Miracle-dz/UniFAR-FTUs huggingface dataset:
RMAF: Relu-Memristor-Like Activation Function for Deep Learning

Activation functions facilitate deep neural networks by introducing non-linearity to the learning process. The non-linearity feature gives the neural network the ability to learn complex patterns. Recently, the most widely used activation function is the Rectified Linear Unit (ReLU). Though, other various existing activation including hand-designed alternatives to ReLU have been proposed. However, none has succeeded in replacing ReLU due to their existing inconsistencies. In this work, activation function called ReLU-Memristor-like Activation Function (RMAF) is proposed to leverage benefits of negative values in neural networks. RMAF introduces a constant parameter ( $\alpha $ ) and a threshold parameter ( $p$ ) making the function smooth, non-monotonous, and introduces non-linearity in the network. Our experiments show that, the RMAF works better than ReLU and other activation functions on deeper models and across number of challenging datasets. Firstly, experiments are performed by training and classifying on multi-layer perceptron (MLP) over benchmark data such as the Wisconsin breast cancer, MNIST, Iris and Car evaluation. RMAF achieves high performance of 98.74%, 99.67%, 98.81% and 99.42% respectively, compared to Sigmoid, Tanh and ReLU. Secondly, experiments were performed on convolution neural network (ResNet) over MNIST, CIFAR-10 and CIFAR-100 data and observed the proposed activation function achieves higher performance accuracy of 99.73%, 98.77% and 79.82% respectively than Tanh, ReLU and Swish. Additionally, we experimented our work on deep networks i.e. squeeze network (SqueezeNet), Dense connected neural network (DenseNet121) and ImageNet dataset, which RMAF produced the best performance. We note that, the RMAF converges faster than the other functions and can replace ReLU in any neural network due to the efficiency, scalability and its similarity to both ReLU and Swish.
A 3-Stage Machine Learning-Based Novel Object Grasping Methodology

The automatic grasping of objects previously unseen by a robotic system is a difficult task—of which there is currently no robust solution. The research presented in this article improves upon previous works that employ depth data and learning techniques to generate and select from a pool of hypothesised grasps by focusing on the pruning and selection process. In this work, a vision-based, sampling methodology that generates candidate grasps through a convolutional neural network is proposed. Each candidate grasp is assessed using scores derived from the candidate itself and other related input modalities—such as the centre of gravity of the object. The final selection is determined by a learning algorithm. To overcome human bias, objective measures of grasp performance are established that comprehensively measure the error introduced by the grasp trial itself. The proposed metrics are empirically demonstrated to quantify grasp quality, offer useful criteria for network training and provide better descriptive power than traditional measures of grasp outcome. Experimentation showed that the proposed methodology can generate a meaningful, final grasp within 1.3 seconds. Trials quantitatively demonstrate a small-object-in-isolation performance of 99%. For unknown objects, this equates to a 10% improvement relative to other similar methodologies. Testing also showed that grasp performance was improved by 5% when implementing the proposed metrics—compared to the baseline.
Fast Probabilistic Voltage Control for Distribution Networks With Distributed Generation Using Polynomial Surrogates

Renewable distributed generation will be a key component of future power distribution networks. In order to control the voltage conditions for distribution networks integrated with distributed generation (DG) units, it is vital to quantify the impacts of control variables on voltage magnitudes under consumption and generation uncertainties. To do so, we need to run, for each control action, several power flow simulations for various consumption and generation realizations. This is computationally infeasible for systems with many uncertain inputs. In this work, we address this challenge by developing surrogates, or metamodels, that analytically estimate the random voltage as a function of input variables which include random parameters (consumption and generation levels) and control actions (power factors). Specifically, we propose a model reduction method for building these surrogates, which reduces the number of simulations needed for training. This method identifies and includes only the consumption and generation variables that are influential on the voltage at a given bus. Using this ‘reduced’ surrogate, we then develop a sensitivity-based approach for probabilistic voltage control. We demonstrate the computational efficacy of the control approach on a IEEE 69-bus system with a large number of correlated input parameters. The highlights of computational efficiency in this case study include (1) accurate probabilistic power flow analysis using surrogates constructed by only 500 training simulations for a system with more than 150 random parameters, and (2) successful surrogate-based voltage control approach which only requires 150 additional simulation samples, as opposed to the conventional perturb-and-observe voltage control which needs more than 500,000 samples.
Electronic Medical Records and Physician Productivity: Evidence from Panel Data

