dataesr/scientific-paragraphs-categorization huggingface dataset:
The ratio of the molar fraction/ (1-molar fraction) is calculated. For protein levels, all top N peptides log2(Ratio) are grouped together to obtain classical fold changes for biological interpretations. 10. A key question in proteomics data analysis is the distinction between noteworthy (or significant) results from other observations, which are false positives, that is, acquired by random chance. Indeed, the large amount of data arising from proteomics technologies is associated with an increase in the possibility to observe atypical "by chance" values in the dataset. In this context, statistical methodologies generally assume that all variations in the data are due to random fluctuations and, accordingly, derive a probability to observe variations that are greater than those present in the data.
Random fluctuations can be modelled in two different ways. In the first, a mathematical function is chosen (often normal or student laws) and a statistical hypothesis is used to discriminate "significant" from "nonsignificant" observations, based on a predefined error rate (generally 5%). In the second, random permutations of the original dataset are performed to define empirical distributions, which will be used to assess potential random fluctuations. It is a remarkably interesting approach, especially when the theoretical probability distributions of the studied parameters are not demonstrated, as is the case with the bSLIM output dataset.
Acknowledgments We would like to thank  Dr. Bernard Delanghe  ( Thermo Fisher Scientific ) for providing us with the table structure of the ". pdresult" file that allowed us to develop the present workflow. NS received a thesis grant from  CRI-Paris. This work was supported by the  ARN , grant  ANR-18-CE44-0014. The English text was edited by  Alex Edelman & Associates  (http://www. alexedelman. com/).
These traits were selected a priori given their established role in litter decay rates and nutrient cycling  (Bakker et al. , 2011). For SLA, three representative leaves for each individual were chosen at ∼60% of the height, and showing no signs of disease. Leaves were then transported to the laboratory for morphological and chemical trait analyses. Pictures of the leaves were taken immediately after returning from the field and then analysed with ImageJ software to obtain the leaf area (cm 2 ). Leaves were then dried at 60 • C to constant mass and weighted to attain leaf dry mass (mg). SLA (cm 2 g -1 ) was calculated from these variables as the ratio between leaf area/dry mass. LDMC (mg g -1 ) was calculated as the ratio of leaf dry mass to fresh mass. To determine LNC and LCC, leaf material was analysed with an elemental analyser to determine leaf C concentration (mg g -1 ) and leaf N concentration (mg g -1 ) (LECO Corporation, Minnesota, USA).
For the latter, our interview questions for each participant started with an initial point of herbaceous plants emerging, with subsequent questions targeting processes taken to identify plants and make decisions on the management of this plant community. The questions continued until we reached a pre-determined end point of healthy coffee plants. We then used a cognitive mapping software, Decision Explorer (Banxia Software Ltd. , 2014), to develop a mental model for each participant. In these maps, the initial and end points were set, with subsequent complexity in managing the herbaceous community captured as key concepts coded by giving common labels to recurring themes  (Bryman, 2004; Özesmi, & Özesmi, 2004)  for a total of 45 common concepts across the interviews. Through an iterative process, these coded labels reflected farmer-identified concepts, termed 'variables' , in the decision-making process on herbaceous community management.
Statistical analysis Statistical analysis was performed in R software version 3. Note: Also presented are adjusted model R 2 and p-value associated with linear regression between canopy openness (as a proxy for shade tree coverage) and the corresponding herbaceous community indices (n = 45 plots). Indices that have been log-transformed are marked with an asterisk (*). the total abundance of species per plot (plants m -2 ). The Functional Diversity package  (Laliberté et al. , 2015)  was employed to determine FR ic , FE ve , FD iv , and FD Q using the four leaf traits (SLA, LDMC, LNC and LCC) of the herbaceous community per plot. To determine the relationship between canopy openness and functional trait indices, we used linear regressions with farm as a random effect. Principal component analysis (PCA) was employed using the 'vegan' r package  (Oksanen et al.
