Miracle-dz/UniFAR-FTUs huggingface dataset:
Profit or growth: what to focus on? The Fleury case

Abstract This teaching case reflects the strategic choices of the Fleury Group in the face of the Brazilian economic scenario and the particularities of the private health services sector in Brazil. The teaching case invites the reader to reflect on challenges related to a strategic orientation for profitability, growth, or both. The Fleury Group serves as a rich source of information for academic analysis, especially about business strategy and organizational ambidexterity. Thus, this teaching case can be used in executive programs and graduate programs (specialization or leading to a Ph.D.), in themes related to strategy and innovation.
Comparing 1-dimensional and 2-dimensional spectral feature representations in voice pathology detection using machine learning and deep learning classifiers

The present study investigates the use of 1-dimensional (1-D) and 2-dimensional (2-D) spectral feature representations in voice pathology detection with several classical machine learning (ML) and recent deep learning (DL) classifiers. Four popularly used spectral feature representations (static mel-frequency cepstral coefficients (MFCCs), dynamic MFCCs, spectrogram and mel-spectrogram) are derived in both the 1-D and 2-D form from voice signals. Three widely used ML classifiers (support vector machine (SVM), random forest (RF) and Adaboost) and three DL classifiers (deep neural network (DNN), long short-term memory (LSTM) network, and convolutional neural network (CNN)) are used with the 1-D feature representations. In addition, CNN classifiers are built using the 2-D feature representations. The popularly used HUPA database is considered in the pathology detection experiments. Experimental results revealed that using the CNN classifier with the 2-D feature representations yielded better accuracy compared to using the ML and DL classifiers with the 1-D feature representations. The best performance was achieved using the 2-D CNN classifier based on dynamic MFCCs that showed a detection accuracy of 81%.
Weakly-Supervised Neural Full-Rank Spatial Covariance Analysis for a Front-End System of Distant Speech Recognition

This paper presents a weakly-supervised multichannel neural speech separation method for distant speech recognition (DSR) of real conversational speech mixtures. A blind source separation (BSS) method called neural full-rank spatial covariance analysis (FCA) can precisely separate multichannel speech mixtures by using a deep spectral model without any supervision. The neural FCA, however, requires that the number of sound sources is ﬁxed and known in advance. This requirement complicates its utilization for a front-end system of DSR for multi-speaker conversations, in which the number of speakers changes dynamically. In this paper, we propose an extension of neural FCA to handle a dynamically changing number of sound sources by taking temporal voice activities of target speakers as auxiliary information. We train a source separation network in a weakly-supervised manner using a dataset of multichannel audio mixtures and their voice activities. Experimental results with the CHiME-6 dataset, whose task is to recognize conversations at dinner parties, show that our method outperformed a conventional BSS-based system in word error rates.
Stochastic coherent adaptive large eddy simulation of forced isotropic turbulence

The stochastic coherent adaptive large eddy simulation (SCALES) methodology is a novel approach to the numerical simulation of turbulence, where a dynamic grid adaptation strategy based on wavelet threshold filtering is utilized to solve for the most ‘energetic’ eddies. The effect of the less energetic unresolved motions is simulated by a model. Previous studies have demonstrated excellent predictive properties of the SCALES approach for decaying homogeneous turbulence. In this paper the applicability of the method is further explored for statistically steady turbulent flows by considering linearly forced homogeneous turbulence at moderate Reynolds number. A local dynamic subgrid-scale eddy viscosity model based on the definition of the kinetic energy associated with the unresolved motions is used as closure model. The governing equations for the wavelet filtered velocity field, along with the additional evolution equation for the subgrid-scale kinetic energy, are numerically solved by means of a dynamically adaptive wavelet collocation method. It is demonstrated that adaptive simulations closely match results from a reference pseudo-spectral fully de-aliased direct numerical simulation, by using only about 1% of the corresponding computational nodes. In contrast to classical non-adaptive large eddy simulation, the agreement with direct solution holds for the mean flow statistics as well as in terms of energy and enstrophy spectra up to the dissipative wavenumbers range.
MRI Distortion Correction and Robot-to-MRI Scanner Registration for an MRI-Guided Robotic System

