Miracle-dz/UniFAR-FTUs huggingface dataset:
Bone Health and Its Positive Relationships with Body Composition in Malaysian Schoolchildren: Findings from a Cross-Sectional Study

Background: Optimal bone health is vital in children to prevent osteoporosis later in life, and body composition plays a crucial role in it. However, the literature reports contradictory results when considering the relationship between body composition and bone health in children. This study aimed to examine the bone health and its relationship with body composition in Malaysian schoolchildren. Methods: In this cross sectional study, body composition data (weight, height, body fat percentage [% fat], fat mass, fat free mass, visceral fat, waist circumference [WC] and body mass index-for-age [BMI z-score]) and bone health data (Z-score and broadband ultrasound attenuation [BUA]) were collected from 415 schoolchildren aged 9–12 years, cluster sampled from randomly selected primary schools in Kuala Lumpur, Malaysia. Results: Girls generally had significantly higher height, body fat percentage, fat mass, visceral fat and Z-score as compared to boys. A steady increase of the mean BUA value was observed with increasing age in both sexes. The mean BUA value of the present study across the population was significantly higher than that of schoolchildren from Nigeria (p < 0.001), Colombia (p < 0.001) and Spain (p = 0.002). Significant positive correlations were found between all the body composition variables and bone outcome variables across the population. Further, BUA was significantly correlated with weight (β = 0.172; p = 0.001), height (β = 0.299; p < 0.001), % fat (β = 0.131; p = 0.007), fat mass (β = 0.130; p = 0.007), fat free mass (β = 0.209; p < 0.001), visceral fat (β = 0.127, p = 0.008), WC (β = 0.165; p = 0.001) and BMI z-score (β = 0.162; p = 0.001), after controlling for sex, age and ethnicity. Similarly, after confounders adjusted, Z-score was significantly predicted by weight (β = 0.160; p = 0.001), height (β = 0.310; p < 0.001), % fat (β = 0.104; p = 0.032), fat mass (β = 0.107; p = 0.026), fat free mass (β = 0.218; p < 0.001), visceral fat (β = 0.107, p = 0.026), WC (β = 0.145; p = 0.002) and BMI z-score (β = 0.150; p = 0.002). Conclusions: Our findings have revealed that body composition variables were positive correlated with bone outcome variables, suggesting that adipose tissue acts to stimulate bone growth. Further clinical and molecular studies in the future is recommended to fully illustrate the complex interactions between adiposity and bone health.
MalPaCa Feature Combination: Which packet header features and combination thereof are the most generalizable, private and easy to extract to cluster malicious behavior?

MalPaCa is an unsupervised clustering tool, which the main purpose is to cluster unidirectional network connections based on network behavior. The clustering is only based on non-intrusive (private) packet features such as transport and network header fields, and thus it has a strong potential usecase. This paper focuses on feature extraction and finding the best combinations that provide best clustering results. The features should be generalizable to a wide range of malware families and follow an easy extraction process. To expand the research one additional packet-based feature is found, TCP flags, as well different variants of previously extracted features were employed, which improves the efficacy of the tool. Finally, a grid search is performed to determine the best combination of the features.
A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks

Athletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community’s interest because of its capability to provide real-time biofeedback to coaches and athletes when on the field and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with sufficient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, sufficiently small for real-world scenarios, easy to use, and achieves the specified communication range. The system’s high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice.
Extremum-Seeking Adaptive-Droop for Model-Free and Localized Volt-VAR Optimization

In an active power distribution system, Volt-VAR optimization (VVO) methods are employed to achieve network-level objectives such as minimization of network power losses. The commonly used model-based centralized and distributed VVO algorithms perform poorly in the absence of a communication system and with model and measurement uncertainties. In this paper, we proposed a model-free local Volt-VAR control approach for network-level optimization that does not require communication with other decision-making agents. The proposed algorithm is based on extremum-seeking approach that uses only local measurements to minimize the network power losses. To prove that the proposed extremum-seeking controller converges to the optimum solution, we also derive mathematical conditions for which the loss minimization problem is convex with respect to the control variables. Local controllers pose stability concerns during highly variable scenarios. Thus, the proposed extremum-seeking controller is integrated with an adaptive-droop control module to provide a stable local control response. The proposed approach is validated using IEEE 4-bus and IEEE 123-bus systems and achieves the loss minimization objective while maintaining the voltage within the pre-specific limits even during highly variable DER(distributed energy resources) generation scenarios.
Understanding the Distributions of Aggregation Layers in Deep Neural Networks

