Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context

We present an algorithm for finding temporally consistent occlusion boundaries in videos to support segmentation of dynamic scenes. We learn occlusion boundaries in a pairwise Markov random field (MRF) framework. We first estimate the probability of an spatio-temporal edge being an occlusion boundary by using appearance, flow, and geometric features. Next, we enforce occlusion boundary continuity in a MRF model by learning pairwise occlusion probabilities using a random forest. Then, we temporally smooth boundaries to remove temporal inconsistencies in occlusion boundary estimation. Our proposed framework provides an efficient approach for finding temporally consistent occlusion boundaries in video by utilizing causality, redundancy in videos, and semantic layout of the scene. We have developed a dataset with fully annotated ground-truth occlusion boundaries of over 30 videos ($5000 frames). This dataset is used to evaluate temporal occlusion boundaries and provides a much needed baseline for future studies. We perform experiments to demonstrate the role of scene layout, and temporal information for occlusion reasoning in dynamic scenes.Comment: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference o

Non-Destructive Quality Analysis of In-shell Pecans Using Microwave Dielectric Spectroscopy

The application of non-destructive pecan quality assessment would allow reliable quality checks of pecan batches. This research analyzed pecan samples using electromagnetic waves in the radio/microwave range from 100 MHz to 2.5 GHz with an open air transmission-type measurement device. Samples of the Maramec, Kanza, Pawnee, and Giles pecan cultivars were analyzed for the signal attenuation and phase shift using a network analyzer. Samples of each cultivar varied in both overall quality and moisture content. Several physical quality parameters of each sample were also measured. Measures of quality were correlated with the signal attenuation and phase shift values at 101 measured frequencies. Results suggest there is a linear correlation between both total kernel mass and edible kernel mass with both signal attenuation and phase shift measurements in the 400 to 500 MHz and 1.00 to 1.10 GHz ranges. Results can be applied to design an automatic pecan grading machine.Biosystems and Agricultural Engineerin

Parameters and Drivers for a Successful and Sustainable Performance of Photovoltaic Manufacturer

The Photovoltaic Industry is at a crossroads for change. Improving the sustainability of this complex system requires a thorough understanding of the entire life cycle of the solar module production. The product life cycle is thereby divided into the value added steps of raw material extraction, outsourced production, in-house production, operation, and recycling. Furthermore, the following report distinguishes between social, ecological, and economic sustainability. The report offers a compacted matrix with all parts of sustainability and each life cycle stage in order toshow companies of the photovoltaic industry the sensible areas. This should be a first step for improving the sustainability in the whole life cycle of a solar module

Narrative review of the role of artificial intelligence to improve aortic valve disease management

Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery

On-device Real-time Custom Hand Gesture Recognition

Most existing hand gesture recognition (HGR) systems are limited to a predefined set of gestures. However, users and developers often want to recognize new, unseen gestures. This is challenging due to the vast diversity of all plausible hand shapes, e.g. it is impossible for developers to include all hand gestures in a predefined list. In this paper, we present a user-friendly framework that lets users easily customize and deploy their own gesture recognition pipeline. Our framework provides a pre-trained single-hand embedding model that can be fine-tuned for custom gesture recognition. Users can perform gestures in front of a webcam to collect a small amount of images per gesture. We also offer a low-code solution to train and deploy the custom gesture recognition model. This makes it easy for users with limited ML expertise to use our framework. We further provide a no-code web front-end for users without any ML expertise. This makes it even easier to build and test the end-to-end pipeline. The resulting custom HGR is then ready to be run on-device for real-time scenarios. This can be done by calling a simple function in our open-sourced model inference API, MediaPipe Tasks. This entire process only takes a few minutes.Comment: 5 pages, 6 figures; Accepted to ICCV Workshop on Computer Vision for Metaverse, Paris, France, 202

Hand hygiene improvement of individual healthcare workers: results of the multicentre PROHIBIT study

