scholarly journals Circuit convergence study using machine learning compact models

2021 ◽  
Author(s):  
Zheng-Kai Yang ◽  
Ming-Hsien Hsu ◽  
Chung Yuan Chang ◽  
Ya-Wen Ho ◽  
PO-NING LIU ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Transient Analysis ◽  
New Technologies ◽  
The Other ◽  
Square Root ◽  
Activation Functions ◽  
Model Merging ◽  
Convergence Study ◽  
Model Formation

Machine learning (ML) compact device models (CM) have emerged as an alternative to physics-based CMs. ML CMs can find a mathematical model close to the device characteristics without much prior knowledge, which saves the time of model formation. Additionally, versatile capabilities such as process-awareness, model merging, and fitting new technologies, promote the usage of ML CMs. While ML CMs draw great attention in CAD, their convergence in SPICE has not been carefully studied. Here different activation functions are used to create ML CMs, and then the circuit convergence is tested. We found that inverse square root unit (ISRU) activation has the best convergence. Besides, gate-to-source and gate-to-drain capacitance is founded to benefit the convergence in transient analysis. The circuit convergence rate is 100% for ISRU, sigmoid, and tanh when the capacitor is present. On the other hand, ISRU significantly outperforms other activation functions in DC sweep, achieving 81% convergence. If quasi-static transient analysis is employed to replace DC sweep, 100% convergence is achieved by ISRU. Due to its superior convergence, ISRU is the most promising for future ML CMs in SPICE.

Data Mining for Model Identification

2011 ◽  
pp. 438-444
Author(s):  
Diego Liberati
Keyword(s):  
Mathematical Model ◽  
New Technologies ◽  
A Priori ◽  
Large Data ◽  
Point Of View ◽  
The Other ◽  
Gene Expressions ◽  
Other Hand ◽  
Hybrid Dynamics ◽  
Non Linear

In many fields of research, as well as in everyday life, it often turns out that one has to face a huge amount of data, without an immediate grasp of an underlying simple structure, often existing. A typical example is the growing field of bio-informatics, where new technologies, like the so-called Micro-arrays, provide thousands of gene expressions data on a single cell in a simple and fast integrated way. On the other hand, the everyday consumer is involved in a process not so different from a logical point of view, when the data associated to his fidelity badge contribute to the large data base of many customers, whose underlying consuming trends are of interest to the distribution market. After collecting so many variables (say gene expressions, or goods) for so many records (say patients, or customers), possibly with the help of wrapping or warehousing approaches, in order to mediate among different repositories, the problem arise of reconstructing a synthetic mathematical model capturing the most important relations between variables. To this purpose, two critical problems must be solved: 1 To select the most salient variables, in order to reduce the dimensionality of the problem, thus simplifying the understanding of the solution 2 To extract underlying rules implying conjunctions and/or disjunctions between such variables, in order to have a first idea of their even non linear relations, as a first step to design a representative model, whose variables will be the selected ones When the candidate variables are selected, a mathematical model of the dynamics of the underlying generating framework is still to be produced. A first hypothesis of linearity may be investigated, usually being only a very rough approximation when the values of the variables are not close to the functioning point around which the linear approximation is computed. On the other hand, to build a non linear model is far from being easy: the structure of the non linearity needs to be a priori known, which is not usually the case. A typical approach consists in exploiting a priori knowledge to define a tentative structure, and then to refine and modify it on the training subset of data, finally retaining the structure that best fits a cross-validation on the testing subset of data. The problem is even more complex when the collected data exhibit hybrid dynamics, i.e. their evolution in time is a sequence of smooth behaviors and abrupt changes.

Ecological Innovations in the Automotive Sector. Part I: Construction Innovations

2018 ◽  
Vol 19 (11) ◽  
pp. 30-35
Author(s):  
Marta Wójcik
Keyword(s):  
Automotive Industry ◽  
Diesel Engines ◽  
New Technologies ◽  
Negative Impact ◽  
The Other ◽  
Automotive Sector ◽  
Environmental Aspects ◽  
Car Industry ◽  
Human Transport ◽  
Sector Part

The automotive sector is one of the fastest growing sectors of economy. The increasing amount of cars both in Polish and world roads results in the immeasurable benefits associated with the goods and human transport. On the other hand, this phenomenon caused the contamination of the environment. During the fuel combustion in petrol or diesel engines, the harmful gases, for example CO2, NOx and SOx are emitted. Apart from the negative impact on the environment, the emission of the aforementioned gases results in the deterioration of human conditions, as well as, the development of civilization diseases. In order to minimalize the harmful influence of an automotive industry on the environment, new technologies which can reduce the consumption of fuel or limit the fumes emission are developed. The first part of paper presents new solutions in an automotive sector which influence on the decline of the negative impact of automobiles on the environment. Additionally, proposed solutions affect the development of a car industry, taking into consideration environmental aspects.

scholarly journals Geocomputing, New Technologies and Historical Analysis: Tools for a Changing Planet

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 9
Author(s):  
Sebastiano Trevisani
Keyword(s):  
Quantitative Analysis ◽  
Philosophy Of Science ◽  
New Technologies ◽  
Historical Analysis ◽  
Holistic Approach ◽  
Research Topic ◽  
The Other ◽  
Historical Records ◽  
The Earth ◽  
The Relationship

Modern Earth Scientists need also to interact with other disciplines, apparently far from the Earth Sciences and Engineering. Disciplines related to history and philosophy of science are emblematic from this perspective. From one side, the quantitative analysis of information extracted from historical records (documents, maps, paintings, etc.) represents an exciting research topic, requiring a truly holistic approach. On the other side, epistemological and philosophy of science considerations on the relationship between geoscience and society in history are of fundamental importance for understanding past, present and future geosphere-anthroposphere interlinked dynamics.

scholarly journals Machine Learning in Medical Emergencies: a Systematic Review and Analysis

2021 ◽  
Vol 45 (10) ◽  
Author(s):  
Inés Robles Mendo ◽  
Gonçalo Marques ◽  
Isabel de la Torre Díez ◽  
Miguel López-Coronado ◽  
Francisco Martín-Rodríguez
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Emergency Services ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
The Other ◽  
Other Hand ◽  
Increasing Demand ◽  
Commercial Applications

AbstractDespite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

scholarly journals Trigonometric Inference Providing Learning in Deep Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6704
Author(s):  
Jingyong Cai ◽  
Masashi Takemoto ◽  
Yuming Qiu ◽  
Hironori Nakajo
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Trigonometric Approximation ◽  
Model Parameters ◽  
Training Algorithms ◽  
Activation Functions ◽  
Classical Training ◽  
Sum Formula

Despite being heavily used in the training of deep neural networks (DNNs), multipliers are resource-intensive and insufficient in many different scenarios. Previous discoveries have revealed the superiority when activation functions, such as the sigmoid, are calculated by shift-and-add operations, although they fail to remove multiplications in training altogether. In this paper, we propose an innovative approach that can convert all multiplications in the forward and backward inferences of DNNs into shift-and-add operations. Because the model parameters and backpropagated errors of a large DNN model are typically clustered around zero, these values can be approximated by their sine values. Multiplications between the weights and error signals are transferred to multiplications of their sine values, which are replaceable with simpler operations with the help of the product to sum formula. In addition, a rectified sine activation function is utilized for further converting layer inputs into sine values. In this way, the original multiplication-intensive operations can be computed through simple add-and-shift operations. This trigonometric approximation method provides an efficient training and inference alternative for devices with insufficient hardware multipliers. Experimental results demonstrate that this method is able to obtain a performance close to that of classical training algorithms. The approach we propose sheds new light on future hardware customization research for machine learning.

scholarly journals Chromatin Manipulation and Editing: Challenges, New Technologies and Their Use in Plants

