Real Talk on the Metaphysics of Gender

Gender classifications often are controversial. These controversies typically focus on whether gender classifications align with facts about gender kind membership: Could someone really be nonbinary? Is Chris Mosier really a man? I think this is a bad approach. Consider the possibility of ontological oppression, which arises when social kinds operating in a context unjustly constrain the behaviors, concepts, or affect of certain groups. Gender kinds operating in dominant contexts, I argue, oppress trans and nonbinary persons in this way: they marginalize trans men and women, and exclude nonbinary persons. As a result, facts about membership in dominant gender kinds should not settle gender classification practices

Learning Multi-Scale Representations for Material Classification

The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors. In computer vision, success stories of learned features have been predominantly reported for object recognition tasks. In this paper, we investigate if and how feature learning can be used for material recognition. We propose two strategies to incorporate scale information into the learning procedure resulting in a novel multi-scale coding procedure. Our results show that our learned features for material recognition outperform hand-crafted descriptors on the FMD and the KTH-TIPS2 material classification benchmarks

New Trends in Development of Services in the Modern Economy

The services sector strategic development unites a multitude of economic and managerial aspects and is one of the most important problems of economic management. Many researches devoted to this industry study are available. Most of them are performed in the traditional aspect of the voluminous calendar approach to strategic management, characteristic of the national scientific school. Such an approach seems archaic, forming false strategic benchmarks. The services sector is of special scientific interest in this context due to the fact that the social production structure to the services development model attraction in many countries suggests transition to postindustrial economy type where the services sector is a system-supporting sector of the economy. Actively influencing the economy, the services sector in the developed countries dominates in the GDP formation, primary capital accumulation, labor, households final consumption and, finally, citizens comfort of living. However, a clear understanding of the services sector as a hyper-sector permeating all spheres of human activity has not yet been fully developed, although interest in this issue continues to grow among many authors. Target of strategic management of the industry development setting requires substantive content and the services sector target value assessment

DHARMAKIRTI, DAVIDSON, AND KNOWING REALITY

If we distinguish phenomenal effects from their noumenal causes, the former being our conceptual(ized) experiences, the latter their grounds or causes in reality as it is independent of our experience, then two contradictory positions with regards to the relationship between these two can be distinguished: either phenomena are identical with their noumenal causes, or they are not. Davidson is among the most influential modern defenders of the former position, metaphysical non-dualism. Dharmakirti\u27 strict distinction between ultimate and conventional reality, on the other hand, may be one of the most rigorously elaborated theories of the opposite position, metaphysical dualism. Despite this fundamental difference, their theories about the connection between phenomena and their noumenal causes are surprisingly similar in important respects. Both Dharmakirti in his theory of apoha and Davidson in his theory of triangulation argued that the content of words or concepts depends on a process involving at least two communicating beings and shared noumenal stimuli. The main point of divergence is the nature of classification, but ultimately Dharmakirti\u27s and Davidson\u27s conclusions on the noumenal-phenomenal relationship turn out to be complementary more than contradictory, and an integrative reconstruction suggests a middle path between dualism and non-dualism

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Psychiatry beyond the brain: externalism, mental health, and autistic spectrum disorder

Externalist theories hold that a comprehensive understanding of mental disorder cannot be achieved unless we attend to factors that lie outside of the head: neural explanations alone will not fully capture the complex dependencies that exist between an individual’s psychiatric condition and her social, cultural, and material environment. Here, we firstly offer a taxonomy of ways in which the externalist viewpoint can be understood, and unpack its commitments concerning the nature and physical realization of mental disorder. Secondly, we apply a strongly externalist approach to the case of Autistic Spectrum Disorder, and argue that this condition can be illuminated by appeal to the hypothesis of extended cognition. We conclude by briefly considering the significance this strongly externalist approach may have for psychiatric practice and pedagogy

Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

Cultural heritage and sustainable development targets : a possible harmonisation? Insights from the European Perspective

The Agenda 2030 includes a set of targets that need to be achieved by 2030. Although none of the 17 Sustainable Development Goals (SDGs) focuses exclusively on cultural heritage, the resulting Agenda includes explicit reference to heritage in SDG 11.4 and indirect reference to other Goals. Achievement of international targets shall happen at local and national level, and therefore, it is crucial to understand how interventions on local heritage are monitored nationally, therefore feeding into the sustainable development framework. This paper is focused on gauging the implementation of the Sustainable Development Goals with reference to cultural heritage, by interrogating the current way of classifying it (and consequently monitoring). In fact, there is no common dataset associated with monitoring SDGs, and the field of heritage is extremely complex and diversified. The purpose for the paper is to understand if the taxonomy used by different national databases allows consistency in the classification and valuing of the different assets categories. The European case study has been chosen as field of investigation, in order to pilot a methodology that can be expanded in further research. A cross‐comparison of a selected sample of publicly accessible national cultural heritage databases has been conducted. As a result, this study confirms the existence of general harmonisation of data towards the achievement of the SDGs with a broad agreement of the conceptualisation of cultural heritage with international frameworks, thus confirming that consistency exists in the classification and valuing of the different assets categories. However, diverse challenges of achieving a consistent and coherent approach to integrating culture in sustainability remains problematic. The findings allow concluding that it could be possible to mainstream across different databases those indicators, which could lead to depicting the overall level of attainment of the Agenda 2030 targets on heritage. However, more research is needed in developing a robust correlation between national datasets and international targets