A Brief Review of the Relationship between Addiction and Memory Systems

This essay will reexamine research on the relationship between human memory and addiction. This paper will review several studies that discussed how memory systems in the human brain are involved in the acquisition of behavior that is learned and is associated with the development of drug addiction and drug relapse. Additional information reveals that when individuals make the transition from recreational drug or impulsive use to compulsive drug abuse, which may result in a neuroanatomical change in areas of the brain from cognitive control guided by the hippocampus/dorsomedial striatum towards conditioned control of behavior managed by the dorsolateral striatum (DLS) [1]. This review also looked at studies that involved experiments with humans and lower animals, which suggested that the hippocampus mediates a cognitive/spatial type of memory, while the dorsal striatum manages stimulus-response (S-R) habit memory, and the amygdala governs the classical conditioning form of learning and stimulus-affective-associative relationships [1]. Overall, these studies utilize the hypothesis of the memory systems view of addiction, and the involvement of learning and memory in the context of drug addiction, which was proposed by them [2]. This theory has been proposed in response to drug addiction research and includes alcohol, amphetamine, and cocaine [1]. The research also explains how stress and anxiety can play a role in how strong emotional excitement can lead to dependent habit memory in rodents and humans [1]. .

Fault-Tolerant Design of Spaceborne Mass Memory System

A fault-tolerant spaceborne mass memory architecture is presented based on entirely commercial-off-theshelf components.The highly modularized and scalable memory kernel supports the hierarchical design and is well suited to redundancy structure.Error correcting code(ECC) and periodical scrubbing are used to deal with bit errors induced by single event upset.For 8-bit wide devices, the parallel Reed Solomon(10, 8) can perform coder/decoder calculations in one clock cycle, achieving a data rate of several Gb/...

Diffusion induced by bounded noise in a two-dimensional coupled memory system

The diffusion behavior driven by bounded noise under the influence of a coupled harmonic potential is investigated in a two-dimensional coupled-damped model. With the help of the Laplace analysis we obtain exact descriptions for a particle’s two-time dynamics which is subjected to a coupled harmonic potential and a coupled damping. The time lag is used to describe the velocity autocorrelation function and mean square displacement of the diffusing particle. The diffusion behavior for the time lag is also discussed with respect to the coupled items and the amplitude of bounded noise.

An efficient labeled memory system for learned indexes

The appearance and wide use of memory hardware bring significant changes to the conventional vertical memory hierarchy that fails to handle contentions for shared hardware resources and expensive data movements.To deal with these problems,existing schemes have to rely on inefficient scheduling strategies that also cause extra temporal,spatial and bandwidth overheads.Based on the insights that the shared hardware resources trend to be uniformly and hierarchically offered to the requests for co-located applications in memory systems,we present an efficient abstraction of memory hierarchies,called Label,which is used to establish the connection between the application layer and underlying hardware layer.Based on labels,our paper proposes LaMem,a labeled,resource-isolated and cross-tiered memory system by leveraging the way-based partitioning technique for shared resources to guarantee QoS demands of applications,while supporting fast and low-overhead cache repartitioning technique.Besides,we customize LaMem for the learned index that fundamentally replaces storage structures with computation models as a case study to verify the applicability of LaMem.Experimental results demonstrate the efficiency and efficacy of LaMem.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

The complex roles of m^(6)A modifications in neural stem cell proliferation, differentiation, and self-renewal and implications for memory and neurodegenerative diseases

N6-methyladenosine(m^(6)A), the most prevalent and conserved RNA modification in eukaryotic cells, profoundly influences virtually all aspects of mRNA metabolism. mRNA plays crucial roles in neural stem cell genesis and neural regeneration, where it is highly concentrated and actively involved in these processes. Changes in m^(6)A modification levels and the expression levels of related enzymatic proteins can lead to neurological dysfunction and contribute to the development of neurological diseases. Furthermore, the proliferation and differentiation of neural stem cells, as well as nerve regeneration, are intimately linked to memory function and neurodegenerative diseases. This paper presents a comprehensive review of the roles of m^(6)A in neural stem cell proliferation, differentiation, and self-renewal, as well as its implications in memory and neurodegenerative diseases. m^(6)A has demonstrated divergent effects on the proliferation and differentiation of neural stem cells. These observed contradictions may arise from the time-specific nature of m^(6)A and its differential impact on neural stem cells across various stages of development. Similarly, the diverse effects of m^(6)A on distinct types of memory could be attributed to the involvement of specific brain regions in memory formation and recall. Inconsistencies in m^(6)A levels across different models of neurodegenerative disease, particularly Alzheimer's disease and Parkinson's disease, suggest that these disparities are linked to variations in the affected brain regions. Notably, the opposing changes in m^(6)A levels observed in Parkinson's disease models exposed to manganese compared to normal Parkinson's disease models further underscore the complexity of m^(6)A's role in neurodegenerative processes. The roles of m^(6)A in neural stem cell proliferation, differentiation, and self-renewal, and its implications in memory and neurodegenerative diseases, appear contradictory. These inconsistencies may be attributed to the timespecific nature of m^(6)A and its varying effects on distinct brain regions and in different environments.

