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Tree-Based Batch Mode Reinforcement Learning

Tree-Based Batch Mode Reinforcement Learning

successor state of the system, and by determining the control policy from this Q-function. The Q-function approximation may be obtained from the limit of a sequence of (batch mode) super- vised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) and two newly proposed ensemble al- gorithms, namely extremely and totally randomized trees. We study their performances on several examples and find that the ensemble methods based on regression trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the to- tally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accuracy results are provided by the extremely randomized trees.
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Performance Investigation of Batch Mode Microbial
Fuel Cells Fed With High Concentration of Glucose

Performance Investigation of Batch Mode Microbial Fuel Cells Fed With High Concentration of Glucose

Glucose-Fed MFC with a quite wide range of substrate concentrations is operated under batch mode at a proper external load. The anodic chamber is flushed with nitrogen gas for at least 30 min before each experiment to remove dissolved oxygen, leading to an anoxic condition in the reactor. Fresh ferricyanide dissolved in PBS was added once the voltage output dropped abruptly to a relative low value, which was due to the evaporation loss or reduction of ferricyanide concentration. When the MFC still could not restore its function the cathodic and anodic electrolytes should be replaced, indicating the end of one complete cycle of operation and the start of a new cycle. The overall performance of MFC is evaluated through the start up time, power output, COD removal rate and Ec et al.
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A Comparative Study of Modified Lignite Fly Ash for the Adsorption of Nickel from Aqueous Solution by Column and Batch Mode Study

A Comparative Study of Modified Lignite Fly Ash for the Adsorption of Nickel from Aqueous Solution by Column and Batch Mode Study

higher in single system than binary and tertiary system which shows the competitive adsorption between the metal ions. Compared to FAN-MO, FAN had higher adsorption ability for the removal of Ni(II) ions from aqueous solution. In column studies, the removal efficiency of Ni(II) ions were less compared to batch mode studies for both FAN and FAN-MO, but the time taken for adsorption was only 10 minutes. With these conditions, we found the column study was not much suitable compared to batch mode studies for removal of Ni(II) ions onto FAN and FAN-MO. The experimental results show that this can be an up-scalable solution and represent a step in investigating the process of complex treatment of wastewater containing dyes and heavy metals.
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Enhanced co-generation of cellulosic ethanol and methane with the starch/sugar-rich waste mixtures and Tween 80 in fed-batch mode

Enhanced co-generation of cellulosic ethanol and methane with the starch/sugar-rich waste mixtures and Tween 80 in fed-batch mode

Fed-batch mode is considered as a favorable way to increase cell contents and facilitate the ethanol concen- tration accumulation [44, 45]. The fed-batch and fed- batch + Tween 80 have been chosen as an instrument to conduct the following experiments of gradually feeding biomass into the fermentation tank to reduce the above- mentioned negative effect of batch mode at high solids loading. During fed-batch SSF, the feeding mode of sub- strates, yeast, and enzymes have great effect on the entire reaction process. Liu et al. found that all the addition of yeast at the beginning of SSF achieved higher ethanol productivity [46]. Gao et  al. evaluated that all cellulase added at 0 h was more favorable to the fermentation pro- cess [47]. In this study, all enzymes and yeast were added at the start of SSF and the optimal substrates feeding methods are shown in Table 2. Because pre-saccharified DER required a certain amount of water, partial SCB and all DER were added at 0 h to make a minimum ini- tial solid loading. With the initial solids loading of 21.2%, 22.9%, and 24% for the solids loading of 36% (Fig.  3a), 40% (Fig. 3b) and 44% (Fig. 3c), respectively, the fermen- tation rates were higher than that of batch mode at the first 8  h due to the increment of system liquidity and exposing more available catalytic sites [48].
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Engineering Escherichia coli to increase plasmid DNA production in high cell-density cultivations in batch mode

Engineering Escherichia coli to increase plasmid DNA production in high cell-density cultivations in batch mode

The performance of strain VH33 Δ (recA deoR nupG) under well defined conditions was evaluated in small- scale bioreactors. Such experiments allowed the attain- ment of high cell-densities in batch mode, something that cannot be achieved in shake flask due to the lack of pH and dissolved oxygen tension control. Two groups of cultivations were carried out: using low (5 g/L) and high (100 g/L) initial glucose concentrations. For comparison, the commercial strain DH5α was cultivated under the same conditions. Results of cultivations using low initial glucose concentration can be seen in Figure 3. The results of bioreactor cultivation using 5 g/L of initial glucose are similar to those of shake flask: DH5α strain produced 13.09 ± 0.34 mg/L of pDNA and 0.66 ± 0.02 g/L of acetate, which started to accumulate 4 h after inocu- lation (Figure 3A). The pDNA supercoiled fraction was nearly constant and higher than 80%, and the Y p/x value
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Introduction to Stata... 2 Stata Sessions Running Stata in Batch Mode...2 Running Stata Interactively...3 Data Management...