SAR data and preprocessing To construct our SAR amplitude time series, we used the Google Earth Engine Sentinel-1 ground range detected (GRD) data set. These data are preprocessed following the workflow of  Filipponi (2019)  to obtain the σ 0 backscatter coefficient in geographic coordinates at a resolution of 20 x 22 m and a pixel size of 10 x 10 m. We then applied the module of Vollrath et al. (2020) using the SRTM 30 m digital elevation model (DEM) to carry out an angular radiometric slope correction based on the volume scattering model of  Hoekman and Reiche (2015). This has the effect of converting from σ 0 (normalised in the ellipsoid plane) to γ 0 (normalised in the plane perpendicular to local satellite look direction). The aim of this step is to reduce the effects of topography on the SAR backscatter.
1d. The length of the "co-event" period was defined as 6 months for the Hiroshima and Zimbabwe events. For the three Nepal inventories, this was reduced to 5 months in order to allow a sufficient pre-event images to be acquired following the satellite launch in 2014 and sufficient post-event images to be acquired before the end of July, since few Sentinel-1 images are available over Nepal in August, September and October 2015. Unfortunately, insufficient data were acquired on the ascending orbit over BG and BK, so we only present results based on the descending track data for these two inventories. In this figure and throughout the manuscript, we refer to SAR data according to the event, track number and orbit direction, for example, ascending track 72 over Zimbabwe will be referred to as Z072A.
Combining tracks Sentinel-1 acquires data on an ascending track, (moving northwards and looking east) and a descending track (moving southwards and looking west). Therefore, we can generate two sets of dates for each landslide inventory (excluding Bhote Kosi and Buri Gandaki where only descending track SAR data were available). This has several advantages. First, landslides that were not assigned a date using data from one track may be better dated by the second, increasing the number of landslides in the inventory for which a date can be assigned. In particular, landslides that are masked due to foreshortening or layover in one track may be better imaged in the other track. This was advantageous in Trishuli, where both ascending and descending tracks were available and steep slopes meant that large numbers of landslides were masked in each case.
Vegetation In order to assess the effect that vegetation cover has on the methods we propose here, we compared the number of correctly timed, incorrectly timed and untimed landslides with different values of pre-event NDVI (Fig. 5 a-c ). We took the maximum 255 NDVI value for each pixel in the year preceding the event and used Sentinel-2 data for Zimbabwe and Hiroshima and Landsat 8 for Trishuli. In all three inventories, the majority of mapped landslides occurred in vegetated areas (0. 6 < N DV I < 0. 8). In all three cases, a landslide in a more vegetated area was more likely to be assigned a date and this date was more likely to be correct.
However, it is worth noting that of the 47 landslides correctly timed across the two events using the C1 and C2 combined, only 3 had already been timed using the combined amplitude-based methods in Sect. 3. 1, suggesting that the incorporation of coherence methods could increase sensitivity, if these could be made more reliable. Currently, only the Sentinel-1 SAR constellation acquires SAR data with sufficient coverage and acquisition frequency for use in landslide timing studies. These data are acquired at C-band, which usually has low coherence in vegetated areas. Lband data is better suited to InSAR-coherence-based landslide detection in vegetated areas  (Burrows et al. , 2020). The planned NASA-ISRO NISAR mission has a similar acquisition strategy to Sentinel and will acquire L-band SAR.
Code and data availability. Sentinel-1 GRD and Sentinel-2 data are available open-access from ESA Copernicus and were accessed through Google Earth Engine (https://developers. google. com/earth-engine/datasets/catalog/sentinel, last access 19 Jan 2022). Images from the USGS Landsat archive were accessed through Google Earth Engine (https://developers. google. com/earth-engine/datasets/catalog/landsat, last access 19 Jan 2022). Landslide polygons were obtained from  Roback et al. (2018)  for Nepal (available at https://doi. org/10. 5066/F7DZ06F9) and from  Emberson et al. (2021)  for Japan and Zimbabwe. Google Earth Engine and Python codes used in generating the time series and detecting landslide timings will be provided if the manuscript is accepted for publication. Images were produced using Python Matplotlib  (Hunter, 2007)  and PyGMT  (Uieda et al.
