Text Difficulty,Working Memory Capacity and Mind Wandering During Chinese EFL Learners’Reading

This experimental study investigated how text difficulty and different working memory capacity(WMC)affected Chinese EFL learners’reading comprehension and their tendency to engage in task-unrelated thoughts,that is,mind wandering(MW),in the course of reading.Sixty first-year university non-English majors participated in the study.A two-factor mixed experimental design of 2(text difficulty:difficult and simple)×2(WMC:high/large and low/small)was employed.Results revealed that 1)the main and interaction effects of WMC and text difficulty on voluntary MW were significant,whereas those on involuntary MW were not;2)while reading the easy texts,the involuntary MW of high-WMC individuals was less frequent than that of low-WMC ones,whereas while reading the difficult ones,the direct relationship between WMC and involuntary MW was not found;and that 3)high-WMC individuals had a lower overall rate of MW and better reading performance than low-WMC individuals did,but with increasing text difficulty,their rates of overall MW and voluntary MW were getting higher and higher,and the reading performance was getting lower and lower.These results lend support to WM theory and have pedagogical implications for the instruction of L2 reading.

Short-Term Memory Capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations

We study the short-term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and to modern languages. To model it, we consider the number of words between any two contiguous interpunctions Ip, because this parameter can model how the human mind memorizes “chunks” of information. Since IP can be calculated for any alphabetical text, we can perform experiments—otherwise impossible— with ancient readers by studying the literary works they used to read. The “experiments” compare the IP of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of similar short-term memory capacity) and by defining an “overlap index”. We also define the population of universal readers, people who can read any New Testament text in any language. Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

SMConf: One-Size-Fit-Bunch, Automated Memory Capacity Configuration for In-Memory Data Analytic Platform

Spark is the most popular in-memory processing framework for big data analytics.Memory is the crucial resource for workloads to achieve performance acceleration on Spark.The extant memory capacity configuration approach in Spark is to statically configure the memory capacity for workloads based on user’s specifications.However,without the deep knowledge of the workload’s system-level characteristics,users in practice often conservatively overestimate the memory utilizations of their workloads and require resource manager to grant more memory share than that they actually need,which leads to the severe waste of memory resources.To address the above issue,SMConf,an automated memory capacity configuration solution for in-memory computing workloads in Spark is proposed.SMConf is designed based on the observation that,though there is not one-size-fit-all proper configuration,the one-size-fit-bunch configuration can be found for in-memory computing workloads.SMConf classifies typical Spark workloads into categories based on metrics across layers of Spark system stack.For each workload category,an individual memory requirement model is learned from the workload’s input data size and the strong-correlated configuration parameters.For an ad-hoc workload,SMConf matches its memory requirement signature to one of the workload categories with small-sized input data and determines its proper memory capacity configuration with the corresponding memory requirement model.Experimental results demonstrate that,compared to the conservative default configuration,SMConf can reduce the memory resource provision to Spark workloads by up to 69%with the slight performance degradation,and reduce the average turnaround time of Spark workloads by up to 55%in the multi-tenant environments.

A Chessboard Model of Human Brain and An Application on Memory Capacity

The famous claim that we only use about 10% of the brain capacity has recently been challenged. Researchers argue that we are likely to use the whole brain, against the 10% claim. Some evidence and results from relevant studies and experiments related to memory in the field of neuroscience lead to the conclusion that if the rest 90% of the brain is not used, then many neural pathways will degenerate. What is memory? How does the brain function? What would be the limit of memory capacity? This article provides a model established upon the physiological and neurological characteristics of the human brain, which can give some theoretical support and scientific explanation to explain some phenomena. It may not only have theoretically significance in neuroscience, but can also be practically useful to fill in the gap between the natural and machine intelligence.

Influence of Working Memory Capacity on the Frequency of Self-Repairs

This article explored the influence of working memory capacity on the frequency of self-repairs.The narrative task and listening span task were used.Twenty post-graduate students participated in this study.Overall,the results of this study illustrated that the working memory is a factor of self-repairs.Speakers who have higher working memory capacity produce lesser self-repairs.This finding provides teachers with a new insight into second language teaching;that is,teachers can improve the amount of lexical knowledge when teaching students who have lower working memory in order to help them produce more accurate language during the process of L2 speech production.

On the Current Error Based Sampled-data Iterative Learning Control with Reduced Memory Capacity

The design of iterative learning controller(ILC) requires to store the system input, output or control parameters of previous trials for generating the input of the current trial. In order to apply the iterative learning controller for a real application and reduce the memory size for implementation, a current error based sampled-data proportional-derivative(PD) type iterative learning controller is proposed for control systems with initial resetting error, input disturbance and output measurement noise in this paper.The proposed iterative learning controller is simple and effective. The first contribution in this paper is to prove the learning error convergence via a rigorous technical analysis. It is shown that the learning error will converge to a residual set if a forgetting factor is introduced in the controller. All the theoretical results are also shown by computer simulations. The second main contribution is to realize the iterative learning controller by a digital circuit using a field programmable gate array(FPGA) chip applied to repetitive position tracking control of direct current(DC) motors. The feasibility and effectiveness of the proposed current error based sampleddata iterative learning controller are demonstrated by the experiment results. Finally, the relationship between learning performance and design parameters are also discussed extensively.

On Memory Capacity of the Probabilistic Logic Neuron Network

In this paper,the memory capacity of Probabilistic Logic Neuron(PLN)network is discussed. We obtain two main results:(1)the method for constructing a PLN network with a given memory capacity;(2)the relationship between the memory capacity and the size of a PLN network.We show that the memory capacity of a PLN network depends on not only the number of input ports of its element but also the number of elements themselves.The results provide a new method for designing a PLN network.

