Pairwise Measures Research Articles

SISTA: Learning Optimal Transport Costs Under Sparsity Constraints

In this paper, we describe a novel iterative procedure called SISTA to learn the underlying cost in optimal transport problems. SISTA is a hybrid between two classical methods, coordinate descent ("S"-inkhorn) and proximal gradient descent ("ISTA"). It alternates between a phase of exact minimization over the transport potentials and a phase of proximal gradient descent over the parameters of the transport cost. We prove that this method converges linearly, and we illustrate on simulated examples that it is significantly faster than both coordinate descent and ISTA. We apply it to estimating a model of migration, which predicts the flow of migrants using country-specific characteristics and pairwise measures of dissimilarity between countries. This application demonstrates the effectiveness of machine learning in quantitative social sciences.

Aromatherapy Pillow Design Using QFD Method to Cure Pinched Nerve

The therapeutic hot pillow is a blend of science and ancient health techniques concoction that are Able to cure various ailments and maintain the body. Heat therapy can open blood vessels wider, thus increasing blood flow and supply of oxygen and nutrients to reduce pain in joints, muscles, and ligaments. Warm temperatures can also reduce the potential for muscle spasms. The specific purpose of this product design is to cure neck pain. The sub-issue of the Aromatherapy Pillow product / device specification will find a sub-solution with steps from Nigel Cross, Determining product characteristics with Quality Function Deployment (QFD). Evaluate alternatives by using Pairwise Comparison and AHP measurements to calculate the weights of each attribute and determine the ranking of each alternative. Value Engineering is the final step of the design process in order to increase of the value of the product for the consumer and reduce the cost to be paid by the manufacturer. Value engineering steps are to list the product components and identify the function of each component, determine the value of the identified function, calculate the cost of each component, find ways to reduce costs without reducing value, and Ultimately Evaluate alternatives.

Polarized Politics of Pandemic Response and the COVID-19 Connectedness Across the U.S. States

This paper investigates the state-level differences in government and community responses to the COVID-19 pandemic, leading to different growth trajectories of COVID-19 cases and their connectedness across the U.S. states. Our regression analysis shows that higher growth trajectories are observed in the states that implemented the lax government and community response to the pandemic. Moving to the analysis of spillovers/connectedness of COVID-19 cases across the states, we apply the Diebold-Yilmaz connectedness methodology to the growth rates of COVID-19 cases. Using the total directional connectedness measures, we find that the states with lax government and community response generated connectedness of COVID-19 cases to others. These findings are also supported by the secondary regression analysis of pairwise connectedness measures over time. Finally, the travel intensity between the pairs of states, indirectly measured by the data on smartphone location exposure, contributes significantly to the pairwise directional connectedness of COVID-19 across the states.

Late acceptance-based heuristic algorithms for identifying critical nodes of weighted graphs

Identifying critical nodes is an efficient way to analyze and apprehend the properties, structures, and functions of complex networks, which is a challenging NP-hard problem. This paper studies a node-weighted version of the critical node problem (NWCNP) that involves minimization of pairwise connectivity measure of a given node-weighted graph via the removal of a subset of nodes (i.e., critical nodes), subject to a budgetary constraint. In this paper, we present two effective iterated local search algorithms for NWCNP. Our proposed algorithms iterate through two complementary search procedures. A local search procedure adopting a constrained neighborhood and late acceptance strategy is employed to find a local optimum from a given starting solution. Then, a destructive–constructive perturbation procedure is used to escape from a local optimum. We conduct extensive computational experiments with two categories of 32 benchmark instances that reveal our proposed algorithms can achieve the best upper bounds for 28 instances and are highly competitive compared to baseline algorithm. In particular, our algorithms achieved better performance on the first category of instances with random weighting than that on the second category of instances with logarithmic weighting. We also investigate the influence of history length and perturbation coefficient on the performance of the proposed algorithms.

The Influence of Various Cerebral and Extracerebral Pathologies on Apparent Diffusion Coefficient Values in the Fetal Brain.

