dataesr/scientific-paragraphs-categorization huggingface dataset:
Integration of omics data Considering the analysis of omics data, all the studies presented performed some sort of dimensionality reduction before applying different statistical analysis. This is a limitation of most existing multiomics data integration approaches since they have not been implemented to deal with large matrices. Therefore, development of new integrative methods/tools must consider efficient handling of large data sets  (Subramanian et al. , 2020). Most presentations studied the relationship between environmental exposures and health outcomes using omic data (single or multi-omics) as an intermediate layer in a mediation analysis fashion. Some other studies used Pearson correlation to study the relationships between omics and exposome data. Different combinations of statistical tools were proposed for omics data integration in analytical frameworks.
These tools taken individually were not novel but their combination for integrating exposome, multi-omics and outcomes was strongly relevant and novel. We note that when one wants to include multi-omics layers in the analysis, the analyst needs to make several decisions in terms of pre-processing the data, reducing dimensionality, testing association between multiple sets of data using different techniques, and so on. This can lead to a large number of potential pipelines to analyse such data, each leading to potentially different results. This is a common problem in most kinds of data modelling, but it is aggravated in multi-omics analyses due to the availability of multiple sets of high-dimensional data. Moreover, we note that other methods previously used for exposome and omics data may also be of interest when analysing such data, but they were not presented during this event.
Causal models Causal models have gained popularity in environmental epidemiology  (Bind 2019) , including for mixtures  (Bellavia et al. 2019). Indeed, causal questions are what ultimately drive interventions and policy change. Causal mediation analysis with exposome data can help us prioritize environmental factors that have the greatest impacts on health. In the challenge, examples of causal models applied to exposome-health associations included mediation analysis using omics data, g-computation methods and the use of causal random forest. With regards to mediation analysis with omics data, we note that our data was cross-sectional. In such a setting, results from mediation analyses with omics data should be interpreted with caution, since the omics markers might be a consequence of the health outcome or of the exposure (one or the other, not both).
Final remarks We note that this article does not provide an exhaustive list of potential methods to study the exposome. In addition, the provided dataset was limited in power to study interactions between exposures Still, this is a realistic setting for most epidemiological datasets with exposome data. Other approaches to be explored in the future include trying to integrate a priori knowledge in the analysis, for example from experimental toxicological data, in order to find chemical interactions, or to a priori group exposures with similar biological targets. These knowledgedriven approaches can also be used to reduce the dimensionality in the omics dataset. Other future challenges to be addressed in the exposome field include developing analysis strategies for longitudinal exposome datasets.
The longitudinal component is actually key in the exposome definition, as the exposome tries to capture the totality of exposures across a lifespan. The present dataset only included two periods, pregnancy and childhood, and thus it only partially covered the lifetime exposome. The strength of this event was the application of various methods on the same, well-characterized HELIX dataset. Indeed, it was possible for approaches focusing on the same outcome, child BMI, to find the same chemicals as main predictors (PCBs, metals). Although biological interpretation from these analyses should be avoided due to the partially simulated nature of the data and the heterogeneity in the methods to deal with the confounder structure (e. g. , multi-centre structure), this exposome dataset could serve as a reference to test novel methods in the future.
We also acknowledge that the way we imputed part of the data may have diluted some complex association patterns that may be present in the real dataset. Although we tested some models and obtained similar results in the real and imputed dataset, this may not be the case with the most flexible models, which can potentially capture complex association in the real dataset but not in the simulated dataset. Finally, the work herein has resulted in computational algorithms with associated code made available to the community with an open-source licence, allowing for reproducible research and applications to other similar research questions based on exposome and even multi-omics data. This event was thought-provoking and highlighted the importance of networking between researchers in a multi-disciplinary environment.
Funding The data for the challenge were issued from a study from the  European Community 's  Seventh Framework Programme  ( FP7/2007-206 ) under grant agreement no  308333  ( HELIX  project) and the  H2020-EU. 3. 1. 2. - Preventing Disease Programme  under grant agreement no  874583  ( ATHLETE  project). LMaitre is funded by a  Juan de la Cierva-Incorporación fellowship  ( IJC2018-035394-I ) awarded by the  Spanish Ministerio de Economía, Industria y Competitividad. ISGlobal  and the Exposome hub acknowledges support from the  Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa   2019-2023 "  Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program. JBGuimbaud was supported by a  CIFRE PhD fellowship  (# 2020/1297 ) from  Meersens.
