dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and descriptive statistics The data used in this paper comes from a survey conducted by EMS in 2017 to collect information on potential elements relative to individual well-being in the Strasbourg metropolitan area  [3]. The online survey collected 2,405 responses from 33 municipalities (63% from the municipality of Strasbourg and 37% from 32 smaller-sized towns and villages in the rest of EMS). Including the neighboring German town of  Kehl [4] , just across the Rhine, the agglomeration has 0. 55 million inhabitants. More insights and data about this European metropolis are given in Appendix 2 (Table  A1 ). Out of this database, we got 984 observations suitable for our study. On such a reduced number, it was possible to consider additional variables-on individual style life or some specificities of the EMS perimeter-to be introduced in our main econometric estimation.
Given that low categories and very high categories have very few observations for these measures, we created a new scale by merging categories 1-4 into one group, 5-7 into a second group and 8-10 for the last group. The first variable SWB3 is the Feeling of Well-Being: SBW3 5 1 if the individual feels bad, SWB3 5 2 if medium and SBW3 5 3 if the individual feels good. Table  2  reports the distribution of the Feeling of Well-being (SWB3). We note that most individuals (57. 52%) feel satisfied (i. e. medium) with their situation. The variable Environmental Satisfaction (SWB3_ENV) is defined similarly. Its distribution is reported in Table  3 , indicating that 66. 72% of individuals are satisfied with local environmental quality. The final dataset included 667 observations for this variable. Social Life Satisfaction, defined in the same way as the two other subjective variables, is presented in Table  4.
We observe that 58. 63 % of the individuals are very satisfied with their social life. The final dataset included only 307 individuals for this subjective measure. However, those few observations may help us consolidate analyses using the two other subjective measures: Feeling of Well-Being and Environmental Satisfaction. Analysis results with Social Life Satisfaction are presented in Appendix 2 (Table  A7 ). Table  A2  reports the distribution of a couple of subjective variables Feeling of Well-Being-Social Life Satisfaction. We note a consistency in the answers reported by individuals. For example, when they feel good, i. e. SBW3 5 3, they also tend to be very satisfied with their social life, i. e. SWB3_SOCIAL 5 3. Concerning the distribution of the couple Feeling of Well-Being-Environmental Satisfaction (Table  A3 ), we remark that when individuals feel good, they should be demanding a good environmental quality as they tend to be unsatisfied with the observed environmental quality.
We use the median income of the French municipalities in 2018 from INSEE. This variable is measured in Euros per consumption unit (CU)  [5]. By combining with data on family composition, we define the variable "median monthly household income" at the municipality level. We note that the utility of individual i is proxied by her self-reported subjective well-being in our data. Let U * i denote individual i's unobserved (or latent) well-being at the time of the survey, and we consider the following conditional model: U * i ¼ X 0 i β þ ε i1 ; (2) where ε i1 is an unobserved error term assumed normally distributed Ɲ(0, σ 2 ε ). We can link U * i to the observed measures of individual self-reported subjective well-being by using the following formulation of the ordered probit model: U i ¼ 8 > > > > < > > > > : 1 if U * i ≤ c 1 2 if c 1 < U * i ≤ c 2 3 if c 2 < U * i (3) where U i corresponds to the subjective well-being (either Feeling of Well-Being SWB3, Environmental Satisfaction SWB3_ENV or Social Life Satisfaction SWB3_SOCIAL) created from the reported answers by individual i on a scale of 1-10, and U * i is defined by equation (2).
Policy-makers should give strong attention to public facilities (an essential element of local public action) and improve environmental quality. If they care about the population's happiness, they have to reorient current policies in this direction. Of course, through the inquiry in 2017 giving this database, the Strasbourg agglomeration development council aimed to provide such evidence to the local administration. Nevertheless, the results were a bit upsetting for many people in the administrative and political circles, who generally prioritize economic and demographic development, while the citizens' responses to the inquiry have revealed a strong focus on the quality of everyday life in their neighborhood. Notes 1. Most studies on subjective well-being underscore a phenomenon of relative standing  (Luttmer, 2005; Senik, 2005; Clark et al.
