Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUATION OF MINERALIZED PLASMATIC MATRIX GRAFT AFTER SURGICAL REMOVAL OF IMPACTED MANDIBULAR THIRD MOLARS (RANDOMIZED CLINICAL STUDY)

The purpose of the study was to evaluate mineralized plasmatic matrix (MPM) as a graft material in reducing the complications associated with the surgical extraction of impacted mandibular third molar. Patients & Methods: The study was conducted on selected 26 patients. Their ages ranged from 19 to 38 years old with mean (SD) of 28.0 (6.67). The patients were selected and divided randomly into two groups (13 patients each). All the patients were subjected to surgical removal of impacted mandibular third molar by the same surgical team and using the same surgical protocol for both groups. After surgical extraction, the sockets in the first (study) group filled with mineralized Plasmatic matrix (MPM), while in the second (control) group, the socket closed without any graft. The patients were evaluated pre and post surgery at 1 st , 3 rd , 5 th , 7 th & 15 days to evaluate pain, trismus, facial swelling and thermal sensitivity of lower second molar. Bone density was evaluated immediately and at 1, 3 and 6 months postoperatively. The results showed; that the pain, trismus, and facial swelling in addition to thermal sensitivity were higher in the control group when compared with the MPM group, with statistically significant differences in some evaluation periods. Bone density was higher in MPM group than in control group with high significant difference (P< 0.01). The conclusion: of this study showed that MPM application after surgical removal of impacted mandibular third molar was more effective as a graft material for reducing the surgical complications through the whole postoperative period.
Sequence Analysis of Recent H7 Avian Inﬂuenza Viruses Associated with Three Different Outbreaks in Commercial Poultry in the United States

The hemagglutinin (HA) and neuraminidase (NA) genes of H7 avian inﬂuenza virus (AIV) isolated between 1994 and 2002 from live-bird markets (LBMs) in the northeastern United States and from three outbreaks in commercial poultry have been characterized. Phylogenetic analysis of the HA and NA genes demonstrates that the isolates from commercial poultry were closely related to the viruses circulating in the LBMs. Also, since 1994, two distinguishing genetic features have appeared in this AIV lineage: a deletion of 17 amino acids in the NA protein stalk region and a deletion of 8 amino acids in the HA1 protein which is putatively in part of the receptor binding site. Furthermore, analysis of the HA cleavage site amino acid sequence, a marker for pathogenicity in chickens and turkeys, shows a progression toward a cleavage site sequence that fulﬁlls the molecular criteria for highly pathogenic AIV.
Survey on Microservice Architecture - Security, Privacy and Standardization on Cloud Computing Environment

— Microservices have been adopted as a natural solution for the replacement of monolithic systems. Some technologies and standards have been adopted for the development of microservices in the cloud environment; API and REST have been adopted on a large scale for their implementation. The purpose of the present work is to carry out a bibliographic survey on the microservice architecture focusing mainly on security, privacy and standardization aspects on cloud computing environments. This paper presents a bundle of elements that must be considered for the construction of solutions based on microservices.
The Role of Age in Intracerebral Hemorrhage: An Intricate Relationship

Strokes are one of the leading causes of death in the United States, and intracerebral hemorrhage is the deadliest type of stroke. Age is a strong risk factor for intracerebral hemorrhages and it also affects the body in numerous ways—including changes to the cardiovascular and central nervous systems— that interplay with multiple risk factors. Understanding the role of age in risk and outcomes can guide treatment and future clinical trials. We reviewed the literature for intracerebral hemorrhage risk factors. This review aimed to identify the role of age, as well as characterize the most commonly used age cut-off points in the literature. Current review of the literature suggests the age cut-off for mortality and morbidity vary between 60-80 years of age, with the most common values of 65 or 70 years. In addition to age as a determinant of outcomes, it increases the risk of multiple chronic health conditions and comorbidities including hypertension, diabetes, and treatment with anticoagulants which contribute to the pathology of intracerebral hemorrhages. The interaction of these chronic conditions, age, and intracerebral hemorrhage is evident; however, the exact mechanism and extent of impact remain unclear. The ambiguity of these connections may be further obscured by the current recommendations, individual patient preferences, and literature supported trials of treatment options for aging patients.
Ideal Cardiovascular Health Associations With Biomarkers and Subclinical Disease and Impact on Incidence of Cardiovascular Disease in the Framingham Offspring Study Epidemiology

