AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories

Training large AI models typically requires large-scale datasets in the machine learning process, making training and parameter-tuning process both time-consuming and costly. Some researchers address this problem by carefully synthesizing a very small number of highly representative and informative samples from real-world datasets. This approach, known as Dataset Distillation (DD), proposes a perspective for data-efficient learning. Despite recent progress in this field, the performance of existing methods still cannot meet expectations, and distilled datasets cannot effectively replace original datasets. In this paper, unlike previous methods that focus solely on improving the effectiveness of student distillation, we recognize and leverage the important mutual influence between expert and student models. We observed that the smoothness of expert trajectories has a significant impact on subsequent student parameter alignment. Based on this, we propose an effective DD framework named AST, standing for Alignment with Smooth and high-quality expert Trajectories. We devise the integration of clipping loss and gradient penalty to regulate the rate of parameter changes in expert trajectory generation. To further refine the student parameter alignment with expert trajectory, we put forward representative initialization for the synthetic dataset and balanced inner-loop loss in response to the sensitivity exhibited towards randomly initialized variables during distillation. We also propose two enhancement strategies, namely intermediate matching loss and weight perturbation, to mitigate the potential occurrence of cumulative errors. We conduct extensive experiments on datasets of different scales, sizes, and resolutions. The results demonstrate that the proposed method significantly outperforms prior methods

A Survey on Federated Unlearning: Challenges, Methods, and Future Directions

In recent years, the notion of ``the right to be forgotten" (RTBF) has evolved into a fundamental element of data privacy regulations, affording individuals the ability to request the removal of their personal data from digital records. Consequently, given the extensive adoption of data-intensive machine learning (ML) algorithms and increasing concerns for personal data privacy protection, the concept of machine unlearning (MU) has gained considerable attention. MU empowers an ML model to selectively eliminate sensitive or personally identifiable information it acquired during the training process. Evolving from the foundational principles of MU, federated unlearning (FU) has emerged to confront the challenge of data erasure within the domain of federated learning (FL) settings. This empowers the FL model to unlearn an FL client or identifiable information pertaining to the client while preserving the integrity of the decentralized learning process. Nevertheless, unlike traditional MU, the distinctive attributes of federated learning introduce specific challenges for FU techniques. These challenges lead to the need for tailored design when designing FU algorithms. Therefore, this comprehensive survey delves into the techniques, methodologies, and recent advancements in federated unlearning. It provides an overview of fundamental concepts and principles, evaluates existing federated unlearning algorithms, reviews optimizations tailored to federated learning, engages in discussions regarding practical applications, along with an assessment of their limitations, and outlines promising directions for future research

5.IEEE-IGARSS in Seoul, Korea

Chinese Academy of SciencesForest Fire Monitoring CenterThe burned area is an important parameters for modelling the carbon cycles. The remote sensing tenology is a only way to monitor it at large scale region. In general, two temporal vegetation index difference was used to detect the burned area. But this technology is difficult to be applied to large-scale region owing to the BRDF effect, atmospheric contamination, geolocation errors, phenological changes, vegetation regrowth and others. In this paper, a new approach was proposed to detect the burned area using MODIS data, which is based on the vector-change technology, and combines the MODIS 500 and 250 meter resolution bands data to find 250 meter resolution burned area. The method adequately uses the spectral and multi-spatial resolution character of MODIS data that can resist the noise pollution and improve the detection accuracy. The detection results are very corresponding with the visual interpretation under different background. Since the method needs no prior knowledge, it could also be applied in large region scale. Based on this algorithm, the burned area dataset covering all China from 2000 to 2004 were produced. © 2005 IEEE.Project 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

Therapeutic effects and mechanism of Atractylodis rhizoma in acute lung injury: Investigation based on an Integrated approach