Magnetic resonance imaging (MRI) guided robotic procedures require safe robotic instrument navigation and precise target localization. This depends on reliable tracking of the instrument from MR images, which requires accurate registration of the robot to the scanner. A novel differential image based robot-to-MRI scanner registration approach is proposed that utilizes a set of active fiducial coils, where background subtraction method is employed for coil detection. In order to use the presented preoperative registration approach jointly with the real-time high speed MRI image acquisition and reconstruction methods in real-time interventional procedures, the effects of the geometric MRI distortion in robot to scanner registration is analyzed using a custom distortion mapping algorithm. The proposed approach is validated by a set of target coils placed within the workspace, employing multi-planar capabilities of the scanner. Registration and validation errors are respectively 2.05 mm and 2.63 mm after the distortion correction showing an improvement of respectively 1.08 mm and 0.14 mm compared to the results without distortion correction.
Difficult Decisions for Older Canadians Receiving Home Care, and Why They Are So Difficult: A Web-Based Decisional Needs Assessment

Background. Older adults receiving home care services often face decisions related to aging, illness, and loss of autonomy. To inform tailored shared decision making interventions, we assessed their decisional needs by asking about the most common difficult decisions, measured associated decisional conflict, and identified factors associated with it. Methods. In March 2020, we conducted a cross-sectional survey with a pan-Canadian Web-based panel of older adults (≥65 y) receiving home care services. For a difficult decision they had faced in the past year, we evaluated clinically significant decisional conflict (CSDC) using the 16-item Decisional Conflict Scale (score 0–100) with a >37.5 cutoff. To identify factors associated with CSDC, we performed descriptive, bivariable, and multivariable analyses using the stepwise selection method with an assumed entry and exit significance level of 0.15 and 0.20, respectively. Final model selection was based on the Bayesian information criterion. Results. Among 460 participants with an average age of 72.5 y, difficult decisions were, in order of frequency, about housing and safety (57.2%), managing health conditions (21.8%), and end-of-life care (8.3%). CSDC was experienced by 14.6% (95% confidence interval [CI]: 11.5%, 18.1%) of respondents on all decision points. Factors associated with CSDC included household size = 1 (OR [95% CI]: 1.81 [0.99, 3.33]; P = 0.27), household size = 3 (2.66 [0.78, 8.98]; P = 0.83), and household size = 4 (6.91 [2.23, 21.39]; P = 0.014); preferred option not matching the decision made (4.05 [2.05, 7.97]; P < 0.001); passive role in decision making (5.13 [1.78, 14.77]; P = 0.002); and lower quality of life (0.70 [0.57, 0.87]; P<0.001). Discussion. Some older adults receiving home care services in Canada experience CSDC when facing difficult decisions. Shared decision-making interventions could mitigate associated factors. Highlights This is the first study in Canada to assess the decisional needs of older adults receiving care at home and to identify their most common difficult decisions. Difficult decisions most frequently made were about housing and safety. The most significant decisional conflict was experienced by people making decisions about palliative care. When their quality-of-life score was low, older adults experienced clinically significant decision conflict. Graphical Abstract This is a visual representation of the abstract.
Simulation-based value stream map for manual steel fabrication workstations

Industrial steel fabrication encounters numerous difficulties in having efficient steel production. These difficulties are usually caused by the presence of bottlenecks that are not easily identified using traditional methods. Value Stream Map (VSM) is a lean tool that can be used to identify process inefficiencies and plan to minimize non-value-added activities. However, traditional VSM is not designed to be applied in a non-repetitive work environment especially when workers are highly involved in the process such as in manual steel fabrication processes. Therefore, the typical VSM requires modification to achieve the desired outcomes. Hence, the goal of the current study is to produce a current state VSM and integrate it with a simulation model to plan for an optimized future state. The scope of this study is limited to the fitting and welding workstations since they represent the typical manual processes in the steel fabrication industry. The outcomes of this study will provide the ability to identify waste and improvement opportunities. Also, it will allow for precise quantification of the improvement gain and time savings. Decision-makers in the steel industry will have accurate information about the processes in fabrication plants. Also, they will be able to make evidence-based decisions that will ensure reduced waste and cost for steel operations.
Leveraging Simultaneous Translation for Enhancing Transcription of Low-resource Language via Cross Attention Mechanism

This work addresses automatic speech recognition (ASR) of a low-resource language using a translation corpus, which includes the simultaneous translation of the low-resource language. In multi-lingual events such as international meetings and court proceedings, simultaneous interpretation by a human is often available for speeches of low-resource languages. In this setting, we can assume that the content of its back-translation is the same as the transcription of the original speech. Thus, the former is expected to enhance the later process. We formulate this framework as a joint process of ASR and machine translation (MT) and implement it with a combination of cross attention mechanisms of the ASR encoder and the MT encoder. We evaluate the proposed method using the spoken language translation corpus of the Extraordinary Cham-bers in the Courts of Cambodia (ECCC), achieving a significant improvement in the ASR word error rate (WER) of Khmer by 8 . 9 % relative. The effectiveness is also confirmed in the Fisher-CallHome-Spanish corpus with the reduction of WER in Spanish by 1 . 7 % relative. 1
Fusion sampling networks for skeleton-based human action recognition

