Implicit Prediction Research Articles

Based on Deep Reinforcement Learning to path planning in uncertain ocean currents for Underwater Gliders

This paper studies the application of Deep Reinforcement Learning (DRL) in the field of underwater glider (UG) formation path planning and solves the path planning of glider formation in uncertain ocean currents. Facing the uncertain ocean current environment, based on the observation and collection of ocean current data by the overall ocean observation system, the glider formation needs to have the ability of path planning. The original DRL algorithm network architecture is simple, and the feature extraction ability is limited. According to the characteristics of ocean current and path planning, a Deep Convolutional Neural Network for uncertain ocean current environment (Duo-CNN) network architecture with a dual-branch structure is proposed. Based on the DRL, the glider model and the interaction between the glider and the environment are used to guide the path planning through the implicit prediction of the known ocean current data. A solution for formation construction, obstacle avoidance, and path planning of glider formation in a dynamic uncertain ocean current environment is provided. The simulation verifies the effectiveness of the path planning of the glider formation using the DRL of the dual-branch Duo-CNN architecture. Compared with other architectures with feature extraction ability, Duo-CNN has advantages.

Implicit HbA1c Achieving 87% Accuracy within 90 Days in Non-Invasive Fasting Blood Glucose Measurements Using Photoplethysmography.

To reduce the error induced by overfitting or underfitting in predicting non-invasive fasting blood glucose (NIBG) levels using photoplethysmography (PPG) data alone, we previously demonstrated that incorporating HbA1c led to a notable 10% improvement in NIBG prediction accuracy (the ratio in zone A of Clarke's error grid). However, this enhancement came at the cost of requiring an additional HbA1c measurement, thus being unfriendly to users. In this study, the enhanced HbA1c NIBG deep learning model (blood glucose level predicted from PPG and HbA1c) was trained with 1494 measurements, and we replaced the HbA1c measurement (explicit HbA1c) with "implicit HbA1c" which is reversely derived from pretested PPG and finger-pricked blood glucose levels. The implicit HbA1c is then evaluated across intervals up to 90 days since the pretest, achieving an impressive 87% accuracy, while the remaining 13% falls near the CEG zone A boundary. The implicit HbA1c approach exhibits a remarkable 16% improvement over the explicit HbA1c method by covering personal correction items automatically. This improvement not only refines the accuracy of the model but also enhances the practicality of the previously proposed model that relied on an HbA1c input. The nonparametric Wilcoxon paired test conducted on the percentage error of implicit and explicit HbA1c prediction results reveals a substantial difference, with a p-value of 2.75 × 10-7.

Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples

Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.

Data-driven implicit design preference prediction model for product concept evaluation via BP neural network and EEG

Customer preference-involved product concept evaluation is a vital stage for developing new products. One of the key factors in this stage is to mine the potential relationships between customers’ needs and technical requirements. In most new product development (NPD), design characteristics (DCs) are employed as performance indicators to describe the technical requirements of a scheme. Previous studies have focused on evaluating the degree to which a scheme meets the performance value of DCs, disregarding the relationship between DCs and potential user preferences. This oversight leads to an inability to accurately predict customer satisfaction with the candidate schemes based on their preferences. Previous research, which was aimed at ensuring the preference value of schemes by utilizing mapping data between design preference (DP) and DCs, overlooked users' psychological states and thus failed to accurately capture their cognitive preferences for the schemes. To address these issues, an electroencephalogram (EEG) data-driven, implicit DP prediction approach is proposed. The method characterizes the implicit cognitive relationship between DP and DCs by mining EEG data and utilizes a back propagation (BP) neural network to predict DP. First, a user's DP word pairs were constructed based on the approach of statistical analysis, and the best-worst method (BWM) was introduced to obtain the importance of DCs. Second, an experiment was designed based on the extracted preference word pairs to collect EEG data that reflect a user's DC preference, record the user's stimulation state toward the DCs, and label their DP based on the segmentation results of preference evaluation. Third, a BP neural network was employed to construct a preference prediction model for DCs, and the reliability of the proposed preference prediction model was validated by using the forklift design example. The prediction results further exhibited the potential association between user preferences and DCs that can be acquired by EEG data mining. The results also provide support for designers to promptly acquire design concepts that satisfy user requirements (URs), thereby enhancing the efficiency and quality of product development in a highly competitive market.

