Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic variation in early fitness traits across European populations of silver birch (Betula pendula)

Abstract Given that the ecological niche of tree species is typically narrower for earlier life stages, intraspecific genetic variation at early fitness traits may greatly influence the adaptive response of tree populations to changing environmental conditions. In this study, we evaluated genetic variation in early fitness traits among 12 populations of Betula pendula from a wide latitudinal range in Europe (41–55°N). We first conducted a chamber experiment to test for population differences in germination and the effect of pre-chilling treatment on seed dormancy release. We then established three common gardens spread across the species latitudinal range in order to evaluate levels of quantitative genetic variation and genotype-by-environment interaction at different early life traits. Our results showed significant variation in chamber germination rates among populations (0–60 %), with southern populations exhibiting lower germination. Pre-chilling treatments did not generally improve germination success. Population seedling emergence rates in the field were correlated with chamber germination rates, though being an order of magnitude lower, with an average ranging from 0 to 1.3 % across gardens. Highly significant variation was found in field emergence rates among populations, and between seed-crop years within populations, but not among families within populations. Populations differed in seedling height, diameter, slenderness and budburst date, with significant among-family variation. Population latitude was positively associated with chamber germination rate and with seedling emergence rate in one of the central field sites. Overall, genetic, environmental and demographic factors seem to influence the observed high levels of variation in early fitness traits among B. pendula populations. Our results suggest limited regeneration capacity for the study species under drier conditions, but further field trials with sufficient replication over environments and seed crops will improve our understanding of its vulnerability to climate change.
Semantic Alignment: Finding Semantically Consistent Ground-Truth for Facial Landmark Detection

Recently, deep learning based facial landmark detection has achieved great success. Despite this, we notice that the semantic ambiguity greatly degrades the detection performance. Specifically, the semantic ambiguity means that some landmarks (e.g. those evenly distributed along the face contour) do not have clear and accurate definition, causing the inconsistent annotations (random errors) introduced by annotators. Accordingly, these inconsistent annotations, which are usually provided by public databases, commonly work as the (inaccurate) groundtruth to supervise network training, leading to the degraded accuracy. To our knowledge, very little research has investigated this problem. In this paper, we propose a novel probabilistic model which introduces a latent variable, i.e. ‘real’ groundtruth which is semantically consistent, to optimize. This framework couples two parts (1) training landmark detection CNN and (2) searching the ‘real’ groundtruth. These two parts are alternatively optimized: the searched ‘real’ groundtruth supervises the CNN training; and the trained CNN assists the searching of ‘real’ groundtruth. In addition, to correct or recover the unconfidently predicted landmarks due to occlusion and low quality, we propose a global heatmap correction unit (GHCU) to correct outliers by considering the global face shape as a constraint. Extensive experiments on both image-based (300V and AFLW) and video-based (300VW) databases demonstrate that our method effectively improves the landmark detection accuracy and achieves state-of-the-art performance.
GEVR

In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-based" group decision model to provide venue recommendations for groups of mobile users. Our study leverages a real-world dataset collected using the OutWithFriendz mobile app for group event planning, which contains 625 users and over 500 group events. We first develop a novel "social-based" group location prediction model, which adaptively applies different group decision strategies to groups with different social relationship strength to aggregate each group member's location preference, to predict where groups will meet. Evaluation results show that our prediction model not only outperforms commonly used and state-of-the-art group decision strategies with over 80% accuracy for predicting groups' final meeting location clusters, but also provides promising qualities in cold-start scenarios. We then integrate our prediction model with the Foursquare Venue Recommendation API to construct an event venue recommendation framework for groups of mobile users. Evaluation results show that GEVR outperforms the comparative models by a significant margin.
A phylogenomic tree inferred with an inexpensive PCR‐generated probe kit resolves higher‐level relationships among Neptis butterflies (Nymphalidae: Limenitidinae)

Recent advances in obtaining reduced representation libraries for next‐generation sequencing permit phylogenomic analysis of species‐rich, recently diverged taxa. In this study, we performed sequence capture with homemade PCR‐generated probes to study diversification among closely related species in a large insect genus to examine the utility of this method. We reconstructed the phylogeny of Neptis Fabricius, a large and poorly studied nymphalid butterfly genus distributed throughout the Old World. We inferred relationships among 108 Neptis samples using 89 loci totaling up to 84 519 bp per specimen. Our taxon sample focused on Palearctic, Oriental and Australasian species, but included 8 African species and outgroups from 5 related genera. Maximum likelihood and Bayesian analyses yielded identical trees with full support for almost all nodes. We confirmed that Neptis is not monophyletic because Lasippa heliodore (Fabricius) and Phaedyma amphion (Linnaeus) are nested within the genus, and we redefine species groups for Neptis found outside of Africa. The statistical support of our results demonstrates that the probe set we employed is useful for inferring phylogenetic relationships among Neptis species and likely has great value for intrageneric phylogenetic reconstruction of Lepidoptera. Based on our results, we revise the following two taxa: Neptis heliodore comb. rev. and Neptis amphion comb. rev.
Attitude and Cruise Control of a VTOL Tiltwing UAV

