Notion Of Proximity Research Articles

On the Public and the Human: Kenneth Frampton’s References to the Space of Appearance

The term “the space of appearance,” which originates from the writings of Hannah Arendt, resonated strongly within the writings of Kenneth Frampton. In this article, I trace how Frampton understands this notion. As he has been mostly concerned with the democratic necessity to establish a public realm, he laments the loss of architecture’s ability to establish such a realm. Arendt’s notion informs him to turn to the local and thus can be understood as informative with regards to his engagement with “critical regionalism” and his attempt to define an architecture resisting neocapitalist urbanisation and its inherent placelessness. By introducing Martin Heidegger’s distinction between space and place, he connects Arendt’s “appearance” to bounded spaces and the notion of proximity. Frampton, I claim, nevertheless does not align the “space of appearance” with particular spatial typologies, but develops his understanding of the potential (or lost potential) of architecture through the reflection on specific projects.

POWER BOOSTING FOR ORDERED MULTIPLE HYPOTHESES WITH APPLICATION TO GENOME-WIDE ASSOCIATION STUDIES

A method for addressing the multiplicity problem is proposed in the setting where the hypotheses test sites may be arranged in some order based on a notion of proximity, such as SNPs of a chromosome in genetic association studies. It is shown that this method is able to control family-wise error rate in the weak sense and numerical evidence shows that this method controls false discovery rate in the strong sense under sparsity. The method is applied to some genome- wide association studies data with asthma and it is argued that this Power Boosting method may be combined with existing error- rate controlling methods in order to improve true positive rates at controllable and possibly negligible cost to the nominal level of error- rate control.

Physical Distance, Ethical Proximity

I develop Levinas’s analysis of “proximity” to explain how successful faceless class dialogues are possible despite physical social distancing during the COVID-19 pandemic. I first examine features of Levinas’s notion of proximity within his idiosyncratic approach to “ethics.” Second, I turn to Levinas’s examination of intentionality and questioning in relation to the hermeneutic priority of questioning. Third, I detail some successes and failures in attempts to embody Levinasian proximity in online or masked discussions with students. I draw out contrasts between experiences at two different institutions as well as between curricular and extracurricular experiences. I do this to expose my own vulnerability in this essay itself. Given pandemic conditions as well as Levinas’s theory of proximity, I found that many masked or virtual class discussions—but especially extracurricular group discussions, such as a philosophy club and the Black Student Union meetings—maintained a closeness of community despite social distancing.

Causation in International Crimes Cases: (Re)Conceptualizing the Causal Linkage

This working paper discusses the meaning of causation in international criminal law. Based on theoretical and comparative analyses, the paper argues for a normative interpretation of causality. As a normative concept, causality should be interpreted in light of: the collective and organized nature of international crimes; the notion of risk-taking; the hydraulic relation between acts reus and mens rea; and the notion of proximity.

Proximity-Based Unification: An Efficient Implementation Method

Unification is a central concept in logic systems based on the resolution principle. As well, in knowledge representation, proximity relations (i.e., reflexive, symmetric, fuzzy binary relations) are useful for introducing semantics into a syntactic level by modeling the semantic closeness of different syntactic objects and managing vague or imprecise information. Proximity relations, in combination with the unification algorithm, make possible expressing certain forms of approximate reasoning in a logic programming framework. In this article, we use proximity relations in the context of a (fuzzy) logic programming system, called Bousi ~ Prolog, as a way of solving the limitations introduced by similarity relations (i.e., transitive proximity relations) to correctly represent fuzzy information. Recently, we introduced an accurate definition of proximity between expressions (terms or atomic formulas) and a new unification algorithm able to manage proximity relations properly. However, the so-called weak unification algorithm, which is an extension of Martelli and Montanari's unification algorithm supported by the new notion of proximity, does not have an efficient implementation. In this article, we present a method that facilitates such an efficient implementation, including an adaptation of the weak SLD resolution rule based on the new unification algorithm, and its integration and implementation into the fuzzy logic programming system Bousi ~ Prolog. A performance analysis to show its efficiency is also presented.

