dataesr/scientific-paragraphs-categorization huggingface dataset:
For locations outside the United States, we used vital registration data where available. Locations without vital registration data had weekly pneumonia seasonality predicted based on latitude from a model pooling all available data (Supplementary Information). Testing per capita. We considered diagnostic testing for active SARS-CoV-2 infections as a predictor of the ability for a state to identify and isolate active infections. We assumed that higher rates of testing were negatively associated with SARS-CoV-2 transmission. Our primary sources for US testing data were compiled by the COVID Tracking Project (Supplementary Information). Unless testing data existed before the first confirmed case in a state, we assumed that testing was non zero after the date of the first confirmed case. Before producing predictions of testing per capita, we smoothed the input data by using the same smoothing algorithm used for smoothing daily death data before modeling (previously described).
Generally, these came from state government official orders or press releases. To determine the expected change in mobility due to SDMs, we used a Bayesian, hierarchical meta-regression model with random effects by location on the composite mobility indicator to estimate the effects of social distancing policies on changes in mobility (Supplementary Information). Mobility. We used four data sources on human mobility to construct a composite mobility indicator. Those sources were Facebook, Google, SafeGraph and Descartes Labs (Supplementary Information). Each source takes a slightly different approach to capturing mobility, so before constructing a composite mobility indicator, we standardized these different data sources (Supplementary Information). Briefly, this first involved determining the change in a baseline level of mobility for each location by data source.
Then, we determined a location-specific median ratio of change in mobility for each pairwise comparison of mobility sources, using Google as a reference and adjusting the other sources by that ratio. The time series for mobility was estimated using a Gaussian process regression model using the standardized data sources to get a composite indicator for change in mobility for each location day. We calculated the residuals between our predicted composite mobility time series and input composite time series, and then applied a first-order random walk to the residuals. The random walk was used to predict residuals from 1 January 2020 to 1 January 2021, which were then added to the mobility predictions to produce a final time series with uncertainty: 'past' changes in mobility from 1 January 2020 to 28 September 2020 and projected mobility from 28 September 2020 to 1 January 2021.
Masks. We performed a meta-analysis of 40 peer-reviewed scientific studies in an assessment of mask effectiveness for preventing respiratory viral infections (Supplementary Information). The studies were extracted from a preprint publication  24. In addition, we considered all articles from a second meta-analysis  23  and one supplemental publication  41. These studies included both persons working in health care and the general population, especially family members of those with known infections. The studies indicate overall reductions in infections due to masks preventing exhalation of respiratory droplets containing viruses, as well as some prevention of inhalation by those uninfected. The resulting meta-regression calculated log-transformed relative risks and corresponding log-transformed standard errors based on raw counts and used a continuity correction for studies with zero counts in the raw data (0.
We estimated the proportion of people who self-reported always wearing a face mask when outside in public for both US and global locations using data from PREMISE (US), the Kaiser Family Foundation (US), YouGov (non-US) and Facebook (non-US) surveys (Supplementary Information). We used the same smoothing model as for COVID-19 deaths and testing per capita to produce estimates of observed mask use. This smoothing process averaged each data point with its neighbors. The level of mask use starting on 21 September 2020 (the last day of processed and analyzed data) was assumed to be flat. Among states without state-specific data, a within-the-US regional average was used.
acknowledgements We thank the various  Departments of Health  and frontline health professionals who are not only responding to this epidemic daily, but also providing the necessary data to inform this work. IHME warmly acknowledges the support of these and others (http://www. healthdata. org/covid/acknowledgements/)who have made our COVID-19 estimation efforts possible. The authors are also grateful to  B. Bell  for insightful discussions about methodology. This work was supported by the  Bill & Melinda Gates Foundation , as well as funding from the  state of Washington  and the  National Science Foundation  (award no. FAIN:  2031096 ) with partial funds awarded to R. C. R. , A. Y. A. and S. D. B. We also extend a note of particular thanks to  J. Stanton  and  J. Nordstrom  for their generous support.
Code availability All code used for these analyses was custom created for this study and is publicly available online (https://github. com/ihmeuw/covid-model-seiir-pipeline/ and https://github. com/ihmeuw/covid-model-deaths-spline/). Analyses were carried out using R version 3. 6. 1, Python 3. 8 and R-INLA version 20. 01. 29. 9000. All maps presented in this study are generated by the authors using RStudio (R Version 3. 6. 3) and ArcGIS Desktop 10. 6, and no permissions were required to publish them. Administrative boundaries were retrieved from the.
