Publications related to Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

Chemical machine learning with kernels: The key impact of loss functions

Volkan Cevher, Sandip De, Junhong Lin, Van Quang Nguyen

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature spent a considerable effort in designing representations that ca ...

2018

An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data

Kshitij Sharma, Valérie Chavez

The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in Statistics, called the Extreme Value Theory (EVT). EVT claims that th ...

2017

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Xin Liu, Hanjie Pan, Feng Xue, Jiaqi Liu

Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-dr ...

Ieee2017

Parameter identification of a lithium-ion cell single-particle model through non-invasive testing

Mario Paolone, Dimitri Torregrossa, Emil Namor

Physics-based models of electrochemical cells are of great interest for the future battery management systems (BMSs), due to their accuracy and capability to predict cell physical states. One of their main disadvantages, when compared to equivalent circuit ...

Elsevier Science Bv2017

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We study jump-penalized estimators based on least absolute deviations which are often referred to as Potts estimators. They are estimators for a parsimonious piecewise constant representation of noisy data having a noise distribution which has heavier tail ...

Oxford University Press2017

Probabilistic Amplitude Demodulation features in Speech Synthesis for Improving Prosody

Philip Neil Garner, Milos Cernak

Abstract Amplitude demodulation (AM) is a signal decomposition technique by which a signal can be decomposed to a product of two signals, i.e, a quickly varying carrier and a slowly varying modulator. In this work, the probabilistic amplitude demodulation ...

Idiap2016

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Philip Neil Garner, Milos Cernak

Amplitude demodulation (AM) is a signal decomposition technique by which a signal can be decomposed to a product of two signals, i.e, a quickly varying carrier and a slowly varying modulator. In this work, the probabilistic amplitude demodulation (PAD) fea ...

2016

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Philip Neil Garner, Milos Cernak

Amplitude demodulation (AM) is a signal decomposition technique by which a signal can be decomposed to a product of two signals, i.e, a quickly varying carrier and a slowly varying modulator. In this work, the probabilistic amplitude demodulation (PAD) fea ...

2016

Stream temperature modeling in mountainous environments

Aurélien Gallice

Stream temperature is one of the key variables affecting the habitat suitability of numerous aquatic species. Over the past decades, research efforts on this topic have concentrated on low-land rivers of North-America, whereas mountainous environments have ...

EPFL2016

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

Sohrab Mirshams Shahshahani

We construct a one parameter family of nite time blow ups to the co-rotational wave maps problem from

S^2\times R

to

S^2

parameterized by

\nu \epsilon (\frac{1}{2},1]

. The longitudinal function

u(t,\alpha)

which is the main object of study will be ...

2016

Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

Graph Chatbot

Chat with Graph Search

Chemical machine learning with kernels: The key impact of loss functions

An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Parameter identification of a lithium-ion cell single-particle model through non-invasive testing

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Probabilistic Amplitude Demodulation features in Speech Synthesis for Improving Prosody

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Stream temperature modeling in mountainous environments

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

Chemical machine learning with kernels: The key impact of loss functions

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Probabilistic Amplitude Demodulation features in Speech Synthesis for Improving Prosody

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Stream temperature modeling in mountainous environments

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Parameter identification of a lithium-ion cell single-particle model through non-invasive testing