Miracle-dz/UniFAR-FTUs huggingface dataset:
A Design of Marker-Based Augmented Reality System Structure using Object Removal Technique

Recently, augmented reality is divided into broadly marker based and markerless based as part of HCI (Human Computer Interaction). Markerless based is augmented object using natural features in real-world environment. On the other hand, Marker based is use to calculate easily the coordinates and exactly augmented object using flat marker of rectangular. However, marker-based image is provided due to the presence of the marker in a markerless, giving users a more less realistic and immersive view. In this paper, We research about combined diminished reality and augmented reality for Marker-Based Augmented Reality System Structure using Object Removal Tchnique in order to increase realistic and immersive view
A generalized version of Chow-Liu algorithm for data mining.

: We propose a learning algorithm for nonparametric estimation and on-line prediction for general stationary ergodic sources. We divide the real space R into a set A of ﬁnite subsets, transform a given sequence in R into the sequence in A to encode the latter using universal coding for ﬁnite sequences with distortion. We prepare inﬁnitely many such A , and mixture the estimated measure to obtain a measure of sequences in R which may be either discrete or continuous. If the sequence is emitted by a stationary ergodic source, then the Kullback-Leibler information divided by the sequence length n converges to zero as n goes to inﬁnity. In particular, for continuous sources, the method does not require existence of a probability density function. In this sense, this paper extends Ryabko’s universal measure. The measure can be used for online prediction to estimate next data given the past sequence.
Residual Sum-Product Networks

Tractable yet expressive density estimators are a key building block of probabilistic machine learning. While sum-product networks (SPNs) offer attractive inference capabilities, obtaining structures large enough to ﬁt complex, high-dimensional data has proven challenging. In this paper, we present a residual learning approach to ease the learning of SPNs, which are deeper and wider than those used previously. The main trick is to ensemble SPNs by explicitly reformulating sum nodes as residual functions. This adds references to substructures across the SPNs at different depths, which in turn helps to improve training. Our experiments demonstrate that the resulting residual SPNs (ResSPNs) are easy to optimize, gain performance from considerably increased depth and width, and are competitive to state-of-the-art SPN structure learning approaches. To combat overﬁtting, we introduce an iterative pruning technique that compacts models and yields better generalization.
A Two-Stage Framework for Multiple Sound-Source Localization

Humans usually perceive the world through information in different modalities, e.g., vision and hearing. By leveraging the relevance and complementary between audio and vision, humans can clearly distinguish different sound sources and infer which object is making sound. In contrast, machines have been proven capable of separately processing audio and visual information using deep neural networks. But can they benefit from joint audiovisual learning? Recent works mainly focus on establishing multi-modal relationship based on temporally synchronized audio and visual signals [1, 3, 8]. This synchronization works effectively for simple scenes [2, 9], i.e., the single-source conditions. However, in unconstrained videos, various sounds are usually mixed, where the scene-level supervision is too coarse to provide the precise alignment between each sound and visual source pair. To tackle this problem, [6, 7] establish audiovisual clusters to associate sound-object pairs, but require pre-determined number of clusters, which is difficult in unconstrained scenarios, thus greatly affecting alignment performance. [2, 9, 11] further apply audiovisual learning into sound localization, but mainly focus on simple scenes, usually unable to find source-specific objects from mixed audio. [13] constructs a pretext task then localizes sound through energy of each pixel. To sum up, existing dominant methods mostly lack the ability to analyze complex audiovisual scenes, and fail to effectively utilize the latent alignment between sound and visual source pairs in unconstrained videos. This is because there are majorly two challenges in complex audiovisual scene analysis: one is how to distinguish different soundsources, the other is how to ensure the established soundobject alignment is fairly satisfactory without one-to-one annotations. To address these challenges, we develop a twostage audiovisual learning framework. At the first stage, we employ a multi-task framework consisting of classification and audiovisual correspondence to provide the reference of audiovisual content for the second stage. At the second stage, based on the classification predictions, we use the operation of Class Activation Mapping (CAM) [14, 10, 4] to extract class-specific feature representations as the potential sound-object pairs (Fig. 1), then perform alignment in Shouting Boating Stream Audiovisual Pair
EFFECT OF TAPING ON SCAPULAR POSTURE AND SHOULDER RANGE OF MOTION IN SUBACROMIAL IMPINGEMENT SYNDROME

Background: Alteration in upper body posture is associated with shoulder impingement due to changes in scapular orientation, increased thoracic kyphosis and concomitant imbalance of glenohumeral and scapulothoracic muscles. Mechanical correction of scaular and thoracic posture can reduce impingement. Recently use of kinesiotapes has been increasingly popular. There have been some studies reporting the effect of kinesiotapes on muscle activation and pain reduction in subacromial impingement syndrome. Kinesiotapes can also be used to correct scapular and thoracic posture in Subacromial Impingement Syndrome.
Keratocystic Odontogenic Tumor of the Maxilla Revealed By a Diffused Cellulitis: A Case Report

