736 research outputs found
Labeled Subgraph Entropy Kernel
In recent years, kernel methods are widespread in tasks of similarity
measuring. Specifically, graph kernels are widely used in fields of
bioinformatics, chemistry and financial data analysis. However, existing
methods, especially entropy based graph kernels are subject to large
computational complexity and the negligence of node-level information. In this
paper, we propose a novel labeled subgraph entropy graph kernel, which performs
well in structural similarity assessment. We design a dynamic programming
subgraph enumeration algorithm, which effectively reduces the time complexity.
Specially, we propose labeled subgraph, which enriches substructure topology
with semantic information. Analogizing the cluster expansion process of gas
cluster in statistical mechanics, we re-derive the partition function and
calculate the global graph entropy to characterize the network. In order to
test our method, we apply several real-world datasets and assess the effects in
different tasks. To capture more experiment details, we quantitatively and
qualitatively analyze the contribution of different topology structures.
Experimental results successfully demonstrate the effectiveness of our method
which outperforms several state-of-the-art methods.Comment: 9 pages,5 figure
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
This PhD thesis thoroughly examines the utilization of deep learning
techniques as a means to advance the algorithms employed in the monitoring and
optimization of electric power systems. The first major contribution of this
thesis involves the application of graph neural networks to enhance power
system state estimation. The second key aspect of this thesis focuses on
utilizing reinforcement learning for dynamic distribution network
reconfiguration. The effectiveness of the proposed methods is affirmed through
extensive experimentation and simulations.Comment: PhD thesi
Euclid preparation XXVIII. Forecasts for ten different higher-order weak lensing statistics
Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale structure. The aim of the Higher-Order Weak Lensing Statistics (HOWLS) project is to assess, compare, and combine the constraining power of ten different HOS on a common set of Euclid-like mocks, derived from N-body simulations. In this first paper of the HOWLS series, we computed the nontomographic (Ωm, Ï8) Fisher information for the one-point probability distribution function, peak counts, Minkowski functionals, Betti numbers, persistent homology Betti numbers and heatmap, and scattering transform coefficients, and we compare them to the shear and convergence two-point correlation functions in the absence of any systematic bias. We also include forecasts for three implementations of higher-order moments, but these cannot be robustly interpreted as the Gaussian likelihood assumption breaks down for these statistics. Taken individually, we find that each HOS outperforms the two-point statistics by a factor of around two in the precision of the forecasts with some variations across statistics and cosmological parameters. When combining all the HOS, this increases to a 4.5 times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with Euclid. The data used in this analysis are publicly released with the paper
OBSERVATIONAL CAUSAL INFERENCE FOR NETWORK DATA SETTINGS
Observational causal inference (OCI) has shown significant promise in recent years, both as a tool for improving existing machine learning techniques and as an avenue to aid decision makers in applied areas, such as health and climate science. OCI relies on a key notion, identification, which links the counterfactual of interest to the observed data via a set of assumptions. Historically, OCI has relied on unrealistic assumptions, such as the âno latent confoundersâ assumption. To address this, Huang and Valtorta (2006) and Shpitser and Pearl (2006) provided sound and complete algorithms for identification of causal effects in causal directed acyclic graphs with latent variables. Nevertheless, these algorithms can only handle relatively simple causal queries.
In this dissertation, I will detail my contributions which generalize identification theory in key directions. I will describe theory which enables identification of causal effects when i) data do not satisfy the âindependent and identically distributedâ assumption, as in vaccine or social network data, and ii) the intervention of interest is a function of other model variables, as in off-line, off-policy learning, iii) when these two complicated settings intersect. Additionally, I will highlight some novel ways to conceive of interventions in networks. I will conclude with a discussion of future directions
Conditional Invertible Generative Models for Supervised Problems
Invertible neural networks (INNs), in the setting of normalizing flows, are a type of unconditional generative likelihood model. Despite various attractive properties compared to other common generative model types, they are rarely useful for supervised tasks or real applications due to their unguided outputs. In this work, we therefore present three new methods that extend the standard INN setting, falling under a broader category we term generative invertible models. These new methods allow leveraging the theoretical and practical benefits of INNs to solve supervised problems in new ways, including real-world applications from different branches of science. The key finding is that our approaches enhance many aspects of trustworthiness in comparison to conventional feed-forward networks, such as uncertainty estimation and quantification, explainability, and proper handling of outlier data
BEYOND CLASSICAL CAUSAL MODELS: PATH DEPENDENCE, ENTANGLED MISSINGNESS AND GENERALIZED COARSENING
Classical causal models generally assume relatively simple settings like static observations, complete observability and independent and identically distributed (i.i.d.) data samples. For many systems of scientific interest, such assumptions are unrealistic. More recent work has explored models with complex properties including (time-invariant) temporal dynamics, data dependence, as well as missingness within the causal inference framework. Inspired by these advances, this dissertation goes beyond these classical causal inference models to explore the following complications that can arise in some causal systems â (i) path dependence, whereby systems exhibit state-specific causal relationships and a temporal evolution that could be counterfactually altered, (ii) entangled missingness, where missingness occurs in data together with causal dependence and finally, (iii) generalized coarsening, where systems entail causal processes operating at multiple timescales, and estimands of interest lie at a timescale different from that in which data is observed. In particular, we use the
language of graphical causal models and discuss an important component of the causal inference pipeline, namely identification, which links the counterfactual of interest to the observed data via a set of assumptions. In some cases, we also discuss estimation,
which allows us to obtain identified parameters from finite samples of data. We illustrate the use of these novel models on observational data obtained from biomedical and clinical settings
Potential Outcome and Decision Theoretic Foundations for Statistical Causality
In a recent paper published in the Journal of Causal Inference, Philip Dawid
has described a graphical causal model based on decision diagrams. This article
describes how single-world intervention graphs (SWIGs) relate to these
diagrams. In this way, a correspondence is established between Dawid's approach
and those based on potential outcomes such as Robins' Finest Fully Randomized
Causally Interpreted Structured Tree Graphs. In more detail, a reformulation of
Dawid's theory is given that is essentially equivalent to his proposal and
isomorphic to SWIGs.Comment: 54 pages, 7 Figures, 3 Tables. Some more minor edits and correction
Measurement of the Triple-Differential Cross-Section for the Production of Multijet Events using 139 fb^{-1} of Proton-Proton Collision Data at \sqrt{s} = 13 TeV with the ATLAS Detector to Disentangle Quarks and Gluons at the Large Hadron Collider
At hadron-hadron colliders, it is almost impossible to obtain pure samples in either quark-
or gluon-initialized hadronic showers as one always deals with a mixture of particle jets.
