Parton energy loss at strong coupling and the universal bound

The apparent universality of jet quenching observed in heavy ion collisions at RHIC for light and heavy quarks, as well as for quarks and gluons, is very puzzling and calls for a theoretical explanation. Recently it has been proposed that the synchrotron--like radiation at strong coupling gives rise to a universal bound on the energy of a parton escaping from the medium. Since this bound appears quite low, almost all of the observed particles at high transverse momentum have to originate from the surface of the hot fireball. Here I make a first attempt of checking this scenario against the RHIC data and formulate a "Universal Bound Model" of jet quenching that can be further tested at RHIC and LHC.Comment: 8 pages, 2 figures, invited plenary talk given at "Hard Probes 2008" Conference, 8-14 June 2008, Illa da Toxa, Galicia, Spai

Toma de Decisiones Participativas y Manejo de Conflictos Internos de la Institución Educativa Secundaria Caminaca, UGEL Azángaro, Puno 2017

La presente investigación titulada Toma de decisiones participativas y manejo de conflictos internos de la institución educativa secundaria Caminaca, UGEL Azángaro, Puno 2017 El problema general fue existe relación entre la toma de decisiones participativas y manejo de conflictos internos de los docentes de la institución educativa secundaria Caminaca, Azángaro, Puno 2018. El objetivo general fue determinar la relación existente entre la toma de decisiones participativas y el manejo de conflictos internos de los docentes de la institución educativa secundaria Caminaca, Azángaro, Puno 2018 y los objetivos específicos fueron indicar el nivel de toma de decisiones participativas que tienen los docentes de la institución educativa secundaria Caminaca, Azángaro, Puno 2018; identificar el nivel de manejo de conflictos internos que tienen los docentes de la institución educativa secundaria Caminaca, Azángaro, Puno 2018 y determinar la correlación entre la toma de decisiones participativas y manejo de conflictos internos de los docentes de la institución educativa secundaria Caminaca, Azángaro, Puno 2018. La hipótesis comprobada fue existe correlación entre la toma de decisiones participativa y manejo de conflictos internos de los docentes de la institución educativa secundaria Caminaca, Azángaro, Puno 2018. Sus variables fueron la independiente que fue la toma de decisiones participativa y la dependiente el manejo de conflictos internos. Su diseño fue descriptivo correlacional. Las técnicas utilizadas fueron las encuestas y los instrumentos fueron cuestionarios. Su población fue de 30 profesores. Su conclusión principal fue que tenemos una Correlación de Pearson de 0,7 que un correlación positiva alta, además tiene una significancia de 0,05 que nos permite comprobar la hipótesis alterna positiva que existe relación positiva entre variables y rechazar la hipótesis nula

Denoising diffusion models for out-of-distribution detection

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://github.com/marksgraham/ddpm-ood

Privacy Distillation:Reducing Re-identification Risk of Multimodal Diffusion Models

Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a text-to-image generative model to teach another model without exposing it to identifiable data. Here, we are interested in the privacy issue faced by a data provider who wishes to share their data via a multimodal generative model. A question that immediately arises is ``How can a data provider ensure that the generative model is not leaking identifiable information about a patient?''. Our solution consists of (1) training a first diffusion model on real data (2) generating a synthetic dataset using this model and filtering it to exclude images with a re-identifiability risk (3) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with privacy distillation can effectively reduce re-identification risk whilst maintaining downstream performance

A 3D generative model of pathological multi-modal MR images and segmentations

Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there has been an increased interest in using these models for data augmentation and synthetic data sharing, using architectures such as generative adversarial networks (GANs) or diffusion models (DMs). Nonetheless, the application of synthetic data to tasks such as 3D magnetic resonance imaging (MRI) segmentation remains limited due to the lack of labels associated with the generated images. Moreover, many of the proposed generative MRI models lack the ability to generate arbitrary modalities due to the absence of explicit contrast conditioning. These limitations prevent the user from adjusting the contrast and content of the images and obtaining more generalisable data for training task-specific models. In this work, we propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations, where the user can condition on specific pathological phenotypes and contrasts. The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies. We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data.Comment: Accepted for publication at the 2023 Deep Generative Models (DGM4MICCAI) MICCAI workshop (Vancouver, Canada

Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models

Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a text-to-image generative model to teach another model without exposing it to identifiable data. Here, we are interested in the privacy issue faced by a data provider who wishes to share their data via a multimodal generative model. A question that immediately arises is ``How can a data provider ensure that the generative model is not leaking identifiable information about a patient?''. Our solution consists of (1) training a first diffusion model on real data (2) generating a synthetic dataset using this model and filtering it to exclude images with a re-identifiability risk (3) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with privacy distillation can effectively reduce re-identification risk whilst maintaining downstream performance

Default mode network maturation and environmental adversities during childhood

Default mode network (DMN) plays a central role in cognition and brain disorders. It has been shown that adverse environmental conditions impact neurodevelopment, but how these conditions impact in DMN maturation is still poorly understood. This article reviews representative neuroimaging functional studies addressing the interactions between DMN development and environmental factors, focusing on early life adversities, a critical period for brain changes. Studies focused on this period of life offer a special challenge: to disentangle the neurodevelopmental connectivity changes from those related to environmental conditions. We first summarized the literature on DMN maturation, providing an overview of both typical and atypical development patterns in childhood and early adolescence. Afterward, we focused on DMN changes associated with chronic exposure to environmental adversities during childhood. This summary suggests that changes in DMN development could be a potential allostatic neural feature associated with an embodiment of environmental circumstances. Finally, we discuss about some key methodological issues that should be considered in paradigms addressing environmental adversities and open questions for future investigations

Denoising diffusion models for out-of-distribution detection

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck - such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://github.com/marksgraham/ddpm-ood

Regional dynamics of the resting brain in amyotrophic lateral sclerosis using fALFF and ReHo analyses

Resting state functional magnetic resonance imaging (rs-fMRI) has been playing an important role in the study of amyotrophic lateral sclerosis (ALS). Although functional connectivity is widely studied, the patterns of spontaneous neural activity of the resting brain are important mechanisms that have been used recently to study a variety of conditions but remain less explored in ALS. Here we have used fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) to study the regional dynamics of the resting brain of non-demented ALS patients compared with healthy controls. As expected, we found the sensorimotor network (SMN) with changes in fALFF and ReHo but also found the default mode (DMN), frontoparietal (FPN), salience (SN) networks altered and the cerebellum, although no structural changes between ALS patients and controls were reported in the regions with fALFF and ReHo changes. We show an altered pattern in the spontaneous low frequency oscillations that is not confined to the motor areas and reveal a more widespread involvement of non-motor regions, including those responsible for cognition