Material recognition methods use image context and local cues for pixel-wise classification. In many cases only a single image is available to make a material prediction. Image sequences, routinely acquired in applications such as mutliview stereo, can provide a sampling of the underlying reflectance functions that reveal pixel-level material attributes. We investigate multi-view material segmentation using two datasets generated for building material segmentation and scene material segmentation from the SpaceNet Challenge satellite image dataset. In this paper, we explore the impact of multi-angle reflectance information by introducing the \textit{reflectance residual encoding}, which captures both the multi-angle and multispectral information present in our datasets. The residuals are computed by differencing the sparse-sampled reflectance function with a dictionary of pre-defined dense-sampled reflectance functions. Our proposed reflectance residual features improves material segmentation performance when integrated into pixel-wise and semantic segmentation architectures. At test time, predictions from individual segmentations are combined through softmax fusion and refined by building segment voting. We demonstrate robust and accurate pixelwise segmentation results using the proposed material segmentation pipeline.

Yeqi Liu, Chuanyang Gong, Ling Yang, Yingyi Chen * Abstract Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatiotemporal relationships at different times and the long-term dependence of the temporal relationships between different series. Attention-based recurrent neural networks (RNN) can effectively represent the dynamic spatiotemporal relationships between exogenous series and target series, but it only performs well in one-step time prediction and short-term time prediction. The first phase produces violent but decentralized response weight, while the second phase leads to stationary and concentrated response weight. Secondly, we employ multiple attentions on target series to boost the long-term dependence. Finally, we study the performance of deep spatial attention mechanism and provide experiment and interpretation. Our methods outperform nine baseline methods on four datasets in the fields of energy, finance, environment and medicine, respectively. Keywords: Time series prediction; Spatiotemporal relationship; Attention mechanism; Dual-stage two-phase model; Deep attention network. 1 Introduction Recent developments in the Internet of Things and Big Data have led to the continuous expansion of data scale(Le & Ge, 2019). Hence, the long-term prediction of multivariate time series has more practical significance, e.g., it is more significant to forecast the weather of one or more days than to forecast the weather of the next hour in the future. However, the long-term prediction of multivariate time series is still a challenging problem, which is mainly reflected in the feature representation and selection mechanism of spatiotemporal relationships between different series.

The operator of the tsunami-wrecked Fukushima nuclear plant began removing fuel Monday from a cooling pool at one of three reactors that melted down in the 2011 disaster, a milestone in what will be a decades-long process to decommission the facility. Tokyo Electric Power Co. said workers started removing the first of 566 used and unused fuel units stored in the pool at Unit 3. The fuel units in the pool located high up in reactor buildings are intact despite the disaster, but the pools are not enclosed, so removing the units to safer ground is crucial to avoid disaster in case of another major earthquake similar to the one that caused the 2011 tsunami. TEPCO says the removal at Unit 3 will take two years, followed by the two other reactors, where about 1,000 fuel units remain in the storage pools. Removing fuel units from the cooling pools comes ahead of the real challenge of removing melted fuel from inside the reactors, but details of how that might be done are still largely unknown. Removing the fuel in the cooling pools was delayed more than four years by mishaps, high radiation and radioactive debris from an explosion that occurred at the time of the reactor meltdowns, underscoring the difficulties that remain.

The operator of the tsunami-wrecked Fukushima nuclear plant has begun removing fuel from a cooling pool at one of three reactors that melted down in the 2011 disaster, a milestone in the decades-long process to decommission the plant. Tokyo Electric Power Co (Tepco) said on Monday that workers started removing the first of 566 used and unused fuel units stored in the pool at Unit 3. The fuel units in the pool located high up in reactor buildings are intact despite the disaster, but the pools are not enclosed so removing the units to safer ground is crucial to avoid disaster in case of another major quake. Tepco said the removal at Unit 3 would take two years, followed by the two other reactors. The step comes ahead of the real challenge of removing melted fuel from inside the reactors, but details of how that might be done are still largely unknown. Removing the fuel in the cooling pools was delayed five years by mishaps, high radiation and radioactive debris from an explosion that occurred at the time of the reactor meltdown, underscoring the difficulties that remain.

