Batch Version Research Articles

Synthesis of N-heterocyclic amides from imidazoheterocycles through convergent paired electrolysis.

An efficient ring opening of imidazoheterocycles induced by a direct C-H azidation resulting in an unusual formation of N-heterocyclic amides has been successfully developed through convergent paired electrolysis. A broad scope of pyridylbenzamides could be obtained in moderate to excellent yields under exogenous-oxidant, electrolyte- and metal-free electrochemical conditions. The methodology was transferred to continuous flow conditions resulting in notable improvements particularly in terms of cost-efficiency over traditional batch versions.

Electrochemical Trifluoromethylalkoxylation of Endocyclic Enamides in Batch and Flow

AbstractA regioselective electrochemical oxytrifluoromethylation of endocyclic enamides is reported. The 1,2‐difunctionalization proceeds under mild conditions and employs the inexpensive bench stable Langlois’ reagent. This multicomponent strategy gives access to a wide range of α,β‐substituted amines by means of various alcohols as nucleophiles in high yields and with good functional group tolerance. In addition, an electrochemically induced C−H trifluoromethylation of enamides has been developed. A continuous flow protocol using a commercial microflow electrochemical reactor was also developed with improved reaction performance and efficiency over traditional batch versions without any supporting electrolyte.

Optimistic online caching for batched requests

In this paper, we investigate ‘optimistic’ online caching policies, distinguished by their use of future request predictions derived, for example, from machine learning models. Traditional online optimistic policies, grounded in the Follow-The-Regularized-Leader (FTRL) algorithm, incur a higher computational cost compared to classic policies like Least Frequently Used (LFU) and Least Recently Used (LRU). This is due to each cache state update necessitating the resolution of a constrained optimization problem. To address this problem, we introduce and analyze the ‘batched’ version of two distinct FTRL-based optimistic policies. In this approach, the cache updates occur less frequently, thereby amortizing the update cost over time or over multiple requests. Rather than updating the cache with each new request, the system accumulates a batch of requests before modifying the cache content. First, we present a batched version of the Optimistic Bipartite Caching (OBC) algorithm, that works for single requests, then we introduce a new optimistic batched caching policy, the Per-Coordinate Optimistic Caching (PCOC) algorithm, derived from the per-coordinate-based FTRL. We demonstrate that these online algorithms maintain ‘vanishing regret’ in the batched case, meaning their average performance approaches over time that of an optimal static file allocation, regardless of the sequence of file requests. We then compare the performance of these two strategies with each other and against optimistic versions of LFU and LRU. Our experimental results indicate that this batched optimistic approach outperforms traditional caching policies on both stationary and real-world file request traces.

Block-wise primal-dual algorithms for large-scale doubly penalized ANOVA modeling

For multivariate nonparametric regression, doubly penalized ANOVA modeling (DPAM) has recently been proposed, using hierarchical total variations (HTVs) and empirical norms as penalties on the component functions such as main effects and multi-way interactions in a functional ANOVA decomposition of the underlying regression function. The two penalties play complementary roles: the HTV penalty promotes sparsity in the selection of basis functions within each component function, whereas the empirical-norm penalty promotes sparsity in the selection of component functions. To facilitate large-scale training of DPAM using backfitting or block minimization, two suitable primal-dual algorithms are developed, including both batch and stochastic versions, for updating each component function in single-block optimization. Existing applications of primal-dual algorithms are intractable for DPAM with both HTV and empirical-norm penalties. The validity and advantage of the stochastic primal-dual algorithms are demonstrated through extensive numerical experiments, compared with their batch versions and a previous active-set algorithm, in large-scale scenarios.

