Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Powers: Hardness Results, Good Characterizations and Efficient Algorithms

Given a graph H = (VH , EH) and a positive integer k, the k-th power of H , written H, is the graph obtained from H by adding new edges between any pair of vertices at distance at most k in H ; formally, H = (VH , {xy | 1 ≤ dH(x, y) ≤ k}). A graph G is the k-th power of a graph H if G = H, and in this case, H is a k-th root of G. For the cases of k = 2 and k = 3, we say that H and H is the square, respectively, the cube of H and H is a square root of G = H, respectively, a cube root of G = H. In this thesis we study the computational complexity for recognizing k-th powers of general graphs as well as restricted graphs. This work provides new NPcompleteness results, good characterizations and efficient algorithms for graph powers. The main results are the following. • There exist reductions proving the NP-completeness for recognizing k-th powers of general graphs for fixed k ≥ 2, recognizing k-th powers of bipartite graphs for fixed k ≥ 3, recognizing k-th powers of chordal graphs, and finding k-th roots of chordal graphs for all fixed k ≥ 2. • The girth of G, girth(G), is the smallest length of a cycle in G, – For all fixed k ≥ 2, recognizing of k-th powers of graphs with girth at most 2⌊ 2 ⌋+ 2 is NP-complete. – There is a polynomial time algorithm to recognize if G = H for some graph H of girth at least 6. This algorithm also constructs a square root of girth at least 6 if one exists. – There exists a good characterization of squares of a graph having girth at least 7. This characterization not only leads to a simple algorithm to compute a square root of girth at least 7 but also shows such a square root, if it exists, is unique up to isomorphism. – There is a good characterization of cubes of a graph having girth at least 10 that gives a recognition algorithm in time O(nm) for such graphs. Moreover, this algorithm constructs a cube root of girth at least 10 if it exists.
Automated Performance Test Generation and Comparison for Complex Data Structures - Exemplified on High-Dimensional Spatio-Temporal Indices

There exist numerous approaches to index either spatio-temporal or high-dimensional data. None of them is able to efficiently index hybrid data types, thus spatio-temporal and highdimensional data. As the best high-dimensional indexing techniques are only able to index point-data and not now -relative data and the best spatio-temporal indexing techniques suffer from the curse of dimensionality, this thesis introduces the Spatio-Temporal Pyramid Adapter (STPA). The STPA maps spatio-temporal data on points, now -values on the median of the data set and indexes them with the pyramid technique. For high-dimensional and spatiotemporal index structures no generally accepted benchmark exists. Most index structures are only evaluated by custom benchmarks and compared to a tiny set of competitors. Benchmarks may be biased as a structure may be created to perform well in a certain benchmark or a benchmark does not cover a certain speciality of the investigated structures. In this thesis, the Interface Based Performance Comparison (IBPC) technique is introduced. It automatically generates test sets with a high code coverage on the system under test (SUT) on the basis of all functions defined by a certain interface which all competitors support. Every test set is performed on every SUT and the performance results are weighted by the achieved coverage and summed up. These weighted performance results are then used to compare the structures. An implementation of the IBPC, the Performance Test Automation Framework (PTAF) is compared to a classic custom benchmark, a workload generator whose parameters are optimized by a genetic algorithm and a specific PTAF alternative which incorporates the specific behavior of the systems under test. This is done for a set of two high-dimensional spatio-temporal indices and twelve variants of the R-tree. The evaluation indicates that PTAF performs at least as good as the other approaches in terms of minimal test cases with a maximized coverage. Several case studies on PTAF demonstrate its widespread abilities.
Wavelet analysis for ground penetrating radar applications: a case study