The COVID-pandemic has become the greatest burden of disease worldwide and in Mexico, a ecting more vulnerable groups in society, such as people with mental disorders (MD). This research aims to analyze the governance processes in the formulation of healthcare policies for people with MD in the face of the COVID- pandemic. An analytical qualitative study, based on semi-structured interviews with key informants in the healthcare system was conducted in. The study followed the theoretical-methodological principles of the Governance Analytical Framework (GAF). The software ATLAS. ti-V. was used for inductive thematic analysis, classifying themes and their categories. To ensure the proper interpretation of the data, a process of triangulation among the researchers was carried out. The findings revealed that in Mexico, the federal Secretary of Health issued guidelines for mental healthcare, but there is no defined national policy.
We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more flexible and general regression relationships than classical linear ones through the use of generalized linear models (GLMs) and also captures the inherent structural dependencies or relationships of the data through this regularization based on the graph Laplacian. We use a publicly available dataset from the National Centers for Environmental Information (NCEI) in the United States of America and perform statistical inferences of future CO 2 emissions in 59 counties. We empirically show how the proposed method outperforms widely used methods, such as the ordinary least squares (OLS) and ridge regression for this challenging problem.
Introduction Statistical models for spatio-temporal data are invaluable tools in environmental applications, providing insights, predictions, and actionable information for understanding and managing complex environmental phenomena  [1]. Such models help uncover complex patterns and trends, providing insights into how environmental variables change geographically and temporally. Many environmental datasets are collected at specific locations and times, leaving gaps in information. Statistical models help interpolate and map values between observation points, providing a complete spatial and temporal picture of the phenomenon being studied. Moreover, environmental applications frequently require predicting future values or conditions. Statistical models allow for accurate predictions by capturing the spatial and temporal dependencies present in the data.
Contributions Graph signal processing is a rapidly developing field that lies at the intersection between signal processing, machine learning and graph theory. In recent years, graph-based approaches to machine learning problems have proven effective at exploiting the intrinsic structure of complex datasets  [7]. Recently, graph penalties were applied successfully to the reconstruction of a time-varying graph signal  [8, 9]  or to the regression with a simple linear model  [10, 11]. In these works, the results highlight that regularization based on the graph structure could have an advantage over more traditional norm-based ones in situations where the data or variables have inherent structural dependencies or relationships. The main advantage of graph penalties is that they take into account the underlying graph structure of the variables, capturing dependencies and correlations that might not be adequately addressed by norm-based penalties.
The authors show from several real-world datasets that the time difference signal (i. e. , E[y t ] -E[y t-1 ] in our case) exhibits smoothness on the graph, even if signals E[y t ] are not smooth on the graph. The proposed model can be simply rewritten as follows in order to take into account this property: β = arg min β V(y; β) + γ 1 β β + γ 2 g -1 β Φ Lg -1 (Φβ) , (15) With this general formulation, several cases can be considered: •  D h =            -I N 0 N. 0 N I N -I N 0 N. 0 N 0 N I N -I N 0 N. 0 N. 0 N. 0 N I N -I N 0 N 0 N. 0 N I N -I N 0 N. 0 N I N            , allows to transform the mean vector into the time difference mean vector. Proposition 1. When the response variables are considered to be normally distributed, i. e. , y ∼ N (Φβ, Σ), then the solution that minimizes the cost function defined in Equation (  15 ) is given by β = Φ Σ -1 Φ + γ 1 I NP + γ 2 Φ LΦ -1 Φ Σ -1 y (16) Proof.
