Monitoring and assesment of ecosystems in China

Institute of Geographical Scienceand Natural Resources Reseach, Chinese Academy of Sciences2005 International Symposium on Environmental Mornitoring in East Asia -Remote Sensing and Forests-,Hosted The EMEA Project, Kanazawa University 21st=Century COE Program -Environmental Monitoring and Predicition of Long- and Short- Term Dynamics of Pan-Japan Sea Area- ,予稿集, EMEA 2005 in Kanazawa, 国際学術研究公開シンポジウム『東アジアの環境モニタリング』－リモートセンシングと森林－,年月日：200511月28日～29日, 場所：KKRホテル金沢, 金沢大学自然科学研究科, 主催：金沢大学EMEAプロジェクト, 共催：金沢大学21世紀COEプログラム「環日本海域の環境変動と長期・短期変動予測

6.EMEA International Symposium in Kanazawa, Japan

Chinese Academy of SciencesProject Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教

Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition

The integration of human emotions into multimedia applications shows great potential for enriching user experiences and enhancing engagement across various digital platforms. Unlike traditional methods such as questionnaires, facial expressions, and voice analysis, brain signals offer a more direct and objective understanding of emotional states. However, in the field of electroencephalography (EEG)-based emotion recognition, previous studies have primarily concentrated on training and testing EEG models within a single dataset, overlooking the variability across different datasets. This oversight leads to significant performance degradation when applying EEG models to cross-corpus scenarios. In this study, we propose a novel Joint Contrastive learning framework with Feature Alignment (JCFA) to address cross-corpus EEG-based emotion recognition. The JCFA model operates in two main stages. In the pre-training stage, a joint domain contrastive learning strategy is introduced to characterize generalizable time-frequency representations of EEG signals, without the use of labeled data. It extracts robust time-based and frequency-based embeddings for each EEG sample, and then aligns them within a shared latent time-frequency space. In the fine-tuning stage, JCFA is refined in conjunction with downstream tasks, where the structural connections among brain electrodes are considered. The model capability could be further enhanced for the application in emotion detection and interpretation. Extensive experimental results on two well-recognized emotional datasets show that the proposed JCFA model achieves state-of-the-art (SOTA) performance, outperforming the second-best method by an average accuracy increase of 4.09% in cross-corpus EEG-based emotion recognition tasks

Majority Vote for Distributed Differentially Private Sign Selection

Privacy-preserving data analysis has become more prevalent in recent years. In this study, we propose a distributed group differentially private Majority Vote mechanism, for the sign selection problem in a distributed setup. To achieve this, we apply the iterative peeling to the stability function and use the exponential mechanism to recover the signs. For enhanced applicability, we study the private sign selection for mean estimation and linear regression problems, in distributed systems. Our method recovers the support and signs with the optimal signal-to-noise ratio as in the non-private scenario, which is better than contemporary works of private variable selections. Moreover, the sign selection consistency is justified by theoretical guarantees. Simulation studies are conducted to demonstrate the effectiveness of the proposed method.Comment: 41 pages, 5 figure

Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China

Large-scale assessments of the potential for food production and its impact on biogeochemical cycling require the best possible information on the distribution of cropland. This information can come from ground-based agricultural census data sets and/or spaceborne remote sensing products, both with strengths and weaknesses. Official cropland statistics for China contain much information on the distribution of crop types, but are known to significantly underestimate total cropland areas and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but supply little information on crop type or management. We combined county-scale agricultural census statistics on total cropland area and sown area of 17 major crops in 1990 with a fine-resolution land-cover map derived from 1995–1996 optical remote sensing (Landsat) data to generate 0.5° resolution maps of the distribution of rice agriculture in mainland China. Agricultural census data were used to determine the fraction of crop area in each 0.5° grid cell that was in single rice and each of 10 different multicrop paddy rice rotations (e.g., winter wheat/rice), while the remote sensing land-cover product was used to determine the spatial distribution and extent of total cropland in China. We estimate that there were 0.30 million km2 of paddy rice cropland; 75% of this paddy land was multicropped, and 56% had two rice plantings per year. Total sown area for paddy rice was 0.47 million km2. Paddy rice agriculture occurred on 23% of all cultivated land in China