Introduction to Stata... 2 Stata Sessions Running Stata in Batch Mode...2 Running Stata Interactively...3 Data Management...

You can execute Stata command files interactively or in batch mode. By default, the Stata session is organized in the interactive mode, in which you type one command at a time at the command line. However, we recommend using Stata in batch mode so that you have a record of your command files; to run batch mode, you prepare all of your commands in a script before starting, and tell Stata to run the complete script. The sections below describe how to use Stata in both modes.

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Before You Begin... 2 Running SAS in Batch Mode... 2 Printing the Output of Your Program... 3 SAS Statements and Syntax... 3

Before You Begin... 2 Running SAS in Batch Mode... 2 Printing the Output of Your Program... 3 SAS Statements and Syntax... 3

This command processes all the commands in the file called census.sas and normally creates two new files, a log file (a file which has the extension .log) and a list file (a file which has the extension .lst). In this example, the log file is called census.log, and it contains an annotated version of your SAS program, including error messages and other messages regarding the execution of your program. The second file created in this example, census.lst, contains the SAS output, which lists the results produced by the SAS program. Alternatively, sometimes to force SAS to run the program in batch mode, you need to specify using the option of “noterminal”. The command is written as follows:
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A multi-armed bandit approach for batch mode active learning on information networks

A multi-armed bandit approach for batch mode active learning on information networks

In this work, we presented a novel and effective active learning algorithm for heterogeneous information networks. We focused on batch mode learning, which we have shown to be more effective on information networks than ful- ly sequential learning. By establishing a correspondence between batch mode active learning on information networks and combinatorial multi-armed ban- dit, we proposed an expected error reduction based algorithm that combines simple strategies we dubbed primary learners to form query sets. Our algo- rithm employs a novel error expectation measure on networks that is highly adaptable to different classification tasks. Results for classification tasks on real world HINs demonstrated that our algorithm outperforms existing meth- ods when applied to both homogeneous and heterogeneous network classifi- cation models. In addition to being adaptable and performant, our algorithm also provides insight into the network structures that are important for the given classification task.
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BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification

BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification

Active learning algorithms automatically identify the salient and exemplar instances from large amounts of unlabeled data and thus reduce human annotation effort in inducing a classification model. More recently, Batch Mode Active Learning (BMAL) techniques have been proposed, where a batch of data samples is selected simultaneously from an un- labeled set. Most active learning algorithms assume a flat label space, that is, they consider the class labels to be in- dependent. However, in many applications, the set of class labels are organized in a hierarchical tree structure, with the leaf nodes as outputs and the internal nodes as clusters of outputs at multiple levels of granularity. In this paper, we propose a novel BMAL algorithm (BatchRank) for hi- erarchical classification. The sample selection is posed as an NP-hard integer quadratic programming problem and a convex relaxation (based on linear programming) is derived, whose solution is further improved by an iterative truncated power method. Finally, a deterministic bound is established on the quality of the solution. Our empirical results on sev- eral challenging, real-world datasets from multiple domains, corroborate the potential of the proposed framework for real- world hierarchical classification applications.
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Batch Mode Active Learning Methods for the Interactive Classification of Remote Sensing Images

Batch Mode Active Learning Methods for the Interactive Classification of Remote Sensing Images

