Do two promising structural materials corrode at very high temperatures when in contact with "liquid metal fuel breeders" in fusion reactors? Researchers of Tokyo Institute of Technology (Tokyo Tech), National Institutes for Quantum Science and Technology (QST), and Yokohama National University (YNU) now have the answer. This high-temperature compatibility of reactor structural materials with the liquid breeder—a lining around the reactor core that absorbs and traps the high energy neutrons produced in the plasma inside the reactor—is key to the success of a fusion reactor design.

Rayleigh-Taylor instability (RTI) occurs at the interface of two media when the heavier fluid is accelerated into the lighter fluid and is a prototypical hydrodynamic event present in many physical events. In high energy physics, this manifests itself across a wide range of length scales from nuclear confinement fusion at micron-scale to supernova explosion at terra scales. RTI can also be viewed as a baroclinic instability prevalent in engineering, geophysics, and astrophysics, a pedagogic description of which is given in Sengupta {\it et al., Comput. Fluids,} {\bf 225}, 104995 (2021) with respect to the experimental results of Read, {\it Physica D}, {\bf 12} 45-58 (1984). Here, a recently proposed non-overlapping parallel algorithm is used to solve this three-dimensional canonical problem, having the unique property of not distinguishing between sequential and parallel computing, using 4.19 billion points and a refined time step of $7.69 \times 10^{-8} sec$. The problem achieves the

Nonlinear multiscale gyrokinetic simulations of a Joint European Torus edge pedestal are used to show that electron-temperature-gradient (ETG) turbulence has a rich three-dimensional structure, varying strongly according to the local magnetic-field configuration. In the plane normal to the magnetic field, the steep pedestal electron temperature gradient gives rise to anisotropic turbulence with a radial (normal) wavelength much shorter than in the binormal direction. In the parallel direction, the location and parallel extent of the turbulence are determined by the variation in the magnetic drifts and finite-Larmor-radius (FLR) effects. The magnetic drift and FLR topographies have a perpendicular-wavelength dependence, which permits turbulence intensity maxima near the flux-surface top and bottom at longer binormal scales, but constrains turbulence to the outboard midplane at shorter electron-gyroradius binormal scales. Our simulations show that long-wavelength ETG turbulence does not

To harness the forces that power the sun to produce substantial clean energy on Earth, researchers heat fuel to such a high temperature that atoms melt into electrons and nuclei to form a hot, gaseous soup called plasma. Roughly 20 times the temperature of the sun's core at 200 million degrees Celsius, the plasma can rip through any material on Earth, so it must be confined by magnetic fields—but it can only be controlled for short periods. Researchers have been able to exert this control for decades, without understanding the precise physics of how it works. Now, in a first step to prolonged control, researchers at Japan's National Institute for Fusion Science have discovered that the underlying mechanism mirrors the unlikely biological predator-prey model.

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. To address these challenges, both academia and industry turn to artificial intelligence technology to realize network intelligence. To this end, a considerable number of novel and creative machine learning-based (ML-based) research works have been put forward in recent few years. Nevertheless, there are still enormous challenges faced by the intelligent optimization of data center networks (DCNs), especially in the scenario of online real-time dynamic processing of massive heterogeneous services and traffic data. To best of our knowledge, there is a lack of systematic and original comprehensively investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we comprehensively investigate the application of machine learning to data

Specialized accelerators are increasingly used to meet the power-performance goals of emerging applications such as machine learning, image processing, and graph analysis. Existing accelerator programming methodologies using APIs have several limitations: (1) The application code lacks portability to other platforms and compiler frameworks; (2) the lack of integration of accelerator code in the compiler limits useful optimizations such as instruction selection and operator fusion; and (3) the opacity of the accelerator function semantics limits the ability to check the final code for correctness. The root of these limitations is the lack of a formal software/hardware interface specification for accelerators. In this paper, we use the recently developed Instruction-Level Abstraction (ILA) for accelerators to serve this purpose, similar to how the Instruction Set Architecture (ISA) has been used as the software/hardware interface for processors. We propose a compiler flow termed D2A

Transformers have emerged to be successful in a number of natural language processing and vision tasks, but their potential applications to medical imaging remain largely unexplored due to the unique difficulties of this field. In this study, we present UTNetV2, a simple yet powerful backbone model that combines the strengths of the convolutional neural network and Transformer for enhancing performance and efficiency in medical image segmentation. The critical design of UTNetV2 includes three innovations: (1) We used a hybrid hierarchical architecture by introducing depthwise separable convolution to projection and feed-forward network in the Transformer block, which brings local relationship modeling and desirable properties of CNNs (translation invariance) to Transformer, thus eliminate the requirement of large-scale pre-training. (2) We proposed efficient bidirectional attention (B-MHA) that reduces the quadratic computation complexity of self-attention to linear by introducing an

As a fundamental problem in ubiquitous computing and machine learning, sensor-based human activity recognition (HAR) has drawn extensive attention and made great progress in recent years. HAR aims to recognize human activities based on the availability of rich time-series data collected from multi-modal sensors such as accelerometers and gyroscopes. However, recent deep learning methods are focusing on one view of the data, i.e., the temporal view, while shallow methods tend to utilize the hand-craft features for recognition, e.g., the statistics view. In this paper, to extract a better feature for advancing the performance, we propose a novel method, namely multi-view fusion transformer (MVFT) along with a novel attention mechanism. First, MVFT encodes three views of information, i.e., the temporal, frequent, and statistical views to generate multi-view features. Second, the novel attention mechanism uncovers inner- and cross-view clues to catalyze mutual interactions between three

Autonomous vehicles can improve mobility and road safety; however, these require large deep-learning models with high energy costs.

