Miracle-dz/UniFAR-FTUs huggingface dataset:
Bell's Inequality: Excluding Local Realism from a Physical Theory of Nature

An overview of the history of Bell’s inequality is given, beginning with the so-called EPR paradox originally put forward by Einstein, Podolsky and Rosen and the "impossibility proof" of von Neumann. Bell’s inequality, which ultimately shifted the discussion from arguments based on philosophically introduced postulates to the domain of experimental physics, is presented together with experimental attempts from the early 1970s to 2010s to violate this inequality. We introduce three historical loopholes in the experimental schemes which have prevented experimentalists from being able to fully disprove the possibility of a hidden-variables description of nature. Recent experimental data indicate that all the main loopholes are now closed for photons (albeit not simultaneously in one experiment). Besides the class of stochastic local realism that have been tested experimentally, some other possible classical interpretations are mentioned.
An Extended EWMA Mean Chart

Abstract In this paper, we extend the exponentially weighted moving average (EWMA) technique to double exponentially weighted moving average (DEWMA) technique. We show that DEWMA mean charts perform better than EWMA mean charts in detecting small mean shifts ranging from 0.1 to 0.5 of the process standard deviation, and that the two types of charts perform similarly when mean shifts are larger than 0.5 standard deviation. The design of DEWMA mean charts is also discussed.
Numeral System Change in Arithmetic and Matricial Formalism

The main goal of this paper is to present a method to tackle the numeral system change problem using matricial formalism. In a previous work, we have described an approach which permits to use matricial formalism and matricial calculation in writing numeration and arithmetic. The present paper is focused on the study of the problem of numeral system change in the framework of this approach. The cases of integer numbers and of more general numbers are given.
THE EFFECT OF DEEP BRAIN STIMULATION ON MOTOR FUNCTIONS AND QUALITY OF LIFE IN THE TREATMENT OF DYSTONIA

Background/Aims: The present study aims to evaluate the levels of depression and health-related quality of life (HRQoL) after surgical globus pallidus internus deep brain stimulation (Gpi-DBS) treatment in patients with generalized and segmental dystonia refractory to medical treatment. Methods: Seventeen patients diagnosed with primary and secondary dystonia who were selected for GPi-DBS by the neurology, neurosurgery, and psychiatry council were included in the study. The patients were divided into two groups: primary dystonia and secondary dystonia. All patients were evaluated using the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) to assess dystonia-related loss of function and disability, the Short Form 36 (SF-36) to assess HRQoL levels, and the Beck Depression Inventory (BDI) to assess depression levels before Gpi-DBS and 1 year after Gpi-DBS, retrospectively. Results: In this study, significant improvement in BFMDRS scores and significant improvement in HRQoL levels were observed after 1 year of Gpi-DBS treatment in all dystonia types. Moreover, significant improvement was found in dystonia severity and disability levels, and HRQoL levels in primary dystonia and secondary dystonia subgroups. Conclusions: We suggest that Gpi-DBS improves dystonia symptoms, and increases HRQoL levels in dystonia. In addition, this study is the first in Turkey to evaluate the effect of Gpi-DBS on HRQoL levels in dystonia. However, we believe that further research with more participants is also needed in Turkey in this regard.
Multi-Agent Reinforcement Learning Control of a Hydrostatic Wind Turbine-Based Farm

This paper leverages multi-agent reinforcement learning (MARL) to develop an efficient control system for a wind farm comprising a new type of wind turbines with hydrostatic transmission. The primary motivation for hydrostatic wind turbines (HWT) is increased reliability, and reduced manufacturing, operating, and maintaining costs by removing troublesome components and reducing nacelle weight. Nevertheless, the high system complexity of HWT and the wake effect pose significant challenges for the control of HWT-based wind farms. We therefore propose a MARL algorithm named multi-agent policy optimization (MAPO), which allows agents (turbines) to gradually improve their control policies by repeatedly interacting with the environment to learn an optimal operation curve for wind farms. Simulation results based on a wind farm simulator, FAST.Farm, show that MAPO outperforms the greedy policy and a popular learning-based method, multi-agent deep deterministic policy gradient (MADDPG), in terms of power generation.
Adjectival passives and adjectival participles in English

The article analyses English adjectival participles, arguing that (i) there is a previously unrecognized type of adjectival participle which expresses an in-progress situation; (ii) Themes in adjectival participles are initially merged outside the participle’s projection; (iii) the standard claim that adjectival participles lack implicit Agents is wrong; (iv) unaccusative-based and transitive-based participles are generated by distinct rules, suggesting that the latter are genuinely passive.
Development of the Perceived Salient Organizational Career Management (OCM) Practices Scale in Southern China

