Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentinel-5P TROPOMI NO<sub>2</sub> retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data

Abstract. Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 v1.2–v1.3 data, however, have revealed that TROPOMI's tropospheric vertical column densities (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are the following. (1) The NO2-v2.2 data are based on version-2 level-1b (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase in the NO2 slant columns of 3 % to 4 %, most of which ends up as a small increase in the stratospheric columns. (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which led to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage. (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase in the tropospheric NO2 columns over polluted scenes of order 15 %. (4) An outlier removal was implemented in the spectral fit, which increases the number of good-quality retrievals over the South Atlantic Anomaly region and over bright clouds where saturation may occur. (5) Snow/ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid and high latitudes in wintertime. This has brought these tropospheric NO2 closer to Ozone Monitoring Instrument (OMI) observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complex, in particular for the tropospheric and total columns.
The Effect of Transformation Leadership Style and Compensation on Organizational Citizenship Behavior (OCB) and Their Implications on the Performance of Police Apparatus at the Ditreskrimsus Polda Aceh

The purpose of this study was to examine the influence of transformation leadership style and compensation on organizational citizenship behavior (OCB) and their implications on the performance of the police apparatus at the Directorate of Special Criminal Investigation of the Aceh Regional Police (Ditreskrimsus Polda Aceh), Indonesia. This research was conducted on the Ditreskrimsus Polda Aceh involving all 129 police officers as respondents. Results found that Transformation Leadership Affects OCB, Transformation Leadership Affects Performance, Compensation Affects OCB, Compensation Affects Performance, OCB Affects Performance, Transformation Leadership Affects OCB through OCB, and Transformation Leadership Affects Performance through OCB. This explains that all hypotheses have a significant effect. OCB was also found as a variable that was able to mediate in the model, partially. Therefore, these results prove that the performance improvement model at the Aceh Police Ditreskrimsus is a function of strengthening transformation leadership, compensation, and OCB.
Calculation of an Indicator for Early Death Using Atomic Bomb Survivors’ Data

Background: A comprehensive, traceable, and easy-to-understand radiation risk indicator is desired for radiological protection. The early-onset hypothesis could be used for this purpose.Materials and Methods: An indicator for early death (IED) was developed and calculated using the epidemiological dataset from the 14th Report of the Life Span Study (LSS) of Hiroshima and Nagasaki. By clarifying the calculation process, IED for all-cause mortality was estimated. In addition, the characteristics of IED for solid cancer mortality and cardiovascular mortality as well as those of men and women, and their dependence on age at exposure were investigated for detailed analysis.Results and Discussion: The IED for all-cause mortality was estimated to be approximately 4 years for an acute radiation exposure of 1 Gy regardless of the fitting dose range. The cumulative death rate for all solid cancers also indicated the early-death tendency (approximately 7–10 years at 1 Gy). Although, there is a slight difference in the characteristics of the risk obtained from the LSS study and this study, it is considered that the IED in a unit of years can also be used to show the overall picture of risk due to radiation exposure.Conclusion: We developed and calculated the indicator for early death, IED, for the cumulative mortality rate of all causes of death, all solid cancers, and circulatory diseases. The quantitative values of IED were estimated to be 4 years for all causes of death, 7–10 years for all solid cancers. IED has an advantage for intuitively understanding the meaning of radiation risk since it can be obtained by a simple and traceable method.
Do patients with major depressive disorder and generalized anxiety disorder experience more somatosensorial amplification, hypochondriasis, and fear of COVID-19?