Lipid bilayer membranes are the fundamental biological barriers that permit life. The bilayer dynamics largely participates in orchestrating cellular workings and is characterized by substantial stability together with extreme plasticity that allows controlled morphological/topological changes. Modeling and understanding the topological change of vesicle-like membrane at the scale of a full cell has proved an elusive aim. We propose and discuss here a continuum model able to encompass the fusion/fission transition of a bilayer membrane at the scale of a Large Unilamellar Vesicle and evaluate the minimal free energy path across the transition, inspired by the idea that fusion/fission-inducing proteins have evolved in Nature towards minimal work expenditure. The results predict the correct height for the energetic barrier and provide the force field that, by acting on the membrane, can induce the transition. They are found in excellent agreement, in terms of

The U.S. fusion community has actively called for an immediate design effort for a cost-effective pilot plant to generate electricity in the 2040s. This effort and related community recommendations are documented in the 2020 report of the Fusion Energy Sciences Advisory Committee entitled "Powering the Future: Fusion & Plasmas."

MAST-U has recently started operating with a Super-X divertor, designed to increase total flux expansion and neutral trapping, both predicted through simple analytic models and SOLPS calculations to reduce the plasma and impurity density detachment thresholds. In this study, utilising the SOLPS-ITER code, we are quantifying the possible gain allowed by the MAST-U Super-X configuration, in terms of access to detachment. We show that a significant reduction of the upstream density detachment threshold (up to a factor 1.6) could be achieved in MAST-U, mainly through an increased total flux expansion, neutral trapping being found very similar between the different configurations. We also show that variations of the strike-point angle are complex to interpret in such a tightly baffled geometry, and that a more "vertical target" does not necessarily imply a lower detachment threshold. As in previous calculations for TCV, we quantify the role of neutral effects through developing and applying

A laser compressing an aluminum crystal provides a clearer view of a material's plastic deformation, potentially leading to the design of stronger nuclear fusion materials and spacecraft shields.

The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spatial consistency represented by continuous disparity or depth changes in the homogeneous region of an image. The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness. Our formulation expands the constraint scopes of sparse LiDAR projection points with the guidance of image information to optimize the cost volume of pixels as much as possible. We applied LGSM to semi-global matching and AD-Census on both simulated and real datasets. When

Gaussian Process Regression (GPR) is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student's t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Mat\'ern kernel for the prior probability, and Student's t-distribution for the likelihood is shown to give

Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous random variable or vector. Although the case of continuous random variables and the problem of pdf fusion frequently arise in multisensor signal processing, statistical inference, and machine learning, a universally accepted method for pdf fusion does not exist. The diversity of approaches, perspectives, and solutions related to pdf fusion motivates a unified presentation of the theory and methodology of the field. We discuss three different approaches to fusing pdfs. In the axiomatic approach, the fusion rule is defined indirectly by a set of properties (axioms). In the optimization approach, it is the result of minimizing an objective function that involves an information-theoretic divergence or a distance measure. In the supra-Bayesian

Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous random variable or vector. Although the case of continuous random variables and the problem of pdf fusion frequently arise in multisensor signal processing, statistical inference, and machine learning, a universally accepted method for pdf fusion does not exist. The diversity of approaches, perspectives, and solutions related to pdf fusion motivates a unified presentation of the theory and methodology of the field. We discuss three different approaches to fusing pdfs. In the axiomatic approach, the fusion rule is defined indirectly by a set of properties (axioms). In the optimization approach, it is the result of minimizing an objective function that involves an information-theoretic divergence or a distance measure. In the supra-Bayesian

Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract

Autonomous vehicles use multiple sensors, large deep-learning models, and powerful hardware platforms to perceive the environment and navigate safely. In many contexts, some sensing modalities negatively impact perception while increasing energy consumption. We propose EcoFusion: an energy-aware sensor fusion approach that uses context to adapt the fusion method and reduce energy consumption without affecting perception performance. EcoFusion performs up to 9.5% better at object detection than existing fusion methods with approximately 60% less energy and 58% lower latency on the industry-standard Nvidia Drive PX2 hardware platform. We also propose several context-identification strategies, implement a joint optimization between energy and performance, and present scenario-specific results.

Gaussian Process Regression (GPR) is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student's t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Mat\'ern kernel for the prior probability, and Student's t-distribution for the likelihood is shown to give

Clean and limitless energy supply can be provided by creating the process powering the sun called nuclear fusion. Recreating the sun on Earth has been proven to be immensely complex and challenging. Ray Chandra investigated the physics behind different ways to protect the surface of the walls of fusion reactors from the extremely hot plasma inside.

