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# Fusion

19.03.2021
05:24 A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning. (arXiv:2103.10346v1 [cs.LG])

Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers. Novel paradigms, such as federated learning (FL), are suitable for decentralized model training across devices or silos that simultaneously act as both data producers and learners. Unlike centralized learning (CL) techniques, relying on big-data fusion and analytics located in energy hungry data centers, in FL scenarios devices collaboratively train their models without sharing their private data. This article breaks down and analyzes the main factors that influence the environmental footprint of FL policies compared with classical CL/Big-Data algorithms running in data centers. The proposed analytical framework takes into account both learning and communication energy costs, as well as the carbon equivalent emissions; in addition, it models both vanilla and decentralized FL policies driven by consensus. The framework is evaluated in an

05:24 Finite-word-length FPGA implementation of model predictive control for ITER resistive wall mode control. (arXiv:2103.10146v1 [eess.SY])

In advanced tokamak scenarios, active feedback control of unstable resistive wall modes (RWM) may be required. A RWM is an instability due to plasma kink at higher plasma pressure, moderated by the presence of a resistive wall surrounding the plasma. We address the dominant kink instability associated with the main nonaxisymmetric (n = 1) RWM, described by the CarMa model. Model predictive control (MPC) is used, with the aim of enlarging the domain of attraction of the unstable RWM modes subject to power-supply voltage constraints. The implementation of MPC is challenging, because the related quadratic programming (QP) on-line optimization problems must be solved at a sub-ms sampling rate. Using complexity-reduction pre-processing techniques and a primal fast gradient method (FGM) QP solver, sufficiently short computation times for ITER are reachable using a standard personal computer (PC). In this work we explore even faster finite-word-length (FWL) implementation using

18.03.2021
08:19 Proposal of A Linked Mirror Configuration for Magnetic Confinement Experiment. (arXiv:2103.09457v1 [physics.plasm-ph])

A new linked mirror device for magnetic confinement experiment is proposed. The new linked mirror device consists of two straight magnetic mirrors connected by two half-torus. The structure of the configuration as a whole is three dimensional because the two linear mirror sections are not parallel. The angle between the two mirror sections generates rotational transform which results in good magnetic confinement of toroidally processing passing particles. In this way the usual loss cone of the traditional linear mirror machines is eliminated. The single particle confinement is similar to that of tokamaks with most of particles well confined. The calculated neoclassical confinement is very good and is even better than that of an equivalent tokamak. Potentially the proposed linked mirror configuration can allow MHD stable high beta equilibria with good plasma confinement suitable for neutron sources and magnetic fusion reactors.

08:19 Nonlinear coupling of reversed shear Alfven eigenmode and toroidal Alfven eigenmode during current ramp. (arXiv:2103.09446v1 [physics.plasm-ph])

Two novel nonlinear mode coupling processes for reversed shear Alfven eigenmode (RSAE) nonlinear saturation are proposed and investigated. In the first process, RSAE nonlinearly couples to a co-propagating toroidal Alfven eigenmode (TAE) with the same toroidal and poloidal mode numbers, and generates a geodesic acoustic mode (GAM). In the second process, RSAE couples to a counter-propagating TAE and generates an ion acoustic wave quasi-mode (IAW). The condition for the two processes to occur is favored during current ramp. Both processes contribute to effectively saturate the Alfvenic instabilities, as well as nonlinearly transfer of energy from energetic fusion alpha particles to fuel ions in burning plasmas.

17.03.2021
04:06 Multiple-isotope pellet cycles captured by turbulent transport modelling in the JET tokamak. (arXiv:2103.09222v1 [physics.plasm-ph])

For the first time the pellet cycle of a multiple-isotope plasma is successfully reproduced with reduced turbulent transport modelling, within an integrated simulation framework. Future nuclear fusion reactors are likely to be fuelled by cryogenic pellet injection, due to higher penetration and faster response times. Accurate pellet cycle modelling is crucial to assess fuelling efficiency and burn control. In recent JET tokamak experiments, deuterium pellets with reactor-relevant deposition characteristics were injected into a pure hydrogen plasma. Measurements of the isotope ratio profile inferred a Deuterium penetration time comparable to the energy confinement time. The modelling successfully reproduces the plasma thermodynamic profiles and the fast deuterium penetration timescale. The predictions of the reduced turbulence model QuaLiKiz in the presence of a negative density gradient following pellet deposition are compared with GENE linear and nonlinear higher fidelity modelling.

04:06 Gumbel-Attention for Multi-modal Machine Translation. (arXiv:2103.08862v1 [cs.CL])

Multi-modal machine translation (MMT) improves translation quality by introducing visual information. However, the existing MMT model ignores the problem that the image will bring information irrelevant to the text, causing much noise to the model and affecting the translation quality. In this paper, we propose a novel Gumbel-Attention for multi-modal machine translation, which selects the text-related parts of the image features. Specifically, different from the previous attention-based method, we first use a differentiable method to select the image information and automatically remove the useless parts of the image features. Through the score matrix of Gumbel-Attention and image features, the image-aware text representation is generated. And then, we independently encode the text representation and the image-aware text representation with the multi-modal encoder. Finally, the final output of the encoder is obtained through multi-modal gated fusion. Experiments and case analysis

16.03.2021
06:45 Fusion yield of plasma with velocity-space anisotropy at constant energy. (arXiv:2103.07834v1 [physics.plasm-ph])

Velocity-space anisotropy can significantly modify fusion reactivity. The nature and magnitude of this modification depends on the plasma temperature, as well as the details of how the anisotropy is introduced. For plasmas that are sufficiently cold compared to the peak of the fusion cross-section, anisotropic distributions tend to have higher yields than isotropic distributions with the same thermal energy. At higher temperatures, it is instead isotropic distributions that have the highest yields. However, the details of this behavior depend on exactly how the distribution differs from an isotropic Maxwellian. This paper describes the effects of anisotropy on fusion yield for the class of anisotropic distribution functions with the same energy distribution as a 3D isotropic Maxwellian, and compares those results with the yields from bi-Maxwellian distributions. In many cases, especially for plasmas somewhat below reactor-regime temperatures, the effects of anisotropy can be

06:45 Effects of Helium massive gas injection level on disruption mitigation on EAST. (arXiv:2103.07645v1 [physics.plasm-ph])

In this study, NIMROD simulations are performed to investigate the effects of massive helium gas injection level on the induced disruption on EAST tokamak. It is demonstrated in simulations that two different scenarios of plasma cooling (complete cooling and partial cooling) take place for different amounts of injected impurities. For the impurity injection above a critical level, a single MHD activity is able to induce a complete core temperature collapse. For impurity injection below the critical level, a series of multiple minor disruptions occur before the complete thermal quench (TQ).

