Risk cognition, agricultural cooperatives training, and farmers' pesticide overuse : evidence from Shandong Province, China

Introduction: Pesticides are widely and excessively used in the world. Reducing pesticide overuse is an important measure to protect the environment and human health. Methods: Based on the survey data of 518 farmers in Shandong Province, China, using the Logit model to empirically test the effect of risk cognition on farmers' pesticide overuse behavior and the moderating effect of cooperatives training on the effect of risk cognition on farmers' pesticide overuse behavior. Results and discussion: We found that 21.24% of farmers overused pesticides. The three dimensions of risk cognition have significant negative effects on farmers' behavior of excessive pesticide use, among which the human health risk cognition has the largest impact (0.74), followed by food safety risk cognition (0.68) and ecological environment risk cognition (0.63). Cooperatives training has a positive moderating effect on the relationship between risk cognition and pesticide overuse behavior, that is, when risk cognition matches farmers participating in cooperatives training, the effect on reducing pesticide overuse is more significant. Years of education, planting scale and detection frequency of pesticide residues have significant effects on farmers' pesticide overuse. Conclusions: The government should help farmers reduce pesticide overuse by improving risk cognition, developing agricultural cooperatives and perfecting guarantee conditions

Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text

Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples. These approaches either train a language model from scratch or fine-tune a pre-trained language model using ID examples, and then take perplexity as output by the language model as OoD scores. In this paper, we analyse the complementary characteristics of both OoD detection methods and propose a multi-level knowledge distillation approach to integrate their strengths, while mitigating their limitations. Specifically, we use a fine-tuned model as the teacher to teach a randomly initialized student model on the ID examples. Besides the prediction layer distillation, we present a similarity-based intermediate layer distillation method to facilitate the student's awareness of the information flow inside the teacher's layers. In this way, the derived student model gains the teacher's rich knowledge about the ID data manifold due to pre-training, while benefiting from seeing only ID examples during parameter learning, which promotes more distinguishable features for OoD detection. We conduct extensive experiments over multiple benchmark datasets, i.e., CLINC150, SST, 20 NewsGroups, and AG News; showing that the proposed method yields new state-of-the-art performance.Comment: 11 page

Exploiting satellite SAR for archaeological prospection and heritage site protection

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian “Valley of the Kings” (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves

MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features

To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one

FSD: An Initial Chinese Dataset for Fake Song Detection

Singing voice synthesis and singing voice conversion have significantly advanced, revolutionizing musical experiences. However, the rise of "Deepfake Songs" generated by these technologies raises concerns about authenticity. Unlike Audio DeepFake Detection (ADD), the field of song deepfake detection lacks specialized datasets or methods for song authenticity verification. In this paper, we initially construct a Chinese Fake Song Detection (FSD) dataset to investigate the field of song deepfake detection. The fake songs in the FSD dataset are generated by five state-of-the-art singing voice synthesis and singing voice conversion methods. Our initial experiments on FSD revealed the ineffectiveness of existing speech-trained ADD models for the task of song deepFake detection. Thus, we employ the FSD dataset for the training of ADD models. We subsequently evaluate these models under two scenarios: one with the original songs and another with separated vocal tracks. Experiment results show that song-trained ADD models exhibit a 38.58% reduction in average equal error rate compared to speech-trained ADD models on the FSD test set.Comment: Submitted to ICASSP 202

ReliTalk: Relightable Talking Portrait Generation from a Single Video

Recent years have witnessed great progress in creating vivid audio-driven portraits from monocular videos. However, how to seamlessly adapt the created video avatars to other scenarios with different backgrounds and lighting conditions remains unsolved. On the other hand, existing relighting studies mostly rely on dynamically lighted or multi-view data, which are too expensive for creating video portraits. To bridge this gap, we propose ReliTalk, a novel framework for relightable audio-driven talking portrait generation from monocular videos. Our key insight is to decompose the portrait's reflectance from implicitly learned audio-driven facial normals and images. Specifically, we involve 3D facial priors derived from audio features to predict delicate normal maps through implicit functions. These initially predicted normals then take a crucial part in reflectance decomposition by dynamically estimating the lighting condition of the given video. Moreover, the stereoscopic face representation is refined using the identity-consistent loss under simulated multiple lighting conditions, addressing the ill-posed problem caused by limited views available from a single monocular video. Extensive experiments validate the superiority of our proposed framework on both real and synthetic datasets. Our code is released in https://github.com/arthur-qiu/ReliTalk

Detection of streptavidin using liquid crystal based whispering gallery mode microbubble

Protein is a complex chemical substance essential for human survival. Traditional protein detection methods, such as colorimetry, electrochemical analysis, and enzyme-linked immunosorbent assays, have shown good specificity and accuracy for the protein detection. However, all these methods require specialized instruments, and the detection procedures are laborious and time-consuming. As a result, a rapid, sensitive, label-free protein detection method is urgently needed. Herein, we have developed an ultra-sensitive biosensor for the detection of low-concentration protein molecules, employing liquid crystal (LC)-amplified optofluidic resonator. Since the orientations of LCs highly depend on the surface biomolecular binding processes, LCs can be employed to realize the extremely sensitive detection of biomolecules. Immobilized protein molecules interfere with the orientation of LCs by reducing the vertical anchoring force from the alignment layer in which the spectral wavelength shift was monitored as a sensing parameter. A biosensing platform based on an LC-amplified optofluidic whispering gallery mode (WGM) resonator was designed and studied accordingly. Due to the simultaneous interaction of the WGM and the LCs in the optofluidic resonator, the changes caused by the injection of protein molecules will be amplified, resulting in a shift in the resonance wavelength. Total wavelength shifts scale proportionally to the molecular concentrations of the protein within a certain range. The detection limit for streptavidin (SA) can reach as low as the femtometer level, which is significantly higher than the detection limit in the classic polarized optical microscope (POM) method visible with the naked eye. In addition to SA, the LC-based optofluidic resonator can also be applied to detect a variety of protein molecules. Our study demonstrates that LC-amplified optofluidic resonator can provide a novel solution for ultrasensitive real-time characterization of biosensing and biomolecular interactions