dataesr/scientific-paragraphs-categorization huggingface dataset:
Qualitative Comparison: Fig. 8  illustrates generated saliency maps of different methods on challenging cases: cluttered background and foreground with a similar appearance (1 st -2 nd rows), human in the scene (3 rd -5 th rows), and low contrast on the depth map (6 th -7 th rows). Compared to the SOTA models, our HiDAnet yields results closer to the groundtruth masks. For the motorbike in the 1 st row, our model can selectively remove the background region (board). For the sculpture in the 2 nd row, our network pays local attention to the foreground and thus the hollow part can be detailed. We can also accurately extract the human with large deformations (3 rd -5 th rows). RGB Depth GT Ours SPnet DFMnet DCFnet BBSnet DASnet TriTrans JLDCF Dataset Size NLPR NJU2K STERE SIP Metric Mb F β ↑ E m ↑ F β ↑ E m ↑ F β ↑ E m ↑ F β ↑ E m ↑ Res2Net50 + Robustness against Depth Noise: Tab.
IV reports the robustness analysis on the depth quality. To make a fair comparison, we conduct experiments and compare with the SOTA SPnet  [13]  and CMINet  [12]  under the same inferior condition with a simulated Gaussian noise on depth. We further evaluate the performances on the simulated noisy testing dataset. The noise level is defined by the conventional metrics RMSE and δ 1. While RMSE and δ 1 are 0, we report the performance tested with the vanilla dataset (without noise). Drop ∆ denotes the performance degradation by % under the simulated depth noise. Note that CMINet designs a multi-scale mutual information minimization during the encoding stage and lately merge multi-modal features at the semantic level, yielding an unsatisfactory performance while dealing with noisy datasets (drop 2.
A. Comparison with Vanilla Channel Attention We propose granularity-based attention (GBA) referring to geometric priors, which differs from the traditional channel attention on the pooling strategies. Formally, let z ∈ R C be the    squeezed spatial information from feature x ∈ R H×W ×C. Accordingly, we can obtain three variations of average pooling: Dataset DES NLPR NJU2K Metric RMSE δ 1 M ↓ F β ↑ S m ↑ E m ↑ RMSE δ 1 M ↓ F β ↑ S m ↑ E m ↑ RMSE δ 1 M ↓ F β ↑ S m ↑ E m ↑ # Description DES NLPR NJU2K STERE M ↓ F β ↑ M ↓ F β ↑ M ↓ F β ↑ M ↓ F β ↑ I Vanilla Global (I) z = ∑ ∑ x(. ) H ×W ; (II) z = ∑ ∑ x(. ) • m i () H ×W ; (III) z = ∑ ∑ x(. ) • m i (. ) ∑ ∑ m i (. ) (9) where (I) denotes the vanilla global average pooling, (II) is the global pooling with local region m i (. ), and (III) is our proposed GBA module that applies local pooling with local region m i (.
While augmenting the number of thresholds to T = 2, we observe from the 5 th column that the scene can be better discretized. The fine-grained details contribute to the clearer boundary generation as shown in the 6 th column. We further augment the number of thresholds to T = 3 and observe the over-discretization, leading to the misunderstanding on the depth map. Thus, it results in lower quality salient masks as shown in the 8 th column. Thus, we perform the experiments with different numbers of thresholds T. Tab. VII shows that the best overall performance is achieved with T = 2 thresholds, thus with n = 3 regions. It can be considered as a scene discretization into three parts: close, middle, and far regions. Our plain HiDAnet is with T = 2 thresholds and achieves the best performance. We also discover that the sensitivity to thresholding varies from one dataset to another, especially the NLPR dataset which is not highly sensitive to the granularity.
D. Ablation study on Key Components Tab. VIII presents a thorough ablation study for each key component. We observe that by gradually adding proposed modules, our network leads to better performance. We also conduct experiments by replacing our proposed modules with several SOTA counterparts. Specifically, we compare our Granularity-Based Attention with the DEDA module proposed in  [6]. Both our GBA and DEDA belong to the mask-guided attention modules. Specifically, DEDA leverages the depth map to dynamically learn the masked-guided attention map which is supervised by the ground truth. The learned attention map refers to the contrast to guide RGB learning. Differently, our mask is statically computed by the Otsu threshold by maximizing inter-class variance. The computed local regions refer to the fine-grained details which are further integrated with semantics cues.
ACKNOWLEDGEMENTS We gratefully acknowledge  Zhuyun Zhou ,  Jilai Zheng ,  Renato Martins  and  Jingjing Wang  for discussion and proofreading. Part of the calculations were performed using HPC resources from  DNUM-CCUB (Université de Bourgogne ). This research is supported in part by the  French National Research Agency  through  ANR CLARA  ( ANR-18-CE33-0004 ), the  French   ADVANCES  project  ISITE-BFC  project ( ANR-15-IDEX-0003 ), and is financed by the  French Conseil Régional de Bourgogne-Franche-Comté.
Two important consequences can be drawn from this observation: (1) The sole data of the T 2 distribution cannot be informative of the demographic model, if we don't know if it is structured or not, since whatever it is, there is always a panmictic model which will provide exactly this T 2 distribution. Indeed, it is sufficient to choose the inverse of the coalescence rate as the size change. (2) What software like PSMC infers, on data from a single diploid genome and however long it may be, is the IICR associated with the demographic model, which is usually not the change in size of the population when it is structured. Taking the second consequence further, as a proof of concept we built a constant size demographic model based solely on a symmetric island model, with the number of islands also constant, where only the migration parameter is allowed to vary.
We then showed that in the simple case of a population structured in islands, then adding the information of the T 3 distribution to the T 2 distribution is enough to distinguish this model from the panmictic model having the same T 2 distribution, thus the same IICR  (Grusea et al. , 2018). This result provides theoretical evidence that a sample size strictly greater than two is sufficient to distinguish an island structured model from a panmictic model, but initial attempts to move into practice have not yet been successful, because of the precision required, which often blends into the noise of the real data. Structure and IICR: sampling strategy. Exploratory work was then done  (Chikhi et al. , 2018) , using simulated data, to find out what signatures are left on the IICR produced by different types of structured models, and thus indirectly (or directly when dealing with software of the same type as PSMC) on the false signals of size changes that these models generate.
These explanations allow to numerically determine the theoretical IICRs of a large number of structured models, such as the continent-islands model in Figure  6 , with possible changes in demographic parameters, such as subpopulation sizes or migration rates. 20 , and migration rates proportional to sizes between islands and the continent (no migration between islands). We find, as in the simulations in  Chikhi et al. (2018) , the importance of sampling location, as well as the obvious false signals of population size changes that software such as PSMC could infer, here the demographic model being constant over time. Also as a proof of concept, an extended fictional model of human evolution was proposed, integrating Neanderthals alongside modern humans in the same constant size structured model, with only the migration coefficients allowed to change.
The challenges of complexity and computation time, even in the simplest case of the symmetric island model, have been overcome thanks to the work of Armando Arredondo, part of his PhD thesis  (Arredondo Soto, 2021) , with the design and realization of a software for inferring such parameters, called SNIF (Structured Non-stationary Inferential Framework), presented in  Arredondo et al. (2021). Testing this software on simulated data revealed a first problem of identifiability between subpopulation sizes and migration rates. Second, if we want to obtain an acceptable level of precision for the estimation of the migration rates, the number of different values over time (this number is called the number of "components") should not be too large, generally not more than 5 or 6. On the other hand, the estimated number of subpopulations is extremely reliable.
A synthesis of these first results can be seen on Figure  9. An application on human data also allows to find a good model in symmetrical islands which explains surprisingly well the graph produced by the PSMC, see Figure  10. This method has already been used to contribute to the study of the evolution of species of microcebes, Malagasy lemuriform primates (Microcebus murinus and Microcebus ravelobensis). The results are published in  (Teixeira et al. , 2021). Other data on other species of mammals are being analysed using this software.
