Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering-Based Travel Pattern for Individual Travel Prediction of Frequent Passengers by Using Transit Smart Card

Individual travel prediction is very important for the construction of intelligent urban transportation systems. Previous studies mainly focus on the improvement of algorithms, but pay little attention to the mining of data information. In this paper, the concept of the travel pattern is introduced into the field of individual travel prediction of frequent bus passengers. The travel pattern of passengers refers to the trip with similar boarding time and similar boarding and alighting stations of the same person. Through clustering the travel pattern by DBSCAN algorithm, the regularity of passenger travel can be better exploited and travel information can be integrated into a unified unit as well. In the process of prediction, we first predict whether the passenger will travel, and then, if so, predict the probability distribution of the next trip conditional on the previous one. The proposed method is tested using the Automatic Fare Collection data of Chengdu’s frequent bus passengers in May 2019. Based on travel pattern, the average accuracy of travel information prediction is about 41%, which is 13% higher than the method without using travel pattern. Furthermore, this paper also discusses the influence of spatial threshold in clustering on the prediction results.
Rural urbanisation and home gardening in southern Mexico: agrobiodiversity loss and alternative pathways

ABSTRACT In southern Mexico, the home garden has played a key role in the livelihood security of rural families and in the conservation of agrobiodiversity. This article examines how, as a dominant trend, rural urbanisation has reduced the dependence on home gardening as a livelihood strategy, with a consequent decline in agrobiodiversity. Drawing upon a mixed methods approach involving household surveys and life stories, the article contributes to a better understanding of long-term dynamics in the relationship between agrobiodiversity management and people’s livelihoods, and provides novel evidence on alternative pathways that allow households to maintain greater home garden agrobiodiversity.
The Impacts of Incentives on Seat Yielding Behavior on the Bus

The global target to achieve inclusive public transportation includes the availability of courtesy seating for vulnerable individuals such as elderly people, people with disabilities, pregnant women, and children. The frequent usage of courtesy seats by passengers who are not vulnerable individuals is an issue pertaining to public transportation. It is important to encourage the travelers to yield seats in the presence of vulnerable individuals because the latter might get seriously injured by falling if they stay on a moving vehicle. This study explores the influence of incentives and situational factors affecting individuals’ decisions to yield seats to vulnerable people. Through an online questionnaire survey, 404 valid responses were obtained. The generalized linear mixed model (GLMM) with repeated measures was used to identify the influential factors affecting the self-reported decisions of yielding seats to vulnerable individuals. The results indicate that the situational factors (health, crowd, vulnerable passenger type, standing time after yielding seats), incentive types, and incentive amounts are significant factors affecting the decisions to yield seats. The study’s findings also indicate that travelers are more sensitive to people with disabilities and pregnant women than to elderly people and children. The practical implications, including campaigning, education interventions, and incentive-based mechanism, were suggested to encourage travelers to yield seats to vulnerable individuals.
U.S. external imbalances: an empirical approach

This paper analyzes the behaviour of the U.S. net external position since the end of the Bretton Woods system and examines alternative paths for future adjustment. We estimate a present value expression that relates the current external imbalance with future expected portfolio return differentials and net exports growth. We find that the return differentials will be the main channel of external adjustment. Half-lives for future adjustments paths towards a balanced position are around 10 years, with these paths having been delayed due to the COVID-19 crisis. The U.S.external adjustment would also involve a significant depreciation of the dollar.
ELIMINATION OF IMPULSIVE NOISE IN IMAGES BY MEANS OF THE USE OF SUPPORT VECTOR MACHINES

ABSTRACT In this work we present an efficient way to cancel the impulsive noise into images by using the Support Vector Machines (SVM). The suppression of impulsive noise is a classic problem in nonlinear processing, and the SVM are especially useful in this type of processing. In this new approach we use the classification and the regression based on SVM. By using the classifier we select the noisy pixels into the images and by using the regression we obtain a reconstruction value based on the neighboring pixels. The results obtained are comparable and, a lot of times, better than those from another "state-of-art" techniques. Besides, this new technique can be applied successfully to images with high noise ratios while maintaining the visual quality and the low reconstruction error. 1. INTRODUCTION Some times the images that we received have added an impulsive noise. This impulsive noise can be due to a noisy transmission channel or to imperfections of the sensor with which we obtain the images so that in some points a saturation takes place. The linear techniques are little effective in the reduction of this type of noise and as alternative they nonlinear techniques appear. A well-known nonlinear method is the median filter. The main disadvantage of this method is that it is applied on all the points of the image regardless of they are noisy or not. Then a blurring effect is produced specially with high rates of noise. This defect is common to other techniques dedicated to the elimination of impulsive noise. In [1] appears a technique in which substitution in the considered noisy pixels is made and the detection of these noisy pixels is implemented by means of the comparison with certain thresholds. The noisy pixels are replaced by means of a modified median filter, that in that work is called Rank Ordered Mean (ROM) and that does not use the noisy pixel to calculate the median. In this work, we made a similar scheme but the detection and the substitution of noisy pixels is made with SVM. Our method provides excellent results in "Peak Signal to Noise Ratio" (PSNR), that measures the reconstruction error, and in visual quality and maintenance of the edges even for very high rates of noise.
FROM LOCAL TO GLOBAL STABILIZATION: A HYBRID SYSTEM APPROACH?