Abstract. After a few seconds of an action, the human eye only needs a few photos to judge, but the action recognition network needs hundreds of frames of input pictures for each action. This results in a large number of floating point operations (ranging from 16 to 100 G FLOPs) to process a single sample, which hampers the implementation of graph convolutional networks (GCN)-based action recognition methods when the computation capabilities are restricted. A common strategy is to retain only the portions of the frames, but this results in the loss of important information in the discarded frames. Furthermore, the selection progress of key frames is too independent and lacks connections with other frames. To solve these two problems, we propose a fusion sampling network to generate fused frames to extract key frames. Temporal aggregation is used to fuse adjacent similar frames, thereby reducing information loss and redundancy. The concept of self-attention is introduced to strengthen the long-term association of key frames. The experimental results on three benchmark datasets show that the proposed method achieves performance levels that are competitive with state-of-the-art methods while using only 16.7% of the number of frames (∼50 and 300 frames in total). On the NTU 60 dataset, the number of FLOPs and Params with a single-channel input are 3.776 G and 3.53 M, respectively. This would greatly reduce the excessive computational power cost in practical applications due to the large amount of data processed by action recognition.
Coexistence of Native and Invasive Freshwater Turtles: The Llobregat Delta (NE Iberian Peninsula) as a Case Study

The global degradation of wetlands is increasing their susceptibility to invasions, which is greatly determined by a niche overlap between native and invasive species. We analyze its role in regulating the coexistence of the native Mediterranean stripe-necked terrapin Mauremys leprosa and the invasive Red-eared Slider Trachemys scripta elegans in a coastal wetland. We analyzed both water chemistry and landscape attributes, using variance-partitioning analysis to isolate the variance explained by each set of variables. Then, the influence of environmental variables on species co-occurrence patterns was assessed by using latent variable models (LVM), which account for correlation between species that may be attributable to biotic interactions or missing environmental covariates. The species showed a very low niche overlap, with clear differences in their response to environmental and landscape filters. The distribution of T. s. elegans was largely explained by landscape variables, preferring uniform landscapes within the daily movement buffer, whereas at larger scales, it was associated with a high diversity of habitats of small and uniform relative sizes. A high percentage of the distribution of M. leprosa was unexplained by the measured variables and may be related to the competitive exclusion processes with T. s. elegans. The species was positively related with large patches with high perimeter values or ecotone area at medium spatial scales, and it was benefited from a marked heterogeneity in the patches’ size at larger scale. According to latent variable models, both species had wide eutrophication and salinity tolerance ranges, but they showed different environmental preferences. T. s. elegans was related to eutrophic freshwater environments, whereas M. leprosa was related to more saline and less eutrophic waters. Our results suggest that M. leprosa modifies its habitat use in order to avoid interaction with the T. s. elegans. Thus, management actions aimed at removing the invasive species from the territory and promoting habitat heterogeneity might be needed to protect M. leprosa and avoid local extinctions.
“A Lot of my Day is Spent Looking at Injustice”: Reflections on Decolonizing Teaching and Relationship Building across Difference

Abstract Working toward decolonizing pedagogy involves discomfort, as one pushes back against the status quo, challenging colonial embeddedness in education and meaningfully integrating Indigenous literature and scholarship. In this study, we conducted a thematic analysis of our respective reflections written throughout our involvement in a post-secondary decolonizing pedagogy and syllabi project. This analysis demonstrates our unique positionalities and the importance of working together across difference. We situate our analysis in a discussion of decolonization and consider what it means to be an ally and an accomplice. Then, we present and reflect upon the two emergent themes (1) relationality and (2) emotions, and the subthemes of: (a) frustration, fear, and discomfort; (b) disappointment; (c) time and energy required to do this work; and (d) hope. We conclude by sharing “takeaways” for others to reflect upon as they pursue their own decolonizing curricula efforts.
The effect of the royal wedding on the UK stock market

