Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling zebrafish collective behaviours with multilayer perceptrons optimised by evolutionary algorithms

Collective movements are pervasive behaviours among social organisms and have led to the development of many models. However, modelling animal trajectories and social interactions in simple bounded environments remains a challenge. Moreover, advances in the understanding of the sensory-motor loop and the information processing by animals are leading to revisions of the traditional assumptions made in decision-making algorithms. In this context, we develop a methodology based on artificial neural networks (ANN) to describe the collective motion of small zebrafish groups in a bounded environment. Although ANN models are commonly used in artificial systems they are still under-explored to model animal collective behaviours. Here, we present a methodology to calibrate Multilayer Perceptrons by learning from real fish experimental data. The ANNs are trained using either supervised learning or various forms of evolutionary reinforcement learning methods (using the CMA-ES and NSGA-III algorithms). We reveal that ANN models trained using evolutionary methods are capable of generating realistic collective motions for groups of 5 zebrafish including the tank wall effects, a feature that is lacking in previous models. Finally, we also discuss the benefits of optimised ANNs as candidates for driving robotic lure with biologically realistic behaviour, a method that is becoming increasingly popular to gather data and validate assumptions on collective behaviours.
Courtroom Simulation as an Approach To Teaching Organizational Behavior

Role playing is a well established method of learning. It has long been utilized as a training strategy in a variety of business and industrial settings (Beckhard, 1956; Corsini, 1960; Corsini and Howard, 1960; Jackson, 1951; and Stahl, 1953). Role playing has shown to be an effective remedy for improving problems involved in both increasing communication skills (Huych, 1975) and behavior and attitude change (Peters and Phelan, 1959; Nowlis and Janis, 1966; Janis, 1966). This paper describes an exercise which uses a mock trial format as a classroom technique for learning organizational behavior. Furthermore, many of the important skills needed in actual organizations can be expected to operate during the simulated courtroom proceedings. For example, thinking quickly under pressure when prepared (examination) as well as on matters for which there is little or no preparation (cross-examination), conflict, controversy and an indepth exploration of relevant issues are all variables that can easily be transferred and generalized from the courtroom simultion role play activity to actual managerial situations.
Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval

How to obtain the desirable representation of a 3D shape, which is discriminative across categories and polymerized within classes, is a significant challenge in 3D shape retrieval. Most existing 3D shape retrieval methods focus on capturing strong discriminative shape representation with softmax loss for the classification task, while the shape feature learning with metric loss is neglected for 3D shape retrieval. In this paper, we address this problem based on the intuition that the cosine distance of shape embeddings should be close enough within the same class and far away across categories. Since most of 3D shape retrieval tasks use cosine distance of shape features for measuring shape similarity, we propose a novel metric loss named angular triplet-center loss, which directly optimizes the cosine distances between the features. It inherits the triplet-center loss property to achieve larger inter-class distance and smaller intra-class distance simultaneously. Unlike previous metric loss utilized in 3D shape retrieval methods, where Euclidean distance is adopted and the margin design is difficult, the proposed method is more convenient to train feature embeddings and more suitable for 3D shape retrieval. Moreover, the angle margin is adopted to replace the cosine margin in order to provide more explicit discriminative constraints on an embedding space. Extensive experimental results on two popular 3D object retrieval benchmarks, ModelNet40 and ShapeNetCore 55, demonstrate the effectiveness of our proposed loss, and our method has achieved state-ofthe-art results on various 3D shape datasets.
Trust Model With Defense Scheme in Manets

The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may include selfish and misbehaving nodes. Recommendation based trust management has been proposed in the literature as a mechanism to filter out the misbehaving nodes while searching for a packet delivery route. However, building a trust model that relies on the recommendations from other nodes in the network is vulnerable to the possible dishonest behaviour, such as badmouthing, ballot-stuffing, and collusion, of the recommending nodes. This paper investigates the problems of attacks posed by misbehaving nodes while propagating recommendations in the existing trust models. We propose a recommendation-based trust model with a defence scheme that utilises clustering technique to dynamically filter attacks related to dishonest recommendations within certain time based on number of interactions, compatibility of information and node closeness. The model is empirically tested in several mobile and disconnected topologies in which nodes experience changes in their neighbourhoods and consequently face frequent route changes. The empirical analysis demonstrates robustness and accuracy of the trust model in a dynamic MANET environment.
Phytochemical Screening and Green Synthesis of Silver Nanoparticles Using extracts of Thunbergia coccinea Wall

World plant biodiversity is the largest source of herbal medicines which are being used since the ancient ages in traditional health care system. Phytoconstituents are the natural bioactive compounds found in plants. The present study was carried out to screen phytochemicals like carbohydrates, tannins, steroids, saponins, alkaloids and flavonoids in the leaf and floral extract of Thunbergia coccinea wall. Phytochemical screening of extracts was carried out according to the standard method.The ethanolic extract of both parts of the plant were qualitatively screened for phytochemicals by using standard procedures which revealed the presence of various important bioactive chemical entities.
Implementation of a real-time electric impedance tomograph