For a given country, official data sources and unofficial ones (e. g. , assessments performed by NGOs) may often be inconsistent, and none of them may represent an accurate representation of the real situation  [31]. Data however has been collected systematically by the Land Matrix Initiative since 2009. This database can considered to be the most comprehensive on LSLAs  [32]. It is maintained by a consortium of research and development partners. The database is a structured collection of information coming from several different sources, such as government data, public press, scientific publications and voluntary contributions from specific individuals or institutes. In order to maintain a specific focus on large scale land acquisitions, only deals accounting for more than 200 hectares of leased or sold land are included in the database  [16].
The database focuses on agricultural international investments, but increasingly covers domestic deals and other sectors such as mining and forestry. Even though contributions to the Land Matrix database are numerous and heterogeneous, and that the number of listed land deals is constantly increasing, such a collection cannot still be considered exhaustive. The Land Matrix acknowledges that some data biases are unavoidable, given differences in government policies regarding data openness, freedom and strength of press, presence of active farmers organizations or existence of dedicated land observatories, or specific scientific studies, having been undertaken  [22]. Land Matrix data has been used in numerous studies, focusing on different topics such as water  [33, 34] , the commons  [14, 35] , food security  [36] , labour markets  [37] , and biofuels  [38].
For our topic of specific interest is the work on determinants of land acquisitions  [39, 40]. Both Arezki et al. [39]  and Lay and Nolte  [40] , using a gravity model approach, already found that LSLA are often related to weak governance systems and overlapping land rights, and that rich investor countries target poorer economies with abundant land and water resources. Authors in  [41]  however also showed that in many cases, investors aim at areas which are already supporting a considerable populations density, hence completion of land resources is likely to follow. Nevertheless, data science techniques have rarely been used to extract new knowledge from the database. In such a complex scenario, the application of data mining and network analysis techniques would allow to better characterize relations among countries and help to understand the dynamics of the land trade market.
In literature, only a couple of works can be found that propose approaches in this direction. Seaquist et al. [42]  combined data from the Land Matrix and GRAIN (www. grain. org, another LSLA database compiling information on deals from press articles and scientific publications) in order to build a land trade network, which is then analyzed focusing on its basic structural characteristics (e. g. , betweenness, clustering coefficient, assortativity). However, the version of the Land Matrix database used in our work (database retrieved on 16th January 2020 on https://landmatrix. org) includes three times the number of entries with respect to the version analyzed in  [42] , which dates back to 2012, i. e. , 2, 931 transnational deals vs 1, 006, making most analyses obsolete. Furthermore, data quality has increased significantly since its initial years  [22] , thanks to feedback from various side and new research results becoming available.
In  [44] , we published some preliminary results regarding the use of network analysis techniques for the analysis and characterization of LSLAs phenomena. The aim of this work is to push forward this promising approach based on complex network analysis, by providing a wider quantitative and qualitative analysis structured in the following main contributions: • the modeling of three land trade networks out of the Land Matrix Initiative database: a multi-sector network, a network centered on the mining sector, and a network centered on the agriculture one; • the definition of a score, namely LSLA-score, which allows to rank the countries based on their investing/target role in the land trade network; • a structural analysis of the three land trade networks; • an analysis of the land trade context which takes into account the LSLA-score ranking and the correlation between network features (i.
e. , resulting in multiple edges in the network). In order to provide a reliable representation of the current land trade situation, we model the land trade network upon deals that are labeled as in operation in the database. We recall that the Land Matrix database also includes abandoned deals and deals currently in their startup phase (i. e. , land has been acquired but the production phase has not started yet), that have been disregarded in our analysis. We also excluded deals corresponding to terminated contracts, whose land may currently be part of new deals.
Data and limitations All the transnational land trade networks used in this work (i. e. , the multi-sector one and the two sector-specific land trade sub-networks) have been generated by using the public Land Matrix Initiative database (https://landmatrix. org/data/). Main characteristics of these datasets are summarized in Table  1. The Python code, as well as the original Land Matrix data and the land trade networks used in the context of this work are publicly available online at https:// gitlab. irstea. fr/roberto. interdonato/lsla-networkanalysis. The snapshot of the database used for this work has been downloaded on January 16th, 2020. The only filter applied at downloading time is the one about the Deal scope being transnational, while the subset of deals having Current implementation status equal to In operation (production) was selected in a post-processing phase.
