dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability • The original dataset used for this study is available here: https ://prod-dcd-datasets-cache-zipfiles. s3. eu-west-1. amazonaws. com/gwbz3fsgp8-1. zip • The beat signal dataset created is available here: https://www. kaggle. com/datasets/marcjuniornkengue/ecg-heart beat-covid-19 • The Dataset -level explanation module: https://github. com/marcusnk237/dataset_gradcam_plus_plus.
Algorithm 1 1 Key features frequenciesInput: Dataset D, model M, layer L, features names dictionary label. (continued ) Algorithm 1 Key features frequencies Initialize array I, array keys_features 1. for each data d in D do 2. Initialize array G c 3. G 10. F[index] ← F[index] +. 1 length(I) 11. if index not in F then 12. F[index] ←. 1 length(I) 13. return F 14. end Table Output: Key features frequencies dictionary F (continued on next column) c ← gradcamplusplus (d,M,L), when gradcampluplus is the GradCAM++ function 4. maxG c ← max (G c ) 5. indxValue ← Gc. index(maxG c ), when index if the function to get value index in an array 6. Store indxValue in I 7. keys_features ← Mapping IndexValue with label keys 8. for index in keys_features do 9 if index in F then.
[24] [25] [26]  A familiarization session was conducted seven days before the first testing session to allow participants to become habituated to the protocol. Furthermore, a healthy nutritional intake and hydration strategy were provided simultaneously. A consecutive dietary record was completed over seven days to assess the adequacy of nutrient intake. Everyone's dietary composition was estimated using the food composition tables published by the Tunisian National Institute of Statistics in 1978 and the Bilnut 4 software package (SCDANutrisoft, Cerelles, France). Estimated daily nutriment data showed that total calorie, macronutrient, and micronutrient intakes were within reference ranges for healthy adults (data not shown).
Data analysis Variables distribution was assessed by the one-sample Kolmogorov-Smirnov test, and data were normally distributed. A two-tailed paired sample t-test was used to compare mean differences in the Control and Post-cupping results. Additionally, the effect size (ES) was calculated for pairwise comparison. The magnitude of ES was interpreted as trivial (< 0. 2), small (0. 2-0. 6), moderate (0. 6-1. 2), large (1. 2-2. 0), and very large (! 2. 0). All statistical analyses were conducted using the statistical package for the social sciences (SPSS, version 18. 0, SPSS Inc, Chicago, IL, USA). The level of significance was set at p < 0. 05.
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clients or between the clients and the central server. In this paper, we challenge this claim by introducing a simple but still very effective membership inference attack algorithm, which relies only on a single training step. In contrast to the popular honest-but-curious model, we investigate a framework with a dishonest central server. Our strategy is applicable to models with ReLU activations and uses the properties of this activation function to achieve perfect accuracy. Empirical evaluation on visual classification tasks with MNIST, CIFAR10, CIFAR100 and CelebA datasets show that our method provides perfect accuracy in identifying one sample in a training set with thousands of samples.
Previous Work Privacy of FL has been an active area of research since this field developed. We focus on privacy at training time and in particular the task of membership inference conducted by the CS. Most literature either assumes a client-side attacker  [3] ,  [4]  or, that a malicious CS behaves honest-but-curious  [5] ,  [6] ,  [7]. Much work has also been devoted to reconstructing input from gradients  [4] , [8],  [9]. This problem is rather trivial if the gradient at a single input is available, but becomes challenging when a whole batch is processed, the typical scenario in FedSGD, where each client reports its gradient computed on one mini-batch to the CS. An active attacker is studied in  [10] ,  [11] , as well as the recent works  [12] ,  [13] ,  [14] ,  [15]. The focus of these works is the reconstruction of individual data samples in the client dataset when the FedSGD strategy is being used.
Setup and Attacker Model Our method relies crucially on the following assumptions. 1) The model f(x; θ) uses ReLU activations and the structure depicted in Fig. 1  can be embedded in f. 2) The client uses a form of stochastic gradient descent, e. g. , SGD, Adam  [23]  to minimize a loss function. 3) The function client(), i. e. , all training hyperparameters (batch size, optimizer, …) are known to the CS. While certainly adaptable to other settings, we will detail and showcase our strategy for FedAvg in the task of image classification. Thus, we will additionally assume the following. 4) The dataset D = {(x n , y n )} n=1,. ,N consists of images x n and discrete labels y n ∈ {1,. , L}. 5) The model outputs logits and we use cross-entropy with softmax, i. e. , the client attempts to minimize the loss function at the training samples D, where (x, y; θ) = -log exp(f(x; θ) y ) L l=1 exp(f(x; θ) l ) (1) = -f(x; θ) y + log L l=1 exp(f(x; θ) l ).
