December 18, 2017, 02:00 PM
Yue Wu: Seismic Event Detection with Deep Learning
[Monday, December 18, 2017 at 2:00 PM in Wegmans Hall 2506] Automatic event detection from time series signals has wide applications, such as abnormal event detection in video surveillance and event detection in geophysical data. Traditional detection methods detect events primarily by the use of similarity and correlation in data. Those methods can be inefficient and yield low accuracy. In recent years, because of the significantly increased computational power, machine learning techniques have revolutionized many science and engineering domains. In particular, the performance of object detection in 2D image data has been significantly improved due to the deep neural network. In this study, we apply a deep-learning-based method to the detection of events from time series seismic signals. However, a direct adaptation of the similar ideas from 2D object detection to our problem faces two challenges. The first challenge is that the duration of earthquake event varies significantly; The other is that the proposals generated are temporally correlated. To address these challenges, we propose a novel cascaded region-based convolutional neural network to capture earthquake events in different sizes, while incorporating contextual information to enrich features for each individual proposal. To achieve a better generalization performance, we use densely connected blocks as the backbone of our network. Because of the fact that some positive events are not correctly annotated, we further formulate the detection problem as a learning-from-noise problem. To verify the performance of our detection methods, we employ our methods to seismic data generated from a bi-axial earthquake machine located at Rock Mechanics Laboratory, and we acquire labels with the help of experts. Through our numerical tests, we show that our novel detection techniques yield high accuracy. Therefore, our novel deep-learning-based detection methods can potentially be powerful tools for locating events from time series data in various applications.
December 20, 2017, 01:30 PM
Joseph Izraelevitz: Concurrency Implications of Nonvolatile Byte-Addressable Memory
[Wednesday, December 20, 2017 at 1:30 PM in Goergen 109]
In the near future, storage technology advances are expected to provide nonvolatile byte-addressable memory (NVM) for general purpose computing. These new technologies provide high density storage and speeds only slightly slower than DRAM, and are consequently presumed by industry to be used as main memory storage. We believe that the common availability of fast NVM storage will have a significant impact on all levels of the computing hierarchy. Such a technology can be leveraged by an assortment of common applications, and will require significant changes to both operating systems and systems library code. Existing software for durable storage is a poor match for NVM, as it both assumes a larger granularity of access and a higher latency overhead.
Our thesis is that exploiting this new byte-addressable and nonvolatile technology requires a significant redesign of current systems, and that by designing systems that are tailored to NVM specifically we can realize performance gains. This thesis extends existing system software for understanding and using nonvolatile main memory. In particular, we propose to understand durability as a shared memory construct, instead of an I/O construct, and consequently will focus particularly on concurrent applications.
The work covered here builds theoretical and practical infrastructure for using nonvolatile main memory. At the theory level, we explore what it means for a concurrent data structure to be correct when its state can reside in nonvolatile memory, propose novel designs and design philosophies for data structures that meet these correctness criteria, and demonstrate that all nonblocking data structures can be easily transformed into persistent, correct, versions of themselves. At the practical level, we explore how to give programmers systems for manipulating persistent memory in a consistent manner, thereby avoiding inconsistencies after a crash. Combining these two ideas, we also explore how to compose data structure operations into larger, consistent operation in persistence.