Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobility-Aware AR Computation Tasks Offloading Based on Offline RL over Terahertz System

Augmented reality (AR) technology is increasingly applied in the Internet of vehicles (IoV). As it poses significant challenges to the communication and computation capabilities of AR terminal devices, this paper investigates combining terahertz (THz) and mobile edge computing (MEC) integrated with reinforcement learning (RL) to optimize the performance of AR services for the IoV. Though the RL-based MEC offloading has been validated to have excellent performance, it has an intrinsic disadvantage where its training phase needs inappropriate offloading strategies to be tested frequently and poses risks to safe driving. Moreover, although THz communication can provide high transmission data rates, its characteristics that different transmission distances lead to different available bandwidth, pose a unique challenge for the communication resources allocation. Motivated by these, we propose a novel static data-driven AR tasks offloading framework to solve resources allocation problem for the IoV with THz communication. To deal with the challenge that different users have different available THz bandwidth, we propose a distance-aware fair THz bandwidth allocation algorithm. Moreover, the offline RL, instead of online RL, is utilized to train the AR task offloading decision agent, where interaction with the real world is not required, completely safeguarding the system's safety. To address the typical issue of out-of-distribution (ODD) actions encountered in offline RL, we adopt conservative Q-learning (CQL) to train and obtain the optimal AR computation task offloading strategy to enhance users' experience. Simulation results show that the proposed solution outperforms state-of-the-art algorithms in terms of algorithm stability and user reward.
Demographic Trends, Young People's Attitudes towards Marriage and Socio-economic Changes Related to Family Formation in Greece and in the European Union: A Comparative Analysis Based on Official and Survey Research Data

The study is dealing with the evolution and the current patterns on marriage and family formation in Greece, as well as the related young population matters, by analyzing the demographic trends during the Post World War II, the temporary attitudes and behavior, especially of young people and the socio-economic factors associated with these trends. To meet these tasks, secondary and survey data are employed, demographic indicators are constructed and descriptive plus non parametric statistics are used. The similarities and differences across Europe are identified as Greece is partly following the southeastern demographic pattern and attitudes towards marriage and family formation, but also the particularities even from the other Mediterranean-Southern countries. The demographic and attitudinal differences among societies are due to the different time period that each of these passed through the certain phases of social and economic transformations.
Relations between cell body size, axon diameter and axon conduction velocity of triceps surae alpha motoneurons during the postnatal development in the cat

Triceps surae α‐motoneurons in cats of different postnatal ages were stained intracellularly with horseradish peroxidase (HRP) and studied light microscopically. In individual neurons, the mean diameter of the cell body and the intramedullary axon diameter were measured and related to the axon conduction velocity.
Research on System Partition Method of High Proportion Renewable Energy Power System Considering Dynamic Frequency Spatiotemporal Characteristics

A system partition method considering the spatiotemporal characteristics of dynamic frequency is proposed in this paper. The partition method can reflect the difference in the spatial distribution of system frequency can be obtained by using sample distance as the basis for partition clustering, and the frequency support weakness of the power grid can be quickly obtained by analyzing the frequency characteristics of the center points in each partition. Thus the system monitoring data demand may significantly reduce, since there is no need for comprehensive scanning of the frequency data of each node in the system.
Biomedical Image Classification using Deep Reinforcement Learning

The amalgamated tool of two renowned tools deep learning and reinforcement learning is a powerful representation for deep neural networks that improves the basic reinforcement learning framework. With the use of deep learning’s representational power, it learns from the agent’s activities on how to increase the expected reward. Recent work has shown a lot of success of deep reinforcement learning in different domains such as video games, robotics, finance, medical and computer vision. In this project, various DRL models and methods for planning medical image analysis are discussed. This study covers the fundamentals of reinforcement learning. DRL algorithms can address the problems with limited and inconsistent annotated medical imaging data, which has been a major obstacle to the implementation of deep learning models in clinical settings. DRL algorithms support these models for the reward function, interactions between agents, and the environment. There has been an extensive amount of research being done in this area, and it has the potential to enhance the utilisation of deep learning in medical imaging.
Hybrid RIS-Assisted MIMO Integrated Sensing and Communication System

