bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sketch2Pose
Authors: Brodt Kirill , Bessmeltsev Mikhail 
Published: July 2022

Artists frequently capture character poses via raster sketches, then use these drawings as a reference while posing a 3D character in a specialized 3D software --- a time-consuming process, requiring specialized 3D training and mental effort. We tackle this challenge by proposing the first system for automatically inferring a 3D character pose from a single bitmap sketch, producing poses consistent with viewer expectations. Algorithmically interpreting bitmap sketches is challenging, as they contain significantly distorted proportions and foreshortening. We address this by predicting three key elements of a drawing, necessary to disambiguate the drawn poses: 2D bone tangents, self-contacts, and bone foreshortening. These elements are then leveraged in an optimization inferring the 3D character pose consistent with the artist's intent. Our optimization balances cues derived from artistic literature and perception research to compensate for distorted character proportions. We demonstrate a gallery of results on sketches of numerous styles. We validate our method via numerical evaluations, user studies, and comparisons to manually posed characters and previous work.
Code and data for our paper are available at http://www-labs.iro.umontreal.ca/bmpix/sketch2pose/.
The Value of 320-Slice Spiral Computed Tomography Perfusion Imaging in Staging and Long-Term Dynamic Evaluation of Breast Cancer
Authors: Geng Hequn , Zhang Zhilin , Zhang Xiaochen , Liang Zhandong , Li Yong , Cui Shujun 
Published: July 2022

The value of 320-slice spiral computed tomography (CT) perfusion imaging in staging and long-term dynamic evaluation of breast cancer was explored. 120 breast cancer patients who underwent preoperative CT examination and were confirmed by surgery and pathology were selected. All patients underwent preoperative TNM staging of breast cancer, with 120 cases in each stage. According to the results of 320-slice spiral CT, the postoperative pathology and surgical methods were compared and analyzed. CT diagnosis of breast cancer showed that T1 sensitivity was 71% and accuracy was 61%, T2 sensitivity was 74% and accuracy was 64%, T3 sensitivity was 94% and the accuracy was 84%, and the T4 sensitivity was 100% and the accuracy was 91%. The sensitivity of N1 stage was 71%, and the accuracy was 61%; and the sensitivity of N2 ~ N3 stage was 81%, and the accuracy was 76%. There were 7 cases of M1 with distant metastasis, the sensitivity was 71%, and the accuracy was 71%. At T1 stage, blood flow (BF) was

39.2
±
16.7

mL/min/100 g, blood volume (BV) was

2.66
±
1.4

mL/100 g, mean transit time (MTT) was

8.16
±
2.7

s, and permeability surface (PS) was

16.6
±
9.7

mL/min/100 g. 320-slice spiral CT perfusion imaging technology provided a new diagnostic mode for everyone, which can quantitatively identify breast cancer with multiple parameters, which was of great significance for clinical auxiliary diagnosis.
WNT Medulloblastoma Limbo: How Low Can We Go?
Authors: Remke Marc , Ramaswamy Vijay 
Published: July 2022

SummarySurvival rates for average-risk medulloblastoma exceed 80%; however, long-term sequelae are substantial. A study from Mumbai, India evaluated the role of omission of craniospinal irradiation. Albeit unsuccessful, this study raises the crucial question of how low therapy can be safely de-escalated with the intent of improving quality of survival.See related article by Gupta et al., p. 4180
In-depth analysis of dynamic degree load balancing technique in public cloud for heterogeneous cloudlets
Authors: Joshi Aparna Shashikant , Devi Munisamy Shyamala 
Published: July 2022

<span lang="EN-US">Load <span lang="EN-US">balancing is one of the challenges of the distributed computing worldview. With the enormous development in clients and their interest for different administrations on the distributed computing stage, compelling or productive asset usage in the cloud climate has turned into an urgent concern. Load balancing is critical to keeping cloud computing running smoothly. This study examines the research using four scheduling algorithms: dynamic degree balance CPU based (D2B_CPU), dynamic degree balanced membership based (D2B_Membership), dynamic degree memory balanced allocation (D2MBA) and hybrid dynamic degree balance (HDDB) algorithm. Central processing unit (CPU) utilisation, bandwidth utilisation, and memory utilisation are used as performance measures to verify the performance of these algorithms. The CloudSim simulation programme was used to simulate these algorithms. The primary goal of this work is to aid in the future construction of new algorithms by researching the behaviour of various existing algorithms.</span></span>
Applying the green Ca2Al3O6F:Eu2+ oxyfluoride phosphorus on white emitting diodes
Authors: Liem Bui Van , Nguyen Thi Dieu An 
Published: July 2022

