Estimating the win probability in a hockey game

When a hockey game is being played, its data comes continuously. Therefore, it is possible to use the stream mining method to estimate the win probability (WP) of a team once the game begins. Based on 8 seasons’ data of NHL from 2003-2014, we provide three methods to estimate the win probability in a hockey game. Win probability calculation method based on statistics is the first model, which is built based on the summary of the historical data. Win probability calculation method based on data mining classification technique is the second model. In this model, we implemented some data classification algorithms on our data and compared the results, then chose the best algorithm to build the win probability model. Naive Bayes, SVM, VFDT, and Random Tree data classification methods have been compared in this thesis on the hockey dataset. We used stream mining technique in our last model, which is a real time prediction model, which can be interpreted as a trainingupdate- training model. Every 20 events in a hockey game are split as a window. We use the last window as the training data set to get decision tree rules used for classifying the current window. Then a parameter can be calculated by the rules trained by these two windows. This parameter can tell us which rule is better than another to train the next window. In our models the variables time, leadsize, number of shots, number of misses, number of penalties are combined to calculate the win probability. Our WP estimates can provide useful evaluations of plays, prediction of game result and in some cases, guidance for coach decisions.Master of Science (M.Sc.) in Computational Science

AgentBench: Evaluating LLMs as Agents

Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting. Our extensive test over 27 API-based and open-sourced (OSS) LLMs shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and OSS competitors. We identify the typical reasons of failures in environments and LLMs, showing that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents. Training on code and high quality multi-turn alignment data could improve agent performance. Datasets, environments, and an integrated evaluation package for AgentBench are released at \url{https://github.com/THUDM/AgentBench}.Comment: 55 page

Functionalization of RNA-DNA Hybrid Nanostructures by Targeting AP Site with ATMND and Its Derivatives

DNA self-assembly has been explored as an effective approach for nanoconstruction. And Hermann’s group have reported to build a novel nanostructure incorporating L-shape RNA which guided by DNA strands to self-assemble into a hybrid RNA-DNA branched motifs. AP site, as a common lesion in DNA, has been demonstrated to be a good target position to interact with small molecular ligands like ATMND. This project introduced ATMND and designed derivatives of it to the hybrid nanostructures built by Hermann’s group and detected different selectivity and affinity to different bases opposite AP site. This work demonstrated the feasibility to combine RNA, DNA and small molecules to construct new self-assembled nanostructures and provided a rational pathway to modify and functionalize nano materials

A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information

Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid

Association of peroxisome proliferator-activated receptor delta and additional gene–smoking interaction on cardiovascular disease

Aims: To investigate the impact of peroxisome proliferator–activator receptor delta (PPARD) gene polymorphism and additional gene–smoking interaction on cardiovascular disease (CVD) risk based on this Chinese population. Methods: A total of 1048 subjects (617 males, 431 females) with a mean age of 52.9 ± 14.1 years old were selected, including 520 CVD patients and 528 normal control subjects. The logistic regression model was used to examine the association between three SNPs and CVD risk, odds ratio (OR), and 95% confident interval (95%CI) were calculated. Generalized multifactor dimensionality reduction (GMDR) was employed to investigate the gene–smoking interaction. Results: Genotypes of variants in rs2016520 and rs9794 were associated with decreased CVD risk, and CVD risk was significantly lower in carriers of C allele of the rs2016520 polymorphism than those with the TT genotype (TC+CC versus TT), adjusted OR (95%CI) = 0.71 (0.56–0.86). In addition, we also found that CVD risk was also significantly lower in carriers of the G allele of the rs9794 polymorphism than those with the CC genotype (CG+ GG versus CC), adjusted OR (95%CI) = 0.69 (0.53–0.86). GMDR analysis suggested a potential gene–environment interaction between rs2016520 and smoking. Overall, the two-locus models had a cross-validation consistency of 10 of 10, and had the testing accuracy of 62.17%, and never smokers with TC or CC of the rs2016520 genotype have the lowest CVD risk, compared to smokers with TT of rs2016520, OR (95%CI) was 0.42 (0.23–0.66). Conclusions: The minor allele of rs2016520 and rs9794 in PPAR-δ and interaction between rs2016520 and non-smoking were associated with decreased risk of CVD

Hot Air Drying of Seabuckthorn (<i>Hippophae rhamnoides</i> L.) Berries: Effects of Different Pretreatment Methods on Drying Characteristics and Quality Attributes

