Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Learning Approach for Heart Attack Prediction

A heart attack also known as cardiac arrest, diversify various conditions impacting the heart and became one of the chief-reason for death worldwide over the last few decades. Approximately, 31% of total deaths globally are due to CVDs. It constitutes the pinnacle of chronic processes which involve complex interactions between risk factors which can and cannot be improved. Most of the instances or cases of cardiovascular diseases can be allocated to revisable risk factors where most of the instances are considered preventable. ML became the enhancing approach for the evolution of predictive models in health care industries and was decided to test various algorithms to check what extent their prediction scores estimate or ameliorate upon the results acquired. Researchers deploy various machine learning and data mining techniques over a set of enormous data of cardiovascular patients to attain the prediction for heart attacks before their occurrence for helping healthcare industries and professionals. This research comprises various Supervised ML classifiers like, Gradient Boosting, Decision Tree, Random Forest and Logistic Regression that have been used to deploy a model for Myocardial Infarction prediction. It uses the existing datasets from the Framingham database and others from the database of the UCI Heart repository. This research intends to ideate the prediction for probabilities of occurrence of a heart attack in the patients. These classifiers have been deployed in pipeline approach of machine learning to attain the prediction using both ways i.e., without optimizations and feature transformations as well as vice-versa. The results impersonate that the Gradient Boosting classifier is achieving the highest accuracy score in such a way that prediction used by our model is of binary form in where 1 means a chance of heart attack and 0 means no chance. Some of the most influential attributes are chest pain type among which the typical angina is the most influential and asymptotic chest pain is least, cholesterol level in which the level greater than 200mg/dl are more prone, increased heart rate, thal, and age. It is concluded that premature heart attack is preventable in 80% of the total cases just by using a healthy diet along with regular exercises and not using tobacco products also the person who drinks more than 5 glasses of water daily are less likely to develop attacks. The medical checkup of Blood-pressure level, cholesterol level and heart rate on daily basis along with meditation can help you prevent the major heart attacks.
Counting Essential Graphs

The contribution describes a method how to enumerate essential graphs. The method is based on two concepts, boundary vertices and symmetric vertices. Boundary vertices generalize the concept of terminal vertices to graphs with directed and undirected edges. Symmetric vertices are deﬁned recursively as vertices that have symmetric neighbors. Classes of symmetric vertices allow to count labeled essential graphs. Layers of boundary vertices together with symmetry allow to ﬁnd maximal essential graphs which are canonical representatives and can be used for counting unlabeled essential graph structures. Possible applications of the work are discussed. One goal is to enable the introduction of appropriate prior distributions in the model space for learning conditional independence structures.
Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits

A major obstacle to forming posterior distributions in machine learning is the difﬁculty of evaluating partition functions. Monte-Carlo approaches are unbiased, but can suffer from high variance. Variational methods are biased, but tend to have lower variance. We develop an approximate inference procedure that allows explicit control of the bias/variance tradeoff, interpolating between the sampling and the variational regime. We use a normalizing ﬂow to map the integrand onto a uniform distribution. We then randomly sample regions from a partition of this uniform distribution and ﬁt simpler, local variational approximations in the image of these regions through the ﬂow. When a partition with only one region is used, we recover standard variational inference, and in the limit of an inﬁnitely ﬁne partition we recover Monte-Carlo sampling. We show experiments validating the effectiveness of our approach.
Perfectionism and Professional Development: Cross-Examining Experienced and Novice EFL Practitioners

The role of teacher variables has become a major trend of study in the ELT literature. To this end, the focus of this descriptive study was to consider whether there is any significant relationship between novice and experienced EFL teachers’ perfectionism and professional development. In doing so, 60 novice and 60 experienced EFL teachers, aged 20-50, who were graduate and/or undergraduate students of EFL related fields participated in this study. The selection was through nonrandom convenience sampling; that is, the teachers who were willing to participate in the study and were teaching at language schools which were available to the researchers were chosen. The data were collected through two questionnaires: Multidimensional Perfectionism Scale (MPS), Teaching, and Learning International Survey (TALIS). Once the researchers had both questionnaires filled by the participants, they conducted the relevant descriptive and inferential statistical analyses. The results revealed that both novice and experienced EFL teachers’ perfectionism was a significant predictor of their professional development. The main imply location of this study is that teacher education centers may wish to invest upon promoting teachers’ perfectionism in order to promote their professional development.
Combining an HTN planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic Hierarchical Task Network (HTN) planner and a constraint satisfaction solver for the Vehicle Routing Problem (VRP) in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the paper describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The VRP solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the HTN planner computes an scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the VRP solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the VRP. The paper presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different conﬁgurations of the VRP solver and showing the suitability of using this approach.
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