<span lang="EN-GB"><span lang="EN-GB">Ca<sub>2</sub>Al<sub>3</sub>O<sub>6</sub>F:Eu<sup>2+</sup>, a new green-emitting and its photoluminescence (PL) characteristics for white-emitting diodes, have been analyzed and generated (w-LEDs). This phosphorus displays a strong absorption range between </span></span>ultraviolet (UV) and blue region, along with a wide green emission range of 502 nm. The procedure for concentration suppression and Eu<sup>2+ </sup>luminous longevity has been investigated using Ca<sub>2</sub>Al<sub>3</sub>O<sub>6</sub>F:Eu<sup>2+</sup> phosphors. Key characteristics for manufacturing w-LED lamps, such as photoluminesce based on temperature, microstructure, morphology, CIE value and quantum efficiency, were also investigated in Ca<sub>2</sub>Al<sub>3</sub>O<sub>6</sub>F:Eu<sup>2+</sup>. The findings show that Ca<sub>2</sub>Al<sub>3</sub>O<sub>6</sub>F:Eu<sup>2+</sup>, is a suitable option for almost UV-excited w-LEDs as a green component.
Brain seizures detection using machine learning classifiers based on electroencephalography signals: a comparative study
Authors: Hashem Attia Atef , Said Ashraf Mahroos 
Published: July 2022

The <span>paper demonstrates various machine learning classifiers, they have been used for detecting epileptic seizures quickly and accurately through electroencephalography (EEG), in real time. Symptoms of epilepsy are caused abnormal brain activity. Analyzing and detecting epileptic seizures presents many challenges because EEG signals are non-stationary, and the patterns of the seizure vary for each patient. Moreover, the EEG signals are noisy, and this affect the process of seizure detection. On the other hand, Machine learning algorithms are very accurate, adaptive and generalize very well when provided with diverse and big training data and can easily analyze complex structure of the EEG signal despite the noisiness when compared to other methods. With this approach the features of epileptic seizures can be learned and used to correctly identify other seizure cases. The demonstration states a comparison between various classifiers, including random forests, K-nearest neighbors (K-NN), decision trees, support vector machine (SVM), logistic regression and naïve bayes. Different performance metrics is used such as accuracy, receiver operating characteristics (ROC), mean absolute error (MAE), root-mean-square error (RMSE) and most importantly detection time for each algorithm. The Bonn university dataset has been used for demonstration process for the classification of the epileptic seizure.</span>
Received signal strength indication based clustering and aggregating data using Q-learning in mobile Ad Hoc network
Authors: Reddy Surya Narayana , Mungara Jitendranath 
Published: July 2022

Clustering <span>is a significant idea for extending the scalability and enhancing the energy in the mobile ad-hoc network (MANET). In addition, the clustering concept is used to diminishes the cost of communication. The re-clustering procedure makes expensive, and frequent re-clustering procedure makes extra routing overhead and extra energy utilization. To solve these issues, received signal strength indication (RSSI) based clustering and aggregating data (RCAD) using Q-learning in MANET is proposed. In this approach, we build the clusters by node RSSI. The fuzzy logic system (FLS) is used to select the cluster head (CH) by the node mobility and node utilization energy. Q-learning-based data-aggregation for improving mobile node routing efficiency in MANET. Here, we can find an optimum next-hop node utilizing their Q-values established on the rewards (RD). Since the RD rule is used to decide the best solution for the Q-learning technique. This RD is computed by present bandwidth (PB), present energy (PE), present packet delivery (PDD), and hop count (HC) parameter for selecting the data aggregator from sender to receiver. The experimental outcomes illustrate that the RCAD approach increases 155 CH round and raises 24% cluster lifetime in the MANET.</span>
Comparative analysis and prediction of coronary heart disease
Authors: Prusty Sashikanta , Patnaik Srikanta , Kumar Dash Sujit 
Published: July 2022

