A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions

The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content. The LLM-generated text detection aims to discern if a piece of text was produced by an LLM, which is essentially a binary classification task. The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, zero-shot methods, fine-turning LMs methods, adversarial learning methods, LLMs as detectors, and human-assisted methods. In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research. We also delve into prevalent datasets, elucidating their limitations and developmental requirements. Furthermore, we analyze various LLM-generated text detection paradigms, shedding light on challenges like out-of-distribution problems, potential attacks, and data ambiguity. Conclusively, we highlight interesting directions for future research in LLM-generated text detection to advance the implementation of responsible artificial intelligence (AI). Our aim with this survey is to provide a clear and comprehensive introduction for newcomers while also offering seasoned researchers a valuable update in the field of LLM-generated text detection. The useful resources are publicly available at: https://github.com/NLP2CT/LLM-generated-Text-Detection

Effects of nitrogen addition and plant litter manipulation on soil fungal and bacterial communities in a semiarid sandy land

The plant and soil microbial communities are influenced by variability in environmental conditions (e.g., nitrogen addition); however, it is unclear how long-term nitrogen addition and litter manipulation affect soil microbial communities in a semiarid sandy grassland. Therefore, we simulated the impact of N addition and litter manipulation (litter removal, litter doubling) on plant and soil microbial communities in Horqin grassland, northern China through an experiment from 2014 to 2019. Our results revealed that in the case of non-nitrogen (N0), litter manipulation significantly reduced vegetation coverage (V) (p < 0.05); soil bacterial communities have higher alpha diversity than that of the fungi, and the beta diversity of soil fungi was higher than that of the bacteria; soil microbial alpha diversity was significantly decreased by nitrogen addition (N10) (p < 0.05); N addition and litter manipulation had significantly interactive influences on soil microbial beta diversity, and litter manipulation (C0 and C2) had significantly decreased soil microbial beta diversity (p < 0.05) in the case of nitrogen addition (N10) (p < 0.05). Moreover, bacteria were mostly dominated by the universal phyla Proteobacteria, Actinobacteria, and Acidobacteria, and fungi were only dominated by Ascomycota. Furthermore, the correlation analysis, redundancy analysis (RDA), and variation partitioning analysis indicated that the soil fungi community was more apt to be influenced by plant community diversity. Our results provide evidence that plant and soil microbial community respond differently to the treatments of the 6-year N addition and litter manipulation in a semiarid sandy land

MiR-34a Enhances Chondrocyte Apoptosis, Senescence and Facilitates Development of Osteoarthritis by Targeting DLL1 and Regulating PI3K/AKT Pathway

Background/Aims: Osteoarthritis (OA) is the prevalent degenerative disease caused by various factors. MicroRNAs are important regulators in the inflammation and immune response. The aim of this study was to investigate the effect of microRNA-34a (MiR-34a) on the death of chondrocytes, senescence, as well as its role in OA progression. Methods: A series of experiments involving CCK-8, flow cytometry, β-galactosidase staining and wound healing assays were conducted to determine the cellular capabilities of proliferation, cell apoptosis, senescence and the ability of cells to recover from injury, respectively. Binding sites between miR-34a and delta-like protein 1 (DLL1) were identified using a luciferase reporter system, whereas mRNA and protein expression of target genes was determined by RT-PCR and immunoblot, respectively. OA model was generated via surgery. Results: We found that miR-34a expression was increased in the cartilage of OA patients. In rat chondrocytes and chondrosarcoma cells, miR-34a transfections noticeably inhibited the expression of DLL1, triggered cell death and senescence, suppressed proliferation, and prevented scratch assay wound closure. However, transfection of a miR-34a inhibitor displayed adverse effects. Additionally, secretion and expression of factors associated with cartilage degeneration were altered via miR-34a. Moreover, miR-34a directly inhibits DLL1 mRNA. Furthermore, concentrations of DLL1, total PI3K, and p-AKT declined in chondrocytes that overexpress miR-34a. DLL1 overexpression elevated PI3K and p-AKT levels, and eliminated cell death triggered by a miR-34a mimic. In vivo, miR-34a remarkably inhibited miR-34a up-regulation, while enhanced the level of DLL1 expression. In the knee joints of surgery-induced OA rats, articular chondrocyte death and loss of cartilage were attenuated via miR-34a antagomir injection. Conclusions: These findings indicate that miR-34a contributes to chondrocyte death, causing OA progression through DLL1 and modulation of the PI3K/AKT pathway

