Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Correlates of Emergent Behaviors in Agent-Based Simulation Models Using Inverse Reinforcement Learning

In large agent-based models, it is difficult to identify the correlate system-level dynamics with individual-level attributes. In this paper, we use inverse reinforcement learning to estimate compact representations of behaviors in large-scale pandemic simulations in the form of reward functions. We illustrate the capacity and performance of these representations identifying agent-level attributes that correlate with the emerging dynamics of large-scale multi-agent systems. Our experiments use BESSIE, an ABM for COVID-like epidemic processes, where agents make sequential decisions (e.g., use PPE/refrain from activities) based on observations (e.g., number of mask wearing people) collected when visiting locations to conduct their activities. The IRL-based reformulations of simulation outputs perform significantly better in classification of agent-level attributes than direct classification of decision trajectories and are thus more capable of determining agent-level attributes with definitive role in the collective behavior of the system. We anticipate that this IRL-based approach is broadly applicable to general ABMs.
Investigation of Cannabis sativa Phytochemicals as Anti-Alzheimer’s Agents: An In Silico Study

Cannabis sativa is a medicinal plant that has been known for years and is used as an Ayurvedic medicine. This plant has great potential in treating various types of brain diseases. Phytochemicals present in this plant act as antioxidants by maintaining synaptic plasticity and preventing neuronal loss. Cannabidiol (CBD) and Tetrahydrocannabinol (THC) are both beneficial in treating Alzheimer’s disease by increasing the solubility of Aβ42 amyloid and Tau aggregation. Apart from these therapeutic effects, there are certain unknown functions of these phytochemicals in Alzheimer’s disease that we want to elucidate through this study. In this research, our approach is to analyze the effect of phytochemicals in Cannabis sativa on multiple culprit enzymes in Alzheimer’s disease, such as AChE (Acetylcholinesterase), BChE (Butyrylcholinesterase), γ-secretase, and BACE-1. In this study, the compounds were selected by Lipinski’s rule, ADMET, and ProTox based on toxicity. Molecular docking between the selected compounds (THCV, Cannabinol C2, and Cannabidiorcol) and enzymes mentioned above was obtained by various software programs including AutoDock Vina 4.2, AutoDock, and iGEMDOCK. In comparison to Donepezil (BA = −8.4 kcal/mol, Ki = 1.46 mM), Rivastigmine (BA = −7.0 kcal/mol, Ki = 0.02 mM), and Galantamine (BA = −7.1, Ki = 2.1 mM), Cannabidiorcol (BA = −9.4 kcal/mol, Ki = 4.61 mM) shows significant inhibition of AChE. On the other hand, Cannabinol C2 (BA = −9.2 kcal/mol, Ki = 4.32 mM) significantly inhibits Butyrylcholinesterase (BuChE) in comparison to Memantine (BA = −6.8 kcal/mol, Ki = 0.54 mM). This study sheds new light and opens new avenues for elucidating the role of bioactive compounds present in Cannabis sativa in treating Alzheimer’s disease.
Multicore EDFA for long-distance transmission

A multicore Er3+-doped fiber amplifier (MC-EDFA) can be realized by using an amplification medium such as bundled or multi-element Er3+-doped fiber (EDF) and multicore EDF, and employing a discrete pumping or cladding pumping method. In this paper, we report three recently developed MC-EDFAs, which represent all the amplification forms realizable using the amplification medium and pumping method. We also report the importance of an MC-EDFA for constructing a practical long-haul multicore fiber transmission system based on the facts of the progress made on the transmission experiments, and describe further progress on the MC-EDFA.
Using qualitative data to inform the adaptation of a stroke preparedness health intervention

