Kinds Of Distances Research Articles

Quantum algorithm for computing distances between subspaces

Geometry and topology have generated impacts far beyond their pure mathematical primitive, providing a solid foundation for many applicable tools. Typically, real-world data are represented as vectors, forming a linear subspace for a given data collection. Computing distances between different subspaces is generally a computationally challenging problem with both theoretical and applicable consequences, as, for example, the results can be used to classify data from different categories. Fueled by the fast-growing development of quantum algorithms, we consider such problems in the quantum context and provide a quantum algorithm for estimating two kinds of distance: Grassmann distance and ellipsoid distance. Under appropriate assumptions and conditions, the speedup of our quantum algorithm is exponential with respect to both the dimension of the given data and the number of data points. Some extensions regarding estimating different kinds of distance are then discussed as a corollary of our main quantum algorithmic method.

Part VII–Critiquing Africas/Diasporas: Intersecting Dialogues—In Conversation with Claire Diao, Leyla Bouzid, Mati Diop, and Cornélia Glele

Abstract: Journalist/film critic Falila Gbadamassi, in conversation with several film practitioners and activists, discovers the myriad interests, concerns, and burning issues that propel their desire to plunge into this vast cinematic landscape. Franco-Burkinabe film journalist, programmer, and activist Claire Diao explores the role that cinema plays as a link between her two cultures. Franco-Tunisian Leyla Bouzid, talks about her film A Tale of Love and Desire (2021, Tunisia) in the context of her own lived experiences, to have a double culture and at the same time have a kind of distance, to be able to question these identities and interrogate representations of it. Franco-Senegalese Mati Diop's relationship to cinema is intertwined with her Africanity. At the same time that her films pay homage to the generation of her uncle Djibril Diop Mambety and her father Wasis Diop, she continues her cinematic journey in her own way, using the tools of her generation. Beninese film organizer and consultant Cornélia Glele navigates behind the scenes with The Woman King (dir. Gina Prince-Bythewood, 2022, United States and Benin) team as she teaches the actresses the fundamentals of the Fon language of the "Agodjiés" of Benin.

Cultural versus demic diffusion in agricultural expansions according to three definitions of dispersal distances

Mathematical models of agricultural spread use distances between birthplaces of parents and their children (often called “birthplace distances”). However, the difficulty to find those distances for pre-industrial farmers has often led to the use of other kinds of distances. One example is the distance between the birthplace and the place of residence of each individual (“residence distances”). Another example is the separation between the birthplaces of parents (“mating distances”). It is poorly known to what extent the latter two distances are valid approximations to birthplace distances. In order to address this question, we have prepared a database with the three distances for a specific pre-industrial agricultural population (the Yanomamö). For the spread of the Neolithic in Europe, all three kinds of distances yield spread rates consistent with the archaeological data, as well as cultural effects below 50% (so demic diffusion was more important than cultural diffusion). The three kinds of distances also yield estimations for the percentage of early farmers who interbred with hunter-gatherers that are consistent with the corresponding estimation from genetics. There is wide agreement for the cultural and demic effects in other expansions of agriculture and/or herding (the spread of the Neolithic in Asia, Bantu, and Khoikhoi expansions; the spread of rice in Asia, etc.) and using distances measured for other populations. We conclude that estimates are largely insensitive to the kind of distances used. This implies that the conclusions drawn so far in the literature using these three kinds of distances are robust.

설교에서 거리 문제: 미적 거리의 관점에서

This paper aims to investigate distance in preaching in terms of aesthetic distance. To begin with, the author examines the discussions of the distance in preaching by Fred Craddock and by contemporary North American homileticians who developed Craddock’s idea of distance in preaching further by applying aesthetic distance. Craddock pointed out that there are three kinds of distance in preaching according to the twin task of interpretation in preaching: 1) Distance between the text and preacher, 2) Distance between the text and listener, and 3) Distance between preacher and listener. Craddock claimed that these distances are overcomethrough a dual hermeneutical process that is constructed by distance and participation in and through hearing the sermon inductively, namely overhearing. Particularly, he argued that the distance between the text and listener, as well as the distance between preacher and listener, should be maintained to protect the listener’s autonomous and independent participation in the preaching event.<BR> A group of scholars of performance homiletics, including Charles Bartow, Jana Childers, and Michael Brothers, developed Craddock’s theory further by applying aesthetic distance, a concept of aesthetic experience in performance theory. These scholars approach hearer’s experience in preaching, which Craddock described as overhearing, as an aesthetic experience, and understand distance in preaching as aesthetic distance. Their discussions of distance in preaching contribute to understanding of the listener as an interpretive subject, equal to the preacher, and comprehending that the experience of the gospel is constructed through the listener’s autonomous participation. The author points out that, despite its benefits, these scholars’ understanding of the experience of the gospel in preaching is too anthropocentric, and argues that God should be considered as the ultimate subject of such experience in preaching as a revelatory event.

