Miracle-dz/UniFAR-FTUs huggingface dataset:
Dead or Alive: Continuous Data Profiling for Interactive Data Science

Profiling data by plotting distributions and analyzing summary statistics is a critical step throughout data analysis. Currently, this process is manual and tedious since analysts must write extra code to examine their data after every transformation. This inefficiency may lead to data scientists profiling their data infrequently, rather than after each transformation, making it easy for them to miss important errors or insights. We propose continuous data profiling as a process that allows analysts to immediately see interactive visual summaries of their data throughout their data analysis to facilitate fast and thorough analysis. Our system, AutoProfiler, presents three ways to support continuous data profiling: (1) it automatically displays data distributions and summary statistics to facilitate data comprehension; (2) it is live, so visualizations are always accessible and update automatically as the data updates; (3) it supports follow up analysis and documentation by authoring code for the user in the notebook. In a user study with 16 participants, we evaluate two versions of our system that integrate different levels of automation: both automatically show data profiles and facilitate code authoring, however, one version updates reactively (“live”) and the other updates only on demand (“dead”). We find that both tools, dead or alive, facilitate insight discovery with 91% of user-generated insights originating from the tools rather than manual profiling code written by users. Participants found live updates intuitive and felt it helped them verify their transformations while those with on-demand profiles liked the ability to look at past visualizations. We also present a longitudinal case study on how AutoProfiler helped domain scientists find serendipitous insights about their data through automatic, live data profiles. Our results have implications for the design of future tools that offer automated data analysis support.
Supplemental Information : Combined simulation and experimental study of large deformation of red blood cells in microfluidic systems

1 Governing Equations for RBC and DPD Models The membrane model consists of points {rn, n ∈ 1.N} which are the vertices of surface triangulation (Figure S1). The area of triangle α ∈ 1. Π formed by vertices (l, m, n) is given by Aα = |(rm − rl)× (rn − rl)|/2. The length of the link i ∈ 1.S connecting vertices m and n is given by Li = |rm − rn|. The in-plane free energy of the membrane Fin-plane = ∑
Supplemental Information : Streaming Variational Inference for Bayesian Nonparametric Mixture Models

where the inequality follows by Jensen’s inequality [1]. The approximation is tight when q̂(z1:n) and q̂(θ\k) approach Dirac measures. Eq. (6) is that of the standard mean field update for q̂(θk) [2]. Since the q(θk) distributions are unknown for all k, we could perform coordinate ascent and cycle through these updates for each of the θk given the other θ\k and q̂(z1:n). Conveniently, since the q̂(z1:n) is already optimized and the θks are conditionally independent given the assignments in the mixture model we can perform a single mean field update for each θk, q̂n(θk) ∝ p(xn|znk, θk)q̂n−1(θk). (7)
Supplemental Information: Computational Analysis of Deposition and Translocation of Inhaled Nicotine and Acrolein in the Human Body with E-cigarette Puffing Topographies

Computational Analysis of Deposition and Translocation of Inhaled Nicotine and Acrolein in the Human Body with E-cigarette Puffing Topographies Ahmadreza Haghnegahdar1, Yu Feng1,*, Xiaole Chen2, Jiang Lin3 1 School of Chemical Engineering, Oklahoma State University, Stillwater, OK, 74078, USA 2School of Energy and Environment, Southeast University, Nanjing, 210096, China 3 Zhejiang University of Science and Technology, Hangzhou, 310023, China
ADDITIVE MAIN EFFECTS AND MULTIPLICATIVE INTERACTIONS ANALYSIS ON YIELD PERFORMANCES OF RICE GENOTYPES IN COASTAL SALINE REGIONS, TRA VINH PROVINCE

The study aims at analyzing the pattern of genotype x environment (GxE) interaction for grain yield of 12 genotypes by AMMI mode. Eight-site yield trial in east-coastal region of Tra Vinh province involved in study. The GxE interaction influenced the relative ranking of the genotypes under salt stress environment condition. Among the genotypes tested, in 2009-2010 Winter- Spring, almost all varieties tested exhibited their adaptability to 8 locations of Tra Vinh province. The selected genotypes could be listed as OM10041, OM8927, OM4488, OM10040, OM5894, and OM7348. They expressed their stable yield over 8 sites. Especially, OM10041 is now considered as a promising genotype that gives yield of 7.8 ton/ha. In addition, there are still some elite lines as AS996, OM10029, OM8104, OM10383, OM5953 and OM10029 available to further develop. Two genotypes could be recommended to grow under unfavorable conditions as OM4488 and OM7347.
Polyadenosine diphosphate-ribose polymerase inhibitors: advances, implications, and challenges in tumor radiotherapy sensitization

