Publications (20)
Dataset Distillation as Pushforward Optimal Quantization
Hong Ye Tan, Emma Slade
Dataset distillation aims to find a synthetic training set such that training on the synthetic data achieves similar performance to training on real data, with orders of magnitude…
A Monte Carlo global analysis of the Standard Model Effective Field Theory: the top quark sector
Nathan P. Hartland, Fabio Maltoni, Emanuele R. Nocera +4
We present a novel framework for carrying out global analyses of the Standard Model Effective Field Theory (SMEFT) at dimension-six: SMEFiT. This approach is based on the Monte Car…
A Monte Carlo analysis of the SMEFT in the top quark sector
Emma Slade
We present a framework for carrying out global analyses of the Standard Model Effective Field Theory: SMEFiT. This approach is based on the Monte Carlo replica method, widely used…
GNisi: A graph network for reconstructing Ising models from multivariate binarized data
Emma Slade, Sonya Kiselgof, Lena Granovsky +1
Ising models are a simple generative approach to describing interacting binary variables. They have proven useful in a number of biological settings because they enable one to repr…
Combined SMEFT interpretation of Higgs, diboson, and top quark data from the LHC
Jacob J. Ethier, Giacomo Magni, Fabio Maltoni +6
We present a global interpretation of Higgs, diboson, and top quark production and decay measurements from the LHC in the framework of the Standard Model Effective Field Theory (SM…
Towards global fits in EFT's and New Physics implications
Emma Slade
I discuss recent progress on fits to dimension-six operators in the Standard Model Effective Theory (SMEFT). I focus on the top quark sector of the SMEFT, as well as the theoretica…
Mining of Single-Class by Active Learning for Semantic Segmentation
Hugues Lambert, Emma Slade
Several Active Learning (AL) policies require retraining a target model several times in order to identify the most informative samples and rarely offer the option to focus on the…
Parton distributions from high-precision collider data
The NNPDF Collaboration, Richard D. Ball, Valerio Bertone +13
We present a new set of parton distributions, NNPDF3.1, which updates NNPDF3.0, the first global set of PDFs determined using a methodology validated by a closure test. The update…
Beyond permutation equivariance in graph networks
Emma Slade, Francesco Farina
In this draft paper, we introduce a novel architecture for graph networks which is equivariant to the Euclidean group in -dimensions. The model is designed to work with graph ne…
Precision determination of the strong coupling constant within a global PDF analysis
Richard D. Ball, Stefano Carrazza, Luigi Del Debbio +5
We present a determination of the strong coupling constant based on the NNPDF3.1 determination of parton distributions, which for the first time includes constraints fr…
The small-x gluon from forward charm production: implications for a 100 TeV proton collider
Rhorry Gauld, Juan Rojo, Emma Slade
We review the constraints on the small-x gluon PDF that can be derived by exploiting the forward D meson production data from the LHCb experiment at and 13 TeV. We t…
Constraining the SMEFT with Bayesian reweighting
Samuel van Beek, Emanuele R. Nocera, Juan Rojo +1
We illustrate how Bayesian reweighting can be used to incorporate the constraints provided by new measurements into a global Monte Carlo analysis of the Standard Model Effective Fi…
Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopy
Christian John Hurry, Jinjie Zhang, Olubukola Ishola +2
Developing computer vision for high-content screening is challenging due to various sources of distribution-shift caused by changes in experimental conditions, perturbagens, and fl…
Self-supervised learning of multi-omics embeddings in the low-label, high-data regime
Christian John Hurry, Emma Slade
Contrastive, self-supervised learning (SSL) is used to train a model that predicts cancer type from miRNA, mRNA or RPPA expression data. This model, a pretrained FT-Transformer, is…
Cuts for two-body decays at colliders
Gavin P. Salam, Emma Slade
Fixed-order perturbative calculations of fiducial cross sections for two-body decay processes at colliders show disturbing sensitivity to unphysically low momentum scales and, in t…
Computing Tools for the SMEFT
Editors, :, Jason Aebischer +22
The increasing interest in the phenomenology of the Standard Model Effective Field Theory (SMEFT), has led to the development of a wide spectrum of public codes which implement aut…
Data efficiency in graph networks through equivariance
Francesco Farina, Emma Slade
We introduce a novel architecture for graph networks which is equivariant to any transformation in the coordinate embeddings that preserves the distance between neighbouring nodes.…
Symmetry-driven graph neural networks
Francesco Farina, Emma Slade
Exploiting symmetries and invariance in data is a powerful, yet not fully exploited, way to achieve better generalisation with more efficiency. In this paper, we introduce two grap…
Deep reinforced active learning for multi-class image classification
Emma Slade, Kim M. Branson
High accuracy medical image classification can be limited by the costs of acquiring more data as well as the time and expertise needed to label existing images. In this paper, we a…
Direct photon production and PDF fits reloaded
John M. Campbell, Juan Rojo, Emma Slade +1
Direct photon production in hadronic collisions provides a handle on the gluon PDF by means of the QCD Compton scattering process. In this work we revisit the impact of direct phot…