306 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en BiotecnologĂa, IngenierĂa y TecnologĂa QuĂmicaLĂnea de InvestigaciĂłn: IngenierĂa, Ciencia de Datos y BioinformáticaClave Programa: DBICĂłdigo LĂnea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
Tools for efficient Deep Learning
In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption.
We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work.
This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C.
Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets.
All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces
Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches
Traditional networking devices support only fixed features and limited configurability.
Network softwarization leverages programmable software and hardware platforms to remove those limitations.
In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms.
This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0.
P4 is the most popular technology to implement programmable data planes.
However, programmable data planes, and in particular, the P4 technology, emerged only recently.
Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking.
The research of this thesis focuses on two open issues of programmable data planes.
First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet.
Second, it enables BIER in high-performance P4 data planes.
BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet.
The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study.
Two more peer-reviewed papers contain additional content that is not directly related to the main results.
They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts
Asymptotic analysis and efficient random sampling of directed ordered acyclic graphs
Directed acyclic graphs (DAGs) are directed graphs in which there is no path
from a vertex to itself. DAGs are an omnipresent data structure in computer
science and the problem of counting the DAGs of given number of vertices and to
sample them uniformly at random has been solved respectively in the 70's and
the 00's. In this paper, we propose to explore a new variation of this model
where DAGs are endowed with an independent ordering of the out-edges of each
vertex, thus allowing to model a wide range of existing data structures. We
provide efficient algorithms for sampling objects of this new class, both with
or without control on the number of edges, and obtain an asymptotic equivalent
of their number. We also show the applicability of our method by providing an
effective algorithm for the random generation of classical labelled DAGs with a
prescribed number of vertices and edges, based on a similar approach. This is
the first known algorithm for sampling labelled DAGs with full control on the
number of edges, and it meets a need in terms of applications, that had already
been acknowledged in the literature.Comment: 32 pages, 12 figures. For the implementation of the algorithms, see
https://github.com/Kerl13/randda
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
Discovering causal relationship using multivariate functional data has
received a significant amount of attention very recently. In this article, we
introduce a functional linear structural equation model for causal structure
learning when the underlying graph involving the multivariate functions may
have cycles. To enhance interpretability, our model involves a low-dimensional
causal embedded space such that all the relevant causal information in the
multivariate functional data is preserved in this lower-dimensional subspace.
We prove that the proposed model is causally identifiable under standard
assumptions that are often made in the causal discovery literature. To carry
out inference of our model, we develop a fully Bayesian framework with suitable
prior specifications and uncertainty quantification through posterior
summaries. We illustrate the superior performance of our method over existing
methods in terms of causal graph estimation through extensive simulation
studies. We also demonstrate the proposed method using a brain EEG dataset.Comment: 36 pages, 2 figures, 7 table
Optimizing NOTEARS Objectives via Topological Swaps
Recently, an intriguing class of non-convex optimization problems has emerged
in the context of learning directed acyclic graphs (DAGs). These problems
involve minimizing a given loss or score function, subject to a non-convex
continuous constraint that penalizes the presence of cycles in a graph. In this
work, we delve into the optimization challenges associated with this class of
non-convex programs. To address these challenges, we propose a bi-level
algorithm that leverages the non-convex constraint in a novel way. The outer
level of the algorithm optimizes over topological orders by iteratively
swapping pairs of nodes within the topological order of a DAG. A key innovation
of our approach is the development of an effective method for generating a set
of candidate swapping pairs for each iteration. At the inner level, given a
topological order, we utilize off-the-shelf solvers that can handle linear
constraints. The key advantage of our proposed algorithm is that it is
guaranteed to find a local minimum or a KKT point under weaker conditions
compared to previous work and finds solutions with lower scores. Extensive
experiments demonstrate that our method outperforms state-of-the-art approaches
in terms of achieving a better score. Additionally, our method can also be used
as a post-processing algorithm to significantly improve the score of other
algorithms. Code implementing the proposed method is available at
https://github.com/duntrain/topo.Comment: 39 pages, 12 figures, ICML 202
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
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