In Search of the Long-Tail: Systematic Generation of Long-Tail Knowledge via Logical Rule Guided Search

Since large language models have approached human-level performance on many tasks, it has become increasingly harder for researchers to find tasks that are still challenging to the models. Failure cases usually come from the long-tail distribution - data that an oracle language model could assign a probability on the lower end of its distribution. Current methodology such as prompt engineering or crowdsourcing are insufficient for creating long-tail examples because humans are constrained by cognitive bias. We propose a Logic-Induced-Knowledge-Search (LINK) framework for systematically generating long-tail knowledge statements. Grounded by a symbolic rule, we search for long-tail values for each variable of the rule by first prompting a LLM, then verifying the correctness of the values with a critic, and lastly pushing for the long-tail distribution with a reranker. With this framework we construct a dataset, Logic-Induced-Long-Tail (LINT), consisting of 200 symbolic rules and 50K knowledge statements spanning across four domains. Human annotations find that 84% of the statements in LINT are factually correct. In contrast, ChatGPT and GPT4 struggle with directly generating long-tail statements under the guidance of logic rules, each only getting 56% and 78% of their statements correct. Moreover, their "long-tail" generations in fact fall into the higher likelihood range, and thus are not really long-tail. Our findings suggest that LINK is effective for generating data in the long-tail distribution while enforcing quality. LINT can be useful for systematically evaluating LLMs' capabilities in the long-tail distribution. We challenge the models with a simple entailment classification task using samples from LINT. We find that ChatGPT and GPT4's capability in identifying incorrect knowledge drop by ~3% in the long-tail distribution compared to head distribution

SIF datasets (50 m) and demos of network structure designs on the study of the STP-SIF issue

<p>The SIF datasets (i.e., SIF2019 and SIF2020) accompany the paper "Regional-Scale Cotton Yield Forecast via Data-Driven Spatio-Temporal Prediction (STP) of Solar-Induced Chlorophyll Fluorescence (SIF)" that was published in <a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425723004121">Remote Sensing of Environment</a> on October 20, 2023. They have a spatial resolution of about 50 m and a monthly temporal resolution. Each of them has seven bands, corresponding to April to October. Please refer to our previous work, "Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network", which was published in <a href="https://www.sciencedirect.com/science/article/pii/S0168169922005737">Computers and Electronics in Agriculture</a> on August 14, 2022, for the development and detailed description.</p><p>The geographic reference (ESPG: 4326 (WGS_1984)) is the same for the two dataset, conforming to that in the geotiff file.</p><p><strong>Citation</strong>:</p><p>[1] Kang, X., Huang, C., Zhang, L., Wang, H., Zhang, Z., Lv, X., 2023. Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF). Remote Sensing of Environment 299, 113861. doi:10.1016/j.rse.2023.113861</p><p>[2] Kang, X., Huang, C., Zhang, L., Zhang, Z., Lv, X., 2022. Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network. Computers and Electronics in Agriculture 201, 107260. doi:10.1016/j.compag.2022.107260</p><p>[3] Kang, X., Huang, C., Chen, J.M., Lv, X., Wang, J., Zhong, T., Wang, H., Fan, X., Ma, Y., Yi, X., Zhang, Z., Zhang, L., Tong, Q., 2023. The 10-m cotton maps in Xinjiang, China during 2018-2021. Sci Data 10, 688. doi:10.1038/s41597-023-02584-3</p><p>[4] Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., Tong, Q., 2023. Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science 13, 1048479. doi:10.3389/fpls.2022.1048479</p&gt

Intravenous immunoglobulin accompanied with high-dose methylprednisolone therapy for 17 children with anti-N-methyl-D-aspartate receptor encephalitis: Clinic and nursing

Objective: An increasing number of pediatric patients are being diagnosed with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, whose treatment requires immunotherapy through nursing interventions. This study aimed to analyze the clinical features and long-term prognosis of pediatric anti-NMDAR encephalitis and to gather nursing experiences of immunotherapy. Methods: Seventeen children diagnosed with anti-NMDAR encephalitis were admitted to the pediatric department. They were subjected to a therapy of intravenous immunoglobulin (IVIG) accompanied with high-dose methylprednisolone (HDMP). Multidisciplinary cooperation and intensive care were used to manage them. The effects of nursing intervention and therapy were repeatedly assessed and analyzed throughout the course of treatment and recovery. Results: None of the patients manifested adverse drug reaction (ADR) during IVIG administration. At the first administration of HDMP, ADRs were promptly and efficiently treated in four patients (24%; i.e., one case each of hyperglycosemia, hypertension, aggravated symptoms, and gastrointestinal bleed). Two patients underwent rehabilitation, and six patients received hyperbaric oxygenation during hospitalization. Nine patients with indwelling gastric tubes experienced four times of unplanned extubation. Hospital stay ranged from 11 days to 59 days, with the mean duration of 26 days. Discharge evaluation revealed that 16 patients who scored 0–2 on the modified Rankin scale presented obvious remission, and one patient who had a mRS score of 4 exhibited less improvement. The mRS scores of hospitalization, discharge, and six-month follow-up displayed statistically significant differences. Conclusions: Nursing interventions of immunotherapy ensures the security of IVIG administration. Multidisciplinary cooperation promotes remission. Our findings can serve as reference for healthcare teams

Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha−1. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R2, RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R2, RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC

Numerical and Experimental Study on Thermal Comfort of Human Body by Split-Fiber Air Conditioner

The thermal comfort of an enclosed room with air conditioner and air-distribution duct coupling can be studied, and the parameters of a split-fiber air conditioner can be optimized on the basis of studying the thermal comfort of various parts of the human body. In this paper, a room model with a distributed air conditioner was proposed. First, the rationality of the three thermal comfort characterization models of predict mean vote (PMV), predicted percentage of dissatisfied (PPD), and percentage of dissatisfied (PD) were verified through experiments and simulations. Then, the temperature and thermal comfort of various parts of the human body were explored when the air-distribution duct had different openings and different positions of the air outlet. The simulation results showed that compared with other situations, when the split-fiber air conditioner had three rows of holes (5-o’clock, 6-o’clock, 7-o’clock) and the air outlet was located in the middle of the right wall of the human body, the PMV, PPD, and PD of the measuring points around the human body fluctuated less, the indoor temperature field distribution fluctuated less, and there was no wind feeling around the human body, which can better meet the needs of human thermal comfort

Numerical and Experimental Study on Thermal Comfort of Human Body by Split-Fiber Air Conditioner

The thermal comfort of an enclosed room with air conditioner and air-distribution duct coupling can be studied, and the parameters of a split-fiber air conditioner can be optimized on the basis of studying the thermal comfort of various parts of the human body. In this paper, a room model with a distributed air conditioner was proposed. First, the rationality of the three thermal comfort characterization models of predict mean vote (PMV), predicted percentage of dissatisfied (PPD), and percentage of dissatisfied (PD) were verified through experiments and simulations. Then, the temperature and thermal comfort of various parts of the human body were explored when the air-distribution duct had different openings and different positions of the air outlet. The simulation results showed that compared with other situations, when the split-fiber air conditioner had three rows of holes (5-o’clock, 6-o’clock, 7-o’clock) and the air outlet was located in the middle of the right wall of the human body, the PMV, PPD, and PD of the measuring points around the human body fluctuated less, the indoor temperature field distribution fluctuated less, and there was no wind feeling around the human body, which can better meet the needs of human thermal comfort

MUC20 regulated by extrachromosomal circular DNA attenuates proteasome inhibitor resistance of multiple myeloma by modulating cuproptosis

Abstract Background Proteasome inhibitors (PIs) are one of the most important classes of drugs for the treatment of multiple myeloma (MM). However, almost all patients with MM develop PI resistance, resulting in therapeutic failure. Therefore, the mechanisms underlying PI resistance in MM require further investigation. Methods We used several MM cell lines to establish PI-resistant MM cell lines. We performed RNA microarray and EccDNA-seq in MM cell lines and collected human primary MM samples to explore gene profiles. We evaluated the effect of MUC20 on cuproptosis of PI-resistant MM cells using Co-immunoprecipitation (Co-IP), Seahorse bioenergetic profiling and in vivo assay. Results This study revealed that the downregulation of Mucin 20 (MUC20) could predict PI sensitivity and outcomes in MM patients. Besides, MUC20 attenuated PI resistance in MM cells by inducing cuproptosis via the inhibition of cyclin-dependent kinase inhibitor 2 A expression (CDKN2A), which was achieved by hindering MET proto-oncogene, receptor tyrosine kinase (MET) activation. Moreover, MUC20 suppressed MET activation by repressing insulin-like growth factor receptor-1 (IGF-1R) lactylation in PI-resistant MM cells. This study is the first to perform extrachromosomal circular DNA (eccDNA) sequencing for MM, and it revealed that eccDNA induced PI resistance by amplifying kinesin family member 3 C (KIF3C) to reduce MUC20 expression in MM. Conclusion Our findings indicated that MUC20 regulated by eccDNA alleviates PI resistance of MM by modulating cuproptosis, which would provide novel strategies for the treatment of PI-resistant MM

Ultrasound Treatment Enhanced Semidry-Milled Rice Flour Properties and Gluten-Free Rice Bread Quality

The structural and functional properties of physical modified rice flour, including ultrasound treated rice flour (US), microwave treated rice flour (MW) and hydrothermal treated rice flour (HT) were investigated with wet-milled rice flour (WF) used as a positive control. The results showed the presence of small dents and pores on the rice flour granules of US and MW while more fragments and cracks were showed in HT. XRD and FTIR revealed that moderate ultrasonic treatment promoted the orderly arrangement of starch while hydrothermal treatment destroyed the crystalline structure of rice flour. In addition, the significant decrease of gelatinization enthalpy and the narrowing gelatinization temperature were observed in US. Compared to that of SF, adding physical modified rice flour led to a batter with higher viscoelasticity and lower tan δ. However, the batter added HT exhibited highest G′ and G″ values and lowest tan δ, which led to a harder texture of bread. Texture analysis demonstrated that physical modified rice flour (except HT) reduced the hardness, cohesion, and gumminess of rice bread. Especially, the specific volume of bread with US increased by 15.6% and the hardness decreased by 17.6%. This study suggested that ultrasound treatment of rice flour could improve texture properties and appearance of rice bread