Engineers are working to develop a new, economically viable and safe source of low carbon electricity through nuclear fusion.

We report on the enhanced core electron density fluctuation with long radial correlation length when the mean flow collapses in high-collisionality \emph{H}-mode plasmas on DIII-D tokamak. This long-radial-range-correlation (LRRC) fluctuation has a radially elongated, streamer-like mode structure ($k_{r}\rho_{s}=0.1-0.3$ and $k_{\theta}\rho_{s}=1-4$) and spans a broad radial scale in the mid-radius region ($0.35<\rho<0.8$). It also shows intermittent features, long-term memory effect, and the characteristic spectrum of self-organized criticality. The amplitude and the radial correlation length of LRRC turbulence increase substantially when the shearing rate of the mean flow is reduced below the turbulent scattering rate of LRRC turbulence. The enhancement of LRRC turbulence is also associated with an apparent degradation of normalized energy confinement time. These findings constitute the first experimental observation of long-radial-range turbulent transport events in

Hasegawa-Wakatani system, commonly used as a toy model of dissipative drift waves in fusion devices is revisited with considerations of phase and amplitude dynamics of its triadic interactions. It is observed that a single resonant triad can saturate via three way phase locking where the phase differences between dominant modes converge to constant values as individual phases increase in time. This allows the system to have approximately constant amplitude solutions. Non-resonant triads show similar behavior only when one of its legs is a zonal wave number. However when an additional triad, which is a reflection of the original one with respect to the $y$ axis is included, the behavior of the resulting triad pair is shown to be more complex. In particular, it is found that triads involving small radial wave numbers (large scale zonal flows) end up transferring their energy to the subdominant mode which keeps growing exponentially, while those involving larger radial wave numbers (small

ARTIFICIAL INTELLIGENCE DeepMind Has Trained an AI to Control Nuclear Fusion Amit Katwala | Wired “DeepMind’s AI was able to autonomously figure out how to create these [plasma] shapes by manipulating the magnetic coils in the right way—both in the simulation and when the scientists ran the same experiments for real inside the TCV tokamak […]

Researchers from the EUROfusion consortium – 4,800 experts, students and staff from across Europe, co-funded by the European

The London-based AI lab, which is owned by Alphabet, says it has trained an AI system to control and sculpt a superheated plasma in a nuclear fusion reaction.

The London-based AI lab, which is owned by Alphabet, says it has trained an AI system to control and sculpt a superheated plasma in a nuclear fusion reaction.

The main concern of the present paper is the study of the multi-scale dynamics of thermonuclear fusion plasmas via a multi-species Fokker-Planck kinetic model. One of the goals is the generalization of the standard Fokker-Planck collision operator to a multispecies one, conserving mass, total momentum and energy, as well as satisfying Boltzmann's H-theorem. Secondly, the paper investigates in more details the reduced model used for the electron description in present simulations, and which considers the electrons in a thermodynamic equilibrium (adiabatic regime), whereas the ions are kept kinetic. On the one hand, we perform some mathematical asymptotic limits to obtain in the electron/ion low mass ratio limit the above-mentioned electron adiabatic regime. On the other hand, we develop a numerical scheme , based on a Hermite spectral method, and perfom numerical simulations to illustrate and investigate in more details this asymptotics.

The main concern of the present paper is the study of the multi-scale dynamics of thermonuclear fusion plasmas via a multi-species Fokker-Planck kinetic model. One of the goals is the generalization of the standard Fokker-Planck collision operator to a multispecies one, conserving mass, total momentum and energy, as well as satisfying Boltzmann's H-theorem. Secondly, the paper investigates in more details the reduced model used for the electron description in present simulations, and which considers the electrons in a thermodynamic equilibrium (adiabatic regime), whereas the ions are kept kinetic. On the one hand, we perform some mathematical asymptotic limits to obtain in the electron/ion low mass ratio limit the above-mentioned electron adiabatic regime. On the other hand, we develop a numerical scheme , based on a Hermite spectral method, and perfom numerical simulations to illustrate and investigate in more details this asymptotics.

devz123@gmail.com(Dev Kundaliya) / Computing.co.uk Latest updatesGoogles DeepMind trained an AI to control nuclear fusion - Fusing hydrogen atoms is safer and more efficient than nuclear fission, but also much more complex Google-backed DeepMind has trained a machine learning algorithm to control the hot plasma inside a tokamak nuclear fusion reactor. It might sound like the start of an 80s blockbuster, but the system could give ...

ZDNet Latest NewsGoogles AI sibling DeepMind controls plasma shapes for nuclear fusion - DeepMind learns how to control the settings of magnetic devices used in nuclear fusion. ...

EPFL’s Swiss Plasma Center (SPC) has several years of experience in plasma physics and plasma control approaches. DeepMind is a scientific discovery company, acquired by Google in 2014, that is...

Most energy-producing technologies used today are unsustainable, as they cause significant damage to our planet's natural environment. In recent years, scientists worldwide have thus been trying to devise alternative energy solutions that take advantage of abundant and natural resources.

