A Selection Theory and Methodology for Heterogeneous Supercomputing

New Technologies, Tools and Approaches for Improving Crop Breeding

Pooling idle computing resources from stationary and computationally powerful vehicles at a nearby parking lot is one way to meet increasing computation demands from mobile devices at the network's edge. This new computing paradigm is called vehicular cloud (VC). Despite the research in performance modeling of VC having gained increasing interest in recent years, a tractable solution for job completion time under concurrent task execution has yet to be seen. Thus, this work investigates the computation capabilities of a VC by analytically determining job completion time when the VC simultaneously executes several tasks. Under several stochastics assumptions, we derive the probability density functions of completion times of the tasks, which also include the job completion time and their first moments. Finally, we present the numerical results for the analysis and the simulation results to show the correctness of the work.

: Our recent work on the modeling human-like multi-task behaviors has led to unexpected insights into a possible mechanism contributing to individual differences in human performance. We begin by outlining our modeled conflict resolution strategy for the graceful sharing of perceptual, cognitive, and motor resources among multiple in-process tasks. We then discuss how that same mechanism might drive individual differences in concurrent task execution. The modeled strategy, in this case operating on subtask structure, may be one contributor to how individual operators differentially decompose and interleave concurrent tasks. We further suggest that the same mechanisms and structures might evolve over time as an operator learns to concurrently engage multiple related tasks. Theory building for multi-task behaviors as explored through model development has opened a new line of inquiry into potential mechanisms for individual differences in performance. Our recent work on the modeling human-like multi-task behaviors has led to unexpected insights into a possible mechanism contributing to individual differences in human performance. We begin by outlining our modeled conflict resolution strategy for the graceful sharing of perceptual, cognitive, and motor resources among multiple in-process tasks. We then discuss how that same mechanism might drive individual differences in concurrent task execution. The modeled strategy, in this case operating on subtask structure, may be one contributor to how individual operators differentially decompose and interleave concurrent tasks. We further suggest that the same mechanisms and structures might evolve over time as an operator learns to concurrently engage multiple related tasks. Theory building for multi-task behaviors as explored through model development has opened a new line of inquiry into potential mechanisms for individual differences in performance.

Emergent relations and competing behavior: Effects on interruptions, accuracy and reaction time.

Concurrency control allows multiple tasks that share data objects to be concurrently executed in a serializable order, thus significantly improving computation progress. However, to accumulate forward progress on energy-harvesting intermittent systems while achieving data consistency across power cycles, existing approaches based on the checkpointing paradigm typically require system suspension at runtime. The runtime overheads incurred by suspension will be more manifest when more tasks are suspended and resumed during checkpointing, offsetting the computation progress improved by concurrent task execution. This paper presents a multiversion concurrency control design, which enables concurrent task execution without system suspension during checkpointing, while maintaining the serializability of task execution and ensuring data consistency after system recovery. We integrated our design into FreeRTOS running on a Texas Instruments device. Experimental results show that, at the very best, our design can double computation progress by reducing the runtime overheads incurred by system checkpointing, especially when tasks are executed with high concurrency.

Mobile Edge Computing (MEC) is a promising solution to enhance the computing capability of resource-limited networks. A fundamental problem in MEC is efficiently offloading tasks from user devices to edge servers. However, there still exists a gap to deploy in real-world environments: 1) traditional centralized approaches needs complete information of edge network, ignoring the communication costs generated by synchronization, 2) previous works do not consider concurrent computation on edge servers, which may cause dynamic changes in the environment, and 3) the scheduling algorithm should deliver individualized decisions for different users independently and with high efficiency To solve this mismatch, we studied a multi-user task offloading problem where user devices make offloading decisions independently. We consider the concurrent execution of tasks and formulate a non-divisible and delay-aware task offloading problem to jointly minimize the dropped task ratio and long-term latency. We propose a decentralized task scheduling algorithm based on DRL that makes offloading decisions without knowing the information of other user devices. We employ Double-DQN, Dueling-DQN, Prioritized Replay Memory, and Recurrent Neural Network (RNN) techniques to improve the algorithm's performance. The results of simulation experiments show that our method can significantly reduce the long-term latency and dropped task ratio compared to the baseline algorithms.