Impaired prediction of ongoing events in posttraumatic stress disorder

The ability to make accurate predictions about what is going to happen in the near future is critical for comprehension of everyday activity. However, predictive processing may be disrupted in Posttraumatic Stress Disorder (PTSD). Hypervigilance may lead people with PTSD to make inaccurate predictions about the likelihood of future danger. This disruption in predictive processing may occur not only in response to threatening stimuli, but also during processing of neutral stimuli. Therefore, the current study investigated whether PTSD was associated with difficulty making predictions about near-future neutral activity. Sixty-three participants with PTSD and 63 trauma controls completed two tasks, one testing explicit prediction and the other testing implicit prediction. Higher PTSD severity was associated with greater difficulty with predictive processing on both of these tasks. These results suggest that effective treatments to improve functional outcomes for people with PTSD may work, in part, by improving predictive processing.

A Cross-Modal Investigation of Statistical Learning in Developmental Dyslexia

ABSTRACT Structural patterns existing in language can be exploited for implicit prediction of sequences in speech and visual input via a process termed statistical learning (SL). Despite extensive examination of SL in dyslexia, whether SL problems arise from modality-constrained learning processes or from global learning processes is still unknown, nor is it clear how SL can be supported. Purpose The present study used the triplet paradigm to explore SL among young adults with dyslexia and among typical readers across auditory and visual modalities and tested whether information from one sensory modality can assist SL in a different sensory modality. Method Participants performed auditory and visual SL tasks under conditions in which a consistent visual/auditory cue respectively accompanied the auditory/visual triplets or under conditions in which no cross-modal information was presented. Results SL performance was poorer in the dyslexia group than among typical readers across visual and auditory modalities. Furthermore, both groups improved their SL abilities under conditions in which cues were consistent with triplet boundaries compared to under conditions lacking cross-modal information Conclusions These findings suggest that SL impairments observed in dyslexia stem from a domain-general deficiency and that cross-modal information can be recruited to support SL in dyslexia.

Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model.

In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer's credit is the standard to evaluate the loan amount and interest rate, and the ability to quickly identify customer information has become a research hotspot. Based on the bank credit application scenario, this paper realizes function extraction and data processing for customer basic attribute data and download transaction data. Then, a linear regression model with penalty and a neural network prediction model are proposed to improve the accuracy of bankruptcy assessment and achieve local optimization. In this way, the implicit risk prediction and control of customer credit are improved, and the default risk of bank loans is significantly reduced. According to the characteristics of the collected sample data, the most appropriate penalty linear regression prediction algorithm is selected and the experimental analysis is carried out to improve the risk management level of banks. The experimental results show that the improved logistic regression and neural network model has obvious advantages in the prediction effect for four models.

An Active Inference Account of Touch and Verbal Communication in Therapy.

This paper offers theoretical explanations for why “guided touch” or manual touch with verbal communication can be an effective way of treating the body (e.g., chronic pain) and the mind (e.g., emotional disorders). The active inference theory suggests that chronic pain and emotional disorders can be attributed to distorted and exaggerated patterns of interoceptive and proprioceptive inference. We propose that the nature of active inference is abductive. As such, to rectify aberrant active inference processes, we should change the “Rule” of abduction, or the “prior beliefs” entailed by a patient’s generative model. This means pre-existing generative models should be replaced with new models. To facilitate such replacement—or updating—the present treatment proposes that we should weaken prior beliefs, especially the one at the top level of hierarchical generative models, thereby altering the sense of agency, and redeploying attention. Then, a new prior belief can be installed through inner communication along with manual touch. The present paper proposes several hypotheses for possible experimental studies. If touch with verbal guidance is proven to be effective, this would demonstrate the relevance of active inference and the implicit prediction model at a behavioral level. Furthermore, it would open new possibilities of employing inner communication interventions, including self-talk training, for a wide range of psychological and physical therapies.

