Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF BURN SEVERITY ON NON-NATIVE PLANTS: A CASE STUDY IN YOSEMITE NATIONAL PARK, CALIFORNIA

EFFECTS OF BURN SEVERITY ON NON-NATIVE PLANTS: A CASE STUDY IN YOSEMITE NATIONAL PARK, CALIFORNIA Kristen M. Kaczynski , Susan W. Beatty, Jan W. van Wagtendonk, Kristin N. Marshall Department of Geography, University of Colorado, Campus Box 260, Boulder, CO 80309, USA US Geological Survey, Western Ecological Research Center, Yosemite Field Station, El Portal, California 95318, USA Graduate Degree Program in Ecology and Department of Forestry Range and Watershed Stewardship, Colorado State University, Fort Collins, Colorado 80523, USA Corresponding author: Tel. :001-303-319-4342, email: Kristen.Kaczynski@colostate.edu. ABSTRACT
Dynamic Reconfiguration of Functional Topology in Human Brain Networks: From Resting to Task States

Task demands evoke an intrinsic functional network and flexibly engage multiple distributed networks. However, it is unclear how functional topologies dynamically reconfigure during task performance. Here, we selected the resting- and task-state (emotion and working-memory) functional connectivity data of 81 health subjects from the high-quality HCP data. We used the network-based statistic (NBS) toolbox and the Brain Connectivity Toolbox (BCT) to compute the topological features of functional networks for the resting and task states. Graph-theoretic analysis indicated that under high threshold, a small number of long-distance connections dominated functional networks of emotion and working memory that exhibit distinct long connectivity patterns. Correspondently, task-relevant functional nodes shifted their roles from within-module to between-module: the number of connector hubs (mainly in emotional networks) and kinless hubs (mainly in working-memory networks) increased while provincial hubs disappeared. Moreover, the global properties of assortativity, global efficiency, and transitivity decreased, suggesting that task demands break the intrinsic balance between local and global couplings among brain regions and cause functional networks which tend to be more separated than the resting state. These results characterize dynamic reconfiguration of large-scale distributed networks from resting state to task state and provide evidence for the understanding of the organization principle behind the functional architecture of task-state networks.
Research on the influence of we-chat applet on improving user experience

As a service application developed for users, we-chat applets can be used without downloading and installation, which realizes the immediate response between offline enterprises and users, and more importantly, it provides users with a convenient platform to find products and improve the cost-effective channel for customers. Therefore, it is beneficial to improve the user experience in the future by studying how to use wechat applets Realize the long-term market competitiveness of enterprises.
Gender differences in Predicting STEM Choice by Affective States and Behaviors in Online Mathematical Problem Solving: Positive-Affect-to-Success Hypothesis

This study aims to identify effective affective states and behaviors of middle-school students' online mathematics learning in predicting their choices to study science, technology, engineering, and mathematics (STEM) in higher education based on a positive-affect-to-success hypothesis. The dataset (591 students and 316,974 actions) was obtained from the ASSISTments project. In the ASSISTments intelligent tutoring system, students completed mathematical problem-solving tasks, and the data was processed to infer their action-level affective states and behaviors, which were averaged to form student-level measures. The students' future STEM choice was predicted by the student- and action-level affective states and behaviors using logistic regression (LR), ordinary least squares regressions with standardized scores (ORz), and random forest with permutation importance and SHAP values (RFPS). The results revealed that student- and action-level gaming behavior consistently predict STEM choice. In addition to gaming, female students are more likely to study STEM if they are less bored and more off-task, and male students if more concentrated and less frustrated. ORz generates theoretically plausible results and identifies sufficiently distinguishable affective states and behaviors. Suggestions for educational practice and research are provided for adaptive teaching.
NNLO QCD corrections to production of a spin-2 particle with non-universal couplings in the DY process

We study the phenomenological impact of the interaction of spin-2 fields with those of the Standard Model in a model independent framework up to next-to-nextto-leading order in perturbative Quantum Chromodynamics. We use the invariant mass distribution of the pair of leptons produced at the Large Hadron Collider to demonstrate this. A minimal scenario where the spin-2 fields couple to two gauge invariant operators with different coupling strengths has been considered. These operators being not conserved show very different ultraviolet behaviour increasing the searches options of spin-2 particles at the colliders. We find that our results using the higher order quantum corrections stabilise the predictions with respect to renormalisation and factorisation scales. We also find that corrections are appreciable which need to be taken into account in such searches at the colliders.
Improving Anchor-based Explanations

