Academic literature on the topic 'Scalable memory bank'

Among the electrical problems observed from the solar irradiation variability, the electrical energy quality and the energetic dispatch guarantee stand out. The great revolution in batteries technologies has fostered its usage with the installation of photovoltaic system (PVS). This work presents a proposition for voltage regulation for residential prosumers using a set of scalable power batteries in passive mode, operating as a consumer device. The mitigation strategy makes decisions acting directly on the demand, for a storage bank, and the power of the storage element is selected in consequence of the results obtained from the power flow calculation step combined with the prediction of the solar radiation calculated by a recurrent neural network Long Short-Term Memory (LSTM) type. The results from the solar radiation predictions are used as subsidies to estimate, the state of the power grid, solving the power flow and evidencing the values of the electrical voltages 1-min enabling the entry of the storage device. In this stage, the OpenDSS (Open distribution system simulator) software is used, to perform the complete modeling of the power grid where the study will be developed, as well as simulating the effect of the overvoltages mitigation system. The clear sky day stored 9111 Wh/day of electricity to mitigate overvoltages at the supply point; when compared to other days, the clear sky day needed to store less electricity. On days of high variability, the energy stored to regulate overvoltages was 84% more compared to a clear day. In order to maintain a constant state of charge (SoC), it is necessary that the capacity of the battery bank be increased to meet the condition of maximum accumulated energy. Regarding the total loading of the storage system, the days of low variability consumed approximately 12% of the available capacity of the battery, considering the SoC of 70% of the capacity of each power level.

Abstract Motivation The rapid development of single-cell RNA sequencing (scRNA-seq) makes it possible to study the heterogeneity of individual cell characteristics. Cell clustering is a vital procedure in scRNA-seq analysis, providing insight into complex biological phenomena. However, the noisy, high-dimensional and large-scale nature of scRNA-seq data introduces challenges in clustering analysis. Up to now, many deep learning-based methods have emerged to learn underlying feature representations while clustering. However, these methods are inefficient when it comes to rare cell type identification and barely able to fully utilize gene dependencies or cell similarity integrally. As a result, they cannot detect a clear cell type structure which is required for clustering accuracy as well as downstream analysis. Results Here, we propose a novel scRNA-seq clustering algorithm called scNAME which incorporates a mask estimation task for gene pertinence mining and a neighborhood contrastive learning framework for cell intrinsic structure exploitation. The learned pattern through mask estimation helps reveal uncorrupted data structure and denoise the original single-cell data. In addition, the randomly created augmented data introduced in contrastive learning not only helps improve robustness of clustering, but also increases sample size in each cluster for better data capacity. Beyond this, we also introduce a neighborhood contrastive paradigm with an offline memory bank, global in scope, which can inspire discriminative feature representation and achieve intra-cluster compactness, yet inter-cluster separation. The combination of mask estimation task, neighborhood contrastive learning and global memory bank designed in scNAME is conductive to rare cell type detection. The experimental results of both simulations and real data confirm that our method is accurate, robust and scalable. We also implement biological analysis, including marker gene identification, gene ontology and pathway enrichment analysis, to validate the biological significance of our method. To the best of our knowledge, we are among the first to introduce a gene relationship exploration strategy, as well as a global cellular similarity repository, in the single-cell field. Availability and implementation An implementation of scNAME is available from https://github.com/aster-ww/scNAME. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Despite advances in antiviral drugs, Cytomegalovirus (CMV) infections remain a significant cause of morbidity and mortality in immunocompromised individuals. We have recently demonstrated in hematopoietic stem cell transplant (HSCT) recipients that adoptively-transferred virus-specific T cells, generated from healthy 3rd party donors and administered as an "ready to administer" product, can be curative, even in patients with drug-refractory CMV infections. However, broader implementation has been hindered by the postulated need for extensive panels of T cell lines representing a diverse HLA profile, as well as the complexities of large scale manufacturing for widespread clinical application. To address these potential issues, we have developed a decision tool that identified a short list of donors who provide HLA coverage for >90% of the stem cell transplant population. Furthermore, to generate banks of CMV-specific T cells from these donors, we have created a simple, robust, and linearly scalable manufacturing process. To determine whether these advances would enable the widespread application of "ready to administer" T cells, we generated CMV cell banks (Viralym-C™) from 9 healthy donors selected by our decision tool, and initiated a fixed-dose (2x107 cells/m2) Phase I clinical trial for the treatment of drug-refractory CMV infections in pediatric and adult HSCT recipients. To generate the Viralym-C™ banks, we stimulated donor peripheral blood mononuclear cells (PBMCs) with overlapping peptide libraries spanning the immunodominant CMV antigens pp65 and IE1. Cells were subsequently expanded in a G-Rex device, resulting in a mean fold expansion of 103±12. The lines were polyclonal, comprising both CD4+ (21.3±6.7%) and CD8+ (74.8±6.9%) T cells, and expressed central CD45RO+/CD62L+ (58.5±4.2%) and effector memory markers CD45RO+/CD62L- (35.3±12.2%). Furthermore, the lines generated were specific for the target antigens (IE1: 419±100; pp65 1070±31 SFC/2x105, n=9). To date, we have screened 12 patients for study participation, and from our bank of just 9 lines we have successfully identified a suitable line for all patients within 24 hours. Of these, 6 patients have been infused; 5 received a single infusion and 1 patient required 2 infusions for sustained benefit. There were no immediate infusion-related toxicities; and despite the HLA disparity between the Viralym-C lines and the patients infused, there were no cases of de novo or recurrent graft versus host disease (GvHD). One patient developed a transient fever a few hours post-infusion, which spontaneously resolved. Based on viral load, measured by quantitative PCR, or symptom resolution (in patients with disease), Viralym-C™ cells controlled active infections in all 5 evaluable patients; 4 patients had complete responses, and 1 patient had a partial response within 4 weeks of cell infusion. One patient with CMV retinitis had complete resolution of symptoms following Viralym-C™ infusion. In conclusion, our results demonstrate the feasibility, preliminary safety and efficacy of "ready to administer" Viralym-C™ cells that have been generated from a small panel of healthy, eligible CMV seropositive donors identified by our decision support tool. These data suggest that cost-effective, broadly applicable T cell anti-viral therapy may be feasible for patients following HSCT and potentially other conditions. Disclosures Tzannou: ViraCyte LLC: Consultancy. Leen:ViraCyte LLC: Equity Ownership, Patents & Royalties. Kakarla:ViraCyte LLC: Employment.

PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.

Existing user-level network interfaces deliver high bandwidth, low latency performance to applications, but are typically unable to support diverse styles of communication and are unsuitable for use in multiprogrammed environments. Often this is because the network abstraction is presented at too high a level, and support for synchronisation is inflexible. In this paper we present a new primitive for in-band synchronisation: the Tripwire. Tripwires provide a flexible, efficient and scalable means for synchronisation that is orthogonal to data transfer. We describe the implementation of a non-coherent distributed shared memory network interface, with Tripwires for synchronisation. This interface provides a low-level communications model with gigabit class bandwidth and very low overhead and latency. We show how it supports a variety of communication styles, including remote procedure call, message passing and streaming.

In this paper, a new architecture for radix-2n serial–serial multiplication/reduction for the finite field GF(2m) is presented. The input operands are serially entered one digit at a time and the output result is computed serially one digit at a time. The reduction polynomial is also fed serially to the structure so that changing the reduction polynomial will not require rewriting or rewiring the structure. The structure utilizes a serial transfer which reduces the bus width needed to transfer data back and forth between memory and multiplication unit. The structure possesses features of regularity, modularity and scalability which are a design requirement for an efficient utilization of FPGA resources. Also, a systolic scalable area efficient design which provides a 50% reduction in hardware without degrading the speed performance is proposed. A radix-4 version of the proposed architecture has been designed, simulated and synthesized using Xilinx ISE 10.1 targeting a Xilinx Virtex-5 FPGA.

Within the framework of multi-temporal Synthetic Aperture Radar (SAR) interferometric processing, image coregistration is a fundamental operation that might be extremely time-consuming. This paper explores the possibility of addressing fast and accurate SAR image geometric coregistration, with sub-pixel accuracy and in the presence of a complex 3-D object scene, by exploiting the parallelism offered by shared-memory architectures. An efficient and scalable processor is proposed by designing a parallel algorithm incorporating thread-level parallelism for solving the inherent computationally intensive problem. The adopted functional scheme is first mathematically framed and then investigated in detail in terms of its computational structures. Subsequently, a parallel version of the algorithm is designed, according to a fork-join model, by suitably taking into account the granularity of the decomposition, load-balancing, and different scheduling strategies. The developed parallel algorithm implements parallelism at the thread-level by using OpenMP (Open Multi-Processing) and it is specifically targeted at shared-memory multiprocessors. The parallel performance of the implemented multithreading-based SAR image coregistration prototype processor is experimentally investigated and quantitatively assessed by processing high-resolution X-band COSMO-SkyMed SAR data and using two different multicore architectures. The effectiveness of the developed multithreaded prototype solution in fully benefitting from the computing power offered by multicore processors has successfully been demonstrated via a suitable experimental performance analysis conducted in terms of parallel speedup and efficiency. The demonstrated scalable performance and portability of the developed parallel processor confirm its potential for operational use in the interferometric SAR data processing at large scales.

In this paper, two new high performance bidirectional mixed radix-2n serial-serial multipliers for the finite field GF (2m) are presented. The input operands are serially entered one digit at a time for the first operand and two digits at a time for the second operand. The output result is computed serially one digit at a time. The reduction polynomial is also fed serially to the structure in the same manner so that changing the reduction polynomial will not require rewriting or rewiring the structure. The structures utilize a serial transfer which reduces the bus width needed to transfer data back and forth between memory and multiplication unit. The structures possess features of regularity, modularity and scalability which are a design requirement for an efficient utilization of FPGA resources. The new twin pipe design has improved the area-time performance by ~37% when compared with the best existing radix-2n serial-serial multipliers for the finite field GF (2m) . Furthermore, it is the first twin pipe bidirectional radix-2n serial-serial multiplier for the finite field GF (2m) reported in the literature. The twin pipe multiplier can be used to perform two successive K-digit multiplications in 2K + 6 cycles without truncating the results. As a consequence, a new data can be fed into the multiplier every K + 3 cycles. A radix-4 version of the proposed architecture has been designed, simulated and synthesized using Xilinx ISE 10.1 targeting a Xilinx Virtex-5 FPGA.

Adaptive techniques can be applied to improve performance of a beamformer in a cluttered environment. The sequential implementation of an adaptive beamformer, for many sensors and over a wide band of frequencies, presents a serious computational challenge. By coupling each transducer node with a microprocessor, in-situ parallel processing applied to an adaptive beamformer on a distributed system can glean advantages in execution speed, fault tolerance, scalability, and cost. In this paper, parallel algorithms for Subspace Projection Beamforming (SPB), using QR decomposition on distributed systems, are introduced for in-situ signal processing. Performance results from parallel and sequential algorithms are presented using a distributed system testbed comprised of a cluster of computers connected by a network. The execution times, parallel efficiencies, and memory requirements of each parallel algorithm are presented and analyzed. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of future advanced beamforming algorithms in a scalable fashion.

The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing.