Miracle-dz/UniFAR-FTUs huggingface dataset:
Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

This paper presents new formulations and algorithms for multichannel extensions of non-negative matrix factorization (NMF). The formulations employ Hermitian positive semidefinite matrices to represent a multichannel version of non-negative elements. Multichannel Euclidean distance and multichannel Itakura-Saito (IS) divergence are defined based on appropriate statistical models utilizing multivariate complex Gaussian distributions. To minimize this distance/divergence, efficient optimization algorithms in the form of multiplicative updates are derived by using properly designed auxiliary functions. Two methods are proposed for clustering NMF bases according to the estimated spatial property. Convolutive blind source separation (BSS) is performed by the multichannel extensions of NMF with the clustering mechanism. Experimental results show that 1) the derived multiplicative update rules exhibited good convergence behavior, and 2) BSS tasks for several music sources with two microphones and three instrumental parts were evaluated successfully.
Converting Neural Network Language Models into back-off language models for efficient decoding in automatic speech recognition

Neural Network Language Models (NNLMs) have achieved very good performance in large-vocabulary continuous speech recognition (LVCSR) systems. Because decoding with NNLMs is very computationally expensive, there is interest in developing methods to approximate NNLMs with simpler language models that are suitable for fast decoding. In this work, we propose an approximate method for converting a feedforward NNLM into a back-off n-gram language model that can be used directly in existing LVCSR decoders. We convert NNLMs of increasing order to pruned back-off language models, using lower-order models to constrain the n-grams allowed in higher-order models. In experiments on Broadcast News data, we find that the resulting back-off models retain the bulk of the gain achieved by NNLMs over conventional n-gram language models, and give significant accuracy improvements as compared to existing methods for converting NNLMs to back-off models. In addition, the proposed approach can be applied to any type of non-back-off language model to enable efficient decoding.
Gamma-delta T lymphocytes and 25-hydroxy vitamin D levels as key factors in autoimmunity and inflammation: the case of zoledronic acid-induced acute phase reaction

Zoledronic acid (ZA) infusion for osteoporosis is frequently associated with the onset of an acute phase reaction (APR) secondary to the activation of γδ T cell receptor (TCR) lymphocytes (γδ T cells) and to low vitamin D levels, similar to what is observed in chronic inflammation and autoimmunity. In this study we investigated whether the phenotype of γδ T cells is associated with APR and 25-OH vitamin D (25-OHvD) levels. For flow-cytometry analysis, peripheral blood samples were obtained from 52 osteoporotic women prior to 5 mg ZA intravenous infusion and from nine women (five with APR) one week later. Twenty-six/52 (50%) patients reported APR and APR+ cases had a higher percentage of central memory Th1-like γδ T cells. One week after ZA infusion, APR was associated with a decreased percentage of central memory Th1-like γδ T cells, an increase in the percentage and activation of effector memory Th1-like γδ T cells, and an increase in Th17-like γδ T cells. Lower 25-OHvD levels were significantly associated with APR, but no correlation was found between 25-OHvD level and γδ T cell percentage or subsets. In conclusion, patients experiencing APR related to ZA infusion have lower 25-OHvD levels and we suggest that the higher percentage of central memory Th1-like γδ T cells and the expansion of effector memory Th1-like and Th17-like γδ T cells are associated with the occurrence of APR.
Convolutional Neural Networks for Speech Recognition

Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structure such as local connectivity, weight sharing, and pooling in CNNs exhibits some degree of invariance to small shifts of speech features along the frequency axis, which is important to deal with speaker and environment variations. Experimental results show that CNNs reduce the error rate by 6%-10% compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks.
A Regression Approach to Speech Enhancement Based on Deep Neural Networks

