Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term outcomes after anatomic total shoulder arthroplasty in patients with osteoarthritis versus osteonecrosis.

Background The outcomes of anatomic total shoulder arthroplasty (TSA) for glenohumeral osteoarthritis (OA) have been extensively reported in the literature. However, there is a paucity of short-term outcomes data on perioperative and post-operative outcomes in patients receiving TSA for either glenoid or humeral head osteonecrosis (ON). We compared the short-term outcomes of TSA performed on patients who had glenohumeral OA and those who had ON. Specifically, we compared: (I) demographics; (II) length-of-stay (LOS); and (III) 30-day postoperative complication rates. Methods Data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) was used to select 7,066 patients with a diagnosis of glenohumeral OA or ON who underwent TSA between January 1st, 2008 and December 31st, 2015. Propensity score matching was performed based on age, sex, race, and body mass index (BMI) with a 3:1 ratio of OA to ON patients. As a result, 387 patients who had a primary diagnosis of OA were compared with 129 patients who had ON. Analyses addressed perioperative complications and hospital disposition factors-including LOS and 30-day post-operative complications-using bivariate and logistic regression models. Results Overall complication rates were low in both groups, at 6.1% and 5.4% for the ON and OA cohorts respectively. The ON cohort had more comorbidities, including higher Charlson/Devo and American Society of Anesthesiologists (ASA) scores (P<0.05). Hospital disposition factors were not statistically different between the 2 groups. While not statistically significant, the ON cohort had a longer average (21.1% longer, P>0.05), an increased risk for developing any complication [odds ratio (OR) =2.07; 95% confidence interval (CI), 0.94 to 4.57; P=0.07], and a 134% higher risk for developing a minor complication compared to the OA (OR =2.34; 95% CI, 1.01 to 5.42; P=0.047). Conclusions Although the patient populations who develop ON or shoulder OA have differences, there are not statistically or clinically meaningful differences in the short-term outcomes after anatomic TSA. Future studies are required to examine if differences exist in the long-term follow-up between the two groups.
Absit invidia verbo: Comparing Deep Learning methods for offensive language

This document describes our approach to building an Offensive Language Classifier. More specifically, the OffensEval 2019 competition required us to build three classifiers with slightly different goals: - Offensive language identification: would classify a tweet as offensive or not. - Automatic categorization of offense types: would recognize if the target of the offense was an individual or not. - Offense target identification: would identify the target of the offense between an individual, group or other. In this report, we will discuss the different architectures, algorithms and pre-processing strategies we tried, together with a detailed description of the designs of our final classifiers and the reasons we choose them over others. We evaluated our classifiers on the official test set provided for the OffenseEval 2019 competition, obtaining a macro-averaged F1-score of 0.7189 for Task A, 0.6708 on Task B and 0.5442 on Task C.
Factors of missed appointments at an academic medical center in Taiwan

Background: Missed appointments mean appointments neither attended nor canceled by patients. Missed appointments belong to one of the important subjects of hospital management because they would incur the inactivity of medical professionals and devices, occupy the health resources for other patients, and thus impair the quality of healthcare services. The aim of this study was to explore the factors of missed appointments at the outpatient department of an academic medical center in Taiwan. Methods: This was a cross-sectional study based on registration records of an academic medical center in Northern Taiwan in 2015. Fifteen variables of patients, appointments, and weathers were taken into analysis. Logistic regression was used to calculate the adjusted odds ratio of each variable. For nonfirst visits, we further built a logistic regression model with the five most influential variables and the personal attendance pattern of the previous three appointments. Results: Of 2 132 577 eligible appointments in 2015, the overall no-show rate was 16.9%. The influential factors included the following: (1) patient characteristics: younger than 40 years, ⩽6 visits, and a no-show rate between 50% and 75% in the previous year; (2) appointment characteristics: Saturdays, evenings, on the last third of the waiting list, only one appointment on the same day, online appointments, appointment-to-visit intervals (wait time) in 7 to 14 days, appointments to obstetrics/gynecology or pediatrics, first-time appointments, and the interval between the appointed visit and the previous visit in <7 days; and (3) weather characteristics: warm weathers and heavy rains. For nonfirst appointments, the influences in decreasing order were heavy rain, shorter interval between the appointed visit and the previous visit to the same department, higher no-show rate in the previous year, total absence in the personal attendance pattern of the previous three appointments, longer wait time, and children. Conclusion: The factors of missed appointments were multifaceted. Further measures could be undertaken accordingly to enhance healthcare efficiency.
Transition to chaos in an acoustically driven cavity flow

