Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Plant Defenses and Plant Nutrients on the Performance of Specialist and Generalist Herbivores of Datura: A Macroevolutionary Study

Macroevolutionary patterns in the association between plant species and their herbivores result from ecological divergence promoted by, among other factors, plants’ defenses and nutritional quality, and herbivore adaptations. Here, we assessed the performance of the herbivores Lema trilineata daturaphila, a trophic specialist on Datura, and Spodoptera frugiperda, a polyphagous pest herbivore, when fed with species of Datura. We used comparative phylogenetics and multivariate methods to examine the effects of Datura species’ tropane alkaloids, leaf trichomes, and plant macronutrients on the two herbivores´ performances (amount of food consumed, number of damaged leaves, larval biomass increment, and larval growth efficiency). The results indicate that species of Datura do vary in their general suitability as food host for the two herbivores. Overall, the specialist performs better than the generalist herbivore across Datura species, and performance of both herbivores is associated with suites of plant defenses and nutrient characteristics. Leaf trichomes and major alkaloids of the Datura species are strongly related to herbivores’ food consumption and biomass increase. Although hyoscyamine better predicts the key components of the performance of the specialist herbivore, scopolamine better predicts the performance of the generalist; however, only leaf trichomes are implicated in most performance components of the two herbivores. Nutrient quality more widely predicts the performance of the generalist herbivore. The contrasting effects of plant traits and the performances of herbivores could be related to adaptive differences to cope with plant toxins and achieve nutrient balance and evolutionary trade-offs and synergisms between plant traits to deal with a diverse community of herbivores.
Solving Approximate Wasserstein GANs to Stationarity

Generative Adversarial Networks (GANs) are one of the most practical strategies to learn data distributions. A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions. Unfortunately, minimizing the Wasserstein distance between the data distribution and the generative model distribution is a challenging problem as its objective is non-convex, non-smooth, and even hard to compute. In this work, we propose to use a smooth approximation of the Wasserstein GANs. We show that this smooth approximation is close to the original objective. Moreover, obtaining gradient information of this approximate formulation is computationally effortless and hence one can easily apply first order optimization methods to optimize this objective. Based on this observation, we proposed a class of algorithms with guaranteed theoretical convergence to stationarity. Unlike the original non-smooth objective, our proposed algorithm only requires solving the discriminator to approximate optimality. We applied our method to learning Gaussian mixtures on a grid and also to learning MNIST digits. Our method allows the use of powerful cost functions based on latent representations of the data, where this latent representation could also be optimized adversarially.
Characterization of polarizing filters for the EnVisS camera: procedure and results

In the framework of an ESA space mission, called Comet Interceptor, scheduled for launch in 2029 some polarizers have been tested and characterized. These polarizers are considered for being mounted on the EnVisS (Entire Visible Sky) instrument. EnVisS is a fish-eye camera that will dynamically acquire images of a comet and the surrounding all-sky coma in the visible range exploiting the spacecraft spinning. The spacecraft will perform a fly-by of the comet, venturing very near to its nucleus. Inside the EnVisS instrument, before reaching the sensor, the acquired light will cross one of the 3 selected scientific filters, i.e. one broadband and two polarizers. The determination of the optical properties of these filters is crucial for the correct prediction of the performance of the camera. The Padua branch of the CNR-IFN (Italian National Research Council – Institute for Photonics and Nanotechnologies) has a long experience in metrology for space instrumentation and has developed a laboratory system for reflectance and transmittance optical measurements. The set-up is composed of a broadband light source, a rotator stage for allocating the samples, and a spectrometer. According to the purpose of the measurement, the structure of this setup can be arranged by adding other elements along the ray path. This system allows measuring wavelength dependent transmissivity and reflectivity properties for optical components such as mirrors, lenses and filters in the UV, visible and NIR spectral range. The polarizing filters under selection for the EnVisS instrument are commercially available components based on the wire grid technology. We have measured their optical transmissivity and reflectivity. In this paper, we present the employed instruments, the step-by-step procedure and the results compared to the nominal performance of the polarizers.
A novel modified Khatter’s approach for solving Neutrosophic Data Envelopment Analysis

