Data Memory Research Articles

Latency and quality-aware task offloading in multi-node next generation RANs

Next-Generation Radio Access Network (NG-RAN) is an emerging paradigm that provides flexible distribution of cloud computing and radio capabilities at the edge of the wireless Radio Access Points (RAPs). Computation at the edge bridges the gap for roaming end users, enabling access to rich services and applications. In this paper, we propose a multi-edge node task offloading system, i.e., QLRan, a novel optimization solution for latency and quality tradeoff task allocation in NG-RANs. Considering constraints on service latency, quality loss, edge capacity, and task assignment, the problem of joint task offloading, latency, and Quality Loss of Result (QLR) is formulated in order to minimize the User Equipment (UEs) task offloading utility, which is measured by a weighted sum of reductions in task completion time and QLR cost. The QLRan optimization problem is proved as a Mixed Integer Nonlinear Program (MINLP) problem, which is a NP-hard problem. To efficiently solve the QLRan optimization problem, we utilize Linear Programming (LP)-based approach that can be later solved by using convex optimization techniques. Additionally, a programmable NG-RAN testbed is presented where the Central Unit (CU), Distributed Unit (DU), and UE are realized by USRP boards and fully container-based virtualization approaches. Specifically, we use OpenAirInterface (OAI) and Docker software platforms to deploy and perform the NG-RAN testbed for different functional split options. Then, we characterize the performance in terms of data input, memory usage, and average processing time with respect to QLR levels. Simulation results show that our algorithm performs significantly improves the network latency over different configurations.

A systematic review of the application of a diary for people with dementia: how to maximise the effect of diary use?

Diaries have been widely used among people with dementia as a data collection tool and memory aid. An understanding of their usage is conducive to the subsequent development of intervention measures, but comprehensive evidence is still lacking. This review synthesises the existing literature to obtain an in-depth understanding of the positive and negative effects of diary usage among people with dementia and the influencing factors. The data were collected from eight databases from the inception of each database to November 2020. A thematic synthesis method was used to sort and summarise the included studies. Ten studies were chosen for the review. The following three analytical themes were identified: (i) positive changes; (ii) factors affecting the record; and (iii) negative feelings. Diaries can cause positive changes in people with dementia, but the specific patterns of stimulation and changes over time remain to be explored. Furthermore, researchers should encourage patients and their families to have a correct understanding of the diary, improve their motivation and social support, fully use the positive effects of the diary, and reduce the occurrence of negative effects. Medical staff should help caregivers improve their awareness of the usefulness and ease of the diary and encourage them to provide support and companionship to people with dementia while exhibiting a positive attitude.

Amyloid‐β accumulation is independently related to executive function in cognitively unimpaired adults

AbstractBackgroundThe shift from a symptom‐based to a biological definition of Alzheimer’s disease (AD), within the NIA‐AA research framework, has highlighted the importance of biomarkers that are indicative of the neuropathological changes underlying AD. The independent associations of amyloid (Aβ), tau, and neurodegeneration with different cognitive domains in cognitively unimpaired (CU) need to be studied in deeply phenotyped cohorts. In the current study we investigated how the distinctive pathologies and neurodegeneration of AD, according to the AT(N) system, are related to cognition in four cognitive domains in CU adults.MethodWe included 316 CU individuals from the prospective BioFINDER‐2 study. The results were validated in CU participants from the ADNI study (n=376). All cases had CSF biomarkers (Aβ42 and P‐tau181), MRI (cortical thickness of AD‐susceptible regions), Aβ‐PET (neocortical uptake) and tau‐PET (entorhinal cortical uptake) as well as neuropsychological test data (including tests for i) memory, ii) executive function, iii) verbal function, iv) visuospatial function). Multivariable linear regression models, adjusted for age, sex and education, were performed, first using CSF Aβ42, P‐tau181 and cortical thickness and secondly using Aβ‐PET, tau‐PET and cortical thickness.ResultIn BioFINDER‐2, abnormal Aβ‐status was independently associated with executive function, regardless of biomarker (CSF Aβ42 β=0.128, p=0.024; Aβ‐PET β=0.124, p=0.049), but not memory or any other cognitive domains. Tau, on the other hand, was independently related to memory (CSF P‐tau181 β=0.132, p=0.018; tau‐PET β=0.189, p=0.002). Cortical thickness was independently associated with executive function (CSF‐based model, β= ‐0.161, p=0.013; PET‐based model, β= ‐0.186, p=0.005) and verbal function (CSF‐based model, β= 0.163, p=0.011; PET‐based model, β= 0.156, p=0.018). In a second step, only participants without evidence of tau pathology or neurodegeneration were included, again resulting in an association between Aβ and executive function (CSF Aβ42, β=0.189, p=0.005; Aβ‐PET, β=0.146, p=0.023), but not with other cognitive domains. Results were overall replicated in the ADNI cohort.ConclusionThese findings suggest an independent relationship between early Aβ accumulation and worse executive function in CU individuals while tau is associated with memory. This finding contributes to our understanding of how cognitive deficits are manifested in early AD, especially in regard to Aβ accumulation.

