Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitivity and degree assortativity explained: The bipartite structure of social networks

Dynamical processes, such as the diffusion of knowledge, opinions, pathogens, "fake news," innovation, and others, are highly dependent on the structure of the social network in which they occur. However, questions on why most social networks present some particular structural features, namely, high levels of transitivity and degree assortativity, when compared to other types of networks remain open. First, we argue that every one-mode network can be regarded as a projection of a bipartite network, and we show that this is the case using two simple examples solved with the generating functions formalism. Second, using synthetic and empirical data, we reveal how the combination of the degree distribution of both sets of nodes of the bipartite network-together with the presence of cycles of lengths four and six-explain the observed values of transitivity and degree assortativity coefficients in the one-mode projected network. Bipartite networks with top node degrees that display a more right-skewed distribution than the bottom nodes result in highly transitive and degree assortative projections, especially if a large number of small cycles are present in the bipartite structure.
Sampling-Free Learning of Bayesian Quantized Neural Networks

Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this paper, we propose Bayesian quantized networks (BQNs), quantized neural networks (QNNs) for which we learn a posterior distribution over their discrete parameters. We provide a set of efficient algorithms for learning and prediction in BQNs without the need to sample from their parameters or activations, which not only allows for differentiable learning in QNNs, but also reduces the variance in gradients. We evaluate BQNs on MNIST, Fashion-MNIST, KMNIST and CIFAR10 image classification datasets, compared against bootstrap ensemble of QNNs (E-QNN). We demonstrate BQNs achieve both lower predictive errors and better-calibrated uncertainties than E-QNN (with less than 20% of the negative log-likelihood).
OKpi: All-KPI Network Slicing Through Efficient Resource Allocation

Networks can now process data as well as transporting it; it follows that they can support multiple services, each requiring different key performance indicators (KPIs). Because of the former, it is critical to efficiently allocate network and computing resources to provide the required services, and, because of the latter, such decisions must jointly consider all KPIs targeted by a service. Accounting for newly introduced KPIs (e.g., availability and reliability) requires tailored models and solution strategies, and has been conspicuously neglected by existing works, which are instead built around traditional metrics like throughput and latency. We fill this gap by presenting a novel methodology and resource allocation scheme, named OKpi, which enables high-quality selection of radio points of access as well as VNF (Virtual Network Function) placement and data routing, with polynomial computational complexity. OKpi accounts for all relevant KPIs required by each service, and for any available resource from the fog to the cloud. We prove several important properties of OKpi and evaluate its performance in two real-world scenarios, finding it to closely match the optimum.
Brain Perfusion Mediates the Relationship Between miRNA Levels and Postural Control

Abstract Transcriptomics, regional cerebral blood flow (rCBF), and a virtual reality-based spatial motor task were integrated using mediation analysis in a novel demonstration of “imaging omics. ” Data collected in National Collegiate Athletic Association (NCAA) Division I football athletes cleared for play before in-season training showed significant relationships in 1) elevated levels of miR-30d and miR-92a to elevated putamen rCBF, 2) elevated putamen rCBF to compromised Balance scores, and 3) compromised Balance scores to elevated microRNA (miRNA) levels. rCBF acted as a consistent mediator variable (Sobel’s test P < 0.05) between abnormal miRNA levels and compromised Balance scores. Given the involvement of these miRNAs in inflammation and immune function and that vascular perfusion is a component of the inflammatory response, these findings support a chronic inflammatory model in these athletes with 11 years of average football exposure. rCBF, a systems biology measure, was necessary for miRNA to affect behavior.
Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Machine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established.
The coupling method in extreme value theory

A coupling method is developed for univariate extreme value theory , providing an alternative to the use of the tail empirical/quantile processes. Emphasizing the Peak-over-Threshold approach that approximates the distribution above high threshold by the Generalized Pareto distribution, we compare the empirical distribution of exceedances and the empirical distribution associated to the limit Generalized Pareto model and provide sharp bounds for their Wasser-stein distance in the second order Wasserstein space. As an application , we recover standard results on the asymptotic behavior of the Hill estimator, the Weissman extreme quantile estimator or the probability weighted moment estimators, shedding some new light on the theory.
Self-falsifiable hierarchical detection of overlapping communities on social networks