06:45 Bayesian modelling of multiple plasma diagnostics at Wendelstein 7-X. (arXiv:2103.07582v1 [physics.plasm-ph])

Consistent inference of the electron density and temperature has been carried out with multiple heterogeneous plasma diagnostic data sets at Wendelstein 7-X. The predictive models of the interferometer, Thomson scattering and helium beam emission spectroscopy systems have been developed in the Minerva framework and combined to a single joint model. The electron density and temperature profiles are modelled by Gaussian processes with their hyperparameters. The model parameters such as the calibration factor of the Thomson scattering system and the model predictive uncertainties are regarded as additional unknown parameters. The joint posterior probability distribution of the electron density and temperature profiles, hyperparameters of the Gaussian processes and model parameters is explored by Markov chain Monte Carlo algorithms. The posterior samples drawn from the joint posterior distribution are numerically marginalised over the hyperparameters and model parameters to obtain the

06:45 Damage to Relativistic Interstellar Spacecraft by ISM Impact Gas Accumulation. (arXiv:2103.07517v1 [physics.space-ph])

As part of the NASA Starlight collaboration, we look at the implications of radiation effects from impacts with the interstellar medium (ISM) on a directed energy driven relativistic spacecraft. The spacecraft experiences a stream of MeV/nucleon impacts along the forward edge primarily from hydrogen and helium nuclei. The accumulation of implanted slowly diffusing gas atoms in solids drives damage through the meso-scale processes of bubble formation, blistering, and exfoliation. This results in macroscopic changes to material properties and, in the cases of blistering and exfoliation, material erosion via blister rupture and delamination. Relativistic hydrogen and helium at constant velocity will stop in the material at a similar depth, as predicted by Bethe-Bloch stopping and subsequent simulations of the implantation distribution, leading to a mixed hydrogen and helium system similar to that observed in fusion plasma-facing components (PFC's). However, the difference in location of

06:45 Radar Camera Fusion via Representation Learning in Autonomous Driving. (arXiv:2103.07825v1 [cs.CV])

Radars and cameras are mature, cost-effective, and robust sensors and have been widely used in the perception stack of mass-produced autonomous driving systems. Due to their complementary properties, outputs from radar detection (radar pins) and camera perception (2D bounding boxes) are usually fused to generate the best perception results. The key to successful radar-camera fusion is accurate data association. The challenges in radar-camera association can be attributed to the complexity of driving scenes, the noisy and sparse nature of radar measurements, and the depth ambiguity from 2D bounding boxes. Traditional rule-based association methods are susceptible to performance degradation in challenging scenarios and failure in corner cases. In this study, we propose to address rad-cam association via deep representation learning, to explore feature-level interaction and global reasoning. Concretely, we design a loss sampling mechanism and an innovative ordinal loss to overcome the

15.03.2021
09:40 Juggling With Representations: On the Information Transfer Between Imagery, Point Clouds, and Meshes for Multi-Modal Semantics. (arXiv:2103.07348v1 [cs.CV])

The automatic semantic segmentation of the huge amount of acquired remote sensing data has become an important task in the last decade. Images and Point Clouds (PCs) are fundamental data representations, particularly in urban mapping applications. Textured 3D meshes integrate both data representations geometrically by wiring the PC and texturing the surface elements with available imagery. We present a mesh-centered holistic geometry-driven methodology that explicitly integrates entities of imagery, PC and mesh. Due to its integrative character, we choose the mesh as the core representation that also helps to solve the visibility problem for points in imagery. Utilizing the proposed multi-modal fusion as the backbone and considering the established entity relationships, we enable the sharing of information across the modalities imagery, PC and mesh in a two-fold manner: (i) feature transfer and (ii) label transfer. By these means, we achieve to enrich feature vectors to multi-modal

09:40 Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion. (arXiv:2103.07074v1 [cs.CV])

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation. Particularly, real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. In this work, we concentrate on the essential visual task, semantic segmentation, for large-scale point cloud data collected in reality. On the one hand, to reduce the ambiguity in nearby points, we augment their local context by fully utilizing both geometric and semantic features in a bilateral structure. On the other hand, we comprehensively interpret the distinctness of the points from multiple resolutions and represent the feature map following an adaptive fusion method at point-level for accurate semantic segmentation. Further, we provide specific ablation studies and intuitive visualizations to validate our key modules. By comparing with state-of-the-art networks on

12.03.2021
10:54 Integrated Age Estimation Mechanism. (arXiv:2103.06546v1 [cs.CV])

Machine-learning-based age estimation has received lots of attention. Traditional age estimation mechanism focuses estimation age error, but ignores that there is a deviation between the estimated age and real age due to disease. Pathological age estimation mechanism the author proposed before introduces age deviation to solve the above problem and improves classification capability of the estimated age significantly. However,it does not consider the age estimation error of the normal control (NC) group and results in a larger error between the estimated age and real age of NC group. Therefore, an integrated age estimation mechanism based on Decision-Level fusion of error and deviation orientation model is proposed to solve the problem.Firstly, the traditional age estimation and pathological age estimation mechanisms are weighted together.Secondly, their optimal weights are obtained by minimizing mean absolute error (MAE) between the estimated age and real age of normal people. In the

11.03.2021
19:11 Not so fast, supernova: Highest-energy cosmic rays detected in star clusters

For decades, researchers assumed the cosmic rays that regularly bombard Earth from the far reaches of the galaxy are born when stars go supernova—when they grow too massive to support the fusion occurring at their cores and explode.

06:31 The m/n=1/1 Instabilities in An EAST NBI Discharge. (arXiv:2103.05845v1 [physics.plasm-ph])

An analysis of precession fishbone, diamagnetic fishbone and internal kink mode in Tokamak plasmas are presented via solving the fishbone dispersion relation. Applying the dispersion relation to EAST discharge experiment, excitation of precession fishbone due to Neutral Beam Injection is successfully explained. Real frequency and growth rate of diamagnetic fishbone and internal kink mode are calculated.

10.03.2021
09:12 HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks. (arXiv:2103.05338v1 [cs.LG])

We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding capabilities to decode in a streaming way the hand, left or right, from fNIRS data. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed of $~1\,$Hz, outperforming standard methods. Since HemCNN does not require baseline correction and the convolution operation is invariant to time translations, our method can help to unlock fNIRS for a variety of real-time tasks. Mobile brain imaging and mobile brain machine interfacing can benefit from this to develop real-world neuroscience and practical human neural interfacing based on BOLD-like signals for the evaluation, assistance and rehabilitation of force generation, such as fusion of fNIRS with EEG signals.

09:12 DISC: A Dynamic Shape Compiler for Machine Learning Workloads. (arXiv:2103.05288v1 [cs.DC])

Many recent machine learning models show dynamic shape characteristics. However, existing AI compiler optimization systems suffer a lot from problems brought by dynamic shape models, including compilation overhead, memory usage, optimization pipeline and deployment complexity. This paper provides a compiler system to natively support optimization for dynamic shape workloads, named DISC. DISC enriches a set of IR to form a fully dynamic shape representation. It generates the runtime flow at compile time to support processing dynamic shape based logic, which avoids the interpretation overhead at runtime and enlarges the opportunity of host-device co-optimization. It addresses the kernel fusion problem of dynamic shapes with shape propagation and constraints collecting methods. This is the first work to demonstrate how to build an end-to-end dynamic shape compiler based on MLIR infrastructure. Experiments show that DISC achieves up to 3.3x speedup than TensorFlow/PyTorch, and 1.8x than

09:12 Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing. (arXiv:2103.05267v1 [cs.LG])

Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliability issues resulting from limb position change. One method to address this is dual-stage classification, in which the limb position is first determined using additional sensors to select between multiple position-specific gesture classifiers. While improving performance, this also increases model complexity and memory footprint, making a dual-stage classifier difficult to implement in a wearable device with limited resources. In this paper, we present sensor fusion of accelerometer and EMG signals using a hyperdimensional computing model to emulate dual-stage classification in a memory-efficient way. We demonstrate two methods of encoding accelerometer features to act as keys for retrieval of position-specific parameters from multiple models stored in superposition. Through validation on a dataset of 13

09.03.2021
11:58 RFN-Nest: An end-to-end residual fusion network for infrared and visible images. (arXiv:2103.04286v1 [cs.CV])

