Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithmic random duality theory - large scale CLuP

Based on our \bl{\textbf{Random Duality Theory (RDT)}}, in a sequence of our recent papers \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19}, we introduced a powerful algorithmic mechanism (called \bl{\textbf{CLuP}}) that can be utilized to solve \textbf{\emph{exactly}} NP hard optimization problems in polynomial time. Here we move things further and utilize another of remarkable RDT features that we established in a long line of work in \cite{StojnicCSetam09,StojnicCSetamBlock09,StojnicISIT2010binary,StojnicDiscPercp13,StojnicUpper10,StojnicGenLasso10,StojnicGenSocp10,StojnicPrDepSocp10,StojnicRegRndDlt10,Stojnicbinary16fin,Stojnicbinary16asym}. Namely, besides being stunningly precise in characterizing the performance of various random structures and optimization problems, RDT simultaneously also provided an almost unparallel way for creating computationally efficient optimization algorithms that achieve such performance. One of the keys to our success was our ability to transform the initial \textbf{\emph{constrained}} optimization into an \textbf{\emph{unconstrained}} one and in doing so greatly simplify things both conceptually and computationally. That ultimately enabled us to solve a large set of classical optimization problems on a very large scale level. Here, we demonstrate how such a thinking can be applied to CLuP as well and eventually utilized to solve pretty much any problem that the basic CLuP from \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19} can solve.
Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking

We address the indoor localization problem, where the goal is to predict user's trajectory from the data collected by their smartphone, using inertial sensors such as accelerometer, gyroscope and magnetometer, as well as other environment and network sensors such as barometer and WiFi. Our system implements a deep learning based pedestrian dead reckoning (deep PDR) model that provides a high-rate estimation of the relative position of the user. Using Kalman Filter, we correct the PDR's drift using WiFi that provides a prediction of the user's absolute position each time a WiFi scan is received. Finally, we adjust Kalman Filter results with a map-free projection method that takes into account the physical constraints of the environment (corridors, doors, etc.) and projects the prediction on the possible walkable paths. We test our pipeline on IPIN'19 Indoor Localization challenge dataset and demonstrate that it improves the winner's results by 20\% using the challenge evaluation protocol.
Therapeutic activities of naringenin on efavirenz-induced sleep-like disorder in the midbrain of white albino mice

Objective(s): Efavirenz, has proven to be effective in suppressing human immunodeficiency virus (HIV) viral load; however, complaints of sleep disorders including hallucination, and insomnia have greatly contributed to non-adherence to antiretroviral therapy. This study aimed at investigating therapeutic activities of naringenin on efavirenz-induced sleep disorder. Materials and Methods: Sixty mice were divided into six groups of control, combination antiretroviral therapy (cART), efavirenz, naringenin, naringenin/efavirenz and naringenin/cART. Efavirenz, cART, and naringenin were administered orally and daily at 15 mg/kg, 24 mg/kg and 50 mg/kg, respectively for 28 days. Post neurobehavioral test, oxidative stress, histology and immunohistochemistry for dopamine were carried out after administration process. Results: Efavirenz (P<0.0001) and cART (P<0.01) significantly increased immobility during open field (P<0.01), escape time in seconds (sec) in Morris water maze (P<0.001) and numbers of head-twitch response (HTR) (P<0.0001). Similarly, there was a significant increase in malondialdehyde (MDA) (P<0.0001) and decreased superoxide dismutase (SOD) (P<0.001) and reduced glutathione (GSH) (P<0.001); however, naringenin-treated groups potentiated anti-oxidant function by reducing oxidative stress (P<0.01). Histological evaluation demonstrated severe neurodegeneration, vacuolization and pyknosis in efavirenz and cART compared to naringenin groups. Dopaminergic neurons using immunohistochemial antibody (tyrosine hydroxylase) staining showed poor immunoreactivity in efavirenz and cART in contrast to naringenin groups. Conclusion: Efavirenz and cART have the potential of inducing sleep disorder possibly due to their capability to trigger inflammation and deplete dopamine level. However, naringenin has proven to be effective in ameliorating these damages.
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed. However, there is no summary on uncertainty estimation in deep object detection, and existing methods are either built with different network architectures and uncertainty estimation methods, or evaluated on different datasets with a wide range of evaluation metrics. As a result, a comparison among methods remains challenging, as does the selection of a model that best suits a particular application. This paper aims to alleviate this problem by providing a review and comparative study on existing probabilistic object detection methods for autonomous driving applications. First, we provide an overview of practical uncertainty estimation methods in deep learning, and then systematically survey existing methods and evaluation metrics for probabilistic object detection. Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets. Finally, we present a discussion of the remaining challenges and future works. Code has been made available at https://github.com/asharakeh/pod_compare. git.
Growth instabilities of vicinal crystal surfaces during molecular beam epitaxy

