Miracle-dz/UniFAR-FTUs huggingface dataset:
Uterine MÜllerian Adenosarcomas: About a Retropspective Case Series Analysis of 13 Patients

Aim: The purposes of this study is to identify the incidence of uterine adenosarcomas in Lower Normandy. In addition, we sought the clinical, morphological, and immunohistochemical features of this uncommon mixed mesenchymal tumors. Patients and methods: 13 cases of uterine adenosarcomas were retrieved the last 23 years from the charts of the Francois Baclesse Cancer Center and thanks to the data obtained from the cancer registries of the department of Manche and Calvados in Lower Normandy (France). All cases were reviewed by the authors. Results: 13 cases were observed beginning with the first case reported in our geographic area in 1989. Follow-up was available in 11 cases, only one young patient died, but from another non related previous carcinoma. All other patients are actually alive with no evidence of disease with a median follow-up of 10 years, only one patient with short follow-up shows a very ominous local evolution with a massive intraabdominal evolution in the context of an heterologous adenosarcoma with a rhabdomyosarcomatous component.
English-Medium Instruction in a State University and Students’ English Self-Efficacy Beliefs

Bir Devlet Universitesindeki Ingiliz Dilinde Ogretim Uygulamalari ve Ogrencilerin Ingilizce Ozyeterlik Inanclari Oz Turkiye'deki universiteler, uluslararasi rekabet guclerini gelistirmek ve boylece kurumlari icin prestij elde etmek icin giderek artan bir sekilde ogretim dili olarak Ingilizce’yi benimsemektedir. Bu calisma, ogretim dili kismen ya da tamamen Ingilizce olan programlara kayitli ogrencilerin Ingilizce ozyeterlik inanclarini ve Ingiliz dilinde ogretim uygulamalari hakkindaki goruslerini incelemeyi amaclamistir. Bu calisma karma yontem desenine sahip olup arastirma sorularina cevap vermek icin hem nicel hem de nitel veriler toplanmistir. Calisma, lisans ogrencilerine yonelik olarak 2017-2018 egitim ogretim yili bahar yariyilinda ogrencilerin dagitilan anketleri doldurmalari yoluyla gerceklestirilmistir. Bu baglamda, veriler Ingilizce ile ilgili Ozyeterlik Inanci Olcegi ve arastirmacilar tarafindan gelistirilen acik uclu sorulardan olusan bir anket araciligiyla toplanmis ve universite genelinde yabanci dil hazirlik okulu ile lisans duzeyindeki tum bolumlere uygulanmasi hedeflenmistir. Calisma 674 ogrencinin katilimi ile gerceklesmistir. Toplanan veriler istatistiksel ve tematik olarak analiz edilmistir. Calismada ogrencilerin Ingilizce’ye iliskin ozyeterlik inanclarina ve yabanci dilde alan bilgisi kazanma deneyimlerine iliskin sonuclar ortaya konulmustur. Acik uclu sorulardan elde edilen bulgular, nicel verilerden elde edilen bulgularin desteklenmesini saglamistir. Ogrencilerin oz-yeterlik inanclarinin ve bu calismada incelenen ogretim dili Ingilizce olan programlar ile ilgili goruslerinin, universite yoneticilerinin kurumlarinin dil politikasini gelistirmelerine veya yeniden yapilandirmalarina dair kararlarina isik tutacagina inanilmaktadir. Anahtar Kelimeler: Lisans ogrencileri, ozyeterlik inanci, yabanci dil olarak Ingilizce, ogretim dili olarak Ingilizce, yuksekogretim
Developing Content for an Online Virtual Interactive Simulation Case for Cultural Competency of Nursing Students in Caring for Puerto Ricans in New York City: A Community Based Participatory Research Approach

With growing cultural diversity in the United States (U.S.), health disparities continue to exist among many ethnic minority populations impacting the U.S. economy. Health disparities are health differences that are noted in a particular cultural group in respect to higher rates of diseases and deaths in comparison to others. These cultural groups have common attributes and can be based on race, ethnicity, disability, sexual orientation, income, residential location and many others. One such example is individuals of Puerto Rican heritage, the second largest Hispanic group living in the U.S. mainland. Puerto Ricans are identified to have multiple health disparities in comparison to other Hispanic and non-Hispanic population groups living in the U.S. Among other factors, common cultural health care beliefs and practices of individuals impact health outcomes. Healthcare professionals like nurses are expected to provide culturally competent care to vulnerable populations with known health disparities. Culturally competent care refers to delivering care congruent with patients’ cultural beliefs and practices. Therefore, it is important to educate health professionals regarding caring for vulnerable populations. The purpose of this community-based participatory research (CBPR) study was to develop content for an educational tool, an online virtual interactive simulation (OVIS) case for developing cultural competency of nursing students in caring for the Puerto Rican population of New York City (NYC). The content development for OVIS was guided by the framework for Cultural Competency Simulation Experiences (CCSE), which was developed as a part of this dissertation. The CCSE framework guided the content development of OVIS using a CBPR approach. A community advisory board was developed which consisted of cultural, clinical and educational experts, residing in New York and Puerto Rico.
Understanding the taxonomic homogenization of road-influenced plant assemblages in the Qionglai mountain range: A functional and phylogenetic perspective