In the image fusion field, the design of deep learning-based fusion methods is far from routine. It is invariably fusion-task specific and requires a careful consideration. The most difficult part of the design is to choose an appropriate strategy to generate the fused image for a specific task in hand. Thus, devising learnable fusion strategy is a very challenging problem in the community of image fusion. To address this problem, a novel end-to-end fusion network architecture (RFN-Nest) is developed for infrared and visible image fusion. We propose a residual fusion network (RFN) which is based on a residual architecture to replace the traditional fusion approach. A novel detail-preserving loss function, and a feature enhancing loss function are proposed to train RFN. The fusion model learning is accomplished by a novel two-stage training strategy. In the first stage, we train an auto-encoder based on an innovative nest connection (Nest) concept. Next, the RFN is trained using the

05.03.2021
08:30 New plasma regimes with small ELMs and high confinement at the Joint European Torus. (arXiv:2103.02679v1 [physics.plasm-ph])

New plasma regimes with high confinement, low core impurity accumulation and small Edge localized mode (ELMs) perturbations have been obtained close to ITER conditions in magnetically confined plasmas from the Joint European torus (JET) tokamak. Such regimes are achieved by means of optimized particle fuelling conditions which trigger a self-organize state with a strong increase in rotation and ion temperature and a decrease of the edge density. An interplay between core and edge plasma regions leads to reduced turbulence levels and outward impurity convection. These results pave the way to an attractive alternative to the standard plasmas considered for fusion energy generation in a tokamak with metallic wall environment such as the ones expected in ITER

08:30 Model-based image adjustment for a successful pansharpening. (arXiv:2103.03062v1 [eess.IV])

A new model-based image adjustment for the enhancement of multi-resolution image fusion or pansharpening is proposed. Such image adjustment is needed for most pansharpening methods using panchromatic band and/or intensity image (calculated as a weighted sum of multispectral bands) as an input. Due various reasons, e.g. calibration inaccuracies, usage of different sensors, input images for pansharpening: low resolution multispectral image or more precisely the calculated intensity image and high resolution panchromatic image may differ in values of their physical properties, e.g. radiances or reflectances depending on the processing level. But the same objects/classes in both images should exhibit similar values or more generally similar statistics. Similarity definition will depend on a particular application. For a successful fusion of data from two sensors the energy balance between radiances/reflectances of both sensors should hold. A virtual band is introduced to compensate for

08:30 A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis. (arXiv:2103.02636v1 [cs.CL])

Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge public perception. Multimodal sentiment analysis offers an innovative solution to computationally understand and harvest sentiments from videos by contextually exploiting audio, visual and textual cues. In this paper, we, firstly, present a first of its kind Persian multimodal dataset comprising more than 800 utterances, as a benchmark resource for researchers to evaluate multimodal sentiment analysis approaches in Persian language. Secondly, we present a novel context-aware multimodal sentiment analysis framework, that simultaneously exploits acoustic, visual and textual cues to more accurately determine the expressed sentiment. We employ both decision-level (late) and feature-level (early) fusion methods to integrate affective cross-modal

01:19 Nuclear engineering researchers develop new resilient oxide dispersion strengthened alloy

Researchers have recently shown superior performance of a new oxide dispersion strengthened (ODS) alloy they developed for use in both fission and fusion reactors.

04.03.2021
23:00 Nuclear engineering researchers develop new resilient oxide dispersion strengthened alloy

Texas A&M University researchers have recently shown superior performance of a new oxide dispersion strengthened (ODS) alloy they developed for use in both fission and fusion reactors.

19:41 Physicists trap ultracold plasma in a magnetic bottle for the 1st time

The breakthrough technique supercools plasma with lasers before trapping them in a magnetic field; allowing physicists to study the northern lights, white dwarves and nuclear fusion in ever greater detail.

03.03.2021
17:02 Fusion startup plans reactor with small but powerful superconducting magnets

Commonwealth Fusion Systems announces site for compact reactor

06:56 Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression. (arXiv:2103.01305v1 [physics.plasm-ph])

The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier

02.03.2021
18:34 Nuclear fusion: Building a star on Earth is hard, which is why we need better materials

Nuclear fusion is the process that powers the Sun and all other stars. During fusion, the nuclei of two atoms are brought close enough together that they fuse together, releasing huge amounts of energy.

09:29 Simulation of runaway electron generation in fusion grade tokamak and suppression by impurity injection. (arXiv:2103.00325v1 [physics.plasm-ph])

During disruptions in fusion-grade tokamaks like ITER, large electric fields are induced following the thermal quench (TQ) period which can generate a substantial amount of Runaway Electrons (REs) that can carry up to 10 MA current with energies as high as several tens of MeV [1-3] in current quench phase (CQ). These runaway electrons can cause significant damage to the plasma-facing-components due to their localized energy deposition. To mitigate these effects, impurity injections of high-Z atoms have been proposed [1-3]. In this paper, we use a self-consistent 0D tokamak disruption model as implemented in PREDICT code [6] which has been upgraded to take into account the effect of impurity injections on RE dynamics as suggested in [4-5]. Dominant RE generation mechanisms such as the secondary avalanche mechanism as well as primary RE-generation mechanisms namely Dreicer, hot-tail, tritium decay and Compton scattering (from {\gamma}-rays emitted from activated walls) have been taken

09:29 The Labeled Multiple Canonical Correlation Analysis for Information Fusion. (arXiv:2103.00359v1 [cs.CV])

The objective of multimodal information fusion is to mathematically analyze information carried in different sources and create a new representation which will be more effectively utilized in pattern recognition and other multimedia information processing tasks. In this paper, we introduce a new method for multimodal information fusion and representation based on the Labeled Multiple Canonical Correlation Analysis (LMCCA). By incorporating class label information of the training samples,the proposed LMCCA ensures that the fused features carry discriminative characteristics of the multimodal information representations, and are capable of providing superior recognition performance. We implement a prototype of LMCCA to demonstrate its effectiveness on handwritten digit recognition,face recognition and object recognition utilizing multiple features,bimodal human emotion recognition involving information from both audio and visual domains. The generic nature of LMCCA allows it to take as

01.03.2021
05:13 Zoetrope Genetic Programming for Regression. (arXiv:2102.13388v1 [stat.ML])

The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression.The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.

05:13 Zoetrope Genetic Programming for Regression. (arXiv:2102.13388v1 [stat.ML])

The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression.The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.

25.02.2021
16:28 Scientists use supercomputers to study reliable fusion reactor design, operation

Nuclear fusion, the same kind of energy that fuels stars, could one day power our world with abundant, safe, and carbon-free energy. Aided by supercomputers Summit at the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) and Theta at DOE's Argonne National Laboratory (ANL), a team of scientists strives toward making fusion energy a reality.

13:47 An aggressive market-driven model for US fusion power development

Electricity generated by fusion power plants could play an important role in decarbonizing the U.S. energy sector by

08:20 Position Location for Futuristic Cellular Communications -- 5G and Beyond. (arXiv:2102.12074v1 [cs.IT])

With vast mmWave spectrum and narrow beam antenna technology, precise position location is now possible in 5G and future mobile communication systems. In this article, we describe how centimeterlevel localization accuracy can be achieved, particularly through the use of map-based techniques. We show how data fusion of parallel information streams, machine learning, and cooperative localization techniques further improve positioning accuracy.

