A Dynamic Epistemic Framework for Conformant Planning

In this paper, we introduce a lightweight dynamic epistemic logical framework for automated planning under initial uncertainty. We reduce plan verification and conformant planning to model checking problems of our logic. We show that the model checking problem of the iteration-free fragment is PSPACE-complete. By using two non-standard (but equivalent) semantics, we give novel model checking algorithms to the full language and the iteration-free language.Comment: In Proceedings TARK 2015, arXiv:1606.0729

Unsupervised Feature Learning by Deep Sparse Coding

In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovation of the framework is that it connects the sparse-encoders from different layers by a sparse-to-dense module. The sparse-to-dense module is a composition of a local spatial pooling step and a low-dimensional embedding process, which takes advantage of the spatial smoothness information in the image. As a result, the new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. Combining the feature representations from multiple layers, DeepSC achieves the state-of-the-art performance on multiple object recognition tasks.Comment: 9 pages, submitted to ICL

GaKCo: a Fast GApped k-mer string Kernel using COunting

String Kernel (SK) techniques, especially those using gapped $k$-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size ($\Sigma$) or allow more mismatches ($M$). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to $O(\Sigma^{M})$. We propose a \textbf{fast} algorithm for calculating \underline{Ga}pped $k$-mer \underline{K}ernel using \underline{Co}unting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger $\Sigma$ and $M$, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the $\Sigma^{M}$ term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets), respectively. GaKCo is shared as an open source tool at \url{https://github.com/QData/GaKCo-SVM}Comment: @ECML 201

A comparison of PM exposure related to emission hotspots in a hot and humid urban environment: Concentrations, compositions, respiratory deposition, and potential health risks

Particle number concentration, particle size distribution, and size-dependent chemical compositions were measured at a bus stop, alongside a high way, and at an industrial site in a tropical city. It was found that the industry case had 4.93 × 107–7.23 × 107 and 3.44 × 104–3.69 × 104 #/m3 higher concentration of particles than the bus stop and highway cases in the range of 0.25–0.65 μm and 2.5–32 μm, respectively, while the highway case had 6.01 × 105 and 1.86 × 103 #/m3 higher concentration of particles than the bus stop case in the range of 0.5–1.0 μm and 5.0–32 μm, respectively. Al, Fe, Na, and Zn were the most abundant particulate inorganic elements for the traffic-related cases, while Zn, Mn, Fe, and Pb were abundant for the industry case. Existing respiratory deposition models were employed to analyze particle and element deposition distributions in the human respiratory system with respect to some potential exposure scenarios related to bus stop, highway, and industry, respectively. It was shown that particles of 0–0.25 μm and 2.5–10.0 μm accounted for around 74%, 74%, and 70% of the particles penetrating into the lung region, respectively. The respiratory deposition rates of Cr and Ni were 170 and 220 ng/day, and 55 and 140 ng/day for the highway and industry scenarios, respectively. Health risk assessment was conducted following the US EPA supplemented guidance to estimate the risk of inhalation exposure to the selected elements (i.e. Cr, Mn, Ni, Pb, Se, and Zn) for the three scenarios. It was suggested that Cr poses a potential carcinogenic risk with the excess lifetime cancer risk (ELCR) of 2.1–98 × 10− 5 for the scenarios. Mn poses a potential non-carcinogenic risk in the industry scenario with the hazard quotient (HQ) of 0.98. Both Ni and Mn may pose potential non-carcinogenic risk for people who are involved with all the three exposure scenarios