As an increasingly prevalent form of human activity, roads drive the taxonomic homogenization of mountain plant assemblages, threatening global biodiversity. However, little is known about how mountain roads impact functional and phylogenetic beta diversity and how these effects are related to taxonomic homogenization. To understand the mechanism of taxonomic homogenization triggered by mountain roads, we used species absence/presence data from 76 plots (2 m*50 m) and values for 12 traits measured on 978 species from the interior and roadside communities in the Qionglai mountain range, one of the temperate regions with the highest plant species richness in the world. We used a structural equation modeling approach (SEM) to consider several surrogates of road disturbance (changes in soil physicochemical properties and the presence or absence of roads) and the causal relationship between three facets of beta diversity (taxonomic beta diversity, TBD; functional beta diversity, FBD and phylogenetic beta diversity, PBD). The results suggest that TBD, FBD and PBD respond inconsistently to mountain roads, despite strong positive correlations between the three facets of plant beta diversity in the study area. Compared with the interior community, the βtotal. tax and βtotal. func of the roadside community decreased by 2.54% and 2.22%, respectively, which were related to the reduction of species and trait richness differences and replacements; however, we did not find the same results when assessing the changes in βtotal. phy, which represents tip-weighted PBD (twPBD). Furthermore, the largest effect of roads on beta diversity was reflected in basal-weighted PBD (bwPBD), which decreased by 9.97%, indicating that those species with fewer extant relatives and longer evolutionary histories are more sensitive to mountain roads. Therefore, it is necessary to take targeted protection measures for ancient species in roadside communities. In addition, we believe that it is still necessary to take measures to prevent the further dispersal of nonnative species, although the presence of non-native species in roadside plots has led to small changes in three facets of beta diversity. There were causal relationships between the three facets of beta diversity, but their intensity and sign different in the SEM of different components of beta diversity (i.e., richness difference and replacement). Our findings suggest that the homogenization of community species composition at the landscape scale arises by a combination of adaptive responses of the functional traits of organisms to environmental consistency (e.g., reduced the differences in soil variables) caused by roads and resorting or reassembly of community clades composition due to environmental filtering. These results contribute to our comprehensive understanding of the impact of mountain roads on plant diversity, which highlights the complex relationship between human pressure and biodiversity loss.
Normative values of resting heart rate variability in young male contact sport athletes: Reference values for the assessment and treatment of concussion

Objective The objective of this study was to identify the main determinants of heart rate variability (HRV) in male athletes aged 14 to 21 years who practice competitive contact sports and to integrate these determinants with the aim of defining normative values of short-term HRV in the time and frequency domains. Methods Participants (n = 369) were aged 14 to 21 years and included 221 football players and 148 ice hockey players. HRV was measured for 5 min at rest, and standard HRV parameters in the time and frequency domains were calculated. Heart rate (HR), age, body mass index (BMI), number of sports weekly practices (WSP) and concussion history (mTBI) were considered determinants potentially able to influence HRV. Results Multiple regression analysis revealed that HR was the primary determinant of standard HRV parameters. The models accounted for 13% to 55% of the total variance of HRV and the contribution of HR to this model was the strongest (β ranged from −0.34 to −0.75). HR was the only determinant that significantly contributes to all HRV parameters. To counteract this dependence, we calculated HRV corrected by the mean RR interval (RRm). Such corrections do not remove any physiological differences in HRV; they simply remove the mathematical bias. HRV parameters were therefore normalized, and their normative limits were developed relative to the mean heart rate. After correction, the correlation coefficients between HR and all corrected HRV parameters were not statistically significant and ranged from −0.001 to 0.045 (p > 0.40 for all). The automatically corrected HRV calculator, which recalculates standard HRV parameters and converts them into corrected parameters in addition to determining whether a given value is within normal limits, facilitates clinical interpretation. Conclusion This study provides for the first time corrected normative values of short-term and resting state HRV parameters in competitive contact sport athletes aged 14 to 21 years. These values were developed independently of the major determinants of HRV. The baseline values for HRV parameters given here could be used in clinical practice when assessing and monitoring cerebral concussions. They may assist in decision making for a safe return to play.
Spectrum of Adult Congenital Heart Diseases : Jordanian Experience

