Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between asthma and enamel defects in primary and young permanent teeth – A systematic review

Childhood respiratory diseases, such as asthma, are important public health problems worldwide and could be associated with tooth enamel defects. This study aimed to verify the relationship between asthma and enamel defects in teeth, to answer the following question: “Could asthma in children be significantly associated with enamel defects in deciduous dentition and young permanent teeth? ” PUBMED‐MEDLINE, EMBASE, LILACS, and COCHRANE databases were systematically searched and assessed articles (2000–2021) were cautiously scored according to a predetermined criterion. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses were considered. Twenty‐two articles were critically appraised and used as a basis for conclusions. The relationship between asthma and enamel defects was confirmed in the majority of appraised papers, one with a high level of evidence, nine with a moderate level, and four with a low level. Out of the eight manuscripts investigating the influence of asthma medication on enamel defects, only three (one with high, one moderate, and another with a low level of evidence) suggested an association. It can be concluded that asthma is closely connected with enamel defects in young permanent teeth. However, as most of the papers appraised were of cross‐sectional or case‐control design, further well‐designed clinical investigations with a prospective design are welcome to reinforce our findings.
Efficient Sparse Representation for Learning With High-Dimensional Data

Due to the capability of effectively learning intrinsic structures from high-dimensional data, techniques based on sparse representation have begun to display an impressive impact on several fields, such as image processing, computer vision, and pattern recognition. Learning sparse representations isoften computationally expensive due to the iterative computations needed to solve convex optimization problems in which the number of iterations is unknown before convergence. Moreover, most sparse representation algorithms focus only on determining the final sparse representation results and ignore the changes in the sparsity ratio (SR) during iterative computations. In this article, two algorithms are proposed to learn sparse representations based on locality-constrained linear representation learning with probabilistic simplex constraints. Specifically, the first algorithm, called approximated local linear representation (ALLR), obtains a closed-form solution from individual locality-constrained sparse representations. The second algorithm, called ALLR with symmetric constraints (ALLRSC), further obtains a symmetric sparse representation result with a limited number of computations; notably, the sparsity and convergence of sparse representations can be guaranteed based on theoretical analysis. The steady decline in the SR during iterative computations is a critical factor in practical applications. Experimental results based on public datasets demonstrate that the proposed algorithms perform better than several state-of-the-art algorithms for learning with high-dimensional data.
Osseodensification Versus Subtractive Drilling Techniques in Bone Healing and Implant Osseointegration: Ex Vivo Histomorphologic/Histomorphometric Analysis in a Low-Density Bone Ovine Model.

PURPOSE The aim of this study was to qualitatively and quantitatively assess the effect of osteotomy preparation by conventional, subtractive, or osseodensification instrumentation on osteotomies, treated with or without endosteal implants, and healing capacity. MATERIALS AND METHODS Seven sheep were used, and 56 osteotomies were made in the left and right ilium of the sheep (n = 8/sheep [4 per side/time point (3 and 6 weeks)]). Two different instrumentation techniques were used: (1) conventional/regular drilling in a three-step series of a 2-mm pilot and 3.2-mm and 3.8-mm twist drills and (2) osseodensification drilling with a Densah Bur 2.0-mm pilot and 2.8-mm and 3.8-mm multi-fluted tapered burs. Drilling was performed at 1,100 rpm with saline irrigation. RESULTS Qualitative histomorphometric evaluation of the osteotomies after 3 and 6 weeks did not indicate any healing impairment due to the instrumentation. In all samples, histologic examination suggested bone remodeling and growth (empty and treated with an implant), irrespective of preparation technique. Osteotomies prepared using the osseodensification instrumentation showed the existence of bone chips autografted into the trabecular spaces along the length of the osteotomy wall. CONCLUSION The osseodensification group yielded higher osseointegration rates, as distinguished through qualitative assessment, bone-to-implant contact, and bone-area-fraction occupancy, indicating an increased osteogenic potential in osteotomies prepared using the osseodensification technique.
Non–human primate epidural ECoG analysis using explainable deep learning technology

