dataesr/scientific-paragraphs-categorization huggingface dataset:
Gommers R, Oliphant TE, et al. : SciPy 1. 0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020; 17(3): 261-272. PubMed Abstract | Publisher Full Text | Free Full Text 10. Anytree. 2016. spatial statistic, and other fields into a unified API. The usefulness of the package is demonstrated by an example of re-analyzing the zebrafish brain region and neuron traces. The tool should be very useful in gaining insights from the microscopy imaging data. The package is adequately documented, and the Docker file makes it easier to use for many users. Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed.
Data We employ two data sources in our study: the UK Office for National Statistics (ONS) for mortality and population data, and the Google COVID-19 Community Mobility Reports (GCMR) for mobility data. The ONS reports the weekly number of deaths registered in England and Wales, broken down by age group, sex and region of usual residence (Office for National Statistics, 2021). In the ONS classification, weekly data do not refer to calendar weeks, but to rolling 7-day periods, from Saturday to Friday, and we keep this notation throughout the article. For our purposes, we retrieve the weekly number of deaths by region for the years 2015-2020. We consider a total of ten regions based on the NUTS-1 (Nomenclature of Territorial Units for Statistics) subdivision, namely Wales plus nine regions in England  (North East, North West, Yorkshire and The Humber, East Midlands, West Midlands, East, London, South East and South West).
Daily relative change is estimated as the percentage change with respect to the corresponding baseline weekday for any given report date. To protect users' privacy, absolute mobility values are not available. We obtain mobility data for each category of the GCMR for 108 subnational regions (the GCMR's sub_region_1 variable), covering a geographic area inhabited by 99% of the population of England and Wales, from February 15 (the first available date in the dataset) to August 14, 2020. We aggregate the GCMR data by week (from Saturday to Friday, for consistency with mortality data) and region (taking the weighted average across all counties belonging to a given region, with weights equal to their population sizes) to make it comparable to the mortality data. Relying on a fixed baseline period ignores yearly seasonality of movement, which may be affected by weather patterns, national holidays, vacation periods, etc.
e. α 1 = 1). Exposures over weeks and years are obtained by linear interpolation of mid-year populations in years t = 2015, …, 2019. For the year 2020, the mid-year population is still not available at the time of writing, therefore we keep constant the 2019 mid-year population given the impact of the COVID-19 pandemic on fertility, mortality and migration. Whereas the coefficients α w capture the typical pattern of mortality in registered week w (including seasonal behavior of weekly mortality data, periodic bank holidays effects, etc. ), the smooth time component aims to describe long-term mortality trends. Given that mortality data are recorded by registration rather than occurrence, such a specification provides a better description of the data than including trigonometric functions for the seasonal pattern of deaths.
Estimation of the model described in Equation (  1 ) is performed in R (R Development Core Team, 2020) using the mgcv package  (Wood, 2019, pp. 8-31). For each region independently, we model data for the pre-pandemic period (from week 2015-01 to week 2020-06, corresponding to a total of m = 267 observations) and predict the expected number of deaths dw for the COVID-19 period (from week 2020-07 to week 2020-33). We use week 2020-06 as last estimated week before prediction for consistency with the Google mobility data, for which the baseline period runs from week 2020-02 to week 2020-06. Consequently, the effect of the pandemic is not captured by the time trend of the model, and dw,2020 for the predicted weeks could be interpreted as the number of deaths that would have occurred in 2020 in the absence of the COVID-19 pandemic.
Discussion Local and national governments around the world have implemented a variety of policies aimed at reducing social contacts to curb the transmission of and deaths from COVID-19. We investigated the relationship between excess mortality and changes in human mobility to assess the effectiveness of non-pharmaceutical interventions. Specifically, we leveraged digital data derived from the Google COVID-19 Community Mobility Reports  (Google LLC, 2021)  to explore the association between mobility and excess mortality at the regional level in England and Wales. We found a strong positive relationship between the mobility of Google Maps users and population-level excess mortality, which is considered to be the best indicator of the impact of the pandemic on mortality (National Academies of Sciences, Engineering, and Medicine, 2020).