Learning and Prediction Procedure As discussed in the previous section, our proposed estimator in  (15)  results from a regression model with a penalization function over the graph, which depends on some hyperparameters, i. e. , γ = {γ 1 , γ 2 }. Cross-validation techniques are the most commonly used strategies for the calibration of such hyperparameters, as they allow us to obtain an estimator of the generalization error of a model  [19]. In this paper, a cross-validation technique is used by partitioning the dataset into train, validation and test sets. Only the train and validation sets are used to obtain the selected parameters/hyperparameters set. Finally, the model with the selected set is evaluated using the test set. Cross validation (CV) is a resampling method that uses different portions of the data to test and train a model through different iterations.
Resampling may be useful while working with iid data. However, as opposed to the latter, time-series data usually posses temporal dependence, and therefore, one should respect the temporal structure while performing CV in that context. To that end, we follow the procedure of forward validation (we refer to it as time series CV) originally due to  [20]. More specifically, the dataset is partitioned as follows D train = {x t , y t } ρ train T t=1 , D val = {x t , y t } (ρ train +ρ val )T t=ρ train T+1 and D test = {x t , y t } T t=(ρ train +ρ val )T+1 , where ρ train and ρ val correspond to the percentage of the dataset used for training and validation, respectively. In this paper, we set ρ val = 1-ρ train 2 to have the same number of data in both the validation and test sets. The set of hyperparameters and parameters are obtained by minimizing the generalization error approximated using the validation set.
Numerical Study-CO 2 Prediction in the United States In this section, we empirically assess the benefit of using our proposed penalized regression model over graph for the prediction of CO 2 in the United States. For this purpose, the CO 2 emission levels were obtained from the Vulcan project (https://vulcan. rc. nau. edu/ (accessed on 1 August 2023))  [21]  and more especially the dataset (https://daac. ornl. gov/ cgi-bin/dsviewer. pl?ds_id=1810 (accessed on 1 August 2023)), which provides emissions on a 1 km by 1 km regular grid with an hourly time resolution for the 2010-2015 time period. More specifically, the response variable vector y t corresponds to the CO 2 emissions for the t-th day after 1 January 2011 at N = 59 different counties on the east coast of the United States of America (see Appendix B for the full list of selected counties).
On the other hand, among the explanatory variables presented in detail below, there are weather data from weather daily information available on the platform https://www. ncdc. noaa. gov/ghcnd-data-access (accessed on 1 August 2023) of National Centers for Environmental Information (NCEI) in the United States of America. NCEI manages one of the largest archives of atmospheric, coastal, geophysical, and oceanic research in the world.
By comparing the values obtained with these variables in Table  2 , we can clearly see the benefit of using an auto-regressive structure in the regression model by the introduction of such lagged response variables. In Figure  6 , the weekly RMSE is depicted as a function of time for three different counties. These weekly RMSEs are obtained by aggregating the daily forecasted values from the proposed regression model which is trained on 50% of the dataset. It is interesting to observe that the weekly RMSE does not explode with time but rather stays quite stable with respect to time. In order to ensure that the previously observed conclusions are not too sensitive to the specific 59 chosen counties, we compute the RMSE on the three different sets for the different regression models by randomly selecting 2 counties for each of the 19 states.
Conclusions In this paper, we propose a novel GLM-based spatio-temporal mixed-effect model with graph penalties. This graph penalization allows us to take into account the inherent structural dependencies or relationships of the data. Another advantage of this model is its ability to model more complicated and realistic phenomena through the use of generalized linear models (GLMs). To illustrate the performance of our model, a publicly available dataset from the National Centers for Environmental Information (NCEI) in the United States of America is used, where we perform statistical inference of future CO 2 emissions over 59 counties. We show that the proposed method outperforms widely used methods, such as the ordinary least squares (OLS) and ridge regression models. In the future, we will further study how to improve this model to this specific CO 2 prediction.
