Miracle-dz/UniFAR-FTUs huggingface dataset:
PIFA – Planar Inverted F Antenna

The Inverted F Antenna (IFA) typically consists of a rectangular planar element located above a ground plane, a short circuiting plate or pin, and a feeding mechanism for the planar element. The Inverted F antenna is a variant of the monopole where the top section has been folded down so as to be parallel with the ground plane. This is done to reduce the height of the antenna, while maintaining a resonant trace length. This parallel section introduces capacitance to the input impedance of the antenna, which is compensated by implementing a short-circuit stub. The stub’s end is connected to the ground plane through a via. The ground plane of the antenna plays a significant role in its operation. Excitation of currents in the printed IFA causes excitation of currents in the ground plane. The resulting electromagnetic field is formed by the interaction of the IFA and an image of itself below the ground plane. Its behavior as a perfect energy reflector is consistent only when the ground plane is infinite or very much larger in its dimensions than the monopole itself. In practice the metallic layers are of comparable dimensions to the monopole and act as the other part of the dipole. ♦ The antenna/ground combination will behave as an asymmetric dipole, the differences in current distribution on the two-dipole arms being responsible for some distortion of the radiation pattern. ♦ In general, the required PCB ground plane length is roughly one quarter (λ/4) of the operating wavelength. ♦ If the ground plane is much longer than λ/4, the radiation patterns will become increasingly multilobed. ♦ On the other hand, if the ground plane is significantly smaller than λ/4, then tuning becomes increasingly difficult and the overall performance degrades. ♦ The optimum location of the IFA in order to achieve an omni-directional far-field pattern and 50Ω impedance matching was found to be close to the edge of the Printed Circuit Board.
RusTEE: Developing Memory-Safe ARM TrustZone Applications

In the past decade, Trusted Execution Environment (TEE) provided by ARM TrustZone is becoming one of the primary techniques for enhancing the security of mobile devices. The isolation enforced by TrustZone can protect the trusted applications running in the TEE against malicious software in the untrusted rich execution environment (REE). However, TrustZone cannot completely prevent vulnerabilities in trusted applications residing in the TEE, which can then be used to attack other trusted applications or even the trusted OS. Previously, a number of memory corruption vulnerabilities have been reported on different TAs, which are written in memory-unsafe languages like C. Recently, various memory-safe programming languages have emerged to mitigate the prevalent memory corruption bugs. In this paper, we propose RusTEE, a trusted application mechanism that leverages Rust, a newly emerged memory-safe language, to enhance the security of TAs. Though the high-level idea is quite straight-forwarding, we resolve several challenges on adopting Rust in mobile TEEs. Specifically, since Rust currently does not support any TrustZone-assisted TEE systems, we extend the existing Rust compiler for providing such support. Also, we apply comprehensive security mechanisms to resolve two security issues of trusted applications, namely, securely invoking high-privileged system services and securely communicating with untrusted REE. We implement a prototype of RusTEE as the trusted applications’ SDK, which supports both emulator and real hardware devices. The experiment shows that RusTEE can compile applications with close-to-C performance on the evaluated platforms.
Effects of Cancer, Coronary Artery Disease and other Comorbidities on COVID-19 Related Mortality: A Meta-analysis and Meta-regression

Objective: To investigate Coronavirus Disease 2019 (COVID-19) associated mortality, the prevalence of different symptoms, and the prevalence and association between comorbidities and their effects on outcomes. Methods: We performed a systematic literature search and meta-analysis on studies that assess COVID-19 patients' symptoms, comorbidities, and outcomes using pooled event rate (PER) and pooled event means (PEM). The primary outcome was the pooled all- cause short-term mortality. The secondary outcomes were length of hospital stay and symptom presentation. Meta-regression and leave-one-out analysis were conducted for mortality. Results: 56 articles met our inclusion criteria with a total of 9074 patients. The PEM for age was 49.6 years. The PER for female gender was 46.79%. The PER for smoking, hypertension, cardiac comorbidities, diabetes was 10.96%, 24.47%, 20.30%; 12.34% respectively. The PER for CAD, COPD, history of cancer and chronic liver disease was 5.44%, 3.96%, 3.75% and 3.08%. The PER for fever, cough, sore throat and headache was 79.29%, 56.48%, 11.10%, 8.16%, respectively. The PER for diarrhea, chest pain, fatigue and vomiting was 11.32%, 13.43%, 27.72% and 11.98%, respectively. PEM for hospital stay was 10.9 days (95% CI 7.3- 16.1 days). The PER for hospital mortality was 11.17% (95% CI, 6.67% - 17.89%). Hospital mortality was significantly and positively associated with cardiac comorbidity and COPD. Age and cancer were not associated with higher hospital mortality. Conclusion: Fever and cough are the most common presenting symptoms with estimated PER of 79.29% and 56.48% respectively. Hospital mortality is significantly and positively associated with cardiac comorbidities, CAD, and COPD, while not being significantly associated with patient age or cancer.
Assessing the impact of gout

