Map Reconstruction of radio observations with Conditional Invertible Neural Networks

In radio astronomy, the challenge of reconstructing a sky map from time ordered data (TOD) is known as an inverse problem. Standard map-making techniques and gridding algorithms are commonly employed to address this problem, each offering its own benefits such as producing minimum-variance maps. However, these approaches also carry limitations such as computational inefficiency and numerical instability in map-making and the inability to remove beam effects in grid-based methods. To overcome these challenges, this study proposes a novel solution through the use of the conditional invertible neural network (cINN) for efficient sky map reconstruction. With the aid of forward modeling, where the simulated TODs are generated from a given sky model with a specific observation, the trained neural network can produce accurate reconstructed sky maps. Using the five-hundred-meter aperture spherical radio telescope (FAST) as an example, cINN demonstrates remarkable performance in map reconstruction from simulated TODs, achieving a mean squared error of $2.29\pm 2.14 \times 10^{-4}~\rm K^2$, a structural similarity index of $0.968\pm0.002$, and a peak signal-to-noise ratio of $26.13\pm5.22$ at the $1\sigma$ level. Furthermore, by sampling in the latent space of cINN, the reconstruction errors for each pixel can be accurately quantified.Comment: Accepted for publication in Research in Astronomy and Astrophysics (RAA); 20 pages, 10 figure

An End-to-End Multi-Task Learning to Link Framework for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. Existing approaches to ECPE generally adopt a two-stage method, i.e., (1) emotion and cause detection, and then (2) pairing the detected emotions and causes. Such pipeline method, while intuitive, suffers from two critical issues, including error propagation across stages that may hinder the effectiveness, and high computational cost that would limit the practical application of the method. To tackle these issues, we propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner. Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i.e., the links are directional. Emotion extraction and cause extraction are incorporated into the model as auxiliary tasks, which further boost the pair extraction. Experiments are conducted on an ECPE benchmarking dataset. The results show that our proposed model outperforms a range of state-of-the-art approaches.Comment: 7 pages, 3 figures, 5 table

Investigating Impulse Buying Behavior in Live Streaming Commerce: The Role of Social Presence

Live streaming is changing the paradigm of people’s entertainment and consumption. It has been adopted by many small individual sellers to improve their market performance, leading to the emergence of live streaming commerce. Although existing literature has paid attention to consumer purchase behavior in live streaming commerce, little knowledge on impulse buying can be available. Drawing on social presence theory and cognitive-affective framework, this paper attempts to develop a theoretical model to investigate how social presence affects consumers’ urge to buy impulsively through the mediating mechanism of cognitive state (i.e., product risk) and affective state (i.e., affective intensity). This paper is expected to advance knowledge on consumers’ impulse buying in live streaming commerce

Effects of habitat usage on hypoxia avoidance behavior and exposure in reef-dependent marine coastal species

Reef habitat in coastal ecosystems is increasingly being augmented with artificial reefs (ARs) and is simultaneously experiencing increasing hypoxia due to eutrophication and climate change. Relatively little is known about the effects of hypoxia on organisms that use complex habitat arrangements and how the presence of highly preferred AR habitat can affect the exposure of organisms to low dissolved oxygen (DO). We performed two laboratory experiments that used video recording of behavioral movement to explore 1) habitat usage and staying duration of individuals continuously exposed to 3, 5, and 7 mg/L dissolved oxygen (DO) in a complex of multiple preferred and avoided habitat types, and 2) the impact of ARs on exposure to different DO concentrations under a series of two-way replicated choice experiments with or without AR placement on the low-oxygen side. Six common reef-dependent species found in the northeastern sea areas of China were used (i.e., rockfish Sebastes schlegelii and Hexagrammos otakii, filefish Thamnaconus modestus, flatfish Pseudopleuronectes yokohamae, sea cucumber Stichopus japonicus, and crab Charybdis japonica). Results showed that lower DO levels decreased the usage of preferred habitats of the sea cucumber and the habitat-generalist filefish but increased the habitat affinity to preferred habitat types for the two habitat-specific rockfishes. Low DO had no effect on the crab’s habitat usage. In the choice experiment, all three fish species avoided 1 mg/L, and the rockfish S. schlegelii continued to avoid the lower DO when given choices involving pairs of 3, 5, and 7 mg/L, while H. otakii and the flatfish showed less avoidance. The availability of ARs affected exposure to low DO for the habitat-preferring rockfishes but was not significant for the flatfish. This study provides information for assessing the ecological effects and potential for adaptation through behavioral movement for key reef-dependent species under the increasing overlap of ARs and hypoxia anticipated in the future

Learning Diverse Tone Styles for Image Retouching

Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing methods deploy a deterministic model to learn the retouching style from a specific expert, making it less flexible to meet diverse subjective preferences. Besides, the intrinsic diversity of an expert due to the targeted processing on different images is also deficiently described. To circumvent such issues, we propose to learn diverse image retouching with normalizing flow-based architectures. Unlike current flow-based methods which directly generate the output image, we argue that learning in a style domain could (i) disentangle the retouching styles from the image content, (ii) lead to a stable style presentation form, and (iii) avoid the spatial disharmony effects. For obtaining meaningful image tone style representations, a joint-training pipeline is delicately designed, which is composed of a style encoder, a conditional RetouchNet, and the image tone style normalizing flow (TSFlow) module. In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass. After training, the TSFlow can generate diverse image tone style vectors by sampling from the Gaussian distribution. Extensive experiments on MIT-Adobe FiveK and PPR10K datasets show that our proposed method performs favorably against state-of-the-art methods and is effective in generating diverse results to satisfy different human aesthetic preferences. Source code and pre-trained models are publicly available at https://github.com/SSRHeart/TSFlow

信息工程——面向自然语言的轨迹可视化系统

This project proposes a natural language oriented trajectory mining system, which seeks to design a location mining model for the problem of location sparsity and dynamic transformation of multiple geographic locations in natural language, and make the trajectory more intuitive by presenting the specific route on the map