Pairwise Approach Research Articles

Inferring Causal Interactions in Financial Markets Using Conditional Granger Causality Based on Quantile Regression

Granger causality analysis emerges as a typical method for inferring causal interactions in economics variables. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one economic variable to another and indirect ones acting through a third economic variable. In order to differentiate direct Granger causality from indirect one, a conditional Granger causality measure is derived based on the parametric dynamic quantile regression model. The proposed method can characterize the causal interactions on the entire distribution in more detail. Simulations are carried out to illustrate that the proposed tests have reasonable size and power against a variety of empirically plausible alternatives in finite samples. An economic application considers the causal relations between the stock trading volume, the stock returns, and the exchange rates for both domestic and cross-country markets by using the daily data of three financial markets: China, Japan, and South Korea. The results demonstrate that the causal effects of stock returns and stock trading volume that exert on the exchange rate are not direct at high quantile in China; besides, no direct causality is found running from stock returns to stock trading volume at high quantile in South Korea. In contrast to Granger causality in mean and Granger causality in quantile, our results provide a comprehensive and theoretically consistent extension of Granger causality to capture causal patterns in general multivariate systems, financial markets in particular.

A Pairwise Convex Hull Approach for Effective Representation of Uncertainty for System Analysis and Its Application to Power Grids

Several convex sets have been applied to characterize the uncertainty in power grids, such as the interval set, the budget-constrained set, the ellipsoidal set, and the convex hull. Of these sets, the convex hull (CH) is the smallest convex set that covers the raw data and is more compact than other convex sets. However, the task of constructing the convex hull becomes exponentially expensive for high-dimensional data and the number of hyperplanes required to represent the set is quite large. To cope with the challenge of high-dimensional data sets, a set of pairwise convex hulls (PW-CH) is proposed in this brief. The proposed pairwise convex hulls set is computationally efficient, since it is linearly expensive for high-dimensional data. It is then theoretically shown that the proposed pairwise convex hulls contain the original full convex hull (F-CH) and are contained in the box sets. In addition, it is numerically shown that the volume of the proposed pairwise convex hull is smaller than for other convex sets. The proposed pairwise convex hull is quite general and can be incorporated into other convex sets to improve their compactness. To illustrate its effectiveness, the proposed PW-CH is applied to electric power grids with renewable resources.

AN APPROACH AND A TOOL FOR MERGING A SET OF MODELS IN PAIRWISE WAY

Model merging addresses the problem of combining information from a set of models into a single one. This task is considered to be an important step in various software engineering practices. When many (more than two) models need to be merged, the most practiced technique is to perform the merge in a pairwise way, without considering the order of merging. The problem with this technique is that the resulting quality is not guaranteed because it is influenced by such an order. In this paper, we propose a pairwise approach for model merging aiming to provide better results by taking into account the order of merging. This approach proposes to combine models in an iterative process until obtaining only one model. At each iteration, we first compare each pair of models in order to measure the similarity between them and to identify the correspondences between their component elements. This is performed using two heuristic-based operators respectively named compare and match. After that, we identify the most similar pairs of models and merge them using a proposed operator. We have implemented our approach as tool support called 3M and evaluated it on a set of case studies.

Revenue allocation in Formula One: a pairwise comparison approach

ABSTRACT A model is proposed to allocate Formula One World Championship prize money among the constructors. The methodology is based on pairwise comparison matrices, allows for the use of any weighting method, and makes possible to tune the level of inequality. We introduce an axiom called scale invariance, which requires the ranking of the teams to be independent of the parameter controlling inequality. The eigenvector method is revealed to violate this condition in our dataset, while the row geometric mean method always satisfies it. The revenue allocation is not influenced by the arbitrary valuation given to the race prizes in the official points scoring system of Formula One and takes the intensity of pairwise preferences into account, contrary to the standard Condorcet method. Our approach can be used to share revenues among groups when group members are ranked several times.

