Miracle-dz/UniFAR-FTUs huggingface dataset:
International Trade and Finance Association International Trade and Finance Association Working Papers 2008

This paper focuses on the importance of accounting harmonisation on foreign activities from a macroeconomic perspective. International Financing Reporting Standards (IFRS) adoption is considered to reduce information costs among countries and is, therefore, an important way to encourage international trade flows and investments. Moreover, heterogeneity in trade and FDI determinants among different European countries (well-established capitalist countries in the “West” and post-communist countries in the “East”) is analysed since transition economies present a lower development of market institutions and, therefore, of financial systems. The effect of IFRS adoption is analysed from a gravity framework. The fixed-effects vector decomposition (FEVD) procedure, recently proposed by Plumper and Troeger (2007), is used to estimate panel data characterised by the presence of time invariant variables, or variables which vary rarely in time. The results provide evidence that benefits exist in terms of trade and FDI when IFRS are adopted. Furthermore, the positive effect of adopting uniform accounting standards on foreign activities in Europe is higher in transition economies. Finally, this effect also differs in countries because of behavioural factors such as unfamiliarity aversion. This paper was presented at the 18th International Conference of the International Trade and Finance Association, meeting at Universidade Nova de Lisboa, Lisbon, Portugal, May 23, 2008.
Altered neurotransmitter levels with post-traumatic stress disorder.

AIM The aim of this study was to observe the changes in the monoamine neurotransmitter levels of patients with post-traumatic stress disorder (PTSD) and explore whether the neurotransmitter level is correlated with the severity of PTSD. MATERIAL AND METHODS A total of 15 PTSD patients without any physical injuries were enrolled into the study. Another 15 cases of pre-restoration patients were recruited as controls who experienced traumatic events but did not experience PTSD or had a Posttraumatic stress disorder Check List-Civilian version (PCL-C) score lower than 12 points. The levels of plasma monoamine neurotransmitters, including norepinephrine, 5-hydroxytryptamine, and dopamine, were tested using enzyme-linked immunosorbent assays. RESULTS No significant differences were found between the monoamine neurotransmitter levels of the PTSD and the control groups (p > 0.05). A correlation study confirmed that the monoamine neurotransmitter levels and the PCL-C scores of the PTSD group were not significantly correlated (p > 0.05). CONCLUSION No neurotransmitter changes are seen in PTSD caused by simple mental trauma, and the PCL-C scores do not associate with PTSD.
Decreased growth in angus steers with a short TG-microsatellite allele in the P1 promoter of the growth hormone receptor gene.

A polymorphic TG-repeat microsatellite is located 90 base pairs upstream from a major transcription start site in the bovine growth hormone receptor gene. A shorter allele with 11 consecutive TG is common in Bos indicus cattle, whereas longer 16- to 20-TG-repeat alleles predominate in Bos taurus breeds. The purpose of this study was to compare growth and carcass traits between Angus steers that had two of the longer growth hormone receptor alleles with their half-siblings that had one short allele and one of the longer alleles. We genotyped 64 Angus sires with respect to the poly-TG microsatellite and identified six bulls that were heterozygous in that they had one short 11-TG allele and one of the longer alleles. We then grouped 125 steer progeny of these six heterozygous bulls according to their genotypes: only the longer 16- to 20-TG-repeat alleles were found in 73 steer progeny (long/long homozygotes), whereas a short 11-TG allele was paired with one of the longer alleles in 52 progeny (short/long heterozygotes). Contrasts for the long/long homozygotes vs the short/long heterozygotes were significant for weaning weight (17 +/- 4 kg; P < .001) and carcass weight (14 +/- 5 kg; P < .01). Approaching significance (P = .03) was the contrast for USDA marbling score (-.3 +/- .2). No significant differences (P > .05) were detected for contrasts in birth weight (.3 +/- .6 kg), longissimus muscle area (-.2 +/- 1 cm2), or carcass fat depth (-.01 +/- .07 cm). This polymorphism has potential for use in DNA marker-assisted selection programs.
C1GALT1 Promotes Invasive Phenotypes of Hepatocellular Carcinoma Cells by Modulating Integrin β1 Glycosylation and Activity

