Occupancy detection is one of the key elements in improving the energy performance of buildings. Due to their nature, occupancy detection models could be trained on old building data and adapted to new buildings for faster onboarding. We explore and analyse the transfer learning framework applied to occupancy detection. We use a combination of Long-short Term Memory neural network and convolutional neural network architectures and test the transfer learning framework on three datasets. The results show that the transferred models perform better than non-transferred models in almost all metric and dataset combinations.

Interest in research of the navigation problem for Unmanned Aerial Vehicles (UAVs) is on the rise. The aim of such a task is reaching a goal position while avoiding obstacles on the way. In this paper, we propose a different approach to Deep Reinforcement Learning (DRL) of navigation decision making process by introducing the reward function based of Artificial Potential Fields (APF). The validation of the proposed approach is performed by the comparison to the state-of-the-art approach. In terms of training performance, success rate, memory usage and the inference time, our approach, though sparser in terms of perceived information about the environment, yield better results.

Implementation of credit scoring models is a demanding task and crucial for risk management. Wrong decisions can significantly affect revenue, increase costs, and can lead to bankruptcy. Together with the improvement of machine learning algorithms over time, credit models based on novel algorithms have also improved and evolved. In this work, novel deep neural architectures, Stacked LSTM, and Stacked BiLSTM combined with SMOTE oversampling technique for the imbalanced dataset were developed and analyzed. The reason for the lack of publications that utilize Stacked LSTM-based models in credit scoring lies exactly in the fact that the deep learning algorithm is tailored to predict the next value of the time series, and credit scoring is a classification problem. The challenge and novelty of this approach involved the necessary adaptation of the credit scoring dataset to suit the time sequence nature of LSTM-based models. This was particularly crucial as, in practical credit scoring datasets, instances are not correlated nor time dependent. Moreover, the application of SMOTE to the newly constructed three-dimensional array served as an additional refinement step. The results show that techniques and novel approaches used in this study improved the performance of credit score prediction.

In this paper, error performance analysis for M-ary phase shift keying (PSK) system in the inverse gamma two-ray with diffuse power (IG/TWDP) composite fading channel is presented. Using Fourier series approach, the average symbol error probability (ASEP) expression is derived in terms of hypergeometric functions, which can be evaluated using standard software packages. Derived expression is used to investigate degradation of error performance cased by shadowing, in regard to those obtained by considering only the TWDP multipath fading. All obtained results are verified by Monte-Carlo simulation.

To maximize the impact of precision medicine approaches, it is critical to accurately identify genetic variants in cancer-associated genes with functional consequences. Yet, our knowledge of rare variants conferring clinically relevant phenotypes and the mechanisms through which they act remains highly limited. A tumor suppressor gene exemplifying the challenge of variant interpretation is VHL. VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here, we optimize and apply Saturation Genome Editing (SGE) to assay nearly all possible single nucleotide variants (SNVs) across VHL’s coding sequence. To delineate mechanisms, we quantify mRNA dosage effects over time and compare effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types, and reveal novel mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered in both germline testing and tumor profiling and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype-phenotype relationships across complete genes.

Road infrastructure management is an extremely important task of traffic engineering. For the purpose of efficient management, it is necessary to determine the efficiency of the traffic flow through PAE 85%, AADT and other exploitation parameters on the one hand, and the number of different types of traffic accidents on the other. In this paper, a novel TrIT2F (trapezoidal interval type-2 fuzzy) PIPRECIA (pivot pairwise relative criteria importance assessment)-TrIT2F MARCOS (measurement of alternatives and ranking according to compromise solution) was developed in order to, in a defined set of 14 road segments, identify the most efficient one for data related to light goods vehicles. Through this the aims and contributions of the study can be manifested. The evaluation was carried out on the basis of seven criteria with weights obtained using the TrIT2F PIPRECIA, while the final results were presented through the TrIT2F MARCOS method. To average part of the input data, the Dombi and Bonferroni operators have been applied. The final results of the applied TrIT2F PIPRECIA-TrIT2F MARCOS model show the following ranking of road segments, according to which Vrhovi–Šešlije M-I-103 with a gradient of −1.00 represents the best solution: A5 > A8 > A2 > A1 > A4 > A3 > A6 > A12 > A13 = A14 > A11 > A7 > A9 > A10. In addition, the validation of the obtained results was conducted by changing the values of the four most important criteria and changing the size of the decision matrix. Tests have shown great stability of the developed TrIT2F PIPRECIA-TrIT2F MARCOS model.