(2) 6) FedAvg is used, i. e. , the response φ to the CS is the full parameter vector after training. The client trains for E epochs using J mini-batches with batch size B = N J. 7) The structure depicted in Fig. 1  can be embedded in the last layers of f, obtaining b as a logit in the output. The CS is given the target sample s = (x t , y t ), where t ∈ {0, 1}. Note that we have s ∈ D if t = 1, while for t = 0, s = (x 0 , y 0 ) is another sample from the dataset, not present in D. Using s, the CS needs to produce a parameter vector θ, which is used by the client to compute the response φ = client(θ, D). This response is used by the CS, to obtain an estimate T of t. If T = t the CS correctly identified membership. Remark 2. 1. Note, that FedAvg is more challenging for an attacker, as FedSGD is essentially a special case of FedAvg.
Training All parameters are listed in Table  1. For each run, we randomly select N samples D from the training set (MNIST, CIFAR10, CIFAR100, or CelebA). For half the runs, we randomly pick an element s ∈ D, for the other half, we randomly pick an element from the remaining samples, thus ensuring s ∈ D. We then spawn a flower-client and a flowerserver, implementing our dishonest strategy. Finally, after every run, we record ∆, as given by (  9 ), as well as whether s ∈ D or not. A failure of our method occurs if ∆ ≥ ξ when s ∈ D (False Positive) or if ∆ < ξ and s ∈ D (False Negative). We perform 400 runs on the training set, using the fixed threshold ξ = 0. 1. 2. The datasets can be found at: CIFAR10/100, MNIST, CelebA  [25]  3. We use the notation F 2 = F ) F to denote functional composition.
All experiments concluded without a single false decision by the CS, achieving an over-all accuracy of 100%. We experienced very occasional failures of our method on the CIFAR100 and CelebA datasets during exploration. When investigating, we noticed that there are 14 exact duplicate images in the training set of the CIFAR100 dataset and 82 exact duplicates in the training set of the CelebA dataset. More details on these duplicates are provided in the discussion (Section 4. 1). In addition, there are several near duplicate images present in the CelebA dataset. These images are indistinguishable for a human and large portions of the images are identical. These (near) duplicates lead to very occasional false positives of our method. We did not observe a single failure that could not be attributed to one of these (near) duplicates.
Duplicates As our strategy may appear to fail if exact duplicates are present in the dataset, we performed a search for such images in all four datasets (MNIST, CIFAR10, CIFAR100, CelebA). Both MNIST and CIFAR10 are free of exact duplicate images. We (re)discovered the exact duplicates in the CIFAR100 dataset that were pointed out by  [26]. We found 14 and 82 duplicates in the training sets of CIFAR100 and CelebA, respectively. The testing sets contain 2 and 7 duplicates, respectively. Additionally, 9 of the 14 duplicates in the CIFAR100 training set, as well as 51 of the 82 duplicates in the CelebA training set occur with different labels. Both duplicates in the testing set of CIFAR100 are differently labeled each time. To our surprise, we also found that the training and testing sets of CIFAR100 and CelebA contain identical images.
In fact, most commonly, the same entity operates the CS and provides the client application binary. In this case, that entity has complete knowledge of any detection code on the client side and will thus win the arms-race alluded to above. • Other optimizers, apart from SGD and Adam were not studied and are left for future work. • Similarly, we did not consider FL strategies other than FedAvg, containing FedSGD as a special case. However, as long as a optimization relies on gradient computation and the parameter vector is exchanged between the CS and the client, our attacker should be viable. • We considered only one client. As pointed out in Section 1. 1, this is not a limitation for the strategy from the attacker's point of view, because the CS can simply target one client at a time, crafting the parameters sent to that client accordingly.
Simulation Results For the validation of the hypotheses, a simulation has been carried out consisting on a single-phase VSI connected to a Rectifier/R-C non-linear load, see Fig. 5. The architecture and the control scheme are explained in  [16]  and no changes in the power calculation had been introduced. Simulation parameters are listed in Table  4 , where R is the resistance and C the capacitance after the rectifier, and   ;  ;   are the damping factors of the local control loop conditioners for the calculation of active and reactive power. In  [16]  only a 3rd harmonic component was introduced in the voltage output of the VSI. The results of the calculated THD for current, voltage, active and reactive power, following the FFT tool in MATLAB/Simulink, are listed in Table  6. After that, a set of SSFP at 0.