Control Method for Continuous Grain Drying Based on Equivalent Accumulated Temperature Mechanism and Artificial Intelligence.

Grain drying is a complex heat and mass transfer process, which has the characteristics of a significant delay, multidisturbance, nonlinearity, strong coupling, and parameter uncertainty. Artificial intelligence (AI) control technology is suitable for solving such complex control problems. In this paper, the mechanism and data dual-drive with equivalent accumulated temperature (EAT) mutual-window AI-control method for continuous grain drying were proposed, and a control system was established. The experimental verification was carried out on the test platform of continuous grain drying. The results show that the method has the ability of implicit prediction, high accuracy, strong stability and self-adaptive ability, and the maximum control deviation of moisture at the outlet of the dryer is −0.58–0.3%.

Application of Data Mining Technology in Public Welfare Sports Education in the Era of Artificial Intelligence

Data mining refers to extracting the implicit prediction information from a massive dataset. It has very application prospects. Some data mining tools can develop things. The purpose of this article mainly discusses the public welfare sports education in the artificial intelligence era. The article discusses the research background and significance, development of education data mining, and decision tree technology and enumerates the application of education data mining in real life. The concept of educational data mining is given, and several common typical decision tree algorithms and their connections and differences are described; then, the concepts of multivalue decision tables and decision trees are discussed in detail. This article aims to build a nonprofit physical education system to manage and analyze the attendance data of students’ physical health assessment, so as to improve the enthusiasm of students to exercise, such as BP neural network, decision tree classification algorithm, and cluster analysis, discusses the calculation and analysis process of the relevant body side data of the sports teaching platform, and emphatically discusses and analyzes the application effect of data mining technology in public welfare sports teaching. In addition, this article has built a public welfare physical education system, allowing us to clearly understand various factors that affect students’ exercise and the relationship between various project indicators. Based on these data, educators can adjust technical means. Experimental results can efficiently and conveniently understand the pass rate of students in various sports. The pass rates of students in the six tests, grip strength, and sitting forward bending were 58%, 65%, 78%, 78%, 85%, and 65%, respectively. Using mathematical methods and computer technology, we can dig out valuable education management information from massive education data, so as to provide a reference for improving school enrollment.

Multiplex network embedding for implicit sentiment analysis

As one of the hot research directions in natural language processing, sentiment analysis has received continuous and extensive attention. Different from explicit sentiment words indicating sentiment polarity, implicit sentiment analysis is a more challenging problem due to the lack of sentiment words, which makes it inadequate to use traditional sentiment analysis method to judge the polarity of implicit sentiment. This paper takes sentiment analysis as a special sign link prediction problem, which is different from traditional text-based method. In particular, by performing the word graph-based text level information embedding and heterogeneous social network information embedding (i.e. user social relationship network embedding, and user-entity sentiment network embedding), the proposed scheme learns the highly nonlinear representations of network nodes, explores early fusion method to combine the strength of these two types of embedding modeling, optimizes all parameters simultaneously and creates enhanced context representations, leading to better capture of implicit sentiment polarity. The proposed method has been examined on real-world dataset, for implicit sentiment link prediction task. The experimental results demonstrate that the proposed method outperforms state-of-the-art schemes, including LINE, node2vec, and SDNE, by 20.2%, 19.8%, and 14.0%, respectively, on accuracy, and achieves at least 14% gains on AUROC. For sentiment analysis accuracy, the proposed method achieves AUROC of 80.6% and accuracy of 78.3%, which is at least 31% better than other models. This work can provide useful guidance on the implicit sentiment analysis.

