Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability-Based Mechanistic–Empirical Pavement Design with Statistical Methods

“Reliability,” as defined in the Mechanistic–Empirical Pavement Design Guide (MEPDG), is an aggregated indicator defined as the probability that each of the key performance measures will be less than a selected critical level over the design period. Being such a complex system, the MEPDG—which is not a single closed-form design equation—cannot depend on classic reliability methods. Monte Carlo simulation is suitable for a robust reliability analysis but is impractical because of the extensive computation time required for any reasonable analysis with the MEPDG. The development of surrogate models for performance predictions becomes an option to represent the comprehensive modeling capability of the MEPDG efficiently. Improvements to the MEPDG reliability model based on several statistical methods are described. Five tasks are detailed. First, the MEPDG is calibrated to local conditions to reduce potential bias and variation from the national calibration. Then, risk analysis and screen analysis are conducted to determine the significant variables to include in surrogate models. Next, a comprehensive experimental design effectively plans MEPDG simulations. Then, response surface models of MEPDG are built through regression analysis. Finally, probabilistic design is achieved by Monte Carlo simulation. The result of these efforts is a state-specific MEPDG-based probabilistic pavement design tool kit named ReliME. This framework provides engineers more flexibility in data acquisition, design alternatives, optimization, and quality control. The framework's successful application in Arkansas could easily be used in other states and incorporated into future mechanistic–empirical pavement design software.
The impact of a short auditory training on L2 pronunciation in languages with different orthographic depth

It is sometimes assumed that the pronunciation of an L2 is more predictable and thus easier to learn if its orthography is transparent. This study aims to find out whether this assumption holds true in the first stages of L2 learning in languages with different orthographic depth. The study also examines the effect that a short auditory training (supported by simultaneous orthographic input) has on L2 pronunciation. A central finding was that the pronunciation of an L2 with a transparent orthography was not easier to learn for a naïve learner when compared to an L2 with an opaque orthography. A second finding was that even a short period of auditory training can introduce a significant improvement of a naïve L2 learner’s speech. Further, the results show that mimicking one specific native speaker could be an effective strategy for pronunciation learning. This method should be studied in more detail in future research.
Lightweight representation learning for efficient and scalable recommendation

Over the past decades, recommendation has become a critical component of many online services such as media streaming and e-commerce. Recent advances in algorithms, evaluation methods and datasets have led to continuous improvements of the state-of-the-art. However, much work remains to be done to make these methods scale to the size of the internet. Online advertising offers a unique testbed for recommendation at scale. Every day, billions of users interact with millions of products in real-time. Systems addressing this scenario must work reliably at scale. We propose an efficient model (LED, for Lightweight Encoder-Decoder) reaching a new trade-off between complexity, scale and performance. Specifically, we show that combining large-scale matrix factorization with lightweight embedding fine-tuning unlocks state-of-the-art performance at scale. We further provide the detailed description of a system architecture and demonstrate its operation over two months at the scale of the internet. Our design allows serving billions of users across hundreds of millions of items in a few milliseconds using standard hardware.
Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds

This paper presents a novel self-supervised approach to reconstruct human shape and pose from noisy point cloud data. Relying on large amount of dataset with ground-truth annotations, recent learning-based approaches predict correspondences for every vertice on the point cloud; Chamfer distance is usually used to minimize the distance between a deformed template model and the input point cloud. However, Chamfer distance is quite sensitive to noise and outliers, thus could be unreliable to assign correspondences. To address these issues, we model the probability distribution of the input point cloud as generated from a parametric human model under a Gaussian Mixture Model. Instead of explicitly aligning correspondences, we treat the process of correspondence search as an implicit probabilistic association by updating the posterior probability of the template model given the input. A novel self-supervised loss is further derived which penalizes the discrepancy between the deformed template and the input point cloud conditioned on the posterior probability. Our approach is very flexible, which works with both complete point cloud and incomplete ones including even a single depth image as input. Compared to previous self-supervised methods, our method shows the capability to deal with substantial noise and outliers. Extensive experiments conducted on various public synthetic datasets as well as a very noisy real dataset (i.e. CMU Panoptic) demonstrate the superior performance of our approach over the state-of-the-art methods.
Identifying Latent Stochastic Differential Equations

