Optimizing Scrubbing by Netlist Analysis for FPGA Configuration Bit Classification and Floorplanning

Existing scrubbing techniques for SEU mitigation on FPGAs do not guarantee an error-free operation after SEU recovering if the affected configuration bits do belong to feedback loops of the implemented circuits. In this paper, we a) provide a netlist-based circuit analysis technique to distinguish so-called critical configuration bits from essential bits in order to identify configuration bits which will need also state-restoring actions after a recovered SEU and which not. Furthermore, b) an alternative classification approach using fault injection is developed in order to compare both classification techniques. Moreover, c) we will propose a floorplanning approach for reducing the effective number of scrubbed frames and d), experimental results will give evidence that our optimization methodology not only allows to detect errors earlier but also to minimize the Mean-Time-To-Repair (MTTR) of a circuit considerably. In particular, we show that by using our approach, the MTTR for datapath-intensive circuits can be reduced by up to 48.5% in comparison to standard approaches

Quantum dynamics of two bosons in an anharmonic trap: Collective vs internal excitations

This work deals with the effects of an anharmonic trap on an interacting two-boson system in one dimension. Our primary focus is on the role of the induced coupling between the center of mass and the relative motion as both anharmonicity and the (repulsive) interaction strength are varied. The ground state reveals a strong localization in the relative coordinate, counteracting the tendency to fragment for stronger repulsion. To explore the quantum dynamics, we study the system's response upon (i) exciting the harmonic ground state by continuously switching on an additional anharmonicity, and (ii) displacing the center of mass, this way triggering collective oscillations. The interplay between collective and internal dynamics materializes in the collapse of oscillations, which are explained in terms of few-mode models.Comment: 8 pages, 7 figure

Generation of annotated multimodal ground truth datasets for abdominal medical image registration

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom, therefore the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed. Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging (MRI), computed tomography (CT), and cone beam CT (CBCT) images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation. Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.Comment: 12 pages, 5 figures. This work has been published in the International Journal of Computer Assisted Radiology and Surgery volum

Endothelin potentiates TRPV1 via ETA receptor-mediated activation of protein kinase C

© 2007 Plant et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

The impact of cognitive reserve on delayed neurocognitive recovery after major non-cardiac surgery: an exploratory substudy

IntroductionDelayed neurocognitive recovery is a common and severe complication after surgery and anesthesia with an adverse impact on daily living, morbidity, and mortality. High cognitive reserve may mitigate the development of delayed neurocognitive recovery, however, supporting data is lacking. We aimed to assess the association between cognitive reserve and delayed neurocognitive recovery in the early postoperative period.MethodsThis is a substudy of two prospective observational studies. Adult patients undergoing elective major non-cardiac surgery, who were fluent in German, were eligible for study participation. Patients with any pre-existing central nervous system disorders were excluded. Cognitive reserve was assessed using the Cognitive Reserve Index questionnaire. Delayed neurocognitive recovery was defined as a decline in cognitive function compared with baseline assessments and was evaluated with a battery of neuropsychological tests on the day of hospital admission and between day three post procedure and before hospital discharge.ResultsA total of 67 patients with a median age of 67 [IQR: (63–73)] years were included in our analysis. We found delayed neurocognitive recovery in 22.4% of patients. There was a significant association between Cognitive Reserve Index questionnaire total score and the occurrence of delayed neurocognitive recovery in the early postoperative period [OR = 0.938, (95% CI, 0.891; 0.988), p = 0.015].ConclusionHigher cognitive reserve in elderly patients undergoing major non-cardiac surgery decreases the risk for subsequent delayed neurocognitive recovery in the early postoperative period

Hyperangulated blades or direct epiglottis lifting to optimize glottis visualization in difficult Macintosh videolaryngoscopy: a non-inferiority analysis of a prospective observational study