20 10 -9 m 2 s -1 , at 25°C). The STE was recorded in the time domain for 16 different gradient values. PFG-NMR measurements were conducted as described above with a varying diffusion time. For the long-range effective diffusion coefficients, the PFG-NMR measurements with diffusion time 1200 ms are used. The NMR experiments are carried out at 25°C with temperature controlled with water flow. The STE signals were then transformed into the frequency domain, the resulting NMR peaks integrated, and the peak integrals plotted as a function of the gradient value. NMR data processing is done within the Bruker NMR software Topspin to extract intensities. Further analysis and fitting of intensities to the Stejskal-Tanner equation are done with MATLAB software. Tomographic Imaging. An X-ray nano-tomography experiment with 54 nm spatial resolution (voxel size 54 nm) was done using 50 nm outermost zone width Fresnel zone plate optics and was performed in 32-ID-C in the advanced photon source (APS) synchrotron at Argonne National Laboratory.
e. , larger than zero and smaller or equal to one, γ tot is the sum of all event frequencies, and N is the total number of possible events. The event occurring at a given step is chosen according to the weighted probability of each event. The event j is selected according to the following rule ∑ ∑ γ ρ γ γ < ≤ = = + i j i i j i 1 2 tot 1 1 (7) where ρ 2 is a random number in the interval (0,1]. This process allows for the simulation of a system with various event The Journal of Physical Chemistry C frequencies, including a mixture of fast and slow events. In the present case, only events related to the transport of a single type of molecule (O 2 ) are considered. However, the script is written in a general form to be able to work with many event types if necessary. It is worth noting that, in this work, an approach upgraded from the ones previously reported was used.
ADA are used as inputs to select movements with potential risk where focus the analysis at a local scale, based on traditional methods (i. e. basically photointerpretation and field work). Both the medium-resolution, free data, acquired by Sentinel-1 and the high-resolution data acquired by COSMO-SkyMed will be used in this project action. Susceptibility and risk analyses associated to landslides will be performed integrating the information of the detected active zones (ADA) with the delimitation of geological phenomena. Two analyses will be performed in the whole area: the first one with the Sentinel-1 images, the second one with COSMO-SkyMed images. Finally, at a few selected areas in each country action protocols based on satellite monitoring as a support of conventional monitoring will be proposed to Civil Protection Authorities.
InSaR results on the French area In charge of the first actions of the Interreg MOMPA project, the CTTC provided its expertise in the field of satellite interferometry (InSAR) to detect and monitor movements related to geohazards in the project area. From CTTC we have been provided by two kinds of maps derived from InSAR technique: the Deformation Activity Map (DAM) and the Active Deformation Areas MAP. The ADA map is the main input to focus deeper analysis on the most significant moving areas, which have a potential impact in terms of risk. The methodology used by the CTTC to obtain the DAM and ADA is described in  [1] , here we summarize the main characteristics of the two maps: • The Deformation Activity Map, (that correspond to a type of velocity map) has been generated starting from Sentinel-1 data (a set of SAR images acquired at different times).
To determine whether two points are adjacent, the distance between them must be less than 2 times their radius of influence. Based on the full-resolution dimensions of pixel images, the radius of influence of the dots was set at 26 m. In MOMPA the velocity threshold to select the active points has been set to 3 times the standard deviation of the velocity map. For each ADA, the following parameters are resumed: the number of aggregate active points, the average, maximum and minimum values of velocities, and the average value of cumulative deformations within each ADA. Thus, in the French area, CTTC provided a dataset with a selection of 299 ADA (figure  3 ). All of those areas have been analysed from the cross-checking of historical data, bibliographic data, analysis on google maps and searches in different databases (especially www.
