Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphFill: Deep Image Inpainting using Graphs

We present a novel coarser-to-finer approach for deep graphical image inpainting that utilizes GraphFill, a graph neural network-based deep learning framework, and a lightweight generative baseline network. We construct a pyramidal graph for the input-masked image by reducing it into superpixels, each representing a node in the graph. The proposed pyramidal approach facilitates the transfer of global context from coarser to finer pyramid levels, enabling GraphFill to estimate plausible information for unknown node values in the graph. The estimated information is used to fill in the masked region, which a Refine Network then refines. Furthermore, we propose a resolution-robust pyramidal graph construction method, allowing for efficient inpainting of high-resolution images with relatively fewer computations. Our proposed GAN-based network is trained in adversarial settings on Places365 and CelebA-HQ datasets and demonstrates competitive performance compared to existing methods while using fewer learning parameters. We conduct thorough ablation studies to evaluate the effectiveness of each component in the Graph-Fill Network for improved performance. Our proposed lightweight model for image inpainting is efficient in real-world scenarios, as it can be easily deployed on mobile devices with limited resources.
Chemical Analysis and Investigation of Antimicrobial and Antibiofilm Activities of Prangos trifida (Apiaceae)

Plants of the genus Prangos are intensively investigated as potential new sources of bioactive isolated products. In this work, the chemical composition of volatile constituents (essential oils and headspace volatiles) and dichloromethane extracts, as well as antimicrobial and antibiofilm activities of essential oils and MFDEs (methanol fractions of dichloromethane extracts) of Prangos trifida from Serbia, were investigated. Volatiles of roots, leaves, stems and fruits, and fatty acids and phytosterols in dichloromethane extracts of roots and fruits were analyzed by GC-FID-MS, whereas coumarins in MFDEs by LC–MS and some isolated coumarins by 1H-NMR. Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations/minimum fungicidal concentrations (MBCs/MFCs) of essential oils and MFDEs were determined against 13 microorganisms. Antibiofilm activity was assessed against four microorganisms. Additionally, congo red and ergosterol binding assays were conducted to elucidate selected mechanisms of antibiofilm action in the case of Candida albicans. Total of 52 volatile constituents, 16 fatty acids, eight phytosterols and 10 coumarins were identified. Essential oils demonstrated significant activity, surpassing that of commercial food preservatives, against six tested molds from the Aspergillus, Penicillium and Trichoderma genera, as well as against bacteria Staphylococcus aureus and Bacillus cereus. Most of the oils strongly inhibited the formation of biofilms by S. aureus, Listeria monocytogenes and Escherichia coli. MFDEs exhibited noteworthy effects against B. cereus and the tested Aspergillus species, particularly A. niger, and significantly inhibited C. albicans biofilm formation. This inhibition was linked to a marked reduction in exopolysaccharide production, while antifungal mechanisms associated with ergosterol remained unaffected.
Dialogue System for Early Mental Illness Detection: Toward a Digital Twin Solution

Mental health disorder rates have increased in recent years. In this research, we aim to address the barriers of stigma, accessibility, and affordability in mental healthcare by designing and developing a dialogue system that analyses the mental status of individuals. Additionally, it gives them personalized feedback based on the severity of the mental health problem. We propose a framework based on the concept of a Digital Twin for mental health, which incorporates recent advancements in technology to assess and classify the severity of mental health problems. The chatbot framework was designed in collaboration with a clinical psychiatrist and utilizes pre-trained BERT models, fine-tuned on the E-DAIC dataset, for the detection of various severity levels. The results of this study demonstrate the potential for our method to accurately detect signs of mental health problems with 69% accuracy, and high acceptability and usability with a score of 84.75%.
Interest In Buying Millennial Generation Electric Motorcycles with Theory of Planned Behaviour and Environmental Concern as Determining Factors

The solution of environmental problems is to use environmentally friendly products. This research uses the theory of planned behaviour, norm activation model, and moral norm as mediating variables to test consumers' interest in purchasing electric motorcycle. Data collected from 157 respondents who are the millennial generation and analyse with SmartPLS 3.0. Based on research findings, environmental concern has a significant effect on perceived behavioural control, green purchase attitude, and green purchase intention. Perceived behavioural control has a significant effect on green purchase attitude. Moral norms and green purchase attitude do not have a significant effect on green purchase intention. Then moral norms cannot mediate between subjective norms and green purchase intention. Research findings explain that environmental factors are the determining factor for someone to adopt an electric motorcycle.
Multimodal Remote Sensing Image Matching via Learning Features and Attention Mechanism

