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In multi-label learning, the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data. The classification performance and robustness of the model are effectively improved. Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation. It is well known that the correlation between labels is asymmetric. However, existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels. Based on this, this paper proposes a Causality-driven Common and Label-specific Features Learning, named CCSF algorithm. Firstly, the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels. Then, in the optimization, both -norm and -norm are used to select the corresponding features, respectively. Finally, it is compared with six state-of-the-art algorithms on nine datasets. The experimental results prove the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes. Many methods only focus attention on label correlations and ignore the instance interrelations between the original feature space and low dimensional space. Additionally, very few techniques consider how to constrain the projection matrix to identify specific and common features in the feature space. In this paper, we propose a new approach of semi-supervised multi-label dimensionality reduction learning by instance and label correlations (SMDR-IC, in short). Firstly, we reformulate MDDM which incorporates label correlations as a least-squares problem so that the label propagation mechanism can be effectively embedded into the model. Secondly, we investigate instance correlations using the k-nearest neighbor technique, and then present the l 1 -norm and l 2 , 1 -norm regularization terms to identify the specific and common features of the feature space. Experiments on the massive public multi-label data sets show that SMDR-IC has better performance than other related multi-label dimensionality reduction methods. [ABSTRACT FROM AUTHOR]

In recent years, the range of applications that utilize multiband imaging has significantly expanded. However, it is difficult to utilize multichannel heterogeneous images to achieve a spectral complementarity advantage and obtain accurate depth prediction based on traditional systems. In this study, we investigate CFNet, an iterative prediction network, for disparity prediction with infrared and visible light images based on common features. CFNet consists of several components, including a common feature extraction subnetwork, context subnetwork, multimodal information acquisition subnetwork, and a cascaded convolutional gated recurrent subnetwork. It leverages the advantages of dual-band (infrared and visible light) imaging, considering semantic information, geometric structure, and local matching details within images to predict the disparity between heterogeneous image pairs accurately. CFNet demonstrates superior performance in recognized evaluation metrics and visual image observations when compared with other publicly available networks, offering an effective technical approach for practical heterogeneous image disparity prediction.

Whereas many theories of decision making predict that presenting or not presenting common features of choice alternatives should not affect choice, in this research, the authors show that common features can be a powerful driver of choice behavior. They conjecture that consumers often hold expectations about the features that choice alternatives have in common, and they demonstrate that presenting (vs. omitting information about) a common feature increases the choice probability of the alternative that would have been expected to perform worse on the common feature, given its performance on differentiating features. This effect occurs because performance on the common feature is judged not at face value but relative to an expectation about which product should perform best on that feature. The effect holds even though performance on the common feature is clearly the same when alternatives are presented side by side. Finally, the authors demonstrate four boundary conditions of this effect. [ABSTRACT FROM AUTHOR]

Some believe that Mulavi's influence on Attar is so great that they consider Masnavi to be completely influenced by Attar's works in terms of form, format, and anecdotes. For this reason, this study seeks to introduce the attraction and rapture and show the common features of these two mystics in this regard. In the mystics' thought, attraction is a special gift given to man by God. Although in mystical thought, mystics invite people to a continuous effort to reach spiritual perfection, in their views, effort without grace (providence) is fruitless. Many eminent and influential Sufis have given originality and importance to the effort and struggle of the seeker and considered rapture and grace as secondary. Some other great mystics such as Molana and Attar consider attraction as the principle and believe that if there is no attraction, the seeker's effort will not lead anywhere. The obtained results indicate that Mulavi and Attar have a greater desire for rapture in order to complete the mystic's religion and sharia and to reach God. They have many features in common in this respect. And in their view, the seeker's efforts are necessary only to the extent that they let the light of rapture shine on the seeker and the seeker reaches the goal. [ABSTRACT FROM AUTHOR]

