Preface

This volume collects papers presented at the 30th Annual Conference on Mathematical Foundations of Programming Semantics (MFPS XXX), held on the campus of Cornell University, Ithaca, New York, USA, from Thursday, June 12 through Sunday, June 15, 2014. The MFPS conferences are devoted to those areas of mathematics, logic, and computer science that are related to models of computation in general and to the semantics of programming languages in particular. The series particularly stresses providing a forum where researchers in mathematics and computer science can meet and exchange ideas about problems of common interest. As the series also strives to maintain breadth in its scope, the conference strongly encourages participation by researchers in neighboring areas

Exploring Randomly Wired Neural Networks for Climate Model Emulation

Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts, but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this manuscript, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them to their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the serially connected dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired dense layers and assess the impact on model performance for models with 1 million and 10 million parameters. We find average performance improvements of 4.2% across model complexities and prediction tasks, with substantial performance improvements of up to 16.4% in some cases. Furthermore, we find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models

Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset

Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic -- primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a re-evaluation of the top-performing candidate models post-retraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data (a few thousand samples) in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135-times speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs)

Discovering New Interpretable Conservation Laws as Sparse Invariants

Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the Sparse Invariant Detector (SID), an algorithm that auto-discovers conservation laws from differential equations. Its algorithmic simplicity allows robustness and interpretability of the discovered conserved quantities. We show that SID is able to rediscover known and even discover new conservation laws in a variety of systems. For two examples in fluid mechanics and atmospheric chemistry, SID discovers 14 and 3 conserved quantities, respectively, where only 12 and 2 were previously known to domain experts.Comment: The codes are available here: https://github.com/KindXiaoming/si

Um modelo de suporte de QoS para aplicações de tempo real

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológco. Programa de Pos-Graduação em Engenharia Elétrica.Este trabalho apresenta o desenvolvimento de um modelo computacional para possibilitar que a arquitetura SALE possa atender as necessidades de tempo real das empresas virtuais. O modelo foi desenvolvido com a utilização das especificações do RT-CORBA (uma extensão do padrão CORBA para tempo real) para a definição de alguns componentes de sua estrutura, assim como a utilização de especificações de QoS para capturar as necessidades do usuário. Incluído na estrutura do modelo, foi desenvolvida uma heurística para adaptação de deadlines perdidos, que é baseada na diminuição do deadline de uma tarefa por um coeficiente de ajuste a fim de aumentar a prioridade desta, fazendo desta forma, com que a freqüência de escalonamento das tarefas seja elevada. Como validação da proposta foram feitas simulações de uso do modelo em um simulador já existente, que foi adaptado para o correto uso, onde um número de tarefas periódicas eram preestabelecidas e tentavam executar respeitando seus deadlines em um servidor. Os resultados obtidos com estas simulações mostraram que a heurística de adaptação de deadline proposta no modelo, quando analisa em seu desempenho individual de atuação sobre uma tarefa específica, atinge resultados satisfatórios, visto que superou a abordagem utilizada para comparação (escalonamento EDF) com rendimentos mais expressivos

Gestión administrativa y satisfacción del usuario de la Institución Educativa Pública 80412 de Pacasmayo, 2021

Esta investigación tuvo como propósito determinar la relación entre la gestión administrativa y la satisfacción del usuario en la institución educativa pública 80412 de Pacasmayo. Es una investigación aplicada con diseño no experimental y nivel correlacional; la muestra de 220 padres de familia, se usó como instrumentos dos cuestionarios, uno para la gestión administrativa y otro para la satisfacción del usuario. Los resultados se procesaron con la estadística descriptiva e inferencial, usando Excel 2019 y SPSS v 26 para identificar la correlación existente entre las variables objeto de estudio, se observó que Rho de Spearman = 0,865, hallándose una correlación positiva y significativa, con significancia = 0,000 inferior al 5%; es decir, que la gestión administrativa se relaciona significativamente con la satisfacción del usuario de la institución educativa pública 80412 de Pacasmayo, además la dimensión planificación se relaciona con la satisfacción del usuario (Spearman fue 0,663); la dimensión organización se relaciona con la satisfacción del usuario (Spearman fue 0,856); la dimensión dirección se relaciona con la satisfacción del usuario (Spearman fue 0,856); la dimensión dirección se relaciona con la satisfacción del usuario (Spearman fue 0,504) y la dimensión control se relaciona con la satisfacción del usuario (Spearman fue de 0,878)

Land cover change impacts on atmospheric chemistry: simulating projected large-scale tree mortality in the United States

Land use and land cover changes impact climate and air quality by altering the exchange of trace gases between the Earth's surface and atmosphere. Large-scale tree mortality that is projected to occur across the United States as a result of insect and disease may therefore have unexplored consequences for tropospheric chemistry. We develop a land use module for the GEOS-Chem global chemical transport model to facilitate simulations involving changes to the land surface, and to improve consistency across land–atmosphere exchange processes. The model is used to test the impact of projected national-scale tree mortality risk through 2027 estimated by the 2012 USDA Forest Service National Insect and Disease Risk Assessment. Changes in biogenic emissions alone decrease monthly mean O₃ by up to 0.4 ppb, but reductions in deposition velocity compensate or exceed the effects of emissions yielding a net increase in O₃ of more than 1 ppb in some areas. The O₃ response to the projected change in emissions is affected by the ratio of baseline NO[subscript x]: VOC concentrations, suggesting that in addition to the degree of land cover change, tree mortality impacts depend on whether a region is NO[subscript x]-limited or NO[subscript x]-saturated. Consequently, air quality (as diagnosed by the number of days that 8 h average O₃ exceeds 70 ppb) improves in polluted environments where changes in emissions are more important than changes to dry deposition, but worsens in clean environments where changes to dry deposition are the more important term. The influence of changes in dry deposition demonstrated here underscores the need to evaluate treatments of this physical process in models. Biogenic secondary organic aerosol loadings are significantly affected across the US, decreasing by 5–10 % across many regions, and by more than 25 % locally. Tree mortality could therefore impact background aerosol loadings by between 0.5 and 2 µg m⁻³. Changes to reactive nitrogen oxide abundance and partitioning are also locally important. The regional effects simulated here are similar in magnitude to other scenarios that consider future biofuel cropping or natural succession, further demonstrating that biosphere–atmosphere exchange should be considered when predicting future air quality and climate. We point to important uncertainties and further development that should be addressed for a more robust understanding of land cover change feedbacks.National Science Foundation (U.S.) (Grant AGC-1238109

Drivers of Innovation in Education and Training in Food Science and Technology

info:eu-repo/semantics/publishedVersio