The evolution and advancements in Information and Communication Technologies (ICT) have enabled large scale distributed computing with a huge chunk of applications for massive number of users. This has obviously generated large volumes of data, thus severely burdening the processing capacity of computers as well as the inflexible traditional networks. State-of-the-art methods for addressing datacenter level performance fixes are yet found wanting for sufficiently addressing this huge processing, storage, and network movement with proprietary protocols for this voluminous data. In this chapter the works have focused on addressing the backend server performance through effective reducer placement, intelligent compression policy, handling slower tasks and in-network performance boosting techniques through effective traffic engineering, traffic classification, topology discovery, energy minimization and load balancing in datacenter-oriented applications. Hadoop, the defacto standard in distributed big data storage and processing, has been designed to store data with its Hadoop Distributed File System (HDFS) and processing engine MapReduce large datasets reliably. However, the processing performance of Hadoop is critically dependent on the time taken to transfer data during the shuffle generated during MapReduce. Also, during concurrent execution of tasks, slower tasks need to be properly identified and efficiently handled to improve the completion time of jobs. To overcome these limitations, three contributions have been made; (i) Compression of generated map outputs at a suitable time when all the map tasks are yet to be completed to shift the load of network onto the CPU; (ii) Placing the reducer onto the nodes where the computation done is highest based on a couple of counters, one maintained at the rack level and another at node level, to minimize the run-time data copying; and (iii) Placing the slower map tasks onto the nodes where the computation done is highest and network is handled by prioritizing. Software defined networking (SDN) has been a boon for next generation networking owing to the separation of control plane from the data plane. It has the capability to address the network requirements in a timely manner by setting flows for every to and fro data movement and gathering large network statistics at the controller to make informed decisions about the network. A core issue in the network for the controller is traffic classification, which can substantially assist SDN controllers towards efficient routing and traffic engineering decisions. This chapter presents a traffic classification scheme utilizing three classifiers namely Feed-forward Neural Network (FFNN), Logistic Regression (LR), Naïve Bayes and employing Particle Swarm Optimization (PSO) for improved traffic classification with less overhead and without overlooking the key Quality of Service (QoS) criterion. Also lowering energy minimization and link utilization has been an important criterion for lowering the operating cost of the network and effectively utilizing the network. This issue has been addressed in the chapter by formulating a multi-objective problem while simultaneously addressing the QoS constraints by proposing a metaheuristic, since no polynomial solution exists and hence an evolutionary based metaheuristic (Clonal Selection) based energy optimization scheme, namely, Clonal Selection Based Energy Minimization (CSEM) has been devised. The obtained results show the efficacy of the proposed traffic classification scheme and CSEM based solution as compared with the state-of-the-art techniques. SDN has been a promising newer network paradigm but security issues and expensive capital procurement of SDN limit its full deployment hence moving to a hybrid SDN (h-SDN) deployment is only logical moving forward. The usage of both centralized and decentralized paradigms in h-SDN with intrinsic issues of interoperability poses challenges to key issues of topology gathering by the controller for proper allocation of network resources and traffic engineering for optimum network performance. State-of-the-art protocols for topology gathering, such as Link Layer Discovery Protocol (LLDP) and Broadcast Domain Discovery Protocol (BDDP) require a huge number of messages and such schemes only gather link information of SDN devices leaving out legacy switches’ (LS) links which results in sub-optimal performance. This chapter provides novel schemes which unearth topology discovery by requiring fewer messages and gathering link information of all the devices in both single and multi-controller environments (might be used when scalability issue is prevalent in h-SDN). Traffic engineering problems in h-SDN are addressed by proper placement of SDN nodes in h-SDN by utilizing the analyzing key criterion of traffic details and the degree of a node while lowering the link utilization in real-time topologies. The results of the proposed schemes for topology discovery and SDN node placement demonstrate the merits as compared with the state-of-the-art protocols.