Comparative Transcriptome Analysis Reveals Putative Genes Responsible for High Theacrine Content in Kucha (Camellia kucha (Chang et Wang) Chang)

The purine alkaloid theacrine, synthesized in a high amount in Kucha (Camellia kucha (Chang et Wang) Chang), has shown multiple pharmacological effects such as anti-depression, dehydration and hypnotic activities. It is critical to better understand the theacrine metabolism at the molecular level for future breeding programs. To identify the putative genes involved in theacrine biosynthesis, we carried out comparative transcriptome analysis between Kucha (Kucha-6 and Kucha-11) and conventional varieties (Yinghong 9 and Qingxin 1). HPLC showed that Kucha synthesized more than 27-fold theacrine in comparison to conventional varieties. A total of ~ 671.61 million high quality clean reads were yielded, and 71.42 ~ 76.87% of these were aligned to the reference genome for each library. We identified 19,095 differentially expressed genes (DEGs) by a pairwise approach, among which 1,142 common DEGs between Kucha and conventional varieties were screened. Functional annotation allowed the identified of 20 oxidoreductase (OR) genes and seven methyltransferase (MT) genes with enhanced expression in Kucha, which might be associated with theacrine diversity in the examined materials. Particularly, two N-methyltransferase-encoding genes (TEA010054 and TEA022559) may catalyze the final methylation step during theacrine synthesis in Kucha. The information generated will be helpful in identifying candidate genes and breeding tea plant with high theacrine content.

RBPR: A hybrid model for the new user cold start problem in recommender systems

The recommender systems aim to predict potential demands of users by analyzing their preferences and provide personalized recommendation services. User preferences can be inferred from explicit or implicit feedback data. Most existing collaborative filtering (CF) methods rely heavily on explicit feedback data. However, these methods perform poorly when rating data is sparse. In this paper, we deal with the extreme case of sparse data, i.e., the new user cold start problem. In order to overcome this problem, we propose a novel CF ranking model, which combines a rating-oriented approach of Probabilistic Matrix Factorization (PMF) and a pairwise ranking-oriented approach of Bayesian Personalized Ranking (BPR) together. Therefore, our proposed model makes full use of the explicit and implicit feedback data. Experiments on the constructed new user cold start datasets based on four public datasets demonstrate the effectiveness of the proposed model for cold start recommendation. Code for the proposed method is available in https://gitee.com/xia_zhaoqiang/recomender-systems-rbpr.

Patterns of phylogenetic beta diversity measured at deep evolutionary histories across geographical and ecological spaces for angiosperms in China

AbstractAimUnderstanding patterns and drivers of phylogenetic beta diversity in a region is important to understanding the origin and maintenance of the regional species diversity and variation in species diversity between local sites. Here we used a phylogenetic beta diversity metric quantifying deep evolutionary histories to explore geographical and ecological patterns of phylogenetic beta diversity in angiosperm assemblages across China.LocationChina.TaxonFlowering plants (angiosperms).MethodsChina was divided into 100 × 100 km grid cells. Species composition in each grid cell was documented. We used a basal‐weighted metric (Dpw) to quantify phylogenetic beta diversity among angiosperm assemblages with two sampling approaches (neighbourhood approach and pairwise approach). Dpw was related to latitude and climatic conditions of angiosperm assemblages and to geographical and climatic distances between angiosperm assemblages across China.ResultsWe found that the southeastern portion of China has much higher Dpw than the northwestern portion of China. The line of high versus low values of Dpw across China is generally consistent with the line of 500‐mm precipitation per year. Our study shows that precipitation is associated with Dpw more strongly than temperature, and Dpw decreases with latitude, particularly in the eastern part of China.Main conclusionsThe emergence of the observed pattern of basal‐weighted phylogenetic beta diversity is at least partly because the southeastern portion of China retains a large number of Tertiary relicts, making it a biogeographical museum, whereas many Tertiary relicts went extinct from the northwestern portion of China, particularly the Tibetan Plateau, due to the uplift of the plateau and the Himalayas, which makes the region a biogeographical grave for Tertiary relicts.

