Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-population differential evolution with adaptive parameter control for global optimization

Differential evolution (DE) is one of the most successful evolutionary algorithms (EAs) for global numerical optimization. Like other EAs, maintaining population diversity is important for DE to escape from local optima and locate a near-global optimum. Using a multi-population algorithm is a representative method to avoid early loss of population diversity. In this paper, we propose a multi-population DE algorithm (MPDE) which manipulates multiple sub-populations. Different sub-populations in MPDE exchange information via a novel mutation operation instead of migration used in most multi-population EAs. The mutation operation is helpful to balance the fast convergence and population diversity of the proposed algorithm. Moreover, the performance of MPDE is further improved by an adaptive parameter control scheme designed based on the multi-population approach. Each sub-population in MPDE evolves with its own set of control parameters, and a learning strategy is used to adaptively adjust the parameter values. A set of benchmark functions is used to test the proposed MPDE algorithm. The experimental results show that MPDE performs better than, or at least comparably, to the classical single population DE with fixed parameter values and three existing state-of-the-art DE variants.
Large scale visual SLAM with single fisheye camera

In this paper, an Extended Kalman Filter (EKF) - based visual SLAM algorithm using single fisheye lens camera to build a large scale sense is presented. The primary contribution of this work is the adaption of MonoSLAM from conventional perspective cameras and wide angle cameras to fisheye lens cameras in which the Polynomial Camera Model [10] is adopted to obtain 3D information. For data association, pSIFT [1] is implemented, which is designed dedicatedly for feature matching among fisheye images. In the experiment, we implement sub-mapping algorithm [5] and optimization jointly over all the frames with 3D feature position and camera poses estimated by MonoSLAM. The result confirms that our algorithm can work with fisheye camera well.
Molecular classification of commercial Spirulina strains and identification of their sulfolipid biosynthesis genes.

Cyanobacterial strains of the genus Spirulina have recently been identified as an excellent source of sulfolipids, some of which possess anti-HIV properties. Thus, to investigate the distribution of sufolipid biosynthesis pathways in Spirulina, a genetic screening/phylogentic study was performed. Five different strains of Spirulina [Spirulina (Jiangmen), Spirulina sp. , S. platensis, S. maxima, and Spirulina seawater] sourced from different locations were initially classified via 16S rDNA sequencing, and then screened for the presence of the sulfolipid biosynthesis genes sqdB and sqdX via a PCR. To assess the suitability of these strains for human consumption and safe therapeutic use, the strains were also screened for the presence of genes encoding nonribosomal peptide synthases (NRPSs) and polyketide synthases (PKSs), which are often associated with toxin pathways in cyanobacteria. The results of the 16S rDNA analysis and phylogenetic study indicated that Spirulina sp. is closely related to Halospirulina, whereas the other four Spirulina strains are closely related to Arthrospira. Homologs of sqdB and sqdX were identified in Spirulina (Jiangmen), Spirulina sp. , S. platensis, and the Spirulina seawater. None of the Spirulina strains screened in this study tested positive for NRPS or PKS genes, suggesting that these strains do not produce NRP or PK toxins.
Uncertain Programming Models for Shortest Path Problem with Uncertain Arc Lengths

Shortest path problem is a fundamental problem in network optimization and combinational optimization. The existing literature mainly concerned with the problem in deterministic, stochastic or fuzzy environments by using different tools. Different from the existing works, we investigate shortest path problem by regarding arc lengths as uncertain variables which are employed to describe the behavior of uncertain phenomena. According to different decision criteria, three concepts of path are proposed in uncertain environment, and three types of uncertain programming models are formulated. Furthermore, these models are concerted into deterministic optimization models in several special cases. keywords: Uncertain programming; Shortest path problem; Uncertainty measure; Uncertain variable.
Macrophage arginase-1 controls bacterial growth and pathology in hypoxic tuberculosis granulomas