The process of aggregation is ubiquitous in almost all the deep nets' models. It functions as an important mechanism for consolidating deep features into a more compact representation while increasing the robustness to overfitting and providing spatial invariance in deep nets. In particular, the proximity of global aggregation layers to the output layers of DNNs means that aggregated features directly influence the performance of a deep net. A better understanding of this relationship can be obtained using information theoretic methods. However, this requires knowledge of the distributions of the activations of aggregation layers. To achieve this, we propose a novel mathematical formulation for analytically modeling the probability distributions of output values of layers involved with deep feature aggregation. An important outcome is our ability to analytically predict the Kullback-Leibler (KL)-divergence of output nodes in a DNN. We also experimentally verify our theoretical predictions against empirical observations across a broad range of different classification tasks and datasets.
Structured Model Pruning of Convolutional Networks on Tensor Processing Units

The deployment of convolutional neural networks is often hindered by high computational and storage requirements. Structured model pruning is a promising approach to alleviate these requirements. Using the VGG-16 model as an example, we measure the accuracy-efficiency trade-off for various structured model pruning methods and datasets (CIFAR-10 and ImageNet) on Tensor Processing Units (TPUs). To measure the actual performance of models, we develop a structured model pruning library for TensorFlow2 to modify models in place (instead of adding mask layers). We show that structured model pruning can significantly improve model memory usage and speed on TPUs without losing accuracy, especially for small datasets (e.g., CIFAR-10).
Control Lyapunov Functions for Compliant Hybrid Zero Dynamic Walking

The ability to realize nonlinear controllers with formal guarantees on dynamic robotic systems has the potential to enable more complex robotic behaviors -- yet, realizing these controllers is often practically challenging. To address this challenge, this paper presents the end-to-end realization of dynamic bipedal locomotion on an underactuated bipedal robot via hybrid zero dynamics and control Lyapunov functions. A compliant model of Cassie is represented as a hybrid system to set the stage for a trajectory optimization framework. With the goal of achieving a variety of walking speeds in all directions, a library of compliant walking motions is compiled and then parameterized for efficient use within real-time controllers. Control Lyapunov functions, which have strong theoretic guarantees, are synthesized to leverage the gait library and coupled with inverse dynamics to obtain optimization-based controllers framed as quadratic programs. It is proven that this controller provably achieves stable locomotion; this is coupled with a theoretic analysis demonstrating useful properties of the controller for tuning and implementation. The proposed theoretic framework is practically demonstrated on the Cassie robot, wherein 3D walking is achieved through the use of optimization-based torque control. The experiments highlight robotic walking at different speeds and terrains, illustrating the end-to-end realization of theoretically justified nonlinear controllers on dynamic underactuated robotic systems.
Chiral phonons and pseudoangular momentum in nonsymmorphic systems

Chiral phonons are the ones with nonzero polarization and can be observed only via a selective coupling with valley electrons and circularly polarized photons. In such process, a new physical quantity, i.e., pseudo-angular momentum (PAM), is required to meet the selection rule. However, phonon PAM was thought to be quantized and can be only defined in the symmorphic systems. In this work, we generalized the definition of PAM to three-dimensional non-symmorphic systems, which show distinct different properties compared with the one in symmorphic systems, e.g., PAM can be non-quantized and q-dependent but still be an observable quantity by experiments. Such new definitions and discoveries can help us to obtain chiral phonons in a broader class of systems with a nonzero group velocity and to convey information like chirality and angular momentum in solids as expected. Materials are also offered to understand the new definition and for further experimental detection.
Simulations of Torus Reverberation Mapping Experiments with SPHEREx

Reverberation mapping (RM) is an efficient method to investigate the physical sizes of the broad line region (BLR) and dusty torus in an active galactic nucleus (AGN). The Spectro-Photometer for the History of the Universe, Epoch of Reionization and Ices Explorer (SPHEREx) mission will provide multi-epoch spectroscopic data at optical and near-infrared wavelengths. These data can be used for RM experiments for bright AGNs. We present results of a feasibility test using SPHEREx data in the SPHEREx deep regions for the torus RM measurements. We investigate the physical properties of bright AGNs in the SPHEREx deep field. Based on this information, we compute the efficiency of detecting torus time lags in simulated light curves. We demonstrate that, in combination with the complementary optical data with a depth of ∼ 20 mag in B−band, lags of ≤ 750 days for tori can be measured for more than ∼ 200 bright AGNs. If high signal-to-noise ratio photometric data with a depth of ∼ 21−22 mag are available, RM measurements can be applied for up to ∼900 objects. When complemented by well-designed early optical observations, SPHEREx can provide a unique dataset for studies of the physical properties of dusty tori in bright AGNs.
A Systematic Survey of Text Worlds as Embodied Natural Language Environments