Matching multimodal remote sensing images (RSIs) remains an ongoing challenge due to the significant nonlinear radiometric differences and geometric distortions, resulting in matches exhibiting one-to-many matches or mismatches. To tackle this challenge, we propose a novel approach for multimodal RSI matching called modality-independent consistency matching (MICM), which leverages the capabilities of deep convolutional neural networks and the transformer attention mechanism to improve the matching performance. The proposed MICM method consists of three key steps. First, a Unet-like feature extraction backbone network is employed to learn multiscale invariant features from multimodal RSIs, enabling the extraction of rich and evenly distributed feature keypoints. Second, a hybrid approach combining local learning features with the transformer attention mechanism is introduced to aggregate learning features, facilitating both detailed capture and long-range modeling to enhance the representation ability of the features. Third, a feature consistency correlation strategy is adopted to maximize the number of correct corresponding feature points, ensuring reliable matching performance. The performance of the proposed method has been extensively evaluated on both the same scene and different scene multimodal RSIs, which are captured from various imaging modes, wavebands, and platforms. The results show the superior matching performance of the proposed MICM method compared to commonly used and state-of-the-art handcrafted- and learning-based methods when evaluated on both the same scene and different scene datasets. The proposed method serves as a valuable reference for addressing common challenges in multimodal RSI matching.
Chapter 6 Micronutrient Constraints to Crop Production in the Near East Potential Significance and Management Strategies

This review focuses on the Near East, a region of the world where there is a dearth of information on micronutrients in soils and plants. The long history of cultivated agriculture in the region and the peculiar characteristics of the soils and the climate predispose towards problems with micronutrient deficiencies. Over 2 decades ago, a global study on micronutrients indicated deficiencies of iron (Fe) and zinc (Zn), and the likelihood of excess levels of boron (B), in some countries of the Near East. This review primarily addresses two focal points in the region, Pakistan on one side and the Syria/Lebanon/Turkey region on the other, reflecting the zones of activity of the two authors. It brings together and summarises pub- lished work in the areas of crop and soil micronutrient availability, their behaviour in soils in relation to crop growth, and strategies to deal with either deficiency or toxicity, including crop selection for tolerance and subsequent genetic manipula- tion. The review highlights the implications of micronutrient constraints in the soil- plant-human-animal continuum. While intensification of agricultural production is likely to accentuate micronutrient deficiencies in the region, other developments may counteract this trend. Nevertheless, as the trend for land use intensification increases, and as other nutrient constraints are eliminated, micronutrients will inevitably assume greater significance in future agriculture of the Near East.
Data and methods for Gazetteer Independent Toponym Resolution

Data and Methods for Gazetteer Independent Toponym Resolution Grant Hollis DeLozier, M.A. The University of Texas at Austin, 2016 Supervisor: Jason Baldridge This thesis looks at the computational task of Toponym Resolution from multiple perspectives. In its common form the task requires transforming a place name–e.g. Washington–into some grounded representation of that place, typically a point (latitude, longitude) geometry. In recent years Toponym Resolution (TR) systems have advanced beyond heuristic techniques into more complex machine learned classifiers and impressive gains have been made. Despite these advances, a number of issues remain with the task. This thesis looks at aspects of typical TR approaches in a critical light and proposes solutions and new methods. In particular, I’m critical of
Technical communication on engine fault detection with small datasets

Predictive maintenance is becoming an increasingly important tool in the marine industry, where unexpected engine failures are costly, particularly due to the resulting downtime. Acoustic sensors have the potential to detect developing damage before critical failures occur based on anomalies in the sound signature. However, in order to determine the baseline engine condition using learning algorithms, a comprehensive set of data in both normal and fault conditions is required, which is difficult to obtain.In this paper, we propose an approach that uses transfer learning and synthetic data for fault detection in marine engines on small datasets. The synthetic data generation methods investigated in this paper include WaveGAN, WaveNet and SpecGAN. These deep-learning-based networks can be used to find patterns in audio files and use them to generate new data that is indistinguishable from the original data to the human ear. Using the synthetic data, several unsupervised transfer learning techniques were investigated, including k-means and autoencoder.The algorithms were evaluated using real data from a river cruise ship engine and test measurements on a small electric motor under laboratory conditions. The combination and parameterisation of the methods should be thoroughly investigated for a final implementation of the method in an industrial setting. Overall, the approach proposed in this paper gives promising results for anomaly detection with small datasets, but needs to be further improved to achieve the best possible results.
Students’ Perspectives of Transdisciplinary Financial Literacy Education in Ontario

