Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of hyperspectral images using a propagation filter and convolutional neural network

ABSTRACT Deep Learning models have been successfully applied to the classification of hyperspectral images (HSIs), among which Convolutional Neural Network (CNN) can extract more efficient spatial–spectral features to improve classification accuracy compared to traditional classifiers such as Support Vector Machine. However, in the case of limited labelled samples with inevitable noise, simply applying CNN for HSIs classification could be unsatisfactory due to the issue of overfitting. To address the problem, data preprocessing techniques such as filtering methods can be used to effectively remove the noise and enhance the spatial–spectral features. Compared to typical filters like Gabor, Propagation Filter (PF) can effectively smooth the HSIs and preserve the edges without causing the cross-region mixing problem, which could significantly compromise the performance of CNN based HSIs classification models. Therefore, in this paper a new model which combine Propagation Filter with CNN (PF-CNN) is proposed. It makes use of the advantages of PF and CNN to effectively handle the cross-region mixing issue for HSIs classification. The experimental results show that the proposed algorithm outperforms other state-of-the-art HSIs classification models. The source code of the proposed model is available at https://github.com/XinweiJiang/Propagation-Filter-CNN.
Delay Analysis of Mobile Edge Computing Using Poisson Cluster Process Modeling: A Stochastic Network Calculus Perspective

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.
Overexpression of c-Myc-dependent heterogeneous nuclear ribonucleoprotein A1 promotes proliferation and inhibits apoptosis in NOTCH1-mutated chronic lymphocytic leukemia cells

Abstract Background: NOTCH1 mutation is an essential molecular biologic aberration in chronic lymphocytic leukemia (CLL). CLL patients with NOTCH1 mutation have shown an unfavorable survival and a poor response to chemoimmunotherapy. This study aims to present the mechanisms of adverse prognosis caused by NOTCH1 mutation from the perspective of the splicing factor heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1). Methods: The microarray data in Gene Expression Omnibus datasets were analyzed by bioinformatics and the function of hnRNPA1 was checked by testing the proliferation and apoptosis of CLL-like cell lines. Afterward, quantitative reverse transcription-polymerase chain reaction and Western blotting were applied to explore the relationship among NOTCH1, c-Myc, and hnRNPA1. Results: RNA splicing was found to play a vital part in NOTCH1-mutated CLL cells; hence, hnRNPA1 was selected as the focus of this study. Higher expression of hnRNPA1 validated in primary NOTCH1-mutated CLL samples could promote proliferation and inhibit apoptosis in CLL. The expression of hnRNPA1 increased when NOTCH1 signaling was activated by transfection with NOTCH1 intracellular domain (NICD)-overexpressed adenovirus vector and declined after NOTCH1 signaling was inhibited by NOTCH1-shRNA. Higher expression of c-Myc was observed in NICD-overexpressed cells and hnRNPA1 expression was downregulated after applying c-Myc inhibitor 10058-F4. Moreover, in NICD-overexpressed cells, hnRNPA1 expression decreased through c-Myc inhibition. Conclusion: Overexpression of c-Myc-dependent hnRNPA1 could promote proliferation and inhibit apoptosis in NOTCH1-mutated CLL cells, which might partly account for the poor prognosis of patients with NOTCH1 mutation.
Transformers for Low-resource Neural Machine Translation

: The recent advances in neural machine translation enable it to be state-of-the-art. However, although there are signiﬁcant improvements in neural machine translation for a few high-resource languages, its performance is still low for less-resourced languages as the amount of training data signiﬁcantly affects the quality of the machine translation models. Therefore, identifying a neural machine translation architecture that can train the best models in low-data conditions is essential for less-resourced languages. This research modiﬁed the Transformer-based neural machine translation architectures for low-resource polysynthetic languages. Our proposed system outperformed the strong baseline in the automatic evaluation of the experiments on the public benchmark datasets.
Identifying and Ranking Criteria of Good Governance in Project-Based Organizations