In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide range of additive noises in real-world situations, a large training set that encompasses many possible combinations of speech and noise types, is first designed. A DNN architecture is then employed as a nonlinear regression function to ensure a powerful modeling capability. Several techniques have also been proposed to improve the DNN-based speech enhancement system, including global variance equalization to alleviate the over-smoothing problem of the regression model, and the dropout and noise-aware training strategies to further improve the generalization capability of DNNs to unseen noise conditions. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the conventional MMSE based technique. It is also interesting to observe that the proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general. Furthermore, the resulting DNN model, trained with artificial synthesized data, is also effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.
Cleft data from surgical camps on rails: A doorstep health care delivery

Aim: The aim of the following study is to investigate the epidemiology of cleft lip and palate (CL/P) patients in a given population. Patients and Methods: Data from cleft camps conducted at Life Line Express Hospital-on-train in Madhya Pradesh, India from year 2003 to 2007 was analyzed. A total of 839 patients of non-syndromic CL/P were included in the study. The data was analyzed for age of the patients, type of cleft, laterality of the defect, their relation to the gender of the patient and presence of cleft in first degree relatives. Result: Nearly 59.8% of the patients were in the age group of 5-18 years. There was an overall male predominance. Unilateral clefts were recorded in 83.3% and bilateral clefts in 14.7% of cases. Only 2% of the patients had isolated cleft palate (CPO) and females had 2.3 times higher risk for CPO than males. Of unilateral cleft lip, left side was more common (67.3%). Nearly 4.9% patients had history of familial inheritance among first degree relatives. Conclusion: In India, vast territories lack basic medical amenities and all modern medical facilities are limited to cities. "Cleft surgery at doorstep" is an extremely effective strategy in treating the "hidden" cleft population and collecting a meaningful epidemiological data from a "virgin" territory. We also stress upon the need to include facial clefts as a notifiable disease in our country to understand the gender, regional and ethnic variations of the disease.
Combination of Language Models for Word Prediction: An Exponential Approach

This paper proposes an exponential interpolation to merge a part-of-speech-based language model and a word-based <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-gram language model to accomplish word prediction tasks. In order to find a set of mathematical equations to properly describe the language modeling, a model based on partial differential equations is proposed. With the appropriate initial conditions, it was found an interpolation model similar to the traditional maximum entropy language model. Improvements in keystroke saved and perplexity over the word-based <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-gram language model and two other traditional interpolation models is obtained, considering three different languages. The proposed interpolation model also provides additional improvement in hit rate parameter.
Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition

Although great progress has been made in automatic speech recognition, significant performance degradation still exists in noisy environments. Recently, very deep convolutional neural networks (CNNs) have been successfully applied to computer vision and speech recognition tasks. Based on our previous work on very deep CNNs, in this paper this architecture is further developed to improve recognition accuracy for noise robust speech recognition. In the proposed very deep CNN architecture, we study the best configuration for the sizes of filters, pooling, and input feature maps: the sizes of filters and poolings are reduced and dimensions of input features are extended to allow for adding more convolutional layers. Then the appropriate pooling, padding, and input feature map selection strategies are investigated and applied to the very deep CNN to make it more robust for speech recognition. In addition, an in-depth analysis of the architecture reveals key characteristics, such as compact model scale, fast convergence speed, and noise robustness. The proposed new model is evaluated on two tasks: Aurora4 task with multiple additive noise types and channel mismatch, and the AMI meeting transcription task with significant reverberation. Experiments on both tasks show that the proposed very deep CNNs can significantly reduce word error rate (WER) for noise robust speech recognition. The best architecture obtains a 10.0% relative reduction over the traditional CNN on AMI, competitive with the long short-term memory recurrent neural networks (LSTM-RNN) acoustic model. On Aurora4, even without feature enhancement, model adaptation, and sequence training, it achieves a WER of 8.81%, a 17.0% relative improvement over the LSTM-RNN. To our knowledge, this is the best published result on Aurora4.
Prognostic implications of active discoid lupus erythematosus and malar rash at the time of diagnosis of systemic lupus erythematosus: Results from a prospective cohort study

