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The use of public transportation or simply moving about in streets are gendered issues. Women and girls often engage in multi-purpose, multi-stop trips in order to do household chores, work, and study ('trip chaining'). Women-headed households are often more prominent in urban settings and they tend to work more in low-paid/informal jobs than men, with limited access to transportation subsidies. Here we present recent results on urban mobility from a gendered perspective by uniquely combining a wide range of datasets, including commercial sources of telecom and open data. We explored urban mobility of women and men in the greater metropolitan area of Santiago, Chile, by analyzing the mobility traces extracted from the Call Detail Records (CDRs) of a large cohort of anonymized mobile phone users over a period of 3 months. We find that, taking into account the differences in users' calling behaviors, women move less than men, visiting less unique locations and distributing their time less equally among such locations. By mapping gender differences in mobility over the 52 comunas of Santiago, we find a higher mobility gap to be correlated with socio-economic indicators, such as a lower average income, and with the lack of public and private transportation options. Such results provide new insights for policymakers to design more gender inclusive transportation plans in the city of Santiago., Comment: 32 pages, 4 figures

Inferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location. Supplementary Information The online version contains supplementary material available at 10.1140/epjds/s13688-021-00284-9.

The power of the press to shape the informational landscape of a population is unparalleled, even now in the era of democratic access to all information outlets. However, it is known that news outlets (particularly more traditional ones) tend to discriminate who they want to reach, and who to leave aside. In this work, we attempt to shed some light on the audience targeting patterns of newspapers, using the Chilean media ecosystem. First, we use the gravity model to analyze geography as a factor in explaining audience reachability. This shows that some newspapers are indeed driven by geographical factors (mostly local news outlets) but some others are not (national-distribution outlets). For those which are not, we use a regression model to study the influence of socioeconomic and political characteristics in news outlets adoption. We conclude that indeed larger, national-distribution news outlets target populations based on these factors, rather than on geography or immediacy.

We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic., Fine-grained studies of epidemic spread and of the effect of nonpharmaceutical interventions are still needed to underpin demographic and socio-economic effects. Here, the authors study the spatial and temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data.

Fighting the COVID-19 pandemic, most countries have implemented non-pharmaceutical interventions like wearing masks, physical distancing, lockdown, and travel restrictions. Because of their economic and logistical effects, tracking mobility changes during quarantines is crucial in assessing their efficacy and predicting the virus spread. Unlike many other heavily affected countries, Chile implemented quarantines at a more localized level, shutting down small administrative zones, rather than the whole country or large regions. Given the non-obvious effects of these localized quarantines, tracking mobility becomes even more critical in Chile. To assess the impact on human mobility of the localized quarantines, we analyze a mobile phone dataset made available by Telefónica Chile, which comprises 31 billion eXtended Detail Records and 5.4 million users covering the period February 26th to September 20th, 2020. From these records, we derive three epidemiologically relevant metrics describing the mobility within and between comunas. The datasets made available may be useful to understand the effect of localized quarantines in containing the COVID-19 pandemic.

Even in developed countries with an active free press, news coverage can be dominated by only a few players. This can lead to a reduction of topical and community diversity. Ownership structures might further limit coverage by implicitly or explicitly biasing editorial policies. In this paper, we apply ecological diversity measures to quantify the health of the Chilean online news ecology using extensive ownership and social media data. Results indicate that high levels concentration characterizes the Chilean media landscape in terms of ownership and topical coverage. Our methods reveal which groups of outlets and ownership exert the greatest influence on news coverage and can be generalized to any nation's news system.

In this keynote, I will present some results we have obtained using location data from mobile phones interacting with information (news) websites, mobile apps like Pokemon GO and Twitter, and large physical spaces like shopping malls. I will draw some conclusions and generally discuss about the properties of mobile phone data for location-based research to finally close with some remarks about privacy and data security.

