Sequential Structure Research Articles

Learning Sequential Force Interaction Skills

Learning skills from kinesthetic demonstrations is a promising way of minimizing the gap between human manipulation abilities and those of robots. We propose an approach to learn sequential force interaction skills from such demonstrations. The demonstrations are decomposed into a set of movement primitives by inferring the underlying sequential structure of the task. The decomposition is based on a novel probability distribution which we call Directional Normal Distribution. The distribution allows infering the movement primitive’s composition, i.e., its coordinate frames, control variables and target coordinates from the demonstrations. In addition, it permits determining an appropriate number of movement primitives for a task via model selection. After finding the task’s composition, the system learns to sequence the resulting movement primitives in order to be able to reproduce the task on a real robot. We evaluate the approach on three different tasks, unscrewing a light bulb, box stacking and box flipping. All tasks are kinesthetically demonstrated and then reproduced on a Barrett WAM robot.

Attentive convolutional gated recurrent network: a contextual model to sentiment analysis

Considering contextual features is a key issue in sentiment analysis. Existing approaches including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) lack the ability to account and prioritize informative contextual features that are necessary for better sentiment interpretation. CNNs present limited capability since they are required to be very deep, which can lead to the gradient vanishing whereas, RNNs fail because they sequentially process input sequences. Furthermore, the two approaches treat all words equally. In this paper, we suggest a novel approach named attentive convolutional gated recurrent network (ACGRN) that alleviates the above issues for sentiment analysis. The motivation behind ACGRN is to avoid the vanishing gradient caused by deep CNN via applying a shallow-and-wide CNN that learns local contextual features. Afterwards, to solve the problem caused by the sequential structure of RNN and prioritizing informative contextual information, we use a novel prior knowledge attention based bidirectional gated recurrent unit (ATBiGRU). Prior knowledge ATBiGRU captures global contextual features with a strong focus on the previous hidden states that carry more valuable information to the current time step. The experimental results show that ACGRN significantly outperforms the baseline models over six small and large real-world datasets for the sentiment classification task.

Associations between area socioeconomic status, individual mental health, physical activity, diet and change in cardiometabolic risk amongst a cohort of Australian adults: A longitudinal path analysis.

Presumed pathways from environments to cardiometabolic risk largely implicate health behaviour although mental health may play a role. Few studies assess relationships between these factors. This study estimated associations between area socioeconomic status (SES), mental health, diet, physical activity, and 10-year change in glycosylated haemoglobin (HbA1c), comparing two proposed path structures: 1) mental health and behaviour functioning as parallel mediators between area SES and HbA1c; and 2) a sequential structure where mental health influences behaviour and consequently HbA1c. Three waves (10 years) of population-based biomedical cohort data were spatially linked to census data based on participant residential address. Area SES was expressed at baseline using an established index (SEIFA-IEO). Individual behavioural and mental health information (Wave 2) included diet (fruit and vegetable servings per day), physical activity (meets/does not meet recommendations), and the mental health component score of the 36-item Short Form Health Survey. HbA1c was measured at each wave. Latent variable growth models with a structural equation modelling approach estimated associations within both parallel and sequential path structures. Models were adjusted for age, sex, employment status, marital status, education, and smoking. The sequential path model best fit the data. HbA1c worsened over time. Greater area SES was statistically significantly associated with greater fruit intake, meeting physical activity recommendations, and had a protective effect against increasing HbA1c directly and indirectly through physical activity behaviour. Positive mental health was statistically significantly associated with greater fruit and vegetable intakes and was indirectly protective against increasing HbA1c through physical activity. Greater SES was protective against increasing HbA1c. This relationship was partially mediated by physical activity but not diet. A protective effect of mental health was exerted through physical activity. Public health interventions should ensure individuals residing in low SES areas, and those with poorer mental health are supported in meeting physical activity recommendations.

Moral dilemmas involving anthropological and ethical dimensions in healthcare curriculum.