Development of High-Speed Image Acquisition and Processing System for Real-Time Plasma Control on EAST

In order to realize advanced plasma control based on visible cameras, a high-speed image acquisition and processing system has been developed recently on Experimental Advanced Superconducting Tokamak (EAST). This system is optimized in many ways to achieve high-speed, real-time, low-latency performance, and to load multiple acquisition cards simultaneously. The acquisition rate of this system can be close to 10 000 FPS when the frame size is set as 320 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times240$ </tex-math></inline-formula> and the pixel depth is set as 8 bits. DMA is used for high-speed data transmission; the memory copy function is optimized for reducing the time cost on data memory reading and writing. Besides, the visualization subsystem based on the Python web can communicate with the acquisition machines and also can synthesize data of multiple acquisition machines in real time to perform image fusion and access display. In addition, a thermal event recognition function based on visible imaging is also included in this system. The convolutional neural network (CNN) model of hot spots and multifaceted asymmetric radiation from the edge (MARFE) detection for EAST plasma discharge has been developed so far. The detection results can be visualized in quasi-real time. In terms of data storage, a new data storage format is designed; a GUI for off-line data analysis and processing based on MATLAB is provided.

Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

The MapReduce framework manages Big Data sets by splitting the large datasets into a set of distributed blocks and processes them in parallel. Data compression and in-memory file systems are widely used methods in Big Data processing to reduce resource-intensive I/O operations and improve I/O rate correspondingly. The article presents a performance-efficient modular and configurable decision-making robust service relying on data compression and in-memory data storage indicators. The service consists of Recommendation and Prediction modules, predicts the execution time of a given job based on metrics, and recommends the best configuration parameters to improve Hadoop and Spark frameworks' performance. Several CPU and data-intensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means.

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

Efficient Group K Nearest-Neighbor Spatial Query Processing in Apache Spark

Aiming at the problem of spatial query processing in distributed computing systems, the design and implementation of new distributed spatial query algorithms is a current challenge. Apache Spark is a memory-based framework suitable for real-time and batch processing. Spark-based systems allow users to work on distributed in-memory data, without worrying about the data distribution mechanism and fault-tolerance. Given two datasets of points (called Query and Training), the group K nearest-neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum of distances to every point of the Query. This spatial query has been actively studied in centralized environments and several performance improving techniques and pruning heuristics have been also proposed, while, a distributed algorithm in Apache Hadoop was recently proposed by our team. Since, in general, Apache Hadoop exhibits lower performance than Spark, in this paper, we present the first distributed GKNN query algorithm in Apache Spark and compare it against the one in Apache Hadoop. This algorithm incorporates programming features and facilities that are specific to Apache Spark. Moreover, techniques that improve performance and are applicable in Apache Spark are also incorporated. The results of an extensive set of experiments with real-world spatial datasets are presented, demonstrating that our Apache Spark GKNN solution, with its improvements, is efficient and a clear winner in comparison to processing this query in Apache Hadoop.

Simpler and Faster Development of Tumor Phylogeny Pipelines.

In the recent years, there has been an increasing amount of single-cell sequencing studies, producing a considerable number of new data sets. This has particularly affected the field of cancer analysis, where more and more articles are published using this sequencing technique that allows for capturing more detailed information regarding the specific genetic mutations on each individually sampled cell. As the amount of information increases, it is necessary to have more sophisticated and rapid tools for analyzing the samples. To this goal, we developed plastic (PipeLine Amalgamating Single-cell Tree Inference Components), an easy-to-use and quick to adapt pipeline that integrates three different steps: (1) to simplify the input data, (2) to infer tumor phylogenies, and (3) to compare the phylogenies. We have created a pipeline submodule for each of those steps and developed new in-memory data structures that allow for easy and transparent sharing of the information across the tools implementing the above steps. While we use existing open source tools for those steps, we have extended the tool used for simplifying the input data, incorporating two machine learning procedures-which greatly reduce the running time without affecting the quality of the downstream analysis. Moreover, we have introduced the capability of producing some plots to quickly visualize results.