One-armed bandit problem and the mirror descent algorithm

We consider the application of the mirror descent algorithm (MDA) to the one-armed bandit problem in the minimax statement as applied to data processing. This problem is also known as the game with nature, where the player's payoff function is the mathematical expectation of the total income. The player must determine the most effective method of the two available and provide that it is predominantly used. In this case, the a priori efficiency of one of the methods is known. This article proposes a modification of the MDA that allows to improve the efficiency of control through the use of additional a priori information. The proposed strategy retains the characteristic property of strategies for one-armed bandits - if a known action is applied once, it will be applied until the end of the control. Modifications for the algorithm for one-by-one processing and for its batch version are considered. Batch processing is interesting in that the total processing time is determined by the number of batches and not the original amount of data, if it is possible to provide parallel processing of data in batches. For the proposed algorithms, using the Monte-Carlo simulation, the optimal values of the tunable parameters were calculated and the minimax risk estimates were obtained.

Optimization of a Pyrometallurgical Process to Efficiently Recover Valuable Metals from Commercially Used Lithium-Ion Battery Cathode Materials LCO, NCA, NMC622, and LFP

With an ever-growing demand for critical raw materials for the production of lithium-ion batteries and a price increase of respective commodities, an ever louder call from the industry for efficient recycling technologies can be noticed. So far, state-of-the-art industry-scaled pyrometallurgical recycling technologies all suffer from the same bottleneck of lithium slagging. At the Chair of Thermal Processing Technology at Montanuniversitaet Leoben, a novel reactor was developed to recover lithium and phosphorus via the gas phase in a pyrometallurgical process. Critical elements such as Li, Ni, Co, and Mn of the commercially used cathode materials LCO (LiCoO2), LFP (LiFePO4), NCA (LiNi0.8Co0.15Al0.05O2), and NMC622 (LiNi0.6Mn0.2Co0.2) were analyzed in a batch version of the so-called InduRed reactor concept. The analyses underline that the reactor concept is highly suitable for an efficient recovery for the metals Ni and Co and that slagging of Li can not only be largely prohibited, but the elements lithium and phosphorous can even be recovered from the gas phase. Plant engineering issues were also considered for further development toward a continuous process. The MgO crucible used shows significant diffusion of various elements from the battery material, which is why the choice of crucible material still requires in-depth research.

Two-Armed Bandit Problem and Batch Version of the Mirror Descent Algorithm

Two-Armed Bandit Problem and Batch Version of the Mirror Descent Algorithm

Cascaded Least Square Algorithm for Strong Clutter Removal in Airborne Passive Radar

Owing to the continuous random and nonperiodic characteristics of the passive signal, the random range sidelobe coupling (RRSC) effects of the strong clutter (i.e., the direct path signal and the strong multipath signal) will exacerbate the performance of space-time adaptive processing (STAP) algorithm in airborne passive radar (APR). This article analyzes the influence of RRSC caused by strong clutter on detection range cell of interest and illustrates that strong clutter should be suppressed before applying the STAP algorithm. A cascaded least square (CLS) algorithm based on the directional dependence of clutter Doppler frequency is proposed for side-looking APR, where the first least square (LS) algorithm is performed to achieve the amplitude estimation of the Doppler-shift echo signal, and the channel amplitude and phase error correction factor can be obtained by the estimated amplitude information of the direct patch signal and the APR geometric configuration. The second LS is employed to further estimate the amplitude information of the strong clutter, and the resulting weight vector can be utilized for clutter cancelation. To make the CLS algorithm more practical, a batch version of the CLS (CLS-B) algorithm is proposed in this article. The advantage of the method is that it cannot only cancel clutter along the clutter ridge but also effectively prevent the suppression of targets falling in the clutter Doppler band. In addition, the proposed CLS algorithm performs well in the presence of the channel amplitude and phase error. A series of simulations are conducted to test the proposed algorithms.

