Miracle-dz/UniFAR-FTUs huggingface dataset:
Ophiostomatoid fungi associated with pines infected by Bursaphelenchusxylophilus and Monochamusalternatus in China, including three new species

Abstract The activity of the pine wood nematode Bursaphelenchusxylophilus leads to extremely serious economic, ecological and social losses in East Asia. The nematode causes pine wilt disease, which is currently regarded as the most important forest disease in China. The pathogenic nematode feeds on dendrocola fungi to complete its cycle of infection. As the vector of the nematode, the Japanese pine sawyer (Monochamusalternatus) also carries dendrocola fungi. Pine woods, infected by B.xylophilus and tunnelled by M.alternatus, are also inhabited by ophiostomatoid fungi. These fungi are well known for their association with many bark and ambrosia beetles. They can cause sapstain and other serious tree diseases. The aims of our study were to investigate and identify the ophiostomatoid communities associated with the epidemic pine wood nematode and the pine sawyer in Pinusmassoniana and P.thunbergii forests, which are the main hosts of the pine wood nematode in China. Two hundred and forty strains of ophiostomatoid fungi were isolated from nematode and sawyer–infected trees in the coastal Shandong and Zhejiang Provinces, representing newly and historically infected areas, respectively. Six ophiostomatoid species were identified on the basis of morphological, physiological and molecular data. For the latter, DNA sequences of the internal transcribed spacer (ITS1–5.8S–ITS2) region and partial b-tubulin gene were examined. The ophiostomatoid species included one known species, Ophiostomaips, three novel species, viz. Ophiostomaalbumsp. nov., Ophiostomamassonianasp. nov. and Sporothrixzhejiangensissp. nov. and two species whose identities are still uncertain, Ophiostomacf.deltoideosporum and Graphilbumcf.rectangulosporium, due to the paucity of the materials obtained. The ophiostomatoid community was dominated by O.ips. This study revealed that a relatively high species diversity of ophiostomatoid fungi are associated with pine infected by B.xylophilus and M.alternatus in China.
On the Relation between Semifield-Valued FCA and the Idempotent Singular Value Decomposition

In this paper we investigate into analogue of a Singular Value Decomposition for rectangular matrices over a complete idempotent semifields (i-SVD), many of whose instances—the complete schedule algebra or completed max-plus semifield, the tropical algebra, and the max-times algebra—are useful in a range of applications. We carry on further the task of eliciting the relation between i-SVD and the extension of Formal Concept Analysis to complete idempotent semifields ($\mathcal{K}$-FCA) started in prior work. We find out that for each relation with entries considered in a complete idempotent semifield, for each of the four types of Galois connections defined by the relation, there are two basis of left- and right-singular vectors to choose from for reconstructing the matrix. These are each of the join-dense or meet-dense sets object or attribute concepts of the concept lattice created by the connection. We suggest why these can never form an orthonormal basis, and refer to prior art describing the spectral lattices of such matrices for meaningful representations in the idempotent settings.
Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation

Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for Designers (XAID), specifically for game designers. By focusing on a specific user group, their needs and tasks, we propose a human-centered approach for facilitating game designers to co-create with AI/ML techniques through XAID. We illustrate our initial XAID framework through three use cases, which require an understanding both of the innate properties of the AI techniques and users’ needs, and we identify key open challenges.
On the Efficiency of Online Social Learning Networks

A social learning network (SLN) emerges when users exchange information on educational topics with structured interactions. The recent proliferation of massively scaled online (human) learning, such as massive open online courses (MOOCs), has presented a plethora of research challenges surrounding SLN. In this paper, we ask: how efficient are these networks? We propose a method in which the SLN efficiency is determined by comparing user benefit in the observed network to a benchmark of maximum utility achievable through optimization. Our method defines the optimal SLN through utility maximization subject to a set of constraints that can be inferred from the network, and given multiple solutions searches for the one closest to the observed network so as to require the least amount of change to user behavior in practice. Through evaluation on four MOOC discussion forum data sets and optimizing over millions of variables, we find that the SLN efficiency can be rather low (from 76% to 90% depending on the specific parameters and data set), which indicates that much can be gained through optimization. We find that the gains in global utility (i.e., average across users) can be obtained without making the distribution of local utilities (i.e., utility of individual users) less fair. We also propose an algorithm for realizing the optimal network through curated news feeds in online SLN.
Octree-based Convolutional Autoencoder Extreme Learning Machine for 3D Shape Classification