Kernel-Based Ensemble Learning in Python

We propose a new supervised learning algorithm for classification and regression problems where two or more preliminary predictors are available. We introduce KernelCobra, a non-linear learning strategy for combining an arbitrary number of initial predictors. KernelCobra builds on the COBRA algorithm introduced by Biau et al. (2016), which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalise this idea by using a kernel to better encapsulate the proximity information. Such a smoothing kernel provides more representative weights to each of the training points which are used to build the aggregate and final predictor, and KernelCobra systematically outperforms the COBRA algorithm. While COBRA is intended for regression, KernelCobra deals with classification and regression. KernelCobra is included as part of the open source Python package Pycobra (0.2.4 and onward), introduced by Srinivasa Desikan (2018). Numerical experiments were undertaken to assess the performance (in terms of pure prediction and computational complexity) of KernelCobra on real-life and synthetic datasets.

Comparing functional connectivity matrices: A geometry-aware approach applied to participant identification

Understanding the correlation structure associated with multiple brain measurements informs about potential “functional groupings” and network organization. The correlation structure can be conveniently captured in a matrix format that summarizes the relationships among a set of brain measurements involving two regions, for example. Such functional connectivity matrix is an important component of many types of investigation focusing on network-level properties of the brain, including clustering brain states, characterizing dynamic functional states, performing participant identification (so-called “fingerprinting”) understanding how tasks reconfigure brain networks, and inter-subject correlation analysis. In these investigations, some notion of proximity or similarity of functional connectivity matrices is employed, such as their Euclidean distance or Pearson correlation (by correlating the matrix entries). Here, we propose the use of a geodesic distance metric that reflects the underlying non-Euclidean geometry of functional correlation matrices. The approach is evaluated in the context of participant identification (fingerprinting): given a participant’s functional connectivity matrix based on resting-state or task data, how effectively can the participant be identified? Using geodesic distance, identification accuracy was over 95% on resting-state data, and exceeded the Pearson correlation approach by 20%. For whole-cortex regions, accuracy improved on a range of tasks by between 2% and as much as 20%. We also investigated identification using pairs of subnetworks (say, dorsal attention plus default mode), and particular combinations improved accuracy over whole-cortex participant identification by over 10%. The geodesic distance also outperformed Pearson correlation when the former employed a fourth of the data as the latter. Finally, we suggest that low-dimensional distance visualizations based on the geodesic approach help uncover the geometry of task functional connectivity in relation to that during resting-state. We propose that the use of the geodesic distance is an effective way to compare the correlation structure of the brain across a broad range of studies.

Clustering of semantically enriched short texts

The paper is devoted to the issue of clustering small sets of very short texts. Such texts are often incomplete and highly inconclusive, so establishing a notion of proximity between them is a challenging task. In order to cope with polysemy we adapt the SenseSearcher algorithm (SnS), by Kozlowski and Rybinski in Computational Intelligence 33(3): 335–367, 2017b. In addition, we test the possibilities of improving the quality of clustering ultra-short texts by means of enriching them semantically. We present two approaches, one based on neural-based distributional models, and the other based on external knowledge resources. The approaches are tested on SnSRC and other knowledge-poor algorithms.

Topological spaces via phenotype–genotype spaces

Researchers hope that establishing a notion of proximity using topology will help to clarify the biological processes underlying the evolution of living organisms. The simple model presented here, using RNA shapes, can carry over to more general and complex genotype–phenotype systems. Proximity is an important component of continuity, in both real-world and topological terms. Consequently, phenotype spaces provide an appropriate setting for modeling and investigating continuous and discontinuous evolutionary change.

Relative entropy and proximity of quantum field theories

We study the question of how reliably one can distinguish two quantum field theories (QFTs). Each QFT defines a probability distribution on the space of fields. The relative entropy provides a notion of proximity between these distributions and quantifies the number of measurements required to distinguish between them. In the case of nearby conformal field theories, this reduces to the Zamolodchikov metric on the space of couplings. Our formulation quantifies the information lost under renormalization group flow from the UV to the IR and leads us to a quantification of fine-tuning. This formalism also leads us to a criterion for distinguishability of low energy effective field theories generated by the string theory landscape.

