Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (a.k.a. embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms. Moreover, existing HNE algorithms, though mostly claimed generic, are often evaluated on different datasets. Understandable due to the application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data. Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and etc. from different sources, towards handy and fair evaluations of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for eleven popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
Stability and Instability Divergence Conditions for Dynamical Systems

A novel method for stability and instability study of autonomous dynamical systems using the flow and divergence of the vector field is proposed. A relation between the method of Lyapunov functions and the proposed method is established. Bendixon and Bendixon-Dulac theorems for $n$th dimensional systems are extended. Based on the proposed method, the state feedback control law is designed. The control signal is obtained from the partial differential inequality. The examples illustrate the application of the proposed method and the existing ones.
The Wigner's Semicircle Law of Weighted Random Networks

The spectral graph theory provides an algebraical approach to investigate the characteristics of weighted networks using the eigenvalues and eigenvectors of a matrix (e.g., normalized Laplacian matrix) that represents the structure of the network. However, it is difficult for large-scale and complex networks (e.g., social network) to represent their structure as a matrix correctly. If there is a universality that the eigenvalues are independent of the detailed structure in large-scale and complex network, we can avoid the difficulty. In this paper, we clarify the Wigner's Semicircle Law for weighted networks as such a universality. The law indicates that the eigenvalues of the normalized Laplacian matrix for weighted networks can be calculated from the a few network statistics (the average degree, the average link weight, and the square average link weight) when the weighted networks satisfy the sufficient condition of the node degrees and the link weights.
Boosting Deep Hyperspectral Image Classification with Spectral Unmixing

Recent advances in neural networks have made great progress in addressing the hyperspectral image (HSI) classification problem. However, the overfitting effect, which is mainly caused by complicated model structure and small training set, remains a major concern when applying neural networks to HSIs analysis. Reducing the complexity of the neural networks could prevent overfitting to some extent, but it declines the networks' ability to extract more abstract features. Enlarging the training set is also difficult. To tackle the overfitting problem, we propose an abundance-based multi-HSI classification method. By applying an autoencoder-based spectral unmixing technique, different HSIs are firstly converted from the spectral domain to the abundance domain. After that, the abundance representations from multiple HSIs are collected to form an enlarged dataset. Lastly, a simple classifier is trained, which makes predictions over all the involved datasets. Taking advantage of the spectral unmixing, transforming the spectral features to the abundance features significantly simplifies the classification tasks. This enables the use of a simple network as the classifier, thus alleviating the overfitting effect. Moreover, as much dataset-specific information is eliminated by the spectral unmixing, a compatible classifier suitable for different HSIs is trained. A several times enlarged training set is constructed by assembling the abundances from different HSIs. The effectiveness of the proposed method is verified by the ablation study and the comparative experiments. On four datasets, the proposed method provides comparable results with two state-of-the-art methods, but using a much simpler model.
EOLO: Embedded Object Segmentation only Look Once

In this paper, we introduce an anchor-free and single-shot instance segmentation method, which is conceptually simple with 3 independent branches, fully convolutional and can be used by easily embedding it into mobile and embedded devices. Our method, refer as EOLO, reformulates the instance segmentation problem as predicting semantic segmentation and distinguishing overlapping objects problem, through instance center classification and 4D distance regression on each pixel. Moreover, we propose one effective loss function to deal with sampling a high-quality center of gravity examples and optimization for 4D distance regression, which can significantly improve the mAP performance. Without any bells and whistles, EOLO achieves 27.7$\%$ in mask mAP under IoU50 and reaches 30 FPS on 1080Ti GPU, with a single-model and single-scale training/testing on the challenging COCO2017 dataset. For the first time, we show the different comprehension of instance segmentation in recent methods, in terms of both up-bottom, down-up, and direct-predict paradigms. Then we illustrate our model and present related experiments and results. We hope that the proposed EOLO framework can serve as a fundamental baseline for a single-shot instance segmentation task in Real-time Industrial Scenarios.
Gender inequities throughout STEM: Women with higher grades drop STEM majors while men persist

Efforts to promote equity and inclusion using evidence-based approaches are vital to correct long-standing societal inequities that have disadvantaged women and discouraged them from pursuing studies, e.g., in many STEM disciplines. We use 10 years of institutional data at a large public university to investigate trends in the majors that men and women declare, drop after declaring, and earn degrees in as well as the GPA of the students who drop or earn a degree. We find that the majors with the lowest number of students also have the highest rates of attrition. Moreover, we find alarming GPA trends, e.g., women who drop majors on average earn higher grades than men who drop those majors, and in some STEM majors, women who drop the majors were earning comparable grades to men who persist in those majors. These quantitative findings call for a better understanding of the reasons students drop a major and for making learning environments equitable and inclusive.
SPES: A Two-Stage Query Equivalence Verifier