08:20 DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques. (arXiv:2102.12191v1 [eess.IV])

Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated widely to classify cervical pap cells. Most of the existing researches require pre-segmented images to obtain good classification results, whereas accurate cervical cell segmentation is challenging because of cell clustering. Some studies rely on handcrafted features, which cannot guarantee the classification stage's optimality. Moreover, DL provides poor performance for a multiclass classification task when there is an uneven distribution of data, which is prevalent

08:20 Position Location for Futuristic Cellular Communications -- 5G and Beyond. (arXiv:2102.12074v1 [cs.IT])

With vast mmWave spectrum and narrow beam antenna technology, precise position location is now possible in 5G and future mobile communication systems. In this article, we describe how centimeterlevel localization accuracy can be achieved, particularly through the use of map-based techniques. We show how data fusion of parallel information streams, machine learning, and cooperative localization techniques further improve positioning accuracy.

23.02.2021
15:01 Daily briefing: Fusion reactor set for fuel test run

06:29 Stochastic fluctuation and transport in the edge tokamak plasmas with the resonant magnetic perturbation field. (arXiv:2102.10733v1 [physics.plasm-ph])

The stochastic layer formation by the penetration of the resonant magnetic perturbation (RMP) field has been considered as a key mechanism in the RMP control of the edge localized mode (ELM) in tokamak plasmas. Here, we provide experimental observations that the fluctuation and transport in the edge plasmas become more stochastic with the more penetration of the RMP field into the plasma. The results support the importance of the stochastic layer formation in the RMP ELM control experiments.

22.02.2021
17:24 Heat loss control method in fusion reactors

The core of a fusion reactor is incredibly hot. Hydrogen that inevitably escapes from it must be cooled on its way to the wall, as otherwise, the reactor wall would be damaged. Researchers from the Dutch institute DIFFER and EPFL's Swiss Plasma Center have developed a strict measurement and control method for the cooling of very hot particles escaping from fusion plasmas.

15:22 Fuel for world’s largest fusion reactor ITER is set for test run

19.02.2021
09:30 Federated Depression Detection from Multi-SourceMobile Health Data. (arXiv:2102.09342v1 [cs.CY])

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.Although researchers hope that artificial intelligence can contribute to the diagnosis and treatment of depression, the traditional centralized machine learning needs to aggregate patient data, and the data privacy of patients with mental illness needs to be strictly confidential, which hinders machine learning algorithms clinical application.To solve the problem of privacy of the medical history of patients with depression, we implement federated learning to analyze and diagnose depression. First, we propose a general multi-view federated learning framework using multi-source data,which can extend any traditional machine learning model to support federated learning across different institutions or parties.Secondly, we adopt late fusion methods to solve the problem of

18.02.2021
05:08 A Mutual Reference Shape for Segmentation Fusion and Evaluation. (arXiv:2102.08939v1 [eess.IV])

This paper proposes the estimation of a mutual shape from a set of different segmentation results using both active contours and information theory. The mutual shape is here defined as a consensus shape estimated from a set of different segmentations of the same object. In an original manner, such a shape is defined as the minimum of a criterion that benefits from both the mutual information and the joint entropy of the input segmentations. This energy criterion is justified using similarities between information theory quantities and area measures, and presented in a continuous variational framework. In order to solve this shape optimization problem, shape derivatives are computed for each term of the criterion and interpreted as an evolution equation of an active contour. A mutual shape is then estimated together with the sensitivity and specificity of each segmentation. Some synthetic examples allow us to cast the light on the difference between the mutual shape and an average

17.02.2021
07:43 An AutoML-based Approach to Multimodal Image Sentiment Analysis. (arXiv:2102.08092v1 [cs.LG])

Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message polarity by creating context and connections. Multimodal sentiment analysis approaches intend to leverage information of both textual and image content to perform an evaluation. Despite recent advances, current solutions still flounder in combining both image and textual information to classify social media data, mainly due to subjectivity, inter-class homogeneity and fusion data differences. In

16.02.2021
10:18 INGRID: an interactive grid generator for 2D edge plasma modeling. (arXiv:2102.07040v1 [physics.plasm-ph])

A new grid generator for tokamak boundary plasma is presented. INGRID (Interactive grid generator) is a Python-based code that can generate grids for boundary plasma modeling for variety of configurations with one or two X-points in the domain. This includes single-null, double-null, and variety of topologically near-snowflake configurations. Users can generate grids through utilization of the INGRID Python package: either interactively via a parameter-file driven GUI, or by embedding in a non-interactive script-controlled workflow. The INGRID workflow consists of three phases: (i) import of magnetic field data and analysis of the magnetic configuration, (ii) creation of a skeleton grid ("Patch Map") via line tracing method, and (iii) refinement of the prototypical grid into a final grid. Performance of INGRID is analyzed by comparison with an existing grid generator. The results demonstrate INGRID's capability to produce grids for boundary plasma modeling in a robust and user-friendly

10:18 Affective State Recognition through EEG Signals Feature Level Fusion and Ensemble Classifier. (arXiv:2102.07127v1 [cs.HC])

Human affects are complex paradox and an active research domain in affective computing. Affects are traditionally determined through a self-report based psychometric questionnaire or through facial expression recognition. However, few state-of-the-arts pieces of research have shown the possibilities of recognizing human affects from psychophysiological and neurological signals. In this article, electroencephalogram (EEG) signals are used to recognize human affects. The electroencephalogram (EEG) of 100 participants are collected where they are given to watch one-minute video stimuli to induce different affective states. The videos with emotional tags have a variety range of affects including happy, sad, disgust, and peaceful. The experimental stimuli are collected and analyzed intensively. The interrelationship between the EEG signal frequencies and the ratings given by the participants are taken into consideration for classifying affective states. Advanced feature extraction

10:18 Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. (arXiv:2102.06883v1 [eess.IV])

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation

15.02.2021
05:59 Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model. (arXiv:2102.06260v1 [cs.CV])

Earth observation offers new insight into anthropogenic changes to nature, and how these changes are effecting (and are effected by) the built environment and the real economy. With the global availability of medium-resolution (10-30m) synthetic aperture radar (SAR) Sentinel-1 and multispectral Sentinel-2 imagery, machine learning can be employed to offer these insights at scale, unbiased to the reporting of companies and countries. In this paper, I introduce DeepSentinel, a data pipeline and experimentation framework for producing general-purpose semantic embeddings of paired Sentinel-1 and Sentinel-2 imagery. I document the development of an extensible corpus of labelled and unlabelled imagery for the purposes of sensor fusion research. With this new dataset I develop a set of experiments applying popular self-supervision methods and encoder architectures to a land cover classification problem. Tile2vec spatial encoding with a self-attention enabled ResNet model outperforms deeper

13.02.2021
23:40 New machine learning theory raises questions about nature of science

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.

12.02.2021
18:35 New machine learning theory raises questions about nature of science

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.