Relative entropy and proximity of quantum field theories

We study the question of how reliably one can distinguish two quantum field theories (QFTs). Each QFT defines a probability distribution on the space of fields. The relative entropy provides a notion of proximity between these distributions and quantifies the number of measurements required to distinguish between them. In the case of nearby conformal field theories, this reduces to the Zamolodchikov metric on the space of couplings. Our formulation quantifies the information lost under renormalization group flow from the UV to the IR and leads us to a quantification of fine-tuning. This formalism also leads us to a criterion for distinguishability of low energy effective field theories generated by the string theory landscape.

Proximity-based unification theory

Similarity-based Logic Programming has been proposed to enhance the Logic Programming paradigm with similarity relations, in order to represent and manage vague or imprecise information. A similarity relation is a reflexive, symmetric, transitive fuzzy binary relation, extending the standard notion of equivalence relation with the purpose of weakening the concept of equality. As is reported, similarity relations have significant limitations, due to the transitivity restriction, that can lead to representing fuzzy information incorrectly in some situations.Recently we have proposed the use of proximity relations (i.e., reflexive, symmetric, fuzzy binary relations) in the context of a logic programming system, called Bousi∼Prolog, as a way of solving this problem and generalizing later approaches exclusively based on similarity relations. However, a naive combination of proximity relations and the existing unification algorithms may cause incompleteness problems. Hence, in this paper, we introduce an accurate definition of proximity between expressions (terms or atomic formulas) and a new unification algorithm able to manage proximity relations properly. The so-called weak unification algorithm is an extension of Martelli and Montanari's unification algorithm supported by the new notion of proximity. As major relevant properties, we prove that the weak unification algorithm terminates and it is sound (i.e., it produces a weak unifier of two expressions, if they are unifiable) and complete (i.e., it is able to compute a weak most general unifier for two unifiable expressions).

Soft Approach to Identification of Cohesive Clusters in Two Gene Representations

The approach to identify clusters of genes represented both by expression values and Gene Ontology annotations, where cluster membership should not be in conflict with any of the representations is presented in the paper. The method enables to identify the genes that are differently clustered in different representations, what can lead to further analysis and interesting conclusions. The approach is based on the fuzzy clustering algorithms and the notion of proximity as the aggregation operation at the higher level than similarity matrices is performed. The approach is verified on two datasets: a small synthetic and real-world gene dataset.

Using proximity to compute semantic relatedness in RDF graphs

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terns in RDF graphs based on the notion of proximity. It proposes a formal definition of proximity in terms of the set paths connecting two concept nodes, and an algorithm for finding this set and computing proximity with a given error margin. This algorithm was implemented on a tool called Shakti that extracts relevant ontological data for a given domain from DBpedia - a community effort to extract structured data from the Wikipedia. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are also reported.

High dimensional data clustering through fuzzy possibilistic C-means with symmetry-based distance measure

One of the difficult tasks in data clustering is clustering the high dimensional data. Clustering high dimensional data has been a major concern owing to the intrinsic sparsity of the data points. Several recent research results signifies that in case of high dimensional data, even the notion of proximity or clustering possibly will not be significant. Fuzzy C-means (FCM) and possibilistic C-means (PCM) has the capability to handle the high dimensional data, whereas FCM is sensitive to noise and PCM requires appropriate initialisation to converge to nearly global minimum. Hence to overcome this issue a fuzzy possibilistic C-means (FPCM) with symmetry-based distance measure has been proposed which can find out the number of clusters that exist in a dataset. In addition with a good fuzzy partitioning of the data, a novel fuzzy cluster validity index called FSym-index is used which depends on the symmetry-based distance. Symmetry-based distance provides a measure of integrity of clustering on several fuzzy partitions of a dataset. If the value of FSym-index is larger, the accuracy also becomes high with less execution time.