It now remains to implement this algorithm in the inference software. IICR k : derivation and first results. Coming back to the coalescences times T k of such a sample of size k, their IICR (noted here IICR k ) for k > 2 is theoretically easily computable thanks to the infinitesimal generator of equation (  3 ) and the extensions exposed in section 3. Indeed, the IICR k of the first time T k of coalescence of k lineages can be defined in the same way as the  IICR k (t) = f T k (t) P(T k > t). While we know that in the panmictic case we have ∀k ≥ 2, ∀t > 0, IICR k (t) = 1 k 2 IICR 2 (t), this is not the case for a structured model (as we formally showed for the symmetric island model in  Grusea et al. (2018) ). There already exist powerful methods to estimate the IICR k of real genomic data of sample size k, notably the extensions of the PSMC, called MSMC (for Multiple Sequentially Markov Coalescent, see  Schiffels and Durbin (2013) ).
Conclusion and prospects In summary, the IICR of T 2 , despite its intrinsic limitations, on the one hand because it is a distribution of a variable which is not directly observable, and on the other hand because it is based on a sample of size 2, proves to be an extremely fertile object of modeling. The matrix writing of this time function (it should be noted that the mathematical objects studied in our work are part of a general formalism coming from the theory of phase-type distributions, see for example  Hobolth et al. (2019) ) facilitates precise numerical calculations  (Rodríguez et al. , 2018) , and makes accessible powerful inference methods, such as the recently developed SNIF program  (Arredondo et al. , 2021). The IICR also sheds new light on a concept with which it is naturally associated, that of effective size, which is the source of an abundant literature (see, for example  Charlesworth (2009) , or the recent Waples (2022) for a complete review), and is subject to many different, sometimes contradictory, interpretations.
Acknowledgements As we said in the preamble, the results presented here are the result of a direct collaboration of many researchers in biology and mathematics, a list of which can hardly be exhaustive and can be found here:  Armando Arredondo Soto ,  Simon Boitard ,  Alexandre Changenet ,  Lounès Chikhi ,  Josué Corujo Rodríguez ,  Simona Grusea ,  Max Halford ,  Marine Ha-Shan ,  Alexane Jouniaux ,  Inês Lourenço ,  Beatriz Mourato ,  Khoa N'Guyen ,  Cyriel Paris ,  Didier Pinchon ,  Willy Rodríguez Valcarce ,  Jordi Salmona ,  Patricia Santos ,  Rémi Tournebize. But it should also be stressed that these results would not have been possible without the direct or indirect collaboration of a multitude of researchers that it is impossible to mention here, and of which  Camille Noûs  is the representative.
Consequently, DA approaches have been specifically designed (e. g. ,  Aydogdu et al. , 2019; Sampson et al. , 2021)  and implemented in the context of neXtSIM by  Cheng et al. (2023). Another drawback of using a Lagrangian framework in a sea ice model is that coupling to existing ocean and atmospheric model components, which are virtually all Eulerian, requires the use of a coupler on a fixed exchange grid  (Boutin et al. , 2023). As such a coupling is essential for climate simulations, a new version of neXtSIM based on an Eulerian framework is being developed under the Scale-Aware Sea Ice Project (SASIP; https://sasip-climate. github. io/). It is in the light of these developments that this study is inscribed. In particular, the dynamics-only sea ice MEB model of  Dansereau et al. (2016 Dansereau et al.
Acknowledgements. The authors acknowledge the support of the  Scale-Aware Sea Ice Project   (SASIP)  funded by  Schmidt Futures - a philanthropic initiative that seeks to improve  societal outcomes through the development of emerging science and technologies. YC has also been funded by the  UK Natural Environment Research Council   LTS-S award   NE/R016518/1. CEREA is a member of  Institut Pierre-Simon Laplace (IPSL).
This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i. e. , basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energydensity physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the stateof-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy.
This means that we have to look at lossy methods, which bring the fidelity of the reduced data into question. Fidelity and reduction are directly in competition with each other, and so it is important to consider what exactly is required of a reduced dataset in order for it to serve as a scientifically useful surrogate. Lossy compressors should be flexible with regard to the structure of the data, generalize to arbitrarily high dimensions, and allow control of errors both in the original degrees of freedom and in downstream QoIs. Compressing data in the same high-dimensional space where it is defined can make more of the data's spatial correlations visible to the compression algorithm, resulting in higher compression ratios. Similarly, compression algorithms should make use of as much of the data's spatial structure as possible.
Compressing nonuniform or unstructured data as though it was defined on uniform grid risks obscuring redundancies and patterns in the data, resulting in lower compression ratios. Another design goal is the control of errors incurred by compression algorithms. Often, scientists are concerned with the change to the QoIs from the compressed data, hoping to make sure that all of the features in the QoIs are preserved to a high enough accuracy. The mathematics required to relate errors in the raw data to errors in QoIs is nontrivial, especially for QoIs that are nonlinear and/or obtained by complex postprocessing. Empirical approaches can provide estimates for, but not guaranteed bounds on, QoI errors by extrapolating from previously encountered datasets and QoIs. We can often look at feature extraction as a method to understand specific QoIs, which must be controlled during the reduction, but, sometimes, we need strict mathematical control of more than the features, such as requiring the magnetic field has zero divergences; this strict control of the error during reduction must be mathematically guaranteed.
This dynamic nature adds another layer of difficulty in managing these massive datasets. Steering experiments in near real time is becoming critical, demanding further automation of the experimental scientific workflow. For example, scientists often run simulations and experiment analyses on different (sometimes geographically distributed) computing resources to simulate different components of the same physical phenomena. These codes need to interact with each other, and often, they must exchange data with analytical or visualization processes in near real time to help scientists understand the simulation results in a timely fashion. As a result, the need to automate these efforts has grown. Research in this field requires moving large amounts of data from the point of origin (e. g. , simulations, experiments, and instruments) to HPC facilities that can perform reduction, analysis, and visualization.
H. Visualization and Data Understanding One of the primary challenges facing scientists is extracting understanding from the large amounts of data produced by simulations, experiments, and observational facilities. The use of data across the entire lifetime ranging from real time to post hoc analysis is complex and varied, typically requiring a collaborative effort across multiple teams of scientists. The rapid growth in the rate and size of data generated at these facilities makes gaining understanding even more difficult. This section highlights examples of simulation and experimental plasma data collection and visualization. A comprehensive review paper on the subject would be lengthy and in all probability obsolete in several years. In the 1970s, plasma datasets of 10 MB seemed enormous.
Computers and visualization software were in their infancy. Now, TB datasets are becoming common. More than 50% of the human brain is devoted to visual processing  [143] , so the use of visualization to understand such large and complex data is essential. There is a wide range of software available for analysis and visualization of scientific data. Many packages (e. g. , Python, Interactive Data Language (IDL), MATLAB, and Mathematica) have easy-to-use visualization routines built in. A number of tools for 3-D visualization are also available. Tools such as VisIt  [144]  and ParaView  [145]  have a broad range of capabilities. These tools can be used for post hoc processing and in situ processing using LibSim  [146]  and Catalyst  [147]. A service-based approach to analysis and visualization  [148]  builds upon a hardware-portable visualization toolkit (VTK-m)  [149]  and a data model (Fides)  [150]  that can be integrated into automated workflows, such as EFFIS.
The magnetic energy is converted into heat, flow, and waves. One outstanding question in this experiment, and in general, is where in the large volume of plasma does this occur? The experiments were carried out in the LAPD at the University of California at Los Angeles (UCLA)  [153]. This, coupled to computer-controlled probe drives, allows the collection of volumetric datasets. The problem required the acquisition of a large amount of data. Three-axis magnetic pickup coils measured the magnetic field from which the vector magnetic field and plasma currents are derived. Other quantities measured with different sensors were the plasma flow, electron temperature, plasma density, and plasma potential. They were measured at over 42 000 spatial locations and 7000 timesteps (δt = 0. 32 µs. The measurement volume was 30 cm on a side in (δx = δy = 0.