Good governance plays a key role in the growth and success of organizations and helps them to achieve their strategic goals. A review of the research literature shows that the term good governance is mostly used in relation to governments, and there is no proper understanding of the role of good governance in non-governmental organizations. Due to the significant growth of project-oriented organizations, there is a gap in the literature to explain the position of good governance in project-based organizations. For this reason, there is no good understanding of the term good governance and its application in project-based organizations. The purpose of this study is to identify and prioritize the criteria of good governance in Iranian project-based organizations. To this end, a hybrid approach based on meta-synthesis, thematic analysis, and multi-criteria decision-making methods was used to identify and prioritize good governance criteria in project-based organizations. In the first phase, using the meta-synthesis method, an extensive review of the good governance literature from 2012 to 2020 was conducted and different criteria of good governance were extracted. In the second phase, the thematic analysis method was used to identify good governance criteria through interviews with 10 experts active in project-based organizations. In the third phase, the best-worst method (BWM) method was used to weigh and prioritize the good governance criteria. The results of data analysis indicated that accountability is the most important criterion, and the planning to respond to risks and uncertainties is the most important sub-criterion of the research. Finally, some managerial implications for managers of project-based organizations and some future research directions were provided.
Extensions of the Ekop Model

Easley, Kiefer, O'Hara and Paperman (1996) introduced a model that enables one to estimate the probability of informed trading in a stock using as input data the numbers of buyer and seller initiated trades over a period. Empirical testing reported in Venter and de Jongh (2004) indicated that this model does not fit data well and they formulated a model based on the Poisson Inverse Gaussian (PIG) distribution that fitted better. This model allowed for random daily news noise impacts on the trading intensities of the liquidity and informed traders. These news noise impacts were assumed to be independent over successive days. Further investigation of the model indicated that the independence assumption may not be realistic. In this paper we reformulate the model in terms of Poisson Log Normally (PLN) distributed news noise impacts with serial correlation. We show how the much more complicated likelihood function and other items of interest for inference purposes may be computed by means of efficient importance sampling.
Developing and Experimenting on Approaches to Explainability in AI Systems

: There has been a sharp rise in research activities on explainable artiﬁcial intelligence (XAI), especially in the context of machine learning (ML). However, there has been less progress in developing and implementing XAI techniques in AI-enabled environments involving non-expert stakeholders. This paper reports our investigations into providing explanations on the outcomes of ML algorithms to non-experts. We investigate the use of three explanation approaches (global, local, and counterfactual), considering decision trees as a use case ML model. We demonstrate the approaches with a sample dataset, and provide empirical results from a study involving over 200 participants. Our results show that most participants have a good understanding of the generated explanations.
Seed germination and physiological responses of quinoa to selenium priming under drought stress

The early stages of quinoa germination are sensitive to drought stress. For this purpose, a study entitled the effect of selenium in different concentrations on germination characteristics and some antioxidant enzymes of quinoa under drought stress conditions with polyethylene glycol (PEG 6000) was investigated. The first experimental factor was seed priming with selenium (from two sources: sodium selenate and selenium nanoparticles: SeNPs ≈ 33.4 nm) at 0.5, 1.5, 3, 4.5, 6 mg·L−1 concentrations, besides, no priming treatment was used as control. The second factor was drought stress with PEG 6000 in concentrations 0, –0.4, –0.8, and –1.2 MPa. Drought stress with accumulation of reactive oxygen species (ROS) had a negative effect on most of the measured traits. In seeds that were primed with appropriate selenium concentrations, germination parameters and antioxidant enzyme activity as well as proline and protein content increased compared to the control treatment. Under conditions of severe stress (–1.2 MPa), the highest activity of catalase (CAT), superoxide dismutase (SOD), and ascorbate peroxidase (APX) enzymes was observed in prime with selenium nanoparticles at concentrations of 4.5, 6.0 and 4.5 mg·L−1, respectively. Concentrations higher than 3 mg·L−1 of selenium nanoparticles and concentrations of 3 mg·L−1 sodium selenate had the highest accumulation of photosynthetic pigments under control (stress-free) conditions. The present study shows that selenium priming can reduce the harmful effects of drought stress on quinoa by altering germination properties and biochemical properties.
Prognostic-based Maintenance Optimization in Complex Systems with Resource Limitation Constraints