ABSTRACT This study developed a scale of perceived salient organizational career management practices based on a survey of 420 social workers from social work organizations in Guangdong province, China. A two-factor structure OCM scale was constructed using items reflecting the components of organizational career management (OCM) practices. The scale showed sufficient reliability and validity. The results demonstrated the necessity of designing promotion ladders, relevant training, career counseling and other OCM measures to assist social workers in their career progression. Practice Points The perceived salient OCM scale helps social work managers to understand how social workers perceive OCMpractices in social work organizations. Social work managers can specifically target OCM practices to social workers, and a systemic career progression ladder will facilitate a proactive and flexible response to social workers’ aspirations. Organizations should organize their OCM practices systematically, and the entire OCM system should be monitored and reviewed methodically.
An ACA-MFS Approach For The Analysis Of Sound Propagation In Sonic Crystals

Some efficient methods are available in the literature to solve the problem of sound propagation in the presence of sonic crystal structures, such as those based in the Multiple Scattering Theory (MST) or in the Finite Element Method (FEM). More recently, the Method of Fundamental Solutions (MFS) and the Boundary Element Method (BEM) have also been applied for that purpose. In this paper, a new strategy based on the use of the MFS is proposed to tackle problems of sound propagation around 3D sonic crystals with constant geometry along the axis of the scatterers (2.5D). The problem is solved in the frequency domain, and the sound field is synthesized as a summation of simpler 2D problems. To allow the solution of large-scale problems, with a high number of scatterers, an Adaptive-CrossApproximation approach is proposed and incorporated in the MFS algorithm, rendering the calculation much faster and with very significant savings in terms of computational requirements. Examples are presented, and the calculation times and RAM requirements are compared with those provided by a classic MFS formulation.
Impact of COVID-19 on the Factors Influencing on-Time Software Project Delivery: An Empirical Study

The objective of this research paper is to investigate the impact of COVID-19 on the factors influencing on-time software project delivery in different Software Development Life Cycle (SDLC) models such as Agile, Incremental, Waterfall, and Prototype models. Also to identify the change of crucial factors with respect to different demographic information that influences on-time software project delivery. This study has been conducted using a quantitative approach. We surveyed Software Developers, Project Managers, Software Architect, QA Engineer and other roles using a Google form. Python has been used for data analysis purposes. We received 72 responses from 11 different software companies of Bangladesh, based on that we find that Attentional Focus, Team Stability, Communication, Team Maturity, and User Involvement are the most important factors for on-time software project delivery in different SDLC models during COVID-19. On the contrary, before COVID-19 Team Capabilities, Infrastructure, Team Commitment, Team Stability and Team Maturity are found as the most crucial factors. Team Maturity and Team Stability are found as common important factors for both before and during the COVID-19 scenario. We also identified the change in the impact level of factors with respect to demographic information such as experience, company size, and different SDLC models used by participants. Attentional focus is the most important factor for experienced developers while for freshers all factors are almost equally important. This study finds that there is a significant change among factors for on-time software project delivery before and during the COVID-19 scenario. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
Confidence Region and Intervals for Sparse Penalized Regression Using Variational Inequality Techniques

LIANG YIN: CONFIDENCE REGION AND INTERVALS FOR SPARSE PENALIZED REGRESSION USING VARIATIONAL INEQUALITY TECHNIQUES. (Under the direction of Shu Lu and Yufeng Liu.) With the abundance of large data, sparse penalized regression techniques are commonly used in data analysis due to the advantage of simultaneous variable selection and prediction. By introducing biases on the estimators, sparse penalized regression methods can often select a simpler model than unpenalized regression. A number of convex as well as non-convex penalties have been proposed in the literature to achieve sparsity. Despite intense work in this area, it remains unclear on how to perform valid inference for sparse penalized regression with a general penalty. In this work, by making use of state-of-the-art optimization tools in variational inequality theory, we propose a unified framework to construct confidence intervals for sparse penalized regression with a wide range of penalties, including the well-known least absolute shrinkage and selection operator (LASSO) penalty and the minimax concave penalty (MCP). We study the inference for two types of parameters: the parameters under the population version of the penalized regression and the parameters in the underlying linear model. Theoretical convergence properties of the proposed methods are obtained. Simulated and real data examples are presented to demonstrate the validity and effectiveness of the proposed inference procedure.
Decidual Stromal Cell Ferroptosis Associated with Abnormal Iron Metabolism Is Implicated in the Pathogenesis of Recurrent Pregnancy Loss

