Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Densest Subgraphs Discovery in Large Dynamic Graphs by Greedy Approximation

Densest subgraph detection has become an important primitive in graph mining tasks when analyzing communities and detecting events in a wide range of application domains. Currently, it is a challenging and practically crucial research issue to develop efficient densest subgraphs mining approaches that can handle both very large and continuously evolving graphs. Although large-scale or dynamic methods have been proposed to find the densest subgraphs, there is still a lack of a promising method to deal with large-scale and dynamically evolving graphs. In this paper, the problem is formulated and proved to be NP-Hard, an incremental greedy approximation approach is proposed, and its running time is O(m+n). In order to find the densest subgraph effectively by heuristically merging the local densest subgraphs, firstly, the edge flow of a dynamic graph is divided into several subgraphs in a given period of $T $ . Secondly, a local candidate set is generated by local denser subgraph discovery. Third, the global densest subgraph candidates are collected by heuristically merging. Last, the densest subgraphs are induced from the global densest subgraph candidates with constraints by static densest subgraph discovery algorithm. This incremental approach enables us to scale up the existing densest subgraph discovery algorithms, which focus mainly on small and static graphs and thus can handle very large dynamic graphs. Experiments on real-world networks with billions of nodes for comprehensive evaluations present excellent improvement in efficiency and accuracy: it reduces about 25% running time on average and presents a more accurate estimation of the structure of a graph with more compact subgraphs than the static method.It also performs well when dealing with graphs of varying densities.
Classification Algorithm Analysis for Breast Cancer

Breast cancer in women is a type of disease that is the main cause of death in women according to world breast cancer data. Therefore, early detection of breasts is needed significantly to improve life. If a woman has been identified, then rehabilitation and treatment on an incentive basis are needed to reduce the worse. This study used a dataset collected by the University of Wisconsin Hospitals, Madison (https://atapdata.ai/). This research conducted experiments using several data mining classification strategies to predict breast cancer using machine learning algorithms. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes, Random Forest, Decision Tree, Deep Learning (H2O), and Neural Network are used to classify algorithms. From these algorithms’ classification, we compare accuracy, best classification, and compare algorithm performance with curve ROC (RapidMiner Studio Core) to see which performance algorithm has the best quality for classification. From the analysis results, the deep learning algorithm with Tanh and Exprectifier activation function has a good accuracy of 93.14%, and the best classification with 89.62%. In addition, deep learning has found the best quality from the ROC curve results on the dataset used in this research.
A Dual-Band Epsilon-Negative Material Design Using Folded-Wire Structures

A novel design of epsilon-negative (ENG) material which can exhibit negative permittivity at two nearby bands is presented in this paper. With the unit shape as an unsealed rectangle frame, the components of this ENG material are two folded wires with different dimensions. Each folded-wire structure can achieve electric resonance at a certain frequency band by appropriately adjusting its dimensions. Its electromagnetic properties are then retrieved to demonstrate that the dual-band electric resonances can be obtained in our sample. With the aid of numerical simulation, the transmission property of the proposed dual-band epsilon-negative material is further investigated. And the result shows that the stop band for each folded wire (permittivity ε < 0) is easily adjusted by changing the designing parameters, without influence on the other nearby resonance frequency. It provides an advancement of dual-band and low mutual coupling resonances design. It is also promising that our approach will be effective to make this kind of dual-band ENG material applied into the areas of reconfigurable antennas and their arrays.
Development of the Passive Perfusion Probe for Non-Invasive Blood Perfusion Measurement

A non-invasive blood perfusion system has been developed and tested in a phantom tissue and an animal model. The system uses a small sensor with a laminated flat thermocouple to measure the heat transfer response to an arbitrary thermal event (convective or conductive) imposed on the tissue surface. Blood perfusion and contact resistance are estimated by comparing heat flux data with a mathematical model of the tissue. The perfusion system was evaluated for repeatability and sensitivity using both a phantom tissue test stand and exposed rat liver tests. Perfusion in the phantom tissue tests was varied by controlling the flow of water into the phantom tissue test section, and the perfusion in the exposed liver tests was varied by temporarily occluding blood flow through the portal vein. The phantom tissue tests indicated that the probe can be used to detect small changes in perfusion (0.009 ml/ml/s). The probe qualitatively tracked the changes in the perfusion of the liver model due to occlusion of the portal vein.
THE LAGRANGIAN DERIVATION OF KANE’S EQUATIONS