The pathogenesis of ulcerative colitis (UC) and Crohn's disease (CD) is still unknown, but the importance of genetic susceptibility has been clearly shown by epidemiological data from family and twin studies. Linkage studies have identified two susceptibility loci for inflammatory bowel disease (IBD) on chromosomes 12 and 16. Importantly, these linkages have been replicated by independent investigators, and studies of positional candidates within these regions continue, together with fine mapping strategies. Regions of 'suggestive' linkage on chromosomes 1, 3, 4, 6, 7, 10, 22 and X have also been reported in individual studies. Other important candidate genes investigated include the interleukin-1 receptor antagonist, MUC3 and genes of the human leukocyte antigen (HLA) system. The apparently conflicting data in different studies from around the world may be explained by ethnic differences, case mix and genetic heterogeneity. Replicated class II HLA associations include HLA DRB1*0103 and DR2 (DRB1*1502), involved in UC susceptibility, and HLA DRB1*03 and DR4 as resistance alleles for CD and UC respectively. Animal studies have provided insights from targeted mutations and quantitative trait locus analysis. The goals of continuing research include narrowing the regions of linkages and analysis of candidate genes, and possibly the application of newly developed methods using single nucleotide polymorphisms. Advances in IBD genetics hold the potential to provide knowledge about the disease pathogenesis at the molecular level, with ensuing benefits for clinical practice.

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

The rapid growth of low-altitude air traffic demands airspace evaluation frameworks that are scalable, flexible, and efficient. However, existing airspace partitioning strategies, primarily designed for sparse, long-distance civil aviation, are ill-suited to the dense and complex low-altitude environment. Moreover, the heterogeneous nature of low-altitude conditions cannot be adequately captured. To address this challenge, we propose a novel low-altitude airspace evaluation framework centered on a hierarchical voxel-based partitioning strategy. This strategy explicitly accommodates the diverse operational requirements of drones across different airspace layers. We couple this with an efficient multi-resolution airspace unit encoding mechanism that dynamically aggregates and evaluates airspace availability. To demonstrate the practical utility of our framework, we further develop an energy-aware, multi-scale route-planning algorithm that operates seamlessly across the hierarchical representation. Simulation results show that our method significantly improves computational efficiency in airspace evaluation, while the proposed planner achieves higher energy efficiency compared to conventional approaches like A*.

Two prominent theories of lifespan development, socioemotional selectivity theory and selection, optimization, and compensation theory, make similar predictions for differences in the goal representations of younger and older adults. Our purpose was to test whether the goals of younger and older adults differed in ways predicted by these two theories. Older adults and two groups of younger adults (college students and non-students) listed their current goals, which were then coded by independent raters. Observed age group differences in goals generally supported both theories. Specifically, when compared to younger adults, older adults reported more goals focused on maintenance/loss prevention, the present, emotion-focus and generativity, and social selection, and less goals focused on knowledge acquisition and the future. However, contrary to prediction, older adults also showed less goal focusing than younger adults, reporting goals from a broader set of life domains (e.g., health, property/possessions, friendship).

Basing on research background and status quo of cost driver's selection and optimization theory, the model of warranty cost driver selection which include design phase warranty cost driver, production phase warranty cost driver, after-sale service stage warranty cost driver is building in the paper with Value Engineering theory. The paper also expatiates on judgmental methods aiming at computing results and optimization methods of warranty cost drivers in different stages. The model releases from selecting pattern of warranty cost drivers rely on complex calculations and simple empirical judgment. The scientific practical warranty cost drivers management means are introduced, and they are more suitable to enterprise's actual operation.

