Visual saliency modeling for images and videos is treated as two independent tasks in recent computer vision literature. On the one hand, image saliency modeling is a well-studied problem and progress on benchmarks like SALICON and MIT300 is slowing. For video saliency prediction on the other hand, rapid gains have been achieved on the recent DHF1K benchmark through network architectures that are optimized for this task. Here, we take a step back and ask: Can image and video saliency modeling be approached via a unified model, with mutual benefit? We find that it is crucial to model the domain shift between image and video saliency data and between different video saliency datasets for effective joint modeling. We identify different sources of domain shift and address them through four novel domain adaptation techniques-Domain-Adaptive Priors, Domain-Adaptive Fusion, Domain-Adaptive Smoothing and Bypass-RNN-in addition to an improved formulation of learned Gaussian priors. We integrate these techniques into a simple and lightweight encoder-RNN-decoder-style network, UNISAL, and train the entire network simultaneously with image and video saliency data. We evaluate our method on the video saliency datasets DHF1K, Hollywood-2 and UCF-Sports, as well as the image saliency datasets SALICON and MIT300. With one set of parameters, our method achieves state-of-the-art performance on all video saliency datasets and is on par with the state-of-the-art for image saliency prediction, despite a 5 to 20-fold reduction in model size and the fastest runtime among all competing deep models. We provide retrospective analyses and ablation studies which demonstrate the importance of the domain shift modeling. The code is available at https://github.com/rdroste/unisal.

All six NXE:3100, 0.25 NA EUV exposure systems are in use at customer sites enabling device development and cycles of learning for early production work in all lithographic segments; Logic, DRAM, MPU, and FLASH memory. NXE EUV lithography has demonstrated imaging and overlay performance both at ASML and end-users that supports sub27nm device work. Dedicated chuck overlay performance of <2nm has been shown on all six NXE:3100 systems.The key remaining challenge is productivity, which translates to a cost-effective introduction of EUVL in high-volume manufacturing (HVM). High volume manufacturing of the devices and processes in development is expected to be done with the third generation EUV scanners -the NXE:3300B. The NXE:3300B utilizes an NA of 0.33 and is positioned at a resolution of 22nm which can be extended to 18nm with off-axis illumination. The subsystem performance is improved to support these imaging resolutions and overall productivity enhancements are integrated into the NXE platform consistent with 125 wph. Since EUV reticles currently do not use a pellicle, special attention is given to reticle-addeddefects performance in terms of system design and machine build including maintenance procedures.In this paper we will summarize key lithographic performance of the NXE:3100 and the NXE:3300B, the NXE platform improvements made from learning on NXE:3100 and the Alpha Demo Tool, current status of EUV sources and development for the high-power sources needed for HVM.Finally, the possibilities for EUV roadmap extension will be reviewed.

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Objectives Operators performing fetal growth scans are usually aware of the gestational age of the pregnancy, which may lead to expected‐value bias when performing biometric measurements. We aimed to evaluate the incidence of expected‐value bias in routine fetal growth scans and assess its impact on standard biometric measurements. Methods We collected prospectively full‐length video recordings of routine ultrasound growth scans coupled with operator eye tracking. Expected value was defined as the gestational age at the time of the scan, based on the estimated due date that was established at the dating scan. Expected‐value bias was defined as occurring when the operator looked at the measurement box on the screen during the process of caliper adjustment before saving a measurement. We studied the three standard biometric planes on which measurements of head circumference (HC), abdominal circumference (AC) and femur length (FL) are obtained. We evaluated the incidence of expected‐value bias and quantified the impact of biased measurements. Results We analyzed 272 third‐trimester growth scans, performed by 16 operators, during which a total of 1409 measurements (354 HC, 703 AC and 352 FL; including repeat measurements) were obtained. Expected‐value bias occurred in 91.4% of the saved standard biometric plane measurements (85.0% for HC, 92.9% for AC and 94.9% for FL). The operators were more likely to adjust the measurements towards the expected value than away from it (47.7% vs 19.7% of measurements; P < 0.001). On average, measurements were corrected by 2.3 ± 5.6, 2.4 ± 10.4 and 3.2 ± 10.4 days of gestation towards the expected gestational age for the HC, AC, and FL measurements, respectively. Additionally, we noted a statistically significant reduction in measurement variance once the operator was biased (P = 0.026). Comparing the lowest and highest possible estimated fetal weight (using the smallest and largest biased HC, AC and FL measurements), we noted that the discordance, in percentage terms, was 10.1% ± 6.5%, and that in 17% (95% CI, 12–21%) of the scans, the fetus could be considered as small‐for‐gestational age or appropriate‐for‐gestational age if using the smallest or largest possible measurements, respectively. Similarly, in 13% (95% CI, 9–16%) of scans, the fetus could be considered as large‐for‐gestational age or appropriate‐for‐gestational age if using the largest or smallest possible measurements, respectively. Conclusions During routine third‐trimester growth scans, expected‐value bias frequently occurs and significantly changes standard biometric measurements obtained. © 2019 the Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, we propose to address the problem of self-supervised representation learning with multi-modal ultrasound video-speech raw data. For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer. In order to learn meaningful representations, the model needs to identify such correlation and at the same time understand the underlying anatomical features. We designed a framework to model the correspondence between video and audio without any kind of human annotations. Within this framework, we introduce cross-modal contrastive learning and an affinity-aware self-paced learning scheme to enhance correlation modelling. Experimental evaluations on multi-modal fetal ultrasound video and audio show that the proposed approach is able to learn strong representations and transfers well to downstream tasks of standard plane detection and eye-gaze prediction.