PurposeIt is unknown if direct epiglottis lifting or conversion to hyperangulated videolaryngoscopes, or even direct epiglottis lifting with hyperangulated videolaryngoscopes, may optimize glottis visualization in situations where Macintosh videolaryngoscopy turns out to be more difficult than expected. This study aims to determine if the percentage of glottic opening (POGO) improvement achieved by direct epiglottis lifting is non-inferior to the one accomplished by a conversion to hyperangulated videolaryngoscopy in these situations.MethodsOne or more optimization techniques were applied in 129 difficult Macintosh videolaryngoscopy cases in this secondary analysis of a prospective observational study. Stored videos were reviewed by at least three independent observers who assessed the POGO and six glottis view grades. A linear mixed regression and a linear regression model were fitted. Estimated marginal means were used to analyze differences between optimization maneuvers.ResultsIn this study, 163 optimization maneuvers (77 direct epiglottis lifting, 57 hyperangulated videolaryngoscopy and 29 direct epiglottis lifting with a hyperangulated videolaryngoscope) were applied exclusively or sequentially. Vocal cords were not visible in 91.5% of the cases with Macintosh videolaryngoscopy, 24.7% with direct epiglottis lifting, 36.8% with hyperangulated videolaryngoscopy and 0% with direct lifting with a hyperangulated videolaryngoscope. Conversion to direct epiglottis lifting improved POGO (mean + 49.7%; 95% confidence interval [CI] 41.4 to 58.0; p < 0.001) and glottis view (mean + 2.2 grades; 95% CI 1.9 to 2.5; p < 0.001). Conversion to hyperangulated videolaryngoscopy improved POGO (mean + 43.7%; 95% CI 34.1 to 53.3; p < 0.001) and glottis view (mean + 1.9 grades; 95% CI 1.6 to 2.2; p < 0.001). The difference in POGO improvement between conversion to direct epiglottis lifting and conversion to hyperangulated videolaryngoscopy is: mean 6.0%; 95% CI −6.5–18.5%; hence non-inferiority was confirmed.ConclusionWhen Macintosh videolaryngoscopy turned out to be difficult, glottis exposure with direct epiglottis lifting was non-inferior to the one gathered by conversion to hyperangulated videolaryngoscopy. A combination of both maneuvers yields the best result.Clinical trial registrationClinicalTrials.gov, NCT03950934

A fast algorithm for genome-wide haplotype pattern mining

<p>Abstract</p> <p>Background</p> <p>Identifying the genetic components of common diseases has long been an important area of research. Recently, genotyping technology has reached the level where it is cost effective to genotype single nucleotide polymorphism (SNP) markers covering the entire genome, in thousands of individuals, and analyse such data for markers associated with a diseases. The statistical power to detect association, however, is limited when markers are analysed one at a time. This can be alleviated by considering multiple markers simultaneously. The <it>Haplotype Pattern Mining </it>(HPM) method is a machine learning approach to do exactly this.</p> <p>Results</p> <p>We present a new, faster algorithm for the HPM method. The new approach use patterns of haplotype diversity in the genome: locally in the genome, the number of observed haplotypes is much smaller than the total number of possible haplotypes. We show that the new approach speeds up the HPM method with a factor of 2 on a genome-wide dataset with 5009 individuals typed in 491208 markers using default parameters and more if the pattern length is increased.</p> <p>Conclusion</p> <p>The new algorithm speeds up the HPM method and we show that it is feasible to apply HPM to whole genome association mapping with thousands of individuals and hundreds of thousands of markers.</p

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained

Interdependence in Action: People with Visual Impairments and their Guides Co-constituting Common Spaces

Prior work on AI-enabled assistive technology (AT) for people with visual impairments (VI) has treated navigation largely as an independent activity. Consequently, much effort has focused on providing individual users with wayfinding details about the environment, including information on distances, proximity, obstacles, and landmarks. However, independence is also achieved by people with VI through interacting with others, such as in collaboration with sighted guides. Drawing on the concept of interdependence, this research presents a systematic analysis of sighted guiding partnerships. Using interaction analysis as our primary mode of data analysis, we conducted an empirical, qualitative study with 4 couples, each made up of person with a vision impairment and their sighted guide. Our results show how pairs used interactional resources such as turn-taking and body movements to both co-constitute a common space for navigation, and repair moments of rupture to this space. This work is used to present an exemplary case of interdependence and draws out implications for designing AI-enabled AT that shifts the emphasis away from independent navigation, and towards the carefully coordinated actions between people navigating togethe