For the first time, artificial intelligence has been used to control the super-hot plasma inside a fusion reactor, offering a new way to increase stability and efficiency

Human-machine interaction has been around for several decades now, with new applications emerging every day. One of the major goals that remain to be achieved is designing an interaction similar to how a human interacts with another human. Therefore, there is a need to develop interactive systems that could replicate a more realistic and easier human-machine interaction. On the other hand, developers and researchers need to be aware of state-of-the-art methodologies being used to achieve this goal. We present this survey to provide researchers with state-of-the-art data fusion technologies implemented using multiple inputs to accomplish a task in the robotic application domain. Moreover, the input data modalities are broadly classified into uni-modal and multi-modal systems and their application in myriad industries, including the health care industry, which contributes to the medical industry's future development. It will help the professionals to examine patients using different

Tech Times : TechGoogle’s DeepMind Can Now Assist Autonomously in Optimizing Nuclear Fusion Plasma Configs - Researchers assisted in teaching DeepMind a new algorithmic program that can control the TCVs magnetic coils. ...

EPFL's Swiss Plasma Center (SPC) has decades of experience in plasma physics and plasma control methods. DeepMind is a scientific discovery company acquired by Google in 2014 that's committed to "solving intelligence to advance science and humanity." Together, they have developed a new magnetic control method for plasmas based on deep reinforcement learning, and applied it to a real-world plasma for the first time in the SPC's tokamak research facility, TCV. Their study has just been published in Nature.

Scientists have achieved a remarkable breakthrough in the conceptual design of twisty stellarators, experimental magnetic facilities that could reproduce on Earth the fusion energy that powers the sun and stars. The breakthrough shows how to more precisely shape the enclosing magnetic fields in stellarators to create an unprecedented ability to hold the fusion fuel together.

DeepMind’s streak of applying its world-class AI to hard science problems continues. In collaboration with the Swiss Plasma Center at EPFL—a university in Lausanne, Switzerland—the UK-based AI firm has now trained a deep reinforcement learning algorithm to control the superheated soup of matter inside a nuclear fusion reactor. The breakthrough, published in the journal Nature,…

Laser-driven ion acceleration has been studied to develop a compact and efficient plasma-based accelerator, which is applicable to cancer therapy, nuclear fusion, and high energy physics. Osaka University researchers, in collaboration with researchers at National Institutes for Quantum Science and Technology (QST), Kobe University, and National Central University in Taiwan, have reported direct energetic ion acceleration by irradiating the world's thinnest and strongest graphene target with the ultra-intense J-KAREN laser at Kansai Photon Science Institute, QST in Japan. Their findings are published in Nature's Scientific Reports.

Advanced in-cabin sensing technologies, especially vision based approaches, have tremendously progressed user interaction inside the vehicle, paving the way for new applications of natural user interaction. Just as humans use multiple modes to communicate with each other, we follow an approach which is characterized by simultaneously using multiple modalities to achieve natural human-machine interaction for a specific task: pointing to or glancing towards objects inside as well as outside the vehicle for deictic references. By tracking the movements of eye-gaze, head and finger, we design a multimodal fusion architecture using a deep neural network to precisely identify the driver's referencing intent. Additionally, we use a speech command as a trigger to separate each referencing event. We observe differences in driver behavior in the two pointing use cases (i.e. for inside and outside objects), especially when analyzing the preciseness of the three modalities eye, head, and finger.

Heavy/high Z metal materials are the preferred materials for plasma facing components in International Thermonuclear Experimental Reactor (ITER) due to their excellent properties. However, at thermonuclear fusion relevant temperatures, the accumulation of heavy/high-Z particles in the core region may significantly cool the plasmas, deteriorating the plasma performance and leading to H to L-mode back transition and even further to radiative collapse.

Human brain is continuously inundated with the multisensory information and their complex interactions coming from the outside world at any given moment. Such information is automatically analyzed by binding or segregating in our brain. While this task might seem effortless for human brains, it is extremely challenging to build a machine that can perform similar tasks since complex interactions cannot be dealt with single type of integration but requires more sophisticated approaches. In this paper, we propose a new model to address the multisensory integration problem with individual event-specific layers in a multi-task learning scheme. Unlike previous works where single type of fusion is used, we design event-specific layers to deal with different audio-visual relationship tasks, enabling different ways of audio-visual formation. Experimental results show that our event-specific layers can discover unique properties of the audio-visual relationships in the videos. Moreover, although

Contemporary ultraintense, short-pulse laser systems provide extremely compact setups for the production of high-flux neutron beams, such as required for nondestructive probing of dense matter, research on neutron-induced damage in fusion devices or laboratory astrophysics studies. Here, by coupling particle-in-cell and Monte Carlo numerical simulations, we examine possible strategies to optimize neutron sources from ion-induced nuclear reactions using 1-PW, 20-fs-class laser systems. To improve the ion acceleration, the laser-irradiated targets are chosen to be ultrathin solid foils, either standing alone or preceded by a near-critical-density plasma to enhance the laser focusing. We compare the performance of these single- and double-layer targets, and determine their optimum parameters in terms of energy and angular spectra of the accelerated ions. These are then sent into a converter to generate neutrons, either traditionally through $(p,n)$ reactions on beryllium nuclei or through