This letter presents the mathematical modeling, controller design, and flight testing of an overactuated vertical take-off and landing tiltwing unmanned aerial vehicle (UAV). Based on simplified aerodynamics and first principles, a dynamical model of the UAV is developed which captures key aerodynamic effects including propeller slipstream on the wing and poststall characteristics of the airfoils. The model-based steady-state flight envelope and the corresponding trim-actuation is analyzed and the overactuation of the UAV solved by optimizing for, e.g., power-optimal trims. The developed control system is composed of two controllers. First, a low-level attitude controller based on dynamic inversion and a daisy-chaining approach to handle allocation of redundant actuators. Second, a higher level cruise controller to track a desired vertical velocity. It is based on a linearization of the system and lookup tables to determine the strong and nonlinear variation of the trims throughout the flight envelope. We demonstrate the performance of the control system for all flight phases (hover, transition, cruise) in extensive flight tests.
Deep Learning and Word Embeddings for Tweet Classification for Crisis Response

Tradition tweet classification models for crisis response focus on convolutional layers and domain-specific word embeddings. In this paper, we study the application of different neural networks with general-purpose and domain-specific word embeddings to investigate their ability to improve the performance of tweet classification models. We evaluate four tweet classification models on CrisisNLP dataset and obtain comparable results which indicates that general-purpose word embedding such as GloVe can be used instead of domain-specific word embedding especially with Bi-LSTM where results reported the highest performance of 62.04% F1 score.
Conditioning a Recurrent Neural Network to synthesize musical instrument transients

A recurrent Neural Network (RNN) is trained to predict sound samples based on audio input augmented by control parameter information for pitch, volume, and instrument identification. During the generative phase following training, audio input is taken from the output of the previous time step, and the parameters are externally controlled allowing the network to be played as a musical instrument. Building on an architecture developed in previous work, we focus on the learning and synthesis of transients - the temporal response of the network during the short time (tens of milliseconds) following the onset and offset of a control signal. We find that the network learns the particular transient characteristics of two different synthetic instruments, and furthermore shows some ability to interpolate between the characteristics of the instruments used in training in response to novel parameter settings. We also study the behaviour of the units in hidden layers of the RNN using various visualisation techniques and find a variety of volume-specific response characteristics.
Metrics and compactifications of Teichmüller spaces of flat tori

Using the identification of the symmetric space $\mathrm{SL}(n,\mathbb{R})/\mathrm{SO}(n)$ with the Teichm\"uller space of flat $n$-tori of unit volume, we explore several metrics and compactifications of these spaces, drawing inspiration both from Teichm\"uller theory and symmetric spaces. We define and study analogs of the Thurston, Teichm\"uller, and Weil-Petersson metrics. We show the Teichm\"uller metric is a symmetrization of the Thurston metric, which is a polyhedral Finsler metric, and the Weil-Petersson metric is the Riemannian metric of $\mathrm{SL}(n,\mathbb{R})/\mathrm{SO}(n)$ as a symmetric space. We also construct a Thurston-type compactification using measured foliations on $n$-tori, and show that the horofunction compactification with respect to the Thurston metric is isomorphic to it, as well as to a minimal Satake compactification.
Genetics of Nonalcoholic Fatty Liver Disease: From Pathogenesis to Therapeutics

Abstract Here, the authors review the remarkable genetic discoveries that have illuminated the biology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH). The authors integrate genes associated with NAFLD and NASH into regulatory pathways to elucidate the disease pathogenesis. They review the evidence for molecular mediators of chronic liver damage, which suggests that convergent pathophenotypes, including inflammation and fibrosis, share common genetic modifiers. They further demonstrate that genes involved in the genetic susceptibility of NAFLD and NASH participate in cross-phenotype associations with diseases of the metabolic syndrome, including type 2 diabetes, obesity, and cardiovascular disease. However, immune-related loci associated with NAFLD and NASH exhibit some level of pleiotropy influencing disparate phenotypes, such as premature birth or sepsis. They finally focus on the translation of current genetic knowledge of NAFLD and NASH toward precision medicine. They provide evidence of genetic findings that can be leveraged to identify therapeutic targets.
Characterization of thallus mechanical and physiological traits of tropical fucoids: A preliminary study

