Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodology to Reduce Distortion Using a Hybrid Thermal Welding Process

Welding is a thermal process which results in high strain and stress values in the material and leads to its change of phase. This might cause significant distortions in the welded structure, which often becomes a relevant design and manufacturing issue. This study deals with a line-heating-based forming process that is applied at the moment of the welding operation, with the final objective of minimizing distortion. A FEM (finite element method) based on a thermo-elastoplastic approach is used here. The computational method is first calibrated in three stages: heatline forming calibration, flame heat source calibration, and the weld process. The final model presented in this work simulates a hybrid process called htTTT (high-temperature thermal transient tensioning) which was optimized over large T welds to minimize the final distortion of the components.
Relationship between Photosynthetic Capacity and Microcystin Production in Toxic Microcystis Aeruginosa under Different Iron Regimes

Blooms of harmful cyanobacteria have been observed in various water bodies across the world and some of them can produce intracellular toxins, such as microcystins (MCs), which negatively impact aquatic organisms and human health. Iron participates significantly in cyanobacterial photosynthesis and is proposed to be linked to MC production. Here, the cyanobacteria Microcystis aeruginosa was cultivated under different iron regimes to investigate the relationship between photosynthetic capacity and MC production. The results showed that iron addition increased cell density, cellular protein concentration and the Chl-a (chlorophyll-a) content. Similarly, it can also up–regulate photosynthetic capacity and promote MC–leucine–arginine (MC–LR) production, but not in a dose–dependent manner. Moreover, a significant positive correlation between photosynthetic capacity and MC production was observed, and electron transport parameters were the most important parameters contributing to the variation of intracellular MC–LR concentration revealed by Generalized Additive Model analysis. As the electron transport chain was affected by iron variation, adenosine triphosphate production was inhibited, leading to the alteration of MC synthetase gene expression. Therefore, it is demonstrated that MC production greatly relies on redox status and energy metabolism of photosynthesis in M. aeruginosa. In consequence, more attention should be paid to the involvement of photosynthesis in the regulation of MC production by iron variation in the future.
Active Learning for LF-MMI Trained Neural Networks in ASR

This paper investigates how active learning (AL) effects the training of neural network acoustic models based on Lattice-free Maximum Mutual Information (LF-MMI) in automatic speech recognition (ASR). To fully exploit the most informative examples from fresh datasets, different data selection criterions based on the heterogeneous neural networks were studied. In particular, we examined the relationship among the transcription cost of human labeling, example informativeness and data selection criterions for active learning. As a comparison, we tried both semi-supervised training (SST) and active learning to improve the acoustic models. Experiments were performed for both the small-scale and large-scale ASR systems. Experimental results suggested that, our AL scheme can beneﬁt much more from the fresh data than the SST in reducing the word error rate (WER). The AL yields 6 ∼ 13% relative WER reduction against the baseline trained on a 4000 hours transcribed dataset, by only selecting 1.2K hrs informative utterances for human labeling via active learning.
Should Code-switching Models Be Asymmetric?

Since the work of Joshi [1], most models of code-switching (C-S) have assumed asymmetry of the participating languages. While there exist patterns of language mixing in which a dominant or matrix language (ML) may not be discernible, these more complex signatures are rarely modeled [2, 3]. We use a series of metrics to characterize the switching in corpora as asymmetrical (insertional C-S) or symmetrical (alternational C-S). We test the efﬁcacy of a linguistic model that assumes no ML in predicting the syntax of C-S in three Spanish–English corpora that vary according to whether the ML is Spanish, English or indeterminate. Our results show that the same constraints on the grammatical junctures and on the directionality of switching hold irrespective of the symmetry of the data. The length of the alternating language spans varies according to POS with noun phrases comprising the shortest spans. This suggests that insertional C-S may be subsumed under alternational C-S, as sponta-neous borrowing. These results invite researchers to reconsider the linguistic theories they adopt and to expand the typology of training data used in creating language models and processing tools for C-S.
G-Eyenet: A Convolutional Autoencoding Classifier Framework for the Detection of Glaucoma from Retinal Fundus Images

Glaucoma is one of the leading causes of visual impairment in the world. It deteriorates the optic nerve fibers over time, and cannot be cured once it reaches the later stages. Hence early detection is of utmost importance for the aging society. In this paper, we propose a novel deep learning multi-model network termed G-EyeNet for glaucoma detection from retinal fundus images. G-EyeNet consists of a deep convolutional autoencoder and a traditional convolutional neural network (CNN) classifier sharing the encoder framework. The multi - model network is jointly optimized for minimizing both image reconstruction error and the classification error based on a multi-task learning procedure. Extensive training experiments are performed on publicly available datasets HRF, DRISHTI-GS, RIM ONE v.3. and evaluated on the publicly available DRIONS-DB dataset. Unsupervised training of the encoder framework helps in learning a good distribution of the input, which helps in classification. This architecture is especially effective when the training dataset is small, which is usually the case in medical imaging. Experimental results show that an area under the curve (AUC) of the receiver operator characteristic (ROC) curve of 0.923 is achieved, which is better compared to the state-of-the-art deep learning algorithms.
Does level of minority presence and hospital reimbursement policy influence hospital referral region health rankings in the United States

