1,316 research outputs found
Antitrust Analysis for the Internet Upstream Market: a BGP Approach
In this paper we study concentration in the European Internet upstream access market. Measurement of market concentration depends on correctly defining the market, but this is not always possible as Antitrust authorities often lack reliable pricing and traffic data. We present an alternative approach based on the inference of the Internet Operators interconnection policies using micro-data sourced from their Border Gateway Protocol tables. Firstly we propose a price-independent algorithm for defining both the vertical and geographical relevant market boundaries, then we calculate market concentration indexes using two novel metrics. These assess, for each undertaking, both its role in terms of essential network facility and of wholesale market dominance. The results, applied to four leading Internet Exchange Points in London, Amsterdam, Frankfurt and Milan, show that some vertical segments of these markets are extremely competitive, while others are highly concentrated, putting them within the special attention category of the Merger Guidelines
Antitrust Analysis for the Internet Upstream Market: A BGP Approach
In this paper we study concentration in the European Internet upstream access market. The possibility of measuring market concentration depends on a correct definition of the market itself; however, this is not always possible, since, as it is the case of the Internet industry, very often Antitrust authorities lack reliable pricing and traffic data. This difficulty motivates our paper. We present an alternative approach based on the inference of the Internet Operators interconnection policies using micro-data sourced from their Border Gateway Protocol tables. We assess market concentration following a two step process: firstly we propose a price-independent algorithm for defining both the vertical and geographical relevant market boundaries, then we calculate market concentration indexes using two novel metrics. These assess, for each undertaking, both itsrole in terms of essential network facility and of wholesale market dominance. The results, applied to four leading Internet Exchange Points in London, Amsterdam, Frankfurt and Milan, show that some vertical segments of these markets are highly concentrated, while others are extremely competitive. According to the Merger Guidelines some of the estimated market concentration values would immediately fall within the special attention category.Technology and Industry, Other Topics
Information flow between resting state networks
The resting brain dynamics self-organizes into a finite number of correlated
patterns known as resting state networks (RSNs). It is well known that
techniques like independent component analysis can separate the brain activity
at rest to provide such RSNs, but the specific pattern of interaction between
RSNs is not yet fully understood. To this aim, we propose here a novel method
to compute the information flow (IF) between different RSNs from resting state
magnetic resonance imaging. After haemodynamic response function blind
deconvolution of all voxel signals, and under the hypothesis that RSNs define
regions of interest, our method first uses principal component analysis to
reduce dimensionality in each RSN to next compute IF (estimated here in terms
of Transfer Entropy) between the different RSNs by systematically increasing k
(the number of principal components used in the calculation). When k = 1, this
method is equivalent to computing IF using the average of all voxel activities
in each RSN. For k greater than one our method calculates the k-multivariate IF
between the different RSNs. We find that the average IF among RSNs is
dimension-dependent, increasing from k =1 (i.e., the average voxels activity)
up to a maximum occurring at k =5 to finally decay to zero for k greater than
10. This suggests that a small number of components (close to 5) is sufficient
to describe the IF pattern between RSNs. Our method - addressing differences in
IF between RSNs for any generic data - can be used for group comparison in
health or disease. To illustrate this, we have calculated the interRSNs IF in a
dataset of Alzheimer's Disease (AD) to find that the most significant
differences between AD and controls occurred for k =2, in addition to AD
showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for
publication in Brain Connectivity in its current for
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an
effective approach, especially by using nuclear medicine imaging techniques
such as Positron Emission Topography (PET). In various literature it has been
found that PET images can be better modeled as signals (e.g. uptake of
florbetapir) defined on a network (non-Euclidean) structure which is governed
by its underlying graph patterns of pathological progression and metabolic
connectivity. In order to effectively apply deep learning framework for PET
image analysis to overcome its limitation on Euclidean grid, we develop a
solution for 3D PET image representation and analysis under a generalized,
graph-based CNN architecture (PETNet), which analyzes PET signals defined on a
group-wise inferred graph structure. Computations in PETNet are defined in
non-Euclidean, graph (network) domain, as it performs feature extraction by
convolution operations on spectral-filtered signals on the graph and pooling
operations based on hierarchical graph clustering. Effectiveness of the PETNet
is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset,
which shows improved performance over both deep learning and other machine
learning-based methods.Comment: Jiaming Guo, Wei Qiu and Xiang Li contribute equally to this wor
Multi-Scale Information, Network, Causality, and Dynamics: Mathematical Computation and Bayesian Inference to Cognitive Neuroscience and Aging
The human brain is estimated to contain 100 billion or so neurons and 10 thousand times as many connections. Neurons never function in isolation: each of them is connected to 10, 000 others and they interact extensively every millisecond. Brain cells are organized into neural circuits often in a dynamic way, processing specific types of information and providing th
Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications
Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic
resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of
Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity
underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the
use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to
cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers
have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic,
and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity
across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power
and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.
Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral
reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains
in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminative. This may cause the ignorance of subtle but critical network
changes that are of investigation values across populations. In this paper, we
propose to improve the discriminative power of BN models for continuous
variables from two different perspectives. This brings two general
discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the
first framework, we employ Fisher kernel to bridge the generative models of GBN
and the discriminative classifiers of SVMs, and convert the GBN parameter
learning to Fisher kernel learning via minimizing a generalization error bound
of SVMs. In the second framework, we employ the max-margin criterion and build
it directly upon GBN models to explicitly optimize the classification
performance of the GBNs. The advantages and disadvantages of the two frameworks
are discussed and experimentally compared. Both of them demonstrate strong
power in learning discriminative parameters of GBNs for neuroimaging based
brain network analysis, as well as maintaining reasonable representation
capacity. The contributions of this paper also include a new Directed Acyclic
Graph (DAG) constraint with theoretical guarantee to ensure the graph validity
of GBN.Comment: 16 pages and 5 figures for the article (excluding appendix
Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease
Alzheimer's disease is the most common neurodegenerative disease. The aim of
this study is to infer structural changes in brain connectivity resulting from
disease progression using cortical thickness measurements from a cohort of
participants who were either healthy control, or with mild cognitive
impairment, or Alzheimer's disease patients. For this purpose, we develop a
novel approach for inference of multiple networks with related edge values
across groups. Specifically, we infer a Gaussian graphical model for each group
within a joint framework, where we rely on Bayesian hierarchical priors to link
the precision matrix entries across groups. Our proposal differs from existing
approaches in that it flexibly learns which groups have the most similar edge
values, and accounts for the strength of connection (rather than only edge
presence or absence) when sharing information across groups. Our results
identify key alterations in structural connectivity which may reflect
disruptions to the healthy brain, such as decreased connectivity within the
occipital lobe with increasing disease severity. We also illustrate the
proposed method through simulations, where we demonstrate its performance in
structure learning and precision matrix estimation with respect to alternative
approaches.Comment: Accepted to Biometrics January 202
Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease
Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative
disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration.
There are no effective treatments for Alzheimer's disease at a late stage,
urging for early intervention. However, existing statistical inference
approaches of AD subtype identification ignore the pathological domain
knowledge, which could lead to ill-posed results that are sometimes
inconsistent with the essential neurological principles. Integrating systems
biology modeling with machine learning, we propose a novel pathology steered
stratification network (PSSN) that incorporates established domain knowledge in
AD pathology through a reaction-diffusion model, where we consider non-linear
interactions between major biomarkers and diffusion along brain structural
network. Trained on longitudinal multimodal neuroimaging data, the biological
model predicts long-term trajectories that capture individual progression
pattern, filling in the gaps between sparse imaging data available. A deep
predictive neural network is then built to exploit spatiotemporal dynamics,
link neurological examinations with clinical profiles, and generate subtype
assignment probability on an individual basis. We further identify an
evolutionary disease graph to quantify subtype transition probabilities through
extensive simulations. Our stratification achieves superior performance in both
inter-cluster heterogeneity and intra-cluster homogeneity of various clinical
scores. Applying our approach to enriched samples of aging populations, we
identify six subtypes spanning AD spectrum, where each subtype exhibits a
distinctive biomarker pattern that is consistent with its clinical outcome.
PSSN provides insights into pre-symptomatic diagnosis and practical guidance on
clinical treatments, which may be further generalized to other
neurodegenerative diseases
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