Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional capacity and associated factors in the elderly : a population study *

Objective: To analyze the association of impaired functional capacity with health conditions and sociodemographic and behavioral factors among elderly residents in a community of northeastern Brazil. Methods: A cross-sectional population-based study, conducted with 316 elderly residents in Lafaiete Coutinho-BA. Functional capacity was evaluated by hierarchical scaling, by verifying association with health conditions and sociodemographic and behavioral factors by the technique of multinomial logistic regression. Results: The dependence for independent activities of daily living (IADL) was associated with the age group ≥ 80 years, use of two or more medications and cognitive impairment. Dependency in basic activities of daily living and IADL was associated with those in the age group ≥ 80 years, with a lack of participation in religious activities, hospitalization in the past 12 months, cognitive impairment and who were overweight. Conclusion: There was an association with impaired functional capacity, health conditions and sociodemographic factors among elderly residents in a community in northeastern Brazil.
Revising quantum optical phenomena in adatoms coupled to graphene nanoantennas

Abstract Graphene flakes acting as photonic nanoantennas may sustain strong electromagnetic field localization and enhancement. To exploit the field enhancement, quantum emitters such as atoms or molecules should be positioned in such close proximity to the flake that electron tunneling might influence the optical and electronic properties of the system. However, tunneling is usually not considered if the optical coupling mechanism between quantum emitters and nanoantennas is at focus. This work presents a framework for describing the electron dynamics in hybrid systems consisting of graphene nanoflakes coupled both electronically and optically to adatoms and subject to external illumination. Our framework combines the single-particle tight-binding approach with a nonlinear master equation formalism that captures both optical and electronic interactions. We apply the framework to demonstrate the impact of electron tunneling between the adatom and the flake on emblematic quantum optical phenomena: degradation of coherent Rabi oscillations and quenching of Purcell spontaneous emission enhancement in two-level adatoms in proximity of triangular graphene nanoflakes.
Use of Weight Estimation in Vertical Radial Plume Mapping Method

The U.S. Environmental Protection Agency published a new technology of Other Test Method (OTM) 10 to quantify fugitive gaseous pollution sources. A multi-beam configuration, open path, and Path-Integrated Optical Remote Sensing (PI-ORS) system were used by it. The Vertical Radial Plume Mapping (VRPM) method described in the OTM-10 utilizes multiple non-overlapping beam paths and was designed to measure the mass flux through the vertical plane downwind of the emission source. VRPM applied the Smoothed Basis Function Minimization (SBFM) method to the measured PIC data and wind data. SBFM assumed that the concentration distribution on the downwind plane is a two-dimensional Gaussian distribution, and fitted the measured five path-integrated concentration (PIC) data to the distribution function by minimizing the Sum of Squared Errors (SSE) function. The product of the mass concentration obtained by the fitting and the wind speed perpendicular to the measurement plane was integrated over the entire plane to calculate the mass flux through the plane. This study intended to use the weight estimation method to apply the historical fitting information to the SBFM method. The specific implementation step was to apply the appropriate function of the difference between the historical predicted PIC data and the measured PIC data to the SSE function to improve the accuracy of flux calculation. Experiments showed that When a decreasing exponential functional form was chosen, the root mean square error of the flux fit was greatly improved.
Towards Learning Generalizable Code Embeddings Using Task-agnostic Graph Convolutional Networks

Code embeddings have seen increasing applications in software engineering (SE) research and practice recently. Despite the advances in embedding techniques applied in SE research, one of the main challenges is their generalizability. A recent study finds that code embeddings may not be readily leveraged for the downstream tasks that the embeddings are not particularly trained for. Therefore, in this article, we propose GraphCodeVec, which represents the source code as graphs and leverages the Graph Convolutional Networks to learn more generalizable code embeddings in a task-agnostic manner. The edges in the graph representation are automatically constructed from the paths in the abstract syntax trees, and the nodes from the tokens in the source code. To evaluate the effectiveness of GraphCodeVec , we consider three downstream benchmark tasks (i.e., code comment generation, code authorship identification, and code clones detection) that are used in a prior benchmarking of code embeddings and add three new downstream tasks (i.e., source code classification, logging statements prediction, and software defect prediction), resulting in a total of six downstream tasks that are considered in our evaluation. For each downstream task, we apply the embeddings learned by GraphCodeVec and the embeddings learned from four baseline approaches and compare their respective performance. We find that GraphCodeVec outperforms all the baselines in five out of the six downstream tasks, and its performance is relatively stable across different tasks and datasets. In addition, we perform ablation experiments to understand the impacts of the training context (i.e., the graph context extracted from the abstract syntax trees) and the training model (i.e., the Graph Convolutional Networks) on the effectiveness of the generated embeddings. The results show that both the graph context and the Graph Convolutional Networks can benefit GraphCodeVec in producing high-quality embeddings for the downstream tasks, while the improvement by Graph Convolutional Networks is more robust across different downstream tasks and datasets. Our findings suggest that future research and practice may consider using graph-based deep learning methods to capture the structural information of the source code for SE tasks.
Situational judgement test performance and subsequent misconduct in medical students

