Miracle-dz/UniFAR-FTUs huggingface dataset:
MPAN: Multi-Part Attention Network for Point Cloud Based 3D Shape Retrieval

3D shape retrieval is an important researching field due to its wide applications in computer vision and multimedia fields. With the development of deep learning technology, great progress has been made in recent years and lots of methods have achieved promising 3D shape retrieval results. Due to the effective description of point cloud data on structural information for 3D shapes, lots of methods based on point cloud data format are proposed for better shape representation. However, most of them focus on extracting a global descrisptor from the whole 3D shape while the local features and detailed structural information are ignored, which negatively affect the effectiveness of shape descriptors. In addition, these methods also ignore the correlations among different parts of point clouds, which may introduce redundant information to the final shape descriptors. In order to address these issues, we propose a Multi-part attention network (MPAN) for 3D model retrieval based on point cloud. Firstly, we segment a 3D shape into multiple parts by employing a pre-trained PointNet++ segmentation model. After extracting the local features from them, we introduce a novel self-attention mechanism to explore the correlations between different parts. Meanwhile, by considering the structural relevance of them, the redundancy for representing 3D shapes is removed while the effective information is utilized. Finally, informative and discriminative shape descriptors, considering both local features and spatial correlations, are generated for 3D shape retrieval task. To validate the effectiveness of our method, we conduct several experiments on the public 3D shape benchmark, ShapeNetPart dataset. Experimental results and comparisons with state-of-the-art methods demonstrate the superiority of our proposed method.
Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics

The inference of Gene Regulatory Networks (GRNs) from gene expression data is a hard and widely addressed scientific challenge with potential industrial and health-care use. Discrete and continuous models of GRNs are often used (i) to understand the process, and (ii) to predict the values of the relevant variables. Here, we propose a procedure to infer models of GRNs from data where (i) the data is binarized, (ii) a Boolean model is created using a Cartesian Genetic Programming technique, (iii) the obtained Boolean model is converted to a system of ordinary differential equations, and (iv) an Evolution Strategy defines the parameters of the continuous model. As a result, we expect to reduce the effect of noise and to improve biological interpretability. The proposed method is applied to two ODE systems that describe the circadian rhythm network dynamic, with 5 and 10 state variables. The models created by the proposed procedure are able to reproduce the behavior observed in the original data.
Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data

Classification problems can be modeled as multi-objective optimization problems. MOCA-I is a multi-objective local search designed to solve these problems, particularly when the data are imbalanced. However, this algorithm has been tuned by hand in order to be efficient on particular datasets. In this paper, we propose a methodology to automatically conFigure a multi-objective algorithm for solving a supervised partial classification problem. This methodology is based on a multi-objective approach of automatic algorithm configuration and requires a clear definition of the experimental protocol. Therefore, we present a k-fold cross-validation protocol to train and test the configuration model. To the best of our knowledge, it is the first time that multi-objective automatic algorithm configuration is performed on optimization algorithms to solve classification problems. Experimental results on real imbalanced datasets show that our approach can find efficient configurations of MOCA-I with less effort in comparison with the ones found exhaustively by hand.
Multifactorial Genetic Fuzzy Data Mining for Building Membership Functions

Association mining is a famous data mining technology because its form is explainable by human beings. Innovating fuzzy set theory to associations mining provides a solution to quantitative database, where membership function plays an important role in mining fuzzy associations. Genetic algorithm (GA) has been successfully applied to the optimization of membership functions. Based on the spirit of divide-andconquer, GA optimizes the membership functions for each item separately. Nevertheless, the cooperation among different items in the course of evolution was never considered. Evolutionary multitasking optimization (EMO) is an emerging searching paradigm which dedicates to solving multiple tasks simultaneously for improving the search efficiency. This study introduces the EMO into genetic fuzzy data mining to address the above issue. Specifically, this study incorporates a state-of the-art genetic fuzzy data mining method, the structure-based representation genetic algorithm, with the well-known multifactorial evolutionary algorithm (MFEA). A series of experiments is conducted to validate the effectiveness and efficiency of the proposed method. The results indicate that the proposed method improves the structure-based representation genetic algorithm in terms of convergence speed and solution quality on all sizes of datasets. The results also show that the proposed method is about 20 times faster than the structure-based representation genetic algorithm with respect to the exploited number of evaluations.
Multi-Objective Optimisation of Multi-Output Neural Trees

