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We study the Sobolev critical Schrödinger-Bopp-Podolsky system −∆u + Φu = λu + µ|u|p−2u + |u|4u in R³, −∆Φ + ∆²Φ = 4πu² in R³, under the mass constraint ∫R³ u² dx = c for some prescribed c > 0, where 2 < p < 8/3, µ > 0 is a parameter, and λ ∈ R is a Lagrange multiplier. By developing a constraint minimizing approach, we show that the above system admits a local minimizer. Furthermore, we establish the existence of normalized ground state solutions. [ABSTRACT FROM AUTHOR]

In this article, we study the initial-boundary value problem for a p-Laplacian parabolic equation with logarithmic nonlinearity on compact metric graphs. Firstly, we apply the Galerkin approximation technique to obtain the existence of a unique local solution. Secondly, by using the potential well theory with the Nehari manifold, we establish the existence of global solutions that decay to zero at infinity for all p > 1, and solutions that blow up at finite time when p > 2 and at infinity when 1 < p ≥ 2. Furthermore, we obtain decay estimates of the global solutions and lower bound on the blow-up rate. [ABSTRACT FROM AUTHOR]

Personalized rating prediction is an important research problem in recommender systems. Although the latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this article, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users' preferences and items' features from different aspects, and also estimate the aspect importance of a user toward an item. Then, the aspect importance is integrated into a novel aspectaware latent factormodel (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects inMATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user's preferences and the targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets. Results show that (1) our method achieves significant improvement compared to strong baseline methods, especially for users with only few ratings; (2) item visual features can improve the prediction performance-the effects of item image features on improving the prediction results depend on the importance of the visual features for the items; and (3) our model can explicitly interpret the predicted results in great detail. [ABSTRACT FROM AUTHOR]

Sodium dodecyl sulfate (SDS) facilitates multiwalled carbon nanotube (MWCNT) debuncflmg and enhances nanotube stability in the aqueous environment by adsorbing on the nanotube surfaces, thereby increasing repulsive electrostatic forces and steric effects. The resulting SDS-wrapped MWCNTs are utilized in industrial applications and have been widely employed in environmental studies. In the present study, MWCNTs adsorbed SDS during ultrasonicadon to form stable MWCNTs suspensions. Desorption of SDS from MWCNTs surfaces was then investigated as a function of Suwannee River Humic Add (SRHA) and background electrolyte concentrations. Due to hydrophobic effects and x--x interactions, MWCNTs exhibit higher affinity for SRHA than SDS. In the presence of SRHA, SDS adsorbed on MWCNTs was displaced. Cations (Na+, Ca2+) decreased SDS desorption from MWCNTs due to charge screening effects. Interestingly, the presence of the divalent calcium cation facilitated multilayered SRHA adsorption on MWCNTs through bridging effects, while monovalent sodium reduced SRHA adsorption. Results of the present study suggest that properties of MWCNTs wrapped with commercial surfactants will be altered when these materials are released to surface waters and the surfactant coating will be displaced by natural organic matter (NOM). Changes on their surfaces will significantly affect MWCNTs fate in aquatic environments. [ABSTRACT FROM AUTHOR]

Background/aim: The aim of this study was to investigate the therapeutic effect of intestinal trefoil factor (ITF) on necrotizing enterocolitis (NEC) by observing the pathological changes and detecting the protein level differences in Caspase-3, Bax, and Bcl-2 in an NEC neonate rat model. Materials and methods: A Wistar rat model of NEC was established and 30 one-day-old neonate Wistar rats were randomly divided into three groups including a normal control (group A), NEC rats treated with 0.2 ml physiological saline through intraperitoneal (i.p.) injection (group B), and NEC rats treated with 0.2 mg ITF by i.p injection (group C). Results: Compared with group B, there were statistically significant differences in Caspase-3, Bax, and Bcl-2 levels in groups A and C (P < 0.05). Moreover, there was a significant difference in the Bcl-2 level between groups A and B (P < 0.05). Conclusion: ITF alleviated injury of the intestinal tract in neonate rats with NEC and this mechanism was possibly related to a reduction in the expression of Caspase-3 and Bax and the increase in Bcl-2 expression. [ABSTRACT FROM AUTHOR]

