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# Fusion

**08:00**Arxiv.org CS Machine-Learning enabled analysis of ELM filament dynamics in KSTAR. (arXiv:2201.07941v1 [physics.plasm-ph])

The emergence and dynamics of filamentary structures associated with edge-localized modes (ELMs) inside tokamak plasmas during high-confinement mode is regularly studied using Electron Cyclotron Emission Imaging (ECEI) diagnostic systems. Such diagnostics allow us to infer electron temperature variations, often across a poloidal cross-section. Previously, detailed analysis of these filamentary dynamics and classification of the precursors to edge-localized crashes has been done manually. We present a machine-learning-based model, capable of automatically identifying the position, spatial extend, and amplitude of ELM filaments. The model is a deep convolutional neural network that has been trained and optimized on an extensive set of manually labeled ECEI data from the KSTAR tokamak. Once trained, the model achieves a $93.7\%$ precision and allows us to robustly identify plasma filaments in unseen ECEI data. The trained model is used to characterize ELM filament dynamics in a single

**07:49**Arxiv.org Physics Machine-Learning enabled analysis of ELM filament dynamics in KSTAR. (arXiv:2201.07941v1 [physics.plasm-ph])

The emergence and dynamics of filamentary structures associated with edge-localized modes (ELMs) inside tokamak plasmas during high-confinement mode is regularly studied using Electron Cyclotron Emission Imaging (ECEI) diagnostic systems. Such diagnostics allow us to infer electron temperature variations, often across a poloidal cross-section. Previously, detailed analysis of these filamentary dynamics and classification of the precursors to edge-localized crashes has been done manually. We present a machine-learning-based model, capable of automatically identifying the position, spatial extend, and amplitude of ELM filaments. The model is a deep convolutional neural network that has been trained and optimized on an extensive set of manually labeled ECEI data from the KSTAR tokamak. Once trained, the model achieves a $93.7\%$ precision and allows us to robustly identify plasma filaments in unseen ECEI data. The trained model is used to characterize ELM filament dynamics in a single

**07:49**Arxiv.org Physics Momentum Conservation in Current Drive and Alpha-Channeling-Mediated Rotation Drive. (arXiv:2201.07853v1 [physics.plasm-ph])

Alpha channeling uses waves to extract hot ash from a fusion plasma, while transferring energy from the ash to the wave. Intriguingly, it has been proposed that the extraction of this charged ash could create a radial electric field, efficiently driving ExB rotation. However, existing theories ignore the response of the nonresonant particles, which play a critical role in enforcing momentum conservation in quasilinear theory. Because cross-field charge transport and momentum conservation are fundamentally linked, this non-consistency throws the whole effect into question. Here, we review recent developments that have largely resolved this question of rotation drive by alpha channeling. We build a simple, general, self-consistent quasilinear theory for electrostatic waves, applicable to classic examples such as the bump-on-tail instability. As an immediate consequence, we show how waves can drive currents in the absence of momentum injection even in a collisionless plasma. To apply this

**10:38**Arxiv.org CS Coupled Support Tensor Machine Classification for Multimodal Neuroimaging Data. (arXiv:2201.07683v1 [stat.ML])

Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities. Multimodal modeling helps to explain the interdependence between heterogeneous data sources, discovers new insights that may not be available from a single modality, and improves decision-making. Recently, coupled matrix-tensor factorization has been introduced for multimodal data fusion to jointly estimate latent factors and identify complex interdependence among the latent factors. However, most of the prior work on coupled matrix-tensor factors focuses on unsupervised learning and there is little work on supervised learning using the jointly estimated latent factors. This paper considers the multimodal tensor data classification problem. A

**10:38**Arxiv.org CS RunnerDNA: Interpretable indicators and model to characterize human activity pattern and individual difference. (arXiv:2201.07370v1 [cs.CY])

Human activity analysis based on sensor data plays a significant role in behavior sensing, human-machine interaction, health care, and so on. The current research focused on recognizing human activity and posture at the activity pattern level, neglecting the effective fusion of multi-sensor data and assessing different movement styles at the individual level, thus introducing the challenge to distinguish individuals in the same movement. In this study, the concept of RunnerDNA, consisting of five interpretable indicators, balance, stride, steering, stability, and amplitude, was proposed to describe human activity at the individual level. We collected smartphone multi-sensor data from 33 volunteers who engaged in physical activities such as walking, running, and bicycling and calculated the data into five indicators of RunnerDNA. The indicators were then used to build random forest models and recognize movement activities and the identity of users. The results show that the proposed

**10:38**Arxiv.org CS Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition. (arXiv:2201.07335v1 [math.NA])

Rayleigh-Taylor instability is a classical hydrodynamic instability of great interest in various disciplines of science and engineering, including astrophyics, atmospheric sciences and climate, geophysics, and fusion energy. Analytical methods cannot be applied to explain the long-time behavior of Rayleigh-Taylor instability, and therefore numerical simulation of the full problem is required. However, in order to capture the growth of amplitude of perturbations accurately, both the spatial and temporal discretization need to be extremely fine for traditional numerical methods, and the long-time simulation may become prohibitively expensive. In this paper, we propose efficient reduced order model techniques to accelerate the simulation of Rayleigh-Taylor instability in compressible gas dynamics. We introduce a general framework for decomposing the solution manifold to construct the temporal domain partition and temporally-local reduced order model construction with varying Atwood

**10:26**Arxiv.org Math Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition. (arXiv:2201.07335v1 [math.NA])

Rayleigh-Taylor instability is a classical hydrodynamic instability of great interest in various disciplines of science and engineering, including astrophyics, atmospheric sciences and climate, geophysics, and fusion energy. Analytical methods cannot be applied to explain the long-time behavior of Rayleigh-Taylor instability, and therefore numerical simulation of the full problem is required. However, in order to capture the growth of amplitude of perturbations accurately, both the spatial and temporal discretization need to be extremely fine for traditional numerical methods, and the long-time simulation may become prohibitively expensive. In this paper, we propose efficient reduced order model techniques to accelerate the simulation of Rayleigh-Taylor instability in compressible gas dynamics. We introduce a general framework for decomposing the solution manifold to construct the temporal domain partition and temporally-local reduced order model construction with varying Atwood

**10:26**Arxiv.org Physics Investigation of Langdon effect on the nonlinear evolution of SRS from the early-stage inflation to the late-stage development of secondary instabilities. (arXiv:2201.07722v1 [physics.plasm-ph])

In a laser-irradiated plasma, the Langdon effect can result in a super-Gaussian electron energy distribution function (EEDF), imposing significant influences on the stimulated backward Raman scattering (SRS). In this work, the influence of a super-Gaussian EEDF on the nonlinear evolution of SRS is investigated by three wave model simulation and Vlasov-Maxwell simulation for plasma parameters covering a wide range of k{\lambda}De from 0.19 to 0.48 at both high and low intensity laser drives. In the early-stage of SRS evolution, it is found that besides the kinetic effects due to electron trapping [Phys. Plasmas 25, 100702 (2018)], the Langdon effect can also significantly widen the parameter range for the absolute growth of SRS, and the time for the absolute SRS to reach saturation is greatly shorten by Langdon effect within certain parameter region. In the late-stage of SRS, when secondary instabilities such as decay of the electron plasma wave to beam acoustic modes, rescattering, and

**09:20**Arxiv.org Statistics Coupled Support Tensor Machine Classification for Multimodal Neuroimaging Data. (arXiv:2201.07683v1 [stat.ML])

Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities. Multimodal modeling helps to explain the interdependence between heterogeneous data sources, discovers new insights that may not be available from a single modality, and improves decision-making. Recently, coupled matrix-tensor factorization has been introduced for multimodal data fusion to jointly estimate latent factors and identify complex interdependence among the latent factors. However, most of the prior work on coupled matrix-tensor factors focuses on unsupervised learning and there is little work on supervised learning using the jointly estimated latent factors. This paper considers the multimodal tensor data classification problem. A

**06:03**Arxiv.org Physics Impact of transport models on local measurements in W7-X using synthetic diagnostics with EMC3-EIRENE and comparison to experimental observations in the W7-X island scrape-off layer. (arXiv:2201.06341v1 [physics.plasm-ph])

Modelling the scrape-off layer of a stellarator is challenging due to the complex magnetic 3D geometry. The here presented study analyses simulations of the scrape-off layer (SOL) of the stellarator Wendelstein 7-X (W7-X) using the EMC3-EIRENE code for the magnetic standard configuration. Comparing with experimental observations, the transport model is validated. Based on the experimentally observed strike line width, the anomalous transport coefficients, used as input to the code are determined to around 0.2 m 2 /s. This is however in disagreement with upstream measurements, where such small cross-field transport leads to temperatures higher than measured experimentally. Agreement can be improved by using spatially varying transport coefficients. Various differences remain, even with spatially varying transport coefficients. The future implementation of drifts into the transport model are expected to help overcome the discrepancies, and thus the development of SOL transport models

**06:03**Arxiv.org Physics Skyrmions-based logic gates in one single nanotrack completely reconstructed via chirality barrie. (arXiv:2201.06182v1 [cond-mat.mtrl-sci])

Logic gates based on magnetic elements are promising candidates for the logic-in-memory applications with nonvolatile data retention, near-zero leakage and scalability. In such spin-based logic device, however, the multi-strip structure and fewer functions are obstacles to improving integration and reducing energy consumption. Here we propose a skyrmions-based single-nanotrack logic family including AND, OR, NOT, NAND, NOR, XOR, and XNOR which can be implemented and reconstructed by building and switching Dzyaloshinskii-Moriya interaction (DMI) chirality barrier on a racetrack memory. Besides the pinning effect of DMI chirality barrier on skyrmions, the annihilation, fusion and shunting of two skyrmions with opposite chirality are also achieved and demonstrated via local reversal of DMI, which are necessary for the design of engineer programmable logic nanotrack, transistor and complementary racetrack memory.

**06:03**Arxiv.org Physics Electromagnetic instabilities and plasma turbulence driven by electron-temperature gradient. (arXiv:2201.05670v1 [physics.plasm-ph])

Electromagnetic (EM) instabilities and turbulence driven by the electron-temperature gradient are considered in a local slab model of a tokamak-like plasma. The model describes perturbations at scales both larger and smaller than the flux-freezing scale $d_e$, and so captures both electrostatic and EM regimes of turbulence. The well-known electrostatic instabilities -- slab and curvature-mediated ETG -- are recovered, and a new instability is found in the EM regime, called the Thermo-Alfv\'enic instability (TAI). It exists in both a slab version (sTAI, destabilising kinetic Alfv\'en waves) and a curvature-mediated version (cTAI), which is a cousin of the (electron-scale) kinetic ballooning mode (KBM). The cTAI turns out to be dominant at the largest scales covered by the model (greater than $d_e$ but smaller than $\rho_i$), its physical mechanism hinging on the fast equalisation of the total temperature along perturbed magnetic field lines (in contrast to KBM, which is pressure

**11:38**Arxiv.org CS A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI. (arXiv:2201.05373v1 [eess.IV])

Brain tumors analysis is important in timely diagnosis and effective treatment to cure patients. Tumor analysis is challenging because of tumor morphology like size, location, texture, and heteromorphic appearance in the medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively. The deep boosted feature space is achieved through the customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types. The dynamic features are extracted

**11:26**Arxiv.org Physics Impact of the improved parallel kinetic coefficients on the helium and neon transport in SOLPS-ITER for ITER. (arXiv:2201.05572v1 [physics.plasm-ph])

New Grad's-Zhdanov module is implemented in the SOLPS-ITER code and applied to ITER impurity transport simulations. Significant difference appears in the helium transport due to improved parallel kinetic coefficients. As a result 30\% decrease of the separatrix-averaged helium relative concentration is observed for the constant helium source and pumping speed. Change of the impurity behaviour is discussed. For the neon changes are less pronounced. For the first time the ion distribution functions are studied in the ITER Scrape-off layer conditions to reveal the origin of the kinetic coefficient improvements and theory limitations.

**11:26**Arxiv.org Physics DEMO ion cyclotron heating: status of ITER-type antenna design. (arXiv:2201.05557v1 [physics.plasm-ph])

The ITER ICRF system will gain in complexity relative to the existing systems on modern devices, and the same will hold true for DEMO. The accumulated experience can help greatly in designing an ICRF system for DEMO. In this paper the current status of the pre-conceptual design of the DEMO ICRF antenna and some related components is presented. While many aspects strongly resemble the ITER system, in some design solutions we had to take an alternative route to be able to adapt to DEMO specific. One of the key points is the toroidal antenna extent needed for the requested ICRF heating performance, achieved by splitting the antenna in halves, with appropriate installation. Modelling of the so far largest ICRF antenna in RAPLICASOL and associated challenges are presented. Calculation are benchmarked with TOPICA. Results of the analysis of the latest model and an outlook for future steps are given.

**17:52**Phys.org Avoiding chains of magnetic islands may lead to fusion paradise

To create the conditions needed for fusion reactions, tokamak reactors contain a plasma in magnetic fields. These magnetic fields can contain tubular areas called magnetic islands. Plasma particles move extra quickly across these islands. This prevents the plasma from reaching the high temperatures necessary for fusion energy production. Fusion plants must therefore minimize the size of these regions. For the first time, researchers have observed the spontaneous formation of a structure in the plasma with multiple magnetic islands. Known as "heteroclinic islands," they do not merge into each other while embedded in a larger magnetic field tube. These structures form areas of higher temperatures at each island center that are directly observed by local measurements. Additional measurements of the magnetic field perturbation caused by these special island chains further confirm their existence in experiments and are consistent with simulation results.

**10:40**Arxiv.org Physics Gyrokinetic modelling of anisotropic energetic particle driven instabilities in tokamak plasmas. (arXiv:2201.03836v1 [physics.plasm-ph])

Energetic particles produced by neutral beams are observed to excite energetic-particle-driven geodesic acoustic modes (EGAMs) in tokamaks. We study the effects of anisotropy of distribution function of the energetic particles on the excitation of such instabilities with ORB5, a gyrokinetic particle-in-cell code. Numerical results are shown for linear electrostatic simulations with ORB5. The growth rate is found to be sensitively dependent on the phase-space shape of the distribution function. The behavior of the instability is qualitatively compared to the theoretical analysis of dispersion relations. Realistic neutral beam energetic particle anisotropic distributions are obtained from the heating solver RABBIT and are introduced into ORB5 as input distribution function. Results show a dependence of the growth rate on the injection angle. A qualitative comparison to experimental measurements is presented and few disagreements between them are found, being the growth rate in the

**04:10**ScienceDaily.com Common household cleaner can boost effort to harvest fusion energy on Earth

Path-setting findings demonstrate for the first time a novel regime for confining heat in stellarators. The demonstration could advance the twisty design as a blueprint for future fusion power plants.

**01:17**Phys.org Common household cleaner can boost effort to harvest fusion energy on Earth

Scientists have found that adding a common household cleaning agent—the mineral boron contained in such cleaners as borax—can vastly improve the ability of some fusion energy devices to contain the heat required to produce fusion reactions on Earth the way the sun and stars do.

**12:39**Technology.org Seeing the plasma edge of fusion experiments in new ways with artificial intelligence

MIT researchers are testing a simplified turbulence theory’s ability to model complex plasma phenomena using a novel machine-learning

**06:43**Arxiv.org CS United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI. (arXiv:2201.02629v1 [eess.IV])

Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is invisible or insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI. The UAL first utilizes a multi-view aware encoder to extract multi-modality NCMRI information for liver tumor segmentation and detection. In this encoder, a novel edge dissimilarity feature pyramid module is designed to facilitate the complementary

**06:09**Arxiv.org Physics MEASUREMENT OF THE HIGGS BRANCHING RATIO BR($H\rightarrow\gamma\gamma$) AT 3 TeV CLIC. (arXiv:2201.03203v1 [physics.acc-ph])

In this paper we address the potential of a 3 TeV centre-of-mass energy Compact Linear Collider (CLIC) to measure the branching fraction of the Higgs boson decay to two photons, BR($H\rightarrow\gamma\gamma$). Since photons are massless, the Higgs boson coupling to photons is realized through higher order processes involving heavy particles either from the Standard Model or beyond. Any deviation of the measured BR($H\rightarrow\gamma\gamma$), and consequently of the Higgs coupling $g_{H\gamma\gamma}$ from the predictions of the Standard Model, may indicate New Physics. The Higgs decay to two photons is thus an interesting probe of the Higgs sector. This study is performed using simulation of the detector for CLIC and by considering all relevant physics and beam-induced processes in a full reconstruction chain. It is shown that the product of the Higgs production cross-section in $W^+W^-$ fusion and BR($H\rightarrow\gamma\gamma$) can be measured with a relative statistical

**09:21**Arxiv.org CS Cross-Modality Deep Feature Learning for Brain Tumor Segmentation. (arXiv:2201.02356v1 [eess.IV])

Recent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using deep convolutional neural networks. However, different from the RGB image data that are very widespread, the medical image data used in brain tumor segmentation are relatively scarce in terms of the data scale but contain the richer information in terms of the modality property. To this end, this paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. The proposed cross-modality deep feature learning framework consists of two learning processes: the cross-modality feature transition (CMFT) process and the cross-modality feature fusion (CMFF) process, which aims at learning rich feature representations by transiting knowledge

**09:21**Arxiv.org CS Multi-modal data fusion of Voice and EMG data for Robotic Control. (arXiv:2201.02237v1 [cs.RO])

Wearable electronic equipment is constantly evolving and is increasing the integration of humans with technology. Available in various forms, these flexible and bendable devices sense and can measure the physiological and muscular changes in the human body and may use those signals to machine control. The MYO gesture band, one such device, captures Electromyography data (EMG) using myoelectric signals and translates them to be used as input signals through some predefined gestures. Use of this device in a multi-modal environment will not only increase the possible types of work that can be accomplished with the help of such device, but it will also help in improving the accuracy of the tasks performed. This paper addresses the fusion of input modalities such as speech and myoelectric signals captured through a microphone and MYO band, respectively, to control a robotic arm. Experimental results obtained as well as their accuracies for performance analysis are also presented.

