Pairwise Data Research Articles

Rapid size change associated with intra-island evolutionary radiation in extinct Caribbean \u201cisland-shrews\u201d

BackgroundThe Caribbean offers a unique opportunity to study evolutionary dynamics in insular mammals. However, the recent extinction of most Caribbean non-volant mammals has obstructed evolutionary studies, and poor DNA preservation associated with tropical environments means that very few ancient DNA sequences are available for extinct vertebrates known from the region’s Holocene subfossil record. The endemic Caribbean eulipotyphlan family Nesophontidae (“island-shrews”) became extinct ~ 500 years ago, and the taxonomic validity of many Nesophontes species and their wider evolutionary dynamics remain unclear. Here we use both morphometric and palaeogenomic methods to clarify the status and evolutionary history of Nesophontes species from Hispaniola, the second-largest Caribbean island.ResultsPrincipal component analysis of 65 Nesophontes mandibles from late Quaternary fossil sites across Hispaniola identified three non-overlapping morphometric clusters, providing statistical support for the existence of three size-differentiated Hispaniolan Nesophontes species. We were also able to extract and sequence ancient DNA from a ~ 750-year-old specimen of Nesophontes zamicrus, the smallest non-volant Caribbean mammal, including a whole-mitochondrial genome and partial nuclear genes. Nesophontes paramicrus (39-47 g) and N. zamicrus (~ 10 g) diverged recently during the Middle Pleistocene (mean estimated divergence = 0.699 Ma), comparable to the youngest species splits in Eulipotyphla and other mammal groups. Pairwise genetic distance values for N. paramicrus and N. zamicrus based on mitochondrial and nuclear genes are low, but fall within the range of comparative pairwise data for extant eulipotyphlan species-pairs.ConclusionsOur combined morphometric and palaeogenomic analyses provide evidence for multiple co-occurring species and rapid body size evolution in Hispaniolan Nesophontes, in contrast to patterns of genetic and morphometric differentiation seen in Hispaniola’s extant non-volant land mammals. Different components of Hispaniola’s mammal fauna have therefore exhibited drastically different rates of morphological evolution. Morphological evolution in Nesophontes is also rapid compared to patterns across the Eulipotyphla, and our study provides an important new example of rapid body size change in a small-bodied insular vertebrate lineage. The Caribbean was a hotspot for evolutionary diversification as well as preserving ancient biodiversity, and studying the surviving representatives of its mammal fauna is insufficient to reveal the evolutionary patterns and processes that generated regional diversity.

Cumuliform cloud formation control using parameter-predicting convolutional neural network

Physically-based cloud simulation is an effective approach for synthesizing realistic cloud. However, generating clouds with desired shapes requires a time-consuming process for selecting the appropriate simulation parameters. This paper addresses such a problem by solving an inverse cloud forming problem. We propose a convolutional neural network, which has the ability of solving nonlinear optimization problems, to estimate the spatiotemporal simulation parameters for given cloud images. The cloud formation process is then simulated by using computational fluid dynamics with these control parameters as initial states. The proposed parameter-predicting model consists of three components, including the feature extraction network, the adversarial network and the parameter generation network. These subnetworks form two parallel branches for different functionality-feature extraction and parameter estimation. To solve the challenge of estimating high-dimensional spatiotemporal simulation parameters, we adapt an encoder and decoder network to compress these parameters into a low-dimensional latent space. We train the proposed deep learning model with pairwise data of time series parameters and the corresponding synthetic images, which are rendered by the density fields of the synthesized clouds under different illuminations. In the practice, our method can simulate physically plausible cloud evolution processes and generate clouds with desired shapes for the real-world and synthetic images.

Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS

Transcriptional switch (TS) is a widely observed phenomenon caused by changes in the relative expression of transcripts from the same gene, in spatial, temporal or other dimensions. TS has been associated with human diseases, plant development and stress responses. Its investigation is often hampered by a lack of suitable tools allowing comprehensive and flexible TS analysis for high-throughput RNA sequencing (RNA-Seq) data. Here, we present deepTS, a user-friendly web-based implementation that enables a fully interactive, multifunctional identification, visualization and analysis of TS events for large-scale RNA-Seq datasets from pairwise, temporal and population experiments. deepTS offers rich functionality to streamline RNA-Seq-based TS analysis for both model and non-model organisms and for those with or without reference transcriptome. The presented case studies highlight the capabilities of deepTS and demonstrate its potential for the transcriptome-wide TS analysis of pairwise, temporal and population RNA-Seq data. We believe deepTS will help research groups, regardless of their informatics expertise, perform accessible, reproducible and collaborative TS analyses of large-scale RNA-Seq data.