Cardiovascular disease (CVD) <span>is now one of the leading causes of death worldwide and was also thought to be a serious illness in the mid and old ages. Artificial intelligence and machine learning have a huge impact on the healthcare areas. As a result, getting a familiar individual with data processing techniques suitable for numerical health data. Although, the most often used algorithms for classification tasks will be incredibly advantageous in terms of time management. In particular here, a common procedure has been proposed for predicting cardiovascular disease. Accordingly, we herein consider nine typical classifiers of both machine learning and deep learning technology for the comparative analysis and prediction of coronary heart failure. These models are computationally inexpensive and easy to build. Moreover, these classifiers are tested and compared using a confusion matrix in the Jupyter notebook, yielding classification measures such as accuracy, f1-score, recall, and precision. As a result, the logistic regression classifier gives the maximum possible accuracy, precision, and f1-score of 90.78%, 90.24%, and 91.35% respectively.</span>
Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes
Authors: Tang Siyi , Razeghi Orod , Kapoor Ridhima , Alhusseini Mahmood I. , Fazal Muhammad , Rogers Albert J. , Rodrigo Bort Miguel , Clopton Paul , Wang Paul J. , Rubin Daniel L. , Narayan Sanjiv M. , Baykaner Tina 
Published: July 2022

Background:
Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes.

Methods:
Consecutive patients who underwent catheter ablation between 2015 and 2017 with panoramic left atrial electrogram before ablation and clinical follow-up for at least 1 year following ablation were included. Convolutional neural network and a novel multimodal fusion framework were developed for predicting 1-year atrial fibrillation recurrence after catheter ablation from electrogram, ECG signals, and clinical features. The models were trained and validated using 10-fold cross-validation on patient-level splits.

Results:
One hundred fifty-six patients (64.5±10.5 years, 74% male, 42% paroxysmal) were analyzed. Using electrogram signals alone, the convolutional neural network achieved an area under the receiver operating characteristics curve (AUROC) of 0.731, outperforming the existing APPLE scores (AUROC=0.644) and CHA2DS2-VASc scores (AUROC=0.650). Similarly using 12-lead ECG alone, the convolutional neural network achieved an AUROC of 0.767. Combining electrogram, ECG, and clinical features, the fusion model achieved an AUROC of 0.859, outperforming single and dual modality models.

Conclusions:
Deep neural networks trained on electrogram or ECG signals improved the prediction of catheter ablation outcome compared with existing clinical scores, and fusion of electrogram, ECG, and clinical features further improved the prediction. This suggests the promise of using machine learning to help treatment planning for patients after catheter ablation.
Against the Wind of Change: An Auto-ethnographic Account on Gender Relations in the Diplomatic Sector of Post-communist States
Authors: Kostadinova Tonka 
Published: July 2022

Summary
The article sets оut to investigate gender-related challenges in the diplomatic work of post-communist diplomatic institutions, with a focus on Bulgaria’s Ministry of Foreign Affairs. It draws on the author’s own experience as a diplomat and takes an auto-ethnographic perspective in order to elucidate from within the daily routines, working life and gender relations at Bulgaria’s Foreign Office. The research provides unique first-hand access to the institutional and micro-political context in which Eastern European diplomats perform and develop. It brings greater nuances to the understanding of gender and diplomatic work and unpacks important issues that have remained invisible in mainstream diplomacy studies such as the complex and sensitive contemporary interactions between gender, diplomacy, and the national security and intelligence departments.
The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model “Wind Stress Shaving” Experiments
Authors: Yu Sungduk , Fedorov Alexey V. 
Published: July 2022

Abstract
Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño–Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these “wind stress shaving” experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus “shaving off” wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker eastern Pacific (EP) El Niño events. Removing most wind burst–related wind stress anomalies reduces ENSO mean amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is highlighted by the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.
Whence the Action? the Persistence and Aggravation of Violent Crime at Addresses, Streets, and Neighborhoods
Authors: O'Brien Dan , Ciomek Alex 
Published: July 2022

Objectives Researchers have long studied the persistence of violence and aggravation from disorder to violence in communities. Recently this work has begun to consider how these phenomena might operate simultaneously at multiple geographic scales. We examine the role of neighborhoods, streets, and addresses in these phenomena, presenting and assessing a five-part typology for cross-scale interactions. Methods We calculated six measures of physical disorder, social disorder, and violent crime from administrative records for all parcels (i.e., addresses) in Boston, MA, for 2011–2016. Multilevel models used these measures to predict public violence and gun-related events in the following year at all three geographical scales and with cross-scale interactions. Results Persistence was common at all scales. Aggravation from disorder to crime was greatest for addresses. Nearly all significant cross-level interactions involved addresses. The most common interactions were reinforced persistence, when persistence of violence at an address was reinforced by violence in the street or neighborhood; and mediated persistence, when persistence at a higher geographic scale operated through addresses with disorder. Conclusions The study suggests that action is greatest at addresses, but streets and neighborhoods offer critical context. It also provides a framework for future work assessing the complementarity of communities and places.
Automated image quality assessment of mammography phantoms: a systematic review
Authors: Alawaji Zeyad , Tavakoli Taba Seyedamir , Rae William 
Published: July 2022