DOI: 10.21276/sjds.2017. 4.12. 2 Abstract: Keratocystic odontogenic tumor (KCOT) is a unique cyst because of its locally aggressive behavior, high recurrence rate, and histological characteristic appearance. The maxillary presentation is rare. This paper reports a case of maxillary KCOT successfully treated with radical enucleation. A 23-year-old female patient was referred to our department to search for a possible dental etiology to a right upper facial cellulitis. Radiology showed a well-limited unilocular radiolucent area, between the teeth 15 and 16 which were displaced. Initially, a lateral periodontal cyst was suspected. An enucleation of the cyst was performed. Histology concluded on a maxillary KCOT. A follow up at 9 months showed complete healing with no signs of recurrence. No consensus exists concerning KCOT management. The most conservative treatment used is enucleation. However, it had a high recurrence rate of 56% up to 10 years after treatment compared with other methods which explain the need for long-term follow-up.
MDA-Net: Memorable Domain Adaptation Network for Monocular Depth Estimation

Monocular depth estimation is a challenging task that aims to predict a corresponding depth map from a given single RGB image. Recent deep learning models have been proposed to predict the depth from the image by learning the alignment of deep features between the RGB image and the depth domains. In this paper, we present a novel approach, named Memorable Domain Adaptation Network (MDA-Net), to more effectively transfer domain features for monocular depth estimation by taking into account the common structure regularities (e.g., repetitive structure patterns, planar surfaces, symmetries) in domain adaptation. To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-speciﬁc information between RGB image domain and the depth domain. More speciﬁcally, in the SOM module, we develop a Memorable Bank of Filters (MBF) unit to learn a set of ﬁlters that memorize the structure-aware image-depth residual pattern, and also an Attention Guided Controller (AGC) unit to control the ﬁlter selection in the MBF given image features queries. Given the query image feature, the trained SOM module is able to adaptively select the best customized ﬁlters for cross-domain feature transferring with an optimal structural disparity between image and depth. In summary, we focus on addressing this structure-speciﬁc domain adaption challenge by proposing a novel end-to-end multi-scale memorable network for monocular depth estimation. The experiments show that our MDA-Net demonstrates the superior performance compared to the existing supervised monocular depth estimation approaches on the challenging KITTI and NYU Depth V2 benchmarks.
Kernel-based Approach for Learning Causal Graphs from Mixed Data

A causal graph can be generated from a dataset using a particular causal algorithm, for instance, the PC algorithm or Fast Causal Inference (FCI). This paper provides two contributions in learning causal graphs: an easy way to handle mixed data so that it can be used to learn causal graphs using the PC algorithm/FCI and a method to evaluate the learned graph structure when the true graph is unknown. This research proposes using kernel functions and Kernel Alignment to handle mixed data. The two main steps of this approach are computing a kernel matrix for each variable and calculating a pseudo-correlation matrix using Kernel Alignment. The Kernel Alignment matrix is used as a substitute for the correlation matrix that is the main component used in computing a partial correlation for the conditional independence test for Gaussian data in the PC Algorithm and FCI. The advantage of this idea is that is possible to handle more data types when there is a suitable kernel function to compute a kernel matrix for an observed variable. The proposed method is successfully applied to learn a causal graph from mixed data containing categorical, binary, ordinal, and continuous variables. We also introduce the Modal Value of Edges Existence (MVEE) method, a new method to evaluate the structure of learned graphs represented by Partial Ancestral Graph (PAG) when the true graph is unknown. MVEE produces an agreement graph as a proxy to the true graph to evaluate the structure of the learned graph. MVEE is successfully used to choose the best-learned graph when the true graph is unknown.
Immunohistochemical Assessment of Inflammation and Regeneration in Morphine-Dependent Rat Brain

Background Opioids are amongst the most common abused drugs. Pathologic studies on opioid abuse are limited since the evaluation of inflammation and regeneration in brain tissue is not as simple as other tissues of the body. Thus, the present study aimed to determine the relationship between the dependence on morphine and inflammatory and regenerative processes. Methods In this experimental study, 48 male wistar rats were divided into 6 groups. The dependent groups (3 groups) received 0.4 mg/ml morphine in drinking water for 7, 28, and 56 days. The control groups (3 groups) received sucrose solution in drinking water for the same period. The histopathological studies of the brain sample were done. The slides were stained by hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining method. The areas of brain were evaluated in terms of lymphocytic infiltration and glial scar. Findings A significant difference was observed in the mean number of cells in the glial scar of the dependent group 3 (dependent for 56 days) among the control group (P = 0.040). Further, a significant relationship was reported between the increased duration of morphine use and the number of created scar glial cells. Furthermore, a significant increase in the number of astrocytes was observed in the affected areas. Conclusion After long-term use, opioids can result in increased number of astrocytes and creating glial scar centers in the affected areas in response to the inflammation.
Second Order Bias Corrected Efficient GMM Estimator

