Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative induction of decision trees

Currently many problems related to data mining and knowledge discovery have two relevant characteristics: they produce data that is distributed over several locations, while also generating large volumes of data that need to be classified in an online fashion. Examples of such applications are related to bioinformatics, e-commerce, and sensor data. Regarding classification by means of decision trees, some efficient approaches have been proposed, which are centralized and based on restructuring the decision tree using new instances. However, there are some issues. First, most proposed approaches require that new instances are fully labeled. Second, in some environments, the agent in charge of the classification task cannot re-induce the classifier or restructure the decision tree each time it observes a new instance. Moreover, because this agent does not see the whole dataset, the induced classifier is not likely to be very accurate unless information is exchanged among the agents that are, each, in charge of pieces of the data. Thus, a decrease in accuracy may occur because attributes and classes may be misrepresented in the training dataset used so far. Instead of re-inducing the classification model with arbitrary frequency in a centralized way, this paper proposes an approach based on reinforcement learning that allows agents to go on using the existing classifier as basis for some exploration in the space of possible classifications. We use a quality assessment of the learned model in order to let each agent decide when it is time to get a new model, either by borrowing it from another agent, or by inducing a new classifier. Results using UCI datasets with various characteristics show that this method can be used as a compromise between costly methods for re-inducing the classifier at all times, and using only a static and centralized classification model.
Optically Defined Modal Sensors Incorporating Spiropyran-Doped Liquid Crystals with Piezoelectric Sensors

We integrated a piezoelectric sensing layer lamina containing liquid crystals (LC) and spiropyran (SP) in a LC/SP mixture to create an optically reconfigurable modal sensor for a cantilever beam. The impedance of this LC/SP lamina was decreased by UV irradiation which constituted the underlying mechanism to modulate the voltage externally applied to the piezoelectric actuating layer. Illuminating a specific pattern onto the LC/SP lamina provided us with a way to spatially modulate the piezoelectric vibration signal. We showed that if an UV illuminated pattern matches the strain distribution of a specific mode, a piezoelectric modal sensor can be created. Since UV illumination can be changed in situ in real-time, our results confirm for the first time since the inception of smart sensors, that an optically tailored modal sensor can be created. Some potential applications of this type of sensor include energy harvesting devices, bio-chips, vibration sensing and actuating devices.
Constraint-Based Design of Extensible Networks

Advances in technology are making it increasingly attractive to incorporate specialized processing inside networks. Extensible networks enable network plugins to be dynamically loaded in routers, providing customized processing to individual application sessions. Examples of such processing include video compression, decompression and transcoding, general format conversions, and group information distribution. This paper addresses the question of how to design extensible networks to provide the bandwidth and processing resources needed to handle an expected traffic demand. The design of extensible networks is complicated by the fact that, in addition to dimensioning links to support traffic, processing resources must be allocated to support an expected traffic load. Since the use of extensible networks is driven by the applications that use them, the characteristics of those applications must be carefully considered in the network design process. This paper develops a framework for the design and configuration of cost-effective extensible networks that seamlessly integrates application characteristics and processing requirements into a general constraint-based network design methodology. We have incorporated these ideas into an interactive software design tool called the Extensible Network Planner(XNP), which enables convenient specification of network designs, rapid exploration of alternative designs and evaluation of those alternatives against automatically computed cost bounds. Constraint-Based Design of Extensible Networks Sumi Choi and Jonathan Turner Department of Computer Science and Engineering, Campus Box 1045 Washington University, St. Louis, MO 63130-4899 fsyc1, jstg@arl.wustl. edu
Synopsis Text Mining for Information Retrieval

Introduction Nowadays, large quantity of data is being accumulated in the data repository. Usually there is a huge gap from the stored data to the knowledge that could be constructed from the data. This transition won't occur automatically, that's where Data Mining comes into picture. In Exploratory Data Analysis, some initial knowledge is known about the data, but Data Mining could help in a more in-depth knowledge about the data. Seeking knowledge from massive data is one of the most desired attributes of Data Mining. Manual data analysis has been around for some time now, but it creates a bottleneck for large data analysis. Fast developing computer science and engineering techniques and methodology generates new demands to mine complex data types. A number of Data Mining techniques (such as association, clustering, classification) are developed to mine this vast amount of data. Previous studies [18] on Data Mining focus on structured data, such as relational and transactional data. However, in reality, a substantial portion of the available information is stored in text databases (or document databases), which consists of large collections of documents from various sources, such as news articles, books, digital libraries and Web pages. Text databases are rapidly growing due to the increasing amount of information available in electronic forms, such as electronic publications, e-mail, CD-ROMs, and the World Wide Web (which can also be viewed as a huge, interconnected, dynamic text database). Data stored in text databases is mostly semi-structured, i.e., it is neither completely unstructured nor completely structured. For example, a document may contain a few also contain some largely unstructured text components, such as abstract and contents. In recent database research, studies have been done to model and implement semi-structured data. Information Retrieval techniques, such as text indexing, have been developed to handle the unstructured documents. But, traditional Information Retrieval Synopsis-2 techniques become inadequate for the increasingly vast amount of text data. Typically, only a small fraction of the many available documents will be relevant to a given individual or user. Without knowing what could be in the documents, it is difficult to formulate effective queries for analyzing and extracting useful information from the data. Users need tools to compare different documents, rank the importance and relevance of the documents, or find patterns and trends across multiple documents. Thus, Text Mining has become an increasingly popular and essential theme in Data Mining. Text Mining, also known …
Psychosocial functioning in pediatric cancer.