Objective. With the development in the field of neural networks, explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results. Approach. We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment. Main results. The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements. Significance. As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.
NVJPFSI at FinCausal 2021 Span-based Causality Extraction Task

We describe our NVJPFSI (NVIDIA Japan Financial Services with AI) system for span-based causality extraction from ﬁnancial news documents submitted as part of the Fin-Causal 2021 Workshop. We investigated a list of pretrained language models, such as ALBERT-xxlarge, BERT-large, and RoBERTa-large models ﬁne-tuned under SQuAD2.0. A grid-based ensemble learning algorithm is further introduced to combine n-best predictions from ﬁve checkpoints. We show impressive re-sults of F1 (94.77%) and exact match (87.62%) scores through applying these models individually and in grid-based ensemble learning.
Sayer: Using Implicit Feedback to Optimize System Policies

We observe that many system policies that make threshold decisions involving a resource (e.g., time, memory, cores) naturally reveal additional, or implicit feedback. For example, if a system waits X min for an event to occur, then it automatically learns what would have happened if it waited < X min, because time has a cumulative property. This feedback tells us about alternative decisions, and can be used to improve the system policy. However, leveraging implicit feedback is difficult because it tends to be one-sided or incomplete, and may depend on the outcome of the event. As a result, existing practices for using feedback, such as simply incorporating it into a data-driven model, suffer from bias. We develop a methodology, called Sayer, that leverages implicit feedback to evaluate and train new system policies. Sayer builds on two ideas from reinforcement learning---randomized exploration and unbiased counterfactual estimators---to leverage data collected by an existing policy to estimate the performance of new candidate policies, without actually deploying those policies. Sayer uses implicit exploration and implicit data augmentation to generate implicit feedback in an unbiased form, which is then used by an implicit counterfactual estimator to evaluate and train new policies. The key idea underlying these techniques is to assign implicit probabilities to decisions that are not actually taken but whose feedback can be inferred; these probabilities are carefully calculated to ensure statistical unbiasedness. We apply Sayer to two production scenarios in Azure, and show that it can evaluate arbitrary policies accurately, and train new policies that outperform the production policies.
OptDebug: Fault-Inducing Operation Isolation for Dataflow Applications

Fault-isolation is extremely challenging in large scale data processing in cloud environments. Data provenance is a dominant existing approach to isolate data records responsible for a given output. However, data provenance concerns fault isolation only in the data-space, as opposed to fault isolation in the code-space---how can we precisely localize operations or APIs responsible for a given suspicious or incorrect result? We present OptDebug that identifies fault-inducing operations in a dataflow application using three insights. First, debugging is easier with a small-scale input than a large-scale input. So it uses data provenance to simplify the original input records to a smaller set leading to test failures and test successes. Second, keeping track of operation provenance is crucial for debugging. Thus, it leverages automated taint analysis to propagate the lineage of operations downstream with individual records. Lastly, each operation may contribute to test failures to a different degree. Thus OptDebug ranks each operation's spectra---the relative participation frequency in failing vs. passing tests. In our experiments, OptDebug achieves 100% recall and 86% precision in terms of detecting faulty operations and reduces the debugging time by 17x compared to a naïve approach. Overall, OptDebug shows great promise in improving developer productivity in today's complex data processing pipelines by obviating the need to re-execute the program repetitively with different inputs and manually examine program traces to isolate buggy code.
George: Learning to Place Long-Lived Containers in Large Clusters with Operation Constraints