Thallus mechanical strength, as well as production of secondary metabolites as defensive compounds, of tropical macroalgae is often essential for protection from herbivory. In tropical macroalgae, thallus mechanical strength is negatively correlated with productivity — a trade‐off between productivity and thallus toughness. The tropical fucoids, Turbinaria ornata which has defensive morphological traits against herbivory and Sargassum ilicifolium which is expanding its distribution in Japanese waters, were examined to determine thallus traits related to mechanical strength and productivity and their pair‐wise relationships were also examined. These traits that are directly or indirectly related to the trade‐off between productivity and thallus toughness were compared to data for various other temperate macroalgae by regression analyses. We found two strong positive correlations between thallus mechanical strength and thallus mass or thallus thickness, confirming that higher levels of mechanical strength for tropical fucoids is associated with higher biomass or thallus thickness. Also, negative correlations between thallus toughness and productivity were found indicating structural and physiological trade‐offs. However, the tropical fucoids exhibited relatively high productivity regardless of their higher level of thallus toughness. These traits of the tropical fucoids slightly deviate from the typical conservative strategy with higher thallus toughness and lower productivity as a trade‐off between productivity and thallus toughness.
Quaternion Non-Negative Matrix Factorization: Definition, Uniqueness, and Algorithm

This article introduces quaternion non-negative matrix factorization (QNMF), which generalizes the usual non-negative matrix factorization (NMF) to the case of polarized signals. Polarization information is represented by Stokes parameters, a set of 4 energetic parameters widely used in polarimetric imaging. QNMF relies on two key ingredients: (i) the algebraic representation of Stokes parameters thanks to quaternions and (ii) the exploitation of physical constraints on Stokes parameters. These constraints generalize non-negativity to the case of polarized signals, encoding positive semi-definiteness of the covariance matrix associated which each source. Uniqueness conditions for QNMF are presented. Remarkably, they encompass known sufficient uniqueness conditions from NMF. Meanwhile, QNMF further relaxes NMF uniqueness conditions requiring sources to exhibit a certain zero-pattern, by leveraging the complete polarization information. We introduce a simple yet efficient algorithm called quaternion alternating least squares (QALS) to solve the QNMF problem in practice. Closed-form quaternion updates are obtained using the recently introduced generalized HR calculus. Numerical experiments on synthetic data demonstrate the relevance of the approach. QNMF defines a promising generic low-rank approximation tool to handle polarization, notably for blind source separation problems arising in imaging applications.
Benzene Removal from Aqueous Solutions by Heterogeneous Catalytic Ozonation Process with Magnesium Oxide Nanoparticles

ABSTRACT The application of a heterogeneous catalytic ozonation process with magnesium oxide (MgO) nanoparticles for the degradation of benzene in aqueous solutions was investigated by a Central Composite Design to optimize experimental conditions. Analysis of variance (ANOVA) was used for studying the significance and adequacy of the model. Four independent variables including pH (3–12), catalyst dose (0.1–0.5 g), ozonation time (5–50 min), and benzene concentration (10–200 mg/L) were transformed to coded values, and subsequently, second-order quadratic model was built to predict the responses. The ANOVA of the benzene degradation in solutions by COP/MgO nanoparticles showed an F-value of 48.76 for the quadratic model.
Inference for stochastic kinetic models from multiple data sources for joint estimation of infection dynamics from aggregate reports and virological data

Influenza and respiratory syncytial virus (RSV) are the leading etiological agents of seasonal acute respiratory infections (ARI) around the world. Medical doctors typically base the diagnosis of ARI on patients' symptoms alone, and do not always conduct virological tests necessary to identify individual viruses, which limits the ability to study the interaction between multiple pathogens and make public health recommendations. We consider a stochastic kinetic model (SKM) for two interacting ARI pathogens circulating in a large population and an empirically motivated background process for infections with other pathogens causing similar symptoms. An extended marginal sampling approach based on the Linear Noise Approximation to the SKM integrates multiple data sources and additional model components. We infer the parameters defining the pathogens' dynamics and interaction within a Bayesian hierarchical model and explore the posterior trajectories of infections for each illness based on aggregate infection reports from six epidemic seasons collected by the state health department, and a subset of virological tests from a sentinel program at a general hospital in San Luis Potosi, Mexico. We interpret the results based on real and simulated data and make recommendations for future data collection strategies. Supplementary materials and software are provided online.
High-quality Instance-aware Semantic 3D Map Using RGB-D Camera

