Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation of The Impact of Amelogenesis Imperfecta (AI) on Children and Adolescents

Background: Amelogenesis Imperfecta (AI) is an inherited dental condition affecting enamel, which can result in significant tooth discolouration and enamel breakdown, requiring lifelong dental care. The possible impact of this condition on children and young adults is not known. Aims and Objectives: The aim of the study was to explore the impact of AI on children and young adults through in-depth interviewing and subsequent Framework Analysis. The information derived from this was then used to construct a questionnaire. Methods: This research comprised of two parts, combining qualitative and quantitative methodology, in order to develop a questionnaire to distribute to a large cohort of AI patients. The first part involved semi-structured in-depth interviews with 7 AI patients and common themes and concepts were then identified using Framework Analysis. The second part of the study was the development of a questionnaire based on the themes and subthemes identified from part one of the research. This questionnaire was then distributed to 61 AI patients mixed between three cohorts of AI patients: pre, mid, and post-treatment. Results: Children and adolescents with AI exhibited concerns regarding the aesthetics and function of their dentition. Patients also expressed a high level of concern regarding comments by other people and self consciousness associated with this. A small number of AI patients highlighted the effect of their dental treatment and health on their personal life. Conclusion: The results indicate that there are marked impacts on children and young adults as a result of AI. These include aesthetics, function, and psycho-social aspects.
Effects of whole body vibration on spinal proprioception of healthy subjects and patients with low back pain

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Ill-Timed: The Effect of Early Chronic Illness Onset on Young Adult Psychosocial Development

Chronic illness affects nearly half of all American adults, yet this experience is often regarded as socially normative for older adults. In this study, I examined chronic illness onset early in the life course and its effects on mastery, a person’s self-perception as capable of coping with and managing life’s circumstances, and depressive symptoms as informed by the life course perspective and the stress process model. Using multilevel modeling of American Changing Lives Survey (ACLS) data, I examined the following questions: What is the relationship between early onset chronic illness and mastery? Second, what is the relationship between early onset chronic illness and depressive symptoms? Does mastery mediate the relationship between early onset chronic illness and depressive symptoms? Is early onset chronic illness (24-35) more strongly associated with decreased mastery and increased depressive symptoms than illness onset at the more socially normative life stages of mid-life (36-64) and late-life (65 years and older)? Lastly, does mastery mediate or moderate the relationship between timing of illness onset and depressive symptoms? Through this study, I aim to contribute to sociological knowledge of whether and how chronic illness impacts mastery and depression among young adults. I argue that ill-timed chronic illness impacts young adults’ sense of control over their lives, which has enduring psychological and social consequences. Findings support that healthy and chronically ill young adults do not significantly differ on mastery, but ill young adults report significantly higher depressive symptoms than healthy same age peers. Mastery moderates the effects of timing of illness onset on depressive symptoms with older adults reaping greater benefit from mastery against depressive symptoms than young adults with early onset illness. These findings suggest that early onset chronic illness positions people at greater risk for poor mental health outcomes and that the chronic illness experience and its effects are not uniform across the life course. Consequently, work in this area must consider age as an important context in which the life event of chronic illness onset occurs. INDEX WORDS: Chronic illness, Young adults, Depressive symptoms, Life course, Mental health, Mastery ILL-TIMED: THE EFFECT OF EARLY CHRONIC ILLNESS ONSET ON YOUNG ADULT PSYCHOSOCIAL DEVELOPMENT
Controlled drug delivery : designing technologies for the future

Despite the public desire for a magic bullet-a drug that cures the ailment and is easy to take-most drugs require a sustained release to the target area as opposed to a burst, hence the need for controlled release devices. This volume devotes separate sections to current work in each of the key aspects for developing these devics, including the route of administration, drug delivery vehicles, drug targeting, and modulated drug delivery.
Using Feature Extraction From Deep Convolutional Neural Networks for Pathological Image Analysis and Its Visual Interpretability

