Модели осложненных предложений с сопоставительными оборотами в хакасском языке

Introduction. This article, based on the material of the Khakass language, describes models of complicated sentences with comparative constructions. Notably, many issues related to the description of complicated sentences in Khakass have not received proper coverage yet, which determines the relevance of this study. The article aims to identify and analyze the structural-semantic types, as well as ways of expressing complicated sentences with comparative constructions in the Khakass language. The research is based on a solid sample of examples from the texts of fiction of various genres, folklore, and journalistic texts, as well as recordings of oral speech. Results. The research shows that there are three types of complicated sentences in Khakass, including a comparative model based on the equality of compared features, a comparative-gradation model, and a substitution model. Each model is described in terms of its basic semantics and ways of formalizing the relationship between dependent and main parts, with specific examples illustrating their use. Each model varies in semantic and structural terms. The comparative model of equal features and the comparative-gradation model have two variants, the substitution model has five. In a comparative model based on equality of features, equal relations between two given events are expressed, while the comparison-gradation model compares the degree of significance of given events, with the action in the main part becoming significant. The substitution model may be of two types: substitutive per se and substitutive-preferential. In substitutive models proper, the actions of the main part do not meet the speaker’s expectation, while in the other model, preference is given to the main event.

How Hard to Tell? Complexity of Belief Manipulation Through Propositional Announcements

Consider a set of agents with initial beliefs and a formal operator for incorporating new information. Now suppose that, for each agent, we have a formula that we would like them to believe. Does there exist a single announcement that will lead all agents to believe the corresponding formula? This paper studies the problem of the existence of such an announcement in the context of model-preference definable revision operators. First, we provide two characterisation theorems for the existence of announcements: one in the general case, the other for total partial orderings. Second, we exploit the characterisation theorems to provide upper bound complexity results. Finally, we also provide matching optimal lower bounds for the Dalal and Ginsberg operators.

Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis

In a recent publication in the Journal of Intelligence, Dennis McFarland mischaracterized previous research using latent variable and psychometric network modeling to investigate the structure of intelligence. Misconceptions presented by McFarland are identified and discussed. We reiterate and clarify the goal of our previous research on network models, which is to improve compatibility between psychological theories and statistical models of intelligence. WAIS-IV data provided by McFarland were reanalyzed using latent variable and psychometric network modeling. The results are consistent with our previous study and show that a latent variable model and a network model both provide an adequate fit to the WAIS-IV. We therefore argue that model preference should be determined by theory compatibility. Theories of intelligence that posit a general mental ability (general intelligence) are compatible with latent variable models. More recent approaches, such as mutualism and process overlap theory, reject the notion of general mental ability and are therefore more compatible with network models, which depict the structure of intelligence as an interconnected network of cognitive processes sampled by a battery of tests. We emphasize the importance of compatibility between theories and models in scientific research on intelligence.

The Power of the Hybrid Model for Mean Estimation

We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees. In particular, we study the utility of hybrid model estimators that compute the mean of arbitrary realvalued distributions with bounded support. When the curator knows the distribution’s variance, we design a hybrid estimator that, for realistic datasets and parameter settings, achieves a constant factor improvement over natural baselines.We then analytically characterize how the estimator’s utility is parameterized by the problem setting and parameter choices. When the distribution’s variance is unknown, we design a heuristic hybrid estimator and analyze how it compares to the baselines. We find that it often performs better than the baselines, and sometimes almost as well as the known-variance estimator. We then answer the question of how our estimator’s utility is affected when users’ data are not drawn from the same distribution, but rather from distributions dependent on their trust model preference. Concretely, we examine the implications of the two groups’ distributions diverging and show that in some cases, our estimators maintain fairly high utility. We then demonstrate how our hybrid estimator can be incorporated as a sub-component in more complex, higher-dimensional applications. Finally, we propose a new privacy amplification notion for the hybrid model that emerges due to interaction between the groups, and derive corresponding amplification results for our hybrid estimators.

The influence of consumer brand loyalty on brand remanufacturing market strategy

This paper analyzed how consumer brand loyalty influences brand remanufacturers’ market strategy when independent remanufacturers (IRs) enter the market and when they do not. The authors developed and analyzed four market models, “no IR enter and no brand remanufacture (n),” “no IR enter and brand remanufacture (nR),” “IR enter and no brand manufacture (nr),” and “IR enter and brand remanufacture (nrR)”. They then analyzed the results using sensitivity analysis and comparative analysis. The research found that new product prices, brand remanufactured product prices, and general re-product prices are positively correlated with brand loyalty; with the increasing level of brand loyalty, brand manufacturers’ profit increases, while the IR’s profit is lowered. Further, when the manufacturing costs of new products are high, the model preference for the brand manufacturers is always [Formula: see text]; when the costs are lower, the level of the consumer brand loyalty affects the model preference. In models nr and nrR, brand loyalty may increase the brand consumer surplus, but reduces the average consumer surplus, total consumer surplus, and total social welfare.

Distributionin of Data Handling in Cloud Asset

In Cloud computing, task scheduling is one of the technique of specifying and assigning job to assets that finish the job. It may be virtual computing elements like threads & processors or data flows, which is planned on hardware resources like processors. The planning operation is performed by a scheduler. Schedulers are enabled various customers to properly communicate system funds or attain excellent service quality. Scheduling is essential for computing and the notion of planning allows multitasking computers with single CPU as inner portion of a computer system's execution model. Preference will be provided based on the requirements and goals of the user. Multiple computing parts comprise of many parallel applications while duties of execution are relied on other duties. We have studied few related articles in this paper, which is presented in the following section.

Rented vs. owner-occupied housing and monetary policy

The aim of this paper is to show how housing tenure (rented vs.cowner-occupied) affects monetary policy. I propose a dynamic stochastic general equilibrium model with housing, both owned and rented. First, I analyze how, in the model, preference parameters, fiscal incentives, and institutional factors determine the rental market share and the residential debt-to-GDP ratio. Then, within this framework, I study how the transmission and optimality of monetary policy differ depending on these factors. From a positive perspective, impulse responses illustrate differences in the monetary transmission mechanism. I find that of all factors, tax incentives generate the largest differences. In normative terms, results show that when the relative size of the rental market is larger, monetary policy is more stabilizing. An optimal monetary policy analysis also suggests that in this case, monetary policy should respond more aggressively to inflation and disregard output, because the financial accelerator effects are weaker.

An Analysis of Global and Regional Mainstreaminess for Personalized Music Recommender Systems

The music mainstreaminess of a listener reflects how strong a person’s listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives, we show country-specific differences and study how taking into account music mainstreaminess influences the quality of music recommendations. In this paper, we first propose 11 novel mainstreaminess measures characterizing music listeners, considering both a global and a countryspecific basis for mainstreaminess. To this end, we model preference profiles (as a vector over artists) for users, countries, and globally, incorporating artist frequency, listener frequency, and a newly proposed TF-IDF-inspired weighting function, which we call artist frequency–inverse listener frequency (AF-ILF). The resulting preference profile for each user u is then related to the respective country-specific and global preference profile using fraction-based approaches, symmetrized Kullback-Leibler divergence, and Kendall’s τ rank correlation, in order to quantify u’s mainstreaminess. Second, we detail country-specific peculiarities concerning what defines the countries’ mainstream and discuss the proposed mainstreaminess definitions. Third, we show that incorporating the proposed global and country-specific mainstreaminess measures into the music recommendation process can notably improve accuracy of rating prediction.