Significance Tuberculosis (TB) granulomas represent sites of both bacterial containment and tissue pathology. Macrophage killing of Mycobacterium tuberculosis (Mtb) in granulomas to contain infection must be regulated to prevent collateral tissue damage. Nitric oxide synthase-2 (NOS2) and arginase-1 (Arg1), macrophage enzymes metabolizing l-arginine, play key roles in this process. NOS2 produces reactive nitrogen intermediates to kill Mtb, whereas Arg1 regulates NOS2 activity via substrate competition. Arg1 activity could predominate in hypoxic regions of granulomas where NOS2 activity likely is suboptimal. Here we show that Arg1 plays a central role in restricting bacterial growth and restraining tissue damage within granulomas in TB and other chronic inflammatory diseases. These findings point to the modulation of Arg1 activity as a potential host-directed therapy for TB. Lung granulomas develop upon Mycobacterium tuberculosis (Mtb) infection as a hallmark of human tuberculosis (TB). They are structured aggregates consisting mainly of Mtb-infected and -uninfected macrophages and Mtb-specific T cells. The production of NO by granuloma macrophages expressing nitric oxide synthase-2 (NOS2) via l-arginine and oxygen is a key protective mechanism against mycobacteria. Despite this protection, TB granulomas are often hypoxic, and bacterial killing via NOS2 in these conditions is likely suboptimal. Arginase-1 (Arg1) also metabolizes l-arginine but does not require oxygen as a substrate and has been shown to regulate NOS2 via substrate competition. However, in other infectious diseases in which granulomas occur, such as leishmaniasis and schistosomiasis, Arg1 plays additional roles such as T-cell regulation and tissue repair that are independent of NOS2 suppression. To address whether Arg1 could perform similar functions in hypoxic regions of TB granulomas, we used a TB murine granuloma model in which NOS2 is absent. Abrogation of Arg1 expression in macrophages in this setting resulted in exacerbated lung granuloma pathology and bacterial burden. Arg1 expression in hypoxic granuloma regions correlated with decreased T-cell proliferation, suggesting that Arg1 regulation of T-cell immunity is involved in disease control. Our data argue that Arg1 plays a central role in the control of TB when NOS2 is rendered ineffective by hypoxia.
Online quadrotor trajectory generation and autonomous navigation on point clouds

In this paper, we present a framework for online generation of safe trajectories directly on point clouds for autonomous quadrotor flight. Considering a quadrotor operateing in unknown environments, we use a 3-D laser range finder for state estimation and simultaneously build a point cloud map of the environment. Based on the incrementally built point cloud map, we utilize the property of the fast nearest neighbor search in KD-tree and adopt the sampling-based path finding method to generate a flight corridor with safety guarantee in 3-D space. A trajectory generation method formulated in quadratically constrained quadratic programming (QCQP) is then used to generate trajectories that constrained entirely within the corridor. Our method runs onboard within 100 milliseconds, making it suitable for online re-planning. We integrate the proposed planning method with laser-based state estimation and mapping modules, and demonstrate the autonomous quadrotor flight in unknown indoor and outdoor environments.
Soft-decision redundant residue number system based error correction coding

Soft-decision-based redundant residue number system (RRNS)-assisted error control coding is proposed and its performance is evaluated. An RRNS(n,k) code is a maximum-minimum distance block code, exhibiting identical distance properties to Reed-Solomon (RS) codes. Hence their error correction capability is given by t=(n-k)/2. Different bit mapping methods are proposed, which result in systematic and non-systematic RRNS encoders. We show that the classic Chase algorithm can be invoked, in order to contrive soft-decision detection for RRNS codes and to exploit the soft channel outputs, which provide the relative reliability of each of the received binary digits. We found that soft decision-based RRNS decoding is at least 1.5 dB better compared to hard decision-assisted RRNS decoding.
Atomic Spatial Processes

The emergence of compact GPS systems and the establishment of open data initiatives has resulted in widespread availability of spatial data for many urban centres. These data can be leveraged to develop data-driven intelligent resource allocation systems for urban issues such as policing, sanitation, and transportation. We employ techniques from Bayesian non-parametric statistics to develop a process which captures a common characteristic of urban spatial datasets. Specifically, our new spatial process framework models events which occur repeatedly at discrete spatial points, the number and locations of which are unknown a priori. We develop a representation of our spatial process which facilitates posterior simulation, resulting in an interpretable and computationally tractable model. The framework's superiority over both empirical grid-based models and Dirichlet process mixture models is demonstrated by fitting, interpreting, and comparing models of graffiti prevalence for both downtown Vancouver and Manhattan.
African Transnationalism in China: At the Interface of Local, Transnational, Bilateral and Multilateral Responses

The growing presence of a diversifying group of Africans in China raises broader issues of their status and permanence. The politics associated with African transnationalism in China are evident in Chinese and African government responses and the transnational African voice. This article looks at facets of an African transnational presence and some key responses at a local, international and transnational level to suggest an evolving state of Sino-African relations in which African multilateralism and the transnational African actor play a greater part.
Minority threat and criminal sentencing: examining juveniles in the adult criminal justice system