Exercise preconditioning alleviates ischemia-induced memory deficits by increasing circulating adiponectin

Cerebral ischemia is a major health risk that requires preventive approaches in addition to drug therapy.Physical exercise enhances neurogenesis and synaptogenesis,and has been widely used for functional rehabilitation after stroke.In this study,we determined whether exercise training before disease onset can alleviate the severity of cerebral ischemia.We also examined the role of exercise-induced circulating factors in these effects.Adult mice were subjected to 14 days of treadmill exercise training before surgery for middle cerebral artery occlusion.We found that this exercise pre-conditioning strategy effectively attenuated brain infarct area,inhibited gliogenesis,protected synaptic proteins,and improved novel object and spatial memory function.Further analysis showed that circulating adiponectin plays a critical role in these preventive effects of exercise.Agonist activation of adiponectin receptors by Adipo Ron mimicked the effects of exercise,while inhibiting receptor activation abolished the exercise effects.In summary,our results suggest a crucial role of circulating adiponectin in the effects of exercise pre-conditioning in protecting against cerebral ischemia and supporting the health benefits of exercise.

Recombinant chitinase-3-like protein 1 alleviates learning and memory impairments via M2 microglia polarization in postoperative cognitive dysfunction mice

Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life of patients.To date,there are no viable treatment options for postoperative cognitive dysfunction.The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research.To identify the signaling mechanisms contributing to postoperative cognitive dysfunction,we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset,which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus3 days after tibial fracture.The dataset was enriched in genes associated with the biological process"regulation of immune cells,"of which Chill was identified as a hub gene.Therefore,we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fractu re surgery.Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 124 hours post-surgery,and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests.In addition,protein expression levels of proinflammatory factors(interleukin-1βand inducible nitric oxide synthase),M2-type macrophage markers(CD206 and arginase-1),and cognition-related proteins(brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B)were measured in hippocampus by western blotting.Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment,downregulated interleukin-1βand nducible nitric oxide synthase expression,and upregulated CD206,arginase-1,pNR2B,and brain-derived neurotropic factor expression compared with vehicle treatment.Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1.Collectively,our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus.Therefore,recombinant chitinase-3-like protein1 may have therapeutic potential fo r postoperative cognitive dysfunction.

Regulator of G protein signaling 6 mediates exercise-induced recovery of hippocampal neurogenesis,learning,and memory in a mouse model of Alzheimer’s disease

Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.

A Survey of Phase Change Memory Systems

As the scaling of applications increases, the demand of main memory capacity increases in order to serve large working set. It is difficult for DRAM （dynamic random access memory） based memory system to satisfy the memory capacity requirement due to its limited scalability and high energy consumption. Compared to DRAM, PCM （phase change memory） has better scalability, lower energy leakage, and non-volatility. PCM memory systems have become a hot topic of academic and industrial research. However, PCM technology has the following three drawbacks： long write latency, limited write endurance, and high write energy, which raises challenges to its adoption in practice. This paper surveys architectural research work to optimize PCM memory systems. First, this paper introduces the background of PCM. Then, it surveys research efforts on PCM memory systems in performance optimization, lifetime improving, and energy saving in detail, respectively. This paper also compares and summarizes these techniques from multiple dimensions. Finally, it concludes these optimization techniques and discusses possible research directions of PCM memory systems in future.