Implicit action prediction constrains observed biological action reconstruction

Voluntary movements include a predictive control of the sensory-motor consequences of executed or observed actions. The motor system predicts further steps of actions relying on its pure observation. This study seeks to disclose the interference of an implicit motor prediction effect during actions reconstruction. Videos of human actions directed to objects were presented to volunteers. Subsequently, they combined four static frames of those videos randomly arranged on the screen. Such combination could be chronological (from the beginning to the end of the action) or reverse (from the end to the beginning of the action). The observed actions were also biological (human movement) or non-biological (movement of objects). The grasping began with the actor's hand in a resting position over a table (Experiment I), or with his hand in contact with the object (Experiment II). In the first experiment, participants presented lower accuracy in the biological condition rearranging in chronological order. In the second experiment, however, the accuracy was lower in reverse order. The interpretation of such results is that the implicit predictive mechanisms interfered in the rearrangement of the frames. As an example: the expected movement after a grasping action whose outcome is capping a bottle would be the withdrawal of the hand. Therefore, combining frames of a recent seen action, volunteers present less accuracy if the first frame to be placed is counterintuitive.

Study on the performance evaluation and prediction model of self-compacting concrete in steel shell immersed tube

In order to study the quality control and evaluation methods of self-compacting concrete (SCC) pumping process in Shenzhen-Zhongshan Bridge and similar projects, sample test is performed on self-compacting concrete mixture collected from the pumping field of the E1-E4 steel shell immersed tube; Then a database base on relationship between the variation parameters and the target performance is established. On this basis, the Grey system theory is adopted to analyze the parameter sensitivity of the SCC pumping performance to the different kinds of variables. The results show that variables are related to target performance and some of the variables have a significant influence. Using the powerful data mining capability of support-vector machine and Bayesian statistical inference in the case of uncertain exact mathematical relationship between independent variables and dependent variables, implicit and explicit prediction models of variation of SCC pumping performance are respectively established by pumping distance, number of elbows, pumping time and environmental temperature as the control parameters. Finally, the comparisons between the measured data and calculation result prove that both models have good prediction accuracy and stability.

Change in a probabilistic representation of meaning can account for N400 effects on articles: A neural network model

Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.

Is the central-marginal hypothesis a general rule? Evidence from three distributions of an expanding mangrove species, Avicennia germinans (L.) L.

The central‐marginal hypothesis (CMH) posits that range margins exhibit less genetic diversity and greater inter‐population genetic differentiation compared to range cores. CMH predictions are based on long‐held “abundant‐centre” assumptions of a decline in ecological conditions and abundances towards range margins. Although much empirical research has confirmed CMH, exceptions remain almost as common. We contend that mangroves provide a model system to test CMH that alleviates common confounding factors and may help clarify this lack of consensus. Here, we document changes in black mangrove (Avicennia germinans) population genetics with 12 nuclear microsatellite loci along three replicate coastlines in the United States (only two of three conform to underlying “abundant‐centre” assumptions). We then test an implicit prediction of CMH (reduced genetic diversity may constrain adaptation at range margins) by measuring functional traits of leaves associated with cold tolerance, the climatic factor that controls these mangrove distributional limits. CMH predictions were confirmed only along the coastlines that conform to “abundant‐centre” assumptions and, in contrast to theory, range margin A. germinans exhibited functional traits consistent with greater cold tolerance compared to range cores. These findings support previous accounts that CMH may not be a general rule across species and that reduced neutral genetic diversity at range margins may not be a constraint to shifts in functional trait variation along climatic gradients.

Estimación de distribuciones diamétricas para Pinus patula con la función Weibull

