Contextual Hierarchical Part-Driven Conditional Random Field Model for Object Category Detection

Even though several promising approaches have been proposed in the literature, generic category-level object detection is still challenging due to high intraclass variability and ambiguity in the appearance among different object instances. From the view of constructing object models, the balance between flexibility and discrimination must be taken into consideration. Motivated by these demands, we propose a novel contextual hierarchical part-driven conditional random field (CRF) model, which is based on not only individual object part appearance but also model contextual interactions of the parts simultaneously. By using a latent two-layer hierarchical formulation of labels and a weighted neighborhood structure, the model can effectively encode the dependencies among object parts. Meanwhile, beta-stable local features are introduced as observed data to ensure the discriminative and robustness of part description. The object category detection problem can be solved in a probabilistic framework using a supervised learning method based on maximum a posteriori (MAP) estimation. The benefits of the proposed model are demonstrated on the standard dataset and satellite images

Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation

Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies. This strategy limits the model's ability to leverage information from distant context. We overcome this limitation with a novel Document Flattening (DocFlat) technique that integrates Flat-Batch Attention (FBA) and Neural Context Gate (NCG) into Transformer model to utilize information beyond the pseudo-document boundaries. FBA allows the model to attend to all the positions in the batch and learns the relationships between positions explicitly and NCG identifies the useful information from the distant context. We conduct comprehensive experiments and analyses on three benchmark datasets for English-German translation, and validate the effectiveness of two variants of DocFlat. Empirical results show that our approach outperforms strong baselines with statistical significance on BLEU, COMET and accuracy on the contrastive test set. The analyses highlight that DocFlat is highly effective in capturing the long-range information.Comment: 15 pages, 8 figures, accepted by EACL 202

Asymptotic behavior of solutions of a Fisher equation with free boundaries and nonlocal term

We study the asymptotic behavior of solutions of a Fisher equation with free boundaries and the nonlocal term (an integral convolution in space). This problem can model the spreading of a biological or chemical species, where free boundaries represent the spreading fronts of the species. We give a dichotomy result, that is, the solution either converges to $1$ locally uniformly in $\mathbb{R}$, or to $0$ uniformly in the occupying domain. Moreover, we give the sharp threshold when the initial data $u_0=\sigma \phi$, that is, there exists $\sigma^*>0$ such that spreading happens when $\sigma>\sigma^*$, and vanishing happens when $\sigma\leq \sigma^*$

Asymptotic behavior of solutions of a Fisher equation with free boundaries and nonlocal term

We study the asymptotic behavior of solutions of a Fisher equation with free boundaries and the nonlocal term (an integral convolution in space). This problem can model the spreading of a biological or chemical species, where free boundaries represent the spreading fronts of the species. We give a dichotomy result, that is, the solution either converges to 1 locally uniformly in R, or to 0 uniformly in the occupying domain. Moreover, we give the sharp threshold when the initial data u0 = σφ, that is, there exists σ ∗ > 0 such that spreading happens when σ > σ , and vanishing happens when σ ≤