Miracle-dz/UniFAR-FTUs huggingface dataset:
Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

Objectives: To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis—by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis—was performed to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28–79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.
Gaining More from Less Data in out-of-domain Question Answering Models

We propose text augmentation techniques for Question Answering task in NLP that involves using synonyms with stochasticity on out-of-domain datasets (DuoRC and RACE and RelationExtraction) that are set to be 400 times smaller than the in-domain datasets (SQUAD, NewsQA, NaturalQuestions). We address ways to improve extraction of generalized information from out-of-domain or less available datasets from large pre-trained models like BERT or its variation DistiIBERT which is used here with also being able to benefit from producing QA applications across domains. It is found that augmenting less available QA datasets in ways described, indicate improvement in generalization, but not all augmentations strategies are equally good. We find that these augmentations are helpful in achieving better performance on out-of-domain data.
Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.
CONSTRUCTING HYBRID NANOSTRUCTURES OUT OF NOBLE METALS AND SEMICONDUCTORS

Title of Document: CONSTRUCTING HYBRID NANOSTRUCTURES OUT OF NOBLE METALS AND SEMICONDUCTORS Huizhi Bai, Ph.D., 2015 Directed By: Professor and Chair, Janice Reutt-Robey, Department of Chemistry and Biochemistry Fabrication of complex hybrid nanostructures with tunable properties is desirable to fulfill functional applications in multidisciplinary areas. Manipulation of pre-designed nanostructure building blocks composed of distinct materials to achieve finite control over crystallinity, morphology, and composition is a major challenge. This dissertation aims to address the topic and create material with new optical properties. Questions explored are: How to realize delicate control on crystallinity of hybrid nanostructures through unconventional synthetic routes? How to achieve precise hybridized nanostructures with designed geometry, topology and composition? How do these features affect optical properties? Specifically, this dissertation contains recent efforts on fabrication and characterization of functional hybrid nanostructures made from metal and semiconducting materials. I first present a critical review of a monocrystalline nonepitaxially grown metallic @ semiconducting core @ shell hybrid nanostructures. This includes a comprehensive description of the novel nonepitaxial synthetic route, emphasizing critical experimental steps, anticipation of challenges, and ending with my perspective. This systematic review should expand knowledge of the newly developed nonepitaxial method and spread technical aspects of the experiments. I then introduce an anisotropically shaped semiconducting nanocrystal with binary alloy composition. The rod-shaped ensemble has exhibited tunable bright band gap fluorescence that is dependent on dimension. This work is the first to achieve binary semiconducting alloy nanocrystals with anisotropic shapes. Interestingly, the electronic behavior within the rod-shaped semiconducting nanocrystals is altered due to gradient element distribution of the binary material, which is of fundamental interest and potential practical importance. Lastly, a core – metallic satellites-styled nanoparticle assembly structure will be discussed. Control over Ag nanoparticles as surrounding satellites in terms of size, shape and quantity is achieved via a facile synthetic route, and a collective electronic (dipole – dipole coupling) behavior within the metallic assembly is observed, and supported by numerical simulation. This work provides a new facile pathway to achieve wellcontrolled silica – Ag hybrid nanostructures. CONSTRUCTING HYBRID NANOSTRUCTURES OUT OF NOBLE METALS AND SEMICONDUCTORS
A GIS-based spatiotemporal study of the variability of water quality in the Dubai Creek, UAE

This paper presents the outcomes of a study on the water quality of Dubai Creek which aimed to assess its eutrophication status. Field water quality data from stations along the creek collected in 2012 and 2013 were used. Ordinary least squares (OLS) and spatial autocorrelation analyses were used as part of geographic information system (GIS)-based exploratory regression analysis to study the relationship between chlorophyll- a and nutrients, specifically total nitrogen and phosphate. Multiple logistic regression analysis was used to study the vulnerability of the creek to eutrophication. Results showed unique trends of spatiotemporal variability of chlorophyll- a and nutrients. OLS modeling showed high correlation between field and modeled chlorophyll- a values between Al Garhoud Bridge and Sanctuary stations, located about 2 km upstream and downstream of the Sewage Treatment Plant (STP) Outfall station. Furthermore, results showed the lower half of the creek was more vulnerable to eutrophication than the upper, which was believed to be due to the location of the STP station, poor flushing, shallow water depth, and irregular circulation patterns in the creek. Accordingly, this study recommends development of a mitigation plan in order to control the levels of nutrients in the creek.
The XPD /ERCC2, RAD51 and hOGG1 gene polymorphisms in Turkish patients with advanced-stage gastric cancer

