Miracle-dz/UniFAR-FTUs huggingface dataset:
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The present study aims to highlight the main models for evaluating territorial competitiveness used at international and European level starting from the following premise: despite the difficulties encountered in the process of defining and understanding the competitiveness term and of numerous and different scientific approaches, there are, though, common elements that can be found at the level of the majority of models for evaluating national, regional and local competitiveness. The paper turns to bibliographic study and comparative analysis, highlighting the main theoretical approaches of competitiveness and the models developed on their basis. Although the indices used within the models present certain specific characteristics derived from the theoretical approach on which it is based, but also implied by the selected aggregation level, there are several central elements that can be found at the level o the majority of evaluation models such as: economic performances, investments level, institutions, infrastructure, education, health and population's welfare.
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Decomposing the Composition Effect This paper proposes a decomposition of the composition effect, i.e. the part of the observed between-group difference in the distribution of some economic outcome that can be explained by differences in the distribution of covariates. Our decomposition contains three types of components: (i) the “direct contributions” of each covariate due to between-group differences in the respective marginal distributions, (ii) several “two way” and “higher order” interaction effects due to the interplay between two or more covariates’ marginal distributions, and (iii) a “dependence effect” accounting for between-group differences in dependence patterns among the covariates. Our methods can be used to decompose differences in arbitrary distributional features, like quantiles or inequality measures, and allows for general nonlinear relationships between the outcome and the covariates. It can easily be implemented in practice using standard econometric techniques. An application to wage data from the US illustrates the empirical relevance of the decomposition’s components. JEL Classification: C13, C18, C21, J31
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De Fleur (1956) provides the earliest evidence of diminishing returns. He finds a common logarithmic pattern for leaflets dropped and message recalled in field experiment. Since then, many researchers have applied logarithmic or square root patterns to capture the effect of diminishing returns with their advertising response modeling across different media. But discussions with managers support the notion that the diminishing returns to incremental dollars spent on one medium (say, television) are not likely to be the same as those for equivalent dollars spent on other media (e.g., Print). But if diminishing returns indeed vary across media, how does that change the resulting allocation recommendation? To address this issue, we derive a dynamic model that captures the notion of differential diminishing returns and disentangles it from closely related notions of differential carryovers and differential ad effectiveness. Second, we develop a systematic method to estimate the model’s parameters using market data and illustrate empirically that all three effects, diminishing returns, carryover and ad effectiveness vary across the four media employed. Finally, we investigate the normative implications for managerial decision-making. Here, we additionally account for varying media buying efficiencies across media. Taken together, the approach and its illustration should provide managers with a better toolkit to allocate their multimedia budgets.
Culture-Documented Invasive Mold Infections at MD Anderson Cancer Center in Houston, Texas, Pre– and Post–Hurricane Harvey

Abstract Background Extensive floodwater damage following hurricane Harvey raised concerns of excess mold infections in immunocompromised patients. This study sought to evaluate the impact of hurricane Harvey on the incidence of culture-positive invasive mold infections (cIMIs) in patients treated at MD Anderson Cancer Center (MDACC; Houston, TX). Methods All mold-positive culture results in the Microbiology Laboratory at MDACC in a 12-month period before and after hurricane Harvey were reviewed. cIMI cases were defined according to European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group criteria. Rates and causative agents of cIMIs pre- and post-Harvey were compared. In addition, we evaluated institution-wide trends in the use of systemically administered mold-active antifungal agents by segmented regression analysis. Results Inpatient cIMI rates per 1000 patient-days were comparable in the pre- and post-Harvey observation period (0.17 vs 0.21, P = .36). During both surveillance periods, the vast majority of cIMI cases were due to Aspergillus spp. , Fusarium spp. , or Mucorales. No emergence of unusual mold infections was seen, and the relative frequencies of mold genera recovered from cultures at the MDACC Microbiology Laboratory remained largely unaltered. The overall use of posaconazole was significantly higher in the post-Harvey period and the use of both voriconazole and liposomal amphotericin B began to increase significantly immediately after Harvey. Conclusions Our monocentric study employing stringent culture-based definitions of mold infections found no excess cases of IMIs in MDACC’s immunosuppressed patient population in the aftermath of a major flooding event. Increased use of some mold-active antifungals in the aftermath of hurricane Harvey was observed institutionally.
Instance Segmentation of Biological Images Using Harmonic Embeddings