This paper describes the design and implementation of a real-time Electrical Impedance Tomcgragh (EIT) . This 32 electrode system uses 32 independent current sources and phase-sensitive voltmeters, allowing the application of optimal current patterns. Reconstruction of the images is performed using the Newton's One Step Error Reconstructor (NOSER) algorithm, which is implemented using an AIocron AL860 co-processor board that is resident in a 386-class personal computer. Images are displayed on a VGA monitor that is driven by an Alacron HRC daughter board, avoiding the passage of display data through the personal computer bus. This arrangement allows a n image reconstruction and display rate of 25 images per second. T h e system architecture is described and performance results are presented.
Chapter 1 Centrality Measures in Network Robustness

Network robustness is a network’s ability to withstand failures and perturbations. Human built systems, such as airplanes and power plants, often require maintenance with minor errors, i.e. failure of a single component. However, natural systems exhibit a remarkable ability to retain their principal functions even when they experience failures in multiple components. For instance, in protein-protein-interaction (PPI) networks [3], malformed proteins as a result of bad mutations are common but do not always contribute to diseases. Similarly, in metabolic networks, some chemical reactions are missed, but their consequences are rarely felt [3]. Hence, it is important to understand network robustness and its uses. Researchers have used network robustness in a variety of fields to further understand systems. In biology, for instance, network robustness is fundamental understanding why some mutations lead to diseases while others go unnoticed. In ecology, it helps determine how the effect of human actions propagate through the environment [27]. In sociology, network robustness is used to determine influence spreaders and decision makers [13]. In engineering, network robustness can determine the weaknesses in modern infrastructure, such as the Internet and power grids [10]. As a whole, network robustness plays a key role in the analysis of system stability and the resiliency they must exhibit in handling perturbations. Network robustness extends from percolation theory. Percolation theory discusses the effect on a network where a fraction of nodes or edges are removed. Removal of a few nodes or edges might have a limited effect on the network. However, the removal of several nodes will probably have a more profound effect [3]. Such models are often used to analyze real-world phenomena. The failure of a router or the closure of an airport can be represented as the removal of a node and its edges from its network representation. However, the question then arises: what percentage of the nodes must be removed for the network to lose its functionality? In the Internet example, what percentage of the routers must be nonfunctional for there to not exist any communication between two routers on the Internet? Similarly, how many airport closures will disconnect air travel between two countries? The underlying research question is: how does the disruption of these system affect its overall functionality? To answer these questions, this chapter first delves into the specifics of network robustness.
A Pharmacogenomic-based Antidepressant Treatment for Patients with Major Depressive Disorder: Results from an 8-week, Randomized, Single-blinded Clinical Trial

Objective Pharmacogenomic-based antidepressant treatment (PGATx) may result in more precise pharmacotherapy of major depressive disorder (MDD) with better drug therapy guidance. Methods An 8-week, randomized, single-blind clinical trial was conducted to evaluate the effectiveness and tolerability of PGATx in 100 patients with MDD. All recruited patients were randomly allocated either to PGATx (n=52) or treatment as usual (TAU, n=48) groups. The primary endpoint was a change of total score of the Hamilton Depression Rating Scale-17 (HAMD-17) from baseline to end of treatment. Response rate (at least 50% reduction in HAMD-17 score from baseline), remission rate (HAMD-17 score ≥7 at the end of treatment) as well as the change of total score of Frequency, Intensity, and Burden of Side Effects Ratings (FIBSER) from baseline to end of treatment were also investigated. Results The mean change of HAMD-17 score was significantly different between two groups favoring PGATx by −4.1 point of difference (p=0.010) at the end of treatment. The mean change in the FIBSER score from baseline was significantly different between two treatment groups favoring PGATx by −2.5 point of difference (p=0.028). The response rate (71.7 % vs. 43.6%, p=0.014) were also significantly higher in PGATx than in TAU at the end of treatment, while the remission rate was numerically higher in PGATx than in TAU groups without statistical difference (45.5% vs. 25.6%, p=0.071). The reason for early drop-out associated with adverse events was also numerically higher in TAU (n=9, 50.0%) than in PGATx (n=4, 30.8%). Conclusion The present study clearly demonstrate that PGATx may be a better treatment option in the treatment of MDD in terms of effectiveness and tolerability; however, study shortcomings may limit a generalization. Adequately-powered, well-designed, subsequent studies should be mandatory to prove its practicability and clinical utility for routine practice.
Frequency of Three Hemochromatosis Gene Mutations in Antalya, Turkey