Research on the structure and content of parental input to children with autism spectrum disorders (ASD) is an important, yet largely neglected area of study. In particular, it is not clear which input features are similar or different between children with ASD and typically developing (TD) children. We analysed transcripts of spontaneous conversations between six ASD children and their mothers, who were followed over a period of between 12 and 26 months, for common features in the composition and function of the shared lexical items in the mothers' speech to their children with ASD. Results first indicated the small core lexicon shared by all the mothers (8.27%) made up a total of 78.1% of the mothers' actual speech, similar to what's been found in maternal speech to TD 2-year-old children. Results also showed that, in contrast to maternal speech to TD children, the core lexicon of the mothers of ASD children was composed of slightly more content words (particularly verbs) than function words (68.48% versus 31.52%). These results suggest that mothers restrict their choice of lexical items to a small pool of highly frequent words when conversing with their children, ASD as well as TD. The use of more content words in maternal speech to children with ASD may reflect these mothers' explicit efforts to orient and direct their ASD children's attention to an ongoing activity. [ABSTRACT FROM AUTHOR]

The Russian school of forensic firearms investigation traditionally recognizes common and individual features of traces on bullets and cartridge cases. The first are characteristics inherent in all weapons of the same model and describing their details in general: shape, size, location, relative position. The second type are individual characteristics, which are unique and present only in one firearm. The individual features are used for forensic identification, while the common can be used only for the identification of a firearm’s type and model. The Western (West Europe and the USA) methodology of forensic ballistic identification recognizes the third type of traits – subclass characteristics. These marks are the result of manufacturing processes and can be present in a group of sequentially produced parts. Conventionally they can be placed between class and individual characteristics. One of the problems in contemporary firearms identification is the wrong recognition of subclass marks as individual marks and, as a result, giving false-positive conclusions of identification. The article discusses the problem of subclass features, gives examples, presents a review of the literature. The influence of various technological processes on the possibility of showing up of these marks is described.

Objectives: The 8p11 myeloproliferative syndrome (EMS) is an extremely rare, generally aggressive haematologic malignancies. This study provided the clinical outcomes and therapeutic strategies for EMS patients confirmed with CEP110-FGFR1 fusion. Methods: We report here a case of translocation (8;9) (p12;q33)/CEP110-FGFR1 who received allo-HSCT and achieved molecular remission. We searched the PubMed database for relevant medical literatures published between 1992 and 2018. We generalized the laboratory results, clinical features, therapeutic outcomes for EMS with confirmed CEP110-FGFR1 fusion. Results: We identified 16 EMS cases with CEP110-FGFR1 fusions including our patient. The observed common syndrome features were characterized as follows: a male predominance, fatigue (35.7%), tonsil hypertrophy (41.7%), lymphadenopathy (53.8%), hepatosplenomegaly (54.5%). leukocytosis (greater than 20.0 × 109/L, 71.4%), coexisting of eosinophilia and monocytosis (93.3%), and frequent progression to acute leukaemia. High incidence of tonsil hypertrophy and monocytosis may be a feature of EMS with CEP110/FGFR1 fusions. The CR rate for EMS was 23.1%. One patient treated with highly selective FGFR kinase inhibitor, INCB054828, achieved complete molecular remission rapidly. Allo-HSCT was performed in 8 patients. The median survival time for those patients was 9.0 (95%CI 5.599–12.601) months, with a range between 5 and 27 months. Allogeneic HSCT could improve survival in selected patients. Conclusion: FGFR1 and RUNX1 may be potential therapeutic targets for clinical trials. More accumulation of cases is also needed to determine whether allo-HSCT could be an optimal approach. [ABSTRACT FROM AUTHOR]

The idea of inspiration or divine revelation is echoed mostly in the cultures of nations. In the Greek mythology, there are springs or fountains called Pieren, the place where poets went to and must drink from to receive the poetical inspiration. The magical water of this place supplies poets with eloquence and makes them able to create images, metaphors, similes and other rhetorical devices. Similarly, in Roman and many European literatures, there are Muses, the goddess of poetry, these muses are responsible to urge poets and stimulate them to hover in the world of poetry. In fact, every poet has his own muse that he visits when finds himself unable to write anymore. In pre-Islam Arab literature, it can be found a similar idea of Pieren or muses theme, Arab believe strongly that the poet is someone has supernatural power because he is the tongue of the tribe who registers its deeds and fights with his poetry against the poets of other tribes. Actually, the Arabian tribes were in struggle against each other for domination the grassy prairies that their camels pasture in. Arabs of pre-Islamic era believe that there is a mysterious valley near Abqar Mountain in Yemen, every poet roams through this valley having the inspiration that helps him in his poetry. This study tries to find out the comparative aspect between the Caedomn's Hymn the first poem that is written by anonymous man called Caedmon in the old English period and Muallaqat written by the renowned poet, Imru Al-Qais in Arab Pre-Islamic period. In fact, Caedmon's Hymn and Arab poem are written in the close period. [ABSTRACT FROM AUTHOR]