Lineage tracing for multiple lung cancer by spatiotemporal heterogeneity using a multi-omics analysis method integrating genomic, transcriptomic, and immune-related features

IntroductionThe distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) holds clinical significance in staging, therapeutic intervention, and prognosis assessment for multiple lung cancer. Lineage tracing by clinicopathologic features alone remains a clinical challenge; thus, we aimed to develop a multi-omics analysis method delineating spatiotemporal heterogeneity based on tumor genomic profiling.MethodsBetween 2012 and 2022, 11 specimens were collected from two patients diagnosed with multiple lung cancer (LU1 and LU2) with synchronous/metachronous tumors. A novel multi-omics analysis method based on whole-exome sequencing, transcriptome sequencing (RNA-Seq), and tumor neoantigen prediction was developed to define the lineage. Traditional clinicopathologic reviews and an imaging-based algorithm were performed to verify the results.ResultsSeven tissue biopsies were collected from LU1. The multi-omics analysis method demonstrated that three synchronous tumors observed in 2018 (LU1B/C/D) had strong molecular heterogeneity, various RNA expression and immune microenvironment characteristics, and unique neoantigens. These results suggested that LU1B, LU1C, and LU1D were MPLC, consistent with traditional lineage tracing approaches. The high mutational landscape similarity score (75.1%), similar RNA expression features, and considerable shared neoantigens (n = 241) revealed the IPM relationship between LU1F and LU1G which were two samples detected simultaneously in 2021. Although the multi-omics analysis method aligned with the imaging-based algorithm, pathology and clinicopathologic approaches suggested MPLC owing to different histological types of LU1F/G. Moreover, controversial lineage or misclassification of LU2’s synchronous/metachronous samples (LU2B/D and LU2C/E) traced by traditional approaches might be corrected by the multi-omics analysis method. Spatiotemporal heterogeneity profiled by the multi-omics analysis method suggested that LU2D possibly had the same lineage as LU2B (similarity score, 12.9%; shared neoantigens, n = 71); gefitinib treatment and EGFR, TP53, and RB1 mutations suggested the possibility that LU2E might result from histology transformation of LU2C despite the lack of LU2C biopsy and its histology. By contrast, histological interpretation was indeterminate for LU2D, and LU2E was defined as a primary or progression lesion of LU2C by histological, clinicopathologic, or imaging-based approaches.ConclusionThis novel multi-omics analysis method improves the accuracy of lineage tracing by tracking the spatiotemporal heterogeneity of serial samples. Further validation is required for its clinical application in accurate diagnosis, disease management, and improving prognosis

Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China

Most methods used for crop classification rely on the ground-reference data of the same year, which leads to considerable financial and labor cost. In this study, we presented a method that can avoid the requirements of a large number of ground-reference data in the classification year. Firstly, we extracted the Normalized Difference Vegetation Index (NDVI) time series profiles of the dominant crops from MODIS data using the historical ground-reference data in multiple years (2006, 2007, 2009 and 2010). Artificial Antibody Network (ABNet) was then employed to build reference NDVI time series for each crop based on the historical NDVI profiles. Afterwards, images of Landsat and HJ were combined to obtain 30 m image time series with 15-day acquisition frequency in 2011. Next, the reference NDVI time series were transformed to Landsat/HJ NDVI time series using their linear model. Finally, the transformed reference NDVI profiles were used to identify the crop types in 2011 at 30 m spatial resolution. The result showed that the dominant crops could be identified with overall accuracy of 87.13% and 83.48% in Bole and Manas, respectively. In addition, the reference NDVI profiles generated from multiple years could achieve better classification accuracy than that from single year (such as only 2007). This is mainly because the reference knowledge from multiple years contains more growing conditions of the same crop. Generally, this approach showed potential to identify crops without using large number of ground-reference data at 30 m resolution

Synergetic Effect of Nano-ZnO and Trinidad Lake Asphalt for Antiaging Properties of SBS-Modified Asphalt

