1,249 research outputs found
Endowment structures, industrial dynamics, and economic growth
Economic Theory&Research,Political Economy,Economic Growth,Debt Markets,Emerging Markets
Marshallian externality, industrial upgrading, and industrial policies
A growth model with multiple industries is developed to study how industries evolve as capital accumulates endogenously when each industry exhibits Marshallian externality (increasing returns to scale) and to explain why industrial policies sometimes succeed but sometimes fail. The authors show that, in the long run, the laissez-faire market equilibrium is Pareto optimal when the time discount rate is sufficiently small or sufficiently large. When the time discount rate is moderate, there exist multiple dynamic market equilibria with diverse patterns of industrial development. To achieve Pareto efficiency, it would require the government to identify the industry target consistent with the comparative advantage and to coordinate in a timely manner, possibly for multiple times. However, industrial policies may make people worse off than in the market equilibrium if the government picks an industry that deviates from the comparative advantage of the economy.Water and Industry,Economic Theory&Research,Industrial Management,Industrial Economics,Common Property Resource Development
Orbital Angular Momentum Waves: Generation, Detection and Emerging Applications
Orbital angular momentum (OAM) has aroused a widespread interest in many
fields, especially in telecommunications due to its potential for unleashing
new capacity in the severely congested spectrum of commercial communication
systems. Beams carrying OAM have a helical phase front and a field strength
with a singularity along the axial center, which can be used for information
transmission, imaging and particle manipulation. The number of orthogonal OAM
modes in a single beam is theoretically infinite and each mode is an element of
a complete orthogonal basis that can be employed for multiplexing different
signals, thus greatly improving the spectrum efficiency. In this paper, we
comprehensively summarize and compare the methods for generation and detection
of optical OAM, radio OAM and acoustic OAM. Then, we represent the applications
and technical challenges of OAM in communications, including free-space optical
communications, optical fiber communications, radio communications and acoustic
communications. To complete our survey, we also discuss the state of art of
particle manipulation and target imaging with OAM beams
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
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