Multi‐source domain adaptation (MDA) aims to leverage knowledge from multiple source domains to improve the classification performance on target domains. Different degrees of distribution discrepancies between every two domains pose a huge challenge to MDA tasks. Most works focus on extracting features shared by all domains, which is critical but not enough to reduce distribution discrepancies. In this paper, we propose a method named as common‐specific feature learning (CSFL). Constituting a framework of feature learning, CSFL explores a subspace where the combination of common and specific features makes learned representations comprehensive. Based on this framework, we conduct a metric learning method for learning a discriminative feature representation. Considering redundant information caused by source domains is likely to hurt the performance, we impose an effective low‐rank constraint to remove the redundant information. Further, we adopt structure consistent constraint to preserve the local structure in each domain. CSFL has obtained about 1–5% improvement of mean accuracy, compared to the state‐of‐the‐art shallow methods. Further, compared with 90.2% and 89.4% of the best baseline deep method, CSFL achieves mean accuracy of 90.8% and 89.7% on the Office‐31 and ImageCLEF‐DA datasets respectively. The encouraging results validate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Software product line (SPL) is one of the most recent and effective reuse approaches. SPL derives several products from the core artefacts. SPL engineering includes two processes: domain engineering, which identifies the common and variable features to develop the core artefacts, and application engineering, which reuses the core artefacts to derive products. Once the artefacts are reused across multiple products, quality assessment is necessary to prevent inconsistencies from spreading across all SPL products. There are several frameworks and standards, as ISO/IEC 25010:2011, to evaluate quality characteristics. In this study, the authors provide an overview of the SPL quality assessment. Therefore, they perform a systematic mapping to compile and synthesise data regarding the quality characteristics assessed in studies from 2000 to 2019. The results include the identification of 346 metrics applied in 16 software properties to evaluate three quality characteristics of the ISO/IEC 25010:2011. Additionally, they find the domain engineering evaluation frequently occurs regarding the maintainability characteristic. Moreover, they provide analyses of the: (i) metrics used by programming paradigm, (ii) metrics used by software properties, (iii) software properties evaluated for each quality characteristic, (iv) tools used to extract metrics, (v) systems used as benchmarks, and (vi) datasets used for extracting metrics. [ABSTRACT FROM AUTHOR]

This article identifies similarities among the first-person narratives of the Brontë sisters' mature fiction. It is suggested that the sisters had very close relationships with each other both within the family and in their professional lives as novelists. The latter reveal themselves in the shared types of images taken from the natural world, which draw on observation of the landscape around Haworth. These include the recurrence of animal, bird, plant and weather imagery. There are also other very different patterns of imagery, including that of slavery. Peculiar to the Brontë sisters' novels are the recurrence of physiognomic detail and their shared love of home. [ABSTRACT FROM AUTHOR]

Incorrect predictions or underestimation of a city's solar potential can result from neglecting common features of photovoltaic (PV) panels from remote sensing images. This paper proposes an improved approach to address the challenge of accurately segmenting PV panels from remote sensing images using deep learning methods. The proposed method incorporates common features of PV panels and a constraint refinement module (CRM) to perform the localization of PV panel regions and shape regularization more accurately. Specifically, the method uses a color loss function based on prior knowledge of color to refine the predicted region with correct color information among confusing objectives, and a shape loss function based on multi-layer shape targets calculation to refine the initial segments and constrain the edge information of predicted regions. Different CRMs are embedded into the four refined initial segment modules, respectively, to improve the detection IoU of PV panels by up to 7.44%. The best CRM-integrated model performs the best IoU of 74.66% when segmenting PV panels. The proposed method has important implications for urban PV panel segmentation at the city level and provides a promising solution for remote sensing image-based PV plate segmentation tasks in challenging scenarios. • We proposed an improved feature-oriented segmentation method to enhance the accuracy and contextual information of PV panels. • We incorporated common features of PV panels and CRM to perform PV panel region localization and shape regularization. • We designed a color constraint refinement module via prior knowledge to refine the predicted region among confusing targets. • We designed a shape constraint refinement module based on multi-layer shape targets to constrain the edge of predicted areas. [ABSTRACT FROM AUTHOR]

This paper examines the short and long-term co-movement of large emerging market economies -- the BRICS countries -- by applying the econometric techniques and the tests proposed in the common-feature literature. Despite their dissimilarities, given the rising trade linkages among the BRICS over the last 20 years one should expect their cycles to be synchronized. Our empirical findings fully support this hypothesis. The evidence holds also for the co-movement between the BRICS and developed economies, the US and the Eurozone, which may reflect the effects of globalization.