Service-level mobile traffic data enables research studies and innovative applications with a potential to shape future service-oriented communication systems and beyond. However, real-world datasets reporting measurements at the individual service level are hard to access as such data is deemed commercially sensitive by operators. APPSHOT is a model for generating synthetic high-fidelity city-scale snapshots of service level mobile traffic. It can operate in any geographical region and relies solely on easily available spatial context information such as population density, thus allowing the generation of new and open traffic datasets for the research community. The design of APPSHOT is informed by an original characterization of servicelevel mobile traffic data. APPSHOT is a novel conditional GAN design instantiated by a convolutional neural network generator and two discriminators.
APPSHOT builds upon and substantially extends CartaGenie, a previous model we proposed for generating snapshots of aggregate city-scale mobile traffic  [16]. Similarly to CartaGenie, APPSHOT leverages generative adversarial networks (GANs) and convolutional neural networks (CNNs) towards high-fidelity traffic data generation capturing spatial correlations for the given time. More specifically, it is a conditional model that takes contextual attributes for a target city as multi-channel image input to control the output traffic data from the model to match with that city while capturing stochasticity in traffic data. APPSHOT significantly extends CartaGenie in multiple ways ( §V) for generation of service-level mobile traffic snapshots. First, it faithfully models correlations among services and their relative proportions (with respect to overall traffic) via joint generation of traffic for all services as different channels of an image and using adversarial training with two discriminators.
Moreover, it operates at a smaller 'patch' level to allow generating service snapshots for regions of different dimensions. Finally, APPSHOT employs overlapping patch based learning with slide-by-1-pixel method to ensure correct modeling of hotspots, better generalization to new cities and absence of artefacts when sewing up patches into a city-scale traffic map. Overall, these enhancements allow producing dependable service-level traffic snapshots that cannot be synthesized via the original CartaGenie. To train and test APPSHOT, we use an operator-provided mobile traffic dataset, spanning the ten most significant mobile services for ten cities in a major European country, and we augment it with corresponding context data from public sources ( §III). Our evaluation results ( §VII) considering five different fidelity metrics show that APPSHOT generalizes to unseen cities and yields service-level traffic snapshots for different peak periods that are significantly superior to a range of baseline approaches, including CartaGenie.
We additionally evaluate APPSHOT through a representative use case ( §VIII) on resource sharing efficiency with network slicing  [3]  and show that it provides similar results to those obtained with real (ground-truth) traffic data. In summary, our key contributions are as follows: (i) We propose a novel conditional deep generative model, APPSHOT, which to the best of our knowledge is the first method for synthesizing dependable city-scale and service-level mobile traffic data in the literature (discussed in §II). Upon publication of this work, we will make a synthetic service-level mobile traffic dataset gener-ated using APPSHOT available to the research community at https://github. com/netsys-edinburgh/AppShot/. (ii) We present a novel analysis of service-level mobile traffic data across multiple cities and derive insights that inform our design of APPSHOT ( §IV).
(iii) Using an operator provided multi-city and multi-service mobile traffic measurement dataset, we show that APPSHOT synthesizes realistic service-level traffic snapshots solely using contextual input, outperforms all baseline approaches, and effectively generalizes to cities not seen during training. We also demonstrate the utility of APPSHOT for downstream applications through a use case. The remainder of this paper is structured as follows. The next section discusses related work from the networking and computer vision domains. In §III, we elaborate on the mobile traffic and context data relevant to our work. Then in §IV, we conduct an analysis of the aforementioned data, including service-level traffic characteristics and correlation between traffic and context. The proposed generative model APPSHOT is described in detail in §V.
The few related works that exist in the mobile networking context  [16] ,  [19] -  [21]  focus on overall traffic across all services. Di Francesco et al. [19]  propose an approach for assembling a cellular dataset for a given region by integrating multiple sources of data, including census data for population distribution, base station locations and estimation of data demand per subscriber. For the data demand, they simply model this as a probability distribution based on operator provided data on overall mobile traffic across all services and then sample from it. We consider this approach as a baseline in our evaluations and highlight its limitations in handling traffic correlations. In another mobile traffic related work, Bo et al. [20]  target generation of mobile traffic patterns for a region focusing on hotspots through geotagged Twitter data for that region.
III. MOBILE TRAFFIC AND CONTEXT DATA For the purpose of modeling, analysis and evaluation in this work, we make use of a real-world mobile traffic dataset collected in the production network of a major mobile network operator in Europe. We also gather data for a variety of contextual attributes for the target regions from public sources. Mobile Traffic Data. Our traffic dataset spans 10 major cities in a European country (referred henceforth as CITY 1-CITY 10), where it covers the mobile demands of the whole subscriber base of the operator, amounting to around 30% of the local user population. This data was obtained by monitoring individual IP data flow sessions in the operator's network over the General Packet Radio Service (GPRS) Tunneling Protocol User plane (GTP-U). To infer the services corresponding to the traffic flows, the operator employs a combination of proprietary and commercial traffic classification tools on top of Deep Packet Inspection (DPI) probes, which allows identifying a very wide range of mobile services with a high degree of accuracy  [11].
