Remote sensing of glacier change in the Central Qinghai-Tibet Plateau and the relationship with changing climate

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

Interpreting AI for Networking: Where We Are and Where We Are Going

In recent years, artificial intelligence (AI) techniques have been increasingly adopted to tackle networking problems. Although AI algorithms can deliver high-quality solutions, most of them are inherently intricate and erratic for human cognition. This lack of interpretability tremendously hinders the commercial success of AI-based solutions in practice. To cope with this challenge, networking researchers are starting to explore explainable AI (XAI) techniques to make AI models interpretable, manageable, and trustworthy. In this article, we overview the application of AI in networking and discuss the necessity for interpretability. Next, we review the current research on interpreting AI-based networking solutions and systems. At last, we envision future challenges and directions. The ultimate goal of this article is to present a general guideline for AI and networking practitioners and motivate the continuous advancement of AI-based solutions in modern communication networks

PerturbScore: Connecting Discrete and Continuous Perturbations in NLP

With the rapid development of neural network applications in NLP, model robustness problem is gaining more attention. Different from computer vision, the discrete nature of texts makes it more challenging to explore robustness in NLP. Therefore, in this paper, we aim to connect discrete perturbations with continuous perturbations, therefore we can use such connections as a bridge to help understand discrete perturbations in NLP models. Specifically, we first explore how to connect and measure the correlation between discrete perturbations and continuous perturbations. Then we design a regression task as a PerturbScore to learn the correlation automatically. Through experimental results, we find that we can build a connection between discrete and continuous perturbations and use the proposed PerturbScore to learn such correlation, surpassing previous methods used in discrete perturbation measuring. Further, the proposed PerturbScore can be well generalized to different datasets, perturbation methods, indicating that we can use it as a powerful tool to study model robustness in NLP.Comment: Accepted by Findings of EMNLP202

Field and Laboratory Studies on Pathological and Biochemical Characterization of Microcystin-Induced Liver and Kidney Damage in the Phytoplanktivorous Bighead Carp

Field and experimental studies were conducted to investigate pathological characterizations and biochemical responses in the liver and kidney of the phytoplanktivorous bighead carp after intraperitoneal (i.p.) administration of microcystins (MCs) and exposure to natural cyanobacterial blooms in Meiliang Bay, Lake Taihu. Bighead carp in field and laboratory studies showed a progressive recovery of structure and function in terms of histological, cellular, and biochemical features. In laboratory study, when fish were i.p. injected with extracted MCs at the doses of 200 and 500 μg MC-LReq/kg body weight, respectively, liver pathology in bighead carp was observed in a time dose-dependent manner within 24 h postinjection and characterized by disruption of liver structure, condensed cytoplasm, and the appearance of massive hepatocytes with karyopyknosis, karyorrhexis, and karyolysis. In comparison with previous studies on other fish, bighead carp in field study endured higher MC doses and longer-term exposure, but displayed less damage in the liver and kidney. Ultrastructural examination in the liver revealed the presence of lysosome proliferation, suggesting that bighead carp might eliminate or lessen cell damage caused by MCs through lysosome activation. Biochemically, sensitive responses in the antioxidant enzymes and higher basal glutathione concentrations might be responsible for their powerful resistance to MCs, suggesting that bighead carp can be used as biomanipulation fish to counteract cyanotoxin contamination

Why do mothers of young infants choose to formula feed in China? Perceptions of mothers and hospital staff

In China the exclusive breastfeeding rate remains low and infant formula is widely used. This study aimed to elicit and compare mothers’ and hospital staff perceptions of the reasons that shaped mothers’ decision to formula feed. In-depth interviews with 50 mothers, and four focus group discussions with 33 hospital staff, were conducted in Hangzhou and Shenzhen in November 2014. Responses given by the mothers and hospital staff showed a number of commonalities. The perception of "insufficient breast milk" was cited by the majority of women (n = 37, 74%) as the reason for formula feeding. Mothers’ confidence in breastfeeding appears to be further reduced by maternal mothers or mothers-in-law’s and ?confinement ladies misconceptions about infant feeding. Inadequate breastfeeding facilities and limited flexibility at their workplace was another common reason given for switching to formula feeding. A substantial proportion of mothers (n = 27, 54%) lacked an understanding of the health benefits of breastfeeding. Antenatal education on breastfeeding benefits for expectant mothers and their families is recommended. Moreover, mothers should be provided with breastfeeding support while in hospital and be encouraged to seek professional assistance to deal with breastfeeding problems after discharge. Employers should also make work environments more breastfeeding-friendly

MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification

Vision Transformer (ViT) models have recently emerged as powerful and versatile models for various visual tasks. Recently, a work called PMF has achieved promising results in few-shot image classification by utilizing pre-trained vision transformer models. However, PMF employs full fine-tuning for learning the downstream tasks, leading to significant overfitting and storage issues, especially in the remote sensing domain. In order to tackle these issues, we turn to the recently proposed parameter-efficient tuning methods, such as VPT, which updates only the newly added prompt parameters while keeping the pre-trained backbone frozen. Inspired by VPT, we propose the Meta Visual Prompt Tuning (MVP) method. Specifically, we integrate the VPT method into the meta-learning framework and tailor it to the remote sensing domain, resulting in an efficient framework for Few-Shot Remote Sensing Scene Classification (FS-RSSC). Furthermore, we introduce a novel data augmentation strategy based on patch embedding recombination to enhance the representation and diversity of scenes for classification purposes. Experiment results on the FS-RSSC benchmark demonstrate the superior performance of the proposed MVP over existing methods in various settings, such as various-way-various-shot, various-way-one-shot, and cross-domain adaptation.Comment: SUBMIT TO IEEE TRANSACTION