Conclusion and further works The objective of MOMPA is to improve risk management, to contribute to evolve from a reactive approach to a proactive one. The innovation element of the project is applying the interferometric SAR (InSAR) technique, based on satellite imagery, for the enhancement of geological risk management. This technique, which is sensitive to small terrain deformations, can be used for an early detection of movements. In the French area, satellites images processing gives a dataset with a selection of 1,635 ADAs. All those areas have been closely cross-checking and finally 6 sites have been chosen to be cross-checked by on-field analyses. Two of them were presented in this paper. Work is still on progress, but the first results show encouraging results mainly on large areas.
Introduction Context. Many problems in software verification are encoded as reachability queries of some undesired condition-a bug, the exploitation of a vulnerability, etc. When a verification engine establishes that a certain buggy location in the program is reachable, an input triggering the bug is reported to the developer so that it can be fixed. In the case of techniques based on an under-approximation of program behaviors, like Symbolic Execution (SE)  [9]  or Bounded Model Checking (BMC)  [13] , we even have in principle the guarantee that the reported issue is real (correctness): there are no false positives. Problem. Yet, things are more subtle in practice, as some bugs can be triggered reliably whereas others only happen in very specific and highly improbable initial This work has been partially supported by ANR (grant ANR-20-CE25-0009-TAVA) and ERC (grant agreement 771527-BROWSEC).
If the canary has been tampered with, the program exits to prevent exploitation (Fig. 1b , line 11). Here, SSP prevents the attacker from overwriting the return address of victim, as doing so also overwrites the canary with 0x61616161. This will be detected at line 10 of Fig. 1b  with probability 1 -2 -32 on a 32-bit architecture: the only way to pass through it is to have the canary value equal to 0x61616161. Hence, the buffer overflow in this program is not exploitable anymore. Unfortunately, this standard reachability query is satisfiable with the canary global random value equal to 0x61616161 and controlled input equal to e. g. ,  42. And indeed, binary-level SE tools Angr  [46]  or Binsec  [23]  do report the bug as reachable (cf. Table  1 ). Yet, this answer is unsatisfying as this only happens with a very low probability: it may not be considered a plausible attack.
e. , a path is executable if and only if its path constraint is satisfiable. Conversely, BMC  [13]  considers the program as a whole and builds a SMT formula expressing that one of the paths of length at most k leads to. It is equivalent to the disjunction of the path constraints of these paths. The target is reachable in k steps at most if and only if this formula is satisfiable. These algorithms are detailed in Fig. 2 , where GetPredicate turns a path into its path constraint and GetPaths(k) yields all paths below size bound k.
With robust reachability, when only the input buffer is controlled and not the stack canary, Binsec/RSE fails to prove or disprove exploitability in 24 h. However, if we mark the canary as controlled, Binsec/RSE finds an exploit in about 15 min. This suggests the canary brings a real protection against exploitation. CVE-2019-14192 in U-boot. U-boot is an open-source boot-loader, popular for embedded boards. When booting over Network File System (NFS), U-boot does not validate the length field of some network packets. This length is sub-tracted 16 and used as a size to be copied. If a malicious packet declares a length of less than 16, computation underflows and leads to a buffer overflow. We encode the situation as follows: the input network packet is controlled, the IP address of the victim is constant, the NFS state machine is initialized to expect the appropriate packet type and all other values are uncontrolled.
Experimental Evaluation Research Questions. We now seek to investigate in a more systematic way the following research questions: Protocol. We base our analysis on a set of 46 reachability problems on binary executables from various architectures (i686-windows-pc, i686-linux-gnu and armv7-linux-gnu) presented in Table  3. The average trace length for reachable problem instances is 809 instruction-long, with a maximum of 18k instructions. The problems fall into two categories: real code and synthetic examples (e. g. code designed to be analysed). For each executable, Binsec/RSE determines if a certain location is robustly reachable from a certain initial state. If this is the case a model is output by Binsec/RSE, and compared to a ground truth obtained by manual analysis. Tests were run on Intel Xeon E-2176M(12)@4.
