Multidimensional clustering approaches for pareto-frontiers

In Data Mining large and increasing sets of data are becoming more and more common. In order to avoid losing the overview on these data-sets, preference queries are a very popular method to reduce quantities of data to high relevant information. Together with clustering methods like k-means, confusing sets of objects can be constituted and presented clearer in order to get a better overview. In this report we present on the one hand the Pareto-dominance as a very suitable and promising approach to cluster objects over better-than relationships. In order to meet someones desires, one can tip the balance of the final results to the more favored dimension if no decision for allocating objects is possible. On the other hand we introduce based on the Pareto-dominance an advanced clustering approach exploiting the Borda Social Choice voting rule to manage distances of different domains by equally weights during the clustering process

Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering

WPA2-Personal is widely used to protect Wi-Fi networks against illicit access. While attackers typically use GPUs to speed up the discovery of weak network passwords, attacking random passwords is considered to quickly become infeasible with increasing password length. Professional attackers may thus turn to commercial high-end FPGA-based cluster solutions to significantly increase the speed of those attacks. Well known manufacturers such as Elcomsoft have succeeded in creating world\u27s fastest commercial FPGA-based WPA2 password recovery system, but since they rely on high-performance FPGAs the costs of these systems are well beyond the reach of amateurs. In this paper, we present a highly optimized low-cost FPGA cluster-based WPA-2 Personal password recovery system that can not only achieve similar performance at a cost affordable by amateurs, but in comparison our implementation would also be more than $5$ times as fast on the original hardware. Since the currently fastest system is not only significantly slower but proprietary as well, we believe that we are the first to present the internals of a highly optimized and fully pipelined FPGA WPA2 password recovery system. In addition we evaluated our approach with respect to performance and power usage and compare it to GPU-based systems

Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network

This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the parameter set with the best result was used for the final training. In the final step, we report on our efforts of implementing the testing stage in open_iA, which allows users to segment datasets with the fully trained model within reasonable time. The model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. Despite being trained on a limited number of datasets, it exhibits a satisfactory level of prediction accuracy

Validity of a Novel Digitally Enhanced Skills Training Station for Freehand Distal Interlocking

Background and Objectives: Freehand distal interlocking of intramedullary nails is technically demanding and prone to handling issues. It requires precise placement of a screw through the nail under fluoroscopy guidance and can result in a time consuming and radiation expensive procedure. Dedicated training could help overcome these problems. The aim of this study was to assess construct and face validity of new Digitally Enhanced Hands-On Surgical Training (DEHST) concept and device for training of distal interlocking of intramedullary nails. Materials and Methods: Twenty-nine novices and twenty-four expert surgeons performed interlocking on a DEHST device. Construct validity was evaluated by comparing captured performance metrics—number of X-rays, nail hole roundness, drill tip position and drill hole accuracy—between experts and novices. Face validity was evaluated with a questionnaire concerning training potential and quality of simulated reality using a 7-point Likert scale. Results: Face validity: mean realism of the training device was rated 6.3 (range 4–7). Training potential and need for distal interlocking training were both rated with a mean of 6.5 (range 5–7), with no significant differences between experts and novices, p 0.234. All participants (100%) stated that the device is useful for procedural training of distal nail interlocking, 96% wanted to have it at their institution and 98% would recommend it to colleagues. Construct validity: total number of X-rays was significantly higher for novices (20.9 6.4 versus 15.5 5.3, p = 0.003). Success rate (ratio of hit and miss attempts) was significantly higher for experts (novices hit: n = 15; 55.6%; experts hit: n = 19; 83%, p = 0.040). Conclusion: The evaluated training device for distal interlocking of intramedullary nails yielded high scores in terms of training capability and realism. Furthermore, construct validity was proven by reliably discriminating between experts and novices. Participants indicate high further training potential as the device may be easily adapted to other surgical tasks

Validity of a Novel Digitally Enhanced Skills Training Station for Freehand Distal Interlocking.