We introduce Octree-based Convolutional Autoencoder Extreme Learning Machine (OCA-ELM) for 3D shape classification. This approach combines Convolutional Autoencoder Extreme Learning Machine (CAE-ELM) with octreebased con- volution to generate feature maps from several types of geometric data, and extract discriminative features with Extreme Learning Machine Autoencoder (ELM-AE). The extracted features can then be used for various computer graphics applications, such as 3D shape classification. Compared with other 3D classification methods, the proposed OCA-ELM has superior classification performance. Experiments on ModelNet40 show that OCA-ELM outperforms state-of-the-art CNN-based methods and surpasses CAE-ELM in classification accuracy by 3.69%, demonstrating the effectiveness of our method.
Alcohol-related changes in the intestinal microbiome influence neutrophil infiltration, inflammation and steatosis in early alcoholic hepatitis in mice

Background Alcohol-induced intestinal dysbiosis disrupts homeostatic gut-liver axis function and is essential in the development of alcoholic liver disease. Here, we investigate changes in enteric microbiome composition in a model of early alcoholic steatohepatitis and dissect the pathogenic role of intestinal microbes in alcohol-induced liver pathology. Materials and methods Wild type mice received a 10-day diet that was either 5% alcohol-containing or an isocaloric control diet plus a single binge. 16S rDNA sequencing defined the bacterial communities in the cecum of alcohol- and pair-fed animals. Some mice were treated with an antibiotic cocktail prior to and throughout alcohol feeding. Liver neutrophils, cytokines and steatosis were evaluated. Results Acute-on-chronic alcohol administration induced shifts in various bacterial phyla in the cecum, including increased Actinobacteria and a reduction in Verrucomicrobia driven entirely by a reduction in the genus Akkermansia. Antibiotic treatment reduced the gut bacterial load and circulating bacterial wall component lipopolysaccharide (LPS). We found that bacterial load suppression prevented alcohol-related increases in the number of myeloperoxidase- (MPO) positive infiltrating neutrophils in the liver. Expression of liver mRNA tumor necrosis factor alpha (Tnfα), C-X-C motif chemokine ligand 1 (Cxcl1) and circulating protein monocyte chemoattractant protein-1 (MCP-1) were also reduced in antibiotic-treated alcohol-fed mice. Alcohol-induced hepatic steatosis measured by Oil-Red O staining was significantly reduced in antibiotic treated mice. Genes regulating lipid production and storage were also altered by alcohol and antibiotic treatment. Interestingly, antibiotic treatment did not protect from alcohol-induced increases in serum aminotransferases (ALT/AST). Conclusions Our data indicate that acute-on-chronic alcohol feeding alters the microflora at multiple taxonomic levels and identifies loss of Akkermansia as an early marker of alcohol-induced gut dysbiosis. We conclude that gut microbes influence liver inflammation, neutrophil infiltration and liver steatosis following alcohol consumption and these data further emphasize the role of the gut-liver axis in early alcoholic liver disease.
Forecasting QoS Attributes Using LSTM Networks

Many modern software systems and applications are built using heterogeneous services provided by a range of devices, from high-power devices located in the Cloud to potentially resource-constrained and/or mobile services from IoT devices at the edge of the network. The large growth in the number of these services has led to some functionally similar services. When selecting services, a critical criterion is Quality of Service (QoS), which includes factors such as response time, location and cost. As the value of dynamic QoS attributes vary with time, there is a need to accurately forecast future QoS values to identify if a service may be about to fail. In this paper, we propose using an LSTM-based neural network to forecast future QoS values. We evaluate the use of an LSTM network against the existing state of the art in experiments using an established web service dataset and a new dataset collected by deploying services on low power IoT devices, which we publicly release. This mixture of datasets covers the heterogeneity that would be expected in a typical IoT environment.
Patch-Based Non-rigid 3D Reconstruction from a Single Depth Stream

We propose an approach for 3D reconstruction and tracking of dynamic surfaces using a single depth sensor, without any prior knowledge of the scene. It is robust to rapid inter-frame motions due to the probabilistic expectation-maximization non-rigid registration framework. Our pipeline subdivides each input depth image into non-rigidly connected surface patches, and deforms it towards the canonical pose by estimating a rigid transformation for each patch. The combination of a data term imposing similarity between model and data, and a regularizer enforcing as-rigid-as-possible motion of neighboring patches ensures that we can handle large deformations, while coping with sensor noise. We employ a surfel-based fusion technique, which lets us circumvent the repeated conversion between mesh and signed distance field representations which are used by related techniques. Furthermore, a robust keyframe-based scheme allows us to keep track of correspondences throughout the entire sequence. Through a variety of qualitative and quantitative experiments, we demonstrate resistance to larger motion and achieving lower reconstruction errors than related approaches.
NetEgg: A Scenario-Based Programming Toolkit for SDN Policies