Currently a variety of novel scenarios have appeared within nursing practice such as confidentiality of a patient victim of abuse, justice in insolvent patients, poorly informed consent delivery, non-satisfactory medicine outputs, or the possibility to reject a recommended treatment. These scenarios presuppose skills that are not usually acquired during the degree. Thus, the implementation of teaching approaches that promote the acquisition of these skills in the nursing curriculum is increasingly relevant. The article analyzes an academic model which integrates in the curriculum a series of specific theoretical concepts together with practical skills to acquire the basic ethic assessment competency. The project includes designing two subjects, General Anthropology and Ethics-Bioethics, with an applied approach in the nursing curriculum. The sequential structure of the curriculum in both subjects is constituted by three learning domains (theoretical, practical, and communicative) with different educational strategies. No significant ethical considerations as this is a discussion paper. The model was structured from the anthropology's concepts and decision-making process, applied to real situations. The structure of the three domains theoretical-practical-communicative is present in each session. It is observed that theoretical domain fosters the capacity for critical analysis and subsequent ability to judge diverse situations. The practical domain reflected two significant difficulties: students' resistance to internalizing moral problems and the tendency to superficial criticism. The communicative domain has frequently shown that the conflicting points are in the principles to be applied. We conclude that this design achieves its objectives and may provide future nursing professionals with ethical competences especially useful in healthcare practice. The three domains of the presented scheme are associated with the same process used in decision making at individual levels, where the exercise of clinical prudence acquires particular relevance.

Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution.

Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.

Leilões para conversão de dívida em investimento: uma proposta da teoria de leilões para implantação dos termos de ajuste de conduta (TAC) da Anatel

Desde a privatização da Telebrás em 1998, o Brasil tem assistido a uma impressionante expansão nas telecomunicações. Paralelamente, cresceram assustadoramente as dívidas das operadoras de telecomunicação, com elevadas multas regulatórias. Em 2012, a Anatel aprovou a celebração de termos de ajustamento de conduta (TAC) para a conversão de dívida das operadoras em projetos de investimento. A questão crucial é como determinar a taxa de conversão. Este trabalho propõe um mecanismo de leilão para a determinação endógena dessa taxa. Nesse leilão as operadoras propõem taxas de conversão de dívida em um certo investimento, e a operadora que oferecer a taxa mais baixa (menos reais de dívida deduzidos para cada real investido) vence o leilão. Ademais, discute-se como a estrutura sequencial do problema permite desenhar um mecanismo inédito de leilões sequenciais que tem a característica de criar assimetria entre os jogadores, gerando maior bem-estar social e garantindo a implementação dos projetos selecionados.

A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem

Combinatorial optimization problems have been widely used in various fields. And many types of combinatorial optimization problems can be generalized into the model of unconstrained binary quadratic programming (UBQP). Therefore, designing an effective and efficient algorithm for UBQP problems will also contribute to solving other combinatorial optimization problems. Pointer network is an end-to-end sequential decision structure and combines with deep learning technology. With the utilization of the structural characteristics of combinatorial optimization problems and the ability to extract the rule behind the data by deep learning, pointer network has been successfully applied to solve several classical combinatorial optimization problems. In this paper, a pointer network based algorithm is designed to solve UBQP problems. The network model is trained by supervised learning (SL) and deep reinforcement learning (DRL) respectively. Trained pointer network models are evaluated by self-generated benchmark dataset and ORLIB dataset respectively. Experimental results show that pointer network model trained by SL has strong learning ability to specific distributed dataset. Pointer network model trained by DRL can learn more general distribution data characteristics. In other words, it can quickly solve problems with great generalization ability. As a result, the framework proposed in this paper for UBQP has great potential to solve large scale combinatorial optimization problems.