Situational judgement tests (SJTs) have been widely adopted, internationally, into medical selection. It was hoped that such assessments could identify candidates likely to exhibit future professional behaviours. Understanding how performance on such tests may predict the risk of disciplinary action during medical school would provide evidence for the validity of such SJTs within student selection. It would also inform the implementation of such tests within student recruitment.
FRCRN: Boosting Feature Representation Using Frequency Recurrence for Monaural Speech Enhancement

Convolutional recurrent networks (CRN) integrating a convolutional encoder-decoder (CED) structure and a recurrent structure have achieved promising performance for monaural speech enhancement. However, feature representation across frequency context is highly constrained due to limited receptive fields in the convolutions of CED. In this paper, we propose a convolutional recurrent encoder-decoder (CRED) structure to boost feature representation along the frequency axis. The CRED applies frequency recurrence on 3D convolutional feature maps along the frequency axis following each convolution, therefore, it is capable of catching long-range frequency correlations and enhancing feature representations of speech inputs. The proposed frequency recurrence is realized efficiently using a feedforward sequential memory network (FSMN). Besides the CRED, we insert two stacked FSMN layers between the encoder and the decoder to model further temporal dynamics. We name the proposed framework as Frequency Recurrent CRN (FRCRN). We design FRCRN to predict complex Ideal Ratio Mask (cIRM) in complex-valued domain and optimize FRCRN using both time-frequency-domain and time-domain losses. Our proposed approach achieved state-of-the-art performance on wideband bench-mark datasets and achieved 2nd place for the real-time fullband track in terms of Mean Opinion Score (MOS) and Word Accuracy (WAcc) in the ICASSP 2022 Deep Noise Suppression (DNS) challenge.
Training Strategies for Improved Lip-Reading

Several training strategies and temporal models have been recently proposed for isolated word lip-reading in a series of independent works. However, the potential of combining the best strategies and investigating the impact of each of them has not been explored. In this paper, we systematically investigate the performance of state-of-the-art data augmentation approaches, temporal models and other training strategies, like self-distillation and using word boundaries indicators. Our results show that time masking is the most important augmentation followed by mixup and Densely-Connected Temporal Convolutional Networks (DC-TCN) are the best temporal model for lip-reading of isolated words. Using self-distillation and word boundaries is also beneficial but to a lesser extent. A combination of all the above methods results in a classification accuracy of 93.4%, which is an absolute improvement of 4.6% over the current state-of-the-art performance on the LRW dataset. The performance can be further improved to 94.1% by pre-training on additional datasets. An error analysis of the various training strategies reveals that the performance improves by increasing the classification accuracy of hard-to-recognise words.
Deep Neural Network (DNN) Audio Coder Using A Perceptually Improved Training Method

A new end-to-end audio coder based on a deep neural network (DNN) is proposed. To compensate for the perceptual distortion that occurred by quantization, the proposed coder is optimized to minimize distortions in both signal and perceptual domains. The distortion in the perceptual domain is measured using the psychoacoustic model (PAM), and a loss function is obtained through the two-stage compensation approach. Also, the scalar uniform quantization was approximated using a uniform stochastic noise, together with a compression-decompression scheme, which provides simpler but more stable learning without an additional penalty than the softmax quantizer. Test results showed that the proposed coder achieves more accurate noise-masking than the previous PAM-based method and better perceptual quality then the MP3 audio coder.
Online Learning with Probabilistic Feedback

Online learning with expert advice is widely used in various machine learning tasks. It considers the problem where a learner chooses one from a set of experts to take advice and make a decision. In many learning problems, experts may be related, henceforth the learner can observe the losses associated with a subset of experts that are related to the chosen one. In this context, the relationship among experts can be captured by a feedback graph, which can be used to assist the learner’s decision-making. However, in practice, the nominal feedback graph often entails uncertainties, which renders it impossible to reveal the actual relationship among experts. To cope with this challenge, the present work develops a novel online learning algorithm to deal with uncertainties while making use of the uncertain feedback graph. The proposed algorithm is proved to enjoy sublinear regret under mild conditions. Experiments on real datasets are presented to demonstrate the effectiveness of the novel algorithm.
Selective Multi-Task Learning For Speech Emotion Recognition Using Corpora Of Different Styles