In database-as-a-service platforms, automated verification of query equivalence helps eliminate redundant computation across queries (i.e., overlapping sub-queries). State-of-the-art tools for automated detection of query equivalence adopt two different approaches. The first technique is based on reducing queries to algebraic expressions and proving their equivalence using an algebraic theory. The limitations of this approach are threefold. It cannot prove the equivalence of queries with significant differences in the attributes of their relational operators. It does not support certain widely-used SQL features. Its verification procedure is computationally intensive. The second technique is based on deriving the symbolic representation of the queries and proving their equivalence using the satisfiability modulo theory. The limitations of this approach are twofold. It only proves equivalence of queries under set semantics. It cannot prove the equivalence of queries with significant structural differences in their abstract syntax trees. In this paper, we present a novel two-stage approach to automated verification of query equivalence that addresses the limitations of these individual techniques. The first stage consists of reducing queries to a novel algebraic representation and then normalizing the resulting algebraic expressions to minimize structural differences. The second stage consists of applying a verification algorithm to convert the normalized algebraic expressions to a novel query pair symbolic representation and proving their equivalence under bag semantics using satisfiability modulo theory. We implement our two-stage approach in SPES. SPES proves the equivalence of a larger set of query pairs (90/232) under bag semantics compared to the state-of-the-art tools based on algebraic (30/232) under bag semantics and symbolic approaches (67/232) under set semantics.
On the comparison of incompatibility of split systems across different taxa sizes

The concept of $k$-compatibility measures how many phylogenetic trees it would take to display all splits in a given set. A set of trees that display every single possible split is termed a \textit{universal tree set}. In this note, we find $A(n)$, the minimal size of a universal tree set for $n$ taxa. By normalising the $k$-compatibility using $A(n)$, one can then compare incompatibility of split systems across different taxa sizes. We demonstrate this application by comparing two SplitsTree networks of different sizes derived from archaeal genomes.
Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?

In this paper, we address the problem of using sentiment analysis tools ‘off-the-shelf’, that is when a gold standard is not available for retraining. We evaluate the performance of four SE-specific tools in a cross-platform setting, i.e., on a test set collected from data sources different from the one used for training. We find that (i) the lexicon-based tools outperform the supervised approaches retrained in a cross-platform setting and (ii) retraining can be beneficial in within-platform settings in the presence of robust gold standard datasets, even using a minimal training set. Based on our empirical findings, we derive guidelines for reliable use of sentiment analysis tools in software engineering.
A novel LMI-based Method for Robust Stabilization of Fractional-order Interval Systems with $1\le\alpha<2$

Abstract: This paper deals with the problem of robust dynamic output feedback stabilization of interval fractionalorder linear time invariant (FO-LTI) systems with the fractional order . In this study, a new formulation based on the null-space analysis of the system matrices is proposed using linear matrix inequalities (LMIs). The applied uncertain model is the most complete model of linear interval systems, in which all of the systems matrices are interval matrices. A robust dynamic output feedback controller is designed that asymptotically stabilizes the interval FO-LTI system, where no limiting constraint is assumed on the state space matrices of the uncertain system. Eventually, a numerical example with simulations is presented to demonstrate the effectiveness and correctness of the theoretical results.
Coupling of localization and depth data for mapping using Intel RealSense T265 and D435i cameras

We propose to couple two types of Intel RealSense sensors (tracking T265 and depth D435i) in order to obtain localization and 3D occupancy map of the indoor environment. We implemented a python-based observer pattern with multi-threaded approach for camera data synchronization. We compared different point cloud (PC) alignment methods (using transformations obtained from tracking camera and from ICP family methods). Tracking camera and PC alignment allow us to generate a set of transformations between frames. Based on these transformations we obtained different trajectories and provided their analysis. Finally, having poses for all frames, we combined depth data. Firstly we obtained a joint PC representing the whole scene. Then we used Octomap representation to build a map.
Antisense inhibition of phospholipase A2: a new approach for already tested therapeutic targets for the treatment of sepsis*.

