Progressive Neural Architecture Search

We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures in order of increasing complexity, while simultaneously learning a surrogate model to guide the search through structure space. Direct comparison under the same search space shows that our method is up to 5 times more efficient than the RL method of Zoph et al. (2018) in terms of number of models evaluated, and 8 times faster in terms of total compute. The structures we discover in this way achieve state of the art classification accuracies on CIFAR-10 and ImageNet.Comment: To appear in ECCV 2018 as oral. The code and checkpoint for PNASNet-5 trained on ImageNet (both Mobile and Large) can now be downloaded from https://github.com/tensorflow/models/tree/master/research/slim#Pretrained. Also see https://github.com/chenxi116/PNASNet.TF for refactored and simplified TensorFlow code; see https://github.com/chenxi116/PNASNet.pytorch for exact conversion to PyTorc

Do citizens enjoy talking politics? : How political and social dispositions shape our attitudes towards political conversations.

Viele Ideen zur Verbesserung moderner Demokratien bauen auf die aktive Teilnahme von Bürger*innen in politischen Gesprächen miteinander. Aber wie gerne reden Menschen im Alltag überhaupt über Politik? In unserer Studie stellen wir fest, dass positive Einstellungen zu politischen Alltagsgesprächen tatsächlich nicht weit verbreitet sind. Die Gründe dafür sind eher sozialer als politischer Natur.Many ideas for improving modern democracy are built on the active engagement of citizens in political conversations with each other. In our study, however, we find that few people actually have a positive attitude towards political talk. In explaining this phenomenon, the social aspects outweigh the political ones

Application of repeat-pass TerraSAR-X staring spotlight interferometric coherence to monitor pasture biophysical parameters: limitations and sensitivity analysis

This paper describes the potential and limitations of repeat-pass synthetic aperture radar interferometry (InSAR) to retrieve the biophysical parameters of intensively managed pastures. We used a time series of eight acquisitions from the TerraSAR-X Staring Spotlight (TSX-ST) mode. The ST mode is different from conventional Stripmap mode; therefore, we adjusted the Doppler phase correction for interferometric processing. We analyzed the three interferometric pairs with an 11-day temporal baseline, and among these three pairs found only one gives a high coherence. The results show that the high coherence in different paddocks is due to the cutting of the grass in the month of June, however the temporal decorrelation in other paddocks is mainly due to the grass growth and high sensitivity of the X-band SAR signals to the vegetation cover. The InSAR coherence (over coherent paddocks) shows a good correlation with SAR backscatter (R2dB=0.65, p<0.05) and grassland biophysical parameters (R2Height=0.55, p<0.05, R2Biomass=0.75,p<0.05). It is thus possible to detect different management practices (e.g., grazing, mowing/cutting) using SAR backscatter (dB) and coherence information from high spatial short baseline X-band imagery; however, the rate of decorrelation over vegetated areas is high. Initial findings from the June pair show the possibility of change detection due to the grass growth, grazing, and mowing events by using InSAR coherence information. However, it is not possible to automatically categorize different paddocks undergoing these changes based only on the SAR backscatter and coherence values, due to the ambiguity caused by tall grass flattened by the wind

Planted: a dataset for planted forest identification from multi-satellite time series

Protecting and restoring forest ecosystems is critical for biodiversity conservation and carbon sequestration. Forest monitoring on a global scale is essential for prioritizing and assessing conservation efforts. Satellite-based remote sensing is the only viable solution for providing global coverage, but to date, large-scale forest monitoring is limited to single modalities and single time points. In this paper, we present a dataset consisting of data from five public satellites for recognizing forest plantations and planted tree species across the globe. Each satellite modality consists of a multi-year time series. The dataset, named \PlantD, includes over 2M examples of 64 tree label classes (46 genera and 40 species), distributed among 41 countries. This dataset is released to foster research in forest monitoring using multimodal, multi-scale, multi-temporal data sources. Additionally, we present initial baseline results and evaluate modality fusion and data augmentation approaches for this dataset

Comparison of ASCA and ROSAT Cluster Temperatures: A2256, A3558 and AWM7

We address the consistency between ASCA and ROSAT spatially-resolved cluster temperature measurements, which is of significant interest given the recent ASCA reports of temperature gradients in several hot clusters. We reanalyze ROSAT PSPC data on A2256 (originally analyzed by Briel & Henry) using the newer calibration and a technique less sensitive to the calibration uncertainties, and find a temperature decline with radius in good agreement with ASCA's. We also present ASCA temperature maps and radial profiles of A3558 and AWM7 and compare them to the published ROSAT results. In A3558, we detect an asymmetric temperature pattern and a slight radial decline. We do not find any significant temperature variations in AWM7, except around the cD galaxy. Radial temperature profiles of these two clusters are in a qualitative agreement with ROSAT. However, while their ASCA average temperatures agree with other high-energy instruments, ROSAT temperatures are lower by factors of 1.7 and 1.25, respectively. We find that including realistic estimates of the current ROSAT systematic uncertainties enlarges the temperature confidence intervals so that ROSAT measurements are consistent with others for these clusters as well. Due to the limited energy coverage of ROSAT PSPC, its results for the hotter clusters are highly sensitive to calibration uncertainties. We conclude that at the present calibration accuracy, there is no disagreement between ASCA and other instruments. On the scientific side, a ROSAT temperature underestimate for A3558 may be responsible for the anomalously high gas to total mass fraction found by Bardelli et al in this system.Comment: SIS data added for A3558 which increased significance of the temperature structure. Accepted for ApJ. Latex, 8 pages, 4 figure