Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematics and phylogeny of the Zygodactylidae (Aves, Neognathae) with description of a new species from the early Eocene of Wyoming, USA

Zygodactylidae are an extinct lineage of perching birds characterized by distinct morphologies of the foot and wing elements. Although the clade has a complex taxonomic history, current hypotheses place Zygodactylidae as the sister taxon to Passeriformes (i.e., songbirds). Given the rather sparse fossil record of early passeriforms, the description of zygodactylid taxa is important for inferring potentially ancestral states in the largest radiation of living birds (i.e., the ∼6,000 species of extant passeriforms). Despite the exceptional preservation of many specimens and considerable species diversity in Zygodactylidae, the relationships among species have not been previously evaluated in a phylogenetic context. Herein, we review the fossil record of Zygodactylidae from North America and describe five new well-preserved fossils from the early Eocene Green River Formation of Wyoming. Two specimens are identified as representing a new species and the first records of the taxon Zygodactylus outside Europe. Anatomical comparisons with previously named taxa and the results of phylogenetic analysis including newly described specimens and previously named zygodactylid taxa provide the first hypothesis of the species-level relationships among zygodactylids. The monophyly of Zygodactylidae is supported in these new analyses. However, the monophyly of Primozygodactylus and the taxonomic distinction between Zygodactylus and Eozygodactylus remain unresolved and would likely benefit from the description of additional specimens.
Surface Integral Equation-Based Characteristic Mode Formulation for Penetrable Bodies

A novel surface integral equation (SIE)-based theory of characteristic modes (TCM) formulation is proposed for homogeneous penetrable bodies. Analytical expressions for the characteristic eigenvalues are presented and good agreement between the numerical and analytical results is observed for both lossless and lossy, as well as for dielectric and magnetodielectric objects. The new formulations, without any postprocessing methods, avoid spurious modes and resonances appearing in existing SIE-based TCM formulations. For lossy objects, the obtained eigenvalues are complex. The real part of an eigenvalue is related to the ratio of the reactive and radiated power, as in the original TCM formulation for perfect electric conductor structures, and the imaginary part gives the ratio between the dissipated and radiated power.
Osmoregulation in the Plotosidae Catfish: Role of the Salt Secreting Dendritic Organ

Unlike other marine teleosts, the Plotosidae catfishes reportedly have an extra-branchial salt secreting dendritic organ (DO). Salinity acclimation [brackishwater (BW) 3aaa, seawater (SWcontrol) 34aaa, and hypersaline water (HSW) 60aaa] for 14 days was used to investigate the osmoregulatory abilities of Plotosus lineatus through measurements of blood chemistry, muscle water content (MWC), Na+/K+-ATPase (NKA) specific activity and ion transporter expression in gills, DO, kidney and intestine. Ion transporter expression was determined using immunoblotting, immunohistochemistry (IHC) and quantitative polymerase chain reaction (qPCR). HSW elevated mortality, plasma osmolality and ions, and hematocrit, and decreased MWC indicating an osmoregulatory challenge. NKA specific activity and protein levels were significantly higher in DO compared to gill, kidney and intestine at all salinities. NKA specific activity increased in kidney and posterior intestine with HSW but only kidney showed correspondingly higher NKA α-subunit protein levels. Since DO mass was greater in HSW, the total amount of DO NKA activity expressed per gram fish was greater indicating higher overall capacity. Gill NKA and V-ATPase protein levels were greater with HSW acclimation but this was not reflected in NKA activity, mRNA or ionocyte abundance. BW acclimation resulted in lower NKA activity in gill, kidney and DO. Cl- levels were better regulated and the resulting strong ion ratio in BW suggests a metabolic acidosis. Elevated DO heat shock protein 70 levels in HSW fish indicate a cellular stress. Strong NKA and NKCC1 (Na+:K+:2Cl- cotransporter1) co-localization was observed in DO parenchymal cells, which was rare in gill ionocytes. NKCC1 immunoblot expression was only detected in DO, which was highest at HSW. Cystic fibrosis transmembrane regulator Cl- channel (CFTR) localize apically to DO NKA immunoreactive cells. Taken together, the demonstration of high NKA activity in DO coexpressed with NKCC1 and CFTR indicates the presence of the conserved secondary active Cl- secretion mechanism found in other ion transporting epithelia suggesting a convergent evolution with other vertebrate salt secreting organs. However, the significant osmoregulatory challenge of HSW indicates that the DO may be of limited use under more extreme salinity conditions in contrast to the gill based ionoregulatory strategy of marine teleosts.
Lower Bounds on Substructure Antenna  $Q$ -Factor

Calculation of the lower bound on <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor for currents within a fixed region (equivalently on a perfectly conducting structure) is used to derive corresponding bounds for currents located within any subregion (substructure). Both tuned and untuned <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factors are studied using eigenvalue techniques and the Poincaré separation theorem. We show that the tuned <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor of a substructure is bounded from below by the minimum <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor of the full structure. Furthermore, we derive that all modal untuned <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factors of a substructure are bounded from below by the corresponding modal untuned <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factors of the full structure. Mathematical results are demonstrated through numerical examples involving substructures produced at random, by heuristic design and by genetic optimization.
A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems

Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators. In this paper, we propose a neural APE system that encodes the source (src) and machine translated (mt) sentences with two separate encoders, but leverages a shared attention mechanism to better understand how the two inputs contribute to the generation of the post-edited (pe) sentences. Our empirical observations have showed that when the mt is incorrect, the attention shifts weight toward tokens in the src sentence to properly edit the incorrect translation. The model has been trained and evaluated on the official data from the WMT16 and WMT17 APE IT domain English-German shared tasks. Additionally, we have used the extra 500K artificial data provided by the shared task. Our system has been able to reproduce the accuracies of systems trained with the same data, while at the same time providing better interpretability.
UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities

Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients’ data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications’ communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.
Selection of Significant Brain Regions Based on MvGTDA and TS-DLF for Emotion Estimation

In this paper, we propose a novel method for estimating human emotion using functional brain images. The final goal of our study is contribution to affective brain computer interfaces (aBCIs), which use neuropsychological signals. In the proposed method, we newly derive multiview general tensor discriminant analysis (MvGTDA) in order to reveal significant brain regions and accurately estimate human emotion evoked by visual stimuli. This is because it is important to find activation of multiple brain regions for estimating emotional states. Since we regard a “Brodmann area” as a “view” and introduce $L_{1}$ -norm regularization for these views, MvGTDA can eliminate non-crucial Brodmann areas and select significant ones. Moreover, in general studies on functional brain images based on machine learning methodologies, there is an overfitting problem caused by a small sample size. Therefore, revealing significant Brodmann areas based on MvGTDA has another important role, i.e., solving the overfitting problem. By inputting estimation results respectively obtained from the significant areas and the MvGTDA-based feature, tensor-based supervised decision-level fusion (TS-DLF) integrates them and outputs the final estimation result of the user’s emotion. In experiments, we showed the effectiveness of our method by using actual functional brain images and we revealed the significant brain regions in emotional states.
MIMO-OFDM Radar Using a Linear Frequency Modulated Carrier to Reduce Sampling Requirements

The requirement of fast analog-to-digital (AD) and digital-to-analog (DA) converters is a challenge for high-resolution orthogonal frequency division multiplexing (OFDM) radars. This paper introduces a method to increase the modulation bandwidth of an OFDM signal by using an additional frequency modulated continuous wave carrier to reduce the requirements for the AD/DA converters while maintaining a high unambiguous velocity range. Multiplexing of transmit antennas with OFDM is still possible with the combined modulation. The presented approach is verified by simulations and measurements. A reduction of AD/DA converter bandwidth by factor eight is achieved.
Dual-Channel Dielectric Resonator Filter and Its Application to Doherty Power Amplifier for 5G Massive MIMO System

In this paper, a dual-channel dielectric resonator (DR) filter is presented for the first time. Two bandpass filters (BPFs) are integrated as one dual-channel filter with two input ports and two output ports. By using a common quad-mode DR shared by two second-order BPFs, only one metallic cavity is needed, which greatly reduces the circuit size. Frequency control of the DR is investigated and electromagnetic field properties at HEH11 and HEE11 resonant pairs are studied for guiding the dual-channel filter realization. By properly arranging the input and output feeding probes, two of the resonant modes are excited for one channel and other two for the other channel. Since the feeding probes of one channel filter do not excite the resonant modes of the other one, isolation between two filters is achieved. Three transmission zeros are generated near the passband, resulting in high selectivity. For verification, the dual-channel filter is implemented, which features good bandpass responses and offers a significant size reduction in comparison with conventional two DR filters. What is more, the dual-channel filter with the impedance transformation function is integrated with a $2 \times 2$ Doherty power amplifier for 5G massive multiple-input multiple-output system application, featuring high efficiency, good filtering responses, and high integration.
Factors That Moderate or Mediate Pregnancy Complications in Women with Anxiety and Depression

Background: Maternal anxiety and depression is noted in approximately 10-20% of pregnant women and can impact pregnancy and neonatal health, however, little is known about the factors. This study explored the roles of specific risk factors such as age, marital status, stress, substance use and ethnicity as either a moderator or a mediator to better understand the relationship between maternal anxiety or depression and major pregnancy complications and birth outcomes. Methods: A cohort of 646 women were studied longitudinally over three time points in early and late pregnancy and early postpartum. Maternal anxiety and depression were considered primary predictors with pregnancy complications i.e., gestational diabetes, edema and birth outcomes, i.e., low birth weight or preterm birth, as outcome variables. Socio-demographic factors were examined as moderator variables and psychosocial and behavioral factors were considered as mediator variables. For moderating and mediating analyses, a series of logistic regression analyses were performed. Results: Almost one-quarter of the women reported to have two or more pregnancy complications, 5.7% had a pre-term birth and 7.6% had a low birth weight baby. Stress in late pregnancy showed both a partial and a full mediating pathway between maternal anxiety and depression and pregnancy complications such as gestational diabetes, hypertension and edema. History of sexual abuse, single or divorced marital status, younger maternal age and Aboriginal ancestry had moderating effects in early and late pregnancy for adverse pregnancy and birth outcomes (i.e., low birth weight). Conclusion: Identifying mothers with stress, history of abuse, unpartnered, younger and of Indigenous ancestry will help clinicians and policy makers target timely interventions for pregnant women with anxiety and depression symptoms.
Thermal analysis and tracking process in hybrid composites of silicone rubber