Objective Cutaneous lupus erythematosus (CLE) may have prognostic implications for systemic lupus erythematosus (SLE). We aimed to determine the impact of discoid lupus erythematosus (DLE) and malar rash on SLE disease activity. Methods Data were analyzed from the Toronto Lupus Clinic prospective cohort study. We compared SLE patients with active DLE or malar rash at SLE diagnosis to SLE patients who never developed CLE. Outcomes were assessed at one and five years, including Adjusted Mean Systemic Lupus Erythematosus Disease Activity Index 2000 (AMS). Results A total of 524 SLE patients (284 without CLE, 65 with DLE, and 175 with malar rash) were included. Mean AMS scores in patients without CLE at one and five years were 5.96 ± 5.06 and 4.00 ± 3.52, which did not differ significantly from scores at one (6.93 ± 5.31, p = 0.17) and five years (4.29 ± 2.62, p = 0.63) in the DLE group. In patients with malar rash, AMS scores at one (8.30 ± 6.80, p < 0.001) and five years (5.23 ± 3.06, p = 0.004) were higher than controls without CLE. Conclusions Malar rash may be a marker of more severe systemic disease over time, while DLE has no significant impact on general SLE disease activity.
The assessment of mental disorders in primary health care clinics in the Gaza Strip

Ten primary health care clinics in the Gaza Strip were randomly selected from the five regions that form the Gaza Strip (Southern region, Gaza City, Middle region, Khan-Younis and Rafah). The objective of the present study was to investigate the prevalence of mental health problems among patients attending primary health care clinics in the Gaza Strip. Six hundred and sixty-one randomly selected primary health care patients from the five regions were surveyed using the HSCL-25. Seventy-three per cent of patients visiting primary care clinics in the Gaza Strip had psychiatric symptoms consistent with psychiatric disorders. The prevalence of mental health problems among females was higher (76.8%) than males (67%). Previously married and single patients reported more distressing symptoms than married people. The results also revealed that the prevalence of mental health problems was higher among patients living in refugee camps. Civic status was not associated with mental health problems.
Severe vitamin D deficiency increases the risk for moderate to severe disease activity in Chinese patients with SLE

Objective We aimed to investigate the prevalence of vitamin D deficiency in Chinese lupus patients and to assess the association between vitamin D levels and disease severity. Methods Serum levels of 25OHD3 in 121 patients with systemic lupus erythematosus (SLE) and 150 healthy controls were measured by electrochemiluminescence immunoassay. Data regarding demographics and clinical parameters were collected. Disease activity of SLE was evaluated according to the SLE Disease Activity Index (SLEDAI) score and irreversible organ damage by the Systemic Lupus International Collaborating Clinic/American College of Rheumatology, SLICC/ACR Damage Index (SDI). The multivariate logistic regression model was used to investigate the association between the degree of vitamin D deficiency and SLEDAI or SDI scores. Results The prevalence of vitamin D insufficiency (25OHD3 <30 ng/ml) and severe deficiency (25OHD3 <10 ng/ml) in SLE patients was 62.81% and 34.71%, respectively. Logistic regression analysis indicated that the cut-off point of 25OHD3 concentration was 10 ng/ml where its level was correlated with increased SLEDAI (OR 6.420, p = 0.006), but not with the SDI. In addition, hydroxychloroquine treatment lowered the SLEDAI increased by the severe 25OHD3 deficiency (OR 0.280, p = 0.008). Moreover, long disease duration (OR 1.014, p = 0.008) predicted moderate to severe organ damage. Conclusions Vitamin D deficiency is highly prevalent in patients with SLE. Severe deficiency increases the risk for moderate to severe disease activity, but not for organ damage.
Urinary osteoprotegerin: a potential biomarker of lupus nephritis disease activity

