05094v1 [math.AP]) The present article proposes a diffuse interface model for compressible multicomponent flows with transport phenomena of mass, momentum and energy (i.e., mass diffusion, viscous dissipation and heat conduction). The model is reduced from the seven-equation Baer-Nuziato type model with asymptotic analysis in the limit of instantaneous mechanical relaxations. The main difference between the present model and the Kapila's five-equation model consists in that different time scales for pressure and velocity relaxations are assumed, the former being much smaller than the latter. Thanks to this assumption, the velocity disequilibrium is retained to model the mass diffusion process. Aided by the diffusion laws, the final model still formally consists of five equations. The proposed model satisfy two desirable properties : (1) it respects the laws of thermodynamics, (2) it is free of the spurious oscillation problem in the vicinity of the diffused interface zone. The mass diffusion, viscous 10.03.2022 08:32 The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach. (arXiv:2203.04921v1 [cs.LG]) In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly health care industry contains many data consisting of patient and disease-related information. By using the machine learning technique, we can look for hidden data patterns to predict various diseases. Recently CVDs, or cardiovascular disease, have become a leading cause of death around the world. The number of death due to CVDs is frightening. That is why many researchers are trying their best to design a predictive model that can save many lives using the data mining model. In this research, some fusion models have been constructed to diagnose CVDs along with its severity. Machine learning(ML) algorithms like artificial neural network, SVM, logistic regression, decision tree, random forest, and AdaBoost have been applied to the heart disease dataset to predict disease. Randomoversampler was implemented 08:32 Visibility-Inspired Models of Touch Sensors for Navigation. (arXiv:2203.04751v1 [cs.RO]) This paper introduces mathematical models of touch sensors for mobile robotics based on visibility. Serving a purpose similar to the pinhole camera model for computer vision, the introduced models are expected to provide a useful, idealized characterization of task-relevant information that can be inferred from their outputs or observations. This allows direct comparisons to be made between traditional depth sensors, highlighting cases in which touch sensing may be interchangeable with time of flight or vision sensors, and characterizing unique advantages provided by touch sensing. The models include contact detection, compression, load bearing, and deflection. The results could serve as a basic building block for innovative touch sensor designs for mobile robot sensor fusion systems. 08:32 Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion. (arXiv:2203.04586v1 [eess.IV]) Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and FLAIR can be obtained simultaneously in a single scanning process. However, acquiring contrast enhanced modalities such as T1ce requires additional time, cost, and injection of contrast agent. As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e.g., brain tumor segmentation) for performance enhancing. In this work, we propose an end-to-end framework named Modality-Level Attention Fusion Network (MAF-Net), wherein we innovatively conduct patchwise contrastive learning for extracting multi-modal latent features and dynamically assigning attention weights to fuse different modalities. Through extensive experiments on BraTS2020, our proposed MAF-Net is found to yield superior T1ce synthesis performance (SSIM of 08:32 Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring. (arXiv:2203.04396v1 [eess.SP]) Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, Motion Artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this problem, based on combining PPG signals with inertial sensor data. Until know, both commercial and reasearch solutions are computationally efficient but not very robust, or strongly dependent on hand-tuned parameters, which leads to poor generalization performance. % In this work, we tackle these limitations by proposing a computationally lightweight yet robust deep learning-based approach for PPG-based HR estimation. Specifically, we derive a diverse set of Temporal Convolutional Networks (TCN) for HR estimation, leveraging Neural Architecture Search (NAS). Moreover, we also introduce ActPPG, an adaptive algorithm that selects among multiple 09.03.2022 06:04 Reactive collisions between electrons and BeT$^+$: Complete set of thermal rate coefficients up to 5000 K. (arXiv:2203.04122v1 [physics.atom-ph]) Rate coefficients for the dissociative recombination, vibrational excitation and vibrational de-excitation of the BeT$^{+}$ion for all vibrational levels of its ground electronic state ($ X\ensuremath{^{1}\Sigma^{+}},v_{i}^{+}=0,\dots,27$) are reported, including in the calculation the contribution of super-excited states of the BeT complex pertaining to three electronic symmetries -$^{2}\Pi$,$^{2}\Sigma^{+}$, and$^{2}\Delta$. These data are suitable for the kinetic modeling of beryllium and tritium containing plasma, as encountered in magnetic fusion devices with beryllium walls (JET, ITER). In the present study we restrict ourselves to incident electron energies from 10$^{-3}$up to$2.7$eV, and to electron temperatures between$100$and$5000$K, respectively. Together with our earlier and closely related studies on the BeH$^{+}$and BeD$^{+}$systems, this present work completes the isotopic coverage for the beryllium monohydride ions. The vibrational energy (rather than the 08.03.2022 13:12 LLNL scientists confirm thermonuclear fusion in a sheared-flow Z-pinch In findings that could help advance another “viable pathway” to fusion energy, research led by Lawrence Livermore National Laboratory (LLNL) 09:56 LLNL scientists confirm thermonuclear fusion in a sheared-flow Z-pinch In findings that could help advance another “viable pathway” to fusion energy, research led by Lawrence Livermore National 00:46 Scientists confirm thermonuclear fusion in a sheared-flow Z-pinch device In findings that could help advance another "viable pathway" to fusion energy, research led by Lawrence Livermore National Laboratory (LLNL) physicists has proven the existence of neutrons produced through thermonuclear reactions from a sheared-flow stabilized Z-pinch device. 