Towards Transaction as a Service

This paper argues for decoupling transaction processing from existing two-layer cloud-native databases and making transaction processing as an independent service. By building a transaction as a service (TaaS) layer, the transaction processing can be independently scaled for high resource utilization and can be independently upgraded for development agility. Accordingly, we architect an execution-transaction-storage three-layer cloud-native database. By connecting to TaaS, 1) the AP engines can be empowered with ACID TP capability, 2) multiple standalone TP engine instances can be incorporated to support multi-master distributed TP for horizontal scalability, 3) multiple execution engines with different data models can be integrated to support multi-model transactions, and 4) high performance TP is achieved through extensive TaaS optimizations and consistent evolution. Cloud-native databases deserve better architecture: we believe that TaaS provides a path forward to better cloud-native databases

On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process

Wi-Fi fingerprinting becomes a dominant solution for large-scale indoor localization due to its major advantage of not requiring new infrastructure and dedicated devices. The number and the distribution of Reference Points (RPs) for the measurement of localization fingerprints like RSSI during the offline phase, however, greatly affects the localization accuracy; for instance, the UJIIndoorLoc is known to have the issue of uneven spatial distribution of RPs over buildings and floors. Data augmentation has been proposed as a feasible solution to not only improve the smaller number and the uneven distribution of RPs in the existing fingerprint databases but also reduce the labor and time costs of constructing new fingerprint databases. In this paper, we propose the multidimensional augmentation of fingerprint data for indoor localization in a large-scale building complex based on Multi-Output Gaussian Process (MOGP) and systematically investigate the impact of augmentation ratio as well as MOGP kernel functions and models with their hyperparameters on the performance of indoor localization using the UJIIndoorLoc database and the state-of-the-art neural network indoor localization model based on a hierarchical RNN. The investigation based on experimental results suggests that we can generate synthetic RSSI fingerprint data up to ten times the original data -- i.e., the augmentation ratio of 10 -- through the proposed multidimensional MOGP-based data augmentation without significantly affecting the indoor localization performance compared to that of the original data alone, which extends the spatial coverage of the combined RPs and thereby could improve the localization performance at the locations that are not part of the test dataset.Comment: 10 pages, 6 figures, under review for journal publicatio

Simulation Design of a Tomato Picking Manipulator

Simulation is an important way to verify the feasibility of design parameters and schemes for robots. Through simulation, this paper analyzes the effectiveness of the design parameters selected for a tomato picking manipulator, and verifies the rationality of the manipulator in motion planning for tomato picking. Firstly, the basic parameters and workspace of the manipulator were determined based on the environment of a tomato greenhouse; the workspace of the lightweight manipulator was proved as suitable for the picking operation through MATLAB simulation. Next, the maximum theoretical torque of each joint of the manipulator was solved through analysis, the joint motors were selected reasonably, and SolidWorks simulation was performed to demonstrate the rationality of the material selected for the manipulator and the strength design of the joint connectors. After that, the trajectory control requirements of the manipulator in picking operation were determined in view of the operation environment, and the feasibility of trajectory planning was confirmed with MATLAB. Finally, a motion control system was designed for the manipulator, according to the end trajectory control requirements, followed by the manufacturing of a prototype. The prototype experiment shows that the proposed lightweight tomato picking manipulator boasts good kinematics performance, and basically meets the requirements of tomato picking operation: the manipulator takes an average of 21 s to pick a tomato, and achieves a success rate of 78.67%

Mitochondrial Genome of an 8,400-Year-Old Individual from Northern China Reveals a Novel Sub-Clade under C5d

Ancient DNA studies have always refreshed our understanding of the human past that can’t be tracked by modern DNA alone. Until recently, ancient mitochondrial genomic studies in East Asia are still very limited. Here, we retrieved the whole mitochondrial genome of an 8,400-year- old individual from Inner Mongolia, China. Phylogenetic analyses show that the individual belongs to a previously undescribed clade under haplogroup C5d that was most probably originated in northern Asia and may have a very low frequency in extant populations that is not yet sampled. We further characterized the demographic history of mitochondrial haplogroups C5 and C5d, and found that C5 experienced a sharp increase in population size starting from around 4,000 years before present (BP). The time when intensive millet farming was built by populations who are associated with the lower Xiajiadian culture and was widely adopted in northern China. We caution that people related to haplogroup C5 may added this farming technology to their original way of life and that the various subsistence may provide abundant food sources and may further contribute to the increase of the population size

Immunomodulatory glycan LNFPIII alleviates hepatosteatosis and insulin resistance through direct and indirect control of metabolic pathways

Parasitic worms express host-like glycans to attenuate the immune response of human hosts. The therapeutic potential of this immunomodulatory mechanism in controlling metabolic dysfunction associated with chronic inflammation remains unexplored. We demonstrate here that administration of Lacto-N-fucopentaose III (LNFPIII), a LewisX containing immunomodulatory glycan found in human milk and on parasitic helminths, improves glucose tolerance and insulin sensitivity in diet-induced obese mice. This effect is mediated partly through increased Il-10 production by LNFPIII activated macrophages and dendritic cells, which reduces white adipose tissue inflammation and sensitizes the insulin response of adipocytes. Concurrently, LNFPIII treatment up-regulates nuclear receptor Fxr-α (or Nr1h4) to suppress lipogenesis in the liver, conferring protection against hepatosteatosis. At the signaling level, the extracellular signal-regulated kinase (Erk)-Ap1 pathway appears to mediate the effects of LNFPIII on both inflammatory and metabolic pathways. Our results suggest that LNFPIII may provide novel therapeutic approaches to treat metabolic diseases

Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks

In a wireless sensor network (WSN), reducing the energy consumption of battery-powered sensor nodes is key to extending their operating duration before battery replacement is required. Message bundling can save on the energy consumption of sensor nodes by reducing the number of message transmissions. However, bundling a large number of messages could increase not only the end-to-end delays and message transmission intervals, but also the packet error rate (PER). End-to-end delays are critical in delay-sensitive applications, such as factory monitoring and disaster prevention. Message transmission intervals affect time synchronization accuracy when bundling includes synchronization messages, while an increased PER results in more message retransmissions and, thereby, consumes more energy. To address these issues, this paper proposes an optimal message bundling scheme based on an objective function for the total energy consumption of a WSN, which also takes into account the effects of packet retransmissions and, thereby, strikes the optimal balance between the number of bundled messages and the number of retransmissions given a link quality. The proposed optimal bundling is formulated as an integer nonlinear programming problem and solved using a self-adaptive global-best harmony search (SGHS) algorithm. The experimental results, based on the Cooja emulator of Contiki-NG, demonstrate that the proposed optimal bundling scheme saves up to 51.8% and 8.8% of the total energy consumption with respect to the baseline of no bundling and the state-of-the-art integer linear programming model, respectively

GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark