dataesr/scientific-paragraphs-categorization huggingface dataset:
However, our metric robustly achieves competitive performance in most distortion types. Further, in certain types of distortions, our metric significantly outperforms state-of-the-art blind LFIQA metrics, and achieves competitive performance with existing FR metrics, e. g. , LN distortion in Win5-LID database and VDSR distortion in NBU-LF1. 0 database. The above analyses fully demonstrate the robustness of our metric. However, the performance of JPEG2k distortion in Win5-LID database and Zhang's distortion in NBU-LF1. 0 database is somewhat unsatisfactory. The reasons behind deserve deeper investigation. For JPEG2k distortion in Win5-LID database, JPEG2k distorted LFIs are generated by adopting JPEG2k on each SAI, in which angular information does not directly participate in the generation process.
In this case, spatial distortion is much more severe compared to angular distortion. However, our metric focuses on modeling the angular-spatial effect dominated by angular information. This may be the reason for the suboptimal performance of JPEG2k distortion. For Zhang's distortion in NBU-LF1. 0 database, Zhang's distortion is a depth estimation-based angular distortion, in which different regions in a scene have different angular reconstruction quality according to the estimated depth. However, our metric is a block-based metric and assumes that all regions are of the same quality during training, which is not conducive to evaluate Zhang's distortion. Our previous work DeeBLiF also performs unsatisfactorily on Zhang's distortion due to the same reason.
E. Performance of Cross-Database Validation Since the generalization ability is a crucial factor for designing an effective IQA metric, we investigate the crossdatabase performance of our proposed metric in this subsection. We conduct the cross-database experiments following most previous works  [33] ,  [35] ,  [39]. Specifically, we first train our metric on the whole Win5-LID database due to its diversity and complexity in terms of image categories and distortion types. Then, we evaluate the performance on each distortion type that is shared between the Win5-LID database and other databases, including NBU-LF1. 0 (NN), LFDD (JPEG2k), VALID10bit (HEVC), and SHU (JPEG2k). The results are shown in TABLE III. We can find that our metric still performs well on HEVC and JPEG2k even when trained on another database, implying a relatively good This article has been accepted for inclusion in a future issue of this journal.
Content is final as presented, with the exception of pagination. generalization ability on compression distortions. However, our metric shows relatively poor performance on the NN distortion. The reason for this result has been discussed in  [39] , that is, the NN distortion is implemented differently in the Win5-LID and NBU-LF1. 0 databases. For Win5-LID database, the LFI containing 9×9 SAIs is reconstructed in five different distortion levels, each of which involves 50, 40, 30, 20, and 10 randomly selected SAIs to reconstruct the LFI, respectively. For NBU-LF1. 0 database, the 9×9 SAIs are first down-sampled to 5×5, 3×3, and 2×2 fixed SAIs, respectively, and then the NN interpolation is used for reconstruction. We can easily find that the distortion levels between these two databases do not overlap, which leads to relatively poor performance on the NN distortion.
F. Ablation Studies To further explore the effectiveness of each module of our metric, we conduct ablation experiments in this subsection. TABLE IV reports the experimental results on the Win5-LID and NBU-LF1. 0 databases, where w/o is the abbreviation of without, and Aux. Learn. denotes the auxiliary learning branch. The table demonstrates a significant decrease in the performance of our metric on both databases when the AEM module is excluded, indicating the crucial role of capturing angular information for LFIQA. In addition, we can also find that using auxiliary learning without DRS module (i. e. , w/o DRS) performs even worse than not using auxiliary learning (i. e. , w/o Aux. Learn. ). A possible explanation is that if all regions are introduced for auxiliary learning, the regions with insufficient angular-spatial information will lead to decreased performance.
G. Hyperparameter Analyses Since the performance of our metric is affected by the number of the selected discriminative regions, i. e. , N, we perform hyperparameter experiments on the Win5-LID and NBU-LF1. 0 databases to investigate the impact of its value. As shown in TABLE V, the performance is compared when N is set to 1, 5, 10, 20, 50, 100, 200, and w/o. Here, w/o represents that all regions are incorporated into the learning process. From the table we can see that the performance is relatively poor if the value of N is too large or too small. The main reason could be that insufficient discriminative regions lead to inadequate information for auxiliary training, while excessive discriminative regions introduce too many low-quality predictions, ultimately impeding the learning process.
H. Time Complexity Analyses Time complexity is an important factor for an IQA metric as it affects the efficiency and practicality of the metric in real-world applications. Therefore, we conduct an analysis of the time complexity of our metric in comparison to other state-of-the-art metrics. All metrics are executed using the same hardware configurations as mentioned in Section III-E. Following  [33] , we measure the time complexity of each metric by testing a single LFI from the Win5-LID database,    still within an acceptable range. Considering the outstanding performance, our proposed metric achieves a better trade-off between computational efficiency and prediction accuracy.
I. Illustrative Examples To provide a more intuitive example of quality evaluation, we present some illustrative predictions of our proposed metric, along with four state-of-the-art metrics, on the Win5-LID, NBU-LF1. 0, and LFDD databases. Due to space limitations, we only display the central viewpoint of each LFI. As shown in Fig. 4 , we present a series of LFIs with diverse MOS labels, distortion types and reference scenes, along with the corresponding predictions from five metrics. It can be found that despite the diverse characteristics of LFIs, our proposed metric exhibits superior performance in accurately predicting the LFI quality compared to other metrics.
This work was supported in part by the  National Natural Science Foundation of China  under Grant  62101344  and Grant  62171294 ; in part by the  Natural Science Foundation of Guangdong Province, China , under Grant  2022A1515010159  and Grant  2020A1515010959 ; in part by the  Key Project of DEGP  under Grant  2018KCXTD027 ; in part by the  Key Project of Shenzhen Science and Technology Plan  under Grant  20220810180617001 ; and in part by the  China Scholarship Council.
A. Motivation The fifth-generation (5G) wireless communication is becoming a reality and being deployed worldwide  [1] -  [3]. It enables various functionalities that are shared among three pillar paradigms, including enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (uRLLC), and massive machine type communications (mMTC), each of which is oriented to satisfy different aspects of communication requirements. Specifically, eMBB is intended for supporting high data traffic services, such as video streaming applications and mobile augmented reality, with expected peak data rates of 10 and 20 gigabit-per-second, as well as average data rates of 50 and 100 megabit-per-second in the downlink and uplink, respectively. The uRLLC use case involves mission-critical applications, such as autonomous driving and remote robotic operation, with a reliability of 99.
D. Vision It is foreseen that the HMIMO technology has the potential to drive two possible paradigm shifts from conventional 5G to future 6G, namely, the extremely large spatial multiplexing holographic radios and the smart radio environment. Based on these insights, we envision a broad range of HMIMO applications in future 6G, as illustrated in Fig. 3. It shows a space-air-ground-sea integrated communication network, in which we demonstrate several scenarios, such as smart cities, remote mountain areas, forest, desert and sea. HMIMO can be applied to each case by deploying HMIMO surfaces as active transceivers or passive reflectors. For instance, in outdoor communications of smart cities, the HMIMO surfaces can be mounted on building surfaces, serving as base stations (BSs) for data transmissions to user equipments (UEs) located at offices, homes, schools, factories, etc.
This is truly beneficial in satisfying communication requirements for emergencies occurring in remote areas, e. g. , forest fires, rescues in remote mountain areas, desert and sea. Moreover, the HMIMO empowered satellites are capable of assisting wireless monitoring in remote areas, such as desertification monitoring. The benefits of HMIMO also show great potential in promoting physical layer security of wireless communications that are critical for realizing secure data exchanges in military applications. For example, an early warning aircraft can utilize HMIMO surfaces to confront enemy interference and wiretaps, offering an accurate and timely early warning detection, as well as assisting a timely situational awareness report and military command feedback. Far beyond the above visions, it is foreseen that the powerful HMIMO technology can be applied to a multitude of cases and scenarios in assisting communication paradigm shifts and supporting newly emerged upper layer applications.
