Miracle-dz/UniFAR-FTUs huggingface dataset:
On complete Ricci-flat metrics on open Kahler manifolds

This paper has two purposes. First it partially extends the result in the author's previous work concerning the asymptotic expansion of the Tian-Yau metrics, by considering a slightly larger class of quasi-projective manifolds. This text is also intended to provide a quick introductory reference to the study of Ricci-flat metrics on open manifolds.
Motion Planning of Planar Snake Robots in Viscous Environments

In this paper, a systematic motion planning algorithm for planar under-actuated hyper-redundant snake robots in viscous environments is presented. The proposed algorithm generates shape trajectories, i.e., continuous trajectories in the base (shape) space of the snake, that ensure the tracking of desired trajectories defined in the fiber (motion) space of the snake. The proposed motion planning algorithm is formulated as an optimization problem which minimizes the joint velocities. This cost function is also utilized to avoid singular configurations. Finally, the proposed algorithm is validated through simulations using a five-link snake robot.
Two small-scale inertial ranges in rotating helical turbulence and the return to isotropy

We present numerical evidence of how three-dimensionalization occurs at small scale in rotating turbulence with helical forcing using a Beltrami (ABC) flow. The Zeman scale `Ω at which the inertial and eddy turn-over times are equal is more than one order of magnitude larger than the dissipation scale, with the relevant domains (large-scale inverse cascade of energy, dual regime in the direct cascade of energy E and helicity H, and dissipation) each moderately resolved. The grid has 3072 points, with Rossby and Reynolds numbers of respectively 0.07 and 2.7× 10. At scales smaller than the forcing, a helical wave-modulated inertial law for the energy and helicity spectra is followed beyond `Ω by Kolmogorov spectra for E and H with a small bottleneck. Looking at the two-dimensional slow manifold, we also show that the helicity spectrum breaks down at `Ω, a clear sign of recovery of three-dimensionality in the small scales.
User Terminal Segments for Low-Earth Orbit Satellite Constellations: Commercial Systems and Innovative Research Ideas

Despite the rapid and continuous development of communication and networking technologies, the goal of universal connectivity (i.e., the ability to communicate with any user at any time and in any place) is still unrealized. In 2019, a study <xref ref-type="bibr" rid="ref1">[1]</xref> found that 40% of Earth’s regions lack network coverage, which means that there are still 4 billion people on the planet without Internet access. Moreover, even as the COVID-19 pandemic increased the need for online working, learning, and accessing services—and, thus, increased the number of Internet users—2.9 billion people remain offline in developing countries <xref ref-type="bibr" rid="ref2">[2]</xref>. Satellite communications (SatCom) have been emerging as a potential and indispensable solution to extending broadband coverage to underserved areas <xref ref-type="bibr" rid="ref2">[2]</xref>.
Re-evaluation of the anticarcinogenic effect of metformin in a chemically-induced hepatocellular carcinoma model not associated with diabetes

ABSTRACT Hepatocellular carcinoma (HCC) is the fourth cause of cancer mortality worldwide. Metformin is an antidiabetic drug that showed a prophylactic effect in the early stages of some cancers. This study aimed to reevaluate the anticarcinogenic effect of metformin in a cancer model not associated with diabetes. Also, to evaluate the effect of pharmacological antidiabetic dose of metformin (used in cancer studies) at the early stages of HCC versus the sub-pharmacological dose. HCC was induced by a single i.p injection of diethylnitrosamine followed by 16 biweekly i.p injections of carbon-tetrachloride. Metformin was i.p injected (25 or 250 mg/kg) post HCC induction for four weeks. The low dose of metformin suppressed HCC by reducing liver biomarkers levels, and the number and size of hepatic nodules. Also, metformin (25 mg/kg) downstaged HCC via activating Bax and suppressing Bcl-2, VEGF, and PCNA. Mice treated with metformin (250 mg/kg) exhibited lactic acidosis, death, and insignificant differences in nodules’ number and size and immunohistochemical markers compared to the HCC group. This study concluded that the pharmacological dose of metformin cannot be used at advanced stages of HCC, while the sub-pharmacological dose of metformin showed a promising anticarcinogenic effect in chemically-induced HCC model not associated with diabetes.
Adaptive Output Regulation for Discrete-time Linear Systems with an Uncertain Exosystem