**09:44**Arxiv.org Physics An algorithm for coalescence of classical objects and formation of planetary systems. (arXiv:2201.02195v1 [astro-ph.EP])

Isaac Newton formulated the central difference algorithm (Eur. Phys. J. Plus (2020) 135:267) when he derived his second law. The algorithm is under various names ("Verlet, leap-frog,...") the most used algorithm in simulations of complex systems in Physics and Chemistry, and it is also applied in Astrophysics. His discrete dynamics has the same qualities as his exact analytic dynamics for contineus space and time with time reversibility, symplecticity and conservation of momentum, angular momentum and energy. Here the algorithm is extended to include the fusion of objects at collisions. The extended algorithm is used to obtain the self-assembly of celestial objects at the emergence of planetary systems. The emergence of twelve planetary systems is obtained. The systems are stable over very long times, even when two "planets" collide or if a planet is engulfed by its sun.

**09:32**Arxiv.org Statistics Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation. (arXiv:2201.02172v1 [stat.AP])

Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However, TRISO failure probabilities are small and the associated computational models are expensive. We used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models. With multifidelity modeling, we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) models. For the 1D TRISO models, we considered three multifidelity modeling strategies: only Kriging, Kriging LF prediction plus Kriging correction, and deep neural network (DNN) LF prediction plus Kriging correction. While the results across these multifidelity modeling strategies compared satisfactorily, strategies employing information fusion from two LF models consistently called the HF model least often. Next, for

**21:56**LiveScience.com China's $1 trillion 'artificial sun' fusion reactor just got five times hotter than the sun

The experimental fusion reactor sustained the temperatures for a record-breaking 17 minutes.

**16:23**Phys.org Seeing the plasma edge of fusion experiments in new ways with artificial intelligence

To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

**05:55**Arxiv.org CS A Versatile SPH Modeling Framework for Coupled Microfluid-Powder Dynamics in Additive Manufacturing: Binder Jetting, Material Jetting, Directed Energy Deposition and Powder Bed Fusion. (arXiv:2201.01677v1 [cs.CE])

The present work proposes a versatile computational modeling framework for simulating coupled microfluid-powder dynamics problems involving thermo-capillary flow and reversible phase transitions. A liquid and a gas phase are interacting with a solid phase that is assumed to consist of a substrate and several arbitrarily-shaped mobile rigid bodies while simultaneously considering surface tension and wetting effects. All phases are spatially discretized using smoothed particle hydrodynamics because its Lagrangian nature is beneficial in the context of dynamically changing interface topologies. The proposed modeling framework is especially suitable for meso- and microscale modeling of complex physical phenomena in additive manufacturing processes such as binder jetting, material jetting, directed energy deposition, and powder bed fusion. To this end, the generality and robustness of the computational modeling framework is demonstrated by several application-motivated examples in three

**05:55**Arxiv.org CS Monitoring Energy Trends through Automatic Information Extraction. (arXiv:2201.01559v1 [cs.CL])

Energy research is of crucial public importance but the use of computer science technologies like automatic text processing and data management for the energy domain is still rare. Employing these technologies in the energy domain will be a significant contribution to the interdisciplinary topic of ``energy informatics", just like the related progress within the interdisciplinary area of ``bioinformatics". In this paper, we present the architecture of a Web-based semantic system called EneMonIE (Energy Monitoring through Information Extraction) for monitoring up-to-date energy trends through the use of automatic, continuous, and guided information extraction from diverse types of media available on the Web. The types of media handled by the system will include online news articles, social media texts, online news videos, and open-access scholarly papers and technical reports as well as various numeric energy data made publicly available by energy organizations. The system will utilize

**05:55**Arxiv.org CS Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms. (arXiv:2201.01446v1 [cs.DC])

High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve $1-2$ nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding

**17:36**Phys.org Chinese tokamak facility achieves 120-million-degree C for 1,056 seconds

Researchers working at China's tokamak facility have announced that the team was able to hold 120-million-degree Celsius plasma for 1,056 seconds. In their announcement to the press, they noted that their achievement was a new record for holding superheated plasma.

**15:49**WhatReallyHappened.com ‘Artificial Sun’ hits record temperature

A clean energy experiment known as the ‘Artificial Sun’ reached a temperature of 70,000,000C, and held it for more than 17 minutes, during trials in China. The program aims to mimic natural reactions occurring within stars. On Thursday, Chinese scientists set a new record, as their Experimental Advanced Superconducting Tokamak (EAST), known as the ‘Artificial Sun’, reached and sustained a temperature five times hotter than the Sun, state-run Xinhua News Agency reported. The high-temperature plasma operation sustained temperatures of 70,000,000C for 1,056 seconds, more than 17 minutes, Xinhua claimed. By comparison, our own Sun is believed to be around 15,000,000C at its core.

**11:17**Arxiv.org CS Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space. (arXiv:2105.07800v2 [cs.CV] UPDATED)

Visual place recognition is one of the essential and challenging problems in the fields of robotics. In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments. We achieve this by first designing a novel deep learning architecture to generate the static semantic segmentation and recover the static image directly from the corresponding dynamic image. We then innovatively leverage the spatial-pyramid-matching model to encode the static semantic segmentation into feature vectors. In parallel, the static image is encoded using the popular Bag-of-words model. On the basis of the above multi-modal features, we finally measure the similarity between the query image and target landmark by the joint similarity of their semantic and visual codes. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach for place recognition in

**09:40**Arxiv.org Physics On Boundary Conditions for Lattice Kinetic Schemes for Magnetohydrodynamics Bounded by Walls with Finite Electrical Conductivities. (arXiv:2112.13930v1 [physics.comp-ph])

Magnetohydrodynamic (MHD) flow of liquid metals through conduits play an important role in the proposed systems for harnessing fusion energy, and various other engineering and scientific problems. The interplay between the magnetic fields and the fluid motion gives rise to complex flow physics, which depend on the electrical conductivity of the bounding walls. An effective approach to represent the latter is via the Shercliff boundary condition for thin conducting walls relating the induced magnetic field and its wall normal gradient at the boundary via a parameter referred to as the wall conductance ratio (Shercliff, JA, J. Fluid Mech. 1, 644 (1956)). Within the framework of the highly parallelizable lattice Boltzmann (LB) method, a lattice kinetic scheme for MHD involving a vector distribution function for the magnetic fields was proposed by Dellar (Dellar, PJ, J. Comp. Phys. 179, 95 (2002)). However, the prior LB algorithms only accounted for limiting special cases involving

**09:00**Arxiv.org CS An Empirical Study of Adder Neural Networks for Object Detection. (arXiv:2112.13608v1 [cs.CV])

Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations, which are more energy efficient than traditional convolutional neural networks built with multiplications. Compared with classification, there is a strong demand on reducing the energy consumption of modern object detectors via AdderNets for real-world applications such as autonomous driving and face detection. In this paper, we present an empirical study of AdderNets for object detection. We first reveal that the batch normalization statistics in the pre-trained adder backbone should not be frozen, since the relatively large feature variance of AdderNets. Moreover, we insert more shortcut connections in the neck part and design a new feature fusion architecture for avoiding the sparse features of adder layers. We present extensive ablation studies to explore several design choices of adder detectors. Comparisons with state-of-the-arts are conducted on COCO and

**07:53**Arxiv.org Physics High-Uniformity Calculation Method of Four-Coil Configuration in Large-Caliber Magnetic Field Immunity Testing System. (arXiv:2112.13670v1 [physics.ins-det])

Power electronic equipment regulated by the International Thermonuclear Experimental Reactor (ITER) organization must pass the relevant steady-state magnetic field immunity test. The main body of magnetic field immunity test is magnetic field generator coil. Through mathematical derivation in this paper, the magnetic field calculation formulas of four-coil configuration under ideal and actual models are obtained. The traditional method of magnetic field performance calculation is compared with the general formula method under the ideal model. A global parameter optimization method based on Lagrange Multiplier by KKT conditions is proposed to obtain the coil parameters of high-uniformity magnetic field. The magnetic field distribution in the uniform zone is revealed by the finite element method. The model analysis is proved to be correct and effective by experimental results. The research of this paper provides a practical scheme for the coil design with high magnetic field and

**07:53**Arxiv.org Physics The Preliminary design of DC Magnet Power Supply System for ITER Static Magnetic Field Test facility. (arXiv:2112.13669v1 [physics.ins-det])

ITER (International Thermonuclear Experimental Reactor) static magnetic field (SMF) test facility requires a DC power supply with low voltage, high current, and high stability. Due to the limitation ofswitching loss, there is a contradiction between the output current capability and the output ripple. Large output current usually leads to low switching frequency, and low switching frequency will generate a large number of harmonics. To solve the problems, a topology based on the interleaving parallel buck converter is used and tested in this paper. Moreover, the topology is realized with only a small number of switching metal-oxide-semiconductor field effect transistors (MOSFETs). This article introduces the system design scheme and control method in detail. The analysis of harmonic and simulation are carried out. The validity of proposed scheme and control strategy were confirmed by experiments, the power supply system can supply large current of 15kA and has ability of low ripple.