Seabuckthorn berries are difficult to dry because the outermost surface is covered with a dense wax layer, which prevents moisture transfer during the drying process. In this study, uses of ultrasonic-assisted alkali (UA), pricking holes in the skin (PH) and their combination (UA + PH) as pretreatment methods prior to hot air drying and their effects on drying characteristics and quality attributes of seabuckthorn berries were investigated. Selected properties include color, microstructure, rehydration capacity, as well as total flavonoids, phenolics and ascorbic acid contents. Finally, the coefficient of variation method was used for comprehensive evaluation. The results showed that all pretreatment methods increased the drying rate; the combination of ultrasonic-assisted alkali (time, 15 min) and pricking holes (number, 6) (UA15 + PH6) had the highest drying rate that compared with the control group, the drying time was shortened by 33.05%; scanning electron microscopy images revealed that the pretreatment of UA could dissolve the wax layer of seabuckthorn berries, helped to form micropores, which promoted the process of water migration. All the pretreatments reduced the color difference and increased the lightness. The PH3 samples had the highest value of vitamin C content (54.71 mg/100 g), the UA5 and PH1 samples had the highest value of total flavonoid content (11.41 mg/g) and total phenolic content (14.20 mg/g), respectively. Compared to other pretreatment groups, UA15 + PH6 achieved the highest quality comprehensive score (1.013). Results indicate that UA15 + PH6 treatment is the most appropriate pretreatment method for improving the drying characteristics and quality attributes of seabuckthorn berries

Diacylglycerol kinase zeta positively controls the development of iNKT-17 cells.

Invariant natural killer T (iNKT) cells play important roles in bridging innate and adaptive immunity via rapidly producing a variety of cytokines. A small subset of iNKT cells produces IL-17 and is generated in the thymus during iNKT-cell ontogeny. The mechanisms that control the development of these IL-17-producing iNKT-17 cells (iNKT-17) are still not well defined. Diacylglycerol kinase ζ (DGKζ) belongs to a family of enzymes that catalyze the phosphorylation and conversion of diacylglycerol to phosphatidic acid, two important second messengers involved in signaling from numerous receptors. We report here that DGKζ plays an important role in iNKT-17 development. A deficiency of DGKζ in mice causes a significant reduction of iNKT-17 cells, which is correlated with decreased RORγt and IL-23 receptor expression. Interestingly, iNKT-17 defects caused by DGKζ deficiency can be corrected in chimeric mice reconstituted with mixed wild-type and DGKζ-deficient bone marrow cells. Taken together, our data identify DGKζ as an important regulator of iNKT-17 development through iNKT-cell extrinsic mechanisms

Visual Detection of Water Content Range of Seabuckthorn Fruit Based on Transfer Deep Learning

To realize the classification of sea buckthorn fruits with different water content ranges, a convolution neural network (CNN) detection model of sea buckthorn fruit water content ranges was constructed. In total, 900 images of seabuckthorn fruits with different water contents were collected from 720 seabuckthorn fruits. Eight classic network models based on deep learning were used as feature extraction for transfer learning. A total of 180 images were randomly selected from the images of various water content ranges for testing. Finally, the identification accuracy of the network model for the water content range of seabuckthorn fruit was 98.69%, and the accuracy on the test set was 99.4%. The program in this study can quickly identify the moisture content range of seabuckthorn fruit by collecting images of the appearance and morphology changes during the drying process of seabuckthorn fruit. The model has a good detection effect for seabuckthorn fruits with different moisture content ranges with slight changes in characteristics. The migration deep learning can also be used to detect the moisture content range of other agricultural products, providing technical support for the rapid nondestructive testing of moisture contents of agricultural products

Online Detection and Classification of Moldy Core Apples by Vis-NIR Transmittance Spectroscopy

Apple moldy core disease is a common internal fungal disease. The online detection and classification of apple moldy core plays a vital role in apple postharvest processing. In this paper, an online non-destructive detection system for apple moldy core disease was developed using near-infrared transmittance spectroscopy in spectral range of 600–1100 nm. A total of 120 apple samples were selected and randomly divided into a training set and a test set based on the ratio of 2:1. First, basic parameters for detection of apples with moldy core were determined through detection experiments of samples in a stationary state. Due to the random distribution of the diseased tissue inside diseased apples, stationary detection cannot accurately identify the diseased tissue. To solve this problem, the spectra of apples in motion state transmitted forward by the transmission line were acquired. Three placement orientations of the apple in the carrying fruit cup were tested to explore the influence of fruit orientation on spectral characteristics and prediction. According to the performance of the model, the optimal preprocessing method and modeling method were determined (fixed orientation model and arbitrary orientation model). SPA was used to select the characteristic wavelengths to further improve the online detection speed. The overall results showed that the multi-spectra model using mean spectra of three orientations was the best. The prediction accuracies of multi-spectra model using SPA for three orientations were 96.7%, 97.5% and 97.5% respectively. As a conclusion, the arbitrary orientation model was beneficial to improve the online detection of apple moldy core disease