We present a method for learning latent stochastic differential equations (SDEs) from high dimensional time series data. Given a high-dimensional time series generated from a lower dimensional latent unknown Itô process, the proposed method learns the mapping from ambient to latent space, and the underlying SDE coefficients, through a self-supervised learning approach. Using the framework of variational autoencoders, we consider a conditional generative model for the data based on the Euler-Maruyama approximation of SDE solutions. Furthermore, we use recent results on identifiability of latent variable models to show that the proposed model can recover not only the underlying SDE coefficients, but also the original latent variables, up to an isometry, in the limit of infinite data. We validate the method through several simulated video processing tasks, where the underlying SDE is known, and through real world datasets.
Robust multiple change-point detection for multivariate variability using data depth

In this paper, we introduce two robust, nonparametric methods for multiple change-point detection in the variability of a multivariate sequence of observations. We demonstrate that changes in ranks generated from data depth functions can be used to detect changes in the variability of a sequence of multivariate observations. In order to detect more than one change, the first algorithm uses methods similar to that of wild-binary segmentation. The second algorithm estimates change-points by maximizing a penalized version of the classical Kruskal Wallis ANOVA test statistic. We show that this objective function can be maximized via the well-known PELT algorithm. Under mild, nonparametric assumptions both of these algorithms are shown to be consistent for the correct number of change-points and the correct location(s) of the change-point(s). We demonstrate the efficacy of these methods with a simulation study, where we compare our new methods to several competing methods. We show our methods outperform existing methods in this problem setting, and our methods can estimate changes accurately when the data are heavy tailed or skewed.
FIMP DM in the extended hyperchargeless Higgs triplet model

We perform an exclusive study on the Feebly Interacting Massive Particle (FIMP) dark matter candidate in an extended hyperchargeless (Y = 0) Higgs triplet model. The additional Z2 odd neutral fermion singlet plays the role of dark matter with support from two other vector-like fermion doublets. The mixing between the neutral component of a doublet and singlet fermions controls the current relic density through the Freeze-in mechanism, whereas the additional doublet fermion helps to get the neutrino mass and mixing angles. We obtain a broad region of the parameter spaces satisfying the current relic density and neutrino mass and mixing angles. ∗ prtmdas9@gmail.com † mkdas@tezu.ernet. in ‡ psnk2235@iacs.res.in 1 ar X iv :2 10 7. 01 57 8v 2 [ he pph ] 2 7 N ov 2 02 1
Open-Set Classification in a Few-shot Setting

In many applications, we have only a few samples of each class from which to learn to categorize them (few-shot classification). In these settings, it is very challenging to identify samples that do not lie in any of the known categories (open-set classification). The challenge of learning a good abstraction for a class with very few samples makes it extremely difficult to detect samples from the unseen categories; consequently, open-set recognition has received minimal attention in the few-shot setting. However, it is very critical in many applications like environmental monitoring where the number of labeled examples for each class is limited, while the number of classes can be very large. Most open-set few-shot classification methods consider uniform probability for open class samples, but this approach is often inaccurate, especially for fine-grained categorization. Instead, we propose a novel exemplar reconstruction based meta-learning strategy for jointly detecting open class samples, as well as, categorizing samples from seen classes via metric based classification. The exemplars, which act as representatives of a class, can either be provided in the training dataset or estimated in the feature domain. Our framework, named Reconstructing Exemplar based Few-shot Open-set ClaSsifier (ReFOCS), has been tested on a wide variety of datasets and shown to outperform multiple state-of-the-art methods.
Diagnostic accuracy of hysteroscopy in relation to histopathology in patients with abnormal uterine bleeding at a tertiary care centre, Secunderabad, India

Background: Judicious use of hysteroscopy to manage abnormal uterine bleeding (AUB) adds a new dimension in handling this perplexing problem. Hysteroscopy combined with histological examination is the new “Gold Standard” for evaluating a case of abnormal uterine bleeding Objective: To study Diagnostic accuracy of hysteroscopy in relation to histopathology in patients with abnormal uterine bleeding Methods: A hospital based diagnostic accuracy study was carried out among 100 women with abnormal uterine bleeding. All patients underwent the procedure of hysteroscopy. Samples were collected in all patients for histopathology confirmation of the hysteroscopy findings. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy were calculated for hysteroscopy in comparison to gold standard histopathology. Results: Majority of the women belonged to 40-49 years (41%). Majority (37%) reported after one year of occurrence of the symptoms. Majority (54%) had menorrhagia. Hysteroscopy was found to be having good sensitivity and specificity in comparison to gold standard histopathology. Sensitivity=97.7%; specificity=78.5%, positive predictive value=78.1%; negative predictive value=97.7%; diagnostic accuracy=87% for hysteroscopy in comparison to histopathology. Conclusion: Hysteroscopy is simple to perform and provides direct visualization of the uterus cavity and the endo-cervical canal. In abnormal uterine bleeding, quick and safe diagnosis is possible by hysteroscopy. In cases of endometrial polyp and sub mucous myoma which are pedunculated structures, diagnostic accuracy is greater with hysteroscopy and can be treated during the procedure itself. Thus, Hysteroscopic guided biopsy and histopathology is considered as new “Gold Standard” in diagnosis and often treatment of abnormal uterine bleeding.
Surgical Management and Outcomes of Wilms Tumor in Rwanda: A Retrospective Study of Patients Operated on at the University Teaching Hospital of Kigali-Rwanda