was associated with higher circulating concentrations of natriuretic peptides (N-terminal pro-atrial natriuretic peptide and B-type natriuretic peptide) and lower blood concentrations of plasminogen activator inhibitor-1, aldosterone, C-reactive protein, D-dimer, fibrinogen, homocysteine, and growth differentiation factor-15 levels ( P <0.001 for all), as well as lower odds of subclinical disease (odds ratio, 0.74 per 1-unit increase in CVH score; 95% confidence interval, 0.68–0.80). The incidence of CVD (267 events over 16 years) was inversely associated with the CVH score in age- and sex-adjusted models (hazard ratio, 0.77 per 1-unit increase in CVH score; 95% confidence interval, 0.70–0.86), which was slightly attenuated upon adjustment for biomarkers and subclinical disease (hazard ratio, 0.87; 95% confidence interval, 0.78–0.97). Conclusion —In our prospective community-based study, the inverse association between an ideal cardiovascular health score and CVD incidence was partly attributable to its favorable impact on CVD biomarker levels and subclinical disease. ( Circulation . 2014;130:1676-1683.)
Sucrose-stimulated Salivary pH as an Adjunct to Caries Risk Assessment

Background: Child and maternal sucrose-stimulated salivary pH (SSS pH) levels have the potential to be associated with childhood caries. Aim: This study investigated the relationships among child and maternal SSS pH and child caries diagnosis, severity, and risk. Design: SSS pH levels were measured from 202 pediatric subjects and 175 mothers. Early childhood caries (ECC) and severe ECC (SECC) diagnoses, caries risk assessment (CRA) results, and caries severity scores were recorded. The associations between child and maternal SSS pH and the child-caries risk, diagnosis, and severity were respectively assessed using regression models. Results: Children with SSS pH <5.6 had higher odds to be diagnosed with ECC or SECC (aOR=7.27), and higher odds to present with moderate to extensive caries severity (aOR=5.63). Child SSS pH was associated with multiple risk factors on a CRA. When child SSS pH was adjusted for age, gender, and race/ethnicity as a predictor for SECC and ECC, the sensitivity and specificity estimates increased. Maternal and child SSS pH were positively associated. Conclusions: Dentists should consider the use of child-SSS pH as an inexpensive adjunct to the CRA and know that maternal and child SSS pH are significantly associated.
Scholars' Mine Scholars' Mine Dynamic Simulation of a MEMS Cantilever Switch Dynamic Simulation of a MEMS Cantilever Switch

The dynamic behavior of a micro-electro-mechanical-system (MEMS) cantilever switch is investigated. Over-actuation of the switch can degrade bounce characteristics and reduce the lifetime of the contacts. This work concerns the development of a control system that limits the number of switch bounces and reduces the impact force on the beam tip. A limited mass-spring analysis of the tip-position is given and an associated control approach is applied. Input limiting, state-feedback, and adaptive control methods are compared. All results demonstrate improved switch bounce characteristics for the simplified beam model with the adaptive showing the best performance improvement. A comprehensive finite element analysis is shown that predicts the dynamic beam behavior along the entire length. This approach produces a realistic model of the beam during switching, especially the tip displacement. A versatile control system is proposed that uses finite-element-analysis simulation and adaptive control. The feasibility of this dynamic control system is also discussed.
The NumericalCertification package in Macaulay2

The package NumericalCertification implements methods for certifying numerical approximations of solutions for a given system of polynomial equations. For certifying regular solutions, the package implements Smale’s α -theory and Krawczyk method. For a singular solution, we implement soft veriﬁcation using the iterative deﬂation method. We demonstrate the functionalities of the package focusing on interaction with current numerical solvers in Macaulay2 .
Convolutional Fine-Grained Classification With Self-Supervised Target Relation Regularization

Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such target coding schemes are less flexible to model inter-class correlation and are sensitive to sparse and imbalanced data distribution as well. In light of this, this paper introduces a novel target coding scheme – dynamic target relation graphs (DTRG), which, as an auxiliary feature regularization, is a self-generated structural output to be mapped from input images. Specifically, online computation of class-level feature centers is designed to generate cross-category distance in the representation space, which can thus be depicted by a dynamic graph in a non-parametric manner. Explicitly minimizing intra-class feature variations anchored on those class-level centers can encourage learning of discriminative features. Moreover, owing to exploiting inter-class dependency, the proposed target graphs can alleviate data sparsity and imbalanceness in representation learning. Inspired by recent success of the mixup style data augmentation, this paper introduces randomness into soft construction of dynamic target relation graphs to further explore relation diversity of target classes. Experimental results can demonstrate the effectiveness of our method on a number of diverse benchmarks of multiple visual classification, especially achieving the state-of-the-art performance on three popular fine-grained object benchmarks and superior robustness against sparse and imbalanced data. Source codes are made publicly available at https://github.com/AkonLau/DTRG.
SmartControllerJS: A JavaScript library to turn smartphones into controllers for web-based interactive experiments

We introduce SmartControllerJS, a new JavaScript library for fast, cost-eﬀective designing of web applications controlled via everyday smartphones. At its core, SmartControllerJS establishes a connection between two webpages, one page running on a desktop browser and the other on the user’s smartphone. The smartphone webpage loads a controller interface allowing users to control a web application running on their computer’s browser. The SmartControllerJS framework enables fast iteration loops when designing interactive user experiments because it has minimal friction and allows for scaling, while having no running costs. We ﬁrst describe how this library is built, how it can be used, and provide interactive examples. We then present two games designed for public screens along with results from user studies evaluating acceptability and ease of use. Finally, we implement a custom controller based on user feedback and introduce connection monitoring tools. We believe SmartControllerJS can accelerate the design of interactive experiments for researchers in Human-Computer Interaction, and be a useful tool for educational projects. All our code is available at SmartControllerJS and links to all demos can be found in Table I. To explore our demos, we recommend reading this work on a desktop computer with your smartphone in hand.
A violation of the Tsirelson bound in the pre-quantum theory of trace dynamics

The term Bell’s theorem refers to a set of closely related results which imply that quantum mechanics is incompatible with local hidden variable theories. Bell’s inequality is the statement that if measurements are performed independently on two space-like separated particles of an entangled pair, the assumption that outcomes depend on hidden variables implies an upper bound on the correlations between the outcomes. Quantum mechanics predicts correlations which violate this upper bound. The CHSH inequality is a speciﬁc Bell inequality in which classical correlation (i.e. if local hidden variables exist) can take the maximum value of 2. Quantum mechanics violates this bound, allowing for a higher bound on the correlation, which can take the maximum value 2 √ 2, known as the Tsirelson bound. Popescu and Rohrlich showed that the assumption of relativistic causality allows for an even higher bound on the CHSH correlation, this value being 4. Why is the bound coming from causality higher than the Tsirelson bound? Are there relativistic causal dynamical theories which violate the Tsirelson bound? In the present paper we answer this question in the aﬃrmative. We show that the pre-quantum theory of trace dynamics, from which quantum theory is emergent as a thermodynamic approximation, permits the CHSH correlation to take values higher than 2 √ 2. We interpret our ﬁndings to suggest that quantum theory is approximate, and emergent from the more general theory of trace dynamics.
Template matching with white balance adjustment under multiple illuminants

In this paper, we propose a novel template matching method with a white balancing adjustment, called N-white balancing, which was proposed for multi-illuminant scenes. To reduce the influence of lighting effects, N-white balancing is applied to images for multi-illumination color constancy, and then a template matching method is carried out by using adjusted images. In experiments, the effectiveness of the proposed method is demonstrated to be effective in object detection tasks under various illumination conditions.
Adaptive Active Contours for the 3D Segmentation of Computed Tomography Images

—Computed Tomography is one of the most valuable modalities of medical imaging and is used in the diagnosis of a wide range of pathologies. In many cases, it is necessary to obtain a precise segmentation and a 3D visualization of certain organs, tissues, nodules or regions in the tomography. However, manual delimitation is a very time-consuming process and advanced applications are needed to perform this process automatically. Active contours intend to ﬁt an initial approximation to the most relevant edges around the starting contour. Most approaches of active contours assume that all points in the initial approximation are close to the ﬁnal solution and that similar conditions can be set to all the sections of the contour, which is frequently a false assumption. For that reason, we have developed a new approach, in which different terms are used and adapted according to the evolution of the contour. Balloon, regularizing and attraction terms are combined and extended to 3D, avoiding the need for a close initial approximation and reducing human intervention. Furthermore, a multiscale framework is added to tackle the heterogeneity of the images and the variability of the approximations. The combination of these terms allows obtaining a more precise segmentation of the tomography.
Multiband gravitational wave cosmography with dark sirens