Fuzzy DEMATEL analysis of barriers to Blockchain-based life cycle assessment in China

The emerging Blockchain technology can drastically improve the effectiveness and efficiency of life cycle assessment, which is widely used for assessing the environmental impact of products and processes. However, Blockchain adoption is impeded by various barriers including systems-related, external, intra-organizational and inter-organizational barriers. So far, no research has analyzed how these barriers interact with each other for better decision-making in life cycle assessment. This research narrows the knowledge gap by prioritizing the important barriers using a fuzzy Decision-Making Trial and Evaluation of Laboratory (DEMATEL) method. Pairwise comparison data were collected from three representative organizations in China, which all have Blockchain implementation experiences. The results show that the key cause barriers are immaturity of technology, and technical challenges for collecting supply chain data in real time. The prominent barriers include lack of new organizational policies for using technology, and lack of government policy/regulation guidance and support, among others. Managerial implications are discussed based on the results and findings.

Unsupervised nonlinear adaptive manifold learning for global and local information

In this paper, we propose an Unsupervised Nonlinear Adaptive Manifold Learning method (UNAML) that considers both global and local information. In this approach, we apply unlabeled training samples to study nonlinear manifold features, while considering global pairwise distances and maintaining local topology structure. Our method aims at minimizing global pairwise data distance errors as well as local structural errors. In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data, we add a feature approximate error that can be used to learn a linear extractor. Also, we add a feature approximate error that can be used to learn a linear extractor. In addition, we use a method of adaptive neighbor selection to calculate local structural errors. This paper uses the kernel matrix method to optimize the original algorithm. Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

Search and rescue under the forest canopy using multiple UAVs

We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared with the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.

Prioritizing Factors Affecting Health Service Quality using Integrated Fuzzy DEMATEL and ANP: A Case of Iran

Background:Quality of services is one of the important factors affecting the success of service organizations. However, due to limited resources, organizations should identify the most important dimensions of service quality and take some measures to improve them.Objective:To identify and prioritize the factors affecting service quality from the viewpoints of all health care stakeholders in the teaching hospitals and the headquarters affiliated to Shiraz University of Medical Sciences.Methods:This is an applied, cross-sectional and descriptive-analytical study conducted in the teaching hospitals and the headquarters affiliated to Shiraz University of Medical Sciences. The study population consisted of all health care stakeholders, including patients, patients' families and relatives, general population, health care policymakers and managers, and health service providers such as matrons and supervisors. Six people of each studied stakeholder group were selected as the study sample using a purposive sampling method (n=30). A pair-wise comparison matrix designed based on the SERVQUAL dimensions was used for making pair-wise comparisons and collecting the required data. The collected pair-wise comparison data were analyzed through Fuzzy DEMATEL and ANP techniques using Super Decision 2.2 and Excel 2007.Results:The results showed that Access (NW=0.39018) and Tangibility (NW=0.05873) had the highest and lowest priorities.Conclusion:According to the results of the present study and from the studied health care stakeholders' viewpoint, access to care was the most affecting and most important dimension to be improved. Therefore, in order to improve patients' access to the health care services, some suggestions can be made such as providing appropriate facilities for patients' families and relatives, offering special discounts to the poor, providing easier access to the emergency entrance, providing parking and accommodation for patients and their relatives, employing and using the appropriate number of staff, and delivering 24-hour and high-quality health care services.