Since 2016, through an association between Telefonica R&D and the Institute of Data Science in Chile, a group of researchers and myself have been working with trillions of digital traces left behind when people use their mobile phones. All of this work has been done under the general umbrella term of "data science for social good", and we have worked on anything from population displacement after external events like earthquakes, how people started using public spaces after the introduction of a popular mobile game, to actual social inclusion of people of different socio-economic backgrounds mixing in shopping malls or reading certain kinds of news, or patterns arising from gendered data sets. We will show how data in the private sector made us learn important social lessons such as how parks can become more secure when people went out to play Pokemon Go, how certain malls are hubs of social inclusion, how gender segregates the city and how different demographics keep themselves in their own informational filter bubble. However, even after all this benefits, the relationship with industry has never been fluid, and involves a lot of small and not so small compromises and "battles". In this talk, I will present a technical history of the work we've done with X/CDRs for social good including practical aspects of accessing and sharing data, the balance of research and industrial innovation, and issues of transactions costs while still providing value for the company itself, government, the university and society. I will also recount experiences about what it meant for a company like Telefonica and a research university like us to travel together in a very interesting context of huge data, incredible insights, privacy considerations, money, corporate interests, university expectations, and data-driven discovery.

The always increasing mobile connectivity affects every aspect of our daily lives, including how and when we keep ourselves informed and consult news media. By studying a DPI (deep packet inspection) dataset, provided by one of the major Chilean telecommunication companies, we investigate how different cohorts of the population of Santiago De Chile consume news media content through their smartphones. We find that some socio-demographic attributes are highly associated to specific news media consumption patterns. In particular, education and age play a significant role in shaping the consumers behaviour even in the digital context, in agreement with a large body of literature on off-line media distribution channels.

The social inclusion aspects of shopping malls and their effects on our understanding of urban spaces have been a controversial argument largely discussed in the literature. Shopping malls offer an open, safe and democratic version of the public space. Many of their detractors suggest that malls target their customers in subtle ways, promoting social exclusion. In this work, we analyze whether malls offer opportunities for social mixing by analyzing the patterns of shopping mall visits in a large Latin-American city: Santiago de Chile. We use a large XDR (Data Detail Records) dataset from a telecommunication company to analyze the mobility of $387,152$ cell phones around $16$ large malls in Santiago de Chile during one month. We model the influx of people to malls in terms of a gravity model of mobility, and we are able to predict the customer profile distribution of each mall, explaining it in terms of mall location, the population distribution, and mall size. Then, we analyze the concept of social attraction, expressed as people from low and middle classes being attracted by malls that target high-income customers. We include a social attraction factor in our model and find that it is negligible in the process of choosing a mall. We observe that social mixing arises only in peripheral malls located farthest from the city center, which both low and middle class people visit. Using a co-visitation model we show that people tend to choose a restricted profile of malls according to their socio-economic status and their distance from the mall. We conclude that the potential for social mixing in malls could be capitalized by designing public policies regarding transportation and mobility., submitted to peer review

The 27 th 2016 ACM international conference on Hypertext and Social Media will be held in Halifax, Canada, from July 10 to 13. It will be collocated with UMAP 2016, the 24th international conference on User Modeling, Adaptation and Personalization. This newsletter article briefly introduces the conference and its venue.

There are many representations of planar graphs but few are as elegant as Turan’s (1984): it is simple and practical, uses only four bits per edge, can handle multi-edges and can store any specified embedding. Its main disadvantage has been that “it does not allow efficient searching” (Jacobson, 1989). In this paper we show how to add a sublinear number of bits to Turan’s representation such that it supports fast navigation, thus overcoming this disadvantage. Other data structures for planar embeddings may be asymptotically faster or smaller but ours is simpler, and that can be a theoretical as well as a practical advantage: e.g., we show how our structure can be built efficiently in parallel.

