1,478 research outputs found
An advanced tool for Preventive Voltage Security Assessment
peer reviewedThis paper deals with methods for the preventive assessment of voltage security with respect
to contingencies. We describe a computing tool for the determination of secure operation limits, together with methods for contingency filtering. Examples from two very different real-life systems are provided. We outline extensions in the field of preventive control
Improving GPU Simulations of Spiking Neural P Systems
In this work we present further extensions and improvements
of a Spiking Neural P system (for short, SNP systems) simulator on graphics
processing units (for short, GPUs). Using previous results on representing SNP
system computations using linear algebra, we analyze and implement a compu-
tation simulation algorithm on the GPU. A two-level parallelism is introduced
for the computation simulations. We also present a set of benchmark SNP sys-
tems to stress test the simulation and show the increased performance obtained
using GPUs over conventional CPUs. For a 16 neuron benchmark SNP system
with 65536 nondeterministic rule selection choices, we report a 2.31 speedup of
the GPU-based simulations over CPU-based simulations.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
Molecular sciences address a wide range of problems involving molecules of
different types and sizes and their complexes. Recently, geometric deep
learning, especially Graph Neural Networks, has shown promising performance in
molecular science applications. However, most existing works often impose
targeted inductive biases to a specific molecular system, and are inefficient
when applied to macromolecules or large-scale tasks, thereby limiting their
applications to many real-world problems. To address these challenges, we
present PAMNet, a universal framework for accurately and efficiently learning
the representations of three-dimensional (3D) molecules of varying sizes and
types in any molecular system. Inspired by molecular mechanics, PAMNet induces
a physics-informed bias to explicitly model local and non-local interactions
and their combined effects. As a result, PAMNet can reduce expensive
operations, making it time and memory efficient. In extensive benchmark
studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy
and efficiency in three diverse learning tasks: small molecule properties, RNA
3D structures, and protein-ligand binding affinities. Our results highlight the
potential for PAMNet in a broad range of molecular science applications.Comment: Published in Scientific Reports (DOI: 10.1038/s41598-023-46382-8
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis
Survival analysis, time-to-event analysis, is an important problem in
healthcare since it has a wide-ranging impact on patients and palliative care.
Many survival analysis methods have assumed that the survival data is centrally
available either from one medical center or by data sharing from multi-centers.
However, the sensitivity of the patient attributes and the strict privacy laws
have increasingly forbidden sharing of healthcare data. To address this
challenge, the research community has looked at the solution of decentralized
training and sharing of model parameters using the Federated Learning (FL)
paradigm. In this paper, we study the utilization of FL for performing survival
analysis on distributed healthcare datasets. Recently, the popular Cox
proportional hazard (CPH) models have been adapted for FL settings; however,
due to its linearity and proportional hazards assumptions, CPH models result in
suboptimal performance, especially for non-linear, non-iid, and heavily
censored survival datasets. To overcome the challenges of existing federated
survival analysis methods, we leverage the predictive accuracy of the deep
learning models and the power of pseudo values to propose a first-of-its-kind,
pseudo value-based deep learning model for federated survival analysis (FSA)
called FedPseudo. Furthermore, we introduce a novel approach of deriving pseudo
values for survival probability in the FL settings that speeds up the
computation of pseudo values. Extensive experiments on synthetic and real-world
datasets show that our pseudo valued-based FL framework achieves similar
performance as the best centrally trained deep survival analysis model.
Moreover, our proposed FL approach obtains the best results for various
censoring settings
Managing Household Waste through Transfer Learning
As the world continues to face the challenges of climate change, it is
crucial to consider the environmental impact of the technologies we use. In
this study, we investigate the performance and computational carbon emissions
of various transfer learning models for garbage classification. We examine the
MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models.
Our findings indicate that the EfficientNetV2 family achieves the highest
accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model
requires more time and produces higher carbon emissions. ResNet50 outperforms
ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger
carbon footprint. We conclude that EfficientNetV2S is the most sustainable and
accurate model with 96.41% accuracy. Our research highlights the significance
of considering the ecological impact of machine learning models in garbage
classification.Comment: 11 pages, 9 figure
Personalized Federated Learning with Multi-branch Architecture
Federated learning (FL) is a decentralized machine learning technique that
enables multiple clients to collaboratively train models without requiring
clients to reveal their raw data to each other. Although traditional FL trains
a single global model with average performance among clients, statistical data
heterogeneity across clients has resulted in the development of personalized FL
(PFL), which trains personalized models with good performance on each client's
data. A key challenge with PFL is how to facilitate clients with similar data
to collaborate more in a situation where each client has data from complex
distribution and cannot determine one another's distribution. In this paper, we
propose a new PFL method (pFedMB) using multi-branch architecture, which
achieves personalization by splitting each layer of a neural network into
multiple branches and assigning client-specific weights to each branch. We also
design an aggregation method to improve the communication efficiency and the
model performance, with which each branch is globally updated with weighted
averaging by client-specific weights assigned to the branch. pFedMB is simple
but effective in facilitating each client to share knowledge with similar
clients by adjusting the weights assigned to each branch. We experimentally
show that pFedMB performs better than the state-of-the-art PFL methods using
the CIFAR10 and CIFAR100 datasets.Comment: Accepted by IJCNN 202
Concurrent stochastic methods for global optimization
The global optimization problem, finding the lowest minimizer of a nonlinear function of several variables that has multiple local minimizers, appears well suited to concurrent computation. This paper presents a new parallel algorithm for the global optimization problem. The algorithm is a stochastic method related to the multi-level single-linkage methods of Rinnooy Kan and Timmer for sequential computers. Concurrency is achieved by partitioning the work of each of the three main parts of the algorithm, sampling, local minimization start point selection, and multiple local minimizations, among the processors. This parallelism is of a coarse grain type and is especially well suited to a local memory multiprocessing environment. The paper presents test results of a distributed implementation of this algorithm on a local area network of computer workstations. It also summarizes the theoretical properties of the algorithm
A Finite-Horizon Approach to Active Level Set Estimation
We consider the problem of active learning in the context of spatial sampling
for level set estimation (LSE), where the goal is to localize all regions where
a function of interest lies above/below a given threshold as quickly as
possible. We present a finite-horizon search procedure to perform LSE in one
dimension while optimally balancing both the final estimation error and the
distance traveled for a fixed number of samples. A tuning parameter is used to
trade off between the estimation accuracy and distance traveled. We show that
the resulting optimization problem can be solved in closed form and that the
resulting policy generalizes existing approaches to this problem. We then show
how this approach can be used to perform level set estimation in higher
dimensions under the popular Gaussian process model. Empirical results on
synthetic data indicate that as the cost of travel increases, our method's
ability to treat distance nonmyopically allows it to significantly improve on
the state of the art. On real air quality data, our approach achieves roughly
one fifth the estimation error at less than half the cost of competing
algorithms
Learning Early Detection of Emergencies from Word Usage Patterns on Social Media
In the early stages of an emergency, information extracted
from social media can support crisis response with evidence-based content.
In order to capture this evidence, the events of interest must be
first promptly detected. An automated detection system is able to activate
other tasks, such as preemptive data processing for extracting eventrelated
information. In this paper, we extend the human-in-the-loop approach
in our previous work, TriggerCit, with a machine-learning-based
event detection system trained on word count time series and coupled
with an automated lexicon building algorithm.We design this framework
in a language-agnostic fashion. In this way, the system can be deployed
to any language without substantial effort. We evaluate the capacity of
the proposed work against authoritative flood data for Nepal recorded
over two years
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