This paper studies the output regulation problem for discrete-time linear systems subject to an uncertain exosystem. The design involves two online estimation algorithms. The first one is to estimate the unknown parameters of the exosystem, and the second one is to estimate the solution to the regulator equations based on the estimated system matrix of the exosystem. Combining these two algorithms gives rise to an iterative solution that converges exponentially to some exact solution to the regulator equations. Finally, by integrating these two algorithms with the feedforward control approach, both dynamic state feedback and dynamic output feedback control laws are synthesized to solve the problem.
School-Based Comprehensive Strength Training Interventions to Improve Muscular Fitness and Perceived Physical Competence in Chinese Male Adolescents

Purpose This research was to see how effective and feasible school-based comprehensive strength training programs are in improving muscular fitness and perceived physical competence in Chinese male adolescents. Methods A total of 123 participants (13.46 ± 0.60 years) were randomized to comprehensive strength training intervention group (CST) (n = 62) and the control group (CON) (n = 61). The training sessions were performed three times a week for ten weeks in CST. Muscular fitness (i.e., muscular strength, power, and muscular endurance) and perceived physical competence were assessed at initial testing and final testing. Results The subjects in the CST significantly improved their mean performance in standing long jump (p < 0.05), vertical jump (p < 0.05), 1 min push-ups (p < 0.05), 1 min sit-ups (p < 0.05), handgrip strength (p < 0.05), and perceived physical competence (p < 0.05) after the intervention. Moreover, the CST were greater in standing long jump (p < 0.05), vertical jump (p < 0.05), 1 min sit-ups (p < 0.05), handgrip strength (p < 0.05), and perceived physical competence (p < 0.05) compared to the CON, but no in 1 min push-ups (p > 0.05). Conclusions The comprehensive strength training interventions designed in this study can significantly increase male adolescents' muscular fitness, especially in the lower extremity muscle power and abdominal core endurance, and can enhance their perceived physical competence.
Lightweight 1-D CNN-Based Timing Synchronization for OFDM Systems With CIR Uncertainty

In this letter, a lightweight one-dimensional convolutional neural network (1-D CNN)-based timing synchronization (TS) method is proposed to reduce the computational complexity and processing delay and hold the timing accuracy in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the TS task is first transformed into a deep learning (DL)-based classification task, and then three iterations of the compressed sensing (CS)-based TS strategy are simplified to form a lightweight network, whose CNN layers are specially designed to highlight the classification features. Besides, to enhance the generalization performance of the proposed method against the channel impulse responses (CIR) uncertainty, the relaxed restriction for propagation delay is exploited to augment the completeness of training data. Numerical results reflect that the proposed 1-D CNN-based TS method effectively improves the TS accuracy, reduces the computational complexity and processing delay, and possesses a good generalization performance against the CIR uncertainty. The source codes of the proposed method are available at https://github.com/qingchj851/CNNTS.
On the Verification of Detectability for Timed Systems

In this paper, we investigate the verification of detectability, a fundamental state estimation property, for partially-observed discrete event systems (DES). Existing works on this topic mainly focus on untimed DES. In many applications, however, real-time information is critical for the purpose of system analysis. To this end, in this paper, we investigate the verification of detectability for timed DES modeled by timed automata. Two notions of detectability, strong detectability and weak detectability, are studied in the dense-time setting, which characterize detectability by time elapsing rather than events steps. We show that verifying strong detectability for timed system is decidable by providing a verifiable necessary and sufficient condition. Furthermore, we show that weak detectability is undecidable in the timed setting by reducing the language universality problem for timed automata to this verification problem. Our results extend the detectability analysis of DES from the untimed setting to a timed setting.
Durable Disadvantage: Gender and the Mark of Unauthorized Status in Immigrants’ Occupational Trajectories