A cold cathode field emission device having a cone shaped emitter (112, 208) formed with a substantially normal (but not absolutely normal) vapor deposition process (109) wherein the substrate (101, 201) need not be rotated with respect to the vapor deposition target. The vapor deposition process forms an encapsulating layer (111, 207) that can either be utilized as an electrode within the completed device, or that can be removed to allow subsequent construction of additional layers.
Triggering of neo-classical tearing modes by mode coupling effects

The formation of magnetic islands with mode numbers (m>1,n≥1) apparently triggered by sawtooth crashes has for a long time been a concern in JET discharges with auxiliary heating as it affects the peak performance, in terms of stored energy or of neutron yield (in DT experiments). The phenomenological association of the sawtooth crash with the onset of resistive modes is particularly relevant in the context of neo-classical tearing modes (NTM) theory that requires the formation of a `seed' island above a critical value. Relying on observations and on an understanding of the role of the collapse phase of the (m = 1,n = 1) instability, we propose as physical mechanism of destabilization of the metastable NTM a forced reconnection process due to electro-dynamic mode coupling. For a definite mode-number congruence and frequency matching conditions a bifurcation pattern appears in the behaviour of the driven mode. Modelling of a JET discharge shows that for perfect frequency matching conditions the triggering of the (m = 3,n = 2) NTM could be due to either two-mode toroidal coupling or three-mode non-linear coupling. The observed mode frequencies in the cases examined, suggest non-linear coupling, with the (m = 3,n = 2) mode being driven by (m = 1,n = 1) and (m = 4,n = 3) modes, the (m = 1,n = 1) being provided either by the sawtooth precursor or by fishbones.
Hidden geometrical structures in integrable models

The bootstrap equations for the ADE series of purely elastic scattering theories have turned out to be intimately connected with the geometry of root systems and the Coxeter element. An informal review of some of this material is given, mentioning also a couple of other contexts -- the Pasquier models, and the simply-laced affine Toda field theories -- where similar structures are encountered. The relevance of twisted Coxeter elements is indicated, and a construction of these elements inspired by the twisted foldings of the affine Toda models is described.
Bilateral Harnack Inequalities for Stochastic Differential Equation with Multiplicative Noise

By constructing a coupling with unbounded time-dependent drift, a lower bound estimate of dimension-free Harnack inequality with power is obtained for a large class of stochastic differential equation with multiplicative noise. The key is an application of the inverse Hölder inequality. Combining this with the well-known upper bound, bilateral dimension-free Harnack inequality with power is established. As a dual inequality, the bilateral shift-Harnack inequalities with power are also investigated for stochastic differential equation with additive noise. Applications to the study of heat kernel inequalities are provided to illustrate the obtained inequalities.
Confidence intervals for proportions.

Ä Ä TO THIS POINT: In the preceding chapters we learned that populations are characterized by numerical descriptive measures called parameters such as the (population) mean , the (population) standard deviation , or a (population) . 5 proportion . The value of a particular population descriptive measure is typically unknown; a reliable estimate p of one of these unknown values is frequently needed. may be very interested in an estimate of the NASA reliability of a crucial component in the space shuttle; a candidate for an elective office may need an estimate of the proportion of voters that will vote for him/her to plan campaign strategy. Estimates of these unknown values are based on sample statistics computed from sample data. Since sample statistics vary in a random manner from sample to sample, estimates based on them will be subject to uncertainty. This uncertainty is reflected in the of a statistic. sampling distribution
Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study