Physician productivity is an important driver of key healthcare outcomes, such as quality of care, treatment costs and patient satisfaction, because physicians influence a vast majority of treatment decisions, and are central to the care delivery process. Thus, it is critical for researchers to understand how transformation technologies, such as electronic medical records (EMRs) impact physician productivity. While researchers and policy makers in the United States have suggested that the implementation of EMRs can have significant beneficial impacts on patient safety, health care quality and overall costs of care delivery, the effects of EMRs on physicians themselves have been understudied in the literature. In this paper, we examine the productivity impacts of EMR implementation on physicians. Our focus is to investigate if productivity impacts of EMR implementation depend on physician specialties and the duration for which the EMR has been implemented. This research is informed by extant work in physician productivity, IT productivity and task-technology fit theory. We use a unique panel dataset comprising 87 physicians specializing in internal medicine, pediatrics and family practice in 12 primary care clinics of an academic hospital in a large state in the western United States. Our dataset contains 3,186 physician-month productivity observations collected over 39 months. We employ random effects model on this panel dataset to estimate the impact of EMR implementation on physician productivity. We find that productivity impacts of EMR are contingent upon physician specialty and the time period for which an EMR has been implemented. Furthermore, we find that the stable stage impacts of EMR on various specialties are different from those in the transitory learning stage. These results emphasize the need for fine-grained analyses of productivity impacts of EMR implementation on physicians. We postulate that the fit provided by an EMR to the task requirements of physicians of various specialties is key to disentangling the productivity dynamics. We contribute to the nascent but emerging stream of literature that examines productivity implications of various information technologies among white color knowledge workers in the service industries.
Bacterial intracellularly active toxins: Membrane localisation of the active domain

Numerous bacterial toxins exert their activity by inactivating or modulating a specific intracellular host target. For this purpose, these toxins have developed efficient strategies to overcome the different host cell defences including specific binding to cell surface, internalisation, passage through the endosome or plasma membrane, exploiting intracellular trafficking and addressing to intracellular targets. Several intracellularly active toxins deliver an active domain into the cytosol that interacts with a target localised to the inner face of the plasma membrane. Thus, the large clostridial glucosylating toxins (LCGTs) target Rho/Ras‐GTPases, certain virulence factors of Gram negative bacteria, Rho‐GTPases, while Pasteurella multocida toxin (PMT) targets trimeric G‐proteins. Others such as botulinum neurotoxins and tetanus neurotoxin have their substrate on synaptic vesicle membrane. LCGTs, PMT, and certain virulence factors from Vibrio sp. show a particular structure constituted of a four‐helix bundle membrane (4HBM) protruding from the catalytic site that specifically binds to the membrane phospholipids and then trap the catalytic domain at the proximity of the membrane anchored substrate. Structural and functional analysis indicate that the 4HBM tip of the Clostridium sordellii lethal toxin (TcsL) from the LCGT family contain two loops forming a cavity that mediates the binding to phospholipids and more specifically to phosphatidylserine.
Pattern Regulation in the Regenerating Limbs of Urodele Amphibians

Pattern formation is the process by which cells in regenerating and developing systesm are spatially organized to form the complex structures of the adult organism. This aspect of morphogenesis has intrigued developmental biologists for over a century. The urodele limb has been used extensively in the study of morphogenesis during regeneration and several models have been proposed to account for a variety of experimental results. The purpose of this paper is to review the experiments that have been published since the turn of the century that deal with patter formation in the regenerating limbs of urodele amphibians and to discuss these results in the context of recent theoretical models.
Poly(L‐lactide)‐Degrading Activity in Various Actinomycetes