No community detection algorithm can be optimal for all possible networks, thus it is important to identify whether the algorithm is suitable for a given network. We propose a multi-step algorithmic solution scheme for overlapping community detection based on an advanced label propagation process, which imitates the community formation process on social networks. Our algorithm is parameter-free and is able to reveal the hierarchical order of communities in the graph. The unique property of our solution scheme is self-falsifiability; an automatic quality check of the results is conducted after the detection, and the fitness of the algorithm for the specific network is reported. Extensive experiments show that our algorithm is self-consistent, reliable on networks of a wide range of size and different sorts, and is more robust than existing algorithms on both sparse and large-scale social networks. Results further suggest that our solution scheme may uncover features of networks’ intrinsic community structures.
Differentially Private Mixed-Type Data Generation For Unsupervised Learning

In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data, and privately train a model for generating synthetic data that will satisfy the same statistical properties as the original data. This learned model can be used to generate arbitrary amounts of publicly available synthetic data, which can then be freely shared due to the post-processing guarantees of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both unlabeled binary data (MIMIC-III) and unlabeled mixed-type data (ADULT). We also introduce new metrics for evaluating the quality of synthetic mixed-type data, particularly in unsupervised settings.
Spontaneous freeze out of dark matter from an early thermal phase transition

We propose a new paradigm for the thermal production of dark matter in the early Universe, in which dark-matter particles acquire their mass and freeze out spontaneously from the thermal bath after a dark phase transition takes place. The decoupling arises because the dark-matter particles become suddenly nonrelativistic and not because of any decay channel becoming kinematically close. We propose a minimal scenario in which a scalar and a fermionic dark matter are in thermal equilibrium with the standard-model bath. We compute the finite temperature corrections to the scalar potential and identify a region of the parameter space where the fermionic dark-matter mass spontaneously jumps over the temperature when the dark phase transition happens. We explore the phenomenological implications of such a model in simple cases and show that the annihilation cross section of dark-matter particles has to be larger by more than 1 order of magnitude as compared to the usual constant-mass weakly interacting massive particle scenario in order to accommodate the correct relic abundance. We show that in the spontaneous freeze out regime a TeV-scale fermionic dark matter that annihilates into leptons through s-wave processes can be accessible to detection in the near future.
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach

Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network compression through a variety of techniques such as pruning and quantization have been proposed to reduce the resource requirement. A key parameter that all existing compression techniques are sensitive to is the compression ratio (e.g., pruning sparsity, quantization bitwidth) of each layer. Traditional solutions treat the compression ratios of each layer as hyper-parameters, and tune them using human heuristic. Recent researchers start using black-box hyper-parameter optimizations, but they will introduce new hyper-parameters and have efficiency issue. In this paper, we propose a framework to jointly prune and quantize the DNNs automatically according to a target model size without using any hyper-parameters to manually set the compression ratio for each layer. In the experiments, we show that our framework can compress the weights data of ResNet-50 to be 836x smaller without accuracy loss on CIFAR-10, and compress AlexNet to be 205x smaller without accuracy loss on ImageNet classification.
Milky Way Satellite Census. I. The Observational Selection Function for Milky Way Satellites in DES Y3 and Pan-STARRS DR1

We report the results of a systematic search for ultra-faint Milky Way satellite galaxies using data from the Dark Energy Survey (DES) and Pan-STARRS1 (PS1). Together, DES and PS1 provide multi-band photometry in optical/near-infrared wavelengths over ∼80% of the sky. Our search for satellite galaxies targets ∼25,000 deg2 of the high-Galactic-latitude sky reaching a 10σ point-source depth of ≳22.5 mag in the g and r bands. While satellite galaxy searches have been performed independently on DES and PS1 before, this is the first time that a self-consistent search is performed across both data sets. We do not detect any new high-significance satellite galaxy candidates, recovering the majority of satellites previously detected in surveys of comparable depth. We characterize the sensitivity of our search using a large set of simulated satellites injected into the survey data. We use these simulations to derive both analytic and machine-learning models that accurately predict the detectability of Milky Way satellites as a function of their distance, size, luminosity, and location on the sky. To demonstrate the utility of this observational selection function, we calculate the luminosity function of Milky Way satellite galaxies, assuming that the known population of satellite galaxies is representative of the underlying distribution. We provide access to our observational selection function to facilitate comparisons with cosmological models of galaxy formation and evolution.
â€˜Pâ€™ MITRALE AND LEFT ATRIAL ENLARGEMENT: COMPARISON WITH ECHOCARDIOGRAPHY