Background and Objectives: Freehand distal interlocking of intramedullary nails is technically demanding and prone to handling issues. It requires precise placement of a screw through the nail under fluoroscopy guidance and can result in a time consuming and radiation expensive procedure. Dedicated training could help overcome these problems. The aim of this study was to assess construct and face validity of new Digitally Enhanced Hands-On Surgical Training (DEHST) concept and device for training of distal interlocking of intramedullary nails. Materials and Methods: Twenty-nine novices and twenty-four expert surgeons performed interlocking on a DEHST device. Construct validity was evaluated by comparing captured performance metrics-number of X-rays, nail hole roundness, drill tip position and drill hole accuracy-between experts and novices. Face validity was evaluated with a questionnaire concerning training potential and quality of simulated reality using a 7-point Likert scale. Results: Face validity: mean realism of the training device was rated 6.3 (range 4-7). Training potential and need for distal interlocking training were both rated with a mean of 6.5 (range 5-7), with no significant differences between experts and novices, p ≥ 0.234. All participants (100%) stated that the device is useful for procedural training of distal nail interlocking, 96% wanted to have it at their institution and 98% would recommend it to colleagues. Construct validity: total number of X-rays was significantly higher for novices (20.9 ± 6.4 versus 15.5 ± 5.3, p = 0.003). Success rate (ratio of hit and miss attempts) was significantly higher for experts (novices hit: n = 15; 55.6%; experts hit: n = 19; 83%, p = 0.040). Conclusion: The evaluated training device for distal interlocking of intramedullary nails yielded high scores in terms of training capability and realism. Furthermore, construct validity was proven by reliably discriminating between experts and novices. Participants indicate high further training potential as the device may be easily adapted to other surgical tasks

Relevance of Host Cell Surface Glycan Structure for Cell Specificity of Influenza A Viruses

first_page settings Order Article Reprints Open AccessHypothesis Relevance of Host Cell Surface Glycan Structure for Cell Specificity of Influenza A Viruses by Markus Kastner 1,†,‡, Andreas Karner 1,†,§ [ORCID] , Rong Zhu 1,† [ORCID] , Qiang Huang 2 [ORCID] , Andreas Geissner 3,4,‖, Anne Sadewasser 5,¶, Markus Lesch 6, Xenia Wörmann 6, Alexander Karlas 6,**, Peter H. Seeberger 3,4 [ORCID] , Thorsten Wolff 5 [ORCID] , Peter Hinterdorfer 1 [ORCID] , Andreas Herrmann 7 and Christian Sieben 8,9,* [ORCID] 1 Institute for Biophysics, Johannes Kepler University Linz, 4020 Linz, Austria 2 State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai 200438, China 3 Department for Biomolecular Systems, Max Planck Institute for Colloids and Interfaces, 14476 Potsdam, Germany 4 Institute of Chemistry and Biochemistry, Freie Universität Berlin, 14195 Berlin, Germany 5 Division of Influenza and other Respiratory Viruses, Robert Koch-Institute, 13353 Berlin, Germany 6 Molecular Biology Department, Max Planck Institute for Infection Biology, 10117 Berlin, Germany 7 Institut für Chemie und Biochemie, Freie Universität Berlin, Altensteinstraße 23a, 14195 Berlin, Germany 8 Nanoscale Infection Biology Group, Department of Cell Biology, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany 9 Institute for Genetics, Technische Universität Braunschweig, 38106 Braunschweig, Germany * Author to whom correspondence should be addressed. † These authors contributed equally to this work. ‡ Current address: Materials Characterization Lab (MCL), Materials Research Institute (MRI), Pennsylvania State University, University Park, PA 16802, USA. § Current address: University of Applied Sciences Upper Austria, School of Medical Engineering and Applied Social Sciences, Garnisonstr. 21, 4020 Linz, Austria. ‖ Current address: Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC V6T 1Z1, Canada. ¶ Current address: Secarna Pharmaceuticals GmbH & Co. KG, Am Klopferspitz 19, 82152 Planegg, Germany. ** Current address: Viral Vectors and Gene Therapeutics, ProBioGen AG, 13086 Berlin, Germany. Viruses 2023, 15(7), 1507; https://doi.org/10.3390/v15071507 Received: 9 May 2023 / Revised: 21 June 2023 / Accepted: 28 June 2023 / Published: 5 July 2023 (This article belongs to the Special Issue Physical Virology - Viruses at Multiple Levels of Complexity) Download Browse Figures Review Reports Versions Notes Abstract Influenza A viruses (IAVs) initiate infection via binding of the viral hemagglutinin (HA) to sialylated glycans on host cells. HA’s receptor specificity towards individual glycans is well studied and clearly critical for virus infection, but the contribution of the highly heterogeneous and complex glycocalyx to virus–cell adhesion remains elusive. Here, we use two complementary methods, glycan arrays and single-virus force spectroscopy (SVFS), to compare influenza virus receptor specificity with virus binding to live cells. Unexpectedly, we found that HA’s receptor binding preference does not necessarily reflect virus–cell specificity. We propose SVFS as a tool to elucidate the cell binding preference of IAVs, thereby including the complex environment of sialylated receptors within the plasma membrane of living cells