Objective: To study the spectrum of adult congenital heart disease with respect to their presentation, and the most appropriate management. Methods: This is a retrospective study looking at catheterization and surgical records of all adult patients (18 years or older) with congenital heart disease who were treated at Queen Alia Heart Institute between January 2004 and April 2014. Their demographic characteristics, mode of presentation, investigations, and subsequent management were booked. Results: Within the study time, 517 patients were diagnosed. In a percentage ratio of 51: 49 males to females respectively. Their ages ranged between 18 and 62 years, mean of 29.5 years. The commonest presenting symptoms were dyspnea, and incidental heart murmurs. One hundred and fifty patients (26%) have had previous cardiac surgery, eleven patients (2%) have had previous catheter based interventions and three patients (0.6%) have had both. Remarkably, congenital valvular lesions constituted 36.2%, while atrial septal defect was seen in 22%of these patients. One hundred sixty six patients (32%) were treated by catheter-based interventions, while the rest (42%) had cardiac surgery. Conclusion: Due to the rapid development in methods of detection, congenital heart diseases are getting easily discovered regardless of the age factor. In adults, suspicions should be taken in consideration when it comes to frequent complaints of effort intolerance, dyspnea or even history of previous cardiac interventions.
HintPose

Most of the top-down pose estimation models assume that there exists only one person in a bounding box. However, the assumption is not always correct. In this technical report, we introduce two ideas, instance cue and recurrent refinement, to an existing pose estimator so that the model is able to handle detection boxes with multiple persons properly. When we evaluated our model on the COCO17 keypoints dataset, it showed non-negligible improvement compared to its baseline model. Our model achieved 76.2 mAP as a single model and 77.3 mAP as an ensemble on the test-dev set without additional training data. After additional post-processing with a separate refinement network, our final predictions achieved 77.8 mAP on the COCO test-dev set.
Contact Surface Estimation via Haptic Perception

Legged systems need to optimize contact force in order to maintain contacts. For this, the controller needs to have the knowledge of the surface geometry and how slippery the terrain is. We can use a vision system to realize the terrain, but the accuracy of the vision system degrades in harsh weather, and it cannot visualize the terrain if it is covered with water or grass. Also, the degree of friction cannot be directly visualized. In this paper, we propose an online method to estimate the surface information via haptic exploration. We also introduce a probabilistic criterion to measure the quality of the estimation. The method is validated on both simulation and a real robot platform.
Thermal conductivity of intrinsic semiconductor at elevated temperature: role of four-phonon scattering and electronic heat conduction

While using first-principles-based Boltzmann transport equation approach to predict the thermal conductivity of crystalline semiconductor materials has been a routine, the validity of the approach is seldom tested for high-temperature conditions. Most previous studies only focused on the phononic contribution, and neglected the electronic part. Meanwhile, the treatment on phonon transport is not rigorous as a few ingredients, such as four-phonon scatterings, phonon renormalization and thermal expansion, are ignored. In this paper, we present a Boltzmann transport equation study on high-temperature thermal conduction in bulk silicon by considering the effects of both phonons and electrons, and explore the role of the missing parts in the previous studies on the thermal conductivity at elevated temperature. For the phonon transport, four-phonon scattering is found to considerably reduce the thermal conductivity when the temperature is larger than 700 K, while the effects of phonon renormalization and thermal expansion on phononic thermal conductivity are negligible. Bipolar contribution to the electronic thermal conductivity calculated from first-principles is implemented for the first time. More than 25% of heat is shown to be conducted by electrons at 1500 K. The computed total thermal conductivity of silicon faithfully reproduces the measured data. The approach presented in this paper is expected to be applied to other high-temperature functional materials, and the results could serve as benchmarks and help to explain the high-temperature phonon and electron transport phenomena.
TimeConvNets: A Deep Time Windowed Convolution Neural Network Design for Real-time Video Facial Expression Recognition

A core challenge faced by the majority of individuals with Autism Spectrum Disorder (ASD) is an impaired ability to infer other people’s emotions based on their facial expressions. With significant recent advances in machine learning, one potential approach to leveraging technology to assist such individuals to better recognize facial expressions and reduce the risk of possible loneliness and depression due to social isolation is the design of computer vision-driven facial expression recognition systems. Motivated by this social need as well as the low latency requirement of such systems, this study explores a novel deep time windowed convolutional neural network design (TimeConvNets) for the purpose of realtime video facial expression recognition. More specifically, we explore an efficient convolutional deep neural network design for spatiotemporal encoding of time windowed video frame subsequences and study the respective balance between speed and accuracy. Furthermore, to evaluate the proposed TimeConvNet design, we introduce a more difficult dataset called BigFaceX, composed of a modified aggregation of the extended Cohn- Kanade (CK+), BAUM-1, and the eNTERFACE public datasets. Different variants of the proposed TimeConvNet design with different backbone network architectures were evaluated using BigFaceX alongside other network designs for capturing spatiotemporal information, and experimental results demonstrate that TimeConvNets can better capture the transient nuances of facial expressions and boost classification accuracy while maintaining a low inference time.
Language-integrated updatable views

