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RST-style document-level discourse parsing remains a difficult task and efficient deep learning models on this task have rarely been presented. In this paper, we propose an attention-based hierarchical neural network model for discourse parsing. We also incorporate tensor-based transformation function to model complicated feature interactions. Experimental results show that our approach obtains comparable performance to the contemporary state-of-the-art systems with little manual feature engineering.

Although increases in cardiovascular load (pressure overload) are known to elicit ventricular remodeling including cardiomyocyte hypertrophy and interstitial fibrosis, the molecular mechanisms of pressure overload or AngII -induced cardiac interstitial fibrosis remain elusive. In this study, serpinE2/protease nexin-1 was over-expressed in a cardiac fibrosis model induced by pressure-overloaded via transverse aortic constriction (TAC) in mouse. Knockdown of serpinE2 attenuates cardiac fibrosis in a mouse model of TAC. At meantime, the results showed that serpinE2 significantly were increased with collagen accumulations induced by AngII or TGF-β stimulation in vitro. Intriguingly, extracellular collagen in myocardial fibroblast was reduced by knockdown of serpinE2 compared with the control in vitro. In stark contrast, the addition of exogenous PN-1 up-regulated the content of collagen in myocardial fibroblast. The MEK1/2- ERK1/2 signaling probably promoted the expression of serpinE2 via transcription factors Elk1 in myocardial fibroblast. In conclusion, stress-induced the ERK1/2 signaling pathway activation up-regulated serpinE2 expression, consequently led accumulation of collagen protein, and contributed to cardiac fibrosis.

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Long DRAM latency is a critical performance bottleneck in current systems. DRAM access latency is defined by three fundamental operations that take place within the DRAM cell array: (i) activation of a memory row, which opens the row to perform accesses; (ii) precharge, which prepares the cell array for the next memory access; and (iii) restoration of the row, which restores the values of cells in the row that were destroyed due to activation. There is significant latency variation for each of these operations across the cells of a single DRAM chip due to irregularity in the manufacturing process. As a result, some cells are inherently faster to access, while others are inherently slower. Unfortunately, existing systems do not exploit this variation.The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make several new observations about latency variation within DRAM. We find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced.Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system. We conclude that the experimen-Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. tal characterization and analysis of latency variation within modern DRAM, provided by this work, can lead to new techniques that improve DRAM and system performance.

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Mobile apps enable ad networks to collect and track users. App developers are given “configurations” on these platforms to limit data collection and adhere to privacy regulations; however, the prevalence of apps that violate privacy regulations because of third parties, including ad networks, begs the question of how developers work through these configurations and how easy they are to utilize. We study privacy regulations-related interfaces on three widely used ad networks using two empirical studies, a systematic review and think-aloud sessions with eleven developers, to shed light on how ad networks present privacy regulations and how usable the provided configurations are for developers. We find that information about privacy regulations is scattered in several pages, buried under multiple layers, and uses terms and language developers do not understand. While ad networks put the burden of complying with the regulations on developers, our participants, on the other hand, see ad networks responsible for ensuring compliance with regulations. To assist developers in building privacy regulations-compliant apps, we suggest dedicating a section to privacy, offering easily accessible configurations (both in graphical and code level), building testing systems for privacy regulations, and creating multimedia materials such as videos to promote privacy values in the ad networks’ documentation.

While online developer forums are major resources of knowledge for application developers, their roles in promoting better privacy practices remain underexplored. In this paper, we conducted a qualitative analysis of a sample of 207 threads (4772 unique posts) mentioning different forms of personal data from the /r/androiddev forum on Reddit. We started with bottom-up open coding on the sampled posts to develop a typology of discussions about personal data use and conducted follow-up analyses to understand what types of posts elicited in-depth discussions on privacy issues or mentioned risky data practices. Our results show that Android developers rarely discussed privacy concerns when talking about a specific app design or implementation problem, but often had active discussions around privacy when stimulated by certain external events representing new privacy-enhancing restrictions from the Android operating system, app store policies, or privacy laws. Developers often felt these restrictions could cause considerable cost yet fail to generate any compelling benefit for themselves. Given these results, we present a set of suggestions for Android OS and the app store to design more effective methods to enhance privacy, and for developer forums(e.g., /r/androiddev) to encourage more in-depth privacy discussions and nudge developers to think more about privacy.