5Hz and at 0. 25Hz components, both with 10% of the fundamental voltage amplitude, were added to the voltage source. Table  5  list these parameters. If the calculation of the   in (28) is analytically performed, it yields approximately a contribution of 1%. However, after carrying out the simulation considering voltage SSFP, the MATLAB/Simulink THD calculated through FFT for the voltage not only slightly decreases, it also shows contributions to THD in frequencies higher than 1Hz and below 50Hz, as can be seen in Table  7. Nevertheless, THD for output current varies, when SSFP in voltage are present, in spite of being conditioned through an 8 th order BPF  [16]. Even more, as Fig. 6  shows, its influence on the reactive power is too strong, considering the variation in THD of reactive power shown in Table  6.
PURPOSE: This study aimed to explore the concept of total keratometry (TK) by analyzing extensive international datasets representing diverse ethnic backgrounds. The primary objective was to quantify the disparities between traditional keratometry (K) and TK values in normal eyes and assess their impact on intraocular lens (IOL) power calculations using various formulas. • DESIGN: Retrospective multicenter intra-instrument reliability analysis. • METHODS: The study involved the analysis of biometry data collected from ten international centers across Europe, the United States, and Asia. Corneal power was expressed as equivalent power and astigmatic vector com-.
PATIENTS AND METHODS • STUDY DESIGN: This retrospective study conformed to ethics codes based on the tenets of the Declaration of Helsinki. Prior ethics approval was obtained (Ärztekammer des Saarlandes, 157/21). An Excel. CSV file (Microsoft Corporation) of exported biometry data collected using the IOLM700 SS-OCT biometry device (Software versions used included 1. 70. 14. 53814 through 1. 90. 12. 05) of patient exams from ten international centers acquired between 2018 and 2022 were included. Study centers from Europe, Asia, and North America are listed in Supplementary Table  1. Every center had a recent software version for the SS-OCT biometer, as TK measurements were required to enter this study. All data acquisition was executed by experienced staff at the study centers. The exported, anonymized biometric data were further studied using the following methods.
Regarding corneal curvature measurements, we recorded the flat and steep anterior (R1 and R2), posterior (PR1 and PR2) and total corneal radii (TR1 and TR2), as well as their meridians. It is of note that while R and PR are real measurable radii, there is no real TR measurable in the human eye. Rather, TR is derived from an equivalent power (TK): First total corneal power is derived from a Gullstrand thick lens cornea model (R, PR, and CCT), then it is converted into a radius (TR) using one specific keratometric index that allows IOL calculation with most established IOL calculation formulas including Haigis. After this conversion to TR, TK can be calculated using the default or preferred keratometric index that is used for K values (eg, 1. 3375 or 1. 332). Corneal curvature data was converted from R and TR to anterior (K1, K2) and total (TK1, TK2) corneal power using a corneal refractive index of n K = 1.
RESULTS • DEMOGRAPHIC DATA: A total of 116,982 measurements of 57,862 right and 59,120 left eyes met criteria for inclusion in our analysis dataset. Supplementary Table  1  shows the descriptive data of the biometric measures of right eyes for the entire study population in terms of axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD, measured from the corneal epithelium to the anterior lens surface), lens thickness (LT), and the radii of curvature for the corneal front and back surfaces. The difference in mean keratometric power K/TK with the IOLM700 between right and left eyes was 0. 05 D (43. 75 ± 1. 60 D/43. 81 ± 1. 62 D in right eyes, and 43. 80 ± 1. 60 D / 43. 86 ± 1. 62 D in left eyes). Table  1  shows that in all datasets at least 91. 13% and on average 93.
The TK concept of the IOLM700 is based on the latter concept. Our study confirms the results of several studies that have demonstrated that K and TK provide comparable values, with no significant differences in healthy, unoperated eyes. 6 , 7  Table  6  reports the mean values of other studies and reveals that only a minority of eyes have a difference of 0. 25 D or greater. Interestingly, confirming the results of our previous studies, measurements obtained with the IOLMaster 700 are different compared to those obtained with other devices, as the IOLM700 APR is usually 1. 12 rather than 1. 20 to 1. 22. 3 , 15  This suggests that, on average, the IOLMaster 700 measures a flatter posterior corneal radius. This was true for the several European, Asian and American eyes in our dataset. In our database, the APR observed with the IOL-Master 700 is 1.