Modality-specific matrix factorization hashing for cross-modal retrieval

Cross-modal retrieval has been attracted attentively in the past years. Recently, the collective matrix factorization was proposed to learn the common representations for cross-modal retrieval based on assumption that the pairwise data from different modalities should have the same common semantic representations. However, this unified common representation could inherently sacrifice the modality-specific representations for each modality because the distributions and representations of different modalities are inconsistent. To mitigate this problem, in this paper, we propose Modality-specific Matrix Factorization Hashing (MsMFH) via alignment, which learns the modality-specific semantic representation for each modality and then aligns the representations via the correlation information. Specifically, we factorize the original feature representations into individual latent semantic representations, and then align the distributions of individual latent semantic representations via an orthogonal transformation. Then, we embed the class label into the hash codes learning via latent semantic space, and obtain hash codes directly by an efficient optimization with a closed solution. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms to many existing cross-modal hashing methods up to 3% in term of mean average precision (mAP).

Incorporating side information into Robust Matrix Factorization with Bayesian Quantile Regression

Matrix Factorization is a widely used technique for modeling pairwise and matrix-like data. It is frequently used in pattern recognition, topic analysis and other areas. Side information is often available, however utilization of this additional information is problematic in the pure matrix factorization framework. This article proposes a novel method of utilizing side information by combining arbitrary nonlinear Quantile Regression model and Matrix Factorization under Bayesian framework. Gradient-free optimization procedure with the novel Surrogate Function is used to solve the resulting MAP estimator. The model performance has been evaluated on real data-sets.

SMAN: Stacked Multimodal Attention Network for Cross-Modal Image–Text Retrieval

This article focuses on tackling the task of the cross-modal image-text retrieval which has been an interdisciplinary topic in both computer vision and natural language processing communities. Existing global representation alignment-based methods fail to pinpoint the semantically meaningful portion of images and texts, while the local representation alignment schemes suffer from the huge computational burden for aggregating the similarity of visual fragments and textual words exhaustively. In this article, we propose a stacked multimodal attention network (SMAN) that makes use of the stacked multimodal attention mechanism to exploit the fine-grained interdependencies between image and text, thereby mapping the aggregation of attentive fragments into a common space for measuring cross-modal similarity. Specifically, we sequentially employ intramodal information and multimodal information as guidance to perform multiple-step attention reasoning so that the fine-grained correlation between image and text can be modeled. As a consequence, we are capable of discovering the semantically meaningful visual regions or words in a sentence which contributes to measuring the cross-modal similarity in a more precise manner. Moreover, we present a novel bidirectional ranking loss that enforces the distance among pairwise multimodal instances to be closer. Doing so allows us to make full use of pairwise supervised information to preserve the manifold structure of heterogeneous pairwise data. Extensive experiments on two benchmark datasets demonstrate that our SMAN consistently yields competitive performance compared to state-of-the-art methods.

Combination of Classifiers With Different Frames of Discernment Based on Belief Functions

Classifier fusion remains an effective method to improve classification performance. In applications, the classifiers learnt using different attributes may work with various frames of discernment (FoD) of classification. There generally exist more or less complementary knowledge among these classifiers. However, how to efficiently combine such classifiers under different FoD is a challenging problem. In this article, we propose a new method for classifier fusion with different FoD based on the belief functions, which allow to well represent and deal with uncertain information. The credal transformation rules are developed to map the various FoD into a common one. It allows to transfer the probability (or mass of belief) of one class in the given FoD not only to several singleton classes but also to the metaclasses (i.e., disjunction of several classes) and the ignorance in other chosen FoD according to a transformation matrix, which is estimated based on the training (pairwise) data by minimizing a certain error criteria. Thus, we can well characterize the uncertainty and imprecision during the transformation of FoD. After that, the outputs of different classifiers represented by basic belief assignments (BBAs) can be transformed to a common FoD. Then, the well-known Dempster's rule is employed to combine these transformed BBA to obtain final classification result under the chosen FoD. Several real data sets are used in the experiment to evaluate the performance of the proposed method. Our experimental results show that this new method can efficiently improve the classification accuracy with respect to other related methods.

Worst-case versus average-case design for estimation from partial pairwise comparisons

Pairwise comparison data arises in many domains, including tournament rankings, web search and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We also consider the noisy sorting subclass of the SST model. We show that when the assignment of items to the topology is arbitrary, these permutation-based models, unlike their parametric counterparts, do not admit consistent estimation for most comparison topologies used in practice. We then demonstrate that consistent estimation is possible when the assignment of items to the topology is randomized, thus establishing a dichotomy between worst-case and average-case designs. We propose two computationally efficient estimators in the average-case setting and analyze their risk, showing that it depends on the comparison topology only through the degree sequence of the topology. We also provide explicit classes of graphs for which the rates achieved by these estimators are optimal. Our results are corroborated by simulations on multiple comparison topologies.

