Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing the financial transparency of European Commission project funding

The Financial Transparency System FTS of the European Commission contains information about grants for European Union projects starting from 2007. It allows users to get an overview on EU funding, including information on beneficiaries as well as the amount and type of expenditure and information on the responsible EU department. The original dataset is freely available on the European Commission website, where users can query the data using an HTML form and download it in CSV and most recently XML format. In this article, we describe the transformation of this data to RDF and its interlinking with other datasets. We show that this allows interesting queries over the data, which were very difficult without this conversion. The main benefit of the dataset is an increased financial transparency of EU project funding. The RDF version of the FTS dataset will become part of the EU Open Data Portal and eventually be hosted and maintained by the European Union itself.
The association between family factors and child behaviour problems using dyadic data.

BACKGROUND Parental psychopathology and family issues are key influence factors to child behaviour problems. This study aimed to investigate the dyadic impact of maternal and paternal depression and perceived family functioning on child behaviour problems. METHODS Both maternal and paternal depression, perceived family functioning and reported child behaviour problems were collected, respectively. Because of the interdependent characteristic of dyadic data, structural equation modelling was used to examine the relationship among all variables and the mutual influence between mother and father. RESULTS Results showed that father-perceived family functioning mediated the relationship between parental depression and child behaviour problems, but mother-perceived family functioning did not show this mediation effect. Meanwhile, maternal and paternal depression influenced both of their own and their partner's perceived family functioning. CONCLUSIONS The findings indicated that paternal psychopathology and family functioning should not be overlooked in child behaviour development. In addition, this study underscored the importance to investigate the different impact of father and mother on child development within a dyadic unit.
A Proposal for Making Eiffel Type-Safe

Statically type-correct Eiffel programs may produce run-time errors because (1) attributes may be redeclared during inheritance, invalidating assignments in the superclass, (2) a formal method argument type may be restricted in violation of the contravariance of function types, and (3) two applications of a generic class are assumed to conform if the actual arguments conform. The third problem is solved by case analysis on the variance of generic parameters. Declaration by association provides a solution to the first two problems, but it suffers from additional difficulties. Type attributes, or generic parameters with default values, are suggested as a replacement for most cases of declaration by association. The special association type used to express type recursion cannot be explained using type attributes, and it appears to be a truly novel construct for typing object-oriented programs. One consequence of this construct is that Eiffel’s conformance hierarchy is a proper subset of its inheritance hierarchy. To appear in the Proceedings of the European Conference on ObjectOriented Programming, 10-14 July, 1989, and in The Computer Journal, September 1989.
MOLECULAR IMAGING IN DRUG DISCOVERY AND DEVELOPMENT

NATURE REVIEWS | DRUG DISCOVERY VOLUME 2 | FEBRUARY 2003 | 123 Imagine seeing a specific molecular target in a live animal, following a drug’s distribution in the same animal and quantitating the drug’s direct effect on the target, all in a matter of minutes. Although this prospect might have seemed utopian a few years ago, enabling technologies, such as novel imaging modalities and molecular probes, are being developed at a rapid pace and should allow these questions to be addressed routinely in the not-too-distant future. The widespread availability of mouse imaging systems is not as far off as many researchers might think. These systems (TABLE 1) are generally cheaper than their clinical counterparts and can be housed in basic science laboratories (see REF. 1 for a review). Given the increasingly common use of mouse models of disease to validate potential drug targets, to assess therapeutic efficacy and to identify and validate biomarkers of drug efficacy and/or safety, the ability to image mouse models noninvasively would have far-reaching applications in drug discovery and development. Historically, in vivo imaging methods have largely relied on imaging gross anatomy, and diseases or treatment effects were mostly detected as structural abnormalities (these methods occasionally being referred to as structural or conventional imaging). With higher spatial resolutions and the advent of imaging agents, it became possible to image at the mesoscale and therefore derive physiological parameters in live subjects (this is often called functional imaging). More recently, it has become possible to image specific molecules and targets, a field that is often referred to as molecular imaging. Indeed, molecular imaging can be used to either image the administered drug directly (for example, following its distribution and target binding) or the target itself (for example, receptor expression and modulation of downstream targets). For the newer molecular imaging tools to be useful, there are two prerequisites: first, they must have the high sensitivity that is required to monitor interactions at a molecular level and also have sufficiently high spatial resolution to image mouse models of human disease; and second, more target-specific molecular probes must become available. The goal of this paper is to briefly review the available imaging modalities, highlight some uses of anatomical and functional imaging and then focus on exciting advances in molecular imaging (FIG. 1) and how they will affect drug discovery and development. For more in-depth reading on specific imaging modalities and diseases, the reader is referred to several recent review articles.
RSCM: Region Selection and Concurrency Model for Multi-Class Weather Recognition

