Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing Organic Food Purchase Intention in an Emergent Market: An Empirical Investigation of Saudi Arabia

The consumption of organic food has increased owing to consumers becoming more concerned about their health. Many people have begun to realize the advantages of using organic instead of conventional food. Consumer-attitude is now the most influential factor affecting consumer purchase intention toward organic food. This study aims to identify factors influencing consumers’ attitudes and purchase intention toward organic food in the Saudi context. The data used here were collected from 147 participants by using structured questionnaires. Structural equation modelling and partial least square (SmartPLS) were used to analyze the data collection. The results show that issues relating to food quality and scepticism influence consumers’ attitudes toward organic food. Interestingly, organic food knowledge and health consciousness are not significant in influencing consumer attitudes toward organic food. Besides, none of the moderating effects were significant (e.g., food safety and price) regarding the relationship between consumer-attitude and purchase intention toward organic food. The results will help marketers and organic food retailers to understand better consumer’s behaviour, build their marketing strategy and make advantageous decisions.
Organic Matter Amendment of Fallow Forest Tree Seedling Nursery Soils Influences Soil Properties and Biomass of a Sorghum Cover Crop

To maintain adequate soil organic matter, bareroot nurseries may add organic amendments, grow and incorporate cover crops between seedling production cycles, or both. We examined the influence of amendment of nursery soils with organic materials on both soil properties and biomass production of a sorghum (Sorghum spp.) cover crop. Level of organic matter, pH, cation exchange capacity, and concentrations of P, K, Ca, and Mg were all significantly influenced by application of chicken manure, sawdust, compost, or leaves. Above-ground biomass of the sorghum cover crop was highest with application of chicken manure at 8,700 kg ha -1 (7,670 lb ac -1) together with sawdust at 12,000 kg ha -1 (10,580 lb ac -1). We observed a negative influence of sawdust on sorghum biomass production, illustrating the importance of maintaining adequate C: N ratios. Our results suggest that integrating a combination of soil amendments and cover crops could potentially increase long-term organic matter levels, helping to sustain nursery soil productivity.
Heavy metal induced ecophysiological function alterations in the euhalophyte Suaeda salsa

Heavy metal accumulation affects the physiological status of plants. Suaeda salsa L. is used to investigate the toxic effects of cadmium (Cd) and lead (Pb) either alone or mixtures under the static test conditions. Cd-Pb mixture exposure can decrease lignin content and weaken the increase. Mitochondrial calcium content significantly reduced at 30 µM Cd and Pb exposure. Cd-Pb mixture exposure can increase calcium content under the same concentration exposure. Soluble sugar levels noted a significant decrease in Cd, Pb and Cd-Pb mixture exposure. The accumulations of Cd, Pb in S. salsa were significantly increased with exposure time. Soluble protein (SP) in S. salsa at 30 µM concentration treatments decreased with exposure time. Heat shock protein 70 (HSP70) was enhanced lightly along with the increase of added Cd-Pb from 30 to 70 &3181;M and then decreased below the controls which present a synergistic effect. Heat shock protein 60 (HSP60) increased slightly with the increase of Cd-Pb from 30 to 110 µM, and then decreased hereafter and significantly inhibited at 150 ƒEM (p<0.05). Moreover, Cd-Pb mixture exposure significantly increased the Rubisco activity under lower concentration and presented antagonistic effect. At the same time, the viability percent decreased as increase Cd-Pb concentration exposure (p0.05), it presents a dose-dependent manner. Mitochondrial cells treated with Cd-Pb exposure obviously reduced the reactive oxygen species (ROS) levels in mitochondrial cells. Key words : Suaeda salsa, heavy metal, ecophysiological function.
Lemma Disambiguation in Multilingual Parallel Corpora