Objective: Mental illnesses are important determinants in response to the pandemic. However, there are only a limited number of studies investigating the effects of COVID-19 in individuals with mental illnesses. We aimed to reveal the fear of COVID-19, somatosensorial amplification levels, anxiety, depression, and stress levels of patients with major depressive disorder (MDD) and generalized anxiety disorder (GAD). Method: We evaluated 500 participants in three groups. This study enrolled 137 patients with MDD, 140 patients with GAD, and 223 healthy controls. Participants were evaluated with the Whiteley Index-7, Somatosensory Amplification Scale (SSAS), Depression Anxiety Stress Scale-21 (DASS-21), and The Fear of COVID-19 Scale. Results: There was a statistically significant difference between the groups' SSAS, DASS-21, Whiteley Index, and The Fear of COVID-19 Scale scores. In the post hoc analysis, Whiteley Index and DASS-21 subscale scores were higher in patient groups than in the control group (p<0.001). Regression analysis revealed that Whiteley Index, COVID-19 Fear, and DASS-S had a significant effect on somatosensorial amplification. Conclusion: The findings of our study show that MDD and GAD patients are susceptible to normal bodily sensations. COVID-19 fear and hypochondriasis appear to be essential determinants of somatosensorial amplification. Accordingly, there is a need to develop effective psychological interventions focused on these factors in MDD and GAD.
Dentists’ Competence and Knowledge on Domestic Violence and How to Improve It—A Review

Domestic violence (DV) is an important public health topic with a high prevalence in society. Dentists are also frontline responders to DV, as they not only treat victims of DV with dental injuries, but they can also screen for the presence of DV because they see patients for regular check-ups. Using the WHO definition, which describes domestic violence as intimate partner violence, 17 papers could be included in our analyses. The results of this review clearly indicated that although dentists, as members of the health care sector, are important frontline responders to DV, they are neither trained adequately at medical school nor do most feel competent enough to ask victims about DV or support them as needed. DV is often not taught at dentistry schools at all. The aims of this review were to provide an overview of existing literature on dentists’ knowledge and beliefs regarding DV, whether and how DV is taught in medical education and to give recommendations on how to improve the education of dentists on this topic. Based on our findings, we recommend that DV education should be mandatory at dentistry schools and in further training for dentists with a focus on communication with victims, how DV can be identified and how to support victims well.
Delivering language intervention at scale: promises and pitfalls

Background: There is now substantial evidence that language interventions delivered to small groups can be effective for improving language skills and hence strengthen-ing the foundation for formal schooling. However, there are remaining challenges when delivering such interventions in naturalistic environments at scale. Method: We re ﬂ ect on three randomised trials designed to evaluate the impact of an early years language programme, prior to the implementation of a large effectiveness trial, delivered in partnership with speech and language professionals. We consider ﬁ ndings within a framework from implementation science. Results: We found that, in contrast to policy-led interventions for reading and mathematics, language interventions are not prioritised in mainstream settings. Aside from this, other obstacles to delivery were the time taken to prepare and to timetable original sessions, lack of communication about the requirements of delivery and the need for language screening. Crucial to success was the support from the class teacher of teaching assistants delivering the intervention. However, feedback was largely positive from most stakeholders, and the intervention was found to have a positive impact on children ’ s language with preliminary evidence for effects on behaviour and on reading comprehension. Conclusions: While many educators recognise the importance of language for communication, the bene ﬁ ts of oral language interventions are only recently becoming prioritised by policy-makers. We propose that challenges to successful delivery and adoption of evidence-based language interventions in mainstream settings can be remedied through better communication with stakeholders and collaboration between researchers and professional colleagues including senior leaders, teachers, teaching assistants, speech and language therapists and psychologists. It is imperative to take account of issues of implementation when designing an intervention and to do this successfully is a multidisciplinary enterprise.
Life Conditions as Mediators of Welfare State Effect on Mental Wellbeing among Oldest Old in Europe

Background: Mental wellbeing is formed by our daily environments, which are, in turn, influenced by public policies, such as the welfare state. This paper looks at how different aspects of life conditions may mediate the welfare state effect on mental wellbeing in oldest old age. Methods: Data were extracted from Round 6 of the European Social Survey (2012). The dataset comprised of 2058 people aged 80 years and older from 24 countries. Mediation analyses determined possible links between the welfare state, including eleven intervening variables representing life conditions and five mental wellbeing dimensions. Results: Our study confirms that the higher the level of welfare state, the better mental wellbeing, irrespective of dimension. Although several life conditions were found to mediate the welfare state effect on mental wellbeing, subjective general health, coping with income and place in society were the most important intervening variables. Conclusions: All three variables centre around supporting autonomy in the oldest old age. By teasing out how the welfare state influences mental wellbeing in the oldest old, we can better understand the many drivers of wellbeing and enable evidence informed age-friendly policy making.
The Portrait of Cyberchondria—A Cross-Sectional Online Study on Factors Related to Health Anxiety and Cyberchondria in Polish Population during SARS-CoV-2 Pandemic