**18:07**SingularityHub.Com Chasing Energy’s Holy Grail: Was 2021 Fusion Power’s Breakthrough Year?

Few technologies encapsulate the techno-utopian dream as much as fusion power, but its promise of limitless renewable energy has always remained tantalizingly out of reach. A flurry of developments in the last six months suggest that might be starting to change. The old joke is that fusion power is 30 years away, and always will […]

**03:37**TechInvestorNews.com New Era Begins: Construction Starts on 47-Acre Fusion Reactor Funded by Google and Bill Gates (Slashdot)

SlashdotNew Era Begins: Construction Starts on 47-Acre Fusion Reactor Funded by Google and Bill Gates - Heating plasma fuel to over 100 million degrees Celsius to create inexpensive and unlimited zero-emissions electricity has been compared to everything from a holy grail to fools gold... writes the Boston Globe, or an expensive delusion diverting scarce money and brainpower from the urgent needs of rapidly addressing climate change. ...

**11:29**Arxiv.org Physics Subcritical transition to turbulence in quasi-two-dimensional shear flows. (arXiv:2112.12190v1 [physics.flu-dyn])

The transition to turbulence in ducts, pipes or other conduits is a crucial phenomenon. It determines the energy consumption and heat or mass exchange in countless processes: whether cooling circuits of heat exchangers, pipelines or chemical reactors to cite but a few. The transition occurs at relatively low flow rates as a response to perturbations exceeding a critical amplitude (such transitions are subcritical) through an intrinsically three-dimensional (3D) mechanism. However, fluid motion can be restricted to two dimensions, if it is stratified, subject to rapid rotation or intense magnetic fields, for example in rotating machines or in the liquid metal cooling circuits of nuclear fusion reactors. Subcritical turbulence has yet to be observed in 2D or quasi-2D flows, let alone a transition to it. Here we use stability analysis and direct numerical simulations on the example of a duct flow driven by the motion of its lateral walls to provide the first evidence of turbulence in

**08:36**Arxiv.org CS Decompose the Sounds and Pixels, Recompose the Events. (arXiv:2112.11547v1 [cs.CV])

In this paper, we propose a framework centering around a novel architecture called the Event Decomposition Recomposition Network (EDRNet) to tackle the Audio-Visual Event (AVE) localization problem in the supervised and weakly supervised settings. AVEs in the real world exhibit common unravelling patterns (termed as Event Progress Checkpoints (EPC)), which humans can perceive through the cooperation of their auditory and visual senses. Unlike earlier methods which attempt to recognize entire event sequences, the EDRNet models EPCs and inter-EPC relationships using stacked temporal convolutions. Based on the postulation that EPC representations are theoretically consistent for an event category, we introduce the State Machine Based Video Fusion, a novel augmentation technique that blends source videos using different EPC template sequences. Additionally, we design a new loss function called the Land-Shore-Sea loss to compactify continuous foreground and background representations.

**16:39**RT.com China fires up its ‘artificial sun’

A new round of nuclear fusion experiments for an advanced superconducting tokamak (EAST), or “Chinese artificial sun,” kicked off at the Hefei Institutes of Physical Science this month, Xinhua News Agency reported on Tuesday. Read Full Article at RT.com

**11:26**Arxiv.org CS Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning. (arXiv:2112.11212v1 [cs.LG])

Variation in the local thermal history during the laser powder bed fusion (LPBF) process in additive manufacturing (AM) can cause microporosity defects. in-situ sensing has been proposed to monitor the AM process to minimize defects, but the success requires establishing a quantitative relationship between the sensing data and the porosity, which is especially challenging for a large number of variables and computationally costly. In this work, we develop machine learning (ML) models that can use in-situ thermographic data to predict the microporosity of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold (/tau) and the maximum radiance (T_{max}). These features are computed, stored for each voxel in the built material, are used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification

**19:24**ScienceDaily.com Speeding the development of fusion energy

Profile of path-setting method to simulate the crazy-quilt movement of free electrons during experimental efforts to harness on Earth the fusion power that drives the sun and stars. The method cracks a complex equation that can enable improved control of the random and fast-moving moving electrons in the fuel for fusion energy.

**05:31**Arxiv.org Physics How Zonal Flow Affects Trapped Electron Driven Turbulence in Tokamaks. (arXiv:2112.10391v1 [physics.plasm-ph])

The role of self-generated zonal flows in the collisionless trapped-electron mode (CTEM) turbulence is a long-standing open issue in tokamak plasmas. Here we show, for the first time, that the zonal flow excitation in the CTEM turbulence is formally isomorphic to that in the ion temperature gradient turbulence. Trapped electrons contribute implicitly only via linear dynamics. Theoretical analyses further suggest that, for short wavelength CTEMs, the zonal flow excitation is weak and, more importantly, not an effective saturation mechanism. Corresponding controlling parameters are also identified theoretically. These findings not only offer a plausible explanation for previous seemingly contradictory simulation results, but can also facilitate controlling the CTEM instability and transport with experimentally accessible parameters.

**18:24**Phys.org Science fiction revisited: Ramjet propulsion

In science fiction stories about contact with extraterrestrial civilisations, there is a problem: What kind of propulsion system could make it possible to bridge the enormous distances between the stars? It cannot be done with ordinary rockets like those used to travel to the moon or Mars. Many more or less speculative ideas about this have been put forward—one of them is the "Bussard collector" or "Ramjet propulsion". It involves capturing protons in interstellar space and then using them for a nuclear fusion reactor.

**08:06**Arxiv.org Physics Design considerations of the European DEMO's IR-interferometer/polarimeter based on TRAVIS simulations. (arXiv:2112.09363v1 [physics.ins-det])

Interferometry is the primary density control diagnostic for large-scale fusion devices, including ITER and DEMO. In this paper we present a ray tracing simulation based on TRAVIS accounting for relativistic effects. The study shows that measurements will over-estimate the plasma density by as much as 20 degree. In addition, we present a measurement geometry, which will enable vertical position control during the plasma's ramp-up phase when gap-reflectometers and neutron cameras are still blind.

**20:58**Phys.org Unraveling a puzzle to speed the development of fusion energy

Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory have developed an effective computational method to simulate the crazy-quilt movement of free electrons during experimental efforts to harness on Earth the fusion power that drives the sun and stars. The method cracks a complex equation that can enable improved control of the random and fast-moving moving electrons in the fuel for fusion energy.

**09:13**Arxiv.org Physics Comprehensive study of nuclear reactions in muon-catalyzed fusion: I. dt$\mu$ system. (arXiv:2112.08399v1 [nucl-th])

Muon catalyzed fusion ($\mu$CF) has recently regained considerable research interest owing to several new developments and applications. In this regard, we performed a comprehensive study on the most important fusion reaction, $(dt\mu)_{J=v=0} \to \alpha + n + \mu + 17.6$ MeV or $(\alpha \mu)_{nl} + n + 17.6$ MeV. For the first time, the coupled-channel Schr\"{o}dinger equation has been solved for the reaction, satisfying the boundary condition for the muonic molecule $(dt\mu)_{J=v=0}$ as the initial state and the outgoing wave in the $\alpha n \mu$ channel. We employ the $dt\mu$- and $\alpha n \mu$-channel coupled three-body model. All the nuclear interactions, the $d$-$t$ and $\alpha$-$n$ potentials, and the $d t$-$\alpha n$ channel-coupling nonlocal tensor potential are chosen to reproduce the observed low-energy astrophysical $S$-factor of the reaction $d+t\to \alpha+n + 17.6 \,{\rm MeV}$, as well as the total cross section of the $\alpha+n$ reaction. The resultant $dt\mu$ fusion

**16:41**Phys.org Toward fusion energy, team models plasma turbulence on the nation's fastest supercomputer

A team modeled plasma turbulence on the nation's fastest supercomputer to better understand plasma behavior