Objectives Urinary biomarkers may help in identification, treatment and assessment of response in patients with lupus nephritis (LN). Osteoprotegerin (OPG) is produced by the kidneys and lymphoid cells and may reflect renal disease activity better. The data on its utility are sparse. Methods Fifty-eight patients with active LN (AN), 24 with active non-renal disease (ANR) and 39 with inactive disease (ID) were included. Median disease duration was 32 (1–204) months and median age was 27 (12–50) years. AN patients were followed up every three months for one year. Urine and serum samples were collected for OPG measurement by ELISA (pg/ml) and urinary values were normalised for creatinine excretion (pg/mg). Urine samples from 24 healthy individuals (HCs) and 20 patients each of rheumatoid arthritis (RA) and diabetic nephropathy (DM) served as controls. Variables were expressed as median (range). Results At baseline, normalised urinary OPG (uOPG) was significantly higher (p < 0.001) in AN (1229 (0–8577)) than ANR (236 (0–14713)), ID (463 (7–4253)), HCs (366 (120–2849)) and DM (350 (127–1577)) but it was not different from RA (1511 (122–8849)). uOPG correlated modestly with rSLEDAI (r = 0.4, p < 0.001) and SLEDAI (r = 0.31, p < 0.001) but not with serum OPG (sOPG). uOPG but not sOPG could differentiate between AN and ANR groups. In the longitudinal study, uOPG and sOPG decreased significantly with treatment at all follow-up visits but the trend of fall in sOPG was erratic. uOPG values at baseline, 3, 6, 9 and 12 months were 1229 (0–8577), 466 (3–4874), 104 (0–1598), 325 (0–4025) and 555 (6–6771) pg/mg, respectively. uOPG but not sOPG rose before conventional markers in three patients who had a relapse of LN. In two patients who developed chronic kidney disease, uOPG remained persistently high. For differentiating AN from ANR patients, uOPG performed the best on receiver operator characteristics analysis (AUC = 0.72) when compared with anti-dsDNA antibodies, C3, C4 and sOPG. Conclusion uOPG is derived from kidneys and helps differentiate active SLE patients with and without LN. It shows modest correlation with disease activity and has a potential to predict poor response to therapy and relapse of LN.
From autoantibody research to standardized diagnostic assays in the management of human diseases – report of the 12th Dresden Symposium on Autoantibodies

Testing for autoantibodies (AABs) is becoming more and more relevant, not only for diagnosing autoimmune diseases (AIDs) but also for the differentiation of defined AID subtypes with different clinical manifestations, course and prognosis as well as the very early diagnosis for adequate management in the context of personalized medicine. A major challenge to improve diagnostic accuracy is to harmonize or even standardize AAB analyses. This review presents the results of the 12th Dresden Symposium on Autoantibodies that focused on several aspects of improving autoimmune diagnostics. Topics that are addressed include the International Consensus on ANA Patterns (ICAP) and the International Autoantibody Standardization (IAS) initiatives, the optimization of diagnostic algorithms, the description and evaluation of novel disease-specific AABs as well as the development and introduction of novel assays into routine diagnostics. This review also highlights important developments of recent years, most notably the improvement in diagnosing and predicting the course of rheumatoid arthritis, systemic sclerosis, idiopathic inflammatory myopathies, and of autoimmune neurological, gastrointestinal and liver diseases; the potential diagnostic role of anti-DFS70 antibodies and tumor-associated AABs. Furthermore, some hot topics in autoimmunity regarding disease pathogenesis and management are described.
Association between shortened telomere length and systemic lupus erythematosus: a meta-analysis

