Is Perception Stimulus-Dependent?

The most natural way to distinguish perception from cognition is by considering perception as stimulus-dependent. Perception is tethered to the senses in a way that cognition is not. Beck Australasian Journal of Philosophy 96(2): 319-334 (2018) has recently argued in this direction. He develops this idea by accommodating two potential counterexamples to his account: hallucinations and demonstrative thoughts. In this paper, I examine this view. First, I detect two general problems with movement to accommodate these awkward cases. Subsequently, I place two very common mental phenomena under the prism of the stimulus-dependence criterion: amodal completion and visual categorization. The result is that the stimulus-dependent criterion is too restrictive, it leaves the notion of perception extremely cramped. I conclude that even the criterion of stimulus-dependence fails to mark a clearly defined border between perception and cognition.

Some Existence and Dependence Criteria of Solutions to a Fractional Integro-Differential Boundary Value Problem via the Generalized Gronwall Inequality

The main intention of the present research study is focused on the analysis of a Caputo fractional integro-differential boundary problem (CFBVP) in which the right-hand side of supposed differential equation is represented as a sum of two nonlinear terms. Under the integro-derivative boundary conditions, we extract an equivalent integral equation and then define new operators based on it. With the help of three distinct fixed-point theorems attributed to Krasnosel’skiĭ, Leray–Schauder, and Banach, we investigate desired uniqueness and existence results. Additionally, the dependence criterion of solutions for this CFBVP is checked via the generalized version of the Gronwall inequality. Next, three simulative examples are designed to examine our findings based on the procedures applied in the theorems.

A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier

Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).

Sales Comparison Approach Indicating Heterogeneity of Particular Type of Real Estate and Corresponding Valuation Accuracy

The article focuses on heterogeneity of goods, namely real estate and consequently deals with market valuation accuracy. The heterogeneity of real estate property is, in particular, that every unit is unique in terms of its construction, condition, financing and mainly location and thus assessing the value must necessarily be difficult. This research also indicates the rate of efficiency of markets across the types based on their level of variability. The research is based on two databases consisting of various types of real estate with specific market parameters. These parameters determine the differences across the types and reveal heterogeneity. The first database has been set on valuations by sales comparison approach and the second one on data of real properties offered on the market. The methodology is based on univariate and multivariate statistics of key variables of those databases. The multivariate analysis is performed by Hotelling T2 control chart and statistics with appropriate numerical characteristics. The results of both databases were joint by weights with regard to the dependence criterion of the variables. The final results indicate potential valuation accuracy across the types. The main contribution of the research is that the evaluation was not only derived from the price deviation or distribution, but it also draws from causes of real property heterogeneity as a whole.

Cloud Model-Based Method for Infrared Image Thresholding

Traditional statistical thresholding methods, directly constructing the optimal threshold criterion using the class variance, have certain versatility but lack the specificity of practical application in some cases. To select the optimal threshold for infrared image thresholding, a simple and efficient method based on cloud model is proposed. The method firstly generates the cloud models corresponding to image background and object, respectively, and defines a novel threshold dependence criterion related with the hyper-entropy of these cloud models and then determines the optimal grayscale threshold by the minimization of this criterion. It is indicated by the experiments that, compared with selected methods, using both image thresholding and target detection, the proposed method is suitable for infrared image thresholding since it performs good results and is reasonable and effective.

Levels of Description: A Novel Approach to Dynamical Hierarchies

We present a novel formal interpretation of dynamical hierarchies based on information theory, in which each level is a near-state-determined system, and levels are related to one another in a partial ordering. This reformulation moves away from previous definitions, which have considered unique hierarchies of structures or objects arranged in aggregates. Instead, we consider hierarchies of dynamical systems: these are more suited to describing living systems, which are not mere aggregates, but organizations. Transformations from lower to higher levels in a hierarchy are redescriptions that lose information. There are two criteria for partial ordering. One is a state-dependence criterion enforcing predictability within a level. The second is a distinctness criterion enforcing the idea that the higher-level description must do more than just throw information away. We hope this will be a useful tool for empirical studies of both computational and physical dynamical hierarchies.