The poly(L-lactide) (PLA)-degrading ability of actinomycetes obtained from culture collections was examined by the formation of clear zones on PLA-emulsified agar plates. Using 41 genera. (105 strains) of actinomycetes with phylogenetic affiliations based on 16S rRNA sequences, PLA degraders were found to be limited to members of the family Pseudonocardiaceae and related genera. They included Amycolatopsis, saccharothrix, Lentzea, Kibdelosporangium, and Streptoalloteichus. A large number of PLA degraders were widely distributed within the genus Saccharothrix. Most strains forming clear zones on PLA-emulsified agar plates also formed clear zones on silk fibroin agar plates. Saccharothrix species showed an ability to degrade PLA films and assimilate degradation products in liquid cultures. No significant change of the molecular weight and polydispersity (M w /M n ) of the remaining film fragments was confirmed. After cultivation for two weeks, many irregular holes/pits on the surface of the film due to the colonization of microorganisms were observed by scanning electron microscopy.
Direct dopamine terminal regulation by local striatal microcircuitry

Regulation of axonal dopamine release by local microcircuitry is at the hub of several biological processes that govern the timing and magnitude of signaling events in reward‐related brain regions. An important characteristic of dopamine release from axon terminals in the striatum is that it is rapidly modulated by local regulatory mechanisms. These processes can occur via homosynaptic mechanisms—such as presynaptic dopamine autoreceptors and dopamine transporters ‐ as well heterosynaptic mechanisms such as retrograde signaling from postsynaptic cholinergic and dynorphin systems, among others. Additionally, modulation of dopamine release via diffusible messengers, such as nitric oxide and hydrogen peroxide, allows for various metabolic factors to quickly and efficiently regulate dopamine release and subsequent signaling. Here we review how these mechanisms work in concert to influence the timing and magnitude of striatal dopamine signaling, independent of action potential activity at the level of dopaminergic cell bodies in the midbrain, thereby providing a parallel pathway by which dopamine can be modulated. Understanding the complexities of local regulation of dopamine signaling is required for building comprehensive frameworks of how activity throughout the dopamine system is integrated to drive signaling and control behavior.
CEO Overconfidence and the Speed of Adjustment of Cash Holdings

We examine the link between CEO overconfidence and speed of adjustment (SOA) of cash holdings for listed US firms. We find a negative effect of overconfident CEOs on the SOA. Further, CEO overconfidence increases the asymmetry in the SOA between firms with excess cash and those with a cash deficit: The SOA is faster (slower) when there is excess cash (deficit). Importantly, we find that the SOA is value-relevant above and beyond cash holding levels. We address endogeneity concerns through difference-in-differences and propensity score matching specifications. Our results are robust to alternative measures of overconfidence, estimation methods, and corporate governance quality.
SCCLRR: A Robust Computational Method for Accurate Clustering Single Cell RNA-Seq Data

Single-cell RNA transcriptome data present a tremendous opportunity for studying the cellular heterogeneity. Identifying subpopulations based on scRNA-seq data is a hot topic in recent years, although many researchers have been focused on designing elegant computational methods for identifying new cell types; however, the performance of these methods is still unsatisfactory due to the high dimensionality, sparsity and noise of scRNA-seq data. In this study, we propose a new cell type detection method by learning a robust and accurate similarity matrix, named SCCLRR. The method simultaneously captures both global and local intrinsic properties of data based on a low rank representation (LRR) framework mathematical model. The integrated normalized Euclidean distance and cosine similarity are used to balance the intrinsic linear and nonlinear manifold of data in the local regularization term. To solve the non-convex optimization model, we present an iterative optimization procedure using the alternating direction method of multipliers (ADMM) algorithm. We evaluate the performance of the SCCLRR method on nine real scRNA-seq datasets and compare it with seven state-of-the-art methods. The simulation results show that the SCCLRR outperforms other methods and is robust and effective for clustering scRNA-seq data. (The code of SCCLRR is free available for academic https://github.com/wzhangwhu/SCCLRR).
A variable-parameter-model-based feedforward compensation method for tracking control

