Miracle-dz/UniFAR-FTUs huggingface dataset:
Reprogenetics: Preimplantational genetics diagnosis

Preimplantational Genetics Diagnosis (PGD) is requested by geneticists and reproductive specialists. Usually geneticists ask for PGD because one or both members of the couple have an increased genetic risk for having an affected offspring. On the other hand, reproductive specialists ask for embryo aneuploidy screening (PGS) to assures an euploid embryo transfer, with the purpose to achieve an ongoing pregnancy, although the couple have normal karyotypes. As embryonic aneuploidies are responsible for pre and post implantation abortions, it is logical to considerer that the screening of the embryonic aneuploidies prior to embryo transfer could improve the efficiency of the in vitro fertilization procedures. Nevertheless, it is still premature to affirm this until well-designed clinical trials were done, especially in women of advanced age where the rate of embryos with aneuploidies is much greater. Although the indications of PGD are similar to conventional prenatal diagnosis (PND), PGD has less ethical objections than the PND. As with the PGD/PGS results only unaffected embryos are transferred, both methods can avoid the decision to interrupt the pregnancy due to a genetic problem; this makes an important difference when compared to conventional prenatal diagnosis.
The Response of Catastrophe Insurance Markets to Extreme Events : A Real Option Approach

This paper connects the traditional financial and insurance literatures in the context of real option theory. We use the analogy between insurance and investment under uncertainty in order to study the general equilibrium in an insurance industry with heterogeneous competing firms. We show that even under the assumption of rationality (defined in the traditional sense) insurance companies should be treated as strategic agents when catastrophic (CAT) events are possible. We derive the equilibrium investment (insurance policies) using a generalization of the standard model of partially reversible investment under uncertainty. Implications of the proposed model for the optimal contract design and future extensions in the framework of dynamic programming approach are discussed. Haas School of Business, UC Berkeley, Berkeley, CA 94720-1900 We are grateful to Thomas Russell for the fruitful discussions.
Randomized gossip algorithms for solving Laplacian systems

We consider the problem of solving a Laplacian system of equations Lx = b in a distributed fashion, where L is the Laplacian of the communication graph. Solving Laplacian systems arises in a number of applications including consensus, distributed control, clock synchronization, localization and calculating effective resistances, to name a few. We leverage our analysis on a randomized variant of Kaczmarz's algorithm to propose a distributed asynchronous gossip algorithm with expected exponential convergence. We quantify the convergence rate depending solely on properties of the network topology, and further propose an accelerated version that scales favorably for larger networks. Our approach naturally extends to least-squares estimation of general linear systems where each row/column is assigned to nodes of a given network. Last but not least, we show that average consensus is a special case in our framework.
Interface Personalization through Inclusive User Modelling Web Service

This paper presents a process of developing personalized applications through user modelling. The process involves understanding users' requirements through survey, formulating those requirements into a user model, integrating the model with different applications and finally conducting user trials to validate the personalization system. We have developed the inclusive user model, which consists of a user interaction simulator and an interface personalization web service. The inclusive user modelling system has found applications in a wide variety of systems for EU and Indian population including a digital TV framework for elderly users, an electronic agricultural advisory system, a weather monitoring system and an emergency warning system. Our user studies confirmed that users prefer and performed better with personalized systems than non-personalized versions.
A Variable Threats Based Self-Organization Scheme for Wireless Sensor Networks

Self-organization is an important issue in wireless sensor networks because of the inherent unreliability of the network. Besides, variable threats in the networks can not be ignored. Extending battery life and enhancing robustness under variable threats are two essential aspects which need to be considered when a self-organization scheme is explored. In order to address these issues, a Redundant Nodes Selection scheme and a variable Threats Probability Estimation scheme are proposed in this paper. RNS is able to select redundant nodes that can be switched off without affecting overall sensing coverage. TPE is able to help a sensor node to choose the most suitable path and avoid high-threat neighbors in order to reduce packet loss. The scenario with RNS extended battery life by 30% to 50%, and postponed the occurrence of first partitioning in the network by 27% to 140%. TPE decreased packet loss by 225% to 400% when a high threat level was involved.
Large Scale Sequential Learning from Partially Labeled Data

The success of data-driven solutions to difficult problems, along with the dropping costs of storing and processing massive amounts of data, has led to growing interest in large scale machine learning. In many cases, statistical learning problems involve sequential data, which exhibits significant sequential correlation. This fact makes the training of sequence classifier be time consuming and the application of sequential learning from large scale data is difficult, especially when the available training data are sparsely labeled. This paper proposed a novel learning approach to build the sequence classifiers from a large amount of partially labeled training data. The mechanism of semi-supervised learning for classifier building from the partially labeled data is embedded in the computing framework of ensemble learning, which is adapted for distributed learning over large scale dataset. For its practical evaluation, we conducted the empirical experiments by using Conditional Random Field (CRF) as the basic learner to detect concepts in large scale document set. The results show that our approach outperforms the best baselines significantly, which demonstrates the effectiveness of the proposed approach.
The social life of visualization

