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Register a new userTurning Time from Enemy into an Ally Using the Pomodoro Technique
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Added by
Xiaofeng Wang
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Time is one of the most important factors dominating agile software development processes in distributed settings. Effective time management helps agile teams to plan and monitor the work to be performed, and create and maintain a fast yet sustainable pace. The Pomodoro Technique is one promising time management technique. Its application and adaptation in Sourcesense Milan Team surfaced various benefits, challenges and implications for distributed agile software development. Lessons learnt from the experiences of Sourcesense Milan Team can be useful for other distributed agile teams to turn time from enemy into an ally.
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Microlensing of gravitationally lensed quasars by the stars in the foreground lens galaxy can be used to probe the nature of dark matter, to determine the mean stellar mass in the lens galaxy, and to measure the internal structure of quasar accretion disks. Until recently, little progress has been made toward using microlensing for these purposes because of the difficulty in obtaining the necessary data and the lack of good analysis methods. In the last few years, both problems have been solved. In particular, Bayesian analysis methods provide a general approach to measuring quantities of physical interest and their uncertainties from microlensing light curves. We discuss the data and the analysis methods and show preliminary results for all three astrophysical applications.
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Autonomous driving shows great potential to reform modern transportation and its safety is attracting much attention from public. Autonomous driving systems generally include deep neural networks (DNNs) for gaining better performance (e.g., accuracy on object detection and trajectory prediction). However, compared with traditional software systems, this new paradigm (i.e., program + DNNs) makes software testing more difficult. Recently, software engineering community spent significant effort in developing new testing methods for autonomous driving systems. However, it is not clear that what extent those testing methods have addressed the needs of industrial practitioners of autonomous driving. To fill this gap, in this paper, we present the first comprehensive study to identify the current practices and needs of testing autonomous driving systems in industry. We conducted semi-structured interviews with developers from 10 autonomous driving companies and surveyed 100 developers who have worked on autonomous driving systems. Through thematic analysis of interview and questionnaire data, we identified five urgent needs of testing autonomous driving systems from industry. We further analyzed the limitations of existing testing methods to address those needs and proposed several future directions for software testing researchers.
Recently, there have been two landmark discoveries of gravitationally lensed supernovae: the first multiply-imaged SN, Refsdal, and the first Type Ia SN resolved into multiple images, SN iPTF16geu. Fitting the multiple light curves of such objects can deliver measurements of the lensing time delays, which are the difference in arrival times for the separate images. These measurements provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Over the next decade, accurate time delay measurements will be needed for the tens to hundreds of lensed SNe to be found by wide-field time-domain surveys such as LSST and WFIRST. We have developed an open source software package for simulations and time delay measurements of multiply-imaged SNe, including an improved characterization of the uncertainty caused by microlensing. We describe simulations using the package that suggest a before-peak detection of the leading image enables a more accurate and precise time delay measurement (by ~1 and ~2 days, respectively), when compared to an after-peak detection. We also conclude that fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data, but that employing a Gaussian Process Regression (GPR) technique is sufficient for determining the uncertainty due to microlensing.
In this work, we use the liquid ammonia method to successfully intercalate potassium atoms into ZrTe5 single crystal, and find a transition from semimetal to semiconductor at low temperature in the intercalated ZrTe5. The resistance anomalous peak is gradually suppressed and finally disappears with increasing potassium concentration. Whilst, the according sign reversal is always observed in the Hall resistance measurement. We tentatively attribute the semimetal-semiconductor transition to the lattice expansion induced by atomic intercalation and thereby a larger energy band gap.
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