Publications associées à The Anatomy of a Cryptocurrency Pump-and-Dump Scheme

Publications associées (30)

Machine Learning Security Against Data Poisoning: Are We There Yet?

Poisoning attacks compromise the training data utilized to train machine learning (ML) models, diminishing their overall performance, manipulating predictions on specific test samples, and implanting backdoors. This article thoughtfully explores these atta ...

Ieee Computer Soc2024

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

Jean-Philippe Thiran, Roser Vinals Terres

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Zenodo2024

Enriching the Computational Toolbox for Organocatalysis

Simone Gallarati

Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

EPFL2023

Machine-learning the electronic density of states: electronic properties without quantum mechanics

Chiheb Ben Mahmoud

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

EPFL2023

Supervised learning and inference of spiking neural networks with temporal coding

Ana Stanojevic

The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...

EPFL2023

DiGress: Discrete Denoising diffusion for graph generation

Pascal Frossard, Volkan Cevher, Igor Krawczuk, Clément Arthur Yvon Vignac, Bohan Wang

This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or ...

2023

DIABLO: A Benchmark Suite for Blockchains

Rachid Guerraoui, Vincent Gramoli, Gauthier Jérôme Timothée Voron

With the recent advent of blockchains, we have witnessed a plethora of blockchain proposals. These proposals range from using work to using time, storage or stake in order to select blocks to be appended to the chain. As a drawback it makes it difficult fo ...

ASSOC COMPUTING MACHINERY2023

Deep learning-based analysis of multiple sclerosis lesions with high and ultra-high field MRI

Francesco La Rosa

Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system and affects almost 3 million people worldwide. There is currently no cure for MS, and its symptoms, starting with fatigue and weakness, often progress over time ...

EPFL2022

Self-Exciting Point Processes: Identification and Control

Michael Mark

This thesis addresses theoretical and practical aspects of identification and subsequent control of self-exciting point processes. The main contributions correspond to four separate scientific papers. In the first paper, we address the challenge of robust ...

EPFL2022

Toward Mobile Distributed Ledgers

Boi Faltings, Sujit Prakash Gujar

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks

Berend Smit, Seyedmohamad Moosavi, Kevin Maik Jablonka, Daniele Ongari

Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can f ...

NATURE RESEARCH2021

Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective

Tong Zhang, Jing Zhang

The advances made in predicting visual saliency using deep neural networks come at the expense of collecting large-scale annotated data. However, pixel-wise annotation is labor-intensive and overwhelming. In this paper, we propose to learn saliency predict ...

2021

Wavelength-Division Multiplexed Optical Cryptocurrency

Maxim Karpov

We propose an optical proof-of-work scheme that feeds data encoded into wavelength-division multiplexed modes through a programmable photonic network. We verify robustness by modeling network dispersion, allowing for energy-efficient optical alternatives t ...

IEEE2021

Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

Sthithpragya Gupta, Aude Billard, Walid Amanhoud, Farshad Khadivar

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning per ...

IEEE2021

Datasets and Models for Historical Newspaper Article Segmentation

Maud Ehrmann, Sofia Ares Oliveira, Raphaël Barman

Dataset and models used and produced in the work described in the paper "Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers": https://infoscience.epfl.ch/record/282863?ln=en ...

2021

Quantifying Endogeneity of Cryptocurrency Markets

Thomas Alois Weber, Michael Mark

We construct a ‘reflexivity’ index to measure the activity generated endogenously within a market for cryptocurrencies. For this purpose, we fit a univariate self-exciting Hawkes process with two classes of parametric kernels to high-frequency trading data ...

2022

ICO investors

Rüdiger Fahlenbrach, Marc André Frattaroli

We conduct a detailed analysis of investors in successful initial coin offerings (ICOs). The average ICO has 4700 contributors. The median participant contributes small amounts and many investors sell their tokens before the underlying product is developed ...

2021

Learning stereo reconstruction with deep neural networks

Stepan Tulyakov

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

EPFL2020

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas, Stamatia Zoi Pagoulatou

Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardiac contractility is still challenging. This paper investigated the potential of estimating aortic systolic ...

Nature Research2020

Essays in Financial Economics

Marc André Frattaroli

This thesis consists of three chapters that study separate subjects in the area of corporate governance and financial intermediation. In the first chapter, I study a protectionist anti-takeover law introduced in 2014 in France that covers a subset of all ...

EPFL2020