Gravitational waves might help resolve the tension between early and late Universe measurements of the Hubble constant, and this possibility can be enhanced with a gravitational wave detector in the decihertz band as we will demonstrate in this study. Such a detector is particularly suitable for the multiband observation of stellar-mass black hole binaries between space and ground, which would significantly improve the source localization accuracy thanks to a long baseline for timing triangulation, hence promoting the"dark siren"cosmology. Proposed decihertz concepts include DECIGO/B-DECIGO, TianGO, and others. We consider here the prospects of multiband observation of dark siren binaries with a variety of network configurations. We find that a multiband observation can uniquely identify a black hole binary to a single galaxy to a cosmological distance, and thus a dark siren behaves as if it had an electromagnetic counterpart. Considering only fully localized dark sirens, we use a Fisher matrix approach to estimate the error in the Hubble constant and matter density parameter. We find that a decihertz detector substantially improves our ability to measure cosmological parameters because it enables host galaxies to be identified out to a larger distance without the systematics from statistical techniques based on comparing the population distribution.
AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy

Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. This is currently a bottleneck for the topic, as available public dataset is still extremely limited in the field of CAI. In this paper, we present and release the first integrated dataset (named AutoLaparo) with multiple image-based perception tasks to facilitate learning-based automation in hysterectomy surgery. Our AutoLaparo dataset is developed based on full-length videos of entire hysterectomy procedures. Specifically, three different yet highly correlated tasks are formulated in the dataset, including surgical workflow recognition, laparoscope motion prediction, and instrument and key anatomy segmentation. In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. The dataset is available at https://autolaparo.github. io.
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks

On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning.
Simple and Tighter Derivation of Achievability for Classical Communication Over Quantum Channels

Achievability in information theory refers to demonstrating a coding strategy that accomplishes a prescribed performance benchmark for the underlying task. In quantum information theory, the crafted Hayashi-Nagaoka operator inequality is an essential technique in proving a wealth of one-shot achievability bounds since it effectively resembles a union bound in various problems. In this work, we show that the pretty-good measurement naturally plays a role as the union bound as well. A judicious application of it considerably simplifies the derivation of one-shot achievability for classical-quantum (c-q) channel coding via an elegant three-line proof. The proposed analysis enjoys the following favorable features. (i) The established one-shot bound admits a closed-form expression as in the celebrated Holevo-Helstrom Theorem. Namely, the error probability of sending $M$ messages through a c-q channel is upper bounded by the minimum error of distinguishing the joint channel input-output state against $(M-1)$ decoupled products states. (ii) Our bound directly yields asymptotic results in the large deviation, small deviation, and moderate deviation regimes in a unified manner. (iii) The coefficients incurred in applying the Hayashi-Nagaoka operator inequality are no longer needed. Hence, the derived one-shot bound sharpens existing results relying on the Hayashi-Nagaoka operator inequality. In particular, we obtain the tightest achievable $\epsilon$-one-shot capacity for c-q channel coding heretofore, improving the third-order coding rate in the asymptotic scenario. (iv) Our result holds for infinite-dimensional Hilbert space. (v) The proposed method applies to deriving one-shot achievability for classical data compression with quantum side information, entanglement-assisted classical communication over quantum channels, and various quantum network information-processing protocols.
Block Discrete Empirical Interpolation Methods

We present two block variants of the discrete empirical interpolation method (DEIM); as a particular application, we will consider a CUR factorization. The block DEIM algorithms are based on the concept of the maximum volume of submatrices and a rank-revealing QR factorization. We also present a version of the block DEIM procedures, which allows for adaptive choice of block size. The results of the experiments indicate that the block DEIM algorithms exhibit comparable accuracy for low-rank matrix approximation compared to the standard DEIM procedure. However, the block DEIM algorithms also demonstrate potential computational advantages, showcasing increased efficiency in terms of computational time.