dataesr/scientific-paragraphs-categorization huggingface dataset:
Further investigation on shrug gestures in co-occurrence with speech in other types of sentences in BP is needed to determine which other functions can be expressed by this gesture. In sum, our findings showed that the acoustic component together with the gesture component are produced to reach the speakers" communicative aim in both languages. The analysis also revealed that questions are produced with more facial movements than are assertions. In addition, BP and MS data presented the same visual cues as questions markers in our visual analysis. The results of this paper support previous investigations on the multimodality of speech production (Gili Fivela, 2018), by showing that there are facial gestures which are pragmatically used to mark the illocutionary force of an utterance, as stated by  Kendon (2004: 5).
However, either early fusion or latter one might be incapable of effectively addressing the aforementioned challenge, as there is a lack of completely-paired multi-modality samples in the whole dataset. The problem setting naturally motivates us to find a latent shared feature space by learning modality-specific projections from the training samples. For this purpose, some tentative works have been proposed by joint dimensionality reduction or alignment learning. In  [2] , principal component analysis (PCA) is used to simultaneously project multi-modal data into a common subspace. Rasti et al. [4]  fused the HSI and Lidar data using total variation component analysis for land-cover and land-use mapping. Hong et al. [5]  jointly embedded the spatial-spectral information for HSI classification.
A. Brief Motivation Although operational optical satellites, e. g. , Sentinel-2 and Landsat-8, enable the MS data openly and largely available on a global scale, the MS data fail to distinguish similar classes due to its few spectral bands. Rather, the HSI is acquired with rich spectral information, enabling identification of the materials more easily and accurately, but its space coverage is far narrower than that of MS data. This naturally motivates us to investigate a general but interesting question -can a limited amount of HS data partially overlapping with MS data improve the classification performance of the extra large-scale and non-overlapped MS data? This is a typical CML-based problem setting.
A. Data Description We conducted the experiments on MS-Lidar and HS data provided by the 2018 IEEE GRSS data fusion contest (DFC2018) during the training phase  [13] , where the MS-Lidar data and HSI were acquired by an Optech Titam MW (14SEN / CON340) with a Lidar sensor and an ITRES CASI 1500 sensor, respectively. The MS-Lidar data was collected from three different wavelengths (1550 nm, 1064 nm, and 532 nm) at a 50 cm ground sampling distance (GSD). It consists of 1202 × 4768 pixels with nine bands (three downsampled RGB bands, three intensity bands, and 3 DEM bands). Note that these bands are stacked  [14]  as the model input. The corresponding HSI with the dimensions of 601 × 2384 × 48 covers the wavelength range from 380 nm to 1050 nm at a GSD of 1 m. The false-color images for the two used data are shown in Fig.
B. Experimental Configuration All models involved in performance comparison are trained on MS-Lidar and HS data and tested only in presence of MS-Lidar data to meet our CML's problem setting. To have pixel-to-pixel aligned on the two different modalities, we downsampled the MS-Lidar and ground truth to the HSI's spatial resolution using the nearest neighbor interpolation. Notably, in the used dataset, the sample distribution between the classes is extremely unbalanced. To provide a more reasonable and meaningful performance analysis. we evenly select 200 samples from each class for training 1 and the rest samples for testing. Furthermore, ten replications were performed for the selection of training and test samples and their averaged results are finally used for the quantitative evaluation.
IV. CONCLUSION In this letter, we investigate a CML-related problem by using the heterogeneous RS data (MS-Lidar and HS data). Concretely, we propose a novel joint sparse subspace learning ( 1 -CoSpace) model, which is an improved version of CoSpace, by simultaneously learning a shared feature subspace and a sparse regression matrix. Benefiting from sparse modeling, the proposed 1 -CoSpace can interpret and mine the intrinsic structure of the data more effectively, resulting in a further performance improvement.
This work is jointly supported by the  German Research Foundation (DFG)  under grant  ZH 498/7-2 , the  Helmholtz Association  under the framework of the  Young Investigators Group SiPEO  ( VH-NG-1018 ), and the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant agreement No. ERC-2016-StG-714087 , Acronym: So2Sat). This work of  N. Yokoya  is also supported by the  Japan Society for the Promotion of Science  ( KAKENHI 18K18067 ). D. Hong  and  X. Zhu  are with the  Remote Sensing Technology Institute (IMF) ,  German Aerospace Center (DLR) ,.
