Software Hint-Driven Data Management for Hybrid Memory in Mobile Systems

Hybrid memory systems, comprised of emerging non-volatile memory (NVM) and DRAM, have been proposed to address the growing memory demand of current mobile applications. Recently emerging NVM technologies, such as phase-change memories (PCM), memristor, and 3D XPoint, have higher capacity density, minimal static power consumption and lower cost per GB. However, NVM has longer access latency and limited write endurance as opposed to DRAM. The different characteristics of distinct memory classes render a new challenge for memory system design. Ideally, pages should be placed or migrated between the two types of memories according to the data objects’ access properties. Prior system software approaches exploit the program information from OS but at the cost of high software latency incurred by related kernel processes. Hardware approaches can avoid these latencies, however, hardware’s vision is constrained to a short time window of recent memory requests, due to the limited on-chip resources. In this work, we propose OpenMem: a hardware-software cooperative approach that combines the execution time advantages of pure hardware approaches with the data object properties in a global scope. First, we built a hardware-based memory manager unit (HMMU) that can learn the short-term access patterns by online profiling, and execute data migration efficiently. Then, we built a heap memory manager for the heterogeneous memory systems that allows the programmer to directly customize each data object’s allocation to a favorable memory device within the presumed object life cycle. With the programmer’s hints guiding the data placement at allocation time, data objects with similar properties will be congregated to reduce unnecessary page migrations. We implemented the whole system on the FPGA board with embedded ARM processors. In testing under a set of benchmark applications from SPEC 2017 and PARSEC, experimental results show that OpenMem reduces 44.6% energy consumption with only a 16% performance degradation compared to the all-DRAM memory system. The amount of writes to the NVM is reduced by 14% versus the HMMU-only, extending the NVM device lifetime.

High-scale random access on DNA storage systems

ABSTRACT Due to the rapid cost decline of synthesizing and sequencing deoxyribonucleic acid (DNA), high information density, and its durability of up to centuries, utilizing DNA as an information storage medium has received the attention of many scientists. State-of-the-art DNA storage systems exploit the high capacity of DNA and enable random access (predominantly random reads) by primers, which serve as unique identifiers for directly accessing data. However, primers come with a significant limitation regarding the maximum available number per DNA library. The number of different primers within a library is typically very small (e.g. ≈10). We propose a method to overcome this deficiency and present a general-purpose technique for addressing and directly accessing thousands to potentially millions of different data objects within the same DNA pool. Our approach utilizes a fountain code, sophisticated probe design, and microarray technologies. A key component is locality-sensitive hashing, making checks for dissimilarity among such a large number of probes and data objects feasible.

An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis

Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.

MI3: Machine-initiated Intelligent Interaction for Interactive Classification and Data Reconstruction

In many applications, while machine learning (ML) can be used to derive algorithmic models to aid decision processes, it is often difficult to learn a precise model when the number of similar data points is limited. One example of such applications is data reconstruction from historical visualizations, many of which encode precious data, but their numerical records are lost. On the one hand, there is not enough similar data for training an ML model. On the other hand, manual reconstruction of the data is both tedious and arduous. Hence, a desirable approach is to train an ML model dynamically using interactive classification, and hopefully, after some training, the model can complete the data reconstruction tasks with less human interference. For this approach to be effective, the number of annotated data objects used for training the ML model should be as small as possible, while the number of data objects to be reconstructed automatically should be as large as possible. In this article, we present a novel technique for the machine to initiate intelligent interactions to reduce the user’s interaction cost in interactive classification tasks. The technique of machine-initiated intelligent interaction (MI3) builds on a generic framework featuring active sampling and default labeling. To demonstrate the MI3 approach, we use the well-known cholera map visualization by John Snow as an example, as it features three instances of MI3 pipelines. The experiment has confirmed the merits of the MI3 approach.