We propose a novel multivariate signal denoising method that performs long-range correlation analysis of multiple modes in input data by considering inherent inter-channel dependencies of the data. That is achieved through a novel and generic multivariate extension of detrended fluctuation analysis (DFA) method - another contribution of this paper. Specifically, our proposed denoising method first obtains data driven multiscale signal representation using multivariate variational mode decomposition (MVMD) method. Then, the proposed generic multivariate DFA is used to reject the predominantly noisy modes based on their randomness scores. Finally, the denoised signal is reconstructed by summing the remaining modes albeit after the removal of the noise traces using the principal component analysis (PCA).
Do Online Courses Provide an Equal Educational Value Compared to In-Person Classroom Teaching? Evidence from US Survey Data Using Quantile Regression

Education has traditionally been classroom-oriented with a gradual growth of online courses in recent times. However, the outbreak of the COVID-19 pandemic has dramatically accelerated the shift to online classes. Associated with this learning format is the question: what do people think about the educational value of an online course compared to a course taken in-person in a classroom? This paper addresses the question and presents a Bayesian quantile analysis of public opinion using a nationally representative survey data from the United States. Our findings show that previous participation in online courses and full-time employment status favor the educational value of online courses. We also find that the older demographic and females have a greater propensity for online education. In contrast, highly educated individuals have a lower willingness towards online education vis-\`a-vis traditional classes. Besides, covariate effects show heterogeneity across quantiles which cannot be captured using probit or logit models.
Species tree estimation under joint modeling of coalescence and duplication: Sample complexity of quartet methods

We consider species tree estimation under a standard stochastic model of gene tree evolution that incorporates incomplete lineage sorting (as modeled by a coalescent process) and gene duplication and loss (as modeled by a branching process). Through a probabilistic analysis of the model, we derive sample complexity bounds for widely used quartet-based inference methods that highlight the effect of the duplication and loss rates in both subcritical and supercritical regimes.
Ubiquitous NH3 supersonic component in L1688 coherent cores

Context. Star formation takes place in cold dense cores in molecular clouds. Earlier observations have found that dense cores exhibit subsonic non-thermal velocity dispersions. In contrast, CO observations show that the ambient large-scale cloud is warmer and has supersonic velocity dispersions. Aims. We aim to study the ammonia (NH3) molecular line profiles with exquisite sensitivity towards the coherent cores in L1688 in order to study their kinematical properties in unprecedented detail. Methods. We used NH3 (1,1) and (2,2) data from the first data release (DR1) in the Green Bank Ammonia Survey (GAS). We first smoothed the data to a larger beam of 1′ to obtain substantially more extended maps of velocity dispersion and kinetic temperature, compared to the DR1 maps. We then identified the coherent cores in the cloud and analysed the averaged line profiles towards the cores. Results. For the first time, we detected a faint (mean NH3(1,1) peak brightness < 0.25 K in TMB), supersonic component towards all the coherent cores in L1688. We fitted two components, one broad and one narrow, and derived the kinetic temperature and velocity dispersion of each component. The broad components towards all cores have supersonic linewidths (ℳS ≥ 1). This component biases the estimate of the narrow dense core component’s velocity dispersion by ≈28% and the kinetic temperature by ≈10%, on average, as compared to the results from single-component fits. Conclusions. Neglecting this ubiquitous presence of a broad component towards all coherent cores causes the typical single-component fit to overestimate the temperature and velocity dispersion. This affects the derived detailed physical structure and stability of the cores estimated from NH3 observations.
Stringy corrections to the entropy of electrically charged supersymmetric black holes with $\mathrm{AdS}_5\times S^5$ asymptotics

We study the leading $\alpha^\prime$ corrections to the entropy of certain black holes with AdS$_5\times S^5$ asymptotics. We find that, in the supersymmetric limit, the entropy does not receive $\alpha^\prime$ corrections. This result strengthens recent calculations that match the index of $\mathcal{N}=4$ Super-Yang-Mills with the corresponding partition function in the supersymmetric limit. In the small temperature regime, we find that the entropy corrections are concordant with the weak gravity conjecture.
360° Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron

In this study, we present a method for all-around depth estimation from multiple omnidirectional images for indoor environments. In particular, we focus on plane-sweeping stereo as the method for depth estimation from the images. We propose a new icosahedron-based representation and ConvNets for omnidirectional images, which we name "CrownConv" because the representation resembles a crown made of origami. CrownConv can be applied to both fisheye images and equirect- angular images to extract features. Furthermore, we propose icosahedron-based spherical sweeping for generating the cost volume on an icosahedron from the extracted features. The cost volume is regularized using the three-dimensional CrownConv, and the final depth is obtained by depth regression from the cost volume. Our proposed method is robust to camera alignments by using the extrinsic camera parameters; therefore, it can achieve precise depth estimation even when the camera alignment differs from that in the training dataset. We evaluate the proposed model on synthetic datasets and demonstrate its effectiveness. As our proposed method is computationally efficient, the depth is estimated from four fisheye images in less than a second using a laptop with a GPU. Therefore, it is suitable for real-world robotics applications. Our source code is available at https://github.com/matsuren/crownconv360depth.
Entropy decay in the Swendsen-Wang dynamics

We study the mixing time of the Swendsen-Wang dynamics for the ferromagnetic Ising and Potts models on the integer lattice ${\mathbb Z}^d$. This dynamics is a widely used Markov chain that has largely resisted sharp analysis because it is non-local, i.e., it changes the entire configuration in one step. We prove that, whenever strong spatial mixing (SSM) holds, the mixing time on any $n$-vertex cube of ${\mathbb Z}^d$ is $O(\log n)$, improving on the previous best known bound of $O(n)$. SSM is a standard condition corresponding to exponential decay of correlations with distance between spins on the lattice and is known to hold in $d=2$ dimensions throughout the high-temperature (single phase) region. Our result follows from a modified log-Sobolev inequality, which expresses the fact that the dynamics contracts relative entropy at a constant rate at each step. The proof of this fact utilizes a new factorization of the entropy in the joint probability space over spins and edges that underlies the Swendsen-Wang dynamics. This factorization leads to several additional results, including mixing time bounds for a number of natural local and non-local Markov chains on the joint space, as well as for the standard random-cluster dynamics.
REPrune: Filter Pruning via Representative Election

Even though norm-based filter pruning methods are widely accepted, it is questionable whether the "smaller-norm-less-important" criterion is optimal in determining filters to prune. Especially when we can keep only a small fraction of the original filters, it is more crucial to choose the filters that can best represent the whole filters regardless of norm values. Our novel pruning method entitled "REPrune" addresses this problem by selecting representative filters via clustering. By selecting one filter from a cluster of similar filters and avoiding selecting adjacent large filters, REPrune can achieve a better compression rate with similar accuracy. Our method also recovers the accuracy more rapidly and requires a smaller shift of filters during fine-tuning. Empirically, REPrune reduces more than 49% FLOPs, with 0.53% accuracy gain on ResNet-110 for CIFAR-10. Also, REPrune reduces more than 41.8% FLOPs with 1.67% Top-1 validation loss on ResNet-18 for ImageNet.
An Obscured, Seyfert 2–like State of the Stellar-mass Black Hole GRS 1915+105 Caused by Failed Disk Winds

We report on Chandra grating spectra of the stellar-mass black hole GRS 1915+105 obtained during a novel, highly obscured state. As the source entered this state, a dense, massive accretion disk wind was detected through strong absorption lines. Photoionization modeling indicates that it must originate close to the central engine, orders of magnitude from the outer accretion disk. Strong, nearly sinusoidal flux variability in this phase yielded a key insight: the wind is blueshifted when its column density is relatively low, but redshifted as it approaches the Compton-thick threshold. At no point does the wind appear to achieve the local escape velocity; in this sense, it is a “failed wind. ” Later observations suggest that the disk ultimately fails to keep even the central engine clear of gas, leading to heavily obscured and Compton-thick states characterized by very strong Fe K emission lines. Indeed, these later spectra are successfully described using models developed for obscured active galactic nuclei (AGNs). We discuss our results in terms of the remarkable similarity of GRS 1915+105 deep in its “obscured state” to Seyfert 2 and Compton-thick AGNs, and we explore how our understanding of accretion and obscuration in massive black holes is impacted by our observations.
Unsupervised Human 3D Pose Representation with Viewpoint and Pose Disentanglement

Learning a good 3D human pose representation is important for human pose related tasks, e.g. human 3D pose estimation and action recognition. Within all these problems, preserving the intrinsic pose information and adapting to view variations are two critical issues. In this work, we propose a novel Siamese denoising autoencoder to learn a 3D pose representation by disentangling the pose-dependent and view-dependent feature from the human skeleton data, in a fully unsupervised manner. These two disentangled features are utilized together as the representation of the 3D pose. To consider both the kinematic and geometric dependencies, a sequential bidirectional recursive network (SeBiReNet) is further proposed to model the human skeleton data. Extensive experiments demonstrate that the learned representation 1) preserves the intrinsic information of human pose, 2) shows good transferability across datasets and tasks. Notably, our approach achieves state-of-the-art performance on two inherently different tasks: pose denoising and unsupervised action recognition. Code and models are available at: \url{this https URL}
Near-complete protein structural modelling of the minimal genome.