, 2016). Improvements in hardware, the availability of larger databases, and algorithmic advances have made possible to accurately build neural networks with more than one hidden layer. These deep neural networks have managed to solve problems that were previously unattainable in the fields of computer vision  (Liu et al. , 2020) , natural language processing  (Young et al. , 2018)  or speech recognition  (Nassif et al. , 2019). However, in mental health, getting accurate results is not enough. Knowing the factors that characterize a given disease is often as important as precisely detecting those patients who suffer the condition. Identifying the relevant factors allows for improved treatments and prevention measures. One scientific field that has put a lot of effort in creating explainable models is the area of statistics.
Among the techniques developed in this field, the lasso algorithm is undoubtedly one of the most widely used  (Tibshirani, 1996). The lasso algorithm performs variable selection by including a regularization term in the loss function of the linear regression. The importance of this technique can be observed in the several extensions that have been developed to deal with variable selection. These techniques, which modify the loss function, include Elastic-Net  (Zou and Hastie, 2003, 2005; Witten et al. , 2014) , Group Lasso  (Zhou and Zhu, 2010; Zhao et al. , 2014)  or recently Sparse Group Lasso  (Simon et al. , 2013; Vincent and Hansen, 2014; Rao et al. , 2016; Laria et al. , 2019). However, in order to use these techniques, a database with a moderate/large size is needed since the regularization parameter is estimated through crossvalidation.
Having a database of this size is not always possible in the mental health field. The proposed dlasso algorithm, a neuronal network with two hidden layers and a regularization term, combines the advantages of lasso and the neural networks. On the one hand, like the lasso linear model, dlasso performs variable selection and provides the weights associated with each selected variable. This makes the neural network explainable. On the other hand, the weights of the neural network are trained through the backpropagation algorithm which, unlike traditional lasso, makes to automatically find the optimal value of the regularization parameter possible. Therefore, dlasso proposes to be a bridge that connects the prominent area of neural networks with that of modern statistics to obtain mutual benefits.
MODEL FORMULATION Consider the usual linear lasso framework, where we have a data matrix X ∈ R N×p containing N observations of dimension p, a response vector y ∈ R N , and the objective is to find β ∈ R p that minimizes, for some λ, the objective function L(β, λ) = N i=1   y i - p j=1 X ij β j   2 + λ||β|| 1. (1) where ||β|| 1 = p i=1 |β i | is the L 1 norm. The general approach chooses the value of λ that minimizes the quadratic error term in L on a separate dataset, using some type of cross-validation over a grid (see, for example,  Friedman et al. , 2010; Friedman, 2012). In order to develop our methodology, the following proposition provides an alternative definition of the lasso problem. Proposition 1. The lasso problem Equation (1) is equivalent to, min w    N i=1   y i - λ 0 λ p j=1 X ij w j   2 + λ 0 ||w|| 1    , with λ, λ 0 > 0.
• The family Adaptability, Partnership, Growth, Affection, and Resolve (Apgar) scale. This five-item scale is used to assess how family members perceive the level of functioning of the family unit  (Smilkstein, 1978). Responses ranged from hardly ever to almost always. • Visual Analoge Scale (Vas) on life satisfaction. This is an ad hoc questionnaire designed by the Department of Translational Psychiatry of the Fundación Jiménez Díaz Hospital and which is part of the electronic questionnaires administered by a digital tool. Parents were asked to rate their own level of satisfaction in different life areas: themselves, family, friends, work and leisure activities. Parents scored these aspects on a scale from 0 to 10, where a higher number means more satisfaction. Once the data were collected, a repeated validation analysis was conducted to test whether the dlasso algorithm was able to predict the severity of parent-reported child inattention, via the SWAN scale, based on the responses that they provided to the 44 previous predictors.
Acknowledgements. We would like to thank the two referees,  Evan Gowan  and  Javier Blasco , for their insightful comments. Catherine Ritz  is warmly thanked for her dedication to the GRISLI ice sheet model. We acknowledge the  Institut Pierre Simon Laplace  for hosting the iLOVECLIM model code under the LUDUS framework project (https://forge. ipsl. jussieu. fr/ludus,last access: 8 October 2021). Financial support. The research leading to these results has received funding from the  SCOR foundation project COASTRISK. https://doi. org/10. 5194/cp-17-2179-2021Clim. Past, 17, 2179-2199, 2021.
