Miracle-dz/UniFAR-FTUs huggingface dataset:
Multichannel-based Learning for Audio Object Extraction

The current paradigm for creating and deploying immersive audio content is based on audio objects, which are composed of an audio track and position metadata. While rendering an object-based production into a multichannel mix is straightforward, the reverse process involves sound source separation and estimating the spatial trajectories of the extracted sources. Besides, cinematic object-based productions are often composed by dozens of simultaneous audio objects, which poses a scalability challenge for audio object extraction. Here, we propose a novel deep learning approach to object extraction that learns from the multichannel renders of object-based productions, instead of directly learning from the audio objects themselves. This approach allows tackling the object scalability challenge and also offers the possibility to formulate the problem in a supervised or an unsupervised fashion. Since, to our knowledge, no other works have previously addressed this topic, we first define the task and propose an evaluation methodology, and then discuss under what circumstances our methods outperform the proposed baselines.
SN4KE: Practical Mutation Testing at Binary Level

Mutation analysis is an effective technique to evaluate a test suite adequacy in terms of revealing unforeseen bugs in software. Traditional source- or IR-level mutation analysis is not applicable to the software only available in binary format. This paper proposes a practical binary mutation analysis via binary rewriting, along with a rich set of mutation operators to represent more realistic bugs. We implemented our approach using two state-of-the-art binary rewriting tools and evaluated its effectiveness and scalability by applying them to SPEC CPU benchmarks. Our analysis revealed that the richer mutation operators contribute to generating more diverse mutants, which, compared to previous works leads to a higher mutation score for the test harness. We also conclude that the reassembleable disassembly rewriting yields better scalability in comparison to lifting to an intermediate representation and performing a full translation.
Predicting Clinical Deterioration in Hospitals

Responding rapidly to a patient who is demonstrating signs of imminent clinical deterioration is a basic tenet of patient care. This gave rise to a patient safety intervention philosophy known as a Rapid Response System (RRS), whereby a patient who meets a pre-determined set of criteria for imminent clinical deterioration is immediately assessed and treated, with the goal of mitigating the deterioration and preventing intensive care unit (ICU) transfer, cardiac arrest, or death. While RRSs have been widely adopted, multiple systematic reviews have failed to find evidence of their effectiveness. Typically, RRS criteria are simple, expert (consensus) defined rules that identify significant physiologic abnormalities or are based on clinical observation.If one can find a pattern in the patient’s data earlier than the onset of the physiologic derangement manifest in the current criteria, intervention strategies might be more effective. In this paper, we apply machine learning to electronic medical records (EMR) to infer if patients are at risk for clinical deterioration. Our models are more sensitive and offer greater advance prediction time compared with existing rule-based methods that are currently utilized in hospitals.Our results warrant further testing in the field; i f successful, hospitals can integrate our approach into their existing IT systems and use the alerts generated by the model to prevent ICU transfer, cardiac arrest, or death, or to reduce the ICU length of stay.
Clinical BCI Challenge-WCCI2020: RIGOLETTO - RIemannian GeOmetry LEarning, applicaTion To cOnnectivity

This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies on Riemannian Geometry, with a twist. Instead of using the classical covariance matrices, we also rely on measures of functional connectivity. Our approach ranked 1st on the task 1 of the competition.
Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery

Image restoration is a typical ill-posed problem, and it contains various tasks. In the medical imaging field, an ill-posed image interrupts diagnosis and even following image processing. Both traditional iterative and up-to-date deep networks have attracted much attention and obtained a significant improvement in reconstructing satisfying images. This study combines their advantages into one unified mathematical model and proposes a general image restoration strategy to deal with such problems. This strategy consists of two modules. First, a novel generative adversarial net(GAN) with WGAN-GP training is built to recover image structures and subtle details. Then, a deep iteration module promotes image quality with a combination of pre-trained deep networks and compressed sensing algorithms by ADMM optimization. (D)eep (I)teration module suppresses image artifacts and further recovers subtle image details, (A)ssisted by (M)ulti-level (O)bey-pixel feature extraction networks (D)iscriminator to recover general structures. Therefore, the proposed strategy is named DIAMOND.
Onoma-to-wave: Environmental sound synthesis from onomatopoeic words

In this paper, we propose a framework for environmental sound synthesis from onomatopoeic words. As one way of expressing an environmental sound, we can use an onomatopoeic word, which is a character sequence for phonetically imitating a sound. An onomatopoeic word is effective for describing diverse sound features. Therefore, using onomatopoeic words for environmental sound synthesis will enable us to generate diverse environmental sounds. To generate diverse sounds, we propose a method based on a sequence-to-sequence framework for synthesizing environmental sounds from onomatopoeic words. We also propose a method of environmental sound synthesis using onomatopoeic words and sound event labels. The use of sound event labels in addition to onomatopoeic words enables us to capture each sound event's feature depending on the input sound event label. Our subjective experiments show that our proposed methods achieve higher diversity and naturalness than conventional methods using sound event labels.
Intrinsic Schreier special objects