Qualitative research methods are often used to develop health interventions, but few researchers report how their qualitative data informed intervention development. Improved completeness of reporting may facilitate the development of effective behavior change interventions. Our objective was to describe how we used qualitative data to develop our stroke education intervention consisting of a pamphlet and video. First, we created a questionnaire grounded in the theory of planned behavior to determine reasons people delay in activating emergency medical services and presenting to the hospital after stroke symptom onset. From our questionnaire data, we identified theoretical constructs that affect behavior which informed the active components of our intervention. We then conducted cognitive interviews to determine emergency department patients’ understanding of the intervention pamphlet and video. Our cognitive interview data provided insight into how our intervention might produce behavior change. Our hope is that other researchers will similarly reflect upon and report on how they used their qualitative data to develop health interventions.
Non-Alcoholic Fatty Liver Disease in Patients with Polycystic Ovary Syndrome: A Systematic Review, Meta-Analysis, and Meta-Regression

Background: The metabolic effects of polycystic ovary syndrome (PCOS) may increase the risk of non-alcoholic fatty liver disease (NAFLD). However, the burden of NAFLD in PCOS has not been unequivocally defined. This systematic review (SR), meta-analysis (MA) assessed NAFLD’s prevalence, and risk factors in patients with PCOS. Methods: A literature search was performed in MEDLINE, Scopus, and Scielo. First, we performed a MA of proportions to estimate the prevalence of NAFLD in PCOS. Second, we performed meta-analyses of precalculated adjusted odds ratios to examine NAFLD risk factors. Finally, we performed a meta-regression to model how the estimated prevalence changed with changes in prespecified variables. Results: We identified 817 articles from the database searches. Thirty-six were included. MA of proportions found a pooled NAFLD prevalence of 43% (95% CI, 35–52%) with high heterogeneity (I2 = 97.2%). BMI, waist circumference, ALT values, HOMA-IR values, free androgen index levels, hyperandrogenism, and triglycerides were associated with significantly higher risk-adjusted odds of NAFLD among patients with PCOS. Meta-regression showed that rises in NAFLD prevalence were mediated through increases in metabolic syndrome prevalence and higher levels of HOMA-IR, free androgen index, and total testosterone. Conclusion: The prevalence of NAFLD (43%) among PCOS patients is high despite their average young age, with several metabolic and PCOS-specific factors influencing its occurrence. Screening programs may aid in detecting metabolic-associated fatty liver disease and prevent its consequences. Further work is required to establish the burden of liver-related outcomes once NAFLD has progressed in the PCOS population.
Theoretical and Empirical Comparison of the Mokken and the Rasch Approach to IRT

The Mokken model of monotone homogeneity, the Mokken model of double monotonicity, and the Rasch model are theoretically and empirically compared. These models are compared with respect to restrictiveness to empirical test data, properties of the scale, and accuracy of measurement. Appli cation of goodness-of-fit procedures to empirical data largely confirmed the expected order of the models according to restrictiveness: Almost all items were in concordance with the model of mono tone homogeneity, and fewer items complied with the model of double monotonicity and the Rasch model. The model of monotone homogeneity was found to be a suitable alternative to more restric tive models for basic testing applications; more sophisticated applications, such as equating and adaptive testing, appear to require the use of para metric models.
The Temperature in Dissipative Particle Dynamics

The two most popular algorithms for dissipative particle dynamics (DPD) are critically discussed. In earlier papers, the Groot–Warren algorithm with λ = 1/2 was recommended over the original Hoogerbrugge–Koelman scheme on the basis of a marked difference in their equilibrium temperatures. We show, however, that both schemes produce identical trajectories. Expressions for the temperatures of an ideal gas and a liquid as functions of the simulation parameters are presented. Our findings indicate that the current DPD algorithms do not possess a unique temperature because of the way in which the dissipative and random forces are included. The commonly used large time steps are beyond the stability limits of the conservative force field integrator.
EEG-based lie detection using ERP P300 in response to known and unknown faces: An overview

