Cross-Domain Few-Shot Classification via Inter-Source Stylization

The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets. Some existing approaches require labelled samples from multiple domains for model training. However, these methods fail when the sample labels are scarce. To overcome this challenge, this paper proposes a solution that makes use of multiple source domains without the need for additional labeling costs. Specifically, one of the source domains is completely tagged, while the others are untagged. An Inter-Source Stylization Network (ISSNet) is then introduced to enhance stylisation across multiple source domains, enriching data distribution and model's generalization capabilities. Experiments on 8 target datasets show that ISSNet leverages unlabelled data from multiple source data and significantly reduces the negative impact of domain gaps on classification performance compared to several baseline methods.Comment: 5 page

Elastic chiral Landau level and snake states in origami metamaterials

In this study, we present a method for generating a synthetic gauge field in origami metamaterials with continuously varying geometrical parameters. By modulating the mass term in the Dirac equation linearly, we create a synthetic gauge field in the vertical direction, which allows for the quantization of Landau levels through the generated pseudomagnetic field. Furthermore, we demonstrate the existence and robustness of the chiral zeroth Landau level. The unique elastic snake state is realized using the coupling between the zeroth and the first Landau levels. Our results, supported by theory and simulations, establish a feasible framework for generating pseudomagnetic fields in origami metamaterials with potential applications in waveguides and cloaking.Comment: 6 pages, 4 figure

Characterization of elastic topological states using dynamic mode decomposition

Elastic topological states have been receiving increased intention in numerous scientific and engineering fields due to their defect-immune nature, resulting in applications of vibration control and information processing. Here, we present the data-driven discovery of elastic topological states using dynamic mode decomposition (DMD). The DMD spectrum and DMD modes are retrieved from the propagation of the relevant states along the topological boundary, where their nature is learned by DMD. Applications such as classification and prediction can be achieved by the underlying characteristics from DMD. We demonstrate the classification between topological and traditional metamaterials using DMD modes. Moreover, the model enabled by the DMD modes realizes the prediction of topological state propagation along the given interface. Our approach to characterizing topological states using DMD can pave the way towards data-driven discovery of topological phenomena in material physics and more broadly lattice systems

Unbiased Scene Graph Generation via Two-stage Causal Modeling

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it overlooks another source of bias, i.e., semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail relationships.Comment: 17 pages, 9 figures. Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligenc

Interdomain plant–microbe and fungi–bacteria ecological networks under different woodland use intensity during the dry and wet season

Microbial communities, which are affected by soil types and climate factors, contribute to maintain the function of terrestrial ecosystems. Recent studies have shown that interdomain relationships in below–aboveground communities may contribute greatly to ecosystem functioning. However, the responses of interactions among plant, soil fungal, and bacterial communities to the change of woodland use and their effects on ecosystem multifunctionality (EMF) remain poorly understood. In this study, the plant–microbe and fungi–bacteria interdomain ecology network (IDEN) based on SparCC pairwise associations were constructed by simultaneous aboveground plant surveys and belowground microbial analyses among four different woodland use intensities (WUI) along different seasons. The effects of different seasons on these relationships were surveyed to probe into the links to EMF. With the increase of woodland use intensity, the plant–microbe network complexity decreased, while the fungus–bacteria network complexity increased. In both dry and wet seasons, ecosystem multifunctionality decreased with the increase of woodland use intensity. Some tree species are the network hubs and may play a pivotal role in the community structure stability of the forest ecosystem. During the dry season, WUI could indirectly affect EMF through plant–microbial network complexity. During the wet season, WUI had a direct effect on EMF. WUI also indirectly affected EMF through plant–microbial network complexity and fungus–bacterial network complexity. Air temperature is the main climatic factor for EMF in the dry season, while soil moisture content is the climatic factor for EMF in the wet season. Our study revealed the important role of the relationship between plants and their associated soil microbial communities (IDENs) in maintaining ecosystem processes and function. Investigating the recovery dynamics of inter-domain ecological networks after extreme disturbances is important for understanding the overall development of ecosystems

Effects of plant diversity, soil microbial diversity, and network complexity on ecosystem multifunctionality in a tropical rainforest

IntroductionPlant diversity and soil microbial diversity are important driving factors in sustaining ecosystem multifunctionality (EMF) in terrestrial ecosystems. However, little is known about the relative importance of plant diversity, soil microbial diversity, and soil microbial network complexity to EMF in tropical rainforests.MethodsThis study took the tropical rainforest in Xishuangbanna, Yunnan Province, China as the research object, and quantified various ecosystem functions such as soil organic carbon stock, soil nutrient cycling, biomass production, and water regulation in the tropical rainforest to explore the relationship and effect of plant diversity, soil microbial diversity, soil microbial network complexity and EMF.ResultsOur results exhibited that EMF decreased with increasing liana species richness, soil fungal diversity, and soil fungal network complexity, which followed a trend of initially increasing and then decreasing with soil bacterial diversity while increasing with soil bacterial network complexity. Soil microbial diversity and plant diversity primarily affected soil nutrient cycling. Additionally, liana species richness had a significant negative effect on soil organic carbon stocks. The random forest model suggested that liana species richness, soil bacterial network complexity, and soil fungal network complexity indicated more relative importance in sustaining EMF. The structural equation model revealed that soil bacterial network complexity and tree species richness displayed the significantly positive effects on EMF, while liana species richness significantly affected EMF via negative pathway. We also observed that soil microbial diversity indirectly affected EMF through soil microbial network complexity. Soil bulk density had a significant and negative effect on liana species richness, thus indirectly influencing EMF. Simultaneously, we further found that liana species richness was the main indicator of sustaining EMF in a tropical rainforest, while soil bacterial diversity was the primary driving factor.DiscussionOur findings provide new insight into the relationship between biodiversity and EMF in a tropical rainforest ecosystem and the relative contribution of plant and soil microibal diversity to ecosystem function with increasing global climate change

Digital image processing to detect subtle motion in stony coral

Coral reef ecosystems support significant biological activities and harbor huge diversity, but they are facing a severe crisis driven by anthropogenic activities and climate change. An important behavioral trait of the coral holobiont is coral motion, which may play an essential role in feeding, competition, reproduction, and thus survival and fitness. Therefore, characterizing coral behavior through motion analysis will aid our understanding of basic biological and physical coral functions. However, tissue motion in the stony scleractinian corals that contribute most to coral reef construction are subtle and may be imperceptible to both the human eye and commonly used imaging techniques. Here we propose and apply a systematic approach to quantify and visualize subtle coral motion across a series of light and dark cycles in the scleractinian coral Montipora capricornis. We use digital image correlation and optical flow techniques to quantify and characterize minute coral motions under different light conditions. In addition, as a visualization tool, motion magnification algorithm magnifies coral motions in different frequencies, which explicitly displays the distinctive dynamic modes of coral movement. Specifically, our assessment of displacement, strain, optical flow, and mode shape quantify coral motion under different light conditions, and they all show that M. capricornis exhibits more active motions at night compared to day. Our approach provides an unprecedented insight into micro-scale coral movement and behavior through macro-scale digital imaging, thus offering a useful empirical toolset for the coral research community

Effects of climate change on the potential distribution of the threatened relict Dipentodon sinicus of subtropical forests in East Asia: Recommendations for management and conservation

Dipentodon sinicus Dunn. (Dipentodonaceae) is a rare and threatened relict plant species usually found co-dominating with other relict plants in subtropical forest patches in highly fragmented habitats of southwestern China, northern Vietnam and northeastern Myanmar of East Asia. To date, its management and conservation strategies in the light of climate change have not been explored. We evaluated effects of climate change on the distribution of climatically suitable areas of D. sinicus as found prevailing during the last glacial maximum (LGM), the mid-Holocene and the present time, and predicted the distribution of climatically suitable habitats in 2070 throughout East Asia. The results as derived from ecological niche modeling (ENM) show the current distribution to be limited to the prehistoric (the mid-Holocene and LGM) refugia, and to indicate decreasing probability of presence and a reducing range of distribution for 2070. In addition, the suitable areas predicted with high probability (0.5–1) only account for on average 9.8% of the total area of potential habitats (threshold‒1) among the models for the year 2070, thereby indicating that D. sinicus is highly vulnerable. Under all the future scenarios for the year 2070, 69–74.2% of potential habitats in China would be outside protected areas. We assess and propose priorities for protected areas, and provide suggestions for conservation management strategies.This study received financial support from Science and Technology Department of Yunnan University, China (2019YNU002), the Ministry of Science and Technology of China (2015FY210200-15), Ajuts a Grups de Recerca Consolidats” (grants nos. 2014-SGR514-GREB and 2017-SGR1116) from the Generalitat de Catalunya (Spain), Applied Basic Research Foundation of Yunnan Province, China (Grant No. 2019FB058), the Environment Research and Technology Development Fund (JPMEERF15S11407) of the Environmental Restoration and Conservation Agency of Japan, and the Kakenhi Grant Number 15H02833.Highlights Abstract Keywords 1. Introduction 2. Material and methods 2.1. Species 2.2. Occurrence data and ecological niche modeling 3. Results 3.1. Model performance and present potential distribution 3.2. Projected distribution during the mid-Holocene (ca. 6000 yr BP) and LGM (ca. 21,000 yr BP) 3.3. Projected distribution under future climate (2070) 4. Discussion 4.1. Effects of climate change on spatial distribution patterns of D. sinicus 5. Recommendations for future conservation efforts and management Declaration of competing interest Acknowledgements Appendix A. Supplementary data Reference