Background. The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. Methods. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic (N = 44). For this study, we identified and analyzed a subset of responses from interviewees (n = 19) who reported using modeling to inform decision making. We used conventional content analysis to analyze themes from this convenience sample with respect to the source of models and their applications, the value of modeling and recommended applications, and hesitancies toward the use of models. Results. Models were used to compare trends in disease spread across localities, estimate the effects of social distancing policies, and allocate scarce resources, with some interviewees depending on multiple models. Decision makers desired more granular models, capable of projecting disease spread within subpopulations and estimating where local outbreaks could occur, and incorporating a broad set of outcomes, such as social well-being. Hesitancies to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling specific to the local context. Conclusions. Decision makers perceived modeling as valuable for informing organizational decisions yet described varied ability and willingness to use models for this purpose. These data present an opportunity to educate organizational decision makers on the merits of decision-support modeling and to inform modeling teams on how to build more responsive models that address the needs of organizational decision makers. Highlights Organizations from a diversity of sectors across North Carolina (including public health, education, business, government, religion, and public safety) have used decision-support modeling to inform decision making during COVID-19. Decision makers wish for models to project the spread of disease, especially at the local level (e.g., individual cities and counties), and to help estimate the outcomes of policies. Some organizational decision makers are hesitant to use modeling to inform their decisions, stemming from doubts that models could reflect nuances of human behavior, concerns about the accuracy and precision of data used in models, and the limited amount of modeling available at the local level.
Non-Gaussian Merton-Black-Scholes theory

This book introduces an analytically tractable and computationally effective class of non-Gaussian models for shocks (regular Levy processes of the exponential type) and related analytical methods similar to the initial Merton–Black–Scholes approach, which the authors call the Merton–Black–Scholes theory.
Construction of Data Resource Sharing Platform for College Students’ Ideological and Political Education Based on Constraint Clustering

Online learning communities have changed the “silo” learning structure of traditional online learning and provided an effective support environment for wisdom sharing and collaborative knowledge building. The construction of a good online learning community has become a core issue in the field of online learning research. The study abstracts the construction of online learning community as a constrained clustering problem and proposes an intelligent construction method for online learning community based on constrained clustering. In particular, the three principles include the combination of limited freedom of choice and continuous iterative improvement, the combination of openness and standardization of the sharing platform, and the combination of public welfare and paid attributes of data resources. This paper discusses how to share valuable basic statistical data and social survey data in the field of college students’ ideological and political education (IPE) and make them public to the whole society by building a data resource sharing platform, so as to improve the utilization of data on the one hand and support comparative research topics in related fields through statistical analysis among survey data on the other hand. The experimental results show that the proportion of low-level posts (KC1 and KC2) of the two groups of students is relatively high (79.4% and 76.5%, respectively), indicating that the students in the learning community have carried out a lot of knowledge sharing and discussed and compared them to a certain extent. The proportion of high-level posts by students in both groups is very small (20.8% and 23.5%, respectively), but the number of high-level (KC3, KC4) posts by the students in the experimental group is significantly higher than that of the students in the control group, indicating that the students in the experimental group have good exchanges and discussions have resulted in meaningful negotiation and testing on some issues.
Some properties of the vector-valued Banach ideal space $E(X)$ derived from those of $E$ and $X$

Let $(\Omega,\Sigma,\mu)$ be an arbitrary measure space, $E$ a Banach idel space (Kothe function space) on $\Omega, X$ a Banach space, and $E(X) $ the "vector-valued Banach ideal space" composed of $E$ and $X$. By a general method based on semi-embeddings, it is proved for certain properties $P$ of Banach spaces that if $E$ and $X$ have $P$ then $E(X)$ has $P$ as well $(\ast)$. Examples for $P$ are the analytic Radon-Nikodým property and the property not to contain $c_0$, thus simplifying results of Bukhavlov. $(\ast)$ is also true for the type $II-\Lambda$-Randon-Nikodým property and the separable complementation property, and somewhat weaker versions of $(\ast)$ hold for the type $I-\Lambda$-Radon-Nikodým property, the property $(P)$ of coste and Lust-Piquard, and for the near Radon-Nikodým property.
The Paradox of Technology Adoption: Exploring the Effect of Cyber Usage and its Effect on Dependence