Iron is necessary for various critical biological processes, but iron overload is also dangerous since labile iron is redox-active and toxic. We found that low serum iron and decidual local iron deposition existed simultaneously in recurrent pregnancy loss (RPL) patients. Mice fed with a low-iron diet (LID) also showed iron deposition in the decidua and adverse pregnancy outcomes. Decreased ferroportin (cellular iron exporter) expression that inhibited the iron export from decidual stromal cells (DSCs) might be the reason for local iron deposition in DSCs from low-serum-iron RPL patients and LID-fed mice. Iron supplementation reduced iron deposition in the decidua of spontaneous abortion models and improved pregnancy outcomes. Local iron overload caused ferroptosis of DSCs by downregulating glutathione (GSH) and glutathione peroxidase 4 levels. Both GSH and cystine (for the synthesis of GSH) supplementation reduced iron-induced lipid reactive oxygen species (ROS) and cell death in DSCs. Ferroptosis inhibitor, cysteine, and GSH supplementation all effectively attenuated DSC ferroptosis and reversed embryo loss in the spontaneous abortion model and LPS-induced abortion model, making ferroptosis mitigation a potential therapeutic target for RPL patients. Further study that improves our understanding of low-serum-iron-induced DSC ferroptosis is needed to inform further clinical evaluations of the safety and efficacy of iron supplementation in women during pregnancy.
Assessing Distribution Shift in Probabilistic Object Detection Under Adverse Weather

Object detection is a safety-critical aspect of autonomous driving, allowing vehicles to identify moving objects in the scene for tracking, prediction and decision making. Current detectors, however, tend to provide point estimates for detected objects, which lack information on the variability of the prediction and how well it fits the model that produced the prediction. Proper uncertainty estimation can be incorporated into traditional object detection pipelines to produce a measure of uncertainty alongside traditional point estimate object predictions. In this work, uncertainty estimates are implemented for LiDAR and camera object detectors using Bayesian theory, and the resulting output distributions are assessed using signal detection theory to generate an uncertainty based classifier that can evaluate its own performance. The classifier can be used to track the ratio of false positive to true positive detections, defined as the anomalous detections ratio. Findings from this work indicate that this novel metric is responsive to degraded driving conditions including night time driving and lens obstructions for the RGB camera, while in LiDAR data, the metric is responsive to snowfall and simulated rain conditions. These results are focused on the classification and regression of vehicle objects, making use of the sizeable ground-truth sets for vehicles that are provided in publicly-available autonomous driving data sets.
Efficient t0$$ {t}_0 $$ ‐year risk regression using the logistic model

In some clinical studies patient survival beyond a specific point in time, t0$$ {t}_0 $$ , say, may be of special interest as it may for instance indicate patient cure. To analyze the t0$$ {t}_0 $$ ‐year risk for such patients may be accomplished using logistic regression with appropriate weights (IPWCC) that may further be augmented (AIPWCC) to improve efficiency. In this paper, we derive the most efficient estimator for this problem, which is different from the AIPWCC based on the full data efficient influence function. We first give the result for a survival endpoint and then generalize to the competing risk setting. The proposed estimators superior behavior is illustrated using simulations as well as applying it to some real data concerning the survival of blood and marrow transplanted patients.
The Role of Immunotherapy or Immuno-Chemotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Simple Summary Surgical resections remain the gold standard for early stages non-small-cell carcinoma (NSCLC) and may be considered for locally advanced tumors. Medical treatment has changed drastically in recent years, especially for advanced stages, for which the development of immunotherapy and molecular targeted therapy significantly increased survival and quality of life. The addition of radical surgical resection following immunotherapy or immuno-chemotherapy is feasible and safe with low surgical-related mortality and morbidity in selected patients with initially unresectable NSCLC. Abstract Many new treatment modalities for non-small-cell carcinoma (NSCLC) have been described in the last two decades. Surgical resections remain the gold standard for early stages and may be considered for locally advanced tumors. Medical treatment has changed drastically in recent years, especially for advanced stages, for which the development of immunotherapy and molecular targeted therapy significantly increased survival and quality of life. The addition of radical surgical resection following immunotherapy or immuno-chemotherapy is feasible and safe with low surgical-related mortality and morbidity in selected patients with initially unresectable NSCLC. However, data from multiple ongoing trials with overall survival as the primary endpoint should be awaited before this strategy is introduced into the standard of care.
A Novel Method for Early Fatigue Damage Diagnosis in 316L Stainless Steel Formed by Selective Laser Melting Technology

