Selective COX-2 inhibitors and risk of myocardial infarction

Selective inhibitors of cyclooxygenase- 2 ( COX- 2, ` coxibs') are highly effective anti-inflammatory and analgesic drugs that exert their action by preventing the formation of prostanoids. Recently some coxibs, which were designed to exploit the advantageous effects of non- steroidal anti-inflammatory drugs while evading their side effects, have been reported to increase the risk of myocardial infarction and atherothrombotic events. This has led to the withdrawal of rofecoxib from global markets, and warnings have been issued by drug authorities about similar events during the use of celecoxib or valdecoxib/ parecoxib, bringing about questions of an inherent atherothrombotic risk of all coxibs and consequences that should be drawn by health care professionals. These questions need to be addressed in light of the known effects of selective inhibition of COX- 2 on the cardiovascular system. Although COX- 2, in contrast to the cyclooxygenase-1 ( COX- 1) isoform, is regarded as an inducible enzyme that only has a role in pathophysiological processes like pain and inflammation, experimental and clinical studies have shown that COX- 2 is constitutively expressed in tissues like the kidney or vascular endothelium, where it executes important physiological functions. COX- 2- dependent formation of prostanoids not only results in the mediation of pain or inflammatory signals but also in the maintenance of vascular integrity. Especially prostacyclin ( PGI(2)), which exerts vasodilatory and antiplatelet properties, is formed to a significant extent by COX- 2, and its levels are reduced to less than half of normal when COX- 2 is inhibited. This review outlines the rationale for the development of selective COX- 2 inhibitors and the pathophysiological consequences of selective inhibition of COX- 2 with special regard to vasoactive prostaglandins. It describes coxibs that are currently available, evaluates the current knowledge on the risk of atherothrombotic events associated with their intake and critically discusses the consequences that should be drawn from these insights. Copyright (C) 2005 S. Karger AG, Basel

Optimising for Interpretability: Convolutional Dynamic Alignment Networks

We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which are optimised to transform their inputs with dynamically computed weight vectors that align with task-relevant patterns. As a result, CoDA Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly, CoDA Nets can be combined with conventional neural network models to yield powerful classifiers that more easily scale to complex datasets such as Imagenet whilst exhibiting an increased interpretable depth, i.e., the output can be explained well in terms of contributions from intermediate layers within the network

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

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

Random and Adversarial Bit Error Robustness: {E}nergy-Efficient and Secure {DNN} Accelerators

Deep neural network (DNN) accelerators received considerable attention in recent years due to the potential to save energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. Furthermore, DNN accelerators have been shown to be vulnerable to adversarial attacks on voltage controllers or individual bits. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, as well as random bit error training (RandBET) or adversarial bit error training (AdvBET) improves robustness against random or adversarial bit errors in quantized DNN weights significantly. This leads not only to high energy savings for low-voltage operation as well as low-precision quantization, but also improves security of DNN accelerators. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays, and achieves robustness against both targeted and untargeted bit-level attacks. Without losing more than 0.8%/2% in test accuracy, we can reduce energy consumption on CIFAR10 by 20%/30% for 8/4-bit quantization using RandBET. Allowing up to 320 adversarial bit errors, AdvBET reduces test error from above 90% (chance level) to 26.22% on CIFAR10