The reconfigurable intelligent surface (RIS), with the ability to program the radio environment, is considered as an essential component of the integrated sensing and communication (ISAC) system. In this paper, we propose a hybrid RIS (HRIS)-assisted multiple-input multiple-output (MIMO) ISAC system, where the HRIS is externally controllable and serves the dual function of reflecting the transmit signal to communication users and receiving radar echoes. Using this new RIS architecture, we characterize the tradeoff between communication and radar sensing performance by jointly designing the base station (BS)'s beamforming vectors and the HRIS's parameters. Our objective is to maximize the signal-to-interference-and-noise ratio (SINR) of radar while ensuring the communication SINR requirement. To solve the corresponding non-convex joint beamforming design problem, we propose an alternating optimization approach, where the auto gradient descent (AGD) is applied to effectively find the best HRIS configuration, and the semidefinite relaxation (SDR) is used to obtain the closed-form BS beamforming solution. Our numerical results are provided to evaluate the quality of radar and communication, as well as the effectiveness of the provided algorithm,
Wavelet Based Random Noise Removal from Color Images Using Python

Image de-noising is an essential field in image processing, encompassing a wide range of applications. This is pre-processing task in which unwanted noise signals are removed using different techniques. Noise are unwanted signals which deteriorate the useful information from the image. These information may be edges, ridges, contours are other fine structures. For different applications these details are important. Noise signals may contaminate the image partially or completely. It depends upon the type of noise and its level. Noise may be categories according to its characteristics. The most frequent types of noise signals encountered in image processing include Additive White Gaussian Noise, Speckle noise, salt and pepper noise, Rician noise, random noise, and more. Noise signals introduced in the images during data acquisition, transmission or due to faulty location. Additive white Gaussian noise is one of the most common noise signal which affect almost all the images in a certain extent. In this chapter we apply de-noising technique which is based on wavelet thresholding. Wavelet transform is widely recognized as one of the most popular transforms in signal and image processing. It is used in various image processing applications. Thresholding is an essential component in wavelet transform, and it is commonly classified into two types: hard thresholding and soft thresholding. In the chapter we apply soft thresholding technique which outperforms hard thresholding technique.
Spaced Learning in Engineering Education: A Multifaceted Approach Centered on Newton's Law of Cooling

This manuscript presents a comprehensive approach to engineering education that incorporates the spacing effect and effective learning techniques. Over four semesters, students embark on an immersive project-based learning journey centered on Newtons' law of cooling, approaching the project from analytical, experimental, electronic instrumentation, and numerical perspectives. Beginning with foundational principles and culminating in advanced numerical implementations, the methodology integrates insights from self-explanation, distributed practice, and practice testing. This holistic approach ensures enduring engagement and mastery. The iterative reintroduction of the project at spaced intervals fortifies students' understanding, enabling them to perceive the entirety of the engineering process, from theoretical underpinnings to practical applications. This multisemester journey offers a promising avenue for engineering students to solidify their knowledge and skills while embracing the principles of cognitive psychology and education.
Design of a Corner-Reflector Reactively Controlled Antenna for Maximum Directivity and Multiple Beam Forming at 2.4 GHz

Electronically steerable passive array radiator (ESPAR) antennas constitute a promising research field and are expected to play important role in future wireless communications. In this paper, a new approach in ESPAR antenna design for base station applications is proposed. A corner-plate reflector is combined with active and passive (reactively loaded) elements in order to implement a corner-reflector ESPAR (CR-ESPAR) configuration. It is shown that when combined with corner reflectors in order to sectorize the coverage area, an ESPAR antenna offers multiple radiation patterns with higher directivity and resolution. A case study of a CR-ESPAR suitable for 2.4 GHz ISM applications is demonstrated, where the performance of the structure is optimized with respect to resonance frequency, input impedance, and multiple switched-beam patterns configuration. The optimization of the array is performed using a Genetic Algorithm (GA) tool as a method of choice, achieving a maximum gain equal to 14 dBi for a 30°3 dB-beamwidth and a gain of 11 dBi for a 45°3 dB-beamwidth, while the VSWR is kept below 1.7 in all cases. Due to its limited physical size, the proposed CR-ESPAR can be used as a portable antenna for deployment in WiFi, WLAN and other applications.
Maternal pre-pregnancy BMI and gestational weight gain, offspring DNA methylation and later offspring adiposity: findings from the Avon Longitudinal Study of Parents and Children