We present a new instance segmentation approach tailored to biological images, where instances may correspond to individual cells, organisms or plant parts. Unlike instance segmentation for user photographs or road scenes, in biological data object instances may be particularly densely packed, the appearance variation may be particularly low, the processing power may be restricted, while, on the other hand, the variability of sizes of individual instances may be limited. The proposed approach successfully addresses these peculiarities. Our approach describes each object instance using an expectation of a limited number of sine waves with frequencies and phases adjusted to particular object sizes and densities. At train time, a fully-convolutional network is learned to predict the object embeddings at each pixel using a simple pixelwise regression loss, while at test time the instances are recovered using clustering in the embedding space. In the experiments, we show that our approach outperforms previous embedding-based instance segmentation approaches on a number of biological datasets, achieving state-of-the-art on a popular CVPPP benchmark. This excellent performance is combined with computational efficiency that is needed for deployment to domain specialists. The source code of the approach is available at https://github.com/kulikovv/harmonic .
Assessment on latitudinal tree species richness using environmental factors in the southeastern United States

The southeastern region of the United States exhibits an unusual trend of decreasing tree species richness (TSR) from higher to lower latitudes over the Florida peninsula. This trend contradicts the widely marked latitudinal diversity gradient where species richness is highest in tropical zones and decreases towards extratropical regions. This study aims to assess the environmental factors that prompt this atypical inverse latitudinal gradient seen in TSR using the USDA Forest Service’s Forest Inventory and Analysis (FIA) database. Fifteen variables under four categories of forested area, groundwater, soil properties, and climate groups were examined to model TSR in the region. Generalized linear models (GLMs) with Poisson distributions first assessed individual variables to test explanatory power then the LASSO regularization method was utilized to extract two subsets of the most influential variables to predict TSR. Forest area and four climate variables (mean annual temperature, precipitation seasonality, mean temperature of coldest quarter, and mean precipitation of driest quarter) were the top five variables during the initial GLM assessment implying their potential individual influence in regulating TSR. Two subsets of LASSO models contained seven and three predictor variables, respectively. Frist subset includes seven predictors, presented in highest to low standardized coefficient, mean temperature of coldest quarter, forested area, precipitation seasonality, mean precipitation of driest quarter, water table depth, spodosol, and available water storage. The other subset further excluded four lowest influential variables from the first set, leaving the top three variables from the first subset. The first subset of the LASSO model predicted TSR with 63.4% explained deviance while the second subset reproduced 60.2% of deviance explained. With only three variables used, the second model outperformed the first model evaluated by the AIC value. We conclude that forest patch area, mean temperature of coldest quarter, and precipitation seasonality are the highly influential variables of TSR among environmental factors in the southeastern region of U.S., but evolutionary or historic cause should be further incorporated to fully understand tree species diversity pattern in this region.
Data Priming Network for Automatic Check-Out

Automatic Check-Out (ACO) receives increased interests in recent years. An important component of the ACO system is the visual item counting, which recognizes the categories and counts of the items chosen by the customers. However, the training of such a system is challenged by the domain adaptation problem, in which the training data are images from isolated items while the testing images are for collections of items. Existing methods solve this problem with data augmentation using synthesized images, but the image synthesis leads to unreal images that affect the training process. In this paper, we propose a new data priming method to solve the domain adaptation problem. Specifically, we first use pre-augmentation data priming, in which we remove distracting background from the training images using the coarse-to-fine strategy and select images with realistic view angles by the pose pruning method. In the post-augmentation step, we train a data priming network using detection and counting collaborative learning, and select more reliable images from testing data to fine-tune the final visual item tallying network. Experiments on the large scale Retail Product Checkout (RPC) dataset demonstrate the superiority of the proposed method, i.e., we achieve 80.51% checkout accuracy compared with 56.68% of the baseline methods. The source codes can be found in https://isrc.iscas. ac.cn/gitlab/research/acm-mm-2019-ACO.
Contribution to adaptive higher order sliding mode controllers and observers: Application to energy management systems. (Contribution à la commande et à l'observation adaptatives par modes glissants d'ordres supérieurs: Application aux systèmes de gestion de l'énergie)

This thesis deals with the development of novel strategies to adapt higher order sliding mode controllers and observers. The implementation of classics first order and higher order sliding mode controllers requires the knowledge of the upper bound of the disturbance or its derivative, which are often not known. The first contribution of this thesis is the design of an adaptive strategy that can ensure the convergence of the sliding variable to a predefined neighborhood of zero without requiring any information of the disturbance or its derivative and without overestimating the adaptive gain. This adaptive strategy is then declined for the design of the first order, second order and integral sliding mode controllers, and for the Levant's differentiator. The second contribution of the thesis is the development of two adaptive strategies for discontinuous higher order sliding mode control. The proposed two algorithms can provide the achievement of n-order sliding mode despite disturbances with unknown upper bounds or with unknown upper bounds of their derivatives. Finally, in order to show the effectiveness of the proposed algorithms, they are successfully applied through simulations to control the wind energy conversion system and the linear induction motor system for cogeneration.
Identification and expression analysis of the GDSL esterase/lipase family genes, and the characterization of SaGLIP8 in Sedum alfredii Hance under cadmium stress

