Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilayer Decomposition Denoising Empowered CNN for Radar Signal Modulation Recognition

Radar signal recognition plays a crucial role in modern electronic reconnaissance systems. With the increasing complexity of electromagnetic environments, radar signals are susceptible to noise interference under low signal-to-noise ratio (SNR) conditions, posing a challenge to accurate radar signal recognition. To address this issue, we propose a multilayer decomposition denoising empowered convolutional neural network (CNN) for radar signal recognition. Specifically, the original radar signals are first processed by multilayer decomposition denoising, which consists of variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) in sequence, termed as VMD-LMD-WT. Then we use the Choi-Williams distribution (CWD) to convert the denoised signals into time-frequency images (TFIs). Finally, an improved CNN with dilated convolution is employed for radar signal recognition. Experiments demonstrate that the multilayer decomposition denoising method effectively improves the SNR of the original signal, which is beneficial for the subsequent recognition task. The recognition accuracy is improved by about 11% compared to that directly using the original signal. Furthermore, compared to the current network models, the proposed CNN network can efficiently extract the signal features and improve the detection accuracy of low probability of intercept (LPI) radar signals. The recognition accuracy reaches 75.3% for 12 signals when the SNR is low to −14dB.
Connecting Academia with the Professional World: Exploring Written Assignments in a Postgraduate Professional Development Program

Master-level postgraduate professional development (PPD) programs have grown rapidly in many parts of the world. Being able to complete written assignments successfully is a significant concern for students pursuing PPD studies. Yet the nature of written assignments in such programs has been under-researched. This paper reports a study conducted in the context of a Master of Education (MEd) program in an English-medium university in Hong Kong. The study addressed the research question How do MEd written assignments prompt students to engage with academia and the professional world? Analyses of the documentary data, comprising a collection of written assignment prompts featuring 132 tasks, revealed three categories of assignments: oriented to the professional world, oriented to academia, and connecting academia and the professional world, with the last category being the largest share (75%). The paper illustrates these categories of assignments and considers implications of the study for pedagogy and future research.
Workshop Result: Teaching Structured Reviews to Environmental Engineering Researchers

As part of the 2019 biennial conference of the Association of Environmental Engineering and Science Professors, a pre-conference workshop on the topic of structured reviews was delivered to 22 participants. The workshop had three objectives, namely: 1) raising awareness about the process of structured reviews; 2) demonstrating the process of structured reviews; and 3) encouraging participants to adopt a structured review format for future research. To achieve these objectives, three activities were undertaken, including: 1) a pre-conference web site was constructed where participants completed mandatory exercises before the on-site portion of the workshop; 2) group-work to learn the structured review process during the on-site portion of the workshop; and 3) voluntary follow-up including mentoring/coaching by workshop conveners to support the use of structured reviews in future research after the completion of the on-site portion of the workshop. Assessment of the knowledge, skills, and attitudes of workshop participants was undertaken using an on-site, anonymous, voluntary readiness assessment test (RAT) as well as an on-site, anonymous, voluntary comprehension assessment test (CAT), administered immediately preceding and following the on-site portion of the workshop, respectively. To support the long-term adoption of structured reviews in research, the workshop moderators provided mentoring/coaching to workshop participants in the months following the completion of the on-site portion of the workshop. The value of this long-term mentoring/coaching was assessed by an additional CAT administered one year after the completion of the on-site portion of the workshop. The purpose of this paper is to share: 1) workshop content and format that could be used by other conveners of similar workshops; 2) results of the analysis of the RAT and CATs; and 3) the author’s experience with mentoring/coaching workshop participants on the use of structured reviews.
The impact of the initial COVID-19 lockdown on the mental health and lifestyle of children in Turkey

The COVID-19 lockdown and subsequent school closures increased children’s mental health difficulties and associated risk of school exclusion. The present study examined the changes in children’s daily habits, and emotional and behavioral symptoms during the first national lockdown in 2020 as compared to before. A total of 854 caregivers living in Turkey with children aged 5 to 12 years completed a survey. The results showed that children spent significantly more time using screens, and less time engaging in physical activity during the lockdown compared to before. Additionally, one in three children were reported as feeling bored. Approximately half of the children were reported to show signs of nervousness, anger, anxiety, worry, restlessness, irritability, argumentativeness, and fear of infection. The children whose caregivers reported more family coexistence difficulty, higher risk of COVID-19 infection, poorer mental health, and high parenting role stress experienced higher levels of emotional and behavioral difficulties during the lockdown as compared to before. Children’s pandemic-related emotional symptoms increased with age. Our findings suggested that the COVID-19 lockdown in Turkey had a negative impact on children’s socioemotional and behavioral wellbeing with pre-adolescents being at higher risk of emotional change during the lockdown. These findings can guide early prevention and intervention programs to address child mental health symptoms in Turkey
Mapping Phytoplankton and Algal Blooms With a Novel Multisensor Water Index