The Lagrangian approach to the development of dynamics equations for a multi-body system, constrained or otherwise, requires solving the forWard kinematics of the system at velocity level in order to derive the kinetic energy of the system. The kinetic-energy expression should then be differentiated multiple times to derive the equations of motion of the system. Among these differentiations, the partial derivative of kinetic energy with respect to the system generalized coordinates is specially cumbersome. In this paper, we will derive this partial derivative using a novel kinematic relation for the partial derivative of angular velocity with respect to the system generalized coordinates. It will be shown that, as a result of the use of this relation, the equations of motion of the system are directly derived in the form of Kane's equations.
Array Pattern Synthesis Based on the Improved LDW-PSO Algorithm

PSO algorithm is intensively applied in array pattern synthesis. While the choice of inertia factor in classical PSO algorithm is a cumbersome task, improper inertia factor will result in slow convergence or falling into the local minima. This paper presents an improved algorithm based on LDW-PSO to avoid converging to the local minima. Simulation results show that the algorithm has faster speed and higher robustness compared with LDW-PSO algorithm.
Picosecond wide-field magneto-optical imaging of magnetization dynamics of amorphous film elements

Time-resolved wide-field magneto-optical microscopy with picosecond time resolution and with phase-locked harmonic excitation is used to image the fundamental dynamic modes of magnetic domain and domain-wall states in soft-magnetic thick film elements. By analyzing mirrored domain states, the pure in-plane and out-of-plane dynamic magnetic response under a continuous microwave excitation is extracted simultaneously. Domain-wall oscillations and local domain response, including transversely aligned and closure domain states, are visualized below, at, and above the resonance frequency of the magnetic elements. Regions of different high-frequency permeability are distinguished from the laterally resolved measurements. Despite nearly constant precessional frequency, a strong mode amplitude variation is found with varying the magnetic bias field, which is correlated to the exhibited change of the individual domain structure. Inhomogeneous out-of-phase precessional modes within the individual domains occur close to a state of domain instability. The imaging results are compared to existing models of flux response. Magnetic domain knowledge and precise dynamic imaging is needed for the understanding of the dynamic magnetization behavior of even basic domain structures beyond the magnetic material's elementary properties.
Distrib uted, Complete, Multi-robot Coverage of Initially Unknown Environments using Repartitioning (Extended Abstract)

We consider the problem of coverage path planning by multiple robots in an environment where the location and geometry of obstacles are initially unknown to the robots. We propose a novel algorithm where the robots initially partition the environment using Voronoi partitioning. Each robot then uses an auction-based algorithm to reallocate inaccessible portions of its initial Voronoi cell to robots in neighboring Voronoi cells so that each robot is responsible for covering a set of contiguous connected regions. We have verified the performance of our algorithm on e-puck robots within the Webots simulator in different environments with different obstacle geometries and shown that it performs complete, non-overlapping coverage.
Fluorescence decay and recovery in organic dye-doped polymer optical fibers

Photostabilities of several dye-doped polymer optical fibers are investigated experimentally. In the spectral fluorescence of those fibers doped with dyes such as fluorescein and pyrromethene, a characteristic double-peak structure has been identified. The photobleaching induced by the irradiation of an Ar laser output is found to be partially or completely reversible. This depends on the type of dyes used and also on the intensity of irradiation. Also, a spectrally asymmetrical decay which corresponds to the differential photobleaching of the two peaks in the fluorescence spectra has been observed.
Near-Field Picometer Vibration Detection Based on Dielectric Microsphere Probe

We propose a near-field picometer vibration detection method based on a dielectric microsphere probe. Using an optical fiber combined with a dielectric microsphere to form a near-field probe. When parallel light is injected into the dielectric microsphere, interaction occurs between the surface of the microsphere and the focused beam in the near field, and at the front end of the dielectric microsphere will generate a highly localized electromagnetic beam, i.e., a photonic nanojet (PNJ). The nanojet of the probe in the near-field can enhance the scattered light of the object under the test surface. We used a differential structure that was formed by two optical fiber probes to detect the picometer vibration in the near field. From the experimental results, we know that the method can detect the picometer vibration signal, while the minimum vibration signal of 20 pm can be detected. The method has the potential to apply in cellular diagnostics, single-molecule detection, and in situ measurements.
Potential of Terminalia catappa Leaf Extract as Bioherbicide on Mikania micrantha Seed Germination

Mikania micrantha is a weed that is commonly found on agricultural land. Environmentally friendly weed control can be done using bioherbicides because the materials used come from nature. Terminalia catappa contains secondary metabolites so that its bioactive compounds can be used as weed control. This study aims to determine the total secondary metabolite compounds of Terminalia catappa leaves and their effect on Mikania micrantha seed germination. This study used a factorial Completely Randomized Design (CRD). The first factor is various types of solvents aquadest, ethanol and methanol. The second factor was the concentration of the extract application 30%, 50%, 70% and repeated 3 times so that there were 27 experimental units. Data were analyzed using analysis of variance if it had a significant effect, further tests were carried out using 5% BNJ. The results showed that the Terminalia catappa leaf extract with distilled aquadest of 70% was able to prevent the seeds from germinating while the methanol and ethanol solvent extracts were able to inhibit the germination of Mikania micrantha seeds at an application concentration of 30%.
Scattering of electromagnetic plane wave by a circular disk on a grounded substrate

