Problemeset cossibilites d'evaluation de procedes des analyses Cluster, III. Appendix: Courte description des Algorithmes analyse Cluster les plus rependues

Es wird eine relativ einfach gehaltene Kurzcharakteristik derjenigen Clusteranalyse-Algorithmen gegeben, die aufgrund eines Literaturüberblicks (SCHNEIDER & SCHEIBLER 1983a) als die in der Fonchung hauptsächlich benutzten Verfahren einzustufen sind. Die Kurzbeschreibung verzichtet im wesentlichen auf statistische Details und verfolgt speziell das Ziel, dem Leser eine Vorstellung von Gemeinsamkeiten und Untenchieden in der Funktionsweise von hierarchischen Clusteranalysen, Optimierungs- bzw. Partitionierungstechniken, Dichteverfahren, "Clumping Techniques" und anderen Prozeduren zu geben.This paper presents a summary of 18 clustering algorithms most frequently applied in reseuch (cf. SCHNEIDER & SCHEIBLEK 1983a). Only a short description of each procedure is provided which aims at highlighting the basic differences and comrnonalities of hierarchical clustering algorithms, iterative partitioning methods, mode seeking techniques, clumping techniques, and other procedures.Les Algorithmes analyse Cluster qui sont decritent par (Schneider & Scheibler 1983) comme etant les procedes les plus rependus dans Ia recherche sont relates ici de facon courte. Le description chematique exclue l'ennumeration des details statistiques et a pour but essentiel de transmettre au lecteur, entre autre, une representation des rapports et des differences dans Je mode de fonction des analyses Cluster hierarchiques, des techniques d'optimation, et des procedes de population «Clumping techniques» etc

Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness

In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to obtain a sound and ({\delta}-)complete decision procedure. Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search. We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Our experiments show that the proposed approach significantly outperforms three state-of-the-art tools, namely AI^2 , Reluplex, and Reluval

Enhancement by postfiltering for speech and audio coding in ad-hoc sensor networks

Enhancement algorithms for wireless acoustics sensor networks~(WASNs) are indispensable with the increasing availability and usage of connected devices with microphones. Conventional spatial filtering approaches for enhancement in WASNs approximate quantization noise with an additive Gaussian distribution, which limits performance due to the non-linear nature of quantization noise at lower bitrates. In this work, we propose a postfilter for enhancement based on Bayesian statistics to obtain a multidevice signal estimate, which explicitly models the quantization noise. Our experiments using PSNR, PESQ and MUSHRA scores demonstrate that the proposed postfilter can be used to enhance signal quality in ad-hoc sensor networks

Ethical issues in autologous stem cell transplantation (ASCT) in advanced breast cancer: A systematic literature review

BACKGROUND: An effectiveness assessment on ASCT in locally advanced and metastatic breast cancer identified serious ethical issues associated with this intervention. Our objective was to systematically review these aspects by means of a literature analysis. METHODS: We chose the reflexive Socratic approach as the review method using Hofmann's question list, conducted a comprehensive literature search in biomedical, psychological and ethics bibliographic databases and screened the resulting hits in a 2-step selection process. Relevant arguments were assembled from the included articles, and were assessed and assigned to the question list. Hofmann's questions were addressed by synthesizing these arguments. RESULTS: Of the identified 879 documents 102 included arguments related to one or more questions from Hofmann's question list. The most important ethical issues were the implementation of ASCT in clinical practice on the basis of phase-II trials in the 1990s and the publication of falsified data in the first randomized controlled trials (Bezwoda fraud), which caused significant negative effects on recruiting patients for further clinical trials and the doctor-patient relationship. Recent meta-analyses report a marginal effect in prolonging disease-free survival, accompanied by severe harms, including death. ASCT in breast cancer remains a stigmatized technology. Reported health-related-quality-of-life data are often at high risk of bias in favor of the survivors. Furthermore little attention has been paid to those patients who were dying. CONCLUSIONS: The questions were addressed in different degrees of completeness. All arguments were assignable to the questions. The central ethical dimensions of ASCT could be discussed by reviewing the published literature