USING FEATURE EXTRACTION FROM DEEP CONVOLUTIONAL NEURAL NETWORKS FOR PATHOLOGICAL IMAGE ANALYSIS AND ITS VISUAL INTERPRETABILITY Wei-Wen Hsu Old Dominion University, 2019 Director: Chung-Hao Chen This dissertation presents a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using the domain knowledge in pathology. In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixelwise classification (segmentation) into three classes directly, a hierarchical framework with the multi-view scheme was designed in the proposed system that performs lesion detection for region proposal at higher magnification first and then conducts lesion classification at lower magnification for each detected lesion. The majority voting scheme was adopted to improve the error tolerance of the system in lesion-wise prediction. For all collected 186 slides, the slide-wise prediction accuracy rate strikes to 95.16% (177/186) in binary classification to predict carcinoma (malignant) or non-carcinoma (benign), and the sensitivity for cases with carcinoma reaches 96.36% (106/110). In multi-classification, the accuracy rate is 92.47% (172/186) when predicting Non-Carcinoma, DCIS, and IDC for each slide. Most importantly, the interpretability for the mechanism of the proposed CAD system is provided from the pathological perspective. The experimental results show that the morphological characteristics and co-occurrence properties
Decision Support System to Improve Railway Track Maintenance Using Track-Quality Index (TQI)

In recent years, studies on track degradation in railways have attracted a great deal of attention . Intensive research activities have been carried out by many organizations with the aim of securing a high level of safety and reliability of the infrastructure system . New technologies and stringent safety standards are constantly being introduced for several reasons, not only to prevent the assets from failure or damage but also to minimize the main sources of problems associated with the performance degradation in terms of quality, comfort and safety of each journey . Following these shortcomings, this research aims to develop a logical model for the deterioration of track geometry and to incorporate the proposed model as basis for optimizing maintenance in practice. For the purpose of analysis, a segment of Northeastern line from Thanon Chira junction to Nong Khai station comprising 320.900 km. and 35 stations, in length will be considered and multivariate statistical analyzes will be employed in some geometric parameters . The results will be used to derive the optimization model for scheduling track maintenance by means of tamping in a given period of time .
Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter

Many studies have utilized global navigation satellite system (such as global positioning system (GPS)) trajectories in order to successfully infer road networks because such data can reveal the geometry and development of a road network, can be obtained in a timely manner, and updated on a low budget. Unfortunately, existing studies for inferring road networks from vehicle traces suffer from low accuracy, especially in dense urban regions and locations with complex structures, such as roundabouts, overpasses, and complex intersections. This study presents a novel two-stage approach for inferring road networks from trajectory points and capturing road geometry with better accuracy. First, a lane structure-aware filter is proposed to cluster vehicle trajectories influenced by high noise and outliers in order to reveal the continuous structure points of lane curves from massive trajectory points. Second, a road tracing operator is utilized to segment the road network geometry by inserting new vertices and segments to a vigorous vertex in the heading of the structure points that are extracted in the first step. Experimental results demonstrate the increased accuracy of the extracted roads and show that the proposed method exhibits strong robustness to noise and various sampling rates.
Proceedings of the EAA Spatial Audio Signal Processing symposium

Higher Order Ambisonics (HOA) is a popular method for rendering spatial audio. However, the desired sound field can be reproduced over a small reproduction area at lower ambisonic orders. This problem can be handled by upscaling B-format signals using several methods both in the time and frequency domain. In this paper, a novel Sequential Multi Stage DNN (SMS-DNN) is developed for upscaling Ambisonic signals. The SMS-DNN allows for training of a very large number of layers since training is performed in blocks consisting of a fixed number of layers. Additionally, the vanishing gradient problem in DNN with a large number of layers is also effectively handled by the proposed SMS-DNN due to its sequential nature. This method does not require prior estimation of the source locations and works in multiple source scenarios. Reconstructed sound field analysis, subjective and objective evaluations conducted on the upscaled Ambisonic sound scenes indicate reasonable improvements when compared to the benchmark HOA reproduction.
A Fraudulent Data Simulation Method Based on Generative Adversarial Networks