Carbonized tracking due to thermal depolymerization on weather-shed materials of polymeric insulators is one of the key insulation failure modes, adversely affecting the reliability of power delivery networks. This paper investigates the tracking process and thermal distribution of micron-AlN and hybrid micron-AlN + nano-silica filled silicone rubber composites. Micron sized aluminum nitride (AlN: 5∼10 μm) and nano-silica (SiO2: 20 nm) particles were procured for fabricating composites in this work. The inclined plane test according to IEC 60587 was used; tracking voltage method 2 was adopted with initial applied voltage of 3 kV and ramping rate of 0.25 kV/h over duration of 240 minutes. Measurement results show hybrid composites exhibit substantially lower tracking length as compared to micron-AlN filled composites. Thermal accumulation is found significantly reduced in hybrid composites which could be due to better thermal stability and thermal conductivity. Moreover, the hybrid set of filler particles may increase the surface area of particles in the composites, provide better scattering, reduce secondary electron collision and impede release of high energy causing thermal depolymerization.
Detection of Cattle Using Drones and Convolutional Neural Networks

Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition

Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition (e.g., categorizing hundreds of subordinate categories of birds) usually requires a comprehensive visual concept organization including category labels and part-level attributes. In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition. Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation. By introducing a novel gated mechanism, our KERL framework incorporates this knowledge representation into the discriminative image feature learning, i.e., implicitly associating the specific attributes with the feature maps. Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus facilitating distinguishing the subtle differences among subordinate categories. ii) Our framework can learn feature maps with a meaningful configuration that the highlighted regions finely accord with the nodes (specific attributes) of the knowledge graph. Extensive experiments on the widely used Caltech-UCSD bird dataset demonstrate the superiority of our KERL framework over existing state-of-the-art methods.
ZIKV Infection Induces an Inflammatory Response but Fails to Activate Types I, II, and III IFN Response in Human PBMC

The recent epidemic in the Americas caused by Zika virus (ZIKV), Asian lineage, spurred the research towards a better understanding of how ZIKV infection affects the host immune response. The aim of this study was to evaluate the effects of Asian and East African ZIKV strain infection on the induction of IFN and proinflammatory and Th2 cytokines in human PBMC. We reported a slight modulation of type II IFN in PBMC exposed to Asian strain, but not to African strain, and a complete lack of type I and III IFN induction by both strains, suggesting the ability of ZIKV to evade the IFN system not only inhibiting the antiviral IFN response but also IFN production. Moreover, we highlighted a polyfunctional immune activation only in PBMC exposed to Asian strain, due to the induction of an inflammatory profile (IL-6, IL-8) and of a Th9 (IL-9) response. Overall, our data show a different ability of the ZIKV Asian strain, with respect to the African strain, to activate host immune response that may have pathogenetic implications for virus spread in vivo, including mother-to-child transmission and induction of severe fetal complications, as birth defects and neurological disorders.
Metatranscriptome Analysis Deciphers Multifunctional Genes and Enzymes Linked With the Degradation of Aromatic Compounds and Pesticides in the Wheat Rhizosphere

Agricultural soils are becoming contaminated with synthetic chemicals like polyaromatic compounds, petroleum hydrocarbons, polychlorinated biphenyls (PCBs), phenols, herbicides, insecticides and fungicides due to excessive dependency of crop production systems on the chemical inputs. Microbial degradation of organic pollutants in the agricultural soils is a continuous process due to the metabolic multifunctionalities and enzymatic capabilities of the soil associated communities. The plant rhizosphere with its complex microbial inhabitants and their multiple functions, is amongst the most live and dynamic component of agricultural soils. We analyzed the metatranscriptome data of 20 wheat rhizosphere samples to decipher the taxonomic microbial communities and their multifunctionalities linked with the degradation of organic soil contaminants. The analysis revealed a total of 21 different metabolic pathways for the degradation of aromatic compounds and 06 for the xenobiotics degradation. Taxonomic annotation of wheat rhizosphere revealed bacteria, especially the Proteobacteria, actinobacteria, firmicutes, bacteroidetes, and cyanobacteria, which are shown to be linked with the degradation of aromatic compounds as the dominant communities. Abundance of the transcripts related to the degradation of aromatic amin compounds, carbazoles, benzoates, naphthalene, ketoadipate pathway, phenols, biphenyls and xenobiotics indicated abundant degradation capabilities in the soils. The results highlighted a potentially dominant role of crop rhizosphere associated microbial communities in the remediation of contaminant aromatic compounds.
CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles

The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users’ identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users’ identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.