8)  3. 6 (0. 3) 3. 27 (0. 5) 1607 (150) 7. 17 (0. 10) 3, 895 (78) 3, 678 (74) 6. 77 (0. 21) 6. 71   Exp2  7. 2 (0. 8)  3. 6 (0. 3) 4. 18 (0. 5) 1748 (150) 7. 17 (0. 10) 3, 895 (78) 3, 724 (74) 6. 85 (0. 22) 6. 70   Exp4  7. 2 (0. 8)  3. 6 (0. 3) 5. 89 (0. 5) 1869 (150) 7. 17 (0. 10) 3, 895 (78) 3, 747 (75) 6. 89 (0. 22) 6  c Results from hydrostatic weighing and X-ray absorption measurements (Tables  A1  and A2  of supporting information). KNIBBE ET AL. 10. 1029/2020JE006651 7 of 26 Name X Si (wt%) a X C (wt%) a P (GPa) b T (K) b  ,0 S (g•cm -3 ) c   ,0 S S (m -1 ) c   , S PT S (m -1 ) c  , S PT (g•cm -3 ) c FESIC model (g•cm -3 ) d Exp1 7. Standard errors are estimated from the precision of the high-precision balance that is used for hydrostatic weighing and the propagation of uncertainties that are inherent to modeling the X-ray absorption data (Section A5 of supporting information).
This contrasts with the increasing influence of Si on the V P of Fe-rich metal. However, experimental data are indicative of a complex mixing behavior between Fe and S  (Morard et al. , 2018; Terasaki et al. , 2019 ; Figure  5 ). According to the nonideal Fe-S mixing model of  Terasaki et al. (2019) , the V P peaks specifically in the 0-10 wt% S domain that is most relevant for Mercury's core, with pressures above ∼5 GPa. The density of Fe-rich liquid metal decreases strongly with S-concentration in the <10 wt% S and >30 wt% S domains. Density and is less sensitive to S in the compositional domain between 10 wt% S and 30 wt% S, according to the nonideal mixing model of  Terasaki et al. (2019). KNIBBE ET AL.
We use for evaluation and comparison the 68 images from the CBSD68 dataset, center-cropped to n = 256 × 256 and Gaussian noise with 3 noise levels ν ∈ {0. 01, 0. 03, 0. 05}. For deblurring, the degradation operator A = H is a convolution performed with circular boundary conditions. As in  [7, 12, 18, 24] , we consider the 8 real-world camera shake kernels of  [45] , the 9 × 9 uniform kernel and the 25 × 25 Gaussian kernel with standard deviation 1. 6. For single image super-resolution (SR), the low-resolution image y ∈ R m is obtained from the high-resolution one x ∈ R n via y = SHx + ν where H ∈ R n×n is the convolution with anti-aliasing kernel. The matrix S is the standard s-fold downsampling matrix of size m × n and n = s 2 × m. As in  [7] , we evaluate SR performance on 4 isotropic Gaussian blur kernels with standard deviations 0.
ACKNOWLEDGMENT We thank the following funding sources for this project: the Marie Skłodowska-Curie action SOLBIOCHEM (project n° 896901), the French National Agency for Research (ANR) project MEGOPE (ANR-22-CE29-0015) and the European Innovation Council (EIC) Pathfinder Project ELOBIO (grant agreement n o 101070856). We gratefully acknowledge to MAX IV synchrotron for access to HIPPIE beamline through run number 20210958.
Acknowledgments The authors acknowledge the financial support of  Ministerio de Ciencia e Innovación and Fondos FEDER  ( PID2021-126509OB-C21  and  PDC2022-133332-C21 ) and support of  Generalitat de Catalunya  ( 2021SGR00138 ). IM acknowledge the financial support of  Ministerio de Ciencia e Innovación and Fondos FEDER  ( PID2021-126509OB-C22 ) AM acknowledges support from project  PROMETEO/2021/006  ( Generalitat Valenciana, Spain ) and  PID2021-123124OB-I00  ( Ministerio de Ciencia e Innovación, Spain). EN acknowledges a grant from  Generalitat Valenciana ,  Santiago Grisolia Program  (ref. GRISOLIAP/2020/004 ). IE acknowledges financial support from a  Beatriu de Pinós Fellowship  ( 2021-BP-00206 ).
Maillot et al. [2016]  compare the performance of different DFN models from the comparison of channeling and equivalent permeability indicators. Zou and Cvetkovic [2020, 2021]  also use DFN models and varying hydraulic boundary conditions to simulate steady-state pumping tests and derive indicators of cumulative and of complementary cumulative distributions to evaluate the impact of fracture inplane heterogeneities on the flow structure at the DFN scale. The first objective of this study is to develop flowbased metrics adapted to in-situ flow testing capacities and suited to emphasize the multiscale nature of the flow structure. To address these issues, the database of fractures and discrete inflows at Forsmark, which includes flow logging and steady-state pumping tests, was analyzed with metrics revisited from the former work of  Maillot et al.
