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About 10,000 papers for “memory storage”

Memory storage behavior.

Chenyi Zhu (9383370), Zhiyi Wang (328693), Yinan Peng (21410787) et al. · Figshare · 2025

<div> Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. The core mechanisms of IRBMO include: elite search behavior, which improves global optimization by guiding the search to expand in more promising directions; collaborative hunting behavior, which quickly identifies key features and promotes collaborative optimization among feature subsets; and memory storage behavior, which leverages historically valid information to improve search efficiency and accuracy. To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. In addition, compared with nine existing feature selection methods, IRBMO demonstrates significant advantages in terms of fitness value. To further enhance the performance, this paper also constructs the V2IRBMO variant by combining the S-shaped and V-shaped transfer functions, which further enhances the robustness and generalization ability of the algorithm. Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. These results demonstrate the potential and wide applicability of IRBMO in feature selection for medical data. </div>

Cited by 0Open access

Memory Mass Storage

Giovanni Campardo, Massimo Iaculo, Federico Tiziani et al. · 2011

Cited by 14

Storage Class Memory

D Hession, N McKelvey, Kevin Curran · Ulster University Research Portal (Ulster University) · 2013

Advances in processor technology have created myriads of related problems for system designers, chief amongst which is the need to improve on the latency between storage, memory and the processor itself. Significant data transfer rates between the processor, L1 cache, L2 cache and main memory are possible but it is at this point that the scenario changes and it is this problem which must be addressed in the years to come if the gains made in processor technology are to be further translated into overall system performance improvements. Storage Class Memory (SCM) offers just the bridge needed to get over the ever widening gap between CPU processing speeds, the need to move large blocks of data quickly and the read/write speeds offered by traditional disk reliant systems. In terms of their predicted cost, speed, retentive abilities(data persistence) and power requirements, the technologies that can be classed as SCM are the most likely candidates to keep Moore's Law true well into the next decade and beyond. This paper provides an overview of SCM and its role in the 'greening' of data centres around the world. The makeup of data centres with their vast banks of HDD's all-consuming power whether idle or in use is not a sustainable model as the world faces into a future of increasing energy costs and decreasing supply of traditional fuel sources.

Cited by 4Open access

Memory storage trends

Вероника Викторовна Ермолаева, M.A. Mileshin, D.R. Tugushev · 2020

This article discusses the latest trends in the development of memory drives.Two leading data storage devices in the computer services market are compared.

Cited by 0Open access

Storage Class Memory

Chung Lam · 2010

This paper describes the development of a new class of solid state memory known as Storage Class Memory (SCM). We will examine various memory technologies that promise to become the emerging SCM standard currently under development in research laboratories and memory manufacturing facilities around the world. We will focus our attention mostly in the enterprise space and extrapolate the future of SCM in this space for the next 10 years.

Cited by 26

Storage Systems for Storage-Class Memory

Haris Volos · 2012

Emerging device technologies including phase-change-memory (PCM), spin-torgue transfer RAM (STT-RAM) and memristors promise high-speed storage. These technologies collectively are termed storage-class memory (SCM) as data can be accessed through ordinary load/store instructions rather than through I/O requests. Hence, user-mode code can access data directly, so there is no need for the OS to mediate every access. Furthermore, existing virtual memory hardware can protect access to individual data pages. The existing OS structure of file systems as a kernellevel service may no longer be necessary with storage class memory, and causes unneeded complexity and lower performance. We propose rewriting the storage stack to create a new flexible, highperformance storage architecture enabled by storage-class memory. The Case for User-mode Storage Systems for SCM Despite rapid advancements in storage technology, the fundamental organization

Cited by 0

Revamping Storage Class Memory With Hardware Automated Memory-Over-Storage Solution

Zhang, Jie, Kwon, Miryeong, Gouk, Donghyun et al. · arXiv (Cornell University) · 2021

Large persistent memories such as NVDIMM have been perceived as a disruptive memory technology, because they can maintain the state of a system even after a power failure and allow the system to recover quickly. However, overheads incurred by a heavy software-stack intervention seriously negate the benefits of such memories. First, to significantly reduce the software stack overheads, we propose HAMS, a hardware automated Memory-over-Storage (MoS) solution. Specifically, HAMS aggregates the capacity of NVDIMM and ultra-low latency flash archives (ULL-Flash) into a single large memory space, which can be used as a working or persistent memory expansion, in an OS-transparent manner. HAMS resides in the memory controller hub and manages its MoS address pool over conventional DDR and NVMe interfaces; it employs a simple hardware cache to serve all the memory requests from the host MMU after mapping the storage space of ULL-Flash to the memory space of NVDIMM. Second, to make HAMS more energy-efficient and reliable, we propose an "advanced HAMS" which removes unnecessary data transfers between NVDIMM and ULL-Flash after optimizing the datapath and hardware modules of HAMS. This approach unleashes the ULL-Flash and its NVMe controller from the storage box and directly connects the HAMS datapath to NVDIMM over the conventional DDR4 interface. Our evaluations show that HAMS and advanced HAMS can offer 97% and 119% higher system performance than a software-based hybrid NVDIMM design, while consuming 41% and 45% lower system energy, respectively.

Cited by 0Open access

Synapses and Memory Storage

Mark Mayford, Steven A. Siegelbaum, Eric R. Kandel · Cold Spring Harbor Perspectives in Biology · 2012

The synapse is the functional unit of the brain. During the last several decades we have acquired a great deal of information on its structure, molecular components, and physiological function. It is clear that synapses are morphologically and molecularly diverse and that this diversity is recruited to different functions. One of the most intriguing findings is that the size of the synaptic response in not invariant, but can be altered by a variety of homo- and heterosynaptic factors such as past patterns of use or modulatory neurotransmitters. Perhaps the most difficult challenge in neuroscience is to design experiments that reveal how these basic building blocks of the brain are put together and how they are regulated to mediate the information flow through neural circuits that is necessary to produce complex behaviors and store memories. In this review we will focus on studies that attempt to uncover the role of synaptic plasticity in the regulation of whole-animal behavior by learning and memory.

Cited by 518Open access