In this paper, the three conventional GMM estimators First-difference, Level and System GMM estimators with respective efficient initial weight matrices are considered to estimate the autoregressive panel data model. It is observed that the bias of first-difference GMM estimator is heigher and the bias of system GMM estimator is lesser among the above mentioned estimators but as variance ratio increases and as autoregressive parameter approaches to one the bias of all the aforementioned estimators increase. Hence to reduce such bias, second order bias correction method is considered. Through Monte-Carlo simulation it is observed that the considered second order bias correction method works well for first-difference and system GMM estimators, specially when the variance ratio is greater than one. Mathematics Subject Classification: 62Fxx, 91Bxx.
Robust Scene Text Recognition Through Adaptive Image Enhancement

Scene text in natural images often has a complex and varied appearance and a variety of degradations, which pose a great challenge to the reliable recognition of text. In this paper, we propose a novel scene text recognition method that introduces an effective, end-to-end trainable text image enhancement network prior to an attention-based recognition network, which adaptively improves the text image and enhances the performance of the whole recognition model. Speciﬁcally, the enhancement network combines a novel hierarchical residual enhancement network, which generates and reﬁnes pixel-wise enhancement details that are added to the input text image, and a spatial rectiﬁcation network regularizing the shape of the text. Through end-to-end training with the recognition network in a weak supervision way with word annotations only, the enhancement network effectively learns to transform the text image to a more favorable form for subsequent recognition. The state-of-the-art results on several standard benchmarks demonstrate the effectiveness of our enhancement-based scene text recognition method.
Electronic isotope shift factors for the Ir 5 d 7 6 s 2 4 F 9 / 2 → ( odd , J = 9 / 2 ) line at 247.587 nm

We present the theoretical calculations of the electronic isotope shift factors of the ( odd , J = 9 / 2 ) line at 247.587 nm, that were recently used to extract nuclear mean square radii and nuclear deformations of iridium isotopes [Mukai et al. (2020)]. The fully relativistic multiconﬁguration Dirac-Hartree-Fock method and the relativistic conﬁguration interaction method were used to perform the atomic structure calculations. Additional properties such as the sharing rule , Landé g factors or phase tracking were employed to ensure an adequate description of the targeted odd level.
Cost-effectiveness Analysis of Preoperative Screening Strategies for Obstructive Sleep Apnea among Patients Undergoing Elective Inpatient Surgery

Background: Obstructive sleep apnea is underdiagnosed in surgical patients. The cost-effectiveness of obstructive sleep apnea screening is unknown. This study’s objective was to evaluate the cost-effectiveness of preoperative obstructive sleep apnea screening (1) perioperatively and (2) including patients’ remaining lifespans. Methods: An individual-level Markov model was constructed to simulate the perioperative period and lifespan of patients undergoing inpatient elective surgery. Costs (2016 Canadian dollars) were calculated from the hospital perspective in a single-payer health system. Remaining model parameters were derived from a structured literature search. Candidate strategies included: (1) no screening; (2) STOP-Bang questionnaire alone; (3) STOP-Bang followed by polysomnography (STOP-Bang + polysomnography); and (4) STOP-Bang followed by portable monitor (STOP-Bang + portable monitor). Screen-positive patients (based on STOP-Bang cutoff of at least 3) received postoperative treatment modifications and expedited definitive testing. Effectiveness was expressed as quality-adjusted life month in the perioperative analyses and quality-adjusted life years in the lifetime analyses. The primary outcome was the incremental cost-effectiveness ratio. Results: In perioperative and lifetime analyses, no screening was least costly and least effective. STOP-Bang + polysomnography was the most effective strategy and was more cost-effective than both STOP-Bang + portable monitor and STOP-Bang alone in both analyses. In perioperative analyses, STOP-Bang + polysomnography was not cost-effective compared to no screening at the $4,167/quality-adjusted life month threshold (incremental cost-effectiveness ratio $52,888/quality-adjusted life month). No screening was favored in more than 90% of iterations in probabilistic sensitivity analyses. In contrast, in lifetime analyses, STOP-Bang + polysomnography was favored compared to no screening at the $50,000/quality-adjusted life year threshold (incremental cost-effectiveness ratio $2,044/quality-adjusted life year). STOP-Bang + polysomnography was favored in most iterations at thresholds above $2,000/quality-adjusted life year in probabilistic sensitivity analyses. Conclusions: The cost-effectiveness of preoperative obstructive sleep apnea screening differs depending on time horizon. Preoperative screening with STOP-Bang followed by immediate confirmatory testing with polysomnography is cost-effective on the lifetime horizon but not the perioperative horizon. The integration of preoperative screening based on STOP-Bang and polysomnography is a cost-effective means of mitigating the long-term disease burden of obstructive sleep apnea. In a Canadian single healthcare payer model, the cost-effectiveness of preoperative obstructive sleep apnea screening differs depending on time horizon. Preoperative screening with STOP-Bang followed by immediate confirmatory testing with polysomnography is cost-effective on the lifetime horizon but not the perioperative horizon.
Evaluating Physical Functioning in Survivors of Critical Illness: Development of a New Continuum Measure for Acute Care*