A Modified Projected K-Means Clustering Algorithm with Effective Distance Measure

Clustering high dimensional data has been a big issue for clustering algorithms because of the intrinsic sparsity of the data points. Several recent research results signifies that in case of high dimensional data, even the notion of proximity or clustering possibly will not be significant. K-Means is one of the basic clustering algorithm which is commonly used in several applications, but it is not possible to discover subspace clusters. The subspaces are explicit to the clusters themselves. In this paper, an algorithm called Modified Projected K-Means Clustering Algorithm with Effective Distance Measure is designed to generalize K-Means algorithm with the objective of managing the high dimensional data. The experimental results confirm that the proposed algorithm is an efficient algorithm with better clustering accuracy and very less execution time than the Standard K-Means and General K-Means algorithms.

Homogeneous genetic algorithms

A new type of genetic algorithm (GA) is developed to mitigate one or both of the following two major difficulties that traditional GAs may suffer: (1) when the number of ‘active genes’ needs to be held constant or kept within some prescribed range, and (2) when the set of genes is much larger than the set of active genes of feasible solutions under consideration. These homogeneous GAs use (unordered) sets to represent ‘active genes’ in chromosomes rather than strings, and a correspondingly natural crossover operator is introduced. ‘Homogeneous’ refers to the fact that, in contrast to traditional GAs where pairs of genes that are ‘close’ have better chances of being preserved under crossover, there is no notion of proximity between pairs of genes. Examples are provided that will demonstrate superior performance of these new GAs for some typical problems in which these difficulties arise.

A Plane Economy of Space Surfaces From the Concepts of Einstein and Euclid

A unification of the sciences is attempted. The “concept”, conceptual structuring of the world, is accounted for as the perceptual product of the means in which the world is actually actively structured as difference states of an energy-energy or energy-matter conversion product whose continually becoming state bears perceptual and cognitive reality born of the objects of sensation, and who present (verses becoming) state is unwitnessible by virtue that it is also the means of mental as well as physiological processes (i.e. is not visible to itself). Hierarchy in conceptual ordering, in model, is an active and flexible reflection of an unwitnessible state as the delivering agent of a constantly becoming state, i.e. the experienced environment; the value of temporally enduring constancy in the former as the agent of the same in the latter: the concept is an ordered set of differences of processes involved in the making of the world. A contrast is drawn between actual (in the frame of nature) and apparent (in the frame of witness) emergences, (the logical verses the illogical respectively) in light of a redefined notion of proximity to the senses in which the actual physical/temporal paths of emergences rather than perceptual senses of closeness, reach-ability or experimental reach-ability are measures of distance. The theory of relativity, reflecting in its’ conceptual construction common interpretations of proximity, is claimed to be overlapped with respect to the physical and the conceptual in a manner in which its’ empirical meaning/realization exaggerates the real size of volumes of space.

Effective Topology from Spacetime Tomography

We recover the effective topology of spacetime using the notion of record from the decoherent histories approach to Quantum Theory. From a series of (gedanken) experiments, we obtain the set of possible events, grouped into sub-sets that corresponds to histories, but with no other information such as (causal) order or any notion of proximity. This corresponds to tomography of the "effective" spacetime, that is done in an operational way. Making certain assumptions about these records, and using the existence of upper bound in the speed of transfer of matter and information, we recover the full partial (causal) order up to certain ambiguities. The partially ordered set of events corresponds to an "effective" causal set which is a discretized version of spacetime with the causal relation as defining feature. We conclude with a derivation of the topology of this effective discretized spacetime.

Neurofuzzy Modeling of Context–Contingent Proximity Relations

The notion of proximity is one of the foundational elements in humans' understanding and reasoning of the geographical environments. The perception and cognition of distances plays a significant role in many daily human activities. Yet, few studies have thus far provided context–contingent translation mechanisms between linguistic proximity descriptors (e.g., “near,”“far”) and metric distance measures. One problem with previous fuzzy logic proximity modeling studies is that they presume the form of the fuzzy membership functions of proximity relations. Another problem is that previous studies have fundamental weaknesses in considering context factors in proximity models. We argue that statistical approaches are ill suited to proximity modeling because of the inherently fuzzy nature of the relations between linguistic and metric distance measures. In this study, we propose a neurofuzzy system approach to solve this problem. The approach allows for the dynamic construction of context–contingent proximity models based on sample data. An empirical case study with human subject survey data is carried out to test the validity of the approach and to compare it with the previous statistical approach. Interpretation and prediction accuracy of the empirical study are discussed.