In European rivers, research and monitoring programmes have targeted metal pollution from bed and floodplain sediments since the mid-20 th century by using various sampling and analysis protocols. We propose to characterise metal contamination trajectories since the 1960s based on the joint use of a large amount of data from dated cores and subsurface sediments along the Rhône River (ca. 512 km, Switzerland-France). For the reconstruction of spatio-temporal trends, enrichment factors (EF) and geo-accumulation (Igeo) approaches were compared. The latter index was preferred due to the recurrent lack of grain-size and lithogenic elements in the dataset. Local geochemical backgrounds were established near (1) the Subalps and (2) the Massif Central to consider the geological variability of the watershed.
, 2014). A dataset of 1340 measures on the river sediment cores -RCS (n samples = 137) was used in this paper, including metals, TOC (Total Organic Carbon) and grain-size data  (Dendievel et al. , 2020b). 2. 1. 4. Subsurface sediment data  Metal concentrations from bed and flood deposits (BFD), monitored by the Rhône-Méditerranée-Corse Water Agency (RMC WA), were integrated at the monitoring stations located nearby the coring sites. These RMC WA stations were monitored at different time-periods: since the end of the 1980s (Arles), or since the beginning of the 1990s (Culoz, Saint-Sorlin-en-Bugey, Chasse-sur-Rhône), and more recently (stations list in Table  1 ). This additional dataset presents 1263 measurements (n samples = 179) including metals and TOC obtained from the "naiades" website (French server for the monitoring of water and sediment quality: http://www.
Metal analysis Two protocols of extraction were performed depending on the samples. An Aqua Regia (AR) extraction procedure was achieved on the six river core samples (RCS) (analysis: ENTPE, UMR 5023 LEHNA). A total extraction (TE) was employed for the fresh bed and flood deposits (BFD) according to the RMC WA procedure for sediment monitoring (analysis: Drôme Department Laboratory). Finally, to have a consistent basis for the data comparison, both extraction methods were used on the geochemical background core samples (GBS) at the INRAE (TE), and at the ENTPE (AR). Precision, analytical and procedural accuracies were controlled by replicate measurements performed on certified materials in order to check digestion and analyses. Repeated measurements (triplicates) on the same samples were achieved to test repeatability (the standard errors associated to these results are provided in the linked dataset: https://doi.
pangaea. de/10. 1594/ PANGAEA. 914477 [Dendievel et al. , 2020b] ). For the AR extraction (RCS, GBS), the samples (0. 1 g) were dried at 105 °C for 12 h and sieved at 63 µm. They were digested with one volume of HNO 3 (2 ml of 60% Merck Suprapur for RCS; 0. 5 ml of 14 M Ultrapur for GBS) and three volumes of HCl (6 ml of 30% Merck Suprapur for RCS; 1. 5 ml of 12 M Ultrapur for GBS). After digestion in a microwave system (CEM 6: 110-180 °C at a pressure of 120 PSI), samples were filtrated at 22 µm by using 25 ml of distilled water. Then, metal concentrations were measured by Atomic Absorption Spectrometry (AAS) with a Perkin Elmer PinAAcle 900 T. Flame absorption was used for  Cr, Pb, and Zn (standard FD T90-112; AFNOR, 1998) , and a graphite furnace was employed for Cd and Cu  (NF EN ISO 15586; AFNOR, 2004).
Data treatment 2. 3. 1. Treatment of undetects and normalisation to grain-size Metal concentrations were greater than the LOQs (limits of quantification), except for Cd for which 78 samples from the BFD (i. e. 25%) were lower than the LOQs (usually 0. 2 mg kg -1 ). To handle Cd leftcensored data, we used a Kaplan-Meyer procedure  (Helsel, 2012). This reliable method is based on counting numbers of data at and below each detected value, and computes the 95% upper confidence limit on the mean (UCL95). It decreases the number of undetected Cd values to 39 (13%). Taking into to account the grain-size was also one of the main issues due to the high heterogeneity of the data. Indeed, potential lithogenic elements were rare, and only acquired recently (since 2010, availability: Al and Fe) and no grain-size was measured on the Rhône River BFD.