Hand hygiene; Intensive care; InterventionHigiene de manos; Cuidados intensivos; IntervenciónHigiene de mans; Cures intensives; IntervencióBackground Traditionally, hand hygiene (HH) interventions do not identify the observed healthcare workers (HWCs) and therefore, reflect HH compliance only at population level. Intensive care units (ICUs) in seven European hospitals participating in the “Prevention of Hospital Infections by Intervention and Training” (PROHIBIT) study provided individual HH compliance levels. We analysed these to understand the determinants and dynamics of individual change in relation to the overall intervention effect. Methods We included HCWs who contributed at least two observation sessions before and after intervention. Improving, non-changing, and worsening HCWs were defined with a threshold of 20% compliance change. We used multivariable linear regression and spearman’s rank correlation to estimate determinants for the individual response to the intervention and correlation to overall change. Swarm graphs visualized ICU-specific patterns. Results In total 280 HCWs contributed 17,748 HH opportunities during 2677 observation sessions. Overall, pooled HH compliance increased from 43.1 to 58.7%. The proportion of improving HCWs ranged from 33 to 95% among ICUs. The median HH increase per improving HCW ranged from 16 to 34 percentage points. ICU wide improvement correlated significantly with both the proportion of improving HCWs (ρ = 0.82 [95% CI 0.18–0.97], and their median HH increase (ρ = 0.79 [0.08–0.97]). Multilevel regression demonstrated that individual improvement was significantly associated with nurse profession, lower activity index, higher nurse-to-patient ratio, and lower baseline compliance. Conclusions Both the proportion of improving HCWs and their median individual improvement differed substantially among ICUs but correlated with the ICUs’ overall HH improvement. With comparable overall means the range in individual HH varied considerably between some hospitals, implying different transmission risks. Greater insight into improvement dynamics might help to design more effective HH interventions in the future.The study was funded by the European Commission 7th Framework Programme

Novel Subclone of Carbapenem-Resistant Klebsiella pneumoniae Sequence Type 11 with Enhanced Virulence and Transmissibility, China.

We aimed to clarify the epidemiologic and clinical importance of evolutionary events that occurred in carbapenem-resistant Klebsiella pneumoniae (CRKP). We collected 203 CRKP causing bloodstream infections in a tertiary hospital in China during 2013-2017. We detected a subclonal shift in the dominant clone sequence type (ST) 11 CRKP in which the previously prevalent capsular loci (KL) 47 had been replaced by KL64 since 2016. Patients infected with ST11-KL64 CRKP had a significantly higher 30-day mortality rate than other CRKP-infected patients. Enhanced virulence was further evidenced by phenotypic tests. Phylogenetic reconstruction demonstrated that ST11-KL64 is derived from an ST11-KL47-like ancestor through recombination. We identified a pLVPK-like virulence plasmid carrying rmpA and peg-344 in ST11-KL64 exclusively from 2016 onward. The pLVPK-like-positive ST11-KL64 isolates exhibited enhanced environmental survival. Retrospective screening of a national collection identified ST11-KL64 in multiple regions. Targeted surveillance of this high-risk CRKP clone is urgently needed

Native Point Defect Measurement and Manipulation in ZnO Nanostructures

This review presents recent research advances in measuring native point defects in ZnO nanostructures, establishing how these defects affect nanoscale electronic properties, and developing new techniques to manipulate these defects to control nano- and micro- wire electronic properties. From spatially-resolved cathodoluminescence spectroscopy, we now know that electrically-active native point defects are present inside, as well as at the surfaces of, ZnO and other semiconductor nanostructures. These defects within nanowires and at their metal interfaces can dominate electrical contact properties, yet they are sensitive to manipulation by chemical interactions, energy beams, as well as applied electrical fields. Non-uniform defect distributions are common among semiconductors, and their effects are magnified in semiconductor nanostructures so that their electronic effects are significant. The ability to measure native point defects directly on a nanoscale and manipulate their spatial distributions by multiple techniques presents exciting possibilities for future ZnO nanoscale electronics

Annealing Effects on the Band Alignment of ALD SiO2 on (Inx Ga1−x )2 O3 for x = 0.25–0.74

The band alignment of Atomic Layer Deposited SiO 2 on (In x Ga1−x) 2 O 3 at varying indium concentrations is reported before and after annealing at 450 °C and 600 °C to simulate potential processing steps during device fabrication and to determine the thermal stability of MOS structures in high-temperature applications. At all indium concentrations studied, the valence band offsets (VBO) showed a nearly constant decrease as a result of 450 °C annealing. The decrease in VBO was −0.35 eV for (In0.25Ga 0.75) 2 O 3 , −0.45 eV for (In0.42Ga 0.58) 2 O 3 , −0.40 eV for (In0.60Ga 0.40) 2 O 3 , and −0.35 eV (In0.74 Ga0.26) 2 O 3 for 450 °C annealing. After annealing at 600 °C, the band alignment remained stable, with <0.1 eV changes for all structures examined, compared to the offsets after the 450 °C anneal. The band offset shifts after annealing are likely due to changes in bonding at the heterointerface. Even after annealing up to 600 °C, the band alignment remains type I (nested gap) for all indium compositions of (In x Ga1−x ) 2 O 3 studied