In this paper we reframe the creation of information visualizations as a kind of interface design, where visualizations provide people with an interface onto a dataset in such a way that they can generate, understand, and ultimately communicate interpretations of the data in the form of narratives to other members of given social settings. The paper describes a three stage create--interpret--capture process [Figure 1] for the design of information visualizations. The work references existing interaction design patterns, interfaces, and theories of organizational behaviour that serve to illustrate the approach we have used.
DeSRL: A Linear-Time Semantic Role Labeling System

This paper describes the DeSRL system, a joined effort of Yahoo! Research Barcelona and Universita di Pisa for the CoNLL-2008 Shared Task (Surdeanu et al., 2008). The system is characterized by an efficient pipeline of linear complexity components, each carrying out a different sub-task. Classifier errors and ambiguities are addressed with several strategies: revision models, voting, and reranking. The system participated in the closed challenge ranking third in the complete problem evaluation with the following scores: 82.06 labeled macro F1 for the overall task, 86.6 labeled attachment for syntactic dependencies, and 77.5 labeled F1 for semantic dependencies.
Quantum-Behaved Particle Swarm Optimization with Cooperative Coevolution for Large Scale Optimization

Quantum-behaved particle swarm optimization (QPSO) has successfully been applied to unimodal and multimodal optimization problems. However, with the emerging and popular of big data and deep machine learning, QPSO encounters limitations with high dimensions. In this paper, QPSO with cooperative co evolution (QPSO_CC) is used to decompose the high dimensional problems into several lower dimensional problems and optimize them separately. The numerical experimental results show that QPSO_CC has comparative or even better performance than other algorithms.
Performance evaluation and comparison of G.729/AMR/fuzzy voice activity detectors

The paper proposes a performance evaluation and comparison of G.729, AMR, and fuzzy voice activity detection (FVAD) algorithms. The comparison was made using objective, psychoacoustic, and subjective parameters. A highly varied speech database was also set up to evaluate the extent to which VADs depend on language, the signal-to-noise ratio (SNR), or the power level.
A FRET-based assay for characterization of alternative splicing events using peptide nucleic acid fluorescence in situ hybridization

We describe a quantitative method for detecting RNA alternative splicing variants that combines in situ hybridization of fluorescently labeled peptide nucleic acid (PNA) probes with confocal microscopy Förster resonance energy transfer (FRET). The use of PNA probes complementary to sequences flanking a given splice junction allows to specifically quantify, within the cell, the RNA isoform generating such splice junction by FRET measure. As a proof of concept we analyzed two alternative splicing events originating from lymphocyte antigen 6 (LY6) complex, locus G5B (LY6G5B) pre-mRNA. These are characterized by the removal of the first intron (Fully Spliced Isoform, FSI) or by retention of such intron (Intron-Retained Isoform, IRI). The use of PNA probe pairs labeled with donor (Cy3) and acceptor (Cy5) fluorophores, suitable to FRET, flanking FSI and IRI specific splice junctions specifically detected both mRNA isoforms in HeLa cells. We have observed that the method works efficiently with probes 5–11 nt apart. The data supports that this FRET-based PNA fluorescence in situ hybridization (FP–FISH) method offers a conceptually new approach for characterizing at the subcellular level not only splice variant isoform structure, location and dynamics but also potentially a wide variety of close range RNA–RNA interactions.
Qualitative Spatial Representation and Reasoning: A Hierarchical Approach

The ability to reason in space is crucial for agents in order to make informed decisions. Current high-level qualitative approaches to spatial reasoning have serious deficiencies in not reflecting the hierarchical nature of spatial data and human spatial cognition. This article proposes a framework for hierarchical representation and reasoning about topological information, where a continuous model of space is approximated by a collection of discrete sub-models, and spatial information is hierarchically represented in discrete sub-models in a rough set manner. The work is based on the Generalized Region Connection Calculus theory, where continuous and discrete models of space are coped in a unified way. Reasoning issues such as determining the mereological (part-whole) relations between two rough regions are also discussed. Moreover, we consider an important problem that is closely related to map generalization in cartography and Geographical Information Science. Given a spatial configuration at a finer level, we show how to construct a configuration at a coarser level while preserving the mereological relations.
Mood state and recall biases: the role of affect

