Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decomposition based Multi-objective Evolutionary Algorithm with ReliefF based Local Search and Solution Repair Mechanism for Feature Selection

Feature selection has two main objectives which are to maximise the classification accuracy and to minimise the number of selected features. Unfortunately, the two objectives are usually in conflict, which makes feature selection a multi-objective problem. MOEA/D (multi-objective optimisation evolutionary algorithm based on decomposition) has shown to be effective in solving multi-objective feature selection, which evolves more diverse fronts than other multi-objective algorithms such as SPEA2 or NSGAII. However, sometimes the feature subsets around the middle of the evolved fronts do not have high classification performance. The goal of this work is to propose a local search for MOEA/D with an expectation of maintaining the front diversity while improving the classification performance of the feature subsets in the evolved fronts. The local search is based on three operators: insert, remove, and swap. The insert/remove operators either add/remove a single feature from the current feature subset, while the swap operator exchanges a selected feature with an unselected feature. The selection of added/removed/swapped features is based on Relief, a well-known measure which considers feature interactions. The experimental results show that the proposed local search can maintain or improve the fronts evolved by MOEA/D-DYN, a state-of-the-art MOEA/D algorithm for feature selection.
Control of Fusarium Wilt of Spinach by Transplanting Seedlings Pretreated with Non-pathogenic Fusarium oxysporum

ホウレンソウ萎ちょう病発生土壌で健全に生育するホウレンソウの根面から分離した非病原性F. oxysporum S3HO3菌株はホウレンソウ萎ちょう病を発病抑制できるが,土壌施用効果は収穫期まで持続しなかった。 これを改善するために,耕種的に本病を軽減できる移植栽培を組み合わせ,ホウレンソウの苗に本菌を前接種した。 この場合,非病原性F. oxysporum S3HO3菌株分生子懸濁液を,播種前の床土1g当たり106 cfuになるように混和すると防除価96. 3で発病抑制効果が最も高く,収穫期まで持続した。 一方,育苗中に本菌分生子懸濁液(107 bud-cells/ml)を灌注接種(床土重量の1/10容(v/w))する方法では,播種10日目(移植5日前)に灌注接種した場合の防除価は87. 1で,これより早い時期の灌注接種よりも発病抑制効果が高かった。 この生育中の灌注接種は,作業上簡便であるが,防除価は播種前の床土に混和接種する方法には及ばなかった。 非病原性F. oxysporum S3HO3菌株はホウレンソウをはじめ,キュウリ,メロン,トマト,ナバナ,シュンギク,ミツバなど6科17作物に対して病原性を示さず,他の作物への影響がないと考えられたので,農家圃場において移植栽培との組み合わせによる萎ちょう病防除試験を実施した。 夏取りの作型において本菌株を播種前に混和接種した床土で育苗したホウレンソウ苗を萎ちょう病汚染圃場に移植した結果,本法は直播(慣行栽培)に比較して発病を顕著に抑制し,移植単独の発病軽減を上回り,太陽熱消毒と同等の防除効果があった。 以上のことから,非病原性F. oxysporumと移植栽培を組み合わせたホウレンソウ萎ちょう病の防除法は実用的であると考えられた。
Fast Multi-Focus Ultrasound Image Recovery Using Generative Adversarial Networks

In conventional ultrasound (US) imaging, it is common to transmit several focused beams at multiple locations to generate a multi-focus image with constant lateral resolution throughout the image. However, this method comes at the expense of a loss in temporal resolution, which is important in applications requiring both high-frame rate and constant lateral resolution. Moreover, relative motions of the target with respect to the probe often exist due to hand tremors or biological motions, causing blurring artifacts in the multi-focus image. This article introduces a novel approach for multi-focus US image recovery based on Generative Adversarial Network (GAN) without a reduction in the frame-rate. Herein, a mapping function between the single-focus US image and multi-focus version for having a constant lateral resolution everywhere is estimated through different GANs. We use adversarial loss functions in addition to Mean Square Error (MSE) to generate more realistic ultrasound images. Moreover, we use the boundary seeking method for improving the stability of training, which is currently the main challenge in using GANs. Experiments on simulated and real phantoms as well as on ex vivo data are performed. Results confirm that having both adversarial loss function and boundary seeking training provides better results in terms of the mean opinion score test. Furthermore, the proposed method enhances the resolution and contrast indexes without sacrificing the frame-rate. As for the comparison with other approaches which are not based on NNs, the proposed approach gives similar results while requiring neither channel data nor computationally expensive algorithms.
A Synergistic Approach for Deep Learning and Knowledge Engineered Solutions

