Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Hyperparameter Meta-Learning with Hypergradient Distillation

Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters. Although such hyperparameters can be optimized using the existing gradient-based hyperparameter optimization (HO) methods, they suffer from the following issues. Unrolled differentiation methods do not scale well to high-dimensional hyperparameters or horizon length, Implicit Function Theorem (IFT) based methods are restrictive for online optimization, and short horizon approximations suffer from short horizon bias. In this work, we propose a novel HO method that can overcome these limitations, by approximating the second-order term with knowledge distillation. Specifically, we parameterize a single Jacobian-vector product (JVP) for each HO step and minimize the distance from the true second-order term. Our method allows online optimization and also is scalable to the hyperparameter dimension and the horizon length. We demonstrate the effectiveness of our method on two different meta-learning methods and three benchmark datasets.
Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges

Malware distribution to the victim network is commonly performed through file attachments in phishing email or downloading illegitimate files from the internet, when the victim interacts with the source of infection. To detect and prevent the malware distribution in the victim machine, the existing end device security applications may leverage sophisticated techniques such as signature-based or anomaly-based, machine learning techniques. The well-known file formats Portable Executable (PE) for Windows and Executable and Linkable Format (ELF) for Linux based operating system are used for malware analysis and the malware detection capabilities of these files has been well advanced for real time detection. But the malware payload hiding in multimedia like cover images using steganography detection has been a challenge for enterprises, as these are rarely seen and usually act as a stager in sophisticated attacks. In this article, to our knowledge, we are the first to try to address the knowledge gap between the current progress in image steganography and steganalysis academic research focusing on data hiding and the review of the stegomalware (malware payload hiding in images) targeting enterprises with cyberattacks current status. We present the stegomalware history, generation tools, file format specification description. Based on our findings, we perform the detail review of the image steganography techniques including the recent Generative Adversarial Networks (GAN) based models and the image steganalysis methods including the Deep Learning opportunities and challenges in stegomalware generation and detection are presented based on our findings.
Pedestrian Wind Factor Estimation in Complex Urban Environments

Urban planners and policy makers face the challenge of creating livable and enjoyable cities for larger populations in much denser urban conditions. While the urban microclimate holds a key role in defining the quality of urban spaces today and in the future, the integration of wind microclimate assessment in early urban design and planning processes remains a challenge due to the complexity and high computational expense of computational fluid dynamics (CFD) simulations. This work develops a data-driven workflow for real-time pedestrian wind comfort estimation in complex urban environments which may enable designers, policy makers and city residents to make informed decisions about mobility, health, and energy choices. We use a conditional generative adversarial network (cGAN) architecture to reduce the computational computation while maintaining high confidence levels and interpretability, adequate representation of urban complexity, and suitability for pedestrian comfort estimation. We demonstrate high quality wind field approximations while reducing computation time from days to seconds.
On Transportation of Mini-batches: A Hierarchical Approach

Mini-batch optimal transport (m-OT) has been successfully used in practical applications that involve probability measures with a very high number of supports. The m-OT solves several smaller optimal transport problems and then returns the average of their costs and transportation plans. Despite its scalability advantage, the m-OT does not consider the relationship between mini-batches which leads to undesirable estimation. Moreover, the m-OT does not approximate a proper metric between probability measures since the identity property is not satisfied. To address these problems, we propose a novel mini-batch scheme for optimal transport, named Batch of Mini-batches Optimal Transport (BoMb-OT), that finds the optimal coupling between mini-batches and it can be seen as an approximation to a well-defined distance on the space of probability measures. Furthermore, we show that the m-OT is a limit of the entropic regularized version of the BoMb-OT when the regularized parameter goes to infinity. Finally, we carry out experiments on various applications including deep generative models, deep domain adaptation, approximate Bayesian computation, color transfer, and gradient flow to show that the BoMb-OT can be widely applied and performs well in various applications.
F ROM SCAN TO R EAL D ATA : S YSTEMATIC G ENER - ALIZATION VIA M EANINGFUL L EARNING