This paper examines student’s cyber usage and its effect on dependence. Cybers technology relates to any computer enabled device and internet use where users communicate on various communication networks. Cyber Overall users throughout the world have highly relied on the Internet, it provides them many benefits, while at the same time dependent on cyber technologies and unable to regulate their use with negative impacts. Method: The study is dependent on a quantitative method based on random distribution of a questionnaire. The study sample consisted of 438 participants, 196 males and 242 females. The study outcomes highlighted vital impacts of internet use on users. Evidence indicates users have high reliance on the internet, which affected their wellbeing. Results show cyber dependence was more evident with females than males, where males are more likely to engage in behaviors that have a direct impact on their wellbeing. In terms of college students’ perception in relation to their major of study and cyber activity. In terms of major, art students experienced higher cyber dependence than users in other majors. It also been evident that “arts” students spend more time using cyber technology than "tech" students. In addition, "arts" students admit that the cyber dependency is negatively impacting their wellbeing and causing them various health issues. Conclusion: Cyber dependence has a negative impact on student’s health. Health Decision makers must address this issue and highlight to users the dangers of cyber dependence. Awareness campaigns are required to address this rapidly growing issue that has escalated recently.
Image segmentation for human motion analysis: methods and applications

Human motion analysis is closely connected with the development of computational techniques capable of automatically identify objects represented in image sequences, track and analyse its movement. Feature extraction is generally the first step in the study of human motion in image sequences which is strictly related to human motion modelling [1]. Next step is feature correspondence, where the problem of matching features between two consecutives image frames is addressed. Finally high level processing can be used in several applications of Computer Vision like, for instance, in the recognition of human movements, activities or poses. This work will focus in the study of image segmentation methods and applications for human motion analysis. Image segmentation methods related to human motion need to deal with several challenges such as: dynamic backgrounds, for instance when the camera is in motion; lighting conditions that can change along the image sequences; occlusion problems, when the subject does not remain inside the workspace; or image sequences with more than one subject in the workspace at the same time. It is not easy to develop methods which can deal with all these problems at once, so it is common to make some assumptions, however each day more robust and accurate methods are being developed. A typical method of image segmentation is background subtraction, which involves the calculi of a reference image followed by the subtraction of each frame of the image sequence from the reference and further threshold of the result [2]. The simplest form is using a timeaveraged background image as reference but it requires a training period absent of foreground objects. Other possibility is describing each pixel in the scene by a mixture of Gaussian distributions, where the weight parameters of the mixture represent the time proportions that those colours stay in the scene, so background components will be the ones with the highest
Laplacian smoothing revisited

We show that the driving force behind the regularizing effect of Laplacian smoothing on surface elements is the popular mean ratio quality measure. We use these insights to provide natural generalizations to polygons and polyhedra. The corresponding functions measuring the quality of meshes are easily seen to be convex and can be used for global optimization-based untangling and smoothing. Using a simple backtracking line-search we compare several smoothing methods with respect to the resulting mesh quality. We also discuss their effectiveness in combination with topology modification.
On the n(log2n) Isomorphism Technique

Abstract : An algorithm is given for deciding isomorphism of two groups of order n (given as multiplication tables) which runs in 0(n to the (log sub 2 n + 0(1)) power) steps where n is the order of the groups. The fact that a group of n is generated by log n element is used. This technique generalizes to isomorphism of quasigroups, latin squares, and some graphs generated from latin squares.
Identification of Pathologic Grading-Related Genes Associated with Kidney Renal Clear Cell Carcinoma

Background Renal epithelium lesions can cause renal cell carcinoma. This kind of tumor is common among all renal cancers with poor prognosis, of which more than 70% belong to kidney renal clear cell carcinoma. As the pathogenesis of KIRC has not been elucidated, it is necessary to be further explored. Methods The Genomic Spatial Event database was used to obtain the analysis dataset (GSE126964) based on the GEO database, and The Cancer Genome Atlas was applied for KIRC data collection. edgeR and limma analyses were subsequently conducted to identify differentially expressed genes. Based on the systems biology approach of WGCNA, potential biomarkers and therapeutic targets of this disease were screened after the establishment of a gene coexpression network. GO and KEGG enrichment used cluster Profiler, enrichplot, and ggplot2 in the R software package. Protein-protein interaction network diagrams were plotted for hub gene collection via the STRING platform and Cytoscape software. Hub genes associated with overall survival time of KIRC patients were ultimately identified using the Kaplan-Meier plotter. Results There were 1863 DEGs identified in total and ten coexpressed gene modules discovered using a WGCNA method. GO and KEGG analysis findings revealed that the most enrichment pathways included Notch binding, cell migration, cell cycle, cell senescence, apoptosis, focal adhesions, and autophagosomes. Twenty-seven hub genes were identified, among which FLT1, HNRNPU, ATP6V0D2, ATP6V1A, and ATP6V1H were positively correlated with OS rates of KIRC patients (p < 0.05). Conclusions In conclusion, bioinformatic techniques can be useful tools for predicting the progression of KIRC. DEGs are present in both KIRC and normal kidney tissues, which can be considered the KIRC biomarkers.