Toward weather condition recognition, we emphasize the importance of regional cues in this paper and address a few important problems regarding appropriate representation, its differentiation among regions, and weather-condition feature construction. Our major contribution is, first, to construct a multi-class benchmark data set containing 65 000 images from six common categories for sunny, cloudy, rainy, snowy, haze, and thunder weather. This data set also benefits weather classification and attribute recognition. Second, we propose a deep learning framework named region selection and concurrency model (RSCM) to help discover regional properties and concurrency. We evaluate RSCM on our multi-class benchmark data and another public data set for weather recognition.
Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach

Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure when dealing with large networks. A growing body of work has been adopting local expansion methods in order to identify the community from a few exemplary seed members. %Very few approaches can systematically demonstrate both high efficiency and effectiveness that significantly stands out amongst the divergent approaches in finding communities. In this paper, we propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm). Different from PageRank-like diffusion methods, LEMON finds the community by seeking a sparse vector in the span of the local spectra such that the seeds are in its support. We show that LEMON can achieve the highest detection accuracy among state-of-the-art proposals. The running time depends on the size of the community rather than that of the entire graph. The algorithm is easy to implement, and is highly parallelizable. Moreover, given that networks are not all similar in nature, a comprehensive analysis on how the local expansion approach is suited for uncovering communities in different networks is still lacking. We thoroughly evaluate our approach using both synthetic and real-world datasets across different domains, and analyze the empirical variations when applying our method to inherently different networks in practice. In addition, the heuristics on how the quality and quantity of the seed set would affect the performance are provided.
Sealing performances of Scotchbond Multi-Purpose-Z100 in Class II restaurations

PURPOSE To evaluate the sealing ability of Class II Scotchbond Multi-Purpose/Z100 composite restorations placed under in vivo and in vitro conditions. MATERIALS AND METHODS For the in vivo part, Class II preparations were cut in posterior teeth which were scheduled for extraction for periodontal reasons. The cervical margins ended in dentin-cementum, 1 mm below the CEJ. The teeth were extracted after 2-3 months of clinical service. The in vitro part was performed in extracted posterior teeth, free from restorations and caries, following the same in vivo protocol. The in vivo and in vitro samples were processed and evaluated for marginal leakage. RESULTS Leakage by dye penetration was observed at the cervical margins in 70% of the in vivo and in 50% of the in vitro specimens. All in vivo samples with no leakage at the cervical margin were restored where the adjacent interproximal tooth was missing. In more than 40% of the restorations placed in vivo, leakage was also found at the occlusal margins. Statistically significant differences were found at the cervical margins between in vivo and in vitro groups (P < 0.05). The in vitro samples can be of only limited value in predicting clinical performances of an enamel-dentin bonding system in Class II restorations.
Using Mutation Analysis to Evolve Subdomains for Random Testing

Random testing is inexpensive, but it can also be inefficient. We apply mutation analysis to evolve efficient subdomains for the input parameters of eight benchmark programs that are frequently used in testing research. The evolved subdomains can be used for program analysis and regression testing. Test suites generated from the optimised subdomains outperform those generated from random subdomains with 10, 100 and 1000 test cases for uniform, Gaussian and exponential sampling. Our subdomains kill a large proportion of mutants for most of the programs we tested with just 10 test cases.
QoS-Aware Deployment of IoT Applications Through the Fog