Traffic data per pixel consists of overall mobile traffic volume for each service across uplink and downlink directions in bits/s, over time. The dataset covers a continuous period of 6 weeks. In this dataset, we consider the top 10 popular services that contribute to more than 80% of the total traffic volume, namely: YouTube (YT), Instagram (INS), SnapChat (SC), WhatsApp (WA), Netflix (NF), Apple Store (AS), iTunes, Facebook (FB), Twitter (TW), and Google Play (GP). As such, the effective total mobile traffic in our study is the sum of traffic due to these top-10 services. Context Data. Our conditional generation model takes advantage of contextual attributes to produce credible synthetic traffic. We gather a wide range of context data from easily accessible public sources, so that the method is applicable as widely as possible.
A. Problem Statement Let χ = {X 1 , X 2 , • • • , X N } be a real-world mobile network traffic dataset that contains sets of observations of mobile traffic, such that each set is collected in a different geographical region, i. e. , city. The data for each city n ∈ {1, • • • , N }, includes observations over a given span of time T n , hence X n = {x n 1 , • • • , x n T n }. The observation at each time slot is composed of traffic due to S different services, i. e. , x n t = {x n t,1 , • • • , x n t,S }. For time slot t ∈ 1, • • • , T n and service s ∈ 1, • • • , S, we represent the mobile traffic observation x n t,s ∈ ℜ H n ×W n as a single channel image, whose pixels map to the spatial units over which network traffic is recorded. Then, each pixel value corresponds to the network traffic value recorded for service s at a specific geographic location; and, H n × W n are the height and width of the city n's dimensions in pixels, respectively; the dimensions may differ between cities.
A. Fidelity Metrics Weighted Error (WErr). This metric quantifies the composition of a synthesized multi-service mobile traffic dataset relative to the corresponding real (ground-truth) data. Suppose in a real dataset made up of traffic from multiple services, the actual percentage of traffic due to a service s among S services with respect to total traffic is r s and its traffic volume is t s. If the traffic volume of the same service in the corresponding synthetically generated dataset is t s , then the Weighted Error (WErr) is defined as: WErr = S s=1 r s |t s -t s | t s. In other words, it is the relative estimation error in traffic volume per service weighted by each service's actual percentage, averaged over all services. Smaller WErr means more accurate service composition in the synthetic dataset.
They, however, target generation of spatial snapshot or spatiotemporal data for total traffic, as with the FDaS baseline above. To apply them to the multi-service traffic generation case studied in this paper and have them as baselines, we train and use multiple separate instances of CartaGenie and SpectraGAN models, one per each service. Data. In addition to the above baselines, we also consider an ideal case for reference, which we refer to as "Data". Metrics for this case are computed by splitting the real dataset (with 30 weekdays) into two distinct subsets (15 weekdays each part), and comparing these subsets of real data against each other. This captures the variability of the dataset within itself, which is a proxy for the 'upper bound' fidelity performance a synthetic data generation model like APPSHOT can achieve.
A. Fidelity and Generalization Throughout this section, we consider a leave-one-city-out evaluation. Specifically, each of the 10 cities in our dataset is taken as a test city in turn while using the data for the remaining 9 cities as the training set. This type of evaluation lets us assess the ability of APPSHOT and various baselines to generalize to unseen cities as well as their ability to handle different sized cities and their differences in traffic/context data value ranges. Following the earlier analysis in §IV, our evaluations focus on weekdays and morning/midday/evening peak hours. For brevity, we mainly show results for the morning peak hour period, unless otherwise specified; but similar conclusions apply for other periods. Correlation between services. As shown in §IV, traffic for different services exhibit strong mutual correlations.
So it is important for the generated traffic data to preserve this feature. To assess APPSHOT on this aspect, for each test city in the dataset, we compute the average of SSIM between traffic snapshots of every pair of services in the real ground-truth data, and similarly in the data synthesized with APPSHOT. We then compute the absolute error in the average pairwise SSIM computed over synthetic data with respect to that on real data. Results shown in in Figure  12a  indicate that APPSHOT yields a small error, within 14% of the real data on average. Composition of different services for different cities. Besides maintaining inherent correlations between traffic for different services, it is also important to ensure that their proportions relative to total traffic are preserved in the synthesized data.