Value of Sample Separation Information in a Sequential Probit Model

We illustrate the estimation and identification of multi-step sequential probit models with and without stepwise sample separation information. The likelihood functions are explicitly derived to ease experimentation with such models. We used data on health, activity limitations, demographic traits and work from the Survey of Income and Program Participation (SIPP) and exactly matched them with Social Security administrative records to showcase our theoretical points. Using a Monte Carlo simulation technique, our results suggest that the correlations in errors across equations may arise due to unobserved individual heterogeneity. Using a novel marginal likelihood approach, we also estimated the above sequential probit model without the sample separation information for the purpose of direct comparison. In terms of both in-sample and jackknife-type out-of-sample predictive analysis, the value of modelling the underlying sequential structure of the determination process in generating correct membership probabilities of belonging to a particular group is confirmed. JEL: C31, C34, C 35, I12, I18

Predicting online shopping behaviour from clickstream data using deep learning

Clickstream data is an important source to enhance user experience and pursue business objectives in e-commerce. The paper uses clickstream data to predict online shopping behavior and target marketing interventions in real-time. Such AI-driven targeting has proven to save huge amounts of marketing costs and raise shop revenue. Previous user behavior prediction models rely on supervised machine learning (SML). Conceptually, SML is less suitable because it cannot account for the sequential structure of clickstream data. The paper proposes a methodology capable of unlocking the full potential of clickstream data using the framework of recurrent neural networks (RNNs). An empirical evaluation based on real-world e-commerce data systematically assesses multiple RNN classifiers and compares them to SML benchmarks. To this end, the paper proposes an approach to measure the revenue impact of a targeting model. Estimates of revenue impact together with results of standard classifier performance metrics evidence the viability of RNN-based clickstream modeling and guide employing deep recurrent learners for campaign targeting. Given that the empirical analysis shows RNN-based and conventional classifiers to capture different patterns in clickstream data, a specific recommendation is to combine sequence and conventional classifiers in an ensemble. The paper shows such an ensemble to consistently outperform the alternative models considered in the study.

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

Complete sequential assignment and secondary structure prediction of the cannulae forming protein CanA from the hyperthermophilic archaeon Pyrodictium abyssi

CanA from Pyrodictium abyssi forms a heat-resistant organic hollow-fiber network together with CanB and CanC. An N-terminally truncated construct of CanA (K1-CanA) gave NMR spectra of good quality that could be assigned by three-dimensional NMR methods on 15N and 13C–15N enriched protein. We assigned the chemical shifts of 96% of all backbone 1HN atoms, 98% of all backbone 15N atoms, 100% of all 13Cα atoms, 100% of all 1Hα atoms, 90% of all 13C′ atoms, and 100% of the 13Cβ atoms. Two short helices and 10 β-strands are estimated from an analysis of the chemical shifts leading to a secondary structure content of K1-CanA of 6% helices, 44% β-pleated sheets, and 50% coils.

Turn-taking and sense making: Children and professionals co-producing and revisiting multimodal books during transition from day-care to school

Abstract This study contributes to current research on technological tools in early childhood education. The activities studied were part of a larger, socioculturally informed and design-based study conducted by the researcher in collaboration with professionals and children in Denmark. Activities are examined where children and professionals use tablets and interactive whiteboards for co-producing and dialogically revisiting multimodal books during transition from day-care to school. Findings show how children, professionals, hardware, software, and institutional practices co-constitute a turn-taking infrastructure that shapes activities. Children are thus allocated rights and obligations to contribute sequentially with photos, audios, and texts to single pages in the common books. In addition, they are attributed ownership to the multimodal records they produced. Through examination of activities over time, the study traces children's sense making of multimodal records as trajectories across time and contexts. It is shown how the joint activities rely on participants' initiation and maintenance of shared understanding, accomplished partly through turn-taking procedures, partly through utterances of sense making. It is discussed how a dynamic tension, sometimes a contradiction, between sequential structure and multi-voiced sense making unfolds dialectically. This points to insights regarding technological tools mediating young children's communicative agency within early childhood educational settings.