[2016]. The second objective is to test the capacity of the latter to discriminate between DFN models otherwise calibrated to similar geological and geometrical observations. A modeling environment is set up in the software "DFN. lab" to generate DFN realizations from a predefined range of DFN models and perform numerical flow simulations in steady-state pumping configuration simulations. The paper is organized as follows: the data and conditions of the Forsmark site are recalled in Section 2. The framework to define the metrics is presented in Section 3. Data analyses are presented in Section 4. In Section 5 the DFN models are defined and finally, several a priori equiprobable DFN models are compared with each other and with the field data and the interpretations and comparisons between data and models are presented.
Site and available data overview The Forsmark site in Sweden, located in crystalline bedrock in south-eastern Sweden and located roughly 130 km north of Stockholm, is selected by the Swedish regulatory authorities to build a nuclear waste repository for spent nuclear fuel at 470 m depth  [SKB, 2011]. Svensk Kärnbränslehantering AB (SKB) is in charge of site investigations, future construction, and long-term safety assessment of the final repository. The database built over the course of the site investigations encompasses data from geological, mechanical, hydrogeological, geochemical, etc. , aspects. Several tens of boreholes each reaching lengths up to about 1 km have been drilled and exhaustively logged. The presented analyses relies on a detailed description of the fracture transmissivity structure in the different boreholes that have been extensively carried out at the Forsmark site by SKB with the Posiva Flow Logs (PFL).
The two unknowns K and h 0 can be deduced from two pairs of hydraulic heads (h 1 and h 2 ) and resulting flow rates (Q 1 and Q 2 ) provided that K is not dependent on the induced pressure (no hydromechanical effects). A combination of sequential and overlapping flow logging allows identifying water conductive fractures down to a spatial resolution of 0. 1 m and flow rates down to 30 ml•h -1. This leads to a measurement limit of approximately T = 10 -10 m 2 /s when expressed in equivalent transmissivity as defined below. The transmissivity of a test section, or the fracture identified by the sequential mode, is defined as: T = K L = ln R r 0 2π • (Q 2 -Q 1 )/(h 2 -h 1 ) (2) with R ∼ 500 m it comes that T ∼ ∆Q/∆h. The ratio ∆Q/∆h is also called a specific capacity (in (m 2 /s)). Martin and Follin [2011]  rightly note that: "The PFL transmissivity value (T in (2)) reported to SICADA (the SKB database) will be close to the local value at the borehole (i.
The blue lines represent power-law fits at small and large scales, respectively. The dark yellow dots in the K g plot are for KFM08A, a borehole that will be discussed in the modeling section. Table  1. List of boreholes (IDCODES in the SICADA database) with PFL data including the total number of inflows, the depth of the uppermost and lowermost inflow, the smallest and the largest PFL transmissivity The evolution of K a (L) and K g (L) reflects the organization of flows along the boreholes. In the first regime, as long as the length of the core sections is smaller than the minimum distance between two inflows, every element contains only one inflow and hence the equivalent permeability is inversely proportional to the core section length: IDCODE Number of inflows (T > 0) Uppermost inflow (m) Lowest inflow (m) Min(T ) (m 2 /s) Max(T ) (m 2 /s) K j = T j L.
Description of the selected models The geo-DFNs rely on site investigations and data interpretations at the Forsmark site  [Darcel et al. , 2009 , Fox et al. , 2007 , Olofsson et al. , 2007]. The fracture density and fracture orientation distributions are measured from borehole logging and core mapping. The number of fractures fully intersecting the boreholes per unit core length P 10 is converted into total surface of fracture per unit volume P 32 by means of stereological rules  [Dershowitz and Herda, 1992 , Darcel et al. , 2003a , Davy et al. , 2006 , Piggott, 1997]. The values of P 32 for the different orientation poles recorded in Forsmark are given in Table  2  with references therein. The multiscale organization of fracture networks is a critical attribute for fracture connectivity and flow  [Bonnet et al.
