Endophytic Fungi of Bitter Melon \u3ci\u3e(Momordica Charantia)\u3c/i\u3e in Guangdong Province, China

Endophytic fungi can mutualistically interact with their host plants by deterring herbivores. Overall 1172 endophytic fungal isolates were recovered from roots, stems, leaves, flowers and fruits of bitter melon, Momordica charantia, at five sites in Guangdong Province. These isolates were identified to 25 genera using morphological and molecular characteristics. The endophyte communities at the five sites were similar. Alternaria alternata, Aspergillus spp., Cladosporium spp., Colletotrichum spp., Nigrospora spp., Penicillium spp., Arthrinium spp., Chaetimium spp., Curvularia spp., Fusarium spp., Phoma spp., and Phomopsis spp. were isolated from at least three of the five sites. The coefficient of similarity for endophytes ranged from 60.6% to 83.3% between any two sites. There were significant differences in the species composition of endophytes recovered from different tissues of bitter melon. Fusarium spp. was the most frequent in root and stem samples, Colletotrichum spp. in leaf samples, A. alternata in flower samples, and Cladosporium spp. in fruit samples. The coefficients of similarity for endophytes were between 42.9% and 80.0% from any two tissues. We found that the composition of endophytes of bitter melon was relatively stable across sites, but differed greatly among tissues. We also found that there were fewer insects such as aphids (Homoptera: Aphididae), leafminers (Lepidoptera, Gracillariidae), and cotton leafworms Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) collected from the leaves of bitter melon at the Huadu site compared to those collected at the Yunfu site. Whether this is related to the endophyte communities isolated from different sites requires further research

Complete boundedness of the Heat Semigroups on the von Neumann Algebra of hyperbolic groups

We prove that $(\lambda_g\mapsto e^{-t|g|^r}\lambda_g)_{t>0}$ defines a completely bounded semigroup of multipliers on the von Neuman algebra of hyperbolic groups for all real number $r$. One ingredient in the proof is the observation that a construction of Ozawa allows to characterize the radial multipliers that are bounded on every hyperbolic graph, partially generalizing results of Haagerup--Steenstrup--Szwarc and Wysocza\'nski. Another ingredient is an upper estimate of trace class norms for Hankel matrices, which is based on Peller's characterization of such norms.Comment: v2: 28 pages, with new examples, new results, motivations and hopefully a better presentatio

Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge

Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to reflect its exact contributions. To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper. Evaluation results show the effectiveness of our model by significantly outperforming the state-of-the-art. We furthermore demonstrate how an effective contextualization method results in improving citation-based summarization of the scientific articles.Comment: SIGIR 201

Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates

Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual series. Furthermore, our algorithm is extended to take into account individual autocorrelation to provide better estimation, using a recent convex relaxation of quadratically constrained quadratic program. Extensive experiments on synthetic and real-world electricity consumption datasets illustrate the effectiveness of our matrix recovery algorithms

TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201