English

The aim of the study was to profoundly gain understanding into specific cultural values that underlie traditional practices which pose as barriers to rural girls’ education in Zambia. A pairwise case study approach was used to identify and describe types of cultural values prevalent in the Bemba and Tonga ethnic groups of Zambia. A total of 28 interviews (16 focus group discussions and 12 key informant interviews) were carried out with community members and school heads in Choma and Kasama, to verify specific proximate barriers associated with school-going girls. Thematic data analysis was performed using NVivo 12. The study identified six broad values that the two ethnic groups mutually desired. Security and protection, purity and propriety of a woman, conformity, respect, generosity and hard work. Conversely, power and possession, and unity were specific to Tongas and Bembas, respectively. The value of “unity” portrayed more impetus than that of “power and possession” to restrain girls from attending school. While both social groups indicated desire to uphold these values, modes of value expression and collective practices were clearly differentiated. The study established that specific practices and norms chocking rural girls’ education are performed through the process of cultural value activation. Further, the study demonstrates that cultural values have underlying associations with rural girls’ education advancement especially at higher ages. Gaining detailed knowledge of cultural values underpinning specific social groups through case study research is important in order to inform the design of more effective girls’ education promotion interventions. Key word: Cultural values, rural girls’ education, ethnic groups, traditional practices, norms, Bemba, Tonga.

Convergence and club convergence of CO2 emissions at state levels: A nonlinear analysis of the USA

This study analyses the convergence of CO2 emissions at state-level in the USA for the period from 1976 to 2014 in a nonlinear and novel empirical framework. In so doing, we have applied Pesaran’s (2007) test of pair-wise approach to testing convergence which gives in general what are the rejection frequencies and thus provides evidence of convergence. At the aggregate level, we also applied Chi-Young et al. (2006) half-life convergence test and the KPSS test with Fourier transformation which states are converging towards a cross-section average. Finally, we also adopted club convergence approach developed by Phillips and Sul (2007) to identify if the states are converging towards a club and last but not least, we applied Schnurbus et al., 2017 test to find if there is possible evidence if club merging. We make two contributions to the literature: (i) we conduct a country-specific analysis by focusing on the US; (ii) we consider both convergence and club convergence. Our overall results from the Pesaran’s (2007) pair-wise approach of convergence indicates that about 35% of the time the null of a unit root is rejected when ADF test is used and about 22% of the time null is rejected when ADF-GLS is used (irrespective of AIC or SBIC criterion). These results are also supported by KPSS stationary test which shows that null is rejected about 70–80% times. However, when Fourier function is incorporated in the KPSS test we find that the null hypothesis of stationarity is rejected only for Florida, Massachusetts, Montana, Nevada, New Mexico, Rhode Island, Texas indicating that only these states are non-convergent. Our overall results from club convergence (after club merging) show that USA states are forming 4 clubs. Our findings provide new insight into the convergence of CO2 emissions at the state level in the USA and thus have profound implications in terms of environmental policy setting and Per Capita Emission (PCE) allocations.

Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application*

AbstractMacroeconomic variables are weighted averages of a large number of components. Our objective is to model and forecast all of theNcomponents of a macro variable. The main feature of our proposal consists of discovering subsets of components that share single common trends while neither assuming pervasiveness nor imposing special restrictions on the serial or cross‐sectional idiosyncratic correlation. We adopt a pairwise approach and study its statistical properties. Our asymptotic theory works both with fixedNandT→∞ and with [T,N]→∞. We show that the pairwise approach can be implemented using three alternative strategies, which take into account alternative characteristics of the data generating process. The paper includes an application to the US CPI broken down into 159 components.