We present novel and efficient approaches to lemmatisation by improving and building on the output of standard lemmatisation tools such as the TreeTagger. Lemmatisation is the process of reducing an inflected word form to its base form and it can be key in the process of reducing sparse data for morphologically rich languages, such as Finnish or German, because highly inflected word forms are relatively rare. A rich morphology also leads to more ambiguities. These ambiguities in context usually do not pose a problem to humans, but they pose a problem to computers. In computational linguistics lemmatisation is performed automatically and often relies on a lexicon known to the system. Ideally each word form is in the lexicon and assigned with a single lemma. However, this is often not the case, as the system is confronted with unseen word forms in a new data set, the lexicon contains erroneous entries or the same word form occurs with more than one lemma candidate because it is ambiguous. The TreeTagger is such a system and a standard POS-tagging tool that can output lemmas to word forms that are contained in the lexicon. We develop two machine learning approaches for lemma disambiguation in German, French, Italian and Finnish that yield better overall performance than preexisting quantitative approaches. We apply our approaches to the Full European Parliament Corpus v. 6, a multilingual, multiparallel corpus. The first approach is a distant-semi supervised approach, which is trained on the unambiguous lemmatisations of lemma candidates of ambiguous lemmas. This approach does not need any manually annotated data for training. The main disadvantage is the low coverage, as a large amount of ambiguous lemmas do not occur unambiguously in the corpus. Therefore, we also developed an active learning pipeline that lets users select the correct lemma in several carefully chosen examples of ambiguous lemmas with lack of evidence for lemma candidates. We draw the observations for manual annotation from precomputed clusters, which help to find minority classes and outliers. The manually disambiguated data is used for a supervised machine learning approach. For the evaluation of the two approaches, we created gold standards for all four languages. The larger part A is for evaluating the distant semi-supervised approach and the smaller part B is for evaluating the active learning pipeline. The distant semi-supervised approach yields results of above 85 % precision, which is above the majority class baseline and exceeds a previously developed quantitative approach applied on the same corpus in recall and F1-score. The results for the active learning pipeline are equally promising with a precision above 90 % for German.
Root morphology of wheat genotypes differing in zinc efficiency

Abstract The root morphology (root length, diameter) of the three wheat genotypes (Triticum aestivum L. cvs Excalibur and Gatcher, and T. turgidum conv. durum (Desf.) McKay cv Durati) grown in zinc (Zn)‐deficient, sandy soil under controlled conditions has been measured by a root scanner coupled to a computer. Wheat plants were supplied with 0, 0.025, 0.05, 0.1, 0.2, or 0.4 mg Zn/kg soil. Excalibur has previously been identified as the Zn‐efficient genotype which can take up more Zn and has higher yield in soils with low plant‐available Zn. Durati is Zn‐inefficient and Gatcher an intermediate genotype with respect to Zn efficiency. Root and shoot dry matter significantly increased at 0.1 mg Zn/kg soil compared to the 0 Zn level. Zinc content in shoots was lower in Durati than in Excalibur and Gatcher at sufficient supply of Zn. Zinc applications had no significant effect on root morphology at two weeks after sowing. At that time, however, the Zn‐efficient genotype Excalibur developed a longer and thinner .
Electrical and optical properties of PbTiO3 thin films on p‐Si grown by metalorganic chemical vapor deposition at low temperature

Metalorganic chemical vapor deposition of PbTiO3 using Pb (tmhd)2, Ti(OC3H7)4, and N2O via thermal pyrolysis at relatively low temperature (∼500 °C) was performed in order to produce PbTiO3 insulator gates with high quality PbTiO3/p‐Si interfaces. Raman spectroscopy showed the several optical phonon modes of the PbTiO3/p‐Si structure. The stoichiometry of the PbTiO3 films was observed by Auger electron spectroscopy. Room‐temperature current‐voltage and capacitance‐voltage measurements clearly revealed metal‐insulator‐semiconductor behavior for the samples with a PbTiO3 insulator gate, and the interface state densities at the PbTiO3/p‐Si were approximately 1011 eV−1 cm−2 at the middle of the Si energy gap.
Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application

Airlines have been making use of highly complex Revenue Management Systems to maximize revenue for decades. Estimating the impact of changing one component of those systems on an important outcome such as revenue is crucial, yet very challenging. It is indeed the difference between the generated value and the value that would have been generated keeping business as usual, which is not observable. We provide a comprehensive overview of counterfactual prediction models and use them in an extensive computational study based on data from Air Canada to estimate such impact. We focus on predicting the counterfactual revenue and compare it to the observed revenue subject to the impact. Our microeconomic application and small expected treatment impact stand out from the usual synthetic control applications. We present accurate linear and deep-learning counterfactual prediction models which achieve respectively 1.1% and 1% of error and allow to estimate a simulated effect quite accurately.
A Change-Point Based Control Chart for Detecting Sparse Changes in High-Dimensional Heteroscedastic Data

Because of the curse-of-dimensionality, high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying distribution and complicated dependency among variables such as heteroscedasticity increase uncertainty of estimated parameters, and decrease the effectiveness of control charts. In addition, the requirement of sufficient reference samples limits the application of traditional charts in high dimension low sample size scenarios (small n, large p). More difficulties appear in detecting and diagnosing abnormal behaviors that are caused by a small set of variables, i.e., sparse changes. In this article, we propose a changepoint based control chart to detect sparse shifts in the mean vector of high-dimensional heteroscedastic processes. Our proposed method can start monitoring when the number of observations is a lot smaller than the dimensionality. The simulation results show its robustness to nonnormality and heteroscedasticity. A real data example is used to illustrate the effectiveness of the proposed control chart in high-dimensional applications. Supplementary material and code are provided online.
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection

The method of deep learning has achieved excellent results in improving the performance of robotic grasping detection. However, the deep learning methods used in general object detection are not suitable for robotic grasping detection. Current modern object detectors are difficult to strike a balance between high accuracy and fast inference speed. In this paper, we present an efficient and robust fully convolutional neural network model to perform robotic grasping pose estimation from an n-channel input image of the real grasping scene. The proposed network is a lightweight generative architecture for grasping detection in one stage. Specifically, a grasping representation based on Gaussian kernel is introduced to encode training samples, which embodies the principle of maximum central point grasping confidence. Meanwhile, to extract multi-scale information and enhance the feature discriminability, a receptive field block (RFB) is assembled to the bottleneck of our grasping detection architecture. Besides, pixel attention and channel attention are combined to automatically learn to focus on fusing context information of varying shapes and sizes by suppressing the noise feature and highlighting the grasping object feature. Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed. The network is an order of magnitude smaller than other excellent algorithms while achieving better performance with an accuracy of 98.9$\%$ and 95.6$\%$ on the Cornell and Jacquard datasets, respectively.
Learning Setup Policies: Reliable Transition Between Locomotion Behaviours

Dynamic platforms that operate over many unique terrain conditions typically require many behaviours. To transition safely, there must be an overlap of states between adjacent controllers. We develop a novel method for training setup policies that bridge the trajectories between pre-trained Deep Reinforcement Learning (DRL) policies. We demonstrate our method with a simulated biped traversing a difficult jump terrain, where a single policy fails to learn the task, and switching between pre-trained policies without setup policies also fails. We perform an ablation of key components of our system, and show that our method outperforms others that learn transition policies. We demonstrate our method with several difficult and diverse terrain types, and show that we can use setup policies as part of a modular control suite to successfully traverse a sequence of complex terrains. We show that using setup policies improves the success rate for traversing a single difficult jump terrain (from 51.3<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate with the best comparative method to 82.2<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>), and traversing a random sequence of difficult obstacles (from 1.9<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> without setup policies to 71.2<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>).
Generalizing Adversarial Examples by AdaBelief Optimizer

Recent research has proved that deep neural networks (DNNs) are vulnerable to adversarial examples—the legitimate input added with imperceptible and well-designed perturbations can fool DNNs easily in the testing stage. However, most of the existing adversarial attacks are difficult to fool adversarially trained models. To solve this issue, we propose an AdaBelief iterative Fast Gradient Sign Method (AB-FGSM) to generalize adversarial examples. By integrating AdaBelief optimization algorithm to I-FGSM, we believe that the generalization of adversarial examples will be improved, relying on the strong generalization of AdaBelief optimizer. To validate the effectiveness and transferability of adversarial examples generated by our proposed AB-FGSM, we conduct the white-box and black-box attacks on various single models and ensemble models. Compared with state-of-the-art attack methods, our proposed method can generate adversarial examples effectively in the white-box setting, and the transfer rate is 7%~21% higher than latest attack methods.
Design and Analysis of Wideband Full-Duplex FR2-IAB Networks

This paper develops a 3GPP-inspired design for the in-band-full-duplex (IBFD) integrated access and backhaul (IAB) networks in the frequency range 2 (FR2) band, which can enhance the spectral efﬁciency (SE) and coverage while reducing the latency. However, the self-interference (SI), which is usually more than 100 dB higher than the signal-of-interest, becomes the major bottleneck in developing these IBFD networks. We design and analyze a subarray-based hybrid beamforming FD-IAB system with the RF beamformers obtained via RF codebooks given by a modiﬁed Linde-Buzo-Gray (LBG) algorithm. The SI is canceled in three stages, where the antenna isolation forms the ﬁrst stage. The second stage consists of the optical domain-based RF cancellation, where cancelers are connected with the RF chain pairs. The third stage is comprised of the digital cancellation via successive interference cancellation followed by minimum mean-squared error baseband receiver. Multiuser interference in the access link is canceled by zero-forcing at the IAB-node transmitter. Simulations show that our staged SI cancellation can enhance the SE. Moreover, the residual SI due to the hardware impairments and channel uncertainty can affect the SE of the FD scheme in the backhaul link.
Transition from shear-dominated to Rayleigh–Taylor turbulence