Characterisation of pollution sources and trends by using river sediment cores and subsurface samples A Principal Component Analysis (PCA) was carried out separately on RCS and BFD samples. The Me/Al ratios were used to avoid the grain-size effect. Then, after the removal of missing values (NA), the data was transformed by using a centred-log ratio procedure ("clr" function from the package "rgr" [v. 1. 1. 15: Garrett, 2018]  in R [v. 3. 5. 2; R Core Team, 2018]) to correct the closure effect in the data matrix  (Aitchison, 1986; Tolosana-Delgado, 2012). A biplot was computed according to the PCA results and showed compositional coordinates according to the main inertia axes. Two pollution indices -Enrichment Factors (EF) and the geo-accumulation Index (Igeo) -were tested to evaluate the degree of contamination in river sediments (e.
g. Grosbois et al. , 2006 Grosbois et al. , , 2012;; Meybeck, 2013; Müller, 1986; Reimann and de Caritat, 2000). These indices were calculated based on the Upper and Middle Rhône geochemical backgrounds (MGB) according to the location of the studied sites, and estimated separately according to each extraction procedure (AR and TE). On the one hand, EFs can take into account the grain-size control by referring to the Al content (considered as a grain-size proxy). On the other hand, the Igeo comprises a safety factor of 1. 5 referring to the changes of metal concentrations linked to natural variations  (Müller, 1986). In addition, the Igeo can be calculated regardless of Al (which is not available for the BFD data before 2010) and provides an independent index, which is useful when Al could be influenced by anthropogenic emissions.
For more details about the EF and Igeo categories, please refer to SI-4. Igeo and EFs spatio-temporal trends were represented using R (v. 3. 5. 2; R Core Team, 2018) and the "ggplot2" package (v. 3. 1. 1; Wickham et al. , 2019). Non-parametric models were plotted based on general additive models (gam) with an appropriate degree of freedom and number of dimensions  (Wood, 2019). Gam (confidence interval = 99%) were plotted according to the six geographical clusters (GC) defined above (Fig. 1 ; Table  1 ). Gam curves were used to define the time of recovery, i. e. the decay time-period between the maximal Igeo level (according to gam) and Igeo < 1 for each metal (i. e. uncontaminated to moderately contaminated class).
The use of EFs and Igeo allow to reconstruct spatial and temporal trends of metal contamination from 1965 to 2018 by combining and modelling (gam) data from river core sediments (RCS) and bed/flood deposits (BFD; Fig. 5 ). EFs and Igeos variations are summarised in Table  3 : these two indices present similar changes over space and time. However, EFs are based on a lower number of samples (n = 209) than the Igeo (n = 316, except for Cd for which n = 235) because the Al data -needed for EFs -is only available on BFD since 2010, which limits the analysis. Thus, in our study, the Igeo provides the best compromise between availability and quality to model metal pollution trajectories using both BFD (subsurface samples) and RCS (dated cores). It should be noted that this combined approach -which takes into account analytical results of different nature -must be used prudently, especially due to the low reliability of subsurface samples data derived from past monitoring activities (already mentioned by  Thomas and Meybeck, 1996).
Conclusion This paper aims at reconstructing metal pollution trends along a large river by combining results from dated cores and subsurface samples acquired through research and monitoring protocols. Major issues were raised up due to the heterogeneity of the original dataset that contains limited grain-size and lithogenic information. According to our study, both Al and Fe were relevant to normalise metal concentrations from dated cores, while only Al was grain-size correlated in background samples. This work identified some metal sources along the Rhône geographical clusters (GC): (1) the leaching of Cu from vineyards, (2) the Cd-Cu-Zn prevalence near the Hercynian Mountains (mining, slope erosion) or after urban/industrial areas, (3) the impact of industrial Pb on the Lower Rhône, and (4) the influence of geogenic Cr and Ni for the most recent deposits.