Adverse life course events associated with unemployment can negatively affect individuals’ future labor market prospects. Unauthorized status, and subsequent unauthorized employment, may operate similarly, marring immigrants’ labor market prospects even after they change legal status. However, it is unclear how and why any durable disadvantage associated with prior unauthorized status operates differently by gender. This is an important shortcoming, given that legal status and gender overlap to influence both migration and stratification. Using longitudinal data from a nationally representative sample of lawful permanent residents, we find durable disadvantage associated with prior exposure to unauthorized status, especially among women. Men with prior exposure to unauthorized status experience persistent occupational disadvantage over time relative to men who were never unauthorized. However, women with exposure to unauthorized status experience widening occupational disadvantage over time relative to women who were never unauthorized. Human capital and legal processes help to explain this pattern.
Using Statistics In Economics

The primary aim of this book is to provide a text for economics students which gives a good balance between statistics and econometrics.The intention is to prepare students for an introductory course in econometrics, while instilling a clear understanding of statistics. It is suitable for first or second year courses in Introductory Statistics or Introductory Statistics and Econometrics on economics degrees. The book is characterized by Leighton Thomas' clear style of explanation, gained through 35 years of teaching experience.
Delivering a Birth Safely – Case Reports of Perineal Infection Prevention among Pregnant Women Living Around Ex-landfills

Background: Pregnant women living around ex-landfills have a higher risk of infection due to slum milieu, poor nutritional status, and fetal head pressure on the perineum during labor which can cause it to tear. Objective: This research aimed to emphasize the importance of limiting prophylaxis antibiotics for the 1st and 2nd degrees of perineal tears due to their negligible risk of infection. Case presentations: This is a report of two primigravid women aged 19 years old (cases 1 and 2) and multiparous women aged 29 and 30 yo (cases 3 and 4) who managed to give birth safely in Puskesmas (Indonesian primary healthcare facilities), despite having low blood pressure and non-adherence to antenatal care. All of them suffered from 2nd degree of perineal tears and received analgesics, iron, vitamin B complex, and vitamin A tablets. The subjects attended puerperium care on days, 14, and 42 postpartum at Puskesmas. Discussion: In March 2020, Puskesmas' healthcare team performed a new 1st and 2nd-degree perineal tears prevention without antibiotics following normal vaginal birth. Standard care consists of personal hygiene, perineal wound care, and education on the importance of nutritious food and adherence to maternal supplementation. All subjects presented with complete perineal wound healing without infection at day-42 postpartum. Conclusion: It can be concluded that 2nd-degree perineal tears infection following normal vaginal birth could be prevented without antibiotics. It is strongly suggested to control the maternal nutritional states, potentially interfering with the maternal ability to heal the perineal wound.
Optimal Connectivity during Multi-agent Consensus Dynamics via Model Predictive Control

In this paper, we solve an optimal consensus control problem of maximizing the state-dependent communication connectivity during a multi-agent consensus dynamics. A proportional-derivative type consensus controller is leveraged to drive agents into a symmetric formation. The asymptotic stability of the closed-loop system dynamics is established using Lyapunov theory, which helps us to deduce an intuitive time-varying gain profile based on a sufficient condition for convergence. Further, a Model Predictive Control approach is adopted to minimize a quadratic cost over a finite prediction horizon by adjusting the controller gains, such that the optimal connectivity is attained on the way with less control efforts, while handling constraints to agents’ states, inputs, turn-rates and disturbances injected into agent velocities. Simulation results with time-varying controller gains demonstrate the impact of our proposed technique.
Watchdog Monitoring for Detecting and Handling of Control Flow Hijack on RISC-V-based Binaries