The evaluation of the performance of decision-making units (DMUs) that use comparable inputs to produce related outputs can be accomplished through a non-parametric linear programming (LP) technique called Data Envelopment Analysis (DEA). However, the observed data are occasionally imprecise, ambiguous, inadequate, and inconsistent which may result in incorrect decision-making when these criteria are ignored. Neutrosophic Set (NS) is an extension of fuzzy sets which is used to represent unclear, erroneous, missing, and wrong information. This paper proposes a neutrosophic version of the DEA model, and a novel solution technique for Neutrosophic DEA (Neu-DEA) model. The possibility mean for triangular neutrosophic number (TNN) is redefined and modified the Khatter’s approach to convert directly the Neu-DEA model into its crisp DEA model. As a result, the Neu-DEA model is simplified to a crisp LP problem with a risk parameter (δ ∈ [0, 1]) that represents the attitude of the decision-maker towards taking risk. The efficiency score of the DMUs is computed by using various risk factors and divided into efficient and inefficient groups. The ranking of DMUs is determined by calculating the mean efficiency score of DMUs, which is based on various risk parameters. A numerical example is illustrated here to describe the suggested approach’s flexibility and authenticity and compared with some of the existing approaches.
FedUR: Federated Learning Optimization Through Adaptive Centralized Learning Optimizers

Introducing adaptiveness to federated learning has recently ushered in a new way to optimize its convergence performance. However, adaptive learning strategies originally designed in centralized machine learning are in naїve extended to federated learning in existing works, which does not necessarily improve convergence performance and further reduce communication overhead as expected. In this paper, we fully investigate those centralized learning-based adaptive learning strategies, and propose an adaptive Federated learning algorithm targeting the model parameter Update Rule, called FedUR. Convergence upper bounds under FedUR are derived from the aspect of both local iterations and global aggregations. Through comparison with the convergence upper bounds of original federated learning, we theoretically analyze how those strategies should be tuned to help federated learning effectively optimize convergence performance and reduce overall communication overhead. Extensive experiments are conducted based on several real datasets and machine learning models, which show that FedUR can effectively increase final convergence accuracy with even lower communication overhead requirement.
Spherical Hands: Toward Underactuated, In-Hand Manipulation Invariant to Object Size and Grasp Location

Minimalist, underactuated hand designs can be modified to produce useful, dexterous, in-hand capabilities without sacrificing their passive-adaptability in power-grasping. Incorporating insight from studies in parallel mechanisms, we implement and investigate the “spherical hand” morphologies: novel, hand topologies with two fingers configured such that the instantaneous screw axes, describing the displacement of the grasped object, always intersect at the same point relative to the palm. This produces the same instantaneous motion about a common point for any object geometry in a stable grasp. Various rotary fingertip designs are also implemented to help maintain stable contact conditions and minimize slip, in order to prove the feasibility of this design in physical hand implementations. The achievable precision manipulation workspaces of the proposed morphologies are evaluated and compared to prior human manipulation data as well as manipulation results with traditional three-finger hand topologies. Experiments suggest that the spherical hands’ design modifications can make the system’s passive reconfiguration more easily predictable, providing insight into the expected object workspace while minimizing the dependence on accurate object and contact modeling. We believe this design can significantly reduce the complexity of planning and executing dexterous manipulation movements in unstructured environments with underactuated hands.
A Kernel Method for Positive 1-in-3-SAT

This paper illustrates the power of Gaussian Elimination by adapting it to Positive 1-in-3-SAT. We derive a general kernelization method for this problem and thus obtain an upper bound for the complexity of its counting version of O(2kR 2^{(1-k)R}) for number of variables R and clauses-to-variables ratio k. Combining this method with previous results gives a time and space complexity for the counting problem of O(4/3|V|2^{3|V|/8}) and O(4/3|V|2^{3|V|/16}).
Comparison Algorithm for Diabetes Classification with Consideration of Mutual Information and Information Feature

Diabetes is a prevalent disease in humans that is caused by excessive sugar levels in the body. If left untreated, it can lead to severe consequences such as paralysis, decay in certain parts of the body, and even death. Unfortunately, early detection of diabetes is difficult, and many cases go untreated until it is too late. However, the development of technology has opened up new possibilities for early detection and treatment of diabetes. One such approach is classification, a commonly used method in the field of Computer Science. Classification is used in various fields, including health, agriculture, and animal diseases, to draw conclusions based on input data using cause-and-effect relationships. Many different learning concepts and methods can be used in classification, with the Decision Tree concept being one of the most popular examples. This study compares several classification methods, including Decision Tree, Random Forest, AdaBoost, and Stochastic Gradient Boost, with feature selections carried out using MI and IF. The study aims to evaluate the effectiveness of these methods and the influence of feature selection on improving their performance. Based on the results of the study, it can be concluded that feature selection using Mutual Information and Importance Feature can improve the classification accuracy in some methods, particularly in Random Forest, AdaBoost, and Stochastic Gradient Boost. However, the Decision Tree algorithm did not show any improvement in accuracy after feature selection. The best classification accuracy was achieved with the Stochastic Gradient Boost method using the original dataset without feature selection, while the Random Forest method showed the highest accuracy after using all the features. Overall, the results suggest that feature selection can be a useful technique for improving the performance of classification algorithms in diabetes prediction. The study suggests that future research could investigate other classification methods, such as Neural Network or Deep Learning, and use optimization algorithms like Genetic Algorithm or Particle Swarm Optimization to improve feature selection results.
Parent and Carer Skills Groups in Dialectical Behaviour Therapy for High-Risk Adolescents with Severe Emotion Dysregulation: A Mixed-Methods Evaluation of Participants’ Outcomes and Experiences

