Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization

For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem can be formalized as a sequence prediction problem, where a number of observations are used to predict the sequence into the future. However, real-world scenarios demand a model of uncertainty of such predictions, as future states become increasingly uncertain and multi-modal -- in particular on long time horizons. This makes modelling and learning challenging. We cast state of the art semantic segmentation and future prediction models based on deep learning into a Bayesian formulation that in turn allows for a full Bayesian treatment of the prediction problem. We present a new sampling scheme for this model that draws from the success of variational autoencoders by incorporating a recognition network. In the experiments we show that our model outperforms prior work in accuracy of the predicted segmentation and provides calibrated probabilities that also better capture the multi-modal aspects of possible future states of street scenes

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach

Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We show that such membership inference attacks can be successfully carried out on complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks with little effect on the utility of the segmentation method. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas.Comment: Accepted to ECCV 2020. Code at: https://github.com/SSAW14/segmentation_membership_inferenc

Pathogen burden, inflammation, proliferation and apoptosis in human in-stent restenosis - Tissue characteristics compared to primary atherosclerosis

Pathogenic events leading to in-stent restenosis (ISR) are still incompletely understood. Among others, inflammation, immune reactions, deregulated cell death and growth have been suggested. Therefore, atherectomy probes from 21 patients with symptomatic ISR were analyzed by immunohistochemistry for pathogen burden and compared to primary target lesions from 20 stable angina patients. While cytomegalovirus, herpes simplex virus, Epstein-Barr virus and Helicobacter pylori were not found in ISR, acute and/or persistent chlamydial infection were present in 6/21 of these lesions (29%). Expression of human heat shock protein 60 was found in 8/21 of probes (38%). Indicated by distinct signals of CD68, CD40 and CRP, inflammation was present in 5/21 (24%), 3/21 (14%) and 2/21 (10%) of ISR cases. Cell density of ISR was significantly higher than that of primary lesions ( 977 +/- 315 vs. 431 +/- 148 cells/mm(2); p < 0.001). There was no replicating cell as shown by Ki67 or PCNA. TUNEL+ cells indicating apoptosis were seen in 6/21 of ISR specimens (29%). Quantitative analysis revealed lower expression levels for each intimal determinant in ISR compared to primary atheroma (all p < 0.05). In summary, human ISR at the time of clinical presentation is characterized by low frequency of pathogen burden and inflammation, but pronounced hypercellularity, low apoptosis and absence of proliferation. Copyright (C) 2004 S. Karger AG, Basel

Performance of hospitals according to the ESC ACCA quality indicators and 30-day mortality for acute myocardial infarction: national cohort study using the United Kingdom Myocardial Ischaemia National Audit Project (MINAP) register

Aims To investigate the application of the European Society of Cardiology Acute Cardiovascular Care Association quality indicators (QI) for acute myocardial infarction for the study of hospital performance and 30-day mortality. Methods and results National cohort study (n = 118,075 patients, n = 211 hospitals, MINAP registry), 2012-13. Overall, 16 of the 20 QIs could be calculated. Eleven QIs had a significant inverse association with GRACE risk adjusted 30-day mortality (all P < 0.005). The association with the greatest magnitude was high attainment of the composite opportunity-based QI (80-100%) vs. zero attainment (odds ratio 0.04, 95% confidence interval 0.04-0.05, P < 0.001), increasing attainment from low (0.42, 0.37- 0.49, P < 0.001) to intermediate (0.15, 0.13-0.16, P < 0.001) was significantly associated with a reduced risk of 30-day mortality. A 1% increase in attainment of this QI was associated with a 3% reduction in 30-day mortality (0.97, 0.97-0.97, P < 0.001). The QI with the widest hospital variation was ′fondaparinux received among NSTEMI′ (interquartile range 84.7%) and least variation ′centre organisation′ (0.0%), with seven QIs depicting minimal variation (<11%). GRACE risk score adjusted 30-day mortality varied by hospital (median 6.7%, interquartile range 5.4-7.9%). Conclusions Eleven QIs were significantly inversely associated with 30-day mortality. Increasing patient attainment of the composite quality indicator was the most powerful predictor; a 1% increase in attainment represented a 3% decrease in 30-day standardised mortality. The ESC QIs for acute myocardial infarction are applicable in a large health system and have the potential to improve care and reduce unwarranted variation in death from acute myocardial infarction

Using Local Context To Improve Face Detection

Most face detection algorithms locate faces by classifying the content of a detection window iterating over all positions and scales of the input image. Recent developments have accelerated this process up to real-time performance at high levels of accuracy. However, even the best of today&apos;s computational systems are far from being able to compete with the detection capabilities of the human visual system. Psychophysical experiments have shown the importance of local context in the face detection process. In this paper we investigate the role of local context for face detection algorithms. In experiments on two large data sets we find that using local context can significantly increase the number of correct detections, particularly in low resolution cases, uncommon poses or individual appearances as well as occlusions

A fabrication guide for planar silicon quantum dot heterostructures

We describe important considerations to create top-down fabricated planar quantum dots in silicon, often not discussed in detail in literature. The subtle interplay between intrinsic material properties, interfaces and fabrication processes plays a crucial role in the formation of electrostatically defined quantum dots. Processes such as oxidation, physical vapor deposition and atomic-layer deposition must be tailored in order to prevent unwanted side effects such as defects, disorder and dewetting. In two directly related manuscripts written in parallel we use techniques described in this work to create depletion-mode quantum dots in intrinsic silicon, and low-disorder silicon quantum dots defined with palladium gates. While we discuss three different planar gate structures, the general principles also apply to 0D and 1D systems, such as self-assembled islands and nanowires.Comment: Accepted for publication in Nanotechnology. 31 pages, 12 figure

{CoSSL}: {C}o-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning

In this paper, we propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL. To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE) for classifier learning. Furthermore, the current evaluation protocol for imbalanced SSL focuses only on balanced test sets, which has limited practicality in real-world scenarios. Therefore, we further conduct a comprehensive evaluation under various shifted test distributions. In experiments, we show that our approach outperforms other methods over a large range of shifted distributions, achieving state-of-the-art performance on benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our code will be made publicly available