The SARS-CoV-2 pandemic has served as a magnifying glass for cyberchondria, while the internet emerged as one of the main sources of medical information and support. The core ambition of this study was to estimate the level of cyberchondria and describe the socio-demographic, clinical and pandemic-related factors affecting its severity amid the SARS-CoV-2 pandemic. A cross-sectional study was performed between 16 May 2020 and 29 December 2020 in Poland within a sample of 538 adult internet users. The online survey tool included a Polish adaptation of the Cyberchondria Severity Scale (CSS-PL) and the Short Health Anxiety Inventory (SHAI), complemented with a set of questions covering sociodemographic, clinical and pandemic-related factors. Participants were clustered according to severity of health anxiety and cyberchondria symptoms. The performed binary logistic regression indicated professional inactivity, having a chronic mental disorder and subjectively limited access to healthcare due to COVID-19 to be key determinants of severe health anxiety and cyberchondria. Cyberchondria might be a remarkable public health issue as large proportion of respondents from the analyzed sample population of internet users met the criteria for severe symptoms. Key determinants of intense cyberchondria corresponded with employment stability, mental resilience and accessibility of healthcare services, which could be greatly challenged amid the pandemic.
Egg Characteristics of Pelung Chickens in Four Different Areas in West Java, Indonesia: technical inputs for conservation

Egg characteristics such as egg size and egg quality affect the hatchability rate of chicken eggs. The study objective was to determine egg characteristics of Pelung chickens that are endangered chickens of Indonesia. A total of 116 eggs from Garut, Bandung, Cianjur and Sukabumi Regencies, West Java Province, Indonesia was selected using a purposive technique. The observed parameters were egg weight, egg specific gravity, and thickness of an eggshell. Data were analyzed using the nonparametric Kruskal-Wallis test followed by the post hoc Mann-Whitney U test. The former was used to determine data differences among research areas, while the latter was applied to compare data differences between the two research areas. The rank Spearman analysis was used to determine the correlation between observed parameters. The study showed that there were significant differences (p > 0.05) among research areas for all observed parameters. Egg weight and thickness of eggshell in the Sukabumi Regency were higher than other regencies, while egg specific gravity in this regency was the highest (p > 0.05). There was a significant positive correlation (p > 0.05) between observed parameters. The findings indicated variation in the rearing management system and the genetic of Pelung chickens in research areas.
A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents

With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human. In the case of humans and artificial AI agents operating in the same environment, we note the significance of comprehension and response to the actions or capabilities of a human from an agent's perspective, as well as the possibility to delegate decisions either to humans or to agents, depending on who is deemed more suitable at a certain point in time. Such capabilities will ensure an improved responsiveness and utility of the entire human-AI system. To that end, we investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior, and associated performance with respect to a certain goal, is used to delegate control between humans and AI agents through the use of an intermediary entity. As we demonstrate, this allows overcoming potential shortcomings of either humans or agents in the pursuit of a goal.
Influence of Color Spaces for Deep Learning Image Colorization

Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing colorization methods rely on different color spaces: RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question:"Is it crucial to correctly choose the right color space in deep-learning based colorization? ". First, we briefly summarize the literature and, in particular, deep learning-based methods. We then compare the results obtained with the same deep neural network architecture with RGB, YUV and Lab color spaces. Qualitative and quantitative analysis do not conclude similarly on which color space is better. We then show the importance of carefully designing the architecture and evaluation protocols depending on the types of images that are being processed and their specificities: strong/small contours, few/many objects, recent/archive images.
Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures

We show direct and conceptually simple reductions between the classical learning with errors (LWE) problem and its continuous analog, CLWE (Bruna, Regev, Song and Tang, STOC 2021). This allows us to bring to bear the powerful machinery of LWE-based cryptography to the applications of CLWE. For example, we obtain the hardness of CLWE under the classical worst-case hardness of the gap shortest vector problem. Previously, this was known only under quantum worst-case hardness of lattice problems. More broadly, with our reductions between the two problems, any future developments to LWE will also apply to CLWE and its downstream applications. As a concrete application, we show an improved hardness result for density estimation for mixtures of Gaussians. In this computational problem, given sample access to a mixture of Gaussians, the goal is to output a function that estimates the density function of the mixture. Under the (plausible and widely believed) exponential hardness of the classical LWE problem, we show that Gaussian mixture density estimation in $\mathbb{R}^{n}$ with roughly $\log n$ Gaussian components given poly $(n)$ samples requires time quasi-polynomial in n. Under the (conservative) polynomial hardness of LWE, we show hardness of density estimation for $n^{\epsilon}$ Gaussians for any constant $\epsilon>0$, which improves on Bruna, Regev, Song and Tang (STOC 2021), who show hardness for at least $\sqrt{n}$ Gaussians under polynomial (quantum) hardness assumptions. Our key technical tool is a reduction from classical LWE to LWE with k-sparse secrets where the multiplicative increase in the noise is only $O(\sqrt{k})$, independent of the ambient dimension n.
Zero-shot Blind Image Denoising via Implicit Neural Representations

Recent denoising algorithms based on the"blind-spot"strategy show impressive blind image denoising performances, without utilizing any external dataset. While the methods excel in recovering highly contaminated images, we observe that such algorithms are often less effective under a low-noise or real noise regime. To address this gap, we propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs), based on our two findings: (1) INR tends to fit the low-frequency clean image signal faster than the high-frequency noise, and (2) INR layers that are closer to the output play more critical roles in fitting higher-frequency parts. Building on these observations, we propose a denoising algorithm that maximizes the innate denoising capability of INRs by penalizing the growth of deeper layer weights. We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
QuLog: Data-Driven Approach for Log Instruction Quality Assessment

In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messages: the developer provides hints to the (unknown) operator, where the cause of an occurred issue is, and vice versa, the operator can report bugs during operation. To fulfil this purpose, developers write log instructions that are structured text commonly composed of a log level (e.g., “info”, “error”), static text (“IP {} cannot be reached”), and dynamic variables (e.g. IP {}). However, opposed to well-adopted coding practices, there are no widely adopted guidelines on how to write log instructions with good quality properties. For example, a developer may assign a high log level (e.g., “error”) for a trivial event that can confuse the operator and increase maintenance costs. Or the static text can be insufficient to hint at a specific issue. In this paper, we address the problem of log quality assessment and provide the first step towards its automation. We start with an in-depth analysis of quality log instruction properties in nine software systems and identify two quality properties: 1) correct log level assignment assessing the correctness of the log level, and 2) sufficient linguistic structure assessing the minimal richness of the static text necessary for verbose event description. Based on these findings, we developed a data-driven approach that adapts deep learning methods for each of the two properties. An extensive evaluation on large-scale open-source systems shows that our approach correctly assesses log level assignments with an accuracy of 0.88, and the sufficient linguistic structure with an F1 score of 0.99, outperforming the baselines. Our study highlights the potential of the data-driven methods in assessing log instructions quality.
A simulation-free seismic survey design by maximizing the spectral gap

Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorithms. These technologies are designed to record as little data as possible in the field, while providing accurate wavefield reconstruction in the areas of the survey that are not recorded. This is achieved by designing randomized subsampling masks that allow for accurate wavefield reconstruction via matrix completion methods. Motivated by these recent results, we propose a simulation-free seismic survey design that aims at improving the quality of a given randomized subsampling using a simulated annealing algorithm that iteratively increases the spectral gap of the subsampling mask, a property recently linked to the quality of the reconstruction. We demonstrate that our proposed method improves the data reconstruction quality for a fixed subsampling rate on a realistic synthetic dataset.