No abstract

Thermochromic windows can smartly modulate the indoor solar irradiation, leading to energy saving for architectural heating and cooling systems. Herein, we integrate the active plasmonic VO 2 nanoparticles in kirigami-inspired reconfigurable elastomers to achieve adaptive, broadband, and highly efficient solar modulation. The smart window promises a UV-visible-NIR traverse state in cold days and a UV-visible-NIR blocked state in hot days to reduce the architectural heating and cooling energy consumption.

In this paper, we consider a realistic scenario on stance detection with more application potential, i.e., zero-shot and few-shot stance detection, which identifies stances for a wide range of topics with no or very few training examples. Conventional data-driven approaches are not applicable to the above zero-shot and few-shot scenarios. For human beings, commonsense knowledge is a crucial element of understanding and reasoning. In the absence of annotated data and cryptic expression of users' stance, we believe that introducing commonsense relational knowledge as support for reasoning can further improve the generalization and reasoning ability of the model in the zero-shot and few-shot scenarios. Specifically, we introduce a commonsense knowledge enhanced model to exploit both the structurallevel and semantic-level information of the relational knowledge. Extensive experiments demonstrate that our model outperforms the state-of-the-art methods on zero-shot and fewshot stance detection task.

Vanadium dioxide (VO2) based thermochromic smart window is considered as the most promising approach for economizing building energy consumption. However, the high phase transition temperature (τc), low luminous transmission (Tlum), and solar modulation (ΔTsol) impose an invertible challenge for commercialization. Currently, smart window research surprisingly assumes that the sunlight radiates in one direction which is obviously not valid as most regions receive solar radiation at various angles in different seasons. For the first time, solar elevation angle is considered and 3D printing technology is employed to fabricate tilted microstructures for modulating solar transmission dynamically. To maximize energy‐saving performance, the architecture of the structures (tilt, thickness, spacing, and width) and tungsten (W) doped VO2 can be custom‐designed according to the solar elevation angle variation at the midday between seasons and tackle the issue of compromised Tlum and ΔTsol with W‐doping. The energy consumption simulations in different cities prove the efficiency of such dynamic modulation. This first attempt to adaptively regulate the solar modulation by considering the solar elevation angle together with one of the best reported thermochromic properties (τc = 40 °C, Tlum(average) = 40.8%, ΔTsol = 23.3%) may open a new era of real‐world‐scenario smart window research.

In this paper, we focus on the task of finegrained text sentiment transfer (FGST). This task aims to revise an input sequence to satisfy a given sentiment intensity, while preserving the original semantic content. Different from conventional sentiment transfer task that only reverses the sentiment polarity (positive/negative) of text, the FTST task requires more nuanced and fine-grained control of sentiment. To remedy this, we propose a novel Seq2SentiSeq model. Specifically, the numeric sentiment intensity value is incorporated into the decoder via a Gaussian kernel layer to finely control the sentiment intensity of the output. Moreover, to tackle the problem of lacking parallel data, we propose a cycle reinforcement learning algorithm to guide the model training. In this framework, the elaborately designed rewards can balance both sentiment transformation and content preservation, while not requiring any ground truth output. Experimental results show that our approach can outperform existing methods by a large margin in both automatic evaluation and human evaluation. Our code and data, including outputs of all baselines and our model are available at https://github.com/luofuli/ Fine-grained-Sentiment-Transfer. 1