e. , each node in our land trade network represents a country), we only take into account the country of origin, disregarding the information about the specific investment company (nevertheless, the use of such information may open interesting analysis scenarios, and is left as future work). Please note that in the Land Matrix database more than one investing company may be listed under the Top parent companies attribute. In these cases, when these companies are based in different countries, we considered the deal as belonging equally to each country with the same (total) deal size. While this assumption introduces an overestimation in the edge weights, it is, in our opinion, the most reasonable one in our analysis scenario. Detailed information about the participation of each company in a given deal (i.
e. , percentage of investment) is not available. Here we chose to consider that a given parent company (and thus a given investing country) is involved in a deal of a certain total size with a given target country, regardless of the actual percentage of investment. This choice was rationalized by the following. Firstly, we used a public database without introducing third-party information that may have been difficult to retrieve and that would not have been available in all cases. Secondly, we oriented our analysis toward considering global trading relations. An alternative solution may have been to divide the total deal size over the number of top parent companies: while this may be reasonable, it would introduce a strong underestimation of the importance of certain deals when multiple companies are involved, which may lead to counter-intuitive results in our scenario.
The same assumption has been made in presence of multiple values in the Intention of investment attribute, i. e. , deals reporting multiple values have been considered has equally belonging to all the intentions of investment. The reason for this choice is the same as in the case of multiple investing countries, i. e. , the choice to only use the public database without introducing third-party information that may be difficult to retrieve and not applicable to all cases. As already mentioned, target countries are identified through the Location 1: Target country attribute of the database. While more than one location may be listed in the database for certain deals (the database includes attributes from Location 1 to Location 21), in all cases (as verified on the snapshot used for this work) they all belong to the same target country, i.
e. , the attributes from Location 2 to Location 21 (where not null) always refer to different areas in the same target country. Please note that the Land Matrix Initiative database is constantly evolving, so that the analyses carried out in this work may give slightly different results with newer (more recent) versions of the database. Nevertheless, while the main focus of this work is in the modeling and characterization of global trading relations between countries, we are confident that the general outcomes of our analysis will stay significant even in presence of updates in the database.
The LSLA-score The modeling of the Land Matrix database as a directed weighted network allows us to provide a rank of the countries based on their status of investing/target countries in the context of large scale land acquisition, we here provide a score, namely the LSLA-score, based on the total surface of acquired and sold land for each country, as reported in the Land Matrix database. Note that in this work we will refer to sold land with a wide meaning, which includes contracts of different nature (e. g. , leasing, rent, etc. ). The LSLA-score s for each country u in the land trade network is defined as follows: sðuÞ ¼ log 10 1 þ P ðv;uÞ2E wðv; uÞ 1 þ P ðu;vÞ2E wðu; vÞ ! ð1Þ To prevent zero or infinite ratios, the values of s(�) are Laplace add-one smoothed. In other words, the LSLA-score is proportional to the weighted in/out-degree ratio of each country, i.
A land reform process, which resulted in the establishment of a Land Commission, has been started by the Liberian government in 2009 with the aim to address this issue. Since  then, the investment activity in the country seems to have drastically slowed down (according to the data registered by the Land Matrix). Regions belonging to the Global North like Western Europe, Northern America and, to a lesser extent, Asia (Except South-Eastern), confirm to be mainly composed by investing countries, that correspond to source nodes in the network (i. e. , no incoming links, normalized LSLA-score around 0. 0). Such leading countries in terms of global foreign direct investment, are highly ranked in terms of land investments  [46]. In other regions a certain balance between the weighted indegree and outdegree can be noted, mainly due to the presence of the BRICS countries (Brazil, Russia, India, China and South Africa).
Data for the quantitative indicators are drawn from national and international statistical sources. Fig  7  shows a heatmap reporting the correlation (Pearson's correlation coefficient) of the LSLA-score, indegree, outdegree and betweenness centrality of the land trade network with respect to such indicators. The heatmap was produced using the seaborn python library. It is easy to observe how land deals are directed towards countries experiencing low development rates (economic, social, governance). This is highlighted by the negative correlation of both LSLA-score and indegree with the last five indicators (WGI, CPI, HDI, GNI, GFSI). This scenario is more evident in the case of LSLA-score, showing correlations in the range -0. 77 � ρ � -0. 61, while indegree shows correlations in the range -0.