This paper is concerned with prognostic information for maintenance optimization in complex systems. At each stage of such a system, we consider redundant components used as backup to guarantee the system’s availability. The Remaining Useful Life (RUL/prognostic information) of components is used to evaluate each component’s redundancy. We address RUL-based maintenance optimization under resource limitation to ensure the availability of the system such that production demands can be satisfied in a given maintenance planning horizon. We propose a mixed-integer linear programming approach to minimize the overall cost. Our numerical results on test instances show the efficiency of the proposed approach to attain optimal solutions.
Social Transmission of Information through Virtual Robotic Agents

Social learning includes simple or complex social mechanisms that allow us to understand cooperation and communication in animals, giving them better chances to survive for longer and thrive as a society. Specifically, certain types of social learning such as observational conditioning and stimulus enhancement have been investigated in the context of social information spread between primates. However, not many studies have utilized such social learning mechanisms to study social learning between humans and artificial agents. In the work described here, we seek to understand if and how simple social learning mechanisms can influence human participants using an online game platform with an immersive first person experience built through Unity. Specifically, we designed a study inspired by experiments in behavioural sciences to investigate whether and to what extent, a robotic agent can influence human’s actions. The study compared two conditions in which the robot showed body-language based emotions in a positive or negative manner that could enhance certain stimuli for the human participants and influence their decision making. From this, we wanted to understand whether these effects are socially learned by humans. Objective (position of player in-game) and Subjective (questionnaires) measures were recorded, and markers using the objective data suggest successful social transmission of information. We believe this approach can make a novel contribution to the field of Human Interaction with Artificial Agents.
Discovery of over 200 new and expanded genetic conditions using GeneMatcher

GeneMatcher is a platform through which various stakeholders can connect with others interested in candidate gene findings. GeneDx, a diagnostic laboratory, has utilized GeneMatcher over the last seven years to successfully facilitate connections between clinicians and researchers, generating fruitful research collaborations. Our ultimate goal in reporting candidate gene findings is to amass sufficient evidence to establish novel disease–gene relationships (DGRs), thus providing diagnostic answers to families and clinicians. Our database of over 300,000 clinical exomes has been a major driver of DGR discovery. Our laboratory accounts for over 20% of total GeneMatcher submissions. Largely fueled by GeneMatcher matches, we have published over 200 articles involving new DGRs or expanded phenotypes for known disease‐causing genes in the past three years. These endeavors require commitments to sharing data and dedicating resources to investigate potential matches. Ultimately, GeneMatcher enables collaboration on a broad scale: we are grateful to the clinicians, researchers, patients, and caregivers who have partnered with us to accelerate the pace of DGR discovery. GeneMatcher opens the door to new partnerships, new discoveries, and families finding answers that otherwise may not have been possible.
Impact of Autonomous Vehicles on Roundabout Capacity

Studying the impact of AVs on our road infrastructure offers a lot of potential in the transportation domain; one of these issues is how capacity will be affected. This paper presents a contribution to this research area by investigating the impact of AVs on the capacity of single-lane roundabouts using a microsimulation model. For the development of the model, a roundabout situated in Győr (Hungary) was selected and field data on the roundabout geometric characteristics as well as traffic volumes were used. Simulations using Vissim were run for various scenarios based on varying input traffic volumes and market penetration rates of AVs to assess queue lengths. The highway capacity manual (HCM) roundabout model was used to estimate the capacity of the existing roundabout. Values of follow-up times and critical gaps were set to decreasing as the penetration rate of AVs increases. The results demonstrated that 20% and 40% AVs in the flow would increase leg capacities by about 10% and 20%, respectively. Furthermore, a reduction in excessive queue lengths was estimated and capacities and queue lengths were calculated by legs. It was found that these are highly influenced by the distribution of flows among legs, and the share of flows in various directions.
Approximate Entropy in Canonical and Non-Canonical Fiction

Computational textual aesthetics aims at studying observable differences between aesthetic categories of text. We use Approximate Entropy to measure the (un)predictability in two aesthetic text categories, i.e., canonical fiction (‘classics’) and non-canonical fiction (with lower prestige). Approximate Entropy is determined for series derived from sentence-length values and the distribution of part-of-speech-tags in windows of texts. For comparison, we also include a sample of non-fictional texts. Moreover, we use Shannon Entropy to estimate degrees of (un)predictability due to frequency distributions in the entire text. Our results show that the Approximate Entropy values can better differentiate canonical from non-canonical texts compared with Shannon Entropy, which is not true for the classification of fictional vs. expository prose. Canonical and non-canonical texts thus differ in sequential structure, while inter-genre differences are a matter of the overall distribution of local frequencies. We conclude that canonical fictional texts exhibit a higher degree of (sequential) unpredictability compared with non-canonical texts, corresponding to the popular assumption that they are more ‘demanding’ and ‘richer’. In using Approximate Entropy, we propose a new method for text classification in the context of computational textual aesthetics.
RAPID-INTEGRATED NEMOV6 HANDOFF IN IEEE802.16E BWA NETWORKS

