Reformers have promoted mixed-member electoral systems as the "best of both worlds." In this volume, internationally recognized political scientists evaluate the ways in which the introduction of a ...mixed-member electoral system affects the configuration of political parties. The contributors examine several political phenomena, including cabinet post allocation, nominations, preelectoral coalitions, split-ticket voting, and the size of party systems and faction systems. Significantly, they also consider various ways in which the constitutional system— especially whether the head of government is elected directly or indirectly— can modify the incentives created by the electoral system. The findings presented here demonstrate that the success of electoral reform depends not only on the specification of new electoral rules per se but also on the political context— and especially the constitutional framework—within which such rules are embedded.
Negretto provides the first systematic explanation of the origins of constitutional designs from an analytical, historical and comparative perspective. Based on analysis of constitutional change in ...Latin America from 1900 to 2008 and four detailed case studies, Negretto shows the main determinants of constitutional choice are the past performance of constitutions in providing effective and legitimate instruments of government and the strategic interests of the actors who have influence over institutional selection. The book explains how governance problems shape the general guidelines for reform, while strategic calculations and power resources affect the selection of specific alternatives of design. It emphasizes the importance of events that trigger reform and the designers' level of electoral uncertainty for understanding the relative impact of short-term partisan interests on constitution writing. Negretto's study challenges predominant theories of institutional choice and paves the way for the development of a new research agenda on institutional change.
•Hierarchical Cell-to-Tissue (HACT) representation: A novel multi-level hierarchical entity-graph representation of a histology image to model the hierarchical composition of the tissue by encoding ...comprehensible histological entities (cells and tissue-regions) as well as the intra- and inter-entity level interactions.•HACT-Net: A hierarchical graph neural network to operate on the hierarchical entity-graph representation to map the tissue structure to tissue functionality.•BReAst Carcinoma Subtyping (BRACS) dataset: Introduce (BRACS) dataset, a large cohort of Haematoxylin & Eosin-stained breast tumor regions-of-interest.•Domain expert comparison: Benchmarking of the proposed methodology with three expert pathologists on the BRACS test set.•Quantitative evaluation: Experimentations on BRACS dataset and public BACH dataset to demonstrate the efficacy of the proposed methodology in breast cancer subtyping compared to state-of-the-art computer-aided diagnostic approaches.•Qualitative evaluation: Demonstration of salient regions in the histopathology image during the inference with HACT-Net.
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Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net.
In this paper, collaborative representation is proposed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be ...approximately represented by its spatial neighborhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collaboration of representation is reinforced by l 2 -norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the stability of the solution can be enhanced. The major advantage of the proposed algorithm is the capability of adaptively modeling the background even when anomalous pixels are involved. A kernel extension of the proposed approach is also studied. Experimental results indicate that our proposed detector may outperform the traditional detection methods such as the classic Reed-Xiaoli (RX) algorithm, the kernel RX algorithm, and the state-of-the-art robust principal component analysis based and sparse-representation-based anomaly detectors, with low computational cost.
Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including spectrum awareness, adaptive transmissions, ...and interference avoidance. Recently, deep learning (DL)-based modulation classification has attracted significant attention due to its superiority in feature extraction and classification accuracy. In DL-based modulation classification, one major challenge is to preprocess a received signal and represent it in a proper format before feeding the signal into deep neural networks. This article provides a comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms. Since a received signal can be represented by either features, images, sequences, or a combination of them, existing algorithms of DL-based modulation classification can be categorized into four groups and are reviewed accordingly in this article. Furthermore, the advantages as well as disadvantages of each signal representation method are summarized and discussed.
Over the past quarter century new ideologies of participation and representation have proliferated across democratic and non-democratic regimes. In Participation without Democracy, Garry Rodan breaks ...new conceptual ground in examining the social forces that underpin the emergence of these innovations in Southeast Asia. Rodan explains that there is, however, a central paradox in this recalibration of politics: expanded political participation is serving to constrain contestation more than to enhance it.
Participation without Democracyuses Rodan's long-term fieldwork in Singapore, the Philippines, and Malaysia to develop a modes of participation (MOP) framework that has general application across different regime types among both early-developing and late-developing capitalist societies. His MOP framework is a sophisticated, original, and universally relevant way of analyzing this phenomenon. Rodan uses MOP and his case studies to highlight important differences among social and political forces over the roles and forms of collective organization in political representation. In addition, he identifies and distinguishes hitherto neglected non-democratic ideologies of representation and their influence within both democratic and authoritarian regimes.Participation without Democracysuggests that to address the new politics that both provokes these institutional experiments and is affected by them we need to know who can participate, how, and on what issues, and we need to take the non-democratic institutions and ideologies as seriously as the democratic ones.
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