Fog computing aims at extending the Cloud by bringing computational power, storage, and communication capabilities to the edge of the network, in support of the IoT. Segmentation, distribution, and adaptive deployment of functionalities over the continuum from Things to Cloud are challenging tasks, due to the intrinsic heterogeneity, hierarchical structure, and very large scale infrastructure they will have to exploit. In this paper, we propose a simple, yet general, model to support the QoS-aware deployment of multicomponent IoT applications to Fog infrastructures. The model describes operational systemic qualities of the available infrastructure (latency and bandwidth), interactions among software components and Things, and business policies. Algorithms to determine eligible deployments for an application to a Fog infrastructure are presented. A Java tool, FogTorch, based on the proposed model has been prototyped.
Rapid Speaker Adaptation using Maximum Likelihood Neural Regression

In this paper, a new method called Maximum Likelihood Neural Regression (MLNR) is introduced for Rapid Speaker Adaptation (RSA). MLNR, which is conceptually simple, adapts the Gaussian means of a speaker independent (SI) model to the data of a new speaker by assuming a non-linear mapping from the SI Gaussian means to the adapted Gaussian means. It performs a non-linear regression between maximum likelihood (ML) estimates of the means and the speaker independent means using General Regression Neural Networks (GRNN). Evaluation on the Wall Street Journal benchmark shows that the suggested scheme outperforms different conventional approaches.
Education and the Poverty Trap in Rural China: Closing the Trap

This is an ambitious attempt to view the relationships involving education and income as forming a system, and one that can generate a poverty trap. The setting is rural China, and the data are from a national household survey for 2002, designed with research hypotheses in mind. The paper shows how and why the returns to education vary according to household and community income. It examines the effects of education on income, innovation, health and happiness, and shows how education can be important in helping people to escape from various dimensions of poverty. The results are brought together to form an empirical model of a poverty trap, and the implications for poverty analysis and for educational policy are considered.
Optimizing mpf queries: decision support and probabilistic inference

Managing uncertain data using probabilistic frameworks has attracted much interest lately in the database literature, and a central computational challenge is probabilistic inference. This paper presents a broad class of aggregate queries, called MPF queries, inspired by the literature on probabilistic inference in statistics and machine learning. An MPF (Marginalize a Product Function) query is an aggregate query over a stylized join of several relations. In probabilistic inference, this join corresponds to taking the product of several probability distributions, while the aggregate operation corresponds to marginalization. Probabilistic inference can be expressed directly as MPF queries in a relational setting, and therefore, by optimizing evaluation of MPF queries, we provide scalable support for probabilistic inference in database systems. To optimize MPF queries, we build on ideas from database query optimization as well as traditional algorithms such as Variable Elimination and Belief Propagation from the probabilistic inference literature. Although our main motivation for introducing MPF queries is to support easy expression and efficient evaluation of probabilistic inference in a DBMS, we observe that this class of queries is very useful for a range of decision support tasks. We present and optimize MPF queries in a general form where arbitrary functions (i.e., other than probability distributions) are handled, and demonstrate their value for decision support applications through a number of illustrative and natural examples.
Optimal Deployment of Geographically Distributed Workflow Engines on the Cloud

When orchestrating Web service workflows, the geographical placement of the orchestration engine (s) can greatly affect workflow performance. Data may have to be transferred across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper, we present a framework that, given a DAG-based workflow specification, computes the optimal Amazon EC2 cloud regions to deploy the orchestration engines and execute a workflow. The framework incorporates a constraint model that solves the workflow deployment problem, which is generated using an automated constraint modelling system. The feasibility of the framework is evaluated by executing different sample workflows representative of scientific workloads. The experimental results indicate that the framework reduces the workflow execution time and provides a speed up of 1.3x-2.5x over centralised approaches.
The Observation of Bacteria and Yeast through Microfluidic Devices

The microbes are intimately to our lives, therefore the researches of behaviors of microbes such as growth, division, anastomosis, transfection, transformation, death etc. are very important. Traditional methods such as freeze-etching method, can only find different growing periods through different cells so there is still no definite evidence for the existence of some biological activities. This would destroy a biological sample as well, so that later experiments can not be carried over smoothly. The effects of microscale heterogeneities cannot be replicated by conventional experimental techniques as well. We have recently incorporated PDMS-based valves and pumps for the analysis of single nonadherent yeast and bacteria cells. Optical trapping facilitated and ensured the encapsulation of single cells within the individual microdroplets. In this way, we have photographed the process of division of Saccharomyces sp. , and Bacillus megaterium.
Artificial societies and information theory : modelling of sub system formation based on Luhmann's autopoietic theory