Noises may significantly disturb ground penetrating radar (GPR) signals, therefore, filtering undesired information using wavelet analysis would be challenging, despite the fact that several methods have been presented. Noises are gathered by probe, particularly from deep locations, and they may conceal reflections, suffering from small altitudes, because of signal attenuation. Multiple engineering fields need data analysis to distinguish valued material, based on information obtained by underground observations. Using wavelets as one of the useful methods for analyzing data is considered in this paper. However, optimal wavelet analysis would be challenging in the realm of exploring GPR signals. There is no doubt that accounting for wavelet function, decomposition level, threshold estimation method and threshold transformation, in the matter of de-noising and investigating signals, is of great importance; they must be chosen with judgment as they influence the results enormously if they are not carefully designated. Multiple wavelet functions are applied to perform de-noising and reconstruction on synthetic noisy signals generated by the finite-difference time-domain (FDTD) method to account for the most appropriate function for the purpose. In addition, various possible decomposition levels, threshold estimation methods and threshold transformations in the de-noising procedure are tested. The optimal wavelet analysis is also evaluated by examining real data acquired from several antenna frequencies which are common in engineering practice.
EXCLUSION IN ALL-PAY AUCTIONS

In a recent paper, Baye, Kovenock, and de Vries (1993) derive a general formula for the seller's expected revenue in an all-pay auction, where the buyers' valuations are common knowledge. They use this formula to show that excluding the buyer with the highest valuation sometimes increases the seller's expected revenue. Lobbying may be modeled as an all-pay auction, and the exclusion result may therefore explain the practice of reducing fields of contestants vying for a political prize. One implication is that a politician may, perversely, exclude the individuals who place the highest value on a political prize. The exclusion result depends critically on the assumption that the politician must award the prize to the highest bidder. We describe an alternate procedure that gives the politician more expected revenue. Although a politician adopting our procedure sometimes increases her expected revenue by excluding potential bidders, she should never exclude the buyer with the highest valuation.
The Journal of Systems and Software

Many enterprises that adopt Agile/Scrum suffer from collaboration issues between Scrum teams that depend on one another to deliver end-to-end functionality. These dependencies delay delivery and as a result deteriorate the business value delivered in such value chains. The objective of our study is to support enterprises that suffer from such dependencies with a governance framework that helps them mitigate collaboration issues between sets of codependent Scrum teams. We ﬁrst identify a set of intervention actions that aim to mitigate the collaboration issues between codependent Scrum teams. Second, we validate the effectiveness of these intervention actions in a large conﬁrmatory industrial case study. This study was held in a large multi-national ﬁnancial institute that worked with a large number of codependent Scrum teams. Third, we triangulate the ﬁndings in three focus groups. We ﬁnally package the intervention actions in a governance framework.Theinterventionactionsledtoadeliverytimereductionfrom29daysto10days. Theparticipants in the focus groups conﬁrmed the causality between the intervention actions and the observed delivery improvement. The empirical results show that the intervention actions, packaged in the governance framework, enable codependent sets of Scrum teams to deliver faster.
Future Changes in Precipitation Extremes Over Canada: Driving Factors and Inherent Mechanism

In this study, future changes in precipitation extremes over 10 climatic regions in Canada and their mechanism under Representative Concentration Pathways (RCPs) throughout the 21st century are investigated by using the Providing Regional Climates for Impacts Studies (PRECIS) model. The performance of PRECIS in hindcasting total and extreme precipitation for the historical period is first evaluated through two experiments driven by the boundary conditions from both ERA‐Interim (1979–2011) and HadGEM2‐ES (1959–2005). The validation results indicate that PRECIS can reasonably reproduce both the magnitudes and spatial patterns of precipitation extremes over Canada. Changes in total and extreme precipitation for two future periods are analyzed to explore how regional climate over different climatic regions would respond to global warming. Mechanism governing changes in precipitation extremes is explored through a comprehensive analysis of potential climate factors and their correlations and interactions with precipitation extremes. There are obvious increasing trends over most regions for the magnitude of precipitation extremes except for the duration indices. Averages of projected precipitation extremes over the climatic regions in Canada are projected to increase under RCP4.5. Such increases under RCP8.5 would be amplified due to higher greenhouse gas emissions. The projected changes in total precipitation are dominated by changes in wind velocity and relative humidity (e.g., changes in horizontal water vapor flux that would have significant effects on the occurrence of precipitation in Canada). In addition, the changes in the majority of precipitation extremes are commonly attributed to the changes in the saturation vapor pressure due to warmer temperature as described by the Clausius‐Clapeyron equation.
Detecting Influence Structures in Multi-Agent Reinforcement Learning Systems