Machine Learning techniques offer innovative solutions to many problems from image recognition to intractably difficult games (i.e., Go). However, researchers and developers often employ machine learning techniques in isolation from knowledge-engineered approaches, typically considered passe. While knowledge-engineered solutions have limitations, they offer valuable insights about the problem space and solution pathways. Herein, we explore a hybrid approach that synergistically leverages both knowledge engineering and machine learning.The mismatch between representational layers of machine-learned systems and explicit, symbolic knowledge in engineered solutions make a hybrid approach appear infeasible. We describe a system of complementary techniques either increase system performance or decrease system implementation cost without reconceptualizing seeming incompatible strategies. This approach applies to problems wherein knowledge- engineered solution can simplify learned information, while a machine learning method can expand problem space boundaries for knowledge-engineered solutions. We illustrate this hybrid approach in Sudoku, enabling empirical assessment of engineering vs. machine learning trade-offs.
Productivity and early calf survival in the porcupine caribou herd

Concerns have been raised that displacement from traditional calving and postcalving areas by petroleum exploration may cause increased calf mortality in the Porcupine Caribou (Rangifer tarandus) Herd. Consequently, we determined productivity and early calf survival by frequently locating 23-53 radio-collared cows and 59-61 radio-collared calves from 1983 to 1985. Seventy-nine to 87% of the adult females gave birth each year, and 68-90% of calves were born within, or adjacent to, a coastal plain area proposed for petroleum leasing and development. Sixteen to 35% of the calves died by 25 June. Fifty-nine to 74% of calf mortality occurred within 48 hours of birth and involved a number of causes in addition to predation. Predation was the major cause of death after the first 48 hours, and mortality increased toward higher terrain away from the coastal plain
Curvelet Transform Domain-Based Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

Hyperspectral unmixing (HU) is an efficient way to extract component information from mixed pixels in remotely sensed imagery. Nonnegative matrix factorization (NMF) based unmixing methods have been widely used due to their ability to extract endmembers (pure spectral signatures) and their corresponding fractional abundances in simultaneous fashion. In this article, we propose a new sparse-constrained NMF method for HU purposes. Unlike most sparse regularizers, imposed on abundances (vectors or matrix) directly, our method imposes sparse constraints on a transformed abundance domain. It is based on the assumption that sparsity, when applied on a well-designed transform domain, leads to sparser representations than those in the corresponding source domain (e.g., natural images are approximately sparse in a wavelet domain). In this regard, we specifically explore sparsity on a curvelet transformed domain of abundances. Moreover, we consider the Chambolle–Pock algorithm to solve the involved optimization model, so as to obtain a fast and stable solution. Our experiments, carried out on both synthetic and real hyperspectral datasets, reveal that our newly proposed transform domain-based sparse NMF unmixing method (hereinafter named as “TS-NMF”) obtains better unmixing results than those achieved by other state-of-the-art NMF-based unmixing methods, with less parameter tuning and better robustness to noise.
Automated Vehicle Parking Occupancy Detection in Real-Time