Background: There is an established evidence-base for dialectical behaviour therapy for adolescents (DBT-A) in the treatment of young people with severe emotion dysregulation and related problems, including repeated self-harm and suicidal behaviours. However, few studies have reported on parental involvement in such treatments. This study aims to explore the outcomes and experiences of participants of a dedicated skills group for parents and carers embedded within an adapted DBT-A programme in the United Kingdom. Method: This study was conducted within a specialist outpatient Child and Adolescent Mental Health Services (CAMHS) DBT programme in the National Health Service (NHS) in London. Participants were parents and carers of adolescents engaged in the DBT-A programme. Participants attended a 6-month parent and carer skills group intervention and completed self-report measures relating to carer distress, communication and family functioning, at pre-intervention and post-intervention. Following the intervention, semi-structured interviews were also completed with a subgroup of participants to explore their experiences of the skills group and how they perceived its effectiveness. Quantitative and qualitative methods were used to analyse the data collected from participants. Results: Forty-one parents and carers completed the intervention. Participants reported a number of statistically significant changes from pre- to post-intervention: general levels of distress and problems in family communication decreased, while perceived openness of family communication and strengths and adaptability in family functioning increased. A thematic analysis of post-intervention interviews examining participant experiences identified six themes: (1) experiences prior to DBT; (2) safety in DBT; (3) experiences with other parents and carers; (4) new understandings; (5) changes in behaviours; and (6) future suggestions. Discussion: Parents and carers who attended a dedicated DBT skills groups, adapted for local needs, reported improvements in their wellbeing, as well as interactions with their adolescents and more general family functioning, by the end of the intervention. Further studies are needed which report on caregiver involvement in DBT.
Swimmer's Stroke Estimation Using CNN and MultiLSTM

Many methods of sports video analysis have been proposed in the computer vision ﬁeld. However, the analysis of swimming videos is a challenging task. This is because there is a lot of noise, such as water splashes, making it difﬁcult to see the swimmer’s motion and detect body parts. Thus, it is difﬁcult to automatically estimate a swimmer’s motion, especially the stroke. In this paper, we introduce a novel approach to automatically estimating the stroke in such situations. Firstly, we detect the swimmer from a swimming video using a projective transformation, background subtraction, and a Kalman ﬁlter. We next create a model that learns a mapping from a window of frames to a point on a one-dimensioned(1D) target signal, which represents a swimmer’s stroke (we call a ’stroke signal’). We use a convolutional neural network (CNN) and multi long short-term memory (Multi-LSTM) which is an expanded model of LSTM. Finally , we estimate swimmer’s stroke from the stroke signal. In a dataset including various environments, the outputs of our system showed higher accuracy than previous ones.
Exploring the Opportunities of Haptic Technology in the Practice of Visually Impaired and Blind Sound Creatives

Visually impaired and blind (VIB) people as a community face several access barriers when using technology. For users of specialist technology, such as digital audio workstations (DAWs), these access barriers become increasingly complex—often stemming from a vision-centric approach to user interface design. Haptic technologies may present opportunities to leverage the sense of touch to address these access barriers. In this article, we describe a participant study involving interviews with twenty VIB sound creatives who work with DAWs. Through a combination of semi-structured interviews and a thematic analysis of the interview data, we identify key issues relating to haptic audio and accessibility from the perspective of VIB sound creatives. We introduce the technical and practical barriers that VIB sound creatives encounter, which haptic technology may be capable of addressing. We also discuss the social and cultural aspects contributing to VIB people’s uptake of new technology and access to the music technology industry.
A Method for Calculating Wind PowerPenetration Limit Considering Wake Effect and Wind Speed Correlation

With the development of wind power technology, accurate analysis of wind power penetration limit is conducive to large-scale wind power safely connected to the power grid. At present, the related research does not consider the influence of wake effect. Therefore, this paper proposes a wind power penetration limit analysis method considering the wake effect and wind speed correlation. Firstly, this paper uses a stochastic simulation technique based on the Latin hypercube sampling (LHS) to simulate the wind speed of multiple wind farms with correlation, and establishes a wind power active output model considering the wake effect, thus the calculation model of wind power penetration limit is established based on the chance constrained programming (CCP) theory and solved by the particle swarm optimization algorithm. Taking the IEEE-30 node system as an example, the feasibility and effectiveness of the above model and algorithm in evaluating wind power access capability are verified.
Slip-flow lattice-Boltzmann simulations in ducts and porous media: A full rehabilitation of spurious velocities