We consider the unsteady regimes of an acoustically-driven jet that forces a recirculating flow through successive reflections on the walls of a square cavity. The specific question being addressed is to know whether the system can sustain states of low-dimensional chaos when the acoustic intensity driving the jet is increased, and, if so, to characterise the pathway and underlying physical mechanisms. We adopt two complementary approaches, both based on data extracted from numerical simulations: (i) We first characterise successive bifurcations through the analysis of leading frequencies. Two successive phases in the evolution of the system are singled out in this way, both leading to potentially chaotic states. The two phases are separated by a drastic simplification of the dynamics that immediately follows the emergence of intermittency. The second phase also features a second intermediate state where the dynamics is simplified due to frequency-locking. (ii) Nonlinear time series analysis enables us to reconstruct the attractor of the underlying dynamical system, and to calculate its correlation dimension and leading Lyapunov exponent. Both these quantities bring confirmation that the state preceding the dynamic simplification that initiates the second phase is chaotic. Poincar\'e maps further reveal that this chaotic state in fact results from a dynamic instability of the system between two non-chaotic states respectively observed at slightly lower and slightly higher acoustic forcing.
Finite Element’s Analysis of Fluid Flow through the Rectangular Channel with Inclined Screens settled at Angles

The present study has been produced to analyze the steady state, laminar and Newtonian fluid flow via the rectangular channel with three equal spaced inclined perforated plates with screen boundary conditions settled at angles from $ - \frac{\pi }{4}$ to $\frac{\pi }{4}$ with κ = 2.2 and η = 0.78. The leading two-dimensional incompossible Navier-Stokes equations are solved by applying the finite element method with least square Galerkin’s scheme using the commercial software COMSOL MultiPhysics 5.4. In the first step, results are contrasted by measuring the stream-wise velocity at the exit wall of the channel with the asymptotic solution provided by Elder [1], which was built on the analysis of fluid flow through the single screen. In the 2nd step we are going to present the velocity contours with surface plot and pressure drop arrow plot in the whole domain vs the inclination of the screens. Finally, dragging forces are calculated at the three perforated plates in terms of angle. The research is significant because it contributed interpolated equations of the maximum velocity at the exit, maximum pressure in the domain and drag force applied by the single screen in terms of inclination.
A Neurorobotics Approach to Investigating the Emergence of Communication in Robots

This paper introduces our approach to building a robot with communication capability based on the two key features: stochastic neural dynamics and prediction error minimization. A preliminary experiment showed that the humanoid robot was able to imitate other's action by means of those key features. In addition, we found that some sorts of communicative patterns emerged between two robots in which the robots inferred the intention of another agent behind the sensory observation.
The Meaning of Aggression Varies Across Culture: Testing the Measurement Invariance of the Refined Aggression Questionnaire in Samples From Spain, the United States, and Hong Kong

Abstract Cultural differences in aggression are still poorly understood. The purpose of this article is to assess whether a tool for measuring aggression has the same meaning across cultures. Analyzing samples from Spain (n = 262), the United States (n = 344), and Hong Kong (n = 645), we used confirmatory factor analysis to investigate measurement invariance of the refined version of the Aggression Questionnaire (Bryant & Smith, 2001). The measurement of aggression was more equivalent between the Chinese and Spanish versions than between these two and the U.S. version. Aggression does not show invariance at the cultural level. Cultural variables such as affective autonomy or individualism could influence the meaning of aggression. Aggressive behavior models can be improved by incorporating cultural variables.
The GREATS H β + [O iii] luminosity function and galaxy properties at z ∼ 8: walking the way of JWST

The James Webb Space Telescope will allow to spectroscopically study an unprecedented number of galaxies deep into the reionization era, notably by detecting [O iii]λλ4959, 5007, and H β nebular emission lines. To efficiently prepare such observations, we photometrically select a large sample of galaxies at z ∼ 8 and study their rest-frame optical emission lines. Combining data from the GOODS Re-ionization Era wide-Area Treasury from Spitzer (GREATS) survey and from HST we perform spectral energy distribution (SED) fitting, using synthetic SEDs from a large grid of photoionization models. The deep Spitzer/IRAC data combined with our models exploring a large parameter space enables to constrain the [O iii] + H β fluxes and equivalent widths for our sample, as well as the average physical properties of z ∼ 8 galaxies, such as the ionizing photon production efficiency with $\log (\xi _\mathrm{ion}/\mathrm{erg}^{-1}\mathrm{Hz})\ge 25.77$. We find a relatively tight correlation between the [O iii] + H β and UV luminosity, which we use to derive for the first time the [O iii]λλ4959, 5007 + H β luminosity function (LF) at z ∼ 8. The z ∼ 8 [O iii] + H β LF is higher at all luminosities compared to lower redshift, as opposed to the UV LF, due to an increase of the [O iii] + H β luminosity at a given UV luminosity from z ∼ 3 to z ∼ 8. Finally, using the [O iii] + H β LF, we make predictions for JWST/NIRSpec number counts of z ∼ 8 galaxies. We find that the current wide-area extragalactic legacy fields are too shallow to use JWST at maximal efficiency for z ∼ 8 spectroscopy even at 1 h depth and JWST pre-imaging to ≳30 mag will be required.
Intrinsic Point Defects in Ultrathin 1T-PtSe2 Layers

