Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Evaluation of Monocular Visual–Inertial SLAM Methods for Freight Railways

Accurate, real-time, seamless, and reliable train localization is one of the most critical tasks for automatic train control systems. With the prospect of replacing the traditional track-side infrastructures, independent on-board sensors have attracted a great deal of interest. Since each rail vehicle is equipped with visual cameras, the visual–inertial simultaneous localization and mapping (VI-SLAM) is a promising approach for train localization due to its simple sensor setups. However, the existing VI-SLAM methods mostly pay attention to indoors, urban, or suburban environments, where freight railways in the industrial scenarios have not been considered or investigated overall. In this article, we seek to explore the potential of VI-SLAM for railways, and provide a comprehensive experimental evaluation of several typical monocular VI-SLAM methods for freight railways. Our datasets are collected on the Chinese Fuyang–Luan railway utilizing a maintenance rail vehicle. The results illustrate that monocular VI-SLAM methods are extremely easy to fail, even within a few minutes. These methods also need to deal with severe scale estimation errors due to constrained motions.
Automation of Trace Analysis

Software Automation approach involves the use of various tools and frameworks that can automatically perform tasks such as code generation, testing, deployment, and monitoring to avoid the repetitive and time-consuming tasks. By automating routine tasks, software developers can focus on more complex and creative aspects of their work, leading to faster delivery of software products. The analysis of trace log is a crucial step in various fields such as software engineering, cyber-security, and network analysis. However, manual analysis of logs can be time-consuming, error-prone, and difficult to scale up for large log file. Therefore, there is a need for automation in the analysis of trace log to improve efficiency, accuracy, and scalability. In recent years, various AI-based and statistical techniques and tools have been developed for automation of trace analysis. These techniques can be used to identify patterns, anomalies, and correlations in trace data, as well as to generate insights and predictions that can be useful for decision-making. The proposed work aims to develop a system that can be used for easy interpretation of analysis reports that identify failed series of the log file and potential causes of system failure. It intends to streamline log analysis and improve software development efficiency.
Low-Resolution DACs Aided Partially-Connected Hybrid Precoding Design for MU-MISO System

The utilization of hybrid beamforming (HBF) and low-resolution (LR) quantizers can greatly alleviate the pressure on energy consumption in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, partially-connected (PC) architectures that restrict the use of phase shifters can result in further deterioration of spectral efficiency (SE) with the configuration of LR quantizers. In this letter, we propose a two-stage hybrid precoding design for a downlink multi-user (MU) multiple-input single-output (MISO) system with LR digital-to-analog converters (DAC) to mitigate the impact of quantization distortion on SE. Simulation results show that the proposed algorithm has a significant performance improvement over conventional precoding algorithms and can be easily extended to the ideal quantization case.
Early life Rainfall and Later Life Human Capital Outcomes in Bangladesh

How does early life rainfall impact later life human capital outcomes in Bangladesh? This paper examines the effect of exogenous rainfall shocks in early-life on health, wealth and education outcomes of Bangladeshi women born between 1952-1988. I link historical rainfall for each woman's birth year and Demographic and Health Survey (DHS) cluster of birth with current outcomes from the 1999/2000, 2004 and 2007 survey rounds of the DHS. This study finds that rural women with 20% higher than mean rainfall in their year and cluster of birth are on average 0.24 cm taller and score 0.053 higher on a wealth index (1-5). I find that 20% higher than mean rainfall in the two seasons prior to birth leads to women attaining 0.097 more years of education. I find excess rainfall has a negative impact on height and education for women born in urban areas, but I give this result less attention due to the low number of observations of women born in urban areas in our sample.
Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review

Purpose Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis. Materials and Methods A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score. Results Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively. Conclusion AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.
NEW MODELING AND ENHANCED CONTROL STRATEGY FOR GRID-CONNECTED FOUR-LEG INVERTER WITHOUT PHASE-LOCKED LOOP AND PARK’S TRANSFORMATION