Acute lung injury (ALI) is characterized by an excessive inflammatory response. Atractylodes lancea (Thunb.) DC. is a traditional chinese medicine with good anti-inflammatory activity that is commonly used clinically for the treatment of lung diseases in China; however, its mechanism of against ALI is unclear. We clarified the therapeutic effects of ethanol extract of Atractylodis rhizoma (EEAR) on lipopolysaccharide (LPS)-induced ALI by evaluation of hematoxylin-eosin (HE) stained sections, the lung wet/dry (W/D) ratio, and levels of inflammatory factors as indicators. We then characterized the chemical composition of EEAR by ultra-performance liquid chromatography and mass spectrometry (UPLC-MS) and screened the components and targets by network pharmacology to clarify the signaling pathways involved in the therapeutic effects of EEAR on ALI, and the results were validated by molecular docking simulation and Western blot (WB) analysis. Finally, we examined the metabolites in rat lung tissues by gas chromatography and mass spectrometry (GC-MS). The results showed that EEAR significantly reduced the W/D ratio, and tumor necrosis factor-α (TNF-α), interleukin-1 beta (IL-1β), interleukin-6 (IL-6) levels in the lungs of ALI model rats. Nineteen components of EEAR were identified and shown to act synergetically by regulating shared pathways such as the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) signaling pathways. Ferulic acid, 4-methylumbelliferone, acetylatractylodinol, atractylenolide I, and atractylenolide III were predicted to bind well to PI3K, AKT and MAPK1, respectively, with binding energies < -5 kcal/mol, although only atractylenolide II bound with high affinity to MAPK1. EEAR significantly inhibited the phosphorylation of PI3K, AKT, p38, and ERK1/2, thus reducing protein expression. EEAR significantly modulated the expression of metabolites such as D-Galactose, D-Glucose, serine and D-Mannose. These metabolites were mainly concentrated in the galactose and amino acid metabolism pathways. In conclusion, EEAR alleviates ALI by inhibiting activation of the PI3K-AKT and MAPK signaling pathways and regulating galactose metabolism, providing a new direction for the development of drugs to treat ALI

Alterations in Cerebellar Functional Connectivity Are Correlated With Decreased Psychomotor Vigilance Following Total Sleep Deprivation

Previous studies have reported significant changes in functional connectivity among various brain networks following sleep restriction. The cerebellum plays an important role in information processing for motor control and provides this information to higher-order networks. However, little is known regarding how sleep deprivation influences functional connectivity between the cerebellum and the cerebral cortex in humans. The present study aimed to investigate the changes in cerebellar functional connectivity induced by sleep deprivation, and their relationship with psychomotor vigilance. A total of 52 healthy men underwent resting-state functional magnetic resonance imaging before and after 36 h of total sleep deprivation. Functional connectivity was evaluated using region of interest (ROI)-to-ROI analyses, using 26 cerebellar ROIs as seed regions. Psychomotor vigilance was assessed using the psychomotor vigilance test (PVT). Decreased functional connectivity was observed between cerebellar seed regions and the bilateral postcentral, left inferior frontal, left superior medial frontal, and right middle temporal gyri. In contrast, increased functional connectivity was observed between the cerebellum and the bilateral caudate. Furthermore, decrease in functional connectivity between the cerebellum and the postcentral gyrus was negatively correlated with increase in PVT reaction times, while increase in functional connectivity between the cerebellum and the bilateral caudate was positively correlated with increase in PVT reaction times. These results imply that altered cerebellar functional connectivity is associated with impairment in psychomotor vigilance induced by sleep deprivation

Tissue-specific transcriptomics reveals a central role of CcNST1 in regulating the fruit lignification pattern in Camellia chekiangoleosa, a woody oil-crop

Fruit lignification is of significant economic importance because it affects the quality of fruit and the production of seed oil. The specified lignification pattern in Camellia chekiangoleosa fruits plays critical roles in its seed oil yield, but little is known about how this lignification process is regulated. Here, we report on a comprehensive tissue-specific transcriptomics analysis conducted for C. chekiangoleosa fruit. By mining the differentially expressed genes, we found that lignin biosynthesis and transcriptional regulation pathways were significantly enriched in the lignified tissues. The homolog of NST-like transcription factor, CcNST1, was highly expressed in lignified seed coat and endocarp tissues; transgenic analyses of CcNST1 in Arabidopsis and hybrid poplar revealed the enhanced lignification levels of various tissues. Gene expression analysis of the transgenic lines uncovered potential downstream genes involved in the regulation of lignin biosynthesis. This work provides a valuable gene expression resource and identified the pivotal role of CcNST1 in regulating the lignin biosynthesis underlying fruit lignification