To systematically evaluate how dispersion methods affect the environmental behaviors of multiwalled carbon nanotubes (MWNTs), MWNTs were dispersed in various solutions (e.g., surfactants, natural organic matter (NOM), and etc.) via ultrasonication (SON) and long-term stirring (LT). The two tested surfactants [anionic sodium dodecyl sulfate (SDS) and nonionic poly(ethylene glycol)-poly(propylene glycol)-poly(ethylene glycol) (PEO-PPO-PEO) triblock copolymers (Pluronic)] could only disperse MWNTs via ultrasonication; while stable aqueous SON/MWNT and LT/MWNT suspensions were formed in the presence of the two model NOMs (Suwannee river humic acid and fulvic acid). Due to the inherent stochastic nature for both methods, the formed MWNT suspensions were highly heterogeneous. Their physicochemical properties, including surface charge, size, and morphology, greatly depended upon the dispersant type and concentration but were not very sensitive to the preparation methods. Aggregation and deposition behaviors of the dispersed MWNTs were controlled by van der Waal and electrostatic forces, as well as other non-DLVO forces (e.g., steric, hydrophobic forces, etc.). Unlike the preparation method-independent physicochemical properties, LT/NOM-MWNTs and SON/NOM-MWNTs differed in their fathead minnow epithelial cell metabolomics profiles. [ABSTRACT FROM AUTHOR]

Deposition of multiwalled carbon nanotubes (MWNTs) on model environmental surfaces was investigated using a quartz crystal microbalance with dissipation monitoring (QCM-D). Deposition behaviors of MWNTs on positively and negatively charged surfaces were in good agreement with Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, although hydrophobic interactions dominated MWNTs deposition on a hydrophobic polystyrene surface. Initial deposition rates (rf) and deposition attachment efficiencies (αD) depended on solution ionic strengths (IS) and surface electrostatic properties. Identical rf and αD values at constant IS on similar surfaces suggested that deposition was insensitive to surface morphology (i.e., bare crystal surface vs coated surface). The dissipation unit (D) was used with frequency (f) to investigate nanoparticle deposition: ∣ΔD/Δf∣ values varied for deposition on different surfaces, indicating that the nature of MWNT association with surfaces varied despite constant rf and αD values. [ABSTRACT FROM AUTHOR]

Colloidally stable C60 suspensions produced via extended mixing in water (aq/nC60) are highly heterogeneous with respect to particle size and morphology. Ultraviolet-visible (UV-vis) absorption spectra of aq/nC60 are often used as a supplemental tool to dynamic light scattering (DLS), transmission electron microscopy (TEM), and other analytical methods to characterize aq/nC60. In the present study, the UV-vis spectra provide information about the average particle size and the interactions between C60 and water. We report that the decrease in relative absorption in the 240-290 nm range is a function of magnetic stirring time, that the average size (Zave) of an aq/nC60 suspension determines the position of absorbance maximum of its 360 nm band, and that the methods used to prepare and fractionate nC60 affect the extent of the blue shift in this band that occurs due to a decrease in Zave. We also confirm that the broad absorption band in the 400-600 nm region is a result of C60 aggregation. [ABSTRACT FROM AUTHOR]

The existence of solutions for semilinear equations with Dirichlet condition are established under the assumption that the nonlinearity is of linear growth and the asymptotic behavior of its primitive at infinity stays away from the Fučík spectrum. [ABSTRACT FROM AUTHOR]

Active learning (AL) attempts to maximize a model's performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted signiicant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various ields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in ields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to ill this gap. We provide a formal classiicationmethod for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions. [ABSTRACT FROM AUTHOR]

Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers' prior knowledge and experience. And due to the limitations of humans' inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search (NAS) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions. [ABSTRACT FROM AUTHOR]