ABSTRACTBACKGROUND AND PURPOSEThe changing MRI signal accompanying brain maturation in fetal brains can be quantified on apparent diffusion coefficient (ADC) maps. Deviations from the natural course of ADC values may reflect structural pathology. The purpose of this study was to determine the influence of fetal pathologies on the ADC values in different regions of the fetal brain and their evolution with increasing gestational age.METHODSThis was a retrospective study of 291 fetuses evaluated between the 14th and the 40th week of gestation using diffusion‐weighted imaging (DWI). Fetuses with normal MRI findings but sonographically suspected pathology or fetuses with abnormalities not affecting the brain were analyzed in the control group and compared to fetuses suffering from different pathologies like hydrocephalus/ventriculomegaly, brain malformations, infections, ischemia/hemorrhage, diaphragmatic hernias, and congenital heart disease. Pairwise ADC measurements in each side of the white matter (WM) of the frontal, parietal, and occipital lobes, in the basal ganglia and the cerebellum, as well as a single measurement in the pons were performed and were plotted against gestational age.RESULTSIn the control group, brain maturation followed a defined gradient, resulting in lower ADC values in the most mature regions. Each disorder group experienced abnormal patterns of evolution of the ADC values over time deviating from the expected course.CONCLUSIONSThe ADC values in different regions of the fetal brain and their evolution with increasing gestational age are influenced by pathologies compromising the cerebral maturation.

Entanglement and dynamics of diffusion-annihilation processes with Majorana defects

Coupling a many-body system to a thermal environment typically destroys the quantum coherence of its state, leading to an effective classical dynamics at the longest time scales. We show that systems with anyon-like defects can exhibit universal late-time dynamics that is stochastic, but fundamentally non-classical, because some of the quantum information about the state is topologically protected from the environment. Our coarse-grained model describes one-dimensional systems with domain-wall defects carrying Majorana modes. These defects undergo Brownian motion due to coupling with a bath. Since the fermion parity of a given pair of defects is nonlocal, it cannot be measured by the bath until the two defects happen to come into contact. We examine how such a system anneals to zero temperature via the diffusion and pairwise annihilation of Majorana defects, and we characterize the nontrivial entanglement structure that arises in such stochastic processes. Separately, we also investigate simplified "quantum measurement circuits" in one or more dimensions, involving repeated pairwise measurement of fermion parities for a lattice of Majoranas. The dynamics of these circuits can be solved by exact mappings to classical loop models. They yield analytically tractable examples of measurement-induced phase transitions, with critical entanglement structures that are governed by nonunitary conformal fixed points. In the system of diffusing and annihilating Majorana defects, the relaxation to the ground state is analogous to coarsening in a classical 1D Ising model via domain wall annihilation. Here, however, configurations are labeled not only by the defect positions but by a nonlocal entanglement structure. The resulting dynamical process is a new universality class for the coarsening of topological domain walls, whose universal properties can be obtained from an exact mapping.

Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification

Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accurately along the geodesic path of the underlying data manifold by exploring the rich contextual information of the sample. In this paper, we propose a lightweight post-processing person re-identification method in which the pairwise measure is determined by the relationship between the sample and the counterpart's context in an unsupervised way. We translate the point-to-point comparison into the bilateral point-to-set comparison. The sample's context is composed of its neighbor samples with two different definition ways: the first order context and the second order context, which are used to compute the pairwise similarity in sequence, resulting in a progressive post-processing model. The experiments on four large-scale person re-identification benchmark datasets indicate that (1) the proposed method can consistently achieve higher accuracies by serving as a post-processing procedure after the content-based person re-identification methods, showing its state-of-the-art results, (2) the proposed lightweight method only needs about 6 milliseconds for optimizing the ranking results of one sample, showing its high-efficiency. Code is available at: https://github.com/123ci/PBCmodel .

Borrowing of information across patient subgroups in a basket trial based on distributional discrepancy.

