Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting models of emergency department crowding.

OBJECTIVES The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. METHODS From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. RESULTS The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. CONCLUSIONS Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours.
Covariance Matrix Estimation for Reinforcement Learning

One of the goals in scaling reinforcement learning (RL) pertains to dealing with high-dimensional and continuous state-action spaces. In order to tackle this problem, recent efforts have focused on harnessing well-developed methodologies from statistical learning, estimation theory and empirical inference. A key related challenge is tuning the many parameters and efﬁciently addressing numerical problems, such that ultimately efﬁcient RL algorithms could be scaled to real-world problem settings. Methods such as Covariance Matrix Adaptation - Evolutionary Strategy (CMAES), Policy Improvement with Path Integral ( PI 2 ) and their variations heavily depends on the covariance matrix of the noisy data observed by the agent. It is well known that covariance matrix estimation is problematic when the number of samples is relatively small compared to the number of variables. One way to tackle this problem is through the use of shrinkage estimators that offer a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the target ) with the aim of minimizing the mean-squared error (MSE). Recently, it has been shown that a Multi-Target Shrink-age Estimator (MTSE) can greatly improve the single-target variation by utilizing several targets simultaneously. Unlike the computationally complex cross-validation (CV) procedure, the shrinkage estimators provide an analytical framework which is an attractive alternative to the CV computing procedure. We consider the application of shrinkage estimators in dealing with a function approximation problem, using the quadratic discriminant analysis (QDA) technique and show that a two-target shrinkage estimator generates improved performance. The approach paves the way for improved value function estimation in large-scale RL settings, offering higher efﬁciency and fewer hyper-parameters.
Institute for International Integration Studies the Macroeconomics of Financial Integration: a European Perspective the Macroeconomics of Financial Integration: a European Perspective the Macroeconomics of Financial Integration: a European Perspective

Disclaimer Any opinions expressed here are those of the author(s) and not those of the IIIS. All works posted here are owned and copyrighted by the author(s). Papers may only be downloaded for personal use only. Abstract This essay addresses the macroeconomics of international financial integration from a European perspective. We first analyse the role of international financial integration in promoting economic convergence among members of the European Union. Next, we analyse the implications of increasing financial linkages, both within Europe and between Europe and other regions in the world economy. Finally, we assess how increased financial integration has altered the economics of external adjustment.
Ligature‐associated bacterial profiles are linked to type 2 diabetes mellitus in a rat model and influenced by antibody treatment against TNF‐α or RAGE

There is a bidirectional relationship between periodontal disease (PD) and type 2 diabetes mellitus (T2D). T2D may lead to ecological perturbations in the oral environment, which may facilitate an altered microbiota. However, previous studies have been inconclusive in determining the effect of T2D on oral bacterial profiles. Therefore, we aimed to evaluate the influence of T2D on the ligature‐associated bacterial profile in a diabetic rat model with PD and investigated the impact of blocking inflammatory pathways with antibodies targeting either Tumor Necrosis Factor α (TNF‐α) or the receptor of advanced glycation end‐products (RAGE). A total of 62 Zucker obese rats (45 T2D) and 17 lean (non‐T2D) were divided into 4 treatment groups; lean with PD, obese with PD, obese with PD and anti‐TNF‐α treatment, and obese with PD with anti‐RAGE treatment. Periodontal disease was ligature induced. Ligature‐associated bacterial profiles were analyzed using Human Oral Microbe Identification Microarray (HOMIM). Ligature‐associated bacterial profiles differed between lean and obese rats. Furthermore, treatment with antibodies against TNF‐α or RAGE had an impact on subgingival bacterial profiles. T2D phenotypes are associated with different ligature‐associated bacterial profiles and influenced by treatment with antibodies against TNF‐α or RAGE.
Failure to acquire new semantic knowledge in patients with large medial temporal lobe lesions

We examined new semantic learning in two profoundly amnesic patients (E.P. and G.P.) whose lesions involve virtually the entire medial temporal lobe (MTL) bilaterally. The patients were given five tests of semantic knowledge for information that could only have been acquired after the onset of their amnesia in 1992 and 1987, respectively. Age‐matched and education‐matched controls (n = 8) were also tested. On tests of recall, E.P. and G.P. each scored 10% correct on a test of 20 easy factual questions (controls = 90%), 2% and 4% correct on 55 questions about news events (controls = 85%), and 0% and 4% correct on a test of 24 famous faces. On three tests of recognition memory for this same material, the patients scored at chance levels. Similarly, the patients were unable to judge whether persons who had been famous for many decades were still living or had died during the past 10 years (E.P. = 53%; G.P. = 50%; controls = 73%; chance = 50%). Lastly, neither patient E.P. nor patient G.P. could draw an accurate floor plan of his current residence, despite having lived there for 10 years and 1 year, respectively. The results demonstrate that the capacity for new semantic learning can be absent, or nearly absent, when there is virtually complete damage to the MTL bilaterally. Accordingly, the results raise the possibility that the acquisition of conscious (declarative) knowledge about the world cannot be supported by structures outside the MTL, even with extended exposure. Published 2004 Wiley‐Liss, Inc.
Networked Flow in musical bands