In this study, we evaluate the performance of five semantic search engines that are available on the web, using 45 criteria, in the form of a researcher-made checklist. Criteria provided in the checklist included both common and semantic features. Common criteria or features are those applicable to all search engines and semantic ones are those only applicable to semantic search engines. Findings show that the selected search engines do not have suitable performance and expected efficiency. DuckDuckGo, has the most points, considering regular features. Cluuz is in the second place with 20 points and Hakia with 18 points was in the third place. Lexxe and Factbites, with scores of 15 and 10 were placed in the next categories in order of their points. In semantic features, DuckDuckGo, with 10/65 points was in the first place. Hakia with 9/99 points was in the second place, and then the search engines Cluuz with 8/66 Points, Lexxe with 8/65 points and Factbites with 7/32 points were allocated to the next levels. The research results also indicated that on the whole, considering ordinary and semantic features, DuckDuckGo with 31/65 points, Cluuz with 28/66, Hakia with 27/99 points, Lexxe with 23/65 points and Factbites with 17/32 points, got the highest scores out of it.

We investigate whether combining forecasts from surveys of expectations is a helpful empirical strategy for forecasting inflation in Brazil. We employ the FGV-IBRE Economic Tendency Survey, which consists of monthly qualitative information from approximately 2000 consumers since 2006, and also the Focus Survey of the Central Bank of Brazil, with daily forecasts since 1999 from roughly 250 professional forecasters. Natural candidates to win a forecast competition in the literature of surveys of expectations are the (consensus) cross-sectional average forecasts (AF). We first show that these forecasts are a bias-ridden version of the conditional expectation of inflation using the no-bias tests proposed in Issler and Lima (J Econom 152(2):153-164, 2009) and Gaglianone and Issler (Microfounded forecasting, 2015). The results reveal interesting data features: Consumers systematically overestimate inflation (by 2.01 p.p., on average), whereas market agents underestimate it (by 0.68 p.p. over the same sample). Next, we employ a pseudo out-of-sample analysis to evaluate different forecasting methods: the AR(1) model, the Granger and Ramanathan (J Forecast 3:197-204, 1984) forecast combination (GR) technique, a Phillips-curve based method, the Capistrán and Timmermann (J Bus Econ Stat 27:428-440, 2009) combination method, the consensus forecast (AF), the bias-corrected average forecast (BCAF), and the extended BCAF. Results reveal that: (i) the MSE of the AR(1) model is higher compared to the GR (and usually lower compared to the AF); and (ii) the extended BCAF is more accurate than the BCAF, which, in turn, dominates the AF. This validates the view that the bias corrections are a useful device for forecasting using surveys. The Phillips-curve based method has a median performance in terms of MSE, and the Capistrán and Timmermann (2009) combination method fares slightly worse. [ABSTRACT FROM AUTHOR]

Introduction: Cerebrovascular accidents constitute the most common cause of disability all over the world. In India prevalence rate is 545 per 100000 and mortality rate is around 7.5 per thousand. Therefore the authors undertook a study on patients who underwent coronary artery bypass surgery (CABG). They were investigated for clinical and/or radiological evidence of cerebrovascular disease (CVD) with the aim to decide on early neurological intervention. Patients and Methods: 210 patients who had undergone CABG were evaluated with neck vessel Doppler using high resolution duplex scanner system and computed tomography (CT) scan as well as MR angiogram (MRA) in addition to assessment of co morbid risk factors. Results: 91% of patients who had undergone CABG had radiological evidence of CVD. The most common risk associated with CAD and CVD was Hypertension (HT), DM, dyslipidemia and combined HT and DM in that order. Neck vessels were normal in 59%. Significant disease was found in only 7.2%. Internal carotids were abnormal in 82%. Infarcts were seen in 82.1%. Discussion: This study reveals patients with CAD have a high degree of asymptomatic CVD. It is mostly due to small vessel disease including internal carotids but not so much with large vessels. Therefore, patients with CAD carry a high risk of vascular cognitive dysfunction. This can be reduced by effective management of the systemic risk factors. Screening for large vessel disease which is commonly done is likely to give a false sense of security. Conclusion: As against western population Indians seem to have small and medium sized vessels disease in the setting of CAD. [ABSTRACT FROM AUTHOR]