In this work the growth instabilities of vicinal crystal surfaces during MBE are studied theoretically. The vicinal surfaces grow in a step-flow mode, where steps present due to a small miscut relative to a high symmetry direction of a crystal, propagate due to a deposition flux. The number of the steps remains constant as nucleation of atomic island is suppressed by the presence of steps. In the first part the models used to describe the step-flow growth are presented. Also the different microscopic processes taking place on a growing surfaces are discussed at length, as the large scale morphology is determined by the relative relevance of these processes. The dynamics of atoms diffusing along the atomic steps, which are of central importance for the step-flow growth, are addressed in particular. The main subject of this thesis are the step meandering instabilities, which lead to a ripple morphology on a growing surface. The atomic steps become wavy due to growth instabilities as they propagate. The wave patterns formed on the steps are in-phase over multiple steps, thus leading to long ripples running in the direction of the step-train. The wavelength of the pattern, i.e. the typical separation of the ripples, is set by the competition between the driving force (the deposition flux) and the relaxation of the steps. In equilibrium the steps are straight. In growth experiments the typical scale of the ripples lies in the nanometer scale l ~ 10-1000 nm. The dynamics of these instabilities are studied employing Monte-Carlo simulations and partial differential equations, describing the time evolution of the steps. A quantitative comparison between these two approaches is made. The results are also related to recent experimental results. In the last part the destabilization of the step-flow growth due to the appearance of new steps is considered. New steps may result from either island nucleation on terraces, or due to the appearance of vacancy islands that are formed when a strongly deformed step crosses itself.
Neural Network iLQR: A New Reinforcement Learning Architecture

As a notable machine learning paradigm, the research efforts in the context of reinforcement learning have certainly progressed leaps and bounds. When compared with reinforcement learning methods with the given system model, the methodology of the reinforcement learning architecture based on the unknown model generally exhibits significantly broader universality and applicability. In this work, a new reinforcement learning architecture is developed and presented without the requirement of any prior knowledge of the system model, which is termed as an approach of a "neural network iterative linear quadratic regulator (NNiLQR)". Depending solely on measurement data, this method yields a completely new non-parametric routine for the establishment of the optimal policy (without the necessity of system modeling) through iterative refinements of the neural network system. Rather importantly, this approach significantly outperforms the classical iterative linear quadratic regulator (iLQR) method in terms of the given objective function because of the innovative utilization of further exploration in the methodology. As clearly indicated from the results attained in two illustrative examples, these significant merits of the NNiLQR method are demonstrated rather evidently.
The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling

We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource Speech Benchmark 2021: a suite of 4 black-box, zero-shot metrics probing for the quality of the learned models at 4 linguistic levels: phonetics, lexicon, syntax and semantics. We present the results and analyses of a composite baseline made of the concatenation of three unsupervised systems: self-supervised contrastive representation learning (CPC), clustering (k-means) and language modeling (LSTM or BERT). The language models learn on the basis of the pseudo-text derived from clustering the learned representations. This simple pipeline shows better than chance performance on all four metrics, demonstrating the feasibility of spoken language modeling from raw speech. It also yields worse performance compared to text-based 'topline' systems trained on the same data, delineating the space to be explored by more sophisticated end-to-end models.
Differentiable Computational Geometry for 2D and 3D machine learning

With the growth of machine learning algorithms with geometry primitives, a high-efficiency library with differentiable geometric operators are desired. We present an optimized Differentiable Geometry Algorithm Library (DGAL) loaded with implementations of differentiable operators for geometric primitives like lines and polygons. The library is a header-only templated C++ library with GPU support. We discuss the internal design of the library and benchmark its performance on some tasks with other implementations.
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding

To capture higher-order structural features, most GNN-based algorithms learn node representations incorporating k-hop neighbors' information. Due to the high time complexity of querying k-hop neighbors, most graph algorithms cannot be deployed in a giant dense temporal network to execute millisecond-level inference. This problem dramatically limits the potential of applying graph algorithms in certain areas, especially financial fraud detection. Therefore, we propose Asynchronous Propagation Attention Network, an asynchronous continuous time dynamic graph algorithm for real-time temporal graph embedding. Traditional graph models usually execute two serial operations: first graph querying and then model inference. Different from previous graph algorithms, we decouple model inference and graph computation to alleviate the damage of the heavy graph query operation to the speed of model inference. Extensive experiments demonstrate that the proposed method can achieve competitive performance while greatly improving the inference speed. The source code is published at a Github repository.
Comparison of Macular Thickness Measurements Using Swept-Source and Spectral-Domain Optical Coherence Tomography in Healthy and Diabetic Subjects

