A parallel supercomputer implementation of a biological inspired neural network and its use for pattern recognition

Abstract : A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP’s Mammouth parallel with 64 notes (128 cores)

Phosphorylation du CTD de l'ARN polymérase II et impact de l'histone H2A.Z sur le positionnement des nucléosomes chez S. cerevisiae

La phosphorylation du domaine C-terminal de l’ARN polymérase II permet à ce complexe protéique d’exécuter la transcription des gènes, en plus de coupler à la transcription des événements moléculaires comme la maturation des ARNm. Mes résultats montrent que même si cette phosphorylation suit un patron similaire à l’ensemble des gènes, il existe des exceptions pouvant être dues à des mécanismes alternatifs de phosphorylation du CTD. Le présent ouvrage s’intéresse également au rôle qu’occupe la variante d’histone H2A.Z dans l’organisation de la chromatine. Des études précédentes on montré que le positionnement de certains nucléosomes le long de l’ADN serait influencé par H2A.Z et aurait une influence sur la capacité de transcrire les gènes. Par une approche génomique utilisant les puces à ADN, j’ai cartographié l’impact de la délétion de H2A.Z sur la structure des nucléosomes. Enfin, des résultats intéressants sur la dynamique d’incorporation de H2A.Z à la chromatine ont été obtenus.RNA Polymerase II is the molecular complex responsible for the transcription of class II genes. Proper transcription and associated events such as mRNA processing are thought to require the phosphorylation of its C-terminal domain. Here I show that this phosphorylation follows a similar pattern for most of the genes, althought some exceptions exist. These exceptions could be explained by alternative phosphorylation mechanisms. Also, this work provides data on how the variant histone H2A.Z influences chromatin structure. Previous studies have shown a role for H2A.Z in the positioning of some nucleosomes along the DNA, which would impact the ability to transcribe genes. Here I used a microarray technology to profile nucleosome positions in a genome-wide manner. My data provide further evidence that H2A.Z influences nucleosome positioning. Interesting results regarding the dynamics of H2A.Z incorporation into chromatin are also shown

FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs

Here, we introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability integral transform penalty to reduce model misspecification. We demonstrate that learning the joint dynamics of IV surfaces and prices produces market scenarios that are consistent with historical features and lie within the sub-manifold of surfaces that are essentially free of static arbitrage. Finally, we demonstrate that delta hedging using the simulated surfaces generates profit and loss (P&L) distributions that are consistent with realised P&Ls.Comment: 30 pages, 12 figures, 5 table

Learning the Efficient Frontier

The efficient frontier (EF) is a fundamental resource allocation problem where one has to find an optimal portfolio maximizing a reward at a given level of risk. This optimal solution is traditionally found by solving a convex optimization problem. In this paper, we introduce NeuralEF: a fast neural approximation framework that robustly forecasts the result of the EF convex optimization problem with respect to heterogeneous linear constraints and variable number of optimization inputs. By reformulating an optimization problem as a sequence to sequence problem, we show that NeuralEF is a viable solution to accelerate large-scale simulation while handling discontinuous behavior.Comment: Accepted at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

A parallel supercomputer implementation of a biological inspired neural network and its use for pattern recognition

Abstract : A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP’s Mammouth parallel with 64 notes (128 cores)

Designing a resilience-based intervention program for children with cancer and their families: a study protocol

Background: Advances in pediatric oncology have significantly increased survival rates, yet have introduced challenges in managing long-term treatment side effects. This study process introduces an interdisciplinary clinical intervention program rooted in the family resilience framework, aimed at improving well-being across the cancer trajectory for children and their families, especially those in Canadian communities far from specialized oncology centers with limited access to resources. Methods: Employing an intervention mapping approach, this program collaboratively involves patients, families, professionals, and researchers. It aims to identify vulnerability factors, establish a logic model of change, and devise comprehensive strategies that include professional interventions alongside self-management tools. These strategies, tailored to address biopsychosocial and spiritual challenges, are adapted to the unique contexts of communities distant from specialized cancer treatment centers. A mixed-methods approach will evaluate program effectiveness. Expected results: Anticipated outcomes include the empowerment of families with self-management tools and professional support, designed to mitigate biopsychosocial and spiritual complications. By addressing the specific needs and limitations of these communities, the program strives to improve the overall health and well-being of both undergoing treatment and survivorship phases. Discussion: By focusing on comprehensive care that includes both professional interventions and self-management, this initiative marks a significant shift toward a holistic, family-centered approach in pediatric oncology care for remote communities. It underlines the necessity of accessible interventions that confront immediate and long-term challenges, aiming to elevate the standard of care by emphasizing resilience, professional support, and family empowerment in underserved area