### Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data. (arXiv:1911.08979v1 [q-bio.PE])

Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information…

### TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction. (arXiv:1911.08684v1 [cs.LG])

Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework. First, we formulate a sparsity…

### Corruption Robust Exploration in Episodic Reinforcement Learning. (arXiv:1911.08689v1 [cs.LG])

We initiate the study of multi-stage episodic reinforcement learning under adversarial manipulations in both the rewards and the transition probabilities of the underlying system. Existing efficient algorithms heavily rely on the “optimism under uncertainty” principle which dictates their behavior and does not allow flexibility to perform corruption-robust exploration. We address this by (i) departing from…

### Where is the Bottleneck of Adversarial Learning with Unlabeled Data?. (arXiv:1911.08696v1 [cs.LG])

Deep neural networks (DNNs) are incredibly brittle due to adversarial examples. To robustify DNNs, adversarial training was proposed, which requires large-scale but well-labeled data. However, it is quite expensive to annotate large-scale data well. To compensate for this shortage, several seminal works are utilizing large-scale unlabeled data. In this paper, we observe that seminal works…

### Bayesian Curiosity for Efficient Exploration in Reinforcement Learning. (arXiv:1911.08701v1 [cs.LG])

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy. This contributes to the problem of high sample complexity, as the algorithm wastes effort by repeatedly visiting parts of the state space that have already been explored. We introduce a novel method based…

### Bayesian sparse convex clustering via global-local shrinkage priors. (arXiv:1911.08703v1 [stat.ML])

Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering. Although the weighted $L_1$ norm as the regularization term is usually employed in the sparse convex clustering, this increases the dependence on the data and reduces the estimation accuracy if the sample size is not sufficient. To…

### Graph-Driven Generative Models for Heterogeneous Multi-Task Learning. (arXiv:1911.08709v1 [cs.LG])

We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional network (GCN) with multiple…

### Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding. (arXiv:1911.08699v1 [physics.chem-ph])

With the development of low order scaling methods for performing Kohn-Sham Density Functional Theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of system treated comes an increase in complexity, making it challenging to analyze such large systems…

### Scattering Activities Bounded by Reciprocity and Parity Conservation. (arXiv:1911.08745v1 [physics.optics])

Scattering activities are generally manifest through different optical responses of scattering bodies to circularly polarized light of opposite handedness. Similar to the ubiquitous roles played by scattering theory across different branches of photonics, scattering activities can serve as a fundamental concept to clarify underlying mechanisms of various chiroptical effects, both within and beyond scattering systems.…

### Do disruption index indicators measure what they propose to measure? The comparison of several indicator variants with assessments by peers. (arXiv:1911.08775v1 [cs.DL])

Recently, Wu, Wang, and Evans (2019) and Bu, Waltman, and Huang (2019) proposed a new family of indicators, which measure whether a scientific publication is disruptive to a field or tradition of research. Such disruptive influences are characterized by citations to a focal paper, but not its cited references. In this study, we are interested…