A pairwise pseudo‐likelihood approach for left‐truncated and interval‐censored data under the Cox model

Left truncation commonly occurs in many areas, and many methods have been proposed in the literature for the analysis of various types of left-truncated failure time data. For the situation, a common approach is to conduct the analysis conditional on truncation times, and the method is relatively simple but may not be efficient. In this paper, we discuss regression analysis of such data arising from the proportional hazards model that also suffer interval censoring. For the problem, a pairwise pseudo-likelihood approach is proposed that aims to recover some missing information in the conditional methods. The resulting estimator is shown to be consistent and asymptotically normal. A simulation study is conducted to assess the performance of the proposed method and suggests that it works well in practical situations and is indeed more efficient than the existing method. The approach is also applied to a set of real data arising from an AIDS cohortstudy.

Gender, Land and Food Access in Ghana’s Suburban Cities: A Case of the Adenta Municipality

The disparity in land and food access in Ghana often overlooks the possibility of an underlying gender disparity. This paper explores and interrogates the disparity between land and food access with respect to gender and the evolution of this relationship over the years as a result of the settlement expansion and urban growth within the Adenta Municipality in Ghana. Adopting a mixed pairwise approach of combining spatial analytical tools, vulnerability indexing and resilient indicators, the paper examines the levels and rates of land accessibilities within the stream of modern cities. It assesses the land market system complexities within developing economies and attempts to address the potential threats of gender-land access gaps. The paper finally assigns weights of ranks to model the phenomenon and recommends trends that can facilitate predictions and early cautionary systems for effective urban land governance in Ghana. The paper concludes that though it is noticed that women engage in power structures on a daily basis, this both benefits and burdens them, depending on their socio-cultural status and other factors in terms of access to land and food.

Partly Uncoupled Siamese Model for Change Detection from Heterogeneous Remote Sensing Imagery

This paper addresses the problematic of detecting changes in bitemporal heterogeneous remote sensing image pairs. In different disciplines, multimodality is the key solution for performance enhancement in a collaborative sensing context. Particularly, in remote sensing imagery there is still a research gap to fill with the multiplication of sensors, along with data sharing capabilities, and multitemporal data availability. This study is aiming to explore the multimodality in a multi-temporal set-up for a better understanding of the collaborative sensor wide information completion; we propose a pairwise learning approach consisting on a pseudo-Siamese network architecture based on two partly uncoupled parallel network streams. Each stream represents itself a Convolutional Neural Network (CNN) that encodes the input patches. The overall Change Detector (CD) model includes a fusion stage that concatenates the two encodings in a single multimodal feature representation which is then reduced to a lower dimension using fully connected layers and finally a loss function based on the binary cross entropy is used as a decision layer. The proposed pseudo-Siamese pairwise learning architecture allows to the CD model to capture the spatial and the temporal dependencies between multimodal input image pairs. The model processes the two multimodal input patches at one-time under different spatial resolutions. The evaluation performances on different real multimodal datasets reflecting a mixture of CD conditions with different spatial resolutions, confirm the effectiveness of the proposed CD architecture.

Patterns of plant species richness and growth form diversity in critical habitats of the Nama-Karoo Biome, South Africa

The distribution patterns and determinants of biodiversity in the Nama-Karoo Biome are poorly known. This poses a problem for conservation given the impact that future developments (e.g. uranium mining, fracking) and global climate change are likely to have on the region's biodiversity. This study investigated the potential role that several environmental and climatic variables play in determining species richness, relative cover, and growth form diversity in two habitat types: plains and rocky dolerite hillslopes. The study area extended over a 600 km longitudinal environmental gradient in the Upper Karoo in which mean annual precipitation varied from 100 to 600 mm from west to east. A pairwise floristic survey approach was implemented, using modified Whittaker plots in each habitat type at 30 sites within the shale gas exploration area in the Upper Karoo bioregion. The results showed that total species richness was significantly higher (p < 0.01) in slope habitats than in plains habitats across the environmental gradient. The greatest diversity in both habitat types in terms of species richness and relative cover was in the low woody shrubs growth form. Similar plant associations on slopes aggregated spatially and conformed closely to their respective vegetation types, while those in plains habitats were interspersed with no clear clustering. A combination of soil, environmental and climatic parameters emerged as significant explanatory variables for modelling total species richness and relative cover of the five dominant growth forms. Habitat type was a significant explanatory variable for total species richness and relative cover. In the current vegetation map of South Africa, the vegetation types for the Upper Karoo bioregion are coarse, giving the impression of homogeneity in what is a highly heterogeneous landscape. Further mapping of fine-scale biodiversity patterns is needed in order to better inform conservation and management practices in the Nama-Karoo Biome.