Background The herb Sedum alfredii (S. alfredii) Hance is a hyperaccumulator of heavy metals (cadmium (Cd), zinc (Zn) and lead (Pb)); therefore, it could be a candidate plant for efficient phytoremediation. The GDSL esterase/lipase protein (GELP) family plays important roles in plant defense and growth. Although the GELP family members in a variety of plants have been cloned and analyzed, there are limited studies on the family's responses to heavy metal-stress conditions. Methods Multiple sequence alignments and phylogenetic analyses were performed according to the criteria described. A WGCNA was used to construct co-expression regulatory networks. The roots of S. alfredii seedlings were treated with 100 µM CdCl2 for qRT-PCR to analyze expression levels in different tissues. SaGLIP8 was transformed into the Cd sensitive mutant strain yeast Δycf1 to investigate its role in resistance and accumulation to Cd. Results We analyzed GELP family members from genomic data of S. alfredii. A phylogenetic tree divided the 80 identified family members into three clades. The promoters of the 80 genes contained certain elements related to abiotic stress, such as TC-rich repeats (defense and stress responsiveness), heat shock elements (heat stress) and MYB-binding sites (drought-inducibility). In addition, 66 members had tissue-specific expression patterns and significant responses to Cd stress. In total, 13 hub genes were obtained, based on an existing S. alfredii transcriptome database, that control 459 edge genes, which were classified into five classes of functions in a co-expression subnetwork: cell wall and defense function, lipid and esterase, stress and tolerance, transport and transcription factor activity. Among the hub genes, Sa13F.102 (SaGLIP8), with a high expression level in all tissues, could increase Cd tolerance and accumulation in yeast when overexpressed. Conclusion Based on genomic data of S. alfredii, we conducted phylogenetic analyses, as well as conserved domain, motif and expression profiling of the GELP family under Cd-stress conditions. SaGLIP8 could increase Cd tolerance and accumulation in yeast. These results indicated the roles of GELPs in plant responses to heavy metal exposure and provides a theoretical basis for further studies of the SaGELP family's functions.
Simple BERT Models for Relation Extraction and Semantic Role Labeling

We present simple BERT-based models for relation extraction and semantic role labeling. In recent years, state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. In this paper, extensive experiments on datasets for these two tasks show that without using any external features, a simple BERT-based model can achieve state-of-the-art performance. To our knowledge, we are the first to successfully apply BERT in this manner. Our models provide strong baselines for future research.
The Contribution of Posttraumatic Stress Disorder and Depression to Insomnia in North Korean Refugee Youth

Refugees are exposed to multiple traumatic and stressful events and thereby are at higher risk for developing a variety of psychological sequelae including posttraumatic stress disorder (PTSD). However, the relation of PTSD to other mental health conditions has not been fully revealed in refugee populations. The present study investigated relationships among trauma exposure, PTSD, depression, and insomnia in North Korean refugee youth. Seventy-four refugee youth were assessed for exposure to traumatic events, PTSD, depression, and insomnia symptoms. The results showed high rates of multiple trauma exposures among the refugee youth and high incidences of co-occurring symptoms of PTSD and insomnia in those who have multiple trauma. Furthermore, the overall symptoms and four cluster symptoms of PTSD were strongly correlated with insomnia in addition to depression. In the path model to predict insomnia, PTSD affected insomnia only through depression, indicating that the greater the levels of PTSD suffered, the greater the likelihood for developing sleep problems via depression. The present study indicates how sleep problems relate to trauma-related symptoms, i.e., PTSD and depression in refugee populations, and highlights the need for further investigation of the specific relation between sleep problems and trauma-related symptoms for effective evaluation and intervention.
Risk of acute exacerbation between acetaminophen and ibuprofen in children with asthma