Identifying experts in a specific research field is an essential and practical task in academia and industry. Although some efforts have been attempted for expert finding, they rely on abundant text mining to reveal the target research topic accurately. Furthermore, it is time-consuming to collect and process these text materials for each candidate, and it is difficult to prepare a precise description for a topic that can cover all sub-topics. To this end, in this study, we develop a new expert finding model called TOpic-Sensitive representative Author identification (TOSA), to find representative authors in a specific research topic from a heterogeneous topic-insensitive academic network involving various topics, with a few well-known experts collected previously who are professional in the target topic. Specifically, we first learn the high-order relationships about authors in the heterogeneous academic network, which contain enough topic-related information. Next, an embedding space is found, where the collected experts are close to each other; in other words, the area they gather indicates the features about the collected authoritative experts. Finally, a prototype is calculated based on the collected experts in the embedding space, and we can rank other unlabeled authors by measuring the closeness between them and the prototype. Along this line, in addition to the collected experts in advance, more authoritative experts in the target topic will be identified. Extensive experiments on DBLP and ACM datasets demonstrate that the proposed TOSA model can identify experts who are professional in the query topic directly from a heterogeneous topic-insensitive academic network without text data processing, and its performance is significantly superior to the baseline models. • An expert finding task is reformulated based solely on academic network. • This task no longer requires the collection and processing of topic-related texts. • A novel expert finding model (TOSA) is proposed to solve the reformulated task. • TOSA can find experts based on only a few expertized experts collected in advance. • Experimental results on DBLP and ACM datasets show the superiority of TOSA. [ABSTRACT FROM AUTHOR]

The penetration of Western European culture at the end of the 19th century all over the world and especially in the Far Eastern realm was and still is an interesting topic for debates, comprising a large area, form accurate historical and sociological research until the most fanatical Europhobic scribbles. One of the most significant fields where this process is unfolding is musical culture. It is well-known that Western music was quickly and enthusiastically assimilated in the every-day life of Japanese and Koreans. Starting from the fruitful co-habitation between Western musical culture and Japanese musical tradition, the present essay proposes a discussion on the forms of cultural contamination and also aims at identifying some possible pre-existent features that might or might not facilitate the interpenetration of two distinct musical cultures. [ABSTRACT FROM AUTHOR]

This study investigates some of the common features of the exchange rate dynamics as an indicator of monetary policy coordination in selected Latin American countries-Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela. The empirical results indicate that these countries have long term common trends and do share short term common cycles in their exchange rates. While the results demonstrate a comovement in trend components, all seven countries do not exhibit synchronous comovement in cyclical components. In this direction, we find two groups-a group of five countries (Brazil, Chile, Colombia, Mexico and Peru) that demonstrate a strong comovement and a group of two countries (Argentine and Venezuela) that show just an opposite movement than that of the former group. Our results do not suggest any qualitative differences in the trajectory of common trend and common cycles when considering for different sub-periods. Taking both trend and cyclical behaviors into account our analysis does support for monetary policy coordination between Brazil, Chile, Columbia, Mexico and Peru. Relinquishing exchange rate independence might be costly for Argentina and Venezuela. [ABSTRACT FROM AUTHOR]