OBJECTIVE To describe the emergence of pediatric psycho-oncology and to summarize research on psychosocial aspects of childhood cancer and survivorship. METHODS To review research into illness communication and informed consent, procedural pain, late effects, psychological distress, coping and adjustment, and special risk populations. Methodological challenges, appropriate methodology, and directions for future research are discussed. RESULTS The past 30 years have seen change from avoidance of communication about cancer to an emphasis on straightforward discussion of diagnosis and prognosis. Behavioral research has led to interventions to reduce procedural distress. Late effects have been observed in social functioning. Although average levels of distress in survivors of pediatric cancer are typical, subsets of more vulnerable patients and family members exist. Factors predicting positive and negative coping have been identified. CONCLUSIONS As the numbers of pediatric cancer survivors increase, psychosocial researchers will be better able to conduct longitudinal studies not only of adjustment and its predictors but also of the impact of the emerging medical treatments and interventions to ameliorate late effects of treatment. Additional funding, improving methodology, and multi-institutional cooperation will aid future pediatric psycho-oncology investigators.
Role of endogenous serotonin in modulating genioglossus muscle activity in awake and sleeping rats.

RATIONALE Exogenous serotonin at the hypoglossal motor nucleus (HMN) stimulates genioglossus (GG) muscle activity. However, whether endogenous serotonin contributes to GG activation across natural sleep-wake states has not been determined, but is relevant given that serotonergic neurons have decreased activity in sleep and project to pharyngeal motoneurons. OBJECTIVES To determine the role of endogenous serotonin at the HMN in modulating GG activity across natural sleep-wake states. METHODS Ten rats were implanted with electroencephalogram and neck muscle electrodes to record sleep-wake states, and GG and diaphragm wires for respiratory muscle recordings. Microdialysis probes were implanted into the HMN for perfusion of artificial cerebrospinal fluid and the serotonin receptor antagonist mianserin (100 microM). MEASUREMENTS AND MAIN RESULTS In room air, there was no effect of mianserin on respiratory-related or tonic GG activities across sleep-wake states (p > 0.300). In hypercapnia, however, the normal declines in GG activity from non-REM to REM sleep, and wakefulness to REM sleep, were reduced with mianserin (p < 0.005). These data demonstrate a normally low endogenous serotonergic drive modulating GG activity unless augmented by reflex inputs. We also demonstrated a significant serotonergic drive modulating GG activity in vagotomized rats, but not in vagi-intact rats, under anesthesia, suggesting that previous results in reduced preparations may have been influenced by vagotomy. CONCLUSIONS The results show a minimal endogenous serotonergic drive at the HMN modulating GG activity across sleep-wake states, unless augmented by reflex inputs. This result has implications for pharmacologic strategies aiming to increase GG activity by manipulating endogenous serotonin in patients with obstructive sleep apnea.
The Interpretative Role of an Experiencer: How to Design for Meaningful Transmedia Experiences by Contrasting Ambiguous Vs. Prescribed Qualities

The term transmedia is often used to describe the seamless consumption of a variety of content that is part of the same product across multiple delivery channels. Media convergence, divergence and transmedia are closely related and describe an evolving paradigm of content consumption. In this chapter, we address the challenge to provide a clear distinction between transmedia, convergence and divergence as separate, but intertwined concepts. The multifaceted and interdisciplinary nature of the subject necessitates respecting various interpretations of transmedia storytelling. By collecting and grouping the various perspectives that shape the understanding and expectations of transmedia storytelling, we came up with a conceptual transmedia methodology that is based on both traditional storytelling frameworks such as ‘fictional universes’ and user experience design theories. This stands in contrast to the commercial interpretations of transmedia storytelling, which are often based on multimedia merchandising solutions rather than on rich evolving storylines that run across multiple platforms. The evolving methodology illustrates the complexities of transmedia design, including for example interdisciplinarity, genres, and emergent production models. We focus on user experience design early in the creative writing process, replacing former methods that added transmedia patterns as if an afterthought at the end of a production cycle. 1 Sabiha Ghellal , Department of Mobile Media, Stuttgart Media University, Stuttgart, Germany Email: ghellal@hdm-stuttgart.de 2 Annika Wiklund-Engblom, MediaCity Åbo Akademi University , Vasa, Finland Email: annika.wiklund@abo.fi 3 Damjan Obal, SLO, Faculty of Electrical Engineering and Computer Science University of Maribor, Maribor, Slovenia Email: damjan@edgartells.me 4 Ann Morrison, Department of Architecture, Design and Media Technology, Aalborg University, Aalborg, Denmark Email: morrison@create.aau. dk
Natural variability and sampling errors in solar radiation measurements for model validation over the Atmospheric Radiation Measurement Southern Great Plains region