We consider the problem of quantifying the amount of influence one agent can exert on another in the setting of multi-agent reinforcement learning (MARL). As a step towards a unified approach to express agents’ interdependencies, we introduce the total and state influence measurement functions. Both of these are valid for all common MARL systems, such as the discounted reward setting. Additionally, we propose novel quantities, called the total impact measurement (TIM) and state impact measurement (SIM), that characterize one agent’s influence on another by the maximum impact it can have on the other agents’ expected returns and represent instances of impact measurement functions in the average reward setting. Furthermore, we provide approximation algorithms for TIM and SIM with simultaneously learning approximations of agents’ expected returns, error bounds, stability analyses under changes of the policies, and convergence guarantees. The approximation algorithm relies only on observing other agents’ actions and is, other than that, fully de-centralized. Our work appears to be the first study of determining influence structures in the multi-agent average reward setting with provable convergence guarantees.
PERIOPERATIVE MEDICINE Association of TGF-β 1 with cystatin-C in patients with diabetic nephropathy

Background & Objective: Diabetes is considered a condition that is characterized by an increase in oxidative stress and inflammation. Transforming growth factor-beta (TGF-β ) belongs to the TGF-β subfamily of the TGF - β superfamily. Numerous biological processes, such as cell growth, differentiation, adhesion, proliferation, tissue repair, morphogenesis, and apoptosis, are regulated by the TGF-β subfamily. Five TGF - β isoforms i n this subfamily have been found in vertebrates; however, only TGF-β 1 -3 has been demonstrated to be expressed in mammals. Methodology: The current research comprised of a total of 130 individuals, all of whom were placed into one of three primary groups ; 50 (25 males and 25 females) diabetic patients without nephropathy; 50 (25
Genetic Engineered Ideonella Sakaiensis Bacteria: A Solution of the Legendary Plastic Waste Problem

Plastic waste has been known as the most unsolved environmental problem in this world. Instead of what have been expected, the amount of plastic waste is increasing significantly in years. Nowadays, 6.3 billion metric tons of plastic waste is produced and if present trends continue, there will be 12 billion metric tons of plastic in landfills by 2050. Due to the fact, an effective and environmental friendly solution is urgently needed. The ability of Ideonella sakaiensis bacteria to consume polyethylene terephthalate (PET), the main material of plastic, in only 6 weeks becomes a great potential to solve this problem. These bacteria need to consume PET for their metabolism and they can produce safety secretes from it as well. Nevertheless, the habitat of these bacteria is very limited. With technology of genetic engineering, the genes of Ideonella sakaiensis can be modified with Azotobacter sp. ’s genes that make them survive in areas that usually have much plastic waste, such as soil and water. This combination is expected to optimize the ability of Ideonella sakaiensis as it breaks down the limitation of where the bacteria can survive. As a result, the problem of plastic waste can be solved effectively without damaging the environment and automatically form a sustainability in nature and life.
EEG signal enhancement based on 2K-cGAN

Brain-computer interface technology provides a new connectivity pathway between the human brain and external computers, which can recognize desired commands by decoding task-related neural responses from electroencephalographic (EEG) signals. EEG is a unique information factor that correlates with the subject’s genetic information, and thus is considered to be robust against forgery, demonstrating great potential in identification, and EEG-based biometric identification is being progressively applied. However, while EEG exhibits significant pattern variability among subjects bringing advantages for authentication, it also adds challenges to its data acquisition, and how to acquire training data in a targeted manner is the current challenge. In this paper, we propose a generative adversarial network (2K-cGAN) EEG augmentation method based on 2K standard classifiers, which utilizes a model to generate data in the form of a tensor of EEG signals, and incorporates category labels into the network structure to regulate the training of the generator and the discriminator, while the discriminator is considered as a 2K dimensional standard classifier to categorize the output data, so as to enable the generator to target the generation of data. The proposed network model is trained and generated on two tensor representations of the BCI Competition-IV 2b dataset, and the accuracy of augmentation by different multiples is compared using the LetNet classical classification network, and the experimental results show that the data generated using this paper’s method can maintain feature consistency with the original data as well as capture the unique features of different classes.
A Self-Improved Optimization-Based Artificial Neural Network Model for WPT System for Electric Vehicle Charging