While speech emotion recognition (SER) has been actively studied, the amount and variations of training data are limited compared with speech recognition and speaker recognition tasks. Therefore, it is promising to combine multiple corpora to train a generalized SER model. However, the manner of emotion expression is different according to the settings, task domains, and languages. In particular, there is a mismatch between acted datasets and spontaneous datasets since the former includes much more rich and explicit emotion expressions than the latter. In this paper, we investigate effective combination methods based on multi-task learning (MTL) considering the style attribute. We also hypothesize the neutral expression, which has the largest number of samples, is not affected by the style, and thus propose a selective MTL method that applies MTL to emotion categories except for the neutral category. Experimental evaluations using the IEMOCAP database and a call center dataset confirm the effect of the combination of the two corpora, MTL, and the proposed selective MTL.
Understanding the Inequality of Web Traffic and Engagement in Online Healthcare Communities

The online healthcare community (OHC) is a kind of doctor-patient communication platform, in which doctors can share medical knowledge and provide various kinds of counsel for patients. However, if the OHC's web traffic is concentrated on a small number of doctors, or if only a few doctors are actively involved in the OHC's activities, this will not be conducive to the optimal development of the OHC. This study explores this issue of inequality and makes three main innovations. First, based on data on web traffic and engagement extracted from 139,037 doctors' web pages in one popular OHC, we point out how serious the inequality phenomenon is. Second, we confirm that the Matthew effect indeed exists in this context and leads to greater inequality. Third, we demonstrate that the inequality of psychological or material rewards causes the inequality of web traffic or engagement to become worse; hence, an appropriate reward mechanism should be designed to mitigate the Matthew effect rather than enhance it. Finally, we discuss the managerial implications of these results, as well as avenues for future studies.
Comparative Analysis of the Mitochondrial Genome of Galatheanthemum sp. MT-2020 (Actiniaria Galatheanthemidae) From a Depth of 9,462 m at the Mariana Trench

The hadal zone, which represents the deepest marine habitat on Earth (6,000–11,000 m), is a harsh environment mainly characterized by extremely high hydrostatic pressure, and this habitat is believed to have a high degree of endemism. The deep-sea anemone family Galatheanthemidae comprises two valid species exclusively from the hadal; however, no other information about this family is currently available. In the present study, a sea anemone was collected from a depth of 9,462 m at the Mariana Trench and was defined as Galatheanthemum sp. MT-2020 (Actiniaria Galatheanthemidae). The mitochondrial genome of Galatheanthemum sp. MT-2020 was circular, was 16,633 bp in length, and contained two ribosomal RNA genes, 13 protein-coding genes and two transfer RNA genes. The order of the genes of Galatheanthemum sp. MT-2020 was identical to that of the majority of the species of the order Actiniaria. The value of the AT-skew was the lowest in the whole mitochondrial genome, with a positive GC skew value for the atp8 gene, while other species, except Antholoba achates, had the negative values of the GC skew. Galatheanthemum sp. MT-2020 was clustered with another abyssal species, Paraphelliactis xishaensis, in the phylogenetic tree, and these species diverged in the early Jurassic approximately 200 Mya from the shallow-sea species. The usage ratio of valine, which is one of the five amino acids with the strongest barophilic properties, in the mitochondrial genomes of the two abyssal species was significantly higher than that in other species with habitats above the depth of 3,000 m. The ω (dN/dS) ratio of the genomes was 2.45-fold higher than that of the shallow-sea species, indicating a slower evolutionary rate. Overall, the present study is the first to provide a complete mitogenome of sea anemones from the hadal and reveal some characteristics that may be associated with adaptation to an extreme environment.
Unfolded Protein Response–Related Signature Associates With the Immune Microenvironment and Prognostic Prediction in Osteosarcoma