Concealed information detection is nowadays an essential part of security. Conventional lie detectors are expensive, time-consuming, and their accuracy depends on the subject. Many researchers have focused on investigating concealed information for lie detection using electroencephalography (EEG) to recognize a lie better. This work aimed to provide an overview of scientific studies on EEG-based lie detection in the context of ERP P300 during the presentation of known and unknown faces published in the last five years (2017–2022). To the best of our knowledge, there is no recent available review of the most used methods for EEG data analysis in this field. For that reason, this article was created containing the current most used methods for feature extraction and classification, protocols, and accuracy of individual approaches.
Energy-efficient activity-driven computing architectures for edge intelligence

We present an overview of different methods to increase the energy efficiency of Tiny Machine Learning networks running on resource-constrained edge neural network accelerators. Most commonly reported are accelerator designs that support networks with sparse weight matrices, that skip over zero activations, and that use low-bit precision parameters. We introduce a brain-inspired sparsity, i.e. temporal sparsity, that can further reduce computes needed during network inference. Accelerators that support this sparsity require storage of the network states. This storage is already needed in a recurrent neural network. Activity-driven sensors like the event camera sensor, when interfaced to a neural network, can further reduce the computes by triggering network updates only when necessary, especially for tasks involving temporal changes in the input. The different sparsity methods and the input activity-driven form of computing can pave the way to edge devices with the high energy-efficiency seen of brains when operating in uncontrolled natural environments. These devices are targeted at edge tasks for domains such as wearables, brain-machine interfaces, and Internet-of-Things (IoT) applications.
A Smart System for an Assessment of the Remaining Useful Life of Ball Bearings by Applying Chaos-Based Health Indicators and a Self-Selective Regression Model

Bearings are the most commonly used components in rotating machines and the ability to diagnose their faults and predict their remaining useful life (RUL) is critical for system maintenance. This paper proposes a smart system combined with a regression model to predict the RUL of bearings. The method converts the azimuth signal through low-pass filtering (LPF) and a chaotic mapping system, and uses Euclidean feature values (EFVs) to extract features in order to construct useful health indicators (HIs). In fault detection, the iterative cumulative moving average (ICMA) is used to smooth the HIs, and the Euclidean norm is used to find the time-to-start prediction (TSP). In terms of prediction, this paper uses a self-selective regression model to select the most suitable regression model to predict the RUL of the bearing. The dataset provided by the Center for Intelligent Maintenance Systems (IMS) is applied for performance evaluation; in comparison with previous research, better prediction results can be achieved by applying the proposed smart assessment system. The proposed system is also applied to the PRONOSTIA (also called FEMTO-ST) bearing dataset in this paper, demonstrating that acceptable prediction performance can be obtained.
Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model

Because of the complexity, nonlinearity, and volatility, stock market forecasting is either highly difficult or yields very unsatisfactory outcomes when utilizing traditional time series or machine learning techniques. To cope with this problem and improve the complex stock market’s prediction accuracy, we propose a new hybrid novel method that is based on a new version of EMD and a deep learning technique known as long-short memory (LSTM) network. The forecasting precision of the proposed hybrid ensemble method is evaluated using the KSE-100 index of the Pakistan Stock Exchange. Using a new version of EMD that uses the Akima spline interpolation technique instead of cubic spline interpolation, the noisy stock data are first divided into multiple components technically known as intrinsic mode functions (IMFs) varying from high to low frequency and a single monotone residue. The highly correlated sub-components are then used to build the LSTM network. By comparing the proposed hybrid model with a single LSTM and other ensemble models such as the support vector machine (SVM), Random Forest, and Decision Tree, its prediction performance is thoroughly evaluated. Three alternative statistical metrics, namely root means square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to compare the aforementioned techniques. The empirical results show that the suggested hybrid Akima-EMD-LSTM model beats all other models taken into consideration for this study and is therefore recommended as an effective model for the prediction of non-stationary and nonlinear complex financial time series data.
Non-Coding RNAs in Cell-to-Cell Communication: Exploiting Physiological Mechanisms as Therapeutic Targets in Cardiovascular Pathologies

