Scheduling Large-Scale DNA Sequencing Applications

This chapter studies a load scheduling strategy with near-optimal processing time that is designed to explore the computational characteristics of DNA sequence alignment algorithms, specifically, the Needleman-Wunsch Algorithm. Following the divisible load scheduling theory, an efficient load scheduling strategy is designed in large-scale networks so that the overall processing time of the sequencing tasks is minimized. In this study, the load distribution depends on the length of the sequence and number of processors in the network and, the total processing time is also affected by communication link speed. Several cases have been considered in the study by varying the sequences, communication and computation speeds, and number of processors. Through simulation and numerical analysis, this study demonstrates that for a constant sequence length as the numbers of processors increase in the network the processing time for the job decreases and minimum overall processing time is achieved.

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of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling

School of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling ◽

10.3389/978-2-88963-087-5 ◽

2019 ◽

Keyword(s):

Large Scale ◽

Brain Cells ◽

Neural Bases ◽

Large Scale Networks ◽

Camillo Golgi

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Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

Cerebral Cortex ◽

10.1093/cercor/bhab007 ◽

2021 ◽

Author(s):

Miguel Dasilva ◽

Christian Brandt ◽

Marc Alwin Gieselmann ◽

Claudia Distler ◽

Alexander Thiele

Keyword(s):

Attentional Control ◽

Large Scale ◽

Neuronal Excitability ◽

Cell Types ◽

Receptor Activation ◽

Frontal Eye Field ◽

Control Signals ◽

Cognitive Operations ◽

Eye Field ◽

Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

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Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 ◽

10.1145/2783258.2783385 ◽

2015 ◽

Cited By ~ 26

Author(s):

Michael Mitzenmacher ◽

Jakub Pachocki ◽

Richard Peng ◽

Charalampos Tsourakakis ◽

Shen Chen Xu

Keyword(s):

Large Scale ◽

Large Scale Networks

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How does the position of firms in the supply chain affect their performance? An empirical study

Applied Network Science ◽

10.1007/s41109-021-00364-9 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Siddharth Arora ◽

Alexandra Brintrup

Keyword(s):

Supply Chain ◽

Empirical Data ◽

Large Scale ◽

Individual Performance ◽

Supply Network ◽

Supply Chain Networks ◽

Focal Firm ◽

Cooperative Competition ◽

Large Scale Networks ◽

The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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