Miracle-dz/UniFAR-FTUs huggingface dataset:
Integral Probability Metrics PAC-Bayes Bounds

We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of this bound with the IPM being the total variation metric and the Wasserstein distance. A notable feature of the obtained bounds is that they naturally interpolate between classical uniform convergence bounds in the worst case (when the prior and posterior are far away from each other), and improved bounds in favorable cases (when the posterior and prior are close). This illustrates the possibility of reinforcing classical generalization bounds with algorithm- and data-dependent components, thus making them more suitable to analyze algorithms that use a large hypothesis space.
SketchCleanNet - A deep learning approach to the enhancement and correction of query sketches for a 3D CAD model retrieval system

Search and retrieval remains a major research topic in several domains, including computer graphics, computer vision, engineering design, etc. A search engine requires primarily an input search query and a database of items to search from. In engineering, which is the primary context of this paper, the database consists of 3D CAD models, such as washers, pistons, connecting rods, etc. A query from a user is typically in the form of a sketch, which attempts to capture the details of a 3D model. However, sketches have certain typical defects such as gaps, over-drawn portions (multi-strokes), etc. Since the retrieved results are only as good as the input query, sketches need cleaning-up and enhancement for better retrieval results. In this paper, a deep learning approach is proposed to improve or clean the query sketches. Initially, sketches from various categories are analysed in order to understand the many possible defects that may occur. A dataset of cleaned-up or enhanced query sketches is then created based on an understanding of these defects. Consequently, an end-to-end training of a deep neural network is carried out in order to provide a mapping between the defective and the clean sketches. This network takes the defective query sketch as the input and generates a clean or an enhanced query sketch. Qualitative and quantitative comparisons of the proposed approach with other state-of-the-art techniques show that the proposed approach is effective. The results of the search engine are reported using both the defective and enhanced query sketches, and it is shown that using the enhanced query sketches from the developed approach yields improved search results.
On John's test for sphericity in large panel data models

This paper studies John’s test for sphericity of the error terms in large panel data models, where the number of cross-section units n is large enough to be comparable to the number of times series observations T , or even larger. Based on recent random matrix theory results, John’s test’s asymptotic normality properties are established under both the null and the alternative hypotheses. These asymptotics are valid for general populations, i.e., not necessarily Gaussian provided certain ﬁnite moments. A fantastic phenomenon found in the paper is that John’s test for panel data models possesses a powerful dimension-proof property. It keeps the same null distribution under diﬀerent ( n, T )-asymptotics, i.e., the small or medium panel regime n/T → 0 as T → ∞ , the large panel regime n/T → c ∈ (0 , ∞ ) as T → ∞ , and the ultra-large panel regime n/T → ∞ ( T δ /n = O p (1) , 1 < δ < 2) as T → ∞ . Moreover, John’s test is always consistent except under the alternative of bounded-norm covariance with the large panel regime n/T → c ∈ (0 , ∞ ).
Efficient Adaptive Regret Minimization

In online convex optimization the player aims to minimize her regret against a ﬁxed comparator over the entire repeated game. Algorithms that minimize standard regret may converge to a ﬁxed decision, which is undesireable in changing or dynamic environments. This motivates the stronger metric of adaptive regret, or the maximum regret over any continuous sub-interval in time. Existing adaptive regret algorithms suﬀer from a computational penalty - typically on the order of a multiplicative factor that grows logarithmically in the number of game iterations. In this paper we show how to reduce this computational penalty to be doubly logarithmic in the number of game iterations, and with minimal degradation to the optimal attainable adaptive regret bounds.
A study on eddy-current losses in rotors of surface permanent-magnet synchronous machines

In this paper, we investigate the Eddy current (EC) losses in the rotors of surface permanent-magnet (PM) synchronous machines, which have concentrated armature windings. By the finite-element method (FEM) analysis, it is clear that the EC losses in rotors are apparently different at rated currents if the pole and slot combinations are different. The distribution of the EC density in the rotor is closely related with the asynchronous components of the armature magnetomotive force (MMF). If the armature MMF has a low order of spatial harmonic components, which is not synchronous with the rotor, the EC losses are of large amounts.
Generalized Korn's Inequalities for Piecewise H2 Vector Fields

The purpose of this paper is to construct a new class of discrete generalized Korn's inequalities for piecewise H2 vector fields in three-dimensional space. The resulting Korn's inequalities are different from the standard Korn's inequalities, as they involve the trace-free symmetric gradient operator, in place of the usual symmetric gradient operator. It is anticipated that the new generalized Korn's inequalities will be useful for the analysis of a broad range of finite element methods, including mixed finite element methods and discontinuous Galerkin methods.
Improving Nighttime Driving-Scene Segmentation via Dual Image-Adaptive Learnable Filters

