Forcing small models of conditions on program interleaving for detection of concurrent bugs

2009 ◽  
Author(s):  
Ehud Trainin ◽  
Yarden Nir-Buchbinder ◽  
Rachel Tzoref-Brill ◽  
Aviad Zlotnick ◽  
Shmuel Ur ◽  
...  
Keyword(s):  
Small Models

Nano sensors: Designing and Fabrication, Applications for flexible devices and Future Perspectives

2020 ◽  
Vol 16 ◽  
Author(s):  
Muhammad Bilal Tahir ◽  
Aleena Shoukat ◽  
Tahir Iqbal ◽  
Asma Ayub ◽  
Saff-e Awal ◽  
...  
Keyword(s):  
Self Assembly ◽  
Multidisciplinary Approach ◽  
Future Perspectives ◽  
Flexible Devices ◽  
More Care ◽  
Potential Applications ◽  
Nano Sensors ◽  
Mechanical Sensor ◽  
Small Models ◽  
Near Future

: The field of nanosensors has been gaining a lot of attention due to its properties such as mechanical and electrical ever since its first discovery by Dr. Wolter and first mechanical sensor in 1994. The rapidly growing demand of nanosensors has become profitable for a multidisciplinary approach in designing and fabrication of materials and strategies for potential applications. Frequent stimulating advancements are being suggested and established in recent years and thus heading towards multiple applications including food safety, healthcare, environmental monitoring, and biomedical research. Nanofabrication being an efficient method has been used in different industries like medical pharmaceutical for their complex functional geometry at a lower scale. These nanofabrications apply through different methods. There are five most commonly known methods which are frequently used, including top-down lithography, molecular self-assembly, bottom-up assembly, heat and pull method for fabrication of biosensors, etching for fabrication of nanosensors etc. Nanofabrication help at the nanoscale to design and work with small models. But these models due to their small size and being sensitive need more care for use as well as more training and experience to do work with. All methods used for nanofabrication are good and helpful. But more preferred is molecular self-assembly as it is helpful in mass production. Nanofabrication has become an emerging and developing field and it assumed that in near future our world is known by the new devices of nanofabrication.

scholarly journals Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1511
Author(s):  
Taylor Simons ◽  
Dah-Jye Lee
Keyword(s):  
Neural Networks ◽  
Visual Inspection ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Quality Inspection ◽  
Agricultural Produce ◽  
Resource Limited ◽  
Inspection Tasks ◽  
Computational Resources ◽  
Small Models

There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target embedded visual inspection tasks, like quality inspection sorting on manufactured parts and agricultural produce sorting. We propose a new binarized convolutional layer, called the neural jet features layer, that learns well-known classic computer vision kernels that are efficient to calculate as a group. We show that on visual inspection tasks, neural jet features perform comparably to standard BNN convolutional layers while using less computational resources. We also show that neural jet features tend to be more stable than BNN convolution layers when training small models.

scholarly journals Small models, large cardinals, and induced ideals

2021 ◽  
Vol 172 (2) ◽  
pp. 102889
Author(s):  
Peter Holy ◽  
Philipp Lücke
Keyword(s):  
Large Cardinals ◽  
Small Models

scholarly journals Choosing between identification schemes in noisy-news models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Joshua C. C. Chan ◽  
Eric Eisenstat ◽  
Gary Koop
Keyword(s):  
Bayesian Methods ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Identification Scheme ◽  
Identification Schemes ◽  
The One ◽  
Small Models ◽  
Structural Vector

AbstractThis paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.

scholarly journals Structural Analysis of High-Spin States of S0-S4 at OEC Complex: A Theoretical Approach of Small Models

2016 ◽  
Author(s):  
Eduardo W. Castilho-Almeida ◽  
Diego Paschoal ◽  
Hélio F. dos Santos ◽  
Patrick J. O'Malley ◽  
Wagner B. de Almeida
Keyword(s):  
Structural Analysis ◽  
High Spin ◽  
Theoretical Approach ◽  
Spin States ◽  
High Spin States ◽  
Small Models

scholarly journals A Simple Ensemble Learning Knowledge Distillation

2020 ◽  
Author(s):  
Himel Das Gupta ◽  
Kun Zhang ◽  
Victor S. Sheng
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Performance Enhancement ◽  
Portable Devices ◽  
Computational Power ◽  
Small Machine ◽  
Learning Tasks ◽  
Machine Learning Applications ◽  
Knowledge Distillation ◽  
Small Models

Deep neural network (DNN) has shown significant improvement in learning and generalizing different machine learning tasks over the years. But it comes with an expense of heavy computational power and memory requirements. We can see that machine learning applications are even running in portable devices like mobiles and embedded systems nowadays, which generally have limited resources regarding computational power and memory and thus can only run small machine learning models. However, smaller networks usually do not perform very well. In this paper, we have implemented a simple ensemble learning based knowledge distillation network to improve the accuracy of such small models. Our experimental results prove that the performance enhancement of smaller models can be achieved through distilling knowledge from a combination of small models rather than using a cumbersome model for the knowledge transfer. Besides, the ensemble knowledge distillation network is simpler, time-efficient, and easy to implement.

The present is the key to the paleo-past: Charles R. Knight’s reconstruction of extinct beasts for the Field Museum, Chicago

2021 ◽  
Author(s):  
Renee M. Clary
Keyword(s):  
Nature Of Science ◽  
Scientific Progress ◽  
Life Forms ◽  
Public Understanding ◽  
Correct Position ◽  
Field Museum ◽  
Scientific Staff ◽  
Full Control ◽  
Evolution Of Life ◽  
Small Models

ABSTRACT Although he was legally blind, Charles R. Knight (1874–1953) established himself as the premier paleontological artist in the early 1900s. When the Field Museum, Chicago, commissioned a series of large paintings to document the evolution of life, Knight was the obvious choice. Knight considered himself an artist guided by science; he researched and illustrated living animals and modern landscapes to better understand and represent extinct life forms within their paleoecosystems. Knight began the process by examining fossil skeletons; he then constructed small models to recreate the animals’ life anatomy and investigate lighting. Once details were finalized, Knight supervised assistants to transfer the study painting to the final mural. The Field Museum mural process, a monumental task of translating science into public art, was accompanied by a synergistic tension between Knight, who wanted full control over his artwork, and the museum’s scientific staff; the correct position of an Eocene whale’s tail—whether uplifted or not—documents a critical example. Although modern scientific understanding has rendered some of Knight’s representations obsolete, the majority of his 28 murals remain on display in the Field Museum’s Evolving Planet exhibit. Museum educators contrast these murals with contemporary paleontological knowledge, thereby demonstrating scientific progress for better public understanding of the nature of science.

scholarly journals Small Models for Big Data

2020 ◽  
Vol 107 (4) ◽  
pp. 710-711 ◽  
Author(s):  
Hitesh B. Mistry ◽  
David Orrell
Keyword(s):  
Big Data ◽  
Small Models

Solving Large and Small Models on Microcomputers

1999 ◽  
Vol 32 (2) ◽  
pp. 6218-6223
Author(s):  
Jean-Louis Brillet
Keyword(s):  
Small Models
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