12 ± 0. 02 (95% CI: 1. 08-1. 17). Since anterior and posterior corneal surfaces are actually not spherical but rather aspherical, the reconstructed radii strongly depend on the measurement and reconstruction method, primarily the incorporated zone diameter. Previous studies carried out with Scheimpflug cameras or anterior segment optical coherence tomographers reported APR similar to the Liou Brennan model eye, nonetheless, standard deviation seems to be between 0. 02 and 0. 04 D, independent of the measurement device ( Table  2  and Table 7  ). 3-5 , 16-21  In our dataset, there was no noticeable difference in APR for gender, location (USA, Europe, Asia), or laterality (OD, OS).
110 AMERICAN JOURNAL OF OPHTHALMOLOGY APRIL 2024 Wei et al. 22  found that a larger difference between K and TK was associated with a thinner CCT. This could not be reproduced in our dataset ( Figure  1  A and B). Corneal thinning is typically associated with keratoconus, which should lead to differences in both values (K and TK), but Wei et al. performed preoperative Scheimpflug tomography and excluded eyes with keratoconus, hence, undetected preclinical keratoconus is not likely to explain the difference in both studies. We were able to show that the difference between K and TK can be used to detect eyes after LVC. 15 , 23  While the heteroscedasticity of Figure  2  could not be explained by flat or steep corneal curvature ( Table  3  ), nor by CCT or astigmatism magnitude ( Figure  1  ), eyes with a conspicuous CRW1 index that showed a noticeably larger difference between TK and K values than all other eyes and most likely constitute outliers in flat corneas.
Although the maximum attention was paid to exclude cases with prior corneal refractive surgery or keratoconus, it is possible that in such a large retrospectively multiple-site collected dataset a minority of eyes was mislabeled. When it comes to the clinical impact on IOL power calculation, some studies investigated the differences in the refractive outcomes between K and TK, once these are entered into IOL formulas to calculate the spherical equivalent power. It needs to be noted, that some formulas also use K values only to calculate the vergence of paraxial rays, whereas other formulas use it also to predict the ELP. Hence, formulas are influenced by varying degrees when switching from K to TK values. To our knowledge, no study on normal eyes reports the use of TK values with a double K mode (using TK for the cornea model and K for the ELP calculation), which might be the "cleanest" way to incorporate TK values.
33  Further studies should shed light on the applicability of TK values in astigmatism correction. This study is not without limitations. First, although the maximum attention was paid to exclude cases with prior corneal refractive surgery or keratoconus, it is possible that in such a large retrospectively multiple-site collected dataset a minority of eyes was mislabeled. This may explain the presence of some eyes with extreme K and CCT values, and of some eyes with a conspicuous CRW1 index that showed a noticeably larger difference between TK and K values than all other eyes and most likely constitute outliers in flat corneas seen in Figure  2. Second, due to their undisclosed nature, we were not able to compare predicted refraction of new generation IOL power formulae. Third, we were unable to report refractive results and analyze if K or TK led to more favorable surgery outcomes.
Acknowledgments: This study, referred to as "Investigator  Initiated Study ," was author initiated and has received funding support from  Carl Zeiss Meditec AG ("CZM "). Please note that CZM's involvement in this study is limited to providing financial support, and  CZM  had no control over the study design, data collection, analysis, interpretation, or publication of the results. The findings and conclusions presented in this study are solely those of the investigators. Dr. Wendelstein  reports research support from  Carl Zeiss Meditec AG. He reports personal fees from  Carl Zeiss Meditec AG ,  Alcon Surgical, Rayner Surgical , and  Johnson  and  Johnson Vision  outside of the submitted work. He is supported by an  " ESCRS Peter Barry Fellowship Award. "  Dr. Hoffmann  reports personal fees from  Hoya  and  Johnson  and  Johnson  outside the submitted work.
Funding/Support: This study, referred to as " Investigator Initiated Study ," was author initiated and has received funding support from  Carl Zeiss Meditec AG ( " CZM "). Please note that CZM's involvement in this study is limited to providing financial support, and  CZM  had no control over the study design, data collection, analysis, interpretation, or publication of the results. The findings and conclusions presented in this study are solely those of the investigators. The contribution of  IRCCS-Fondazione Bietti  has been supported by  Fondazione Roma  and the  Italian Ministry of Health. Dr. Wendelstein  was supported by an " European Society of Cataract and Refractive Surgeons (ESCRS)   Peter Barry Fellowship Award " for the publication and its content. All other authors received no financial support for the research, authorship, and/or publication of this article.