Statistical analysis Normally distributed variables were expressed as means ± standard deviation and compared using the Student's t test. Non-normally distributed variables were expressed as median and interquartile range (IQR) and compared using the Mann-Whitney U test. Categorical variables were expressed as counts and percentages and were compared using the chi-square test or Fisher's exact test when needed. A p-value <0. 05 was considered statistically significant. The analyses were performed with the SPSS statistical package, version 11. 0 (SPSS Inc. , Chicago, IL).
12 Pace-matching ≥89% was shown to be a good predictor of successful ablation, with sensitivity and specificity of 95% and 80%, respectively. 13 The technique used to process PVC matching is also of importance, since automated template matching has been shown to be superior to visual matching. 13 echnological advances like PaSo ® software help greatly to obtain a detailed and accurate pace-matching map. 14 fter a reference on the body surface lead is selected, a clinical PVC pattern is acquired. Thereafter, pacing morphologies are acquired and compared to the recorded PVC pattern. An overall correlation percentage is calculated by averaging the specific correlation percentage of each ECG lead. If multifocal PVCs are present, a pattern bank including all PVC morphologies can be obtained to generate a detailed PaSo ® map in order to spot the site of origin of each ectopic beat, as performed in our study.
Limitations This is a single-center study, in a limited number of patients, of the use of PaSo ® software to ablate multifocal PVCs. Its results should be confirmed in larger multicenter studies. To exclude the possibility that non-predominant PVCs represented just a different exit site from the predominant PVC, multifocal PVCs were defined as PVCs coming from different myocardial regions. However, since patients with structural heart disease were included, some with prior myocardial infarction and slow conduction areas, we cannot exclude that the same PVC site of origin could result in two PVC morphologies, exiting from adjacent ventricular regions. In this paper, we aimed to determine the efficacy of an automated pace-mapping-based ablation strategy for multifocal PVCs. However, as shown in Table  2 , activation points could be obtained for most of the PVCs mapped and only three patients had a full procedure based solely on pacemapping.
PROTOTYPE AND AUTOMATED ATTACKS Based on the presented approach, we implement AutoDiff, an automated tool for indifferentiability attacks with two modes: • verification: on input of a candidate distinguisher code , AutoDiff applies our main theorem to check whether  is universal. • synthesis: AutoDiff attempts to generate a candidate universal distinguisher that is then tested as in verification mode. This is a heuristics: it does not guarantee that, if  is universally differentiable, a universal distinguisher will effectively be found. We stress again that the verification of fixed attack codes (Verification Mode) is non-trivial, due to the universal quantification over all simulators inherent to the definition of indifferentiability. In both modes, the users specifies the construction  in the reference theory; the inverse  -1 should also be specified in case  is invertible, but the tool checks for safety that they are effectively inverses one from each other by solving a unification problem.
We evaluate the verification mode by formalising and corroborating existing indifferentiability attacks from the literature, while the synthesis mode is tasked to rediscover indifferentiability attacks without guidance. The benchmarks include constructions presented previously in our paper (Feistel networks, Iterated Even-Mansour ciphers) and some others presented in this section, among others in Figures 7a to 7e. All the experiments were executed on an 8-core machine with 1. 80GHz Intel Core i7-10510U CPU and 16GB of RAM. Our tool is open-source and it is available here: https://github. com/miguel-ambrona/autodiff.
