Social space transformation in Barcelona: neighborhood spaces

In the ideal city of Cerdà, all the controls are rational and beautiful, but with time passing by, urbanization and industrialization have increased the population. The neighborhood courtyard that originally belonged to each Manzana disappeared, buildings of various uses gradually fill up the center of manzana. The government has made efforts to restore part of the inner garden, but it is far from enough. People's outdoor activities are forced to gradually move away from their residences, onto the streets or further squares. The neighborhood space in Manzana is different from the larger social space. Similarly, the buildings inserted in each manzana are also different from ordinary buildings. The free social space that should be facing the neighbors has become a monotonous roof. Now, sharing these roofs can contribute to the social networking of the entire neighborhood.Follow Cerda's will to carry Utopia to the end

Does a Neural Network Really Encode Symbolic Concepts?

Recently, a series of studies have tried to extract interactions between input variables modeled by a DNN and define such interactions as concepts encoded by the DNN. However, strictly speaking, there still lacks a solid guarantee whether such interactions indeed represent meaningful concepts. Therefore, in this paper, we examine the trustworthiness of interaction concepts from four perspectives. Extensive empirical studies have verified that a well-trained DNN usually encodes sparse, transferable, and discriminative concepts, which is partially aligned with human intuition

Generic regularity of conservative solutions to Camassa-Holm type equations

This paper mainly proves the generic properties of the Camassa-Holm equation and the two-component Camassa-Holm equation by Thom's transversality Lemma. We reveal their differences in generic regularity and singular behavior

Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs

In this technical note, we aim to explain a deep neural network (DNN) by quantifying the encoded interactions between input variables, which reflects the DNN's inference logic. Specifically, we first rethink the definition of interactions, and then formally define faithfulness and conciseness for interaction-based explanation. To this end, we propose two kinds of interactions, i.e., the AND interaction and the OR interaction. For faithfulness, we prove the uniqueness of the AND (OR) interaction in quantifying the effect of the AND (OR) relationship between input variables. Besides, based on AND-OR interactions, we design techniques to boost the conciseness of the explanation, while not hurting the faithfulness. In this way, the inference logic of a DNN can be faithfully and concisely explained by a set of symbolic concepts.Comment: arXiv admin note: text overlap with arXiv:2111.0620

Evaluation of the impact of phase change humidity control material on energy performance of office buildings

Phase change humidity control material (PCHCM) is a new kind of composite made of high performance PCM microcapsules and diatomite. The PCHCM composite can moderate the hygrothermal variations by absorbing or releasing both heat and moisture and significantly reduce the peak/valley values of indoor temperature and relative humidity. In this paper, a novel model is developed to evaluate the energy performance of office buildings with PCHCM. The model is validated by a series of experiments, and then applied to investigate the effect of PCHCM on energy consumption in different typical climates worldwide (i.e. Beijing, Paris, Atlanta, and Guangzhou). Results show that high values of energy efficiencies can be obtained in the climates which characterized by a wide amplitude of temperature and humidity difference all day along (Paris and Atlanta). Noteworthy, the highest potential energy saving rate could be up to 19.57% for the office building in Paris