Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-period complete-information games with self-control: a dual-self approach

The existing literature on dual-self has focused on individual decisionmaking problems. We adopt the theoretical framework established by Fudenberg and Levine (American Economic Review, 2006, 96(5): 1449-1476) in 2006 to propose a new dual-self model that expands individual decision problems to multi-player strategic situations, better reflecting human interactions in reality. For the example of two-period complete-information game with multiple equilibria provided in this paper, we analyze it using our proposed dual-self model and compare the results we found with those of economic models without dual-self. Consistent with the Fudenberg-Levine axioms, our model defines a more general concept of self-control cost that takes into account multiplayer interactions. Keywords self-control, dual-self, complete information, game theory Chinese Library Classification F224.32 2010 Mathematics Subject Classification 91A10, 91A20, 91A40, 91A80 õÏ &EÆ¥ g·yμ ÄuV­g· . ©Ûμe∗ v1† x $ Á yk©zéV­g· ïÄ=uéü<ûü ̄K &? ÄuFudenberg ÚLevine (American Economic Review, 2006, 96(5): 1449-1476) in 2006 nØμe§ï áòü<ûüÿÐõ<Æ V­g· . §±O(/xÚïÄ<3y¢ )1¥ pÄ1. |^T . é, &Ee üÏõ­þïÆÐm©Û§¿ Ù¦ÃV­g·N 2LÆ . 1é'. V­g· . 3Fudenberg-Levine . únb ± Ä:þ§JÑ ä5 Nyõ<pÄA g· ¤ Vg. ' c g·§V­g·§ &E§ÆØ
Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009–2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.
Restoration and replication: a case study on the value of computational reproducibility assessment

Open science is vital to the interdisciplinary field of ecology due to its integrative nature and use of longitudinal datasets that build upon earlier data collections. To highlight the importance of open science in the rapidly growing discipline of restoration ecology, we conducted a “computational reproducibility” assessment of a publication on a mining restoration program spanning several decades and over 250 km2 in a global biodiversity hotspot. Open data and code provided alongside the original publication were assessed for consistency with the results and conclusions of the original publication, as were potential limitations in findings due to the methodology. The impacts of inconsistencies and limitations were qualitatively assessed against the key findings from the publication and data were reanalyzed where impacts were potentially significant. Of the six inconsistencies and limitations identified, two had a significant impact on 5 of the 11 key findings of the original publication and one new key finding was made. The impact of this is of high ecological significance as the findings related to key restoration parameters: species richness (similarity of species richness between forest and 25‐year‐old restoration), functional diversity (correlation of species richness and functional diversity), and the restoration trajectory (long‐term trends and restored areas' resilience to disturbance). These outcomes highlight the importance of open data and the value of detailed third‐party data reviews, particularly in restoration ecology which relies on research findings to inform decision‐making and policy and drive adaptive management.
Defining NAD(P)(H) Catabolism

Dietary vitamin B3 components, such as nicotinamide and nicotinic acid, are precursors to the ubiquitous redox cofactor nicotinamide adenine dinucleotide (NAD+). NAD+ levels are thought to decline with age and disease. While the drivers of this decline remain under intense investigation, strategies have emerged seeking to functionally maintain NAD+ levels through supplementation with NAD+ biosynthetic intermediates. These include marketed products, such as nicotinamide riboside (NR) and its phosphorylated form (NMN). More recent developments have shown that NRH (the reduced form of NR) and its phosphorylated form NMNH also increases NAD+ levels upon administration, although they initially generate NADH (the reduced form of NAD+). Other means to increase the combined levels of NAD+ and NADH, NAD(H), include the inhibition of NAD+-consuming enzymes or activation of biosynthetic pathways. Multiple studies have shown that supplementation with an NAD(H) precursor changes the profile of NAD(H) catabolism. Yet, the pharmacological significance of NAD(H) catabolites is rarely considered although the distribution and abundance of these catabolites differ depending on the NAD(H) precursor used, the species in which the study is conducted, and the tissues used for the quantification. Significantly, some of these metabolites have emerged as biomarkers in physiological disorders and might not be innocuous. Herein, we review the known and emerging catabolites of the NAD(H) metabolome and highlight their biochemical and physiological function as well as key chemical and biochemical reactions leading to their formation. Furthermore, we emphasize the need for analytical methods that inform on the full NAD(H) metabolome since the relative abundance of NAD(H) catabolites informs how NAD(H) precursors are used, recycled, and eliminated.
Discovery and characterization of a diffuse astrophysical muon neutrino flux with the iceCube neutrino observatory