An Inter-Subband Processing Algorithm for Complex Clutter Suppression in Passive Bistatic Radar

Clutter suppression is a challenging problem for passive bistatic radar systems, given the complexity of actual clutter scenarios (stationary, time-varying and fractional-order clutter). Such complex clutter induces intense sidelobes in the entire range-Doppler plane and thus degrades target-detection performance, especially for low-observable targets. In this paper, a novel method, denominated as the batch version of the extensive cancellation algorithm (ECA) in the frequency domain (ECA-FB), is presented for the first time, to suppress stationary clutter and its sidelobes. Specifically, in this method, the received signal is first divided into short batches in the frequency domain to coarsen the range resolution, and then the clutter is removed over each batch via ECA. Further, to suppress the time-varying clutter, a Doppler-shifted version of ECA-FB (ECA-FBD) is proposed. Compared with the popular ECA and ECA-B methods, the proposed ECA-FB and ECA-FBD obtained superior complex clutter suppression and slow-moving target detection performance with lower computational complexity. A series of simulation and experimental results are provided to demonstrate the validity of the proposed methods.

Vehicle Routing Optimization System with Smart Geopositioning Updates

Solving the vehicle routing problem (VRP) is one of the best-known optimization issues in the TLS (transport, logistic, spedition) branch market. Various variants of the VRP problem have been presented and discussed in the literature for many years. In most cases, batch versions of the problem are considered, wherein the complete data, including customers’ geographical distribution, is well known. In real-life situations, the data change dynamically, which influences the decisions made by optimization systems. The article focuses on the aspect of geopositioning updates and their impact on the effectiveness of optimization algorithms. Such updates affect the distance matrix, one of the critical datasets used to optimize the VRP problem. A demonstration version of the optimization system was developed, wherein updates are carried out in integration with both open source routing machine and GPS tracking services. In the case of a dynamically changing list of destinations, continuous and effective updates are required. Firstly, temporary values of the distance matrix based on the correction of the quasi-Euclidean distance were generated. Next, the impact of update progress on the proposed optimization algorithms was investigated. The simulation results were compared with the results obtained “manually” by experienced planners. It was found that the upload level of the distance matrix influences the optimization effectiveness in a non-deterministic way. It was concluded that updating data should start from the smallest values in the distance matrix.

Two-armed bandit problem and batch version of the mirror descent algorithm

We consider the minimax setup for the two-armed bandit problem as applied to data processing if there are two alternative processing methods with different a priori unknown efficiencies. One should determine the most efficient method and provide its predominant application. To this end, we use the mirror descent algorithm (MDA). It is well-known that corresponding minimax risk has the order of $N^{1/2$ with $N$ being the number of processed data and this bound is unimprovable in order. We propose a batch version of the MDA which allows processing data by packets that is especially important if parallel data processing can be provided. In this case, the processing time is determined by the number of batches rather than by the total number of data. Unexpectedly, it turned out that the batch version behaves unlike the ordinary one even if the number of packets is large. Moreover, the batch version provides significantly smaller value of the minimax risk, i.e., it considerably improves a control performance. We explain this result by considering another batch modification of the MDA which behavior is close to behavior of the ordinary version and minimax risk is close as well. Our estimates use invariant descriptions of the algorithms based on Gaussian approximations of incomes in batches of data in the domain of ``close'' distributions and are obtained by Monte-Carlo simulations.

Efficient generation of self-avoiding, semiflexible rotational isomeric chain ensembles in bulk, in confined geometries, and on surfaces

