Improved Variable Forgetting Factor Proportionate RLS Algorithm with Sparse Penalty and Fast Implementation Using DCD Iterations

The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

An Unforgettable Trip Into an Intangible Cultural Heritage at Quanzhou

Quanzhou,as the only starting point of the Maritime Silk Road recognized by the United Nations,was praised as"the most prosperous city in the world"by Marco Polo.On July 25th,2021,China's"Quanzhou:Emporium of the World in Song-Yuan China"was added to the UNESCO World Heritage List as a cultural site,bringing the total number of the country's UNESCO World Heritage sites to 56.The twentytwo world heritage sites in Quanzhou.

My Unforgettable Middle School Life

How time flies!My colorful middle school life now comes to an end.Here is what I think about my middle school life.In the past,I was busy with my subjects and worked hard at them.I got on well with my classmates.I was good at English.I liked playing basketball.But now I am interested in history,because it is very interesting.

“Here Comes the New”:Individual and Collective Forgetting in Toni Morrison’s Jazz

This article examines the problem of individual and collective attempts at forgetting the traumatic past in Toni Morrison’s sixth novel Jazz(1992).More specifically,it emphasizes by selected examples psychological and social aspects of willful amnesia which can lend itself useful in helping traumatized(country)individuals to repress painful remembrances,heal mental wounds and build a new identity in a memory-free modern city.Analyzing Jazz’s narrative featuring Joe and Violet Trace,with a particular focus put on the expectations and experiences connected with their migration to and life in the City,the article explores via Paul Connerton’s ruminations on cultural forgetting in modern times-delineated in his book How Modernity Forgets(2009)-the mechanisms of intentional amnesia used in the process of recovering from personal and social traumas resulting from more recent(migration and urban life)and more time-distant(slavery and racism)ordeals.

An Unforgettable Day

It was Sunday today.We decided to have a picnic on the top of the mountain near our school.How exciting1 we were!We set out early in the morning.After we got to the top,we were fascinated by the beautiful scenery.After we had a goodtime on the top of the mountain.

Language Model Using Differentiable Neural Computer Based on Forget Gate-Based Memory Deallocation

A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems.In this study,we apply a DNC to a language model(LM)task.The LM task is one of the reasoning problems,because it can predict the next word using the previous word sequence.However,memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory,which degrades performance.Therefore,we propose a forget gatebased memory deallocation(FMD)method,which searches for the minimum value of elements in a forget gate-based retention vector.The forget gatebased retention vector indicates the retention degree of information stored in each external memory address.In experiments,we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example.For LM tasks,we evaluated DNC using the Penn Treebank and enwik8 LM tasks.Although it does not yield SOTA results in LM tasks,the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character.For the speech recognition rescoring tasks,FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate.

Forget-me-not

Forget-me-not的意思是“勿忘我”。关于它,还有一个美丽动人的故事呢。传说在欧洲中世纪之时,一位英俊的骑士带着心爱的姑娘到海边游目聘怀。海岸上有一丛鲜花极其艳丽,姑娘十分高兴,欲得一束。情郎奋不顾身去采摘,不幸被海浪卷走,临难时他用尽全力把花抛上岸边,并大声喊道:“Forget me not.Forget me not.”便吞没在波涛汹涌的大海之中。姑娘手捧鲜花,痛不欲生,为了表示对恋人的哀悼,她便把这花叫做forget-me-not。后来。

A traffic flow cellular automaton model to considering drivers' learning and forgetting behaviour

It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers＇ memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers＇ intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.

Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

RLS channel estimation with adaptive forgetting factor in space-time coded MIMO-OFDM systems

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

Unforgettable Restaurant: helping older people who are living with disability and dementia return to society

On 25 January 2021,a special restaurant named the‘Unforgettable Restaurant’was opened in a prime locality of Shanghai,China;it emerged from a popular publicly broadcast welfare TV programme of the same name.1 This restaurant’s waiters are older patients with cognitive impairment,and the cumu-lative number of views for the programme has now exceeded 1.4 billion online.23 After the show was broadcast for two consecutive years,the filming party decided to open the restaurant officially,and the waiting duties were assumed by older people living with disability and dementia.