This paper proposes an enhanced voltage-oriented control strategy (EVOC) based on super twisting sliding mode control (STSMC) for a grid-connected four-leg source voltage inverter (GC-FLVSI) in the synchronous rotating frame (dq0–frame) without using a phase-locked loop (PLL) and Park's transformation. The proposed strategy is used not only to control the dq0–axes FLVSI inject currents but also to derive the mathematical model of the GC-FLVSI in the dq0–frame based on the direct instantaneous power control theory (DPC) and to eliminate the impact of PLL and Park's transformation. The principle of the proposed EVOC strategy is analyzed in detail. The STSMC is used for accurate dq0–axes FLVSI inject current control, and it can result in sinusoidal currents with high quality, high robustness against parametric variations, and low chattering with easy implementation. Finally, the superiority of the proposed EVOC-STSMC strategy in terms of complexity, response, steady-state errors, robustness, total harmonic distortion (THDs) mitigation, neutral current reduction, and robustness against parameter variations is verified through comparative analysis with the EVOC strategy based on the PI controller (EVOC-PIC).
Detecting Diabetic Retinopathy Using Deep Learning Techniques

Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to vision loss. Diabetic Macular Edema (DME) is a related condition where fluid accumulates in the macula, leading to retinal thickening. Early detection of both DR and DME is critical for effective treatment. However, current detection methods are timeconsuming and challenging. In this paper, we propose a novel approach using Capsule Networks and GAN to automatically detect and classify DR and DME using retinal images. We also investigate the correlation between the two conditions and develop a cross-disease grading system using image-level supervision. Our proposed model is tested on two publicly available datasets, Messidor and IDRiD, and achieves promising results. Our approach represents a significant advance in the automated detection of DR and DME using deep learning techniques.
Protecting Depositors and Saving Money: Why Deposit Guarantee Schemes in the EU Should Be Able to Support Transfers of Assets and Liabilities When a Bank Fails

In this paper we show that allowing deposit guarantee schemes (DGSs) the option of supporting asset and liability transfers in the event of a bank’s insolvency provides important economic benefits. However, only 12 EU Member States have so far included such “alternative measures” in their DGSs’ toolkits. The number of Member States where alternative measures have been actively used is even more limited. Based on our findings, we argue that giving deposit guarantee schemes in the EU the option of using alternative measures would improve the efficiency and effectiveness of the EU banking crisis management framework. It would speed up the handling of smaller banks’ failures while reducing upfront outlays and final costs for deposit guarantee schemes. It would improve the protection of deposits, thereby safeguarding depositor confidence and overall financial stability. It would also allow access to finance to be better preserved and enhance the level playing field for banks and depositors in the EU. We also argue that, apart from the availability of the option in law, the least cost test and the creditor hierarchy determine the de facto availability and potential magnitude of alternative measures. Currently, however, both the least cost test and the creditor hierarchy limit the possibility of supporting asset and liability transfers and may therefore need to be reformed in order for economically efficient results to be achieved.
The relationship between perceived restorativeness and place attachment for hikers at Jeju Gotjawal Provincial Park in South Korea: the moderating effect of environmental sensitivity

Objectives Jeju Gotjawal Provincial Park provides visitors with opportunities for outdoor recreation and informs visitors of the environmental significance of the park’s ecosystem. This study attempted to examine how the perceived restorativeness of park visitors influenced their place attachment. In addition, the moderating effect of environmental sensitivity on the hypothesized relationship was explored. Methods Using the purposive sampling method, 408 surveys were collected at Jeju Gotjawal Provincial Park. The hypotheses were tested by confirmatory factor analysis, path analysis, and invariance tests using Lisrel 8.70. Results The results indicated that perceived restorativeness had a positive influence on place attachment (place identity and place dependence). Further, the hypothesized relationship was stronger for the visitors with higher environmental sensitivity, compared to those with weaker environmental sensitivity. Conclusion Park managers should consider ways to increase the perceived restorativeness of visitors as they experience the natural environment at the park. Also, since environmental sensitivity played an important role in shaping the perceived restorativeness–place attachment relationship, there is a need for educational programs that can inform visitors of the significance of the natural environment to increase their affection for nature.
Ingredient Detection, Title and Recipe Retrieval from Food Images