El presente trabajo consiste en un experimento en el que se inducen estados de animo mediante la exposicion a unas peliculas (una alegre y otra triste) sin instrucciones concretas por parte de los experimentadores y se realizaran pruebas de memoria inmediata de listas que contenian palabras neutras, con connotaciones alegres y con connotaciones tristes. Los resultados indican que mientras el efecto de la exposicion a las peliculas sobre el recuerdo de palabras fue claro y consistente, los cambios subjetivos del estado de animo fueron debiles, sin que ambos efectos mostraran ning? n tipo de correlacion. Estos resultados son mas favorables a la hipotesis que destaca el papel de la preparacion cognitiva que a la basada en el estado afectivo
Histogram Analysis Using a Scale-Space Approach

A new application of scale-space filtering to the classical problem of estimating the parameters of a normal mixture distribution is described. The technique involves generating a multiscale description of a histogram by convolving it with a series of Gaussians of gradually increasing width (standard deviation), and marking the location and direction of the sign change of zero-crossings in the second derivative. The resulting description, or fingerprint, is interpreted by relating pairs of zero-crossings to modes in the histogram where each mode or component is modeled by a normal distribution. Zero-crossings provide information from which estimates of the mixture parameters are computed. These initial estimates are subsequently refined using an iterative maximum likelihood estimation technique. Varying the scale or resolution of the analysis allows the number of components used in approximating the histogram to be controlled.
The impact of length and solvability of anagrams on performance and metacognitive judgments

Psychology Days in Zadar is an international conference organized biennially by the Department of Psychology of the University of Zadar. It started over 40 years ago and it has evolved over decades into a recognizable psychology conference that attracts many researchers from Croatia and abroad.The 21st Psychology Days conference in Zadar was held on May 24‐26 2018. It was organized by an international organizational‐scientific committee and gathered over 330 active participants from Spain, Slovenia, Bosnia and Herzegovina, Serbia, Macedonia, Hungary, Czech Republic, Austria, Russia, Netherlands, Norway, Turkey and USA. At conference, 130 papers were presented through four invited lectures, two symposia, twelve oral sessions, two poster sessions and four workshops.Encouraged with authors` interest and positive experiences regarding previous editions of Book of Selected Proceedings (after 15th and 20th Psychology Days), we have decided to invite authors who participated at 21st Psychology Days, to send their papers for publishing in the Book of Selected Proceedings of 21st Psychology Days in Zadar.
A Joint Syntactic-Semantic Representation for Recognizing Textual Relatedness

This paper describes our participation in the Recognizing Textual Entailment challenge (RTE-5) in the Text Analysis Conference (TAC 2009). Following the two-stage binary classification strategy, our focus this year is to recognize related Text-Hypothesis pairs instead of entailment pairs. In particular, we propose a joint syntactic-semantic representation to better capture the key information shared by the pair, and also apply a co-reference resolver to group cross-sentential mentionings of the same entities together. For the evaluation, we achieve 63.7% of accuracy on the three-way test, 68.5% on the entailment vs. non-entailment test, and 74.3% on the relatedness recognition. Based on the error analysis, we will work on differentiating entailment and contradiction in the future.
Kinetic Analysis of Guanidine Hydrochloride Inactivation of β-Galactosidase in the Presence of Galactose

Inactivation of purified β-Galactosidase was done with GdnHCl in the absence and presence of varying [galactose] at 50°C and at pH 4.5. Lineweaver-Burk plots of initial velocity data, in the presence and absence of guanidine hydrochloride (GdnHCl) and galactose, were used to determine the relevant K m and V max values, with p-nitrophenyl β-D-galactopyranoside (pNPG) as substrate, S. Plots of ln([P]∞ − [P]t) against time in the presence of GdnHCl yielded the inactivation rate constant, A. Plots of A versus [S] at different galactose concentrations were straight lines that became increasingly less steep as the [galactose] increased, showing that A was dependent on [S]. Slopes and intercepts of the 1/[P]∞ versus 1/[S] yielded k +0 and k' +0, the microscopic rate constants for the free enzyme and the enzyme-substrate complex, respectively. Plots of k +0 and k' +0 versus [galactose] showed that galactose protected the free enzyme as well as the enzyme-substrate complex (only at the lowest and highest [galactose]) against GdnHCl inactivation. In the absence of galactose, GdnHCl exhibited some degree of non-competitive inhibition. In the presence of GdnHCl, galactose exhibited competitive inhibition at the lower [galactose] of 5 mM which changed to non-competitive as the [galactose] increased. The implications of our findings are further discussed.
Approximate Verification of the Symbolic Dynamics of Markov Chains

A finite state Markov chain M is often viewed as a probabilistic transition system. An alternative view - which we follow here - is to regard M as a linear transform operating on the space of probability distributions over its set of nodes. The novel idea here is to discretize the probability value space [0,1] into a finite set of intervals. A concrete probability distribution over the nodes is then symbolically represented as a tuple D of such intervals. The i-th component of the discretized distribution D will be the interval in which the probability of node i falls. The set of discretized distributions is a finite set and each trajectory, generated by repeated applications of M to an initial distribution, will induce a unique infinite string over this finite set of letters. Hence, given a set of initial distributions, the symbolic dynamics of M will consist of an infinite language L over the finite alphabet of discretized distributions. We investigate whether L meets a specification given as a linear time temporal logic formula whose atomic propositions will assert that the current probability of a node falls in an interval. Unfortunately, even for restricted Markov chains (for instance, irreducible and aperiodic chains), we do not know at present if and when L is an (omega)-regular language. To get around this we develop the notion of an epsilon-approximation, based on the transient and long term behaviors of M. Our main results are that, one can effectively check whether (i) for each infinite word in L, at least one of its epsilon-approximations satisfies the specification; (ii) for each infinite word in L all its epsilon approximations satisfy the specification. These verification results are strong in that they apply to all finite state Markov chains. Further, the study of the symbolic dynamics of Markov chains initiated here is of independent interest and can lead to other applications.
Multiregion Graph Cut Image Segmentation

The purpose of our study is two-fold: (1) investigate an image segmentation method which combines parametric modeling of the image data and graph cut combinatorial optimization and, (2) use a prior which allows the number of labels/regions to decrease when the number of regions is not known and the algorithm initialized with a larger number. Experimental verification shows that the method results in good segmentations and runs faster than conventional graph cut methods.
A Preliminary Analysis of Monetary and Inflation Targeting Frameworks for Thailand *

This paper analyzes main characteristics of monetary policy management under inflation targeting and monetary targeting. It also examines the strengths and weaknesses of the two policy strategies in the application to the context of developing countries like Thailand. Although the results from the empirical analysis in this study remain inconclusive to indicate which sort of policy strategies, inflation targeting or monetary targeting, is more suitable for the actual application in Thailand’s case, the findings suggest some specific conditions and important factors that the central bank and concerned authorities have to take into consideration when opting for an appropriate monetary policy framework in the future. In choosing a monetary policy strategy appropriate to a changing structure of Thailand’s economic and financial environment, the monetary authorities need to examine further the issues related to the ability to forecast inflation reasonably well over the medium term, the nature of transmission mechanism of monetary and exchange rate A Preliminary Analysis of Monetary and Inflation Targeting Frameworks for Thailand * Rungsun Hataiseree Monetary Policy Group Bank of Thailand * Background paper to a lecture given to central banks’ delegates at a session of “Study Visit on Option for Exchange Rate Policy”, organized by ESCAP and the Bank of Thailand, on 22nd, November, 1999. An earlier version of this paper was also presented at the Tenth Convention of the Economic Society of Thailand, November 4, 1999, Bangkok. The author wishes to thank seminar participants in particular Chalongphob Sussangkarn, Sataporn Jinachitra for helpful discussion. Special thanks are also due to Atchana Waiquamdee and Suchada Kirakul for their useful comments and suggestions on the previous version of the paper. The views expressed are those of the author and not necessarily those of the Bank of Thailand. policies on inflation, the role of financial innovation and the stability of money demand, a quantitative nature on linkage between instruments and targets of monetary policy, a degree of independence of monetary policy, the nature of the trade-off between the attainment of inflation and other macroeconomic objectives, the nature of shocks affecting the Thai economy in the near term.
The bright and dark sides of individual and group innovation:a special issue introduction

This introduction essay proposes a challenging program for researchers eager to explore factors and process mechanisms contributing to the benefits and costs individuals and groups incur from pursuing innovative approaches. With respect to individual innovation, such moderating factors might be found in the characteristics of the innovative idea, the innovator, co-workers, supervisors, the broader organizational context, and in national culture. Examples of factors that are likely to shape the beneficial and detrimental outcomes of group innovation include knowledge, skills and ability of group members, group tenure, diversity among group members, group processes (clarifying group objectives, participation, constructive management of competing perspectives), and external demands on groups. This Special Issue contains a state-of-the-science paper, three articles dealing with the benefits and costs of individual innovation, and three articles addressing the bright and dark sides of group innovation. Copyright © 2004 John Wiley & Sons, Ltd.