Semantic segmentation on driving-scene images is vital for autonomous driving. Although encouraging performance has been achieved on daytime images, the performance on nighttime images are less satisfactory due to the insufficient exposure and the lack of labeled data. To address these issues, we present an add-on module called dual image-adaptive learnable filters (DIAL-Filters) to improve the semantic segmentation in nighttime driving conditions, aiming at exploiting the intrinsic features of driving-scene images under different illuminations. DIAL-Filters consist of two parts, including an image-adaptive processing module (IAPM) and a learnable guided filter (LGF). With DIAL-Filters, we design both unsupervised and supervised frameworks for nighttime driving-scene segmentation, which can be trained in an end-to-end manner. Specifically, the IAPM module consists of a small convolutional neural network with a set of differentiable image filters, where each image can be adaptively enhanced for better segmentation with respect to the different illuminations. The LGF is employed to enhance the output of segmentation network to get the final segmentation result. The DIAL-Filters are light-weight and efficient and they can be readily applied for both daytime and nighttime images. Our experiments show that DAIL-Filters can significantly improve the supervised segmentation performance on ACDC_Night and NightCity datasets, while it demonstrates the state-of-the-art performance on unsupervised nighttime semantic segmentation on Dark Zurich and Nighttime Driving testbeds. Codes and models are available at https://github.com/wenyyu/IA-Seg.
International Journal of Public Health Science (IJPHS)

Received Dec 14, 2021 Revised May 27, 2022 Accepted Jun 21, 2022 The study aimed to determine trend of Indonesian local food consumption and its association with body mass index (BMI) among female university students. It was cross-sectional study that involved 217 Indonesian female university students (18-25 years old) in April-May 2021 using conventional sampling by a self-administered online questionnaire in Malang City, East Java, Indonesia. Sociodemographic and dietary habits were collected along with body BMI data. Descriptive and Ordinal Logistic Regression were applied to analyze the data. The results showed the average BMI of 217 participants was 21.28+3.30 kg/m2. From 167 local Indonesian food that were identified, local cuisine with high carbohydrates and sugar such as fried noodles, meatballs, mixed vegetable soup, biscuits/pastry, fritters with topping, and iced milk tea with topping had a positive impact on overnutrition. Interestingly, chicken porridge had an inverse association with overnutrition. The ordinal regression results depicted the length of stay in study area had a significant association with BMI. However, meal frequency and snack time consumption also affected BMI. The preferred highcarbohydrate food is significantly associated with overnutrition (p<0.05). It is crucial to adjust nutritional education and people's food menu in reducing high-carbohydrate and sugar food to provide healthy food for university students.
DA LI PROFITABILNOST BANAKA UTIČE NA PRIVREDNI RAST: PRIMERI BANAKA POJEDINIH ZEMALJA ZAPADNOG BALKANA

Banks play an important role in a country’s economy because increasing savings and capital accumulation has a positive effect on economic growth and employment through the banks’ resource transfer function. The purpose of this study is to establish a cause-and-effect relationship between bank profitability and economic growth in three selected countries, including Bosnia and Herzegovina, Serbia, and Croatia. In this research, a panel causality test is applied to examine the cause-and-effect relationship for the time period from the first quarter of 2008 to the fourth quarter of 2020. Empirical findings in this study showed that the profitability of banks in selected developing countries (Bosnia and Herzegovina, Serbia and Croatia) has a positive effect on economic growth. Also, this research provides insight into in-depth analysis in terms of considering several countries through the use of a panel causality test, for the purpose of studying the relationship between bank profitability and economic growth. the first and second profitability indicators (return on assets - ROA and return on equity - ROE) with the indicator of economic growth expressed in real GDP. Therefore, this means that a high level of bank performance through bank performance affects the promotion of economic development, in line with the findings of Ayadi et al. (2010) and Yudistira and Ike (2014). Profitable banks are key drivers of economic growth. The results of the research suggest a positive relationship between bank profitability and economic growth. Therefore, the research results suggest that with an increasing the profitability of banks affects the increase in economic growth. Also, these results show that markets with a higher bank presence have a significantly higher growth rate. This further means that cooperative banks have a significant market share in lending to small and medium-sized enterprises, which are often recognized as drivers of economic development, especially in transition economies.
Improving the prediction of transcription factor binding sites to aid the interpretation of non-coding single nucleotide variants