**05:52**Arxiv.org CS FEAR: Fast, Efficient, Accurate and Robust Visual Tracker. (arXiv:2112.07957v1 [cs.CV])

We present FEAR, a novel, fast, efficient, accurate, and robust Siamese visual tracker. We introduce an architecture block for object model adaption, called dual-template representation, and a pixel-wise fusion block to achieve extra flexibility and efficiency of the model. The dual-template module incorporates temporal information with only a single learnable parameter, while the pixel-wise fusion block encodes more discriminative features with fewer parameters compared to standard correlation modules. By plugging-in sophisticated backbones with the novel modules, FEAR-M and FEAR-L trackers surpass most Siamesetrackers on several academic benchmarks in both accuracy and efficiencies. Employed with the lightweight backbone, the optimized version FEAR-XS offers more than 10 times faster tracking than current Siamese trackers while maintaining near state-of-the-art results. FEAR-XS tracker is 2.4x smaller and 4.3x faster than LightTrack [62] with superior accuracy. In addition, we expand

**06:52**Arxiv.org CS A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies. (arXiv:2112.05593v1 [cs.NI])

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research

**06:52**Arxiv.org CS Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion. (arXiv:2112.05254v1 [cs.LG])

Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extreme events. Although machine learning approaches have shown promising results in long-range climate forecasting, the associated model uncertainties may reduce their reliability. To address this issue, we propose a late fusion approach that systematically combines the predictions from multiple models to reduce the expected errors of the fused results. We also propose a network architecture with the novel denormalization layer to gain the benefits of data normalization without actually normalizing the data. The experimental results on long-range 2m temperature forecasting show that the framework outperforms the 30-year climate normals, and the accuracy can be improved by increasing the number of models.

**06:52**Arxiv.org CS An Experimental Design Perspective on Model-Based Reinforcement Learning. (arXiv:2112.05244v1 [cs.LG])

In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm

**05:55**Arxiv.org Statistics An Experimental Design Perspective on Model-Based Reinforcement Learning. (arXiv:2112.05244v1 [cs.LG])

In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm

**05:55**Arxiv.org Physics Complete 3D MHD simulations of the current quench phase of ITER mitigated disruptions. (arXiv:2112.05600v1 [physics.plasm-ph])

Complete 3D simulations of the current quench phase of ITER disruptions are key to predict asymmetric forces acting into the ITER wall. We present for the first time such simulations for ITER mitigated disruptions at realistic Lundquist numbers. For these strongly mitigated disruptions, we find that the edge safety factor remains above 2 and the maximal integral horizontal forces remain below 1 MN. The maximal integral vertical force is found to be 13 MN and arises in a time scale given by the resistive wall time as expected from theoretical considerations. In this respect, the vertical force arises after the plasma current has completely decayed, showing the importance of continuing the simulations also in the absence of plasma current. We conclude that the horizontal wall force rotation is not a concern for these strongly mitigated disruptions in ITER, since when the wall forces form, there are no remaining sources of rotation.

**05:44**Arxiv.org Math An Experimental Design Perspective on Model-Based Reinforcement Learning. (arXiv:2112.05244v1 [cs.LG])

In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm

**16:38**TheStar.com Thundercat enters the spotlight: How the behind-the-scenes jazz virtuoso ascended to pop stardom

In the somewhat obscure world of jazz-fusion music, Thundercat is a towering figure. The L.A.-based songwriter and bass virtuoso — known for his dazzling solos and incredibly complex chord arrangements — has worked alongside saxophonist Kamasi Washington and experimental producer Flying Lotus to reinvigorate a genre often associated with pretentious audiophiles and weird uncles. But behind the scenes — as a session musician for artists like Snoop Dogg and Erykah Badu, and one of the creative architects behind Kendrick Lamar’s seminal album “To Pimp a Butterfly” — Thundercat has also left a significant mark on contemporary hip hop and R&B. Today, as one of the most in-demand collaborators across multiple genres, Thundercat is poised to enter the mainstream as a bona fide rock star. Since the release of his 2020 album “It Is What It Is,” which took home a Grammy for Best Progressive R&B Album, he has worked with

**08:00**Arxiv.org CS Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection. (arXiv:2112.05053v1 [cs.CV])

Accurate and efficient pedestrian detection is crucial for the intelligent transportation system regarding pedestrian safety and mobility, e.g., Advanced Driver Assistance Systems, and smart pedestrian crosswalk systems. Among all pedestrian detection methods, vision-based detection method is demonstrated to be the most effective in previous studies. However, the existing vision-based pedestrian detection algorithms still have two limitations that restrict their implementations, those being real-time performance as well as the resistance to the impacts of environmental factors, e.g., low illumination conditions. To address these issues, this study proposes a lightweight Illumination and Temperature-aware Multispectral Network (IT-MN) for accurate and efficient pedestrian detection. The proposed IT-MN is an efficient one-stage detector. For accommodating the impacts of environmental factors and enhancing the sensing accuracy, thermal image data is fused by the proposed IT-MN with visual

**07:03**Arxiv.org Physics A new type of stellarator divertor: the hybrid stellarator divertor. (arXiv:2112.05086v1 [physics.plasm-ph])

A new type of stellarator divertor is found. It has features of both a nonresonant divertor (A. Punjabi and A. H. Boozer, Phys. Plasmas 27, 012503 (2020)) as well as a resonant divertor. It has the outermost confining surface with sharp edges and large islands outside the outermost surface. For this reason, we have called it hybrid divertor. This divertor can be configured by adjusting the currents in external coils which produce nonresonant perturbations. We have simulated this divertor using the method developed in (A. H. Boozer and A. Punjabi, Phys. Plasmas 25, 092520 (2018)). The simulation shows that the footprints have fixed locations on the wall and are stellarator symmetric. The magnetic field lines leave and enter the outermost surface through three magnetic turnstiles. The probability exponents of the three turnstiles are 2.1, 2.25, and 4.3. The hybrid divertor confines larger plasma volume, has higher average shear, larger footprints, lower average density of strike points,

**07:03**Arxiv.org Physics Optimal shape of stellarators for magnetic confinement fusion. (arXiv:2112.05049v1 [math.OC])

We are interested in the design of stellarators, devices for the production of controlled nuclear fusion reactions alternative to tokamaks. The confinement of the plasma is entirely achieved by a helical magnetic field created by the complex arrangement of coils fed by high currents around a toroidal domain. Such coils describe a surface called "coil winding surface" (CWS). In this paper, we model the design of the CWS as a shape optimization problem, so that the cost functional reflects both optimal plasma confinement properties, through a least square discrepancy, and also manufacturability, thanks to geometrical terms involving the lateral surface or the curvature of the CWS. We completely analyze the resulting problem: on the one hand, we establish the existence of an optimal shape, prove the shape differentiability of the criterion, and provide the expression of the differential in a workable form. On the other hand, we propose a numerical method and perform simulations of optimal

**07:03**Arxiv.org Physics A new technique for tokamak edge density measurement based on microwave interferometer. (arXiv:2112.04964v1 [physics.plasm-ph])

Novel approach for density measurements at the edge of a hot plasma device is presented - Microwave Interferometer in the Limiter Shadow (MILS). The diagnostic technique is based on measuring the change in phase and power of a microwave beam passing tangentially through the edge plasma. The wave propagation involves varying combinations of refraction, phase change and further interference of the beam fractions. A 3D model is constructed as a synthetic diagnostic for MILS and allows exploring this broad range of wave propagation regimes. The diagnostic parameters, such as its dimensions, frequency and configuration of the emitter and receiver antennas, should be balanced to meet the target range and location of measurements. It can be therefore adjusted for various conditions and here the diagnostic concept is evaluated on a chosen example, which was taken as suitable to cover densities of ~10^15-10^19 m^-3 on the edge of the ASDEX Upgrade tokamak. Based on a density profile with fixed

**07:03**Arxiv.org Physics Comparative studies of hydrogen dissolution and release behavior in zirconate oxides by TDS and TMAP4 analysis. (arXiv:2112.04649v1 [physics.app-ph])

Proton-conducting oxides are potential materials for electrochemical devices such as fuel cells, hydrogen pumps, hydrogen sensors, and the tritium purification and recovery system in nuclear fusion reactors. The hydrogen concentration in oxide materials is important, but its precise measurement is difficult. In this study, thermal desorption spectroscopy (TDS) was used to investigate hydrogen dissolution and release behavior in proton-conducting oxides, yttrium (Y), and cobalt (Co) doped barium-zirconates in the temperature range of 673-1273 K using deuterium (D2) and heavy water (D2O). Specimens were prepared with conventional powder metallurgy: the powder of three zirconates, BaZr0.9Y0.1O3-a (BZY), BaZr0.955Y0.03Co0.015O3-a (BZYC), and CaZr0.9In0.1O2.95 (CZI) was pressed into discs and fired in the air at 1873 K for 20 h. The densities of the sintered BZY, BZYC, and CZI specimens were 98 percent, 99.7 percent, and 99.5 percent of the theoretical densities respectively. XRD, SEM, and