The shift from a fee-for-service payment to a value-based payment scheme, sparked by the Patient Protection and Affordable Care Act, introduced pay-for-performance programs such Hospital Value Based Purchasing. Previous inquiry has not considered how local community factors may affect hospital system performance. This study investigated the association between local health performance and minority population in a hospital referral region (HRR). The primary objective was to ascertain whether community diversity levels are significantly associated to local health performance guided by the ecological model. Secondary data analysis collected from the 2016 American Hospital Association, Area Health Resource File, Commonwealth Fund Scorecard on Local Health System Performance, and the Dartmouth Atlas HRR dataset was used. Our primary findings show that the more diverse a HRR is, the more likely it is to be associated with lower ranking for access and affordability prevention and treatment avoidable hospital use and cost as well as healthy lives. Total performance score was significantly related to a better health ranking on prevention and treatment, hospital use, and cost, as well as healthy lives. This research supports the assertion that communities, particularly minorities in those communities, affect local health care performance in a variety of ways.
An Exploration towards Joint Acoustic Modeling for Indian Languages: IIIT-H Submission for Low Resource Speech Recognition Challenge for Indian Languages, INTERSPEECH 2018

India being a multilingual society, a multilingual automatic speech recognition system (ASR) is widely appreciated. Despite different orthographies, Indian languages share same phonetic space. To exploit this property, a joint acoustic model has been trained for developing multilingual ASR system us-ing a common phone-set. Three Indian languages namely Tel-ugu, Tamil and, Gujarati are considered for the study. This work studies the amenability of two different acoustic modeling approaches for training a joint acoustic model using common phone-set. Sub-space Gaussian mixture models (SGMM), and recurrent neural networks (RNN) trained with connectionst temporal classiﬁcation (CTC) objective function are explored for training joint acoustic models. From the experimental re-sults, it can be observed that the joint acoustic models trained with RNN-CTC have performed better than SGMM system even on 120 hours of data (approx 40 hrs per language). The joint acoustic model trained with RNN-CTC has performed better than monolingual models, due to an efﬁcient data sharing across the languages. Conditioning the joint model with language identity had a minimal advantage. Sub-sampling the features by a factor of 2 while training RNN-CTC models has reduced the training times and has performed better.
Emotion Recognition from Human Speech Using Temporal Information and Deep Learning

Emotion recognition by machine is a challenging task, but it has great potential to make empathic human-machine communications possible. In conventional approaches that consist of feature extraction and classifier stages, extensive studies have devoted their effort to developing good feature representations, but relatively little effort was made to make proper use of the important temporal information in these features. In this paper, we propose a model combining features known to be useful for emotion recognition and deep neural networks to exploit temporal information when recognizing emotion status. A benchmark evaluation on EMO-DB demonstrates that the proposed model achieves a state-of-the-art performance of 88.9% recognition rate.
Noise Robust Acoustic to Articulatory Speech Inversion

In previous work, we have shown that using articulatory features derived from a speech inversion system trained using synthetic data can significantly improve the robustness of an automatic speech recognition (ASR) system. This paper presents results from the first of two steps needed for exploring if the same will hold true for a speech inversion system trained with natural speech. Specifically, we developed a noise robust multi-speaker acoustic to articulatory speech inversion system. A feed forward neural network was trained using contextualized mel-frequency cepstral coefficients (MFCC) as the input acoustic features and six tract-variable (TV) trajectories as the output articulatory features. Experiments were performed on the U. Wisc. X-ray Microbeam (XRMB) database with 8 noise types artificially added at 5 different SNRs. Performance of the system was measured by computing the correlation between estimated and actual TVs. The performance of the multi-condition trained system was compared to the cleanspeech trained system. The effect of speech enhancement on TV estimation was also evaluated. Experiments showed a 10% relative improvement in correlation over the baseline cleanspeech trained system.
Image Memorability: The Role of Depth and Motion