A. Introduction Recent advances in data science and data methods, together with the continued decline in the cost of computing hardware and data acquisition instruments, are not only reinforcing the traditional roles of experimental and observational data but also rapidly changing how the data are used in interpretation, prediction, and optimization problems, as illustrated in Table  I. As the datasets get bigger, computer-aided automation is essential in the workflow of acquisition, data processing, and analysis. Big data, aided by data-driven algorithms, also offer new methods for interpretation, prediction, and control of plasma experiments, which is otherwise difficult if feasible when only small datasets are available. More comprehensive discussions on big data, data analytics, and infrastructure needs can be found in Sections II and VIII and the literature outside the field of plasma physics.
Interpretation, prediction, optimization, and control are too sophisticated for traditional computer programs, which can only repeat the preprogrammed tasks or workflows without glitches. ML methods, such as deep learning and kernel learning, bring several important features that are missing from traditional computer programs. Multilayer NNs, which mimic the way the network of neurons in the human brain processes information, for example, have the ability to learn without being explicitly programmed. The same NN architecture can also be reprogrammed or "retrained" for different datasets or multitasks. In other words, an NN developed for material science, biology, or even outside natural science can be adopted to solve plasma physics problems  [178]. As another example, NNs provide a new tool for the fast solution of repetitive nonlinear curve fitting problems encountered in experimental data fitting  [179].
NNs continue to grow in size and architectural variety from thousands to more than one billion of simple computational units or the "artificial neurons. " In comparison, there are about 100 billion neurons in the human brain. A growing number of libraries and predesigned NN architectures are now available through open sources, such as GitHub. ML process in NNs such as CNN and LSTM is equivalent to iterative tuning a large number of "weight" parameters associated with neurons or nodes. The initial network configurations and states, such as the connections between different neurons, are called hyperparameters. There are many hyperparameters for an NN, including the number of layers; the orders of neuron connections; the number of nodes in a layer (which intrinsically determines the connections); the dimensions of the kernels in convolutional layers; the selection of nonlinear truncation functions such as sigmoids, rectified linear units (ReLUs), and licking ReLUs; orders of pooling operations and their type, with or without loops; and so on.
The initial values for the parameters of the connections, or the weights, are usually set at random within a certain range, by following heuristic rules that are well known as "Xavier initiation"  [180]. CV for hyperparameter selection is also supplemented by heuristics in practice, depending on the different ML approaches, such as SVMs  [181] , Bayesian learning, or GPs  [182]. Laboratory plasmas also provide rich experimental datasets and data varieties to test and develop ML methods or a plasmatrained "artificial brain" that can potentially benefit other scientific fields. Laboratory plasmas are extremely diverse, ranging from microplasmas produced by short-pulse lasers to table-top experiments and to the nearly 30-m-tall ITER experiment. In spite of the difference in plasma density, temperature and data-driven methods are generic for interpretation, prediction, and control problems.
The plasma temperature was below 100 eV in the region. The best-performing NN had 12 hidden layers of 12 neurons that were sandwiched between a 1000-element input vector (the spectra) and the single output node (T e ). Each neuron in the model used an exponential linear unit (ELU) activation function with the exception of the final output neuron, which did not have an activation function so that it could take on any value. An Adam optimizer was used to calculate the changes to the model weights. The Python model construction and training were handled with TensorFlow. The full dataset consisted of 1865 input (spectrum time slices) and output pairs, of which 25% are reserved for evaluation. The rest of 75% were further split to a 3:1 ratio for training and training assessments. In addition to tunability and higher emission intensities, laser-based spectroscopy, such as Thomson scattering, and charged particle spectroscopy, such as charge exchange recombination spectroscopy (CHERS), can overcome the limitations of passive spectroscopy using plasma emission.