Trophy Hunters: War Writing in the Twentieth Century

Trophy Hunters: War Writing in the Twentieth Century

The Generalized Bregman Distance

Recently, a new kind of distance has been introduced for the graphs of two point-to-set operators, one of which is maximally monotone. When both operators are the subdifferential of a proper lower ...

Genome as a Functional Program

We discuss a model of genome as a program with functional architecture and consider the approach to Darwinian evolution as a learning problem for functional programming. In particular we introduce a model of learning for some class of functional programs. This approach is related to information geometry -- the learning model uses some kind of distance in the information space (the reduction graph of the model), we consider statistical sum over paths in the reduction graph and discuss relation of this sum to temperature learning.

Research on thermal response of pool fire of oil tank based on PyroSim

This paper presents a numerical investigation aimed at studying the thermal response of adjacent tank caused by different fire conditions. The analysis involves two-step procedure:(1) PyroSim model to obtain the temperature and the inclination variation of the large pool-fire flame, to determine the ultimate temperature distributions of the target tank, and to compare the ultimate temperature and the fuel ignition temperature, thus to determine the target tank is safety or not,(2) There are three kinds of semi-empirical models for describing the radiation e.g.(i) point source,(ii) Shokri-Beyler and (iii) Mudan models, to determine thermal radiation flux of the target tank. The analysis considered the influence of two parameters: (i) seven kinds of wind, (ii) five kinds of distances between the tanks. The paper compares three kinds of semi-empirical models and PyroSim model, so PyroSim model is more fit for relationships between thermal radiation flux. The simulation results show that the maximum temperature of the target tank is the connection between the wall and the top of the tank; the target tanks have failure risk within 0.3D and 0.6D tank distances and the tank spacing greater than or equal to 0.9D have no failure risk.

The Effect of Distance on Sentence Processing by Older Adults.

In sentences with long-distance dependency relations (“The man whom the police arrested is thin”), there are two kinds of distance between the gap (object position of arrested) and the filler man: linear (the intervening words in linear order), and structural (the intervening nodes in the syntactic tree). Previous studies found that older adults have difficulty comprehending sentences with long-distance dependency relations. However, it is not clear whether they are more disrupted by longer structural distance between gaps and fillers, or longer linear distance. There is a distinction between linear distance and structural distance, in that the former is directly related to working memory whereas the latter is associated with syntactic ability. By examining the effect of linear distance and structural distance on sentence processing by older adults, we can identify whether age-related decline in sentence comprehension is attributed to working memory dysfunction or syntactic decline. For this purpose, structural distance and linear distance were manipulated in Mandarin relative clauses (RCs). 30 older adults and 33 younger adults were instructed to perform a self-paced reading task. We found that both groups performed more slowly as structural distance increased, and less accurately when linear distance increased. More importantly, there was a significant interaction between linear distance and age group in the accuracy of comprehension, with linear distance disrupting older adults more than younger adults in offline processing. The findings suggest that the age-related decline in offline sentence comprehension might be attributable to the decline in working memory, rather than syntactic ability. Practical implications, limitations, and directions for future studies are discussed.