ACKNOWLEDGEMENTS We would like to thank  Pooya Farshim ,  Manuel Barbosa  and  Serdar Erbatur , for very fruitful discussions at the early stages of this project. We are also very thankful to all the members of the  Indian Statistical Institute (ISI) Crypto Team , in Kolkata. With special thanks to  Avik Chakraborti ,  Ashwin Jha  and  Mridul Nandi , for all their interest and their incredibly valuable feedback which significantly improved our work. They deserve credit for proposing some of the constructions on which our tool has been evaluated and for suggesting to explore other domains such as finding collisions on PGV functions. Finally, we would like to thank the anonymous reviewers, for their valuable time and multiple suggestions.
The aim of this paper is to identify the drivers that could increase the financial integration of microfinance institutions with commercial banks. Importantly, financial integration ensures the sustainable growth of microfinance institutions through the provision of more financial resources, if needed. To identify these drivers, we used a panel dataset of 953 microfinance institutions (MFIs) from the MIX market dataset and country-level data from the World Bank for the 2003-2016 period. We applied a panel quantile approach with nonadditive fixed effects. Our results reveal that an increase in financial development slows the financial integration of MFIs. However, considering the transitory aspect of financial integration by each quantile, it appears that financial development positively impacts the financial integration of MFIs.
In other words, agency costs will increase with an increase in microfinance institutions' integration with commercial banks. Within microfinance institutions, large MFIs with a greater commitment to commercial aspects of their activities may cause mission drifts in their clients' portfolios. They could increase moral hazard incentives that highlight the trade-off between financial performance and outreach. The purpose of considering trade-offs in microfinance explains their impact on financial integration. Therefore, in this study, we consider four main driving forces that can impact MFIs' financial integration: financial development, agency costs, financial performance and outreach. The sample set used is the panel dataset of 953 MFIs from the MIX market dataset and country-level data from the World Bank dataset, both collected for the 2003-2016 period.
Data The microfinance institution data are taken from MIX market, which is a microfinance database operated by MIX covering thousands of financial service providers. We use unbalanced panel data with a sample of 953 MFIs on average, with varied numbers of MFIs per year. Table  A1  in the Appendix provides a correlation analysis of the variables used. There is a positive and significant correlation between financial integration and financial development, agency cost and outreach (average loans per gross national income (GNI) and the number of active borrowers). A negative and significant correlation exists between financial integration and financial performance. Table  1  gives more details about the dataset in terms of numbers of MFIs per year and per type of MFI. The full sample consists of 18,102 observations from 234 countries over a period of 19 years  (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017).
Our goal in building this database is to diversify the endogenous characteristics of microfinance institutions. Table  2  provides the descriptive statistics. To generate some explanatory variables to compare with the main dependent variable, financial integration (FI), we applied a logarithmic rule that ensures an upgrade of all values. The mean value of financial integration is equal to 0. 086, with a maximum value of 0. 983. There are some MFIs with zero financial integration in the first percentile. In other words, borrowing is not present in their capital structure. In the highest percentile (p99), financial integration is higher, which indicates a high degree of interaction between commercial banks and MFIs through borrowing tools. When observing the value of financial development, for the first percentile, the level assigned is lower than that observed in the highest percentile (p99).
Giannetti and Ongena (2009)  evaluated the impact of financial integration on firm performance by considering the rate foreign lending in the gross loan portfolio. For the financial integration measure, they used the percentage of foreign lending, which is the ratio of foreign bank loans to total bank loans. In the MIX market database, we cannot distinguish foreign loans from domestic loans in the growing loan portfolio. However, according to the literature review above, a proxy for financial integration is the borrowing level over the total financing sources (liabilities). For example, an analysis of the Kenyan microfinance sector shows the importance of the debt funds rate to deposits and com-pulsory savings 5. We have identified five components of capital sources: domestic debt, international debt, compulsory savings, deposits collected and equity.
For our econometric approach, the panel quantile data specification allows the use of instrumental variables, as we did in one of our previous study  (Kendo & Tchakounte, 2021) , incorporated in the Stata program qregpd developed by  Powell (2016). We run the qregpd by clearly including instruments defined as lag values of initial regressors. The lag gap considered is the first lag for each regressor. The qregpd is a special case of a generalized quantile estimator. The generalized quantile regressions are estimated using the Markov chain Monte Carlo (MCMC) method, which comprises a sequence of draws from the posterior distribution of the model's parameters  (Arellano & Bonhomme, 2011).