La predicción implícita es una herramienta valiosa para conocer el tipo de productos que se pueden obtener del bosque en un tiempo determinado y, de esta forma, planificar el manejo forestal sostenible. En la Sierra Norte de Oaxaca, México, existen rodales coetáneos de Pinus patula, una especie maderable de rápido crecimiento e importancia económica y ecológica. El objetivo de este estudio fue estimar el crecimiento y rendimiento maderable de esta especie a través de dos sistemas de predicción con el enfoque de modelos de distribución en clases diamétricas. La base de datos consistió en 132 distribuciones diamétricas, provenientes de 66 sitios permanentes de muestreo de 400 m2 localizados en Ixtlán de Juárez, Oaxaca, México. La distribución diamétrica del rodal se estimó mediante la función de densidad de probabilidades Weibull con los métodos de predicción de parámetros y predicción de percentiles. Asimismo, se ajustaron modelos de proyección del diámetro mínimo y diámetro medio cuadrático del rodal para recuperar los parámetros y estimar la estructura diamétrica de rodales a medida que cambia la edad. El mejor método fue el de predicción de parámetros, debido al sesgo menor en la predicción del volumen total con respecto a un sistema de proyección explícito diseñado para la especie. Por tanto, es preferente el uso de este sistema de predicción implícita para planificar el manejo de los rodales coetáneos de P. patula.

A conservative implicit scheme for steady state solutions of diatomic gas flow in all flow regimes

An implicit scheme for steady state solutions of diatomic gas flow is presented. The Rykov model equation is solved in the finite volume discrete velocity method framework, in which the translational and rotational degrees of freedom are taken into account. At the cell interface, a difference scheme of the model equation is used to construct a multiscale flux (similar to discrete unified gas-kinetic scheme), so that the cell size is not constrained by the cell Knudsen number. The physical local time step is implemented to preserve the multiscale property in the nonuniform-mesh case. The implicit macroscopic prediction technique is adopted to find a predicted equilibrium state at each time step, making the scheme highly efficient in all flow regimes. Furthermore, an integral error compensation technique with negligible computational cost is proposed, which makes the scheme conservative and allows more flexible discretization for particle velocity space. With the compensation technique, the unstructured velocity-space mesh is used in the test cases, which reduces the velocity mesh number significantly. The present method is proved to be efficient and accurate.

Implicit Argument Prediction as Reading Comprehension

Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples. We present a new model for implicit argument prediction that draws on reading comprehension, casting the predicate-argument tuple with the missing argument as a query. We also draw on pointer networks and multi-hop computation. Our model shows good performance on an argument cloze task as well as on a nominal implicit argument prediction task.

Trust-Based Neural Collaborative Filtering

In order to maintain the strong representational learning of the deep neural network model to learn the interaction function between any users and items, combining the trust relationship between users as a local relationship to enhance the ability to supplement interactive data, to achieve a better recommendation effect. This paper proposes a Trust-based Neural Collaborative Filtering model (TNCF). Firstly, trust information and scoring information are merged through the Generalized Matrix Factorization model (GMF) to obtain recommendations based on trust friend preferences. Then, using the Multi-Layer Perceptron model (MLP), the nonlinear kernel is utilized to learn the interaction function from the data to obtain the recommendation based on the user’s personal taste. Finally, all the interaction results are aggregated for implicit prediction. Compared with the three different baselines on the FilmTrust and Epinions datasets, the experimental consequences reveal that the proposed model improves the recommendation and also performs well on sparse statistics.

The implications of persistent (and permanent) engagement in cyberspace

Abstract The USA is in the midst of its most resounding policy shift on cyber conflict with profound implications for national security and the future of the Internet. The US Department of Defense (DoD) cyber strategy concludes that since US cyber forces are in “persistent engagement” with adversaries, then it is an imperative for them to “defend forward” to “continuously contest” them. The implicit prediction is that adversaries will become less effective, forced to expend more resources on defense and rebuild capabilities and infrastructure. John Bolton, the national security advisor, has boasted of a new policy to use offensive cyber operations to impose costs on adversaries and create the frameworks of deterrence. Over time, proponents suggest, these policies will be stabilizing as adversaries engage over repeated engagements in “tacit bargaining” of what is and is not acceptable leading to “more stable expectations of acceptable and unacceptable behavior”. This article advances existing research by including a deeper discussion the academic and policy background on active defense and cyber deterrence, discussing the implied causal chain of “persistent- engagement stability theory,” and analyzing potential risks, especially specific feedback loops which may amplify or dampen cyber conflict. It concludes with specific policy solutions to help mitigate these risks and a suggestion for a broader theory, “stability-enhancing engagement theory.”