Humans can systematically generalize to novel compositions of existing concepts. There have been extensive conjectures into the extent to which neural networks can do the same. Recent arguments supported by evidence on the SCAN dataset claim that neural networks are inherently ineffective in such cognitive capacity. In this paper, we revisit systematic generalization from the perspective of meaningful learning, an exceptional capability of humans to learn new concepts by connecting them with other previously known knowledge. We propose to augment a training dataset in either an inductive or deductive manner to build semantic links between new and old concepts. Our observations on SCAN suggest that, following the meaningful learning principle, modern sequence-to-sequence models, including RNNs, CNNs, and Transformers, can successfully generalize to compositions of new concepts. We further validate our findings on two real-world datasets on semantic parsing and consistent compositional generalization is also observed. Moreover, our experiments demonstrate that both prior knowledge and semantic linking play a key role to achieve systematic generalization. Meanwhile, inductive learning generally works better than deductive learning in our experiments. Finally, we provide an explanation for data augmentation techniques by concluding them into either inductive-based or deductive-based meaningful learning. We hope our findings will encourage excavating existing neural networks’ potential in systematic generalization through more advanced learning schemes.
LncRNAs specifically overexpressed in endocervical adenocarcinoma are associated with an unfavorable recurrence prognosis and the immune response

Background Cervical cancer is the fourth most common gynecological tumor in terms of both the incidence and mortality of females worldwide. Cervical squamous cell carcinoma (CSCC) accounts for 70–80% of cervical cancers, and endocervical adenocarcinoma (EAC) accounts for 20–25%. Unlike CSCC, EAC has worse clinical outcomes and prognosis. In this study, we explored the relationship between various types of long noncoding RNAs (lncRNAs) and pathological types of cervical cancer. Methods RNA sequencing (RNA-Seq) and clinical data from The Cancer Genome Atlas (TCGA) were used in this study. A single-sample gene set enrichment analysis (ssGSEA) and the ESTIMATE package were used to assess lncRNA activity and immune responses, respectively. RT-qPCR was performed to verify our findings. Results We explored the relationship between various types of lncRNAs and pathological types of cervical cancer. A series of long intergenic noncoding RNAs (lincRNAs) and antisense RNAs, which are the major types of lncRNAs, were identified to be specifically expressed in EAC and associated with a poor recurrence prognosis in patients with cervical cancer, suggesting that they might serve as independent prognostic markers of recurrence in patients with cervical cancer. RT-qPCR was performed to verify the 10 EAC-specific lncRNAs in cervical cancer samples we collected. Furthermore, the overexpression of these lncRNAs was positively correlated with EAC pathology levels but negatively correlated with immune responses in the microenvironment of cervical cancer. Conclusions These lncRNAs potentially represent new biomarkers for the prediction of the recurrence prognosis and help obtain deeper insights into potential immunotherapeutic approaches for treating cervical cancer.
The mediating role of COVID-19-related burden in the association between adverse childhood experiences and emotional exhaustion: results of the egePan – VOICE study

ABSTRACT Background: Adverse childhood experiences (ACEs) increase the risk for mental health problems. However, there is a lack of data targeting the role of ACEs for one of the most prevalent mental health problems in health-care professionals: burnout. Objective: We aimed to assess the relationship between ACEs and the core burnout dimension ‘emotional exhaustion’ (EE). As health-care professionals have been facing particular challenges during the COVID-19 pandemic, we furthermore aimed to assess the role of COVID-19 associated burden in the interplay between ACEs and EE. Methods: During the first lockdown in Germany, a total of 2500 medical healthcare professionals were questioned in a cross-sectional online survey. Questions targeted, among others, sociodemographics, ACEs, COVID-19-associated problems (e.g. increase of workload, worries about relatives and patients) and emotional exhaustion, measured by the respective dimension of the Maslach Burnout Inventory (MBI). Results: In German health-care professionals, ACEs were associated with a higher EE score. The number of experienced ACEs was associated with the majority of assessed COVID-19-associated problems. An increasing number of ACEs predicted higher EE scores, controlling for gender. The association between ACEs and EE was mediated significantly by COVID-19-associated problems. These included maladaptive coping strategies such as increased smoking, drinking and use of antidepressants/tranquilizers, feeling less protected by measures of the employee or the state, a greater feeling of being burdened by COVID-19-associated problems and greater exhaustion and sleep problems. Conclusion: Our findings suggest ACEs as significant risk factor for EE in German health-care professionals. The current pandemic means a significant burden that further pronounces this risk. HIGHLIGHTS Adverse childhood experiences (ACEs) predicted higher scores of emotional exhaustion (EE) in German healthcare professionals during the COVID-19 pandemic. COVID-19 associated problems mediated the association between ACEs and EE. COVID-19 and ACEs add to the risk of EE in healthcare professionals.
HIF-1α Pulmonary Phenotype Wide Association Study Unveils a Link to Inflammatory Airway Conditions