2021 ◽  
Vol 22 (2) ◽  
pp. 512
Author(s):  
Kateryna Fal ◽  
Denisa Tomkova ◽  
Gilles Vachon ◽  
Marie-Edith Chabouté ◽  
Alexandre Berr ◽  
...  
Keyword(s):  
Cell Fate ◽  
Dna Sequence ◽  
Zinc Finger ◽  
Chromatin Structure ◽  
New Technologies ◽  
Dna Interaction ◽  
The Other ◽  
Proof Of Concept ◽  
Epigenetic Marks ◽  
Molecular Events

An ongoing challenge in functional epigenomics is to develop tools for precise manipulation of epigenetic marks. These tools would allow moving from correlation-based to causal-based findings, a necessary step to reach conclusions on mechanistic principles. In this review, we describe and discuss the advantages and limits of tools and technologies developed to impact epigenetic marks, and which could be employed to study their direct effect on nuclear and chromatin structure, on transcription, and their further genuine role in plant cell fate and development. On one hand, epigenome-wide approaches include drug inhibitors for chromatin modifiers or readers, nanobodies against histone marks or lines expressing modified histones or mutant chromatin effectors. On the other hand, locus-specific approaches consist in targeting precise regions on the chromatin, with engineered proteins able to modify epigenetic marks. Early systems use effectors in fusion with protein domains that recognize a specific DNA sequence (Zinc Finger or TALEs), while the more recent dCas9 approach operates through RNA-DNA interaction, thereby providing more flexibility and modularity for tool designs. Current developments of “second generation”, chimeric dCas9 systems, aiming at better targeting efficiency and modifier capacity have recently been tested in plants and provided promising results. Finally, recent proof-of-concept studies forecast even finer tools, such as inducible/switchable systems, that will allow temporal analyses of the molecular events that follow a change in a specific chromatin mark.

The Numerical Analysis of the Velocity and Pressure Distribution of the Oil Film in Heavy Hydrostatic Thrust Bearing

2014 ◽  
Vol 541-542 ◽  
pp. 658-662
Author(s):  
Jian Li ◽  
Yuan Chen ◽  
Yang Chun Yu ◽  
Zhu Xin Tian ◽  
Yu Huang
Keyword(s):  
Mathematical Model ◽  
Numerical Analysis ◽  
Pressure Distribution ◽  
Working Conditions ◽  
Thrust Bearing ◽  
Rotation Speed ◽  
The Other ◽  
Surface Load ◽  
Oil Film ◽  
Volume Method

To study the velocity and pressure distribution of the oil film in a heavy hydrostatic thrust bearing, a mathematical model of the velocity is proposed and the finite volume method (FVM) has been used to simulate the flow field under different working conditions. Some pressure experiments were carried out and the results verified the correctness of the simulation. It is concluded that the pressure distribution varies small under different rotation speed when the surface load on the workbench is constant. But the velocity of the oil film is influenced greatly by the rotation speed. When the rotation speed of the workbench is as quick as enough, the velocity of the oil film on one radial side of the pad will be zero, that is to say the lubrication oil will be drained from the other three sides of the recess.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

scholarly journals Laws and Emerging Technologies

Laws ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 46
Author(s):  
Esther Salmerón-Manzano
Keyword(s):  
Information Technologies ◽  
New Technologies ◽  
Legal Issues ◽  
The Other ◽  
Special Issue ◽  
Legal Challenges ◽  
The World ◽  
Huge Impact ◽  
New Challenges ◽  
The Way

New technologies and so-called communication and information technologies are transforming our society, the way in which we relate to each other, and the way we understand the world. By a wider extension, they are also influencing the world of law. That is why technologies will have a huge impact on society in the coming years and will bring new challenges and legal challenges to the legal sector worldwide. On the other hand, the new communications era also brings many new legal issues such as those derived from e-commerce and payment services, intellectual property, or the problems derived from the use of new technologies by young people. This will undoubtedly affect the development, evolution, and understanding of law. This Special Issue has become this window into the new challenges of law in relation to new technologies.

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