Polyadenosine diphosphate-ribose polymerase (PARP) is a key modifying enzyme in cells, which participates in single-strand break repair and indirectly affects double-strand break repair. PARP inhibitors have shown great potential in oncotherapy by exploiting DNA damage repair pathways, and several small molecule PARP inhibitors have been approved by the U.S. Food and Drug Administration for treating various tumor types. PARP inhibitors not only have significant antitumor effects but also have some synergistic effects when combined with radiotherapy; therefore they have potential as radiation sensitizers. Here, we reviewed the advances and implications of PARP inhibitors in tumor radiotherapy sensitization. First, we summarized the multiple functions of PARP and the mechanisms by which its inhibitors exert antitumor effects. Next, we discuss the immunomodulatory effects of PARP and its inhibitors in tumors. Then, we described the theoretical basis of using PARP inhibitors in combination with radiotherapy and outlined their importance in oncological radiotherapy. Finally, we reviewed the current challenges in this field and elaborated on the future applications of PARP inhibitors as radiation sensitizers. A comprehensive understanding of the mechanism, optimal dosing, long-term safety, and identification of responsive biomarkers remain key challenges to integrating PARP inhibition into the radiotherapy management of cancer patients. Therefore, extensive research in these areas would facilitate the development of precision radiotherapy using PARP inhibitors to improve patient outcomes.
A Measurement Method of Power Transferred to an Electric Vehicle Using Wireless Charging

The increasing number of zero-emission vehicles on the roads demands novel vehicle charging solutions that ensure convenience, safety, increased charging infrastructure availability, and aesthetics. Wireless charging technology is seen as the one that could assure these desirable properties and could be applied not just in conventional implementations but also in off-grid solutions together with roadway energy harvesting systems. Both approaches require proper transfer of energy metering methods. In this paper, a method for measuring the power transferred to the load in a wireless charging system is presented, and its systematic error is assessed in the relevant range of influencing factors. The novelty of the method is that it does not require any metrologically certified measurement instrumentation on the receiver side of the wireless charging system. The error analysis is performed using a numerical simulation. Considered error-influencing factors included secondary side electrical load, coils’ coupling coefficient and quality factor, current and voltage quantization resolution, and compensation topology type (serial-serial (SS) and serial-parallel (SP)). It was determined that the systematic error of the power assessment does not exceed 0.7% for SS and 1.1% for SP topologies when the coupling coefficient is in the range of 0.05 to 0.4 and the quality factor of the resonant system is in the range of 100 to 800.
Cognitive Informatics : Exploring the Theoretical Foundations for Natural Intelligence , Neural Informatics , Autonomic Computing , and Agent Systems

Cognitive informatics (CI) is a new discipline that studies the natural intelligence and internal information processing mechanisms of the brain, as well as the processes involved in perception and cognition. CI provides a coherent set of fundamental theories, and contemporary mathematics, which form the foundation for most information and knowledge based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, computer/software engineering, and knowledge engineering. This editorial addresses the objectives of the International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), and explores the domain of CI and its interdisciplinary nature. The historical evolvement of CI and its theoretical foundations are elucidated. The coverage of this inaugural issue and recent advances in CI are reviewed. This editorial demonstrates that the investigation into CI will result in fundamental findings towards the development of next generation information and computing technologies.
Estimating the impact of water quality on surrounding property values in Upper Big Walnut Creek Watershed in Ohio for dynamic optimal control