: Motivated by the categorical-algebraic analysis of split epimorphisms of monoids, we study the concept of a special object induced by the intrinsic Schreier split epimorphisms in the context of a regular unital category with binary coproducts, comonadic covers and a natural imaginary splitting in the sense of our article [20]. In this context, each object comes naturally equipped with an imaginary magma structure. We analyse the intrinsic Schreier split epimorphisms in this setting, showing that their properties improve when the imaginary magma structures happen to be associative. We compare the intrinsic Schreier special objects with the protomodular objects, and characterise them in terms of the imaginary magma structure. We furthermore relate them to the Engel property in the case of groups and Lie algebras.
Social Capital and Digital Divide: Implications for Mobile Health Policy in Developing Countries

Digital divide has been a major obstacle for mobile health services for the elderly in developing countries; to assess the potential solution to narrow digital divide among the elderly, we use data from the China Health and Retirement Longitudinal Study (CHARLS) and test for a causal role of social capital in digital access among elderly individuals in China. To handle endogenous problems associated with social capital, we introduce instrumental variable (IV) estimates in our models. Our data analysis shows that social capital facilitates increased digital access. We distinguish between two digital access patterns, an infrastructure pattern and a personal device pattern, and find that the causal effect of social capital is determined by the personal device pattern. Therefore, since family members and relatives increase digital access among elderly people, we propose a family-centered mobile health policy in developing countries.
Antibiotic Therapy in Dentistry

Dental caries, pulpal necrosis, trauma, and periodontal diseases can result in dental infections which could have severe consequences that affect both soft and hard tissues of the oral cavity. Dental infections commonly present with symptoms of pain, fever, and swelling. Surgical and endodontic treatments are the early management of infected teeth, followed by antibiotic therapy. Some alternative methods also exist for treating infection such as low-level laser therapy and photodynamic therapy. Antibiotics are generally used in dental procedures to treat odontogenic infections, nonodontogenic infections, local infection, focal infection, and prophylaxis. Antibiotic prophylaxis is prescribed for patients with immunosuppressed conditions, infective endocarditis, metabolic disorders, and patients with prosthetic joints. To reduce the complications of unnecessary antibiotic prescriptions especially bacterial resistance, comprehensive guidelines should be established. It has been noted that only about 12% of dentists adequately and correctly prescribe antibiotics, which shows the importance of comprehensive guidelines. Antibiotics prescription may result in some adverse effects such as hypersensitivity reactions and dermatological and allergic disorders. Furthermore, unnecessary prescription of antibiotics could result in several serious sequelae, for example, bacterial resistance, gastric and hematological problems, and diversion of bacterial microbiota. The present review attempts to summarize the indications of antibiotic therapy in dentistry and discuss the common types of antibiotics that are routinely used in dental practice based on pharmacologic classes. Moreover, types of antibiotics that are considered safe during pregnancy and childhood are also reviewed.
Social influence of COVID-19: An observational study on the social impact of post-COVID-19 lockdown on everyday life in Kerala from a community perspective

BACKGROUND: The current novel coronavirus pandemic started as a simple outbreak in December 2019 from Wuhan, China, and it has now become a global threat. The governments from most of the countries including India have already taken strict precautionary measures to reduce the coronavirus spread such as social distancing, closure of schools, colleges, airports, restaurants, shopping malls, and other places where the people might gather. An increase in the levels of anxiety, aggression, depression, forgetfulness, and hallucinations are possible psychological effects of isolation. Too little is understood of the social impact of the pandemic. AIM: To study the social impact of post-COVID-19 lockdown in Kerala from a community perspective. MATERIALS AND METHODS: This cross-sectional survey was conducted among 700 families (50 families from each district) from all the 14 districts of Kerala from during April-May 2020 using respondent-driven sampling. The data were analyzed and the categorical variables have been presented as percentages and proportions. RESULTS: Anxiety (44%) and fear (44.3%) were reported by many of the study participants. The survey also picked up an increase in the prevalence of domestic violence (13.7%) during the period. The most common social change brought about by the lockdown was an improvement in the hygiene practices among the study population. CONCLUSION: The present study highlights the positive social changes brought about as a result of the COVID-19 lockdown. Further studies need to be conducted on a larger scale to assess the psycho-behavioural impact of COVID-19 on the wider population.
Thirdhand smoke beliefs and behaviors among families of primary school children in Shanghai

