Child psychiatrists, pediatricians, and other child clinicians need to have a solid understanding of child language development. There are at least four important reasons that make this necessary. First, slowing, arrest, and deviation of language development are highly associated with, and complicate the course of, child psychopathology. Second, language competence plays a crucial role in emotional and mood regulation, evaluation, and therapy. Third, language deficits are the most frequent underpinning of the learning disorders, ubiquitous in our clinical populations. Fourth, clinicians should not confuse the rich linguistic and dialectal diversity of our clinical populations with abnormalities in child language development. The challenge for the clinician becomes, then, how to get immersed in the captivating field of child language acquisition without getting overwhelmed by its conceptual and empirical complexity. In the past 50 years and since the seminal works of Roger Brown, Jerome Bruner, and Catherine Snow, child language researchers (often known as developmental psycholinguists) have produced a remarkable body of knowledge. Linguists such as Chomsky and philosophers such as Grice have strongly influenced the science of child language. One of the major tenets of Chomskian linguistics (known as generative grammar) is that children’s capacity to acquire language is “hardwired” with “universal grammar”—an innate language acquisition device (LAD), a language “instinct”—at its core. This view is in part supported by the assertion that the linguistic input that children receive is relatively dismal and of poor quality relative to the high quantity and quality of output that they manage to produce after age 2 and that only an advanced, innate capacity to decode and organize linguistic input can enable them to “get from here (prelinguistic infant) to there (linguistic child).” In “Constructing a Language,” Tomasello presents a contrasting theory of how the child acquires language: It is not a universal grammar that allows for language development. Rather, human cognition universals of communicative needs and vocal-auditory processing result in some language universals, such as nouns and verbs as expressions of reference and predication (p. 19). The author proposes that two sets of cognitive skills resulting from biological/phylogenetic adaptations are fundamental to the ontogenetic origins of language. These sets of inherited cognitive skills are intentionreading on the one hand and pattern-finding, on the other. Intention-reading skills encompass the prelinguistic infant’s capacities to share attention to outside events with other persons, establishing joint attentional frames, to understand other people’s communicative intentions, and to imitate the adult’s communicative intentions (an intersubjective form of imitation that requires symbolic understanding and perspective-taking). Pattern-finding skills include the ability of infants as young as 7 months old to analyze concepts and percepts (most relevant here, auditory or speech percepts) and create concrete or abstract categories that contain analogous items. Tomasello, a most prominent developmental scientist with research foci on child language acquisition and on social cognition and social learning in children and primates, succinctly and clearly introduces the major points of his theory and his views on the origins of language in the initial chapters. In subsequent chapters, he delves into the details by covering most language acquisition domains, namely, word (lexical) learning, syntax, and morphology and conversation, narrative, and extended discourse. Although one of the remaining domains (pragmatics) is at the core of his theory and permeates the text throughout, the relative paucity of passages explicitly devoted to discussing acquisition and proBOOK REVIEWS

Constructing a language: A usage-based theory of language acquisition

In this paper, a SEIR epidemic model for the COVID-19 is built according to some general control strategies, such as hospital, quarantine and external input Based on the data of Hubei province, the particle swarm optimization (PSO) algorithm is applied to estimate the parameters of the system We found that the parameters of the proposed SEIR model are different for different scenarios Then, the model is employed to show the evolution of the epidemic in Hubei province, which shows that it can be used to forecast COVID-19 epidemic situation Moreover, by introducing the seasonality and stochastic infection the parameters, nonlinear dynamics including chaos are found in the system Finally, we discussed the control strategies of the COVID-19 based on the structure and parameters of the proposed model

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SEIR modeling of the COVID-19 and its dynamics.

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Particle Swarm Optimization (PSO) is among the most universally applied population-based metaheuristic optimization algorithms. PSO has been successfully used in various scientific fields, ranging from humanities, engineering, chemistry, medicine, to advanced physics. Since its introduction in 1995, the method has been widely investigated, which led to the development of hundreds of PSO versions and numerous theoretical and empirical findings on their convergence and parameterization. However, so far there is no detailed study on the proper choice of PSO swarm size, although it is widely known that population size crucially affects the performance of metaheuristics. In most applications, authors follow the initial suggestion from 1995 and restrict the population size to 20–50 particles. In this study, we relate the performance of eight PSO variants to swarm sizes that range from 3 up to 1000 particles. Tests are performed on sixty 10- to 100-dimensional scalable benchmarks and twenty-two 1- to 216-dimensional real-world problems. Although results do differ for the specific PSO variants, for the majority of considered PSO algorithms the best performance is obtained with swarms composed of 70–500 particles, indicating that the classical choice is often too small. Larger swarms frequently improve efficiency of the method for more difficult problems and practical applications. For unimodal problems slightly lower swarm sizes are recommended for the majority of PSO variants, but some would still perform best with hundreds of particles.

Population size in Particle Swarm Optimization

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years.

