Air Quality Prediction with 1-Dimensional Convolution and Attention on Multi-modal Features

Air pollution, especially from particulate matters, has become a serious problem in many countries. To cope with these abrupt pollutions, there have been several studies to predict the temporal concentration of air pollution using deep neural networks. However, these studies have difficulties in predicting accurately since the air quality is complexly correlated with various types of multi-modal features over a long time. In this paper, we propose a new architecture to predict air qualities of particulate matters incorporating deeply stacked 1 dimensional CNN with residual connection [I] and attention mechanism [2]. Specifically, 1-dimensional CNN extracts highlevel features with large receptive fields and attention mechanism captures complex correlation among various features. Through extensive experiments with Seoul air pollution data and public benchmarks, we verify our architecture achieves state-of-the-art result in nib.; and P.Mio prediction.N

Phase transition in a random NK landscape model

An analysis for the phase transition in a random NK landscape model is given. For the fixed ratio model, NK(n, k, z), Gao and Culberson [17] showed that a random instance generated by NK(n, 2,z)withz>z0 = 27−7 √ 5 4 is asymptotically insoluble. Based on empirical results, they conjectured that the phase transition occurs around the value z = z0. We prove that an instance generated by NK(n, 2,z)with z < z0is soluble with positive probability by providing a variant of the unit clause algorithm. Using branching process arguments, we also reprove that an instance generated by NK(n, 2,z)withz>z0is asymptotically insoluble. The results show the phase transition around z = z0 for NK(n, 2,z). In the course of the analysis, we introduce a generalized random 2-SAT formula, which is of self interest, and show its phase transition phenomenon

Phase transition in a random NK landscape model

An analysis for the phase transition in a random NK landscape model, NK(n,k,z), is given. This model is motivated from population genetics and the solubility problem for the model is equivalent to a random (k + 1)-SAT problem. Gao and Culberson [Y. Gao, J. Culberson, An analysis of phase transition in NK landscapes, Journal of Artificial Intelligence Research 17 (2002) 309–332] showed that a random instance generated by NK(n, 2,z) with z>z0 = 27−7√5 4 is asymptotically insoluble. Based on empirical results, they conjectured that the phase transition occurs around the value z = z0. We prove that an instance generated by NK(n, 2,z)with z<z0 is soluble with positive probability by providing a polynomial time algorithm. Using branching process arguments, we prove again that an instance generated by NK(n, 2,z)with z>z0 is asymptotically insoluble. The results show the phase transition around z = z0 for NK(n, 2,z). In the course of the analysis, we introduce a generalized random 2-SAT formula, which is of self interest, and show its phase transition phenomenon

Lower and upper bounds for linkage discovery

For a real-valued function f defined on {0,1}n , the linkage graph of f is a hypergraph that represents the interactions among the input variables with respect to f . In this paper, lower and upper bounds for the number of function evaluations required to discover the linkage graph are rigorously analyzed in the black box scenario. First, a lower bound for discovering linkage graph is presented. To the best of our knowledge, this is the first result on the lower bound for linkage discovery. The investigation on the lower bound is based on Yao's minimax principle. For the upper bounds, a simple randomized algorithm for linkage discovery is analyzed. Based on the Kruskal-Katona theorem, we present an upper bound for discovering the linkage graph. As a corollary, we rigorously prove that O(n [superscript 2]logn) function evaluations are enough for bounded functions when the number of hyperedges is O(n), which was suggested but not proven in previous works. To see the typical behavior of the algorithm for linkage discovery, three random models of fitness functions are considered. Using probabilistic methods, we prove that the number of function evaluations on the random models is generally smaller than the bound for the arbitrary case. Finally, from the relation between the linkage graph and the Walsh coefficients, it is shown that, for bounded functions, the proposed bounds are eventually the bounds for finding the Walsh coefficients.ICT at Seoul National UniversityBrain Korea 21 Projec

Homeostasis-inspired continual learning:learning to control structural regularization

Abstract Learning continually without forgetting might be one of the ultimate goals for building artificial intelligence (AI). However, unless there are enough resources equipped, forgetting knowledge acquired in the past is inevitable. Then, we can naturally pose a fundamental question about how to control what knowledge and how much of it to forget to improve the overall accuracy. To give a clear answer to it, we propose a novel trainable network termed homeostatic meta-model. The proposed neuromorphic framework is a natural extension of the conventional concept Synaptic Plasticity (SP) for further optimizing the accuracy of continual learning. In the preceding works on SP and its variations, though they seek important network parameters for structural regularization, they care less about the intensity of regularization (IoR). Per contra, this work reveals that a careful selection of IoR during continual training can remarkably improve the accuracy of tasks. The proposed method balances IoR between newly learned knowledge and the previously-acquired ones rather than biasing it to a specific task or evenly balancing. To obtain effective and optimal IoRs for the real-time continual learning circumstances, we propose a homeostasis-inspired meta learning architecture. The proposed meta-model automatically controls the IoRs by capturing important parameters from the previous tasks and the current learning direction. We provide experimental results considering various types of continual learning tasks showing that the proposed method notably outperforms the conventional methods in terms of learning accuracy and knowledge forgetting. We also show that the proposed method is relatively stable and robust compared to the existing SP-based methods. Furthermore, the IoR generated by our model interestingly appears to be proactively controlled within a specific range, which resembles a negative feedback mechanism of homeostasis in synapses

Characteristics of Particulate Matter and Volatile Organic Compound Emissions from the Combustion of Waste Vinyl

Vinyl samples were burned in a controlled environment to determine the characteristics of particulate matter (PM) and volatile organic compound (VOC) emissions during the combustion process. Open burning of plastic or vinyl products poses several environmental and health risks in developed and developing countries, due to the release of high concentrations of harmful pollutants. The production of fine and ultrafine particles was significant. At a heat flux of 25 kW/m2, the production of PM of 0.35 μm in size was highest at 63.0 μg/m3. In comparison, at fluxes of 35 and 50 kW/m2, the production of PM of 0.45 μm in size was highest with values of 67.8 and 87.7 μg/m3, respectively. Benzene, acetone, and other toxic compounds were also identified in the analyses

Synergistic Effect of Partially Fluorinated Ether and Fluoroethylene Carbonate for High-Voltage Lithium-Ion Batteries with Rapid Chargeability and Dischargeability

The roles of a partially fluorinated ether (PFE) based on a mixture of 1,1,1,2,2,3,3,4,4-nonafluoro-4-methoxybutane and 2-(difluoro(methoxy)methyl)-1,1,1,2,3,3,3-heptafluoropropane on the oxidative durability of an electrolyte under high voltage conditions, the rate capability of the graphite and 5 V-class LiNi0.4Mn1.6O4 (LNMO) electrodes, and the cycling performance of graphite/LNMO full cells are examined. Our findings indicate that the use of PFE as a co-solvent in the electrolyte yields thermally stable electrolytes with self-extinguishing ability. Furthermore, the PFE combined with fluoroethylene carbonate (FEC) effectively alleviates the oxidative decomposition of the electrolyte at the high-voltage LNMO cathode, allowing reversible electrochemical reactions at the graphite anodes and LNMO cathodes at high rates. In striking contrast to other electrolytes, the FEC-PFE electrolyte allows the rapid charging of high-density LNMO cathodes and graphite anodes at high rates. Moreover, the combination of PFE, which mitigates electrolyte decomposition at high voltages, and FEC, which stabilizes the anode-electrolyte interface, enables the reversible cycling of high-voltage full cells (graphite/LNMO) with a capacity retention of 70.3% and a high Coulombic efficiency of 99.7% after 100 cycles at 1C rate at 30 ??C