Rule-based explanations are a popular method to understand the rationale behind the answers of complex machine learning (ML) classifiers. Recent approaches, such as Anchors, focus on local explanations based on if-then rules that are applicable in the vicinity of a target instance. This has proved effective at producing faithful explanations, yet anchor-based explanations are not free of limitations. These include long overly specific rules as well as explanations of low fidelity. This work presents two simple methods that can mitigate such issues on tabular and textual data. The first approach proposes a careful selection of the discretization method for numerical attributes in tabular datasets. The second one applies the notion of pertinent negatives to explanations on textual data. Our experimental evaluation shows the positive impact of such methods on the quality of anchor-based explanations.
A Systematic Assessment of National Artificial Intelligence Policies: Perspectives from the Nordics and Beyond

Echoing the evolving interest and impact of artificial intelligence on society, governments are increasingly looking for ways to strategically position themselves as both innovators and regulators in this new domain. One of the most explicit and accessible ways in which governments outline these plans is through national strategy and policy documents. We follow a systematic search strategy to identify national AI policy documents across twenty-five countries. Through an analysis of these documents, including topic modelling, clustering, and reverse topic-search, we provide an overview of the topics discussed in national AI policies and contrast the differences between countries. Furthermore, we analyse the frequency of eleven ethical principles across our corpus. Our paper outlines implications of the differences between geographical and cultural clusters in relation to the future development of artificial intelligence applications.
3D Object Detection in Point Clouds

In this project, we study VoteNet, the state-of-the-art neural network that detects 3D objects in point clouds. We ﬁrst survey related literature to understand what makes VoteNet better than other existing models. We then conduct an error analysis to identify where VoteNet could be improved. We ﬁnally propose three testing probes on VoteNet as an attempt for improvement. We report our evaluation results and suggest future research directions for 3D object detection in point clouds
A Novel Approach for Univariate Outlier Detection

- In many applications outlier detection is an important task . In the process of Knowledge Discovery in Databases, isolation of outlying data is important. This isolation process improves the quality of data and reduces the impact of outlying data on the existing values. Numerous methods are available in the detection process of outliers in univariate data sets. Most of these methods handle one outlier at a time. In this paper, Grubb’s statistics, sigma rule and fence rules deal more than one outliers at a time. In general, when multiple outliers are present, presence of such outliers prevents us from detecting other outliers. Hence, as soon as outliers are found, removing outlier is an important task. Multiple outliers are evaluated on different data sets and proved that results are effective. Separate procedures are used for detecting outliers in continuous and discrete data. Experimental results show that our method works well for different data .
Animated Transitions to Support Visualization of Missing Data

Large datasets often contain missing data, which needs to be dealt with when visualizing the data, either by deletion or by depicting missing data in a certain way. These visualizations are often interactive. Animated transitions can be implemented to smoothen the switch between views. In the context of the IKON project, which goal it is to facilitate knowledge transfer, animated transitions and missing data visualizations for a line chart will get conceptualized, implemented and evaluated. The general goal of the thesis is to enhance the way how users work with visualizations consisting missing data. Additionally, more information is tried be to collected regarding the user’s interaction with the chosen missing data visualizations. This thesis explores the theory behind missing data, why data can be missing and what can be done to handle missing, including imputation methods and visualization techniques. It also examines already existing visualization techniques and studies, presenting their ﬁndings regarding preference of visualization types and diﬀerent criteria such as accuracy and conﬁdence in data. Next, this thesis delves into the theory of animated transitions for data graphics. This includes the diﬀerent types of transitions in data graphics and rules which should be followed to create successful animated transitions. Two diﬀerent studies about animated transitions are be presented and their results are summarized, showing the possible positive eﬀect animations can have for transitions.
Mapping of grain alkali digestion trait using a Cheongcheong/Nagdong doubled haploid population in rice

We performed a molecular marker-based analysis of quantitative trait loci for traits that determine the quality of appearance of grains using 120 doubled haploid lines developed by anther culture from the F1 cross between ``Cheongcheong`` (Oryza sativa L. ssp. Indica) and ``Nagdong`` (Oryza sativa L. ssp. Japonica). We therefore calculated the alkali digestion value (ADV), used to indirectly measure gelatinization temperature, to evaluate the quality of cooked rice in 2013 and 2014. The ADV score of frequency distribution was higher milled rice than brown rice. In total, nine different quantitative trait loci (QTLs) were found on 5 chromosomes in 2013 and 2014. Also, chromosome 5, 8 were detected over two years. We conclude that selected molecular markers from this QTL analysis could be exploited in future rice quality. In conclusion, we investigated ADV of brown and milled rice in CNDH population. This study found nine QTLs related to the ADV of brown and milled rice. The detected one marker can be used to select lines with desirable eating-quality traits because ADV is closely associated with the eating quality of cooked rice. Therefore, it will be useful to collect resources and distinguishable in many varieties for rice breeding program.
Applying Data Mining for Advertisement in Social Networks and Improving CTR