ABSTRACT Purpose: To establish normative data for macular thickness in Chinese aged 30 to 80 years using the swept-source optical coherence tomography (SS-OCT) device. Methods: The study included 290 normal eyes, 430 NDR eyes and 150 DR eyes of community residents aged 30 to 80 years in Guangzhou, China. Mean macular thicknesses in Early Treatment Diabetic Retinopathy Study (ETDRS) subfields, central point thicknesses (CPT), and macular volume was measured by SS-OCT (Triton DRI OCT, Topcon, Tokyo, Japan) and Spectral-Domain OCT (SD-OCT; Heidelberg Engineering, Heidelberg, Germany). We assessed agreement between SS-OCT and SD-OCT measurements by intraclass correlation coefficients (ICC) and Bland-Altman plots. We established a conversion equation relating central subfield (CSF), CPT and macular volume between the two OCT devices. Results: Macular thickness measurements in SS-OCT were significantly thinner than in SD-OCT. The mean CSF thickness in normal eyes measured by SS-OCT and SD-OCT were 227.8 ± 19.4 μm and 260.0 ± 19.7 μm (p < .0001). CSF thickness was a signiﬁcantly difference between genders (SS-OCT: male 237.2 ± 18.8 μm vs female 222.0 ± 17.5 μm, p < .0001). In all three groups, the agreement between SS-OCT and SD-OCT was excellent (all ICC > 0.9). The conversion equations for CSF, CPT and macular volume from SS-OCT to SD-OCT were derived, with over 95% of the predicted values fell within 10% of the actual measurements in DR and NDR eyes. Conclusion: We propose SS-OCT CSF thicknesses of 275 μm for males and 260 μm for females as the minimum criteria for macular edema in Chinese aged 30 to 80 years based on 2 SDs above the mean CSF. SS-OCT measurements were significantly thinner than SD-OCT. We derived equations from converting SS-OCT measurements to SD-OCT equivalents.
Fate of the conformal order

We use holographic correspondence to study transport of the conformal plasma in ${\mathbb R}^{2,1}$ in a phase with a spontaneously broken global $Z_2$ symmetry. The dual black branes in a Poincare patch of asymptotically $AdS_4$ have "hair" -- a condensate of the order parameter for the broken symmetry. This hair affects both the hydrodynamic and the nonhydrodynamic quasinormal modes of the black branes. Nonetheless, the shear viscosity of the conformal order is universal, the bulk viscosity vanishes and the speed of the sound waves is $c_s^2=\frac 12$. We compute the low-lying spectrum of the non-hydrodynamic modes. We identify a quasinormal mode associated with the fluctuations of the $Z_2$ order parameter with the positive imaginary part. The presence of this mode in the spectrum renders the holographic conformal order perturbatively unstable. Correspondingly, the dual black branes violate the correlated stability conjecture.
Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains

We prove a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains. Working with bounded and $\pi$-canonical kernels, we show that we can recover the convergence rate of Arcones and Gin{\'e} who proved a concentration result for U-statistics of independent random variables and canonical kernels. Our result allows for a dependence of the kernels $h_{i,j}$ with the indexes in the sums, which prevents the use of standard blocking tools. Our proof relies on an inductive analysis where we use martingale techniques, uniform ergodicity, Nummelin splitting and Bernstein's type inequality. Assuming further that the Markov chain starts from its invariant distribution, we prove a Bernstein-type concentration inequality that provides sharper convergence rate for small variance terms.
Emotional Intelligence and Aggressive Behaviors in Adolescents: A Systematic Review and Meta-Analysis

Adolescent aggression is a global public health with long-lasting and costly emotional, social, and economic consequences, and it is of vital importance to identify those variables that can reduce these behaviors in this population. Therefore, there is a need to establish the protective factors of aggressive behavior in adolescence. While some research has demonstrated the relationship between emotional intelligence (EI) and various aggressive responses in adolescence, indicating that EI—or the ability to perceive, use, understand, and regulate emotions—could be considered a protective factor for the development of aggressive behavior in adolescence, the strength of this effect is not clear. The aim of the present study was to conduct a systematic review of the literature concerning the relationship between aggressive behavior and EI in adolescents and provide a reliable estimate of the relationship between both constructs through a meta-analysis. For this purpose, we searched for relevant articles in English and Spanish in Medline, PsycINFO, and Scopus, obtaining 17 selectable articles based on the search terms used in research in the adolescent population. These studies provide scientific evidence of the relationship between the level of EI assessed from the three theoretical models of EI (performance-based ability model, self-report ability model, and self-report mixed model) and various aggressive responses, showing that adolescents with higher levels of EI show less aggressive behavior. Implications for interventions and guidelines for future research are discussed.
Distance-based Data Cleaning: A Survey (Technical Report)