Background: Osteosarcoma is a highly malignant bone tumor commonly occurring in adolescents with a poor 5-year survival rate. The unfolded protein response (UPR) can alleviate the accumulation of misfolded proteins to maintain homeostasis under endoplasmic reticulum stress. The UPR is linked to the occurrence, progression, and drug resistance of tumors. However, the function of UPR-related genes (UPRRGs) in disease progression and prognosis of osteosarcoma remains unclear. Methods: The mRNA expression profiling and corresponding clinical features of osteosarcoma were acquired from TARGET and GEO databases. Consensus clustering was conducted to confirm different UPRRG subtypes. Subsequently, we evaluated the prognosis and immune status of the different subtypes. Functional analysis of GO, GSEA, and GSVA was used to reveal the molecular mechanism between the subtypes. Finally, four genes (STC2, PREB, TSPYL2, and ATP6V0D1) were screened to construct and validate a risk signature to predict the prognosis of patients with osteosarcoma. Result: We identified two subtypes according to the UPRRG expression patterns. The subgroup with higher immune scores, lower tumor purity, and active immune status was linked to a better prognosis. Meanwhile, functional enrichment revealed that immune-related signaling pathways varied markedly in the two subtypes, suggesting that the UPR might influence the prognosis of osteosarcoma via influencing the immune microenvironment. Moreover, prognostic signature and nomogram models were developed based on UPRRGs, and the results showed that our model has an excellent performance in predicting the prognosis of osteosarcoma. qPCR analysis was also conducted to verify the expression levels of the four genes. Conclusion: We revealed the crucial contribution of UPRRGs in the immune microenvironment and prognostic prediction of osteosarcoma patients and provided new insights for targeted therapy and prognostic assessment of the disease.
Realizing High Photovoltaic Power Densities With Tracking-Integrated Concentrator Photovoltaics

Concentrating photovoltaics (CPV) have long been held up as a solution to low power density in photovoltaics, but due to the requirement of sun tracking have been largely unable to realize high power densities in practically useful settings. The emerging concept of tracking-integrated CPV, in which the sun tracking apparatus is incorporated into the module itself, has the potential to finally achieve this goal by allowing CPV use in building integrated or rooftop settings. In this article, we will provide a status update on TI-CPV and an evaluation of its technical and economic potential with focus on diffuse light collection. We will seek to demonstrate how TI-CPV concepts that are now nearing commercialization are viable to offer, for the first time, the chance for CPV to actually deliver high power densities and high-efficiency utilization of the solar resource in practical settings such that it represents one of the best prospects for CPV to finally gain a foothold in large commercial markets. We identify TI-CPV designs with integrated mechanical tracking and diffuse light transmittance as the closest at resent to commercial feasibility, as the transmitted light offers a potential valuable secondary output. A semi-empirical performance model of such a system yields an annual electrical output of >300 kWh/m2 and 59.4 million lux-hours optical output that are equivalent to 593.4 kWh/m2 if that light would otherwise be provided by LED lamps with 100 lumen/W luminous efficacy. This would indicate that full-system capex of up to $1,600/kW could be viable relative to conventional rooftop PV systems, providing a benchmark for future manufacturing and design improvements.
Surgical Site Infection Following Single-Port Appendectomy: A Systematic Review of the Literature and Meta-Analysis

Introduction Surgical site infections (SSIs) are one of the most common postoperative complications after appendectomy leading to recurrent surgery, prolonged hospital stay, and the use of antibiotics. Numerous studies and meta-analyses have been published on the effect of open versus conventional laparoscopic appendectomy (CLA) reporting faster postoperative recovery and less postoperative pain for CLA. A development from CLA has been the single-port appendectomy (SPA), associated with a better cosmesis but seemingly having a higher risk of wound infections. The aim of this systematic literature review and meta-analysis is to investigate whether reduced port or SPA alters the ratio of SSIs. Methods Pubmed, Embase, and Cochrane databases were screened for suitable articles. All articles published between January 1, 2002, and March 23, 2022, were included. Articles regarding children below the age of 18 were excluded as well as manuscripts that investigated solemnly open appendectomies. Articles were screened for inclusion criteria by two independent authors. Incidence of SSI was the primary outcome. Duration of operation and length of hospital stay were defined as secondary outcomes. Results A total of 25 studies were found through a database search describing 5484 patients. A total of 2749 patients received SPA and 2735 received CLA. There was no statistical difference in the rate of SSI (P = 0.98). A total of 22 studies including 4699 patients reported the duration of operation (2223 SPA and 2476 CLA). There was a significantly shorter operation time seen in CLA. The length of hospital stay was reported in 23 studies (4735 patients: 2235 SPA and 2500 CLA). A shorter hospital stay was seen in the SPA group (P < 0.00001). Separately performed analysis of randomized controlled trials could not confirm this effect (P = 0.29). Discussion SPA is an equally safe procedure considering SSI compared to CLA and does not lead to an increased risk of SSI. A longer operation time for SPA and a minor difference in the length of stay does lead to the use of SPA in selected patients only.