The management of accounts receivable is crucial to a company's profitability and overall growth, as it directly influences financial performance. Specifically, firms with longer cash conversion cycle periods exhi...
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The rapid integration of digital technologies has precipitated a substantial transition from traditional in-person commerce to digital trade, even encompassing physical goods. This transformation not only simplifies t...
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Fractional factorial(FF)designs are commonly used for factorial experiments in many *** some prior knowledge has shown that some factors are more likely to be significant than others,Li,et al.(2015)proposed a new patt...
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Fractional factorial(FF)designs are commonly used for factorial experiments in many *** some prior knowledge has shown that some factors are more likely to be significant than others,Li,et al.(2015)proposed a new pattern,called the individual word length pattern(IWLP),which,defined on a column of the design matrix,measures the aliasing of the effect assigned to this column and effects involving other *** this paper,the authors first investigate the relationships between the IWLP and other popular criteria for regular FF *** we know,fractional factorial split-plot(FFSP)designs are important both in theory and *** another contribution of this paper is extending the IWLP criterion from FF designs to FFSP *** authors propose the IWLP of a factor from the whole-plot(WP),or sub-plot(SP),denoted by the I_w WLP and Is WLP respectively,in the FFSP *** authors further propose combined word length patterns C_(w) WLP and Cs WLP,in order to select good designs for different *** new criteria C_(w) WLP and Cs WLP apply to the situations that the potential important factors are in WP or SP,*** examples are presented to illustrate the selected designs based on the criteria established here.
This research addresses the challenge of low-resolution CCTV footage in criminal investigations through the application of generative artificial intelligence (AI). Leveraging Generative Adversarial Networks (GANs) and...
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Universities have been expanding the datascience programs for undergraduate students, with the simultaneous goal of reaching and retaining students from underrepresented groups in the datascience workforce. The set ...
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The emergence of Large Language Models(LLMs)has renewed debate about whether Artificial Intelligence(AI)can be conscious or *** paper identifies two approaches to the topic and argues:(1)A“Cartesian”approach treats ...
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The emergence of Large Language Models(LLMs)has renewed debate about whether Artificial Intelligence(AI)can be conscious or *** paper identifies two approaches to the topic and argues:(1)A“Cartesian”approach treats consciousness,sentience,and personhood as very similar terms,and treats language use as evidence that an entity is *** approach,which has been dominant in AI research,is primarily interested in what consciousness is,and whether an entity possesses it.(2)An alternative“Hobbesian”approach treats consciousness as a sociopolitical issue and is concerned with what the implications are for labeling something sentient or *** both enables a political disambiguation of language,consciousness,and personhood and allows regulation to proceed in the face of intractable problems in deciding if something“really is”sentient.(3)AI systems should not be treated as conscious,for at least two reasons:(a)treating the system as an origin point tends to mask competing interests in creating it,at the expense of the most vulnerable people involved;and(b)it will tend to hinder efforts at holding someone accountable for the behavior of the systems.A major objective of this paper is accordingly to encourage a shift in *** place of the Cartesian question-is AI sentient?-I propose that we confront the more Hobbesian one:Does it make sense to regulate developments in which AI systems behave as if they were sentient?
Adam has become one of the most favored optimizers in deep learning problems. Despite its success in practice, numerous mysteries persist regarding its theoretical understanding. In this paper, we study the implicit b...
Bayesian modelling helps applied researchers to articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate posterior inference, researchers ca...
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Bayesian modelling helps applied researchers to articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate posterior inference, researchers can now easily build, use, and revise complicated Bayesian models for large and rich data. These capabilities, however, bring into focus the problem of model criticism. Researchers need tools to diagnose the fitness of their models, to understand where they fall short, and to guide their revision. In this paper, we develop a new method for Bayesian model criticism, the holdout predictive check (HPC). Holdout predictive check are built on posterior predictive check (PPC), a seminal method that checks a model by assessing the posterior predictive distribution on the observed data. However, PPC use the data twice—both to calculate the posterior predictive and to evaluate it—which can lead to uncalibrated p-values. Holdout predictive check, in contrast, compare the posterior predictive distribution to a draw from the population distribution, a heldout dataset. This method blends Bayesian modelling with frequentist assessment. Unlike the PPC, we prove that the HPC is properly calibrated. Empirically, we study HPC on classical regression, a hierarchical model of text data, and factor analysis.
Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations ***...
The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized home automation, yet traditional air-conditioning (AC) systems still struggle with energy inefficiency. Our research pres...
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