Background: Computerized image analysis is a viable technique for evaluating image quality as a complement to human observers. Purpose: To systematically review the image analysis software used in the assessment of 2D image quality using mammography phantoms. Material and Methods: A systematic search of multiple databases was performed from inception to July 2020 for articles that incorporated computerized analysis of 2D images of physical mammography phantoms to determine image quality. Results: A total of 26 studies were included, 12 were carried out using direct digital imaging and 14 using screen film mammography. The ACR phantom (model-156) was the most frequently evaluated phantom, possibly due to the lack of accepted standard software. In comparison to the inter-observer variations, the computerized image analysis was more consistent in scoring test objects. The template matching method was found to be one of the most reliable algorithms, especially for high-contrast test objects, while several algorithms found low-contrast test objects to be harder to distinguish due to the smaller contrast variations between test objects and their backgrounds. This was particularly true for small object sizes. Conclusion: Image analysis software was in agreement with human observers but demonstrated higher consistency and reproducibility of quality evaluation. Additionally, using computerized analysis, several quantitative metrics such as contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) could be used to complement the conventional scoring method. Implementing a computerized approach for monitoring image quality over time would be crucial to detect any deteriorating mammography system before clinical images are impacted.
Hip Arthroscopy for the Treatment of Femoroacetabular Impingement Syndrome in Hips With Mild Osteoarthritis (Tönnis Grade 1): A Matched Comparative Cohort Analysis at Minimum 5-Year Follow-up
Authors: Sivasundaram Lakshmanan , Rice Morgan W. , Horner Nolan S. , Alter Thomas D. , Ephron Christopher G. , Nho Shane J. 
Published: July 2022

Background: There is a paucity of information in the literature on midterm outcomes from the arthroscopic treatment of femoroacetabular impingement syndrome (FAIS) with concomitant labral treatment in patients with mild osteoarthritis (OA) using modern surgical techniques. Purpose: To compare outcomes of hip arthroscopy for the treatment of FAIS between patients with mild OA (Tönnis grade 1) and patients without OA (Tönnis grade 0) at minimum 5-year follow-up. Study Design: Cohort study; Level of evidence, 3. Methods: Patients were identified who underwent primary hip arthroscopy for FAIS with routine capsular closure between January 2012 and December 2015. Patients with Tönnis grade 1 were matched 1:3 by age, sex, and body mass index to patients without OA. The Hip Outcome Score–Activities of Daily Living (HOS-ADL), HOS–Sports Subscale (HOS-SS), modified Harris Hip Score, and 12-item International Hip Outcome Tool were collected preoperatively and at 5 years postoperatively and compared between groups using an independent t test. Survivorship rate and percentage achievement of a Patient Acceptable Symptom State (PASS) or minimal clinically important difference (MCID) were compared using a Fisher exact test. Results: A total of 50 patients (54 hips) with Tönnis grade 1 were matched to 162 patients (162 hips) with Tönnis grade 0. The mean ± SD age and body mass index of the Tönnis grade 1 group were 44.5 ± 9.6 years and 28.5 ± 5.5, respectively. Patient-reported outcome (PRO) scores improved significantly for both groups from presurgery to 5 years postoperatively for all PROs ( P≤ .03). There were no significant differences in preoperative PROs between the groups. Patients with Tönnis grade 1 had significantly lower postoperative scores on the HOS-ADL (74.7 ± 22.6 vs 83.0 ± 20.1; P = .04) and HOS-SS (58.8 ± 33.7 vs 71.8 ± 29.3; P = .03) than patients with grade 0. Patients with Tönnis grade 1 also had significantly lower rates of achievement of the MCID (57.1% vs 80.2%; P < .01) and PASS (34.1% vs 53.4%; P = .03) for any PRO when compared with patients with Tönnis grade 0. Gross survivorship was significantly lower for Tönnis grade 1 versus grade 0 (77.8% vs 96.9%; P < .001). Conclusion: Patients with Tönnis grade 1 arthritis experienced significant improvement in PROs after hip arthroscopy for the treatment of FAIS. However, they had significantly lower postoperative HOS-ADL and HOS-SS scores with significantly lower rates of achievement on the MCID and PASS, with a significantly lower gross survivorship rate at a minimum 5 years postoperatively in comparison with those with Tönnis grade 0 changes.