Online cloud services are widely deployed as Long-Running Applications (LRAs) hosted in containers. Placing LRA containers turns out to be particularly challenging due to the complex interference between co-located containers and the operation constraints in production clusters such as fault tolerance, disaster avoidance and incremental deployment. Existing schedulers typically provide APIs for operators to manually specify the container scheduling requirements and offer only qualitative scheduling guidelines for container placement. Such schedulers, do not perform well in terms of both performance and scale, while also requiring manual intervention. In this work, we propose George, an end-to-end generalpurpose LRA scheduler by leveraging the state-of-the-art Reinforcement Learning (RL) techniques to intelligently schedule LRA containers. We present an optimal container placement formulation for the first time with the objective of maximizing container placement performance subject to a set of operation constraints. One fundamental challenge in scheduling is to categorically satisfy different operation constraints in practice; specifically, to guarantee hard constraints and ensure soft constraints violations within a pre-defined threshold. We design a novel projection-based proximal policy optimization (PPPO) algorithm in combination with an Integer Linear optimization technique to intelligently schedule LRA containers under operation constraints. In order to reduce the training time, we apply transfer learning technique by taking advantage of the similarity in different LRA scheduling events. We prove theoretically that our proposed algorithm is effective, stable, and safe. We implement George as a plug-in service in Docker Swarm. Our in-house cluster demonstrates that George can maximize the LRA performance while enforcing the hard constraints and the soft constraints with a pre-defined threshold. The experiments show that George improves LRA performance and scale tremendously by requiring less than 1 hour scheduling time in a large cluster with 2K containers and 700 machines, 16x faster than existing schedulers. Compared with state-of-the-art alternatives, George also achieves 26% higher container performance with up to 70% lower constraint violation.
Response to the ‘visual studies now’ questionnaire for Visual Studies

I understand the entanglements of our historical and contemporary eras through the exploration of space at multiple scales, which is best demonstrated through some examples. In my research, I explored the dividing practices of Bombay’s lunatic asylum and hospitals to show how colonialism was manifested in space. This examination revealed the many divisions within the city’s ‘European’ population, even as the colonial regime was encouraging native populations to overcome their resistance to sharing space with diverse religious and ethnic communities in new native public hospitals. Many of Bombay’s philanthropists hailed from the small Parsi community. Making special provisions for their own community in Bombay’s native hospitals, the financial support of Parsi philanthropists allowed Parsis to separate themselves out from other native communities through the construction of wards for the exclusive use of Parsi patients (Figure 1).
Editorial: a call for more research on authenticity in corporate social responsibility programs

as evidenced by the Business roundtable’s (2019) revised ‘Statement of the Purpose of a Corporation,’ which commits to companies serving a broader set of stakeholders than just shareholders – including customers, employees, suppliers, and the community – companies are putting more emphasis on corporate social responsibility (CSr). there is little doubt that around much of the globe, there are more expectations from consumers for companies to follow responsible practices (see taylor 2018). research has been conducted on promoting corporate social responsibility in advertising for many decades and much has been learned about what drives consumer reaction to such campaigns (e.g., Bergkvist and Zhou 2019; Chen 2020). Key variables in terms of length of commitment to a program (Ellen et al. 2006), fit between the program and the company (Ellen et al. 2006; Elving 2013; Kim and Choi 2020; lee et al. 2020; Schaefer et al. 2020), and perceived authenticity (Schaefer et al. 2019; Kim and lee 2019) to a CSr program have been found to be associated with higher effectiveness of ads for to a CSr initiative. in general, it has been found that length and size of commitment, fit between the company and the initiative, and perceived authenticity of the company’s commitment to the program are positively correlated with consumer reaction to communications pertaining to the CSr initiative (Bergkvist and taylor 2016; Bergkvist and Zhou 2019). From an ethics standpoint, an issue that persists in doing research on promotion of CSr initiatives is that authenticity of a program is difficult to measure objectively. as a result, researchers have generally relied on consumer perceptions of the authenticity of a CSr program (lee et al. 2012; Öberseder et al. 2014), and perceptions may not always match reality. indeed, it would take considerable effort for a typical consumer to be able to effectively gauge the degree to which a company’s commitment to a CSr program is primarily profit motivated versus being based on a sincere desire to contribute to a cause. Without doubt, there can sometimes be ‘win-win’ situations in which CSr initiatives help people and/or society, but it is highly likely there are others where the company gains more than the cause. Such concerns are not new, as in the past some firms have even been accused of spending more promoting a CSr program than on the CSr program itself. indeed, some ‘old hands’ in the marketing field believe that a shorter-term profit motive still underlies many CSr initiatives. the complexity of being able to decipher corporate motives leave an odd situation where consumer perceptions of authenticity help a company regardless of whether the company’s commitment to the cause is sincere and/or altruistic or not. Moreover, there is a history of some companies who are effective at promoting themselves as good corporate citizens scoring high on consumer perceptions of being socially responsible in spite of not https://doi.org/10.1080/02650487.2021. 1986310
Investigation on extendable multiport DC–DC boost converter for hybrid renewable energy systems