We present a mapping system capable of constructing detailed instance-level semantic models of room-sized indoor environments by means of an RGB-D camera. In this work, we integrate deep-learning based instance segmentation and classification into a state of the art RGB-D SLAM system. We leverage the pipeline of ElasticFusion \cite{whelan2016elasticfusion} as a backbone, and propose modifications of the registration cost function to make full use of the instance class labels in the process. The proposed objective function features tunable weights for the depth, appearance, and semantic information channels, which can be learned from data. The resulting system is capable of producing accurate semantic maps of room-sized environments, as well as reconstructing highly detailed object-level models. The developed method has been verified through experimental validation on the TUM RGB-D SLAM benchmark and the YCB video dataset. Our results confirmed that the proposed system performs favorably in terms of trajectory estimation, surface reconstruction, and segmentation quality in comparison to other state-of-the-art systems.
Islamic Spirituality, Resilience and Achievement Motivation of Yemeni Refugee Students: A Proposed Conceptual Framework

The present study aims to improve Yemeni refugee students’ achievement motivation by introducing the construct of Islamic perspective of spirituality and resilience. The issue of achievement motivation of students has been a concern in educational psychology circles. However, it appears to be mostly not concerned on refugee students. From the previous literature review finding, the study supports the belief that Islamic spirituality effects on refugee students because it encompasses the whole aspects of the person life. It also describes the role of positive outcomes of resilience on refugee students. To achieve this, a new theoretical framework is proposed to describe how spirituality from Islamic perspective and resilience can impact on achievement motivation of Yemeni refugee students. This contribution to the body of literature research aims to support theoretical and empirical research and provide a more explanation of the correlation between these three concepts. The study has particular implication for research, to help parents and teachers to improve achievement motivation through improve Islamic spirituality and resilience of refugee students. Finally, the paper concludes by calling for future research based on the gaps identified in the current study to investigate the relationship between Islamic spirituality, resilience, and achievement motivation. https://doi.org/10.26803/ijlter.19. 4.19
Bounds from ISW-galaxy cross-correlations on generalized covariant Galileon models

Several modified cosmological models exist, which also try to address the tensions between data and predictions of the Λ-CDM model. Galileon models are particular scalar tensor theories that represent one such possibility. While it is commonly understood that there may be inconsistencies between predictions of some Galileon models and observations, in particular concerning ISW-galaxy cross-correlations, there is no proof yet that these models are completely ruled out. Indeed, by using a specific background in the generalized covariant Galileon theory known as the the tracker solution, here we show that, after imposing all standard theoretical stability constraints, it is still possible to identify a region in the parameter space of the model that allows for positive ISW-galaxy cross-correlations. By a physical interpretation in terms of a chi-square analysis, we confirm the expectation that in this viable region the predictions of generalized covariant Galileon theory on the tracker solution background have higher likelihood when they approach the physics of the Λ-CDM model.
Towards Resilient 5G: Lessons Learned from Experimental Evaluations of LTE Uplink Jamming

Energy, water, health, transportation and emergency services act as backbones for our society. Aiming at high degrees of efficiency, these systems are increasingly automated, depending on communication systems. However, this makes these Critical Infrastructures prone to cyber attacks, resulting in data leaks, reduced performance or even total system failure. Beyond a survey of existing vulnerabilities, we provide an experimental evaluation of targeted uplink jamming against Long Term Evolution (LTE)'s air interface. Primarily, our implementations of smart attacks on the LTE Physical Uplink Control Channel (PUCCH), the Physical Uplink Shared Channel (PUSCH) as well as on the radio access procedure are outlined and tested. In exploiting the unencrypted resource assignment process, these attacks are able to target and jam specific UE resources, effectively denying uplink access. Evaluation results reveal the criticality of such attacks, severely destabilizing Critical Infrastructures, while minimizing attacker exposure. Finally we derive possible mitigations and recommendations for 5G stakeholders, which serve to improve the robustness of mission critical communications and enable the design of resilient next generation mobile networks.
ENHANCEMENT OF MECHANICAL AND TRIBOLOGICAL PROPERTIES OF AA 6082-T6 COMPOSITES REINFORCED WITH TIN, GRAPHITE AND ZINC

In the continuous search of new lightweight materials for automotive engine parts and industrial applications, new materials with superior properties is always needed. Among various materials, the aluminium alloys are preferred due to their light weight and superior properties. In this work, the AA 6082-T6 was chosen as base material along with Tin, Graphite and Zinc as matrix about 2.5 wt. % in each. The work is mainly focused on analysis of various mechanical, metallurgical and tribological properties. In heat treated AA 6082-T6 with Sn + Graphite + Zn, only few surface defects were found with fine grain size, the elevated hardness and ultimate tensile strength higher than AA 6082-T6 about 8% and 10% respectively. The wear and friction ratios were also reduced up to 8% for the same combination. These results provide the importance of the novel process insight to prepare composite matrix materials having superior properties than existing one for automotive piston material applications.