This architecture has gained popularity among object detection algorithms. Faster R-CNN  [10]  is composed of the following four parts: • feature extraction module, this can be a VGGnet  [14] , Mobilnet  [16] , or Resnet  [13] ; • region proposal module to generate the bounding boxes around the object; • classification layer to detect the class of the object-for example, cat, dog, etc. ; • regression layer to make the prediction more precise. The computational speed of the network depends on the feature extraction module and the size of the region proposal module. Both SSD  [12]  and YOLO  [11]  are single-stage detectors. They are significantly faster than two-stage detectors (region-based methods), such as Faster R-CNN  [10]. However, in cases when the objects have not so much variability, neither interclass nor intraclass Faster R-CNN  [10]  is a well-suited network.
In our problem, we are interested in naturally cluttered home environments, where the subject intends to grasp an object, such as in kitchens. The vision analysis system we propose has to be designed to recognise objects to grasp in the video, similar to the grasping-in-the-wild (GITW) dataset  [18]. This dataset was recorded in natural environments by several healthy volunteers and we made it publicly available on the CNRS NAKALA platform. The objects here, seen from the glass-mounted camera, are quite small. Their surface merely represents 10% of the whole video frame. Hence, Faster R-CNN  [10]  is a better choice than the SSD  [12]  and YOLO  [11]. This is due to the fact that Faster R-CNN achieves higher mean average precision (mAP) than them, as reported by Huang et al. [19]  for small objects.
Another approach used in Vitis AI  [23]  and Apache TVM VTA  [24]  is to divide the computation into a series of matrix-matrix multiplications and create a customized ISA (instruction set architecture) to execute these operations efficiently. The resulting system might have higher memory bandwidth requirements and longer latency, but can be easily reprogrammed to infer a different network during different steps of an image processing application. Apache TVM VTA  [24]  is an open, generic, and customizable deep learning accelerator with a complete TVM-based compiler stack. It is an end-to-end hardware-software deep learning system stack that combines TVM and VTA. It contains the hardware design drivers, a just-in-time (JIT) runtime, and an optimizing compiler stack based on TVM. The main advantages of the quantization are reduced complexity of the circuit, efficient use of dedicated hardware resources, reduced on-chip memory requirements, reduced off-chip memory bandwidth, and smaller power dissipation.
Dataset The GITW  [18]  dataset contains egocentric videos recorded by a camera on the eye tracker glasses. It includes the gaze points of where the person was looking at each moment. The videos were recorded in the wild, in real kitchens, by different subjects, and every video was recorded by a subject who grasped a kitchen object. The acquisition device used was Tobii Glasses 2 (eye tracker) with an egocentric scene camera. The Tobii Glasses video resolution is HD (1280 pixels × 720 pixels), and the video frame rate is 25 fps. There are 16 different kitchen objects in the videos: bowl, plate, wash liquid, vinegar bottle, milk bottle, oil bottle, glass, lid, saucepan, frying pan, and mug. Different subjects recorded the dataset in five different kitchens. The videos were short, around 10 s long.
The GITW  [18]  dataset contains 404 videos overall. The dataset is freely available for research. We carried out the time measurements on a subset of the GITW dataset, containing fifteen videos of "grasping a bowl" actions, recorded by four different subjects. The kitchen environments are of different complexity, from a scene with just a few objects, such as the BowlPlace1 videos, to a highly cluttered scene, such as BowlPlace4. The class bowl object had a strong inner variance: different colours, the material of the bowl object, and even a transparent one. The lighting conditions and the visibility are different. Moreover, sometimes, we obtained strong blurring effects due to the camera motion, which was worn on the person's body.