In the RS domain, AL was applied to the detection of subsurface targets, such as landmines and unexploded ordnance in [29]-[30]. Some preliminary works about the use of AL for RS classification problems can be found in [12], [31]-[32]. The technique proposed in [12] is based on MS and selects the most uncertain sample for each binary SVM in a One-Against-All (OAA) multiclass architecture (i.e., querying h = n samples, where n is the number of classes). In [31], two batch mode AL techniques for multiclass RS classification problems are proposed. The first technique is MS by closest support vector (MS-cSV), which considers the smallest distance of the unlabeled samples to the n hyperplanes (associated to the n binary SVMs in a (OAA) multiclass architecture) as the uncertainty value. At each iteration, the most uncertain unlabeled samples, which do not share the closest SV, are added to the training set. The second technique, called entropy query-by bagging (EQB), is based on the selection of unlabeled samples according to the maximum disagreement between a committee of classifiers. The committee is obtained by bagging: first different training sets (associated to different EQB predictors) are drawn with replacement from the original training data. Then, each training set is used to train the OAA SVM architecture to predict the different labels for each unlabeled sample. Finally, the entropy of the distribution of the different labels associated to each sample is calculated to evaluate the disagreement among the classifiers on the unlabeled samples. The samples with maximum entropy (i.e., those with maximum disagreement among the classifiers) are added to the current training set. In [32], an AL technique is presented, which selects the unlabeled sample that maximizes the information gain between the a posteriori probability distribution estimated from the current training set and the training set obtained by including that sample into it. The information gain is measured by the Kullback–Leibler (KL) divergence. This KL-Maximization (KL-Max) technique can be implemented with any classifier that can estimate the posterior class probabilities. However this technique can be used to select only one sample at each iteration.
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BIOGAS PRODUCTION KINETIC FROM VINASSE WASTE IN BATCH MODE ANAEROBIC DIGESTION

BIOGAS PRODUCTION KINETIC FROM VINASSE WASTE IN BATCH MODE ANAEROBIC DIGESTION

ABSTRACT Bottom product of distillation unit from bioethanol industry is often called as vinasse waste. Anaerobic treatment is one of good choice to convert vinasse into biogas. The purpose of this research was to study the biogas production kinetc from vinasse waste in batch mode anaerobic digestion. The kinetic model of biogas production was developed through modified Gompertz equation. Meanwhile, the kinetic of biodegradability of organic material was developed based on first order kinetic reaction. The researh resulted the kineticconstant of biogasproduction were biogas production potential (A),maximum biogas production rate (U), and minimum time to produce biogas (λ) of83,982 mL/(kg COD), 19,71 mL/(kg COD.day), and1.004 days, respectively. Kinetic constant of organic biodegradability material (k) was-0,059day -1 . Kinetic model could be used to design volume of batch digesteranaerobic with the formula V digester = 3 * ym (1-exp(-k*t)) * m.
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A Semi Supervised Batch Mode Active Learning Strategy for Improved Statistical Machine Translation

A Semi Supervised Batch Mode Active Learning Strategy for Improved Statistical Machine Translation

The availability of substantial, in-domain parallel corpora is critical for the develop- ment of high-performance statistical ma- chine translation (SMT) systems. Such corpora, however, are expensive to pro- duce due to the labor intensive nature of manual translation. We propose to al- leviate this problem with a novel, semi- supervised, batch-mode active learning strategy that attempts to maximize in- domain coverage by selecting sentences, which represent a balance between domain match, translation difficulty, and batch di- versity. Simulation experiments on an English-to-Pashto translation task show that the proposed strategy not only outper- forms the random selection baseline, but also traditional active learning techniques based on dissimilarity to existing training data. Our approach achieves a relative im- provement of 45.9% in BLEU over the seed baseline, while the closest competitor gained only 24.8% with the same number of selected sentences.
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Phenol Biosorption from Polluted Aqueous Solutions by Ulva Lactuca Alga Using Batch Mode Unit

Phenol Biosorption from Polluted Aqueous Solutions by Ulva Lactuca Alga Using Batch Mode Unit

This paper is related to study the using sea lettuce (Ulva lactuca) as a low-cost adsorbent for removing the phenol compounds from aqueous solutions by adsorption under different operating conditions in a batch unit. The SEM and FTIR tests were performed to determine the morphological characteristics and the functional groups existing on the adsorbent material, respectively, while the surface area was identified by means of two techniques which were blue color method and BET method. The results of the adsorption experiments showed that the efficiency of the removal process is inversely proportional with initial concentration of phenol, pH and temperature; while the efficiency was directly proportional to adsorbent amount, agitation speed and treatment time. The results showed that the percentage of removal of phenol from processed water solutions ranged from 25.446% to 90.125%. The Langmuir and Freundlich isotherm models were chosen to estimate the amounts of phenol adsorption by the sea lettuce powder. The kinetic study shows that the adsorption was obeyed pseudo second order also the thermody- namic parameters were calculated.
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Batch Migration Guide

Batch Migration Guide

Transend Migrator has the ability to migrate more than one account at a time, and this is called Concurrent Migrations. During a batch process, Transend Migrator will migrate each account in entry order, which is defined in the Batch Mode Data table. It starts at the top and works through the list until the list is complete. If your license allows, you may migrate more than one account at a time. This allows you to complete your project more quickly.

