YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018

Angiotensin II Type 1 Receptor Autoantibodies in Primary Aldosteronism

Primary aldosteronism (PA) is the most common form of endocrine hypertension. Agonistic autoantibodies against the angiotensin II type 1 receptor (AT1R-Abs) have been described in transplantation medicine and women with pre-eclampsia and more recently in patients with PA. Any functional role of AT1R-Abs in either of the two main subtypes of PA (aldosterone-producing adenoma or bilateral adrenal hyperplasia) requires clarification. In this review, we discuss the studies performed to date on AT1R-Abs in PA

Angiotensin II Type 1 Receptor Autoantibodies in Primary Aldosteronism

Primary aldosteronism (PA) is the most common form of endocrine hypertension. Agonistic autoantibodies against the angiotensin II type 1 receptor (AT(1)R-Abs) have been described in transplantation medicine and women with pre-eclampsia and more recently in patients with PA. Any functional role of AT(1)R-Abs in either of the two main subtypes of PA (aldosterone-producing adenoma or bilateral adrenal hyperplasia) requires clarification. In this review, we discuss the studies performed to date on AT(1)R-Abs in PA

Experimental Study of Oblique Pedestrian Streams

The intersecting of pedestrian streams is a common phenomenon which would lead to the pedestrian deceleration, stopping, and even threat to the safety of walking. The organization of pedestrian flow is a critical factor which influences the intersection traffic. The aim of this paper is to study the characteristics of oblique pedestrian streams by a set of pedestrian experiments. Two groups of experiment participants, three volume levels and five intersecting angles were tested. The qualitative analysis and quantitative analysis methods were applied to find out the relationship between the pedestrian streams angle and pedestrian characteristics. The results indicated that the mean and median speed, exit traffic efficiency decreased initially and increased afterwards with the increase of intersecting angles when the volume was 1,000 p/h/m and 3,000 p/h/m, while the speed standard deviation changing inversely. However, these four factors show the opposite variation tendency in volume 5,000 p/h/m. Meanwhile, the quadratic function was selected to fit them. It is found that the worst speeds of pedestrian streams were 131° and 122° in volume 1,000 p/h/m and 3,000 p/h/m, respectively, and the greatest influence on pedestrian streams was 125° in volume 5,000 p/h/m. The results of this research can help establish the foundation for the organization and optimization of intersecting pedestrian streams.</p

Observation of coherent oscillation in single-passage Landau-Zener transitions

Landau-Zener transition (LZT) has been explored in a variety of physical systems for coherent population transfer between different quantum states. In recent years, there have been various proposals for applying LZT to quantum information processing because when compared to the methods using ac pulse for coherent population transfer, protocols based on LZT are less sensitive to timing errors. However, the effect of finite range of qubit energy available to LZT based state control operations has not been thoroughly examined. In this work, we show that using the well-known Landau-Zener formula in the vicinity of an avoided energy-level crossing will cause considerable errors due to coherent oscillation of the transition probability in a single-passage LZT experiment. The data agree well with the numerical simulations which take the transient dynamics of LZT into account. These results not only provide a closer view on the issue of finite-time LZT but also shed light on its effects on the quantum state manipulation.Comment: 10 pages,5 figure

Primary aldosteronism: Pathophysiological mechanisms of cell death and proliferation

Primary aldosteronism is the most common surgically curable form of hypertension. The sporadic forms of the disorder are usually caused by aldosterone overproduction from a unilateral adrenocortical aldosterone-producing adenoma or from bilateral adrenocortical hyperplasia. The main knowledge-advances in disease pathophysiology focus on pathogenic germline and somatic variants that drive the excess aldosterone production. Less clear are the molecular and cellular mechanisms that lead to an increased mass of the adrenal cortex. However, the combined application of transcriptomics, metabolomics, and epigenetics has achieved substantial insight into these processes and uncovered the evolving complexity of disrupted cell growth mechanisms in primary aldosteronism. In this review, we summarize and discuss recent progress in our understanding of mechanisms of cell death, and proliferation in the pathophysiology of primary aldosteronism

SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a \emph{situation model}, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose \emph{SMART}, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes, and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to improve the performance of SMART further. To rigorously study this task, we carefully curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17\% in the OOD evaluation, demonstrating its superior generalization ability

Exploring the Relationship between Architecture and Adversarially Robust Generalization

Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization gap on unseen testing adversaries, deemed as the adversarially robust generalization problem. Despite the preliminary understandings devoted to adversarially robust generalization, little is known from the architectural perspective. To bridge the gap, this paper for the first time systematically investigated the relationship between adversarially robust generalization and architectural design. Inparticular, we comprehensively evaluated 20 most representative adversarially trained architectures on ImageNette and CIFAR-10 datasets towards multiple `p-norm adversarial attacks. Based on the extensive experiments, we found that, under aligned settings, Vision Transformers (e.g., PVT, CoAtNet) often yield better adversarially robust generalization while CNNs tend to overfit on specific attacks and fail to generalize on multiple adversaries. To better understand the nature behind it, we conduct theoretical analysis via the lens of Rademacher complexity. We revealed the fact that the higher weight sparsity contributes significantly towards the better adversarially robust generalization of Transformers, which can be often achieved by the specially-designed attention blocks. We hope our paper could help to better understand the mechanism for designing robust DNNs. Our model weights can be found at http://robust.art