SummaryBasket trials have emerged as a new class of efficient approaches in oncology to evaluate a new treatment in several patient subgroups simultaneously. In this article, we extend the key ideas to disease areas outside of oncology, developing a robust Bayesian methodology for randomized, placebo-controlled basket trials with a continuous endpoint to enable borrowing of information across subtrials with similar treatment effects. After adjusting for covariates, information from a complementary subtrial can be represented into a commensurate prior for the parameter that underpins the subtrial under consideration. We propose using distributional discrepancy to characterize the commensurability between subtrials for appropriate borrowing of information through a spike-and-slab prior, which is placed on the prior precision factor. When the basket trial has at least three subtrials, commensurate priors for point-to-point borrowing are combined into a marginal predictive prior, according to the weights transformed from the pairwise discrepancy measures. In this way, only information from subtrial(s) with the most commensurate treatment effect is leveraged. The marginal predictive prior is updated to a robust posterior by the contemporary subtrial data to inform decision making. Operating characteristics of the proposed methodology are evaluated through simulations motivated by a real basket trial in chronic diseases. The proposed methodology has advantages compared to other selected Bayesian analysis models, for (i) identifying the most commensurate source of information and (ii) gauging the degree of borrowing from specific subtrials. Numerical results also suggest that our methodology can improve the precision of estimates and, potentially, the statistical power for hypothesis testing.

Method comparison with repeated measurements — Passing–Bablok regression for grouped data with errors in both variables

Passing–Bablok regression relies on the slopes of the connecting lines between pairwise measurements. We introduce the Block–Passing–Bablok regression for grouped data, where repeated measurements with errors in both variables are available.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

Representational Rényi Heterogeneity.

A discrete system’s heterogeneity is measured by the Rényi heterogeneity family of indices (also known as Hill numbers or Hannah–Kay indices), whose units are the numbers equivalent. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational Rényi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the Rényi heterogeneity is both tractable and semantically relevant. This method requires neither a priori binning nor definition of a distance function on the observable space. We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices.

Entrainment2Vec: Embedding Entrainment for Multi-Party Dialogues

Entrainment is the propensity of speakers to begin behaving like one another in conversation. While most entrainment studies have focused on dyadic interactions, researchers have also started to investigate multi-party conversations. In these studies, multi-party entrainment has typically been estimated by averaging the pairs' entrainment values or by averaging individuals' entrainment to the group. While such multi-party measures utilize the strength of dyadic entrainment, they have not yet exploited different aspects of the dynamics of entrainment relations in multi-party groups. In this paper, utilizing an existing pairwise asymmetric entrainment measure, we propose a novel graph-based vector representation of multi-party entrainment that incorporates both strength and dynamics of pairwise entrainment relations. The proposed kernel approach and weakly-supervised representation learning method show promising results at the downstream task of predicting team outcomes. Also, examining the embedding, we found interesting information about the dynamics of the entrainment relations. For example, teams with more influential members have more process conflict.

Incorporating Discrete Constraints Into Random Walk-Based Graph Matching

Graph matching is a fundamental problem in theoretical computer science and artificial intelligence, and lays the foundation for many computer vision and machine learning tasks. Approximate algorithms are necessary for graph matching due to its NP-complete nature. Inspired by the usage in network-related tasks, random walk is generalized to graph matching as a type of approximate algorithm. However, it may be inappropriate for the previous random walk-based graph matching algorithms to utilize continuous techniques without considering the discrete property. In this paper, we propose a novel random walk-based graph matching algorithm by incorporating both continuous and discrete constraints in the optimization process. Specifically, after interpreting graph matching by random walk, the continuous constraints are directly embedded in the random walk constraint in each iteration. Further, both the assignment matrix (vector) and the pairwise similarity measure between graphs are iteratively updated according the discrete constraints, which automatically leads the continuous solution to the discrete domain. Comparisons on both synthetic and real-world data demonstrate the effectiveness of the proposed algorithm.