Understanding what makes an image memorable or forgettable is one of the interesting problems in the domain of computer vision and cognitive science. The initial studies have demonstrated that memorability is an inherent characteristic of an image and have found many high-level image properties (such as emotions, saliency, object statistics, popularity, aesthetics, etc.) which influence image memorability. This paper sheds light on the relationship between image memorability and two image features: motion and depth which, to the best of our knowledge, is still unexplored. Experimental analysis reveals that motion and depth cues have a positive influence in determining image memorability. Further, the paper presents a novel deep learning model, FOD-MemNet, to exploit motion and depth cues along with object features to predict image memorability. Experimental results demonstrate that the proposed FOD-MemNet model outperforms the current state-of-the-art model by achieving a rank correlation of 0.655 which is near to human consistency $(\rho=0.68)$.
Analysis of an Irrigation Pump Driver Fed by Solar PV Panels (Part II)

this paper is an extension of the work presented as (part I) in Ref [1]. This paper (Part II) deals to increase the utilization of the daylight period during the morning and evening hours by increasing the number of photovoltaic (PV) panels. At the same time it will be able to use the maximum power extracted in high illumination hours at noon. This can be done by increasing the motor speed during this period, and reduce it at low illumination periods. Hence, a continuous irrigation achieved and efficient utilization of PV power approved. Also, this work deals with sizing of PV panels and driving motor ratings due to the depth of the Well and the required irrigation water flow.
Ultra-Wide Field Imaging Characteristics of Primary Retinal Vasculitis: Risk Factors for Retinal Neovascularization

ABSTRACT Aim: To evaluate patterns of retinal vasculitis with ultra-wide field imaging (UWF) and ascertain the risk factors for retinal neovascularization. Methods: Consecutive patients of retinal vasculitis were included prospectively. Patients with retinal vasculitis secondary to uveitis were excluded. UWF was done for all the patients. Retinal involvement was classified into three zones and area of capillary non-perfusion was stratified into clock hours. Results: Two hundred patients were included, 85% (n = 170) were male. Mean age was 28.99 ± 10.56 years. Clinical examination revealed 65% cases (n = 130) to be bilateral, while UWF angiography detected 72.5% (n = 145) to have bilateral involvement. Retinal neovascularization was present in 47% (n = 188). Presence of posterior disease had very high odds ratio for development of retinal neovascularization as compared to cases restricted to retinal periphery (OR = 45.03, CI = 6.10–332.30, p = < 0.001). Conclusion: UWF imaging is useful in detecting retinal vasculitis, which is otherwise obscure to clinical examination and assessing risk factors for retinal neovascularization.
Cavity Resonator Filters in Shielded Quarter-Mode Substrate Integrated Waveguide Technology

The substrate integrated waveguide (SIW) technology has high performance but is relatively bulky compared to other planar technologies. Compact topologies like the quarter-mode technology aim to reduce the size of SIW components, but suffer from additional losses. A modified version of the quarter-mode resonator topology, called shielded quarter-mode, improves the performance of microwave filters with a small increase of the component area. This paper presents three different microwave filters operating at 4 GHz as an example of application for the shielded quarter-mode topology.
Analysis and Impact of Social Media and it's Privacy on Big Data

There are millions of data been generated in various forms around us regularly which is approximately 2.5 quintillion bytes on the daily basis and most of the data are coming from social sites. Social sites are one of the biggest contributor of big data generation. Big data is a technology which deals with extremely large data which is used to analyze different patterns, associations, trends and largely in human behavior and interactions computationally where social media data came in use. As sharing information as per one's daily basis activities and many other forms online is the main objective of social media whereas, users are unable to understand the process and they keep on uploading their personal data without having the idea that all this is contributing big data and is continually collecting at various platforms which might affect users privacy. In this paper, here analyzed big data privacy in social media and some major issues related its privacy and heavy rate of growing data per day.
Lateral visual occlusion does not change walking trajectories

Difficulties with walking are often reported following brain damage that causes a lateralized loss of awareness on one side. Whether lateralized loss of awareness has a direct causal impact on walking is unknown. A review of the literature on visually guided walking suggests several reasons why a lateralized loss of visual awareness might be expected to lead to difficulties walking. Here, we isolated and examined the effect of lateralized vision loss on walking behavior in real and virtual environments. Healthy young participants walked to a target placed within a real room, in a virtual corridor, or on a virtual ground plane. In the ground-plane condition, the scene either was empty or contained three obstacles. We reduced vision on one side by occluding one eye (Experiment 1 and 2) or removing one hemifield, defined relative to either the head or trunk (Experiment 2), through use of eye patching (Experiment 1) and a virtual-reality system (Experiment 2). Visual-field restrictions did not induce significant deviations in walking paths in any of the occlusion conditions or any of the environments. The results provide further insight into the visual information that guides walking in humans, and suggest that lateralized vision loss on its own is not the primary cause of walking difficulties.
The impact of music on the bioelectrical oscillations of the brain

