Optimatization of sample points for monitoring arable land quality by simulated annealing while considering spatial variations

This presentation was given as part of the GIS Day@KU symposium on November 16, 2016. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2016/.Arable land is the basis of food production, the most valuable input in agricultural production, and an important factor in sustainable agricultural development and national food security. In China, the reduction and degradation of arable land due to industrialization and urbanization has gradually emerged as one of the most prominen challenges. In this context, the long-term dynamic monitoring of arable land quality becomes important for protecting arable land resources. However, little consideration has been given to optimizing sample points number and layout in previous monitoring studies on arable land quality. When considering the optimization of sample points, various strategies are needed, depending on the indicators. In addition, the distributio of soil properties displays spatial variations. However, existing sampling studies have paid little attention to spatial variations during scenarios with multiple indicators.Therefore, it is necessary to further investigate how to improve the efficiency and accuracy of arable land quality monitoring and evaluation by optimizing the number and layout of sample points when there are spatial variations in multiple indicators.Platinum Sponsors: KU Department of Geography and Atmospheric Science. Gold Sponsors: Enertech, KU Environmental Studies Program, KU Libraries. Silver Sponsors: Douglas County, Kansas, KansasView, State of Kansas Data Access & Support Center (DASC) and the KU Center for Global and International Studies

Emotion Recognition by Video: A review

Video emotion recognition is an important branch of affective computing, and its solutions can be applied in different fields such as human-computer interaction (HCI) and intelligent medical treatment. Although the number of papers published in the field of emotion recognition is increasing, there are few comprehensive literature reviews covering related research on video emotion recognition. Therefore, this paper selects articles published from 2015 to 2023 to systematize the existing trends in video emotion recognition in related studies. In this paper, we first talk about two typical emotion models, then we talk about databases that are frequently utilized for video emotion recognition, including unimodal databases and multimodal databases. Next, we look at and classify the specific structure and performance of modern unimodal and multimodal video emotion recognition methods, talk about the benefits and drawbacks of each, and then we compare them in detail in the tables. Further, we sum up the primary difficulties right now looked by video emotion recognition undertakings and point out probably the most encouraging future headings, such as establishing an open benchmark database and better multimodal fusion strategys. The essential objective of this paper is to assist scholarly and modern scientists with keeping up to date with the most recent advances and new improvements in this speedy, high-influence field of video emotion recognition

A Comparative Study of Mammalian Diversification Pattern

Although mammals have long been regarded as a successful radiation, the diversification pattern among the clades is still poorly known. Higher-level phylogenies are conflicting and comprehensive comparative analyses are still lacking. Using a recently published supermatrix encompassing nearly all extant mammalian families and a novel comparative likelihood approach (MEDUSA), the diversification pattern of mammalian groups was examined. Both order- and family-level phylogenetic analyses revealed the rapid radiation of Boreoeutheria and Euaustralidelphia in the early mammalian history. The observation of a diversification burst within Boreoeutheria at approximately 100 My supports the Long Fuse model in elucidating placental diversification progress, and the rapid radiation of Euaustralidelphia suggests an important role of biogeographic dispersal events in triggering early Australian marsupial rapid radiation. Diversification analyses based on family-level diversity tree revealed seven additional clades with exceptional diversification rate shifts, six of which represent accelerations in net diversification rate as compared to the background pattern. The shifts gave origin to the clades Muridae+Cricetidae, Bovidae+Moschidae+Cervidae, Simiiformes, Echimyidae, Odontoceti (excluding Physeteridae+Kogiidae+Platanistidae), Macropodidae, and Vespertilionidae. Moderate to high extinction rates from background and boreoeutherian diversification patterns indicate the important role of turnovers in shaping the heterogeneous taxonomic richness observed among extant mammalian groups. Furthermore, the present results emphasize the key role of extinction on erasing unusual diversification signals, and suggest that further studies are needed to clarify the historical radiation of some mammalian groups for which MEDUSA did not detect exceptional diversification rates

On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions

As Federated Learning (FL) has gained increasing attention, it has become widely acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the overall framework when learning over a sequence of tasks results in the phenomenon known as ``catastrophic forgetting''. Consequently, much FL research has centered on devising federated increasing learning methods to alleviate forgetting while augmenting knowledge. On the other hand, forgetting is not always detrimental. The selective amnesia, also known as federated unlearning, which entails the elimination of specific knowledge, can address privacy concerns and create additional ``space'' for acquiring new knowledge. However, there is a scarcity of extensive surveys that encompass recent advancements and provide a thorough examination of this issue. In this manuscript, we present an extensive survey on the topic of knowledge editing (augmentation/removal) in Federated Learning, with the goal of summarizing the state-of-the-art research and expanding the perspective for various domains. Initially, we introduce an integrated paradigm, referred to as Federated Editable Learning (FEL), by reevaluating the entire lifecycle of FL. Secondly, we provide a comprehensive overview of existing methods, evaluate their position within the proposed paradigm, and emphasize the current challenges they face. Lastly, we explore potential avenues for future research and identify unresolved issues.Comment: 7 pages, 1 figure, 2 tabel