Supplemental Digital Content is available in the text. Objectives: Evaluation of physical functioning is central to patient recovery from critical illness—it may enable the ability to determine recovery trajectories, evaluate rehabilitation efficacy, and predict individuals at highest risk of ongoing disability. The Physical Function in ICU Test-scored is one of four recommended physical functioning tools for use within the ICU; however, its utility outside the ICU is poorly understood. The De Morton Mobility Index is a common geriatric mobility tool, which has had limited evaluation in the ICU population. For the field to be able to track physical functioning recovery, we need a measurement tool that can be used in the ICU and post-ICU setting to accurately measure physical recovery. Therefore, this study sought to: 1) examine the clinimetric properties of two measures (Physical Function in ICU Test-scored and De Morton Mobility Index) and 2) transform these measures into a single measure for use across the acute care continuum. Design: Clinimetric analysis. Setting: Multicenter study across four hospitals in three countries (Australia, Singapore, and Brazil). Patients: One hundred fifty-one ICU patients. Interventions: None. Measurements and Main Results: Physical function tests (Physical Function in ICU Test-scored and De Morton Mobility Index) were assessed at ICU awakening, ICU, and hospital discharge. A significant floor effect was observed for the De Morton Mobility Index at awakening (23%) and minimal ceiling effects across all time points (5–12%). Minimal floor effects were observed for the Physical Function in ICU Test-scored across all time points (1–7%) and a significant ceiling effect for Physical Function in ICU Test-scored at hospital discharge (27%). Both measures had strong concurrent validity, responsiveness, and were predictive of home discharge. A new measure was developed using Rasch analytical principles, which involves 10 items (scored out of 19) with minimal floor/ceiling effects. Conclusions: Limitations exist for Physical Function in ICU Test-scored and De Morton Mobility Index when used in isolation. A new single measure was developed for use across the acute care continuum.
Normothermia in Patients With Sepsis Who Present to Emergency Departments Is Associated With Low Compliance With Sepsis Bundles and Increased In-Hospital Mortality Rate*

Supplemental Digital Content is available in the text. Objectives: To investigate the impact of normothermia on compliance with sepsis bundles and in-hospital mortality in patients with sepsis who present to emergency departments. Design: Retrospective multicenter observational study. Patients: Nineteen university-affiliated hospitals of the Korean Sepsis Alliance participated in this study. Data were collected regarding patients who visited emergency departments for sepsis during the 1-month period. The patients were divided into three groups based on their body temperature at the time of triage in the emergency department (i.e., hypothermia [< 36°C] vs normothermia [36–38°C] vs hyperthermia [> 38°C]). Interventions: None. Measurements and Main Results: Of 64,021 patients who visited emergency departments, 689 with community-acquired sepsis were analyzed (182 hyperthermic, 420 normothermic, and 87 hypothermic patients). The rate of compliance with the total hour-1 bundle was lowest in the normothermia group (6.0% vs 9.3% in hyperthermia vs 13.8% in hypothermia group; p = 0.032), the rate for lactate measurement was lowest in the normothermia group (62.1% vs 73.1% vs 75.9%; p = 0.005), and the blood culture rate was significantly lower in the normothermia than in the hyperthermia group (p < 0.001). The in-hospital mortality rates in the hyperthermia, normothermia, and hypothermia groups were 8.5%, 20.6%, and 30.8%, respectively (p < 0.001), but there was no significant association between compliance with sepsis bundles and in-hospital mortality. However, in a multivariate analysis, compared with hyperthermia, normothermia was significantly associated with an increased in-hospital mortality (odds ratio, 2.472; 95% CI, 1.005–6.080). This association remained significant even after stratifying patients by median lactate level. Conclusions: Normothermia at emergency department triage was significantly associated with an increased risk of in-hospital mortality and a lower rate of compliance with the sepsis bundle. Despite several limitations, our findings suggest a need for new strategies to improve sepsis outcomes in this group of patients.