Base on the accurate inverse of a system, the feedforward compensation method can compensate the tracking error of a linear system dramatically. However, many control systems have complex dynamics and their accurate inverses are difficult to obtain. In the paper, a variable parameter model is proposed to describe a system and a multi-step adaptive seeking approach is used to obtain its parameters in real time. Based on the proposed model, a variable-parameter-model-based feedforward compensation method is proposed, and a disturbance observer is used to overcome the influence of the model uncertainty. Theoretical analysis and simulation results show that the variable-parameter-model-based feedforward compensation method can obtain better performance than the traditional feedforward compensation.
Histological localization of psoralens in fruits of Psoralea bituminosa

Linear furanocoumarins were found on the surface of Psoralea bituminosa (Leguminosae) fruits in concentrations up to 1% of the total furanocoumarins. Most of the remainder was on the surface of the embryos and very young seedlings, with ca. 4% of the total furanocoumarins within the embryos, and very little in the seed coat and fruit. Psoralen was present in the highest concentration, over twice that of xanthotoxin. There was very little bergapten, whose distribution differed from that of the xanthotoxin and psoralen, ca. 20% being on the surface and the rest in dead tissues, i.e., fruit and seed covers. Evidence was obtained, through autofluorescence, that these coumarins are localized in discrete cells of the embryo, on the embryo surface, and on the fruit surface. Key words: Psoralea, Leguminosae, fruits, furanocoumarins, autofluorescence, histochemistry.
3D SiP Simulation Technology and Application

This paper mainly outlines a 3D SiP (System-in-package) project simulation flow, describes the 3D SiP simulation technology detailed. Generally, the 3D SiP simulation consists of two sections, the first section is to import the design data into a full wave 3D EM simulation tool, to extract the S-Parameter for nets. Then in the second section, put the S-Parameter into a 2D simulation tool, to get the signal waveform or eye diagram. In this paper, we detailed describe the 3D SiP simulation requirements and processes, simulation flow, design data translation, 3D EM model extraction, eye diagram simulation and etc. Finally we get the simulation result and present the conclusion and optimization suggestions.
Reactive Power Compensation and Switching Optimization in Matrix Converter using Model Predictive Current Control Approach

This research paper aims to investigate Matrix Converter's use for Power Factor Improvement, Reactive Power Compensation, and novel switching optimization. Initially, the introduction of the matrix converter and its different topologies is presented and its various control techniques are analyzed. The Model Predictive Control of current in the direct matrix converter is investigated in three distinct parts. The first part deals with the control of output current through which distortion-free output current is obtained at the load side. The second part deals with the control of reactive power at the input side of the load so that reactive power at the input is minimized for achieving near unity power factor. The third part is related to switching optimization. In this part, Matrix converter’ switching is optimized to a level that unnecessary switching states are eliminated and only the essential switching is allowed. This optimization significantly reduced Total Harmonic Distortion in the waveforms. Finally, in the end, all three parts are combined into one cost function. Each part has its own customizable weighting factor. Filter at the input side of the matrix converter is added to reduce input current distortion. Simulations for this control technique are done in MATLAB/Simulink and its analysis and results are presented in the paper.
Re-Setting the Circadian Clock Using Exercise against Sarcopenia

Sarcopenia is defined as the involuntary loss of skeletal muscle mass and function with aging and is associated with several adverse health outcomes. Recently, the disruption of regular circadian rhythms, due to shift work or nocturnal lifestyle, is emerging as a novel deleterious factor for the development of sarcopenia. The underlying mechanisms responsible for circadian disruption-induced sarcopenia include molecular circadian clock and mitochondrial function associated with the regulation of circadian rhythms. Exercise is a potent modulator of skeletal muscle metabolism and is considered to be a crucial preventative and therapeutic intervention strategy for sarcopenia. Moreover, emerging evidence shows that exercise, acting as a zeitgeber (time cue) of the skeletal muscle clock, can be an efficacious tool for re-setting the clock in sarcopenia. In this review, we provide the evidence of the impact of circadian disruption on skeletal muscle loss resulting in sarcopenia. Furthermore, we highlight the importance of exercise timing (i.e., scheduled physical activity) as a novel therapeutic strategy to target circadian disruption in skeletal muscle.
Heart Rate Variability in College Football Players throughout Preseason Camp in the Heat