Green Communication: An Effective Approach to Minimize Risk of Forgetfulness from Mobile Phone Usage

The usage of mobile-phone among children increased significantly. Children are in their growing phase and cells of their body are rapidly dividing, therefore propagation of electro-magnetic (EM) radiation occurs quickly in children. The aim of the present study was to evaluate the extent of mobile-phone usage as well as its possible health effect. A total number of 455 (398 children and 57 adults, 396 urban and 59 rural) students of age group ranging from 10-29 years participated in this study. An “Information Gathering Chronological (IGC) model” was used for the collection and evaluation of information. The four major parameters, i.e. demographic and public uniqueness, mobile-phone consumption patterns, grievance of the “forgetfulness” symptom to the subjects and awareness about the safety measures were included to get the concise information from participants. We have observed that the prevalence of “forgetfulness” was 23.95% among mobile-phone users. The incidence of overall “forgetfulness” symptoms was 23.59%, 17.46%, 25.00% and 37.50% in low (LU), normal (NU), moderate (MU) and heavy (HU) mobile-phone users respectively. A trend for risk for “forgetfulness” was observed in HU as compared to LU in overall mobile-phone users. Three folds and nearly five folds increased risk for “forgetfulness” was found among HU as compared to LU in children (p ≤ 0.0210) and urban area mobile-phone users respectively. No significant difference for “forgetfulness” symptoms was found in other categories (i.e. adult and rural mobile-phone users). These results suggested that the incidences of “forgetfulness” among children from urban area mobile-phone users were significantly increased.

Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors

The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they need.At this stage,there are a lot of intelligent tutoring systems.In these systems,students􀆳learning actions are tracked in real⁃time,and there are a lot of available data.From these data,personalized education that suits each student can be mined.To improve the quality of education,some models for predicting students􀆳next practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep learning.However,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the knowledge.Although some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning sequences.The main contribution of this paper is in two aspects.One is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political combinations.The other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting behavior.This paper proposes an adaptive algorithm for the original DKT model.By using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students􀆳learning.The model proposed in this paper has been experimented on two public data sets to improve the original accuracy.

概述forget it在口语中的用法

在日常使用中,我们经常会遇到forget(about)it这个短语。但在不同的场合和语境中,forget(about)it具有多种含义和不同的用法。在参考常用辞书及相关文献的基础上,本文就此作一概述。1.用于回答别人的道谢,表示“不用谢”“不客气”“算不了什么”。例如:Thank you very much for your help.—Forget it.多谢你的帮助。——不用谢。

Forgetting In Creative Problem Solving for College Students

As more and more benefits of forgetting have been found in recent studies,whether forgetting could promote individuals ability of creative problem solving remains a controversial debate.This article discusses the effect of two types of forgetting,the retrieval-induced forgetting(RIF)and the forgetting during incubation,in benefiting creative problem solving by introducing and analysing the relevant experiments.The results reveal that retrieval-induced forgetting only works when previous mental fixations occurred and the promotion varies when solving different types of problems.The level of RIF is irrelevant to the performance in solving closed-ended creative problems and high level of RIF even impairs the creativity when solving open-ended problems.And forgetting during incubation cannot explain the incubation effect.The spreading activation of relevant information or the unconscious work is more likely to be the possible reasons.In conclusion,the current article brings about the discussions about the work conditions and effects of forgetting in creative problem solving.

On the effects of contextualized explanation and Ebbinghaus Forgetting Curve on the teaching and learning of English vocabulary

This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.

口语中forget it的用法

在非正式语体的口语中,forget it是个出现频率较高的语句,多用祈使句。它适用于多种语境。本文结合实例谈谈它的各种用法。一、用以回答感谢,表示“区区小事,何足挂齿”。 -I＇m most grateful to you for your help. 你帮了我的忙,我十分感激。 - Oh,It’s nothing,forget it.噢,这么点小事,别客气。

A FILIPINO’S FEELINGS ABOUT CHINA--Tagalog writer Marco Antonio V.Lopez’s unforgettable memories of China

Marco Antonio V.Lopez considers his three stints working in Chinese universities teaching Filipino language and culture the most special achievements of his life.Lopez,a renowned Tagolog novelist,has twice won the prestigious Carlos Palanca Memorial Award for Literature,the longest-running literary contest in the Philippines and has since served as a judge for the award.For Lopez,“China”means civilization,friendship,and unforgettable memories across two decades.

Unforgettable Moments in the Global Fight Against COVID-19 Pandemic

The online photography exhibition“Zooming in on COVID-19:Unforgettable Moments in the Global Fight Against the Pandemic”opened in Beijing on June 29.This online exhibition,fueled by new media and modern technologies,provides a multi-lingual and multi-terminal viewing experience to global audiences.