Drowsy/distracted driving has become one of the leading causes of traffic crash. Only certain particular drowsy/distracted driving behaviors have been studied by previous studies, which are mainly based on dedicated sensor devices such as bio and visual sensors. The objective of this study is to extract the common features for identifying drowsy/distracted driving through a set of common vehicle motion parameters. An intelligent vehicle was used to collect vehicle motion parameters. Fifty licensed drivers (37 males and 13 females, M = 32.5 years, SD = 6.2) were recruited to carry out road experiments in Wuhan, China and collecting vehicle motion data under four driving scenarios including talking, watching roadside, drinking and under the influence of drowsiness. For the first scenario, the drivers were exposed to a set of questions and asked to repeat a few sentences that had been proved valid in inducing driving distraction. Watching roadside, drinking and driving under drowsiness were assessed by an observer and self-reporting from the drivers. The common features of vehicle motions under four types of drowsy/distracted driving were analyzed using descriptive statistics and then Wilcoxon rank sum test. The results indicated that there was a significant difference of lateral acceleration rates and yaw rate acceleration between “normal driving” and drowsy/distracted driving. Study results also shown that, under drowsy/distracted driving, the lateral acceleration rates and yaw rate acceleration were significantly larger from the normal driving. The lateral acceleration rates were shown to suddenly increase or decrease by more than 2.0 m/s 3 and the yaw rate acceleration by more than 2.5°/s 2 . The standard deviation of acceleration rate (SDA) and standard deviation of yaw rate acceleration (SDY) were identified to as the common features of vehicle motion for distinguishing the drowsy/distracted driving from the normal driving. In order to identify a time window for effectively extracting the two common features, a double-window method was used and the optimized “Parent Window” and “Child Window” were found to be 55 s and 6 s, respectively. The study results can be used to develop a driving assistant system, which can warn drivers when any one of the four types of drowsy/distracted driving is detected. [ABSTRACT FROM AUTHOR]

This article introduces the concept of co-nonlinearity. Co-nonlinearity is an example of a common feature in time series [Engle, Robert F., and Sharon Kozicki. 1993. 'Testing for Common Features.' Journal of Business & Economic Statistics 11 (4): 369-380] and an extension of the concept of common nonlinear components [Anderson, Heather M., and Farshid Vahid. 1998. 'Testing Multiple Equation Systems for Common Nonlinear Components.' Journal of Econometrics 84 (1): 1-36]. If some time series follow a nonlinear process but where a linear relationship between the levels of these series removes the nonlinearity, such a relationship is defined as co-nonlinear. In this article I show how to determine the number of such co-nonlinear relationships. Furthermore, I show how to formulate hypothesis tests on the co-nonlinear relationships in a full maximum likelihood framework. The framework for identifying co-nonlinear relationships is illustrated in a system of Norwegian interest rates. [ABSTRACT FROM AUTHOR]

In this study, we evaluate the performance of five semantic search engines available on the web, using 45 criteria in the form of a researcher-made checklist. Criteria provided in the checklist include both common and semantic features. Common criteria or features are those applicable to all search engines and semantic ones are those applicable only to semantic search engines. Findings show that the selected search engines do not have suitable performance and expected efficiency. DuckDuckGo gained the highest points, considering regular features. Cluuz is in the second place with 20 points and Hakia with 18 points was in the third place. Lexxe and Factbites, with scores of 15 and 10 were placed in the next categories according to their points. In semantic features, DuckDuckGo with 10/65 points was in the first place; Hakia with 9/99 points was in the second place, and then the search engines Cluuz with 8/66 Points, Lexxe with 8/65 points and Factbites with 7/32 points were allocated to the next levels. The results also indicated that on the whole, considering ordinary and semantic features, DuckDuckGo with 31/65, Cluuz with 28/66, Hakia with 27/99, Lexxe with 23/65 and Factbites with 17/32 points got the highest scores. [ABSTRACT FROM AUTHOR]

In light of the long-standing vision of economic and monetary integration in the ASEAN (Association of Southeast Asian Nations) region and the importance of coordinating monetary policies to achieve it, the objective of this article is to assess the monetary policy synchronization among the founding members of the ASEAN, that is, Indonesia, Malaysia, the Philippines, Singapore and Thailand. Due to the importance of exchange rate movements to monetary policies, we approach this issue from a currency exchange rate perspective. Specifically, multivariate trend–cycle decomposition is employed to investigate common trends and common cycles among the exchange rates of these countries during the period 1976–2012. Our analysis reveals that the real exchange rates of Malaysia, the Philippines, Singapore and Thailand share common cycles in the short term and have common trends in the long term, but the Indonesian currency does not share these relationships. Thus, our results augur well for the synchronization of monetary policies among Malaysia, the Philippines, Singapore and Thailand. In contrast, the relatively turbulent dynamics of the Indonesian rupiah evident in frequent bouts of stark depreciation separated by periods of steady depreciation over the past three decades raise questions regarding the readiness of Indonesia for participating in a monetary alliance with the ASEAN-4 nations. [ABSTRACT FROM PUBLISHER]