Crit Care Med 2012 Vol. 40, No. 7 an end-expiratory transpulmonary pressure similar to that previously obtained in animals with intact chest wall. Although at the present time there are few clinical studies investigating a safe threshold of transpulmonary pressure (6, 7, 9), the computation of transpulmonary pressure, which is now available at the bedside, is essential to discriminate patients who could benefit from higher PEEP level due to an increase in chest wall elastance compared to those at risk of hyperinflation due to low chest wall elastance. Davide Chiumello, MD Dipartimento di Anestesia, Rianimazione (Intensiva e Subintensiva) e Terapia del Dolore Fondazione IRCCS Ca’ Granda – Ospedale Maggiore Policlinico Milan, Italy
MRI pattern of hepatic metastases from uveal melanoma: can it be correlated with patients' prognosis?

Poster: "ECR 2017 / C-1287 / MRI pattern of hepatic metastases from uveal melanoma: can it be correlated with patients' prognosis? " by: "J. P. Zawaideh1, L. Bacigalupo2, F. Paparo1, F. belli1, C. Defferari1, A. De Censi1, C. Mosci1, G. A. Rollandi2; 1Genova/IT, 2Genoa/IT"
Etomidate in critically ill patients . Is it safe ?

tine diagnostic testing yet. Flow cytometric measurement of fibrocytes, either alone or in combination with other (soluble) biomarkers, might in the future prove valuable as a routine tool to identify patients with ALI/ARDS at risk of poor outcome; however, the detection of fibrocytes in peripheral blood would be more convenient for routine testing than BAL measurements. In idiopathic pulmonary fibrosis, increased fibrocyte numbers have been found in blood, which even further increased during acute exacerbation and predicted higher mortality (11). So far only ten patients with ARDS have been analyzed for blood fibrocytes and showed no significant difference compared with healthy control subjects (11). Therefore, further studies are required to determine whether blood fibrocyte analysis is suitable for predicting outcome in ALI/ARDS. In conclusion, the study by Quesnel et al (15) provides us with interesting data, which definitely warrants further, welldesigned studies in which detailed fibrocyte immunophenotyping should be performed on sequential BAL and peripheral blood samples from patients with ALI/ ARDS. Such studies will not only provide important insight into the origin and role of (BAL) fibrocytes in the pathobiology of ALI/ARDS, but might also teach us whether clinical decisions can be based on fibrocyte analysis. In other words, can the fibrocyte turn into a fibroguide for treatment adjustment in ALI/ARDS? Willem A. Dik, PhD Department of Immunology Erasmus MC University Medical Center Rotterdam, The Netherlands
Effect of Payment Mechanisms on Diabetes Management by Private Primary Care Clinics in Kuala Lumpur, Malaysia: a Qualitative Study on Provider Behavior

Background: Flooding is one of many natural disasters that influence children’s development. The declining of academic performance is recognized as a sign of potential risk of developmental disorders of children. Consequently, some supports are required by children to enhance their ability to face difficulties. This study aimed to investigate the relationship between contextual factors (family support, peer support, community support, school support and culture) and resilience in elementary school-aged children exposed to flooding in Indonesia. Methods: A cross-sectional design was employed. The final participants were 162 children ages 9–12 years old from grade 3 rd to 6 th and were exposed to flooding in Undar Andir Village, Serang District, Indonesia in 2013. The instruments were self-administered questionnaires. The Connor-Davidson Resilience Scale- 10, Multidimensional Scale of Perceived Social Support, The After-School Environment Scale and The Cultural Competence Self-Assessment Tool were utilized in this study based upon the validity and reliability test. Mean, standard deviation, Pearson correlation and multiple regression were employed for data analysis. Results: The study showed that there were significant correlated between family support (r = .388, p < .01), peer support (r = .445, p < .01), community support (r = .350, p < .01) and culture (r = .255, p < .01) and resilience of children who exposed to flooding. In contrast, school support was not significantly correlated with resilience. Final statistical analysis with multiple regression showed that in contextual factors, family support (Beta= .291, p<.01), peer support (Beta= .260, p< .01), and culture (Beta= .239, p < .01) were recognized as predictors of resilience of children exposed to flooding. Conclusion: Family support, peer support, community support and culture were significantly predicting the level of resilience in elementary school-aged children who were exposed to floods. Therefore, establishing a nursing intervention program based on local cultural approach is required. The program can be formed to build up the relationships between children and family as well as their friends.
Deciphering the Sepsis Riddle : We Can Learn From Star Trek *