Distributed Semi-Supervised Sparse Statistical Inference

This paper is devoted to studying the semi-supervised sparse statistical inference in a distributed setup. An efficient multi-round distributed debiased estimator, which integrates both labeled and unlabelled data, is developed. We will show that the additional unlabeled data helps to improve the statistical rate of each round of iteration. Our approach offers tailored debiasing methods for $M$-estimation and generalized linear model according to the specific form of the loss function. Our method also applies to a non-smooth loss like absolute deviation loss. Furthermore, our algorithm is computationally efficient since it requires only one estimation of a high-dimensional inverse covariance matrix. We demonstrate the effectiveness of our method by presenting simulation studies and real data applications that highlight the benefits of incorporating unlabeled data.Comment: 41 pages, 4 figure

Low-coercive-field ferroelectric hafnia with mobile domain walls

The high coercive field ($\mathcal{E}_c$) of hafnia-based ferroelectrics presents a major obstacle to their applications. The ferroelectric switching mechanisms in hafnia that dictate $\mathcal{E}_c$, especially those related to nucleation-and-growth at the domain wall (DW), have remained elusive. Through deep-learning-assisted multiscale simulations, we determine the finite-temperature thermodynamics and switching mechanisms for diverse types of 180$^\circ$ DWs, revealing a complex, stress-sensitive mobility landscape. The propagation velocities for mobile DW types under various thermal conditions can be characterized with a single creep equation, featuring a creep exponent of 2. This unconventional critical exponent results from the nucleation of a half-unit-cell-thin, elliptically-shaped critical nucleus. Our multiscale approach not only reproduces the experimental thickness ($d$) scaling, $\mathcal{E}_c\propto d^{-\frac{2}{3}}$, but also predicts that $\mathcal{E}_c$ of HfO$_2$ can be engineered to $\approx$0.1 MV/cm, even lower than perovskite ferroelectrics. The theoretical lower bound of $\mathcal{E}_c$ afforded by ferroelectric hafnia offers opportunities to realize power-efficient, high-fidelity ferroelectric nanoelectronics.Comment: 19 pages, 4 figure

Spatial and temporal patterns of carbon emissions from forest fires in China from 1950 to 2000

Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 111 (2006): D05313, doi:10.1029/2005JD006198.We have estimated the emission of carbon (C) and carbon-containing trace gases including CO2, CO, CH4, and NMHC (nonmethane hydrocarbons) from forest fires in China for the time period from 1950 to 2000 by using a combination of remote sensing, forest fire inventory, and terrestrial ecosystem modeling. Our results suggest that mean annual carbon emission from forest fires in China is about 11.31 Tg per year, ranging from a minimum level of 8.55 Tg per year to a maximum level of 13.9 Tg per year. This amount of carbon emission is resulted from the atmospheric emissions of four trace gases as follows: (1) 40.66 Tg CO2 with a range from 29.21 to 47.53 Tg, (2) 2.71 Tg CO with a range from 1.48 to 4.30 Tg, (3) 0.112 Tg CH4 with a range from 0.06 to 0.2 Tg, and (4) 0.113 Tg NMHC with a range from 0.05 to 0.19 Tg. Our study indicates that fire-induced carbon emissions show substantial interannual and decadal variations before 1980 but have remained relatively low and stable since 1980 because of the application of fire suppression. Large spatial variation in fire-induced carbon emissions exists due to the spatial variability of climate, forest types, and fire regimes.This work has been supported by NASA Interdisciplinary Science Program (NNG04GM39C), China’s Ministry of Science and Technology (MOST) 973 Program (2002CB412500), Chinese Academy of Sciences ODS Program, and NSFC International Cooperative Program (40128005)