, 2018). In this study, a DEM at a 10 m resolution derived from the IGN (French national institute for geographic information) BD Alti product was used. Additionally, a DEM of the bed over which the rock glacier is flowing was interpolated from manually drawn contour lines based on surface DEM. These contour lines of the bed extend the contour lines of the terrain surrounding the rock glacier below its surface, using local constraints from existing geophysical data  (Bodin et al. , 2009). For this operation, we assume that the rather simple overall morphology of the Laurichard rock glacier (a single relatively narrow tongue) and its overimposed position above surrounding terrain (bedrock and other debris slopes, called "bedrock" below) allow to estimate the lateral thickness variability of the rock glacier.
Passive seismic monitoring Since December 2017, an array of six seismometers (named C00 to C05) has been set up on the lower part of the Laurichard rock glacier (Figure  1e ). They are located around 50 m apart, covering the whole area of the rock glacier front. The seismometers (Mark Products L4C, one vertical component) are coupled with the top of relatively large, stable and flat boulders, and sheltered by a plastic tube to shield off any influence of rain, wind and snow. They are connected together to one digitizer (Nanometrics Centaur, sampling rate 200 Hz) with wires insulated by sheath to protect from weather and rockfalls. This passive seismological network records continuously ambient noise together with microseismicity. Because of a rough field context (climatic conditions and surface instability, subjecting sensors to tilting), and although frequently required site visits in order to releveling sensors, the longest periods of usable data have been recorded from only two seismometers: C00 (located around 100 m upstream the front, on the right side of the rock glacier) and C05 (located near the front, on the left side).
2 m). In addition, a Common Middle Point (CMP) survey was performed along the western part of the transverse profile using unshielded bi-static 100 MHz antennas, in order to assess locally the electromagnetic wave velocity distribution within the glacier. Fig. 5 (1a) shows the CMP data after trace by trace amplitude normalization and gain amplification using a dynamic automatic gain control computed on a 100 ns time window. After the direct air and ground waves, numerous events exhibiting a hyperbola shape can be recognized from 40 ns to 225 ns in the CMP data. These hyperbolas have been analyzed considering a semblance analysis (Fig. 5 (1b)), which yields the stacking velocity versus propagation time where a semblance is maximum. The picked maximum of the velocity distribution shows variations ranging from 14 cm/ns to 11 cm/ns with a mean velocity of 12 cm/ns.
Multichannel Analysis of Surface Waves (MASW) In order to better understand the seismic wavefield and constrain the S-wave velocity distribution at the site, we analyzed the surface Rayleigh waves, which dominate the vertical seismic records used in the tomography. For this, we used a far offset shot and computed the semblance map of the velocity and frequency of the waves dominating the seismic record (Figure  7a ), obtained using the Geopsy package  (Wathelet et al. , 2004). The semblance map shows several continuous modes, while the fundamental dispersion curve was picked from 14 Hz to 30 Hz, as indicated by the black line. The presence of several other modes is due to the presence of strong contrasts within the rock glacier and at the interface between the rock glacier and the bedrock.
The dispersion curve was inverted using the Geopsy/dinver package  (Wathelet et al. , 2004) , where a global neighborhood algorithm optimization method is implemented. The model was parametrized using four layers, the top three searching for linear velocity gradients in each layer. With the available frequency range and the velocity distribution, the resolution at large depths (> 15 m) is rather poor. The Vs profiles displayed in Figure  7b  shows a large variability but the best fitting models all converge towards an interface located at 5 m depth with a superficial velocity of 155 m/s followed by a linear increase of velocity reaching 750 m/s at a depth of 7 m. The best model also shows another deeper interface, at 15 m depth, which could be the bedrock interface, despite the low resolution at this depth.