88 research outputs found
CULTURAL ADAPTATION OF CHINESE STUDENTS AS THE NEED OF THEIR EDUCATION ABROAD PROCESS
The article is devoted to a sharp problem of educational practice: ways of cultural adaptation of foreign (in particular, Chinese) students in new host society as a condition of their successful education abroad. Chinese students now form the largest abroad students` community in main countries of the world. As the Chinese way of living and the education traditions differ a lot from the countries` they usually would like to continue their education at, cultural adaptation is seen in the article as the need for a comfortable education process both for the students came and the University host them. The Main methods of Chinese students` cultural adaptation process are under consideration of the research, paying attention to the period (stage) a student finds himself / herself at that also can be seen as the aim of the research. As to the methods, the ones traditionally used in social studies and general research work were used: ethnographic descriptions and census data, scientific method to collect empirical evidence, method of analysis, etc. As the result was presented a set of methods that can be used by a host University on condition of the Chinese students` cooperation to level down the cultural shock period for the student and to speed up his/her entering the host culture society.
Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization
We focus on a replanning scenario for quadrotors where considering time
efficiency, non-static initial state and dynamical feasibility is of great
significance. We propose a real-time B-spline based kinodynamic (RBK) search
algorithm, which transforms a position-only shortest path search (such as A*
and Dijkstra) into an efficient kinodynamic search, by exploring the properties
of B-spline parameterization. The RBK search is greedy and produces a
dynamically feasible time-parameterized trajectory efficiently, which
facilitates non-static initial state of the quadrotor. To cope with the
limitation of the greedy search and the discretization induced by a grid
structure, we adopt an elastic optimization (EO) approach as a
post-optimization process, to refine the control point placement provided by
the RBK search. The EO approach finds the optimal control point placement
inside an expanded elastic tube which represents the free space, by solving a
Quadratically Constrained Quadratic Programming (QCQP) problem. We design a
receding horizon replanner based on the local control property of B-spline. A
systematic comparison of our method against two state-of-the-art methods is
provided. We integrate our replanning system with a monocular vision-based
quadrotor and validate our performance onboard.Comment: 8 pages. Published in International Conference on Robotics and
Automation (ICRA) 2018. IEEE copyrigh
Lifting the Veil: Unlocking the Power of Depth in Q-learning
With the help of massive data and rich computational resources, deep
Q-learning has been widely used in operations research and management science
and has contributed to great success in numerous applications, including
recommender systems, supply chains, games, and robotic manipulation. However,
the success of deep Q-learning lacks solid theoretical verification and
interpretability. The aim of this paper is to theoretically verify the power of
depth in deep Q-learning. Within the framework of statistical learning theory,
we rigorously prove that deep Q-learning outperforms its traditional version by
demonstrating its good generalization error bound. Our results reveal that the
main reason for the success of deep Q-learning is the excellent performance of
deep neural networks (deep nets) in capturing the special properties of rewards
namely, spatial sparseness and piecewise constancy, rather than their large
capacities. In this paper, we make fundamental contributions to the field of
reinforcement learning by answering to the following three questions: Why does
deep Q-learning perform so well? When does deep Q-learning perform better than
traditional Q-learning? How many samples are required to achieve a specific
prediction accuracy for deep Q-learning? Our theoretical assertions are
verified by applying deep Q-learning in the well-known beer game in supply
chain management and a simulated recommender system
On-Device Model Fine-Tuning with Label Correction in Recommender Systems
To meet the practical requirements of low latency, low cost, and good privacy
in online intelligent services, more and more deep learning models are
offloaded from the cloud to mobile devices. To further deal with cross-device
data heterogeneity, the offloaded models normally need to be fine-tuned with
each individual user's local samples before being put into real-time inference.
In this work, we focus on the fundamental click-through rate (CTR) prediction
task in recommender systems and study how to effectively and efficiently
perform on-device fine-tuning. We first identify the bottleneck issue that each
individual user's local CTR (i.e., the ratio of positive samples in the local
dataset for fine-tuning) tends to deviate from the global CTR (i.e., the ratio
of positive samples in all the users' mixed datasets on the cloud for training
out the initial model). We further demonstrate that such a CTR drift problem
makes on-device fine-tuning even harmful to item ranking. We thus propose a
novel label correction method, which requires each user only to change the
labels of the local samples ahead of on-device fine-tuning and can well align
the locally prior CTR with the global CTR. The offline evaluation results over
three datasets and five CTR prediction models as well as the online A/B testing
results in Mobile Taobao demonstrate the necessity of label correction in
on-device fine-tuning and also reveal the improvement over cloud-based learning
without fine-tuning
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