Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds

This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges.

Hierarchical Class-Based Curriculum Loss

Classification algorithms in machine learning often assume a flat label space. However, most real world data have dependencies between the labels, which can often be captured by using a hierarchy. Utilizing this relation can help develop a model capable of satisfying the dependencies and improving model accuracy and interpretability. Further, as different levels in the hierarchy correspond to different granularities, penalizing each label equally can be detrimental to model learning. In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels based on their levels in the hierarchy, learned implicitly by the training paradigm. We theoretically show that the proposed hierarchical class-based curriculum loss is a tight bound of 0-1 loss among all losses satisfying the hierarchical constraints. We test our loss function on real world image data sets, and show that it significantly outperforms state-of-the-art baselines.

Quantitative evaluation of industrial and commercial management’s promotion to economic growth based on hierarchical constraints

In order to improve the quantitative evaluation ability of industrial and commercial management on promoting economic growth, the design method of quantitative evaluation model of industrial and commercial management on promoting economic growth based on hierarchical constraints is proposed. the descriptive statistical analysis method to realize big data fusion mining for quantitative evaluation of business administration’s promotion to economic growth is used, and a hierarchical constraint parameter analysis model to realize node deployment model design of hierarchical constraint parameter analysis model is designed, fuzzy control and adaptive feature parameter identification are realized in the process of quantitative evaluation of industrial and commercial administration’s promotion to economic growth. Convergence control is realized in the process of quantitative evaluation based on pattern recognition and association distributed fusion. Fuzzy degree identification and optimization prediction of quantitative evaluation of industrial and commercial administration’s promotion to economic growth are realized in hierarchical constraint parameter analysis model. The simulation results show that the output accuracy and system stability of the quantitative evaluation of the promotion of business administration to economic growth are good.

Balzac Retranslated

Literary translation is tricky. Hardly ever do you hear a critic say that the translation of a book is “good”. In the best of cases, people pretend that, even though they have been reading a translation, they were in fact reading Balzac, or Dostoevsky, or any other author of universal renown. For those who are able to read the original text, the translation is more often than not rejected as “inaccurate”, “stylistically inadequate”, “loose”, “overly free”, “not doing justice to the original”, or simply “bad”. James Payn even claimed that Balzac “is not translatable, or when translated is not readable” (67). Yet Balzac was translated and retranslated many times in a variety of languages and in many ways. In this paper, the word retranslation will be used for the realisation of a new translation from the original source language into a target language in which a translation already exists, and relay translation for translations done from a translated source. As for the term translation, it will be extended, in the sense that Patrick O'Neill gives this term (6), to include adaptations such as movies, TV series, or even graphic novels, in any language, because adaptations, whatever the medium, are subjected to the same constraints as translations, creating effectively a new “language” to transfer the author’s story and message. Thus this paper will focus on how Balzac's novels have been extended when translated and/or adapted to other media, taking in consideration Roulet’s Discourse Analysis parameters (2001 44), that is, the hierarchical constraints related to the text structure; the linguistic constraints related to the syntactic or lexical norms of the language or linguistic variety that is being considered; and the situational constraints of the receiving culture. To do so, an analysis of the hierarchical constraints of translating, retranslating or adapting Balzac’s La Cousine Bette will be carried out, as well as of the linguistic constraints related to the translation of gender in Balzac’s short story Sarrasine, or to the translation of accents and other oral features in various novels; and finally of the situational constraints related to translating Balzac into English in the Victorian era, and into Chinese at the turn of the 20th century. From these parameters a new, prismatic view of Balzac’s creations will emerge, embodying the dialogue that translators and retranslators enable between cultures.

Y2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences

Jointly learning representations of 3D shapes and text is crucial to support tasks such as cross-modal retrieval or shape captioning. A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels. Hence the method suffers from a lack of detailed geometry. To resolve this issue, we propose Y2Seq2Seq, a view-based model, to learn cross-modal representations by joint reconstruction and prediction of view and word sequences. Specifically, the network architecture of Y2Seq2Seq bridges the semantic meaning embedded in the two modalities by two coupled “Y” like sequence-tosequence (Seq2Seq) structures. In addition, our novel hierarchical constraints further increase the discriminability of the cross-modal representations by employing more detailed discriminative information. Experimental results on cross-modal retrieval and 3D shape captioning show that Y2Seq2Seq outperforms the state-of-the-art methods.