This study aimed at using the Networked Flow (NF) model to investigate group collaboration in the context of musical bands. We analyzed the relationship between flow, social presence, structural dynamics and performance as they related to 15 bands in a rehearsal room. Flow was measured using the Flow State Scale; social presence was assessed with the Networked Minds Social Presence scale; and interpersonal communication structure (exchange of gazes and verbal orders) was assessed by means of Social Network Analysis (SNA). In addition, we considered: (a) a subjective measure of performance, rated by each member on an ad-hoc questionnaire; and (b) an expert rating of performance, based on the evaluation of audio-video recordings of each group. Findings showed the multifaceted nature of the relationship between social presence and flow. Group flow score was a significant predictor of self-reported performance, but not of expert-evaluated performance. Moreover, several correlations were found between flow, social presence and patterns of interpersonal coordination (both implicit and explicit). Specifically, SNA reveals that flow was positively related to exchanges of gazes and negatively associated with exchanges of orders. Overall, this study contributes to further elucidating the complex interplay between group flow and intersubjective dynamics in music collaboration.
Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature

[1] Land surface temperature (LST) is involved in many land surface processes such as evapotranspiration, net radiation, or air temperature modeling. In this paper we present an improved methodology to retrieve LST from Landsat 4 TM, Landsat 5 TM, and Landsat 7 ETM+ using four atmospheric databases covering different water vapor ranges (from 0 to 8 g cm−2) to build the LST retrieval models and using both water vapor and air temperature as input variables. We also compare this with LST retrievals using only water vapor or only air temperature, as well as with an existing LST retrieval algorithm. In order to validate the results, we have selected 77 Landsat images taken between 2002 and 2006 (Catalonia, northeast of the Iberian Peninsula) and two sources of water vapor (radiosounding data and remote sensing estimations) and air temperature (radiosounding data and air temperature modeling). The best results using radiosounding data are obtained when both air temperature and water vapor are present in the LST retrieval models with a mean RMSE of 0.9 K, followed by only water vapor models with a mean RMSE of 1.5 K and only air temperature models with a mean RMSE of 5.6 K. The results obtained using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 water vapor product and at-satellite-pass air temperature modeling as input data also show that this kind of input data offers best results, with a mean RMSE of 0.9 K, followed by water vapor models with a mean RMSE of 2.1 K and only air temperature models with a mean RMSE of 5.6 K. Similar errors when using radiosounding or modeled water vapor and air temperature as input data suggest the avoidance of radiosounding data to retrieve LST over extensive areas. Finally, when comparing the presented methodology with another methodology also using water vapor and air temperature as input data, the improvement is of more than 0.5 K.
Does job insecurity threaten who you are? Introducing a social identity perspective to explain well-being and performance consequences of job insecurity

Summary This paper introduces a social identity perspective to job insecurity research. Worrying about becoming jobless, we argue, is detrimental because it implies an anticipated membership of a negatively evaluated group—the group of unemployed people. Job insecurity hence threatens a person's social identity as an employed person. This in turn will affect well-being and job performance. A three-wave survey study amongst 377 British employees supports this perspective. Persons who felt higher levels of job insecurity were more likely to report a weaker social identity as an employed person. This effect was found to be stable over time and also held against a test of reverse causality. Furthermore, social identity as an employed person influenced well-being and in-role job performance and mediated the effect of job insecurity on these two variables over time. Different to the expectations, social identity as an employed person and organisational proactivity were not connected. The findings deliver interesting evidence for the role of social identity as an employed person in the relationships between job insecurity and its consequences. Theoretically, this perspective illustrates the individual and group-related nature of job insecurity and offers a novel way of connecting work situations with individual well-being, behaviour and attitudes. Copyright © 2017 John Wiley & Sons, Ltd.
How Knowledge Transfer Impacts Performance: A Multi-Level Model of Benefits and Liabilities