Background: Evidence suggests that in utero exposure to undernutrition and overnutrition might affect adiposity in later life. Epigenetic modification is suggested as a plausible mediating mechanism. Methods: We used multivariable linear regression and a negative control design to examine offspring epigenome-wide DNA methylation in relation to maternal and offspring adiposity in 1018 participants. Results: Compared with neonatal offspring of normal weight mothers, 28 and 1621 CpG sites were differentially methylated in offspring of obese and underweight mothers, respectively [false discovert rate (FDR)-corrected P-value < 0.05), with no overlap in the sites that maternal obesity and underweight relate to. A positive association, where higher methylation is associated with a body mass index (BMI) outside the normal range, was seen at 78.6% of the sites associated with obesity and 87.9% of the sites associated with underweight. Associations of maternal obesity with offspring methylation were stronger than associations of paternal obesity, supporting an intrauterine mechanism. There were no consistent associations of gestational weight gain with offspring DNA methylation. In general, sites that were hypermethylated in association with maternal obesity or hypomethylated in association with maternal underweight tended to be positively associated with offspring adiposity, and sites hypomethylated in association with maternal obesity or hypermethylated in association with maternal underweight tended to be inversely associated with offspring adiposity. Conclusions: Our data suggest that both maternal obesity and, to a larger degree, underweight affect the neonatal epigenome via an intrauterine mechanism, but weight gain during pregnancy has little effect. We found some evidence that associations of maternal underweight with lower offspring adiposity and maternal obesity with greater offspring adiposity may be mediated via increased DNA methylation.
Enhancing Abstractive Text Summarization with Proximal Policy Optimization

In the realm of Natural Language Processing (NLP), Abstract Text Summarization (ATS) holds a crucial position, involving the transformation of lengthy textual content into concise summaries while retaining essential information. This research paper delves into the utilization of advanced algorithms such as Seq-Seq Transfer Learning, Pegasus, and Proximal Policy Optimization for the abstraction text summarization process. Specifically, this study incorporates cutting-edge techniques like deep neurotemporal models and Reinforcement Learning (RL), with a focus on Preorder Language Models (PTLM). The integration of knowledge into the development process further enhances the efficacy of ATS applications. The paper meticulously examines the recent advancements in abstract concept extraction, addressing both historical and existing challenges while proposing innovative solutions. Emphasizing the significance of data and metrics in abstract ATS, this research incorporates the widely used ROUGE metric for evaluation. By comparing influential models, the study provides a comprehensive overview of the field’s evolution. In addition, it explores the integration of transformative graph-based Transformer architecture and PTLM, which have revolutionized NLP. The research paper concludes by offering valuable insights into the future of abstract text summarization, paving the way for further exploration and innovation in the domain.
Decoding Cinematic Fortunes: A Machine Learning Approach to Predicting Film Success

The film industry, a dynamic mix of artistry and commerce, confronts the persistent challenge of predicting a movie's success amid the inherent uncertainty of audience reception. This research utilizes the power of machine learning and data science to unravel the elusive traits that contribute to a film's profitability and popularity. Leveraging a rich dataset encompassing diverse features such as production budget, actors, directors, and genre, our study aims to discern the intricate dynamics that shape a movie's performance. The methodology involves rigorous data preprocessing, feature engineering, and the application of four machine learning models, Radial SVM, random forest, Extreme Gradient Boosting (XGBoost), and KNN. Through a comprehensive analysis, we identify key factors influencing movie success, emphasizing the importance of budget, genre, and actor popularity. The results showcase the effectiveness of Random Forest and XGBoost models in predicting a movie's success, while performance tuning further enhances their accuracy. The feature importance analysis underscores the pivotal roles of budget, genre, and actor popularity in determining a movie's fate. Correlation mapping sheds light on the relationships between variables, providing valuable insights into the multifaceted nature of movie success. This research offers a pioneering exploration into the integration of machine learning and data science in the film industry, presenting a transformative lens for understanding and predicting success in this dynamic landscape.
Construction and Annotation of Test-Items in DiET

We present an annotation tool, called GLOSS, that manifests the following features: accepts as inputs SGML source documents and/or their database images and produces as output source SGML documents as well as the associated database images; allows for simultaneous opening of more documents; can collapse independent annotation views of the same original document, which also allows for a layer-by-layer annotation process in different annotation sessions and by different annotators, including automatic; offers an attractive interface to the user; permits discourse structure annotation by offering a pair of building operations (adjoining and substitution) and remaking operations (undo, delete parent-child link and tree dismember). Finally we display an example that shows how GLOSS is employed to validate, using a corpus, a theory on global discourse.
Gender bias in schooling: the case for Bhutan