Text Worlds are virtual environments for embodied agents that, unlike 2D or 3D environments, are rendered exclusively using textual descriptions. These environments offer an alternative to higher-fidelity 3D environments due to their low barrier to entry, providing the ability to study semantics, compositional inference, and other high-level tasks with rich action spaces while controlling for perceptual input. This systematic survey outlines recent developments in tooling, environments, and agent modeling for Text Worlds, while examining recent trends in knowledge graphs, common sense reasoning, transfer learning of Text World performance to higher-fidelity environments, as well as near-term development targets that, once achieved, make Text Worlds an attractive general research paradigm for natural language processing.
On the pinned distances problem in positive characteristic

We study the Erdős–Falconer distance problem for a set A⊂F2$A\subset \mathbb {F}^2$ , where F$\mathbb {F}$ is a field of positive characteristic p$p$ . If F=Fp$\mathbb {F}=\mathbb {F}_p$ and the cardinality |A|$|A|$ exceeds p5/4$p^{5/4}$ , we prove that A$A$ determines an asymptotically full proportion of the feasible p$p$ distances. For small sets A$A$ , namely when |A|⩽p4/3$|A|\leqslant p^{4/3}$ over any F$\mathbb {F}$ , we prove that either A$A$ determines ≫|A|2/3$\gg |A|^{2/3}$ distances, or A$A$ lies on an isotropic line. For both large and small sets, the results proved are in fact for pinned distances.
Semantic and Geometric Unfolding of StyleGAN Latent Space

Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to a natural image. This property emerges from the disentangled nature of the latent space. In this paper, we identify two geometric limitations of such latent space: (a) euclidean distances differ from image perceptual distance, and (b) disentanglement is not optimal and facial attribute separation using linear model is a limiting hypothesis. We thus propose a new method to learn a proxy latent representation using normalizing flows to remedy these limitations, and show that this leads to a more efficient space for face image editing.
Fast Compression of MCMC Output

We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates), while imposing equality constraints on the averages of these control variates, using the cube method (an approach that originates from survey sampling). The main advantage of cube thinning is that its complexity does not depend on the size of the compressed sample. This compares favourably to previous methods, such as Stein thinning, the complexity of which is quadratic in that quantity.
Fractional powers approach of operators for higher order abstract Cauchy problems

In this paper we explore the theory of fractional powers of non-negative (and not necessarily self-adjoint) operators and its amazing relationship with the Chebyshev polynomials of the second kind to obtain results of existence, regularity and behavior asymptotic of solutions for linear abstract evolution equations of $n$-th order in time, where $n\geqslant3$. We also prove generalizations of classical results on structural damping for linear systems of differential equations.
Block Bregman Majorization Minimization with Extrapolation

In this paper, we consider a class of nonsmooth nonconvex optimization problems whose objective is the sum of a block relative smooth function and a proper and lower semicontinuous block separable function. Although the analysis of block proximal gradient (BPG) methods for the class of block $L$-smooth functions have been successfully extended to Bregman BPG methods that deal with the class of block relative smooth functions, accelerated Bregman BPG methods are scarce and challenging to design. Taking our inspiration from Nesterov-type acceleration and the majorization-minimization scheme, we propose a block alternating Bregman Majorization-Minimization framework with Extrapolation (BMME). We prove subsequential convergence of BMME to a first-order stationary point under mild assumptions, and study its global convergence under stronger conditions. We illustrate the effectiveness of BMME on the penalized orthogonal nonnegative matrix factorization problem.
Carleman estimate for the Navier–Stokes equations and applications

For linearized Navier–Stokes equations, we first derive a Carleman estimate with a regular weight function. Then we apply it to establish conditional stability for the lateral Cauchy problem and finally we prove conditional stability estimates for the inverse source problem of determining a spatially varying divergence-free factor of a source term.
On the Variance of the Fisher Information for Deep Learning

