9,796 research outputs found
Correlations and Scaling Laws in Human Mobility
Human mobility patterns deeply affect the dynamics of many social systems. In
this paper, we empirically analyze the real-world human movements based GPS
records, and observe rich scaling properties in the temporal-spatial patterns
as well as an abnormal transition in the speed-displacement patterns. We notice
that the displacements at the population level show significant positive
correlation, indicating a cascade-like nature in human movements. Furthermore,
our analysis at the individual level finds that the displacement distributions
of users with strong correlation of displacements are closer to power laws,
implying a relationship between the positive correlation of the series of
displacements and the form of an individual's displacement distribution. These
findings from our empirical analysis show a factor directly relevant to the
origin of the scaling properties in human mobility.Comment: 10 pages, 9 figure
Higher Education of Minority Nationalities in China
China is a unified multinational country, with more than fifty minority nationalities
Plug and Play Robust Distributed Control with Ellipsoidal Parametric Uncertainty System
We consider a continuous linear time invariant system with ellipsoidal parametric uncertainty structured into subsystems. Since the design of a local controller uses only information on a subsystem and its neighbours, we combine the plug and play idea and robust distributed control to propose one distributed control strategy for linear system with ellipsoidal parametric uncertainty. Firstly for linear system with ellipsoidal parametric uncertainty, a necessary and sufficient condition for robust state feedback control is proposed by means of linear matrix inequality. If this necessary and sufficient condition is satisfied, this robust state feedback gain matrix can be easily derived to guarantee robust stability and prescribed closed loop performance. Secondly the plug and play idea is introduced in the design process. Finally by one example of aircraft flutter model parameter identification, the efficiency of the proposed control strategy can be easily realized
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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