Teaching Programming Through The "Modular Arithmetic" Math Project

Project-based approach was integrated into the Computer science programme at New Bulgarian University to enhance the learning process for first year students. A multidisciplinary project based on main concepts of Programming and Discrete mathematics was designed and developed. The concepts and relations between them were presented by concepts maps and the tasks were ordered according to revised Bloom's taxonomy. So it became possible to evaluate the cognitive learning levels of the students from simple remembering of information to analyzing and creating, to improve the self-learning skills, the ability to search for new information, working in teams and communication skills. The students had the opportunity to apply algorithms related to different data structures, memory and time optimisation that are used in the business and to integrate different programming technologies in a project.

Early objective adherence to hypoglossal nerve stimulation therapy.

To assess early adherence to therapy with hypoglossal nerve stimulation therapy. This is a prospective study of consecutive patients with moderate to severe obstructive sleep apnea who underwent implantation of hypoglossal nerve stimulation therapy within a single academic practice and attended a follow-up appointment after greater than 30 days of therapy use. Objective adherence data were extracted from an objective monitoring database and compared to patient characteristics. The study population was 79 participants who were 29.1% female with a mean age of 58.7 ± 12.8 years old, body mass index of 28.9 ± 3.4 kg/m2, and baseline apnea-hypopnea index of 33.8 ± 17.6 events/h. In the first 7 days after device activation, average use was 7.8 h/night, with 91.9% of nights with greater than 4 hours of therapy use and an average of 0.2 pauses in therapy per night. These figures remained stable after 30 days of use: 7.7 h/night, 91.0% of nights longer than 4 hours, and 0.3 pauses per night. Objective evidence of difficulty with acclimatization was associated with age less than 60 years (odds ratio 2.8, 95% confidence interval 1.1-7.1, P = .03) and a history of prior upper airway surgery (3.9, 1.2-11.9, P = .015). Insomnia was present in 31 patients and was not associated with objective evidence of difficulty tolerating therapy. Early adherence to hypoglossal nerve stimulation is excellent (92.4% >4 hours on >70% of nights), suggesting that the acclimatization period is straightforward in most. Younger age and a history of prior upper airway surgery appear to be associated with an increased risk of difficulty with acclimatization. Huyett P. Early objective adherence to hypoglossal nerve stimulation therapy. J Clin Sleep Med. 2022;18(2):631-636.

Effects of Interfacial Termination, Oxidation, and Film Thickness on the Magnetic Anisotropy in Mn2.25Co0.75Ga0.5Sn0.5/MgO Heterostructures.

Perpendicular magnetic anisotropy (PMA) is a determining factor for the realization of nonvolatile information storage devices with high efficiency and thermal stability. In this work, a new spin gapless semiconductor Mn2.25Co0.75Ga0.5Sn0.5 Heusler alloy with an inter-spin zero gap was first designed theoretically. The Mn2.25Co0.75Ga0.5Sn0.5 bulk was prepared successfully in experiment. The effects of interfacial termination, oxidation, and film thickness on the magnetic anisotropy of Mn2.25Co0.75Ga0.5Sn0.5/MgO (MCGS/MgO) heterostructures are investigated systematically by first-principles calculations. The results show that all the Mn(A)Mn(C)GaSn-, Mn(A)Mn(C)CoGaSn-, Mn(B)GaSnI-, and Mn(B)GaSnII-terminated MCGS/MgO heterostructures (called as AC1, AC2, BD1, and BD2 models, respectively) present PMA, which mainly derives from the interfacial and surficial MCGS layers. Furthermore, the PMA of MCGS/MgO heterostructures can be preserved in a large range of interfacial oxidization (up to ±50%). With MCGS thickness increasing from 5 to 16 monolayers, the PMA of MCGS/MgO heterostructures with an AC-type surface decreases significantly. However, the PMA of BD-type surface models is relatively robust to the thickness of the MCGS layer, and the magnetic anisotropy always points to the out-of-plane direction. Therefore, MCGS Heusler alloy is a new promising spin gapless semiconductor candidate for spintronics applications. The robust and tunable PMA in MCGS/MgO heterostructures offers the possibility for developing nonvolatile data memory devices.

VC@Scale: Scalable and high-performance variant calling on cluster environments.