Background Antipyretics are widely prescribed in pediatric practice. Some reports have mentioned that acetaminophen and non-steroid anti-inflammatory drugs may negatively affect asthma control by causing asthma exacerbation (AE). However, many confounding factors can also influence the risks. We assessed the impact of using acetaminophen or ibuprofen on AE in asthmatic children, especially those with strong risk factors. Methods We used the 2010 Taiwan National Health Insurance Research Database and identified 983 children with persistent asthma aged 1–5 years old; among them, 591 used acetaminophen alone and 392 used ibuprofen alone in 2010. Then, we analyzed the risk of AE over 52 weeks in the patients with and without severe AE in the previous year. Results The ibuprofen group had a higher risk of an emergency room (ER) visit or hospitalization for AE (odds ratio (OR) = 2.10, 95% confidence interval (CI) [1.17–3.76], P = 0.01). Among asthmatic children who had severe AE in the previous year, the risk of AE was higher in the ibuprofen group than in the acetaminophen group (OR = 3.28, 95% CI [1.30–8.29], P = 0.01), where as among those who did not, the risks of AE were similar between the acetaminophen and ibuprofen groups (OR = 1.52, 95% CI [0.71–3.25], P = 0.28). Conclusions Among young asthmatic children, use of ibuprofen was associated with a higher risk of AE than acetaminophen, if they had severe AE with ER visit or hospitalization in the previous year. Pediatricians should use antipyretics among children with asthma after a full evaluation of the risk.
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning

Most deraining works focus on rain streaks removal but they cannot deal adequately with heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or rain veiling effect significantly washes out the image, further scenes are relatively more blurry, etc. In this paper, we propose a novel method to address these problems. We put forth a 2-stage network: a physics-based backbone followed by a depth-guided GAN refinement. The first stage estimates the rain streaks, the transmission, and the atmospheric light governed by the underlying physics. To tease out these components more reliably, a guided filtering framework is used to decompose the image into its low- and high-frequency components. This filtering is guided by a rain-free residue image --- its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks. For the second stage, the refinement stage, we put forth a depth-guided GAN to recover the background details failed to be retrieved by the first stage, as well as correcting artefacts introduced by that stage. We have evaluated our method against state of the art methods. Extensive experiments show that our method outperforms them on real rain image data, recovering visually clean images with good details.
Comparison of fish and oil supplements for a better understanding of the role of fat level and other food constituents in determining bioaccessibility

Abstract In order to investigate the effects of fat level and protein and other components on lipid bioaccessibility, the bioaccessibility of total lipids and particular fatty acids (FAs) of fish samples with different fat levels (5.4% w/w, 10.2% w/w, and 16.6% w/w) and cod liver oil supplement in different quantities (82, 154, 313, 604, and 1,027 mg) was determined by an in vitro digestion model. Digestion of the fish and oil (up to 154 mg) samples as measured by TAG disappearance was complete. Lipolysis was impaired by high amounts of oil (313 mg and higher). Bioaccessible FA profiles had similarities with the initial (before digestion) FA profiles. However, total MUFA and oleic acid contents were higher in the bioaccessible fraction. The bioaccessibility of EPA and DHA was generally lower than that of oleic acid and total MUFA. Fat level did not affect FAs’ bioaccessibility. On the other hand, protein and other components may have interfered in lipid bioaccessibility and it was found that the reduction of bioaccessibility was stronger when the ratio of the lipid fraction to the nonlipid fraction (mainly protein) was smaller.
DSNet: An Efficient CNN for Road Scene Segmentation

Road scene understanding is a critical component in an autonomous driving system. Although the deep learning-based road scene segmentation can achieve very high accuracy, its complexity is also very high for developing real-time applications. It is challenging to design a neural net with high accuracy and low computational complexity. To address this issue, we investigate the advantages and disadvantages of several popular CNN architectures in terms of speed, storage and segmentation accuracy. We start from the Fully Convolutional Network (FCN) with VGG, and then we study ResNet and DenseNet. Through detailed experiments, we pick up the favorable components from the existing architectures and at the end, we construct a light-weight network architecture based on the DenseNet. Our proposed network, called DSNet, demonstrates a real-time testing (inferencing) ability (on the popular GPU platform) and it maintains an accuracy comparable with most previous systems. We test our system on several datasets including the challenging Cityscapes dataset (resolution of 1024×512) with an mIoU of about 69.1 % and runtime of 0.0147 second per image on a single GTX 1080Ti. We also design a more accurate model but at the price of a slower speed, which has an mIoU of about 72.6 % on the CamVid dataset.
Detecting Cybersecurity Events from Noisy Short Text

It is very critical to analyze messages shared over social networks for cyber threat intelligence and cyber-crime prevention. In this study, we propose a method that leverages both domain-specific word embeddings and task-specific features to detect cyber security events from tweets. Our model employs a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network which takes word level meta-embeddings as inputs and incorporates contextual embeddings to classify noisy short text. We collected a new dataset of cyber security related tweets from Twitter and manually annotated a subset of 2K of them. We experimented with this dataset and concluded that the proposed model outperforms both traditional and neural baselines. The results suggest that our method works well for detecting cyber security events from noisy short text.