Multi-level Explanation of Deep Reinforcement Learning-based Scheduling

Dependency-aware job scheduling in the cluster is NP-hard. Recent work shows that Deep Reinforcement Learning (DRL) is capable of solving it. It is difficult for the administrator to understand the DRL-based policy even though it achieves remarkable performance gain. Therefore the complex model-based scheduler is not easy to gain trust in the system where simplicity is favored. In this paper, we give the multi-level explanation framework to interpret the policy of DRL-based scheduling. We dissect its decision-making process to job level and task level and approximate each level with interpretable models and rules, which align with operational practices. We show that the framework gives the system administrator insights into the state-of-the-art scheduler and reveals the robustness issue in regards to its behavior pattern.Comment: Accepted in the MLSys'22 Workshop on Cloud Intelligence / AIOp

Functional linear regression: dependence and error contamination

Functional linear regression is an important topic in functional data analysis. It is commonly assumed that samples of the functional predictor are independent realizations of an underlying stochastic process, and are observed over a grid of points contaminated by iid measurement errors. In practice, however, the dynamical dependence across different curves may exist and the parametric assumption on the error covariance structure could be unrealistic. In this article, we consider functional linear regression with serially dependent observations of the functional predictor, when the contamination of the predictor by the white noise is genuinely functional with fully nonparametric covariance structure. Inspired by the fact that the autocovariance function of observed functional predictors automatically filters out the impact from the unobservable noise term, we propose a novel autocovariance-based generalized method-of-moments estimate of the slope function. We also develop a nonparametric smoothing approach to handle the scenario of partially observed functional predictors. The asymptotic properties of the resulting estimators under different scenarios are established. Finally, we demonstrate that our proposed method significantly outperforms possible competing methods through an extensive set of simulations and an analysis of a public financial dataset

Shiitake cultivation as biological preprocessing of lignocellulosic feedstocks – Substrate changes in crystallinity, syringyl/guaiacyl lignin and degradation-derived by-products

Formulation of substrates based on three hardwood species combined with modulation of nitrogen content by whey addition (0–2%) was investigated in an experiment designed in D-optimal model for their effects on biological preprocessing of lignocellulosic feedstock by shiitake mushroom (Lentinula edodes) cultivation. Nitrogen loading was shown a more significant role than wood species for both mushroom production and lignocellulose degradation. The fastest mycelial colonization occurred with no nitrogen supplementation, but the highest mushroom yields were achieved when 1% whey was added. Low nitrogen content resulted in increased delignification and minimal glucan consumption. Delignification was correlated with degradation of syringyl lignin unit, as indicated by a significant reduction (41.5%) of the syringyl-to-guaiacyl ratio after cultivation. No significant changes in substrate crystallinity were observed. The formation of furan aldehydes and aliphatic acids was negligible during the pasteurization and fungal cultivation, while the content of soluble phenolics increased up to seven-fold.publishedVersio

Enabling efficient bioconversion of birch biomass by Lentinula edodes regulatory roles of nitrogen and bark additions on mushroom production and cellulose saccharification

Pretreatment with edible white-rot fungi has advantages in low inputs of energy and chemicals for reducing the recalcitrance of woody biomass for bioethanol production while harvesting protein-rich food. The effectiveness of fungal pretreatment may vary with substrate composition. In this study, birch with or without bark and nitrogen additives were experimentally studied for their effects on shiitake production, substrate lignocellulosic degradation and enzymatic convertibility with cellulolytic enzymes. Whey was added as protein nitrogen and led to successful outcomes, while non-protein nitrogen urea and ammonium-nitrate resulted in mortality of fungal mycelia. The mushroom yields of one harvest were generally comparable between the treatments, averaging 651 g fresh weight per kilogram dry substrate, and high enough as to be profitable. Nitrogen loading (0.5-0.8%, dry mass) negatively affected lignin degradation and enzymatic convertibility and prolonged cultivation/pretreatment time. The added bark (0-20%) showed quadratic correlation with degradation of lignin, xylan and glucan as well as enzymatic digestibility of glucan. Nitrogen loading of < 0.6% led to maximal mass degradation of xylan and lignin at bark ratios of 4-9% and 14-19%, respectively, peak saccharification of glucan at 6-12% and the shortest pretreatment time at 8-13% bark. The designed substrates resulted in 19-35% of glucan mass loss after fungal pretreatment, less than half of the previously reported values. Nitrogen and bark additions can regulate lignocellulose degradation and saccharification of birch-based substrates. The designed substrate composition could considerably reduce cellulose consumption during fungal pretreatment, thus improving bioconversion efficiency

Single-Step Hydrothermal Synthesis of Biochar from H3PO4-Activated LettuceWaste for Efficient Adsorption of Cd(II) in Aqueous Solution

