Explaining Disease: Correlations,Causes, and Mechanisms

Minds and Machines - Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among...     

Virtio network paravirtualization driver: Implementation and performance of a de-facto standard

One of the techniques used to improve I/O performance of virtual machines is paravirtualization. Paravirtualized devices are intended to reduce the performance overhead on full virtualization where all hardware devices are emulated. The interface of a paravirtualized device is not identical to that of the underlying hardware. The OS of the virtual guest machine must be ported in order to use a paravirtualized device. In this paper, the network virtualization done by the Kernel-based Virtual Machine (KVM) is described. The KVM model is different from other Virtual Machines Monitors (VMMs) because the KVM is a Linux kernel model and it depends on hardware support. In this work, the overhead of using such virtual networks is been measured. A paravirtualized model by using the virtio [38] network driver is described, and some performance results of web benchmark on the two models are presented.     

Accelerating Turing Machines

Minds and Machines - Accelerating Turing machines are Turing machines of a sort able to perform tasks that are commonly regarded as impossible for Turing machines. For example, they can determine...     

Neural networks discover a near-identity relation to distinguish simple syntactic forms

Computer simulations show that an unstructured neural-network model [Shultz, T. R., & Bale, A. C. (2001). Infancy, 2, 501–536] covers the essential features␣of infant learning of simple grammars in an artificial language [Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1999). Science, 283, 77–80], and generalizes to examples both outside and inside of the range of training sentences. Knowledge-representation analyses confirm that these networks discover that duplicate words in the sentences are nearly identical and that they use this near-identity relation to distinguish sentences that are consistent or inconsistent with a familiar grammar. Recent simulations that were claimed to show that this model did not really learn these grammars [Vilcu, M., & Hadley, R. F. (2005). Minds and Machines, 15, 359–382] confounded syntactic types with speech sounds and did not perform standard statistical tests of results.     

Reproducibility and the Concept of Numerical Solution

Minds and Machines - In this paper, we show that reproducibility is a severe problem that concerns simulation models. The reproducibility problem challenges the concept of numerical solution and...     

Triviality Arguments Reconsidered

Minds and Machines - Opponents of the computational theory of mind (CTM) have held that the theory is devoid of explanatory content, since whatever computational procedures are said to account for...     

Towards the Ethical Publication of Country of Origin Information (COI) in the Asylum Process

Minds and Machines - This article addresses the question of how ‘Country of Origin Information’ (COI) reports—that is, research developed and used to support decision-making in...     

A Minimalist Epistemology for Agent-Based Simulations in the Artificial Sciences

Minds and Machines - The epistemology of computer simulations has become a mainstream topic in the philosophy of technology. Within this large area, significant differences hold between the various...     

Deceptive Appearances: the Turing Test,Response-Dependence,and Intelligence as an Emotional Concept

Minds and Machines - The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot (Rethinking Turing’s Test, Journal of Philosophy, 2013) has...     

The End of Vagueness: Technological Epistemicism,Surveillance Capitalism,and Explainable Artificial Intelligence

Minds and Machines - Artificial Intelligence (AI) pervades humanity in 2022, and it is notoriously difficult to understand how certain aspects of it work. There is a movement—Explainable...     

The Epigenesis of Meaning in Human Beings,and Possibly in Robots

Minds and Machines - This article addresses a classical question: Can a machine use language meaningfully and if so, how can this be achieved? The first part of the paper is mainly philosophical....     

Publisher Correction to: The Ethics of AI Ethics: An Evaluation of Guidelines

Minds and Machines - In the original publication of this article, the Table 1 has been published in a low resolution. Now a larger version of Table 1 is published in this correction. The publisher...     

Satisficing Revisited

Minds and Machines - In the debate between simple inference heuristics and complex decision mechanisms, we take a position squarely in the middle. A decision making process that extends to both...     

Are Propositional Attitudes Mental States?

Minds and Machines - I present an argument that propositional attitudes are not mental states. In a nutshell, the argument is that if propositional attitudes are mental states, then only minded...     

On-line control of the threshold policy parameter for multiclass systems

Motivated by the problem of Quality of Service (QoS) provisioning, this paper investigates a system with two classes of traffic and addresses the problem of dynamically controlling the buffer threshold used with the threshold policy, such that an objective function is optimized. For the analysis, a Stochastic Fluid Model (SFM) is used to derive unbiased sensitivity estimators of various metrics (workload, loss, throughput and packet expiration) with respect to the buffer threshold. These estimators are computed using information obtained from the sample path of the “real” discrete event system. Thus, one can use these estimators together with stochastic approximation techniques in order to maintain the system at a near-optimum point despite any changes in the incoming traffic or the transmission capacity of the network.     

Gödel, Lucas, and mechanical models of the mind

In Minds, Machines, and Gödel, Lucas offers an argument, based upon Godel's incompleteness theorems, that his mind cannot be modelled by a machine. This argument has generated a variety of alleged refutations, some of which are incompatible with others. It is argued here that the incompatibility of these refutations points to a puzzle or paradox which has not yet been resolved. A solution to this puzzle is presented in which it is argued that the existence of an algorithm, capable of generating a godel sentence for an axiomatic model of that same algorithm, is not incompatible with Godels well-known results. It is further argued that, contrary to received opinion, Gödel's results do not provide grounds for believing that cognitive agents are incapable of proving the consistency of correct formal models of their own cognitive mechanisms. This is shown to be so, even on the assumption that these formal models are known by those agents (on empirical grounds) to be formal models of themselves. Finally, the implications of the above issues for theoretical questions in AI are explored.     

线性时变时滞系统分析与参数辨识的PMCP方法

本文将[1]定义的按段多重chebyshev多项式系应用于线性时变时滞系统分析与参数辨识，提出了系统分析和参数辨识算法。数值例子表明本文提出的算法既简单又有效，大大优于由移位chebyshev多项式系所导出来的相应算法。     

线性时变时滞系统分析与参数辨识的PMCP方法(英文)

本文将文[1]定义的按段多重chebyshev多项式系应用于线性时变时滞系统分析与参数辨识,提出了系统分析和参数辨识算法。数值例子表明本文提出的算法既简单又有效,大大优于由移位chebyshev多项式系所导出的相应算法。     

Hebb-Hopfield neural networks based on one-dimensional sets of neuron states

Neural Networks (NN), which interconnection matrix is the Hebb matrix of Hopfield (HH) [2,3] are considered. Quasi-continuos sets of neuron states are being used for network matrix production. It is shown, that in this case minima of Hopfield energy are at the bottom of deep ditches, corresponding to the basic set of network activity states for the HH NN. The corresponding states can be made to be stable states of the network. When neuron threshold fatigue is introduced, depending of its recent activity state, the network activity becomes cyclic, moving with a constant rate in one of the two possible directions in the ring, depending on the initial conditions. The phenomena described present novel robust types of NN behavior, which have a high probability to be encountered in living neural systems.