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01. Structural Geology, Tectonics and Geodynamics

Biot’s theory (1962) describes the hydro-mechanically coupled physics of fluid-saturated porous rocks. This theory finds many applications in Earth Sciences. A dynamic response of such a system results in the two longitudinal waves and one shear wave. A quasi-static response results in fluid pressure diffusion. Adding plasticity to such a system enables us to model poro-elasto-plastic behavior, for example, associated with fracturing due to fluid injection. We present a three-dimensional numerical implementation of poro-elastic physics using graphical processing units (Alkhimenkov et al., submitted). Only 95 seconds are needed to run 1000 time steps of a model having 1150 3 (1.5 billion) grid cells (Figure 1). This implementation can be used to obtain dynamic (wave propagation) and quasi-static (no inertia) results using the same code. Additionaly, we present a simulation considering an elasto-plastic medium in two dimensions (Figure 2). Currently, we are adding more physics to the present 3-D code to model poro-elasto-plastic responses due to fluid injection in a porous medium.
Distributed Training of Recurrent Neural Networks by FGM Protocol

Artificial Neural Networks are appealing because they learn by example and are strongly supported by statistical and optimization theories. The usage of recurrent neural networks as identifiers and predictors in nonlinear dynamic systems has increased significantly. They can present a wide range of dynamics, due to feedback and are also flexible nonlinear maps. Based on this, there is a need for distributed training on these networks, because of the enormous datasets. One of the most known protocols for distributed training is the Geometric Monitoring protocol. Our conviction is that this is a very expensive protocol regarding the communication of nodes. Recently, the Functional Geometric Protocol has tested training on Convolutional Neural Networks and has had encouraging results. The goal of this work is to test and compare these two protocols on Recurrent Neural Networks.
A chiral gauge-invariant model for Majorana neutrinos

The article investigates the possibility that right-handed neutrinos are Majorana particles embedded in an abelian multiplicative $U(1)_R$ factor, with the Lagrangian in the new factor being invariant under chiral gauge transformations. Majorana neutrinos couple to charged and neutral currents, producing new signatures to (anti)neutrino-electron elastic scattering and an additional term to coherent scattering of neutrinos on atomic nuclei. The model is ultraviolet complete and depends on one extra hypercharge correlating the reactions and providing upper bounds for the new coupling constant.
A hybrid LBM-DEM framework with an improved immersed moving boundary method for modelling complex particle-liquid flows involving adhesive particles.

This paper presents an improved immersed moving boundary model (IBM) for solving complex fluid-particle interactions in a coupled lattice Boltzmann method (LBM) and an adhesive discrete element method (DEM), using the "partially saturated cell" scheme. It is shown that the existing scheme does not well address the contribution of each solid particle to the fluid when multiple particles intersect with the same lattice cell. This issue is completely addressed by modifying the weighting function in the partially covered cells in the present study. Furthermore, a fast linear approximation method with high efficiency and good accuracy is applied to calculate the partially intersected volume between a particle and a lattice cell. Verified with several benchmark cases, the developed hybrid IBM-LBM-DEM numerical framework is capable of describing the flow field between dense particles with a relatively low grid resolution in more details, as well as effectively capturing the adhesive mechanics between microspheres.
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

Graph kernels based on the $1$-dimensional Weisfeiler-Leman algorithm and corresponding neural architectures recently emerged as powerful tools for (supervised) learning with graphs. However, due to the purely local nature of the algorithms, they might miss essential patterns in the given data and can only handle binary relations. The $k$-dimensional Weisfeiler-Leman algorithm addresses this by considering $k$-tuples, defined over the set of vertices, and defines a suitable notion of adjacency between these vertex tuples. Hence, it accounts for the higher-order interactions between vertices. However, it does not scale and may suffer from overfitting when used in a machine learning setting. Hence, it remains an important open problem to design WL-based graph learning methods that are simultaneously expressive, scalable, and non-overfitting. Here, we propose local variants and corresponding neural architectures, which consider a subset of the original neighborhood, making them more scalable, and less prone to overfitting. The expressive power of (one of) our algorithms is strictly higher than the original algorithm, in terms of ability to distinguish non-isomorphic graphs. Our experimental study confirms that the local algorithms, both kernel and neural architectures, lead to vastly reduced computation times, and prevent overfitting. The kernel version establishes a new state-of-the-art for graph classification on a wide range of benchmark datasets, while the neural version shows promising performance on large-scale molecular regression tasks.
Mining Dual Emotion for Fake News Detection