Tekstualisme Dan Kontekstualisme Penafsiran Kontemporer Terhadap Surah Al-Maidah Ayat 38

Not only in the interpretation of classical scholars, the discourses on the interpretation of contemporary scholars are also diverse and often contradictory even though they are based on the same textual basis of the Qur'anic verse. This study intends to identify trends in the interpretation of contemporary scholars regarding the legal sanctions for cutting hands in al-Maidah verse 38. Two figures are studied, namely Ibn 'Asyur and Muhammad Syahrur. The main data objects of this research, namely the book (kitab) entitled al-Tahrîr wa al-Tanwîr by Muhammad Tahir Ibn 'Asyur and al-Kitâb wa al-Qur'ân Qirâ'ah Mu'âsirah by Muhammad Syahrur, and. The research approach used is a philosophical normative approach. The analytical theory used is the typology of textualism and contextualism of interpretation which was coined by Abdullah Saeed. Meanwhile, the nature of the research approach is descriptive-analytic. The results of the study conclude that the interpretation of Ibn 'Asyur regarding al-Ma'idah verse 38 can be categorized as a textual interpretation. This can be seen from his interpretation of the literal meaning of the verse. In addition, Ibn 'Asyur also tends to view the punishment of cutting off hands for thieves to be a deterrent as well as a preventive measure. In contrast to Ibn 'Asyur, Muhammad Syahrur's interpretation of the legal case of cutting off hands for thieves includes contextual interpretation. This can be seen when he understands the verse of cutting off hands for thieves, he gives a meaning that gives space for ijtihad for an area and conditions to enforce punishments that have a deterrent effect, provided that it must not exceed the punishment of cutting off handsas the maximum limit.

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

With the advantage of faster data access than traditional disks, in-memory database systems, such as Redis and Memcached, have been widely applied in data centers and embedded systems. The performance of in-memory database greatly depends on the access speed of memory. With the requirement of high bandwidth and low energy, die-stacked memory (e.g., High Bandwidth Memory (HBM)) has been developed to extend the channel number and width. However, the capacity of die-stacked memory is limited due to the interposer challenge. Thus, hybrid memory system with traditional Dynamic Random Access Memory (DRAM) and die-stacked memory emerges. Existing works have proposed to place and manage data on hybrid memory architecture in the view of hardware. This paper considers to manage in-memory database data in hybrid memory in the view of application. We first perform a preliminary study on the hotness distribution of client requests on Redis. From the results, we observe that most requests happen on a small portion of data objects in in-memory database. Then, we propose the Application-oriented Data Migration called ADM to accelerate in-memory database on hybrid memory. We design a hotness management method and two migration policies to migrate data into or out of HBM. We take Redis under comprehensive benchmarks as a case study for the proposed method. Through the experimental results, it is verified that our proposed method can effectively gain performance improvement and reduce energy consumption compared with existing Redis database.

Organizations Decentered: Data Objects, Technology and Knowledge

Data are no longer simply a component of administrative and managerial work but a pervasive resource and medium through which organizations come to know and act upon the contingencies they confront. We theorize how the ongoing technological developments reinforce the traditional functions of data as instruments of management and control but also reframe and extend their role. By rendering data as technical entities, digital technologies transform the process of knowing and the knowledge functions data fulfil in socioeconomic life. These functions are most of the times mediated by putting together disperse and steadily updatable data in more stable entities we refer to as data objects. Users, customers, products, and physical machines rendered as data objects become the technical and cognitive means through which organizational knowledge, patterns, and practices develop. Such conditions loosen the dependence of data from domain knowledge, reorder the relative significance of internal versus external references in organizations, and contribute to a paradigmatic contemporary development that we identify with the decentering of organizations of which digital platforms are an important specimen.

R code and downstream analysis objects for the scRNA-seq atlas of human breast spanning normal, preneoplastic and tumorigenic states

Breast cancer is a common and highly heterogeneous disease. Understanding the cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into the cancer development in human breast. Recently, a large-scale single-cell RNA expression atlas was constructed of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Details of all the bioinformatic analyses that produced the results described in the study are provided.

ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability

The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.