Developing an ideal and cheap adsorbent for adsorbing heavy metals from aqueous solution has been urgently need. In this study, a novel, effective and low-cost method was developed to prepare the biochar from lettuce waste with H3PO4 as an acidic activation agent at a low-temperature (circa 200°C) hydrothermal carbonization process. A batch adsorption experiment demonstrated that the biochar reaches the adsorption equilibrium within 30 min, and the optimal adsorption capacity of Cd(II) is 195.8 mg⋅g-1at solution pH 6.0, which is significantly improved from circa 20.5 mg⋅g-1 of the original biochar without activator. The fitting results of the prepared biochar adsorption data conform to the pseudo-second-order kinetic model (PSO) and the Sips isotherm model, and the Cd(II) adsorption is a spontaneous and exothermic process. The hypothetical adsorption mechanism is mainly composed of ion exchange, electrostatic attraction, and surface complexation. This work offers a novel and low-temperature strategy to produce cheap and promising carbon-based adsorbents from organic vegetation wastes for removing heavy metals in aquatic environment efficiently

Neural-Symbolic Recommendation with Graph-Enhanced Information

The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale. In this work, we combine the advantages of graph neural networks and propositional logic operations to construct a neuro-symbolic recommendation model with both global implicit reasoning ability and local explicit logic reasoning ability. We first build an item-item graph based on the principle of adjacent interaction and use graph neural networks to capture implicit information in global data. Then we transform user behavior into propositional logic expressions to achieve recommendations from the perspective of cognitive reasoning. Extensive experiments on five public datasets show that our proposed model outperforms several state-of-the-art methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].Comment: 12 pages, 2 figures, conferenc

Spent mushroom substrates for ethanol production – Effect of chemical and structural factors on enzymatic saccharification and ethanolic fermentation of Lentinula edodes-pretreated hardwood

Spent mushroom substrates (SMS) from cultivation of shiitake (Lentinula edodes) on three hardwood species were investigated regarding their potential for cellulose saccharification and for ethanolic fermentation of the produced hydrolysates. High glucan digestibility was achieved during enzymatic saccharification of the SMSs, which was related to the low mass fractions of lignin and xylan, and it was neither affected by the relative content of lignin guaiacyl units nor the substrate crystallinity. The high nitrogen content in SMS hydrolysates, which was a consequence of the fungal pretreatment, was positive for the fermentation, and it ensured ethanol yields corresponding to 84–87% of the theoretical value in fermentations without nutrient supplementation. Phenolic compounds and acetic acid were detected in the SMS hydrolysates, but due to their low concentrations, the inhibitory effect was limited. The solid leftovers resulting from SMS hydrolysis and the fermentation residues were quantified and characterized for further valorisation

Cytological and proteomic analyses of horsetail (Equisetum arvense L.) spore germination

Spermatophyte pollen tubes and root hairs have been used as single-cell-type model systems to understand the molecular processes underlying polar growth of plant cells. Horsetail (Equisetum arvense L.) is a perennial herb species in Equisetopsida, which creates separately growing spring and summer stems in its life cycle. The mature chlorophyllous spores produced from spring stems can germinate without dormancy. Here we report the cellular features and protein expression patterns in five stages of horsetail spore germination (mature spores, rehydrated spores, double-celled spores, germinated spores, and spores with protonemal cells). Using 2-DE combined with mass spectrometry, 80 proteins were found to be abundance changed upon spore germination. Among them, proteins involved in photosynthesis, protein turnover, and energy supply were over-represented. Thirteen proteins appeared as proteoforms on the gels, indicating the potential importance of post-translational modification. In addition, the dynamic changes of ascorbate peroxidase, peroxiredoxin, and dehydroascorbate reductase implied that reactive oxygen species homeostasis is critical in regulating cell division and tip-growth. The diverse expression patterns of proteins in photosynthesis, energy supply, lipid and amino acid metabolism indicated that heterotrophic and autotrophic metabolism were necessary in light-dependent germination of the spores. Twenty-six proteins were involved in protein synthesis and fate, indicating that protein turnover is vital to spore germination. Furthermore, the altered abundance of small G protein Ran, 14-3-3 protein, actin, and Caffeoyl-CoA O-methyltransferase revealed that signaling transduction, vesicle trafficking, cytoskeleton dynamics, and cell wall modulation were critical to cell division and polar growth. These findings lay a foundation toward understanding the molecular mechanisms underlying fern spore asymmetric division and rhizoid polar growth

Neuro-Symbolic Recommendation Model based on Logic Query

A recommendation system assists users in finding items that are relevant to them. Existing recommendation models are primarily based on predicting relationships between users and items and use complex matching models or incorporate extensive external information to capture association patterns in data. However, recommendation is not only a problem of inductive statistics using data; it is also a cognitive task of reasoning decisions based on knowledge extracted from information. Hence, a logic system could naturally be incorporated for the reasoning in a recommendation task. However, although hard-rule approaches based on logic systems can provide powerful reasoning ability, they struggle to cope with inconsistent and incomplete knowledge in real-world tasks, especially for complex tasks such as recommendation. Therefore, in this paper, we propose a neuro-symbolic recommendation model, which transforms the user history interactions into a logic expression and then transforms the recommendation prediction into a query task based on this logic expression. The logic expressions are then computed based on the modular logic operations of the neural network. We also construct an implicit logic encoder to reasonably reduce the complexity of the logic computation. Finally, a user's interest items can be queried in the vector space based on the computation results. Experiments on three well-known datasets verified that our method performs better compared to state of the art shallow, deep, session, and reasoning models.Comment: 17 pages, 6 figure