We see that CartaGenie and SpectraGAN respectively yield unacceptable synthetic traffic maps for Instagram and YouTube for CITY 1, the most challenging target city given its vastly bigger size, population density, traffic volume and hotspots compared to other cities in our dataset (see §IV). In §VII-B, we will further explore the performance of CartaGenie and SpectraGAN relative to APPSHOT. In Figure  13 , also note that Pix2Pix fails to provide meaningful traffic maps for any service and exhibits severe artefacts, consistent with its poor performance in terms of quantitative fidelity metrics as seen above. Performance at different peak periods. We now consider how well APPSHOT generates service level traffic snapshots at different peak periods. Results for different fidelity metrics averaged across all test cities are summarized in the left panel of Table  II.
APPSHOT yields consistently good performance for individual services as well as for total traffic. SpectraGAN, on the other hand, performs significantly worse for some services, and is unstable over time. These results are in line with worse spatial fidelity (in terms of NEMD and SSIM) seen previously with SpectraGAN in Table  II. A key reason for this is its inability to exploit inter-service correlations, unlike APPSHOT. Time domain performance. We employ the L1 distance of autocorrelation between synthetic and real data (AC-L 1 ), also considered in previous work  [21] ,  [22] , to comparatively evaluate the temporal fidelity of the synthetic data between SpectraGAN and APPSHOT. Specifically, we compute this metric by taking the average value of L 1 norm between the corresponding points of the auto-correlations of real and synthetic time-series data, at the pixel level.
VIII. RADIO NETWORK SLICING USE CASE As part of our performance evaluation, we assess the utility of APPSHOT through a downstream application use case of multi-service mobile traffic data. Specifically, we employ synthetic data generated by APPSHOT to effectively feed a recent model for the estimation of the multiplexing efficiency with radio network slicing  [3]. The actual size of each pixel in our dataset is 250 × 250m 2 , which is close to the coverage range of a small cell. We thus assume that the radio network is composed of small cells each matching one pixel. We further assume that each service is associated with an individual slice, i. e. , a dedicated and customized set of network resources and functions that allows achieving strong quality of service (QoS) guarantees to the service providers.
The (minimum) amount of resources needed to serve the overall traffic in absence of slicing (i. e. , when multiplexing across services is possible) is R i (p, t). Then the network slicing efficiency is: We compute the accuracy of estimating multiplexing efficiency with APPSHOT-generated data relative to using real data. We consider low and high coverage cases, respectively corresponding to covering 95% and 99% of demand in each reconfiguration period, i. e. , p = {0. 95, 0. 99}. In other words, more than 95% or 99% of the demand must be accommodated in each slice during a reconfiguration period. We consider a wide range of reconfiguration periods from 2h to 36h. As seen from the results in Table  VI , the APPSHOT-generated data only introduces about 5% error in estimating the multiplexing efficiency compared with the real traffic data for short reconfiguration periods.
IX. CONCLUSIONS We have presented APPSHOT, a novel conditional deep generative model for synthesizing high-fidelity multi-service network traffic data that needs only publicly available context information of target regions. We have used real-world servicelevel mobile traffic data for multiple cities for our evaluation and show that APPSHOT not only outperforms a range of baseline approaches in terms of fidelity and also generalizes well to unseen regions. Our patch-based learning approach and corresponding operations have proved to be effective in generating traffic for cities with different sizes. Also, data augmentation with overlapping patches significantly enhances the performance with respect to handling traffic hotspots and diverse traffic ranges. The APPSHOT neural network architecture and the service level constraints it incorporates significantly enhance the accuracy of service compositions in synthetic traffic, while preserving a strong structural correlation between services.
The impacts of money in US politics have long been debated. Building on principal-agent models, we test whether and to what degree companies' political donations lead to their favored treatment in federal procurement. We expect the impact of donations on favoritism to vary by the strength of control by political principals over their bureaucratic agents. We compile a comprehensive dataset of published federal contracts and registered campaign contributions for 2004-15. We develop risk indices capturing tendering practices and outcomes likely characterized by favoritism. Using fixed effects regressions, matching, and regression discontinuity analyses, we find confirming evidence for our theory. A large increase in donations from $10,000 to $5m (in USD) increases favoritism risks by about 1/4th standard deviation (SD).
2017). It is quite possible but untested that US federal spending is biased We conceptualize this analytical problem using a principalagent framework, considering interactions between permanent bureaucrats on the one hand, and elected politicians and their bureaucratic appointees on the other. We combine two comprehensive datasets to test our hypotheses: data on federal contracting payments covering years 2004-15 3 and data on individual donations by firm employees to political campaigns  (Bonica 2016). Answering our research question requires examining the impact of contractors' political donations on whether a contract was awarded under circumstances that suggest favoritism (e. g. , noncompetitive tendering procedures, or lack of contenders in formally competitive tenders). Hence, our dependent variable is defined at the contract level: using factor analysis, we derive an indicator of favoritism as a latent dimension from seven individual risk factors that each are weakly suggestive of favoritism.