**06:51**Arxiv.org Math Optimal shape of stellarators for magnetic confinement fusion. (arXiv:2112.05049v1 [math.OC])

We are interested in the design of stellarators, devices for the production of controlled nuclear fusion reactions alternative to tokamaks. The confinement of the plasma is entirely achieved by a helical magnetic field created by the complex arrangement of coils fed by high currents around a toroidal domain. Such coils describe a surface called "coil winding surface" (CWS). In this paper, we model the design of the CWS as a shape optimization problem, so that the cost functional reflects both optimal plasma confinement properties, through a least square discrepancy, and also manufacturability, thanks to geometrical terms involving the lateral surface or the curvature of the CWS. We completely analyze the resulting problem: on the one hand, we establish the existence of an optimal shape, prove the shape differentiability of the criterion, and provide the expression of the differential in a workable form. On the other hand, we propose a numerical method and perform simulations of optimal

**20:58**ExtremeTech.com Fusion Experiment Reaches Vital Power Generation Milestone

For the first time, a fusion reaction has generated more power than the fuel absorbed. This puts humanity on the verge of creating usable energy by harnessing the power of the sun. The post Fusion Experiment Reaches Vital Power Generation Milestone appeared first on ExtremeTech.

**05:33**Arxiv.org Physics The GBS code for the self-consistent simulation of plasma turbulence and kinetic neutral dynamics in the tokamak boundary. (arXiv:2112.03573v1 [physics.plasm-ph])

A new version of GBS (Ricci et al. Plasma Phys. Control. Fusion 54, 124047, 2012; Halpern et al. J. Comput. Phys. 315, 388-408, 2016; Paruta et al. Phys. Plasmas 25, 112301, 2018) is described. GBS is a three-dimensional, flux-driven, global, two-fluid turbulence code developed for the self-consistent simulation of plasma turbulence and kinetic neutral dynamics in the tokamak boundary. In the new version presented here, the simulation domain is extended to encompass the whole plasma volume, avoiding an artificial boundary with the core, hence retaining the core-edge-SOL interplay. A new toroidal coordinate system is introduced to increase the code flexibility, allowing for the simulation of arbitrary magnetic configurations (e.g. single-null, double-null and snowflake configurations), which can also be the result of the equilibrium reconstruction of an experimental discharge. The implementation of a new iterative solver for the Poisson and Amp\`ere equations is presented, leading to a

**09:13**Arxiv.org CS Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction. (arXiv:2112.02252v1 [cs.CV])

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge -- it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for both multimodal fusion and multitask learning. At its core, CEN dynamically exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the

**11:59**Arxiv.org CS High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features. (arXiv:2112.01627v1 [eess.IV])

Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics. This technique has been used in numerous fields including materials science, shock physics, inertial confinement fusion, and other national security applications. In many of these applications, however, complications resulting from noise, scatter, complex beam dynamics, etc. prevent the reconstruction of density from being accurate enough to identify the underlying physics with sufficient confidence. As such, density reconstruction from static/dynamic radiography has typically been limited to identifying discontinuous features such as cracks and voids in a number of these applications. In this work, we propose a fundamentally new approach to reconstructing density from a temporal sequence of radiographic images. Using only the robust features identifiable in radiographs, we combine them with the underlying hydrodynamic equations of motion

**11:12**Arxiv.org Physics Mixed Stochastic-Deterministic Time-Dependent Density Functional Theory: Application to Stopping Power of Warm Dense Carbon. (arXiv:2112.01638v1 [physics.plasm-ph])

Warm dense matter (WMD) describes an intermediate phase, between condensed matter and classical plasmas, found in natural and man-made systems. In a laboratory setting, WDM needs to be created dynamically. It is typically laser or pulse-power generated and can be difficult to characterize experimentally. Measuring the energy loss of high energy ions, caused by a WDM target, is both a promising diagnostic and of fundamental importance to inertial confinement fusion research. However, electron coupling, degeneracy, and quantum effects limit the accuracy of easily calculable kinetic models for stopping power, while high temperatures make the traditional tools of condensed matter, e.g. Time-Dependent Density Functional Theory (TD-DFT), often intractable. We have developed a mixed stochastic-deterministic approach to TD-DFT which provides more efficient computation while maintaining the required precision for model discrimination. Recently, this approach showed significant improvement

**11:00**Arxiv.org Math Lattice models from CFT on surfaces with holes I: Torus partition function via two lattice cells. (arXiv:2112.01563v1 [cond-mat.stat-mech])

We construct a one-parameter family of lattice models starting from a two-dimensional rational conformal field theory on a torus with a regular lattice of holes, each of which is equipped with a conformal boundary condition. The lattice model is obtained by cutting the surface into triangles with clipped-off edges using open channel factorisation. The parameter is given by the hole radius. At finite radius, high energy states are suppressed and the model is effectively finite. In the zero-radius limit, it recovers the CFT amplitude exactly. In the touching hole limit, one obtains a topological field theory. If one chooses a special conformal boundary condition which we call "cloaking boundary condition", then for each value of the radius the fusion category of topological line defects of the CFT is contained in the lattice model. The fact that the full topological symmetry of the initial CFT is realised exactly is a key feature of our lattice models. We provide an explicit

**05:40**RFI.fr Governments help arms firms avoid Covid slump: report

Governments around the world have continued to buy arms during the pandemic and some also passed measures to help their big weapons firms, according to the Stockholm International Peace Research Institute (SIPRI). Overall, the 100 top weapons firms saw their profits rise by 1.3 percent on 2019 to a record $531 billion, despite the global economy contracting by more than three percent. "Military manufacturers were largely shielded by sustained government demand for military goods and services," said SIPRI researcher Alexandra Marksteiner in the institute's annual assessment of arms companies. "In much of the world, military spending grew and some governments even accelerated payments to the arms industry in order to mitigate the impact of the Covid-19 crisis." The top five arms firms were all from the United States, Lockheed-Martin -- which counts F-35 fighter jets and various types of missiles among its bestsellers -- consolidating its first place with sales of $58.2 billion.

**06:06**Arxiv.org CS Towards generating citation sentences for multiple references with intent control. (arXiv:2112.01332v1 [cs.CL])

Machine-generated citation sentences can aid automated scientific literature review and assist article writing. Current methods in generating citation text were limited to single citation generation using the citing document and a cited document as input. However, in real-world situations, writers often summarize several studies in one sentence or discuss relevant information across the entire paragraph. In addition, multiple citation intents have been previously identified, implying that writers may need control over the intents of generated sentences to cover different scenarios. Therefore, this work focuses on generating multiple citations and releasing a newly collected dataset named CiteMI to drive the future research. We first build a novel generation model with the Fusion-in-Decoder approach to cope with multiple long inputs. Second, we incorporate the predicted citation intents into training for intent control. The experiments demonstrate that the proposed approaches provide

**06:06**Arxiv.org CS Multi-task fusion for improving mammography screening data classification. (arXiv:2112.01320v1 [eess.IV])

Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive

**00:23**Phys.org Shaping up nicely: Adjusting the plasma edge can improve the performance of a star on Earth

While trying out a new device that injects powder to clean up the walls of the world's largest stellarator, a twisty fusion device known as Wendelstein 7-X (W7-X) in Greifswald, Germany, scientists were pleased to find that the bits of atoms confined by magnetic fields within the device got temporarily hotter after each injection. Researchers led by scientists at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) in collaboration with the Max Planck Institute for Plasma Physics (IPP) in Germany found that pulsed injections of boron carbide, an ingredient in sandpaper, increased the density and temperature of the ultrahot atom fragments, or plasma, leading to better fusion performance.