06:35 Comparing spontaneous and pellet-triggered ELMs via non-linear extended MHD simulations. (arXiv:2102.05850v1 [physics.plasm-ph])

Injecting frozen deuterium pellets into an ELMy H-mode plasma is a well established scheme for triggering edge localized modes (ELMs) before they naturally occur. Based on an ASDEX Upgrade H-mode plasma, this article presents a comparison of extended MHD simulations of spontaneous type-I ELMs and pellet-triggered ELMs allowing to study their non-linear dynamics in detail. In particular, pellet-triggered ELMs are simulated by injecting deuterium pellets into different time points during the pedestal build-up described in [A. Cathey et al. Nuclear Fusion 60, 124007 (2020)]. Realistic ExB and diamagnetic background plasma flows as well as the time dependent bootstrap current evolution are included during the build-up to capture the balance between stabilising and destabilising terms for the edge instabilities accurately. Dependencies on the pellet size and injection times are studied. The spatio-temporal structures of the modes and the resulting divertor heat fluxes are compared in detail

10.02.2021
11:41 On the Universal Transformation of Data-Driven Models to Control Systems. (arXiv:2102.04722v1 [math.OC])

As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often

11:41 Drift reduced Landau fluid model for magnetized plasma turbulence simulations in BOUT++ framework. (arXiv:2102.04976v1 [physics.plasm-ph])

Recently the drift-reduced Landau fluid six-field turbulence model within the BOUT++ framework has been upgraded. In particular, this new model employs a new normalization, adds a volumetric flux-driven source option, the Landau fluid closure for parallel heat flux and a Laplacian inversion solver which is able to capture n=0 axisymmetric mode evolution in realistic tokamak configurations. These improvements substantially extended model's capability to study a wider range of tokamak edge phenomena, and are essential to build a fully self-consistent edge turbulence model capable of both transient (e.g., ELM, disruption) and transport time-scale simulations.

11:41 On the Universal Transformation of Data-Driven Models to Control Systems. (arXiv:2102.04722v1 [math.OC])

As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often

09.02.2021
21:36 US Navy Has Patents on Tech It Says Will Engineer the Fabric of Reality (Slashdot)

SlashdotUS Navy Has Patents on Tech It Says Will Engineer the Fabric of Reality - The U.S. Navy has patents on weird and little understood technology. According to patents filed by the Navy, it is working on a compact fusion reactor that could power cities, an engine that works using inertial mass reduction, and a hybrid aerospace-underwater craft. From a report: Dubbed the UFO patents, ...

08.02.2021
21:41 Scientists propose lithium to cope with high-risk condition in future fusion facilities

Perhaps the greatest technological challenge to harvesting on Earth the fusion energy that powers the sun and stars in future tokamak fusion reactors will be controlling the extreme heat that could strike the exhaust system inside the devices. Such heat flow, or flux, could seriously damage the walls of the divertor at the heart of the exhaust system and shut down fusion reactions in the doughnut-shaped facilities.

09:52 Sparse Normal Means Estimation with Sublinear Communication. (arXiv:2102.03060v1 [stat.ML])

We consider the problem of sparse normal means estimation in a distributed setting with communication constraints. We assume there are $M$ machines, each holding a $d$-dimensional observation of a $K$-sparse vector $\mu$ corrupted by additive Gaussian noise. A central fusion machine is connected to the $M$ machines in a star topology, and its goal is to estimate the vector $\mu$ with a low communication budget. Previous works have shown that to achieve the centralized minimax rate for the $\ell_2$ risk, the total communication must be high - at least linear in the dimension $d$. This phenomenon occurs, however, at very weak signals. We show that once the signal-to-noise ratio (SNR) is slightly higher, the support of $\mu$ can be correctly recovered with much less communication. Specifically, we present two algorithms for the distributed sparse normal means problem, and prove that above a certain SNR threshold, with high probability, they recover the correct support with total

09:52 Sparse Normal Means Estimation with Sublinear Communication. (arXiv:2102.03060v1 [stat.ML])

We consider the problem of sparse normal means estimation in a distributed setting with communication constraints. We assume there are $M$ machines, each holding a $d$-dimensional observation of a $K$-sparse vector $\mu$ corrupted by additive Gaussian noise. A central fusion machine is connected to the $M$ machines in a star topology, and its goal is to estimate the vector $\mu$ with a low communication budget. Previous works have shown that to achieve the centralized minimax rate for the $\ell_2$ risk, the total communication must be high - at least linear in the dimension $d$. This phenomenon occurs, however, at very weak signals. We show that once the signal-to-noise ratio (SNR) is slightly higher, the support of $\mu$ can be correctly recovered with much less communication. Specifically, we present two algorithms for the distributed sparse normal means problem, and prove that above a certain SNR threshold, with high probability, they recover the correct support with total

09:52 Nonlinear MHD simulations of external kinks in quasi-axisymmetric stellarators using an axisymmetric external rotational transform approximation. (arXiv:2102.03080v1 [physics.plasm-ph])

Reduced magnetohydrodynamic (MHD) equations are used to study the nonlinear dynamics of external kinks in quasi-axisymmetric (QA) stellarators with varying fractions of external rotational transform. The large bootstrap currents associated with high beta plasmas typically make QA configurations susceptible to low n external modes, limiting their operational space. The violence of the nonlinear dynamics, and, in particular, when these modes lead to a disruption, is not yet understood. In this paper, the nonlinear phase of external kinks in an unstable QA configuration with an edge safety factor below two is simulated. The nonlinear MHD code JOREK is used, validating the linear dynamics against CASTOR3D. The external rotational transform is approximated axisymmetrically, and varied from the tokamak limit to understand its influence on the nonlinear dynamics. The violence of the kink instability is quantified, and shown to reduce with the increasing external field. At the same time,

09:52 Sparse Normal Means Estimation with Sublinear Communication. (arXiv:2102.03060v1 [stat.ML])

We consider the problem of sparse normal means estimation in a distributed setting with communication constraints. We assume there are $M$ machines, each holding a $d$-dimensional observation of a $K$-sparse vector $\mu$ corrupted by additive Gaussian noise. A central fusion machine is connected to the $M$ machines in a star topology, and its goal is to estimate the vector $\mu$ with a low communication budget. Previous works have shown that to achieve the centralized minimax rate for the $\ell_2$ risk, the total communication must be high - at least linear in the dimension $d$. This phenomenon occurs, however, at very weak signals. We show that once the signal-to-noise ratio (SNR) is slightly higher, the support of $\mu$ can be correctly recovered with much less communication. Specifically, we present two algorithms for the distributed sparse normal means problem, and prove that above a certain SNR threshold, with high probability, they recover the correct support with total

07.02.2021
10:24 New fiber optic temperature sensing approach to keep fusion power plants running

The pursuit of fusion as a safe, carbon-free, always-on energy source has intensified in recent years, with a

05.02.2021
17:36 New fiber optic temperature sensing approach to keep fusion power plants running

The pursuit of fusion as a safe, carbon-free, always-on energy source has intensified in recent years, with a number of organizations pursuing aggressive timelines for technology demonstrations and power plant designs. New-generation superconducting magnets are a critical enabler for many of these programs, which creates growing need for sensors, controls, and other infrastructure that will allow the magnets to operate reliably in the harsh conditions of a commercial fusion power plant.

10:55 Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms. (arXiv:2102.02637v1 [cs.LG])

Data is evolving with the rapid progress of population and communication for various types of devices such as networks, cloud computing, Internet of Things (IoT), actuators, and sensors. The increment of data and communication content goes with the equivalence of velocity, speed, size, and value to provide the useful and meaningful knowledge that helps to solve the future challenging tasks and latest issues. Besides, multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis. It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria in providing insights to big data. On the other hand, the derivations are made for it to go with the approximations to increase the duality of runtime and improve the entire system's potentiality and efficacy. In essence, several fields, including business,

04.02.2021
06:26 Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond. (arXiv:2102.01998v1 [cs.AI])

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms can not manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical

06:26 Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond. (arXiv:2102.01998v1 [cs.AI])

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms can not manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical

02.02.2021
11:52 Information fusion between knowledge and data in Bayesian network structure learning. (arXiv:2102.00473v1 [cs.AI])

Bayesian Networks (BNs) have become a powerful technology for reasoning under uncertainty, particularly in areas that require causal assumptions that enable us to simulate the effect of intervention. The graphical structure of these models can be determined by causal knowledge, learnt from data, or a combination of both. While it seems plausible that the best approach in constructing a causal graph involves combining knowledge with machine learning, this approach remains underused in practice. This paper describes and evaluates a set of information fusion methods that have been implemented in the open-source Bayesys structure learning system. The methods enable users to specify pre-existing knowledge and rule-based information that can be obtained from heterogeneous sources, to constrain or guide structure learning. Each method is assessed in terms of structure learning impact, including graphical accuracy, model fitting, complexity and runtime. The results are illustrated both with

01.02.2021
18:12 America on a new fast track to fusion energy

The giant appropriations bill signed by US President Donald Trump on December 27 contains a little-noticed section that sets the goal of creating a full-fledged fusion industry, a new industrial sector centered on the commercialization of nuclear fusion as an energy source. The envisioned model is the successful public-private partnership that built up America’s commercial space industry. In an exclusive interview with Asia Times, Fusion Industry Association director Andrew Holland tells the inside story to correspondent Jonathan Tenennbaum.