Cancer cell invasion and metastasis are the primary causes of treatment failure and death in hepatocellular carcinoma (HCC). We previously reported that core 1 β1,3-galactosyltransferase (C1GALT1) is frequently overexpressed in HCC tumors and its expression is associated with advanced tumor stage, metastasis, and poor survival. However, the underlying mechanisms of C1GALT1 in HCC malignancy remain unclear. In this study, we found that overexpression of C1GALT1 enhanced HCC cell adhesion to extracellular matrix (ECM) proteins, migration, and invasion, whereas RNAi-mediated knockdown of C1GALT1 suppressed these phenotypes. The promoting effect of C1GALT1 on the metastasis of HCC cells was demonstrated in a mouse xenograft model. Mechanistic investigations showed that the C1GALT1-enhanced phenotypic changes in HCC cells were significantly suppressed by anti-integrin β1 blocking antibody. Moreover, C1GALT1 was able to modify O-glycans on integrin β1 and regulate integrin β1 activity as well as its downstream signaling. These results suggest that C1GALT1 could enhance HCC invasiveness through integrin β1 and provide novel insights into the roles of O-glycosylation in HCC metastasis.
Robust speech recognition using missing data techniques in the prospect domain and fuzzy masks

Missing data theory (MDT) has been applied to handle the problem of noise-robust speech recognition. Conventional MDT-systems require acoustic models that are expressed in the log-spectral rather than in the cepstral domain, which leads to a loss in accuracy. Therefore, we have already introduced a MDT-technique that can be applied in any feature domain that is a linear transform of log-spectra. This MDT-system requires hard decisions about the reliability of each spectral component. When computed from noisy data, misclassification errors in the mask are hardly unavoidable and the recognition rate will significantly degrade. The risk of misclassifications can be reduced by estimating a probability that the component is reliable, e.g. a fuzzy mask. In this paper, we extend our MDT-system to be applied in the probabilistic decision framework. Experiments on the Aurora2 database demonstrate a further increase in recognition accuracy, especially at low SNRs.
Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS)

Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky–Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations.
Sustainability Management with the Sustainability Balanced Scorecard in SMEs: Findings from an Austrian Case Study

Given the economic importance of small and medium sized enterprises (SMEs) and their need for strategic sustainability management, this paper examines the concept of Sustainability Balanced Scorecard (SBSC) with respect to SMEs. This case study describes the development of an SBSC in a middle-sized Austrian brewery. The existing methods used in SBSC creation were adapted to suit the company’s specific needs. The findings show that when developing an SBSC it is important to take specific SME characteristics into account and apply methods that allow for adaptation to the prevailing conditions, such as strategic management, organizational structure and resources. In the case study special attention was paid to strategy analysis and thereby the creation of a solid basis for the SBSC. Furthermore, the results show SBSC development is not a process of distinct consecutive steps, as it is often suggested in the literature, but rather that it is a complex process marked by iterative steps. Moreover, the paper highlights the supporting factors and challenges experienced. Whereas top management support, the presence of a project team and high employee involvement were identified as key supporting factors, a lack of specific resources and blurry strategy formulation exert negative influence on the development process. The results presented here should help other SMEs in their development of an SBSC. The paper stimulates scientific discourse on sustainability and strategic management by highlighting the relevant supporting factors and challenges.
Way of Life of the Educational Organization: Intellectual Model

This problem analytical scientific article informs the reader about nowadays academic standards of secondary school management and their suitability to social, historical, political and economic realities of the development of civilization. It sets out and protects by academic evidence different points of view on the new enough concept of «Way of educational organization life», gives modern approaches to the problem and methods for its solution, justifies the concept. It is built on the rules of the logic of scientific research. The presented intellectual model of Way of life of educational organization is based on the most important elements of intellectual capital, and the latent factors of cumulative approach to the development of the educational organization. As the way of life of the educational institutions is multifaceted and complex phenomenon of reality six main factors of the intellectual model were appointed in the paper. As the way of life of the educational institutions is multifaceted and complex phenomenon of reality six main factors of the intellectual model were appointed in the paper. It is demonstrated that there within the Way of life are internal communications, the basic properties of the laws of development, self-development and interaction with the environment.
Creating Spatially-Shaped Defense Models Using DEVS and Cell-DEVS

In recent years, new techniques for military modeling and simulation provided the practitioner with advanced mechanisms to describe complex applications. Some of the recent efforts in the field tried to address important issues in open research areas, ranging from agent-based modeling, multiresolution/hierarchical models, hybrid models, and composability. We show how to address some of these issues through the application of a formal modeling and simulation technique and its application to the domain of defense applications. Our efforts consider the construction of multimodels, including components that can be defined as spatially-shaped models, using the Cell-DEVS and DEVS formalisms. DEVS is a mathematically sound framework in which a system is modeled by dividing it into a number of components (each of them having a discrete state and interacting with the environment via input/output ports). Cell-DEVS is an extension to DEVS that formulates the execution of cellular models with explicit timing delays. We show how these concepts can be applied to different defense-related spatial models, including a radar transmitter/receiver, a target-seeking device, and land battlefield models.
Drosophila as a model of wound healing and tissue regeneration in vertebrates