Abstract We assess the effect of the recent royal wedding of Prince Harry and Meghan Markle on various sectors of the UK stock market over the period between November 2017 and May 2018. For this purpose, the event study methodology is used to estimate abnormal returns and conduct several robustness tests such as the Corrado ranking test, the Chesney non-parametric conditional distribution approach, the Fama-French five-factor model, the market model, allowing for market integration, and the removal of firm-specific information. In addition, we use various ARCH-type models to capture changes in systematic risk. The results show that the effect of the royal wedding is limited to few sectors. We also find that a positive national and investor mood does not always boost sectoral stock returns. In addition, we observe that announcements related to the royal wedding led to changes in both short-term and long-term systematic risk.
Vascular Brain Lesions, Cognitive Reserve, and Their Association with Cognitive Profile in Persons with Early-Stage Cognitive Decline

Background: Cognitive reserve may protect against the effects of brain pathology, but few studies have looked at whether cognitive reserve modifies the adverse effects of vascular brain pathology. Objective: We determined if cognitive reserve attenuates the associations of vascular brain lesions with worse cognition in persons with subjective concerns or mild impairment. Methods: We analyzed 200 participants aged 50–90 years from the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) study. Cognition was measured using the Montreal Cognitive Assessment and a neuropsychological test battery. High vascular lesion burden was defined as two or more supratentorial infarcts or beginning confluent or confluent white matter hyperintensity. Cognitive reserve proxies included education, occupational attainment, marital status, social activities, physical activity, household income, and multilingualism. Results: Mean age was 72.8 years and 48% were female; 73.5% had mild cognitive impairment and 26.5% had subjective concerns. Professional/managerial occupations, annual household income≥$60,000 per year, not being married/common law, and high physical activity were independently associated with higher cognition. Higher vascular lesion burden was associated with lower executive function, but the association was not modified by cognitive reserve. Conclusion: Markers of cognitive reserve are associated with higher cognition. Vascular lesion burden is associated with lower executive function. However, cognitive reserve does not mitigate the effects of vascular lesion burden on executive function. Public health efforts should focus on preventing vascular brain injury as well as promoting lifestyle factors related to cognitive reserve, as cognitive reserve alone may not mitigate the effects of vascular brain injury.
DewCityGame: Dew Computing-based 5G IoT for Smart City Using Coalition Formation Game

In this article, a smart city architecture with dew computing and machine-to-machine (M2M) communication for the Internet of Things (IoT) is proposed. IoT devices are directly connected to the machines and these machines contain a small amount of computing unit, called dew. The machines combine to form clusters based on distances through a coalition formation game and the machines within the cluster perform the tasks of the IoT nodes. The dew located within each machine can complete a maximum threshold amount of task. If the task exceeds that, then it is transferred to another machine connected to it via M2M communication. In DewCityGame, delay and power consumption of the proposed network are calculated. The delay and power consumption of the proposed optimized dew-enabled M2M communication network has been compared with other non-optimized dew-enabled M2M communication and non-M2M communication network. The proposed optimized dew-enabled M2M communication network has generated ∼15% to 42% less delay and ∼20% to 50% less power consumption than non-optimized dew-enabled M2M communication and non-M2M communication network.
Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.
Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate. Automated recognition of EC can aid physicians in improving diagnostic performance and accuracy. However, the classification of EC is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness. The recent developments of deep learning (DL) and computer-aided diagnosis (CAD) models have been useful for designing accurate EC classification models. In this aspect, this study develops an atom search optimization with a deep transfer learning-driven EC classification (ASODTL-ECC) model. The presented ASODTL-ECC model mainly examines the medical images for the existence of EC in a timely and accurate manner. To do so, the presented ASODTL-ECC model employs Gaussian filtering (GF) as a preprocessing stage to enhance image quality. In addition, the deep convolution neural network- (DCNN-) based residual network (ResNet) model is applied as a feature extraction approach. Besides, ASO with an extreme learning machine (ELM) model is utilized for identifying the presence of EC, showing the novelty of the work. The performance of the ASODTL-ECC model is assessed and compared with existing models under several medical images. The experimental results pointed out the improved performance of the ASODTL-ECC model over recent approaches.
On thermal fluctuations in quantum magnets

The effect of thermal fluctuations on the dynamics of a gapped quantum magnet is studied using inelastic neutron scattering on copper nitrate, a model material for the one-dimensional (1D) bond alternating Heisenberg chain, combined with theoretical and numerical analysis. We observe and interpret the thermally induced central peak due to intraband scattering as well as the thermal development of an asymmetric continuum of scattering. We relate this asymmetric line broadening to hard core constraints and quasi-particle interactions. Our findings are a counter example to recent assertions of universality of line broadening in 1D systems and are to be considered as a new paradigm of behaviour, applicable to a broad range of quantum systems.