There are several reasons to extend the presentation of Navier-Stokes equations to multicomponent systems. Many technological applications are based on physical phenomena that are present neither in pure elements nor in binary mixtures. Whereas Fourier's law must already be generalized in binaries, it is only with more than two components that Fick's law breaks down in its simple form. The emergence of dissipative phenomena affects also the inertial confinement fusion configurations, designed as prototypes for the future fusion nuclear plants hopefully replacing the fission ones. This important topic can be described in much simpler terms than in many textbooks since the publication of the formalism put forward recently by Snider in \textit{Phys. Rev. E} \textbf{82}, 051201 (2010). In a very natural way, it replaces the linearly dependent atomic fractions by the independent set of partial densities. Then, the Chapman-Enskog procedure is hardly more complicated for multicomponent

Record-breaking 59 megajoules of sustained fusion energy demonstrate potential of fusion to deliver safe, low-carbon energy. Researchers from

Scientists in Britain announced Wednesday they had smashed a previous record for generating fusion energy, hailing it as a "milestone" on the path towards cheap, clean power and a cooler planet.

When ITER, the international fusion experiment fires up in 2025, a top priority will be avoiding or mitigating violent disruptions that can seriously damage the giant machine. Scientists at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have built and successfully simulated the prototype of a novel device to mitigate the consequences of a damaging disruption before one can proceed.

Fusion is described as "the process that takes place in the heart of stars and provides the power that drives the universe."

Fusion is described as "the process that takes place in the heart of stars and provides the power that drives the universe."

Fusion energy is a serious, sustainable offer in the long-term energy mix, says George Freeman MP, UK minister for science.

Precision phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are vitally important ingredients in experimental measurements. Machine learning technology has the potential to dramatically optimise simulations for complicated final states. We investigate the use of neural networks to approximate matrix elements, studying the case of loop-induced diphoton production through gluon fusion. We train neural network models on one-loop amplitudes from the NJet C++ library and interface them with the Sherpa Monte Carlo event generator to provide the matrix element within a realistic hadronic collider simulation. Computing some standard observables with the models and comparing to conventional techniques, we find excellent agreement in the distributions and a reduced total simulation time by a factor of thirty.

Recent studies show that deep learning models achieve good performance on medical imaging tasks such as diagnosis prediction. Among the models, multimodality has been an emerging trend, integrating different forms of data such as chest X-ray (CXR) images and electronic medical records (EMRs). However, most existing methods incorporate them in a model-free manner, which lacks theoretical support and ignores the intrinsic relations between different data sources. To address this problem, we propose a knowledge-driven and data-driven framework for lung disease diagnosis. By incorporating domain knowledge, machine learning models can reduce the dependence on labeled data and improve interpretability. We formulate diagnosis rules according to authoritative clinical medicine guidelines and learn the weights of rules from text data. Finally, a multimodal fusion consisting of text and image data is designed to infer the marginal probability of lung disease. We conduct experiments on a

The Joint European Torus fusion experiment in the U.K. has set a new record for generating energy.

Nuclear fusion is the same process that the sun uses to generate heat. Proponents believe it could one day help address climate change by providing an abundant, safe and green source of energy. A team at the Joint European Torus (JET) facility near Oxford in central England generated 59 megajoules of energy for five seconds during an experiment in December, more than doubling a 1997 record, the UK Atomic Energy Authority said. That is about the power needed to power 35,000 homes for the same period of time, five seconds, said JET's head of operations Joe Milnes. The results "are the clearest demonstration worldwide of the potential for fusion energy to deliver safe and sustainable low-carbon energy", the UKAEA said. The donut-shaped machine used for the experiments is called a tokamak, and the JET site is the largest operational one in the world. Inside, just 0.1 milligrammes each of deuterium and tritium -- both are isotopes of hydrogen, with deuterium also called heavy hydrogen --

Researchers have set a record for energy released in a fusion reaction, putting them a step closer to harnessing the power of the stars. Read Full Article at RT.com

Experiment at the Joint European Tokamak in Oxford saw a super-hot plasma sustained for 5 seconds, producing a record 59 megajoules of heat energy

Edge computing and 5G have made it possible to perform analytics closer to the source of data and achieve super-low latency response times, which is not possible with centralized cloud deployment. In this paper, we present a novel fever-screening system, which uses edge machine learning techniques and leverages private 5G to accurately identify and screen individuals with fever in real-time. Particularly, we present deep-learning based novel techniques for fusion and alignment of cross-spectral visual and thermal data streams at the edge. Our novel Cross-Spectral Generative Adversarial Network (CS-GAN) synthesizes visual images that have the key, representative object level features required to uniquely associate objects across visual and thermal spectrum. Two key features of CS-GAN are a novel, feature-preserving loss function that results in high-quality pairing of corresponding cross-spectral objects, and dual bottleneck residual layers with skip connections (a new, network

End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications. This is specifically true in time series domain where problems like disaster prediction, anomaly detection, and demand prediction often do not have a large amount of historical data. Moreover, relying purely on past examples for training can be sub-optimal since in doing so we ignore one very important domain i.e knowledge, which has its own distinct advantages. In this paper, we propose a novel knowledge fusion architecture, Knowledge Enhanced Neural Network (KENN), for time series forecasting that specifically aims towards combining strengths of both knowledge and data domains while mitigating their individual weaknesses. We show that KENN not only reduces data dependency of the overall framework but also improves performance by producing predictions that are better than the ones produced

Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers. However, achieving a rather good performance is not an easy task due to the noisy raw data, underutilized information, and the misalignment of multi-modal sensors. In this paper, we provide a literature review of the existing multi-modal-based methods for perception tasks in autonomous driving. Generally, we make a detailed analysis including over 50 papers leveraging perception sensors including LiDAR and camera trying to solve object detection and semantic segmentation tasks. Different from traditional fusion methodology for categorizing fusion models, we propose an innovative way that divides them into two major classes, four minor classes by a more reasonable taxonomy in the view of the fusion stage. Moreover, we dive deep into the current fusion methods, focusing on the remaining problems and open-up discussions on the potential research

We propose a laser-driven non-thermal near-solid density fast reactor concept with mixed nuclear fusion fuels that is capable of transforming external laser energy efficiently into fusion energy and secondary particles. The reactor is capable of making use of a range of neutronic and aneutronic fuels. Its core part consists of an integrated nanoscopic nuclear fusion fuel based laser-driven accelerator that is capable of producing non-thermal ionic distributions within fuel mixes.

Tech Times : TechKyoto Fusioneering Secures Million-Dollar Funding! Japanese Tech Firm To Enhance Fusion Reactor Deve - Kyoto Fusioneering was able to secure a million-dollar funding for its fusion reactor development enhancement. ...

Video super-resolution (VSR) has many applications that pose strict causal, real-time, and latency constraints, including video streaming and TV. We address the VSR problem under these settings, which poses additional important challenges since information from future frames are unavailable. Importantly, designing efficient, yet effective frame alignment and fusion modules remain central problems. In this work, we propose a recurrent VSR architecture based on a deformable attention pyramid (DAP). Our DAP aligns and integrates information from the recurrent state into the current frame prediction. To circumvent the computational cost of traditional attention-based methods, we only attend to a limited number of spatial locations, which are dynamically predicted by the DAP. Comprehensive experiments and analysis of the proposed key innovations show the effectiveness of our approach. We significantly reduce processing time in comparison to state-of-the-art methods, while maintaining a high

We explore the structure of the space of quasisymmetric configurations identifying them by their magnetic axes, described as 3D closed curves. We demonstrate that this topological perspective divides the space of all configurations into well-separated quasisymmetric phases. Each phase is characterized by the self-linking number (a topological invariant), defining different symmetry configurations (quasi-axisymmetry or quasi-helical symmetry). The phase-transition manifolds correspond to quasi-isodynamic configurations. By considering some models for closed curves (most notably torus unknots), general features associated with these phases are explored. Some general criteria are also built and leveraged to provide a simple way to describe existing quasisymmetric designs. This constitutes the first step in a program to identify quasisymmetric configurations with a reduced set of functions and parameters, to deepen understanding of configuration space, and offer an alternative approach to

In this paper, we develop the kinetic and hydrodynamic theories of the convective mesoscale flows driven by the spatially inhomogeneous electrostatic IC parametric microturbulence in the pedestal plasma with a sheared poloidal flow. The developed kinetic theory predicts the generation of the sheared poloidal convective flow, and of the radial compressed flow with radial flow velocity gradient. The developed hydrodynamic theory of the convective flows reveals the radial compressed convective flow as the dominant factor in the formation of the steep pedestal density profile with density gradient exponentially growing with time. This gradient density growth is limited by the formation of the radial oscillating with time ion outflow of pedestal plasma to SOL.

Compact torus injection is considered as a high promising approach to realize central fueling in the future tokamak device. Recently, a compact torus injection system has been developed for the Experimental Advanced Superconducting Tokamak, and the preliminary results have been carried out. In the typical discharges of the early stage, the velocity, electron density and particles number of the CT can reach 56.0 km/s, 8.73*10^20 m^(-3) and 2.4*10^18 (for helium), respectively. A continuous increase in CT density during acceleration was observed in the experiment, which may be due to the plasma ionized in the formation region may carry part of the neutral gas into the acceleration region, and these neutral gases will be ionized again. In addition, a significant plasma rotation is observed during the formation process which is introduced by the E*B drift. In this paper, we present the detailed system setup and the preliminary platform test results, hoping to provide some basis for the

For the successful generation of ion-beam-driven high energy density matter and heavy ion fusion energy, intense ion beams must be transported and focused onto a target with small spot size. One of the successful approaches to achieve this goal is to accelerate and transport intense ion charge bunches in an accelerator and then focus the charge bunches ballistically in a section of the accelerator that contains a neutralizing background plasma. This requires the ability to control space-charge effects during un-neutralized (non-neutral) beam transport in the accelerator and transport sections, and the ability to effectively neutralize the space charge and current by propagating the beam through background plasma. As the beam intensity and energy are increased in future heavy ion fusion (HIF) drivers and Fast Ignition (FI) approaches, it is expected that nonlinear processes and collective effects will become much more pronounced than in previous experiments. Making use of 3D