We propose an algorithm and a new method to tackle the classification problems. We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each run of evolutionary learning, i.e., each hypothesis (tree) generated bears distinct properties compared to any other hypothesis both in topological space and parameter-space. This leads to a challenging optimisation problem where the aim is to minimise the tree-size and maximise the classification accuracy. Therefore, the Pareto-optimality concerns were met by hypervolume indicator analysis. We used nine benchmark classification learning problems to evaluate the performance of the MONT. As a result of our experiments, we obtained MONTs which are able to tackle the classification problems with high accuracy. The performance of MONT emerged better over a set of problems tackled in this study compared with a set of well-known classifiers: multilayer perceptron, reduced-error pruning tree, naïve Bayes classifier, decision tree, and support vector machine. Moreover, the performances of three versions of MONT’s training using genetic programming, NSGA-II, and NSGA-III suggests that the NSGA-III gives the best Pareto-optimal solution.
Scanning Microwave Microscopy for Biological Applications: Introducing the State of the Art and Inverted SMM

Often, a student comes in excited by a revolutionary idea. When this happens, we invite the student to check the literature carefully and, moreover, to extend the search way back, for more than a century, in fact. For example, encouraged by Albert Einstein, Edward H. Synge introduced the concept of a near-field scanning microscope in the 1928 paper "A Suggested Method for Extending Microscopic Resolution Into the Ultramicroscopic Region" [1]. He claimed to have overcome the ". axiom in microscopy, that the only way to extend resolving power lies in the employment of light of smaller wavelength. " For subwavelength resolution of a biological sample, Synge proposed to place an opaque screen with a 10-nm diameter pinhole within 10 nm of the sample (Figure 1). Light passing through the pinhole and the sample is focused on a photodetector. By moving the screen laterally in 10-nm steps, the sample is imaged with 10-nm resolution, regardless of the wavelength of the light. Later, what he proposed became known as a scanning near-field optical microscope (SNOM).
Robust Microstrip to Empty Substrate-Integrated Waveguide Transition Using Tapered Artificial Dielectric Slab Matrix

In this letter, a novel transition from microstrip (MS) line to empty substrate-integrated waveguide (ESIW) with tapered artificial dielectric slab matrix in compact size is proposed. ESIW exhibits effective characteristics for energy transmission, especially in the millimeter waveband, due to the loss of dielectric. Recently, an MS to ESIW transition without sharp dielectric taper is achieved through an expanded ESIW width. Manufacturing and assembling difficulties can be overcome in this kind of transition, especially for a small thickness. In this letter, in order to apply in thin substrate with a compact size, especially in millimeter waveband, a novel robust transition from MS line to ESIW (with the tapered artificial dielectric slab matrix) is designed, fabricated, and measured in back-to-back configuration. In Ka-band, the return loss and insertion loss for one transition are better than 14.7 and − 0.46 ± 0.17 dB, respectively.
Automated Machine Learning for Information Retrieval in Scientific Articles

The amount of scientific conferences and journal articles continues to increase and new approaches are required to support users in finding relevant publications. This study investigates to what extent a new machine learning (ML) pipeline may preferentially identify links between similar scientific articles. The characteristics of intersections and unions of keywords, contextualized keywords (i.e., synsets) and neighbors are computed and used to train a ML model. Automated machine learning (AutoML) is then applied to ease the search for a new pipeline. Extensive experiments demonstrated that a newly designed ML model achieves an accuracy of 90% on a dataset of approximately 120,000 article pairs. These results suggest that application of ML for proposing new recommendation systems could have in the long term a positive impact in the literature.
A hybrid heuristic for districting problems with routing criteria

In this paper we consider districting problems with routing criteria. Such problems arise when clients in a commercial district need to be attended daily and the time to visit them is limited, for example by working hours of retail businesses. Routing costs are modeled by traveling salesman tours starting from predetermined depot locations. Besides having low routing costs, districts must also satisfy balancing (e.g. of total demand or workload) and contiguity constraints, and may also be required to be geographically compact. We propose a general heuristic for solving such problems. It iteratively builds new solutions by a greedy constructive heuristic, and then improves them by a hybrid alternating tabu search strategy. We further propose an efficient scheme for maintaining high-quality routes under small modifications to districts, for example incurred by neighborhood search operators. In extensive computational experiments we study the different components of our method, and show that it is effective in treating a number of problem variants with different constraints.
Robust Multi-Objective optimization using Conditional Pareto Optimal Dominance