We provide an efficient ready-to-run code gensaw that generates single or large ensembles of self-avoiding, flexible, semiflexible, rotationally isometric or helical chains in the bulk or subject to arbitrary confinement and tethering conditions, where we allow for arbitrary intramolecular bending and dihedral energy functions. The resulting configuration files are provided in various common formats and can be immediately used to do molecular simulations or statistical analysis. We work out analytic expressions for the mean squared end-to-end distance and gyration radius of the semiflexible, helical and rotational isomeric state models with a finite number of bonds and arbitrary interaction potentials for direct comparison and testing of the code in the limiting case of unconfined phantom chains. In addition to the graphics-free linux standalone batch version gensaw that creates configuration and other files for high throughput applications from the command line, we provide an interactive online version gensaw-visualization that serves as platform-independent graphical user interface, and animates the resulting conformations using a remote gensaw server. Program summaryProgram title:gensawCPC Library link to program files:https://doi.org/10.17632/8xfgf88f3w.1Licensing provisions: MITProgramming languages: fortran 90, perl (standalone batch version) or java (interactive online version)Required software:ifort 16+ or gfortran 4+, perl 5+ (standalone batch version), jre 1.8.0+ (interactive online version)Nature of problem: Efficient creation of self-avoiding, semiflexible multibead walks/chains of variable contour length, subject to arbitrary confinement and tethering conditions, characterized by a conformational energy that is composed of arbitrary bending and torsion contributions. The code must produce an ensemble of such chains, using a flexible output format. For the limiting case of chains with zero thickness, the code should reproduce analytic results for the ideal chains.Solution method: We use an adapted enrichment procedure that optimizes its parameters on the fly and derive all analytic results for semiflexible chains with arbitrary bending and torsion potentials for comparison with the created ensembles. While gensaw creates configuration and optionally other files from the command line, gensaw-visualization serves as a platform-independent graphical user interface and allows to visualize and animate the conformations.

COMPUTING COMPONENTS OF CLOUD WEB-SERVICES FOR REMOTE SENSING DATA PROCESSING

The organization of computations in cloud Web services for satellite data processing is considered. Computing component of almost every service is a batch version of the corresponding technology of the PlanetaMonitoring software for processing remote sensing data. The exceptions are the technologies that require interactive communication with user, i.e., supervised classification of remote sensing data and movement tracking of natural environments by the coordinates of identifiable objects, each of which consists of two parts, i.e., an interactive Windows application running on the user's computer and the part hidden in the cloud.

Large-Scale Twin Parametric Support Vector Machine Using Pinball Loss Function

Traditional hinge loss function-based large-scale support vector machine (SVM) algorithms tend to perform poorly in the presence of noise, especially when the model is trained incrementally. In this paper, we propose an efficient stochastic quasi-Newton method-based twin parametric SVM using the pinball loss function (termed as SQN-PTWSVM), which is efficient and more robust to the presence of noise when compared to conventional hinge loss SVM for large-scale data scenarios. To establish the theoretical convergence of the method, a modified version of SQN-PTWSVM, termed as SQN-SPTWSVM, has also been proposed. It overcomes the poor convergence issue faced by stochastic gradient twin SVM thus resulting in a faster and reliable model. In SQN-SPTWSVM, the hyperplanes obtained are stable enough to handle noise and resampling issues that occur frequently in stochastic learning scenarios, leading to better generalization ability of the classifier. The proposed method has been extended to nonlinear scenarios as well. Moreover, batch versions of the proposed algorithms have also been introduced which significantly reduce the training time and memory requirement of SQN-PTWSVM and SQN-SPTWSVM. The experimental results on several benchmark datasets and activity recognition applications have shown that the performance of our method is better than the existing classifiers in terms of speed and accuracy.

An efficient load balancing approach for service function chain mapping

Network function virtualization promises a significant advantage for addressing diverse quality of service requirements via chains of Virtual Network Functions (VNFs). Service Function Chain (SFC) mapping, composed of placing VNF in the physical network and routing traffic through them, is the key problem to meet the requirements. In the recent mission-critical applications, delay guarantee and fault tolerance are the crucial requirements wherein links and servers load balancing greatly facilitate providing the requirements. In this paper, we approach the problem of minimizing the maximum load of links and nodes in SFC mapping. After formulating the problem as a mixed-integer linear program, a batched version of the model is developed to estimate the optimal solution in a reasonable time. Then, we propose a practical solution based on the water-filling algorithm. Simulation results in different scenarios show that the algorithm can yield excellent performance compared to the optimal solution and benchmarks algorithms.

Invariant description for batch version of UCB strategy for multi-armed bandit

We consider a variation of upper confidence bound strategy for multi-armed bandit in batch processing setting. Invariant descriptions with the unit control horizon are obtained for upper bounds in the strategy and for regret. A set of Monte-Carlo simulations are performed for different settings of MABs to determine the minimax regret for multi-armed bandits with different configurations.