The analysis presented in this dissertation aims to break the aforementioned degeneracy by
extracting the underlying fractions of (light) quarks and gluons through a measurement of the
relative production rates of multijet events.
A measurement of the triple-differential multijet cross section at a centre-of-mass energy of
13 TeV using an integrated luminosity of 139 fb â1 of data collected with the ATLAS detector
in proton-proton collisions at the Large Hadron Collider (LHC) is presented. The cross section
is measured as a function of the transverse momentum p T , two categories of pseudorapidity
η rel defined by the relative orientation between the jets, as well as a Jet Sub-Structure (JSS)
observable O JSS , sensitive to the quark- or gluon-like nature of the hadronic shower of the two
leading-p T jets with 250 GeV < p T < 4.5 TeV and |η| < 2.1 in the event.
The JSS variables, which have been studied within the context of this thesis, can broadly be
divided into two categories: one set of JSS observables is constructed by iteratively declustering
and counting the jetâs charged constituents; the second set is based on the output predicted by
Deep Neural Networks (DNNs) derived from the âdeep setsâ paradigm to implement permutation
invariant functions over sets, which are trained to discriminate between quark- and gluon-
initialized showers in a supervised fashion.
All JSS observables are measured based on Inner Detector tracks with p T > 500 MeV
and |η| < 2.5 to maintain strong correlations between detector- and particle-level objects.
The reconstructed spectra are fully corrected for acceptance and detector effects, and the
unfolded cross section is compared to various state-of-the-art parton shower Monte Carlo
models. Several sources of systematic and statistical uncertainties are taken into account that
are fully propagated through the entire unfolding procedure onto the final cross section. The
total uncertainty on the cross section varies between 5 % and 20 % depending on the region of
phase space.
The unfolded multi-differential cross sections are used to extract the underlying fractions
and probability distributions of quark- and gluon-initialized jets in a solely data-driven, model-
independent manner using a statistical demixing procedure (âjet topicsâ), which has originally
been developed as a tool for extracting emergent themes in an extensive corpus of text-based
documents. The obtained fractions are model-independent and are based on an operational
definition of quark and gluon jets that does not seek to assign a binary label on a jet-to-jet basis,
but rather identifies quark- and gluon-related features on the level of individual distributions,
avoiding common theoretical and conceptional pitfalls regarding the definition of quark and
gluon jets.
The total fraction of gluon-initialized jets in the multijet sample is (IRC-safely) measured
to be 60.5 ± 0.4(Stat) â 2.4(Syst) % and 52.3 ± 0.4(Stat) â 2.6(Syst) % in central and forward
region, respectively. Furthermore, the gluon fractions are extracted in several exclusive regions
of transverse momentum
Evolution from the ground up with Amee â From basic concepts to explorative modeling
Evolutionary theory has been the foundation of biological research for about a century
now, yet over the past few decades, new discoveries and theoretical advances have rapidly
transformed our understanding of the evolutionary process. Foremost among them are
evolutionary developmental biology, epigenetic inheritance, and various forms of evolu-
tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led
to the conceptualization of an extended evolutionary synthesis. Starting from abstract
principles rooted in complexity theory, this thesis aims to provide a unified conceptual
understanding of any kind of evolution, biological or otherwise. This is used in the second
part to develop Amee, an agent-based model that unifies development, niche construction,
and phenotypic plasticity with natural selection based on a simulated ecology. Amee
is implemented in Utopia, which allows performant, integrated implementation and
simulation of arbitrary agent-based models. A phenomenological overview over Ameeâs
capabilities is provided, ranging from the evolution of ecospecies down to the evolution
of metabolic networks and up to beyond-species-level biological organization, all of
which emerges autonomously from the basic dynamics. The interaction of development,
plasticity, and niche construction has been investigated, and it has been shown that while
expected natural phenomena can, in principle, arise, the accessible simulation time and
system size are too small to produce natural evo-devo phenomena and âstructures. Amee thus can be used to simulate the evolution of a wide variety of processes
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