Differential Good Arm Identification

This paper targets a variant of the stochastic multi-armed bandit problem called good arm identification (GAI). GAI is a pure-exploration bandit problem with the goal to output as many good arms using as few samples as possible, where a good arm is defined as an arm whose expected reward is greater than a given threshold. In this work, we propose DGAI - a differentiable good arm identification algorithm to improve the sample complexity of the state-of-the-art HDoC algorithm in a data-driven fashion. We also showed that the DGAI can further boost the performance of a general multi-arm bandit (MAB) problem given a threshold as a prior knowledge to the arm set. Extensive experiments confirm that our algorithm outperform the baseline algorithms significantly in both synthetic and real world datasets for both GAI and MAB tasks

Chilling susceptibility in mungbean varieties is associated with their differentially expressed genes

Additional file 4: Table S3. Validation of microarray data by qRT-PCR in mungbean seedlings

How do students from different disciplines perceive the concept of “data”?: A visual elicitation method

Transforming the iSquare draw-and-write technique into the dSquare draw-and-tell approach, we conducted synchronous online visual-elicitation inter-views with 37 college students in six different disciplines to examine how they perceived the concept of “data.” The preliminary findings showed that students across disciplines tend to use group diagrams to present ICT and print materials in their dSquares. When explaining their drawings, students typically relate to functional purposes in academic contexts. While students typically use data in coursework or other work-related contexts in a positive way, students in different disciplines consider data in different original forms and use different types of metaphors to express the concept of data. While science students tend to include files or lab tools and describe data processing through the tools, humanities and social science students tend to include texts or secondary sources and describe data processing or application through personal thinking. Future research suggestions are provided based on the findings

AutoML-GPT: Large Language Model for AutoML

With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users access to a wide range of data preprocessing techniques, feature engineering methods, and model selection algorithms. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. Throughout the process, AutoML-GPT employs advanced techniques for hyperparameter optimization and model selection, ensuring that the resulting model achieves optimal performance. The system effectively manages the complexity of the machine learning pipeline, guiding users towards the best choices without requiring deep domain knowledge. Through our experimental results on diverse datasets, we have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks. Its ability to leverage the vast knowledge encoded in large language models enables it to provide valuable insights, identify potential pitfalls, and suggest effective solutions to common challenges faced during model training

THE KINEMATIC ANALYSIS of BASKETBALL THREE POINT SHOOT AFTER HIGH INTENSITY PROGRAM

The purpose of this study was to analyze kinetic and kinematic characteristics of three points shooting by high speed camera. Basketball players have to finish the high intensity program which was designed from simulative basketball games. The high intensity testing program includes dribbling, sprint, slide, jump shooting and three points shooting. The results of the experiments indicated that elbow, wrist, hip and ankle joints angle velocities would decrease, except the knee joint, after the high intensity program. The knee angle of take-off would also increase. It indicated that the upper limb joints angular velocity would decrease and players had to increase knee joint angular velocity to maintain original power. The time from take-off to ball release also decreased which means that there was a change in the coordinates in knee joint and elbow joint. After high intensity program the elbow and knee joints extension were closed to produce more power for the shot

THE VARIATION OF DOMINANT ELBOW RANGE OF MOTION AMONG DIFFERENT MATURE STAGE FOR BASEBALL PITCHERS

The purpose of this study was to examine the variation of elbow range of motion (ROM) in the dominant arm between different maturity levels in baseball pitchers. Sixty-two pitchers, including 17 early-puberty players, 22 later-puberty players, and 23 adult players, participated in this study. A goniometer was used to assess elbow ROM in the dominant arms, including elbow flexion, hyper-extension, supination, pronation and valgus angles. The results showed that smaller ROM was found in elbow flexion, supination, and pronation, and larger ROM in elbow valgue, in pithers of later puberty level (