Jomelli et al. , 2020 1 ELA calculated using the toe-to-headwall altitude ratio (THAR) method (ratio of 0. 6). 2 Data calculated for ridgetop elevations of 2800 and 3000 m, respectively. 3 Data calculated for ridgetop elevations of 3200 and 3400 m, respectively. 4 Data calculated for ridgetop elevations of 2500 and 2600 m, respectively. 5 ELA calculated using the accumulation area ratio (AAR) method (ratio of 0. 65 ± 0. 05). 6 Value for the reconstructed glacier based on Harper and Humphrey (2003) and using the Mulleres frontal moraine as the glacier boundary. 7 A balance ratio of 0. 6 ± 0. 05 was used for the AAR method. 8 Value for the reconstructed glacier based on Harper and Humphrey (2003) and using the Mouscadous lateral moraine as the glacier boundary. 9 Value for the reconstructed glacier based on Harper and Humphrey (2003) and using the Médecourbe frontal moraine as the glacier boundary.
Innovative products can be highly prospective and apt to disrupt usages profoundly. They can lead to multiple long-term social impacts influencing people's way of life and behaviour. So it is necessary to anticipate them without delay. Due to high uncertainty, designers may face the problem that conventional user-centred methods, which assess design performances from today's users, are not adapted. We think sociologists can help characterise the likely social impacts of future products. So we propose an original framework called the Representation-Usage-Impact (RUI) method to stimulate sociologists' projection and capture relevant knowledge about probable social impacts. The method includes a database structure encoding the knowledge of sociologists for further use in the design process. Its goal is to help designers avoid making choices today that may be regretted in decades.
The knowledge generated by sociologists is then stored in a database system to be used by designers. The method's goal is neither to predict the future nor to provide proven causal links between a product and its longterm impacts. Instead, the goal is to get designers or decision-makers to ask questions about the impacts that a specific design choice could generate in the long term. As the RUI method is still under construction, we present in this paper an initial version focused on the autonomous vehicle (AV) case study within a major automotive group (section 2). To test whether our proposition has the potential to generate sociologically consistent impacts, we detail a protocol to let sociologists evaluate the method (section 3). We then present their feedback (section 4). Finally, we summarize the limitations of the initial version and propose leads to address them.
ICED23 We describe the constituent elements of the method in the following sections. We first define three ontologies for the representations, usages, and impacts (sections 2. 3 to 2. 5). Then we set up a protocol for extracting usages from representations (section 2. 6) and a protocol for generating impacts with the help of sociologists (section 2. 7). At last, we build a database system to store, exploit and update the knowledge (sections 2. 8 and 2. 9).
Consultation modes The consultation modes allow access to the knowledge distributed in the databases. We defined two of them: (1) The causal mode consists of selecting usages to obtain a corresponding list of likely impacts. These usages may, for example, come from a scenario imagined by a designer for which he would like to see potential impacts. (2) The reverse mode consists of selecting impacts and obtaining a list of usages that are likely to cause them. For example, a designer could select impacts that he considers undesirable to learn about usages to avoid. For these two modes, the designer can select a single impact or a single usage to display the contextual elements related to it. For a usage, it is the representations from which it is extracted and the impacts linked to it. For an impact, it is the usages that are linked to it.
ADDITIONAL RESOURCES The following link leads to three additional resources: (1) an extract from the representation database, (2) a video demo of the consultation modes, and (3) details of the questionnaire for sociologists' evaluations with a link to the descriptive video. https://iced2023-anonymous. notion. site/Additional-resources-2c4a9785898b4487bf1c0e27d5f12282 https://doi. org/10. 1017/pds. 2023. 346 Published online by Cambridge University Press.
The trend of moving towards UHD resolution video content (e. g. , 4K and higher video quality standards) leads to extremely dense datasets. These must be delivered to multitudes of users, rapidly and with high-reliability, over broad geographical areas and on a myriad of devices. The demand for such high-bandwidth services creates a persistent need for the M&E industry to create new and innovative technological solutions to continually enhance user experience. This is essential to attract new customers and retain existing clients. There is also growing competition within the M&E market from the increasing numbers of providers offering high-quality content, meaning that producers must improve efficiencies in how they produce and deliver content to remain competitive. The enhanced bandwidth, low-latency and high reliability of 5G technology offers major benefits to the M&E sector in addressing the challenges highlighted here.