AIM To critically evaluate the reporting quality of a random sample of animal studies within the field of endodontics against the Preferred Reporting Items for Animal Studies in Endodontics (PRIASE) 2021 checklist and to investigate the association between the quality of reporting and several characteristics of the selected studies. METHODOLOGY Fifty animal studies related to endodontics were randomly selected from the PubMed database with publication dates from January 2017 to December 2021. For each study, a score of '1' was given when the item of the PRIASE 2021 checklist was fully reported, whereas a score of '0' was given when an item was not reported; when the item was inadequately or partially reported, a score of '0.5' was given. Based on the overall scores allocated to each manuscript, they were allocated into three categories of reporting quality: low, moderate, and high. Associations between study characteristics and reporting quality scores were also analysed. Descriptive statistics, and Fisher's exact tests were used to describe the data and determine associations. The probability value of 0.05 was selected as the level of statistical significance. RESULTS Based on the overall scores, four (8%) and 46 (92%) of the animal studies evaluated were categorised as 'High' and 'Moderate' reporting quality, respectively. A number of items were adequately reported in all studies related to background (Item 4a), relevance of methods/results (7a) and interpretation of images (11e), whereas only one item related to changes in protocol (6d) was not reported in any. No associations were confirmed between reporting quality scores and number of authors, origin of the corresponding author, journal of publication (endodontic specialty versus non- specialty), impact factor or year of publication. CONCLUSIONS Animal studies published in the specialty of endodontics were mostly of 'moderate' quality in terms of the quality of reporting. Adherence to the PRIASE 2021 guidelines will enhance the reporting of animal studies in the expectation that all future publications will be high-quality.

Leveraging second-order information about the loss at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to small-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via a novel and efficient error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression \emph{before} it is fed into the preconditioner, feeding the compression error back into future iterations. Experiments on deep neural networks show that this approach can compress full-matrix preconditioners to up to 99\% sparsity without accuracy loss, effectively removing the memory overhead of full-matrix preconditioners such as GGT and M-FAC. Our code is available at \url{https://github.com/IST-DASLab/EFCP}.

The automotive industry requires ultra-reliable low-latency connectivity for its vehicles, and as such, it is one of the promising customers of 5G ecosystems and their orchestrated network infrastructure. In particular, Multi-Access Edge Computing (MEC) provides moving vehicles with localized low-latency access to service instances. However, given the mobility of vehicles, and various resource demand patterns at the distributed MEC nodes, challenges such as fast reconfiguration of the distributed deployment according to mobility pattern and associated service and resource demand need to be mitigated. In this paper, we present the orchestrated edges platform, which is a solution for orchestrating distributed edges in complex cross-border network environments, tailored to Connected, Cooperative, and Automated Mobility (CCAM) use cases within a 5G ecosystem. The proposed solution enables collaboration between orchestrators that belong to different tiers, and various federated edge domains, with the goal to enable service continuity for vehicles traversing cross-border corridors. The paper presents the prototype that we built for the H2020 5G-CARMEN trials, including the validation of the orchestration design choices, followed by the promising results that span both orchestration (orchestration latency) and application performance-related metrics (client-to-edge and edge-to-edge service data plane latencies).

In this paper, we report on teachers’ and principals’ shared perceptions regarding beliefs, rules, trust, and encouragement of new initiatives. Collectively, these are aspects of leadership for learning (LFL) describing an overall shared climate in schools. We demonstrate how these perceptions on school climate differ across teachers and principals within and across countries. Moreover, we report how different perceptions of school climate are associated with leadership style. We analyze data from 37 countries that participated in the last cycle of the Teaching and Learning International Survey (TALIS) in 2018. To build the measurement model, we employ multigroup multilevel confirmatory factor analysis, whereas multivariate linear regression is used to inspect associations. Overall, principals and teachers differ in their views of school climate. In the majority of the countries, principals report stronger school climate than teachers. We further confirm these perceptual differences between teachers and principals by separately studying the relationships between teacher perceived school climate and principal perceived school climate with relevant leadership variables. In the entire sample, we find that principals’ perceptions of school climate are more strongly and consistently associated with leadership in schools. This relationship is particularly stable for distributed leadership. In the entire sample, leadership styles are weakly positively correlated with teacher perceptions of school climate too; however, this association is less pronounced and less stable within individual countries. The analyses conducted within countries revealed that the distributed leadership rather than instructional leadership shapes teachers’ perceptions of school climate. More discussion is presented on the need for alignment between different perceptions of school climate and leadership styles in the overall organizational quality.

