Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval

Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is finding increasingly broad applications. However, existing efforts face several major issues, with the most critical being the need for accurate supervision, which often demands costly resources and efforts. Cutting-edge studies focus on achieving unsupervised CDR but typically assume that the category spaces across domains are identical, an assumption that is often unrealistic in real-world scenarios. This is because only through dedicated and comprehensive analysis can the category spaces of different domains be confirmed as identical, which contradicts the premise of unsupervised scenarios. Therefore, in this work, we introduce the problem of Universal Unsupervised Cross-Domain Retrieval (U^2CDR) for the first time and design a two-stage semantic feature learning framework to address it. In the first stage, a cross-domain unified prototypical structure is established under the guidance of an instance-prototype-mixed contrastive loss and a semantic-enhanced loss, to counteract category space differences. In the second stage, through a modified adversarial training mechanism, we ensure minimal changes for the established prototypical structure during domain alignment, enabling more accurate nearest-neighbor searching. Extensive experiments across multiple datasets and scenarios, including closet, partial, and open-set CDR, demonstrate that our approach significantly outperforms existing state-of-the-art CDR works and some potentially effective studies from other topics in solving U^2CDR challenges.Comment: 18 pages, 4 figures, ongoing wor

From Government to Market?:A Discrete Choice Analysis of Policy Instruments for Electric Vehicle Adoption (CEIBS Working Paper, No. 039/2020/POM, 2020)

With the calls for policy instruments to shift from “government” to “market”, surging interest leads to a broad debate on the role of market-oriented policy instruments in promoting the adoption of electric vehicles (EVs). As the two prime examples of market-oriented policy instruments, personal carbon trading (PCT) and tradable driving credit (TDC) schemes are theoretically regarded to alter consumers’ EV preferences by both economic and psychological motivations. However, limited studies validate such effects. To fill the gaps, we conduct a discrete choice experimental survey by integrating vehicle, psychological, and policy attributes together. The empirical results from China reveal how consumers make trade-offs between economic and psychological motivations. In particular, although PCT and TDC can stimulate consumers’ EV adoption behaviors through monetary revenues, the positive effect of more revenues from PCT and TDC in promoting EV adoption is not always supported because EV adoption is subject to some psychological attributes, especially perceived norm pressures. It implies that consumers with stricter norms will be driven more by social and moral pressures than by monetary revenues. Even so, PCT and TDC are considered to be more powerful and sustainable than existing financial incentives. These findings not only contribute to the understanding of the interaction between psychological and policy attributes, but also provide insights for policymakers to design novel policy instruments to promote EV adoption

EZH2 RIP-seq Identifies Tissue-specific Long Non-coding RNAs.

BackgroundPolycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic subunit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are characterized by high tissue-specificity; however, little is known about the tissue profile of the EZH2- interacting lncRNAs.ObjectiveHere we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno- precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In addition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, majority of the lncRNAs were firstly reported to be associated with EZH2.ConclusionOur findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation

Federated Learning with New Knowledge: Fundamentals, Advances, and Futures

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid development trend, along with the continuous emergence of new demands for FL in the real world, that prompts us to focus on a very important problem: Federated Learning with New Knowledge. The primary challenge here is to effectively incorporate various new knowledge into existing FL systems and evolve these systems to reduce costs, extend their lifespan, and facilitate sustainable development. In this paper, we systematically define the main sources of new knowledge in FL, including new features, tasks, models, and algorithms. For each source, we thoroughly analyze and discuss how to incorporate new knowledge into existing FL systems and examine the impact of the form and timing of new knowledge arrival on the incorporation process. Furthermore, we comprehensively discuss the potential future directions for FL with new knowledge, considering a variety of factors such as scenario setups, efficiency, and security. There is also a continuously updating repository for this topic: https://github.com/conditionWang/FLNK.Comment: 10 page

STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions. Further scaling them up to higher orders of magnitude is rarely explored. An overarching goal of exploring large-scale models is to train them on large-scale medical segmentation datasets for better transfer capacities. In this work, we design a series of Scalable and Transferable U-Net (STU-Net) models, with parameter sizes ranging from 14 million to 1.4 billion. Notably, the 1.4B STU-Net is the largest medical image segmentation model to date. Our STU-Net is based on nnU-Net framework due to its popularity and impressive performance. We first refine the default convolutional blocks in nnU-Net to make them scalable. Then, we empirically evaluate different scaling combinations of network depth and width, discovering that it is optimal to scale model depth and width together. We train our scalable STU-Net models on a large-scale TotalSegmentator dataset and find that increasing model size brings a stronger performance gain. This observation reveals that a large model is promising in medical image segmentation. Furthermore, we evaluate the transferability of our model on 14 downstream datasets for direct inference and 3 datasets for further fine-tuning, covering various modalities and segmentation targets. We observe good performance of our pre-trained model in both direct inference and fine-tuning. The code and pre-trained models are available at https://github.com/Ziyan-Huang/STU-Net

Dew formation reduction in global warming experiments and the potential consequences

Dew, as an important contribution of non-rainfall water (NRW), plays a vital role in ecosystem processes in arid and semi-arid regions and is expected to be affected by climate warming. Infrared heater warming systems have been widely used to simulate climate warming effects on ecosystems. However, how this warming system affects dew formation has been long ignored and rarely addressed. In a typical alpine grassland ecosystem on the northeast of the Tibetan Plateau, we measured dew amount and duration using three independent methods: artificial condensing surfaces, leaf wetness sensors and in situ dew formation on plants from 2012 to 2017. We also measured plant traits related to dew conditions. The results showed that (1) warming reduced the dew amount by 41.6%-91.1% depending on the measurement method, and reduced dew duration by 32.1 days compared to the ambient condition. (2) Different plant functional groups differed in dew formation. (3) Under the infrared warming treatment, the dew amount decreased with plant height, while under the ambient conditions, the dew amount showed the opposite trend. We concluded that warming with an infrared heater system greatly reduces dew formation, and if ignored, it may lead to overestimation of the effects of climate warming on ecosystem processes in climate change simulation studies