[1] Ground-based radiation measurements are frequently used for validating the performance of a model in simulating clouds. Such important questions are often raised as: (1) How well do the measurements represent model grid mean values? ; (2) How much of model-observation differences can be attributed to inherent sampling errors? ; and (3) What scale does modeling need to be performed in order to capture the cloud variation? We attempt to address these questions using surface solar irradiance data retrieved from the Geostationary Operational Environmental Satellite (GOES) and measured at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. The satellite retrievals are used to mimic ground measurements with various spatial densities and temporal frequencies, from which the sampling errors of the ground observations are quantified and characterized. Most of the differences between point-specific measurements and area-mean satellite retrievals originate from ground sampling errors. We quantify these errors for different months, model grid sizes, and integration intervals. In March 2000, for example, the sampling error is 16 W m−2 for instantaneous irradiances averaged over an area of 10 × 10 km2. It increases to 46 and 64 W m−2 if the model grid size is enlarged to 200 × 200 km2 and 400 × 400 km2, respectively. The sampling uncertainties decrease rapidly as the time-averaging interval increases up to 24 hours and then level off to a relatively small and stable value. Averaging over periods greater than 5 days reduces the error to a magnitude of less than 15 W m−2 over all grid sizes. The sampling error also decreases as the number of ground stations increases inside a grid, but the most substantial reduction occurs as the number of ground sites increases from 1 to 2 or 3 for a grid size of 200 × 200 km2. This means that for computing grid-mean surface solar irradiance, there is no need for an overly dense network of observation stations.
Derivation of an analytical expression of the Gaussian model Statistical Resolution Limit

Statistical Resolution Limit (SRL), defined as the minimal separation to resolve two closely spaced signals, is one of the important tools to evaluate a given system performance. Based on S.T. Smith's formulation of the SRL, this paper provides a methodology to compute an approximate analytical expression of the resolution limit in the Gaussian model case. As an application, we consider the particular case of two sources located in the near field and consider the resolution limit in terms of minimum angular separation. Discussion and numerical illustrations are then given to get more insights on the proposed derivation and to validate our theoretical results.
Geometry of Shimura varieties of Hodge type over finite fields

We present a general and comprehensive overview of recent developments in the theory of integral models of Shimura varieties of Hodge type. The paper covers the following topics: construction of in- tegral models, their possible moduli interpretations, their uniqueness, their smoothness, their properness, and basic stratifications of their spe- cial fibres.
Transgender young people’s narratives of intimacy and sexual health: implications for sexuality education

Abstract Sexuality education as pedagogy is often fraught by the perceived requirement to balance the informational needs of young people with an investment in notions of childhood ‘innocence’. Nowhere is this perhaps more evident than in sexuality education that seeks to be inclusive of transgender young people, often resulting in the failure of such education to address the needs of such students. In an attempt at addressing the relative dearth of information about what transgender young people would like to see covered in sexuality education, in this paper we explore transgender young people’s accounts of intimacy and sexual health and consider what this means for school-based sexuality education. To do this, we analyse discussions of intimacy from the perspectives of transgender young people as narrated in a sample of YouTube videos. We conclude by advocating for an approach to sexuality education that largely eschews the gendering of body parts and gametes, and which instead focuses on function, so as to not only address the needs of transgender young people (who may find normative discussions of genitals distressing), but to also provide cisgender young people with a more inclusive understanding of their own and other people’s bodies and desires.
Visual Grouping in Menu Interfaces

Menu interfaces often arrange options into semantic groups. This semantic structure is then usually conveyed to the user by supplementary visual grouping cues. We investigate whether these visual grouping cues actually help users locate items in menus faster, and whether there is potential for these powerful grouping cues to impede search when used inappropriately. Thirty-six participants performed known-item searches of word menus. These menus differed along three dimensions: (1) whether visual grouping cues were used, (2) whether items were semantically organized, and (3) the number of items belonging to each semantic group. Results show that the usefulness of visual grouping entirely depends on the underlying semantic structure of the menu. When menus were semantically organized, having visual grouping cues delineate the boundaries between large semantic groups resulted in the fastest search times. But when semantically unrelated items were visually grouped together, participants took far longer to locate targets. Menu designers should therefore take great care to avoid visually grouping semantically unrelated items as this has the potential to hinder menu interactions.
Using novobiocin as a specific inhibitor of breast cancer resistant protein to assess the role of transporter in the absorption and disposition of topotecan.