Objective We aimed to evaluate the relationship between telomere length and systemic lupus erythematosus (SLE). Methods PUBMED and EMBASE databases were searched; meta-analyses were performed comparing telomere length in SLE patients and healthy controls, and on SLE patients in subgroups based on ethnicity, sample type, assay method and data type. Results Eight studies including 472 SLE patients and 365 controls were ultimately selected which showed that telomere length was significantly shorter in the SLE group than in the control group (standardized mean difference (SMD) = −0.835, 95% confidence interval (CI) = −1.291 to −0.380, p = 3.3 × 10−4). Stratification by ethnicity showed significantly shortened telomere length in the SLE group in Caucasian, Asian and mixed populations (SMD = −0.455, 95% CI = −0.763 to −0.147, p = 0.004; SMD = −0.887, 95% CI = −1.261 to −0.513, p = 3.4 × 10−4; SMD = −0.535, 95% CI = −0.923 to −0.147, p = 0.007; respectively). Furthermore, telomere length was significantly shorter in the SLE group than in the control group in whole blood and peripheral blood mononuclear cell groups (SMD = −0.361, 95% CI = −0.553 to −0.169, p = 2.3 × 10−4; SMD = −1.546, 95% CI = −2.583 to −0.510, p = 0.003; respectively); a similar trend was observed in leukocyte groups (SMD = −0.699, 95% CI = −1.511 to −0.114, p = 0.092). Meta-analyses based on assay method or data type revealed similar associations. Conclusions Our meta-analysis demonstrated that telomere length was significantly shorter in patients with SLE, regardless of ethnicity, sample type or assay method evaluated.
Single tooth implants: Pretreatment considerations and pretreatment evaluation

Today, implants are considered as a first treatment option to replace missing teeth due to the considerable advantages over the other available options. The ultimate goal of implant treatment is to restore natural esthetics, function, long term health, and patient comfort. Hence, case selection and treatment planning are very crucial to achieve longevity and predictability of the restoration. This article presents a step-by-step protocol for gathering and analyzing the various factors at the pretreatment evaluation stage to set the groundwork for a dentist to consider implant as a restorative option. Clinical Relevance to Interdisciplinary Dentistry To help the restorative dentist to identify the ideal implant receptor site. Sound periodontal and restorative status is prerequisite to consider implant treatment as an option. Restorative driven implant placement is paramount for overall integrity of the dentition.
Sinusoidal disturbance rejection with application to helicopter flight data estimation

This paper is concerned with the problem of eliminating sinusoidal disturbances from data while producing minimal distortion to the underlying data. A particular example of this problem arises in the filtering of helicopter data which are corrupted by sinusoidal disturbances due to rotor motion. It is shown that an optimal solution to the problem can be found using Kalman filtering theory. The properties of the optimal filter are analyzed using recent results on filtering for nonstabilizable systems. These results are then used to motivate a particular near-optimal filter which has enhanced robustness properties relative to the optimal filter. It will also be shown that an identical filter can be derived using recent results on the evaluation of recursive discrete Fourier transforms. This link between time and frequency domain methods leads to a rather complete understanding of the characteristics of the filter. Specific results are presented showing the application of the filter to real helicopter data.
Joint ill in lambs

Polyarthritis (joint ill) is a commonly recognised condition in young lambs, usually 1 to 3 weeks old. The commonest cause of this condition is the bacteria Streptococcus dysgalactiae. Pathogenesis of infection is imperfectly understood and outbreaks are seen in flocks practising standard hygiene with little other neonatal disease. S. dysgalactiae is usually sensitive to penicillin, however treatment of clinical cases is often unrewarding due to irreparable joint damage. A greater understanding of the sources and routes of infection is needed to aid prevention and control.
Test pattern generation for the combinational representation of asynchronous circuits

In this paper we propose a new deterministic automatic test pattern generator (ATPG) for the stuck-at faults of the combinational representation (CR) of asynchronous sequential digital circuits (ASDCs). The modified FAN algorithm is applied to this CR to generate the test patterns. The FAN was extended to handle the complex gates and to be able to work with the CR of ASDCs. The ATPG was tested over a set of benchmark circuits. The test patterns from the presented ATPG will be used in the future to generate the sequence of test patterns for the ASDCs.