Slip flow in ducts and porous media is simulated using lattice-Boltzmann method incorporated with interfacial force models. The dependence of the results on the viscosity, LBM scheme (D3Q15 and D3Q19) and the relaxation time model (single- or multirelaxation time) is investigated. The severity of spurious velocities (arisen from classic and advanced interfacial force models) is discussed that leads to entirely nonphysical results for whole flow rates (0.027 (kappa/kappa(NS))square ducts > (kappa/kappa(NS))porous media (where kappa is the permeability and NS denotes no-slip).
Apsidal motion of two eclipsing binaries: V796 Cyg and V2783 Ori

In this study, the orbital period variations of two eclipsing binary systems showing apsidal motion were studied. Their O - C diagrams were analysed using all reliable eclipse timings and the elements of apsidal motion of two systems were improved. We found periods of apsidal motion of V796 Cyg and V2783 Ori to be 32.7 ± 0.2 years and 415 ± 50 years, respectively.
A Systematic Literature Review of Skyline Query Processing Over Data Stream

Recently, skyline query processing over data stream has gained a lot of attention especially from the database community owing to its own unique challenges. Skyline queries aims at pruning a search space of a potential large multi-dimensional set of objects by keeping only those objects that are not worse than any other. Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. In regard to this, this paper provides a comparative study on the state-of-the-art approaches over the period between 2000 and 2022 with the main aim to help readers understand the key issues which are essential to consider in relation to processing skyline queries over streaming data. Seven digital databases were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) procedures. After applying both the inclusion and exclusion criteria, 23 primary papers were further examined. The results show that the identified skyline approaches are driven by the need to expedite the skyline query processing mainly due to the fact that data streams are time varying (time sensitive), continuous, real time, volatile, and unrepeatable. Although, these skyline approaches are tailored made for data stream with a common aim, their solutions vary to suit with the various aspects being considered, which include the type of skyline query, type of streaming data, type of sliding window, query processing technique, indexing technique as well as the data stream environment employed. In this paper, a comprehensive taxonomy is developed along with the key aspects of each reported approach, while several open issues and challenges related to the topic being reviewed are highlighted as recommendation for future research direction.
On aspects of robustness and sensitivity in missing data methods

Missing data are common wherever statistical methods are applied in practice. They present a problem by demanding that additional untestable assumptions be made about the mechanism leading to the incompleteness of the data. Minimising the strength of these assumptions and assessing the sensitivity of conclusions to their possible violation constitute two important aspects of current research in this area. One attractive approach is the doubly robust (DR) weighting-based method proposed by Robins and colleagues. By incorporating two models for the missing data process, inferences are valid when at least one model is correctly specified. The balance between robustness, efficiency and analytical complexity is one which is difficult to strike, resulting in a split between the likelihood and multiple imputation (MI) school on one hand and the weighting and DR school on the other. We propose a new method, doubly robust multiple imputation (DRMI), combining the convenience of MI with the robustness of the DR approach, and explore the use of our new estimator for non-monotone missing at random data, a setting in which, hitherto, estimators with the DR property have not been implemented. We apply the method to data from a clinical trial comparing type II diabetes drugs, where we also use MI as a tool to explore sensitivity to the missing at random assumption. Finally, we study DRMI in the longitudinal binary data setting and find that it compares favourably with existing methods.
ISU ReD: Research and eData ISU ReD: Research and eData

Extractive summarization has been widely studied, but the summaries generated by most current extractive summarization works usually disregard the article structure of the source document. Furthermore, the produced summaries are sometimes not representative sentences in the article. In this thesis, we propose an extractive summarization algorithm with knowledge graph enhancement that leverages both the source document and a knowledge graph to predict the most representative sentences for the summary. The aid of knowledge graphs enables deep learning models with pre-trained language models to focus on article structure information in the process of generating extractive summaries. Our proposed method has an encoder and a classifier: the encoder encodes the source document and the knowledge graph separately. The classifier inter-encodes the encoded source document and knowledge graph information by the cross-attention mechanism. Then the classifier determines whether the sentences belong to summary sentences or not. The results show that our model produces higher ROUGE scores on the CNN/Daily Mail dataset than the model without the knowledge graph for assistance, compared to the extractive summarization work based on the pre-trained language model.