Abstract Turbulent mixing layers in nature are often characterised by the presence of a mean shear and an unstable buoyancy gradient between two streams of different velocities. Depending on the relative strength of shear versus buoyancy, either the former or the latter may dominate the turbulence and mixing between the two streams. In this paper, we present a phenomenological theory that leads to the identification of two distinct turbulent regimes: an early regime, dominated by mean shear, and a later regime dominated by buoyancy. The main theoretical result consists of the identification of a cross-over timescale that distinguishes between the shear- and the buoyancy-dominated turbulence. This cross-over time depends on three large-scale constants of the flow, namely, the buoyancy difference, the velocity difference between the two streams and the gravitational acceleration. We validate our theory against direct numerical simulations of a temporal turbulent mixing layer compounded with an unstable stratification. We observe that the cross-over time correctly predicts the transition from shear- to buoyancy-driven turbulence, in terms of turbulent kinetic energy production, energy spectra scaling and mixing layer thickness.
A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks

In this work, to limit the number of required attention inference hops in memory-augmented neural networks, we propose an online adaptive approach called $\text{A}^{2}\text{P}$ -memory-augmented neural network (MANN). By exploiting a small neural network classifier, an adequate number of attention inference hops for the input query are determined. The technique results in the elimination of a large number of unnecessary computations in extracting the correct answer. In addition, to further lower computations in $\text{A}^{2}\text{P}$ -MANN, we suggest pruning weights of the final fully connected (FC) layers. To this end, two pruning approaches, one with negligible accuracy loss and the other with controllable loss on the final accuracy, are developed. The efficacy of the technique is assessed by applying it to two different MANN structures and two question answering (QA) datasets. The analytical assessment reveals, for the two benchmarks, on average, 50% fewer computations compared to the corresponding baseline MANNs at the cost of less than 1% accuracy loss. In addition, when used along with the previously published zero-skipping technique, a computation count reduction of approximately 70% is achieved. Finally, when the proposed approach (without zero skipping) is implemented on the CPU and GPU platforms, on average, a runtime reduction of 43% is achieved.
A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs.In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. Incorporating features such as the Inception residual module with depth-wise separable convolutional layers, the proposed model generated a refined prediction map with the following two training stages: (i) Stage I coarsely segmented the major coronary arteries from pre-processed single-channel ICAs and generated the probability map of vessels; (ii) during the Stage II, a three-channel image consisting of the original preprocessed image, a generated probability map, and an edge-enhanced image generated from the preprocessed image was fed to the proposed MIMS U-Net to produce the final segmentation probability map. During the training stage, the probability maps were iteratively and recurrently updated by feeding into the neural network. After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameters to evaluate stenotic level. Experimental results demonstrated that the proposed method achieved an average Dice score of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patient. Moreover, our stenosis detection algorithm achieved a true positive rate of 0.6668 and a positive predictive value of 0.7043.
Privacy Preserving Techniques Applied to CPNI Data: Analysis and Recommendations

With mobile phone penetration rates reaching 90%, Consumer Proprietary Network Information (CPNI) can offer extremely valuable information to different sectors, including policymakers. Indeed, as part of CPNI, Call Detail Records have been successfully used to provide real-time traffic information, to improve our understanding of the dynamics of people's mobility and so to allow prevention and measures in fighting infectious diseases, and to offer population statistics. While there is no doubt of the usefulness of CPNI data, privacy concerns regarding sharing individuals' data have prevented it from being used to its full potential. Traditional de-anonymization measures, such as pseudonymization and standard de-identification, have been shown to be insufficient to protect privacy. This has been specifically shown on mobile phone datasets. As an example, researchers have shown that with only four data points of approximate place and time information of a user, 95% of users could be re-identified in a dataset of 1.5 million mobile phone users. In this landscape paper, we will discuss the state-of-the-art anonymization techniques and their shortcomings.
Feature Selection Using Reinforcement Learning

With the decreasing cost of data collection, the space of variables or features that can be used to characterize a particular predictor of interest continues to grow exponentially. Therefore, identifying the most characterizing features that minimizes the variance without jeopardizing the bias of our models is critical to successfully training a machine learning model. In addition, identifying such features is critical for interpretability, prediction accuracy and optimal computation cost. While statistical methods such as subset selection, shrinkage, dimensionality reduction have been applied in selecting the best set of features, some other approaches in literature have approached feature selection task as a search problem where each state in the search space is a possible feature subset. In this paper, we solved the feature selection problem using Reinforcement Learning. Formulating the state space as a Markov Decision Process (MDP), we used Temporal Difference (TD) algorithm to select the best subset of features. Each state was evaluated using a robust and low cost classifier algorithm which could handle any non-linearities in the dataset.