Under the Toxic Substances Control Act (TSCA), the Environmental Protection Agency (EPA) is required to perform new chemical reviews of nanomaterials identified in premanufacture notices. However, environmental fate models developed for traditional contaminants are limited in their ability to simulate nanomaterials' environmental behavior by incomplete understanding and representation of the processes governing nanomaterial distribution in the environment and by scarce empirical data quantifying the interaction of nanomaterials with environmental surfaces. In this study, the well-known Water Quality Analysis Simulation Program (WASP) was updated to incorporate particle collision rate and particle attachment efficiency to simulate multiwalled carbon nanotube (MWCNT) fate and transport in surface waters. Heteroaggregation attachment efficiencies (αhet) values derived from sediment attachment studies are used to parametrize WASP for simulation of MWCNTs transport in Brier Creek, a coastal plain river located in central eastern Georgia, and a tributary to the Savannah River. Simulations using a constant MWCNT load of 0.1 kg d-1 in the uppermost Brier Creek water segment showed that MWCNTs were present predominantly in the Brier Creek water column, while downstream MWCNT surface and deep sediment concentrations exhibited a general increase with time and distance from the source, suggesting that MWCNT releases could have increasing ecological impacts in the benthic region over long time frames. [ABSTRACT FROM AUTHOR]

Robust principal component analysis (PCA) is one of the most important dimension-reduction techniques for handling high-dimensional data with outliers. However, most of the existing robust PCA presupposes that the mean of the data is zero and incorrectly utilizes the average of data as the optimal mean of robust PCA. In fact, this assumption holds only for the squared ℓ2-norm-based traditional PCA. In this letter, we equivalently reformulate the objective of conventional PCA and learn the optimal projection directions by maximizing the sum of projected difference between each pair of instances based on ℓ2,1-norm. The proposed method is robust to outliers and also invariant to rotation. More important, the reformulated objective not only automatically avoids the calculation of optimal mean and makes the assumption of centered data unnecessary, but also theoretically connects to the minimization of reconstruction error. To solve the proposed nonsmooth problem, we exploit an efficient optimization algorithm to soften the contributions from outliers by reweighting each data point iteratively. We theoretically analyze the convergence and computational complexity of the proposed algorithm. Extensive experimental results on several benchmark data sets illustrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues. In this paper, we present MARLlib, a library designed to address the aforementioned challenge by leveraging three key mechanisms: 1) a standardized multi-agent environment wrapper, 2) an agent-level algorithm implementation, and 3) a flexible policy mapping strategy. By utilizing these mechanisms, MARLlib can effectively disentangle the intertwined nature of the multi-agent task and the learning process of the algorithm, with the ability to automatically alter the training strategy based on the current task's attributes. The MARLlib library's source code is publicly accessible on GitHub: https://github.com/Replicable-MARL/MARLlib. [ABSTRACT FROM AUTHOR]

Spectral clustering is a key research topic in the field of machine learning and data mining. Most of the existing spectral clustering algorithms are built on gaussian Laplacian matrices, which is sensitive to parameters. We propose a novel parameter-free distance-consistent locally linear embedding. The proposed distance-consistent LLE can promise that edges between closer data points are heavier. We also propose a novel improved spectral clustering via embedded label propagation. Our algorithm is built on two advancements of the state of the art. First is label propagation, which propagates a node's labels to neighboring nodes according to their proximity. We perform standard spectral clustering on original data and assign each cluster with k-nearest data points and then we propagate labels through dense unlabeled data regions. Second is manifold learning, which has been widely used for its capacity to leverage the manifold structure of data points. Extensive experiments on various data sets validate the superiority of the proposed algorithm compared to state-of-theart spectral algorithms. [ABSTRACT FROM AUTHOR]