ABSTRACT High-risk Human Papillomaviruses (HPVs) and Epstein – Barr virus (EBV) are present and involved in several types of human carcinomas, including cervical and, head and neck cancers. Nevertheless, their presence and association in the pathogenesis of colorectal cancer is still nascent. The current study explored the association between the high-risk HPVs and EBV and tumor phenotype in colorectal cancers (CRCs) in the Qatari population. We found that high-risk HPVs and EBV are present in 69/100 and 21/100 cases, respectively. Additionally, 17% of the cases showed a copresence of high-risk HPVs and EBV, with a significant correlation only between the HPV45 subtype and EBV (p = .004). While the copresence did not significantly associate with clinicopathological characteristics, we identified that coinfection with more than two subtypes of HPV is a strong predictor of advanced stage CRC, and the confounding effect of the copresence of EBV in such cases strengthens this association. Our results indicate that high-risk HPVs and EBV can co-present in human CRCs in the Qatari population where they could plausibly play a specific role in human colorectal carcinogenesis. However, future studies are essential to confirm their copresence and synergistic role in developing CRCs.

Financial literacy is a critical life skill that is essential for achieving financial security and individual well-being, economic growth and overall sustainable development. Based on the analysis of research on financial literacy, we aim to provide a balance sheet of current research and a starting point for future research with the focus on identifying significant predictors of financial literacy, as well as variables that are affected by financial literacy. The main methods of our research are a systematic literature review, and bibliometric and bibliographical analysis. We establish a chronological path of the financial literacy topic in the scientific research. Based on the analysis of the most cited articles, we develop a comprehensive conceptual framework for mapping financial literacy. We identified a large number of predictors of financial literacy starting with education, gender, age, knowledge, etc. Financial literacy also affects variables such as retirement planning, financial inclusion, return on wealth, risk diversification, etc. We discuss in detail the main trends and topics in financial literacy research by involving financial literacy of the youth, financial literacy from the gender perspective, financial inclusion, retirement planning, digital finance and digital financial literacy. Our research can help policymakers in their pursuit of improving the levels of individual financial literacy by enabling individuals to make better financial decisions, avoid financial stress and achieve their financial goals. It can also help governments in their efforts in achieving sustainable development goals (SDGs).

Leveraging second-order information at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to medium-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via an efficient and simple-to-implement error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression \emph{before} it is fed into the preconditioner, feeding the compression error back into future iterations. Extensive experiments on deep neural networks for vision show that this approach can compress full-matrix preconditioners by up to two orders of magnitude without impact on accuracy, effectively removing the memory overhead of full-matrix preconditioning for implementations of full-matrix Adagrad (GGT) and natural gradient (M-FAC). Our code is available at https://github.com/IST-DASLab/EFCP.

OBJECTIVES Devices such as mobile phones and smart speakers could be useful to remotely identify voice alterations associated with alcohol intoxication, which could be used to deliver just-in-time interventions, but data to support such approaches for the English language are lacking. In this controlled lab study, we compare how well English spectrographic voice features identify alcohol intoxication. METHODS 18 participants (72% male, aged 21-62 y) read a different randomly-assigned tongue twister prior to drinking and each hour for up to 7 hours after drinking a weight-based dose of alcohol. Vocal segments were cleaned and split into 1 second windows. We built support vector machine models for detecting alcohol intoxication, defined as breath alcohol concentration [BrAC] >0.08%, comparing the baseline voice spectrographic signature to each subsequent timepoint and present ensemble examine accuracy with 95% confidence intervals (CIs). RESULTS Alcohol intoxication was predicted with an accuracy of 98% (95% CI 97.1 to 98.6); mean sensitivity = .98; specificity = .97; positive predictive value = .97; and negative predictive value = .98. CONCLUSIONS In this small controlled lab study, voice spectrographic signatures collected from brief recorded English segments were useful in identifying alcohol intoxication. Larger studies using varied voice samples are needed to validate and expand models.