The IceCube Collaboration has previously discovered a flux of high-energy astrophysical neutrinos whose measurement is based on events with interaction vertices contained within the IceCube detector. This thesis presents a complementary measurement of such a flux based on charged-current muon neutrino events whose interaction vertices can be outside the detector volume. Due to the large range of the induced muon the effective area is significantly larger but the field of view is restricted to the Northern Hemisphere. In this thesis IceCube data from 2009 through 2015 have been analyzed using a likelihood approach based on the reconstructed muon energy and zenith angle. The analyzed data consist of about 350, 000 muon neutrinos with a negligible background of atmospheric muons where the majority of these events are atmospheric neutrinos. The here presented analysis improves the statistics compared to previous analyses [1, 2] by almost an order of magnitude. At the highest neutrino energies between 194 TeV and 7.8 PeV a significant astrophysical muon neutrino flux has been measured. While the data is incompatible with a purely atmospheric neutrino flux at 5. 6σ significance, the data are well described by an isotropic, unbroken power-law flux with a normalization at 100 TeV neutrino energy of ( 0.90 −0.27 ) × 10−18 GeV−1 cm−2 s−1 sr−1 and a hard spectral index of γ = 2.13 ± 0.13. The corresponding energy spectrum is harder compared to previous IceCube analyses with lower energy thresholds which may indicate a break in the astrophysical neutrino spectrum of unknown origin. The highest energy event observed has a reconstructed muon energy of (4.5± 1.2) PeV which results in a median muon neutrino energy of 8.7 PeV based on the best-fit neutrino energy spectrum. The probability of this event being of atmospheric origin has been estimated to be less than 0.005%, strongly suggesting an astrophysical origin of this neutrino. The arrival directions of this event and all other events with reconstructed muon energies above 200 TeV have been analyzed, but no correlation with known γ-ray sources was found. Besides the measurement of the astrophysical neutrino flux, the high statistics of atmospheric neutrinos enable to constrain the flux of prompt atmospheric neutrinos originating from heavy meson decays. Since no indications for such a signal was found, the corresponding flux needs to be below 1.06 in units of the flux normalization of the model in Enberg et al. [3].
Analysis of different algorithms on edge and corner detection

Many computer vision applications use edge and corner detectors as primary operators before high level processing, such as object recognition and tracking. For instance, the information associated to the edges of an object in an image which is, in many cases, sufficient to identify the object. In this section, we will give an overview on these two components of vision systems, showing their foundation and describing the most common techniques of implementation
SWBNet: A Stable White Balance Network for sRGB Images

The white balance methods for sRGB images (sRGB-WB) aim to directly remove their color temperature shifts. Despite achieving promising white balance (WB) performance, the existing methods suffer from WB instability, i.e., their results are inconsistent for images with different color temperatures. We propose a stable white balance network (SWBNet) to alleviate this problem. It learns the color temperature-insensitive features to generate white-balanced images, resulting in consistent WB results. Specifically, the color temperatureinsensitive features are learned by implicitly suppressing lowfrequency information sensitive to color temperatures. Then, a color temperature contrastive loss is introduced to facilitate the most information shared among features of the same scene and different color temperatures. This way, features from the same scene are more insensitive to color temperatures regardless of the inputs. We also present a color temperature sensitivity-oriented transformer that globally perceives multiple color temperature shifts within an image and corrects them by different weights. It helps to improve the accuracy of stabilized SWBNet, especially for multiillumination sRGB images. Experiments indicate that our SWBNet achieves stable and remarkable WB performance.
Sojourn time of some reflected Brownian motion in the unit disk

We study the heat diffusion in a domain with an obstacle inside. More precisely, we are interested in the quantity of heat in so far as a function of the position of the heat source at time 0. This quantity is also equal to the expectation of the sojourn time of the Brownian motion, reflected on the boundary of a small disk contained in the unit disk, and killed on the unit circle. We give the explicit expression of this expectation.This allows us to make some numerical estimates and thus to illustrate the behaviour of this expectation as a function of starting point of Brownian motion.
Géométrie Complexe et Courbure Négative

Le theme central de ce memoire est l'etude des varietes kahleriennes (compactes) dont la courbure sectionnelle est strictement negative. Les exemples connus sont de deux types, selon qu'ils admettent ou non une metrique localement symetrique. Le cas localement symetrique correspond a l'etude des reseaux (uniformes) de PU(n,1). Nous nous interessons particulierement a la construction de reseaux non-arithmetiques. Dans le cas non localement symetrique, nous presentons un resultat d'estimation du pincement des metriques kahleriennes sur les surfaces de Mostow-Siu et leurs analogues tri-dimensionnels.
Attention Based Mapping for Plants Leaf to Classify Diseases using Vision Transformer