Even democracies endowed with the most active free press struggle to maintain the diversity of news coverage. Consolidation and market forces may cause only a few dominant players to control the news cycle. Editorial policies may be biased by corporate ownership relations, narrowing news coverage and focus. To an increasing degree, this problem also applies to social media news distribution, since it is subject to the same socio-economic drivers. To study the effects of consolidation and ownership on news diversity, we model the diversity of Chilean coverage on the basis of ownership records and social media data. We create similarity networks of news outlets on the basis of their ownership and the topics they cover. We then examine the relationships between the topology of ownership networks and content similarity to characterize how ownership affects news coverage. A network analysis reveals that Chilean media is highly concentrated both in terms of ownership as well as in terms of topics covered. Our method can be used to determine which groups of outlets and ownership exert the greatest influence on news coverage.

News consumers expect news outlets to be objective and balanced in their reports of events and opinions. However, there is a growing body of evidence of bias in the media caused by underlying political and socio-economic viewpoints. Previous studies have tried to classify the partiality of the media, but there is little work on quantifying it, and less still on the nature of this partiality. The vast amount of content published in social media enables us to quantify the inclination of the press to pre-defined sides of the socio-political spectrum. To describe such tendencies, we use tweets to automatically compute a news outlet's political and socio-economic orientation. Results show that the media have a measurable bias, and illustrate this by showing the favoritism of Chilean media for the ruling political parties in the country. This favoritism becomes clearer as we empirically observe a shift in the position of the mass media when there is a change in government. Even though relative differences in bias between news outlets can be observed, public awareness of the bias of the media landscape as a whole appears to be limited by the political space defined by the news that we receive as a population. We found that the nature of the bias is reflected in the vocabulary used and the entities mentioned by different news outlets. A survey conducted among news consumers confirms that media bias has an impact on the coverage of controversial topics and that this is perceivable by the general audience. Having a more accurate method to measure and characterize media bias will help readers position outlets in the socio-economic landscape, even when a (sometimes opposite) self-declared position is stated. This will empower readers to better reflect on the content provided by their news outlets of choice.

News consumers expect news outlets to be objective and balanced in their reports of events. However, there is a body of evidence of bias in the media caused by underlying political and socio-economic viewpoints. Previous studies have tried to classify the partiality of the media, sometimes giving a quantitative evaluation, but there is little reported on its nature. The vast amount of content in the social media enables us to quantify the inclination of the press to either side of the political spectrum. To describe such tendencies, we use tweets to automatically compute a news outlet's political and socio-economic orientation. We show that the media have a measurable bias, and illustrate this by showing the favoritism of Chilean media for the ruling political parties in this country. We also found that the nature of the bias is reflected in the vocabulary used and the entities mentioned by different news outlets. A survey conducted among news consumers confirms that media bias has an impact on the coverage of controversial topics and that this is perceivable by the general audience. Having a more accurate method to measure and characterize media bias will clarify to the readers where outlets stand within the socio-economic landscape, even when a self-declared position is stated. This will empower readers to better reflect on the content provided by their news outlets of choice.

There are many representations of planar graphs, but few are as elegant as Tur\'an's (1984): it is simple and practical, uses only 4 bits per edge, can handle self-loops and multi-edges, and can store any specified embedding. Its main disadvantage has been that "it does not allow efficient searching" (Jacobson, 1989). In this paper we show how to add a sublinear number of bits to Tur\'an's representation such that it supports fast navigation while retaining simplicity. As a consequence of the inherited simplicity, we offer the first efficient parallel construction of a compact encoding of a planar graph embedding. Our experimental results show that the resulting representation uses about 6 bits per edge in practice, supports basic navigation operations within a few microseconds, and can be built sequentially at a rate below 1 microsecond per edge, featuring a linear speedup with a parallel efficiency around 50\% for large datasets., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941. Conference version presented at WADS 2017

This year was the 25th edition of the ACM Conferences on Hypertext and Hypermedia. Hypertext 2014 took place in Santiago de Chile, a location that is quite far away from the US and particularly from Europe. Nevertheless, the conference was well-attended with about 85 participants. There were two interesting keynote talks, given by César A. Hidalgo from MIT Media Lab and Ricardo Baeza-Yates from Yahoo! Research Labs. The Best Paper Awards went to a study on reader preferences and behavior on Wikipedia and an experimental study on scalable, generic and adaptive focused crawling. The conference was organized at PUC Chile in central Santiago. The evening slots were dedicated to eating, drinking and exploring the city together, which was a great opportunity to get to know the Hypertext community on a more personal level.