In this work, the integration of renewable hybrid energy (RHE) resources using extendable multiport DC–DC boost converter is investigated. Three renewable energy sources such as solar photovoltaic (PV) system, wind energy system and fuel cell (FC) are integrated into the grid via this converter and grid-tied inverter. The output voltage of the multiport DC–DC boost converter is controlled using adaptive neuro fuzzy inference system-based controller. The overall system model is developed and tested in the MATLAB simulation software and also implemented in real time. The overall system is tested for different operating conditions such as change in irradiance condition of the solar PV panel, change in wind speed condition of the wind turbine, change in hydrogen pressure conditions of the FC and sudden change in load conditions and corresponding results are measured and analysed. The efficiency of the proposed system is about 98.21%. Finally, experimental results of the proposed model are also presented to examine the suitability of the system.
Emerging trends: A gentle introduction to fine-tuning

Abstract The previous Emerging Trends article (Church et al., 2021. Natural Language Engineering 27(5), 631–645. ) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.
How does English national end-of-life care policy impact on the experience of older people at the end of life? Findings from a realist evaluation

Abstract Aim: To explore the extent to which national policy in end-of-life care (EOLC) in England influences and guides local practice, helping to ensure that care for older people at the EOL is of a consistently good quality. Background: Whilst policy is recognised as an important component in determining the effectiveness of EOLC, there is scant literature which attempts to interrogate how this happens or to hypothesise the mechanisms linking policy to better outcomes. Method: This article reports on the second phase of a realist evaluation comprising three case studies of clinical commissioning groups, including 98 in-depth interviews with stakeholders, meeting observation and documentary analysis. Findings: This study reveals the key contextual factors which need to be in place at micro, meso and macro levels if good quality EOLC for older people is to be achieved. The findings provide insight into rising local inequalities and reveal areas of dissonance between stakeholder priorities. Whilst patients privilege the importance of receiving care and compassion in familiar surroundings at EOL, there remains a clear tension between this and the medical drive to cure disease and extend life. The apparent devaluing of social care and subsequent lack of resource has impacted significantly on the way in which dying is experienced. Patient experience at EOL, shaped by the care received both formally and informally, is driven by a fragmented health and social care system. Whilst the importance of system integration appears to have been recognised, significant challenges remain in terms of shaping policy to adequately reflect this. This study highlights the priority attached by patients and their families to the social and relational aspect of death and dying and shines a light on the stark disparities between the health and social care systems which became even more evident at the height of the Covid-19 pandemic.
Integrins, cadherins and channels in cartilage mechanotransduction: perspectives for future regeneration strategies

Abstract Articular cartilage consists of hyaline cartilage, is a major constituent of the human musculoskeletal system and has critical functions in frictionless joint movement and articular homoeostasis. Osteoarthritis (OA) is an inflammatory disease of articular cartilage, which promotes joint degeneration. Although it affects millions of people, there are no satisfying therapies that address this disease at the molecular level. Therefore, tissue regeneration approaches aim at modifying chondrocyte biology to mitigate the consequences of OA. This requires appropriate biochemical and biophysical stimulation of cells. Regarding the latter, mechanotransduction of chondrocytes and their precursor cells has become increasingly important over the last few decades. Mechanotransduction is the transformation of external biophysical stimuli into intracellular biochemical signals, involving sensor molecules at the cell surface and intracellular signalling molecules, so-called mechano-sensors and -transducers. These signalling events determine cell behaviour. Mechanotransducing ion channels and gap junctions additionally govern chondrocyte physiology. It is of great scientific and medical interest to induce a specific cell behaviour by controlling these mechanotransduction pathways and to translate this knowledge into regenerative clinical therapies. This review therefore focuses on the mechanotransduction properties of integrins, cadherins and ion channels in cartilaginous tissues to provide perspectives for cartilage regeneration.