158,189 research outputs found
Time Course of Brain Network Reconfiguration Supporting Inhibitory Control
Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color–word Stroop task. Time–frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal–parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation
Effects of Time Horizons on Influence Maximization in the Voter Dynamics
In this paper we analyze influence maximization in the voter model with an
active strategic and a passive influencing party in non-stationary settings. We
thus explore the dependence of optimal influence allocation on the time
horizons of the strategic influencer. We find that on undirected heterogeneous
networks, for short time horizons, influence is maximized when targeting
low-degree nodes, while for long time horizons influence maximization is
achieved when controlling hub nodes. Furthermore, we show that for short and
intermediate time scales influence maximization can exploit knowledge of
(transient) opinion configurations. More in detail, we find two rules. First,
nodes with states differing from the strategic influencer's goal should be
targeted. Second, if only few nodes are initially aligned with the strategic
influencer, nodes subject to opposing influence should be avoided, but when
many nodes are aligned, an optimal influencer should shadow opposing influence.Comment: 22 page
A Search Strategy of Level-Based Flooding for the Internet of Things
This paper deals with the query problem in the Internet of Things (IoT).
Flooding is an important query strategy. However, original flooding is prone to
cause heavy network loads. To address this problem, we propose a variant of
flooding, called Level-Based Flooding (LBF). With LBF, the whole network is
divided into several levels according to the distances (i.e., hops) between the
sensor nodes and the sink node. The sink node knows the level information of
each node. Query packets are broadcast in the network according to the levels
of nodes. Upon receiving a query packet, sensor nodes decide how to process it
according to the percentage of neighbors that have processed it. When the
target node receives the query packet, it sends its data back to the sink node
via random walk. We show by extensive simulations that the performance of LBF
in terms of cost and latency is much better than that of original flooding, and
LBF can be used in IoT of different scales
JiTS: Just-in-Time Scheduling for Real-Time Sensor Data Dissemination
We consider the problem of real-time data dissemination in wireless sensor
networks, in which data are associated with deadlines and it is desired for
data to reach the sink(s) by their deadlines. To this end, existing real-time
data dissemination work have developed packet scheduling schemes that
prioritize packets according to their deadlines. In this paper, we first
demonstrate that not only the scheduling discipline but also the routing
protocol has a significant impact on the success of real-time sensor data
dissemination. We show that the shortest path routing using the minimum number
of hops leads to considerably better performance than Geographical Forwarding,
which has often been used in existing real-time data dissemination work. We
also observe that packet prioritization by itself is not enough for real-time
data dissemination, since many high priority packets may simultaneously contend
for network resources, deteriorating the network performance. Instead,
real-time packets could be judiciously delayed to avoid severe contention as
long as their deadlines can be met. Based on this observation, we propose a
Just-in-Time Scheduling (JiTS) algorithm for scheduling data transmissions to
alleviate the shortcomings of the existing solutions. We explore several
policies for non-uniformly delaying data at different intermediate nodes to
account for the higher expected contention as the packet gets closer to the
sink(s). By an extensive simulation study, we demonstrate that JiTS can
significantly improve the deadline miss ratio and packet drop ratio compared to
existing approaches in various situations. Notably, JiTS improves the
performance requiring neither lower layer support nor synchronization among the
sensor nodes
Adaptive Random Walks on the Class of Web Graph
We study random walk with adaptive move strategies on a class of directed
graphs with variable wiring diagram. The graphs are grown from the evolution
rules compatible with the dynamics of the world-wide Web [Tadi\'c, Physica A
{\bf 293}, 273 (2001)], and are characterized by a pair of power-law
distributions of out- and in-degree for each value of the parameter ,
which measures the degree of rewiring in the graph. The walker adapts its move
strategy according to locally available information both on out-degree of the
visited node and in-degree of target node. A standard random walk, on the other
hand, uses the out-degree only. We compute the distribution of connected
subgraphs visited by an ensemble of walkers, the average access time and
survival probability of the walks. We discuss these properties of the walk
dynamics relative to the changes in the global graph structure when the control
parameter is varied. For , corresponding to the
world-wide Web, the access time of the walk to a given level of hierarchy on
the graph is much shorter compared to the standard random walk on the same
graph. By reducing the amount of rewiring towards rigidity limit \beta \to
\beta_c \lesss im 0.1, corresponding to the range of naturally occurring
biochemical networks, the survival probability of adaptive and standard random
walk become increasingly similar. The adaptive random walk can be used as an
efficient message-passing algorithm on this class of graphs for large degree of
rewiring.Comment: 8 pages, including 7 figures; to appear in Europ. Phys. Journal
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