The example motif of size 4 (frequency of 204, z-score of 1. 26) has two target countries (Russia and Indonesia) and two investing countries (China and the British Virgin Islands). Both investing countries invest in the two target countries. Listed in the Virgin Islands, Wellpoint Pacific Holdings Ltd is referenced in the panama papers and connected to other holding in Panama and Singapore. The database accounts for investments of up to 199,000 ha in Indonesian agriculture. Also originating from the Virgin Islands, Raysun International Corporation, listed in the Offshore leaks and tied to Honk Kong and Chinese interest is investing some 47,000 ha in Russia for food crops and livestock farming. As regards the example motif of size 5 (frequency of 505, z-score of 2. 17), three countries are investors (France, Bermuda and Switzerland), one country is a target for agricultural investments (Brazil) and one country is both target and investor (Argentina).
Concluding remarks and future work In this work we focused on the global problem of large scale land acquisitions, by modeling three land trade networks out of the data contained in the Land Matrix Initiative database: a multi-sector network, a network centered on the mining sector, and a network centered on the agriculture one. These networks were analyzed by means of state of the art network analysis techniques, with the aim to characterize the behavior of different countries on the global land trade market, and to discover recurrent patterns in the land trade dynamics. To this purpose, we defined a score, namely LSLA-score, which proved to be effective in ranking the countries based on their investing/target role in the land trade network. Leveraging on this network representation, we analyzed the land trade scenario from a quantitative and qualitative point of view, also performing an analysis based on the correlation between centrality measures and different country development indicators.
While this work showed how large scale land acquisitions can be effectively studied by leveraging on complex network analysis and mining techniques, research in this domain can still be advanced by addressing several future challenges. Since the results produced by the use of network motifs on the land trade network were highly promising, a future investigation may regard a deeper qualitative analysis of such results. For instance, while in this work we focused on deals in operation, we can suppose that notable statuses of the implementations (e. g. , abandoned ones) may show correlation with specific network motifs. Another challenge regards the introduction of the temporal dimension in the land trade analysis. Temporal information about the deals contained in the Land Matrix database can be exploited to discover temporal sequences associated to specific categories of deals (e.
These include multi-stage networks, end-to-end networks, generative networks or metalearning. As highlighted in  [21] , these techniques are at a stage of minimal investigation with no asserted guidance on how to perform them efficiently and compare them to existing methodologies. Furthermore, DL requires, in general, high computational costs associated with big data training sets, often making them difficult to use in practice, and, therefore, further research is required to establish this direction. In the specific setting of ASV challenges, the idea behind DL methods, particularly the one of Deep Neural Network (DNN) quickly becoming the ''new-state-of-theart'' methods, is to identify the formants structure with the complex function using many layers of perceptrons. This procedure is a high-cost learning procedure that will be replaced in this work by the EMD technique, able to capture formant structure with the requirement of much fewer parameters and can be applied to small and large datasets providing a uniform method in this regard.
Given the great variety of approaches introduced in the literature and the several databases built and considered by researchers, it is hard to identify a uniform, standard technique unifying the presented framework and tackling the explained issues. The desired and sought technique should carry three main properties: first, non-stationarity and non-linearity should be heavily considered since speech is highly affected by these two characteristics. Often, real-world settings can be further corrupted by adverse environments such as noise, which can cause classical Fourier methods to fail to provide reliable and consistent results across different experiments and noise environments. Secondly, the discrimination power of the classifier should be the centre of attention, and new classification methodologies should be proposed and studied.
This work explores six different kernels as outlined in Table  2. Successful SVMs are strictly dependent on the selected hyperparameters of the kernel functions. Optimal selections can be made for performance measurements evaluated through a cross-validation score of the training set. Several methods are available to search for optimal hyperparameters; the grid-search results tend to be the most numerically stable and easy to implement. In this work we set hyperparameter regions as follows: C ∈ {2 -2 , 2 -1 ,. , 2 6  }, r ∈ {2 -5 , 2 -4 ,. , 2 -2 }, d ∈ {1, 2, 3}, ν ∈ {1, 2}. Regarding the grid for α, we adopted the kernlab package approach for R, which uses the sigest function to obtain the grid range for this parameter. The selected values for α corresponds to a trimmed mean of its grid.
VII. EXPERIMENTAL SET-UP There are three classes of experiments shown in table 4 that foresee the use of three datasets, which are described in table 3. We introduce the three datasets, and then the different types of experiments are presented. We first focus on datasets one and two since they consider two specific classes of sentences, respectively. We highlight the use of these two sets to test our novel methodology within a text-dependent and a speaker-dependent verification system (TD-SD-SV) relevant to ASV challenges characterised by these conditions. The first dataset involves a set of sentences constructed to be challenging and reflect a real ASV setting in which sentences are not phonetically balanced. We obtained them from the first text (Inferno) that makes up Dante Alighieri ''The Divine Comedy''.