Recently, research and development actions have been ongoing to overwhelm the issues with Electric Vehicle (EV) battery systems and long-distance driving limits. EV charging stations can be configured as wired and wireless, and their availability is growing. The EV parking spaces are designated for wired power transmissions where the batteries may be charged. The potential of electric sparks or other hazards makes wired charging of EVs unacceptable. Advancements in Wireless Power Transfer (WPT) in EVs demonstrate the capacity to transfer considerable power across short and mid-range distances. The main objective of this study is to predict the efficiency of the EV wireless charging technique. The accuracy of the WPT model in EV charging is increased in this research using the Self-improved Aquila Optimization Algorithm (SI-AOA) based Artificial Neural Network (ANN) model. The SI-AOA method is an enhanced version of the Aquila Optimization Algorithm (AOA), which is used to determine the weights of the ANN classifier most effectively. This strategy is suggested since it is efficient and time-saving. MATLAB/Simulink is used to model the suggested work, and simulation results are presented. In order to ensure the effectiveness of the envisaged approach, the results produced by the suggested model are compared to the results acquired by conventional methods in terms of error metrics.
Non-Intrusive Load Monitoring Based on Diffusion Model for Transfer Learning among Buildings

In China, buildings consume a large amount of electricity, resulting in non-negligible carbon emissions from power generation. Learning electricity usage behavior from loads monitoring has the potential for electricity cost saving and net zero. Non-intrusive load monitoring (NILM) is a technique used to estimate the individual loads within a household by analyzing the overall power consumption. However, most NILM methods require lots of data for training, which is hard to acquire in practice. To address this problem, transfer learning (TL) is used to build a diffusion model based on a large source-domain data set and it is transferred to a target scenario with limited information. The proposed method is validated on REDD and REFIT datasets, outperforming a state-of-the-art benchmark in various metrics. Therefore, the experimental results show performing NILM via the hybrid of diffusion model and TL is promising.
Inspecting Transfer Learning Models for Diabetic Retinopathy Detection

Astonishingly, 285 million individuals of the global diabetic population, exhibit sign of diabetic retinopathy. It has become a prominent global health challenge arising as a complication of diabetes. Diabetic Retinopathy damages the retina's delicate blood vessels leading to blurred vision, dark spots in vision, retinal detachment and ultimately vision loss. To address this concern, we implemented a study to explore the possibilities for automated DR detection using advanced deep learning techniques. We assessed four models namely, EfficientNet-B5, ResNet152V2, EfficientNet-B6 and MobileNetV2, on the Asia Pacific Tele-Ophthalmology Society (APTOS) dataset of retinal images. MobileNetV2 emerged as a top-performing model, closely followed by EfficientNet-B6, with the potential to revolutionize diabetic retinopathy diagnosis and reduce the risk of vision loss. These findings contribute to the field of medical image analysis, offering promise for enhanced early detection of diabetic retinopathy.
Lightweight pedestrian detection algorithm based on GGSnet

In this paper, a lightweight pedestrian detection algorithm named GGSnet algorithm based on improved YOLOv5s is proposed to improve the detection speed and accuracy of pedestrian recognition, and a comprehensive experimental evaluation is carried out on pedestrian recognition tasks. Ghost module is introduced into YOLOv5s to make the model highly computative while maintaining lightweight. GSConv module replaces traditional convolution operation, which can effectively reduce the complexity of the model, improve the computational efficiency, and maintain the accuracy to a certain extent. At the same time, the SIMAM module is introduced to improve the feature extraction capability of the image and further improve the accuracy of pedestrian recognition. Experimental results show that GGSnet achieves high Precision and mAP scores of 0.915 and 0.932, respectively, demonstrating the advantages of the algorithm in accuracy. In addition, GGSnet has a reference count of 3366,526 and a model size of 7.1MB, 48% and 49.3% of YOLOv5s, respectively, which helps achieve efficient pedestrian recognition in environments with limited storage and computing resources. This verifies that GGSnet has potential in the field of pedestrian detection and provides valuable exploration in practical applications. Therefore, the pedestrian recognition algorithm based on GGSnet proposed in this paper can be used as a promising solution and provide new research ideas and technical support for other fields.
Long-term climate change effects on dynamics of microorganisms and carbon in the root-zone