Bu calismada ileri evre mide kanserli Turk hastalarda XPD/ERCC2, RAD51 ve hOGG1 gen polimorfizminin prognoz uzerine etkisinin olup olmadigini degerlendirdik. Calismaya performans durumu 0-2 olan, inoperabl lokal ileri veya uzak metastazli 31 mide kanseri hastasi ile kontrol grubu olarak 29 saglikli birey dahil edildi. DNA serumdan izole edildikten sonra tek nukleotid gen polimorfizmi MassARRAY Analyzer 4 System [Sequenom] kullanilarak analiz edildi. Gozlenen genotip frekanslarinin istatistiksel degeri beklenen genotip frekanslarina kiyasla Hardy-Weinberg kuralina gore belirlendi. Hasta grubunda ortanca yas 68 (yil) ve hastalarin %74’u erkek, %26’si kadin idi. XPD/ERCC2 Asp312Asn [rs1799793] tek nukleotit gen polimorfizmi G23591A, RAD51 [rs1801320] tek nukleotit gen polimorfizmi G135C ve hOGG1 Ser 326 Cys [rs1052133] tek nukleotid gen poliforfizmi tespit edildi. Mide kanserli Turk hastalarda genotipler ile prognoz arasinda anlamli bir iliski kuramadik.
VARIABLES IN NATURAL LANGUAGE: WHERE DO THEY COME FROM?

ive operators to an algebra is still trivial in a sense analogous to that in which it is trivial to add polynomials. To see this, we must realize that semantic interpretation, as the term is usually understood, is not simply a homomorphism, but typically a homomorphism into an algebra of a specific kind. In the terminology of the present paper, it is a homomorphism into an algebra the operators of which are formal, i.e. devoid of content (which means, as explained in the previous chapter, that they only bring the capacities of some elements of the algebra to bear on some other elements). In Peregrin (1992) I called such algebras applicative and I have proven the following theorem: if A is an algebra, B an applicative algebra, h a homomorhpism from A to B, and A an abstractive extension of A, then there is an extension B of B, and a unique extension h of h such that h is a homomorphism from A to B. This means that by employing not only variables, but also the lambda operator, we still only make explicit the implicit semantic capacities. However, while polynomial symbols are bound to stand for operators, abstractive symbols (lambda-terms) may, in the limit case when all variables are ‘lambda-abstracted away’, stand for objects. In this way variables
Detecting Low-Rank Regions in Omnidirectional Images *

Planar low-rank regions commonly found in man-made environments, can be used to estimate a rectifying homography that provides valuable information about the camera and the 3D plane they observe. Methods to recover such a homography exist, but detection of low-rank regions is largely unsolved, especially for omnidirectional cameras where significant distortions make the problem even more challenging. In this paper we address this problem as follows. First we propose a method to generate a low-rank probability map on an omnidirectional image and use it to build a training set in a self-supervised manner to train deep models to predict low-rank likelihood maps for omnidirectional images. Second, we propose to adapt regular CNN operators to equirectangular images and to combine them seamlessly into a network where each layer preserves the properties of the equirectangular representation. Finally, on the new KITTI360 dataset, we show that the rectifying homography of detected low-rank regions in such predicted maps allows to factorize out the camera-plane pose up to certain ambiguities that can be easily overcome.
Analysis of Facial Emotional Characteristics: Impact of Culture and Gender

Emotional expressions are considered universal for a long time. However, some evidence indicates that there are gender and cultural specific variations in emotional expressions, which leads to inaccurate recognition result in the emotion recognition system. Therefore, this thesis aims to identify the particular cultural and gender variation in basic facial expressions (disgust, fear, happiness, surprise, and sad) in terms of action units that are defined by the facial action coding system (FACS). A logit regression is conducted with each emotions’ action unit as independent variables and race (Caucasian and Asian) as the dependent variable. The result reveals that each emotion expression’s specific action units in Caucasian and East Asian are different. This thesis also constructs four average faces regarding culture and gender to evaluate the gender variability of action unit intensity in the same cultural group. The finding indicates less distinction in Caucasian facial behaviors compared with Asian, and women are generally more expressive than men.
Study and development of physical models to evaluate biological effects of ion therapy : the study of local control of prostate cancer

