Publications associées à Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

Publications associées (32)

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak ...

MDPI2024

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Zenodo2024

Network-based kinetic models: Emergence of a statistical description of the graph topology

Matteo Raviola

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

Cambridge Univ Press2024

Assessment of the hydromorphological effectiveness of sediment augmentation measures downstream of dams

Giovanni De Cesare, Christian Mörtl

This article presents assessment methods for the hydromorphological effectiveness of sediment augmentation measures downstream of dams. First, we describe different ways of quantifying hydromorphological effectiveness based on typical objectives of sedimen ...

CRC Press/Balkema2023

Modeling, analysis, and design of complex biological networks

Mohammadomid Oftadeh

Automating experimental procedures has resulted in an unprecedented increase in the volume of generated data, which, in turn, has caused an accumulation of unprocessed data. As a result, the need to develop tools to analyze data systematically has been ris ...

EPFL2023

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Ali H. Sayed

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Equilibria in Network Constrained Energy Markets

Leonardo Massai

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Elsevier2023

Network Alignment With Holistic Embeddings

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong, Thanh Trung Huynh

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. Howeve ...

IEEE COMPUTER SOC2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

EPFL2023

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Florent Gérard Krzakala

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

IEEE2023

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Dimitrios Kaminaris

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. ...

MDPI2023

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

Paolo Ienne, Kubilay Atasu, Jovan Blanusa

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in r ...

Assoc Computing Machinery2023

Acceleration of graph pattern mining and applications to financial crime

Jovan Blanusa

Various forms of real-world data, such as social, financial, and biological networks, can be represented using graphs. An efficient method of analysing this type of data is to extract subgraph patterns, such as cliques, cycles, and motifs, from graphs. For ...

EPFL2023

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

Martin Louis Lucien Rémy Barry

Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

EPFL2023

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Paolo Ienne, Kubilay Atasu, Jovan Blanusa

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

arXiv2023

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Felix Schürmann, Armando Romani

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

2023

Diffusion of energy technologies: the role and dynamics of supply-side information networks

Claudia Rebeca Binder Signer, Romano Tobias Wyss, Gloria Serra Coch, Maria Anna Hecher

The diffusion of energy technologies is key for supporting the energy transition. While social influence and peer effects have been extensively studied on the demand side and found to impact adoption, less focus has been given to the role of information ne ...

2023

Bayesian reconstruction of memories stored in neural networks from their connectivity

Lenka Zdeborová, Florent Gérard Krzakala

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

2023

Global, Passive Detection of Connection Tampering

Louis-Henri Manuel Jakob Merino

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

Assoc Computing Machinery2023