MIMS:Towards a Message Interface Based Memory System

The decades-old synchronous memory bus interface has restricted many innovations in the memory system, which is facing various challenges （or walls） in the era of multi-core and big data. In this paper, we argue that a message- based interface should be adopted to replace the traditional bus-based interface in the memory system. A novel message interface based memory system called MIMS is proposed. The key innovation of MIMS is that processors communicate with the memory system through a universal and flexible message packet interface. Each message packet is allowed to encapsulate multiple memory requests （or commands） and additional semantic information. The memory system is more intelligent and active by equipping with a local buffer scheduler, which is responsible for processing packets, scheduling memory requests, preparing responses, and executing specific commands with the help of semantic information. Under the MIMS framework, many previous innovations on memory architecture as well as new optimization opportunities such as address compression and continuous requests combination can be naturally incorporated. The experimental results on a 16-core cycle-detailed simulation system show that： with accurate granularity message, MIMS can improve system performance by 53.21% and reduce energy delay product （EDP） by 55.90%. Furthermore, it can improve effective bandwidth utilization by 62.42% and reduce memory access latency by 51% on average.

A Novel Multivalued Associative Memory System

Globally coupled map (GCM) model can evolve through chaotic searching into several stable periodic orbits under properly controlled parameters. This can be exploited in information processing such as associative memory and optimization. In this paper, we propose a novel covariance learning rule for multivalue patterns and apply it in memorization of gray scale images based on modified GCM model (S GCM). Analysis of retrieval results are given finally.

A multimode-fused sensory memory system based on a robust self-assembly nanoscaffolded BaTiO_(3):Eu_(2)O_(3) memristor

Biologically inspired neuromorphic sensory memory systems based on memristor have received a lot of attention in the booming artificial intelligence industry due to significant potential to effectively process multi-sensory signals from complex external environments.However,many memristors have significant switching parameters disperse,which is a great challenge for using memristors in bionic neuromorphic sensory memory systems.Herein,a stable ferroelectric memristor based on the Pd/BaTiO_(3):Eu2O_(3)/La0.67Sr0.33MnO_(3)grown on Silicon structure with SrTiO_(3)as buffer layer is presented.The device possesses low coercive field voltage(-1.3-2.1 V)and robust endurance characteristic(~10^(10)cycles)through optimizing the growth temperature.More importantly,an ultra-stable artificial multimodal sensory memory system with visual and tactile functions was reported for the first time by combining a pressure sensor,a photosensitive sensor,and a robotic arm.Utilizing the above system,the sensitivity value of the system is expressed by the conductance of the memristor to realize the gradual change of external stimulus,and multi signals inputs at the same time to this system have faithfully achieved sensory adaptation to multimodal sensors.This work paves the way for future development of memristor-based perception systems in efficient multisensory neural robots.

Improving the Performance and Energy Efficiency of Phase Change Memory Systems

Phase change memory （PCM） is a promising technology for future memory thanks to its better scalability and lower leakage power than DRAM （dynamic random-access memory）. However, adopting PCM as main memory needs to overcome its write issues, such as long write latency and high write power. In this paper, we propose two techniques to improve the performance and energy-efficiency of PCM memory systems. First, we propose a victim cache technique utilizing the existing buffer in the memory controller to reduce PCM memory accesses. The key idea is reorganizing the buffer into a victim cache structure （RBC） to provide additional hits for the LLC （last level cache）. Second, we propose a chip parallelism-aware replacement policy （CPAR） for the victim cache to further improve performance. Instead of evicting one cache line once, CPAR evicts multiple cache lines that access different PCM chips. CPAR can reduce the frequent victim cache eviction and improve the write parallelism of PCM chips. The evaluation results show that, compared with the baseline, RBC can improve PCM memory system performance by up to 9.4% and 5.4% on average. Combing CPAR with RBC （RBC＋CPAR） can improve performance by up to 19.0% and 12.1% on average. Moreover, RBC and RBC＋CPAR can reduce memory energy consumption by 8.3% and 6.6% on average, respectively.

Superpage-Friendly Page Table Design for Hybrid Memory Systems

Page migration has long been adopted in hybrid memory systems comprising dynamic random access memory(DRAM)and non-volatile memories(NVMs),to improve the system performance and energy efficiency.However,page migration introduces some side effects,such as more translation lookaside buffer(TLB)misses,breaking memory contiguity,and extra memory accesses due to page table updating.In this paper,we propose superpagefriendly page table called SuperPT to reduce the performance overhead of serving TLB misses.By leveraging a virtual hashed page table and a hybrid DRAM allocator,SuperPT performs address translations in a flexible and efficient way while still remaining the contiguity within the migrated pages.

Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.