BACKGROUND: Wilms tumor is the most common renal tumor in children and accounts for 6-8% of all childhood malignancies and has a variable survival rate worldwide. The aim of this study was to describe the surgical management and outcomes of care for Wilms tumor patients operated at the University Teaching Hospital of Kigali (CHUK). METHODS: This is a retrospective chart review conducted at CHUK in Rwanda. It includes all children who had a confirmed Wilms tumor diagnosis operated from July 2012 to June 2016. Patient’s demographics, staging, surgical management, and outcomes were analyzed. RESULTS: A total of 58 patients diagnosed with Wilms tumor were identified. 52.6% were female. The median age was four years, interquartile range (IQR): 1-10 years. The majority of the children were stage II (39.7%) and the minority being stage V (5.2%). Treatment offered was in accordance with the Societe Internationale d’ Oncologie Pediatrique (SIOP) protocol; 91.2% of patients received four weeks of preoperative chemotherapy and a median of 15 weeks postoperative chemotherapy (IQR: 8,26). The resection rate was 100% for those with a unilateral tumor. The spillage rate was 15.8%. At the time of the study, the mortality rate was 19.3%, recurrence was 7%, and 12.3% were lost to follow up. CONCLUSION: The introduction of a single national protocol for treating Wilms tumor in Rwanda with a dedicated management team, including the surgical and pediatric oncology services, has led to early outcomes approaching the ones in high-income countries, but efforts also need to include earlier detection of this tumor.
Investigating the Effects of Doodling on Learning Performance: The Daydream Reduction Hypothesis

Previous studies have reported a positive relationship between doodling and working attention and memory processes (Andrade, 2009; Boggs et al., 2017; Kercood & Banda, 2012). The present study aims to investigate why this positive dual-task relationship exists through empirical testing of the Daydream Reduction Hypothesis; stating that doodling improves learning performance due to its ability to reduce daydreaming. In order to test this hypothesis, a close replication of Andrade (2010) was conducted, with an additional shadowing component that manipulated the participants’ ability to daydream. A significant negative relationship was found between shadowing and attention, suggesting that the shadowing component impaired participants’ performance. Results between doodling and attention and memory were found to be non significant. If the shadowing component were substituted for a task less cognitively demanding, it is possible that we would be able to replicate the results of Andrade (2010), and discover why doodling has been observed to improve learning performance.
The application of the wave potential functions to the analysts of transient electromagnetic fields

The time-domain theory of the wave potential functions is applied to the analysis of transient electromagnetic field propagation for the first time. The field is represented in terms of two scalar wave potentials, instead of the six, electric and magnetic, field components. The problem of boundary conditions at dielectric interfaces and at conducting edges is addressed. The current implementation uses finite differences in the 4-D space-time numerical domain.
Cultural Preferences in Hotel Guestroom Lighting Design

This study examined North American and South Korean preferences for different lighting conditions in a hotel guestroom. A sample of 87 North Americans and 88 Koreans (N = 175) rated their preference for different intensities and color temperatures of light within a digitally generated hotel guestroom design. A mixed between–within analysis of variance (Kuehl, 1999) evaluated three dependent variables (preference, arousal, and pleasure) against three independent variables (two culture groups × two light colors × two light intensities). Findings indicated that North American subjects preferred the hotel guestroom with low intensity and warm color lighting the most, whereas the Korean group preferred high intensity and warm color lighting the most. North American participants also perceived dim lighting as more arousing than bright lighting, whereas Korean participants perceived bright lighting as more arousing than dim lighting. Overall, the findings suggest that design criteria for hotel guestroom lighting should be revised to account for distinct cultural differences.
The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans

Molecular characterization of cell types using single cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues and tissue specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, the tissue specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type specific RNA splicing was discovered and analyzed across tissues within an individual.
Impacts of COVID-19 on rural livelihoods in Bangladesh: Evidence using panel data

Rapid assessments have been emerging on the effects of COVID-19, yet rigorous analyses remain scant. Here, rigorous evidence of the impacts of COVID-19 on several livelihood outcomes are presented, with a particular focus on heterogenous effects of COVID-19. We use a household-level panel dataset consisting of 880 data points collected in rural Bangladesh in 2018 and 2020, and employ difference-in-differences with fixed effects regression techniques. Results suggest that COVID-19 had significant and heterogenous effects on livelihood outcomes. Agricultural production and share of production sold were reduced, especially for rice crops. Further, diet diversity and education expenditure were reduced for the total sample. Households primarily affected by (fear of) sickness had a significantly lower agricultural production, share of crop market sales, and lower health and education expenditure, compared to households affected by other COVID-19 effects, such as travel restrictions. In turn, (fear of) sickness and the correlated reduced incidence of leaving the house, resulted in higher off-farm incomes suggesting that households engage in less physically demanding and localized work. Policy-makers need to be cognizant of these heterogenous COVID-19 effects and formulate policies that are targeted at those households that are most vulnerable (e.g., unable/willing to leave the house due to (fear of) sickness).
Effects of obesity and foot arch height on gait mechanics: A cross-sectional study

Foot arch structure contributes to lower-limb joint mechanics and gait in adults with obesity. However, it is not well-known if excessive weight and arch height together affect gait mechanics compared to the effects of excessive weight and arch height alone. The purpose of this study was to determine the influences of arch height and obesity on gait mechanics in adults. In this study, 1) dynamic plantar pressure, 2) spatiotemporal gait parameters, 3) foot progression angle, and 4) ankle and knee joint angles and moments were collected in adults with normal weight with normal arch heights (n = 11), normal weight with lower arch heights (n = 10), obesity with normal arch heights (n = 8), and obesity with lower arch heights (n = 18) as they walked at their preferred speed and at a pedestrian standard walking speed, 1.25 m/s. Digital foot pressure data were used to compute a measure of arch height, the Chippaux-Smirak Index (CSI). Our results revealed that BMI and arch height were each associated with particular measures of ankle and knee joint mechanics during walking in healthy young adults: (i) a higher BMI with greater peak internal ankle plantar-flexion moment and (ii) a lower arch height with greater peak internal ankle eversion and abduction moments and peak internal knee abduction moment (i.e., external knee adduction moment). Our results have implications for understanding the role of arch height in reducing musculoskeletal injury risks, improving gait, and increasing physical activity for people living with obesity.
Family context and individual characteristics in antenatal care utilization among adolescent childbearing mothers in urban slums in Nigeria

Introduction Adolescent pregnancy contributes significantly to the high maternal mortality in Nigeria. Research evidence from developing countries consistently underscores Antenatal Care (ANC) among childbearing adolescents as important to reducing high maternal mortality. However, more than half of pregnant adolescents in Nigeria do not attend ANC. A major gap in literature is on the influence of family context in pregnant adolescent patronage of ANC services. Methods The study utilized a cross-sectional survey with data collected among adolescent mothers in urban slums in three Nigerian states namely, Kaduna, Lagos, and Oyo. The survey used a multi-stage sampling design. The survey covered a sample of 1,015, 1,009 and 1,088 childbearing adolescents from each of Kaduna, Lagos, and Oyo states respectively. Data were analyzed at the three levels: univariate, bivariate and multivariate. Results Overall, about 70 percent of female adolescents in our sample compared with 75 percent in the Demographic and Health Survey (DHS) had any antenatal care (ANC) visit. About 62 percent in our sample compared with 70 percent in the DHS had at least 4 ANC visits, and, about 55 percent in our sample compared with 41 percent of the DHS that had 4 ANC visits in a health facility with skilled attendant (4ANC+). Those who have both parents alive and the mother with post-primary education have higher odds of attending 4ANC+ visits. The odds of attending 4ANC+ for those who have lost both parents is almost 60% less than those whose parents are alive, and, about 40% less than those whose mothers are alive. The influence of mother’s education on 4ANC+ attendance is more significant with large disparity when both parents are dead. Conclusion The study concludes that identifying the role of parents and community in expanding access to ANC services among adolescent mothers is important in improving maternal health in developing countries.