07:31 Development of New Tracking Detector with Fine-grained Nuclear Emulsion for sub-MeV Neutron Measurement. (arXiv:2101.12424v1 [physics.ins-det])

In this study, we have developed a new sub-MeV neutron detector that has a high position resolution, energy resolution, directional sensitivity, and low background. The detector is based on a super-fine-grained nuclear emulsion, called the Nano Imaging Tracker (NIT), and it is capable of detecting neutron induced proton recoils as tracks through topological analysis with sub-micrometric accuracy. We used a type of NIT with AgBr:I crystals of (98 +- 10) nm size dispersed in the gelatin. First, we calibrated the performance of NIT device for detecting monochromatic neutrons with sub-MeV energy generated by nuclear fusion reactions, and the detection efficiency for recoil proton tracks of more than 2 um range was consistently 100\% (the 1 sigma lower limit was 83%) in accordance with expectations by manual based analysis. In addition, recoil energy and angle distribution obtained good agreement with kinematical expectation. The primary neutron energy was reconstructed by using them, and

29.01.2021
08:49 Biomembranes undergo complex, non-axisymmetric deformations governed by Kirchhoff-Love kinematics and revealed by a three dimensional computational framework. (arXiv:2101.11929v1 [q-bio.QM])

Biomembranes play a central role in various phenomena like locomotion of cells, cell-cell interactions, packaging of nutrients, and in maintaining organelle morphology and functionality. During these processes, the membranes undergo significant morphological changes through deformation, scission, and fusion. Modeling the underlying mechanics of such morphological changes has traditionally relied on reduced order axisymmetric representations of membrane geometry and deformation. Axisymmetric representations, while robust and extensively deployed, suffer from their inability to model symmetry breaking deformations and structural bifurcations. To address this limitation, a 3D computational mechanics framework for high fidelity modeling of biomembrane deformation is presented. The proposed framework brings together Kirchhoff-Love thin-shell kinematics, Helfrich-energy based mechanics, and state-of-the-art numerical techniques for modeling deformation of surface geometries. Lipid bilayers

08:49 Theory-based scaling laws of near and far scrape-off layer widths in single-null L-mode discharges. (arXiv:2101.11848v1 [physics.plasm-ph])

Theory-based scaling laws of the near and far scrape-off layer (SOL) widths are analytically derived for L-mode diverted tokamak discharges by using a two-fluid model. The near SOL pressure and density decay lengths are obtained by leveraging a balance among the power source, perpendicular turbulent transport across the separatrix, and parallel losses at the vessel wall, while the far SOL pressure and density decay lengths are derived by using a model of intermittent transport mediated by filaments. The analytical estimates of the pressure decay length in the near SOL is then compared to the results of three-dimensional, flux-driven, global, two-fluid turbulence simulations of L-mode diverted tokamak plasmas, and validated against experimental measurements taken from an experimental multi-machine database of divertor heat flux profiles, showing in both cases a very good agreement. Analogously, the theoretical scaling law for the pressure decay length in the far SOL is compared to

28.01.2021
11:35 Visible spectra of W8+ in an electron-beam ion trap. (arXiv:2101.11193v1 [physics.atom-ph])

To provide spectroscopic data for lowly charged tungsten ions relevant to fusion research, this work focuses on the W8+ ion. Six visible spectra lines in the range of 420-660 nm are observed with a compact electron-beam ion trap in Shanghai. These lines are assigned to W8+ based on their intensity variations as increasing electron-beam energy and the M1 line from the ground configuration in W7+. Furthermore, transition energies are calculated for the 30 lowest levels of the 4f14 5s2 5p4, 4f13 5s2 5p5 and 4f12 5s2 5p6 configurations of W8+ by using the flexible atomic code (FAC) and GRASP package, respectively. Reasonably good agreement is found between our two independent atomic-structure calculations. The resulting atomic parameters are adopted to simulate the spectra based on the collisional-radiative model implemented in the FAC code. This assists us with identification of six strong M1 transitions in 4f13 5s2 5p5 and 4f12 5s2 5p6 configurations from our experiments

26.01.2021
09:55 Plasma steering in ITER to avoid disruptions. (arXiv:2101.10138v1 [physics.plasm-ph])

Steering ITER plasmas is commonly viewed as a way to avoid disruptions and runaway electrons. Plasma steering sounds as safe as driving to work but will be shown to more closely resemble driving at high speed through a dense fog on an icy road. The long time required to terminate an ITER discharge compared to time over which dangers can be foreseen is analogous to driving in a dense fog. The difficulty of regaining plasma control if it is lost resembles driving on an icy road. Disruptions and runaways are three coupled issues -- a solution to one tends to complicate the solution to the other two: loss of position control of the plasma, excessive power deposition particularly on the divertor, and wall melting due to runaway electrons. All three risks must be addressed for ITER to achieve its mission and essentially eliminated before tokamak power plants can be deployed.

09:55 Analytical edge power loss at the lower hybrid resonance: comparison with ANTITER IV and application to ICRH systems. (arXiv:2101.09503v1 [physics.plasm-ph])

In non-inverted heating scenarios, a lower hybrid (LH) resonance can appear in the plasma edge of tokamaks. This resonance can lead to large edge power deposition when heating in the ion cyclotron resonance frequency (ICRF) range. In this paper, the edge power loss associated with this LH resonance is analytically computed for a cold plasma description using an asymptotic approach and analytical continuation. This power loss can be directly linked to the local radial electric field and is then compared to the corresponding power loss computed with the semi-analytical code ANTITER IV. This method offers the possibility to check the precision of the numerical integration made in ANTITER IV and gives insights in the physics underlying the edge power absorption. Finally, solutions to minimize this edge power absorption are investigated and applied to the case of ITER's ion cyclotron resonance heating (ICRH) launcher. This study is also of direct relevance to DEMO.