PURPOSE To investigate the role of intestinal breast cancer resistant protein (BCRP) in the absorption and disposition of topotecan (TPT) using novobiocin (NOV) as a specific inhibitor. METHODS Transporter inhibition specificity of NOV was assessed in cells overexpressing BCRP or Pgp. Sprague-Dawley rats were orally or intravenously dosed with TPT (2 and 1 mg/kg for p.o. and i.v., respectively) with or without oral co-administration of NOV (50 mg/kg). Pharmacokinetic parameters of TPT were obtained by noncompartmental analysis. To assess the role of BCRP in TPT intestinal permeation, rat ileal segment was perfused with 10 microM TPT in the presence or absence of NOV (500 microM), TPT permeability was calculated based on drug appearance in mesenteric blood. To assess the role of BCRP in TPT intestinal secretion, rat ileal segment was perfused with saline in the presence or absence of NOV (500 microM), while TPT was i.v. infused into rat. Intestinal secretion of TPT was calculated based on drug appearance in the perfusate. RESULTS NOV significantly inhibited efflux activity of BCRP, but not Pgp. Coadministration of NOV markedly increased oral TPT AUC(0-720) and Cmax by 3- and 4.5-fold, respectively, and decreased systemic clearance of i.v. injected TPT (from 44.40+/-7.28 without NOV to 29.44+/-1.99 ml/min/kg with NOV). The inclusion of NOV in perfusate significantly increased TPT permeability from 0.81+/-0.30 x10(-6) to 1.26+/-0.12 x10(-6) cm/s, while, the intestinal secretion of TPT was reduced by ~50% when NOV was included in perfusate. CONCLUSIONS The present study establishes in vitro and in vivo inhibition potency and specificity of NOV on BCRP and provides direct evidence that intestinal BCRP plays an important role in limiting the oral absorption and influencing the systemic elimination of TPT.
Proteomic analysis of the effects of ABA treatments on ripening Vitis vinifera berries

The control of ripening of the non-climacteric grapevine fruit is still a matter of debate, but several lines of evidence point to an important role for the hormone abscisic acid (ABA). The effects of ABA treatments on Cabernet Sauvignon berries before and at véraison were studied using a 2-DE proteomic approach. Proteins from whole deseeded berries (before véraison) and berry flesh and skin (at véraison) treated with 0.76 mM ABA and collected 24 h after treatment were separated and analysed. A total of 60 protein spots showed significant variations between treated and control berries, and 40 proteins, mainly related to general metabolism and cell defence, were identified by LC MS/MS. Our results show that ABA acts mainly through the regulation of mostly the same proteins which are involved in the ripening process, and that several of these changes share common elements with the ABA-induced responses in vegetative tissues.
Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment

Identifying and linking named entities across information sources is the basis of knowledge acquisition and at the heart of Web search, recommendations, and analytics. An important problem in this context is cross-document co-reference resolution (CCR): computing equivalence classes of textual mentions denoting the same entity, within and across documents. Prior methods employ ranking, clustering, or probabilistic graphical models using syntactic features and distant features from knowledge bases. However, these methods exhibit limitations regarding run-time and robustness. This paper presents the CROCS framework for unsupervised CCR, improving the state of the art in two ways. First, we extend the way knowledge bases are harnessed, by constructing a notion of semantic summaries for intra-document co-reference chains using co-occurring entity mentions belonging to different chains. Second, we reduce the computational cost by a new algorithm that embeds sample-based bisection, using spectral clustering or graph partitioning, in a hierarchical clustering process. This allows scaling up CCR to large corpora. Experiments with three datasets show significant gains in output quality, compared to the best prior methods, and the run-time efficiency of CROCS.
A Bayesian Reasoning Framework for On-Line Business Information Systems

We describe a Bayesian Reasoning Framework (BRF) that supports business rule operations for on-line information systems. BRF comprises a three-layer environment with business information systems at the top, a middle-ware Bayesian reasoning server, and a Bayesian reasoning engine at the bottom. The top and middle-ware layers communicate via SOAP/XML protocol, while the middle-ware and bottom layers communicate via a Tag-value protocol that fetches business rules from a central repository. BRF is built as a Bayesian Reasoning Agent and tested in a helpdesk system for assigning advisors to users for trouble-shooting in the operation of business information systems. BRF is modeled following a use-case methodology as well as an inference modeling that uses an assignation template from CommonKADS. The concept, design and implementation of BRF for real-world, on-line business information systems are the main contribution of this research project.