Agriculture plays a crucial role in meeting the basic food needs of a growing global population. The growth of grains and vegetables is essential not only for nutrition but also for the world economy. One of the major challenges associated with cultivating plants is the prevalence of diseases on the leaves, which can significantly impact the plant's growth and development. Traditional techniques for diagnosing these illnesses can be expensive and time-consuming. To solve this problem, researchers are investigating the potential of intelligent systems that depend on the advanced deep-learning architecture. In this study, researchers have replaced CNN with a new method called Vision Transformer for classifying plant leaf diseases. There are three vision transformer models are compared in this which are ViT1, ViT2, and pre-trained ViT_b16 and the test accuracy achieved by them are 85.87%, 89.16%, and 94.16%. The results show that our proposed model is able to identify the specific region of the leaf where the disease is present, providing valuable insights for farmers.
Smooth Composite Likelihood Analysis of Length-Biased and Right -Censored Data

This article discusses regression analysis of length-biased and rightcensored failure time data arising from the accelerated failure time model. A key feature of such data is the informative censoring induced by the length-biased sampling, and several methods have been proposed in the literature for their analysis. However, these may be less efficient or apply only to limited situations. We propose a kernel-smoothed composite likelihood method for estimation of covariate effects. The proposed estimators are proved to be consistent and asymptotically normal. Simulation studies conducted to assess the finite sample performance of the method suggest that it works well for practical situations. An illustrative example is provided.
The Relationship between Reticuloruminal Temperature, Reticuloruminal pH, Cow Activity, and Clinical Mastitis in Dairy Cows

Simple Summary Automated monitoring devices are becoming more widespread in the dairy sector. Upon a literature analysis, we found that more research is needed to assess mastitis status, including spontaneous infections with various pathogens and other variations in reticulorumen temperature. Based on the literature review, we aimed to determine the relationship between reticuloruminal temperature, pH, cow activity, and clinical mastitis (CM) in dairy cows. According to our study findings, we concluded that reticuloruminal temperature, reticuloruminal pH, and cow activity could be used as parameters for the early diagnosis of CM in dairy cows. Furthermore, CM occurrences must be tracked over shorter periods so that farmers can receive information to make wise management decisions. Abstract We hypothesized that reticuloruminal temperature, pH as well as cow activity can be used as parameters for the early diagnosis of clinical mastitis in dairy cows. Therefore, we aimed to detect the relationship between these factors and the disease. We randomly selected cows with clinical mastitis and clinically healthy cows (HG) out of 600 milking cows. We recorded the following parameters during the experiment: reticulorumen temperature (RR temp.), reticulorumen pH (RR pH), and cow activity. We used smaXtec boluses (smaXtec animal care technology®, Graz, Austria). In this investigation, reticulorumen data obtained seven days before diagnosis were compared to HG data from the same time period. CM cows were observed on the same days as the healthy cows. The healthy group’s RR pH was 7.32% higher than that of cows with CM. Reticulorumen temperature was also 1.25% higher in the CM group than in the control group. The healthy group had a higher average value for walking activity, which was 17.37% higher than the CM group. The data of reticulorumen pH changes during 24 h showed that during the day, the pH changed from 5.53 to 5.83 in the CM group. By contrast, pH changed from 6.05 to 6.31 in the control group. The lowest reticulorumen pH in the CM group was detected on the third day before diagnosis, which was 15.76% lower than the highest reticulorumen pH detected on the sixth day before diagnosis. The lowest reticulorumen pH in CM cows was detected at 0 and 1 days before diagnosis and it was 1.45% lower than the highest reticulorumen pH detected on the second day before diagnosis. The lowest walking activity in the CM group was detected 0 days before diagnosis, which was 50.60% lower than on the fifth day before diagnosis. Overall, the results confirmed our hypothesis that reticuloruminal temperature, reticuloruminal pH, and cow activity could be used as parameters for the early diagnosis of clinical mastitis in dairy cows.
Exploring the Differences and Influencing
Factors of Public Participation in Environmental
Protection Behavior in the Private and Public
Spheres in China