Towards Optimizing with Large Language Models

In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes. Each of these tasks corresponds to unique optimization domains, and LLMs are required to execute these tasks with interactive prompting. That is, in each optimization step, the LLM generates new solutions from the past generated solutions with their values, and then the new solutions are evaluated and considered in the next optimization step. Additionally, we introduce three distinct metrics for a comprehensive assessment of task performance from various perspectives. These metrics offer the advantage of being applicable for evaluating LLM performance across a broad spectrum of optimization tasks and are less sensitive to variations in test samples. By applying these metrics, we observe that LLMs exhibit strong optimization capabilities when dealing with small-sized samples. However, their performance is significantly influenced by factors like data size and values, underscoring the importance of further research in the domain of optimization tasks for LLMs

EBV-positive Hodgkin lymphoma is associated with suppression of p21cip1/waf1 and a worse prognosis

<p>Abstract</p> <p>Background</p> <p>About 30-50% of Hodgkin lymphomas (HLs) harbor the Epstein-Barr virus (EBV), but the impact of EBV infection on clinical outcomes has been unclear. EBV-encoded small RNAs (<it>EBER</it>s) are presented in all EBV-infected cells, but their functions are still less understood.</p> <p>Results</p> <p><it>EBER1 </it>was transfected into two HL cell lines, KMH2 and L428, and microarrays were used to screen for <it>EBER1</it>-induced changes. We found that <it>EBER1 </it>suppressed <it>p21</it>^cip1/waf1transcription in HL cell lines. In addition, positive regulators of <it>p21</it>^cip1/waf1transcription, such as p53, EGR1, and STAT1, were decreased. Suppression of <it>p21</it>^cip1/waf1in the <it>EBER1</it>⁺HL cell lines was associated with increased resistance to histone deacetylase inhibitors or proteasome inhibitors, drugs known to cause apoptosis by increasing p21^cip1/waf1levels. On biopsy specimens, EBV⁺HLs had weaker expression of both p21^cip1/waf1and active caspase 3. Clinically, suppression of p21^cip1/waf1in EBV⁺HLs was associated with a worse 2-year disease-free survival rate (45% for EBV⁺HLs <it>vs</it>. 77% for EBV^-HLs, <it>p </it>= 0.002).</p> <p>Conclusion</p> <p>Although the underlying mechanisms are still relatively unclear, <it>EBER1 </it>inhibits <it>p21</it>^cip1/waf1transcription and prevents apoptosis through down-regulation of p53, EGR1, and STAT1. The anti-apoptotic activity of <it>EBER1 </it>may be important in the rescue of Reed-Sternberg cells from drug-induced apoptosis and in the clinical behaviors of EBV⁺HLs.</p

High expression FUT1 and B3GALT5 is an independent predictor of postoperative recurrence and survival in hepatocellular carcinoma.

Cancer may arise from dedifferentiation of mature cells or maturation-arrested stem cells. Previously we reported that definitive endoderm from which liver was derived, expressed Globo H, SSEA-3 and SSEA-4. In this study, we examined the expression of their biosynthetic enzymes, FUT1, FUT2, B3GALT5 and ST3GAL2, in 135 hepatocellular carcinoma (HCC) tissues by qRT-PCR. High expression of either FUT1 or B3GALT5 was significantly associated with advanced stages and poor outcome. Kaplan Meier survival analysis showed significantly shorter relapse-free survival (RFS) for those with high expression of either FUT1 or B3GALT5 (P = 0.024 and 0.001, respectively) and shorter overall survival (OS) for those with high expression of B3GALT5 (P = 0.017). Combination of FUT1 and B3GALT5 revealed that high expression of both genes had poorer RFS and OS than the others (P &lt; 0.001). Moreover, multivariable Cox regression analysis identified the combination of B3GALT5 and FUT1 as an independent predictor for RFS (HR: 2.370, 95% CI: 1.505-3.731, P &lt; 0.001) and OS (HR: 2.153, 95% CI: 1.188-3.902, P = 0.012) in HCC. In addition, the presence of Globo H, SSEA-3 and SSEA-4 in some HCC tissues and their absence in normal liver was established by immunohistochemistry staining and mass spectrometric analysis