411 research outputs found
An Adaptive Locally Connected Neuron Model: Focusing Neuron
This paper presents a new artificial neuron model capable of learning its
receptive field in the topological domain of inputs. The model provides
adaptive and differentiable local connectivity (plasticity) applicable to any
domain. It requires no other tool than the backpropagation algorithm to learn
its parameters which control the receptive field locations and apertures. This
research explores whether this ability makes the neuron focus on informative
inputs and yields any advantage over fully connected neurons. The experiments
include tests of focusing neuron networks of one or two hidden layers on
synthetic and well-known image recognition data sets. The results demonstrated
that the focusing neurons can move their receptive fields towards more
informative inputs. In the simple two-hidden layer networks, the focusing
layers outperformed the dense layers in the classification of the 2D spatial
data sets. Moreover, the focusing networks performed better than the dense
networks even when 70 of the weights were pruned. The tests on
convolutional networks revealed that using focusing layers instead of dense
layers for the classification of convolutional features may work better in some
data sets.Comment: 45 pages, a national patent filed, submitted to Turkish Patent
Office, No: -2017/17601, Date: 09.11.201
Assessing Bank performance and the impact of financial restructuring in a macroeconomic framework : a new application
The authors present a simulation model (applied here to Uruguay and implemented in Javelin) that permits analysis of the interaction between a financial system and the economic environment in which it operates. The model allows the user to compute and project the indicators necessary to monitor the performance of a financial institution and to examine how those indicators respond to economic change. Traditionally, economic analysis in the World Bank has focused on eitherthe real or financial sector, but rarely on the interaction between them. The introduction of the extended Revised Minimum Standard Model, or RMSM-X, reflects the Bank's recognition of the importance of incorporating the financial system into the macroeconomy. Nevertheless, the monetary module in the RMSM-X is too aggregated to allow for any meaningful analysis of the viability of a country's financial system, or any institution in particular. By design, the RMSM-X provides only a generic framework, or platform, that then may be adapted to particular cases. The authors develop a tool that uses a time series that shows developing trend lines. The model requires an adequate level of detail and a consistency of content, interpretation, and presentation of the financial and economic data, plus an adequate grouping of banks to ensure that comparisons are between like entities. The model should be useful to financial analysts who need to plan for and forecast the growth and profits of a financial institution, or a group of institutions, and who are interested in capturing the links with the macroeconomy in a fully consistent framework. The model allows the user to compute and project indicators that are necessary to monitor the performance of a financial institution and to examine how these indicators change in response to changes in the macroenvironment. The model should also be valuable to economists interested in assessing the viability of the financial system, particularly in assessing the impact of financial restructuring. When major financial restructuring is involved, model simulations can help policymakers and supervisors to reassure themselves that bank rehabilitation is worth its costs.Banks&Banking Reform,Economic Theory&Research,Environmental Economics&Policies,Financial Economics,Macroeconomic Management
Implicit Theories and Self-efficacy in an Introductory Programming Course
Contribution: This study examined student effort and performance in an
introductory programming course with respect to student-held implicit theories
and self-efficacy. Background: Implicit theories and self-efficacy shed a light
into understanding academic success, which must be considered when developing
effective learning strategies for programming. Research Questions: Are implicit
theories of intelligence and programming, and programming-efficacy related to
each other and student success in programming? Is it possible to predict
student course performance using a subset of these constructs? Methodology: Two
consecutive surveys (N=100 and N=81) were administered to non-CS engineering
students in I\c{s}{\i}k University. Findings: Implicit theories and
self-beliefs are interrelated and correlated with effort, performance, and
previous failures in the course and students explain failure in programming
course with "programming-aptitude is fixed" theory, and also that programming
is a difficult task for themselves.Comment: Programming Education. 8 page
EARLY PERFORMANCE PREDICTION METHODOLOGY FOR MANY-CORES ON CHIP BASED APPLICATIONS
Modern high performance computing applications such as personal computing, gaming, numerical simulations require application-specific integrated circuits (ASICs) that comprises of many cores. Performance for these applications depends mainly on latency of interconnects which transfer data between cores that implement applications by distributing tasks. Time-to-market is a critical consideration while designing ASICs for these applications. Therefore, to reduce design cycle time, predicting system performance accurately at an early stage of design is essential. With process technology in nanometer era, physical phenomena such as crosstalk, reflection on the propagating signal have a direct impact on performance. Incorporating these effects provides a better performance estimate at an early stage. This work presents a methodology for better performance prediction at an early stage of design, achieved by mapping system specification to a circuit-level netlist description.
At system-level, to simplify description and for efficient simulation, SystemVerilog descriptions are employed. For modeling system performance at this abstraction, queueing theory based bounded queue models are applied. At the circuit level, behavioral Input/Output Buffer Information Specification (IBIS) models can be used for analyzing effects of these physical phenomena on on-chip signal integrity and hence performance.