Abstract Racial/ethnic threat has been researched regarding its impact on criminal sentencing. Past research has been fairly mixed, but a significant number of studies suggest that in areas of higher minority concentration, sentencing is generally harsher. Most of the existing research has utilized adult samples in testing this hypothesis. The current research adds to the existing research through the examination of juvenile offenders convicted in the adult criminal justice system. The data included a national sample of 3457 juveniles convicted in criminal court during 1998 across 40 counties within the United States. In this study, the racial/ethnic hypothesis was examined to assess its effect on sentencing among juveniles in the criminal system. The findings suggest some support for minority threat having an effect on sentencing decisions.
Characterization of self-assembled monolayers of thiols on gold using scanning tunneling microscopy

The formation and the structure at the molecular level of self-assembled monolayers of thiophenol and dodecanethiol molecules on gold surfaces were characterized by scanning tunneling microscopy. Highly ordered monolayer films were obtained for both thiols. The results suggest that the molecular ordering and packing depend on the adsorption time and on the solvent dipole moment. Longer adsorption times and polar solvents favor the saturation of surface coverage and
Investigating the Accuracy of Test Code Size Prediction using Use Case Metrics and Machine Learning Algorithms: An Empirical Study

Software testing plays a crucial role in software quality assurance. It is, however, a time and resource consuming process. It is, therefore, important to predict as soon as possible the effort required to test software, so that activities can be planned and resources can be optimally allocated. Test code size, in terms of Test Lines Of Code (TLOC), is an important testing effort indicator used in many empirical studies. In this paper, we investigate empirically the early prediction of TLOC for object-oriented software using use case metrics. We used different machine learning algorithms (linear regression, k-NN, Naïve Bayes, C4.5, Random Forest, and Multilayer Perceptron) to build the prediction models. We performed an empirical study using data collected from five Java projects. The use case metrics have been compared to the well-known Use Case Points (UCP) method. Results show that the use case metrics-based approach gives a more accurate prediction of TLOC than the UCP method.
Autonomous Viewpoint Selection of Robots Based on Aesthetic Composition Evaluation of a Photo

In the field of painting and photography, composition rules are widely used under various circumstances in order to arouse humans' aesthetic evaluation. In this paper, we propose a method to evaluate the aesthetic values of a scene according to some certain composition rules, such as Rule of Third, Diagonal Composition and Triangle Composition. We propose an evaluation function with three factors for each kind of compositions, which is reasonable to describe properties of compositions as the result showed in our questionnaire. We develop an observation position searching method by estimating relationships between targets from different viewpoints of the monitoring robot. Then a score will be obtained for each viewpoint using our evaluation function. By these scores, the best observation position can be determined within a reachable field. With path planning and moving control, the monitoring robot arrives at the best observation position. We suppose our research can provide any hint about common understanding between human beings and robots.
Process of modularity in networks: The art of evaluation

As the assessment standards of the community structure, in this paper, it unfolds the process of modularity. Firstly, it describes the original authority definition of Newman in detail, and popularizes to all kinds of different types networks. With the discovery of overlap feature, the modularity integrates the characteristic property of belonging factor, including extensions on Newman's modularity and the non-fuzzy modularity based on node contribution. Next, some new novel definitions are represented; LC modularity proves the defects of Newman modularity and provides a better choice with density and cohesion, and local modularity shows a way to community in large scale network. And then, the comparisons of the various definitions demonstrate their advantages and disadvantages, and the academic cause is analyzed, it is that the different understanding on the community structure results in the different reference model. In the end, weighted LC modularity is proposed, and some serviceable suggestions on the independence and equity are presented, and it proves that the collective decision-making community pattern surpasses the single self-centered vertex pattern.
Wavelet-transform steganography: algorithm and hardware implementation

Steganography is a powerful method to conceal the existence of secret data inside a cover object. The concealment steps are performed in the spatial domain and/or the transform domain such as wavelet transform. While it is harder to detect, the transform domain steganography involves complex computations. Hence implementing steganography in hardware improves the steganography system performance. The preservation of the entire secret information is one of the main challenges for the transform domain steganography. Errors, introduced by quantisation steps, destroy some of the embedded secret bits. In this paper, we present a novel algorithm to embed and extract the entire secret data in the Haar wavelet-based transform without any secret information loss. This is accomplished by special clipping mechanism as well as modifying the placement of the secret bit in the transform coefficients. The algorithm is implemented in an FPGA-based hardware, and its performance metrics are examined including resources utilisation, power, timing and energy.
Interaction of polyamine gene vectors with RNA leads to the dissociation of plasmid DNA‐carrier complexes

Plasmid DNA (pDNA) dissociation from polyamine gene vectors after cellular uptake has not been well characterized. A more detailed understanding of this process could lead to more efficient gene transfer agents. Since RNA is present in the cytoplasm at high concentrations and due to its structural similarity to DNA, we were interested in its conceivable interaction with polyamine gene vectors.
Interferometric synthetic aperture sonar for high resolution 3-D mapping of the seabed