Despite experimental data linking HIF-1α dysfunction to inflammatory airway conditions, the effect of single nucleotide polymorphisms within the HIF1A gene on these conditions remains poorly understood. In the current study, we complete a phenotype wide association study to assess the link between SNPs with known disease associations and respiratory phenotypes. We report two SNPs of the HIF1A gene, the intronic rs79865957 and the missense rs41508050. In these positions the A and the T allele are significantly associated with allergic rhinitis and acute bronchitis and bronchiolitis, respectively. These findings further support the role of HIF-1α in inflammatory pulmonary conditions and may serve as a basis to refine our understanding of other HIF-1α associated phenotypes.
Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Background and Objectives This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain’s genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer’s disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. Methods We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. Results It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. Conclusions Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.
Goal Missed, Self Hit: Goal-Setting, Goal-Failure, and Their Affective, Motivational, and Behavioral Consequences

Setting high and specific goals is one of the best-established management tools to increase performance and motivation. However, in recent years, potential downsides of goal-setting are being discussed. One possible downside is the high risk of failing the goal. In an approach to integrate research on the consequences of goal-failure and the basic assumptions of goal-setting theory, we investigated whether failure of a high and specific goal has detrimental effects on a person’s affect, self-esteem, and motivation. In Experiment 1, 185 participants received fictitious feedback about attaining or failing an assigned high and specific goal. In Experiment 2 with 86 participants, we manipulated goal-failure through task-difficulty and we included task choice as a behavioral measure of motivation. In both experiments, participants who failed the high and specific goal showed a decrease in affect, self-esteem, and motivation compared to participants who attained that goal. Results indicate that failing a high and specific goal can be damaging for self-related factors that may be crucial for organizational long-term outcomes. We advise organizations to consider potential undesirable effects when using goal-setting interventions.
LitR directly upregulates autoinducer synthesis and luminescence in Aliivibrio logei

LitR is a master-regulator of transcription in the ainS/R and luxS/PQ quorum sensing (QS) systems of bacteria from Vibrio and Aliivibrio genera. Here, we for the first time directly investigated the influence of LitR on gene expression in the luxI/R QS system of psychrophilic bacteria Aliivibrio logei. Investigated promoters were fused with Photorhabdus luminescens luxCDABE reporter genes cassette in a heterological system of Escherichia coli cells, litR A. logei was introduced into the cells under control of Plac promoter. LitR has been shown to upregulate genes of autoinducer synthase (luxI), luciferase and reductase (luxCDABE), and this effect doesn’t depend on presence of luxR gene. To a much lesser degree, LitR induces luxR1, but not the luxR2 — the main luxI/R regulator. Enhanced litR expression leads to an increase in a LuxI-autoinducer synthesis and a subsequent LuxR-mediated activation of the luxI/R QS system. Effect of LitR on luxI transcription depends on lux-box sequence in luxI promoter even in absence of luxR (lux-box is binding site of LuxR). The last finding indicates a direct interaction of LitR with the promoter in the lux-box region. Investigation of the effect of LitR A. logei on luxI/R QS systems of mesophilic Aliivibrio fischeri and psychrophilic Aliivibrio salmonicida showed direct luxR-independent upregulation of luxI and luxCDABE genes. To a lesser degree, it induces luxR A. fischeri and luxR1 A. salmonicida. Therefore, we assume that the main role of LitR in cross-interaction of these three QS systems is stimulating the expression of luxI.
Assessment of knowledge, attitude and practice toward COVID-19 and associated factors among health care workers in Silte Zone, Southern Ethiopia