DISCRETE BIDIRECTIONAL ASSOCIATIVE MEMORY WITH LEARNING FUNCTION

In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.

Application of MBAM Neural Network in CNC Machine Fault Diagnosis

In order to improve the bidirectional associative memory(BAM) performance, a modified BAM model(MBAM) is used to enhance neural network(NN)’s memory capacity and error correction capability, theoretical analysis and experiment results illuminate that MBAM performs much better than the original BAM. The MBAM is used in computer numeric control(CNC) machine fault diagnosis, it not only can complete fault diagnosis correctly but also have fairly high error correction capability for disturbed Input Information sequence.Moreover MBAM model is a more convenient and effective method of solving the problem of CNC electric system fault diagnosis.

A Statistical Theory of Language Translation Based on Communication Theory

We propose the first statistical theory of language translation based on communication theory. The theory is based on New Testament translations from Greek to Latin and to other 35 modern languages. In a text translated into another language, all linguistic variables do numerically change. To study the chaotic data that emerge, we model any translation as a complex communication channel affected by “noise”, studied according to Communication Theory applied for the first time to this channel. This theory deals with aspects of languages more complex than those currently considered in machine translations. The input language is the “signal”, the output language is a “replica” of the input language, but largely perturbed by noise, indispensable, however, for conveying the meaning of the input language to its readers. We have defined a noise-to-signal power ratio and found that channels are differently affected by translation noise. Communication channels are also characterized by channel capacity. The translation of novels has more constraints than the New Testament translations. We propose a global readability formula for alphabetical languages, not available for most of them, and conclude with a general theory of language translation which shows that direct and reverse channels are not symmetric. The general theory can also be applied to channels of texts belonging to the same language both to study how texts of the same author may have changed over time, or to compare texts of different authors. In conclusion, a common underlying mathematical structure governing human textual/verbal communication channels seems to emerge. Language does not play the only role in translation;this role is shared with reader’s reading ability and short-term memory capacity. Different versions of New Testament within the same language can even seem, mathematically, to belong to different languages. These conclusions are everlasting because valid also for ancient Roman and Greek readers.

STUDY ON THE RELATIONSHIP BETWEEN WORKING MEMORY AND EFL LISTENING COMPREHENSION

The present study intends to investigate the effect of working memory on listening process and its relationship with listening comprehension.The results indicate that working memory is an effective predictor for EFL listening comprehension,i.e.learners with larger working memory capacity are more likely to have better abilities in listening comprehension,and that although L1 working memory span is significantly correlated with L2 working memory span,L2 working memory span plays a more effective role in differentiating learners' EFL listening comprehension.Additionally,this study provides some pedagogical implications on EFL teaching.

The effect of the endosymbiont Wolbachia on the behavior of insect hosts

As one of the most successful intracellular symbiotic bacteria,Wolbachia can infect many arthropods and nematodes.Wolbachia infection usually affects the reproduction of their hosts to promote their own proliferation and transmission.Currently,most of the studies focus on the mechanisms of Wolbachia interactions with host reproduction.However,in addition to distribution in the reproductive tissues,Wolbachia also infect various somatic tissues of their hosts,including the brain.This raises the potential that Wolbachia may influence some somatic processes,such as behaviors in their hosts.Sofar,information about the effects of Wolbachia infection on host behavior is still very limited.The present review presents the current literature on different aspects of the influence of Wolbachia on various behaviors,including sleep,learning and memory,mating,feeding and aggression in their insect hosts.We then highlight ongoing scientific efforts in the field that need addressing to advance this field,which can have significant implications for further developing Wolbachia as environmentally friendly biocontrol agents to control insect-borne diseases and agricultural pests.

Performance evaluation of new echo state networks based on complex network

Recently, echo state networks （ESN） have aroused a lot of interest in their nonlinear dynamic system modeling capabilities. In a classical ESN, its dynamic reservoir （DR） has a sparse and random topology, but the performance of ESN with its DR taking another kind of topology is still unknown. So based on complex network theory, three new ESNs are proposed and investigated in this paper. The small-world topology, scale-free topology and the mixed topology of small-world effect and scale-free feature are considered in these new ESNs. We studied the relationship between DR architecture and prediction capability. In our simulation experiments, we used two widely used time series to test the prediction performance among the new ESNs and classical ESN, and used the independent identically distributed （i.i.d） time series to analyze the short-term memory （STM） capability. We answer the following questions： What are the differences of these ESNs in the prediction performance？ Can the spectral radius of the internal weights matrix be wider？ What is the short-term memory capability？ The experimental results show that the proposed new ESNs have better prediction performance, wider spectral radius and almost the same STM capacity as classical ESN＇s.

Modeling deterministic echo state network with loop reservoir

Echo state network (ESN), which efficiently models nonlinear dynamic systems, has been proposed as a special form of recurrent neural network. However, most of the proposed ESNs consist of complex reservoir structures, leading to excessive computational cost. Recently, minimum complexity ESNs were proposed and proved to exhibit high performance and low computational cost. In this paper, we propose a simple deterministic ESN with a loop reservoir, i.e., an ESN with an adjacent-feedback loop reservoir. The novel reservoir is constructed by introducing regular adjacent feedback based on the simplest loop reservoir. Only a single free parameter is tuned, which considerably simplifies the ESN construction. The combination of a simplified reservoir and fewer free parameters provides superior prediction performance. In the benchmark datasets and real-world tasks, our scheme obtains higher prediction accuracy with relatively low complexity, compared to the classic ESN and the minimum complexity ESN. Furthermore, we prove that all the linear ESNs with the simplest loop reservoir possess the same memory capacity, arbitrarily converging to the optimal value.