In increasingly uncertain economic times, education curricula around the world are changing to include the topic of financial literacy for students. This article reports the findings of a financial literacy study that examined the perspectives of students on their experiences with transdisciplinary teaching and learning of personal financial literacy. In this study, 344 post-secondary students reflected on their educational experiences in Ontario secondary schools by completing a quantitative survey composed of questions and self-assessments related to personal financial literacy curricula. While students felt that personal financial literacy education was important, they felt that there was a need for more knowledge and understanding in secondary school. Further, survey data identified variations in the personal financial literacy education students received that was linked to the stream (advanced or general) students were enrolled in. The findings are discussed, with particular attention to how students’ perspectives can inform policy and curriculum design moving forward.
Evaluation of intra‐seasonal oscillation simulations in IPCC AR5 coupled GCMs associated with the Asian summer monsoon

In this study, we systematically evaluate the intra‐seasonal oscillation (ISO) associated with the Asian summer monsoon region in coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). Results show that most of the models simulate reasonable climatological circulation patterns in boreal summer, but insufficient precipitation over the Bay of Bengal (BOB) and western North Pacific summer monsoon region. Most models underestimate the variance of 12–90‐day ISO mode but overestimate the percent variance of 12–90‐day ISO accounting for raw precipitation anomalies. There remains a wide gap between model simulations and observations in the simulations of eastward‐propagating ISO (30–80‐day), while the models perform better in the northward propagation of the 30–80‐day mode and the westward propagation of the 12–24‐day mode.
The Impact of ESG Ratings on Financial Performance of the Companies: Evidence from BRICS Countries

Non-financial factors become the relevant topic in the context of understanding the successful development of companies over the world. The purpose of this paper is to study the relationship between ESG scores and financial performance of firms operating in emerging markets, in particular BRICS countries. This study includes three financial performance indicators to cover three different perspectives: accounting measure (ROA), market performance (TSR) and economic metric (EVA spread). The ESG scores, its pillars and other financial metrics are taken from Refinitiv Eikon. The sample consists of 257 listed companies operating in BRICS countries throughout 2017–2021. The main method of the research is the Fixed Effect method for panel data. The results showed that there is no statistical significance between ESG and ROA. Besides, government pillars negatively affect ROA through CSR that is explained by legitimacy theory. As for TSR, ESG, social and environment pillars have positive effects on market performance measure, following stakeholder theory. Regarding economic performance, ESG and social pillar have negative influence on EVA spread.
Investment Liberalisation and International Trade

This paper estimates the cross-price elasticity of exports with respect to investment costs for bilateral relations between the US and 35 partner countries. We show that the relationship depends on country characteristics as predicted by the Markusen et al. (1996) model. When countries differ in relative factor endowments and trade costs are low, investment liberalisation stimulates exports, whereas when countries are similar in terms of relative factor endowments and size, and trade costs are moderate to high, investment liberalisation reduces exports.
Disintegration and Trade

The gravity model of trade is utilized to assess the impact of disintegration on trade. The analysis is based on three recent disintegration episodes involving the former Soviet Union, Yugoslavia and Czechoslovakia. The results point to a very strong home bias around the time of disintegration, with intra-union trade exceeding normal trade approximately 43 times in the former Soviet Union and Czechoslovakia, and 24 times in the former Yugoslavia. Disintegration was followed by a sharp fall in trade intensity. Nevertheless, there is a considerable hysteresis in economic relations, with trade flows among the former constituent Republics still between two and 30 times greater than normal trade in 1998.
Corporate Equity Ownership and Product-Market Relationships

This paper investigates an unexplored dimension of block-equity ownership: benefits in product-market relationships between corporations. We find significant increases in investment and operating performance for firms that have product-market relationships with their corporate owners. We also find that the size of equity stakes is positively related to measures of asset specificity and growth options, especially when firms can fund subsequent investment with internal funds. Investment of target firms with business relationships with their corporate owners also significantly increases with Tobin's q following the block equity purchases. We find a strong association of investment with Tobin's q for firms in which there is repeated interaction through supply and distribution agreements between the firm and its equity owner. Our evidence indicates that benefits in product-market relationships are an important motivation for corporate equity ownership. The evidence is consistent with the view that an equity position by an outside corporation is important in aligning incentives between contracting firms to expand joint investment opportunities and reduce the costs of generating and maintaining business agreements and alliances.
The Influence of Electronic Word of Mouth on Online Purchasing: A Bhubaneswar Perspective