Abstract We aimed to characterize cardiac-autonomic responses to a 13-day preseason camp in the heat among an American college football team. Players were categorized as linemen (n=10) and non-linemen (n=18). RHR, natural logarithm of the root-mean square of successive differences multiplied by twenty (LnRMSSD), and subjective wellbeing (LnWellness) were acquired daily. Effect sizes±90% confidence interval showed that for linemen, LnRMSSD decreased (moderate) on day 2 (71.2±10.4) and increased (moderate) on day 12 (87.1±11.2) relative to day 1 (77.9±11.2) while RHR decreased (small–moderate) on days 6, 7, and 12 (67.7±9.3–70.4±5.5 b∙min-1) relative to day 1 (77.1±10.1 b∙min-1). For non-linemen, LnRMSSD increased (small–large) on days 3–5, 7, 12, and 13 (83.4±6.8–87.6±8.5) relative to day 1 (80.0±6.5) while RHR decreased (small–large) on days 3–9, 12, and 13 (62.1±5.2–67.9±8.1 b∙min-1) relative to day 1 (70.8±6.2 b∙min-1). Decrements in LnWellness were observed on days 4–10 and 13 for linemen (moderate) and on days 6–9, 12, and 13 for non-linemen (small–moderate). Despite reductions in LnWellness, cardiac-autonomic parameters demonstrated responses consistent with heat-acclimation, which possibly attenuated fatigue-related decrements.
Rodent EEG: Expanding the Spectrum of Analysis

In epilepsy research, the analysis of rodent electroencephalogram (EEG) has been performed by many laboratories with a variety of techniques. However, the acquisition and basic analysis of rodent EEG have only recently been standardized. Since a number of software platforms and increased computational power have become widely available, advanced rodent EEG analysis is now more accessible to investigators working with rodent models of epilepsy. In this review, the approach to the analysis of rodent EEG will be examined, including the evaluation of both epileptiform and background activity. Major caveats when employing these analyses, cellular and circuit-level correlates of EEG changes, and important differences between rodent and human EEG are also reviewed. The currently available techniques show great promise in gaining a deeper understanding of the complexities hidden within the EEG in rodent models of epilepsy.
The Integrin αLβ2 Hybrid Domain Serves as a Link for the Propagation of Activation Signal from Its Stalk Regions to the I-like Domain*

Integrin activation involves global conformational changes as demonstrated by various functional and structural analyses. The integrin β hybrid domain is proposed to be involved in the propagation of this activation signal. Our previous study showed that the integrin β2-specific monoclonal antibody 7E4 abrogates monoclonal antibody KIM185-activated but not Mg2+/EGTA-activated leukocyte function-associated antigen-1 (LFA-1; αLβ2)-mediated adhesion to ICAM-1. Here we investigated the allosteric inhibitory property of 7E4. By using human/mouse chimeras and substitution mutations, the epitope of 7E4 was mapped to Val407, located in the mid-region of the β2 hybrid domain. Two sets of constitutively active LFA-1 variants were used to examine the effect of 7E4 on LFA-1/ICAM-1 binding. 7E4 attenuated the binding of variants that have modifications to regions membrane proximal with respect to the β2 hybrid domain. In contrast, the inhibitory effect was minimal on variants with alterations in the αL I- and β2 I-like domains preceding the hybrid domain. Furthermore, 7E4 abrogated LFA-1/ICAM-1 adhesion of phorbol 12-myristate 13-acetate-treated MOLT-4 cells. Our data demonstrate that interaction between the hybrid and I-like domain is critical for the regulation of LFA-1-mediated adhesion.