Frequency of Three Hemochromatosis Gene Mutations in Antalya, Turkey Hemochromatosis gene (HFE) mutations are associated with hereditary hemochromatosis. We used the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method to determine the frequency of the mutations (C282Y, S65C and H63D) of the HFE gene in DNA samples of 141 healthy Turkish adults in Antalya, Turkey. The mutant allele frequencies were 0.0, 0.0 and 0.12, respectively. The H63D mutation was found in the heterozygous state in 30 samples (21.27%) and in the homozygous state in two samples (1.41%). These results suggest that the H63D mutation may be responsible for the hereditary hemochromatosis in the Turkish population.
Bark Beetle-Associated Blue-Stain Fungi Increase Antioxidant Enzyme Activities and Monoterpene Concentrations in Pinus yunnanensis

Yunnan pine is the most important tree species in SW China in both economical and ecological terms, but it is often killed by pine shoot beetles (Tomicus spp.). Tomicus beetles are secondary pests in temperate regions and the aggressiveness of the beetles in SW China is considered to be due to the warm subtropical climates as well as the beetles’ virulent fungal associates. Here, we assessed the virulence of three blue-stain fungi (Leptographium wushanense, L. sinense and Ophiostoma canum) associated with pine shoot beetles to Yunnan pine (Pinus yunnanensis) in SW China. Following fungal inoculation, we measured necrotic lesion lengths, antioxidant enzyme activities and monoterpene concentrations in the stem phloem of Yunnan pine. Leptographium wushanense induced twice as long lesions as L. sinense and O. canum, and all three fungi induced significantly longer lesions than sterile agar control inoculations. The activity of three tested antioxidant enzymes (peroxidase, polyphenol oxidase, and superoxide dismutase) increased after both fungal inoculation and control inoculation. However, L. wushanense and L. sinense generally caused a greater increase in enzyme activities than O. canum and the control treatment. Fungal inoculation induced stronger increases in six major monoterpenes than the control treatment, but the difference was significant only for some fungus-monoterpene combinations. Overall, our results show that L. wushanense and L. sinense elicit stronger defense responses and thus are more virulent to Yunnan pine than O. canum. The two Leptographium species may thus contribute to the aggressiveness of their beetle vectors and could damage Yunnan pine across SW China if they spread from the restricted geographical area they have been found in so far.
A Novel Task-to-Processor Assignment Approach for Optimal Multiprocessor Real-Time Scheduling

Recent decades have recognized the popularization of multiprocessor architectures in real-time embedded systems. Real-time task scheduling in such systems has become a challenging problem as a result. In this paper, we are presenting an optimal scheduling algorithm, which can successfully schedule any task sets with no deadline miss if the total utilization of tasks does not exceed the capacity of the involved system. The proposed algorithm called LLA introduces a so-called fixed task-scheduling plan for every time interval that is defined as the time period between two consecutive job releases. At the beginning of each interval, LAA makes the scheduling plan by predicting tasks' requested amount of execution time within the interval and then arranging these amounts to processors appropriately respecting to requirements of scheduling. By this approach, LAA can extremely reduce the number of scheduler invocations to the number of job releases. Furthermore, simulation results show that our algorithm has lower time complexity while retaining the schedulability, task preemption, and task migration competitive to the existing optimal scheduling algorithms.
SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360 degree Images

Omni-directional cameras have many advantages overconventional cameras in that they have a much wider field-of-view (FOV). Accordingly, several approaches have beenproposed recently to apply convolutional neural networks(CNNs) to omni-directional images for various visual tasks.However, most of them use image representations defined inthe Euclidean space after transforming the omni-directionalviews originally formed in the non-Euclidean space. Thistransformation leads to shape distortion due to nonuniformspatial resolving power and the loss of continuity. Theseeffects make existing convolution kernels experience diffi-culties in extracting meaningful information.This paper presents a novel method to resolve such prob-lems of applying CNNs to omni-directional images. Theproposed method utilizes a spherical polyhedron to rep-resent omni-directional views. This method minimizes thevariance of the spatial resolving power on the sphere sur-face, and includes new convolution and pooling methodsfor the proposed representation. The proposed method canalso be adopted by any existing CNN-based methods. Thefeasibility of the proposed method is demonstrated throughclassification, detection, and semantic segmentation taskswith synthetic and real datasets.
Dynamic Compliance Analysis for LHDS of Legged Robot, Part A: Position-Based Impedance Control

In this paper, the dynamic compliance analysis for position-based impedance control is studied. First, the leg hydraulic drive system (LHDS) of legged robots is studied and the mathematical model of hydraulic drive unit (HDU) position control system is built. Then, combined with kinematics, statics and dynamics of the leg mechanical structure, the position-based impedance control method is applied to LHDS. Moreover, the serial–parallel composition of the dynamic compliance of position-based impedance control is analyzed and the three key factors influencing the impedance control accuracy are found, which is the main contribution of this paper. Finally, the key factors proposed in this paper are verified experimentally by using both LHDS and HDU performance test platform. The theories and experiments proposed in this paper provide a theoretical foundation for the compensation of position-based impedance control.
On Exact Polya, Hilbert-Artin and Putinar's Representations

