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1. Trainable Time Warping: Aligning Time-series in the Continuous-time Domain

   https://doi.org/10.1109/icassp.2019.8682322  

   Khorram, Soheil; McInnis, Melvin G; Mower Provost, Emily (May 2019, International Conference on Acoustics, Speech, and Signal Processing (ICASSP))

   DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. TTW performs alignment in the continuoustime domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on S5 UCR datasets in time-series averaging and classification. TTW outperforms GTW on 67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for the classification tasks. 

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2. At-the-time and Back-in-time Persistent Sketches

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   Shi, Benwei; Zhao, Zhuoyue; Peng, Yanqing; Li, Feifei; Phillips, Jeff M. (June 2021, Proceedings of the 2021 International Conference on Management of Data (SIGMOD ’21))

   null (Ed.)
3. What time is it: managing time in the internet

   https://doi.org/10.1145/3340301.3341125  

   Mani, Sathiya Kumaran; Barford, Paul; Durairajan, Ramakrishnan; Sommers, Joel (July 2019, Proceedings of the ACM/IRTF/ISOC Applied Networking Research Workshop)

   In this paper, we report on our investigation of how current local time is reported accurately by devices connected to the internet. We describe the basic mechanisms for time management and focus on a critical but unstudied aspect of managing time on connected devices: the time zone database (TZDB). Our longitudinal analysis of the TZDB highlights how internet time has been managed by a loose confederation of contributors over the past 25 years. We drill down on details of the update process, update types and frequency, and anomalies related to TZDB updates. We find that 76% of TZDB updates include changes to the Daylight Saving Time (DST) rules, indicating that DST has a significant influence on internet-based time keeping. We also find that about 20% of updates were published within 15 days or less from the date of effect, indicating the potential for instability in the system. We also consider the security aspects of time management and identify potential vulnerabilities. We conclude with a set of proposals for enhancing TZDB management and reducing vulnerabilities in the system. 

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4. The Optimal Equilibrium for Time-Inconsistent Stopping Problems---The Discrete-Time Case

   https://doi.org/10.1137/17M1139187  

   Huang, Yu-Jui; Zhou, Zhou (January 2019, SIAM Journal on Control and Optimization)
5. The Waiting-Time Paradox

   https://doi.org/10.3389/frym.2020.582433  

   Masuda, Naoki; Porter, Mason A. (February 2021, Frontiers for Young Minds)

   null (Ed.)

   Suppose that you are going to school and arrive at a bus stop. How long do you have to wait before the next bus arrives? Surprisingly, it is longer—possibly much longer—than what you might guess from looking at a bus schedule. This phenomenon, which is called the waiting-time paradox, has a purely mathematical origin. In this article, we explore the waiting-time paradox, explain why it occurs, and discuss some of its implications (beyond the possibility of being late for school). 

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