19 ± 002 1. 06-1. 09 n/a Langenbucher et al. 4 854 IOLM700 1. 12 ± 0. 02 1. 14-1. 16 n/a Wendelstein et al. 3 114 IOLM700 1. 12 ± 0. 02 n/a 1. 08-1. 18 Female short eyes Wendelstein et al. 3 5470 IOLM700 1. 12 ± 0. 02 n/a 0. 96-1. 27 Female normal eyes Wendelstein et al. 3 114 IOLM700 1. 12 ± 0. 02 n/a 1. 06-1. 18 Female long eyes Wendelstein et al. 3 93 IOLM700 1. 13 ± 0. 02 n/a 1. 07-1. 18 Male short eyes Wendelstein et al. 3 4415 IOLM700 1. 12 ± 0. 02 n/a 1. 0-1. 32 Male normal eyes Wendelstein et al. 3 93 IOLM700 1. 12 ± 0. 03 n/a 1. 06-1. 18 Male long eyes Current dataset OD 57,862 IOLM700 1. 12 ± 0. 02 1. 08-1. 17 0. 96-1. 39 Measurement were performed with the Pentacam (Oculus), Casia SS-1000 (Tomey), Sirius (CSO), Anterion (Heidelberg engineering), and IOL Master 700 (.
Acknowledgments Expert assistance of  Benjamin Saint-Pierre  ( Genom'IC facility of the Institut Cochin ) with transcriptomic experiments is gratefully acknowledged. We thank  Alice d'Orfani ,  Lauryn Moali ,  Iman Nasr  and  Laure Detheve , undergraduates in the laboratory for technical help, the  Imag'IC and Cybio core  facilities of the  Institut Cochin , and  Cédric Broussard ,  Virginie Salnot  and  François Guillonneau  for helpful discussions. Jamila Anba-Mondoloni  ( Micalis Institute ) provided valuable comments and suggestions on this work, We also thank the personnel of the  CIC Mère-Enfant Cochin-Necker  for the human decidua. We acknowledge the use of Alphafold and ChimeraX for FabT in silico design, and BioRender for computer-generated models.
Funding CL was supported by  Université Paris Cité (BioSPC ,  n°51809666 ), FRM ( FDT202106012831 ) and  FEMS  ( FEMS Congress Attendance Grant  for poster n°  7875  in 2021 and  FEMS  grant  -LISSSD 2022  n°  LISS-213065 ). This work was supported by  DIM One Health  ( RPH17043DJA ) (AF),  Agence Nationale de la Recherche  ( StaphEscape  project  ANR-16-CE15-0013 ), and the  Fondation pour la Recherche Medicale  ( DBF20161136769 )(AG). IMAG 'IC core facility is supported by the  National Infrastructure France BioImaging  (grant  ANR-10-INBS-04 ). (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The coastal region is not a constant and depends on several physical elements. Analogous to the world trend, the coastal areas of Tamil Nadu are encountering sea level rise and associated shoreline changes. However, the rate of change is not uniform and varies considerably. The study revealed such differences in shoreline changes during the past 30 years and identified erosion and accretion patterns trends in Tamil Nadu's coastal watersheds, from Pulicat (Thiruvallur district) to Kodyar (Kanyakumari district). The study used remotely sensed Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) datasets to analyse shoreline changes from 1988 to 2018. The Digital Shoreline Analysis System (DSAS) tool is used to derive trends of shoreline changes by End Point Rate (EPR) and Net Shoreline Movement (NSM).
Data source The SRTM (Shuttle Radar Topography Mission) DEM and toposheets were used to generate the drainage network, 40-m contour line and the coastal watersheds. The satellite imagery Landsat TM (Thematic Mapper) and Landsat OLI (Operational Land Imager) acquired between 1988 and 2018 were used to trace the shorelines of the Tamil Nadu coast. All satellite datasets acquired with less than 5 percent of cloud cover were processed for geometric corrections and image enhancements using ENVI 5. 6 software. The datasets (Table  1 ) are acquired within the 30-day temporal intervals (Jan-Feb), considering normal sea surface conditions during this interval period and the availability of cloud-free images.