**08:01**Arxiv.org Physics Analysis of the nonlinear dynamics of a chirping-frequency Alfv\'en mode in a Tokamak equilibrium. (arXiv:2112.00474v1 [physics.plasm-ph])

Chirping Alfv\'en modes are considered as potentially harmful for the confinement of energetic particles in burning Tokamak plasmas. In this paper, the nonlinear evolution of a single-toroidal-number chirping mode is analysed by numerical particle simulation. This analysis can be simplified if the different resonant phase-space structures can be investigated as isolated ones. In our simulations, we adopt as constants of motion, the magnetic momentum, and the initial particle coordinates. The analysis is focused on the dynamics of two of them: namely, those yielding the largest drive during, respectively, the linear phase and the nonlinear one. It is shown that, for each resonant structure, a density-flattening region is formed around the respective resonance radius, with radial width that increases as the mode amplitude grows. It is delimited by two large negative density gradients, drifting inward and outward. If the mode frequency were constant, this density flattening would be

**08:01**Arxiv.org Physics Design of a High-Resolution Rayleigh-Taylor Experiment with the Crystal Backlighter Imager on the National Ignition Facility. (arXiv:2112.00085v1 [physics.plasm-ph])

The Rayleigh-Taylor (RT) instability affects a vast range of High Energy Density (HED) length scales, spanning from supernova explosions (10$^{13}$ m) to inertial confinement fusion (10$^{-6}$ m). In inertial confinement fusion, the RT instability is known to induce mixing or turbulent transition, which in turn cools the hot spot and hinders ignition. The fine-scale features of the RT instability, which are difficult to image in HED physics, may help determine if the system is mixing or is transitioning to turbulence. Earlier diagnostics lacked the spatial and temporal resolution necessary to diagnose the dynamics that occur along the RT structure. A recently developed diagnostic, the Crystal Backlighter Imager (CBI), \cite{Hall:2019, DoZonePlate} can now produce an x-ray radiograph capable of resolving the fine-scale features expected in these RT unstable systems. This paper describes an experimental design that adapts a well-characterized National Ignition Facility (NIF) platform to

**11:47**Arxiv.org CS HOTTBOX: Higher Order Tensor ToolBOX. (arXiv:2111.15662v1 [cs.SE])

HOTTBOX is a Python library for exploratory analysis and visualisation of multi-dimensional arrays of data, also known as tensors. The library includes methods ranging from standard multi-way operations and data manipulation through to multi-linear algebra based tensor decompositions. HOTTBOX also comprises sophisticated algorithms for generalised multi-linear classification and data fusion, such as Support Tensor Machine (STM) and Tensor Ensemble Learning (TEL). For user convenience, HOTTBOX offers a unifying API which establishes a self-sufficient ecosystem for various forms of efficient representation of multi-way data and the corresponding decomposition and association algorithms. Particular emphasis is placed on scalability and interactive visualisation, to support multidisciplinary data analysis communities working on big data and tensors. HOTTBOX also provides means for integration with other popular data science libraries for visualisation and data manipulation. The source

**10:38**Arxiv.org Physics Stellarator optimization for nested magnetic surfaces at finite $\beta$ and toroidal current. (arXiv:2111.15564v1 [physics.plasm-ph])

Good magnetic surfaces, as opposed to magnetic islands and chaotic field lines, are generally desirable for stellarators. In previous work, M. Landreman et al. [Phys. of Plasmas 28, 092505 (2021)] showed that equilibria computed by the Stepped-Pressure Equilibrium Code (SPEC) [S. P. Hudson et al., Phys. Plasmas 19, 112502 (2012)] could be optimized for good magnetic surfaces in vacuum. In this paper, we build upon their work to show the first finite-$\beta$, fixed- and free-boundary optimization of SPEC equilibria for good magnetic surfaces. The objective function is constructed with the Greene's residue of selected rational surfaces and the optimization is driven by the SIMSOPT framework [M. Landreman et al., J. Open Source Software 6, 3525 (2021)]. We show that the size of magnetic islands and the consequent regions occupied by chaotic field lines can be minimized in a classical stellarator geometry by optimizing either the injected toroidal current profile, the shape of a perfectly

**10:38**Arxiv.org Physics Universal turbulence scaling law -8/3 at fusion implosion. (arXiv:2111.15360v1 [physics.flu-dyn])

It is shown that the exact explicit solution of the one-dimensional Euler equations for a compressible medium leads to a turbulence energy spectrum with an exponent of -8/3 at fusion implosion. A possible mechanism for the occurrence of anisotropy of this turbulence associated with hydrodynamic instability intensifying the rotation of the medium behind the shock wave front is considered.

**10:38**Arxiv.org Physics A New Paradigm for Fast and Repetitive Chirping of Alfv\'en Eigenmodes. (arXiv:2111.15021v1 [physics.plasm-ph])

A novel 2D nonlinear dynamical paradigm is constructed to interpret the fast and repetitive frequency chirping and amplitude oscillation of Alfv\'en eigenmodes excited by energetic particles in fusion plasmas as observed in global gyrokinetic simulations. In this non-perturbative paradigm of the collisionless phase-space dynamics, wave-particle resonant interactions cause the phase-space structure to continuously twist and fold, leading to the repetitive excitation and decay of the Alfv\'en eigenmode. The radial (perpendicular to the dominant wave-particle interaction) dependence of the mode amplitude and toroidal precessional drifts of the energetic particles leads to the 2D dynamics of wave-particle interactions, which is found to be responsible for the repetitive process of formation and destruction of the mode structure.

**21:17**Phys.org Intense correlationship proved between irradiation damage and performances of tritium breeding materials

Severe irradiation environment would bring damage to the microstructure of the materials and affect the stable operation and tritium recovery of fusion reactor. Therefore, the efficient tritium production from tritium breeding materials is the guarantee for the realization of tritium self-sufficiency in fusion reactor.

**15:44**WhatReallyHappened.com The Race For Nuclear Fusion Is Going Private

For the past 100 years, commercial nuclear fusion has existed in a realm far closer to science fiction than to scientific practice. In fact, when English mathematician and astronomer Arthur Eddington hypothesized that our sun and stars generate their own power through a process of merging atoms to create massive amounts of energy, heat, and light just a century ago, he was very nearly dismissed as a quack. But since that time, nuclear fusion has advanced by leaps and bounds, from thought experiments to lab-tested experiments, and in the last few years, to major breakthroughs that hint that commercial fusion could really finally be just around the corner. Nuclear fusion is sought after as the “holy grail of clean energy” because it is a totally clean energy source with the potential to create essentially limitless power with absolutely zero greenhouse gas emissions if the full power of fusion reactions can be harnessed by humans. “Simply put, nuclear fusion is the process by

**10:15**Arxiv.org CS Designing a Trusted Data Brokerage Framework in the Aviation Domain. (arXiv:2111.13271v1 [cs.AI])

In recent years, there is growing interest in the ways the European aviation industry can leverage the multi-source data fusion towards augmented domain intelligence. However, privacy, legal and organisational policies together with technical limitations, hinder data sharing and, thus, its benefits. The current paper presents the ICARUS data policy and assets brokerage framework, which aims to (a) formalise the data attributes and qualities that affect how aviation data assets can be shared and handled subsequently to their acquisition, including licenses, IPR, characterisation of sensitivity and privacy risks, and (b) enable the creation of machine-processable data contracts for the aviation industry. This involves expressing contractual terms pertaining to data trading agreements into a machine-processable language and supporting the diverse interactions among stakeholders in aviation data sharing scenarios through a trusted and robust system based on the Ethereum platform.

**09:03**Arxiv.org Physics Reciprocating probe measurements in the test divertor operation phase of Wendelstein 7-X. (arXiv:2111.13478v1 [physics.plasm-ph])

Reciprocating probes are a classic and widespread tool for the investigation of the edge and Scrape-Off Layer of magnetic fusion plasmas. In the Wendelstein 7-X (W7-X) stellarator, the Multi-Purpose Manipulator serves as a multi-user platform for probe measurements of various kinds. This paper presents a review on reciprocating probe operation during the first operation phase of W7-X with a test divertor (2017-2018). It gives an overview of the diverse zoo of probe heads and presents lessons learned about probe operation in complex magnetic geometries, operation safety, and probe head design. A few examples of probe measurements with a focus on unexpected observations are presented.

**06:44**Arxiv.org CS Two step clustering for data reduction combining DBSCAN and k-means clustering. (arXiv:2111.12559v1 [physics.comp-ph])

A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for the detection of high data density regions and the k-means algorithm for reduction. The proposed algorithm iterates while successively decrementing the DBSCAN search radius, allowing for an adaptive reduction factor based on the effective data density. The algorithm is demonstrated for a physics simulation application, where a surrogate model for fusion reactor plasma turbulence is generated with neural networks. A training dataset for the surrogate model is created with a quasilinear gyrokinetics code for turbulent transport calculations in fusion plasmas. The training set consists of model inputs derived from a repository of experimental measurements, meaning there is a potential risk of over-representing specific regions of this input parameter space.