Most tokamak devices including ITER exploit the D-T reaction due to its high reactivity, but the wall loading caused by the associated 14MeV neutrons will limit the further development of fusion performance at high beta. To explore p-11B fusion cycle, a tokamak system code is extended to incorporate the relativistic bremsstrahlung since the temperature of electrons is approaching the rest energy of electron. By choosing an optimum p-11B mix and ion temperature, some representative sets of parameters of p-11B tokamak reactor, whose fusion gain exceeds 1, have been found under the thermal wall loading limit and beta limit when synchrotron radiation loss is neglected. However, the fusion gain greatly decreases when the effect of synchrotron radiation loss is considered. Helium ash also plays an important role in the fusion performance, and we have found that the helium confinement time must be below the energy confinement time to keep the helium concentration ratio in an acceptable range.

An optimized compact stellarator with four simple coils is obtained from direct optimization via coil shape. The new stellarator consists of two interlocking coils and two vertical field coils similar to those of the Columbia Non-neutral Torus (CNT)[Pedersen et al. Phys. Rev. Lett. 88, 205002 (2002)]. The optimized configuration has global magnetic well and a low helical ripple level comparable to that of Wendelstein 7-X (W7-X)[Wolf et al. Nucl. Fusion 57, 102020 (2017)]. The two interlocking coils have a smooth three-dimensional shape much simpler than those of advanced stellarators such as W7-X. This result opens up possibilities of future stellarator reactors with simplified coils.

Although the basic concept of a stellarator was known since the early days of fusion research, advances in computational technology have enabled the modelling of increasingly complicated devices, leading up to the construction of Wendelstein 7-X, which has recently shown promising results. However, there has been surprisingly little activity in nonlinear 3D MHD modelling of stellarators. This paper reports on the extension of the JOREK code to 3D geometries and on the first stellarator simulations carried out with it. The first simple simulations shown here address the classic Wendelstein 7-A stellarator using a reduced MHD model previously derived by us. The results demonstrate that stable full MHD equilibria are preserved in the reduced model: the flux surfaces do not move throughout the simulation, and closely match the flux surfaces of the full MHD equilibrium. Further, both tearing and ballooning modes were simulated, and the linear growth rates measured in JOREK are in reasonable

This paper presents LAGOON -- an open source platform for understanding the complex ecosystems of Open Source Software (OSS) communities. The platform currently utilizes spatiotemporal graphs to store and investigate the artifacts produced by these communities, and help analysts identify bad actors who might compromise an OSS project's security. LAGOON provides ingest of artifacts from several common sources, including source code repositories, issue trackers, mailing lists and scraping content from project websites. Ingestion utilizes a modular architecture, which supports incremental updates from data sources and provides a generic identity fusion process that can recognize the same community members across disparate accounts. A user interface is provided for visualization and exploration of an OSS project's complete sociotechnical graph. Scripts are provided for applying machine learning to identify patterns within the data. While current focus is on the identification of bad actors

Dataflow/mapping decides the compute and energy efficiency of DNN accelerators. Many mappers have been proposed to tackle the intra-layer map-space. However, mappers for inter-layer map-space (aka layer-fusion map-space), have been rarely discussed. In this work, we propose a mapper, DNNFuser, specifically focusing on this layer-fusion map-space. While existing SOTA DNN mapping explorations rely on search-based mappers, this is the first work, to the best of our knowledge, to propose a one-shot inference-based mapper. We leverage a famous language model GPT as our DNN architecture to learn layer-fusion optimization as a sequence modeling problem. Further, the trained DNNFuser can generalize its knowledge and infer new solutions for unseen conditions. Within one inference pass, DNNFuser can infer solutions with compatible performance to the ones found by a highly optimized search-based mapper while being 66x-127x faster.

In this paper we propose a vertical stabilization~(VS) control system for tokamak plasmas based on the extremum seeking (ES) algorithm. The gist of the proposed strategy is to inject an oscillating term in the control action and exploit a modified ES algorithm in order to bring to zero the average motion of the plasma along the unstable mode. In this way, the stabilization of the unstable vertical dynamic of the plasma is achieved. The approach is validated by means of both linear and nonlinear simulations of the overall~ITER tokamak magnetic control system, with the aim of demonstrating robust operation throughout the flat-top phase of a discharge and the capability of reacting to a variety of disturbances.

For more than 60 years, scientists have sought to understand and control the process of fusion, a quest to harness the vast amounts of energy released when nuclei in fuel come together. A new paper describes recent experiments that have achieved a burning plasma state in fusion, helping steer fusion research closer than it has ever been to its ultimate goal: a self-sustaining, controlled reaction.

Researchers led by Prof. Xu Guosheng from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences have recently demonstrated a novel "two-step" magnet design strategy to design advanced stellarator with standardized permanent magnet blocks and simple coils. Related results were published on Cell Reports Physical Science.