This thesis develops a theoretical framework for the generation of artificial societies. In particular it shows how sub-systems emerge when the agents are able to learn and have the ability to communicate. This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated originally by the German sociologist Luhmann, with concepts of Shannon's information theory applied to adaptive learning agents. Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational modelling paradigm involving the modelling of phenomena as dynamical systems of interacting agents. The thesis in particular, investigates the functions and properties necessary to reproduce the paradigm of society by using the mentioned ABM approach. Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems can then be composed by agents with a reduced behavioural complexity. For example in society there are people who produce goods and other who distribute them. Both the behaviour and communication is learned by the agent and not imposed. The simulated task is to collect food, keep it and eat it until sated. Every agent communicates its energy state to the neighbouring agents. This results in two subsystems whereas agents in the first collect food and in the latter steal food from others. The ratio between the number of agents that belongs to the first system and to the second system, depends on the number of food resources. Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise by reducing the amount of sensory information or, equivalently, reducing the complexity of the perceived environment from the agent's perspective. Shannon's information theorem is used to assess the performance of the simulated learning agents. A practical measure, based on the concept of Shannon's information ow, is developed and applied to adaptive controllers which use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning. The behavioural complexity is measured with a novel information measure, called Predictive Performance, which is able to measure at a subjective level how good an agent is performing a task. This is then used to quantify the social division of tasks in a social group of honest, cooperative food foraging, communicating agents.
OT-interactions between Focus and Canonical Word order: Deriving the Crosslinguistic Typology of Structural Contrastive Focus*

Takeo Kurafuji, Ad Neelemean, Alan Prince, Luigi Rizzi, Hubert Truckenbrodt, as well as audiences at University of Geneve, Georgetown University, University of Groeningen, OTS-Utrecht, Stuttgart University, and at the JohnsHopkins and University of Maryland’s HOT Workshop. This research was genereously financed through the NSF grant NSF SBR 95 11891. OT-interactions between Focus and Canonical Word order: Deriving the Crosslinguistic Typology of Structural Contrastive Focus*
A Universal Trend of Reduced mRNA Stability near the Translation-Initiation Site in Prokaryotes and Eukaryotes

Recent studies have suggested that the thermodynamic stability of mRNA secondary structure near the start codon can regulate translation efficiency in Escherichia coli, and that translation is more efficient the less stable the secondary structure. We survey the complete genomes of 340 species for signals of reduced mRNA secondary structure near the start codon. Our analysis includes bacteria, archaea, fungi, plants, insects, fishes, birds, and mammals. We find that nearly all species show evidence for reduced mRNA stability near the start codon. The reduction in stability generally increases with increasing genomic GC content. In prokaryotes, the reduction also increases with decreasing optimal growth temperature. Within genomes, there is variation in the stability among genes, and this variation correlates with gene GC content, codon bias, and gene expression level. For birds and mammals, however, we do not find a genome-wide trend of reduced mRNA stability near the start codon. Yet the most GC rich genes in these organisms do show such a signal. We conclude that reduced stability of the mRNA secondary structure near the start codon is a universal feature of all cellular life. We suggest that the origin of this reduction is selection for efficient recognition of the start codon by initiator-tRNA.
Proinsulin: From Hormonal Precursor to Neuroprotective Factor

In the last decade, non-canonical functions have been described for several molecules with hormone-like activities in different stages of vertebrate development. Since its purification in the 1960s, proinsulin has been one of the best described hormonal precursors, though it has been overwhelmingly studied in the context of insulin, the mature protein secreted by the pancreas. Beginning with our discovery of the presence and precise regulation of proinsulin mRNA in early neurulation and neurogenesis, we uncovered a role for proinsulin in cell survival in the developing nervous system. We subsequently demonstrated the ability of proinsulin to prevent pathological cell death and delay photoreceptor degeneration in a mouse model of retinitis pigmentosa. In this review, we focus on the evolution of proinsulin/insulin, beginning with insulin-like peptides expressed in mainly the neurosecretory cells of some invertebrates. We summarize findings related to the regulation of proinsulin expression during development and discuss the possible effects of proinsulin in neural cells or tissue, and its potential as a neuroprotective molecule.
Deep Learning for Hate Speech Detection in Tweets

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.