2,4‐Dichlorophenoxyacetic‐N‐methylmethanamine and haloxyfop‐P‐methyl interaction: Sequential and interval applications to effectively control sourgrass and fleabane

AbstractSourgrass (Digitaria insularis L.) and fleabane (Conyza spp. L.) constitute a major challenge to weed management in soybean [Glycine max (L.) Merr.] production systems in Brazil, especially when both are resistant to glyphosate and present concurrently. The aim of this study was to evaluate the interaction of haloxyfop‐P‐methyl [methyl(R)‐2{4‐{3‐chloro‐5(trifluoromethyl)‐2‐pyridyloxy] phenoxy} propanoate] and 2,4‐D [2,4‐Dichlorophenoxyacetic acid‐N‐methylmethanamine] to control sourgrass at three to four tillers and flowering stages and 12‐ to 15‐cm fleabane at vegetative stage. The experiments were arranged in a randomized complete block design with four replicates and were conducted twice. Treatments were composed of haloxyfop‐P‐methyl and 2,4‐D applied sequentially at 3‐, 6‐, and 12‐d intervals. Treatments also included an untreated check, haloxyfop‐P‐methyl, and 2,4‐D sequentially (without interval) and stand‐alone applications of both herbicides. All treatments across experiments completely controlled fleabane plants, except haloxyfop‐P‐methyl alone. Haloxyfop‐P‐methyl applied ≥6 d before 2,4‐D controlled flowering and three‐ to four‐tiller sourgrass plants at rates of 50 and 100%, respectively. Based on results of this study, haloxyfop‐P‐methyl should be applied ≥6 d before 2,4‐D in a sequential application structure to control fleabane and sourgrass (three or four tillers) when present concurrently.

Managing the structure of tourism experiences: Foundations for tourism design

Understanding the nature of tourism experiences holds the key to effectively managing tourism destinations. Research in psychology, economics, geography, marketing and, more recently, services management, offers new insight into consumer experiences which are defined by moment-to-moment and as discreet (and summarized) events. This paper synthesizes this literature and then identifies emerging tools such as blueprinting and journey mapping which enable design solutions to better manage tourism experiences. Finally, the implications and directions of this new moments-based paradigm for designing tourism experiences are discussed. • Examines the sequential structure of tourism experiences within the trip journey. • The sequential nature of the trip influences its basic nature, including overall satisfaction. • Journey mapping and blueprinting can be used to capture and represent moments-based experiences. • Discusses relationships between experience design, tourism design and destination management.

Matrix factorization with heterogeneous multiclass preference context

The spreading use of the Internet and big data technology has spawned the need for recommendation systems. However, to alleviate public anxiety about privacy, this paper advocates making recommendation with internal context, which refers to the implicit context hidden beneath the rating matrix only. Inspired by a recent work that embeds neighborhood information among users (as represented by multiclass preference context, MPC) into a factorization-based method, we extend it to both user-oriented and item-oriented, and put forward both user-oriented MPC and item-oriented MPC into a generic factorization framework called matrix factorization with heterogeneous MPC (MF-HMPC). In particular, we derive two specific recommendation methods from MF-HMPC with different structures, including MF with dual MPC (MF-DMPC) and MF with pipelined MPC (MF-PMPC), which are corresponding to concurrent structure and sequential structure, respectively. Extensive empirical studies on four public datasets show that our two forms of MF-HMPC outperform the referenced state-of-the-art MF methods as well as two representative deep learning methods. Moreover, we also discover some interesting facts about how these two kinds of internal contextual information complement each other. The main advantage of our methods is that they manage to strike a good balance between user-oriented neighborhood information and item-oriented neighborhood information.

Direct‐Ink Written Shape‐Morphing Film with Rapid and Programmable Multimotion

AbstractShape‐morphing structures are appealing in the material community to inspire a myriad of applications across domains, including soft robots, tissue engineering, and consumer products. However, it is a challenge to create fast response/recovery actuators with complicated and robust deformation due to the slow diffusion speed of water in hydrogel and small modulus of hydrogel. Herein, a facile strategy is proposed to fabricate a versatile patterned film configuration using chitin and poly(dimethylsiloxane) (PDMS). The direct‐ink writing (DIW) technique is leveraged to build simple, flat, patterned films, which can be morphed into complicated multidimensional architectures. Additionally, computational simulations are conducted to predict the deformation behaviors of the actuator. The sequential response structure can be precisely manipulated by the actuation speed of different structures. Moreover, an assembling method is proposed to create multifunction actuators.