Parking occupancy detection systems help to identify the available parking spaces and direct vehicles efficiently to unoccupied lots by reducing time and energy. This paper presents an approach for the design and development of an end-to-end automated vehicle parking occupancy detection system. The novelty of this study lies in the methodology followed for the object detection process using RetinaNet one stage detector and region-based convolutional neural network deep learning technique. The proposed software architecture consists of low coupled components that support scalability and reliability. The developed web-based and mobile-based client applications assist to find parking spaces easily and efficiently. The existing solutions utilize dedicated sensors and depend on manual segmentation of surveillance footage to detect the state of parking spaces. The proposed approach eliminates existing limitations while maintaining reasonable accuracy.
Water Relations of Mountain Ash (Eucalyptus regnans F. Muell.) Forests

Measurements of the vertical gradients of water potential and stomatal resistance were made in adjacent mature and regrowth forests of mountain ash to follow diurnal and seasonal behaviour. The vertical gradient of water potential at dawn in either forest was shown to be consistent with the theoretical static head of -0.1 bar m-1 but the daytime gradients in the regrowth forest were steeper (down to - 0.45 bar m-1) than in the mature forest which did not fall below -0.27 bar m-1. Stomatal resistance measurements could not be related to height but, consistent with the water potential data, the leaves of the mature forest had higher resistance to water loss than did those of the regrowth. The observations are discussed in relation to the problems of water supply to the leaves of tall trees and the possible contribution of stomatal control to the established water yield characteristics of mountain ash forests.
The Analysis of a Cooperative Hyper-Heuristic on a Constrained Real-world Many-objective Continuous Problem

Despite the significant number of strategies proposed for many-objective optimization, most studies lack the evaluation of their algorithms in real-world applications. In this research, we present the analysis of the application of a recently proposed hyper-heuristic for many-objective optimization to the wind turbine design problem. This application is a representative problem for renewable energy, with 32 variables, 22 constraints, and five objectives. In this study, we solved this problem using the cooperative hyper-heuristic (HH-CO), which uses eight MOEAs on its pool, and switches among them during the search. These MOEAs are representative strategies to deal with multi and many-objective optimization; within HH-CO, every MOEA has its internal population. This behavior is particularly suitable for many-objective optimization, where each MOEA has its criteria to select the solutions that will be preserved for the next generation. Besides, the MOEAs exchange information after being applied, with this migration step, all MOEAs update their private information even if they were not employed. We also evaluate the set of eight state-of-the-art multi-objective evolutionary algorithms (MOEAs), adapted to handle constraints. The empirical analysis uses the hypervolume (HV) quality indicator to evaluate the results and, the HH-CO achieved results competitive to the best MOEAs.
NrGO Floating Gate/SiOXNY Tunneling Layer Stack for Nonvolatile Flash Memory Applications

This paper presents an ultra–thin silicon oxynitride (SiO<sub>X</sub>N<sub>Y</sub>, 4 nm) tunneling layer, nitrogen functionalized reduced graphene oxide (NrGO, 3–5 layer) floating gate (FG) and poly (methyl methacrylate) (PMMA, 60 nm) blocking layers based Al/PMMA/NrGO/SiO<sub>X</sub>N<sub>Y</sub>/p–Si/Au, non–volatile flash memory (NVFM) structures. The ultra–thin SiO<sub>X</sub>N<sub>Y</sub>helps in improving the interface with Si, resulting in lower gate leakage current density and considerable enhanced retention characteristics. The nitrogen engineered GO followed by reduction to NrGO under UV illumination attributes to the modification of the physiochemical properties, hence beneficial for non-volatile memory applications. The uniform, stress free and low temperature processing advocates the potential of PMMA as blocking layer for improved memory characteristics. The electrical characterizations on the fabricated Al/PMMA/NrGO/SiO<sub>X</sub>N<sub>Y</sub>/p–Si/Au gate stack demonstrates a memory window (<inline-formula> <tex-math notation="LaTeX">${\delta }\text{W}$ </tex-math></inline-formula>) of ~1.25 V @ ± 3 V and ~2.6 V @ ± 5 V, low gate leakage current density (J) ~10 nA/cm<sup>2</sup>@ −1 V, retention <inline-formula> <tex-math notation="LaTeX">$\sim 3 \times 10^{11}$ </tex-math></inline-formula> sec (> 10 years with extrapolation) and endurance of more than 100 cycles.
Statistically-driven Coral Reef metaheuristic for automatic hyperparameter setting and architecture design of Convolutional Neural Networks