For behavioral circuit-level performance simulation with IBIS models, a netlist must be described consisting of interacting cores and a communication link. Two new netlists, IBIS-ISS and IBIS-AMI-ISS are introduced for this purpose. The cores are represented by a macromodel automatically generated by a developed tool from IBIS models. The generated IBIS models are employed in the new netlists. Early performance prediction methodology maps a system specification to an instance of these netlists to provide a better performance estimate at an early stage of design. The methodology is scalable in nanometer process technology and can be reused in different designs
A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography
Retinal optical coherence tomography (OCT) and optical coherence tomography
angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's
disease (AD). These non-invasive imaging techniques are cost-effective and more
accessible than alternative neuroimaging tools. However, interpreting and
classifying multi-slice scans produced by OCT devices is time-consuming and
challenging even for trained practitioners.
There are surveys on machine learning and deep learning approaches concerning
the automated analysis of OCT scans for various diseases such as glaucoma.
However, the current literature lacks an extensive survey on the diagnosis of
Alzheimer's disease or cognitive impairment using OCT or OCTA. This has
motivated us to do a comprehensive survey aimed at machine/deep learning
scientists or practitioners who require an introduction to the problem. The
paper contains 1) an introduction to the medical background of Alzheimer's
Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging
modalities, 2) a review of various technical proposals for the problem and the
sub-problems from an automated analysis perspective, 3) a systematic review of
the recent deep learning studies and available OCT/OCTA datasets directly aimed
at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the
latter, we used Publish or Perish Software to search for the relevant studies
from various sources such as Scopus, PubMed, and Web of Science. We followed
the PRISMA approach to screen an initial pool of 3073 references and determined
ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis.
We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as
the main issue that is impeding the progress in the field.Comment: Submitted to Computerized Medical Imaging and Graphics. Concept,
methodology, invest, data curation, and writing org.draft by Yasemin Turkan.
Concept, method, writing review editing, and supervision by F. Boray Te
Adaptive Convolution Kernel for Artificial Neural Networks
Many deep neural networks are built by using stacked convolutional layers of
fixed and single size (often 33) kernels. This paper describes a method
for training the size of convolutional kernels to provide varying size kernels
in a single layer. The method utilizes a differentiable, and therefore
backpropagation-trainable Gaussian envelope which can grow or shrink in a base
grid. Our experiments compared the proposed adaptive layers to ordinary
convolution layers in a simple two-layer network, a deeper residual network,
and a U-Net architecture. The results in the popular image classification
datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and ``Faces in the
Wild'' showed that the adaptive kernels can provide statistically significant
improvements on ordinary convolution kernels. A segmentation experiment in the
Oxford-Pets dataset demonstrated that replacing a single ordinary convolution
layer in a U-shaped network with a single 77 adaptive layer can improve
its learning performance and ability to generalize.Comment: 25 page
Adaptive convolution kernel for artificial neural networks
Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 × 3) kernels. This paper describes a method for learning the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagation-trainable Gaussian envelope which can grow or shrink in a base grid. Our experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network, a deeper residual network, and a U-Net architecture. The results in the popular image classification datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and ‘‘Faces in the Wild’’ showed that the adaptive kernels can provide statistically significant improvements on ordinary convolution kernels. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing ordinary convolution layers in a U-shaped network with 7 × 7 adaptive layers can improve its learning performance and ability to generalize.This work was supported by The Scientific and Technological Research Council of Turkey programme ( TUBITAK-1001 no: 118E722 ), Isik University BAP programme, Turkey (no: 16A202 ), and NVIDIA hardware donation of a Tesla K40 GPU unit, Turkey.Publisher's Versio
A study on adaptive locally connected neuron nodel
Bu çalışmada uyarlanır yerel bağlı (odaklanan) nöron modelinin bir incelemesi sunulmuştur. Öncelikle bu modelin varolan diğer nöron modelleri ile ilişkisi incelenmiştir. Daha sonra modelin ileri beslemede çalışması ve geriye yayılım ile eğitilmesi tartışılmıştır. Modelin çalışma prensipleri sentetik sınıflandırma veri kümeleri üzerinde deneylerle gösterilmiştir. Son olarak, basit ve evrişimli ağların saklı katmanlarında odaklı nöronlar kullanılması halinde tam bağlı nöronlara göre daha iyi bir performans elde edilebileceği MNIST, CIFAR10, FASHION gibi popüler imge tanıma veri kümelerinde karşılaştırmalı olarak gösterilmiştir.The manuscript presents a detailed study of adaptive local connected (focusing) neuron model. Our analysis starts with the model’s relation to other neuron models. Then we describe the feed-forward operation and its training with backpropagation gradient descent algorithm. The operation principles of the model were demonstrated with synthetically sampled data sets. Finally, the comparative experiments on popular image recognition datasets such as MNIST, CIFAR10, and FASHION show that using focusing neuron layers can improve the classification performance in some data sets.Publisher's Versio
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