Among two-dimensional (2D) transition metal dichalcogenides (TMDs), platinum diselenide (PtSe2) stands at a unique place in the sense that it undergoes a phase transition from type-II Dirac semimetal to indirect-gap semiconductor as thickness decreases. Defects in 2D TMDs are ubiquitous and play crucial roles in understanding and tuning electronic, optical, and magnetic properties. Here we investigate intrinsic point defects in ultrathin 1T-PtSe2 layers grown on mica through the chemical vapor transport (CVT) method, using scanning tunneling microscopy and spectroscopy (STM/STS) and first-principles calculations. We observed five types of distinct defects from STM topography images and obtained the local density of states of the defects. By combining the STM results with the first-principles calculations, we identified the types and characteristics of these defects, which are Pt vacancies at the topmost and next monolayers, Se vacancies in the topmost monolayer, and Se antisites at Pt sites within the topmost monolayer. Our study shows that the Se antisite defects are the most abundant with the lowest formation energy in a Se-rich growth condition, in contrast to cases of 2D molybdenum disulfide (MoS2) family. Our findings would provide critical insight into tuning of carrier mobility, charge carrier relaxation, and electron-hole recombination rates by defect engineering or varying growth condition in few-layer 1T-PtSe2 and other related 2D materials.
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks

Recently, Deep Neural Networks (DNNs) have been achieving impressive results on wide range of tasks. However, they suffer from being well-calibrated. In decision-making applications, such as autonomous driving or medical diagnosing, the confidence of deep networks plays an important role to bring the trust and reliability to the system. To calibrate the deep networks' confidence, many probabilistic and measure-based approaches are proposed. Temperature Scaling (TS) is a state-of-the-art among measure-based calibration methods which has low time and memory complexity as well as effectiveness. In this paper, we study TS and show it does not work properly when the validation set that TS uses for calibration has small size or contains noisy-labeled samples. TS also cannot calibrate highly accurate networks as well as non-highly accurate ones. Accordingly, we propose Attended Temperature Scaling (ATS) which preserves the advantages of TS while improves calibration in aforementioned challenging situations. We provide theoretical justifications for ATS and assess its effectiveness on wide range of deep models and datasets. We also compare the calibration results of TS and ATS on skin lesion detection application as a practical problem where well-calibrated system can play important role in making a decision.
Reducing health inequalities trough digital options in mental health: A physician’s perspective

Abstract This article explores the physicians’ perspective regarding the potential of computerized cognitive behavioral therapies (cCBTs) to overcome inequalities in the context of mental health care provision. The main benefits were related to the ability of cCBTs to provide care in a convenient and efficient manner, enhancing its accessibility. These aspects were perceived more important than cost-effectivity of treatment, which is often claimed to be the key benefit of cCBTs. Age and general acceptance of CBT were the most significant individual-level separators of perceptions, while the sector in which the physician works was seen as the main structural-level separator.
Spectral analysis of mixing in 2D high-Reynolds flows

We use spectral analysis of Eulerian and Lagrangian dynamics to study the advective mixing in an incompressible 2D bounded cavity flow. A significant property of such a rotational flow at high Reynolds numbers is that mixing in its core is slower than wall-adjacent areas and corner eddies. We explain this property by considering the resonance between frequencies of unsteady motion -- captured by the Koopman spectral analysis of the velocity field -- and the circulation frequency of Lagrangian tracers in the mean flow. In high-Reynolds rotational 2D flows, the vorticity in the rotational core is uniformly distributed, which leads to uniform distribution of circulation periods in the mean flow, i.e., the kinematics in the core of mean flow is like rigid-body rotation. When this ``rigid" core is exposed to velocity fluctuations arising from bifurcations at high Reynolds, it shows more resilience toward resonance in Lagrangian motion and hence mixes more slowly compared to other areas. We also show how our qualitative resonance argument extends to chaotic flows where the classical tools of dynamical systems are not applicable.
Transfer Learning for Indonesian Named Entity Recognition

Transfer learning enables machine learning system to use previously acquired knowledge from source task on target task. Source task and target task should be correlated. Successful transfer learning would improve system performance on target task especially when target data is small. In this paper, the source task is Indonesian part-of-speech (POS) tagging system and the target task is Indonesian named entity recognition (NER) tagging system. A bidirectional gated recurrent unit and conditional random field (GRU-CRF) sequence labeller is crafted as the model for the feature representation transfer to be applied. We compared F1 evaluation results between system that used transfer and system that did not use transfer and saw an improvement around 0.02 on the transferred model when 100% training data was used and around 0.15 when 1% training data was used.
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection

With a single eye fixation lasting a fraction of a second, the human visual system is capable of forming a rich representation of a complex environment, reaching a holistic understanding which facilitates object recognition and detection. This phenomenon is known as recognizing the "gist" of the scene and is accomplished by relying on relevant prior knowledge. This paper addresses the analogous question of whether using memory in computer vision systems can not only improve the accuracy of object detection in video streams, but also reduce the computation time. By interleaving conventional feature extractors with extremely lightweight ones which only need to recognize the gist of the scene, we show that minimal computation is required to produce accurate detections when temporal memory is present. In addition, we show that the memory contains enough information for deploying reinforcement learning algorithms to learn an adaptive inference policy. Our model achieves state-of-the-art performance among mobile methods on the Imagenet VID 2015 dataset, while running at speeds of up to 70+ FPS on a Pixel 3 phone.
Structured 2D Representation of 3D Data for Shape Processing

We represent 3D shape by structured 2D representations of fixed length making it feasible to apply well investigated 2D convolutional neural networks (CNN) for both discriminative and geometric tasks on 3D shapes. We first provide a general introduction to such structured descriptors, analyze their different forms and show how a simple 2D CNN can be used to achieve good classification result. With a specialized classification network for images and our structured representation, we achieve the classification accuracy of 99.7\% in the ModelNet40 test set - improving the previous state-of-the-art by a large margin. We finally provide a novel framework for performing the geometric task of 3D segmentation using 2D CNNs and the structured representation - concluding the utility of such descriptors for both discriminative and geometric tasks.
Joint Load Control and Energy Sharing for Autonomous Operation of 5G Mobile Networks in Micro-Grids

In this paper, we focus on the design of energy self-sustainable mobile networks by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energies. Many papers are available in the literature on this problem, and however, we are approaching this issue from a different angle. In fact, we advocate for future mobile networks with a hierarchical cell structure and powered by energy harvesting hardware. Base stations within the same geographical area are grouped in a micro-grid and operate almost autonomously from the power grid. To achieve this goal, we target the design of the optimal traffic and computational load control method with energy sharing within the micro-grid. We solve the optimization problem using a graph-based method, and we demonstrate, via software simulations, that a combination of load control plus energy sharing represents a viable and economically convenient solution for enabling energy self-sustainability of mobile networks grouped in micro-grids.
A Top–Bottom Clustering Algorithm Based on Crowd Trajectories for Small Group Classification

The study of crowd movement has recently become a popular research topic due to the increasing frequency of public safety issues. Compared with human evacuation experiments and drills, which may have personal safety risks and require a large number of volunteers, a simple and convenient computer simulation has become the mainstream research method. The computer simulation first needs to characterize small groups in the crowd to model the motion state of crowds for more accurate crowd modeling. In this paper, a top–bottom hierarchical clustering algorithm based on off-line crowd trajectories is proposed to provide small group information for crowd motion simulation. First, an unmanned aerial vehicle and a tracking technology are used to capture the pedestrian flow and extract the pedestrian trajectory. Second, a top–bottom hierarchical clustering strategy is proposed to divide the crowd into groups, which solves the problem of the difficulty of ascertaining small groups. This method solves the problem of automatically determining cluster centers by using the improved density peak clustering algorithm combined with a greedy algorithm. One factor of distinguishing small groups is improved by replacing the angle of the direction of motion with the distance difference, thus reducing the computational complexity. Specifically, the mean distance between trajectories based on the Euclidean distance is used for the top-level coarse-grained clustering; then, the improved Hausdorff mean distance is determined in the bottom-level fine-grained clustering. Third, the proposed algorithm is validated by classifying the groups of pedestrians in real videos. The experiments show that the proposed method is applicable and effective.
Material spike formation in high Reynolds number flow separation

We apply the recent frame-invariant theory of separation spike formation to complex unsteady flows including a turbulent separation bubble, an impinging jet, and flows around a freely moving cylinder and a freely rotating ellipse. We show how the theory captures the onset of material spike formation, without any assumption on the flow type (steady, periodic, unsteady) or separation type (on- or off-wall, fixed or moving boundaries). We uncover new phenomena, such as the transition from on-wall to off-wall separation, the merger of initially distinct spikes, and the presence of severe material spikes that remain hidden to previous approaches. These results unveil how an involved network of spikes arises, interacts, and merges dynamically, leading to the final ejection of particles from the wall in highly transient flow separation processes.