With advancements in the machine learning community and an increase in social media usage in our daily lives, we see a steady growth in the number of food pictures shared online by users on a daily basis. Unfortunately, the picture does not adequately convey the intricate recipe that was used to prepare it. Thus, the image-recipe association problem is addressed in this study. Large datasets like Recipe1M+ and Food101 were accessible for our use, however, they do not rank highly for Indian dishes. As a result, a unique dataset that is entirely devoted to Indian cuisine has been developed and utilized. The aim is to develop a website that can take an image of an Indian dish as input, identify its ingredients, and retrieve the dish’s title. Further, this title is used to retrieve the recipe from the web. The task can be extremely challenging for a computer since it must understand the data well enough to produce accurate and precise ingredients. Both the dataset and the code will be made publicly available.
Tether Experiment: PocketQube Deployed from a CubeSat

This paper demonstrates the feasibility of tethering small satellite types such as PocketQube and CubeSats to achieve system stability via gravity gradient torque. By doing so, collisions between the satellite pair can be prevented through sufficient tension between them. A preliminary design of a CubeSat mission, where a PocketQube is deployed from a 3U CubeSat, is presented. This baseline design shows basic calculations of system torque and 25 m-long tether tension. RF and optical communication between the satellites and imaging of the CubeSat from the PocketQube are additional technology experiments on the proposed mission.
Cross-Scale Feature Propagation Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Over the past few years, various strategies have been proposed to improve the multiscale information capture capability of networks, such as encoder–decoder framework, convolution layers with different kernel sizes in parallel, and multiple branches framework. However, many methods only rely on one of the strategies, which limits their performance when processing remote sensing images (RSIs) with large-scale variance. To address this issue and enable the fast and effective extraction of multiscale semantic information, this manuscript introduces a novel cross-scale feature propagation network (CFPNet). Specifically, the multiscale convolution (MSC) module aims to capture fine-grained multiscale context with different receptive fields, and the attention upsample (AUS) module embeds the semantic information of high-level features into low-level features while maintaining spatial details. Besides, the feature semantic enhancement (FSE) module is proposed to aggregate the multilayer features of the decoder to enhance the final feature representation. The experimental results on the Beijing Land-Use (BLU) and GID datasets demonstrate the effectiveness and efficiency of our CFPNet.
A Case Study of Problems and Possibilities in the Establishment of a Guide Training Institute in Uttarakhand, India

This project sought to explore the problems and possibilities experienced during the establishment of a guide training institute in Uttarakhand, India. As originally proposed by the Mountain Shepherds Initiative, a small tourism organization based in Uttarakhand, the aim of the guide training institute was to provide participants with the knowledge and skills needed to gain employment as guides within, and expand the extent of, high quality/low quantity birdwatching tourism within northern Uttarakhand. This project was undertaken in conjunction with the Mountain Shepherds Initiative and the problems and possibilities were identified during a six month stay in the region from May-November, 2010. The problems and possibilities identified concerned the potential for the expansion of birdwatching tourism within the region as well as the process of establishing the training institute. Problems and possibilities associated with the potential for expanding birdwatching tourism within the region were related to three destinationattributes identified by Scott and Thigpen (2003) as of primary concern to birdwatchers: species availability, infrastructure, and contrast to urban environs. Problems and possibilities identified in connection with the process of establishing the guide training institute related to the means by which sites were selected, the creation of a course curriculum, and the initial implementation of guide training through a pilot course. As the tourism industry grows, and guides are held to increasingly high standards, it is hoped that this exploratory study will both inform other organizations wishing to establish guide training programs as well as future research into guide training.
Enhancing Educational Dialogue Act Classification With Discourse Context and Sample Informativeness

Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a well-performed ML model is nontrivial since the sample size is commonly limited for model training. Based on this fact, we posited that the model performance can be further optimized by the design of input features and the selection of informative instances for model training. By reviewing the existing dialogue-related research, we found that contextual information (i.e., the content of preceding utterances) was a potential feature in many text classification tasks but underexplored in the classification of an instructional strategy. In addition, training a well-performed ML model (e.g., deep neural network) to recognize instructional strategies usually requires a large amount of manually annotated data, which are labor intensive. To alleviate the demand of manual annotation, researchers proposed the use of statistical active learning methods to select informative instances, but this method was rarely used for recognizing the instructional strategy in online tutoring dialogue. Therefore, our study aimed to investigate the improvement of automating the classification of dialogue acts—a popular approach to the detection of instructional strategies—from two perspectives. First, we explored whether and to what extent the incorporation of contextual information can boost a model's prediction performance. Then, we investigated the extent to which the recent active learning methods can alleviate the labor-intensive issues in training the ML model for recognizing the instructional strategies. Our study showed that: 1) the ML models trained on the features that included the contextual information achieved better performance than that of the models excluding it; 2) the effectiveness of the contextual information decayed after the ML model achieved an optimal performance; and 3) compared with the random baseline, active learning methods can select informative samples from the training dataset to train ML models, which can alleviate the labor-intensive issues.
Screen-Printed Graphene Film Circuit Model for Microwave Applications

Graphene, a 2D structure with carbon atoms arranged in a hexagonal (honeycomb) lattice, exhibits intriguing mechanical, thermal and electrical properties. Graphene nanoplatelets can be deposited on different substrates through the preparation of inks with a proper combination of solvents and binders. Films loaded with graphene are attractive for many applications as biosensors and chemical sensors as well as flexible electronics applications. In this paper, graphene films loaded with graphene nanoplateletes of 25 wt. % are deposited by screen printing technique on a microstrip circuit and they are characterized in the microwave frequency range. By fitting the measured scattering parameters of graphene-loaded microstrip lines with Cadence AWR software simulations, an equivalent lumped circuit model of the film is obtained.
Error Analysis for Fused Floating-point Square-root and Division based on Goldschmidt Algorithm

This paper presents an error analysis method for floating-point square-root and division based on Goldschmidt algorithm. The proposed analysis is sufficiently concise to guide circuit design and leads to intermediate results for state-of-the-art word length reduction. This error analysis method is applicable to both division and square-root and can help designers achieve a high degree of resource reuse for these two operations. To validate our method, we have designed a fused square-root and division module for IEEE 754 single precision and compare the theoretical error bound with the simulated error bound under 100 trillion random tests. The module not only excels among existing floating-point units, but also achieves a high degree of resource reuse for division and square-root.
Public Sector Unionization and Government Contracting: A Meta-analysis of Four Decades of Empirical Evidence

Within the large body of literature on government contracting, the effect of public sector unionization on contracting out is still unsettled even after decades of research. Previous literature proposes that unionization may both inhibit and motivate contracting out, making the net effect difficult to predict. Through a meta-analysis of 232 effects drawn from 49 existing studies spanning over four decades, we find that jurisdictions with higher levels of public sector unionization generally contract out more in public service delivery. Further metaregression analysis suggests that unionization has a weaker effect when governments engage in intergovernmental contracting but a stronger effect when governments contract out for technical services. Unionization also has a stronger effect on how much a government contracts out than on whether a government contracts out. Overall, unionization is a relevant, but not necessarily robust, factor in driving contracting out, and its exact effect may vary slightly by context.
Effect of tidal volume and positive end‐expiratory pressure on expiratory time constants in experimental lung injury

We utilized a multicompartment model to describe the effects of changes in tidal volume (VT) and positive end‐expiratory pressure (PEEP) on lung emptying during passive deflation before and after experimental lung injury. Expiratory time constants (τE) were determined by partitioning the expiratory flow–volume ( V˙EV) curve into multiple discrete segments and individually calculating τE for each segment. Under all conditions of PEEP and VT, τE increased throughout expiration both before and after injury. Segmented τE values increased throughout expiration with a slope that was different than zero (P < 0. 01). On average, τE increased by 45.08 msec per segment. When an interaction between injury status and τE segment was included in the model, it was significant (P < 0.05), indicating that later segments had higher τE values post injury than early τE segments. Higher PEEP and VT values were associated with higher τE values. No evidence was found for an interaction between injury status and VT, or PEEP. The current experiment confirms previous observations that τE values are smaller in subjects with injured lungs when compared to controls. We are the first to demonstrate changes in the pattern of τE before and after injury when examined with a multiple compartment model. Finally, increases in PEEP or VT increased τE throughout expiration, but did not appear to have effects that differed between the uninjured and injured state.