0, which include ML-driven anomaly detection, AI-based video analysis and mixed-reality assisted manufacturing  [6]. VITAL-5G brings together key stakeholders from the Transport and Logistics (T&L) sector who engage with SME technology partners and MNOs to develop an open testing platform for T&L-related services. Advanced features of 5G are employed to support rich data streams from connected vehicles to solve market-relevant problems in port and warehouse environments  [7]. As a final example, 5G-ERA examines intent-based networking, 5G middleware and standardised APIs for enhanced robot autonomy across different verticals  [8]. The goal of this paper is to report on the 5GMediaHUB Network Applications and associated experimentation tools. The paper provides: 1) an overview of the 5GMediaHUB Experimentation Facility, 2) details of infrastructure deployment at two 5G testbeds, 3) details of the Network Applications and the Network Application Repository, 4) a summary of 5GMediaHUB's use cases, and 5) a report on the use case validation.
D. Infrastructure Layer This layer consists of two 5G testbeds operated by CTTC (Spain) and Telenor (Norway), both of which have been upgraded with a 3GPP Release 16-compliant 5G standalone (SA) Core. The GÉANT/Uninett network will be leveraged for the physical connectivity between the testbeds' domains, with a site-to-site VPN over the link. This will support multi-domain Network Application deployments. As an independently orchestrated 5G network, each testbed runs its own 5G SO and DMS that will be integrated with the facility via the CDSO's Requests Handler module. At the same time, each testbed exposes a Virtual Infrastructure Manager (VIM) and a Kubernetes Cluster to the CDSO's OSM instance for the deployment of Network Applications.
A. Barcelona Infrastructure Deployment The CTTC testbed  [10]  is based on a Cloud Radio Access Network (C-RAN) architecture with a 5G core and a fully virtualized 5G RAN. The testbed leverages various Software Defined Network Controllers e. g. , Amarisoft, and Service Network Mesh to (i) provide dynamic RAN-related modifications and measurements, (ii) implement E2E slicing, and (iii) provide service-level network management. Figure  3  illustrates the high-level design of CTTC's experimentation platform. 5G RAN-Core segment is implemented with Amarisoft 5G Private Node, which provides 5G RAN capabilities and northbound interface (NBI) management out-of-the-box, enabling the establishment of 5G end-to-end services. The Amarisoft NBI exposes a range of telemetry and actuation via web sockets, which may be leveraged for emulating a wide variety of scenarios and management algorithms.
Media Vertical Network Applications: Streaming Network Application: this is a streaming platform that enables transcoding, packaging and streaming of video on demand (VOD) and linear content. It supports packaging of content from most standard video formats. For linear content, live content feeds or traditional TV channels, the platform supports rtsp, rtmp and MPEG2-TS formats. This Network Application is based on the Cloudncoder Platform  [11]  streaming and packaging modules that will be delivered as two independent Kubernetes Pods. The first Pod will run web server instances for hosting mpeg-dash manifests and video segments. The second Pod will run web socket (RFC6455) server instances. It comes with a well-defined API for which primary and secondary devices can send arbitrary messages back and forth in a true full duplex manner.
Vertical-Agnostic Network Applications: PaaS Network Application: This is implemented with Ansible scripts for a Kubespray controller and deployed by Open-Source MANO (OSM) NFVO  [12]. The Ansibles scripts trigger the automatic generation of cloud-init files. The generated cloud-init files will be used to leverage bootstrapping of Kubernetes (K8s) clusters, which are hosted within VNFs and instantiated by ETSI OSM. The VNF generally acts as the K8s controller; scaling-out the K8s clusters involves the instantiation of new VNF hosts and scaling-in their deletion. The OSM is responsible for orchestrating the network resources, whereas the containerized workloads and services VIM (i. e. , Container Infrastructure Service Management module -Kubernetes) provides and manages the application containers.