Conclusion The objective of the paper is to better understand and quantify the flow structure in fractured rocks from PFL flow logs, and also to propose relevant indicators for validating, calibrating or even rejecting hydrological models. The data consists of a series of inflows along each borehole with an equivalent transmissivity measured from pumping tests. We first studied what the inflow distribution tells us about the permeability structure from a series of analysis: distribution of transmissivities as a function of depth, proportion of flowing sections as a function of section scale, scaling of the arithmetically-averaged and geometrically-averaged permeability. We argue that the permeability scaling evolution provides more information on the flow structures than macroscopic values (i.
France-specific incident cases for cardiovascular disease, dementia, and type 2 diabetes were determined for the year 2018 from a database of new beneficiaries of long-term illness coverage (Affectations Longues Durées, ALD) by the French National health insurance system, using the most recent data available. The 2019 incidence data for breast cancer and colon cancer were taken from the French National Cancer Institute. Deaths, death rates, life-expectancy and population count estimates for 2019 were taken from the National Institute of Statistics and Economic Studie  (22). (Table  1 ).
Methods Data spanning the past 20 y (2003-2022) were collected from PEN societies worldwide. These societies embody both foundational and practical applications, primarily addressing various forms of malnutrition, including undernutrition, overnutrition, and micronutrient imbalances. Their core objective is to implement nutritional support strategies, including the utilization of artificial nutrients. Societies that incorporated basic science or other aspects of human nutrition, not solely focused on PEN or clinical nutrition were excluded. Additionally, dietetic societies were also excluded from our study because they are primarily focused on dietitians/nutritionists and lack interdisciplinary representation. We identified a total of 132 societies globally, using various sources: a report from the European Society for Clinical Nutrition and Metabolism (ESPEN), which lists several PEN societies worldwide  [20] , a study by Bolasco  [21]  outlining key nutrition-related societies across different regions.
We were unable to reach 11 societies because of the absence of or our inability to find accessible contact details or website addresses, and 7 societies did not reply. Consequently, our analysis had a final sample of 64 societies (Table  1 ). We obtained information through direct email correspondence with society members and by examining publicly available accessible data on the respective society websites (Supplemental Table  2 ). We used a one-page survey for data collection, which requested details on the society's founding year, the sex of the inaugural chair, and chairs over the past 2 decades. If applicable, we also asked about the sex of national congress presidents and the editor-in-chief positions for associated journals. Society email addresses were obtained directly from their websites.
Notably, not all societies conducted national conferences or congresses, and some did not disclose this information. Additionally, ASPEN co-organized its Nutrition Week conference with other societies from 2003 to 2017; as a result, we excluded the presidents of these events from our analysis. Table  4  outlines the historical data on editor-in-chief positions across 22 journals affiliated with 19 societies. Of these journals, only 5 had a female as their inaugural editor-in-chief, representing the following societies: Asociaci on Argentina de Nutrici on Enteral y Parenteral (AANEP), Austrian Society for Clinical Nutrition (AKE), Asocici on Colombiana de Nutrici on Clínica (ACNC), CMNCTN, and SEEN. Conversely, 11 journals have yet to appoint a female editor-inchief. Overall, over the past 2 decades, across these 22 journals, females have occupied 23 of 123 editor-in-chief positions, accounting for 18.
Acknowledgments We would like to express our sincere gratitude to all PEN societies that provided data for this study, as well as our colleagues within these societies. Your willingness to share information has been invaluable in our efforts to understand and address the sex gap in leadership positions within these organizations. We appreciate your commitment to promoting equity, diversity, and inclusion within your societies and across the field of PEN. We would like to thank  Taiara Scopel Poltronieri  and  Sophie Robertson  for their assistance with the data collection and datachecking process.
This study proposes a novel research framework to examine the effect of digital CSR communications on financial performance while incorporating deep learning techniques to identify firms' CSR communications on social media. Particularly, this research aims to quantify firms' efforts in digital CSR communications by employing cutting-edge deep learning-based natural language processing (NLP) models to detect CSR-related tweets on social media. Utilizing a unique dataset of 65 Chinese public companies in the manufacturing sector between 2015 and 2019, we detected 64,769 long-form tweets posted on WeChat to acquire both digital CSR communications and stakeholder engagement data. Combining financial and secondary data of sample firms, this research reveals the positive but time-lagged influence of digital CSR communications on firms' financial performance, primarily through the lens of agenda-setting theory.