INTRODUCTION There are few reports on the beliefs about thirdhand smoke in Chinese families with primary school children. This study aims to understand the beliefs about thirdhand smoke among parents or grandparents of primary school children in Shanghai and to provide an evidence base to incorporate thirdhand smoke preventative action into tobacco control interventions. METHODS We performed a cross-sectional survey among parents and grandparents of children aged 6–13 years in the Changjiang Road Primary School and recruited 843 participants to make assessments on the ‘beliefs about thirdhand smoke’ (BATHS) scale. Sociodemographic details including age, gender, marital status, education level, personal income and type of home ownership (new house, secondhand house with or without redecoration) and health status of children (whether they suffered from respiratory diseases or not) were investigated. Scale assessment, univariate and multivariate analyses to explore the factors influencing the BATHS scale and subscale scores, were performed using SPSS version 22.0. RESULTS Participants who were aged >65 years were more likely to get lower scores on the BATHS scale (OR=0.476; 95% CI: 0.311–0.728, p=0.001). Undergraduates (OR=1.190; 95% CI: 1.020–1.388, p=0.027) and graduates (OR=1.4490; 95% CI: 1.102–1.906, p=0.008) obtained higher scores. Moreover, the scores of residents living in a secondhand house with redecoration (OR=0.882; 95% CI: 0.782–0.995, p=0.041) and without redecoration (OR=0.801; 95% CI: 0.698–0.919, p=0.002) were lower compared with those of new-house owners. The scores for participants whose children suffered from respiratory diseases in the past six months (OR=1.104; 95% CI: 1.003–1.216, p=0.043) were higher than those whose children had no respiratory diseases. CONCLUSIONS This study shows that younger people, females, those with higher incomes, and higher education levels, were more likely to believe the thirdhand smoke impacts on health and its persistence in the environment. Our findings can guide targeted actions for smoke-free home interventions.
A New Electrochemical Multiplexed Assay for PSA Cancer Marker Detection

In this paper, two simple and sensitive approaches for prostate specific antigen (PSA) detection are presented, by using antibody modified paramagnetic microparticles coupled to multiplexed electrochemical platforms. The first proposed approach is based on 8-sensors screen-printed arrays as candidate for electrochemical transducers and a simple target capturing step by means of antibody-functionalised magnetic beads. In order to improve the performances of the immunosensing, a second approach is developed by using GRAVI-Cell. This is microfluidic-based affinity assay device for running bead-protocols. This innovative system combines a special chip containing eight polymer microchannels, with a portable, computer-controlled instrument. Both immunosensing strategies developed promise to be a sensitive, multiplexed tool for fast and easy PSA analysis.
A New Hope in Spinal Degenerative Diseases: Piezo1

As a newly discovered mechanosensitive ion channel protein, the piezo1 protein participates in the transmission of mechanical signals on the cell membrane and plays a vital role in mammalian biomechanics. Piezo1 has attracted widespread attention since it was discovered in 2010. In recent years, studies on piezo1 have gradually increased and deepened. In addition to the discovery that piezo1 is expressed in the respiratory, cardiovascular, gastrointestinal, and urinary systems, it is also stably expressed in cells such as mesenchymal stem cells (MSCs), osteoblasts, osteoclasts, chondrocytes, and nucleus pulposus cells that constitute vertebral bodies and intervertebral discs. They can all receive external mechanical stimulation through the piezo1 protein channel to affect cell proliferation, differentiation, migration, and apoptosis to promote the occurrence and development of lumbar degenerative diseases. Through reviewing the relevant literature of piezo1 in the abovementioned cells, this paper discusses the effect of piezo1 protein expression under mechanical stress stimuli on spinal degenerative disease, providing the molecular basis for the pathological mechanism of spinal degenerative disease and also a new basis, ideas, and methods for the prevention and treatment of this degenerative disease.
Determinants of online health information-seeking behavior: A cross-sectional survey among residents of an urban settlement in Delhi

Background: In health care, the rapid proliferation of health information on the internet has resulted in more patients turning to the digital media as their first source of health information and acquiring knowledge. The present study was conducted to assess use of the digital medium as a medical information resource in health-related states and to determine their experience and perceptions about the quality and reliability of the information available among the participants. Methodology: The study was done in an urban settlement of Delhi among adults who use any digital media. A sample of 321 were selected though convenient sampling. The information was collected through a semi-structured, self-administered, pre-tested questionnaire which contained questions on socio-demographic profile, internet usage and awareness about Digital India. Bivariate analysis was done to determine the association between various socio-demographic variables associated with internet usage for health information. Results: In the present study, 88.2% (283/321) were using the internet for health information through digital media. This study found out that younger age group (18-30 years), literate and higher socioeconomic group (upper middle and above) population were more likely to access health information via digital media which was found out to be statistically significant. Conclusion: Access to health information through digitization can improve health literacy among the population and help in promoting a preventive aspect to health problems and disease. They can be the building blocks to build “Swasth Bharat (Healthy India)”.
Relationship between knowledge on COVID-19 and psychological distress among students living in quarantine: an email survey