Time varying Clutter Suppression in CP OFDM Based Passive Radar for Slowly Moving Targets Detection

Time-varying clutter suppression is challenging in passive radar. Although some existed methods are robust against the time-varying clutter, they bear heavy computational burden and memory load. In this study, we propose two advanced versions of the average channel response filter on subcarrier (ACRF-C) for time-varying clutter suppression. The methods take advantage of the orthogonal frequency division multiplex (OFDM) modulation with cyclic prefix (CP-OFDM) of the third-party radio sources, and are implemented in the subcarrier domain. The proposed batch version of ACRF-C (ACRF-CB) can effectively suppress time-varying clutter but modulates targets with small Doppler frequency. To detect slowly moving targets, a sliding version of ACRF-C (ACRF-CS) is presented to partially overlap the channel frequency response for filter coefficients estimation. The ACRF-CS yields a good trade-off between time-varying clutter suppression and slowly moving targets detection. Simulation and experimental results following the theoretical analysis validate that the proposed methods can obtain comparable performance to that of the existed methods but possess remarkable computational and memory advantages.

Statistically Robust Evaluation of Stream-Based Recommender Systems

Online incremental models for recommendation are nowadays pervasive in both the industry and the academia. However, there is not yet a standard evaluation methodology for the algorithms that maintain such models. Moreover, online evaluation methodologies available in the literature generally fall short on the statistical validation of results, since this validation is not trivially applicable to stream-based algorithms. We propose a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -fold validation framework for the pairwise comparison of recommendation algorithms that learn from user feedback streams, using prequential evaluation. Our proposal enables continuous statistical testing on adaptive-size sliding windows over the outcome of the prequential process, allowing practitioners and researchers to make decisions in real time based on solid statistical evidence. We present a set of experiments to gain insights on the sensitivity and robustness of two statistical tests-McNemar's and Wilcoxon signed rank-in a streaming data environment. Our results show that besides allowing a real-time, fine-grained online assessment, the online versions of the statistical tests are at least as robust as the batch versions, and definitely more robust than a simple prequential single-fold approach.

G raph O ne

There is a growing need to perform a diverse set of real-time analytics (batch and stream analytics) on evolving graphs to deliver the values of big data to users. The key requirement from such applications is to have a data store to support their diverse data access efficiently, while concurrently ingesting fine-grained updates at a high velocity. Unfortunately, current graph systems, either graph databases or analytics engines, are not designed to achieve high performance for both operations; rather, they excel in one area that keeps a private data store in a specialized way to favor their operations only. To address this challenge, we have designed and developed G raph O ne , a graph data store that abstracts the graph data store away from the specialized systems to solve the fundamental research problems associated with the data store design. It combines two complementary graph storage formats (edge list and adjacency list) and uses dual versioning to decouple graph computations from updates. Importantly, it presents a new data abstraction, GraphView , to enable data access at two different granularities of data ingestions (called data visibility ) for concurrent execution of diverse classes of real-time graph analytics with only a small data duplication. Experimental results show that G raph O ne is able to deliver 11.40× and 5.36× average speedup in ingestion rate against LLAMA and Stinger, the two state-of-the-art dynamic graph systems, respectively. Further, they achieve an average speedup of 8.75× and 4.14× against LLAMA and 12.80× and 3.18× against Stinger for BFS and PageRank analytics (batch version), respectively. G raph O ne also gains over 2,000× speedup against Kickstarter, a state-of-the-art stream analytics engine in ingesting the streaming edges and performing streaming BFS when treating first half as a base snapshot and rest as streaming edge in a synthetic graph. G raph O ne also achieves an ingestion rate of two to three orders of magnitude higher than graph databases. Finally, we demonstrate that it is possible to run concurrent stream analytics from the same data store.

Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions

Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes to maximize the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed.