136 research outputs found
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Technical Change, Income Distribution, and Profitability in Multisector Linear Economies
This paper analyzes the effect of technical change on income distribution and profitability by comparing the long-run outcomes defined by a uniform profit rate in a multisector linear economy. We study three scenarios with (i) fixed real wage; (ii) fixed profit rate; or (iii) fixed wage-profit ratio, and show that any viable capital- using and labor-saving technical change itself (in the absence of power change) would bring about a fall in the rate of profit. Profit rate would not rise unless the technical change is so power-biased against the working-class that the wage-profit ratio can not be maintained. Our result conclusively supports the argument of the falling rate of profit due to a rising organic composition of capital as an underlying economic force
Piecewise deterministic Markov process for condition-based imperfect maintenance models
In this paper, a condition-based imperfect maintenance model based on
piecewise deterministic Markov process (PDMP) is constructed. The degradation
of the system includes two types: natural degradation and random shocks. The
natural degradation is deterministic and can be nonlinear. The damage increment
caused by a random shock follows a certain distribution, and its parameters are
related to the degradation state. Maintenance methods include corrective
maintenance and imperfect maintenance. Imperfect maintenance reduces the
degradation degree of the system according to a random proportion. The
maintenance action is delayed, and the system will suffer natural degradations
and random shocks while waiting for maintenance. At each inspection time, the
decision-maker needs to make a choice among planning no maintenance, imperfect
maintenance and perfect maintenance, so as to minimize the total discounted
cost of the system. The impulse optimal control theory of PDMP is used to
determine the optimal maintenance strategy. A numerical study dealing with
component coating maintenance problem is presented. Relationship with optimal
threshold strategy is discussed. Sensitivity analyses on the influences of
discount factor, observation interval and maintenance cost to the discounted
cost and optimal actions are presented.Comment: 34 pages, 28 figure
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Persistent Exploitation with Intertemporal Reproducible Solution in Pre-industrial Economies
This paper presents an intertemporal model of pre-industrial economies defined with leisure preference to study the condition of the emergence and persistence of exploitation as unequal exchange of labor. We show that pure workers are exploited in any finite periods if there is positive real profit rate, even though labor allocation among agents tends to be equalized in the limit regardless of the saving behaviors. The so-called Fundamental Marxian Theorem and Profit-Exploitation Correspondence Principle are generalized in the intertemporal setting with exploitation in the whole life, and the Class-Exploitation Correspondence Principle is established with exploitation within period
Unsupervised Domain Adaptation with Progressive Adaptation of Subspaces
Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain
by transferring knowledge from labeled source domain with domain shift. Most of
the existing UDA methods try to mitigate the adverse impact induced by the
shift via reducing domain discrepancy. However, such approaches easily suffer a
notorious mode collapse issue due to the lack of labels in target domain.
Naturally, one of the effective ways to mitigate this issue is to reliably
estimate the pseudo labels for target domain, which itself is hard. To overcome
this, we propose a novel UDA method named Progressive Adaptation of Subspaces
approach (PAS) in which we utilize such an intuition that appears much
reasonable to gradually obtain reliable pseudo labels. Speci fically, we
progressively and steadily refine the shared subspaces as bridge of knowledge
transfer by adaptively anchoring/selecting and leveraging those target samples
with reliable pseudo labels. Subsequently, the refined subspaces can in turn
provide more reliable pseudo-labels of the target domain, making the mode
collapse highly mitigated. Our thorough evaluation demonstrates that PAS is not
only effective for common UDA, but also outperforms the state-of-the arts for
more challenging Partial Domain Adaptation (PDA) situation, where the source
label set subsumes the target one
Learning Capacity: A Measure of the Effective Dimensionality of a Model
We exploit a formal correspondence between thermodynamics and inference,
where the number of samples can be thought of as the inverse temperature, to
define a "learning capacity'' which is a measure of the effective
dimensionality of a model. We show that the learning capacity is a tiny
fraction of the number of parameters for many deep networks trained on typical
datasets, depends upon the number of samples used for training, and is
numerically consistent with notions of capacity obtained from the PAC-Bayesian
framework. The test error as a function of the learning capacity does not
exhibit double descent. We show that the learning capacity of a model saturates
at very small and very large sample sizes; this provides guidelines, as to
whether one should procure more data or whether one should search for new
architectures, to improve performance. We show how the learning capacity can be
used to understand the effective dimensionality, even for non-parametric models
such as random forests and -nearest neighbor classifiers
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