With the rapid development of the internet technology, dirty data are commonly observed in various real scenarios, e.g., owing to unreliable sensor reading, transmission and collection from heterogeneous sources. To deal with their negative effects on downstream applications, data cleaning approaches are designed to preprocess the dirty data before conducting applications. The idea of most data cleaning methods is to identify or correct dirty data, referring to the values of their neighbors which share the same information. Unfortunately, owing to data sparsity and heterogeneity, the number of neighbors based on equality relationship is rather limited, especially in the presence of data values with variances. To tackle this problem, distance-based data cleaning approaches propose to consider similarity neighbors based on value distance. By tolerance of small variants, the enriched similarity neighbors can be identified and used for data cleaning tasks. At the same time, distance relationship between tuples is also helpful to guide the data cleaning, which contains more information and includes the equality relationship. Therefore, distance-based technology plays an important role in the data cleaning area, and we also have reason to believe that distance-based data cleaning technology will attract more attention in data preprocessing research in the future. Hence this survey provides a classification of four main data cleaning tasks, i.e., rule profiling, error detection, data repair and data imputation, and comprehensively reviews the state of the art for each class.
Geometry-Aware Universal Mirror-Prox

Mirror-prox (MP) is a well-known algorithm to solve variational inequality (VI) problems. VI with a monotone operator covers a large group of settings such as convex minimization, min-max or saddle point problems. To get a convergent algorithm, the step-size of the classic MP algorithm relies heavily on the problem dependent knowledge of the operator such as its smoothness parameter which is hard to estimate. Recently, a universal variant of MP for smooth/bounded operators has been introduced that depends only on the norm of updates in MP. In this work, we relax the dependence to evaluating the norm of updates to Bregman divergence between updates. This relaxation allows us to extends the analysis of universal MP to the settings where the operator is not smooth or bounded. Furthermore, we analyse the VI problem with a stochastic monotone operator in different settings and obtain an optimal rate up to a logarithmic factor.
Seroprevalence of SARS-CoV-2 antibodies in children of United Kingdom healthcare workers: a prospective multicentre cohort study protocol

Background A novel coronavirus SARS-CoV-2 has been responsible for a worldwide pandemic. Children typically have very mild, or no, symptoms of infection. This makes estimations of seroprevalence in children difficult. Research is therefore required to determine the seroprevalence of SARS-CoV-2 antibodies in children. The primary objective of this study is to report the seroprevalence of SARS-CoV-2 IgM and/or IgG antibodies in healthy children at baseline, 2 months and 6 months. This is the only longitudinal UK study of seroprevalence in an exclusively paediatric population. Determining the changing seroprevalence is of vital public health importance and can help inform decisions around the lifting of paediatric specific social distancing measures such as school closures and the cancellation of routine paediatric hospital services. Methods and analysis 1000 healthy children of healthcare workers aged between 2 and 15 years will be recruited from five UK sites (Belfast, Cardiff, Glasgow, London and Manchester). The children will undergo phlebotomy at baseline, 2 months and 6 months to measure IgM and/or IgG positivity to SARS-CoV-2. A sample size of 675 patients is required to detect a 5% change in seroprevalence at each time point assuming an alpha of 0.05 and a beta of 0.2. Adjusted probabilities for the presence of IgG and/or IgM antibodies and of SARS-CoV-2 infection will be reported using logistic regression models where appropriate. Ethics and dissemination Ethical approval was obtained from the London - Chelsea Research Ethics Committee (REC Reference—20/HRA/1731) and the Belfast Health & Social Care Trust Research Governance (Reference 19147TW-SW). Results of this study will be made available as preprints and submitted for publication in peer-reviewed journals. Trial registration number NCT0434740; Results
Measuring Quadrangle Formation in Complex Networks

The classic clustering coefficient and the lately proposed closure coefficient quantify the formation of triangles from two different perspectives, with the focal node at the centre or at the end in an open triad respectively. As many networks are naturally rich in triangles, they become standard metrics to describe and analyse networks. However, the advantages of applying them can be limited in networks, where there are relatively few triangles but which are rich in quadrangles, such as the protein-protein interaction networks, the neural networks and the food webs. This yields for other approaches that would leverage quadrangles in our journey to better understand local structures and their meaning in different types of networks. Here we propose two quadrangle coefficients, i.e., the i-quad coefficient and the o-quad coefficient, to quantify quadrangle formation in networks, and we further extend them to weighted networks. Through experiments on 16 networks from six different domains, we first reveal the density distribution of the two quadrangle coefficients, and then analyse their correlations with node degree. Finally, we demonstrate that at network-level, adding the average i-quad coefficient and the average o-quad coefficient leads to significant improvement in network classification, while at node-level, the i-quad and o-quad coefficients are useful features to improve link prediction.