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features; 2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.1 2
Force Measurement Using Magnetic Potentiometer

An actuator is a device that accomplishes the actuation, which is defined as the process of converting some form of energy into mechanical force and motion. To control the actuator, it is important to use sensor technology to get positioning or force information. This paper is focused on an approach to measure the force for a class of compliant actuators. The proposed approach uses a potentiometer and Hooke’s law to calculate output force. Since the noise is found in potentiometer sensor, a low-pass filter is proposed to overcome this problem. The advantage of the proposed force estimation approach is that it is based on the cheap potentiometer, and it does not require the force sensor. Experimental test is given to verify the effectiveness of the proposed approach. Copyright © 2012 IFSA.
Warming and ocean acidification may decrease estuarine dissolved
organic carbon export to the ocean

Relative to their surface area, estuaries make a disproportionately large contribution of dissolved organic carbon (DOC) to the global carbon cycle, but it is unknown how this will change under a future climate. As such, the response of DOC fluxes from microbially dominated unvegetated sediments to individual and combined future climate stressors of temperature change (from 1−3 to 1+5 C compared to ambient mean temperatures) and ocean acidification (OA,∼ 2× current CO2 partial pressure, pCO2) was investigated ex situ. Warming alone increased sediment heterotrophy, resulting in a proportional increase in sediment DOC uptake; sediments became net sinks of DOC (3.5 to 8.8 mmol C m−2 d−1) at warmer temperatures (1+3 and 1+5 C, respectively). This temperature response changed under OA conditions, with sediments becoming more autotrophic and a greater sink of DOC (up to 4× greater than under current pCO2 conditions). This response was attributed to the stimulation of heterotrophic bacteria with the autochthonous production of labile organic matter by microphytobenthos. Extrapolating these results to the global area of unvegetated subtidal estuarine sediments, we find that the future climate of warming (1+3 C) and OA may decrease estuarine export of DOC by ∼ 80 % (∼ 150 Tg C yr−1) and have a disproportionately large impact on the global DOC budget.
Approximate Modularity Revisited | SIAM Journal on Computing | Vol. 49, No. 1 | Society for Industrial and Applied Mathematics

Set functions with convenient properties (such as submodularity) appear in application areas of current interest, such as algorithmic game theory, and allow for improved optimization algorithms. It is natural to ask (e.g., in the context of data driven optimization) how robust such properties are, and whether small deviations from them can be tolerated. We consider two such questions in the important special case of linear set functions. One question that we address is whether any set function that approximately satisfies the modularity equation (linear functions satisfy the modularity equation exactly) is close to a linear function. The answer to this is positive (in a precise formal sense) as shown by Kalton and Roberts [Trans. Amer. Math. Soc. , 278 (1983), pp. 803--816] (and further improved by Bondarenko, Prymak, and Radchenko [J. Math. Anal. Appl., 402 (2013), pp. 234--241]). We revisit their proof idea that is based on expander graphs and provide significantly stronger upper bounds by combining it with new techniques. Furthermore, we provide improved lower bounds for this problem. Another question that we address is that of how to learn a linear function h that is close to an approximately linear function f , while querying the value of f on only a small number of sets. We present a deterministic algorithm that makes only linearly many (in the number of items) nonadaptive queries, and thus improve upon a previous algorithm of Chierichetti, Das, Dasgupta, and Kumar [Proceedings of the 56th Symposium on Foundations of Computer Science, 2015, pp. 1143--1162] that is randomized and makes more than a quadratic number of queries. Our learning algorithm is based on the Hadamard transform.
Managing Disruptions in Production with Machine Learning

Changing customer demands lead to increasing product varieties and decreasing delivery times, which in turn pose great challenges for production companies. Combined with high market volatility, they lead to increasingly complex and diverse production processes. Thus, the susceptibility to disruptions in manufacturing rises, turning the task of Production Planning and Control (PPC) into a complex, dynamic and multidimensional problem. Addressing PPC challenges such as disruption management in an efficient and timely manner requires a high level of manual human intervention. In times of digitization and Industry 4.0, companies strive to find ways to guide their workers in this process of disruption management or automate it to eliminate human intervention altogether. This paper presents one possible application of Machine Learning (ML) in disruption management on a real-life use case in mixed model continuous production, specifically in the final assembly. The aim is to ensure high-quality online decision support for PPC tasks. This paper will therefore discuss the use of ML to anticipate production disruptions, solutions to efficiently highlight and convey the relevant information, as well as the generation of possible reaction strategies. Additionally, the necessary preparatory work and fundamentals are covered in the discussion, providing guidelines for production companies towards consistent and efficient disruption management.
Antibacterial and Anti-Biofilm Activities of Culture Filtrates from Schizophyllum commune, Coniothyrium sp., and Fusarium sp.