NEMO basic support protocol was created by IETF NEMO working group to extend basic end-host mobility support in, Mobile IP (MIP) protocol to provide network mobility support. However, the handover latency in this scheme is high, and not suitable for multimedia and real time applications. Many techniques have been proposed to solve this problem by parallelizing the dual layers handover and assigning the network layer handover part to the serving Mobile Router (PAR) or to the Home Agent (HA). These techniques either not fully parallelized or its advantages will be lost if the distance between the Mobile Router and its HA is so long. In this paper, we propose a new technique called Rapid-Integrated NEMO Handover (RINEMO) that keep its advantage whatever the distance between the MR and its HA and present a better parallelizing between the link layer and the network layer handover steps. Analytical results comparing the techniques are provided, showing that our technique have the lowest handover latency and lowest disruption time.
Diagnosis Method for Inverter Open-circuit Fault Based on Switching Function Model

The normal operations of motor drive systems are directly related to the safety and reliability of inverters. Based on the analysis of switching function model for inverter under both normal and fault statuses,a novel simplified rapid diagnosis approach for component failures was proposed in this paper. Due to the different voltages endured by an IGBT under normal and fault modes,the simple hardware circuit was adopted to detect and locate open-circuit faults. To ensure effectiveness and reliability of the proposed diagnosis method,the raise edges' delays of switching signals were used to avoid false diagnosis,and the delay durations were determined considering various factors. The presented diagnosis approach was validated by induction motor variable voltage and variable frequency (VVVF) control system as well as permanent magnet synchronous motor field-orientation vector control system. The experimental results demonstrate the efficiency of the proposed method for diagnosing single component and arm open-circuit faults.
One Year in the Extreme Isolation of Antarctica—Is This Enough to Modulate an “Allergic” Sensitization?

(1) Background: After spending a year wintering in Antarctica, individual expedition members have reported increased or even new allergic reactions to environmental allergens after their return. (2) Methods: Blood samples from five overwintering crews were analyzed using the chip based multiplex ALEX Allergy Explorer (MacroArray Diagnostics GmbH, Austria). (3) Results: About one third of the 39 participants displayed specific IgEs against pollen. In most individuals, kinetics showed a reduction in the specific IgE at the time about nine months after deployment to Antarctica. Five participants had the highest specific IgE levels after returning to the “normal” world. The examination of the specific IgE relative to house dust mites and storage mites showed different kinetics. Six out of 10 had the highest specific IgE concentrations at the inner Antarctic measurement time point. These data corresponded well to the general situation in the stations. At the stations themselves, there were almost no pollen particle load, especially at Concordia. (4) Conclusions: Antarctic long-term confinement can induce an altered immune function, which is in some individuals pronounced after return to the familiar allergen environment. Future prospective studies in larger cohorts are needed to further specify these first results.
High-throughput field crop phenotyping: current status and challenges

In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.
Dynamic Modeling and Simulation of Quadruped Robot Based on Virtual Prototyping Technology

Simulation of energy consumption for walking robot is one of the ways to estimate the energy that will be consumed before constructing the real robot, and provides basis for the design of physical prototyping with less energy consumption and its control system. Based on the analysis of kinematics and Lagrange dynamics equations of quadruped walking robot, the 3D simulation model considering contact between foots and ground was proposed to analyze the simulation of dynamics and energy consumption of whole gait cycle and obtain the dynamic characteristics. Using specific resistance to evaluate energy efficiency of walking robot, the energy consumption during walking was analyzed with different walking velocity and duty factors, and the relationship between specific resistance and gait parameter was proposed. Meanwhile, the force characteristics were analyzed between foot and ground with rigid terrain and three soft soils.