The adjustment of the hyperparameters and network structure of Convolutional Neural Networks (CNNs) composes an important step towards building effective, but still efficient learning models. The selection of the best configuration is a problem-dependent task that involves to explore an enormous and complex search space. Due to this reason, the use of heuristicbased search fits perfectly within this task, seeking to obtain a near to optimal solution in a complex and large exploratory space. This paper presents SCRODeep, a self-adapting algorithm based on a statistically-driven Coral Reef Optimisation algorithm (SCRO), for the selection of the most adequate CNNs architecture in a particular domain. This metaheuristic has been designed to navigate through a search space where the architecture (defining the particular set of layers, including convolutional or pooling layers), and the hyperparameters of the network (i.e. activation functions, number of units or the kernel initializer, among others) are represented, but where the connections weights and bias are inferred using typical CNNs optimisation algorithms. In contrast to other approaches, where the use of a metaheuristic implies in turn to fix a series of hyperparameters (i.e. the mutation probability in a genetic algorithm), our approach follows a selfparametrisation perspective, thus removing the necessity of fixing these values. The method has been tested in the design of CNNs for image classification, showing that SCRODeep is able to find competitive solutions, while the complexity of the architectures found is constrained.
Occurrence and distribution of Cape hakes Merluccius capensis Cast. and M. paradoxus Franca in the Cape of Good Hope area

A brief history of research into, and the fishery for, Cape hake is given. From twelve monthly surveys in 1972/73, abundance of the two species (Merluccius capensis and M. paradoxus) at depth was found to vary, larger individuals of both species being found deeper than the smaller ones. Off the Cape of Good Hope, M. capensis was never found deeper than 440 m, and there was significant species overlap between 150 and 440 m. M. paradoxus was numerically dominant except at the shallowest station sampled (150 m). Little overlap of mature specimens of each species was recorded. The research data were applied to commercial trawl statistics, revealing that M. capensis accounted for only 12,9 per cent by mass and 4,8 per cent by number of the commercially caught hake in the years 1955–1974. Such low percentages justify considering the Cape hake fishery as a single-species entity for the purposes of stock assessment and management.
Sensor Comparison for Corona Discharge Detection Under Low Pressure Conditions

Low pressure environments, situate insulation systems in a challenging position since partial discharges (PDs), corona and arc tracking are more likely to develop. Therefore, specific solutions are required to detect such harmful phenomena before major failure occurrence. This paper deals with three low-cost and small-size sensing methods, i.e., a single loop antenna, a visible-UV imaging sensor and the measurement of the leakage current to detect corona in the early stage, thus anticipating the appearance of severer effects such as arc tracking or disruptive breakdown. The three studied methods can be applied for an on-line monitoring of corona activity under low pressure environments, thus being compatible with predictive maintenance approaches. This on-line monitoring can be used to develop improved electrical protection devices able to detect such effects in an initial stage, thus improving current solutions which are unable to do so. All three studied sensors give consistent linear responses within the studied pressure range, i.e., 10-100 kPa, with almost no drift. The sensitivity of the visible-UV imaging sensor is slightly lower than that of the others, but it has the advantage of directly locating the discharge points. Results presented in this paper can be very useful for the more electrical aircraft (MEA), which is forcing electrical distribution systems to operate at higher voltage levels. Due to the little experience and scarcity of published data, the experimental results presented in this paper can be valuable for a better understanding of the combined action of high voltage and low pressure environments.
Interference mechanisms in conifer-Ericaceae-feathermoss communities