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop closure candidates, which results in less accurate trajectory estimates. This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters. DOOR-SLAM is based on peer-to-peer communication and does not require full connectivity among the robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined with a distributed pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures; and a distributed SLAM front-end that detects inter-robot loop closures without exchanging raw sensor data. The system has been evaluated in simulations, benchmarking datasets, and field experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM produces more inter-robot loop closures, successfully rejects outliers, and results in accurate trajectory estimates, while requiring low communication bandwidth. Full source code is available at https://github.com/MISTLab/DOOR-SLAM.git.

Nagaoka ferromagnetism observed in a quantum dot plaquette.

Engineered, highly controllable quantum systems are promising simulators of emergent physics beyond the simulation capabilities of classical computers1. An important problem in many-body physics is itinerant magnetism, which originates purely from long-range interactions of free electrons and whose existence in real systems has been debated for decades2,3. Here we use a quantum simulator consisting of a four-electron-site square plaquette of quantum dots4 to demonstrate Nagaoka ferromagnetism5. This form of itinerant magnetism has been rigorously studied theoretically6-9 but has remained unattainable in experiments. We load the plaquette with three electrons and demonstrate the predicted emergence of spontaneous ferromagnetic correlations through pairwise measurements of spin. We find that the ferromagnetic ground state is remarkably robust to engineered disorder in the on-site potentials and we can induce a transition to the low-spin state by changing the plaquette topology to an open chain. This demonstration of Nagaoka ferromagnetism highlights that quantum simulators can be used to study physical phenomena that have not yet been observed in any experimental system. The work also constitutes an important step towards large-scale quantum dot simulators of correlated electron systems.

To share or not to share: The importance of peer firm similarity to auditor choice

A firm’s decision on whether to choose the same auditor as a close competitor reflects a trade-off between exercising caution to protect its proprietary information and pursuing the benefits of auditor knowledge derived from providing services to comparable clients. We conduct a survey of audit partners whose responses confirm that clients weigh these competing incentives when selecting an external auditor. Building on our survey evidence from very experienced practitioners, we empirically analyze this phenomenon using archival data. Employing a pairwise similarity measure based on descriptions from regulatory filings, we find that, on average, peer firms are more likely to engage the same auditor when their product offerings are more similar. In instances when the focal firm shares the same auditor with more similar peer firms, the focal firm benefits from enhanced audit quality while also paying lower audit fees. Despite these benefits, we find that peer firms are less likely to share auditors with rivals in settings with greater perceived costs of information leakage as well as embedded auditor-client relationships in which the auditor retains deep client knowledge. Collectively, we provide evidence that, although the upside stemming from auditor knowledge, on average, dominates the downside of greater vulnerability to information leakage, proprietary cost concerns motivate firms to deviate from seeking auditor expertise.

Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness.

Interethnic variability in drug response arises from genetic differences associated with drug metabolism, action and transport. These genetic variations can affect drug efficacy as well as cause adverse drug reactions (ADRs). We retrieved drug-response related single nucleotide polymorphism (SNP) associated data from databases and analyzed to elucidate population specific distribution of 159 drug-response related SNPs in twenty six populations belonging to five super-populations (African, Admixed Americans, East Asian, European and South Asian). Significant interpopulation differences exist in the minor (variant) allele frequencies (MAFs), linkage disequilibrium (LD) and haplotype distributions among these populations. 65 of the drug-response related alleles, which are considered as minor (variant) in global population, are present as the major alleles (frequency ≥0.5) in at least one or more populations. Populations that belong to the same super-population have similar distribution pattern for majority of the variant alleles. These drug response related variant allele frequencies and their pairwise LD measure (r2) can clearly distinguish the populations in a way that correspond to the known evolutionary history of human and current geographic distributions, while D' cannot. The data presented here may aid in identifying drugs that are more appropriate and/or require pharmacogenetic testing in these populations. Our findings emphasize on the importance of distinct, ethnicity-specific clinical guidelines, especially for the African populations, to avoid ADRs and ensure effective drug treatment.