Satisfaction with an expatriate job

PurposeThe purpose of this paper is to focus on satisfaction with an expatriate job and how such satisfaction is linked to leadership. Specifically, this research examines how two different kinds of distances – physical distance and functional distance – between an expatriate and his/her supervisor are related to satisfaction with the expatriate job.Design/methodology/approachThe study was conducted among 290 Finnish expatriates. Moderated hierarchical regression analysis was conducted in order to test the research hypothesis.FindingsThe results show that low functional distance with a supervisor is related to greater satisfaction with the expatriate job. The physical distance is not directly connected to expatriate job satisfaction, but the common effect of the two types of distance shows that among those whose functional distance is low, working in the same country with the leader is linked to greater expatriate satisfaction than recorded among those who were physically distant. Interestingly, expatriates with high functional distance are more satisfied with the expatriate job if they work in a different country to their supervisor.Originality/valueThis study makes a contribution in three areas; first, it addresses the understudied phenomena of international work-specific job satisfaction, specifically satisfaction with an expatriate job. Second, it provides new knowledge on the outcomes of leader distance in the context of expatriation, a work situation that is inherently related to changes in physical location and to organizational relationships. Third, it contributes to leadership literature and highlights the importance of the conditions and the context in which leadership occurs.

Measuring the Distances to Quasars at High Redshifts with Strong Lensing

Abstract Strongly lensed quasars with time-delay measurements are well known to provide the “time-delay distances” , and the angular diameter distances to the lens galaxies D L . These two kinds of distances give stringent constraints on cosmological parameters. In this work, we explore a different use of time-delay observables: under the assumption of a flat universe, strong lensing observations can accurately measure the angular diameter distances to the sources D S . The corresponding redshifts of the quasars may be up to z S ∼ 4 according to the forecast. The high-redshift distances would sample the Hubble diagram between SNe Ia and the cosmic microwave background, model-independently providing direct information on the evolution of the nature of our universe, for example, the dark energy equation of state parameter w(z). We apply our method to the existing lensing system SDSS 1206+4332 and get at z S = 1.789. We also make a forecast for the era of Large Synoptic Survey Telescope. The uncertainty of D S depends on the redshifts of the lens and the source, the uncertainties of D Δt and D L , and the correlation between D Δt and D L . Larger correlation would result in tighter D S determination.

Why the Epistemic Value of Fictional Literature Does Not Depend Crucially on Its Fictionality

Mitchell Greenʼs conception of the thesis of Literary Cognitivism states that literary fiction can be a source of knowledge that depends crucially on its being fictional. By a modal argument the authors show that the criterion of fictionality cannot be crucial to the epistemic value of literary fiction. Rather, it lays in a certain kind of distance, e.g. a temporal, cultural, or interpersonal one. This will be motivated by drawing parallels to Gadamerʼs hermeneutics, especially his conception of fusion of horizons. In doing so, we agree with Green’s characterisation of knowledge that can be gained from engaging with literary fiction, but present a different approach to the source of this knowledge. At the same time, this approach enables us to extend the epistemic value of literary fiction both quantitatively and qualitatively.

“Unable to Determine”: Limits to Metrical Governance in Agricultural Supply chains

Metrics have long served as tools for governing at a distance. In the food industry, major manufacturers have embraced metrics as tools to govern the sustainability of the farms producing their commodity raw materials. This metrical turn has been influenced but also complicated by agricultural datafication, that is, the increasing quantities of data generated on and about farms. Despite the sheer abundance of data that companies might use to measure and drive improvement in on-farm sustainability, they have struggled to collect data suitable for such purposes. Attention to the different kinds of distance and diversity across which metrics are supposed to govern suggests reasons why they may fail to do so, even when wielded by otherwise powerful corporations.

Intimidad perdida. Paredes y cortinas en la reconstrucción de la privacidad contemporánea

The word transparency has lost its naivety. It has become a sign of opacity itself and architecture is no longer able to escape this radical change of meaning. The relation between clearness and architecture has had optimistic scholars or, at least, their theorists, but today transparency is neither a threat nor a promise anymore. Transparency has reached its paroxysm. So, is it still possible to build a filter that protects our overexposed privacy? This paper tries to rediscover the meaning of partition walls and curtains as filters. Even in a time when they have lost their power of separation, we can still discover experiences such as those carried out by Matta-Clark, Anne Holtrop, Luis Callejas or Petra Blaisse. They all work with those walls and curtains with different approaches. Those elements can underline the cultural, symbolic and physical value of the space they are within. Their curtains can produce a new kind of distance between exterior and interior life.