Global Memory Net and the development of digital image information management system: Experience and practice

Global Memory Net (GMNet) is intended to be an effective gateway to the world cultural, historical, and heritage image collections from selected academic educational and research partners in the world. Much of these unique collections of great value to education and research are not currently accessible due to distance, form, and technical barriers. This project is to find new ways to enable users to access and exploit these significant research collections via global network. As GMNet is ending its first 5-year phase in October 2005, it has contributed substantially to the community building in digital library development by ac- commodating numerous collaborators and technical staff from various parts of the world to spend 3 to 5 months as a full-member of the GMNet team in Boston. They have come from different parts of China—such as Sichuan, Hainan, Shanghai and Xi’an; Croatia; and Hanoi, Vietnam. In addition to contribute to the overall system development and enhancement of system function- alities, they have brought valuable sample image collections of their own institutions/countries, and actually developed prototype collections as a part of GMNet. This paper describes the exciting and productive experience of the first of this visiting research group in developing the GMNet’s Version 2.0 PHP-based system under Prof. Chen’s overall supervision. It also describes both the system’s technical level structure—user/Web-based application/data, and complex functionalities with multi-collection, multi-lingual, multi-modal searching capabilities; system management capabilities; as well as provisions for user uploads and retrieval for our own projects. This Version 2.0 system is built on the Linux/Apache/PHP/MySQL platform. What is described in this paper is an actual case which has formed a base for further new development by others in the research group. It demonstrates fully the value of the synergistic collaboration among global partners for universal digital library development. More information can be found in http://www.memorynet.org/.

Aware conflict detection of non-uniform memory access system and prevention for transactional memory

Most transactional memory (TM) research focused on multi-core processors, and others investigated at the clusters, leaving the area of non-uniform memory access (NUMA) system unexplored. The existing TM implementations made significant performance degradation on NUMA system because they ignored the slower remote memory access. To solve this problem, a latency-based conflict detection and a forecasting-based conflict prevention method were proposed. Using these techniques, the NUMA aware TM system was presented. By reducing the remote memory access and the abort rate of transaction, the experiment results show that the NUMA aware strategies present good practical TM performance on NUMA system.

Bio-inspired Actuating System for Swimming Using Shape Memory Alloy Composites

The paper addresses the designs of a caudal peduncle actuator, which is able to furnish a thrust for swimming of a robotic fish. The caudal peduncle actuator is based on concepts of ferromagnetic shape memory alloy （FSMA） composite and hybrid mechanism that can provide a fast response and a strong thrust. The caudal peduncle actuator was inspired by Scomber Scombrus which utilises thunniform mode swimming, which is the most efficient locomotion mode evolved in the aquatic environment, where the thrust is generated by the lift-based method, allowing high cruising speeds to be maintained for a long period of time. The morphology of an average size Scomber Scombrus （length in 310 mm） was investigated, and a 1：1 scale caudal peduncle actuator prototype was modelled and fabricated. The propulsive wave characteristics of the fish at steady speeds were employed as initial design objectives. Some key design parameters are investigated, i.e. aspect ratio （AR） （AR = 3.49）, Reynolds number （Re = 429 649）, reduced frequency （σ = 1.03）, Strouhal number （St = 0.306） and the maximum strain of the bent tail was estimated at ε = 1.11% which is in the range of superelasticity. The experimental test of the actuator was carried out in a water tank. By applying 7 V and 2.5 A, the actuator can reach the tip-to-tip rotational angle of 85° at 4 Hz.

A Holistic Energy-Efficient Approach for a Processor-Memory System

Component overclocking is an effective approach to speed up the components of a system to realize a higher program performance; it includes processor overclocking or memory overclocking. However, overclocking will unavoidably result in increase in power consumption. Our goal is to optimally improve the performance of scientific computing applications without increasing the total power consumption for a processor-memory system. We built a processor-memory energy efficiency model for multicore-based systems, which coordinates the performance and power of processor and memory. Our model exploits performance boost opportunities for a processor-memory system by adopting processor overclocking, processor Dynamic Voltage and Frequency Scaling(DVFS), memory active ratio adjustment, and memory overclocking, according to different scientific applications.This model also provides a total power control method by considering the same four factors mentioned above. We propose a processor and memory Coordination-based holistic Energy-Efficient(CEE) algorithm, which achieves performance improvement without increasing the total power consumption. The experimental results show that an average of 9.3% performance improvement was obtained for all 14 benchmarks. Meanwhile the total power consumption does not increase. The maximal performance improvement was up to 13.1% from dedup benchmark.Our experiments validate the effectiveness of our holistic energy-efficient model and technology.