Relational lenses are a modern approach to the view update problem in relational databases. As introduced by Bohannon et al. [5], relational lenses allow the definition of updatable views by the composition of lenses performing individual transformations. Horn et al. [20] provided the first implementation of incremental relational lenses, which demonstrated that relational lenses can be implemented efficiently by propagating changes to the database rather than replacing the entire database state. However, neither approach proposes a concrete language design; consequently, it is unclear how to integrate lenses into a general-purpose programming language, or how to check that lenses satisfy the well-formedness conditions needed for predictable behaviour. In this paper, we propose the first full account of relational lenses in a functional programming language, by extending the Links web programming language. We provide support for higher-order predicates, and provide the first account of typechecking relational lenses which is amenable to implementation. We prove the soundness of our typing rules, and illustrate our approach by implementing a curation interface for a scientific database application.
On semibounded expansions of ordered groups

We explore "semibounded" expansions of arbitrary ordered groups; namely, expansions that do not define a field on the whole universe. We show that if $\mathcal R=\langle R, <, +, \dots\rangle$ is a semibounded o-minimal structure and $P\subseteq R$ a set satisfying certain tameness conditions, then $\langle \cal R, P\rangle$ remains semibounded. Examples include the cases when $R=\mathbb R$, and $P= 2^{\mathbb Z}$, or $P=\mathbb Z$, or $P$ is an iteration sequence. As an application, we obtain that smooth functions definable in such $\langle \mathcal R, P\rangle$ are definable in $\mathcal R$.
Variational Auto-Encoder: not all failures are equal

We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distribution class used for the observation model.A first theoretical and experimental contribution of the paper is to establish that even in the large sample limit with arbitrarily powerful neural architectures and latent space, the VAE failsif the sharpness of the distribution class does not match the scale of the data.Our second claim is that the distribution sharpness must preferably be learned by the VAE (as opposed to, fixed and optimized offline): Autonomously adjusting this sharpness allows the VAE to dynamically control the trade-off between the optimization of the reconstruction loss and the latent compression. A second empirical contribution is to show how the control of this trade-off is instrumental in escaping poor local optima, akin a simulated annealing schedule.Both claims are backed upon experiments on artificial data, MNIST and CelebA, showing how sharpness learning addresses the notorious VAE blurriness issue.
Machine Learning Approaches to Predicting and Understanding the Mechanical Properties of Steels

The mechanical properties are essential for structural materials. A dataset consists of 102 carbon steel and 258 low-alloy steel was built from the NIMS steel fatigue dataset. And five machine learning algorithms were applied on the dataset to predict the mechanical properties, including fatigue strength, tensile strength, fracture strength, and hardness of steels. Random forest shows a tremendous predictive power of those properties of steels. The prominent features of those properties were selected based on random forest, and symbolic regressions, the tempering temperature and the alloying elements of carbon, chromium, and molybdenum were shown to play essential roles on the mechanical properties. Besides, the mathematic expressions were generated via symbolic regression, and the expression can directly predict the mechanical properties with heat treatment conditions and compositions. The symbolic regression algorithm shows excellent potential in feature selection and expression discovery in materials science
G-VAE: A Continuously Variable Rate Deep Image Compression Framework

Rate adaption of deep image compression in a single model will become one of the decisive factors competing with the classical image compression codecs. However, until now, there is no perfect solution that neither increases the computation nor affects the compression performance. In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model. Unlike the previous solutions that encode progressively or change the internal unit of the network, G-VAE only adds a pair of gain units at the output of encoder and the input of decoder. It is so concise that G-VAE could be applied to almost all the image compression methods and achieve continuously variable rate with negligible additional parameters and computation. We also propose a new deep image compression framework, which outperforms all the published results on Kodak datasets in PSNR and MS-SSIM metrics. Experimental results show that adding a pair of gain units will not affect the performance of the basic models while endowing them with continuously variable rate.
Occlusion aware unsupervised learning of optical flow from video

In this paper, we proposed an unsupervised learning method for estimating the optical flow between video frames, especially to solve the occlusion problem. Occlusion is caused by the movement of an object or the movement of the camera, defined as when certain pixels are visible in one video frame but not in adjacent frames. Due to the lack of pixel correspondence between frames in the occluded area, incorrect photometric loss calculation can mislead the optical flow training process. In the video sequence, we found that the occlusion in the forward (t→t+1) and backward (t→t-1) frame pairs are usually complementary. That is, pixels that are occluded in subsequent frames are often not occluded in the previous frame and vice versa. Therefore, by using this complementarity, a new weighted loss is proposed to solve the occlusion problem. Our method achieves competitive optical flow accuracy compared to the baseline and some supervised methods on KITTI and Sintel benchmarks.