Based on data from the 2013 China General Social Survey (CGSS2013), this study compares the public’s participation in environmental protection behaviors in the public and private spheres in China and the differences between urban and rural areas to investigate the differences in the influence of relevant factors on environmental protection behaviors in different spheres. This study found that 1) Chinese public participation in environmental protection is mainly in the private sphere, especially female’s participation in the private sphere is significantly higher than men’s; 2) compared to urban residents, rural residents are less involved in environmental protection in the private and
A nomogram for intraoperatively predicting non-sentinel lymph node metastases in early breast cancer patients with positive sentinel lymph nodes

Background Individualized decisions are required in early-stage breast cancer patients. We aimed to establish a novel model for predicting non-sentinel lymph node (SLN) metastases in patients with positive SLNs, using preoperative and intraoperative characteristics and inflammatory indicators. Methods The data of 489 patients with invasive breast cancer were retrospectively collected from Xuanwu Hospital between 2014 and 2021. Among them, 96 patients with at least one positive SLN were used to build the predictive model. Univariate and multivariate analyses were performed to identify the risk factors of non-SLN metastases. A nomogram was developed using these risk factors and was validated by calibration curves. The area under the receiver operating characteristics curve (AUC) and decision curve analyses (DCA) were used to compare our novel nomogram with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram. Cross-validation was performed for further internal validation of the predictive model. External validation was conducted using another treatment group (n=46 patients) in Xuanwu Hospital. Results Non-SLN metastases occurred in 42 of the 83 patients with positive SLNs (50.6%). Multivariate stepwise logistic regression indicated that the risk factors were age (P=0.032), number of positive SLNs (P=0.020), number of negative SLNs (P=0.011), resected tumor size (P=0.038), and monocyte count (P=0.012). A predictive model was developed and virtualized by nomogram using these five risk factors. The AUC of our nomogram was 0.867, which was significantly higher than that of the MSKCC model. DCA also showed a superior clinical value for our novel nomogram. After 10-fold cross-validation with 400 times repetitions, the AUC of our model was still 0.830. External validation of our model showed an AUC of 0.727. The model was well-calibrated in the internal and external validation series. Conclusions A five-factor nomogram was developed for predicting non-SLN metastases in early-stage breast cancer patients. This novel tool exhibited good accuracy and could assist clinicians with intraoperative decisions in breast cancer patients with positive SLNs.
New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids

A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be qualified by means of the metrological and diagnosis tests arranged in this calibrator. This synthetic PD calibrator can reproduce PD pulse trains of the same sequence as actual representative defects (cavity, surface, floating potential, corona, SF6 protrusion, SF6 jumping particles, bubbles in oil, etc.) acquired in HV equipment in service or by means of measurements made in HV laboratory test cells. The diagnostic capabilities and PD measurement errors of the PD analyzers using HFCT sensors can be determined. A new time parameter, “PD Time”, associated with any arbitrary PD current pulse i(t) is introduced for calibration purposes. It is defined as the equivalent width of a rectangular PD pulse with the same charge value and amplitude as the actual PD current pulse. The synthetic PD calibrator consists of a pulse generator that operates on a current loop matched to 50 Ω impedance to avoid unwanted reflections. The injected current is measured by a reference measurement system built into the PD calibrator that uses two HFCT sensors to ensure that the current signal is the same at the input and output of the calibration cage where the HFCT of the PD analyzer is being calibrated. Signal reconstruction of the HFCT output signal to achieve the input signal is achieved by applying state variable theory using the transfer impedance of the HFCT sensor in the frequency domain.
Transformer Meets Remote Sensing Video Detection and Tracking: A Comprehensive Survey

Transformer has shown excellent performance in remote sensing field with long-range modeling capabilities. Remote sensing video (RSV) moving object detection and tracking play indispensable roles in military activities as well as urban monitoring. However, transformers in these fields are still at the exploratory stage. In this survey, we comprehensively summarize the research prospects of transformers in RSV moving object detection and tracking. The core designs of remote sensing transformers and advanced transformers are first analyzed. It mainly includes the attention mechanism evolution for specific tasks, the fitting ability design of input mapping, diverse feature representation, model optimization, etc. The architectural characteristics of RSV detection and tracking are then described across two aspects. One is moving object detection for motion-based traditional background subtractions and appearance-based deep learning models. The other is object tracking for single and multiple targets. The research difficulties mainly include the blurred foreground in RSV data, the irregular object movement in traditional background subtraction, and the severe object occlusion in object tracking. Following that, the potential significance of transformers is discussed according to some thorny problems in RSV. Finally, we summarize ten open challenges of transformers in RSV, which may be used as a reference for promoting future research.