When does knowledge transfer benefit performance? Combining field data from a global consulting firm with an agent-based model, we examine how efforts to supplement one's knowledge from coworkers interact with individual, organizational, and environmental characteristics to impact organizational performance. We find that once cost and interpersonal exchange are included in the analysis, the impact of knowledge transfer is highly contingent. Depending on specific characteristics and circumstances, knowledge transfer can better, matter little to, or even harm performance. Three illustrative studies clarify puzzling past results and offer specific boundary conditions: 1 At the individual level, better organizational support for employee learning diminishes the benefit of knowledge transfer for organizational performance. 2 At the organization level, broader access to organizational memory makes global knowledge transfer less beneficial to performance. 3 When the organizational environment becomes more turbulent, the organizational performance benefits of knowledge transfer decrease. The findings imply that organizations may forgo investments in both organizational memory and knowledge exchange, that wide-ranging knowledge exchange may be unimportant or even harmful for performance, and that organizations operating in turbulent environments may find that investment in knowledge exchange undermines performance rather than enhances it. At a time when practitioners are urged to make investments in facilitating knowledge transfer and collaboration, appreciation of the complex relationship between knowledge transfer and performance will help in reaping benefits while avoiding liabilities.
Breakfast Time Blackouts

We present the case of a 16-year-old girl who suffered from repeated episodes of collapse and loss of consciousness which could be provoked by undertaking a stretching manoeuvre comprising a combined breath hold and neck torsion. A review of the literature is provided on other cases of so-called “stretch syncope” which appears to be a rare form of reflex syncope affecting patients in adolescence.
Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance

Abnormal behaviour may indicate important objects and situations in e.g. surveillance applications. This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) which is a parameter-light algorithm for on-line learning and anomaly detection with well-calibrated false alarm rate. The only design parameter in SNN-CAD is the dissimilarity measure. We propose two parameter-free dissimilarity measures based on Hausdorff distance for comparing multi-dimensional trajectories of arbitrary length. One of these measures is appropriate for sequential anomaly detection in incomplete trajectories. The proposed algorithms are evaluated using two public data sets. Results show that high sensitivity to labelled anomalies and low false alarm rate can be achieved without any parameter tuning.
Consensus-based distributed optimization with malicious nodes

We investigate the vulnerabilities of consensus-based distributed optimization protocols to nodes that deviate from the prescribed update rule (e.g., due to failures or adversarial attacks). After characterizing certain fundamental limitations on the performance of any distributed optimization algorithm in the presence of adversaries, we propose a robust consensus-based distributed optimization algorithm that is guaranteed to converge to the convex hull of the set of minimizers of the non-adversarial nodes' functions. We also study the distance-to-optimality properties of our proposed robust algorithm in terms of F-local sets of the graph. We show that finding the largest size of such sets is NP-hard.
Pruning the news feed: Unfriending and unfollowing political content on social media

Social media allow users some degree of control over the content to which they are exposed, through blocking, unfriending, or hiding feeds from other users. This article considers the extent to which they do so for political reasons. Survey data from Pew Research suggests that political unfriending is relatively rare, with fewer than 10% of respondents engaging in the practice. Analysis finds support for the idea that political unfriending is most common among those who talk about politics, those strongest in ideology, those that see the most politics in social media, and those that perceive the greatest political disagreement in their social networks. This suggests that social media are not exacerbating the political information gap as political information on social media is likely still reaching the least politically engaged, whereas the most politically engaged may opt out of political information within social media but still receive it elsewhere.
Downregulation of Cdh1 signalling in spinal dorsal horn contributes to the maintenance of mechanical allodynia after nerve injury in rats

Background Anaphase-promoting complex/cyclosome (APC/C) and its co-activator Cdh1 are important ubiquitin-ligases in proliferating cells and terminally differentiated neurons. In recent years, APC/C-Cdh1 has been reported as an important complex contributing to synaptic development and transmission. Interestingly, cortical APC/C-Cdh1 is found to play a critical role in the maintenance of neuropathic pain, but it is not clear whether APC/C-Cdh1 in spinal dorsal cord is involved in molecular mechanisms of neuropathic pain conditions. Results Immunostaining showed that Cdh1 was mainly distributed in dorsal horn neurons of the spinal cord in rats. Its expression was downregulated in the ipsilateral dorsal horn at 14 days after spared nerve injury. Rescued expression of Cdh1 in spinal cord by intrathecal administration of recombinant lentivirus encoding Cdh1 (Lenti-Cdh1-GFP) significantly attenuated spared nerve injury-induced mechanical allodynia. Furthermore, rescued expression of spinal Cdh1 significantly reduced surface membrane expression of GluR1, but increased the expression of GluR1-related erythropoietin-producing human hepatocellular receptor A4 and its ligand EphrinA1 in dorsal horn of spared nerve injury-treated animals. Conclusions This study indicates that a downregulation of Cdh1 expression in spinal dorsal horn is involved in molecular mechanisms underlying the maintenance of neuropathic pain. Upregulation of spinal Cdh1 may be a promising approach to treat neuropathic pain.
Experiments on the Indonesian plagiarism detection using latent semantic analysis