This study explores the long- and short-run movement between oil prices and the real exchange rates of two large oil-exporting countries - Canada and Norway. Cointegration and serial correlation common features tests are jointly used to identify the long-term common trend and short-term common cycles. Our test results find that oil prices and the real exchange rates of the Canadian Dollar and the Norwegian Krone have two shared trends and one shared cycle. The trend-cycle decomposition shows a great deal of positive comovement among the trend and cyclical components. The two currencies show economic dynamics very similar to crude oil prices. They do not exhibit any qualitative differences in the trajectory of the trend and cycles when controlling for different crude oil prices. Our results indicate that oil price fluctuations play significant role in explaining the exchange rate movements of oil-exporting countries. [ABSTRACT FROM AUTHOR]

This paper proposes a test for common conditionally heteroskedastic (CH) features in asset returns. Following Engle and Kozicki (1993), the common CH features property is expressed in terms of testable overidentifying moment restrictions. However, as we show, these moment conditions have a degenerate Jacobian matrix at the true parameter value and therefore the standard asymptotic results of Hansen (1982) do not apply. We show in this context that Hansen's (1982) J-test statistic is asymptotically distributed as the minimum of the limit of a certain random process with a markedly nonstandard distribution. If two assets are considered, this asymptotic distribution is a fifty-fifty mixture of χ2 H−1 and χ2 H, where H is the number of moment conditions, as opposed to a χ2 H−1. With more than two assets, this distribution lies between the χ2 H− p and χ2 H ( p denotes the number of parameters). These results show that ignoring the lack of first-order identification of the moment condition model leads to oversized tests with a possibly increasing overrejection rate with the number of assets. A Monte Carlo study illustrates these findings. [ABSTRACT FROM AUTHOR]

Abstract: Time series analysis is a common tool in environmental and ecological studies to construct models to explain and forecast serially correlated data. There are several statistical techniques that are used to deal with univariate and multivariate (more than one series) chronological patterns of fisheries data. In this paper, an additive stochastic model is proposed with explicative and predictive features to capture the main seasonal patterns and trends of a fisheries system in the Amazon. The model is constructed on the assumption that the multivariate response variable – vector containing fishery yield of eight periodic species and the total fishery yield – can be decomposed into three terms: an autoregression of the response vector, an exogenous environmental variable (river level), and a seasonal component (significant frequencies obtained by using spectral analysis and the periodogram indicating the regularity of periodic cycles in the natural and fisheries system). The estimation procedure is carried out via maximum likelihood estimation. The model explained, on average, 78% of the variability in yield of the study species. The model represents the optimal solution (minimum mean square mean error) among the class of all multivariate autoregressive processes with exogenous and seasonal variables. Predictions for one period ahead are provided to illustrate how the model works in practice. [Copyright &y& Elsevier]

We propose a multivariate nonlinear econometric time series model, which can be used to examine if there is common nonlinearity across economic variables. The model is a multivariate censored latent effects autoregression. The key feature of this model is that nonlinearity appears as separate innovation-like variables. Common nonlinearity can then be easily defined as the presence of common innovations. We discuss representation, inference, estimation and diagnostics. We illustrate the model for US and Canadian unemployment and find that US innovation variables have an effect on Canadian unemployment, and not the other way around, and also that there is no common nonlinearity across the unemployment variables. [ABSTRACT FROM AUTHOR]