Understanding the molecular basis of wound healing and regeneration in vertebrates is one of the main challenges in biology and medicine. This understanding will lead to medical advances allowing accelerated tissue repair after wounding, rebuilding new tissues/organs and restoring homeostasis. Drosophila has emerged as a valuable model for studying these processes because the genetic networks and cytoskeletal machinery involved in epithelial movements occurring during embryonic dorsal closure, larval imaginal disc fusion/regeneration, and epithelial repair are similar to those acting during wound healing and regeneration in vertebrates. Recent studies have also focused on the use of Drosophila adult stem cells to maintain tissue homeostasis. Here, we review how Drosophila has contributed to our understanding of these processes, primarily through live‐imaging and genetic tools that are impractical in mammals. Furthermore, we highlight future research areas where this insect may provide novel insights and potential therapeutic strategies for wound healing and regeneration. Developmental Dynamics 240:2739–2404, 2011. © 2011 Wiley Periodicals, Inc.
Automatic Problem Localization via Multi-dimensional Metric Profiling

Debugging today's large-scale distributed applications is complex. Traditional debugging techniques such as breakpoint-based debugging and performance profiling require a substantial amount of domain knowledge and do not automate the process of locating bugs and performance anomalies. We present Orion, a framework to automate the problem-localization process in distributed applications. From a large set of metrics, Orion intelligently chooses important metrics and models the application's runtime behavior through pair wise correlations of those metrics in the system, within multiple non-overlapping time windows. When correlations deviate from those of a learned correct model due to a bug, our analysis pinpoints the metrics and code regions (class and method within it) that are most likely associated with the failure. We demonstrate our framework with several real-world failure cases in distributed applications such as: HBase, Hadoop DFS, a campus-wide Java application, and a regression testing framework from IBM. Our results show that Orion is able to pinpoint the metrics and code regions that developers need to concentrate on to fix the failures.
Algebraic Decoding of Quadratic Residue Codes Using Berlekamp-Massey Algorithm

In this paper, an algebraic decoding method is proposed for the quadratic residue codes that utilize the Berlekamp-Massey algorithm. By a modification of the technique developed by He et al., one can express the unknown syndromes as functions of the known syndromes. The unknown syndromes are determined by an efficient algorithm also developed in this paper. With the appearance of unknown syndromes, one obtains the consecutive syndromes that are needed for the application of the Berlekamp-Massey algorithm. The decoding scheme, developed here, is easier to implement than the previous decoding algorithm developed for the Golay code and the (47, 24, 11) QR code. Moreover, it can be extended to decode all codes of the family of binary quadratic residue codes with irreducible generating polynomials.
Quality estimation for Japanese Haiku poems using Neural Network

We propose a method to estimate the artistic quality of Haiku (Japanese style short poem) texts using a machine learning approach. Based on the assumption that the artistry of a text stems from its sound factors as well as its meanings, we first constructed two types of vector models, a word-based model and a syllable-based model, converted from Haiku texts. Next, we conducted machine learning for these two models using a convolutional neural network to construct a Haiku quality estimation function. We then evaluated the precision of quality estimation for 40,000 Japanese Haiku poems obtained from a Haiku community site, assuming that the number of “likes” given from viewers to a Haiku corresponds to its artistic quality. Through the experiment, we confirmed that by conducting a quality estimation based on the consensus between different models, we can improve the precision of quality estimation up to 0.64. We also found that if we evaluate Haiku poems for which we have high confidence in quality estimation certainty, the F-measure of the estimation improved from 0.57 to 0.64.
Curve Analogies

This paper describes a method for learning statistical models of 2D curves, and shows how these models can be used to design line art rendering styles by example. A user can create a new style by providing an example of the style, e.g. by sketching a curve in a drawing program. Our method can then synthesize random new curves in this style, and modify existing curves to have the same style as the example. This method can incorporate position constraints on the resulting curves.
Distributed Online Tracking

In online tracking, an observer S receives a sequence of values, one per time instance, from a data source that is described by a function f. A tracker T wants to continuously maintain an approximation that is within an error threshold of the value f(t) at any time instance t, with small communication overhead. This problem was recently formalized and studied, and a principled approach with optimal competitive ratio was proposed. This work extends the study of online tracking to a distributed setting, where a tracker T wants to track a function f that is computed from a set of functions f1 , . , fm from m distributed observers and respective data sources. This formulation finds numerous important and natural applications, e.g., sensor networks, distributed systems, measurement networks, and pub-sub systems. We formalize this problem and present effective online algorithms for various topologies of a distributed system/network for different aggregate functions. Experiments on large real data sets demonstrate the excellent performance of our methods in practice.
Performance of pico-cell clusters with cooperative receivers

Pico-cell clusters have been proposed for providing high capacities to dense sets of users in next generation networks. We consider the uplink channel and analyze the sum-rate performance of two interfering pico-cell clusters with cooperative multi-cell joint decoding (MJD). The performance of a linear receiver is compared with that of non-linear receivers which allow message passing between the clusters in order to cancel the associated interference. The receiver, in all the above cases, jointly processes all signals across a single cluster. However we consider different message passing schemes, and maximize the sum rate over system parameters, including the load in each cell. Our results are valid in the large-system limit as the number of cells and antennas per cluster each tend to infinity with fixed ratio. Numerical results show that the nonlinear receivers provide significantly larger capacities than linear receivers, especially at high SNRs.
Whole body motion controller with long-term balance constraints

The standard approach to real-time control of humanoid robots relies on approximate models to produce a motion plan, which is then used to control the whole body. Separation of the planning stage from the controller makes it difficult to account for the whole body motion objectives and constraints in the plan. For this reason, we propose to omit the planning stage and introduce long-term balance constraints in the whole body controller to compensate for this omission. The new controller allows for generation of whole body walking motions, which are automatically decided based on both the whole body motion objectives and balance preservation constraints. The validity of the proposed approach is demonstrated in simulation in a case where the walking motion is driven by a desired wrist position. This approach is general enough to allow handling seamlessly various whole body motion objectives, such as desired head motions, obstacle avoidance for all parts of the robot, etc.
Methodological aspects of exhaled breath condensate collection and analysis

The collection and analysis of exhaled breath condensate (EBC) may be useful for the management of patients with chronic respiratory disease at all ages. It is a promising technique due to its apparent simplicity and non-invasiveness. EBC does not disturb an ongoing respiratory inflammation. However, the methodology remains controversial, as it is not yet standardized. The current diversity of the methods used to collect and preserve EBC, the analytical pitfalls and the high degree of within-subject variability are the main issues that hamper further development into a clinical useful technique. In order to facilitate the process of standardization, a simplified schematic approach is proposed. An update of available data identified open issues on EBC methodology. These issues were then classified into three separate conditions related to their influence before, during or after the condensation process: (1) pre-condenser conditions related to subject and/or environment; (2) condenser conditions related to condenser equipment; and (3) post-condenser conditions related to preservation and/or analysis. This simplified methodological approach highlights the potential influence of the many techniques used before, during and after condensation of exhaled breath. It may also serve as a methodological checklist for a more systematical approach of EBC research and development.
Three Statistical Summarizers at CLEF-INEX 2013 Tweet Contextualization Track

According to the organizers, the objective of the 2014 CLEF- INEX Tweet Contextualization Task is: \. The Tweet Contextualization aims at providing automatically information - a summary that explains the tweet. This requires combining multiple types of processing from in- formation retrieval to multi-document summarization including entity linking. " We present three statistical summarizer systems applied to the CLEF-INEX 2014 task. Cortex summarizer uses several sentence selec- tion metrics and an optimal decision module to score sentences from a document source. Artex summarizer uses a simple inner product among the topic-vector and the pseudo-word vector. Reg summarizer is a per- formant graph-based summarizer. The results show that our systems performed well on CLEF-INEX task. Our three systems have obtained the rst rank in the INEX manual evaluation.
Toward computers that recognize and respond to user emotion

Did you like that or not? Did the system’s choice of adaptation aggravate you more, or did it bring about an expression of gratefulness? Does this interest you or bore you? Recognition of the effects an action has on a user is a key part of adapting successfully to users; how can machines be enabled to recognize affective expressions such as frustration, interest, anger, or joy? And, what are guidelines for designing their response, especially given that recognition is likely to not be perfect? This talk will present new technologies under development for sensing and responding appropriately to human affective expressions. Current applications include usability feedback, health behavior change, learning companions, and human-robot interaction.