Bootstrapping quantum process tomography via a perturbative ansatz

Quantum process tomography has become increasingly critical as the need grows for robust verification and validation of candidate quantum processors, since it plays a key role in both performance assessment and debugging. However, as these processors grow in size, standard process tomography becomes an almost impossible task. Here, we present an approach for efficient quantum process tomography that uses a physically motivated ansatz for an unknown quantum process. Our ansatz bootstraps to an effective description for an unknown process on a multi-qubit processor from pairwise two-qubit tomographic data. Further, our approach can inherit insensitivity to system preparation and measurement error from the two-qubit tomography scheme. We benchmark our approach using numerical simulation of noisy three-qubit gates, and show that it produces highly accurate characterizations of quantum processes. Further, we demonstrate our approach experimentally on a superconducting quantum processor, building three-qubit gate reconstructions from two-qubit tomographic data.

Secure Metric Learning via Differential Pairwise Privacy

Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of individuals given a set of correctly labeled pairwise data by domain experts. These important and precisely-labeled pairwise data are often highly sensitive in real world (e.g., patients similarity). This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning. Unlike traditional differential privacy which only applies to independent samples, thus cannot be used for pairwise data, DPP successfully deals with this problem by reformulating the worst case. Specifically, given the pairwise data, we reveal all the involved correlations among pairs in the constructed undirected graph. DPP is then formalized that defines what kind of DML algorithm is private to preserve pairwise data. After that, a case study employing the contrastive loss is exhibited to clarify the details of implementing a DPP- DML algorithm. Particularly, the sensitivity reduction technique is proposed to enhance the utility of the output distance metric. Experiments both on a toy dataset and benchmarks demonstrate that the proposed scheme achieves pairwise data privacy without compromising the output performance much (Accuracy declines less than 0.01 throughout all benchmark datasets when the privacy budget is set at 4).

Multilevel Optimization for Registration of Deformable Point Clouds.

Handling deformation is one of the biggest challenges associated with point cloud registration. When deformation happens due to the motion of an animated object which actively changes its location and general shape, registration of two instances of the same object turns out to be a challenging task. The focus of this work is to address the problem by leveraging the complementary attributes of local and global geometric structures of the point clouds. We define an energy function which consists of local and global terms, as well as a semi-local term to model the intermediate level geometry of the point cloud. The local energy estimates the transformation parameters at the lowest level by assuming a reduced deformation model. The parameters are estimated in a closed form solution, which are then used to assign the initial probability of a stochastic model working at the intermediate level. The global energy term estimates the overall transformation parameters by minimizing a nonlinear least square function via Gauss-Newton optimization framework. The total energy is optimized in a block coordinate descent fashion, updating one term at a time while keeping others constant. Experiments on three publicly available datasets show that the method performs significantly better than several state-of-the-art algorithms in registering pairwise point cloud data.

Learning Domain-Invariant Discriminative Features for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR), referring to matching face images across different domains, is a challenging problem due to the vast cross-domain discrepancy and insufficient pairwise cross-domain training data. This article proposes a quadruplet framework for learning domain-invariant discriminative features (DIDF) for HFR, which integrates domain-level and class-level alignment in one unified network. The domain-level alignment reduces the cross-domain distribution discrepancy. The class-level alignment based on a special quadruplet loss is developed to further diminish the intra-class variations and enlarge the inter-class separability among instances, thus handling the misalignment and adversarial equilibrium problems confronted by the domain-level alignment. With a bidirectional cross-domain data selection strategy, the quadruplet loss-based method prominently enriches the training set and further eliminates the cross-modality shift. Benefiting from the joint supervision and mutual reinforcement of these two components, the domain invariance and class discrimination of identity features are guaranteed. Extensive experiments on the challenging CASIA NIR-VIS 2.0 database, the Oulu-CASIA NIR&VIS database, the BUAA-VisNir database, and the IIIT-D viewed sketch database demonstrate the effectiveness and preferable generalization capability of the proposed method.

