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

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Scoping review indicates heterogeneous methods for developing and integrating patient decision aids in the context of clinical practice guidelines

Objectives: To review the methods used to develop and integrate patient decision aids (PDAs) based on the recommendations of clinical practice guidelines (CPGs). Study Design and Setting: We conducted a scoping review covering bibliographic databases (PubMed, Embase; searched until December 2023), gray literature, references, and expert consultations to identify eligible documents. Documents published from 2000 onwards and describing methods related to guideline-based PDA development or linking CPGs and PDAs were included. Two reviewers independently selected and analyzed the documents. Results were synthesized and presented narratively. Results: Based on 24 included documents, we categorized their methods into 4 topics. For topic (1), the selection of CPG recommendations for which PDAs are (most) needed, we found a total of 14 selection factors across n = 11 documents, with uncertainty/variability in patient preferences and trade-offs between options being the most frequently mentioned. Topic (2) (n = 24) covers methods for developing and/or updating guideline-based PDAs, such as forming a multidisciplinary development group, using CPGs and their evidence summaries along with other sources as the evidence base, and using digital solutions for semi-automated development and updating. Topic (3) (n = 12) covers methods for PDA quality assessment and/or user testing, such as finalizing and approving the PDAs after a review and feedback process from the CPG group and an iterative user testing process. Topic (4) (n = 20) covers methods for linking CPGs and PDAs, often through digital strategies. Conclusion: We identified heterogeneous methods for developing and integrating PDAs based on CPG recommendations. Empirical testing is required to determine the most useful and practically feasible (combination of) methods. CPG organizations should focus on establishing adequate methods for linking CPG and PDA development to foster shared decision-making.</p