Multispectral satellite imageries (MSIs) are strong enough to delineate features of interest by suppressing others. The water index is a method applied to delineate the water features by combining the multispectral band images. This research proposes a novel index for extracting water features from multisensor MSIs named multisensor water index. The proposed index, along with existing indices, is applied on Landsat-8 OLI imageries, a comparative analysis is done, and it is found that the proposed index is outperforming with 99.77% accuracy. The proposed index was validated on other sensors, such as a series of Landsat (5, 7, 8, and 9), Sentinel-2, Resourcesat, and Modis satellite imagery, by mapping water features successfully. Furthermore, the proposed index maps marine pollution by discriminating phytoplankton and algal bloom from the water feature. It is observed that the increase in phytoplankton or algal bloom is the result of eutrophication. The blooming of phytoplankton and algae is on the surface of the water. Thus, it causes shading at the bottom of the marine ecosystem and negatively impacts the growth of seagrasses, dissolved oxygen levels, fish suffocation, water properties, and other bottom habitats. The three test sites, viz., lake Villarrica, lake Okeechobee, and the Atlantic Ocean, have been considered to study increasing phytoplankton and algal bloom. The proposed index successfully discriminates the phytoplankton and algal bloom from the water features.
An Exploration of Artistic Expressions of Everyday Peri-Urban Landscapes as a Method of Socio-Spatial Analysis in Spatial Planning

Everyday landscapes, including peri-urban areas, play a pivotal role in shaping our physical and mental environments. Nevertheless, spatial planning often falls short of integrating contemporary everyday life and lived space. This paper advocates for an expanded approach beyond traditional planning methods, exploring the potential of artistic representations like drawings, photographs, and films to capture the nuances of peri-urban landscapes. Based on a selection of contemporary artistic works in the fields of drawing, photography, and cinema, this paper explores, through an analysis of specific projects and interviews with the artists, the potentials and constraints of these types of representations. This analysis of selected works reveals that these mediums embody a rich and complex collective journey, showcasing the profound connection between the artist and the environment. Structured around the key stages of analysis, documentation, and communication, the discussion elucidates the interrelated nature of these phases within a broader creative process. It underscores the significance of recognizing these artistic representations as integral components in planning processes, emphasizing their potential to provide unique insights into the complexities of peri-urban landscapes. In conclusion, this article proposes a set of recommendations for the incorporation of these artistic mediums within planning processes and urban studies.
Disentangling Greenhouse Warming and Aerosol Cooling to Reveal Earth’s Transient Climate Sensitivity

Earth’s climate sensitivity has been the subject of heated debate for decades, and recently spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of the most likely range of climate sensitivities (1). Here, we present an observation-based study based on the time period 1964 to 2010, which is unique in that it does not rely on global climate models (GCMs) in any way. The study uses surface observations of temperature and incoming solar radiation from approximately 1300 surface sites, along with observations of the equivalent CO2 concentration (CO2,eq) in the atmosphere, to produce a new best estimate for the transient climate sensitivity of 1.9K (95% confidence interval 1.2K – 2.7K). This is higher than other recent observation-based estimates (2, 3), and is better aligned with the estimate of 1.8K and range (1.1K – 2.5K) derived from the latest generation of GCMs. The new estimate is produced by incorporating the observations in an energy balance framework, and by applying statistical methods that are standard in the field of Econometrics, but less common in climate studies. The study further suggests that about a third of the continental warming due to increasing CO2,eq was masked by aerosol cooling during the time period studied.
Replicative aging in yeast involves dynamic intron retention patterns associated with mRNA processing/export and protein ubiquitination

Saccharomyces cerevisiae (baker's yeast) has yielded relevant insights into some of the basic mechanisms of organismal aging. Among these are genomic instability, oxidative stress, caloric restriction and mitochondrial dysfunction. Several genes are known to have an impact on the aging process, with corresponding mutants exhibiting short- or long-lived phenotypes. Research dedicated to unraveling the underlying cellular mechanisms can support the identification of conserved mechanisms of aging in other species. One of the hitherto less studied fields in yeast aging is how the organism regulates its gene expression at the transcriptional level. To our knowledge, we present the first investigation into alternative splicing, particularly intron retention, during replicative aging of S. cerevisiae. This was achieved by utilizing the IRFinder algorithm on a previously published RNA-seq data set by Janssens et al. (2015). In the present work, 44 differentially retained introns in 43 genes were identified during replicative aging. We found that genes with altered intron retention do not display significant changes in overall transcript levels. It was possible to functionally assign distinct groups of these genes to the cellular processes of mRNA processing and export (e.g., YRA1) in early and middle-aged yeast, and protein ubiquitination (e.g., UBC5) in older cells. In summary, our work uncovers a previously unexplored layer of the transcriptional program of yeast aging and, more generally, expands the knowledge on the occurrence of alternative splicing in baker's yeast.
Practical guidance to identify and troubleshoot suboptimal DSC-MRI results

Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.
Disclosure and the Cost of Capital: Evidence from Firms' Response to the Enron Shock

This paper examines the link between disclosure and the cost of capital. We exploit an exogenous cost of capital shock created by the Enron scandal in Fall 2001 and analyze firms' disclosure responses to this shock. These tests are opposite to the typical research design that analyzes cost of capital responses to disclosure changes. In reversing the tests and using an exogenous shock, we mitigate concerns about omitted variables in traditional cross-sectional disclosure studies. We estimate shocks to firms' betas around the Enron events and the ensuing transparency crisis. Our analysis shows that these beta shocks are associated with increased disclosure. Firms expand the number of pages of their annual 10-K filings, notably the sections containing the financial statements and footnotes. The increase in disclosure is particularly pronounced for firms that have positive cost of capital shocks and larger financing needs. We also find that firms respond with additional interim disclosures (e.g., 8-K filings) and that these disclosures are complementary to the 10-K disclosures. Finally, we show that firms' disclosure responses reduce firms' costs of capital and hence the impact of the transparency crisis.
Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome

Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimaging data is high dimensional and has few training samples. To tackle these issues, we present a sparse feedforward deep neural architecture for encoding and decoding the structural connectome of the human brain. We use a sparsely connected element-wise multiplication as the first hidden layer and a fixed transform layer as the output layer. The number of trainable parameters and the training time is significantly reduced compared to feedforward networks. We demonstrate superior performance of this architecture in encoding the structural connectome implicated in Alzheimer’s disease (AD) and Parkinson’s disease (PD) from DTI brain scans. For decoding, we propose recursive feature elimination (RFE) algorithm based on DeepLIFT, layer-wise relevance propagation (LRP), and Integrated Gradients (IG) algorithms to remove irrelevant features and thereby identify key biomarkers associated with AD and PD. We show that the proposed architecture reduces 45.1% and 47.1% of the trainable parameters compared to a feedforward DNN with an increase in accuracy by 2.6 % and 3.1% for cognitively normal (CN) vs AD and CN vs PD classification, respectively. We also show that the proposed RFE method leads to a further increase in accuracy by 2.1% and 4% for CN vs AD and CN vs PD classification, while removing approximately 90% to 95% irrelevant features. Furthermore, we argue that the biomarkers (i.e., key brain regions and connections) identified are consistent with previous literature. We show that relevancy score-based methods can yield high discriminative power and are suitable for brain decoding. We also show that the proposed approach led to a reduction in the number of trainable network parameters, an increase in classification accuracy, and a detection of brain connections and regions that were consistent with earlier studies.
Power Laws of Topography and Gravity Spectra of the Solar System Bodies

When a spacecraft visits a new planetary body, it is useful to know the properties of its shape and gravity field. This knowledge helps predict the magnitude of the perturbations in the motion of the spacecraft due to nonsphericity of a body's gravity field as well as planning for an observational campaign. It has been known for the terrestrial planets that the power spectrum of the gravity field follows a power law, also known as the Kaula rule (Kaula, 1963, https://doi.org/10.1029/RG001i004p00507; Rapp, 1989, https://doi.org/10.1111/j.1365-246X.1989.tb02031.x). A similar rule was derived for topography (Vening Meinesz, 1951). The goal of this study is to generalize the power law dependence of the gravity and topography spectra for solid surface solar system bodies across a wide range of body sizes. Traditionally, it is assumed that the gravity and topography power spectra of planets scale as g−2, where g is the surface gravity. This gravity scaling also works for the minor bodies to first order. However, we find that a better fit can be achieved using a more general scaling based on the body's radius and mean density. We outline a procedure on how to use this general scaling for topography to provide an a priori estimate for the gravity power spectrum. We show that for irregularly shaped bodies the gravity power spectrum is no longer a power law even if their topography spectrum is a power law. Such a generalization would be useful for observation planning in the future space missions to the minor bodies for which little is known.
AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices

Background Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging. Objective This study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD. Methods The Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process. Results Preliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024. Conclusions This systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently. International Registered Report Identifier (IRRID) DERR1-10.2196/52602
AI Ethics

Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.
COMPARACIÓN INTERNACIONAL DE LOS EMPLEOS DEL TIEMPO DE MUJERES Y HOMBRES

Despues de presentar una breve historia sobre los estudios realizados sobre el uso del tiempo y las comparaciones de tipo sincronico y diacronico, el autor analiza los problemas encontrados en dichas comparaciones y analiza las diferencias y similaridades en el uso del tiempo en 22 paises, a la vez que analiza las desigualdades de genero observadas en la distribucion que hombres y mujeres hacen de las 168 horas semanales entre ocio y trabajo.