I am a “trekkie. ” I watched Star Trek as a kid, and now, I watch it with my kids! Captain James Tiberius Kirk, Mr. Spock (science officer), and Dr. Leonard “Bones” McCoy (physician) were in charge of the “USS Enterprise,” the classic “starship” charged with learning as much as possible about the unknown parts of the galaxy and dealing accordingly with any life forms they may encounter. The destiny of the USS Enterprise was mostly decided by the combination of the raw emotion of Kirk with the pure reason of Spock, In view of these concerns, it is reasonable to speculate that methodological refinement of NMES could improve the application of this modality in ICUAW. One alternative approach would be to target the key muscle groups that are needed to perform a specific task, rather than limiting NMES to a single muscle. Theoretically, this could be achieved by alternate stimulation of synergistic muscle groups. In addition, the use of NMES in combination with other interventions, such as anabolic therapies or volitional tasks (functional electrical stimulation), could augment its efficacy. Furthermore, although commercially available stimulators with preprogrammed stimulation protocols simplify the application of NMES in clinical settings, these protocols may not be optimal for use in ICUAW. Stimulation protocols that achieve muscle activation similar to what occurs during voluntary contraction where asynchronous motor unit discharge produces steady forces with relatively low neural firing rates might produce more robust and physiologically relevant outcomes. Theoretically, stimulation protocols could be graded based on individual responsiveness and used to achieve an aerobic training effect as has been reported in some patient populations. If this is possible, NMES could preserve muscle mass, improve muscle quality and function, and restore muscle endurance in patients with ICUAW. Although the data supporting the use of NMES in ICUAW are limited, this form of rehabilitation in critically ill patients holds great potential. Future studies with NMES, however, need to be carefully designed with attention to the aforementioned issues. Investigations should be scientifically rigorous with objective and well-defined physiologically relevant outcome measures, realizing that muscle performance is not merely a function of muscle quantity (size) (13). Functional capacity is critically dependent on muscle quality (force generating capacity) and muscle endurance. We must also reassess the emerging notion that ICU rehabilitation should be reserved for select groups, excluding those with preexisting functional derangements or comorbidities (14), as NMES has been shown to improve functional capacity in a wide range of chronic diseases.
Collagen-induced arthritis as a model of hyperalgesia

Objective. There is a disparity in the animal models used to study pain in rheumatoid arthritis (RA), which tends to be acute in nature, and models used to assess the pathogenesis of RA. The latter models, like human RA, are lymphocyte-driven and polyarthritic. We assessed pain behavior and mechanisms in collagen-induced arthritis (CIA), the model of preclinical arthritis used most commonly in the field of immunology. We then validated the model using anti-tumor necrosis factor (anti-TNF) therapy, which has analgesic effects in models of inflammation as well as in human RA.Methods. CIA was induced in DBA/1 mice by immunization with type 11 collagen at the base of the tail. Swelling and mechanical and thermal hyperalgesia were assessed before and for 28 days after the onset of arthritis. Spontaneous behavior was assessed using an automated activity monitor. Glial activity was assessed by glial fibrillary acidic protein expression, and nerve damage was evaluated by activating transcription factor 3 expression. The actions of anti-TNF therapy on nociception were then evaluated.Results. Arthritis resulted in a decrease in the threshold for thermal and mechanical stimuli, beginning on the day of onset. Decreased spontaneous activity was also observed. A significant increase in the number of hyperplasic spinal cord astrocytes was observed beginning 10 days after the onset of arthritis. Anti-TNF therapy was profoundly analgesic, with an efficacy similar to that of cyclooxygenase 2 inhibition, and reduced astrocyte activity in CIA.Conclusion. This study shows that the CIA model is suitable for testing not only antiinflammatory but also analgesic drugs for potential use in RA, and highlights the importance of using appropriate disease models to assess relevant pain pathways.
Appraisal of antioxidant capacity and phytochemical screening in aqueous and acetone extracts of vegetables grown in Bhimber AJK, Pakistan

The study was carried out to assess the antioxidant capacity and phytochemical constituents of acetone and water extracts of the vegetables irrigated with fresh and waste water such as Raphanus sativus (Radish), Brassica oleracea capitata (Cabbage), Brassica rapa (Turnip), Coriandrum sativum (Coriander) and Spinacia oleracea (Spinach). A concurrent quantitative investigation of total phenolics, flavonols and flavonoids was also made and according to the results, the higher antioxidant capacity was shown by aqueous extracts in all antioxidant methods than acetone extracts. Key words: Raphanus sativus, Brassica oleracea capitata, Brassica rapa, Coriandrum sativum, Spinacia oleracea, antioxidant.