External beam radiotherapy (EBRT) is a therapy technique aiming at treating locoregional tumors with high efficiency. However, many tumors remain uncontrolled. Newest EBRT techniques always aim at increasing the dose to the tumor while sparing the surrounding healthy tissues. Carbon-ion beam therapy is one of these promising techniques. The number of clinical centres offering carbon-ion beam radiotherapy has been increasing over the world for the last decade. This keen interest spread after very promising results from pilot projects at Berkeley (USA), Chiba (Japan) and Darmstadt (Germany). The theoretical advantages of carbon-ionsare better spatial selectivity in dose deposition and better efficiency in cell killing. They have thus the potential to increase the control of tumors, particularly for unresectable radioresistant tumors. In high linear-energy-transfer (LET) radiations, such as carbon-ion beams, biological effects vary along the ion track, hence, to quantify them, specific radiobiological models are needed. There exist several radiobiological models based on very different theoretical approaches and approximations. They were created and improved in each of the pilot institutions. At the current state of knowledge, no convergence between the model results seems to be possible in the very near future. Clinically employed radiobiological models are the Local Effect Model (LEM) developed in Germany and implemented in CE-certified treatment planning systems, the National Institute of Radiological Science (NIRS) model employed in Japanese centres with passive beam delivery systems and the microdosimetric kinetic model (MKM) in Japanese centres with active scanning beam delivery systems
Random Access in Mobile Networks with Beamforming

In the fifth generation (5G) of mobile networks, high data rate and low latency services are supported. In order to fullfill these two requirements, the frequency above 6 GHz is one of options to be used. The frequency above 6 GHz is called frequency range two (FR2) in 3GPP 38 series specification. The attentuation is a critical problem in high frequency system, so beamforming technology is adopted to overcome this problem. For control plane procedure with beamforming, more time is needed to do synchronization and random access procedure with beamforming. This paper proposes random access procedure with beamforming model
View-Guided Point Cloud Completion Supplementary Material 1

In this section, we show more examples of the ShapeNetViPC dataset. The entire dataset contains 38,328 objects from 13 categories, i.e. airplane, bench, cabinet, car, chair, monitor, lamp, speaker, firearm, sofa, table, cellphone, watercraft. For each shape in dataset, we generate three types of point cloud, including the completed point cloud, partial point cloud with and without noise from 24 different perspectives following the ShapeNetRendering[1]. As shown in Figure 1 and Figure 2, for each object, we show the view image in column 1, the partial point cloud, partial point cloud with noise, and completed point cloud (ground-truth) under the same view point as the view image in columns 2-4, respectively. To show self occlusion and inter-object occlusion more clearly, we show these point clouds under another viewpoint in columns 5-7.
Detection of Na , K , and H 2 O in the hazy atmosphere of WASP-6 b Journal Item

We present new observations of the transmission spectrum of the hot Jupiter WASP-6b both from the ground with the Very Large Telescope FOcal Reducer and Spectrograph (FORS2) from 0.45 to 0.83 μm, and space with the Transiting Exoplanet Survey Satellite from 0.6 to 1.0 μm and the Hubble Space Telescope (HST) Wide Field Camera 3 from 1.12 to 1.65 μm. Archival data from the HST Space Telescope Imaging Spectrograph (STIS) and Spitzer are also re-analysed on a common Gaussian process framework, of which the STIS data show a good overall agreement with the overlapping FORS2 data. We also explore the effects of stellar heterogeneity on our observations and its resulting implications towards determining the atmospheric characteristics of WASP-6b. Independent of our assumptions for the level of stellar heterogeneity we detect Na I, K I, and H2O absorption features and constrain the elemental oxygen abundance to a value of [O/H] −0.9 ± 0.3 relative to solar. In contrast, we find that the stellar heterogeneity correction can have significant effects on the retrieved distributions of the [Na/H] and [K/H] abundances, primarily through its degeneracy with the sloping optical opacity of scattering haze species within the atmosphere. Our results also show that despite this presence of haze, WASP-6b remains a favourable object for future atmospheric characterization with upcoming missions such as the James Webb Space Telescope.
Surgeon Scorecards Improve Muscle Sampling on Transurethral Resection of Bladder Tumor and Recurrence Outcomes in Patients with Nonmuscle Invasive Bladder Cancer

From the Department of Urology (AD), University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Department of Urology (AD, JEC, OSK, GBA, JJM), Northwestern University Feinberg School of Medicine, Jesse Brown VAMC, Chicago, Illinois, Department of Urology (BJJ), University of Washington, Seattle, Washington, and Division of Urology (APG), Department of Surgery, NorthShore University HealthSystem, Evanston, Illinois
SCJ-D-16-00041 2..10