Psychological distress is a generic term which refers to “feeling of emotional strain” that affects our normal mental and physical functioning. The aim of this study is to investigate the psychological distress perceived by the Pakistani students living in quarantine and to determine risk and protective factors, including knowledge of COVD-19, among this population. It is a descriptive cross-sectional study conducted from February to May 2020. Students enrolled at different colleges and universities of Pakistan participated in this survey. One-way analysis of variance (ANOVA) is computed for comparing knowledge scores of participants having different levels of psychological distress. A total of 937 participants completed the survey questionnaire, with slightly more male respondents (60.6%) than female (39.4%). The average age of survey participants is 22.0 years (SD = 3.01), with majority (76.2%) belonging to urban areas. The mean COVID-19 knowledge score is 8.91 (SD = 1.69, range: 1–12), suggesting an overall 74.25% precision rate for this knowledge test for individual participant. The participants scored least knowledge regarding the disease transmission, showing a percentage correctness of only 40%. Majority of the participants (57.3%) are likely to be well, while others (42.7%) have shown symptoms of mental distress. The analysis reveals that participants with moderate mental distress (M = 8.81, SD = 2.37) and those with severe mental distress (M = 8.75, SD = 2.69) scored lower than participants who were likely to be well (M = 9.49, SD = 1.71). Our study concludes that a higher knowledge base regarding the disease will help to mitigate distress levels. Our study suggests that in order to deal with this pandemic effectively, the knowledge regarding COVID-19 should be properly conveyed to general public. It is need of the hour to address mental issues of the population aggressively along with providing awareness about COVID-19.
Quality of life in Iranian patients with head-and-neck cancer

BACKGROUND: The goal of treating cancer patients is to cure the patients and improve their quality of life (QoL) during their illness. The aim of this research was to assess the QoL in Iranian patients with head-and-neck cancer by European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Head and Neck 35 (EORTC QLQ-H&N35) and University of Washington Quality of Life Questionnaire (UW-QoL). MATERIALS AND METHODS: In this cross-sectional study, Iranian variation of EORTC QLQ-H&N35 and UW-QoL questionnaires was administered to 210 patients with head-and-neck cancer. Patients who visited the Department of Oncology at Omid Hospital in Isfahan, Shafa Hospital in Kerman, and Emam Khomeini Hospital in Tehran were selected. Kruskal–Wallis test, general linear model multivariate of variance, multiple regression models, and SPSS version 21 were used for statistical analysis. RESULTS: In the present research work, 210 patients with cancer in head and neck were under investigation, such that 128 patients (61%) were male and 82 patients (39%) were female. Only the patients with laryngeal cancer scored worse for dyspnea according to the scores from UW-QoL questionnaires. There were statistically significant differences for pain, swallowing, social eating, social contact, speech, taste/smell, and trismus based on the QLQ-H&N35. Lower QoL was observed in patients with advanced (Stage III + IV) tumors and treated with radiotherapy plus surgical method. CONCLUSION: The study showed that quality of life differs due to location of tumor, stage of cancer, and treatment type.
A Novel Bespoke Hypertrophic Scar Treatment: Actualizing Hybrid Pressure and Silicone Therapies with 3D Printing and Scanning

The treatment of hypertrophic scars (HSs) is considered to be the most challenging task in wound rehabilitation. Conventional silicone sheet therapy has a positive effect on the healing process of HSs. However, the dimensions of the silicone sheet are typically larger than those of the HS itself which may negatively impact the healthy skin that surrounds the HS. Furthermore, the debonding and displacement of the silicone sheet from the skin are critical problems that affect treatment compliance. Herein, we propose a bespoke HS treatment design that integrates pressure sleeve with a silicone sheet and use of silicone gel using a workflow of three-dimensional (3D) printing, 3D scanning and computer-aided design, and manufacturing software. A finite element analysis (FEA) is used to optimize the control of the pressure distribution and investigate the effects of the silicone elastomer. The result shows that the silicone elastomer increases the amount of exerted pressure on the HS and minimizes unnecessary pressure to other parts of the wrist. Based on this treatment design, a silicone elastomer that perfectly conforms to an HS is printed and attached onto a customized pressure sleeve. Most importantly, unlimited scar treating gel can be applied as the means to optimize treatment of HSs while the silicone sheet is firmly affixed and secured by the pressure sleeve.