This article reports a case study of the iterative design and evaluation of a natural language-driven assistive technology, iGraph -Lite, providing people who are blind access to line graphs. Two laboratory-based usability studies involving blind and sighted people are presented with a discussion of the ensuing implementation of changes. Blind participants were found to adopt different graph interrogation strategies than sighted participants. A small field study is then reported in which a blind user who works with graphs took part to determine the degree to which the iGraph -Lite commands would meet the needs of blind graph experts. The final study invited sighted graph experts and novices to visually inspect and explain a set of line graphs comparable to those used in the usability studies. It aimed to highlight the concepts and the range of words sighted people use, to ascertain the appropriateness of the iGraph -Lite lexicon. A set of preliminary guidelines is presented.

We are delighted to bring you this special issue on speech and language processing for assistive technology. It addresses an important research area that is gaining increased recognition from researchers in speech and language processing as a rich and fulfilling area on which to focus their work, and by researchers in assistive technology as the means to dramatically improve communication technologies for individuals with disabilities. This special issue brings a wide swath of approaches and applications highlighting the variety this area offers.

Pok\'emon Go, a location-based game that uses augmented reality techniques, received unprecedented media coverage due to claims that it allowed for greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pok\'emon Go on people's mobility patterns in a city is still largely unknown. In this paper, we perform a natural experiment using data from mobile phone networks to evaluate the effect of Pok\'emon Go on the pulse of a big city: Santiago, capital of Chile. We found significant effects of the game on the floating population of Santiago compared to movement prior to the game's release in August 2016: in the following week, up to 13.8\% more people spent time outside at certain times of the day, even if they do not seem to go out of their usual way. These effects were found by performing regressions using count models over the states of the cellphone network during each day under study. The models used controlled for land use, daily patterns, and points of interest in the city. Our results indicate that, on business days, there are more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play, but favored places close to where they live. Even if the statistical effects of the game do not reflect the massive change in mobility behavior portrayed by the media, at least in terms of expanse, they do show how "the street" may become a new place of leisure. This change should have an impact on long-term infrastructure investment by city officials, and on the drafting of public policies aimed at stimulating pedestrian traffic., Comment: 23 pages, 7 figures. Published at EPJ Data Science

The wavelet tree has become a very useful data structure to efficiently represent and query large volumes of data in many different domains, from bioinformatics to geographic information systems. One problem with wavelet trees is their construction time. In this paper, we introduce two algorithms that reduce the time complexity of a wavelet tree's construction by taking advantage of nowadays ubiquitous multicore machines. Our first algorithm constructs all the levels of the wavelet in parallel in $O(n)$ time and $O(n\lg\sigma + \sigma\lg n)$ bits of working space, where $n$ is the size of the input sequence and $\sigma$ is the size of the alphabet. Our second algorithm constructs the wavelet tree in a domain-decomposition fashion, using our first algorithm in each segment, reaching $O(\lg n)$ time and $O(n\lg\sigma + p\sigma\lg n/\lg\sigma)$ bits of extra space, where $p$ is the number of available cores. Both algorithms are practical and report good speedup for large real datasets., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941

Mathematics accessibility is an important topic for inclusive education. In this paper, we make Wikipedia's repository of mathematical formulas accessible by providing a natural language description of its more than 420,000 formulas using a well-researched sub-language. We also contribute by targeting Spanish speakers, for whom assistive technologies, particularly domain-specific technologies like the one described here, are scarce. Our focus on the semantics of formulas rather than their visual appearance allowed us to generate verbalizations with a precision of approximately 80% of understandable descriptions, as shown in an evaluation with sighted users.

Hypertext 2014 will be held in Santiago de Chile fro mSeptember 1 to 4. We briefly describe the main aspects of the conference and its venue.

The Seventh W4A International Cross-Disciplinary Conference on Web Accessibility was held on Monday 26th and Tuesday 27th May 2010 as part of the Nineteenth International World Wide Web Conference(WWW2010) located at the Raleigh Convention Center in Raleigh, North Carolina, USA. We ran over 2 days, welcomed 69 attendees. We accepted 31.25% of all submissions, each paper was peer reviewed by three members of our programme committee. We published ISBN'ed proceedings as part of the ACM Digital Library, and the best papers have been invited to submit extended versions of their work to Taylor and Francis' New Review of Hypermedia and Multimedia.

Although pictorial renditions of statistical data are ubiquitous, few techniques and standards exist to exchange, search and query these graphical representations. We present several improvements to human-graph interaction including i) a new approach to manage statistical graph knowledge by semantic annotation of graphs that bridges the gap between Web 2.0 social tagging and formal, logic-based approaches, ii) knowledge management and discovery across a non-trivial graph knowledge base and iii) sophisticated question answering that requires background knowledge using Semantic Web technology.

Succinct representations of trees are an elegant solution to make large trees fit in main memory while still supporting navigational operations in constant time. However, their construction time remains a bottleneck. We introduce a practical parallel algorithm that improves the state of the art in succinct tree construction. Given a tree on $$n$$ nodes stored as a sequence of balanced parentheses, our algorithm builds a succinct tree representation in $$On/p+\lg p$$ time, where $$p$$ is the number of available cores. The constructed representation uses $$2n + on$$ bits of space and supports a rich set of operations in $$O\lg n$$ time. In experiments using up to 64 cores and on inputs of different sizes, our algorithm achieved good parallel speed-up. We also present an algorithm that takes $$On/p + \lg p$$ time to construct the balanced parenthesis representation of the input tree required by our succinct tree construction algorithm.

This paper presents the results of an analysis of the lexical term ‘querer’ (want) qua genuine reference to desire. The instances of ‘querer’ to be analyzed were taken from the naturally occurring speech of monolingual speakers of Spanish. Specifically, a database of 24,558 child utterances taken from the CHILDES system was analyzed. This corpus yielded 444 utterances with the desire term ‘querer’. The utterances belong to several children whose ages range from 21 to 46 months. The results of this study support the hypothesis that children consistently refer to desire at around 2 years of age and that children undergo a marked increase in their reference to this concept at 36 months of age, although it is not yet clear whether they start referring to their own desires before those of someone else.

Wavelet trees have become very useful to handle large data sequences efficiently. By the same token, in the last decade, multicore architectures have become ubiquitous, and parallelism in general has become extremely important in order to gain performance. This paper introduces two practical multicore algorithms for wavelet tree construction that run in O(n) time using $\lg \sigma$ processors, where n is the size of the input and σ the alphabet size. Both algorithms have efficient memory consumption. We also present a querying technique based on batch processing that improves on simple domain-decomposition techniques.

We discuss factors in the design and evaluation of natural language-driven assistive technologies that generate descriptions of, and allow interaction with, graphical representations of numerical data. In particular, we provide data in favor of 1) screen-reading technologies as a usable, useful, and cost-effective means of interacting with graphs. The data also show that by carrying out evaluation of Assistive Technologies on populations other than the target communities, certain subtleties of navigation and interaction may be lost or distorted.

Designing graphs and charts visually by means of graphing applications such as OpenOffice or MS Excel is extremely efficient and cost-effective. However, one of the drawbacks of such approach is that graphs are sometimes involuntarily made less accessible by, for instance, using a text box as title. In this paper we evaluate a corpus of 120 ecologically-valid statistical graphs for accessibility problems, discuss possible algorithms to solve these problems and finally propose the OM (Object Model) Principle, which states that any digital object is made more accessible by simply using the application's model for that object: for instance, the TITLE field for the title text.

We present Atlas.txt, a novel data-to-text natural language generation system which enables access to geo-referenced information like online census data. We first discuss initial findings from an accessibility study on geo-referenced data and outline needs requirements for visually-impaired users of such data. We then present work towards realising our data-to-text system and indicate how it aims to address this issue.

Although statistical graphs are ubiquitous, few techniques and standards exist to exchange, search and query these graphical representations. The gsem project aims to improve human-graph interaction by developing new approaches to manage statistical graph knowledge. Two specific objectives of this project are addressed in this paper: (1) improve the efficiency of searching statistical graph knowledge and (2) facilitate sophisticated question answering about statistical graph knowledge. In particular, semantic annotation and query answering across statistical graphs with owl ontologies are described.

Information is often presented in graphical form. Unfortunately, current assistive technologies such as screen readers are not well-equipped to handle these representations. To provide accessibility to graphs published in ``The Daily" (Statistics Canada's main dissemination venue), we have developed iGraph-Lite, a system that provides short verbal descriptions of the information depicted in graphs and a way to also interact with this information.

Numerical information is often presented in graphs to take advantage of the human ability to quickly find visual patterns. Unfortunately, this medium is problematic for people who are blind or otherwise visually-impaired. To provide accessibility to graphs published in The Daily (Statistics Canada's main dissemination venue), we have developed iGraph, a system that provides short verbal descriptions of the information depicted in graphs and a way of also interacting with graphical information.

Statistical graphs are ubiquitous mechanisms for data visualization such that most, if not all, enterprises communicate information through them. However, many graphs are stored as unstructured images or proprietary binary objects, making them difficult to work with beyond the reports in which they are embedded. While graphs can be mapped to more common XML representations, these lack expressive semantics to discover new knowledge about them or to answer queries at various levels of granularity. This paper describes an OWL ontology that facilitates the representation, exchange, reasoning and query answering of statistical graph data. We illustrate the advantages of using an ontological approach to discover and query about time-series statistical graphs.

Much numerical information is visualized in graphs. However, this is a medium that is problematic for people with visual impairments. We have developed a system called iGraph which provides short verbal descriptions of the information usually depicted in graphs. This system was used as a preliminary solution that was validated through a process of User Needs Analysis (UNA). This process provided some basic data on the needs of people with visual impairments in terms of the components and the language to be used for graph comprehension and also validated our initial approach. The UNA provided important directions for the further development of iGraph particularly in terms of interactive querying of graphs

Numerical information is often presented in graphs. However, this medium is problematic for certain audiences such as inexperienced graph readers, people with visual impairments and users of mobile technologies featuring small screens. We have developed a system called iGraph which provides short verbal descriptions with the information depicted in graphs and a way of interacting with it by means of dialogue in natural language. In this paper, we present the general architecture of the system and its representational and querying mechanisms, together with a glimpse of the natural language interface.

This paper outlines our project to help beginners learn to program by showing object visualisations driven by aspect- oriented programming, and presented as part of the Eclipse development platform. The aspect-oriented programming is part of the infrastructure we use to drive the visualisations that help students learn object-oriented programming. Aspect-oriented programming explicitly supports the kind of cross-cutting concerns that allows our system to drive visualisations that emphasise principles of object interaction. Our extensions to Eclipse allow us to provide this educational scaffolding to help learners, without altering the program, the programming language or the libraries.

The purpose of this paper is to discuss the output of a neural network based on a linguistic model for recognizing the levels of involvement of different verbal arguments, assuming the non-discreteness of thematic relations and their non-primitiveness in linguistic theory. The network's output called for hypothesizing that, contrary to the received view, there is no equal level of involvement in verbal arguments, one having to be always more invovolved than the others, even when having the same number of Proto-Agent and Proto-Patients contributing properties.

Reliable data about socio-economic conditions of individuals, such as health indexes, consumption expenditures and wealth assets, remain scarce for most countries. Traditional methods to collect such data include on site surveys that can be expensive and labour intensive. On the other hand, remote sensing data, such as high-resolution satellite imagery, are becoming largely available. To circumvent the lack of socio-economic data at high granularity, computer vision has already been applied successfully to raw satellite imagery sampled from resource poor countries.