A CTR prediction model based on user interest via attention mechanism

Recently, click-through rate (CTR) prediction is a challenge problem in the aspect of online advertising. Some researchers have proposed deep learning-based models that follow a similar embedding and MLP paradigm. However, the corresponding approaches generally ignore the importance of capturing the latent user interest behind user behaviour data. In this paper, we present a novel attentive deep interest-based network model called ADIN. Specifically, we capture the interest sequence in the interest extractor layer, and the auxiliary losses are employed to produce the interest state with the deep supervision. First, we model the dependency between behaviours by using a bidirectional gated recurrent unit (Bi-GRU). Next, we extract the interest evolving process that is related to the target and propose an interest evolving layer. At the same time, attention mechanism is embedded into the sequential structure. Then, the model learns highly non-linear interactions of features based on stack autoencoders. An experiment has been done using four real-world datasets, the proposed model achieves superior performance than the existing state-of-the-art models.

Design of High-Gain Circularly Polarized Antennas Based on Vehicle Application Environment

In this paper, a novel circularly polarized (CP) antenna that achieves high gain by maximizing the radiation aperture utilization is proposed for vehicle applications. The realization of increasing the radiation aperture is based on the application environment, making full utilization of the vehicle roof, a large metal reflector beneath the antenna, which is indispensable in the vehicle application. Developed from the structure of typical microstrip patch antenna, the design extends the radiation patch to cover the side of the dielectric substrate, which increases the size of the radiating patch without increasing the antenna's outline size. To make CP antennas more suitable for special working environments with large reflecting surface, a sequential rotation feeding structure with 90° progressive phase difference is uniquely designed based on the active port impedance of the four-ended radiation structure. This co-design method introducing the active port impedance of a multi-port radiator into the design of feeding network as the loads can effectively guarantee the CP performance of the antenna, and can greatly simplify the optimization work in the subsequent stage. Based on this design idea, a prototype working at 1.48 GHz with a 50-MHz bandwidth is designed for vehicle satellite broadcasting application. Experimental results show that the antenna achieves a high CP gain greater than 7 dBic with a compact size of $50 \times 50 \times 10\,\,mm^{3}$ ($0.247\lambda _{0} \times 0.247\lambda _{0} \times 0.05\lambda _{0}$ , $\lambda _{0} $ is the wavelength of working frequency in free space).

Broadband Circularly Polarized Cross-Dipole Antenna With Multiple Modes

A broadband circularly polarized (CP) cross-dipole antenna with multiple modes is proposed. The antenna is composed of two pairs of crossed strip dipoles as driven elements, four square-slot patches, and four corner-truncated patches as parasitic elements. A pair of vacant-quarter printed rings is used as sequential phase feeding structure to generate a CP mode, which connect to crossed strip dipoles. By using these coupled rotated elements, multiple CP modes can be stimulated to satisfy the need of broadband CP radiation. In addition, four shorted square metal columns are also introduced in the antenna design to generate an additional CP resonance. A verified prototype was fabricated and measured in this paper. The measured results demonstrate the proposed antenna features wideband CP characteristics with a wide impedance bandwidth (IBW) of 95.5% (0.92-2.60 GHz) and a broad axial-ratio bandwidth (ARBW) of 94.4% (0.95-2.65 GHz).

Simulation on Sequential Construction Process and Structure of the Pisa Tower

The leaning of structures happens all around the world and generates impacts on different extents; thus, it is important to learn about the causes behind. In this report, the sequential construction of a typical leaning structure, the Tower of Pisa, is discussed and simulated by using a finite element code, PLAXIS. The simulation is performed on a two-dimensional plane with simplifications taken into consideration in making modeling feasible under limitations. Three distinct models are built with one as a control variable, while the other two models are set up with exact eccentricity. Data are obtained from the analysis and are plotted in a graph to clearly show the relationship between the tilting angle and construction phases. With reasonable and completed simulation, the study is able to show the significant role compressible subsoil plays in impacting the tilting performance of a tall building.