BackgroundRecently many new deep learning–based variant-calling methods like DeepVariant have emerged as more accurate compared with conventional variant-calling algorithms such as GATK HaplotypeCaller, Sterlka2, and Freebayes albeit at higher computational costs. Therefore, there is a need for more scalable and higher performance workflows of these deep learning methods. Almost all existing cluster-scaled variant-calling workflows that use Apache Spark/Hadoop as big data frameworks loosely integrate existing single-node pre-processing and variant-calling applications. Using Apache Spark just for distributing/scheduling data among loosely coupled applications or using I/O-based storage for storing the output of intermediate applications does not exploit the full benefit of Apache Spark in-memory processing. To achieve this, we propose a native Spark-based workflow that uses Python and Apache Arrow to enable efficient transfer of data between different workflow stages. This benefits from the ease of programmability of Python and the high efficiency of Arrow’s columnar in-memory data transformations.ResultsHere we present a scalable, parallel, and efficient implementation of next-generation sequencing data pre-processing and variant-calling workflows. Our design tightly integrates most pre-processing workflow stages, using Spark built-in functions to sort reads by coordinates and mark duplicates efficiently. Our approach outperforms state-of-the-art implementations by >2 times for the pre-processing stages, creating a scalable and high-performance solution for DeepVariant for both CPU-only and CPU + GPU clusters.ConclusionsWe show the feasibility and easy scalability of our approach to achieve high performance and efficient resource utilization for variant-calling analysis on high-performance computing clusters using the standardized Apache Arrow data representations. All codes, scripts, and configurations used to run our implementations are publicly available and open sourced; see https://github.com/abs-tudelft/variant-calling-at-scale.

Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems.

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations, and agent-based models—reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first-passage time distributions. Research has lead us to question whether the observed long-range memory is a result of the actual long-range memory process or just a consequence of the non-linearity of Markov processes. As our most recent result, we discuss the long-range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the auto-regressive fractionally integrated moving average (ARFIMA) sample series. Our newly obtained results seem to indicate that new estimators of self-similarity and long-range memory for analyzing systems with non-Gaussian distributions have to be developed.

Tunable multistate data storage device based on silkworm hemolymph and graphene oxide

A multistate data memory device can store more than one bit in each cell. Such devices have broad application prospects because they provide a simple and low-cost way to improve memory capacity. As a biomaterial, silkworm hemolymph (SH) has the advantages of being easily accessible, biodegradable and biocompatible. In this study, performance-tunable multistate data storage devices with an indium tin oxide (ITO)/graphene oxide (GO)/SH/GO/Al structure were fabricated with SH and GO as dielectric layers. The results show that the proposed device structure has stable nonvolatile and bipolar switching characteristics. The conductivity and ON/OFF current ratio (1–103) of such a device can be adjusted by modifying the GO concentration. With appropriate control of the compliance current (ICC), the device can be switched to six different resistance states, enabling multistate data storage capabilities and allowing high-density data storage to be realized. These devices have a long retention time (104 s) and extended endurance. Their switching behavior is attributed to the formation and fracture of oxygen vacancy conductive filaments. This research introduces a new approach to the development of biocompatible high-density memory devices.

Analysis of Skim Reading on Desktop versus Mobile Screen

Web readers usually skim through the text to keep up with the amount of available content. The effectiveness of skim reading is ensured by keeping the focus on the meaningful part of the text rather than the less meaningful part. To assure if the skim reading shows efficient results for a particular screen resolution, this research presents variations in the memory of significant data when a text is read on a mobile screen or a desktop screen. Moreover, the study aims to understand the knowledge gained from the text at a given time. In total, sixty participants contributed to the study and it is found that, relative to reading the text on a mobile screen, skimming showed improved memory for the ideas defined in the text on a computer screen. A software prototype is developed in this research study to analyze the impact of skim reading on a desktop computer screen versus a mobile device screen. The findings of the study have been interpreted as evidence in support of a skimming process called satisficing.

Analysis of Grain Boundary Dependent Memory Characteristics in Poly-Si One-Transistor Dynamic Random-Access Memory.

A capacitorless one-transistor dynamic random-access memory cell with a polysilicon body (poly-Si 1T-DRAM) has a cost-effective fabrication process and allows a three-dimensional stacked architecture that increases the integration density of memory cells. Also, since this device uses grain boundaries (GBs) as a storage region, it can be operated as a memory cell even in a thin body device. GBs are important to the memory characteristics of poly-Si 1T-DRAM because the amount of trapped charge in the GBs determines the memory's data state. In this paper, we report on a statistical analysis of the memory characteristics of poly-Si 1T-DRAM cells according to the number and location of GBs using TCAD simulation. As the number of GBs increases, the sensing margin and retention time of memory cells deteriorate due to increasing trapped electron charge. Also, "0" state current increases and memory performance degrades in cells where all GBs are adjacent to the source or drain junction side in a strong electric field. These results mean that in poly-Si 1T-DRAM design, the number and location of GBs in a channel should be considered for optimal memory performance.