Neighborhood Granule Classifiers

Classifiers are divided into linear and nonlinear classifiers. The linear classifiers are built on a basis of some hyper planes. The nonlinear classifiers are mainly neural networks. In this paper, we propose a novel neighborhood granule classifier based on a concept of granular structure and neighborhood granules of datasets. By introducing a neighborhood rough set model, the condition features and decision features of classification systems are respectively granulated to form some condition neighborhood granules and decision neighborhood granules. These neighborhood granules are sets; thus, their calculations are intersection and union operations of sets. A condition neighborhood granule and a decision neighborhood granule form a granular rule, and the collection of granular rules constitutes a granular rule library. Furthermore, we propose two kinds of distance and similarity metrics to measure granules, which are used for the searching and matching of granules. Thus, we design a granule classifier by the similarity metric. Finally, we use the granule classifier proposed in this paper for a classification test with UCI datasets. The theoretical analysis and experiments show that the proposed granule classifier achieves a better classification performance under an appropriate neighborhood granulation parameter.

Using a Socio-Technical Model of a Global Software Development Project for Facilitating Risk Management and Improving the Project Structure

Any global software development project needs to deal with distances – geographical, cultural, time zone, etc. – between the groups of developers engaged in the project. To successfully manage the risks caused by such distances, there is a need to explicate and present the distances in a form suitable for manual or semi-automatic analysis, the goal of which is to detect potential risks and find ways of mitigating them. The article presents a technique of modeling a global software development project suitable for such analysis. The project is modeled as a complex socio-technical system that consists of functional components connected to each other through output-input relationships. The components do not coincide with the organizational units of the project, and their teams can be distributed through the geographical and organizational landscape of the project. The modeling technique helps to explicate and represent various kinds of distances between the functional components to determine which of them constitute risk factors. The technique was developed during two case studies, of which the second is used for presenting and demonstrating the new modeling technique in the article.

Influence of thickness of flange plate on the reduction of angular distortion by welding with trailing reverse-side flame line

ABSTRACTIn manufacturing of the steel bridge, fillet welded T-joint is widely used and angular distortion is often generated. So, reduction or control of angular distortion without additional processes to welding is strongly demanded because it takes great time and effort to correct the angular distortion. In this study, the effectiveness of welding with trailing reverse-side flame line heating for preventing angular distortion was investigated through the welding experiment and numerical simulation in submerged arc welding of fillet T-joint with three different thick flange plate. First, the heat source models for numerical analysis of both submerged arc welding and flame line heating were constructed based on the comparison with the measured temperature histories and angular distortion. And then, these heat source models were used in combination with various kinds of distance between two heat sources to make clear the appropriate distance condition for smallest angular distortion was 150 mm, and it does not depend on thickness of flange plate. It was also confirmed that the experimental angular distortions were in good agreement with those calculated. With a focus on the influence of thickness of flange plate, the reduction of angular distortion by welding with trailing reverse-side flame line heating becomes smaller with increasing thickness of flange plate. However, angular distortion could be adequately prevented under the appropriate flame line heating condition in either thickness of flange plate because the welding-induced angular distortion also becomes smaller with increasing thickness of flange plate. Thus, it was concluded that welding with trailing reverse-side flame line heating could be useful for preventing angular distortion of fillet T-joint, which is a component of steel bridge, enough not to correct it after welding.

LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets.

Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the axis and axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS ( axis) and the variation of LTS in structures (the combination of axis and axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems.

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

Multivariate and functional classification using depth and distance

We construct classifiers for multivariate and functional data. Our approach is based on a kind of distance between data points and classes. The distance measure needs to be robust to outliers and invariant to linear transformations of the data. For this purpose we can use the bagdistance which is based on halfspace depth. It satisfies most of the properties of a norm but is able to reflect asymmetry when the class is skewed. Alternatively we can compute a measure of outlyingness based on the skew-adjusted projection depth. In either case we propose the DistSpace transform which maps each data point to the vector of its distances to all classes, followed by k-nearest neighbor (kNN) classification of the transformed data points. This combines invariance and robustness with the simplicity and wide applicability of kNN. The proposal is compared with other methods in experiments with real and simulated data.