Bacterial infections are usually caused by biofilms that are resistant to extreme conditions. Studies have shown that fungal extracts have the potential to inhibit biofilm formation. The aim of this study was to examine the activity of several fungal culture filtrates in inhibiting bacterial growth and biofilm formation. The fungi were identified by molecular method, and the effects of different concentrations (45%, 75%, and 90%), medium pH (6, 7, and 9), temperatures (30 and 37 °C), and incubation times (24, 48, and 72 h) of the filtrates on their anti-biofilm formation properties were investigated. Anti-biofilm assay was performed using the diffusion test and microtiter assay. The parameters examined included the diameter of the inhibition zone of each well and the optical density of the filtrate solution. Molecular identification based on ITS rDNA regions showed that the fungal isolates in this study were Schizophyllum commune (strain JSB2), Coniothyrium sp. (strain JB1-3), and Fusarium sp. (strain JBB2). Antibacterial assay showed that, among the culture filtrates obtained from all fungi, that from Fusarium sp. strain JBB2 exhibits the highest inhibitory activity. Biofilm inhibition assay further revealed optimum fungal culture filtrate activities at pH 7.
Generalized Mixtures of Finite Mixtures and Telescoping Sampling

Within a Bayesian framework, a comprehensive investigation of the model class of mixtures of finite mixtures (MFMs) where a prior on the number of components is specified is performed. This model class has applications in model-based clustering as well as for semi-parametric density approximation, but requires suitable prior specifications and inference methods to exploit its full potential. We contribute to the Bayesian analysis of MFMs by considering a generalized class of MFMs containing static and dynamic MFMs where the Dirichlet parameter of the component weights either is fixed or depends on the number of components. We emphasize the distinction between the number of components $K$ of a mixture and the number of clusters $K_+$, i.e., the number of filled components given the data. In the MFM model, $K_+$ is a random variable and its prior depends on the prior on the number of components $K$ and the mixture weights. We characterize the prior on the number of clusters $K_+$ for generalized MFMs and derive computationally feasible formulas to calculate this implicit prior. In addition we propose a flexible prior distribution class for the number of components $K$ and link MFMs to Bayesian non-parametric mixtures. For posterior inference of a generalized MFM, we propose the novel telescoping sampler which allows Bayesian inference for mixtures with arbitrary component distributions without the need to resort to RJMCMC methods. The telescoping sampler explicitly samples the number of components, but otherwise requires only the usual MCMC steps for estimating a finite mixture model. The ease of its application using different component distributions is demonstrated on several data sets.
Holographic DC SQUID in the presence of dark matter

The gauge-gravity duality has been applied to examine the properties of holographic superconducting quantum device (SQUID), composed of two S-N-S Josephson junctions, influenced by dark sector modelled by the additional U(1)-gauge field coupled to the ordinary Maxwell one. The dark matter sector is known to affect the properties of superconductors and is expected to enter the current-phase relation. The kinetic mixing between two gauge fields provides a mechanism allowing for the conceivable observation of the effect. We find small but visible effect of the dark matter particle traversing the device, which shows up as a change of its maximal current.
Efficient Privacy-Preserving Stochastic Nonconvex Optimization

While many solutions for privacy-preserving convex empirical risk minimization (ERM) have been developed, privacy-preserving nonconvex ERM remains a challenge. We study nonconvex ERM, which takes the form of minimizing a finite-sum of nonconvex loss functions over a training set. We propose a new differentially private stochastic gradient descent algorithm for nonconvex ERM that achieves strong privacy guarantees efficiently, and provide a tight analysis of its privacy and utility guarantees, as well as its gradient complexity. Our algorithm substantially reduces gradient complexity while matching the best previous utility guarantee given by Wang et al. (2017). We extend our algorithm to the distributed setting using secure multi-party computation, and show it is possible for a distributed algorithm to match the privacy and utility guarantees of a centralized algorithm in this setting. Our experiments on benchmark nonconvex ERM problems demonstrate superior performance in terms of both training cost and utility gains compared with previous differentially private methods using the same privacy budgets.