Non-point source pollution and its impact on water quality are of great importance to policy makers, residence and farmers. This paper uses a hedonic property value model to investigate the marginal implicit values of water quality change in Hoover Reservoir in the Upper Big Walnut Creek (UBWC) watershed, Ohio. The estimates are fed into a simple dynamic optimization model which maximizes social welfare while taking into account the damage from production as well as the production profits. This paper uses the Soil and Water Assessment Tool (SWAT) to simulate the quality and quantity of the surface and ground water and uses ArcGIS to link housing transactions in Franklin County and Delaware County with disaggregated flow of nutrient runoff in the watershed. The econometric results indicate that the marginal damage for a one mg/L increase in nitrogen (N) and phosphorus (P) loadings are $7,713.41 and $27,624.05 respectively, and the increase of property value of a one meter increase of secchi depth water clarity is $95,132.07. This paper investigates the effects of multiple water quality parameters on both waterfront and non-waterfront properties, using the yearly maximum loading, sediment, and dissolved oxygen, as well as yearly average secchi disk depth as measurement. A simple dynamic optimization model is included to show the tradeoff between production profits and water quality.
Recommender systems

Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system’s value lies in its ability to make good matches between the recommenders and those seeking recommendations. The developers of the first recommender system, Tapestry [1], coined the phrase “collaborative filtering” and several others have adopted it. We prefer the more general term “recommender system” for two reasons. First, recommenders may not explictly collaborate with recipients, who may be unknown to each other. Second, recommendations may suggest particularly interesting items, in addition to indicating those that should be filtered out. This special section includes descriptions of five recommender systems. A sixth article analyzes incentives for provision of recommendations. Figure 1 places the systems in a technical design space defined by five dimensions. First, the contents of an evaluation can be anything from a single bit (recommended or not) to unstructured textual annotations. Second, recommendations may be entered explicitly, but several systems gather implicit evaluations: GroupLens monitors users’ reading times; PHOAKS mines Usenet articles for mentions of URLs; and Siteseer mines personal bookmark lists. Third, recommendations may be anonymous, tagged with the source’s identity, or tagged with a pseudonym. The fourth dimension, and one of the richest areas for exploration, is how to aggregate evaluations. GroupLens, PHOAKS, and Siteseer employ variants on weighted voting. Fab takes that one step further to combine evaluations with content analysis. ReferralWeb combines suggested links between people to form longer referral chains. Finally, the (perhaps aggregated) evaluations may be used in several ways: negative recommendations may be filtered out, the items may be sorted according to numeric evaluations, or evaluations may accompany items in a display. Figures 2 and 3 identify dimensions of the domain space: The kinds of items being recommended and the people among whom evaluations are shared. Consider, first, the domain of items. The sheer volume is an important variable: Detailed textual reviews of restaurants or movies may be practical, but applying the same approach to thousands of daily Netnews messages would not. Ephemeral media such as netnews (most news servers throw away articles after one or two weeks) place a premium on gathering and distributing evaluations quickly, while evaluations for 19th century books can be gathered at a more leisurely pace. The last dimension describes the cost structure of choices people make about the items. Is it very costly to miss IT IS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives. In everyday life, we rely on
MMGan: a multimodal MR brain tumor image segmentation method

Computer-aided diagnosis has emerged as a rapidly evolving field, garnering increased attention in recent years. At the forefront of this field is the segmentation of lesions in medical images, which is a critical preliminary stage in subsequent treatment procedures. Among the most challenging tasks in medical image analysis is the accurate and automated segmentation of brain tumors in various modalities of brain tumor MRI. In this article, we present a novel end-to-end network architecture called MMGan, which combines the advantages of residual learning and generative adversarial neural networks inspired by classical generative adversarial networks. The segmenter in the MMGan network, which has a U-Net architecture, is constructed using a deep residual network instead of the conventional convolutional neural network. The dataset used for this study is the BRATS dataset from the Brain Tumor Segmentation Challenge at the Medical Image Computing and Computer Assisted Intervention Society. Our proposed method has been extensively tested, and the results indicate that this MMGan framework is more efficient and stable for segmentation tasks. On BRATS 2019, the segmentation algorithm improved accuracy and sensitivity in whole tumor, tumor core, and enhanced tumor segmentation. Particularly noteworthy is the higher dice score of 0.86 achieved by our proposed method in tumor core segmentation, surpassing those of stateof-the-art models. This study improves the accuracy and sensitivity of the tumor segmentation task, which we believe is significant for medical image analysis. And it should be further improved by replacing different loss functions such as cross-entropy loss function and other methods.
Too Big to Fail : Large Samples and the p-Value Problem