Soil and climate equally contribute to changes in the species compositions of Brazilian dry forests across 300 km

Abstract Aims Understanding the factors that control biodiversity distributions at different spatial scales has been a key challenge for conservation efforts. That biodiversity, reflected in differences in species compositions among sites (beta diversity), can be derived from species replacement (turnover) and is driven by multiple factors. Here, we sought to tackle this issue through two questions related to threatened Brazilian seasonally dry forests: (i) what is the contribution of species turnover to beta diversity? and (ii) which factors drive variations in species compositions among forest patches? Methods We sampled tree species and environmental variables (soils and climate) in 17 dry forest patches spaced almost 300 km apart. We used the beta diversity partitioning framework to determine the contribution of turnover. We used redundancy analysis, with properly controlled spatial structure, to assess the contributions of the environmental and spatial factors to the variations of the species composition. Important Findings Beta diversity among the patches was mainly represented by the turnover component (98.2%), with Simpson dissimilarity superior to other regions of the country (means of 0.89 and 0.71 in multiple site and pairwise measures, respectively). The environmental factors measured explained more than space, representing 30.3% of the variation of the species composition, of which 28.1% was nonspatially structured. We suggest that 300 km represents a threshold at which edaphic and climatic predictors have similar effects in determining community turnover (14.9% and 12.6%, respectively, without spatial structure). Thus, conservation strategies should be considered across landscapes to effectively protect tropical forest diversity, as even considering the different climatic aspects covered by the scale, landscaped edaphic varieties are important drivers of species turnover.

Robust Similarity-Based Concept Factorization for Data Representation

Non-negative matrix factorization (NMF) known as learnt parts-based representation has become a data analysis tool for clustering tasks. It provides an alternative learning paradigm to cope with non-negative data clustering. In this paradigm, concept factorization (CF) and symmetric non-negative factorization (SymNMF) are two typically important representative models. In general, they have distinct behaviors: in CF, each cluster is modeled as a linear combination of samples, and vice versa, i.e., sample reconstruction, while SymNMF built on pair-wise sample similarity measure, is to preserve similarity of samples in a low-dimensional subspace, namely similarity reconstruction. In this paper, we propose a similarity-based concept factorization (SCF) as a synthesis of the two behaviors. This design can be formulated as: the similarity of reconstructed samples by CF is close to that of original samples. To optimize it, we develop an optimization algorithm which leverages the alternating direction of multipliers (ADMM) method to solve each sub-problem of SCF. Besides, we take a further step to consider the robust issue of similarity reconstruction and explore a robust SCF model (RSCF), which penalizes the hardest pair-wise similarity reconstruction via $l_\infty $ . Thus, RSCF enjoys similarity preservation, robustness to similarity perturbation, and ability of reconstructing samples. Extensive experiments validate such properties and show that the proposed SCF and RSCF achieve large performance gains as compared to their counterparts.

Calibration-Free Indoor Positioning Using Crowdsourced Data and Multidimensional Scaling

Indoor positioning plays an important role in various location-based services (LBSs). In conventional systems, the process of constructing radio maps for positioning usually involves labor-intensive manual calibrations, which seriously limits the system’s scalability and adaptiveness. In this paper, we propose an efficient calibration-free method by leveraging on crowdsourced WiFi signal data that are captured passively through a WiFi sensing testbed. Since the ground truths of the crowdsourced data are unavailable, the radio maps cannot be directly constructed. In the proposed method, we adopt the multidimensional scaling (MDS) technique to compute the positions of the unlabeled data thereby generating radio maps. In order to enable MDS, we estimate the pairwise distances among the unlabeled data by using an improved trilateration method and a law of cosine (LoC)-based geometrical algorithm without online pairwise measurements. Experimental results show that the accuracy of the proposed method is higher than trilateration-based method and reasonably lower than that of calibration-based method. Meanwhile, the run time of the proposed method is shorter than previous optimization-based methods. The short run time allows the radio maps to be dynamically updated against the environmental variations.