**06:44**Arxiv.org CS PU-Transformer: Point Cloud Upsampling Transformer. (arXiv:2111.12242v1 [cs.CV])

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines. However, point cloud data is inherently sparse and irregular, causing major difficulties for machine perception. In this work, we focus on the point cloud upsampling task that intends to generate dense high-fidelity point clouds from sparse input data. Specifically, to activate the transformer's strong capability in representing features, we develop a new variant of a multi-head self-attention structure to enhance both point-wise and channel-wise relations of the feature map. In addition, we leverage a positional fusion block to comprehensively capture the local context of point cloud data, providing more position-related information about the scattered points. As the first transformer model introduced for point cloud upsampling, we demonstrate the outstanding performance of our approach by comparing with the state-of-the-art CNN-based methods on different benchmarks quantitatively and

**05:33**Arxiv.org Physics Simulation of non-resonant stellarator divertor. (arXiv:2111.12651v1 [physics.plasm-ph])

An efficient numerical method of studying nonresonant stellarator divertors was introduced in Boozer and Punjabi [Phys. Plasmas 25, 092505 (2018)]. This method is used in this paper to study a different magnetic field model of a nonresonant divertor. The most novel and interesting finding of this study is that diffusive magnetic field lines can be distinguished from lines that exit through the primary and the secondary turnstile, and that below some diffusive velocity, all lines exit through only the primary turnstile. The footprints of each family are stellarator symmetric and have a fixed location on the wall for all velocities. The probability exponent of the primary turnstile is d1 = 9/4 and that of the secondary turnstile is d2 = 3/2. This study also addresses the issues of an inadequate separation of the chamber walls from the outermost confining magnetic surface and a marginal step size of the numerical integrations that could compromise the interpretation of the earlier results

**05:33**Arxiv.org Physics Two step clustering for data reduction combining DBSCAN and k-means clustering. (arXiv:2111.12559v1 [physics.comp-ph])

A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for the detection of high data density regions and the k-means algorithm for reduction. The proposed algorithm iterates while successively decrementing the DBSCAN search radius, allowing for an adaptive reduction factor based on the effective data density. The algorithm is demonstrated for a physics simulation application, where a surrogate model for fusion reactor plasma turbulence is generated with neural networks. A training dataset for the surrogate model is created with a quasilinear gyrokinetics code for turbulent transport calculations in fusion plasmas. The training set consists of model inputs derived from a repository of experimental measurements, meaning there is a potential risk of over-representing specific regions of this input parameter space.

**09:12**Arxiv.org CS Sparse Fusion for Multimodal Transformers. (arXiv:2111.11992v1 [cs.CV])

Multimodal classification is a core task in human-centric machine learning. We observe that information is highly complementary across modalities, thus unimodal information can be drastically sparsified prior to multimodal fusion without loss of accuracy. To this end, we present Sparse Fusion Transformers (SFT), a novel multimodal fusion method for transformers that performs comparably to existing state-of-the-art methods while having greatly reduced memory footprint and computation cost. Key to our idea is a sparse-pooling block that reduces unimodal token sets prior to cross-modality modeling. Evaluations are conducted on multiple multimodal benchmark datasets for a wide range of classification tasks. State-of-the-art performance is obtained on multiple benchmarks under similar experiment conditions, while reporting up to six-fold reduction in computational cost and memory requirements. Extensive ablation studies showcase our benefits of combining sparsification and multimodal

**08:22**Arxiv.org Physics Effects of resonant magnetic perturbations on neutral beam heating in a tokamak. (arXiv:2111.11721v1 [physics.plasm-ph])

Effects of resonant magnetic perturbations (RMPs) on tangential neutral beam heating in the EAST tokamak are studied numerically. RMPs with linear resistive magnetohydrodynamics response are used in the modeling. A variety of representing configurations of RMP coil currents are examined and their effects on the NBI heating efficiency are compared, in order to find a parameter window where deleterious effects of RMPs on NBI heating efficiency are minimized. It is found that the internal redistribution of fast ions by RMPs induces local accumulation of fast ions, resulting in higher local fast ion pressure than the case without RMPs. It is also found that the toroidal phasing of the RMP with respect to the fast ion source has slight effects on the steady-state radial profile of fast ions. The dependence of fast ion loss fraction on the RMP up-down phase difference shows similar behavior as the dependence of the radial width of chaotic magnetic field on the phase difference. A statistical

**11:47**Arxiv.org CS Learning Transport Processes with Machine Intelligence. (arXiv:2109.13096v2 [physics.plasm-ph] UPDATED)

We present a machine learning based approach to address the study of transport processes, ubiquitous in continuous mechanics, with particular attention to those phenomena ruled by complex micro-physics, impractical to theoretical investigation, yet exhibiting emergent behavior describable by a closed mathematical expression. Our machine learning model, built using simple components and following a few well established practices, is capable of learning latent representations of the transport process substantially closer to the ground truth than expected from the nominal error characterising the data, leading to sound generalisation properties. This is demonstrated through an idealized study of the long standing problem of heat flux suppression relevant to fusion and cosmic plasmas. Our analysis shows that the result applies beyond those case specific assumptions and that, in particular, the accuracy of the learned representation is controllable through knowledge of the data quality

**11:47**Arxiv.org CS Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition. (arXiv:2111.11051v1 [cs.CV])

Skeleton-based action recognition is widely used in varied areas, e.g., surveillance and human-machine interaction. Existing models are mainly learned in a supervised manner, thus heavily depending on large-scale labeled data which could be infeasible when labels are prohibitively expensive. In this paper, we propose a novel Contrast-Reconstruction Representation Learning network (CRRL) that simultaneously captures postures and motion dynamics for unsupervised skeleton-based action recognition. It mainly consists of three parts: Sequence Reconstructor, Contrastive Motion Learner, and Information Fuser. The Sequence Reconstructor learns representation from skeleton coordinate sequence via reconstruction, thus the learned representation tends to focus on trivial postural coordinates and be hesitant in motion learning. To enhance the learning of motions, the Contrastive Motion Learner performs contrastive learning between the representations learned from coordinate sequence and additional

**11:47**Arxiv.org CS ARMAS: Active Reconstruction of Missing Audio Segments. (arXiv:2111.10891v1 [eess.AS])

Digital audio signal reconstruction of lost or corrupt segment using deep learning algorithms has been explored intensively in the recent years. Nevertheless, prior traditional methods with linear interpolation, phase coding and tone insertion techniques are still in vogue. However, we found no research work on the reconstruction of audio signals with the fusion of dithering, steganography, and machine learning regressors. Therefore, this paper proposes the combination of steganography, halftoning (dithering), and state-of-the-art shallow (RF- Random Forest and SVR- Support Vector Regression) and deep learning (LSTM- Long Short-Term Memory) methods. The results (including comparison to the SPAIN and Autoregressive methods) are evaluated with four different metrics. The observations from the results show that the proposed solution is effective and can enhance the reconstruction of audio signals performed by the side information (noisy-latent representation) steganography provides. This

**11:47**Arxiv.org CS Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach. (arXiv:2111.10484v1 [cs.AI])

False alerts due to misconfigured/ compromised IDS in ICS networks can lead to severe economic and operational damage. To solve this problem, research has focused on leveraging deep learning techniques that help reduce false alerts. However, a shortcoming is that these works often require or implicitly assume the physical and cyber sensors to be trustworthy. Implicit trust of data is a major problem with using artificial intelligence or machine learning for CPS security, because during critical attack detection time they are more at risk, with greater likelihood and impact, of also being compromised. To address this shortcoming, the problem is reframed on how to make good decisions given uncertainty. Then, the decision is detection, and the uncertainty includes whether the data used for ML-based IDS is compromised. Thus, this work presents an approach for reducing false alerts in CPS power systems by dealing uncertainty without the knowledge of prior distribution of alerts.

**11:08**Arxiv.org Physics The data-driven future of high energy density physics. (arXiv:2111.11310v1 [physics.plasm-ph])

The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems are strongly non-linear and very difficult to understand theoretically or optimize experimentally. Here, we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proven far too non-linear for human researchers. From a fundamental perspective, our understanding can be helped by the way in which machine learning models can rapidly discover complex interactions in large data sets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to ~daily), moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and

**05:05**ScienceDaily.com Scientists create insights into perhaps the most extreme state of matter produced on Earth

Exotic laser-produced high-energy-density (HED) plasmas akin to those found in stars and nuclear explosions could provide insight into events throughout the universe. Physicists have discovered a new way to measure and understand these plasmas, among the most extreme states of matter ever produced on Earth. Improved understanding could provide benefits ranging from fine-tuning the high-density plasmas in inertial confinement fusion experiments to better understanding of processes throughout the universe.

**00:32**Phys.org Scientists create insights into one of the most extreme states of matter produced on Earth

Exotic laser-produced high-energy-density (HED) plasmas akin to those found in stars and nuclear explosions could provide insight into events throughout the universe. Physicists at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have discovered a new way to measure and understand these plasmas, among the most extreme states of matter ever produced on Earth. Improved understanding could provide benefits ranging from fine-tuning the high-density plasmas in inertial confinement fusion experiments to better understanding of processes throughout the universe.

**13:49**Nature.Com The start-ups chasing clean, carbon-free fusion energy

Nature is the international weekly journal of science: a magazine style journal that publishes full-length research papers in all disciplines of science, as well as News and Views, reviews, news, features, commentaries, web focuses and more, covering all branches of science and how science impacts upon all aspects of society and life.