SPEED IS A KEY ASPECT OF YOUTH PHYSICAL DEVELOPMENT PROGRAMS AND COMMONLY ASSESSED DURING TALENT IDENTIFICATION TESTING PROTOCOLS, YET, LITTLE IS UNDERSTOOD ABOUT THE FACTORS THAT UNDERPIN THE NATURAL DEVELOPMENT OF MAXIMAL SPEED THROUGHOUT CHILDHOOD AND ADOLESCENCE. THIS ARTICLE REVIEWS THE ANTHROPOMETRIC, KINEMATIC, KINETIC, AND ASYMMETRY VARIABLES THAT CONTRIBUTE TO SPRINT PERFORMANCE, WHILE EXAMINING THE IMPACT THAT GROWTH AND MATURATION MAY HAVE ON ALL FACETS OF MAXIMAL SPRINT PERFORMANCE IN BOYS. CLEAR GUIDANCE IS PROVIDED ON THE PRACTICAL APPLICATIONS FOR THE STRENGTH AND CONDITIONING COACH THAT SHOULD HELP IN DESIGN OF EFFECTIVE SPEED DEVELOPMENT PROGRAMS FOR MALE YOUTH. INTRODUCTION S printing is considered a fundamental movement that underpins successful sports participation (27,36,46). Sprint speed is also recognized as a key component of youth physical development programs (26) and is a common performance assessment in strength and conditioning programs and talent identification batteries (36). The development of speed in boys throughout childhood and adolescence has previously been considered (40); however, the authors noted that little was understood about the factors that underpin natural developments in maximal sprint performance in youth. Given recent developments in the pediatric literature pertaining to the development of maximal sprint performance (22,32–34,45,51,52), this review aims to examine key variables that facilitate the development of maximal sprint speed in boys throughout childhood and adolescence. DEVELOPMENT OF MAXIMAL SPRINT SPEED IN BOYS In boys, maximal sprint speed is known to develop in a nonlinear fashion throughout childhood and adolescence (28), with the suggestion of a preadolescent spurt (5–9 years old) and adolescent spurt (12–14 years old) in performance for boys (58). Although these spurts in sprint performance are based on chronological age, a maturational effect for the development of sprint speed has also been proposed (33,52). Peak height velocity (PHV) is often used as an indication of maturity and is defined as the maximum rate of growth in stature during the adolescent spurt (28). Peak improvements in sprint speed have been reported to occur between 8 and 18 months before the period of PHV (58,63). Conversely, it has been reported that peak changes in sprint speed may occur in time with the period of PHV (43,56); however, decrements in speed may be noted in the 12 months before PHV (43), thus reinforcing the nonlinear nature of speed development. Recent cross-sectional data have suggested that maximal sprint speed does not change between groups of boys whowere between 1 and 3 years pre-PHV, but significant increases in speed are observed between circaand post-PHV groups (33). Researchers have suggested that when monitored over a 2-year period, boys who advance from 13 to 15 years old Address correspondence to Robert W. Meyers, rwmeyers@cardiffmet.ac. uk.
J un 2 02 1 Quantum fields in the future Rindler wedge

We consider interacting massive scalar quantum field theory in the future Rindler wedge. This is a model example of quantum field theory in curved space–time. On this simple example we show how dynamics of correlation functions depends on the choice of initial Cauchy surface, basis of modes and on the choice of initial state build with the use of the corresponding creation and annihilation operators. We show which choice of modes in the future Rindler wedge respects Poincaré symmetry. But we do not restrict our attention only to these modes and the corresponding state.
PointNetLK Revisited

We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not well-represented in the training set, such as unseen object categories, different complex scenes, or unknown depth sensors. In these circumstances, it has often been better to rely on classical non-learning methods (e.g., Iterative Closest Point), which have better generalization ability. Hybrid learning methods, that use learning for predicting point correspondences and then a deterministic step for alignment, have offered some respite, but are still limited in their generalization abilities. We revisit a recent innovation— PointNetLK [1]—and show that the inclusion of an analytical Jacobian can exhibit remarkable generalization properties while reaping the inherent fidelity benefits of a learning framework. Our approach not only outperforms the state-of-the-art in mismatched conditions but also produces results competitive with current learning methods when operating on real-world test data close to the training set.