Background: This study was carried out to determine the sensitivity and specificity of â€˜Pâ€™ Mitrale in diagnosing left atrial enlargement by comparing it with the Gold Standard of echocardiography. Methods: The study was carried out at the department of Physiology, Army Medical College, Rawalpindi and department of Cardiology at Armed Forces Institute of Cardiology / National Institute of Heart Diseases. 50 clinically diagnosed cases of left atrial enlargement were included in the study. ECG of the patients was recorded and â€˜Pâ€™ Mitrale observed. This was followed by echocardiography and using 2-D echocardiogram as a guideline M-mode recording was obtained and left atrial size was calculated. Results: â€˜Pâ€™ Mitrale has a sensitivity of 22.5% and specificity of 100%. The positive and negative predictive value and diagnostic efficacy of the test were also calculated. Conclusion: Sensitivity of ECG is low in detecting left atrial enlargement. However its sensitivity can be increased by combining â€˜Pâ€™ Terminal Force in lead V1 and P/P-R ratio to â€˜Pâ€™ Mitrale. ECG is however still recommended as a routine investigation because of its cost effectiveness and easy availability.
Why ADAM Beats SGD for Attention Models

While stochastic gradient descent (SGD) is still the de facto algorithm in deep learning, adaptive methods like Adam have been observed to outperform SGD across important tasks, such as attention models. The settings under which SGD performs poorly in comparison to Adam are not well understood yet. In this paper, we provide empirical and theoretical evidence that a heavy-tailed distribution of the noise in stochastic gradients is a root cause of SGD's poor performance. Based on this observation, we study clipped variants of SGD that circumvent this issue; we then analyze their convergence under heavy-tailed noise. Furthermore, we develop a new adaptive coordinate-wise clipping algorithm (ACClip) tailored to such settings. Subsequently, we show how adaptive methods like Adam can be viewed through the lens of clipping, which helps us explain Adam's strong performance under heavy-tail noise settings. Finally, we show that the proposed ACClip outperforms Adam for both BERT pretraining and finetuning tasks.
Azimuthal anisotropy in Au plus Au collisions at root S-NN=200 GeV

The results from the STAR Collaboration on directed flow (v(1)), elliptic flow (v(2)), and the fourth harmonic (v(4)) in the anisotropic azimuthal distribution of particles from Au+Au collisions at root s(NN) = 200 GeV are summarized and compared with results from other experiments and theoretical models. Results for identified particles are presented and fit with a blast-wave model. Different anisotropic flow analysis methods are compared and nonflow effects are extracted from the data. For v(2), scaling with the number of constituent quarks and parton coalescence are discussed. For v(4), scaling with v(2)(2) and quark coalescence are discussed.
Factors associated with success in the implementation of information management and technology in the NHS

This literature review was undertaken with the objective of identifying those factors associated with success or failure in the implementation of information technology in the UK National Health Service (NHS). A number of general messages are evident from the literature: • most problems relate to human rather than technical factors; • to achieve real success, work processes must adapt as information technology (IT) is introduced; • realistic expectations and timescales will allow greater benefit realization; • users must be involved as much as possible - training is crucial; • flexibility and communication capabilities are key technical requirements for systems. We distilled three factors influencing the level of success of specific projects: • The organization - its culture and strategy. • The project - appropriate roles, organizational as well as technical change, human resource policies, staged implementation. • The environment - political, technical and commercial. The literature suggests that the organizational and project-related factors must be broadly supportive if organizations are to implement sensible IT in support of their broader aims. Environmental factors can either help or hinder organizations in achieving their goals, but may not prove decisive. Many of the issues and factors would apply equally well in any organization.
IPX-750, A Dopamine Gluconamine That Binds D1/D5 Receptors and Has Anti-Parkinsonian Effects in Three Animal Models, is Transported across the Blood Brain Barrier