In the realm of deep learning, the Fisher information matrix (FIM) gives novel insights and useful tools to characterize the loss landscape, perform second-order optimization, and build geometric learning theories. The exact FIM is either unavailable in closed form or too expensive to compute. In practice, it is almost always estimated based on empirical samples. We investigate two such estimators based on two equivalent representations of the FIM -- both unbiased and consistent. Their estimation quality is naturally gauged by their variance given in closed form. We analyze how the parametric structure of a deep neural network can affect the variance. The meaning of this variance measure and its upper bounds are then discussed in the context of deep learning.
Beyond the Tavis-Cummings model: Revisiting cavity QED with ensembles of quantum emitters

The interaction of an ensemble of N two-level atoms with a single mode electromagnetic field is described by the Tavis-Cummings model. There, the collectively enhanced light-matter coupling strength is given by gN = √ Nḡ1, where ḡ1 is the ensemble-averaged single-atom coupling strength. Formerly, this model has been employed to describe and to analyze numerous cavity–based experiments. Here, we show that this is only justified if the effective scattering rate into non-cavity modes is negligible compared to the cavity’s free-spectral range. In terms of experimental parameters, this requires that the optical depth of the ensemble is low, a condition that is violated in several state-ofthe-art experiments. We give quantitative conditions for the validity of the Tavis-Cummings model and derive a more general Hamiltonian description that takes into account the cascaded interaction of the photons with all consecutive atoms. We show that the predictions of our model can differ quantitatively and even qualitatively from those obtained with the Tavis-Cummings model. Finally, we present experimental data, for which the deviation from the predictions of the Tavis-Cummings model is apparent. Our findings are relevant for all experiments in which optically dense ensembles of quantum emitters are coupled to an optical resonator.
Entropy, Information, and the Updating of Probabilities

This paper is a review of a particular approach to the method of maximum entropy as a general framework for inference. The discussion emphasizes pragmatic elements in the derivation. An epistemic notion of information is defined in terms of its relation to the Bayesian beliefs of ideally rational agents. The method of updating from a prior to posterior probability distribution is designed through an eliminative induction process. The logarithmic relative entropy is singled out as a unique tool for updating (a) that is of universal applicability, (b) that recognizes the value of prior information, and (c) that recognizes the privileged role played by the notion of independence in science. The resulting framework—the ME method—can handle arbitrary priors and arbitrary constraints. It includes the MaxEnt and Bayes’ rules as special cases and, therefore, unifies entropic and Bayesian methods into a single general inference scheme. The ME method goes beyond the mere selection of a single posterior, and also addresses the question of how much less probable other distributions might be, which provides a direct bridge to the theories of fluctuations and large deviations.
Multielement Characterization and Antioxidant Activity of Italian Extra-Virgin Olive Oils

Food product safety and quality are closely related to the elemental composition of food. This study combined multielement analysis and chemometric tools to characterize 237 extra-virgin olive oil (EVOO) samples from 15 regions of Italy, and to verify the possibility of discriminating them according to different quality factors, such as varietal or geographical origin or whether they were organically or traditionally produced. Some elements have antioxidant properties, while others are toxic to humans or can promote oxidative degradation of EVOO samples. In particular, the antioxidant activity of oils’ hydrophilic fraction was estimated and the concentrations of 45 elements were determined by inductively coupled plasma mass spectrometry (ICP-MS). At first, univariate and multivariate analyses of variance were used to compare the element concentrations, and statistically significant differences were found among samples from different regions. Successively, discriminant classification approaches were used to build a model for EVOO authentication, considering, in turn, various possible categorizations. The results have indicated that chemometric methods coupled with ICP-MS have the potential to discriminate and characterize the different types of EVOO, and to provide “typical” elemental fingerprints of the various categories of samples.
3D RegNet: Deep Learning Model for COVID-19 Diagnosis on Chest CT Image

In this paper, a 3D-RegNet-based neural network is proposed for diagnosing the physical condition of patients with coronavirus (Covid-19) infection. In the application of clinical medicine, lung CT images are utilized by practitioners to determine whether a patient is infected with coronavirus. However, there are some laybacks can be considered regarding to this diagnostic method, such as time consuming and low accuracy. As a relatively large organ of human body, important spatial features would be lost if the lungs were diagnosed utilizing two dimensional slice image. Therefore, in this paper, a deep learning model with 3D image was designed. The 3D image as input data was comprised of two-dimensional pulmonary image sequence and from which relevant coronavirus infection 3D features were extracted and classified. The results show that the test set of the 3D model, the result: f1 score of 0.8379 and AUC value of 0.8807 have been achieved.