A technique is described and demonstrated for generating high-resolution 3-D images of the objects on the seabed, combining synthetic aperture sonar with bathymetric (interferometric) processing. This has applications in surveying of oil wells, pipelines and shipwrecks, and in defence in mine hunting. It is shown that the technique is very sensitive to platform motion errors, and ways are discussed by which the errors may be estimated and compensated. Practical results obtained with an experimental system in a test tank are presented and discussed, and a number of areas for further work are identified.
Computational analysis of the role of the hippocampus in memory

The authors draw together the results of a series of detailed computational studies and show how they are contributing to the development of a theory of hippocampal function. A new part of the theory introduced here is a quantitative analysis of how backprojections from the hippocampus to the neocortex could lead to the recall of recent memories. The theory is then compared with other theories of hippocampal function. First, what is computed by the hippocampus is considered. The hypothesis the authors advocate, on the basis of the effects of damage to the hippocampus and neuronal activity recorded in it, is that it is involved in the formation of new memories by acting as an intermediate‐term buffer store for information about episodes, particularly for spatial, but probably also for some nonspatial, information. The authors analyze how the hippocampus could perform this function, by producing a computational theory of how it operates, based on neuroanatomical and neurophysiological information about the different neuronal systems contained within the hippocampus. Key hypotheses are that the CA3 pyramidal cells operate as a single autoassociation network to store new episodic information as it arrives via a number of specialized preprocessing stages from many association areas of the cerebral cortex, and that the dentate granule cell/mossy fiber system is important, particularly during learning, to help to produce a new pattern of firing in the CA3 cells for each episode. The computational analysis shows how many memories could be stored in the hippocampus and how quickly the CA3 autoassociation system would operate during recall. The analysis is then extended to show how the CA3 system could be used to recall a whole episodic memory when only a fragment of it is presented. It is shown how this recall could operate using modified synapses in backprojection pathways from the hippocampus to the cerebral neocortex, resulting in reinstatement of neuronal activity in association areas of the cerebral neocortex similar to that present during the original episode. The recalled information in the cerebral neocortex could then be used by the neocortex in the formation of long‐term memories. © 1994 Wiley‐Liss, Inc.
Magnetic Resonance Imaging as a Noninvasive Method for Longitudinal Monitoring of Infusion Site Reactions Following Administration of a Novel Apomorphine Formulation

Infusion site reactions are common following subcutaneous infusion of drugs. Such reactions can lead to discontinuation of the treatment. Therefore, assessment of such reactions is essential during preclinical safety studies, and magnetic resonance imaging (MRI) can assist in evaluation. Here, in vivo and ex vivo MRI evaluations were used in addition to classical histopathology to assess the infusion site reaction to ND0701, a novel formulation of apomorphine base developed for the treatment of Parkinson’s disease, in comparison to the commercial apomorphine hydrochloride (HCl) formulation. Both formulations, each at two concentrations, were continuously administered subcutaneously for 20 hr to each of 3 male and 3 female domestic pigs. Based on MRI evaluations, there was a gradual decrease in the volume of the subcutaneous lesions over 4 weeks, with smaller lesions and quicker resolution with ND0701 at concentrations 2.5- to 5-fold higher when compared to the commercial apomorphine HCl formulation. Histopathological evaluation of ND0701 revealed only minimal inflammation at the sites of infusion, whereas the commercial apomorphine HCl caused persistent inflammatory reactions and necrosis. This study provides support to the use of MRI in preclinical testing of subcutaneous drugs when evaluating local site reactions.
Discord of measurements in assessing depression among African Americans with cancer diagnoses

This study examined the level of agreement among the Center for Epidemiologic Studies-Depression Scale (CES-D), Hamilton Rating Scale for Depression (HAM-D), Beck Depression Inventory (BDI-II) and Observers' Rating on assessing depression of African American adults with cancer. A total of 75 breast and prostate cancer patients (57 African Americans and 18 Whites) were interviewed and administered the depression measures. Nonparametric tests were performed to examine the level of measurement agreement by group and the symptom items of CES-D, HAM-D and BDI-II to which African American patients responded differently across measures. The four measures showed agreement on approximately 75% of the cases in both racial groups. However, the difference between measures in identifying depressive cases is marked. The item analysis indicated that most measurement disagreements about African American patients occurred on two items: self-report of depression and sleeping disturbance. Measurement discord may be explained by African American's reporting behavior that varies from a self-reported measure to an interviewer-administrated measure of depression. African American patients showed a reluctance to use the word ‘depression’ and a tendency to report sleep disturbance. The findings suggest that accurately assessing depression in these patients requires a consideration of their culturally shaped life experiences.