OPDAI at SemEval-2024 Task 6: Small LLMs can Accelerate Hallucination Detection with Weakly Supervised Data

This paper mainly describes a unified system for hallucination detection of LLMs, which wins the second prize in the model-agnostic track of the SemEval-2024 Task 6, and also achieves considerable results in the model-aware track. This task aims to detect hallucination with LLMs for three different text-generation tasks without labeled training data. We utilize prompt engineering and few-shot learning to verify the performance of different LLMs on the validation data. Then we select the LLMs with better performance to generate high-quality weakly supervised training data, which not only satisfies the consistency of different LLMs, but also satisfies the consistency of the optimal LLM with different sampling parameters. Furthermore, we finetune different LLMs by using the constructed training data, and finding that a relatively small LLM can achieve a competitive level of performance in hallucination detection, when compared to the large LLMs and the prompt-based approaches using GPT-4

Changes in nonlinearity and stability of streamflow recession characteristics under climate warming in a large glaciated basin of the Tibetan Plateau

The accelerated climate warming in the Tibetan Plateau after 1997 has profound consequences in hydrology, geography, and social wellbeing. In hydrology, the change in streamflow as a result of changes in dynamic water storage that originated from glacier melt and permafrost thawing in the warming climate directly affects the available water resources for societies of the most populated nations in the world. In this study, annual streamflow recession characteristics are analyzed using daily climate and hydrological data during 1980–2015 in the Yarlung Zangbo River basin (YRB) of the southern Tibetan Plateau. The recession characteristics are examined in terms of dQ=dt DaQb and the response/ sensitivity of streamflow to changes in groundwater storage. Major results show that climate warming has significantly increased the nonlinearity of the response (b) and streamflow stability [log.a/] in most subbasins of the YRB. These changes in the recession characteristics are attributed to the opposite effects of increases in the available water storage and recession timescale on the recession. Climate warming has increased subbasin water storage considerably due to more recharge from accelerated glacier melting and permafrost thawing after 1997. Meanwhile, the enlarged storage lengthens recession timescales and thereby decreases the sensitivity of discharge to storage. In the recession period when recharge diminished, increased evaporation and the decreased buffering effect of frost soils under warmer temperatures accelerate the initial recession of streamflow. By contrast, enlarged storage and lengthened recession timescales slow down the recession. While reservoir regulations in some basins have helped reduce and even reverse some of these climate warming effects, this short-term remedy can only function before the solid water storage is exhausted should the climate warming continue

Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC

This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77.05% and attains the first place in this task. We first explore the impact of different pre-trained language models. Then we adopt data cleaning, data augmentation, and adversarial training strategies to enhance the model generalization and robustness. For further improvement, we integrate POS information and word semantic representation using a Mixture-of-Experts (MoE) approach. The experimental results show that MoE can overcome the feature overuse issue and combine the context, POS, and word semantic features well. Additionally, we use a model ensemble method for the final prediction, which has been proven effective by many research works

Individuation of wind turbine systematic yaw error through SCADA data

Much attention in the wind energy literature is devoted to condition monitoring [...

Experimental analysis of the effect of static yaw error on wind turbine nacelle anemometer measurements

The operation of wind turbines in real-world environments can be affected by the presence of systematic errors, which might diminish the Annual Energy Production up to 3-4%. Therefore, it is fundamental to leverage the availability of SCADA-collected measurements in order to formulate reliable diagnosis methods. The static yaw error of a wind turbine occurs when, due to wind vane or installation defects, the rotor plane is systematically not perpendicular to the wind flow. The present work is devoted to the experimental analysis of how the presence of a static yaw error affects the wind turbine nacelle anemometer measurements. Measurements collected at the Eolos Wind Research Station at the University of Minnesota are analyzed. The qualifying aspect is that a utility-scale wind turbine has been fully controlled and imposed to set to a non-vanishing yaw error. Furthermore, approximately two rotor diameters south of the turbine there is a meteorological tower which provides unbiased measurements of the environmental conditions. The main result of this work is that, for given wind speed measured by the meteorological mast anemometers, the measurements of the nacelle wind speed changes systematically in presence of the static yaw error. This aspect has up to now been overlooked in the literature. Therefore, the results of this work might stimulate a critical revision of the existing methods for static yaw error diagnosis and the formulation of new ones