In northern boreal forests ground level species occur which may have the capacity to suppress tree seedling regeneration substantially, particularly in late successional stages. We investigated, through an experiment involving sequential manipulations, the effects of three biotic components operating at the ground layer, i.e. aboveground effects of the ericaceous shrub Empetrum hermaphroditum and of the feathermoss species Pleurozium schreberi and the below-ground effects of extramatrical mycorrhizal hyphae, on the seedling establishment, growth and nutrient acquisition of Pinus sylvestris during two growing seasons. Germination and seedling establishment of P. sylvestris were enhanced by reduction of the above ground components of E. hermaphroditum and P. schreberi but were not influenced by temporary disruption of mycorrhizal hyphae. Seedling growth and nitrogen content were, however, significantly increased when hyphal connections were disrupted and above ground influence of P. schreberi and E. hermaphroditum were reduced. Higher shoot:root ratios were also found in seedlings when hyphal connections were disrupted. Seedlings in treatments where P. schreberi was left intact and above ground interference by E. hermaphroditum was reduced had unusually low shoot:root ratios (1.6) after two growing seasons and significantly lower total amounts of nutrients after one growing season than were initially present in the seeds, indicative of a net nutrient drain during the early establishment phase. The experiment could not explain the mechanisms behind this unexpected nutrient drain. Possible rhizosphere interactions immobilizing nutrients and causing seedling nutrient losses are discussed. From the results of this study and other indirect evidence we hypothesize that the three biotic components we investigated could act together to immobilize nutrients and to inhibit tree seedling regeneration and growth in late successional stages of boreal forests.
Synchrony matters more than species richness in plant community stability at a global scale

Significance The stability of ecological communities under ongoing climate and land-use change is fundamental to the sustainable management of natural resources through its effect on critical ecosystem services. Biodiversity is hypothesized to enhance stability through compensatory effects (decreased synchrony between species). However, the relative importance and interplay between different biotic and abiotic drivers of stability remain controversial. By analyzing long-term data from natural and seminatural ecosystems across the globe, we found that the degree of synchrony among dominant species was the main driver of stability, rather than species richness per se. These biotic effects overrode environmental drivers, which influenced the stability of communities by modulating the effects of richness and synchrony. The stability of ecological communities is critical for the stable provisioning of ecosystem services, such as food and forage production, carbon sequestration, and soil fertility. Greater biodiversity is expected to enhance stability across years by decreasing synchrony among species, but the drivers of stability in nature remain poorly resolved. Our analysis of time series from 79 datasets across the world showed that stability was associated more strongly with the degree of synchrony among dominant species than with species richness. The relatively weak influence of species richness is consistent with theory predicting that the effect of richness on stability weakens when synchrony is higher than expected under random fluctuations, which was the case in most communities. Land management, nutrient addition, and climate change treatments had relatively weak and varying effects on stability, modifying how species richness, synchrony, and stability interact. Our results demonstrate the prevalence of biotic drivers on ecosystem stability, with the potential for environmental drivers to alter the intricate relationship among richness, synchrony, and stability.
Reliability and Repeatability Analysis of Indices to Measure Gait Deterioration in MS Patients during Prolonged Walking

Gait deterioration caused by prolonged walking represents one of the main consequences of multiple sclerosis (MS). This study aims at proposing quantitative indices to measure the gait deterioration effects. The experimental protocol consisted in a 6-min walking test and it involved nine patients with MS and twenty-six healthy subjects. Pathology severity was assessed through the Expanded Disability Status Scale. Seven inertial units were used to gather lower limb kinematics. Gait variability and asymmetry were assessed by coefficient of variation (CoV) and symmetry index (SI), respectively. The evolution of ROM (range of motion), CoV, and SI was computed analyzing data divided into six 60-s subgroups. Maximum difference among subgroups and the difference between the first minute and the remaining five were computed. The indices were analyzed for intra- and inter-day reliability and repeatability. Correlation with clinical scores was also evaluated. Good to excellent reliability was found for all indices. The computed standard deviations allowed us to affirm the good repeatability of the indices. The outcomes suggested walking-related fatigue leads to an always more variable kinematics in MS, in terms of changes in ROM, increase of variability and asymmetry. The hip asymmetry strongly correlated with the clinical disability.