Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads

Towards a Protocol for Benchmark Selection in IPC

The planning competition has traditionally played an important role in motivating research and advances in Planning & Scheduling techniques. Despite its pivotal role in the planning community, some aspects of the competition have not been engineered yet. This is the case for the protocol for selecting benchmark instances. Benchmarks are of critical importance, since they can significantly affect competition results. In this paper we describe desirable properties of a selection protocol, discuss methods exploited in past SAT and planning competitions, and identify challenges that organisers of future competitions have to address in order to improve reliability and usefulness of the insights gained by looking at competitions’ results

On the numerical stability of Fourier extensions

An effective means to approximate an analytic, nonperiodic function on a bounded interval is by using a Fourier series on a larger domain. When constructed appropriately, this so-called Fourier extension is known to converge geometrically fast in the truncation parameter. Unfortunately, computing a Fourier extension requires solving an ill-conditioned linear system, and hence one might expect such rapid convergence to be destroyed when carrying out computations in finite precision. The purpose of this paper is to show that this is not the case. Specifically, we show that Fourier extensions are actually numerically stable when implemented in finite arithmetic, and achieve a convergence rate that is at least superalgebraic. Thus, in this instance, ill-conditioning of the linear system does not prohibit a good approximation. In the second part of this paper we consider the issue of computing Fourier extensions from equispaced data. A result of Platte, Trefethen & Kuijlaars states that no method for this problem can be both numerically stable and exponentially convergent. We explain how Fourier extensions relate to this theoretical barrier, and demonstrate that they are particularly well suited for this problem: namely, they obtain at least superalgebraic convergence in a numerically stable manner

Deep Lidar CNN to Understand the Dynamics of Moving Vehicles

Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e.g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the 'observer' vehicle from that of the external 'observed' vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used.Comment: Presented in IEEE ICRA 2018. IEEE Copyrights: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses. (V2 just corrected comments on arxiv submission

DNA methylation at tobacco telomeric sequences

Majerová et al. (Plant Mol Biol, 2011) have recently reported that a considerable fraction of cytosines at tobacco telomeres is methylated. Although the data presented in this report indicate that tobacco telomeric sequences undergo certain levels of DNA methylation, it is not clear whether the methylated sequences are at telomeres, at internal chromosomal loci or at both

155-day Periodicity in Solar Cycles 3 and 4

The near 155 days solar periodicity, so called Rieger periodicity, was first detected in solar flares data and later confirmed with other important solar indices. Unfortunately, a comprehensive analysis on the occurrence of this periodicity during previous centuries can be further complicated due to the poor quality of the sunspot number time-series. We try to detect the Rieger periodicity during the solar cycles 3 and 4 using information on aurorae observed at mid and low latitudes. We use two recently discovered aurora datasets, observed in the last quarter of the 18th century from UK and Spain. Besides simple histograms of time between consecutive events we analyse monthly series of number of aurorae observed using different spectral analysis (MTM and Wavelets). The histograms show the probable presence of Rieger periodicity during cycles 3 and 4. However different spectral analysis applied has only confirmed undoubtedly this hypothesis for solar cycle 3.Comment: 13 pages, 6 figures, to appear in New Astronom

Assessing the Epigenetic Status of Human Telomeres

The epigenetic modifications of human telomeres play a relevant role in telomere functions and cell proliferation. Therefore, their study is becoming an issue of major interest. These epigenetic modifications are usually analyzed by microscopy or by chromatin immunoprecipitation (ChIP). However, these analyses could be challenged by subtelomeres and/or interstitial telomeric sequences (ITSs). Whereas telomeres and subtelomeres cannot be differentiated by microscopy techniques, telomeres and ITSs might not be differentiated in ChIP analyses. In addition, ChIP analyses of telomeres should be properly controlled. Hence, studies focusing on the epigenetic features of human telomeres have to be carefully designed and interpreted. Here, we present a comprehensive discussion on how subtelomeres and ITSs might influence studies of human telomere epigenetics. We specially focus on the influence of ITSs and some experimental aspects of the ChIP technique on ChIP analyses. In addition, we propose a specific pipeline to accurately perform these studies. This pipeline is very simple and can be applied to a wide variety of cells, including cancer cells. Since the epigenetic status of telomeres could influence cancer cells proliferation, this pipeline might help design precise epigenetic treatments for specific cancer types.Spanish Agency of ResearchEuropean Fund for Regional Development European Union BIO2016-78955-