In this investigation, scattering from a perfectly conducting circular disk located on a grounded substrate has been studied rigorously. The incident field is a plane wave. The method of analysis uses a Kobayashi potential. The mathematical formulation produces dual integral equations (DIEs). These DIEs are solved by using the discontinuous properties of Weber–Schafheitlin’s integral. After applying the projection, the resulting expressions reduce to matrix equations for expansion coefficients. The matrix elements are in the form of infinite integrals. The far-field patterns of the scattered field are computed for different incident angles and disk sizes for both E- and H-polarizations. To validate the results, we have computed the solution based on the physical optics approximation. The comparison of the results shows good agreement.
Design of Computer-Aided-Diagnosis (CAD) for Self- Assessment Tuberculosis in Indonesia

Tuberculosis (TB) is one of the highest causes of death in Indonesia. The main reason is lack of the health facilities. Computer-aided diagnosis (CAD) is a tool for early treatment and screening of many diseases, including TB. This paper proposed a design of a CAD system in Indonesia specifically for TB. The design gives the analysis of self-assessment concepts, use-case diagrams, and black-box diagrams. The black box utilizes chest x-ray (CXR) data for the medical image processing (MIP) method, and artificial intelligence (AI) for classification and visualization of the TB. This CAD design of self-assessment of TB has a capability to help the health practitioners read and interpret the diagnosis result more easily.
Hippocampus shape analysis for temporal lobe epilepsy detection in magnetic resonance imaging

There are evidences in the literature that Temporal Lobe Epilepsy (TLE) causes some lateralized atrophy and deformation on hippocampus and other substructures of the brain. Magnetic Resonance Imaging (MRI), due to high-contrast soft tissue imaging, is one of the most popular imaging modalities being used in TLE diagnosis and treatment procedures. Using an algorithm to help clinicians for better and more effective shape deformations analysis could improve the diagnosis and treatment of the disease. In this project our purpose is to design, implement and test a classification algorithm for MRIs based on hippocampal asymmetry detection using shape and size-based features. Our method consisted of two main parts; (1) shape feature extraction, and (2) image classification. We tested 11 different shape and size features and selected four of them that detect the asymmetry in hippocampus significantly in a randomly selected subset of the dataset. Then, we employed a support vector machine (SVM) classifier to classify the remaining images of the dataset to normal and epileptic images using our selected features. The dataset contains 25 patient images in which 12 cases were used as a training set and the rest 13 cases for testing the performance of classifier. We measured accuracy, specificity and sensitivity of, respectively, 76%, 100%, and 70% for our algorithm. The preliminary results show that using shape and size features for detecting hippocampal asymmetry could be helpful in TLE diagnosis in MRI.
Dynamics of asymptotically AdS spaces and holography

This thesis presents a series of studies on the evolution and out-ofequilibrium dynamics of strongly coupled quantum eld theories by means of the AdS/CFT correspondence. We use a handful of analytic and semianalytic techniques to investigate the response of the system due to dierent types of perturbations, some of them leading to thermalization, cooling down or coherent oscillations of the quantum elds. We characterize the processes by studying the evolution of non-local observables such as two-point functions, Wilson loops and entanglement entropy. Our results may be relevant to heavyion collision and condensed matter physics experiments
Normalizing Regular Expression by using Starred Trie Representation

A regular expression is a set of characters that specify a pattern. A regular expression can be defined for every finite automaton, and is used for pattern matching. Here we are considering about normalized and un-normalized regular expression. Methodology has been developed to find out the normalized regular expression of a given un-normalized regular expression. Both normalized and un-normalized regular expression should be equivalent, means they should generate same strings over the given alphabets. Normalized regular expression is less time consuming in pattern matching as compare to un-normalized regular expression. Implementation of methodology is rule based and using starred trie representation. Implementation techniques for both methodologies are described in chapter 4 “Normalization of regular expression”. Both methodologies are developed in Java programming language. This thesis contains introduction of automaton, regular expression, literature review, problem statement, normalization techniqes, implementation environment followed by conclusion and future scope.
An Implicit Method for the Pseudo-Compressibility Incompressible Navier-Stokes Equation