Background: Drought stress is one of the major natural challenges in the main tea-producing regions of China. The tea plant (Camellia sinensis) is a traditional beverage plant whose growth status directly affects tea quality. Recent studies have revealed that microRNAs (miRNAs) play key functions in plant growth and development. Although some miRNAs have been identified in C. sinensis, little is known about their roles in the drought stress response of tea plants. Results: Physiological characterization of Camellia sinensis 'Tieguanyin' under drought stress showed that the malondialdehyde concentration and electrical conductivity of leaves of drought-stressed plants increased when the chlorophyll concentration decreased under severe drought stress. We sequenced four small-RNA (sRNA) libraries constructed from leaves of plants subjected to four different treatments, normal water supply (CK); mild drought stress (T1); moderate drought stress (T2) and severe drought stress (T3). A total of 299 known mature miRNA sequences and 46 novel miRNAs were identified. Gene Ontology enrichment analysis revealed that most of the differentially expressed-miRNA target genes were related to regulation of transcription. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the most highly enriched pathways under drought stress were D-alanine metabolism, sulfur metabolism, and mineral absorption pathways. Real-time quantitative PCR (qPCR) was used to validate the expression patterns of 21 miRNAs (2 up-regulated and 19 down-regulated under drought stress). The observed co-regulation of the miR166 family and their targets ATHB-14-like and ATHB-15- like indicate the presence of negative feedback regulation in miRNA pathways. Conclusions: Analyses of drought-responsive miRNAs in tea plants showed that most of differentially expressedmiRNA target genes were related to regulation of transcription. The results of study revealed that the expressions of phase-specific miRNAs vary with morphological, physiological, and biochemical changes. These findings will be useful for research on drought resistance and provide insights into the mechanisms of drought adaptation and resistance in C. sinensis. [ABSTRACT FROM AUTHOR]

Mechanisms underlying nodulation factor signaling downstream of the nodulation factor receptors (NFRs) have not been fully characterized. In this study, clathrin heavy chain1 (CHC1) was shown to interact with the Rho-Like GTPase ROP6, an interaction partner of NFR5 in Lotus japonicus. The CHC1 gene was found to be expressed constitutively in all plant tissues and induced in Mesorhizobium loti-infected root hairs and nodule primordia. When expressed in leaves of Nicotiana benthamiana, CHC1 and ROP6 were colocalized at the cell circumference and within cytoplasmic punctate structures. In M. loti-infected root hairs, the CHC protein was detected in cytoplasmic punctate structures near the infection pocket along the infection thread membrane and the plasma membrane of the host cells. Transgenic plants expressing the CHC1-Hub domain, a dominant negative effector of clathrin-mediated endocytosis, were found to suppress early nodulation gene expression and impair M. loti infection, resulting in reduced nodulation. Treatment with tyrphostin A23, an inhibitor of clathrin-mediated endocytosis of plasma membrane cargoes, had a similar effect on down-regulation of early nodulation genes. These findings show an important role of clathrin in the leguminous symbiosis with rhizobia. [ABSTRACT FROM AUTHOR]

Nod Factor Receptor5 (NFR5) is an atypical receptor-like kinase, having no activation loop in the protein kinase domain. It forms a heterodimer with NFR1 and is required for the early plant responses to Rhizobium infection. A Rho-like small GTPase from Lotus japonicus was identified as an NFR5-interacting protein. The amino acid sequence of this Rho-like GTPase is closest to the Arabidopsis (Arabidopsis thaliana) ROP6 and Medicago truncatula ROP6 and was designated as LjROP6. The interaction between Rop6 and NFR5 occurred both in vitro and in planta. No interaction between Rop6 and NFR1 was observed. Green fluorescent protein-tagged ROP6 was localized at the plasma membrane and cytoplasm. The interaction between ROP6 and NFR5 appeared to take place at the plasma membrane. The expression of the ROP6 gene could be detected in vascular tissues of Lotus roots. After inoculation with Mesorhizobium loti, elevated levels of ROP6 expression were found in the root hairs, root tips, vascular bundles of roots, nodule primordia, and young nodules. In transgenic hairy roots expressing ROP6 RNA interference constructs, Rhizobium entry into the root hairs did not appear to be affected, but infection thread growth through the root cortex were severely inhibited, resulting in the development of fewer nodules per plant. These data demonstrate a role of ROP6 as a positive regulator of infection thread formation and nodulation in L. japonicus. [ABSTRACT FROM AUTHOR]