Protein tertiary structure prediction has improved dramatically in recent years. A considerable fraction of various proteomes can be modelled in the absence of structural templates. We ask whether our DMPfold method can model all the proteins without templates in the JCVI-syn3.0 minimal genome, which contains 438 proteins. We find that a useful tertiary structure annotation can be provided for all but 10 proteins. The models may help annotate function in cases where it is unknown, and provide coverage for 29 predicted protein-protein interactions which lacked monomer models. We also show that DMPfold performs well on proteins with structures released since initial publication. It is likely that the minimal genome will have complete structural coverage within a few years.
Measuring nuclear density with parity violating electron scattering

The saturation density of nuclear matter $\rho_0$ is a fundamental nuclear physics property that is difficult to predict from fundamental principles. The saturation density is closely related to the interior density of a heavy nucleus, such as $^{208}$Pb. We use parity violating electron scattering to determine the average interior weak charge and baryon densities in $^{208}$Pb. This requires not only measuring the weak radius $R_{\rm wk}$ but also determining the surface thickness of the weak charge density $a$. We obtain $\rho_0=0.150\pm0.010$ fm$^{-3}$, where the 7\% error has contributions form the PREX error on the weak radius, an assumed 10\% uncertainty in the surface thickness $a$, and from the extrapolation to infinite nuclear matter. These errors can be improved with the upcoming PREX II results and with a new parity violating electron scattering experiment, at a somewhat higher momentum transfer, to determine $a$.
Alon–Tarsi numbers of direct products

We provide a general framework on the coefficients of the graph polynomials of graphs which are Cartesian products. As a corollary, we prove that if $G=(V,E)$ is a graph with degrees of vertices $2d(v), v\in V$, and the graph polynomial $\prod_{(i,j)\in E} (x_j-x_i)$ contains an ``almost central'' monomial (that means a monomial $\prod_v x_v^{c_v}$, where $|c_v-d(v)|\leqslant 1$ for all $v\in V$), then the Cartesian product $G\square C_{2n}$ is $(d(\cdot)+2)$-choosable.
Nonlinear adaptive cruise control of vehicular platoons

The paper deals with the design of nonlinear adaptive cruise controllers for vehicular platoons operating on an open road or a ring road. The constructed feedback controllers are nonlinear functions of the distance between successive vehicles and their speeds. It is shown that the proposed novel controllers guarantee safety (collision avoidance), speed positivity, bounded acceleration, speed limits by explicitly characterising the set of allowable inputs and by leveraging barrier function techniques. Moreover, we guarantee global asymptotic stability of the platoon to the desired configuration, as well as string stability. Certain macroscopic properties are also investigated. The main challenges stem from the fact that all previous objectives must be satisfied simultaneously and the fact that the control systems studied do not evolve in a finite-dimensional linear space but rather on specific sets. The efficiency of the nonlinear adaptive cruise controllers is demonstrated by means of numerical examples.
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards

We study an extension of the classic stochastic multi-armed bandit problem which involves multiple plays and Markovian rewards in the rested bandits setting. In order to tackle this problem we consider an adaptive allocation rule which at each stage combines the information from the sample means of all the arms, with the Kullback-Leibler upper confidence bound of a single arm which is selected in round-robin way. For rewards generated from a one-parameter exponential family of Markov chains, we provide a finite-time upper bound for the regret incurred from this adaptive allocation rule, which reveals the logarithmic dependence of the regret on the time horizon, and which is asymptotically optimal. For our analysis we devise several concentration results for Markov chains, including a maximal inequality for Markov chains, that may be of interest in their own right. As a byproduct of our analysis we also establish asymptotically optimal, finite-time guarantees for the case of multiple plays, and i.i. d. rewards drawn from a one-parameter exponential family of probability densities. Additionally, we provide simulation results that illustrate that calculating Kullback-Leibler upper confidence bounds in a round-robin way, is significantly more efficient than calculating them for every arm at each round, and that the expected regrets of those two approaches behave similarly.