Overexpression of FTO inhibits excessive proliferation and promotes the apoptosis of human glomerular mesangial cells by alleviating FOXO6 m6A modification via YTHDF3-dependent mechanisms

Background: N6-methyladenosine (m6A) is a prevalent post-transcriptional modification presented in messenger RNA (mRNA) of eukaryotic organisms. Chronic glomerulonephritis (CGN) is characterised by excessive proliferation and insufficient apoptosis of human glomerular mesangial cells (HGMCs) but its underlying pathogenesis remains undefined. Moreover, the role of m6A in CGN is poorly understood.Methods: The total level of m6A modification was detected using the m6A quantification assay (Colorimetric). Cell proliferation was assessed by EdU cell proliferation assay, and cell apoptosis was detected by flow cytometry. RNA sequencing was performed to screen the downstream target of fat mass and obesity-associated protein (FTO). MeRIP-qPCR was conducted to detect the m6A level of forkhead box o6 (FOXO6) in HGMCs. RIP assay was utilized to indicate the targeting relationship between YTH domain family 3 (YTHDF3) and FOXO6. Actinomycin D assay was used to investigate the stability of FOXO6 in HGMCs.Results: The study found that the expression of FTO was significantly reduced in lipopolysaccharide (LPS)-induced HGMCs and renal biopsy samples of patients with CGN. Moreover, FTO overexpression and knockdown could regulate the proliferation and apoptosis of HGMCs. Furthermore, RNA sequencing and cellular experiments revealed FOXO6 as a downstream target of FTO in regulating the proliferation and apoptosis of HGMCs. Mechanistically, FTO overexpression decreases the level of FOXO6 m6A modification and reduces the stability of FOXO6 mRNA in a YTHDF3-dependent manner. Additionally, the decreased expression of FOXO6 inhibits the PI3K/AKT signaling pathway, thereby inhibiting the proliferation and promoting apoptosis of HGMCs.Conclusion: This study offers insights into the mechanism through which FTO regulates the proliferation and apoptosis of HGMCs by mediating m6A modification of FOXO6 mRNA. These findings also suggest FTO as a potential diagnostic marker and therapeutic target for CGN

Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques

In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl.github.io/}. Our solution focuses on enhancing the ranking stage, where we fine-tune pre-trained multilingual transformer-based models with MIRACL dataset. Our model improvement is mainly achieved through diverse data engineering techniques, including the collection of additional relevant training data, data augmentation, and negative sampling. Our fine-tuned model effectively determines the semantic relevance between queries and documents, resulting in a significant improvement in the efficiency of the multilingual information retrieval process. Finally, Our team is pleased to achieve remarkable results in this challenging competition, securing 2nd place in the Surprise-Languages track with a score of 0.835 and 3rd place in the Known-Languages track with an average nDCG@10 score of 0.716 across the 16 known languages on the final leaderboard

A highly selective electrochemical assay based on the Sakaguchi reaction for the detection of protein arginine methylation state

Protein arginine methylation is a common form of post-translational modification that plays an important role in many bioprocesses. However, research advances in this field have been severely hampered by the lack of a quick and sensitive method for detecting the arginine methylation state of a protein. In this work we propose a direct and sensitive electrochemical method for identifying the arginine methylation state. This novel assay combines an electrochemical technique with the Sakaguchi reaction, which is highly selective towards the arginine methylation state. We show that the presence of a methyl group on the arginine residue of a protein prevents the Sakaguchi reaction, while the unmethylated arginine residue selectively reacts with 8-hydroxyquinoline; the electrical signal of the reaction product is used for electrochemical detection. From this, a highly selective and simple electrochemical sensor has been developed based on (1) the high selectivity of the Sakaguchi reaction towards the arginine methylation state, and (2) the sensitive electrochemical signal generated by the linked 8-hydroxyquinoline. The assay described in this work thus provides a convenient tool for detection of protein arginine methylation, which may facilitate studies of the biological functions of protein arginine methylases and demethylases