Responses of intra-annual runoff to forest recovery patterns in subtropical China

Forest recovery plays a critical role in regulating eco-hydrological processes in forested watersheds. However, characteristics of the intra-annual runoff variation associated with different forest recovery patterns remain poorly understood. In this study, three forest change periods were identified, the baseline period (1961–1985), reforestation period (1986 − 2000) and fruit tree planting period (2001–2016). We selected the magnitude of seasonal runoff (wet and dry seasons) and distribution characteristics, i.e., non-uniformity coefficient (Cv), complete accommodation coefficient (Cr), concentration ratio (Cn), concentration period (Cd), absolute variation ratio (ΔR) and relative variation ratio (Cmax). The pair-wise approach evaluated the intra-annual runoff variation characteristics between forest change periods. Results indicate that reforestation decreased wet season runoff and increased dry season runoff. In contrast, fruit tree planting increased wet season runoff and had no significant effect on dry season runoff. For intra-annual runoff distribution characteristics, reforestation significantly reduced the Cv, Cr, Cn and Cmax. Distribution of the intra-annual runoff in the fruit tree planting period was not significantly different from the baseline. We concluded that reforestation reduced the occurance of extreme water conditions in wet and dry seasons and effectively increased the stability of the intra-annual runoff. In contrast, fruit tree planting increased instability and fluctuation of the intra-annual runoff after reforestation. The characteristics of the intra-annual runoff to fruit tree planting was similar to those of the baseline. Therefore, adopting fruit tree planting practices to regulate intra-annual runoff characteristics may not be a practical approach, and impacts of different reforestation practices should be ascertained in our study region. The implications of this study should guide regional land–water management, and this study adds to the understanding of the impacts gained in forest cover on hydrology.

Decentralized Multi-Agent Path Finding for UAV Traffic Management

The development of a real-world Unmanned Aircraft System (UAS) Traffic Management (UTM) system to ensure the safe integration of Unmanned Aerial Vehicles (UAVs) in low altitude airspace, has recently generated novel research challenges. A key problem is the development of Pre-Flight Conflict Detection and Resolution (CDR) methods that provide collision-free flight paths to all UAVs before their takeoff. Such problem can be represented as a Multi-Agent Path Finding (MAPF) problem. Currently, most MAPF methods assume that the UTM system is a centralized entity in charge of CDR. However, recent discussions on UTM suggest that such centralized control might not be practical or desirable. Therefore, we explore Pre-Flight CDR methods where independent UAS Service Providers (UASSPs) with their own interests, communicate with each other to resolve conflicts among their UAV operations—without centralized UTM directives. We propose a novel MAPF model that supports the decentralized resolution of conflicts, whereby different ‘agents’, here UASSPs, manage their UAV operations. We present two approaches: (1) a prioritization approach and (2) a simple yet practical pairwise negotiation approach where UASSPs agents determine an agreement to solve conflicts between their UAV operations. We evaluate the performance of our proposed approaches with simulation scenarios based on a consultancy study of predicted UAV traffic for delivery services in Sendai, Japan, 2030. We demonstrate that our negotiation approach improves the “fairness” between UASSPs, i.e. the distribution of costs between UASSPs in terms of total delays and rejected operations due to replanning is more balanced when compared to the prioritization approach.