Robust optimization of real-world problems is essential to reduce the significant negative impact of uncertainties and noises present in the environment. Uncertainties in the decision variables are often handled using explicit or implicit averaging methods, in which the fitness of a solution isjudged based on the objective values of neighbouring solutions. Explicit averaging methods are highly reliable but require additional objective function evaluation, which can significantly increases the overall computational cost of an optimization process. On the other hand, implicit averaging techniques are computationally cheap, yet they suffer from low reliability since they use the history of search in a population-based optimization algorithm. This work proposes a conditional Pareto optimal dominance to improve the reliability of robust optimization methods that use implicit averaging methods. The proposed method is applied to Multi-Objective Particle Swarm optimisation. Empirical study with a benchmark suite shows the benefit of the proposed conditional Pareto optimal dominance in locating robust solutions in multi-objective problems.
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On-demand resource provisioning in cloud computing provides tailor-made resource packages (typically in the form of VMs) to meet users’ demands. Public clouds nowadays provide elaborated types of VMs, but have yet to offer the most flexible dynamic VM assembly, which is partly due to the lack of a mature mechanism for pricing tailor-made VMs. This work proposes an efficient randomized auction mechanism based on a novel application of smoothed analysis and randomized reduction, for dynamic VM provisioning and pricing in geo-distributed cloud data centers. To the best of our knowledge, it is the first one in literature that achieves (i) truthfulness in expectation, (ii) polynomial running time in expectation, and (iii) <inline-formula><tex-math notation="LaTeX">$(1-\epsilon)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>ε</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-2822718.gif"/></alternatives></inline-formula>-optimal social welfare in expectation for resource allocation, where <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives><mml:math><mml:mi>ε</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq3-2822718.gif"/></alternatives></inline-formula> can be arbitrarily close to 0. Our mechanism consists of three modules: (1) an exact algorithm to solve the NP-hard social welfare maximization problem, which has polynomial run-time in expectation, (2) a perturbation-based randomized resource allocation scheme which produces an allocation solution that is <inline-formula><tex-math notation="LaTeX">$(1-\epsilon)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>ε</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq4-2822718.gif"/></alternatives></inline-formula>-optimal and (3) an auction mechanism prices the customized VMs using a randomized VCG payment, with a guarantee in truthfulness in expectation. We validate the efficacy of the mechanism through theoretical analysis and trace-driven simulations.
Generating Tree Inputs for Testing using Evolutionary Computation Techniques

Software Testing usually considers programs with parameters ranging over simple types. However, there are many programs using structured types. The main problem to test these programs is that it is not easy to select a relatively small test suite that can find most of the faults in these programs. In this paper we present a framework to generate test suites for unit testing of methods which have trees as parameters. We combine classical mutation testing with Evolutionary Computation techniques to evolve a population of trees. The final goal is to obtain a set of trees, representing, good test cases, that will be used as the test suite to test the corresponding method.
Candida Soluble Cell Wall β-D-Glucan Induces Lung Inflammation in Mice

Bioactivity of cell wall component(s) of fungi has not been fully elucidated, especially in vivo. We isolated Candida soluble beta-D-glucan (CSBG) from Candida albicans (C. albicans). We investigated the effects of airway exposure to CSBG on the immune systems in the airways in mice. CSBG exposure induced neutrophilic and eosinophilic inflammation in the lung, which was concomitant with the increased local expression of proinflammatory cytokines including tumor necrosis factor - α, interleukin (IL)-1 β, IL-6, macrophage inflammatory protein -1 α, macrophage chemoattractant protein -1, RANTES (regulated on activation and normal T cells expressed and secreted), and eotaxin. The lung inflammation with enhanced expression of proinflammatory proteins caused by CSBG was directly related to its structure, since structurally degraded products of CSBG by formic acid induced negligible responses in the lung. CSBG enhanced nuclear localization of phosphorylated signal transducer and activator of transcription (STAT)-6 in the lung. These results suggest that airway exposure to CSBG induces lung inflammation, at least partly, via the enhanced expression of proinflammatory cytokines and the activation of STAT-6 pathway, and can be a proper murine model for fungal lung inflammation.
Multi-Objective Differential Evolution Algorithms for the Protein Structure Prediction Problem

The structural analysis of proteins is an essential step for understanding their biological function. However, the process of the structural determination of these molecules is expensive and time-consuming. In order to reduce these factors, computational methods might be a provocative approach, despite the complexity associated with it. Over the decades, different computational approaches were proposed as well as different energy force fields. As the force fields consider conflicting terms in its composition, multi-objective optimization approaches showed to be suitable to the Protein Structure Prediction problem. In this way, the objective of the current work is to evaluate and compare three multi-objective algorithms, the Non-Dominated Sorting Genetic Algorithm in its second version, the Generalized Differential Evolution in its third version, and the Differential Evolution Multi-Objective. We split the score3 energy function provided by Rosetta into a bi-objective problem. The first objective considers only the non-bonded van der Waals, while the second one is composed of bonded-terms and a secondary structure reinforcement score. Moreover, structural information provided by the Angle Probability List is considered, since this kind of information proved to be reliable in single-objective approaches. Results obtained are analyzed using GDT and RMSD metrics, showing the better capability of Differential Evolution based methods for the problem.
Eigenvector Crossover in jDE100 Algorithm