Dopamine does not penetrate the blood-brain barrier and IPX 750, a dopamine glycoconjugate, is designed to improve dopamine transport across the blood brain barrier. IPX-750 binds to D1/D5 receptors in vitro and eliminates Parkinson’s disease symptoms in three animal models. These studies were conducted to confirm the bioavailability of IPX-750 following intraperitoneal and gavage administration in rats by detecting IPX-750 in plasma, blood cell, liver and brain extracts. The signature mass ions detected by mass spectrometry in blood, brain and liver confirmed the presence of IPX 750 at 90 min post-administration. Blood levels, (cells + plasma) recovered from samples at 30-90 min after intraperitoneal or gavage administration of 200mg/kg, were 1.4-6.2 μM (600-3200 pg/ml). Blood levels achieved at 30-90 min post-gavage administration were 31-96% of those achieved following intraperitoneal injection. IPX-750 associated with blood cells was 40-130% of that in plasma 60-90 min after intraperitoneal or gavage administration. Brain and liver levels of IPX-750 were 59-195 pg/mg tissue at 30-90 min post-gavage and 900-4700 pg/mg tissue post-ip administration. Following gavage administration brain levels were 55%, 331% and 90% of the levels in the liver at 45, 60 and 90 min. Post-administration with calculated brain penetration indices of 0.6, 3.3 and 0.9.
Cardiac effects of infective and obesity-induced inflammation

The cause of cardiac pathology in a significant number of patients with heart failure is unclear. Although chronic inflammatory changes may contribute toward progressive heart failure, whether low-grade chronic systemic inflammation, produced by infective processes or obesity accounts in-part for the development of cardiac dysfunction and heart failure requires further study. In this regard, although lipopolysaccharide (LPS) administration, an inflammatory mediator derived from the walls of gram negative organisms has been shown to produce an increased cardiomyocyte apoptosis, the lowest dose of LPS previously employed is commensurate with doses that produce vascular shock. Hence this does not reflect the impact of inflammatory changes produced by low-grade systemic infections. Moreover, although inflammatory substances have been shown to be released from adipose tissue and obesity is a cause of cardiac dysfunction and heart failure, it is uncertain to what extent inflammatory changes mediate obesity-induced myocardial dysfunction. To clarify the role of LPS and obesity-induced inflammation as potential causes of cardiac damage and dysfunction, in the present dissertation I therefore evaluated the influence of pyrogenic, but non-septic doses of LPS on cardiomyocyte apoptosis and cardiac systolic function in rats and the contribution of inflammation as indexed by circulating high sensitivity C-reactive protein concentrations (hsCRP) to the relationship between obesity and myocardial systolic function in humans. In normal rats, core body temperature (surgically implanted [in the peritoneal cavity], temperature-sensitive radiotransmitters), cardiomyocyte apoptosis (Terminal Deoxynucleotidyl Transferase Mediated dUTP Nick End Labeling [TUNEL]) staining) and left ventricular (LV) systolic function (two-dimensional directed M-mode echocardiography) were evaluated following two doses of LPS (250 μg/kg), derived from Eschericia coli, delivered 24 hours apart. Cardiac assessments were performed 6 hours after the second LPS dose to ensure that cardiac measurements were obtained at the time of a febrile response, whilst the first LPS dose was employed to ensure that a sufficiently long period had occurred for apoptotic cell death to be
Nutrition of Women in Developing Countries

A series of important questions are addressed regarding the nutrition of women in developing countries. The issues covered all into 3 subject areas: economic perspectives on the nutrition of women; the maternal depletion syndrome; and cultural perspectives on the nutrition of women. 3 recent articles offer descriptions or analyses of womens work income status education and/or live conditions and additionally make observations or policy recommendations that go beyond the descriptions and analyses. Family structure influences womens work patterns economic activities and food intake. Jelliffe advised nutritionists in public health years ago to reinforce cultural beliefs and practices that were positive for nutrition to leave neutral practices alone and to change only those beliefs and practices that are negative. A major problem is to distinguish between cultural beliefs that are made as normative statements and the behaviors in which people actually engage. Another problem relates to the function of cultural beliefs. Some culturally defined pregnancy food restrictions are crunched in terms of sympathetic magic and others are intended to restrict fetal size to make delivery easier. An aspect of culture that virtually has been ignored is the perceived nutrient needs and cultural perceptions of appropriate or attractive body size and shape. Thus far there has been little systematic analysis of cultural view of growth and the small amount of available ethnographic reporting is highly anecdotal. Some aspects of adolescent nutrition have been examined systematically but multisectoral studies linking social and cultural factors to nutrition and health in girls and young women are few. A maternal depletion syndrome may exist but convincing data are unavailable at this time. Further it clearly is not a universal phenomenon. Techniques now available could be used to measure the maternal depletion syndrome but what is needed are appropriate study designs that capture the "natural history" of the syndrome with longitudinal observations. Also needed are descriptive epidemiological studies as well as experimental studies to establish the direction of causality especially concerning associations between parity and nutritional status.