Electronic word of mouth (EWom) has become a crucial communication tool in the digital age. The importance of online reviews and ratings on consumers has increased recently. The study's primary goal is to determine how certain eWom characteristics, such as eWom message consistency, eWom content platform, and eWom valency, affect the popularity of a product and the level of customer participation that influences consumers' intentions to make a purchase. The study is intended to know the impact of factors of eWom on the purchase intention of online buyers in Bhubaneswar. The study was tested on 201 respondents through a cluster sampling method with a structured questionnaire. The regression analysis approach was utilized to analyze the objective and obtain the findings. It revealed that the EWom content platform and Information credibility strongly impact buyers' purchase intention. Information credibility plays a vital role in the purchase decisions of buyers.
The Evolution of Indiscriminate Altruism in a Cooperatively Breeding Mammal

Kin selection theory suggests that altruistic behaviors can increase the fitness of altruists when recipients are genetic relatives. Although selection can favor the ability of organisms to preferentially cooperate with close kin, indiscriminately helping all group mates may yield comparable fitness returns if relatedness within groups is very high. Here, we show that meerkats (Suricata suricatta) are largely indiscriminate altruists who do not alter the amount of help provided to pups or group mates in response to their relatedness to them. We present a model showing that indiscriminate altruism may yield greater fitness payoffs than kin discrimination where most group members are close relatives and errors occur in the estimation of relatedness. The presence of errors in the estimation of relatedness provides a feasible explanation for associations between kin discriminative helping and group relatedness in eusocial and cooperatively breeding animals.
A multi-parameter approach to fingerprinting suspended-sediment sources

There is an increasing need for information on the sources of the suspended sediment transported by rivers, but such information is diffi­ cult to obtain using traditional monitoring techniques. The fingerprinting technique offers an alternative approach, although it is important to select appropriate fingerprinting properties. The use of several such properties or tracers with contrasting behavior was used to assist in discriminating be­ tween several potential sources and in providing more definitive and reli­ able results. Examples of a multi-parameter approach to fingerprintin g suspended sediment source in two small catchments in Devon, United Kingdom, are presented. Cluster analysis was used to distinguish statistical­ ly significant source groupings and a mixing model was employed to esti­ mate the relative contributions from these sources to the overall suspended sediment load.
Semi-stable extensions on arithmetic surfaces

Given a line bundle L on a smooth projective curve over the complex numbers, we show that a general extension E of L by the trivial line bundle is very stable: line bundles contained in E have degree much less than half the degree of E. From this result we deduce new inequalities for the successive minima of the euclidean lattice H^1(X,L^{-1}), where L is an hermitian line bundle on the arithmetic surface X.
INTREST Impact of Movement Control Order on Risk-Adjusted Performance of the Malaysian Real Estate Investment Trusts (M-REITs)

The first Movement Control Order (MCO) implemented by the Malaysian government from 18 th March 2020 to 3 rd May 2020 was unprecedented. Since Malaysia was only mildly affected by previous epidemics or pandemics unlike the COVID-19, studies of the pandemic impact on the performance of Malaysian firms and M-REITs in specific were limited. To close this gap, this study compares Sharpe ratio, Treynor ratio and Jensen Alpha ratio of 18 M-REITs for sub-periods before, during and after the MCO. Paired sample t-test results for all these three risk-adjusted performance measures consistently show that M-REITs significantly performed better during the MCO compared to before the MCO as well as after the MCO compared to before the MCO. The results suggest that the business model and portfolio of real estate managed by M-REITs are resilient against both systematic and unsystematic risk factors, and imply that the intrinsic value of M-REITs is not significantly affected by the market uncertainty caused by movement restriction. The findings also bolster investors’ confidence to include M-REITs as part of their diversified investment portfolio to achieve sustainable return performance in the post-Covid period. To remain resilient and sustainable, the management of M-REITs should diversify the portfolio of properties and real estate they managed since the movement restriction had varying repercussions on different types of real estate. Rooms for further diversification are justified by a larger percentage of M-REITs having positive Treynor ratios after the MCO sub-period if compared to positive Sharpe ratios. A well-diversified portfolio managed by an M-REIT can reduce unsystematic risk, which is part of the total risk measured by standard deviation, eventually moving towards a positive Sharpe ratio.
Serverless State Management Systems

Modern cloud developers face many distributed systems complexities when building disaggregated applications from cloud building blocks. We propose a new class of systems, called Serverless State Management Systems (SSMS), that abstracts away these complexities and transparently manages fault-tolerance, deployment, and scaling of a logical cloud application on physical cloud resources. An SSMS, analogous to a DBMS, provides three important abstractions for disaggregated applications: 1) a logical application model, similar to relational algebra, that describes application semantics but abstracts away the deployment details, 2) strong resilient programming primitives, similar to ACID transactions, that simplifies fault-tolerant programming in the cloud, and 3) smart, cost-based optimization schemes that automates scheduling, placement, and other details, similar to a query optimizer. We present a preliminary design for SSMS and associated research challenges.