In view of the positive and negative sample imbalance in the process of establishing anti-fraud rules and models of the third-party payment, this paper simulated the fraudulent transactions by means of generative adversarial networks. In the design of the model, the output of the discriminator is optimized where classification results are set to three categories; the loss function of the GAN model is changed to include two parts: the source loss function and the category loss function. The generated data and the real business data are mixed according to a certain ratio, to train a fraud detection model. Verify the generation effect of this simulation method by comparing the detection effects of different detection models.
A review of studies in taxi mobility and e-hailing taxi service

Taxi mobility has long been a heated research topic in the fields of geography, physics, complex network science, and computer science. Existing studies focus on the spatio-temporal variation of taxi mobility and taxi operation strategy. However, with the advent of e-hailing taxi services, more and more scholars turn to uncover the impacts of the emerging travel mode on human mobility and urban dynamics. For example, some researchers studied the taxi market equilibrium after the emergence of e-hailing services. And others focus on comparing the difference in taxi pricing strategies and policies between traditional taxi market and e-hailing services. In this article, we systematically reviewed existing research on taxi mobility and impacts of e-hailing services on traditional taxi market. Further research is needed so that could help build up a better understanding of taxi mobility and e-hailing services.
Analysis and Optimization of a Novel Power-Split Hybrid Powertrain

Plug-in hybrid electric vehicles have the advantages of both traditional fuel cars and electric vehicles. This paper presents a single planetary gear dual-mode (input- and output-split) hybrid powertrain. Clutches are substituted for a synchronizer to achieve mode switching. To filter six possible topology schemes, a screening method based on kinematics and theoretical efficiency analysis is utilized. Subsequently, a backward model is established by modeling each powertrain component and designing an appropriate control strategy. Finally, a backward simulation method is utilized to select the optimal topologies. To evaluate the performance of the proposed powertrains, two typical power-split powertrains, THS-P610 and Volt-II, are compared with the optimized powertrains. The comparison results show that the proposed powertrains, having a simpler structure than Volt-II, improve the acceleration and fuel economy performances compared with THS-P610 and Volt-II.
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge

Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge. They often assume that writing knowledge can be acquired from the training data alone. However, when people are writing, they not only rely on the data but also consider related knowledge. In this paper, we enhance neural data-to-text models with external knowledge in a simple but effective way to improve the fidelity of generated text. Besides relying on parallel data and text as in previous work, our model attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words. This allows the model to infer relevant facts which are not explicitly stated in the data table from an external knowledge source. Experimental results on twenty-one Wikipedia infobox-to-text datasets show our model, KBAtt, consistently improves a state-of-the-art model on most of the datasets. In addition, to quantify when and why external knowledge is effective, we design a metric, KBGain, which shows a strong correlation with the observed performance boost. This result demonstrates the relevance of external knowledge and sparseness of original data are the main factors affecting system performance.
Low-Dose CT Reconstruction with Multiclass Orthogonal Dictionaries

We propose a high-accuracy CT image reconstruction from low-dose X-ray projection data. A state-of-the-art method exploits dictionary learning for image patches. This method generates an overcomplete dictionary from patches of standard-dose CT images and reconstructs low-dose CT images by minimizing the sum of date fidelity and reg-ularization terms based on sparse representations with the dictionary. However, this method does not take characteristics of each patch into account, such as texture and edges. In this paper, we propose to divide all patches into several classes, and use an individual dictionary with an individual regularization parameter for each class. Moreover, for fast computation, we introduce the orthogonality for each dictionary. Since clustering collects similar patches, accuracy degradation by the orthogonality hardly occurs. Simulation shows the proposed method outperforms the state-of-the-art one in terms of accuracy and speed.
Untangling in Invariant Speech Recognition

Encouraged by the success of deep convolutional neural networks on a variety of visual tasks, much theoretical and experimental work has been aimed at understanding and interpreting how vision networks operate. At the same time, deep neural networks have also achieved impressive performance in audio processing applications, both as sub-components of larger systems and as complete end-to-end systems by themselves. Despite their empirical successes, comparatively little is understood about how these audio models accomplish these tasks.In this work, we employ a recently developed statistical mechanical theory that connects geometric properties of network representations and the separability of classes to probe how information is untangled within neural networks trained to recognize speech. We observe that speaker-specific nuisance variations are discarded by the network's hierarchy, whereas task-relevant properties such as words and phonemes are untangled in later layers. Higher level concepts such as parts-of-speech and context dependence also emerge in the later layers of the network. Finally, we find that the deep representations carry out significant temporal untangling by efficiently extracting task-relevant features at each time step of the computation. Taken together, these findings shed light on how deep auditory models process their time dependent input signals to carry out invariant speech recognition, and show how different concepts emerge through the layers of the network.
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition

Facial action units (AUs) recognition is essential for emotion analysis and has been widely applied in mental state analysis. Existing work on AU recognition usually requires big face dataset with accurate AU labels. However, manual AU annotation requires expertise and can be time-consuming. In this work, we propose a semi-supervised approach for AU recognition utilizing a large number of web face images without AU labels and a small face dataset with AU labels inspired by the co-training methods. Unlike traditional co-training methods that require provided multi-view features and model re-training, we propose a novel co-training method, namely multi-label co-regularization, for semi-supervised facial AU recognition. Two deep neural networks are used to generate multi-view features for both labeled and unlabeled face images, and a multi-view loss is designed to enforce the generated features from the two views to be conditionally independent representations. In order to obtain consistent predictions from the two views, we further design a multi-label co-regularization loss aiming to minimize the distance between the predicted AU probability distributions of the two views. In addition, prior knowledge of the relationship between individual AUs is embedded through a graph convolutional network (GCN) for exploiting useful information from the big unlabeled dataset. Experiments on several benchmarks show that the proposed approach can effectively leverage large datasets of unlabeled face images to improve the AU recognition robustness and outperform the state-of-the-art semi-supervised AU recognition methods.
Deep reinforcement learning in a spatial navigation task: Multiple contexts and their representation

Deep learning has recently been combined with Qlearning (Mnih et al., 2015) to enable learning difficult tasks such as playing video games based only on visual input. Stable learning in the in the deep Q network (DQN) is facilitated by the use of memory replay, which means that previous experiences are stored and sampled from during an offline learning period. We evaluate the DQN’s ability to learn and retain multiple variations of a spatial navigation task in a virtual environment. Task variations are presented in visually distinct contexts by varying light conditions and environmental textures. Replay memory capacity is varied to measure its effect on task retention. The representations of multiple contexts learned by the DQN agents are analyzed and compared. We show that DQN agents learn a preference for common actions early on, irrespective of replay memory capacity. A limited replay memory causes agents to confuse state-values. Furthermore, we find that contexts are quickly forgotten as soon as corresponding experiences are no longer available in the replay memory.
Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization

A key challenge in topic-focused summarization is determining what information should be included in the summary, a problem known as content selection. In this work, we propose a new method for studying content selection in topic-focused summarization called the summary cloze task. The goal of the summary cloze task is to generate the next sentence of a summary conditioned on the beginning of the summary, a topic, and a reference document(s). The main challenge is deciding what information in the references is relevant to the topic and partial summary and should be included in the summary. Although the cloze task does not address all aspects of the traditional summarization problem, the more narrow scope of the task allows us to collect a large-scale datset of nearly 500k summary cloze instances from Wikipedia. We report experimental results on this new dataset using various extractive models and a two-step abstractive model that first extractively selects a small number of sentences and then abstractively summarizes them. Our results show that the topic and partial summary help the models identify relevant content, but the task remains a significant challenge.
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning

Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominantly focus on learning the proper mapping function for visual-semantic embedding, while neglecting the effect of learning discriminative visual features. In this paper, we study the significance of the discriminative region localization. We propose a semantic-guided multi-attention localization model, which automatically discovers the most discriminative parts of objects for zero-shot learning without any human annotations. Our model jointly learns cooperative global and local features from the whole object as well as the detected parts to categorize objects based on semantic descriptions. Moreover, with the joint supervision of embedding softmax loss and class-center triplet loss, the model is encouraged to learn features with high inter-class dispersion and intra-class compactness. Through comprehensive experiments on three widely used zero-shot learning benchmarks, we show the efficacy of the multi-attention localization and our proposed approach improves the state-of-the-art results by a considerable margin.