A maximum spreading speed for magnetopause reconnection

Past observations and numerical modeling find magnetic reconnection to initiate at a localized region and then spread along a current sheet. The rate of spreading has been proposed to be controlled by a number of mechanisms based on the properties within the boundary. At the Earth's magnetopause the spreading speed is also limited by the speed at which a shocked solar wind front can move along the magnetopause boundary. The speed at which a purely north to south rotational discontinuity propagates through the magnetosheath and contacts the magnetopause is measured here using the Block‐Adaptive‐Tree Solar Wind Roe‐Type Upwind Scheme global magnetohydrodynamics model. The propagation speed along the magnetopause is fastest near the nose of the magnetopause and decreases with distance from the subsolar point. The average propagation speed along the dayside magnetopause is 847 km/s. This is significantly larger than observed rates of reconnection spreading at the magnetopause of 30–40 km/s indicating that, for the observed conditions, the speed of front propagation along the magnetopause does not limit or control the spreading rate of reconnection.Published versio

Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters

Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes, allowing for application optimizations. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficiency issues, it is of paramount importance to be able to correlate performance and power figures within the same profiling and analysis tools. For this reason, we present a performance and energy-efficiency study aimed at demonstrating how a single tool can be used to collect most of the relevant metrics. In particular, we show how the same analysis techniques can be applicable on different architectures, analyzing the same HPC application on a high-end and a low-power cluster. The former cluster embeds Intel Haswell CPUs and NVIDIA K80 GPUs, while the latter is made up of NVIDIA Jetson TX1 boards, each hosting an Arm Cortex-A57 CPU and an NVIDIA Tegra X1 Maxwell GPU.The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] and Horizon 2020 under the Mont-Blanc projects [17], grant agreements n. 288777, 610402 and 671697. E.C. was partially founded by “Contributo 5 per mille assegnato all’Università degli Studi di Ferrara-dichiarazione dei redditi dell’anno 2014”. We thank the University of Ferrara and INFN Ferrara for the access to the COKA Cluster. We warmly thank the BSC tools group, supporting us for the smooth integration and test of our setup within Extrae and Paraver.Peer ReviewedPostprint (published version

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

The browser history reveals highly sensitive information about users, such as financial status, health conditions, or political views. Private browsing modes and anonymity networks are consequently important tools to preserve the privacy not only of regular users but in particular of whistleblowers and dissidents. Yet, in this work we show how a malicious application can infer opened websites from Google Chrome in Incognito mode and from Tor Browser by exploiting hardware performance events (HPEs). In particular, we analyze the browsers' microarchitectural footprint with the help of advanced Machine Learning techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines, and in contrast to previous literature also Convolutional Neural Networks. We profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing portals, on two machines featuring an Intel and an ARM processor. By monitoring retired instructions, cache accesses, and bus cycles for at most 5 seconds, we manage to classify the selected websites with a success rate of up to 86.3%. The results show that hardware performance events can clearly undermine the privacy of web users. We therefore propose mitigation strategies that impede our attacks and still allow legitimate use of HPEs

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval

Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to facilitate models to learn external information. However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories. This paper proposes a Multi-granularity Matching Attention Network (MMAN), which contains three modules: a self-matching module, a char-level matching module, and a semantic-level matching module to comprehensively extract features from the query and a query-category interaction matrix. In this way, the model can eliminate the difference in expression between queries and categories for query intent classification. We conduct extensive offline and online A/B experiments, and the results show that the MMAN significantly outperforms the strong baselines, which shows the superiority and effectiveness of MMAN. MMAN has been deployed in production and brings great commercial value for our company.Comment: Accepted by WWW 202

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The Marathon Continues: Texas NIL Has Room to Grow

College athletes are now permitted to profit off their name, image, and likeness. However, while a hodgepodge of different regulations exists state-by-state and Congress continues to drag its feet to pass a federal framework, Texas restricts college athletes from maximizing their name, image, and likeness earning potential. This Comment proposes improvements to Senate Bill 1385 that would allow college athletes in Texas to partner with the same categories of “taboo” products as their respective university and to endorse products from competing brands, provided such endorsement is outside of a university-sponsored event, with an exception allowing unrestricted endorsement of footwear. This Comment encourages Texas to develop a trust system that holds group licensing revenues in trust until the respective students leave the university. College athletes would not only maximize their name, image, and likeness earning potential but also connect with local businesses. At the same time, universities in Texas would continue to position themselves as attractive destinations for top athletes nationwide. These suggested improvements are inspired by existing state and proposed federal legislation and suggestions from federal judges and a Supreme Court Justice

Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report

Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users to also share comments and opinions through textual reviews. In this paper, we introduce a new recommendation approach which exploits the semantic annotation of user reviews to extract useful and non-trivial information about the items to recommend. It also relies on the knowledge freely available in the Web of Data, notably in DBpedia and Wikidata, to discover other resources connected with the annotated entities. We evaluated our approach in three domains, using both DBpedia and Wikidata. The results showed that our solution provides a better ranking than another recommendation method based on the Web of Data, while it improves in novelty with respect to traditional techniques based on ratings. Additionally, our method achieved a better performance with Wikidata than DBpedia