Cardiovascular disease, the leading cause of death worldwide, has been characterized at the molecular level by alterations in gene expression that contribute to the etiology of the disease. Such alterations have been shown to play a critical role in the development of atherosclerosis, cardiac remodeling, and age-related heart failure. Although much is now known about the cellular and molecular mechanisms in this context, the role of epigenetics in the onset of cardiovascular disease remains unclear. Epigenetics, a complex network of mechanisms that regulate gene expression independently of changes to the DNA sequence, has been highly implicated in the loss of homeostasis and the aberrant activation of a myriad of cellular pathways. More specifically, non-coding RNAs have been gaining much attention as epigenetic regulators of various pathologies. In this review, we will provide an overview of the ncRNAs involved in cell-to-cell communication in cardiovascular disease, namely atherosclerosis, cardiac remodeling, and cardiac ageing, and the potential use of epigenetic drugs as novel therapeutic targets.
A hybrid background subtraction approach for moving object detection

Background subtraction is widely used technique for moving object detection from videos. This paper presents a hybrid approach for moving objects detection and shadow removal from color videos obtained using stationary camera. A background subtraction technique based on frame differencing and ViBe algorithm for detecting moving objects has been proposed. Then, a robust algorithm for shadow removal is employed to remove cast shadows. Foreground is reconstructed using graph cut based cleaning and non-recursive blob finding. Comparative experimental results demonstrate that proposed approach performs better in comparison to other state-of-the-art algorithms.
Instabilities and the adiabatic and isothermal blast wave models for supernova remnants

Isenberg as well as lerche and Vasyliunas proposed the existence of an instability to radial perturbations in adiabatic and isothermal models of self-similar supernova blast waves. Their derivations fail to impose the physical conservation laws at the shock (i.e., the Rankine-Hugoniot jump conditions) as boundary conditions, and their claim of an instability is unsubstantiated. Although as analytic demonstration of the stability of the adiabatic self-similar solution does not presently exist, the cumulative result of three decades of gas dynamic experimentation and numerical simulation provides unmistakable evidence for the stabilty of self-similar blast waves.
Geometric evolutions driven by threshold dynamics

where S(·) is a continuous function taking values in the space S of n × n symmetric matrices and v is real-valued. Special cases of (1.2) are motion by curvature, corresponding to S(n) ≡ I and v(n) ≡ 0, and curvature independent motions, corresponding to S(n) ≡ 0. Solutions of these geometric evolution problems typically develop singularities, regardless of the smoothness of the initial data (see [12], [14]). To overcome such difficulties, the level set approach was introduced by Chen, Giga, and Goto [8] and independently by Evans and Spruck [10]. In [10] motion by mean curvature is dealt with while in [8] more general geometric motions are discussed. Their ideas are based on considering interfaces as level sets of the solution of a degenerate parabolic partial differential equation. One approach to (1.1) is that some classes of dynamics which model the microscopic behavior of physical phenomena (for example, threshold dynamics, cellular automata) provide approximate schemes for (1.1). Among others, Bence, Merriman, and Osher [6] first proposed a simple approximation scheme (BMO) for motion by mean curvature. Later, Evans [9] and Barles and Georgelin [2] gave the first analysis of the BMO algorithm. More recently, Ishii, Pires,
EEG-Based Sleep Stage Classification via Neural Architecture Search