T Internet has provided IS researchers with the opportunity to conduct studies with extremely large samples, frequently well over 10,000 observations. There are many advantages to large samples, but researchers using statistical inference must be aware of the p-value problem associated with them. In very large samples, p-values go quickly to zero, and solely relying on p-values can lead the researcher to claim support for results of no practical significance. In a survey of large sample IS research, we found that a significant number of papers rely on a low p-value and the sign of a regression coefficient alone to support their hypotheses. This research commentary recommends a series of actions the researcher can take to mitigate the p-value problem in large samples and illustrates them with an example of over 300,000 camera sales on eBay. We believe that addressing the p-value problem will increase the credibility of large sample IS research as well as provide more insights for readers.
A Missing Traffic Data Imputation Method Based on a Diffusion Convolutional Neural Network–Generative Adversarial Network

Traffic state data are key to the proper operation of intelligent transportation systems (ITS). However, traffic detectors often receive environmental factors that cause missing values in the collected traffic state data. Therefore, aiming at the above problem, a method for imputing missing traffic state data based on a Diffusion Convolutional Neural Network–Generative Adversarial Network (DCNN-GAN) is proposed in this paper. The proposed method uses a graph embedding algorithm to construct a road network structure based on spatial correlation instead of the original road network structure; through the use of a GAN for confrontation training, it is possible to generate missing traffic state data based on the known data of the road network. In the generator, the spatiotemporal features of the reconstructed road network are extracted by the DCNN to realize the imputation. Two real traffic datasets were used to verify the effectiveness of this method, with the results of the proposed model proving better than those of the other models used for comparison.
Multiple imputation for demographic hazard models with left-censored predictor variables: Application to employment duration and fertility in the EU-SILC.

A common problem when using panel data is that an individual's history is incompletely known at the first wave. We show that multiple imputation, the method commonly used for data that are missing due to non-response, may also be used to impute these data that are "missing by design. " Our application is to a woman's duration of fulltime employment as a predictor of her risk of first birth. We multiply-impute employment status two years earlier to "incomplete" cases for which employment status is observed only in the most recent year. We then pool these "completed" cases with the "complete" cases to derive regression estimates for the full sample. Relative to not being fulltime employed, having been fulltime-employed for two or more years is a positive and statistically significant predictor of childbearing whereas having just entered fulltime employment is not. The fulltime-employment duration parameter variances are about one third lower in the multiply-imputed sample than in the complete-data sample, and only in the multiply-imputed sample does the employment-duration coefficient attain statistical significance.
KNOWLEDGE, PERCEPTION, AND ATTITUDE OF PARENTS TOWARD DENTAL TREATMENT GIVEN TO THEIR PRIMARY SCHOOL STUDENTS IN TRIPOLI, LIBYA

Dental treatment is an important topic, especially for primary school students. Parents' knowledge, attitudes, perceptions, and socioeconomic status are potentially significant factors in improving dental treatment. In 2021, parents of ten primary school students in Tripoli participated in a survey of their children during school time. A cross-sectional descriptive study was conducted in ten randomly selected primary schools across Tripoli, the capital of Libya. A total of 293 respondents were approached using a systematic sampling technique. A previously validated and published questionnaire was used with minimal modifications. Knowledge, attitudes, and perceptions were analyzed using a scoring system based on the number of correct or favorable answers provided by parents. According to our results, parents of primary school students exhibited a high knowledge score of 60.4% regarding dental treatment. Approximately 41.7% of participants displayed a positive attitude towards dental treatment, while 40.2% held a negative attitude. Most parents practiced poor oral health care for their children, with 52.2% individuals scoring poorly, indicating that 52.2% had inadequate dental treatment practices. In general, the variables explained a total of 61.6% of the variation in dental treatment. The findings of this study demonstrate the positive impact of knowledge (B=0.192, P-value<0.05), attitude (B=0.339, P-value<0.05), income (B=0.161, P-value<0.05), and education (B=0.134, P-value<0.05) on dental treatment. However, position and perception had an insignificant effect. Respondents varied based on their socioeconomic status, with high education and income respondents differing from those with low education and income. This study highlights the implications and limitations of the research and provides suggestions for future research after the COVID-19 pandemic. The COVID-19 pandemic has given rise to numerous psychological problems among healthcare workers, which are directly related to inadequate knowledge, incorrect attitudes, and inadequate perception. Nevertheless, timely interventions and accurate information have the potential to safeguard the mental well-being of healthcare professionals during the novel coronavirus epidemic.