Single nucleotide variants (SNVs) that occur in transcription factor binding sites (TFBSs) can disrupt the binding of transcription factors and alter gene expression which can cause inherited diseases and act as driver SNVs in cancer. The identification of SNVs in TFBSs has historically been challenging given the limited number of experimentally characterised TFBSs. The recent ENCODE project has resulted in the availability of ChIP-Seq data that provides genome wide sets of regions bound by transcription factors. These data have the potential to improve the identification of SNVs in TFBSs. However, as the ChIP-Seq data identify a broader range of DNA in which a transcription factor binds, computational prediction is required to identify the precise TFBS. Prediction of TFBSs involves scanning a DNA sequence with a Position Weight Matrix (PWM) using a pattern matching tool. This thesis focusses on the prediction of TFBSs by: (a) evaluating a set of locally-installable pattern-matching tools and identifying the best performing tool (FIMO), (b) using the ENCODE ChIP-Seq data to evaluate a set of de novo motif discovery tools that are used to derive PWMs which can handle large volumes of data, (c) identifying the best performing tool (rGADEM), (d) using rGADEM to generate a set of PWMs from the ENCODE ChIP-Seq data and (e) by finally checking that the selection of the best pattern matching tool is not unduly influenced by the choice of PWMs. These analyses were exploited to obtain a set of predicted TFBSs from the ENCODE ChIP-Seq data. The predicted TFBSs were utilised to analyse somatic cancer driver, and passenger SNVs that occur in TFBSs. Clear signals in conservation and therefore Shannon entropy values were identified, and subsequently exploited to identify a threshold that can be used to prioritize somatic cancer driver SNVs for experimental validation.
Dynamic Capabilities in Information Systems Research – A Dynamic Capabilities in Information Systems Research – A Literature Review Literature Review

Information systems (IS) and digital business strategies are increasingly focal to companies navigating the changing modes of working, collaborating and operating in various networked business settings. Dynamic capabilities (DCs) are often asserted as a key for companies’ attaining sustainable competitive advantage in turbulent and uncertain environments. However, the construct of DCs is utilized in multifarious ways and often researched primarily from a managerial perspective. This makes comprehensive, empirical study of DC development in organizations amorphous and difficult. To address this issue, based on a semi-structured literature review, this paper investigates how DCs are conceptualized in strategic IS literature. Further, the aim is to understand to what extent they have been studied empirically with a multilevel perspective. Firstly, the findings suggest terminology used in defining and explaining DCs is inter-laced and tangled. Secondly, the findings point that the suspected gap in multi-level research on DCs within the IS field exists.
Technical Memorandum | FINAL Phase II Climate, Task Literature Review

This Technical Memorandum is developed as part of Task 1 of the Colorado River Water Availability Study, Phase II, Updating Climate Impacted Hydrology (CRWAS-II or Study). The objective of Task 1 is to develop an approach for creating future climate scenarios and model forcings . A principal deliverable for Task 1 is this technical memorandum describing a literature review covering the CMIP5 ensemble and recent work in methods for developing climate scenarios (subsets) and model forcing data, and recommending one or more potential methods for developing climate scenarios. In addition, this technical memorandum is to describe efforts by the Bureau of Reclamation (Reclamation) to analyze and rationalize an observed “wettening” difference in the CMIP5 projections. This analysis is ongoing, so the description in this memorandum is simply a status report.
OUR Journal: ODU Undergraduate Research Journal OUR Journal: ODU Undergraduate Research Journal Improving the Heat Tolerance of Vulnerable Corals through their Improving the Heat Tolerance of Vulnerable Corals through their Algal Symbionts Algal Symbionts

— Tropical coral reefs are one of the most impressive and diverse ecosystems on the face of the earth. Found in warm, tropical waters around the globe, these reefs are major supporters of the immense biodiversity of the area. The health of coral reefs is highly influential on the overall health of the entire ecosystem. In recent years, intensifying climate change has resulted in an accelerated rise in seawater temperatures and the frequency and severity of coral bleaching. Coral bleaching occurs in response to harsh environmental conditions that cause corals to enter a period of extreme stress. During this time, corals expel their algal symbionts – ending the symbiotic relationship with the zooxanthellae. Corals are especially vulnerable to permanent damage in this bleached state. As major supporters of the success of marine ecosystems and coastal communities alike, it is imperative that ecosystems are rehabilitated and protected from future harm. Coral polyps have long generation times, making them unable to keep up with the rate at which the climate is currently changing. Without human intervention, coral reefs will not survive the next century. Studies have indicated that the introduction of thermotolerant strains of Symbiodinium, the algal symbiont associated with corals, improves the heat tolerance of corals. This review examines the use of thermotolerant algal symbionts as a potential long-term rehabilitation and mitigation strategy. The thermal tolerance of zooxanthellae has been shown to transfer to coral species, making these species of corals more resilient to the increasing ocean temperatures and harsher conditions associated with climate change. Experimentally implanting thermotolerant strains of Symbiodinium may serve as a viable solution to the problem of corals’ slow adaptation times. Additional mitigation strategies may be required during the conservation of coral reefs to ensure the overall health of the reef for years to come. Climate change has shown no indication of slowing down in future years. It is critical that mitigation efforts are implemented immediately to identify feasible, long-term solutions to assist vulnerable corals in beating the heat.
Gamification at Workplace: Theories, constructs and conceptual frameworks