Recent emergence of software-defined networks offers an opportunity to design domain-specific programming abstractions aimed at network operators. In this paper, we propose scenario-based programming, a framework that allows network operators to program network policies by describing example behaviors in representative scenarios. Given these scenarios, our synthesis algorithm automatically infers the controller state that needs to be maintained along with the rules to process network events and update state. We have developed the NetEgg scenario-based programming tool, which can execute the generated policy implementation on top of a centralized controller, but also automatically infers flow-table rules that can be pushed to switches to improve throughput. We evaluate the performance of NetEgg based on the computational requirements of our synthesis algorithm as well as the overhead introduced by the generated policy implementation, and we study the usability of NetEgg based on a user study. Our results show that our synthesis algorithm can generate policy implementations in less than a second for all policies we studied, and the automatically generated policy implementations have performance comparable to their hand-crafted implementations. Our user study shows that the proposed scenario-based programming approach can reduce the programming time by 50% and the error rate by 32% compared with an alternative programming approach.
Actinorhizal Signaling Molecules: Frankia Root Hair Deforming Factor Shares Properties With NIN Inducing Factor

Actinorhizal plants are able to establish a symbiotic relationship with Frankia bacteria leading to the formation of root nodules. The symbiotic interaction starts with the exchange of symbiotic signals in the soil between the plant and the bacteria. This molecular dialog involves signaling molecules that are responsible for the specific recognition of the plant host and its endosymbiont. Here we studied two factors potentially involved in signaling between Frankia casuarinae and its actinorhizal host Casuarina glauca: (1) the Root Hair Deforming Factor (CgRHDF) detected using a test based on the characteristic deformation of C. glauca root hairs inoculated with F. casuarinae and (2) a NIN activating factor (CgNINA) which is able to activate the expression of CgNIN, a symbiotic gene expressed during preinfection stages of root hair development. We showed that CgRHDF and CgNINA corresponded to small thermoresistant molecules. Both factors were also hydrophilic and resistant to a chitinase digestion indicating structural differences from rhizobial Nod factors (NFs) or mycorrhizal Myc-LCOs. We also investigated the presence of CgNINA and CgRHDF in 16 Frankia strains representative of Frankia diversity. High levels of root hair deformation (RHD) and activation of ProCgNIN were detected for Casuarina-infective strains from clade Ic and closely related strains from clade Ia unable to nodulate C. glauca. Lower levels were present for distantly related strains belonging to clade III. No CgRHDF or CgNINA could be detected for Frankia coriariae (Clade II) or for uninfective strains from clade IV.
Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection

Person re-identification is an important topic in retail, scene monitoring, human-computer interaction, people counting, ambient assisted living and many other application fields. A dataset for person re-identification TVPR (Top View Person Re-Identification) based on a number of significant features derived from both depth and color images has been previously built. This dataset uses an RGB-D camera in a top-view configuration to extract anthropometric features for the recognition of people in view of the camera, reducing the problem of occlusions while being privacy preserving. In this paper, we introduce a machine learning method for person re-identification using the TVPR dataset. In particular, we propose the combination of multiple k-nearest neighbor classifiers based on different distance functions and feature subsets derived from depth and color images. Moreover, the neighborhood component feature selection is used to learn the depth features’ weighting vector by minimizing the leave-one-out regularized training error. The classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Experimental results show that the proposed methodology outperforms standard supervised classifiers widely used for the re-identification task. This improvement encourages the application of this approach in the retail context in order to improve retail analytics, customer service and shopping space management.
An Entropy Maximization Approach to Optimal Dimensionality Reduction