Broad Graph-Based Non-Negative Robust Continuous Clustering

Recently, the robust continuous clustering (RCC) has been proposed for unsupervised data classification. The RCC algorithm integrates representation learning and clustering by seeking the balance of the distance of data between intra-cluster and inter-cluster. But the inter-cluster distance in RCC highly depend on the pairwise graph of neighbors, which are constructed by the less-expressive original data. This hampers the performance of RCC on complex epically high-dimensional data. Encouraged by the hybrid feature learning and universal approximation capabilities of the broad learning system (BLS), we first propose a broad graph-based robust continuous clustering algorithm to upgrade RCC. The proposed algorithm measures the distance of the pairwise data with the feature learned from the BLS when constructing the graph. Then to further enhance the clustering performance of the RCC on high-dimensional data, we embed the non-negative matrix factorization (NMF) into the broad graph-based RCC algorithm. By resolving the original data into the basis matrix and coefficient matrix and performing the broad graph-based RCC on the coefficient matrix, the proposed approach takes full advantages of the abundant representation from the BLS and the sparsity and interpretability of NMF coefficients. We verified the proposed algorithms on the synthetic dataset, the UCI dataset and some real-world high dimension datasets. All the empirical results show the proposed algorithms outperform the baselines and improve the clustering performance of RCC effectively.

Sparse Partial Least Squares Methods for Joint Modular Pattern Discovery.

The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. However, how to identify the multiple-to-multiple relationships between genomic factors and drug response among pharmacogenomics data is still a challenging issue. Here, we introduce a sparse partial least squares (SPLS) framework with or without the network-regularized penalty to identify joint modular patterns demonstrated with a large-scale pairwise gene-expression and drug-response data. The identified modular patterns reveal some coordinated gene-drug associations. SPLS methods could be applied to many biological problems such as the eQTL analysis, which is designed to discover genetic variants that influence downstream gene expression level. In summary, SPLS-based methods are a set of powerful tools to uncover the associations between different types of features.

Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data.

With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms. In this work, we applied a graph-based semi-supervised learning (GSSL) to classify a severe chronic mental disorder, schizophrenia (SZ). An advantage of GSSL is that it can simultaneously analyse more than two types of data, while many existing models focus on pairwise data analysis. In particular, we applied GSSL to the analysis of single nucleotide polymorphism (SNP), functional magnetic resonance imaging (fMRI) and DNA methylation data, which accounts for genetics, brain imaging (endophenotypes), and environmental factors (epigenomics) respectively. While parameter selection has been an open challenge for most models, another key contribution of this work is that we explored the parameter space to interpret their meaning and established practical guidelines. Based on the practical significance of each hyper-parameter, a relatively small range of candidate values can be determined in a data-driven way to both optimize and speed up the parameter tuning process. We validated the model through both synthetic data and a real SZ dataset of 184 subjects from the Mental Illness and Neuroscience Discovery (MIND) Clinical Imaging Consortium. In comparison to several existing approaches, our algorithm achieved better performance in terms of classification accuracy. We also confirmed the significance of several brain regions associated with SZ.

Learning to Rank in Entity Relationship Graphs

Many real-world data sets are modeled as entity relationship graphs or heterogeneous information networks. In these graphs, nodes represent entities and edges mimic relationships. ObjectRank extends the well-known PageRank authority flow–based ranking method to entity relationship graphs using an authority flow weight vector (W). The vector W assigns a different authority flow–based importance (weight) to each edge type based on domain knowledge or personalization. In this paper, our contribution is a framework for Learning to Rank in entity relationship graphs to learn W, in the context of authority flow. We show that the problem is similar to learning a recursive scoring function. We present a two-phase iterative solution and multiple variants of learning. In pointwise learning, we learn W, and hence the scoring function, from the scores of a sample of nodes. In pairwise learning, we learn W from given preferences for pairs of nodes. To demonstrate our contribution in a real setting, we apply our framework to learn the rank, with high accuracy, for a real-world challenge of predicting future citations in a bibliographic archive—that is, the FutureRank score. Our extensive experiments show that with a small amount of training data, and a limited number of iterations, our Learning to Rank approach learns W with high accuracy. Learning works well with pairwise training data in large graphs.