In order to address the influence of aging on the performance degradation of SBS-modified asphalt, a composite modification of SBS-modified asphalt by nano-zinc oxide (nano-ZnO) and Trinidad Lake asphalt (TLA) was proposed. Several tests were conducted after adding nano-ZnO and TLA to SBS-modified asphalt, including a rotary film oven test (RTFOT), ultraviolet aging (UV), and the pressure aging vessel test (PAV). The conventional physical index, rheological index, and four-component content of SBS-modified asphalt before and after three aging modes were tested, and the characteristic functional groups in SBS-modified asphalt were tracked and analyzed by Fourier transform infrared spectroscopy (FTIR). The results show that the effects of aging on the rheological properties of SBS-modified asphalt are clearly reduced by adding different proportions of nano-ZnO and TLA in the process of thermal oxygen aging and the ultraviolet aging test, and the antiaging ability of SBS-modified asphalt is clearly improved. To improve the conventional performance and rheological properties of SBS-modified asphalt, an incorporation ratio of 3% nano-ZnO + 25% TLA was proposed. At the same time, the increased rate of heavy components and the change index of the colloidal instability index in the SBS-modified asphalt under the blending ratio were significantly lower than the blank SBS-modified asphalt samples in the same aging mode. FTIR spectra also showed that SBS-modified asphalt performance deterioration were mainly caused by long-term aging and ultraviolet aging. The addition of nano-ZnO and TLA effectively reduced the increase of carbonyl groups and the breakage of the C=C double bond in butadiene and synergistically improved the comprehensive aging resistance of SBS-modified asphalt. Therefore, the use of this modification is an effective method to solve the aging problem of SBS-modified asphalt

Influence of Fastener Failure on Dynamic Performance of Subway Vehicle

The track fasteners may be damaged by fatigue and impact load during long-term subway operation, resulting in the failure of the connecting components between the rail and the track plate, and the spacing of rail support becomes larger, resulting in an increase in its dynamic deformation, affecting the subway vehicle’s running performance, and, in severe cases, endangering the vehicle’s running safety. A vehicle-subway track system model was created to study the running performance of subway vehicles when fasteners failed. A multi-rigid, body spring damping system is used to describe the vehicle system. The model for the track system is created using the finite element method (FEM), and the vehicle dynamic performances under various fastener failure scenarios are calculated, as well as the vehicle’s running comfort and safety in various scenarios. The findings show that fastener failure has little impact on the vehicle’s running comfort but it has a significant impact on the vehicle’s wheel unloading ratio

Estimation of different data compositions for early-season crop type classification

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study

Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA

Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop mapping. Eight time series with different lengths (ranging from one month to eight months) were tested. For each time series, we first used the Random Forest (RF) algorithm to calculate the importance score for all features (including multi-spectral data, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and phenological metrics). Subsequently, an extension of the Jeffries–Matusita (JM) distance was used to measure class separability for each time series. Finally, the RF algorithm was used to classify crop types, and the classification accuracy and certainty were used to analyze the influence of the time series length and the number of features on classification performance; the features were added one by one based on their importance scores. Results indicated that when the time series was longer than five months, the top ten features remained stable. These features were mainly in July and August. In addition, the NDVI features contributed the majority of the most significant features for crop mapping. The NDWI and data from multi-spectral bands also contributed to improving crop mapping. On the other hand, separability, classification accuracy, and certainty increased with the number of features used and the time series length, although these values quickly reached saturation. Five months was the optimal time series length, as longer time series provided no further improvement in the classification performance. This result shows that relatively short time series have the potential to identify crops accurately, which allows for early crop mapping over large areas

Influence of Fastener Failure on Dynamic Performance of Subway Vehicle

The track fasteners may be damaged by fatigue and impact load during long-term subway operation, resulting in the failure of the connecting components between the rail and the track plate, and the spacing of rail support becomes larger, resulting in an increase in its dynamic deformation, affecting the subway vehicle’s running performance, and, in severe cases, endangering the vehicle’s running safety. A vehicle-subway track system model was created to study the running performance of subway vehicles when fasteners failed. A multi-rigid, body spring damping system is used to describe the vehicle system. The model for the track system is created using the finite element method (FEM), and the vehicle dynamic performances under various fastener failure scenarios are calculated, as well as the vehicle’s running comfort and safety in various scenarios. The findings show that fastener failure has little impact on the vehicle’s running comfort but it has a significant impact on the vehicle’s wheel unloading ratio