Dynamic mode decomposition of magnetohydrodynamic bubble chain flow in a rectangular vessel

We demonstrate the first application of dynamic mode decomposition (DMD) to bubble flow with resolved dynamic liquid/gas boundaries. Specifically, we have applied DMD to the output of numerical simulations for a system where chains of bubbles ascend through a rectangular liquid metal vessel. Flow patterns have been investigated in the vessel and bubble reference frames. We show how gas flow rate and applied magnetic affect bubble wake flow and larger-scale flow structures within the liquid metal vessel by examining the velocity field mode statistics over trajectory time and total flow time as well as the computed mode velocity fields. The results of this proof-of-concept study indicate that DMD can yield unique insights into various momentum transfer and bubble interaction mechanisms, and that mode analysis can be used to explain the observed flow patterns. In addition, we showcase our own implementation of DMD that combines resilience to data noise, memory efficiency and special pre-processing for input data.

Room temperature tuning of non volatile magnetoelectric memory in Al doped Sr3Co2Fe24O41

Study of magnetoelectric coupling in Type II multiferroics is indeed a fascinating area of research in next-generation nonvolatile data memory devices. To facilitate memory device applications, it is necessary to find a single phase material with substantial magnetoelectric coupling at room temperature. A room temperature nonvolatile memory device in aluminium doped Sr3Co2Fe24O41 is demonstrated here. Aluminium doping in Sr3Co2Fe24O41 not only enhances magnetoelectric coupling but also allows for easy tuning of memory states in the device. Maximum Magnetoelectric coefficient (α) of 59.44 mV/cm-Oe is observed for x = 0.04 in Sr3Co2(Fe1-xAlx)24O41. α could be stored and maintained for memory use for a broad period of time; additionally, the memory states of the device could be switched and tuned with electric pulses, which is an additional benefit of the memory device. This kind of magnetoelectric memory can be beneficial due to its non-destructive read operations, which is the main challenge in FERAM. This conveniently integrable single phase, robust memory system with increased memory density will undoubtedly pave the way for future memory applications.

Cerebral blood flow response rate to task-activation using a novel method can discriminate cognitive impairment from healthy aging

Objective. A new method to classify individuals as ‘responders’ to task-activated cerebral blood flow velocity (CBFv) has recently been developed. This study investigated whether CBFv response rate to task-activation is affected by Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Approach. The 95th thresholds for cross correlation function peak and variance ratio were derived from 270 unstimulated, healthy hemispheres, and were used to classify the presence of a response to task-activation. Thresholds were applied to five cognitive tasks (attention, verbal fluency, language, visuospatial, memory) in CBFv data from 30 healthy older adults (HC), 35 AD and 22 MCI participants. Cumulative response rate (CRR) was calculated from the sum of responses across five tasks, for both hemispheres. Area under the curve (AUC) was derived from receiver operating characteristic (ROC) curve analysis. Main results. The number of responders differed significantly between tasks (p < 0.005) and diagnostic groups (p = 0.011). On post hoc tests there were more responders in the visuospatial (79%–90%) compared to fluency (45%–80%), language (50%–77%), and memory (44%–70%) tasks bilaterally, and responders were greater in the HC (70%) compared to AD (41%) and MCI (23%) groups to at least eight out of ten tasks. At an optimal threshold of 7.5 out of 10 responses, the AUC-ROC distinguished HC from AD and MCI with a; sensitivity 66% and specificity 70% (AUC = 0.72). Significance. Using a novel method to classify responders to cognitive task-activation, HC demonstrated a higher CRR than those with MCI or AD, and a threshold of <8 responses distinguished healthy ageing from dementia.

Hazelcast jet

Jet is an open source, high performance, distributed stream processor built at Hazelcast during the last five years. Jet was engineered with millisecond latency on the 99.99th percentile as its primary design goal. Originally Jet's purpose was to be an execution engine that performs complex business logic on top of streams generated by Hazelcast's In-memory Data Grid (IMDG): a set of in-memory, partitioned and replicated data structures. With time, Jet evolved into a full-fledged, scale-out stream processor that can handle out-of-order streams and provide exactly-once processing guarantees. Jet's end-to-end latency lies in the order of milliseconds, and its throughput in the order of millions of events per CPU-core. This paper presents the main design decisions we made in order to maximize the performance per CPU-core, alongside lessons learned, and an empirical performance evaluation.