eMotions: A Large-Scale Dataset for Emotion Recognition in Short Videos

Nowadays, short videos (SVs) are essential to information acquisition and sharing in our life. The prevailing use of SVs to spread emotions leads to the necessity of emotion recognition in SVs. Considering the lack of SVs emotion data, we introduce a large-scale dataset named eMotions, comprising 27,996 videos. Meanwhile, we alleviate the impact of subjectivities on labeling quality by emphasizing better personnel allocations and multi-stage annotations. In addition, we provide the category-balanced and test-oriented variants through targeted data sampling. Some commonly used videos (e.g., facial expressions and postures) have been well studied. However, it is still challenging to understand the emotions in SVs. Since the enhanced content diversity brings more distinct semantic gaps and difficulties in learning emotion-related features, and there exists information gaps caused by the emotion incompleteness under the prevalently audio-visual co-expressions. To tackle these problems, we present an end-to-end baseline method AV-CPNet that employs the video transformer to better learn semantically relevant representations. We further design the two-stage cross-modal fusion module to complementarily model the correlations of audio-visual features. The EP-CE Loss, incorporating three emotion polarities, is then applied to guide model optimization. Extensive experimental results on nine datasets verify the effectiveness of AV-CPNet. Datasets and code will be open on https://github.com/XuecWu/eMotions

Model-Based Planning and Control for Terrestrial-Aerial Bimodal Vehicles with Passive Wheels

Terrestrial and aerial bimodal vehicles have gained widespread attention due to their cross-domain maneuverability. Nevertheless, their bimodal dynamics significantly increase the complexity of motion planning and control, thus hindering robust and efficient autonomous navigation in unknown environments. To resolve this issue, we develop a model-based planning and control framework for terrestrial aerial bi-modal vehicles. This work begins by deriving a unified dynamic model and the corresponding differential flatness. Leveraging differential flatness, an optimization-based trajectory planner is proposed, which takes into account both solution quality and computational efficiency. Moreover, we design a tracking controller using nonlinear model predictive control based on the proposed unified dynamic model to achieve accurate trajectory tracking and smooth mode transition. We validate our framework through extensive benchmark comparisons and experiments, demonstrating its effectiveness in terms of planning quality and control performance.Comment: Accepted at IROS 202

XAIR: A Framework of Explainable AI in Augmented Reality

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.Comment: Proceedings of the 2023 CHI Conference on Human Factors in Computing System

Comprehensive identification and characterization of lncRNAs and circRNAs reveal potential brown planthopper-responsive ceRNA networks in rice

Brown planthopper (Nilaparvata lugens Stål, BPH) is one of the most destructive pests of rice. Non-coding RNA plays an important regulatory role in various biological processes. However, comprehensive identification and characterization of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in BPH-infested rice have not been performed. Here, we performed a genome-wide analysis of lncRNAs and circRNAs in BPH6-transgenic (resistant, BPH6G) and Nipponbare (susceptible, NIP) rice plants before and after BPH feeding (early and late stage) via deep RNA-sequencing. A total of 310 lncRNAs and 129 circRNAs were found to be differentially expressed. To reveal the different responses of resistant and susceptible rice to BPH herbivory, the potential functions of these lncRNAs and circRNAs as competitive endogenous RNAs (ceRNAs) were predicted and investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Dual-luciferase reporter assays revealed that miR1846c and miR530 were targeted by the lncRNAs XLOC_042442 and XLOC_028297, respectively. In responsive to BPH infestation, 39 lncRNAs and 21 circRNAs were predicted to combine with 133 common miRNAs and compete for miRNA binding sites with 834 mRNAs. These mRNAs predictably participated in cell wall organization or biogenesis, developmental growth, single-organism cellular process, and the response to stress. This study comprehensively identified and characterized lncRNAs and circRNAs, and integrated their potential ceRNA functions, to reveal the rice BPH-resistance network. These results lay a foundation for further study on the functions of lncRNAs and circRNAs in the rice-BPH interaction, and enriched our understanding of the BPH-resistance response in rice

Integrated transcriptomics and metabolomics analysis provide insight into the resistance response of rice against brown planthopper

IntroductionThe brown planthopper (Nilaparvata lugens Stål, BPH) is one of the most economically significant pests of rice. The Bph30 gene has been successfully cloned and conferred rice with broad-spectrum resistance to BPH. However, the molecular mechanisms by which Bph30 enhances resistance to BPH remain poorly understood.MethodsHere, we conducted a transcriptomic and metabolomic analysis of Bph30-transgenic (BPH30T) and BPH-susceptible Nipponbare plants to elucidate the response of Bph30 to BPH infestation.ResultsTranscriptomic analyses revealed that the pathway of plant hormone signal transduction enriched exclusively in Nipponbare, and the greatest number of differentially expressed genes (DEGs) were involved in indole 3-acetic acid (IAA) signal transduction. Analysis of differentially accumulated metabolites (DAMs) revealed that DAMs involved in the amino acids and derivatives category were down-regulated in BPH30T plants following BPH feeding, and the great majority of DAMs in flavonoids category displayed the trend of increasing in BPH30T plants; the opposite pattern was observed in Nipponbare plants. Combined transcriptomics and metabolomics analysis revealed that the pathways of amino acids biosynthesis, plant hormone signal transduction, phenylpropanoid biosynthesis and flavonoid biosynthesis were enriched. The content of IAA significantly decreased in BPH30T plants following BPH feeding, and the content of IAA remained unchanged in Nipponbare. The exogenous application of IAA weakened the BPH resistance conferred by Bph30.DiscussionOur results indicated that Bph30 might coordinate the movement of primary and secondary metabolites and hormones in plants via the shikimate pathway to enhance the resistance of rice to BPH. Our results have important reference significance for the resistance mechanisms analysis and the efficient utilization of major BPH-resistance genes