Search for: All records

An important goal of modern scheduling systems is to efficiently manage power usage. In energy-efficient scheduling, the operating system controls the speed at which a machine is processing jobs with the dual objective of minimizing energy consumption and optimizing the quality of service cost of the resulting schedule. Since machine-learned predictions about future requests can often be learned from historical data, a recent line of work on learning-augmented algorithms aims to achieve improved performance guarantees by leveraging predictions. In particular, for energy-efficient scheduling, Bamas et. al. [NeurIPS '20] and Antoniadis et. al. [SWAT '22] designed algorithms with predictions for the energy minimization with deadlines problem and achieved an improved competitive ratio when the prediction error is small while also maintaining worst-case bounds even when the prediction error is arbitrarily large. In this paper, we consider a general setting for energy-efficient scheduling and provide a flexible learning-augmented algorithmic framework that takes as input an offline and an online algorithm for the desired energy-efficient scheduling problem. We show that, when the prediction error is small, this framework gives improved competitive ratios for many different energy-efficient scheduling problems, including energy minimization with deadlines, while also maintaining a bounded competitive ratio regardless of the prediction error. Finally, we empirically demonstrate that this framework achieves an improved performance on real and synthetic datasets. 

Algorithms with predictions is a recent framework that has been used to overcome pessimistic worst-case bounds in incomplete information settings. In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve improved approximation ratios in settings where the processing times of the jobs are initially unknown. In this paper, we study the speed-robust scheduling problem where the speeds of the machines, instead of the processing times of the jobs, are unknown and augment this problem with predictions. Our main result is an algorithm that achieves a $$\min\{\eta^2(1+\alpha), (2 + 2/\alpha)\}$$ approximation, for any $$\alpha \in (0,1)$$, where $$\eta \geq 1$$ is the prediction error. When the predictions are accurate, this approximation outperforms the best known approximation for speed-robust scheduling without predictions of $2-1/m$, where $$m$$ is the number of machines, while simultaneously maintaining a worst-case approximation of $$2 + 2/\alpha$$ even when the predictions are arbitrarily wrong. In addition, we obtain improved approximations for three special cases: equal job sizes, infinitesimal job sizes, and binary machine speeds. We also complement our algorithmic results with lower bounds. Finally, we empirically evaluate our algorithm against existing algorithms for speed-robust scheduling. 

Paleostress inversion of 141 outcrop-scale faults across the eastern flank of the southern Central Range of Taiwan, where leveling and GPS data suggest a steep gradient in rock uplift rates yields two main kinematic phases of deformation. Phase 1 consists of 93 normal faults that generally dip moderately northeast, whereas phase 2 consists of 48 strike-slip faults that generally dip steeply west-northwest. Both phases record NE-trending subhorizontal extension but different orientations of principal shortening; in phase 1, the principal shortening axis is nearly vertical, whereas in phase 2, it plunges gently to moderately southeast. The northeast extension is consistent with extension directions obtained from GPS and earthquake focal mechanisms in the central part of the southern Central Range. However, these indicators of contemporary deformation also reveal more complicated states of stress along the eastern and western flanks of the range and in the deep crust southwest of the range. We interpret these more complicated stress states as reflecting the “forceful extrusion” of the southern Central Range, where the lower crust is being pinched between more rigid crustal blocks represented by the Peikang High and the Luzon Arc. In this context, the temporal progress from strike-slip to normal faulting observed in outcrops may reflect the advection of the rocks from lower to higher structural levels. The northeast extension normal faults can be interpreted as accommodating the lateral and vertical movement of the crust in the southern Central Range. Based on thermochronological data and the onset of extrusion in southwest Taiwan in the late Pleistocene, we infer that this SW extrusion process may be younger than 0.5 Ma. 

Multi-source entity linkage focuses on integrating knowledge from multiple sources by linking the records that represent the same real world entity. This is critical in high-impact applications such as data cleaning and user stitching. The state-of-the-art entity linkage pipelines mainly depend on supervised learning that requires abundant amounts of training data. However, collecting well-labeled training data becomes expensive when the data from many sources arrives incrementally over time. Moreover, the trained models can easily overfit to specific data sources, and thus fail to generalize to new sources due to significant differences in data and label distributions. To address these challenges, we present AdaMEL, a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic. In addition, AdaMEL is capable of incorporating an additional set of labeled data to more accurately integrate data sources with different attribute importance. Extensive experiments show that our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning. Besides, it is more stable in handling different sets of data sources in less runtime. 

Abstract We performed a rigorous reverberation-mapping analysis of the broad-line region (BLR) in a highly accreting (L/L_Edd= 0.74–3.4) active galactic nucleus, Markarian 142 (Mrk 142), for the first time using concurrent observations of the inner accretion disk and the BLR to determine a time lag for the Hβλ4861 emission relative to the ultraviolet (UV) continuum variations. We used continuum data taken with the Niel Gehrels Swift Observatory in theUVW2 band, and the Las Cumbres Observatory, Dan Zowada Memorial Observatory, and Liverpool Telescope in thegband, as part of the broader Mrk 142 multiwavelength monitoring campaign in 2019. We obtained new spectroscopic observations covering the Hβbroad emission line in the optical from the Gemini North Telescope and the Lijiang 2.4 m Telescope for a total of 102 epochs (over a period of 8 months) contemporaneous to the continuum data. Our primary result states a UV-to-Hβtime lag of ${8.68}_{-0.72}^{+0.75}$days in Mrk 142 obtained from light-curve analysis with a Python-based running optimal average algorithm. We placed our new measurements for Mrk 142 on the optical and UV radius–luminosity relations for NGC 5548 to understand the nature of the continuum driver. The positions of Mrk 142 on the scaling relations suggest that UV is closer to the “true” driving continuum than the optical. Furthermore, we obtain $\log \left(M_{•}/M_{\odot }\right)$= 6.32 ± 0.29 assuming UV as the primary driving continuum.