4,262 research outputs found
Resolving Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations
Human brain anatomy and function display a combination of modular and
hierarchical organization, suggesting the importance of both cohesive
structures and variable resolutions in the facilitation of healthy cognitive
processes. However, tools to simultaneously probe these features of brain
architecture require further development. We propose and apply a set of methods
to extract cohesive structures in network representations of brain connectivity
using multi-resolution techniques. We employ a combination of soft
thresholding, windowed thresholding, and resolution in community detection,
that enable us to identify and isolate structures associated with different
weights. One such mesoscale structure is bipartivity, which quantifies the
extent to which the brain is divided into two partitions with high connectivity
between partitions and low connectivity within partitions. A second,
complementary mesoscale structure is modularity, which quantifies the extent to
which the brain is divided into multiple communities with strong connectivity
within each community and weak connectivity between communities. Our methods
lead to multi-resolution curves of these network diagnostics over a range of
spatial, geometric, and structural scales. For statistical comparison, we
contrast our results with those obtained for several benchmark null models. Our
work demonstrates that multi-resolution diagnostic curves capture complex
organizational profiles in weighted graphs. We apply these methods to the
identification of resolution-specific characteristics of healthy weighted graph
architecture and altered connectivity profiles in psychiatric disease.Comment: Comments welcom
Benchmarking Distributed Stream Data Processing Systems
The need for scalable and efficient stream analysis has led to the
development of many open-source streaming data processing systems (SDPSs) with
highly diverging capabilities and performance characteristics. While first
initiatives try to compare the systems for simple workloads, there is a clear
gap of detailed analyses of the systems' performance characteristics. In this
paper, we propose a framework for benchmarking distributed stream processing
engines. We use our suite to evaluate the performance of three widely used
SDPSs in detail, namely Apache Storm, Apache Spark, and Apache Flink. Our
evaluation focuses in particular on measuring the throughput and latency of
windowed operations, which are the basic type of operations in stream
analytics. For this benchmark, we design workloads based on real-life,
industrial use-cases inspired by the online gaming industry. The contribution
of our work is threefold. First, we give a definition of latency and throughput
for stateful operators. Second, we carefully separate the system under test and
driver, in order to correctly represent the open world model of typical stream
processing deployments and can, therefore, measure system performance under
realistic conditions. Third, we build the first benchmarking framework to
define and test the sustainable performance of streaming systems.
Our detailed evaluation highlights the individual characteristics and
use-cases of each system.Comment: Published at ICDE 201
Open/Closed String Topology and Moduli Space Actions via Open/Closed Hochschild Actions
In this paper we extend our correlation functions to the open/closed case.
This gives rise to actions of an open/closed version of the Sullivan PROP as
well as an action of the relevant moduli space. There are several unexpected
structures and conditions that arise in this extension which are forced upon us
by considering the open sector. For string topology type operations, one cannot
just consider graphs, but has to take punctures into account and one has to
restrict the underlying Frobenius algebras. In the moduli space, one first has
to pass to a smaller moduli space which is closed under open/closed duality and
then consider covers in order to account for the punctures
Open/closed string topology and moduli space actions via open/closed Hochschild actions
In this paper we extend our correlation functions to the open/closed case.
This gives rise to actions of an open/closed version of the Sullivan PROP as
well as an action of the relevant moduli space. There are several unexpected
structures and conditions that arise in this extension which are forced upon us
by considering the open sector. For string topology type operations, one cannot
just consider graphs, but has to take punctures into account and one has to
restrict the underlying Frobenius algebras. In the moduli space, one first has
to pass to a smaller moduli space which is closed under open/closed duality and
then consider covers in order to account for the punctures
Benchmark model to assess community structure in evolving networks
Detecting the time evolution of the community structure of networks is
crucial to identify major changes in the internal organization of many complex
systems, which may undergo important endogenous or exogenous events. This
analysis can be done in two ways: considering each snapshot as an independent
community detection problem or taking into account the whole evolution of the
network. In the first case, one can apply static methods on the temporal
snapshots, which correspond to configurations of the system in short time
windows, and match afterwards the communities across layers. Alternatively, one
can develop dedicated dynamic procedures, so that multiple snapshots are
simultaneously taken into account while detecting communities, which allows us
to keep memory of the flow. To check how well a method of any kind could
capture the evolution of communities, suitable benchmarks are needed. Here we
propose a model for generating simple dynamic benchmark graphs, based on
stochastic block models. In them, the time evolution consists of a periodic
oscillation of the system's structure between configurations with built-in
community structure. We also propose the extension of quality comparison
indices to the dynamic scenario.Comment: 11 pages, 7 figures, 3 table
Idealized computational models for auditory receptive fields
This paper presents a theory by which idealized models of auditory receptive
fields can be derived in a principled axiomatic manner, from a set of
structural properties to enable invariance of receptive field responses under
natural sound transformations and ensure internal consistency between
spectro-temporal receptive fields at different temporal and spectral scales.
For defining a time-frequency transformation of a purely temporal sound
signal, it is shown that the framework allows for a new way of deriving the
Gabor and Gammatone filters as well as a novel family of generalized Gammatone
filters, with additional degrees of freedom to obtain different trade-offs
between the spectral selectivity and the temporal delay of time-causal temporal
window functions.
When applied to the definition of a second-layer of receptive fields from a
spectrogram, it is shown that the framework leads to two canonical families of
spectro-temporal receptive fields, in terms of spectro-temporal derivatives of
either spectro-temporal Gaussian kernels for non-causal time or the combination
of a time-causal generalized Gammatone filter over the temporal domain and a
Gaussian filter over the logspectral domain. For each filter family, the
spectro-temporal receptive fields can be either separable over the
time-frequency domain or be adapted to local glissando transformations that
represent variations in logarithmic frequencies over time. Within each domain
of either non-causal or time-causal time, these receptive field families are
derived by uniqueness from the assumptions.
It is demonstrated how the presented framework allows for computation of
basic auditory features for audio processing and that it leads to predictions
about auditory receptive fields with good qualitative similarity to biological
receptive fields measured in the inferior colliculus (ICC) and primary auditory
cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table
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