In this paper, an advanced variant of the efficient jDE100 variant is proposed. The introduced jDE100e employs an efficient rotationally-invariant Eigenvector crossover. Both algorithms are applied on 10 test problems for a single-objective optimisation CEC 2020 with four dimension levels. The results show that the proposed jDE100e is able to solve some of the problems successfully. A comparison of the proposed jDE100e with the original jDE100 illustrates the superiority of the newly designed algorithm as jDE100e performs significantly better in 16 out of 40 problems, and jDE100 is not able to outperform jDE100e significantly.
A Cone Decomposition Evolutionary Algorithm with Dominance-based Archive for Many-objective Optimization Problems with Irregular Fronts

Most of the existing multi-objective evolutionary algorithms (MOEAs) are designed to solve many-objective optimization problems (MaOPs) with regular fronts. However, their effectiveness for MaOPs with irregular fronts are yet to be improved. In this paper, a cone decomposition evolutionary algorithm with dominance-based archive (CDEA-DA) is presented to extend decomposition-based MOEAs for MaOPs with irregular fronts. In CDEA-DA, an improved cone decomposition strategy is adopted to decompose one MaOP into several scalar subproblems. Then, a dominance-based archive is designed to collect the non-dominated solutions eliminated during evolution, so as to improve the quality of the obtained front. The proposed algorithm is compared with four state-of-the-art algorithms on unconstrained benchmark MaOPs. Empirical results demonstrate that CDEA-DA achieves the superior quality of fronts.
Gain Performance of Single-Pump Phase Sensitive Amplifier in the Quadratic Gain Regime

In optical communication systems, Phase Sensitive Amplifier (PSA) is well known for its superior noise performance in the sub-quantum limit. Therefore, PSAs are highly suitable for long haul transmission systems where amplification and regeneration is critical. In this paper, we analyze the gain performance of a single pump non-degenerate PSA operating in the quadratic gain regime. The variation of gain characteristics with pump power and fiber length has been discussed. It could be seen that for a 5km long fiber, increasing of pump power above 4W subjected to the increasing of amplifier gain while maintaining the relative phase at a stable constant value of 0.001 rad. Therefore, by fine tuning parameters like fiber nonlinearity, pump power and fiber length, a phase stabilized gain performance of the PSA could be achieved when it is operating in the quadratic gain regime.
Multilayered Circular Dielectric Structure SAR Imaging Using Time-Reversal Compressed Sensing Algorithms Based on Nonuniform Measurement

In this letter, an algorithm for synthetic aperture radar imaging of electrically small targets embedded in multilayered cylindrical geometries (e.g., pipes) using nonuniform measurement points is presented. In contrast to previous approaches, this algorithm is able to efficiently handle nonuniform points without exhibiting relevant side lobes in the image. For these purposes, the approach exploits a flexible time-reversal (TR) algorithm enhanced by compressed sensing (CS). The theoretical performance of the algorithm is studied in terms of the point spread function and several images are presented using synthetic full-wave data from CST Microwave Studio. In addition, the approach is empirically validated by performing microwave imaging of a polyvinyl chloride pipe. The results demonstrated that the TR-CS algorithm provides an effective focusing technique for dense nonuniform measurement points, as well as for sparse nonuniform measurement points.
Design of Low-Profile Dual-Band Printed Quadrifilar Helix Antenna With Wide Beamwidth for UAV GPS Applications

In the paper, a low-profile dual-band printed quadrifilar helix antenna (PQHA) with wide beamwidth is presented for UAV GPS applications. The antenna is composed of a hollow dielectric cylinder with four arms of dual-helix metal strips and a hollow dielectric ring with four pairs of circular metal strips. To provide dual-band operation, different lengths of the dual-helix metal strips are applied. By loading the circular metal strips on the dual-helix metal strips, size reduction of the antenna height is realized. Moreover, wide beamwidth is obtained. A miniaturized quad-feed network is also designed to provide equal amplitude signals with sequentially quadrature phases. To validate the proposed structure, a prototype is fabricated. The height of the antenna is 18.5 mm. The experimental results show that good impedance matching ( $\vert S_{11}\vert < -18$ dB) and axial ratio (< 2 dB) are obtained at GPS L1/ L2 bands. At 1.227 GHz, the measured 3-dB axial ratio beamwidths (ARBWs) are 186° and 187° at xoz and yoz planes, respectively. While the values are 167° and 163° at 1.575 GHz. The measured half-power beamwidths (HPBWs) are both more than 120° at the two bands. The results indicate that the proposed PQHA is a good candidate for UAV GPS applications.