In this article we conclude the main scientific studies into the changes in the bioelectrical brainwave activity that occur while listening to music. A brainwave spectral analysis, derived from findings of electroencephalograms, is a powerful tool to obtain deep and objective insights into the effects of music on the brain. This capacity is being investigated in various contexts. Starting with a healthy population, studies also seek to determine the impact of music in such conditions as disorders of consciousness, psychiatric diseases, and chronic conditions, as well as to further explore the role of music for rehabilitation purposes. Supplemental investigations in this field are needed not only to deepen the knowledge of general neurophysiology of listening to music, but also to possibly open new perspectives for its broader use in clinical practices.
Neuroanatomical Substrates for Risk Behavior

Individuals vary substantially in their tendency to take risks. In the past two decades, a large number of neuroimaging studies in humans have explored the neural mechanisms of several cognitive processes that contribute to risk taking. In this article, I focus on functional and structural MRI studies that investigated uncertainty processing, one of the main features of risk behavior. Using decision-making and learning paradigms, these studies implicated a network of brain areas, including posterior parietal cortex, anterior insula, anterior cingulate cortex, and ventrolateral prefrontal cortex, in various aspects of uncertainty processing. Individual differences in behavior under uncertainty are reflected in the function and structure of some of these areas and are integrated into value representations in ventromedial prefrontal cortex and ventral striatum, reinforcing the potential contribution of all of these brain structures to individual tendencies to take risks.
ADMM-Based Distributed Auction Mechanism for Energy Hub Scheduling in Smart Buildings

Energy hub integrates various energy conversion and storage technologies, which can yield complementarity among multiple energy and provide consumers with stable energy services, such as electricity, heating, and cooling. This enables energy hub to be an ideal energy system design for smart and green buildings. This paper proposes a distributed auction mechanism for multi-energy scheduling of an energy hub that serves numbers of building energy users. In the auction, users first submit their demand data to the hub manager. Then, the hub manager allocates energy to users via optimization of energy scheduling based on the users’ data. The auction mechanism is designed to be incentive compatible, meaning that users are incentivized to truthfully submit their demand data. Next, to mitigate the computational burden of the hub manager, a distributed implementation of the auction is developed, in which an algorithm based on alternating direction method of multipliers (ADMM) is adopted to offload auction computation onto the users. Distributed computation offloading may bring in new chances for users to manipulate the auction outcome since the users participate part of the auction computation. It is proven that the proposed distributed auction mechanism can achieve incentive compatibility in a Nash equilibrium, which indicates that rational users will faithfully report demand data and complete the assigned computation as well. Finally, simulation results based on a household energy consumption dataset are presented to evaluate the energy scheduling performance and to verify the incentive compatibility of the auction mechanism.
Multiple nutritional factors and thyroid disease, with particular reference to autoimmune thyroid disease

Hashimoto's thyroiditis (HT) and Graves’ disease (GD) are examples of autoimmune thyroid disease (AITD), the commonest autoimmune condition. Antibodies to thyroid peroxidase (TPO), the enzyme that catalyses thyroid-hormone production and antibodies to the receptor for the thyroid-stimulating hormone, are characteristic of HT and GD, respectively. It is presently accepted that genetic susceptibility, environmental factors, including nutritional factors and immune disorders contribute to the development of AITD. Aiming to investigate the effect of iodine, iron and selenium in the risk, pathogenesis and treatment of thyroid disease, PubMed and the Cochrane Library were searched for relevant publications to provide a narrative review. Iodine: chronic exposure to excess iodine intake induces autoimmune thyroiditis, partly because highly-iodinated thyroglobulin (Tg) is more immunogenic. The recent introduction of universal salt iodisation can have a similar, although transient, effect. Iron: iron deficiency impairs thyroid metabolism. TPO is a haem enzyme that becomes active only after binding haem. AITD patients are frequently iron-deficient since autoimmune gastritis, which reduces iron absorption and coeliac disease which causes iron loss, are frequent co-morbidities. In two-thirds of women with persistent symptoms of hypothyroidism despite appropriate levothyroxine therapy, restoration of serum ferritin above 100 µg/l ameliorated symptoms. Selenium: selenoproteins are essential to thyroid action. In particular, the glutathione peroxidases remove excessive hydrogen peroxide produced there for the iodination of Tg to form thyroid hormones. There is evidence from observational studies and randomised controlled trials that selenium, probably as selenoproteins, can reduce TPO-antibody concentration, hypothyroidism and postpartum thyroiditis. Appropriate status of iodine, iron and selenium is crucial to thyroid health.