A pseudo-compressibility method developed by Rogers is used to solve the incompressible Navier-Stokes equation. Numerical flux is scattered through 3rd order Roe scheme and the 2nd order Harten-Yee TVD scheme separately. In order to accelerate convergence, several implicit methods(ADI-LU, LGS, LU-SGS) are accepted. Efficiency of different methods are shown as a classical flowfield around a cylinder in low Re(Re=200). Roe scheme is used to simulate two low speed unsteady problems: Low Re flowfield around a rotational cylinder(ω=1) and flowfield around a pitching NACA0015 airfoil with identical pitching rate.
Microphthalmia-Associated Transcription Factor: A Differentiation Marker in Uveal Melanoma

Microphthalmia-associated transcription factor (MITF) is an important regulator of melanogenesis and melanocyte development. In cutaneous melanoma, MITF loss has been linked to an increased expression of stem cell markers, a shift in epithelial-to-mesenchymal transition (EMT)-related factors, and increased inflammation. We explored the role of MITF in Uveal Melanoma (UM) using a cohort of 64 patients enucleated at the Leiden University Medical Center. We analysed the relation between MITF expression and clinical, histopathological and genetic features of UM, as well as survival. We performed differential gene expression and gene set enrichment analysis using mRNA microarray data, comparing MITF-low with MITF-high UM. MITF expression was lower in heavily pigmented UM than in lightly pigmented UM (p = 0.003), which we confirmed by immunohistochemistry. Furthermore, MITF was significantly lower in UM with monosomy 3/BAP1 loss than in those with disomy 3/no BAP1 loss (p < 0.001) and with 8q gain/amplification 8q (p = 0.02). Spearman correlation analysis showed that a low MITF expression was associated with an increase in inflammatory markers, hallmark pathways involved in inflammation, and epithelial-mesenchymal transition. Similar to the situation in cutaneous melanoma, we propose that MITF loss in UM is related to de-differentiation to a less favourable EMT profile and inflammation.
The Public Value of Citizens’ Initiatives: Evidence from a Dutch Municipality

Active citizens who take initiative are generally regarded as desirable. However, the precise reasons why citizens’ initiatives are considered valuable and what their value consists of remain unclear, vague, and often unanswered. In this study, we used Q methodology to explore how civil servants, local politicians, and societal actors in a Dutch municipality view the public value of citizens’ initiatives. The analysis reveals four distinct views of the value of citizens’ initiatives: a view that values intangible results, a view that values a hands-on mentality, a view that values acting out of a sense of purpose, and a view that values citizens organizing and acting out of their own interests. Theoretically, we distinguish between material, immaterial, and process-oriented interpretations of values, and empirically this distinction shows that across the four value views, the process-oriented values are the most disagreed upon. Finally, we find common ground between the value views that we label “selfish collectivism. ” This is the view that appreciates citizens’ initiatives for solving problems for the sake of the community, not for their altruism, but because they are self-serving. The strong differences in value views suggest that there is a risk that subsequent policy language and instruments based on these views could lead to conflict between the actors involved.
Simplification of Mannequin Based on Fuzzy Partition of Geometric Information Gain Metric

Focusing on the maintenance of important shape features during the mannequin simplification process, a new simplification method, driven by fuzzy partition of geometric information gain metric is proposed in this paper. The integration of distance, non-planarity, change of the normal has been defined as the geometric information gain metric to measure the geometric information content of each vertex. The geometric information gain metric is subsequently partitioned into three fuzzy sets to indicate the weak, moderate and strong geometry salience of vertices on the underlying mannequin in which thresholding values for fuzzy sets are selected by minimum fuzziness degree principle. The resulted fuzzy sets of geometric information gain metric are then introduced to the edge collapse operation to determine the vertices incident to candidate edges for contraction. The proposed approach is capable of reducing simplification error and maintaining mannequin's feature dimensions as well. Simulation results show that the new algorithm facilitate better approximations with regard to both visual fidelity and geometric errors than Garland’s quadric error metric algorithm.
From transactions to transformations: exploring transformative food retailing

Food retailing is undergoing a major restructuring process that is altering its boundaries, service provision and operations. Digitalisation and other technological advances are shifting the focus from products to services, from offline to online and from physical to virtual. Simultaneously, initiatives such as the United Nations Sustainable Development Goals are exerting pressure on food retailers to address contemporary global challenges, such as promoting healthy and sustainable consumption and production. However, these areas not only challenge food retailers but also provide opportunities for facilitating favourable dietary changes that benefit consumers, companies and society at large. This study introduces transformative food retailing as a construct that shifts attention to the reconfigured role of food retailing and its potential. We identify the shaping forces and characteristics of transformative food retailing and discuss the implications for consumers, food retailers and society at large. This paper is among the first to define and conceptualise food retail as transformative and, as a result, sets a platform for future scholarly research and practice to uncover the full potential of food retailing in serving both consumers and society.