Proliferation-Attenuating and Apoptosis-Inducing Effects of Tryptanthrin on Human Chronic Myeloid Leukemia K562 Cell Line in Vitro

Tryptanthrin, a kind of indole quinazoline alkaloid, has been shown to exhibit anti-microbial, anti-inflammation and anti-tumor effects both in vivo and in vitro. However, its biological activity on human chronic myeloid leukemia cell line K562 is not fully understood. In the present study, we investigated the proliferation-attenuating and apoptosis-inducing effects of tryptanthrin on leukemia K562 cells in vitro and explored the underlying mechanisms. The results showed that tryptanthrin could significantly inhibit K562 cells proliferation in a time- and dose-dependent manner as evidenced by MTT assay and flow cytometry analysis. We also observed pyknosis, chromatin margination and the formation of apoptotic bodies in the presence of tryptanthrin under the electron microscope. Nuclei fragmentation and condensation by Hoechst 33258 staining were detected as well. The amount of apoptotic cells significantly increased whereas the mitochondrial membrane potential decreased dramatically after tryptanthrin exposure. K562 cells in the tryptanthrin treated group exhibited an increase in cytosol cyt-c, Bax and activated caspase-3 expression while a decrease in Bcl-2, mito cyt-c and pro-caspase-3 contents. However, the changes of pro-caspase-3 and activated caspase-3 could be abolished by a pan-caspase inhibitor ZVAD-FMK. These results suggest that tryptanthrin has proliferation-attenuating and apoptosis-inducing effects on K562 cells. The underlying mechanism is probably attributed to the reduction in mitochondria membrane potential, the release of mito cyt-c and pro-caspase-3 activation

Net exchanges of CO2, CH4, and N2O between China's terrestrial ecosystems and the atmosphere and their contributions to global climate warming

Author Posting. © American Geophysical Union, 2011. 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 116 (2011): G02011, doi:10.1029/2010JG001393.China's terrestrial ecosystems have been recognized as an atmospheric CO2 sink; however, it is uncertain whether this sink can alleviate global warming given the fluxes of CH4 and N2O. In this study, we used a process-based ecosystem model driven by multiple environmental factors to examine the net warming potential resulting from net exchanges of CO2, CH4, and N2O between China's terrestrial ecosystems and the atmosphere during 1961–2005. In the past 45 years, China's terrestrial ecosystems were found to sequestrate CO2 at a rate of 179.3 Tg C yr−1 with a 95% confidence range of (62.0 Tg C yr−1, 264.9 Tg C yr−1) while emitting CH4 and N2O at rates of 8.3 Tg C yr−1 with a 95% confidence range of (3.3 Tg C yr−1, 12.4 Tg C yr−1) and 0.6 Tg N yr−1 with a 95% confidence range of (0.2 Tg N yr−1, 1.1 Tg N yr−1), respectively. When translated into global warming potential, it is highly possible that China's terrestrial ecosystems mitigated global climate warming at a rate of 96.9 Tg CO2eq yr−1 (1 Tg = 1012 g), substantially varying from a source of 766.8 Tg CO2eq yr−1 in 1997 to a sink of 705.2 Tg CO2eq yr−1 in 2002. The southeast and northeast of China slightly contributed to global climate warming; while the northwest, north, and southwest of China imposed cooling effects on the climate system. Paddy land, followed by natural wetland and dry cropland, was the largest contributor to national warming potential; forest, followed by woodland and grassland, played the most significant role in alleviating climate warming. Our simulated results indicate that CH4 and N2O emissions offset approximately 84.8% of terrestrial CO2 sink in China during 1961–2005. This study suggests that the relieving effects of China's terrestrial ecosystems on climate warming through sequestering CO2 might be gradually offset by increasing N2O emission, in combination with CH4 emission.This study has been supported by NASA LCLUC Program (NNX08AL73G_S01) , NASA IDS Program (NNG04GM39C), and China’s Ministry of Science and Technology (MOST) 973 Program (2002CB412500)