In this paper different usage of data mining in social networks has been reviewed and a hypothesis has been define that states by implementing data mining in social networks The CLICK THROUGH RATE (CTR) of their advertisement will improve, which can lead to improvement of the revenue of social network websites. Additionally finding of recent study on social network advertisements which has been conducted will be analyzed and evaluated to validate this hypothesis to show that after implementing data mining on advertisement in social networks CTR of an advertisement will be improved. Moreover methods of the performed research such as schema of database tables that was used in this study and other requirements of this study was stated in this paper and findings before and after implementing data mining was compared in form of bar charts. Additionally other relevant studies regarding this subject has been reviewed and the role of data mining to improve click through rate of advertisement in social network was described.
New insights on the use of the Fairtrade social premium and its implications for child education

Fairtrade standards differentiate themselves from other sustainability standards such as Rainforest Alliance and UTZ by demanding that buyers pay to farmers at least a minimum price and a Fairtrade social premium – a sum that is paid to cooperatives in addition to the agreed price to be used in projects to strengthen the cooperative and to benefit the community in the villages more broadly. The latter is often mentioned in the literature as one of the key mechanisms through which Fairtrade engenders changes in the small farm sector. However, no previous study has explicitly analyzed what the social premium is used for, which factors affect the decision-making processes, and whether farmers, workers, and the local communities more broadly benefit from projects implemented with the premium money. In this article, I use multivariate statistical techniques to analyze how patterns in the use of the premium are related to cooperatives’ organizational characteristics. To illustrate the potential benefits of the social premium to the local community, I use regression analyses to evaluate the effects of certification and educational projects financed with the social premium on household education expenditure. I find evidence that living in a village where an educational project was implemented has a positive effect on education expenditure among farmers, but does not have an effect among rural workers.
CoLoRFulNNLO at work : A determination of α

The most precise determination of fundamental parameters of the Standard Model is very important. One such fundamental parameter is the strong coupling of QCD. Its importance can be gauged by taking a look at the various experiments and configurations where it was measured, for an up to date summary see Ref. [1]. The precise measurement of such a parameter is two-fold difficult. First, high quality data with small and well-controlled uncertainties are needed. Second, high precision calculations are needed from the theory side such that theoretical uncertainties are small as well. In a theoretical prediction based on calculation in perturbation theory the uncertainty has two main sources: the omission of higher-order terms which are estimated by the renormalization scale and the numerical stability of the integrations. While the dependence on unphysical scales can be, in principle, decreased by including more and more higher-order contributions in the prediction the numerical uncertainty can be intrinsic to the method used to obtain the theoretical prediction. Beside this, the way of comparison of experiment with theory is also affected by another uncertainty. While an experiment measures color-singlet objects, hadrons, the predictions are made in QCD for colorful ones, partons. The assumption of local partonhadron duality ensures a correspondence between these two up to non-perturbative effects. Non-perturbative effects are power corrections in nature going with some negative power of the collision energy. This means that for an accurate comparison 1) either these effects should be estimated and taken into account 2) or the experiment should have a high enough energy that these contributions become negligible compared to other effects 3) or an observable has to be chosen which is not sensitive to these effects. To take into account these non-perturbative effects we have to choose from phenomenological [2,3] or analytical models [4]. It is worth to note that none of these models are derived from first principles hence there is still room for improvement. Non-perturbative effects derived from first principles would also be favored because these corrections are to be used in comparisons of predictions to experimental measurements. Currently phenomenological models use several parameters fitted to experimental data thus bias is introduced in the measurement of physical parameters. The calculation of non-perturbative corrections from first principles is also advocated because the only available analytical model seems to be ill-suited for the current precision of theoretical calculations as it was shown in Ref. [5]. In this report we show two approaches how the measurement of a physical parameter, the strong coupling, can be carried out with high precision. Because the used observables allow for such measurements these can be considered as interesting subjects to study in a future electron-positron collider.
Electroacupuncture Pretreatment against Cerebral Ischemia/Reperfusion Injury through Mitophagy