One of the last remaining milestones in fusion research before attaining ignition and self-sustaining energy production is creating

One of the last remaining milestones in fusion research before attaining ignition and self-sustaining energy production is creating

The traditional two-state hidden Markov model divides the high frequency coefficients only into two states (large and small states). Such scheme is prone to produce an inaccurate statistical model for the high frequency subband and reduces the quality of fusion result. In this paper, a fine-grained multi-state contextual hidden Markov model (MCHMM) is proposed for infrared and visible image fusion in the non-subsampled Shearlet domain, which takes full consideration of the strong correlations and level of details of NSST coefficients. To this end, an accurate soft context variable is designed correspondingly from the perspective of context correlation. Then, the statistical features provided by MCHMM are utilized for the fusion of high frequency subbands. To ensure the visual quality, a fusion strategy based on the difference in regional energy is proposed as well for lowfrequency subbands. Experimental results demonstrate that the proposed method can achieve a superior performance

Results of a three-dimensional, flux-driven, electrostatic, global, two-fluid turbulence simulation for a 5-field period stellarator with an island divertor are presented. The numerical simulation is carried out with the GBS code, recently extended to simulate plasma turbulence in non-axisymmetric magnetic equilibria. The vacuum magnetic field used in the simulation is generated with the theory of Dommaschk potentials, and describes a configuration with a central region of nested flux surfaces, surrounded by a chain of magnetic islands, similarly to the diverted configurations of W7-X. The heat outflowing from the core reaches the island region and is transported along the magnetic islands, striking the vessel walls, which correspond to the boundary of the simulation domain. The radial transport of particles and heat is found to be mainly driven by a field-aligned coherent mode with poloidal number $m=4$. The analysis of this mode, based on non-local linear theory considerations,

We set up a mapping procedure able to translate the evolution of the radial profile of fast ions, interacting with Toroidal Alfv\'en Eigenmodes, into the dynamics of an equivalent one dimensional bump-on-tail system. We apply this mapping technique to reproduce ITER relevant simulations, which clearly outlined deviations from the diffusive quasi-linear (QL) model. Our analysis demonstrates the capability of the one-dimensional beam-plasma dynamics to predict the relevant features of the non-linear hybrid LIGKA/HAGIS simulations. In particular, we clearly identify how the deviation from the QL evolutive profiles is due to the presence of avalanche processes. A detailed analysis regarding the reduced dimensionality is also addressed, by means of phase-space slicing based on constants of motion. In the conclusions, we outline the main criticalities and outcomes of the procedure, which must be satisfactorily addressed to make quantitative prediction on the observed outgoing fluxes in a

With 192 lasers and temperatures more than three times hotter than the center of the sun, scientists hit—at least for a fraction of a second—a key milestone on the long road toward nearly pollution-free fusion energy.

New details of the record-setting nuclear fusion experiment last year reveal the intricacy and precision needed to achieve it.

An effective disruption mitigation system in a tokamak reactor should limit the exposure of the wall to localized heat losses and to the impact of high current runaway electron beams, and avoid excessive forces on the structure. We evaluate with respect to these aspects a two-stage deuterium-neon shattered pellet injection in an ITER-like plasma, using simulations with the DREAM framework [M. Hoppe et al (2021) Comp. Phys. Commun. 268, 108098]. To minimize the obtained runaway currents an optimal range of injected deuterium quantities is found. This range is sensitive to the opacity of the plasma to Lyman radiation, which affects the ionization degree of deuterium, and thus avalanche runaway generation. The two-stage injection scheme, where dilution cooling is produced by deuterium before a radiative thermal quench caused by neon, reduces both the hot-tail seed and the localized transported heat load on the wall. However, during nuclear operation, additional runaway seed sources from

The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develop and demonstrate a technique to translate brightness measurements of HeI line radiation into local plasma fluctuations via a novel integrated deep learning framework that combines neutral transport physics and collisional radiative theory for the $3^3 D - 2^3 P$ transition in atomic helium. The tenets for experimental validity are reviewed, illustrating that this turbulence analysis for ionized gases is transferable to both magnetized and unmagnetized environments with arbitrary geometries. Based upon fast camera data on the Alcator C-Mod tokamak, we present the first 2-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density revealing shadowing effects in a fusion plasma using a single spectral line.

We address the problem of multi-modal object tracking in video and explore various options of fusing the complementary information conveyed by the visible (RGB) and thermal infrared (TIR) modalities including pixel-level, feature-level and decision-level fusion. Specifically, different from the existing methods, paradigm of image fusion task is heeded for fusion at pixel level. Feature-level fusion is fulfilled by attention mechanism with channels excited optionally. Besides, at decision level, a novel fusion strategy is put forward since an effortless averaging configuration has shown the superiority. The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation. A variant of which produced the winning tracker at the Visual Object Tracking Challenge 2020 (VOT-RGBT2020). The concurrent exploration of innovative pixel- and feature-level

In recent years the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency for mobile devices. This study aims to contribute to this innovative research by evaluating the performance of a multimodal behavioral biometric based user authentication scheme using touch dynamics and phone movement. This study uses a fusion of two popular publicly available datasets the Hand Movement Orientation and Grasp dataset and the BioIdent dataset. This study evaluates our model performance using three common machine learning algorithms which are Random Forest Support Vector Machine and K-Nearest Neighbor reaching accuracy rates as high as 82% with each algorithm performing respectively for all success metrics reported.

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

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

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

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

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

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

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

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

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