Based on local geochemical backgrounds, metal pollution trends were assessed by EFs and Igeo. They presented similar variations over time, but the lack of Al before 2010 resulted in a gap in the EF estimates. Although, the Igeo allows the use of more data to assess pollution trends. The recovery time to decrease from highly to unpolluted Igeo classes varied from 10-20 years to 30-40 years along the Rhône GC. These trends differ from other European rivers due to various geological, depositional and land-use contexts. Regarding environmental implications, we underlined the high influence of certain metals (e. g. Cu) in the risk of a cocktail of organic and inorganic contaminants. Finally, re-using European monitoring data remains challenging and calls for methodological evolutions to improve the reconstruction of pollution trajectories along large and heterogeneous rivers.
Acknowledgments and funding We are very grateful to  Sophia Vauclin  for her relevant discussion and review. We also thank  Myriam Hammada  and  Céline Bégorre  for their participation in analyses, as well as  Gary Lardaux  and  Hervé Piégay  for their help and support for the study of pre-industrial cores ( CH-CO ,  LPP-C1 ,  MGN-C1 ,  EM-5 ) used for the geochemical background (funding:  OHM "Vallée du Rhône"). Finally, we thank the two reviewers for their comments and suggestions which improved the quality of the article. This study was conducted in the framework of the  INTERPOL consortium  for the comparison of the pollution trends on the main French rivers, funded by the  French Office for Biodiversity (OFB). This research was supported by the  Rhône Sediment Observatory (OSR) , through the  Plan Rhône (European Regional Development Fund) , by the  Rhône-Méditerranée-Corse Water Agency (RMC WA) , the  Rhône National Company (CNR) ,  Electricité De France (EDF) , and the regional councils from  Auvergne-Rhône-Alpes, PACA and Occitanie.
Data Availability The dataset of the study (dated cores and samples from gauging stations) including site names, coordinates, grain-size, TOC and metal concentrations is freely available on PANGEA at https://doi. pangaea. de/10. 1594/PANGAEA. 914477 (Dendievel et al. , 2020b). The dataset regarding the median-based geochemical background was co-submitted to Data in Brief (Dendievel et al. , in press).
Applications to impossible differential cryptanalysis from the designer's point of view We complete the work of Sasaki and Todo in  [ST17b]  and we use our modeling techniques for proving partial resistance against impossible differential cryptanalysis for AES and Skinny. More precisely, we show for both ciphers that even when the Sbox details are taken into account, there are no impossible differentials with one active input/output byte for 5-round AES and 13-round Skinny. All of our MILP experiments were done with the Gurobi optimizer  [GO20]. Organization The rest of the paper is organised as follows. Section 2 is dedicated to our different methods for modeling Boolean functions and Sboxes. Then, in Section 3 we present an algorithm to efficiently model linear layers. In Section 4, we run an experiment for illustrating the effectiveness of the previously-introduced methods.
We notably applied Algorithm 2 for generating an initial set of inequalities for the 8-bit Sboxes of AES and Skinny-128. We then obtained 70336 initial inequalities for AES and 8829 for Skinny-128. The running time was of around 15 minutes for AES and 2 hours for Skinny-128 where 90% of the time was spent not for finding new inequalities but for removing not interesting ones (lines 26-28 in Algorithm 2). The difference in these running times is explained by the fact that as the DDT of Skinny-128 is very sparse, there are many more possible spaces a ⊕ Prec(u) than for the DDT of AES. After this, to find a representative set among these initial inequalities (Problem 2 ) we set up a minimization problem and solved it with Gurobi. While for Skinny-128 the problem was solved in just a few seconds providing us with the global minimal (which is thus the same as the QM algorithm), for AES the problem didn't reach the minimum even after 1900975 seconds of search on a 8-core laptop.
However, the solution provided by Gurobi, even if not the minimal one is much better than the one given by Espresso, as it can be seen in Table  3. Furthermore, according to the authors of [AST + 17], QM itself cannot be applied to AES because of its memory complexity. While for Sboxes with sparse DDTs, as the one of Skinny-128, the method of this section provides a quite compact modeling, for Sboxes with low differential uniformity (i. e. highest value in the DDT), as the one of AES the number of obtained inequalities is quite high for practical applications. For this reason, we provide in the following sections new methods for modeling large Sboxes that outperform in most of the cases the methods provided up to now.