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

Particle swarm optimization algorithm: an overview

Effective environment exploration in unknown environment is precondition of constructing the environment map and carrying out other tasks for multi-robot system. Due to its excellent performance, particle swarm optimization (PSO) has been widely used in multi-robot exploration field. To deal with its drawback – easily trapped in local optima, Darwinian PSO (DPSO) optimization is proposed by Tillett et al. [1] with the natural selection function and first used in real world robot exploration by Couceiro et al. [2] , forming the robotic DPSO (RDPSO). To increase the algorithm performance and control its convergence rate, fractional calculus is used to replace inertia component in RDPSO for its “memory” ability and forming the fractional order RDPSO (FORDPSO). This paper presents a formal analysis of RDPSO and studies the influence of the coefficients on FORDPSO algorithm. To satisfy the requirement of dynamically changing robots׳ behaviors during the exploration, fuzzy inferring system is designed to achieve better control coefficients. Experiment results obtained in two complex simulated environments illustrate that biological and sociological inspiration is effective to meet the challenges of multi-robot system application in unknown environment exploration, and the exploration effect of the fuzzy adaptive FORDPSO is better than that of the fixed coefficient FORDPSO. Furthermore, the performance of FORDPSO with different neighborhood topologies are studied and compared with other six PSO variations. All the results demonstrate the effect of the FORDPSO on the multi-robot environment exploration.

Unknown environment exploration of multi-robot system with the FORDPSO

Face recognition has great theory research and application value. It is a very complicated problem which often suffers from variations in lighting condition, facial expression, head pose, glasses, background, and so on. This paper realizes an effective recognition for 108 face images of 27 individuals in complex background, through a biological inspired emergent developmental network (DN). To decrease the influence of complex background on the recognition of the foreground object, another biological inspired mechanism—synapse maintenance, which can dynamically determine which synapse should be removed, weaken or strengthened, is introduced to enhance the image recognition rate. To prevent the quick decay of the learning rate with the increasement of the neuron age, simulating the learning principle of the human brain, a new learning rate is proposed to determine the neuron learning process. Moreover, to exploit the network resource efficiently, neuron regenesis mechanism is designed to regulate the neuron resource dynamically. First, we design two kinds of neuron states to depict the neuron action, then, simulating the work mechanism of the human brain to produce new neurons continuously to learn new knowledge, we design the neuron regenesis mechanism to activate the suppressed old neurons in the developmental network to regenerate and learn new feature, thus to enhance the network usage efficiency. In order to demonstrate the effect of DN on face recognition, we compare and analyze the performances of DN with/without synapse maintenance mechanism, with the neuron regenesis mechanism. Experiment results in semi-constrained dataset and unconstrained dataset illustrate that DN with the synapse maintenance and neuron regenesis mechanism can effectively improve the face recognition rate in complex background. Further, comparing the performance of DN with some state-of-the-art algorithms, experimental results demonstrate the superior performance of the proposed method.

Developmental Network: An Internal Emergent Object Feature Learning

Estimation of face orientation has been a topic of intense research in the recent past. Most of the prior face orientation methods use symbolic methods or handcrafted internal representations which are not sufficiently brain based. An emergent developmental network (DN) is presented to recognize the face orientation, from sensory and motor experience. This work is different in the sense that we focused on mechanisms that enable a system to develop its emergent representations from its operational experience. In this work, internal unsupervised neurons of the DN are used to represent the face orientation, and the competitions among the internal neurons enable them to represent different face orientations. To illustrate the recognition effect, we study and compare the recognition effects among the BP, LVQ, PNN, and DN. Experiment results demonstrate efficiently how such internal neurons represent the face orientation while they are not directly supervised by the external environment. The presented network is developmental which means that the internal representations are directly learned from the signals of the input and motor ports, not designed internally for particular task; hence, the same learning principles are potentially suitable for other sensory modalities. As far as we know, it is the first trial to use the DN to recognize the face orientation.

Emergent face orientation recognition with internal neurons of the developmental network

Natural language acquisition is a crucial research domain of artificial intelligence. To enable the computer acquire and understand the human language and generate the corresponding response, we must research the principle of language acquiring of human being. Most of the prior language acquisition methods use handcrafted internal representation which is not sufficiently brain-based. An emergent developmental network (DN) is presented to acquire the certain speech extracted by mel-frequency cepstrum coefficient (MFCC), from sensory and motor experience. This work is different in the sense that we focused on mechanisms that enable a system to develop its emergent representations from its operational experience. In this work, internal unsupervised neurons of the DN are used to represent the short contexts, and the competitions among the internal neurons enable them to represent different short contexts. To demonstrate the acquisition effect, we study and analyze the influences of different network structure (i.e., different neuron number and weight threshold) on the language acquisition rate. Four speech acquisition experiments demonstrate efficiently how such internal neurons represent the short context while they are not directly supervised by the external environment. The presented network is developmental which means that the internal representations are directly learned from the signals of the input and motor ports, not designed internally for particular task, hence the same learning principles are potentially suitable for other sensory modalities.

Lei Liu

Papers

Particle swarm optimization algorithm: an overview

Unknown environment exploration of multi-robot system with the FORDPSO

Developmental Network: An Internal Emergent Object Feature Learning

Emergent face orientation recognition with internal neurons of the developmental network

How internal neurons represent the short context: an emergent perspective