—Control flow hijacking has been a major challenge in software security. Several means of protections have been developed but insecurities persist. This is because existing pro- tections have sometimes been circumvented while some resilient protections do not cover all applications. Studies have revealed that a holistic way of tackling software insecurity could involve watchdog monitoring and detection via Control Flow Integrity (CFI). The CFI concept has shown a good measure of reliability to mitigate control flow hijacking. However, sophisticated attack techniques in the form of Return Oriented Programming (ROP) have persisted. A flexible protection is desirable, which not only covers as many architecture structures as possible but also mitigates known resilient attacks like ROP. The solution proffered here is a hybrid of CFI and watchdog timing via inter-process signaling (IP-CFI). It is a software-based protection that involves recompilation of the target program. The implementation here is on vulnerable RISC-V-based process but is flexible and could be adapted on other architectures. We present a proof of concept in IP-CFI which when applied to a vulnerable program, ROP is mitigated. The target program incurs a run-time overhead of 1.5%. The code is available.
Hybrid Neuromorphic Systems: An Algorithm-Application-Hardware-Neuroscience Co-Design Perspective: Invited Special Session Paper

Spiking Neural Networks (SNNs) are considered to be the third generation of artificial neural networks due to its unique temporal, event-driven characteristics. By leveraging bio-plausible spike-based computing between neurons in tandem with sparse on-demand computation, SNNs can demonstrate orders of magnitude power efficiency on neuromorphic hardware in contrast to traditional Machine Learning (ML) methods. This paper reviews recent developments in the domain of neuromorphic SNN algorithms from an overarching system science perspective with an end-to-end co-design focus from algorithms to hardware and applications. The paper outlines opportunities at designing hybrid neuromorphic platforms where leveraging benefits of both traditional ML methods and neuroscience concepts in the training and architecture design choice can actualize SNNs to their fullest potential.
First Few Cases Study to Investigate Clinical and Epidemiological Characteristics of COVID-19 in the Early Phase of the Response, Sierra Leone 2020

Background: Sierra Leone reported its first case of COVID-19 on March 30, 2020. This report describes the clinical characteristics and the epidemiological features of the first few cases and contacts of COVID-19 in Sierra Leone. Methods: A prospective case-ascertained study was conducted to identify contacts of laboratory-confirmed COVID-19 cases. Demographic, socio-economic, and clinical presentations were collected. Specimen were collected on day 1, day 7, and days 14-21. Data was analyzed using R, Stata, and Microsoft Excel. Results: The study included 160 cases and 1,003 contacts (56% males, 66% age 15-49 years). Of the total, 101/160 (63.1%) were primary cases, 59/160 (36.9%) secondary cases. Common symptoms reported included cough (39/160, 24.4%), headache (35/160, 21.9%), and fatigue (29/160, 18.1%). However, 55% (88/160) of the cases were asymptomatic at enrollment, and 83% (49/59) of the secondary cases were exposed to asymptomatic primary cases. The secondary infection rate was 5.8%. Almost 97% of the secondary cases tested positive on day 1and 3.4% on day 7. Pre-existing medical conditions among cases included diabetes and obesity. Conclusions: We were able to identify clinical and epidemiological characteristics of 160 COVID-19 cases in the early phase of the pandemic in Sierra Leone. The findings of this study helped the Government of Sierra Leone develop and revise its policies and response strategies in order to reduce preventable COVID-19-related deaths. Early investigational protocols such as the first few cases study can be of great utility during outbreaks of relatively unknown diseases. What is already known on this topic A cluster of unusual pneumonia cases was reported in Wuhan, China, that was later identified to be caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2. However, limited knowledge exists on the epidemiological and clinical characteristics of SARS-CoV-2 and the outbreak dynamics at the time Sierra Leone detected its first case. What this study adds This study helped to understand the key clinical characteristics of COVID-19 cases and epidemiological features of COVID-19 diseases in Sierra Leone. Implications of the study in practice, or policy The findings of this study provided evidence for targeted and specific prevention and control measures to contain the spread of the disease in Sierra Leone.
A Numerical Analyst's Jordan Canonical Form.