However, let us take the example of the Skinny MixColumns operation given by the matrix M Skinny below. In that case, the code generated by (M Skinny |I) has minimum distance 2 and it is equivalently generated by the matrix A Skinny obtained by adding in F 2 the fourth line to the first line of (M |I). M Skinny =     1 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0     A Skinny =     0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 1    . While the naïve XOR modeling of (M Skinny |I) would have needed 2 3 + 2 + 2 2 + 2 2 = 18 inequalities, using the above matrix for the XOR modeling only requires 14 inequalities. To demonstrate that this representation is more efficient in practice compared to the naïve approach, we computed the time it takes for the Gurobi Optimizer  [GO20]  to reach the minimum number of active Sboxes over several rounds of Skinny-128 for the two different modelings of MixColumns.
4 and 2. 5. For the linear layer we also considered two cases: in the first case we model the MixColumns matrix via the naïve XOR modeling and in the second we model it with the improved XOR modeling given by Algorithms 4 and 5. We then considered all four different combinations of these Sbox and MixColumns modelings. For launching the experiment, we chose a random set of input and output difference pairs (δ in , δ out ) and asked Gurobi to verify whether the differential (δ in , δ out ) is possible. We ran this experiment for Skinny-128 and the AES and present in Table  9  the average computation times for each cipher. Quantiles and other statistics are available in Appendix G. For the AES, the computation time is dramatically higher when the input and output are sparse (have much more zeros than ones) than when they are dense, hence the need for two different tables.
This entails that functionally-correct source code is compiled into functionally-correct assembly. While certified compilation is a promising approach to guarantee functional correctness, it entails key limitations described next. These limitations are particularly-relevant for cryptographic code, where sub-optimal execution time can be a deal-breaker for code deployment. First, the optimizations supported by currently-available certified compilers do not produce code competitive with that produced by the most-aggressively-optimizing (non-certifying) compilers. Indeed, the certification requirement limits the set of optimizations that can currently be performed. For example, global value numbering is an optimization that is missing from most compilers; see however  [13]. The preeminent certified compiler, CompCert  [30] , generates code whose performance typically lies between GCC's -O1 and -O2 optimization levels.
In parallel, Appel et al  [8, 9]  have developed general-purpose program logics to reason about functional properties of source-level programs, and applied these program logics to prove functional correctness of a realistic sha-256 implementation; in a follow-up work, Beringer et al  [15]  combine the Foundational Cryptography Framework of  [33]  to build a machine-checked proof of the (elementary) reductionist argument for hmac. Fiat-Crypto  [24]  is a recently proposed framework for the development of high-speed functionally correct implementations of arithmetic libraries for cryptography. Certified compilation is used to convert a high-level algebraic specification of the library functionality into C code that can subsequently be compiled into executable code. Our approach is complementary to Fiat-Crypto, in that our verified compiler can be used to compile the generated C code, carrying the functional correctness and constant-time guarantees to low level code, including code that relies on intrinsics.
Hacl *  [41]  is a library of formally verified, high-speed implementations. Hacl * is included in recent versions of Mozilla Firefox's NSS security engine. It has recently been extended to vectorized implementations  [34]. F * programs from Hacl * library can be compiled into C code using KreMLin  [35]  and then compiled into assembly, for instance, using CompCert. Kremlin is a high-assurance compiler/extractor tool that generates provably correct C code from F * specifications. Additionally KreMLin is the tool used to generate the TLS1. 3 code verified in Project Everest. 5 gain, our verified compiler can be used to compile C code relying on intrinsics generated by KreMLin with the guarantee that functional correctness is preserved. Finally, we mention work on Jasmin  [3, 6] , a new pre-assembly language for the implementation of high-assurance crypto code.
7 ntrinsics. Instruction extensions are usually domain-specific, and they provide relatively complex operations that should be directly available to the programmer, even if the programmer is using a high-level language such as C. For this reason, compilers typically provide a special mechanism to allow a programmer to specifically request the usage of an extended instruction; this mechanism is typically called an intrinsic. Intrinsics in C compilers such as gcc and clang are simply special function names and data types that are handled by the compiler in a different way to normal function declarations/definitions; for the most part, usage of these special functions is passed transparently through various compiler passes, and eventually translated into a single assembly instruction.