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Glycolysis and the regulation of glucose transport in Lactococcus lactis spp. lactis in batch and fed-batch culture

Glycolysis and the regulation of glucose transport in Lactococcus lactis spp. lactis in batch and fed-batch culture

Maintaining a continuous low glucose level such as 13.75 mM, specific glucose uptake rates are low and very inter- estingly, a shift towards mixed acid metabolism takes place. It is known so far for anaerobic culture that homo- lactic metabolism occurs on substrates supporting rapid growth in which significant amounts of glucose remain in the medium, and a mixed acid fermentation occurs when growth rates are lower and in true carbon-limited chemo- stats [6,33]. Working the glucostat fed-batch mode under microaerobic conditions, a situation was arranged in which significant amounts of glucose were always present in the fermentation broth with glucose being the substrate supporting the highest fermentation rates [5]. Therefore, the shift from homolactic to mixed acid fermentation could be directly correlated to the glucose uptake rate and consequently to the flux through glycolysis. Indeed, low glycolytic flux has commonly been ascribed to be the cause of mixed acid product formation [3,6,8,34,35]. It has also been shown, that the glycolytic flux governs prod- uct formation only when the flux cannot meet the ana- bolic demand [4]. In response to increased ATP demand the glycolytic flux increases, but if the latter is restricted, mixed acid formation occurs [4]. This is in agreement with the data for ADP and ATP contents of cells presented in Fig. 8 but, the fact that the cells were growing under the glucostat fed-batch mode and glucose was always availa- ble but at a fixed, low concentration, points out that apart from the ATP consuming processes, sugar transport should also be involved in the mechanism underlying the phenomena, a fact rather ignored in previously published studies.
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Kinetic study of batch and fed-batch enzymatic saccharification of pretreated substrate and subsequent fermentation to ethanol

Kinetic study of batch and fed-batch enzymatic saccharification of pretreated substrate and subsequent fermentation to ethanol

Results: The enzymatic hydrolysis was carried out at elevated solid loading up to 20% (w/v) and a comparison kinetics of batch and fed-batch enzymatic hydrolysis was carried out using kinetic regimes. Under batch mode, the actual sugar concentration values at 20% initial substrate consistency were found deviated from the predicted values and the maximum sugar concentration obtained was 80.78 g/L. Fed-batch strategy was implemented to enhance the final sugar concentration to 127 g/L. The batch and fed-batch enzymatic hydrolysates were fermented with Saccharomyces cerevisiae and ethanol production of 34.78 g/L and 52.83 g/L, respectively, were achieved. Furthermore, model simulations showed that higher insoluble solids in the feed resulted in both smaller reactor volume and shorter residence time.
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Batch  NFS

Batch NFS

• Most of the literature on single-key NFS relies heavily on operations that — for large key sizes — are not handled efficiently by current CPUs and that become much more efficient on ASICs: consider, for example, the routing circuit in [51]. Batch NFS relies much more heavily on massively parallel elliptic-curve scalar multiplication, exactly the operation that is shown in [12], [11], and [17] to fit very well into off-the-shelf graphics cards. The literature supports the view that off-the-shelf hardware is much less cost- effective than ASICs for single-key NFS, but there is no reason to think that the same is true for batch NFS.
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Containerized Batch System Monitoring

Containerized Batch System Monitoring

To comply with the European Data Protection rules, we allow for sensitive data only to be transmitted encrypted. As we use mainly Filebeat and other Beat shippers as log and information forwarders, we use our existing PKI infrastructure as certificate basis to establish encrypted channels from our batch nodes to Logstash parsing nodes or Elastic Search database nodes. Since using the existing PKI with Logstash proved to be cumbersome, we took a shortcut. Instead of sending the extended information directly from the aggregation container to ElasticSearch, we write the extended in- formation in a file and attach a Filebeat to the file. Figure 4 sketches both possible ways for forwarding the data, directly or encrypted via the intermediate Filebeat.
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Batch Scheduling in the SAP Environment

Batch Scheduling in the SAP Environment

Utilizing an external, enterprise-wide business-integrated scheduling solution completes the SAP CCMS offering and enhances SAP implementations in compound environments. It is the recommended way to assure the highest possible service levels by merging the management of all batch processing across your enterprise into a unified manageable business environment.

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OPTIMIZATION METHODS FOR BATCH SCHEDULING

OPTIMIZATION METHODS FOR BATCH SCHEDULING

(1) Discrete time formulations are usually larger, but its simpler model structure tends to reduce the CPU time if a reasonable number of time points is proposed. (2) Discrete time m[r]

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