Abstract : What does it mean to compute an eigendecomposition of an uncertain matrix? Because of measurement errors and roundoff errors, one must typically compute the eigenvalues and eigenvectors not of a single matrix but rather of a ball of matrices whose radius depends on the uncertainty in the data. We approach this problem by asking how to partition the eigenvalues of the matrices in the ball into nonoverlapping groups which cannot themselves be further partitioned. More specifically, we define the dissociation of two subsets sigma sub 1 and sigma sub 2 = sigma x sigma sub 1 of the sets of eigenvalues sigma of a matrix T as the smallest perturbation of T that will make some eigenvalue from sigma sub 1 and some eigenvalue from sigma sub 2 move together and become indistinguishable. The results of this thesis are of two kinds. First, we compute upper and lower bounds on the dissociation which improve bounds in the literature. Both upper and lower bounds are achievable or nearly so. The upper and lower bounds are often close together but occasionally far apart. Our second set of results quantifies this last statement by assuming a probability density on the set of matrices and computing the likelihood that the bounds are far apart. This approach leads to numerous other probabilistic results, such as the distribution of the condition number of a random matrix, and the distribution of the distance from a random matrix to one with a given Jordan form.
Interpretation and Classification of Arrhythmia Using Deep Convolutional Network

Electrocardiogram (ECG) signal analysis can be time-consuming, tedious, and error-prone. Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac abnormalities accurately and efficiently. Recently, deep learning (DL) models have shown unprecedented progress and strong arrhythmia classification capabilities, but their deployment in the medical sector is constrained due to their “black-box” nature. This article proposes a robust explainability method to assist in explaining the underlying decision-making process in deep neural networks (DNNs) and help provide feedback on biases that would benefit in improving DNN models. To achieve these objectives, initially, a DL model is trained on the MIT-BIH Arrhythmia Database and their classification performance is evaluated. The classification findings are then interpreted using the post-hoc explanation methods such as the SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM) that interpret the decision rationale. As these methods are initially proposed for image applications therefore a new masking approach is proposed to cater these post-hoc explainability methods for ECG time-series data. After evaluating these methods for ECG arrhythmia classification, several drawbacks are drawn such as they fail to locate a feature’s importance if there are multiple occurrences of the same feature in a signal and also, SHAP and LIME perform random perturbations that sometimes produce unreliable explanations. Therefore, to overcome the drawbacks associated with these post-hoc explainability methods on time-series data, a novel K-GradCam method is proposed. The proposed K-GradCam method ensemble the benefits of these gradient-based and perturbation-based approaches and has demonstrated advantages over SHAP, Grad-CAM and LIME in terms of interpreting the models’ decisions. To compare the proposed technique with post-hoc explainability methods quantitatively, the confidence index of the proposed method is evaluated using dice loss. The proposed method shares 71% similarity with SHAP and 81% similarity with Grad-CAM methods; however, it shares the benefits of both methods and is computationally faster than SHAP and LIME.
Reduced-Order Adaptive Output Predictors for a Class of Uncertain Dynamical Systems

The primary goal of this paper is to design an adaptive output feedback predictor for a class of unknown, and possibly high-order systems. We present a reduced-order adaptive predictor scheme based on singular perturbation and modal reduction methods. The exponential stability of the prediction error and ultimate boundedness of the tracking error are proved using Lyapunov’s method. The proposed predictor is analytically verified using the double pendulum output tracking problem. Also, this method is evaluated through a set of experiments based on the single pendulum problem. The numerical and experimental studies demonstrate that the proposed reduced-order adaptive output predictor scheme can track the output when the parameters and order of the model are unknown.