Introduction COVID-19 is a pandemic respiratory disease caused by the highly contagious novel coronavirus (SARS-CoV 2). The disease is now quickly spreading around the world, resulting in ongoing coronavirus pandemic. Healthcare workers are more susceptible to COVID-19 infection than the general population due to frequent contact with infected individuals. Objective This study was aimed to assess knowledge, attitude and practice towards COVID-19 and associated factors among health care workers. Methods Facility-based cross-sectional study design was conducted among health care workers in Silte Zone. A total of 379 health professionals were selected using multistage stratified sampling technique. Self-administered questionnaire was used to collect data. Binary logistic regression model was used to see association between outcome and independent variables. Results This study found 74.9%, 84.2% and 68.9% prevalence of adequate knowledge, positive attitude and good practice respectively. Working in comprehensive specialized hospital (AOR = 4.46, 95% CI = 1.46–13.62). having MSC degree (AOR = 10.26, 95% CI = 2.27–46.44), and training on COVID-19 (AOR = 6.59, 95% CI = 2.97–14.65) were strongly associated with knowledge of health care workers. On the other hand, older age (AOR = 3.35, 95% CI = 1.07–10.50), training on COVID-19 (AOR = 3.73, 95% CI = 1.82–7.63), Work experience (AOR = 3.78, 95% CI = 1.46–9.80) and Knowledge (AOR = 5.45, 95% CI = 2.60–11.43) were significantly associated with attitude, whereas source of information from friends or colleagues (AOR = 3.13, 95% CI = 1.28–7.66), working in primary hospital (AOR = 0.36, 95% CI = 0.21–0.620) and having good knowledge (AOR = 1.80, 95% CI = 1.03–3.14) were strongly associated with good practice of health care workers. Conclusion This study found majority of health care workers had good level of knowledge and positive attitude toward COVID-19, but lower proportion of health care workers practices sufficiently in the study area. Type of health facilities, level of education, training on COVID-19, work experience, type of source of information were significantly associated with knowledge, attitude and practice of health care workers. Stakeholders need to focus on interventions that increase preventive practices of health care workers.
The complete mitochondrial genome of Eastern Curlew Numenius madagascariensis (Charadriiformes, Scolopacidae)

Abstract The complete mitochondrial genome of Numenius madagascariensis Linnaeus, 1766 was sequenced in this study. The circular mitogenomes was 17,147 bp in length, containing 13 protein-coding genes, two ribosomal RNAs (12S rRNA and 16S rRNA), 22 transfer RNA genes, and a D-loop region. The overall nucleotide composition was A: 31.0%, T: 25.6%, C: 29.5%, and G: 13.9%. Twenty-eight genes were encoded on the heavy strand, while the remaining nine genes were encoded on the light strand. The common start codon was ATG, and three stop codons and an incomplete stop codon (T–) were used in PCGs. This study improves our comprehension of the mitogenomic characteristics and its phylogenetic relationships within Scolopacidae.
Relationships Between User Knowledge Sharing in Virtual Community with Community Loyalty and Satisfaction

Background Ubiquitous social media usage makes virtual communities important platforms for people to exchange knowledge and information. The long-term sustainable operation of the virtual community depends on the scale and activity of users in the virtual community. However, in existing virtual communities, the number of easily lost users far exceeds the number of loyal users with the high frequency of knowledge sharing, thus there is a community management need to improve the frequency of knowledge sharing and user loyalty. Methods Based on the social capital theory and social exchange theory, this paper constructs a model of the influence of knowledge sharing on the loyalty and satisfaction of users in a virtual community and undertakes an empirical analysis based on survey data from 533 members of virtual communities. Results The results showed that trust, reciprocity, and reputation positively affected the quantity and quality of knowledge sharing, while social connectedness only positively affected the quantity of knowledge sharing; the quantity and quality of knowledge sharing positively affected community loyalty and community satisfaction. Conclusion This study takes the knowledge sharing of virtual community users as a new starting point to cultivate loyal consumers. It has important theoretical and practical value to improve the satisfaction of community users, cultivate loyal community users, and promote the knowledge sharing of members.
Systems science for developing policy to improve physical activity, the Caribbean

Abstract The World Health Organization (WHO) Global Action Plan on Physical Activity recommends adopting a systems approach to implementing and tailoring actions according to local contexts. We held group model-building workshops with key stakeholders in the Caribbean region to develop a causal loop diagram to describe the system driving the increasing physical inactivity in the region and envision the most effective ways of intervening in that system to encourage and promote physical activity. We used the causal loop diagram to inform how the WHO Global Action Plan on Physical Activity might be adapted to a local context. Although the WHO recommendations aligned well with our causal loop diagram, the diagram also illustrates the importance of local context in determining how interventions should be coordinated and implemented. Some interventions included creating safe physical activity spaces for both sexes, tackling negative attitudes to physical activity in certain contexts, including in schools and workplaces, and improving infrastructure for active transport. The causal loop diagram may also help understand how policies may be undermined or supported by key actors or where policies should be coordinated. We demonstrate how, in a region with a high level of physical inactivity and low resources, applying systems thinking with relevant stakeholders can help the targeted adaptation of global recommendations to local contexts.