25.01.2021
05:17 Potential Early Markets for Fusion Energy. (arXiv:2101.09150v1 [physics.soc-ph])

We identify potential early markets for fusion energy and their projected cost targets, based on analysis and synthesis of many relevant, recent studies and reports. Because private fusion companies aspire to start commercial deployment before 2040, we consider potential markets for fusion in 2035, including electricity, process heat, and hydrogen production. We variously consider "business-as-usual" and high-renewables-penetration scenarios, as well as carbon pricing up to 100 \$/tCO$_2$. Key findings are that fusion developers should focus initially on high-priced global electricity markets and include integrated thermal storage in order to maximize revenue and compete in markets with high renewables penetration. Process heat and hydrogen production will be tough early markets for fusion, but may open up to fusion as markets evolve and if fusion's levelized cost of electricity falls below 50 \$/MWh$_\mathrm{e}$. Finally, we discuss potential ways for a fusion plant to increase

05:17 A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion. (arXiv:2101.08969v1 [cs.CR])

In recent years, malware becomes more threatening. Concerning the increasing malware variants, there comes Machine Learning (ML)-based and Deep Learning (DL)-based approaches for heuristic detection. Nevertheless, the prediction accuracy of both needs to be improved. In response to the above issues in the PE malware domain, we propose the DL-based approaches for detection and use static-based features fed up into models. The contributions are as follows: we recapitulate existing malware detection methods. That is, we propose a vec-torized representation model of the malware instruction layer and semantic layer based on Glove. We implement a neural network model called MCC_RCNN (Malware Detection and Recurrent Convolutional Neural Network), comprising of the combination with CNN and RNN. Moreover, we provide a description of feature fusion in static behavior levels. With the numerical results generated from several comparative experiments towards evaluating the Glove-based

22.01.2021
05:56 New developments regarding the JOREK solver and physics based preconditioner. (arXiv:2101.08646v1 [physics.comp-ph])

The JOREK extended magneto-hydrodynamic (MHD) code is a widely used simulation code for studying the non-linear dynamics of large-scale instabilities in divertor tokamak plasmas. Due to the large scale-separation intrinsic to these phenomena both in space and time, the computational costs for simulations in realistic geometry and with realistic parameters can be very high, motivating the investment of considerable effort for optimization. In this article, a set of developments regarding the JOREK solver and preconditioner is described, which lead to overall significant benefits for large production simulations. This comprises in particular enhanced convergence in highly non-linear scenarios and a general reduction of memory consumption and computational costs. The developments include faster construction of preconditioner matrices, a domain decomposition of preconditioning matrices for solver libraries that can handle distributed matrices, interfaces for additional solver libraries, an

20.01.2021
11:22 Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection. (arXiv:2101.07342v1 [eess.IV])

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform

11:22 Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection. (arXiv:2101.07342v1 [eess.IV])

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform

19.01.2021
17:45 A realistic model of the ITER tokamak magnetic fusion device

Tokamaks, devices that use magnetic fields to confine plasma into torus-shaped chamber, could play a crucial role in the development of highly performing nuclear fusion reactors. The ITER tokamak, which is set to be the largest nuclear tokamak in the world, is particularly likely to shape the way in which nuclear reactors will be fabricated in the future.

07:01 Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design. (arXiv:2010.15208v1 [physics.plasm-ph] CROSS LISTED)

A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulations codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g. yield, shape) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the

07:01 CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering. (arXiv:2101.06883v1 [cs.AI])

With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more satisfactory clustering results. However, existing deep clustering methods usually ignore the relationship between the data. Fortunately, the graph convolutional network can handle such relationship, opening up a new research direction for deep clustering. In this paper, we propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN), which contains four main modules: the cross-attention fusion module which innovatively concatenates the Content Auto-encoder module (CAE) relating to the individual data and Graph Convolutional Auto-encoder module (GAE) relating to the relationship between the data in a layer-by-layer manner, and the self-supervised model that highlights the discriminative information for clustering tasks. While the

18.01.2021
09:43 The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements. (arXiv:2101.06053v1 [cs.MM])

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible `in-the-wild' properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's

09:29 Magnetized Rutherford scattering angle for electron-ion collision in plasma. (arXiv:2101.05943v1 [physics.plasm-ph])

Rutherford scattering formula plays an important role in plasma classical transport. It is urgent to need a magnetized Rutherford scattering formula since the magnetic field increases significantly in different fusion areas (e.g. tokamak magnetic field, self-generated magnetic field, and compressed magnetic field). The electron-ion Coulomb collisions perpendicular to the external magnetic field are studied in this paper. The scattering angle is defined according to the electron trajectory and asymptotic line (without magnetic field). A magnetized Rutherford scattering formula is obtained analytically under the weak magnetic field approximation. It is found that the scattering angle decreases as external magnetic field increases. It is easy to find the scattering angle decreasing significantly as incident distance, and incident velocity increasing. It is shown that the theoretical results agree well with numerical calculation by checking the dependence of scattering angle on external

15.01.2021
10:09 A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing. (arXiv:2101.05605v1 [cs.LG])

To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machine setting parameters, such as laser angle and part pose. However, these setting-based models are machine dependent, hence they often do not transfer to analysis of porosity for a different machine. To address the first problem, a physics-informed, data-driven model (PIM), which instead of directly using machine setting parameters to predict porosity levels of printed parts, it first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure. Then, these physical, machine independent effects are used to predict porosity levels according to pass, flag, fail categories instead of focusing on quantitative pore size prediction. With six learning methods evaluation, PIM proved to achieve good performances with prediction error of 10$\sim$26%. Finally,

10:09 Feature reduction for machine learning on molecular features: The GeneScore. (arXiv:2101.05546v1 [q-bio.GN])

We present the GeneScore, a concept of feature reduction for Machine Learning analysis of biomedical data. Using expert knowledge, the GeneScore integrates different molecular data types into a single score. We show that the GeneScore is superior to a binary matrix in the classification of cancer entities from SNV, Indel, CNV, gene fusion and gene expression data. The GeneScore is a straightforward way to facilitate state-of-the-art analysis, while making use of the available scientific knowledge on the nature of molecular data features used.

10:09 Real or Virtual? Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios. (arXiv:2101.05272v1 [cs.HC])

Augmented Reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) data collected in Augmented Reality scenarios. A shallow convolutional neural net classified 3 second data windows from 20 participants in a person-dependent manner with an average accuracy above 70\% if the testing data and training data came from different trials. Person-independent classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a Brain-Computer Interface is high enough for it to be treated as a useful input mechanism for Augmented Reality applications.

09:56 Feature reduction for machine learning on molecular features: The GeneScore. (arXiv:2101.05546v1 [q-bio.GN])

We present the GeneScore, a concept of feature reduction for Machine Learning analysis of biomedical data. Using expert knowledge, the GeneScore integrates different molecular data types into a single score. We show that the GeneScore is superior to a binary matrix in the classification of cancer entities from SNV, Indel, CNV, gene fusion and gene expression data. The GeneScore is a straightforward way to facilitate state-of-the-art analysis, while making use of the available scientific knowledge on the nature of molecular data features used.

09:56 Re-examining the Role of Nuclear Fusion in a Renewables-Based Energy Mix. (arXiv:2101.05727v1 [physics.soc-ph])

Fusion energy is often regarded as a long-term solution to the world's energy needs. However, even after solving the critical research challenges, engineering and materials science will still impose significant constraints on the characteristics of a fusion power plant. Meanwhile, the global energy grid must transition to low-carbon sources by 2050 to prevent the worst effects of climate change. We review three factors affecting fusion's future trajectory: (1) the significant drop in the price of renewable energy, (2) the intermittency of renewable sources and implications for future energy grids, and (3) the recent proposition of intermediate-level nuclear waste as a product of fusion. Within the scenario assumed by our premises, we find that while there remains a clear motivation to develop fusion power plants, this motivation is likely weakened by the time they become available. We also conclude that most current fusion reactor designs do not take these factors into account and, to

09:56 Formation of edge pressure pedestal and reversed magnetic shear due to toroidal rotation in a tokamak equilibrium. (arXiv:2101.05606v1 [physics.plasm-ph])

Toroidal rotation is well known to play significant roles in the edge transport and L-H transition dynamics of tokamaks. Our recent calculation finds that a sufficiently strong localized toroidal rotation can directly bring out the formation of edge pressure pedestal with reversed magnetic shear that is reminiscent of an H-mode plasma, purely through the effects of toroidal rotation on the tokamak MHD equilibrium itself. In particular, the enhanced edge toroidal rotation enables a substantial peaking of the parallel current profile near edge in higher $\beta$ regimes, which leads to the flattening or reversal of the local $q$ (safety factor) profile. Here the formation of pressure pedestal along with the reversed magnetic shear region is shown to be the natural outcome of the MHD tokamak equilibrium in a self-consistent response to the presence of a localized toroidal rotation typically observed in H-mode or QH-mode.