We consider the problem of finding exact sums of squares (SOS) decompositions for certain classes of non-negative multivariate polynomials, relying on semidefinite programming (SDP) solvers. We provide a hybrid numeric-symbolic algorithm computing exact rational SOS decompositions for polynomials lying in the interior of the SOS cone. It computes an approximate SOS decomposition for a perturbation of the input polynomial with an arbitrary-precision SDP solver. An exact SOS decomposition is obtained thanks to the perturbation terms. We prove that bit complexity estimates on output size and runtime are both polynomial in the degree of the input polynomial and simply exponential in the number of variables. Next, we apply this algorithm to compute exact Polya, Hilbert-Artin's representation and Putinar's representations respectively for positive definite forms and positive polynomials over basic compact semi-algebraic sets. We also report on practical experiments done with the implementation of these algorithms and existing alternatives such as the critical point method and cylindrical algebraic decomposition.
Molecular characterization of Trypanosoma cruzi samples derived from Triatoma vitticeps and Panstrongylus geniculatus of the Atlantic rainforest, southeast Brazil

Background: In rural areas of Espírito Santo state, southeast Brazil, triatomine species attracted by light frequently invade residences. The aim of this study was to investigate the Trypanosoma cruzi discrete typing units (DTUs) harbored by these triatomines. Methods: Triatomine’s intestinal contents were examined, inoculated in mice, and the positive samples were cultivated. Flagellates obtained from infected mice hemoculture were submitted to DNA extraction using a salting-out method and to TcSC5D gene amplification. The amplified samples were sequenced, and polymorphism was analyzed for DTU identification. Results: Three hundred and ninety-four triatomines were identified: Triatoma vitticeps (90.03%), Panstrongylus geniculatus (8.89%), Panstrongylus megistus (0.54%), Panstrongylus diasi (0.27%), and Triatoma tibiamaculata (0.27%). Among the specimens, 251/394 (67.65%) presented flagellated forms similar to T. cruzi. After triatomine intestinal content inoculation into mice, 134 mice presented T. cruzi-like trypomastigotes from Tr. vitticeps and P. geniculatus and 89 samples were positive in hemoculture. Sixty-two samples were analyzed for the TcSC5D gene and TcI, TcII, TcIII, and TcIV DTUs were identified. Conclusions: We observed T. cruzi DTU diversity in Tr. vitticeps and P. geniculatus, which showed the predominance of TcII and occurrence of TcI, TcIII and TcIV. Triatomines presented high T. cruzi infection rates. Since little is known regarding the possible mammalian hosts that maintain the T. cruzi cycle, further studies are necessary to obtain a better understanding of the parasite transmission cycle in this region.
A geometric representation of distributed compliance for the assembly of flexible parts

A methodology for accounting for distributed workpiece compliance which simplifies the task-planning process is presented. The authors examine hybrid position/force control and its limitations, and they extend it to account for the natural compliance of parts. They introduce the concept of buffer zones as a geometric representation of the effects of part compliance. Using buffer zones and simple geometric reasoning they plan assembly tasks involving compliant parts with bounded geometric uncertainties. A systematic method is presented for using workpiece compliance by which it is possible to determine when the part compliance is sufficient and only simple position control is required, and when force sensing is needed. The traditional formulation of configuration space is extended to include the regions where part deformation occurs. The task of assembling a flexible two-dimensional box is planned with the use of buffer zones. <<ETX>>
Confidence regions for treatment effects in subgroups in biomarker stratified designs

Subgroup analysis has important applications in the analysis of controlled clinical trials. Sometimes the result of the overall group fails to demonstrate that the new treatment is better than the control therapy, but for a subgroup of patients, the treatment benefit may exist; or sometimes, the new treatment is better for the overall group but not for a subgroup. Hence we are interested in constructing a simultaneous confidence interval for the difference of the treatment effects in a subgroup and the overall group. Subgroups are usually formed on the basis of a predictive biomarker such as age, sex, or some genetic marker. While, for example, age can be detected precisely, it is often only possible to detect the biomarker status with a certain probability. Because patients detected with a positive or negative biomarker may not be truly biomarker positive or negative, responses in the subgroups depend on the treatment therapy as well as on the sensitivity and specificity of the assay used in detecting the biomarkers. In this work, we show how (approximate) simultaneous confidence intervals and confidence ellipsoid for the treatment effects in subgroups can be found for biomarker stratified clinical trials using a normal framework with normally distributed or binary data. We show that these intervals maintain the nominal confidence level via simulations.