M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences

In this paper, we examine the two assumptions of the Bayesian personalized ranking (BPR), a well-known pair-wise method for one-class collaborative filtering (OCCF): (1) a user with the same degree of negative preferences for all her unrated items; and (2) a user always preferring her rated items to all her unrated items. We claim that (A1) and (A2) cause recommendation errors because they do not always hold in practice. To address these problems, we first define fine-grained multi-type pair-wise preferences (PPs), which are more sophisticated than the single-type PP used in BPR. Then, we propose a novel pair-wise approach called M-BPR, which exploits multi-type PPs together in learning users’ more detailed preferences. Furthermore, we refine M-BPR by employing the concept of item groups to reduce the uncertainty of a user’s a single item-level preference. Through extensive experiments using four real-life datasets, we demonstrate that our approach addresses the problems of the original BPR effectively and also outperforms seven state-of-the-art OCCF (i.e., four pair-wise and three point-wise) methods significantly.

A weak‐signal‐assisted procedure for variable selection and statistical inference with an informative subsample

AbstractThis paper is motivated from an HIV‐1 drug resistance study where we encounter three analytical challenges: to analyze data with an informative subsample, to take into account the weak signals, and to detect important signals and also conduct statistical inference. We start with an initial estimation method, which adopts a penalized pairwise conditional likelihood approach for variable selection. This initial estimator incorporates the informative subsample issue. To accounting for the effect of weak signals, we use a key idea of partial ridge regression. We also propose a one‐step estimation method for each of the signal coefficients and then construct confidence intervals accordingly. We apply the proposed method to the Stanford HIV‐1 drug resistance study and compare the results with existing approaches. We also conduct comprehensive simulation studies to demonstrate the superior performance of our proposed method.

Safety requirements for symbiotic human–robot collaboration systems in smart factories: a pairwise comparison approach to explore requirements dependencies

Industry 4.0 is expected to deliver significant productivity gain taking advantage of Internet of things (IoT). Smart solutions, enhanced by IoT, are constantly driving revolutionary approaches in multiple domains. Smart factories are one domain where intelligent integrated robotic systems will revolutionize manufacturing, resulting in a complex ecosystem, where humans, robots and machinery are combined. In this setting, human safety requirements are of paramount importance. This paper focuses on symbiotic human–robot collaboration systems (HRC), where human safety requirements are essential. Hence, it aims to explore and prioritize human safety requirement dependencies, as well as their dependencies with other critical requirements of smart factory operation, as effectiveness and performance. Toward this end, the proposed approach is based on SysML to represent the requirements dependencies and pairwise comparisons, a fundamental decision-making method, to quantify the importance of these dependencies. This model-driven approach is used as the primary medium for conveying traceability among human safety requirements as well as traceability from safety requirements to effectiveness and performance requirements in the system model. The analysis is based on the operational requirements identified in the European project HORSE, which aims to develop a methodological/technical framework for easy adaptation of robotic solutions from small-/medium-sized enterprises. Validation of the results is also performed to further elaborate on human safety requirement dependency exploration. The outcomes of this paper may be beneficial for symbiotic HRC systems in the early design stage. As the system is being developed with an emphasis on human safety, all these requirements that have been assessed with highly prioritized dependencies should be taken into account, whereas those with negligible ones have to be ignored since they do not significantly affect the rest of the process. Since operational requirements may be conflicted and incompatible, this approach may be very useful for other systems as well during the system design phase to find the appropriate solution satisfying the majority of the requirements, giving a priority to the ones with highly ranked dependencies and hence facilitating the implementation phase and afterward the production line. The outcomes may be used as a step in developing a model-driven approach which should be able to support the manufacturing process, facilitating the integration of systems and software modeling, which is increasingly important for robotic systems in smart factories incorporating HRC.

House price convergence in the euro zone: A pairwise approach

The adoption of the euro common currency was expected to lead to convergence for a number of economic and financial variables across national borders, including house prices. We apply a probabilistic pairwise approach to the question of whether home values converge across eight euro zone housing markets. Contrary to previous studies, we find only marginal evidence that euro housing markets converge. Moreover, for what convergence there appears to be, there is no evidence that the adoption of the euro itself played a role in creating such convergence. Finally, Germany, the largest and most dominant economy of the currency union, is rarely found to be convergent with other nations.