Polyploids account for approximately 70% of flowering plants, including many field, horticulture and forage crops. Cottons are a world-leading fiber and important oilseed crop, and a model species for study of plant polyploidization, cellulose biosynthesis and cell wall biogenesis. This study has addressed the concerns of physical mapping of polyploids with BACs and/or BIBACs by constructing a physical map of the tetraploid cotton, Gossypium hirsutum L. The physical map consists of 3,450 BIBAC contigs with an N50 contig size of 863 kb, collectively spanning 2,244 Mb. We sorted the map contigs according to their origin of subgenome, showing that we assembled physical maps for the A- and D-subgenomes of the tetraploid cotton, separately. We also identified the BIBACs in the map minimal tilling path, which consists of 15,277 clones. Moreover, we have marked the physical map with nearly 10,000 BIBAC ends (BESs), making one BES in approximately 250 kb. This physical map provides a line of evidence and a strategy for physical mapping of polyploids, and a platform for advanced research of the tetraploid cotton genome, particularly fine mapping and cloning the cotton agronomic genes and QTLs, and sequencing and assembling the cotton genome using the modern next-generation sequencing technology.

This paper describes a mathematical programming approach to finding an optimal, heterogeneous suite of processors to solve supercomputing problems. This technique, called superconcurrency, works best when the computational requirements are diverse and significant portions of the code are not tightly-coupled. It is also dependent on new methods of benchmarking and code profiling, as well as eventual use of AI techniques for intelligent management of the selected superconcurrent suite.

Many new applications for robots require them to work alongside people as capable members of human-robot teams. These include—in the long term—robots for homes, hospitals, and offices, but already exist in more advanced settings, such as space exploration. The work reported in this paper is part of an ongoing collaboration with NASA JSC to develop Robonaut, a humanoid robot envisioned to work with human astronauts on maintenance operations for space missions. To date, work with Robonaut has mainly investigated performing a joint task with a human in which the robot is being teleoperated. However, perceptive disorientation, sensory noise, and control delays make teleoperation cognitively exhausting even for a highly skilled operator. Control delays in long range teleoperation also make shoulder-to-shoulder teamwork difficult. These issues motivate our work to make robots collaborating with people more autonomous. Our work focuses on a scenario of a human and an autonomous humanoid robot working together shoulder-to-shoulder, sharing the workspace and the objects required to complete a task. A robotic member of such a team must be able to work towards a shared goal, and be in agreement with the human as to the sequence of actions that will be required to reach that goal, as well as dynamically adjust its plan according to the human’s actions. Human-robot collaboration of this nature is an important yet relatively unexplored kind of human-robot interaction. This paper describes our work towards building a dynamic collaborative framework enabling such an interaction. We discuss our architecture and its implementation for controlling a humanoid robot, working on a task with a human partner. Our approach stems from Joint Intention Theory, which shows that for joint action to emerge, teammates must communicate to maintain a set of shared beliefs and to coordinate their actions towards the shared plan. In addition, they must demonstrate commitment to doing their own part, to the others doing theirs, to providing mutual support, and finally—to a mutual belief as to the state of the task. We argue that to this end, the concept of task and action goals is central. We therefore present a goal-driven hierarchical task representation, and a resulting collaborative turn-taking system, implementing many of the above-mentioned requirements of a robotic teammate. Additionally, we show the implementation of relevant social skills supporting our collaborative framework. Finally, we present a demonstration of our system for collaborative execution of a hierarchical object manipulation task by a robot-human team. Our humanoid robot is able to divide the task between the participants while taking into consideration the collaborator’s actions when deciding what to do next. It is capable of asking for mutual support in the cases where it is unable to perform a certain action. To facilitate this interaction, the robot actively maintains a clear and intuitive channel of communication to synchronize goals, task states, and actions, resulting in a fluid, efficient collaboration.