The 8p23.1 duplication syndrome is a relatively rare genomic condition that has been confirmed with molecular cytogenetic methods in only 11 probands and five family members. Here, we describe another prenatal and five postnatal patients with de novo 8p23.1 duplications analyzed with oligonucleotide array comparative genomic hybridization (oaCGH). Of the common features, mild or moderate developmental delays and/or learning difficulties have been found in 11/12 postnatal probands, a variable degree of mild dysmorphism in 8/12 and congenital heart disease (CHD) in 4/5 prenatal and 3/12 postnatal probands. Behavioral problems, cleft lip and/or palate, macrocephaly, and seizures were confirmed as additional features among the new patients, and novel features included neonatal respiratory distress, attention deficit hyperactivity disorder (ADHD), ocular anomalies, balance problems, hypotonia, and hydrocele. The core duplication of 3.68 Mb contains 31 genes and microRNAs of which only GATA4, TNKS, SOX7, and XKR6 are likely to be dosage sensitive genes and MIR124-1 and MIR598 have been implicated in neurocognitive phenotypes. A combination of the duplication of GATA4, SOX7, and related genes may account for the variable penetrance of CHD. Two of the duplications were maternal and intrachromosomal in origin with maternal heterozygosity for the common inversion between the repeats in 8p23.1. These additional patients and the absence of the 8p23.1 duplications in published controls, indicate that the 8p23.1 duplication syndrome may now be considered a pathogenic copy number variation (pCNV) with an estimated population prevalence of 1 in 58,000. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Relations / Medzinarodne Vztahy is the property of University of Economics in Bratislava, Faculty of International Relations and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper has three original contributions. The first is the reconstruction effort of the series of employment and income to allow the creation of a new coincident index for the Brazilian economic actiuity. The second is the construction of a coincident index of the economic activity for Brazil, and from it, (re) establish a chronology of recessions in the recent past of the Brazilian economy. The coincident index follows the methodology proposed by The Conference Board (TCB) and it covers the period 1980:1 to 2007:11. The third is the construction and evaluation of many leading indicators of economic activity for Brazil which fills an important gap in the Brazilian Business-Cycle literature. [ABSTRACT FROM AUTHOR]

Using a reduced rank regression framework as well as information criteria, we investigate the presence of commonalities in the intraday periodicity, a dominant feature in the return volatility of most intraday financial time series. We find that the test has little size distortion and reasonable power even in the presence of jumps. We also find that only three factors are needed to describe the intraday periodicity of 30 U.S. asset returns sampled at the 5-minute frequency. Interestingly, we find that for most series, the models imposing these commonalities deliver better forecasts of the conditional intraday variance than those where the intraday periodicity is estimated for each asset separately. [ABSTRACT FROM PUBLISHER]

Letter identification is a critical front end of the reading process. In general, conceptualizations of the identification process have emphasized arbitrary sets of distinctive features. However, a richer view of letter processing incorporates principles from the field of type design, including an emphasis on uniformities across letters within a font. The importance of uniformities is supported by a small body of research indicating that consistency of font increases letter identification efficiency. We review design concepts and the relevant literature, with the goal of stimulating further thinking about letter processing during reading. [ABSTRACT FROM AUTHOR]

In two studies, we show that features shared by products (common features) do not cancel out during the decision-making process but in fact are consequential as they decrease desire for delay in decision making. In study 1, we show that as the amount of available information about product features increases, decision delay decreases in spite of the additional information being identical across the products. Further, we also find that this effect is partially mediated by information adequacy. In study 2, we show that despite the overall difficulty of making decisions under avoidance-avoidance versus approach-approach conflict, an increase in common features decreases decision delay under both conflict conditions. [ABSTRACT FROM AUTHOR]

This paper examines the joint dynamics of a system of asset returns by describing and implementing a factor multivariate stochastic volatility (factor MSV) model. The foundation for the model discussed here is the work of Doz and Renault (2006). Despite its attractive design, that model has not been adopted widely in the literature, most likely due to the difficulty encountered in its implementation. The main contribution of this paper is to illustrate that this factor MSV model can be implemented easily and with only a few modifications. Specifically, I develop a sequential testing procedure that can account simultaneously for a series of nested hypotheses and structure properly the moment conditions used for estimation. A simulation study suggests that the factor MSV model and estimation strategy presented here is able to recover accurately the number of, and dynamics for, the latent factors that drive the conditional volatility of returns. [ABSTRACT FROM PUBLISHER]

We propose the multivariate representation of univariate and bivariate (possibly nonstationary) periodic models as a benchmark for the imposition of common periodic correlation (CPC) feature restrictions to obtain parameter parsimony. CPCs are short-run common dynamic features that co-vary across the different days of the week and possibly also across weeks and that can be common across different time series. We also show how periodic models can be used to describe interesting dynamic links in the interaction between stock and flow variables. We apply the proposed modeling framework to a dataset of daily arrivals and departures in airport transit data. [ABSTRACT FROM AUTHOR]