Early fatigue damage is an important factor affecting the service safety of 316L stainless steel parts formed by selective laser melting (SLM) technology. Nonlinear ultrasonic testing for early fatigue damage in SLM 316L stainless steel specimens was carried out. A new method for evaluation of early fatigue damage based on nonlinear ultrasonic testing was proposed. Empirical mode decomposition (EMD) was applied to the unsteady ultrasonic testing signal, and the signal was decomposed into multiple intrinsic mode functions (IMFs) that meet certain conditions; then, the specific IMF (ESI) containing the effective fatigue damage information was extracted. Lastly, fast Fourier transform (FFT) was applied to the specific IMF signal to obtain the required information to evaluate the damage in the measured part caused by fatigue. The results of nonlinear ultrasonic testing agreed well with transmission electron microscope experimental analysis and theoretical model of acoustic nonlinearity caused by dislocations. The change in nonlinear ultrasonic testing results reflected the generation and evolution of dislocation structure during the low-cycle fatigue regime of the SLM 316L stainless steel specimen and revealed the early fatigue damage mechanism of this metal part. Compared with the classical FFT method, the EMD-ESI-FFT method is more sensitive in identifying the early damage in SLM 316L stainless parts induced by fatigue loading, which is equivalent to improving the early fatigue damage identification and diagnosis ability and can better ensure the service safety of important metal parts.
Optimization of convolutional neural network hyperparameters using improved competitive gray wolf optimizer for recognition of static signs of Indian Sign Language

Abstract. In the last decade, the success achieved by convolutional neural networks (CNN) has changed the trend of research in the computer vision field from manual features to learned features for various applications. However, designing the architecture of CNN and selecting optimum values of its hyperparameters poses a critical challenge because of their massive and complex search space. Automatically picking up the optimal values of hyperparameters may facilitate the designers of CNN-based deep learning models. Swarm intelligence (SI)-based improved competitive gray wolf optimizer (ICGWO) in continuous search space is proposed for optimizing hyperparameters of custom-designed CNN architecture. To evaluate the performance of the ICGWO, we employ 16 benchmark functions. We also assess the effectiveness of the ICGWO by evaluating it with six state-of-art meta-heuristic algorithms. The experiment results show that the ICGWO can obtain the best accuracy on eleven benchmark functions and second best on two benchmark functions. The optimized CNN recognizes static signs of English alphabets in Indian Sign Language (ISL). We also evaluated the optimized CNN on the publicly available ISL dataset for the English alphabet, where it attains 99.93% validation accuracy. In order to evaluate the efficacy of the proposed variant of CGWO, we optimized the hyperparameters of CNN using the existing SI-based search optimization algorithms namely particle swarm optimization, whale optimization algorithm (WOA), differential evolution, and gray wolf optimizer as well.
Robust decentralized control for large-scale web-winding systems: A linear matrix inequality approach

In this paper, a robust decentralized control scheme is proposed for web-winding systems. The control input for each subsystem is divided into two parts, a reference control input and a control compensation. First, the reference control inputs and the error dynamic models are presented. Then, based on the error dynamic model, a decentralized controller is designed to compute the control compensation, and the relevant sufficient condition for the existence of the decentralized controller is derived in terms of linear matrix inequalities (LMIs). By virtue of regarding some parameters as interval variables, the proposed controller has good robustness with regard to parameter variations and is adapted to the changes of set point. Finally, a three-motor web-winding system is considered as an application example, and simulation and experimental tests illustrate the effectiveness of the proposed controller.
A Note on the Implementation of

The implementation of collapsed Gibbs samplers for non-parametric Bayesian models is non-trivial, requiring considerable book-keeping. Goldwater et al. (2006a) presented an approximation which significantly reduces the storage and computation overhead, but we show here that their formulation was incorrect and, even after correction, is grossly inaccurate. We present an alternative formulation which is exact and can be computed easily. However this approach does not work for hierarchical models, for which case we present an efficient data structure which has a better space complexity than the naive approach.
A simple way to establish a dual-core hollow fiber for laser surgery applications

A dual-core hollow fiber has two separate cores for propagation of light. Such a fiber can have some good applications in laser surgery. The dual-core guide can transmit an infrared laser beam for cutting or ablation while a visible laser beam is simultaneously transmitted as a pilot or aiming beam. The traditional fabrication procedure for a dual-core hollow fiber involves chemical vapor deposition (CVD) growth on silica tubing of an inner cladding layer followed by the deposition of a low index polymer on the outside of the tubing. This will provide a hollow structure that has a clad-core-clad tube. This work provides an alternative approach which involves nesting of two hollow waveguides to establish a dual-core hollow fiber. An Ag/AgI hollow glass fiber is fabricated for transmitting CO2 laser. Another silica glass tube is selected carefully so that its inner diameter is just slightly larger than the outer diameter of the Ag/AgI hollow fiber. The outer surface of the as-selected glass tubing is coated with a low refractive index polymer. The Ag/AgI hollow fiber was inserted into the polymer coated silica glass tubing to establish an air or silicone oil gap between the two tubes. A visible laser beam is transmitted through the outer tube’s core. The CO2 laser beam is transmitted through the inner Ag/AgI hollow fiber. The dual-core hollow fibers show good transmission for both the red aiming beam and the CO2 laser. Therefore this structure can be a good candidate for laser surgery applications.