Differing parental considerations for girls and boys in households are a primary cause of the gender gap in school enrolment and educational attainment in developing countries, particularly in Sub-Saharan Africa and South Asia. While a number of studies have focused on the inequality of educational opportunities in South Asia, little is known about Bhutan. This study uses recent household expenditure data from the Bhutan Living Standard Survey to evaluate the gender gap in the allocation of resources for schooling. The findings, based on cross-sectional as well as household fixed-effect approaches, suggest that girls are less likely to enrol in school but are not allocated fewer resources once they are enrolled.
Economic Behavior under Uncertainty: A Joint Analysis of Risk Preferences and Technology

A method is developed to estimate jointly risk preferences and technology under general conditions. The approach is illustrated in an application to the analysis of U.S. corn-soybean acreage decisions over time. The results provide useful information on the nature of farmers' risk preferences and on the influence of price risk and production risk on acreage allocation and farmers' welfare. Copyright 1996 by MIT Press.
An Analysis of Object Detection in Bad Weather Conditions using Deep Learning Models

Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research that utilizes learning techniques adjustments to frameworks and the significance of datasets. This paper proposed the YOLOv5 model to detect the object in bad weather conditions. The paper presents the comparison between the YOLOv5 model presented in literature and the proposed YOLOv5 model in terms of accuracy, precision, recall and F1-score. The evaluation results prove the better performance of our proposed model.
Comparing ARMA and ARIMA for Modeling Time Series in Wireless Communications

This abstract discusses a comparative take a look at of ARMA (Autoregressive moving common) and ARIMA (Autoregressive integrated transferring average) fashions with regards to modeling for time series analysis within the context of wireless communications. The purpose of the examine is to decide the merits of each version in producing correct prediction effects. The authors examine each fashions considering the criterion of accuracy of prediction based on the mistake residuals and imply absolute blunders. The outcomes offer similarly information of the way ARMA and ARIMA can be efficaciously utilized in modeling time series for wireless communications.
Pattern Analysis Framework for Detection of Cardiac Disorders using VCG Signal Analysis

Bundle Branch Block (BBB), Cardiomyopathy (CMP), and Dysrhythmia (DYS) are interrelated cardiac conditions. They can make a significant impact on the electrical conduction and overall functioning of the cardiac activity. BBB is categorized by delayed or blocked electrical signals in bundle branches, while CMP refers to structural abnormalities or damage to heart muscles. DYS encompasses various irregular heart rhythms. This paper proposes a pattern recognition framework for precise detection of BBB, CMP and DYS using Vectorcar-diography (VCG). The dataset is obtained from Physikalisch Technische Bundesanstalt (PTB) Diagnostic Electrocardiogram (ECG). VCG signals are extracted from ECG and preprocessed using Empirical Mode Decomposition (EMD). Time, spectral and cepstral features are extracted from the preprocessed signals, followed by the application of the Back Elimination Method through which features with the best contribution to the accurate detection are selected. Classification is done using 10-fold cross-validation. The highest accuracy of 97.9% is achieved using KNN (Fine) classifier.
Social Choice and Networks Lecture Date : October 5 , 2010 Lecture : Errors in Binary Voting

According to this definition, there are two candidates, namely −1 and 1, and there are n voters casting their direct votes for each candidate. The function f receives a configuration of votes and determines the outcome. As in the previous lectures, we have some natural assumptions about the function f . Definition 2. A voting scheme is monotone if for each x, y ∈ {−1, 1} we have x ≥ y =⇒ f(x) ≥ f(y). Here x ≥ y means that xi ≥ yi for each i.
Composite K-/Ka-Band Planner Antenna Elements with A Shared-Radiation Patch

This paper proposed a novel scheme of composite planner antenna elements with a shared-radiation patch which can operate in both K-band and Ka-band. Both of the two antennas radiate into free space via the same physical aperture. The simulation results of the proposed antennas demonstrate that, the composite antenna elements can obtain a relative bandwidth of 10.25% of K-band and a relative bandwidth of 15.38% of Ka-band, respectively.
Unveiling Purchasing Patterns in Grocery Store Consumer Segmentation Insight From K - Means Clustering

The purpose of this research was to apply cluster analysis to a real, never-before-published sales data from a grocery store over the course of three months. Cluster analysis, applied to a dataset containing 1,544 samples, reveals four distinct subsets of consumers, distinguished by their age and the total amount they spent on a subset of products. Consumers who are older and who prefer cooking oil products, but buy fewer eggs are identified as a distinct subset of the market. Another demographic consists of younger buyers who are interested in purchasing chicken eggs but do not want to use cooking oil. It is possible to differentiate between these two groups. This study demonstrates the promise of clustering k-means and how the elbow method can produce a useful cluster number, both of which can improve the accuracy of consumer segmentation and the efficacy of retail businesses' efforts to target customers.