Plagiarism is an important task since its number is increasing and the plagiarism technique is getting difficult. It means that there is not only literal plagiarism but also intelligence plagiarism. In order to handle the intelligence plagiarism, we employed latent semantic analysis (LSA) as the term-document representation. The LSA was used in the Heuristic Retrieval (HR) component and Detailed Analysis (DA) component. We conducted several experiments to compare the token type, the text segmentation and the threshold value. The test data were prepared manually from the available Indonesian paper corpus. Experimental results showed that the LSA outperformed the VSM (Vector Space Model), especially in test cases with intelligence plagiarism.
Allometric Equations for Estimating Biomass of Euterpe precatoria, the Most Abundant Palm Species in the Amazon

Allometric models to estimate biomass components such as stem mass Ms, foliage mass Ml, root mass Mr and aboveground mass Ma, were developed for the palm species Euterpe precatoria Mart. , which is the most abundant tree species in the Amazon. We harvested twenty palms including above- and below-ground parts in an old growth Amazonian forest in Brazil. The diameter at breast height D ranged from 3.9-12.7 cm, and the stem height H ranged from 2.3-16.4 m. The D, diameter at ground basis D0, crown diameter CD, H, stem specific gravity ρ, and number of fronds Nf were considered as independent variables and incorporated into a power function model. The best predictors were D 2 Hρ for Ms and Ma, D 2 HNf for Ml, and D for Mr. Slender index (H/D) ranged from 0.56-1.46 m·cm −1 , and the D-H relationship suggested that the stem shape becomes more slender with increasing D. On the other hand, ρ increased with D implying a stiffening of
Excess Body Mass Index Loss at 3 Months: A Predictive Factor of Long-Term Result after Sleeve Gastrectomy

Introduction. Laparoscopic Sleeve Gastrectomy (SG) is considered as successful if the percentage of Excess Body Mass Index Loss (% EBMIL) remains constant over 50% with long-term follow-up. The aim of this study was to evaluate whether early % EBMIL was predictive of success after SG. Methods. This retrospective study included patients who had SG with two years of follow-up. Patients had follow-up appointments at 3 (M3), 6, 12, and 24 months (M24). Data as weight and Body Mass Index (BMI) were collected systematically. We estimated the % EBMIL necessary to establish a correlation between M3 and M24 compared to % EBMIL speeds and calculated a limit value of % EBMIL predictive of success. Results. Data at operative time, M3, and M24 were available for 128 patients. Pearson test showed a correlation between % EBMIL at M3 and that at M24 (r = 0.74; p < 0.0001). % EBMIL speed between surgery and M3 (p = 0.0011) was significant but not between M3 and M24. A linear regression analysis proved that % EBMIL over 20.1% at M3 (p < 0.0001) predicted a final % EBMIL over 50%. Conclusions. % EBMIL at M3 after SG is correlated with % EBMIL in the long term. % EBMIL speed was significant in the first 3 months. % EBMIL over 20.1% at M3 leads to the success of SG.
Serosorting Is Associated with a Decreased Risk of HIV Seroconversion in the EXPLORE Study Cohort

Background Seroadaptation strategies such as serosorting and seropositioning originated within communities of men who have sex with men (MSM), but there are limited data about their effectiveness in preventing HIV transmission when utilized by HIV-negative men. Methodology/Principal Findings Data from the EXPLORE cohort of HIV-negative MSM who reported both seroconcordant and serodiscordant partners were used to evaluate serosorting and seropositioning. The association of serosorting and seropositioning with HIV seroconversion was evaluated in this cohort of high risk MSM from six U.S. cities. Serosorting was independently associated with a small decrease in risk of HIV seroconversion (OR = 0.88; 95%CI, 0.81–0.95), even among participants reporting ≥10 partners. Those who more consistently practiced serosorting were more likely to be white (p = 0.01), have completed college (p = <0.0002) and to have had 10 or more partners in the six months before the baseline visit (p = 0.01) but did not differ in age, reporting HIV-infected partners, or drug use. There was no evidence of a seroconversion effect with seropositioning (OR 1.02, 95%CI, 0.92–1.14). Significance In high risk HIV uninfected MSM who report unprotected anal intercourse with both seroconcordant and serodiscordant partners, serosorting was associated with a modest decreased risk of HIV infection. To maximize any potential benefit, it will be important to increase accurate knowledge of HIV status, through increased testing frequency, improved test technology, and continued development of strategies to increase disclosure.