07.03.2022 12:41 PPPL researcher’s work yields a breakthrough for a promising fusion-energy device For decades, fusion researchers have largely focused on tokamaks, which use a symmetrical, donut-shaped field to contain the 06.03.2022 10:08 Breakthrough for a promising fusion-energy device How can scientists create fusion — the energy that powers the sun and stars — to produce clean 04.03.2022 18:59 Innovative new magnet could facilitate development of fusion and medical devices Scientists at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have designed a new type of magnet that could aid devices ranging from doughnut-shaped fusion facilities known as tokamaks to medical machines that create detailed pictures of the human body. 18:44 Nuclear fusion is one step closer with new AI breakthrough The green energy revolution promised by nuclear fusion is step closer, thanks to the first successful use of artificial intelligence to shape hydrogen plasmas inside a fusion reactor. 17:00 Toronto artist Maylee Todd enters the metaverse on the dreamy future pop record ‘Maloo’ Maylee Todd has always taken an unconventional approach to sharing her music. A few years back, following the release of the album “Act of Love,” Todd toured the country with an art installation called the “Virtual Womb” — an immersive “musical planetarium” that required audience members to enter through a giant fabric vulva. Todd’s latest project, an album inspired by her work as a virtual reality designer, marks yet another idiosyncratic left turn for the artist, who splits her time between Toronto and Los Angeles. “Maloo,” which arrives via Stones Throw Records on Friday, is a collection of “science fiction lullabies” narrated from the perspective of a digital avatar who inhabits a utopian metaverse called “The Age of Energy.” The story follows Maloo’s journey to a planet whose sustainability depends on the mental health and well-being of its inhabitants. The result is many 10:34 Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning. (arXiv:2203.01466v1 [astro-ph.EP]) We report the first-time recovery of a fresh meteorite fall using a drone and a machine learning algorithm. A fireball on the 1st April 2021 was observed over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on site and surveyed 5.1 km2 area over a 4-day period. A convolutional neural network, trained on previously-recovered meteorites with fusion crusts, processed the images on our field computer after each flight. meteorite candidates identified by the algorithm were sorted by team members using two user interfaces to eliminate false positives. Surviving candidates were revisited with a smaller drone, and imaged in higher resolution, before being eliminated or finally being visited in-person. The 70 g meteorite was recovered within 50 m of the calculated fall line using, demonstrating the effectiveness of this methodology which will facilitate the efficient collection of many more observed 05:24 Multi-scale analysis of global electromagnetic instabilities in ITER Pre-Fusion-Power Operation plasmas. (arXiv:2203.01618v1 [physics.plasm-ph]) Global electromagnetic gyrokinetic simulations are performed with the Particle-in-Cell code ORB5 for an ITER Pre-Fusion-Power-Operation (PFPO) plasma scenario, with half-field (2.65 T) and half-current (7.5 MA). We report on a 'multi-scale' analysis of the discharge, considering eigenmodes and instabilies across three scale-lengths. Although the scenario will nominally have neutral beam heating with particles injected with 1 MeV, Alfv\'en eigenmodes are investigated in the absence of such source, and Reversed Shear (RSAE), Toroidal (TAE) and Elliptical (EAE) Alfv\'en eigenmodes are found with weak damping for moderately low toroidal mode numbers ($10 \leq n \leq 35$). At higher toroidal mode numbers ($40 \leq n \leq 70\$), unstable Alfv\'enic modes have been observed close to rational surfaces and are labelled as Beta-induced Alfv\'en eigenmodes (BAE)/Alfv\'enic Ion Temperature Gradient (AITG) modes, since their frequency is associated with the BAE gap and they are driven by the bulk

05:24 Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning. (arXiv:2203.01466v1 [astro-ph.EP])

We report the first-time recovery of a fresh meteorite fall using a drone and a machine learning algorithm. A fireball on the 1st April 2021 was observed over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on site and surveyed 5.1 km2 area over a 4-day period. A convolutional neural network, trained on previously-recovered meteorites with fusion crusts, processed the images on our field computer after each flight. meteorite candidates identified by the algorithm were sorted by team members using two user interfaces to eliminate false positives. Surviving candidates were revisited with a smaller drone, and imaged in higher resolution, before being eliminated or finally being visited in-person. The 70 g meteorite was recovered within 50 m of the calculated fall line using, demonstrating the effectiveness of this methodology which will facilitate the efficient collection of many more observed