Cerebral ischemia/reperfusion (I/R) injury can induce the mitophagy of neurons in the ischemic brain. Electroacupuncture (EA) pretreatment has a protective effect on cerebral ischemia/reperfusion injury. However, its internal mechanism still needs to be further studied. The present study's purpose is to investigate whether mitophagy is involved in neuroprotection elicited by EA pretreatment in a rat model of cerebral ischemia/reperfusion injury. The rats were pretreated with vehicle, EA at the Baihui (GV20) and Shuigou (GV26) acupoints 30 min daily, for 5 days consecutively prior to the focal cerebral ischemia injury induced by the middle cerebral artery occlusion (MCAO) model. Compared to the sham group, the neurological scores, infarction volume, number of autophagosomes, FUNDC1, p62, and the ratio of LC3-II/I were significantly increased but mitochondrial membrane potential and autophagy-related protein p-mTORC1 significantly decreased in the I/R group. However, EA pretreatment significantly reversed these trends. Overall, the results of this study demonstrated that EA pretreatment protected the cerebral ischemia/reperfusion injury which maybe correlated with mitophagy.
An Evaluation of a Wearable Assistive Device for Augmenting Social Interactions

Gaze behaviors contain rich information regarding a person’s emotions and engagements. Reciprocal eye contact can invoke feelings of liking between two strangers. But blind people cannot perceive and establish the eye contact with sighted counterparts, causing their feelings of social isolation and low confidence in conversations. Thus, our research purpose is to let blind people perceive and react gaze behaviors in social interactions. A Social Glasses system has been implemented iteratively to deliver the multisensory feedback channels of the “eye contact”, integrating both visual and tactile feedback. Specifically, the system consists of a Social Glasses device and a tactile wristband, which are worn by a blind person. The Social Glasses simulates the natural gaze for the blind person, aiming at establishing the “eye contact” between blind and sighted people. The tactile wristband enables the blind person to perceive the corresponding tactile feedback when an “eye contact” happens. To test the system, we conducted a user experiment with 40 participants, including 10 blind-sighted pairs (N = 20) and 10 blindfolded-sighted pairs (N = 20), to see how it could help increase the communication quality between blind and sighted people, as well as to suggest implications for its design. Our main findings demonstrated that both the simulated gaze and the tactile feedback were significantly effective to enhance the communication quality in blind-sighted conversations. Overall, we contribute (1) empirical research findings on how a Social Glasses system enhances the communication quality in blind-sighted conversations; and (2) design principles to inform future assistive wearable device for augmenting social interactions.
Power System Oscillation Damping Controller by Using STATCOM With an Energy Storage System

In this paper deals with the design of an adaptive power oscillation damping (POD) controller for a static synchronous compensator (STATCOM) equipped with energy storage. This is achieved using a signal estimation technique based on a modified recursive least square (RLS) algorithm, which allows a fast, selective, and adaptive estimation of the lowfrequency electromechanical oscillations from locally measured signals during power system disturbances. In this research work the proposed method is effective in increasing the damping of the system at the frequencies of interest, also in the case of system parameter uncertainties and at various connection points of the compensator. First, the analysis of the impact of active and reactive power injection into the power system will be carried out using a simple two-machine system model. A control strategy that optimizes active and reactive power injection at various connection points of the STATCOM will be derived using the simplified model. Small-signal analysis of the dynamic performance of the proposed control strategy will be carried out. The effectiveness of the proposed control method to provide power oscillation damping irrespective of the connection point of the device and in the presence of system parameter uncertainties will be verified through simulation and experimental results.
Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm

Chronic renal failure (CRF) is a clinically serious kidney disease. If the patient is not treated in a timely manner, CRF will develop into uremia. However, current diagnostic methods, such as routine blood examinations and medical imaging, have low sensitivity. Therefore, it is important to explore new and effective diagnostic methods for CRF, such as serum spectroscopy. This study proposes a cost-effective and reliable method for detecting CRF based on Fourier transform infrared (FT-IR) spectroscopy and a support vector machine (SVM) algorithm. We measured and analyzed the FT-IR spectra of serum from 44 patients with CRF and 54 individuals with normal renal function. The partial least squares (PLS) algorithm was applied to reduce the dimensionality of the high-dimensional spectral data. The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. The sensitivity of our diagnostic model was 93.75%, the specificity was 100%, and the accuracy was 96.97%. The results demonstrate that FT-IR spectroscopy combined with a pattern recognition algorithm has great potential in screening patients with CRF.