14.01.2021
05:46 Energy-Efficient Node Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks. (arXiv:2101.04780v1 [cs.IT])

We study a heterogeneous wireless sensor network (WSN) where N heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to M heterogeneous fusion centers (FCs) via multi-hop wireless communication. This heterogeneous node deployment problem is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where nodes retain their deployed position, and mobile WSNs where nodes can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal node deployment in static WSNs, we propose an iterative algorithm to deploy nodes. In addition, we study the necessary conditions of the optimal movement-efficient node deployment in mobile WSNs with constrained movement energy, and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed

05:46 Energy-Efficient Node Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks. (arXiv:2101.04780v1 [cs.IT])

We study a heterogeneous wireless sensor network (WSN) where N heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to M heterogeneous fusion centers (FCs) via multi-hop wireless communication. This heterogeneous node deployment problem is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where nodes retain their deployed position, and mobile WSNs where nodes can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal node deployment in static WSNs, we propose an iterative algorithm to deploy nodes. In addition, we study the necessary conditions of the optimal movement-efficient node deployment in mobile WSNs with constrained movement energy, and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed

05:33 Analytic quasi-steady evolution of a marginally unstable wave in the presence of drag and scattering. (arXiv:2101.04875v1 [physics.plasm-ph])

The 1D bump-on-tail problem is studied in order to determine the influence of drag on quasi-steady solutions near marginal stability ($1-\gamma_d/\gamma_L\ll 1$) when effective collisions are much larger than the instability growth rate ($\nu \gg \gamma$). In this common tokamak regime, it is rigorously shown that the paradigmatic Berk-Breizman cubic equation for the nonlinear mode evolution reduces to a much simpler differential equation, dubbed the time-local cubic equation, which can be solved directly. It is found that in addition to increasing the saturation amplitude, drag introduces a shift in the apparent oscillation frequency by modulating the saturated wave envelope. Excellent agreement is found between the analytic solution for the mode evolution and both the numerically integrated Berk-Breizman cubic equation and fully nonlinear 1D Vlasov simulations. Experimentally isolating the contribution of drag to the saturated mode amplitude for verification purposes is explored but

05:33 Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional. (arXiv:2101.04806v1 [cond-mat.stat-mech])

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye towards studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ice Ih and

13.01.2021
08:32 Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion. (arXiv:2101.04631v1 [eess.IV])

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertainty that can limit further advancements. This uncertainty is traditionally mitigated through different ensemble approaches. However, such ensembles are prohibitively costly with deep networks, which are already large in size. Our work focuses on pushing the performance limits of state-of-the-art methods on Gaussian denoising. We propose a model-agnostic approach for reducing epistemic uncertainty while using only a single pretrained network. We achieve this by tapping into the epistemic uncertainty through augmented and frequency-manipulated images to obtain denoised images with varying

08:19 Analysis of deviation from neoclassical ion equilibrium against electron and ion temperature profiles in T-10 tokamak. (arXiv:2101.04180v1 [physics.plasm-ph])

The results of the analysis of the deviation of the force equilibrium for ions from the neoclassical theory prediction, calculated using the direct measurements of the radial electric field, in the view of its possible local and nonlocal correlation with the profiles of electron, Te, and ion, Ti, temperatures in the T-10 tokamak are presented. Local correlations are analyzed by means of the Pearson's correlation. Nonlocal correlations are treated with an inverse problem under the assumption of an integral equation relationship between the deviation and Te and Ti profiles. The discharges with zero, weak and strong auxiliary heating (electron cyclotron resonance heating) are analyzed. It is found that the electrons substantially (not less than ions) contribute to the deviation of the ion equilibrium from the neoclassical theory prediction both in the local and nonlocal models.

12.01.2021
10:55 Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation. (arXiv:2101.03678v1 [cs.LG])

For health prognostic task, ever-increasing efforts have been focused on machine learning-based methods, which are capable of yielding accurate remaining useful life (RUL) estimation for industrial equipment or components without exploring the degradation mechanism. A prerequisite ensuring the success of these methods depends on a wealth of run-to-failure data, however, run-to-failure data may be insufficient in practice. That is, conducting a substantial amount of destructive experiments not only is high costs, but also may cause catastrophic consequences. Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both non-cyclic and cyclic degradation patterns for the first time. It is designed to enrich data from a data-driven way, generating realistic-like time-series to enhance current RUL methods. First, high-quality data generation is ensured through the proposed convolutional recurrent generative adversarial network (CR-GAN), which

10:55 Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems. (arXiv:2101.03367v1 [cs.LG])

Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing. These networked multi-agent systems require fast, communication-efficient and distributed machine learning (ML) to provide mission critical control functionalities. Distributed ML techniques, including federated learning (FL), represent a mushrooming multidisciplinary research area weaving in sensing, communication and learning. FL enables continual model training in distributed wireless systems: rather than fusing raw data samples at a centralized server, FL leverages a cooperative fusion approach where networked agents, connected via URLLC, act as distributed learners that periodically exchange their locally trained model parameters. This article explores emerging opportunities of FL for the next-generation networked industrial systems. Open problems are discussed, focusing on cooperative driving in

10:55 The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics. (arXiv:2101.03203v1 [cs.CV])

The prevalence of Diabetes mellitus (DM) in the Middle East is exceptionally high as compared to the rest of the world. In fact, the prevalence of diabetes in the Middle East is 17-20%, which is well above the global average of 8-9%. Research has shown that food intake has strong connections with the blood glucose levels of a patient. In this regard, there is a need to build automatic tools to monitor the blood glucose levels of diabetics and their daily food intake. This paper presents an automatic way of tracking continuous glucose and food intake of diabetics using off-the-shelf sensors and machine learning, respectively. Our system not only helps diabetics to track their daily food intake but also assists doctors to analyze the impact of the food in-take on blood glucose in real-time. For food recognition, we collected a large-scale Middle-Eastern food dataset and proposed a fusion-based framework incorporating several existing pre-trained deep models for Middle-Eastern food

11.01.2021
06:33 A review for Tone-mapping Operators on Wide Dynamic Range Image. (arXiv:2101.03003v1 [eess.IV])

The dynamic range of our normal life can exceeds 120 dB, however, the smart-phone cameras and the conventional digital cameras can only capture a dynamic range of 90 dB, which sometimes leads to loss of details for the recorded image. Now, some professional hardware applications and image fusion algorithms have been devised to take wide dynamic range (WDR), but unfortunately existing devices cannot display WDR image. Tone mapping (TM) thus becomes an essential step for exhibiting WDR image on our ordinary screens, which convert the WDR image into low dynamic range (LDR) image. More and more researchers are focusing on this topic, and give their efforts to design an excellent tone mapping operator (TMO), showing detailed images as the same as the perception that human eyes could receive. Therefore, it is important for us to know the history, development, and trend of TM before proposing a practicable TMO. In this paper, we present a comprehensive study of the most well-known TMOs, which