III SAMMENDRAG . IV TABLE OF CONTENTS . V LIST OF PAPERS . VI 1. TERRESTRIAL CARBON CYCLING IN FUTURE CLIMATE . 1 2. CLIMATE CHANGE EXPERIMENTS . 4 2.1 THE CLIMAITE EXPERIMENTAL SITE BRANDBJERG . 4 2.2 EFFECTS OF ELEVATED CO2, DROUGHT AND TEMPERATURE ON TERRESTRIAL C-CYCLING . 7 3. STUDYING CARBON CYCLING WITH PULSE-LABELING . 8 3.1 PULSE-LABELING SETUP (PAPER I) . 8 3.2 VALIDATION OF THE DEVELOPED PULSE-LABELING SETUP (PAPER I) . 10 4. TRACING CARBON BELOWGROUND . 12 4.1 BELOWGROUND CARBON POOLS AND THE SOIL MICROBIAL COMMUNITY UNDER CHANGING CLIMATIC CONDITIONS (PAPER II). 12 4.2 CLIMATE CHANGE AS TRIGGER OF SOIL ORGANIC MATTER PRIMING? (PAPER III) . 18 5. OUTLOOK – CARBON BALANCE UNDER FUTURE CLIMATIC CONDITIONS . 22 5.1 SOIL AND ECOSYSTEM CO2 EFFLUX AS AFFECTED BY CLIMATE CHANGE FACTORS (OUTLOOK FOR PAPER IV) . 22 5.1 CONCLUSIONS AND PERSPECTIVES . 24 6. REFERENCES . 26 7. APPENDIX . 31 8. PAPERS . 42 8.1 PAPER I . 43 8.2 PAPER II . 73 8.3 PAPER III. 112 Sabine Reinsch PhD Thesis
A Four-Stage Super Data Envelopment Analysis Model for Shipping Connectivity Efficiency

Digital technologies such as big data, cloud computing and blockchain are crucial to maritime connectivity networks. This study employs the entropy weight method, four-stage super-efficiency DEA model, competitive situation matrix, and K-means clustering analysis to comprehensively assess maritime connectivity efficiency among 44 countries along the Belt and Road Initiative from 2010 to 2021. Findings reveal: (1) Overall efficiency is increasing, with Japan, Singapore, and Qatar consistently leading. (2) Efficiency varies spatially, with East and Southeast Asia showing higher efficiency, and this disparity lessening with environmental considerations. (3) Total factor productivity improves due to technology, though composite and pure technical efficiency need attention. (4) The competitive matrix categorizes countries into four types, highlighting Malaysia’s progress and underscoring the need for scale efficiency improvements. The insights of this study provide implications for the construction of maritime networks, digitalization and intelligentization of shipping under the Belt and Road Initiative, and are of great significance for strengthening global trade and cooperation.
Multi-information Self-attention Autoencoder Sequential recommendation

The field of sequential recommendation plays a crucial role in personalized recommendation systems, aiming to model users’ past interactions and predict their future interactions with items or behaviors. Traditional methods in sequential recommendation typically rely on user behavior history and item attributes for making recommendations, but they overlook the internal relationships and contextual information among items within a sequence. Moreover, existing autoencoder models face limitations in capturing long-term dependencies and effectively modeling contextual information for sequential recommendation tasks. To address these issues, we propose a novel framework called miSAASRec that leverages a multi-information autoencoder with a self-attention module to capture internal relevance and contextual features of the data. This enables miSAASRec to achieve more accurate and comprehensive data encoding, thereby enhancing the performance of the autoencoder. We conducted several experiments to demonstrate the superior performance of our miSAASRec model compared to existing methods, achieving improvements ranging from 7.92% and 32.20% in MRR (Mean Reciprocal Rank) and 9.00% and 28.90% in Recall@10.