The maximum entropy principle is a well established approach to unsupervised optimization. Entropy maximization learning algorithms for single-layered neural networks already exist for the cases in which the number of output neurons is greater or equal to the number of input neurons. These models were successfully employed in various applications, most notably for independent component analysis. In this work, we generalize the maximum entropy principle to a single-layered neural network with fewer output than input neurons. The proposed learning algorithm finds a low-dimensional representation of the data and identifies the independent components within it. In general, such a model must incorporate some prior knowledge of the input distribution; however, we overcome this difficulty using a variational approach. We illustrate the performance of the model through several examples and compare it to other algorithms. While our model achieves similar results to the state-of-the-art algorithm for overdetermined independent component analysis within a similar convergence time, its main advantage lies in its ability to be learned efficiently on-line.
Predicting Concept Drift in Data Streams Using Metadata Clustering

Concept drifts cause data streams to change over time, making classification harder. Drift detection signals when change occurs so remedial action can be taken. However, existing approaches do not allow drift detectors to improve drift detection by using patterns associated with drift across multiple stream metadata. Metadata Drift Predictor (MDP) is a novel framework which allows this by adapting drift sensitivity mid-stream to reduce errors in drift detection. MDP clusters metadata patterns at drift points and uses recurring patterns to encourage drift detection where metadata looks similar to past drift points and/or discourage drift detection where metadata looks different from past drift points. It avoids relying on statistical assumptions of underlying drift patterns. Our experiments demonstrate that MDP works consistently across traditional stream classifiers and drift detectors to reduce delay and false positive drift detection and/or reduce false negative drift detection. We show that MDP can be used to reduce drifts detected in real-world datasets while improving the accuracy that each detected drift provides when compared to other popular meta-learning frameworks.
Event-Triggered Strategy Design for Discrete-Time Nonlinear Quadratic Games With Disturbance Compensations: The Noncooperative Case

In this paper, the event-triggered strategy design problem is addressed for a class of discrete-time nonlinear quadratic noncooperative games subject to matched disturbances. The event-triggered scheme is proposed based on the relative error of the input signals with aim to determine whether such signals should be transmitted to the actuator or not. The disturbance-observer-based game strategy is put forward to compensate the matched disturbance and also optimize the individual cost function for each player. The main purpose of the addressed problem is to design the time-varying strategy parameters such that the upper bound of the individual cost function of each player is minimized unilaterally over a finite horizon $[{0,N}]$ . Sufficient conditions are first established for the existence and uniqueness of the game strategies through backward Riccati-like recursions and then the desired strategy parameters are computed iteratively by utilizing the Moore–Penrose pseudo inverse. Finally, a simulation example is provided to verify the effectiveness of the proposed design method.
5-Azacytidine: A Promoter of Epigenetic Changes in the Quest to Improve Plant Somatic Embryogenesis

Somatic embryogenesis (SE) is a widely studied process due to its biotechnological potential to generate large quantities of plants in short time frames and from different sources of explants. The success of SE depends on many factors, such as the nature of the explant, the microenvironment generated by in vitro culture conditions, and the regulation of gene expression, among others. Epigenetics has recently been identified as an important factor influencing SE outcome. DNA methylation is one of the most studied epigenetic mechanisms due to its essential role in gene expression, and its participation in SE is crucial. DNA methylation levels can be modified through the use of drugs such as 5-Azacytidine (5-AzaC), an inhibitor of DNA methylation, which has been used during SE protocols. The balance between hypomethylation and hypermethylation seems to be the key to SE success. Here, we discuss the most prominent recent research on the role of 5-AzaC in the regulation of DNA methylation, highlighting its importance during the SE process. Also, the molecular implications that this inhibitor might have for the increase or decrease in the embryogenic potential of various explants are reviewed.
Two-Phase Assessment Approach to Improve the Efficiency of Refactoring Identification

To automate the refactoring identification process, a large number of candidates need to be compared. Such an overhead can make the refactoring approach impractical if the software size is large and the computational load of a fitness function is substantial. In this paper, we propose a two-phase assessment approach to improving the efficiency of the process. For each iteration of the refactoring process, refactoring candidates are preliminarily assessed using a lightweight, fast delta assessment method called the Delta Table. Using multiple Delta Tables, candidates to be evaluated with a fitness function are selected. A refactoring can be selected either interactively by the developer or automatically by choosing the best refactoring, and the refactorings are applied one after another in a stepwise fashion. The Delta Table is the key concept enabling a two-phase assessment approach because of its ability to quickly calculate the varying amounts of maintainability provided by each refactoring candidate. Our approach has been evaluated for three large-scale open-source projects. The results convincingly show that the proposed approach is efficient because it saves a considerable time while still achieving the same amount of fitness improvement as the approach examining all possible candidates.