Gamification has been an active area of interest for both academicians and practitioners for the last decade. Gamification has extended its application to many areas, including the workplace. This study aims to shed light on the theoretical scenario of the gamification literature at the workplace. The article reviews the recent literature on gamification in this context and analyses the theories, constructs, and frameworks used to study the phenomenon. There is a lack of focus on the theoretical framework in the existing reviews. We create a broad taxonomy of theories used in the literature of gamification of the workplace. Further, we also propose a causal-chain framework to explain how gamification influences employees in the workplace. The results indicate that gamification at the workplace is still in its nascent stage and requires more rigorous and in-depth research. We believe that the insights generated provide research avenues for future research studies.
New strategies and therapies for the prevention of heart failure in high‐risk patients

Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle‐aged adults (<65 years). Therefore, identification of individuals HF at‐risk (Stage A) or with pre‐HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP‐HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk‐based framework for the prevention of HF. However, traditional risk factors included in the PCP‐HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID‐19) are emerging as important risk‐enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk‐enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co‐transporter‐2 inhibitors that have also been termed the “statins” of HF prevention), intensive blood pressure lowering, and heart‐healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk‐enhancing factors and SDoH, and equitable approaches to interventions for risk‐based prevention of HF.
Improved Algorithm for Distributed Points Positioning Using Uncertain Objects Clustering

Positioning of mobile objects that require communication with some kind of online service application is very challenging task. Proper positioning with minimal deviation ensures that mobile service system (MSS) e.g. taxi service used in this paper, would complete its tasks to the end users and reduce overall travel distance. This paper is focused on development of the algorithm that will find the best location for MSS object and to improve its efficiency when uncertain data clustering is used. If best location for the mobile system object is chosen, then the respond time can be minimized and the given tasks could also be performed in a reasonable time. Improved bisector pruning method was proposed for clustering previous data of MSS objects to find the best location for its application. Cluster centres are used as best locations for MSS objects. In this way, total expected distance from tasks that have been set to the service system is minimized. Therefore, MSS will be improved and capable to fulfil more tasks during the shortest period of time.
Improving implementation of evidence‐based therapies for heart failure

Treatment options for patients with heart failure have improved rapidly over the last few decades. Data from large scale clinical trials demonstrate that medical and device therapies can improve quality of life, reduce hospitalizations for acute heart failure, and reduce mortality. However, the use of many of these therapies in routine practice is remarkably low. There are many reasons for suboptimal implementation of evidence‐based therapies for heart failure, and we believe addressing the large gap between what can be accomplished in clinical trials versus routine practice is a critical and urgent public health issue. In this review, we outline reasons for this implementation gap and review recent studies attempting to address this issue. We also provide recommendations for future interventions and areas of clinical investigation to improve implementation for patients with heart failure.
Quantum Algorithms for Learning a Hidden Graph

We study the problem of learning an unknown graph provided via an oracle using a quantum algorithm. We consider three query models. In the first model (“OR queries”), the oracle returns whether a given subset of the vertices contains any edges. In the second (“parity queries”), the oracle returns the parity of the number of edges in a subset. In the third model, we are given copies of the graph state corresponding to the graph. We give quantum algorithms that achieve speedups over the best possible classical algorithms in the OR and parity query models, for some families of graphs, and give quantum algorithms in the graph state model whose complexity is similar to the parity query model. For some parameter regimes, the speedups can be exponential in the parity query model. On the other hand, without any promise on the graph, no speedup is possible in the OR query model. A main technique we use is the quantum algorithm for solving the combinatorial group testing problem, for which a query-efficient quantum algorithm was given by Belovs. Here we additionally give a time-efficient quantum algorithm for this problem, based on the algorithm of Ambainis et al. for a “gapped” version of the group testing problem.