The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the forecast uncertainties can help users of solar energy to make better solar radiation based decisions related to the stability of the electrical grid. To achieve this, we apply statistical post-processing techniques that determine relationships between observations of global radiation (made within the KNMI network of automatic weather stations in the Netherlands) and forecasts of various meteorological variables from the numerical weather prediction (NWP) model HARMONIE-AROME (HA) and the atmospheric composition model CAMS. Those relationships are used to produce probabilistic forecasts of global radiation. We compare 7 different statistical post-processing methods, consisting of two parametric and five non-parametric methods. We find that all methods are able to generate probabilistic forecasts that improve the raw global radiation forecast from HA according to the root mean squared error (on the median) and the potential economic value. Additionally, we show how important the predictors are in the different regression methods. We also compare the regression methods using various probabilistic scoring metrics, namely the continuous ranked probability skill score, the Brier skill score and reliability diagrams. We find that quantile regression and generalized random forests generally perform best. In (near) clear sky conditions the non-parametric methods have more skill than the parametric ones.

Cyber-physical systems, especially in critical infrastructures, have become primary hacking targets in international conflicts and diplomacy. However, cyber-physical systems present unique challenges to defenders, starting with an inability to communicate. This paper outlines the results of our interviews with information technology (IT) defenders and operational technology (OT) operators and seeks to address lessons learned from them in the structure of our notional solutions. We present two problems in this paper: (1) the difficulty of coordinating detection and response between defenders who work on the cyber/IT and physical/OT sides of cyber-physical infrastructures, and (2) the difficulty of estimating the safety state of a cyber-physical system while an intrusion is underway but before damage can be effected by the attacker. To meet these challenges, we propose two solutions: (1) a visualization that will enable communication between IT defenders and OT operators, and (2) a machine-learning approach that will estimate the distance from normal the physical system is operating and send information to the visualization.

Recent research on Software-Defined Networking (SDN) strongly promotes the adoption of distributed controller architectures. To achieve high network performance, designing a scheduling function (SF) to properly dispatch requests from each switch to suitable controllers becomes critical. However, existing literature tends to design the SF targeted at specific network settings. In this paper, a reinforcement-learning-based (RL) approach is proposed with the aim to automatically learn a general, effective, and efficient SF. In particular, a new dispatching system is introduced in which the SF is represented as a neural network that determines the priority of each controller. Based on the priorities, a controller is selected using our proposed probability selection scheme to balance the trade-off between exploration and exploitation during learning. In order to train a general SF, we first formulate the scheduling function design problem as an RL problem. Then a new training approach is developed based on a state-of-the-art deep RL algorithm. Our simulation results show that our RL approach can rapidly design (or learn) SFs with optimal performance. Apart from that, the trained SF can generalize well and outperforms commonly used scheduling heuristics under various network settings.

The Tangent Works team participated in GEFCom 2017 to test its automatic model building strategy for time series known as Tangent Information Modeller (TIM). Model building using TIM combined with historical temperature shuffling resulted in winning the competition. This strategy involved one remaining degree of freedom, a decision on using a trend variable. This paper describes our modelling efforts in the competition, and furthermore outlines a fully automated scenario where the decision on using the trend variable is handled by TIM. The results show that such a setup would also win the competition.

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially faster than MCMC at practically no compromises in predictive accuracy. In this paper, we address two critical gaps concerning the usage and understanding of VB for MMNL. First, extant VB methods are limited to utility specifications involving only individual-specific taste parameters. Second, the finite-sample properties of VB estimators and the relative performance of VB, MCMC and maximum simulated likelihood estimation (MSLE) are not known. To address the former, this study extends several VB methods for MMNL to admit utility specifications including both fixed and random utility parameters. To address the latter, we conduct an extensive simulation-based evaluation to benchmark the extended VB methods against MCMC and MSLE in terms of estimation times, parameter recovery and predictive accuracy. The results suggest that all VB variants perform as well as MCMC and MSLE at prediction and recovery of all model parameters with the exception of the covariance matrix of the multivariate normal mixing distribution. In particular, VB with nonconjugate variational message passing and the delta-method (VB-NCVMP-Delta) is relatively accurate and up to 15 times faster than MCMC and MSLE. On the whole, VB-NCVMP-Delta is most suitable for applications in which fast predictions are paramount, while MCMC should be preferred in applications in which accurate inferences are most important.

Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventually to the atmosphere. The early detection of such leaks is therefore tantamount to any underwater CCS project. In this work, we propose to use Passive Acoustic Monitoring (PAM) and a machine learning approach to design efficient detectors that can signal the presence of a leakage. We use data obtained from simulation experiments off the Brazilian shore, and show that the detection based on classification algorithms achieve good performance. We also propose a smoothing strategy based on Hidden Markov Models in order to incorporate previous knowledge about the probabilities of leakage occurrences.