With the improvement of quality of life, people are more and more concerned about the quality of sleep. The electroencephalogram (EEG)-based sleep stage classification is a good guide for sleep quality and sleep disorders. At this stage, most automatic staging neural networks are designed by human experts, and this process is time-consuming and laborious. In this paper, we propose a novel neural architecture search (NAS) framework based on bilevel optimization approximation for EEG-based sleep stage classification. The proposed NAS architecture mainly performs the architectural search through a bilevel optimization approximation, and the model is optimized by search space approximation and search space regularization with parameters shared among cells. Finally, we evaluated the performance of the model searched by NAS on the Sleep-EDF-20, Sleep-EDF-78 and SHHS datasets with an average accuracy of 82.7%, 80.0% and 81.9%, respectively. The experimental results show that the proposed NAS algorithm provides some reference for the subsequent automatic design of networks for sleep classification.
Experimental observations of the tearing of an electron current sheet

A neutral magnetic sheet, in which the current is carried mainly by the electrons, is set up in a laboratory plasma. By forcing the current through a thin slot, the ratio of the length to height t of the sheet may be varied; the current is observed to tear when t≳30. The structure of the magnetic islands and their associated currents is fully three dimensional, although a linear two‐dimensional theory gives a very good estimate of the tearing mode growth time. Tearing is accompanied by the generation of significant Hall currents, and magnetic disturbances are observed to propagate at the whistler wave speed.
Association of Single Nucleotide Polymorphisms in Nucleotide-Binding Domain Leucine-Rich Repeat Protein 1 with Clostridioides difficile Colonization or Infection

Introduction Nucleotide-binding domain leucine-rich repeat protein (NLRP) is critical in the inflammasome-activation pathway, which is important for host survival and the clearance of Clostridioides difficile. Therefore, the influence of NLRP1 polymorphisms on C. difficile colonization (CdC) or infection (CDI) was analyzed. Materials and Methods A prospective cohort study consisted of hospitalized adults was conducted from January 2011 to January 2013. Single nucleotide polymorphisms (SNPs) of NLRP1, including rs12150220, rs2670660, rs6502867, rs878329, rs8182352, rs3744717, and rs11078571, were incorporating in analyses. The episodes of CdC and CDI were the primary and secondary outcome, respectively. Results Of the total of 509 eligible patients, 376 (73.9%) had neither CdC nor CDI, 104 (21.8%) had CdC without developing CDI, and 29 (4.3%) developed CDI during the study period. Through multivariate analyses, comorbid diabetes mellitus (adjusted odds ratio [AOR] 1.59, P=0.04) and CC genotype in NLRP1 rs3744717 (AOR 1.70, P=0.02) were recognized as the risk factor of CdC. After adjusting the independent predictors of CDI, in terms of comorbid diabetes mellitus (AOR 3.18, P=0.005) and prior exposure to ceftazidime/ceftriaxone (AOR 2.87, P=0.04) or proton pump inhibitors (AOR 3.86, P=0.001), patients with CC+GC genotype in NLRP1, rs878329 (AOR 2.39, P=0.03) remained a higher risk of CDI. Conclusion For hospitalized adults, the association of CC genotype in NLRP1 rs3744717 and CdC as well as the CC+GC genotype in NLRP1 rs878329 and CDI was respectively evidenced. We believed the prompt identification of patients having specific genotype in NLRP1 would prevent and improve the quality of care in CDI.
Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.
CSR and Sustainable Growth in China’s Technology Firms between 2010 and 2021

In this study, we examine the relationships between CSR (e.g., CSR aimed at employees, customers, suppliers, and governments) and firm sustainable growth, and how dysfunctional competition moderates these relationships. Based on resource dependence theory, we argue that CSR aimed at four chosen groups of stakeholders (e.g., employees, customers, suppliers, and governments) is positively related to firm sustainable growth and that the levels of dysfunctional competition will positively moderate these relationships. Our results, using a sample of technology firms in China, provide support for these arguments. We find that CSR aimed at employees, customers, suppliers, and governments is positively related to firm sustainable growth. In addition, using the contingence approach, we find that CSR aimed at the four chosen groups of stakeholders has a stronger positive relationship with firm sustainable growth when the level of dysfunctional competition is high than when it is low. Our findings have important theoretical and managerial implications, which are discussed in this study.