42,237 research outputs found
Field Induced Jet Micro-EDM
Electrical discharge machining (EDM) is of the potential of
micro/nano meter scale machining capability. However,
electrode wear in micro-EDM significantly deteriorates the
machining accuracy, thus, it needs to be compensated in
process. To solve this problem, a novel micromachining
method, namely field induced jet micro-EDM, is proposed in
this paper, in which the electrical field induced jet is used as
the micro tool electrode. A series of experiments were carried
out to investigate the feasibility of proposed method. Due to
the electrolyte can be supplied automatically by the capillary
effect and the electrostatic field, it is not necessary to use
pump or valves. The problem of electrode wear does not exist
at all in the machining process because of the field induced jet
will be generated periodically. It is also found that the workpiece
material can be effectively removed with a crater size of
about 2 micrometer in diameter. The preliminary experimental results
verified that the field induced jet micro-EDM is an effective
micromachining method
Top-N Recommender System via Matrix Completion
Top-N recommender systems have been investigated widely both in industry and
academia. However, the recommendation quality is far from satisfactory. In this
paper, we propose a simple yet promising algorithm. We fill the user-item
matrix based on a low-rank assumption and simultaneously keep the original
information. To do that, a nonconvex rank relaxation rather than the nuclear
norm is adopted to provide a better rank approximation and an efficient
optimization strategy is designed. A comprehensive set of experiments on real
datasets demonstrates that our method pushes the accuracy of Top-N
recommendation to a new level.Comment: AAAI 201
Manipulation of electronic and magnetic properties of MC (M=Hf, Nb, Sc, Ta, Ti, V, Zr) monolayer by applying mechanical strains
Tuning the electronic and magnetic properties of a material through strain
engineering is an effective strategy to enhance the performance of electronic
and spintronic devices. Recently synthesized two-dimensional transition metal
carbides MC (M=Hf, Nb, Sc, Ta, Ti, V, Zr), known as MXenes, has aroused
increasingly attentions in nanoelectronic technology due to their unusual
properties. In this paper, first-principles calculations based on density
functional theory are carried out to investigate the electronic and magnetic
properties of MC subjected to biaxial symmetric mechanical strains. At the
strain-free state, all these MXenes exhibit no spontaneous magnetism except for
TiC and ZrC which show a magnetic moment of 1.92 and 1.25 /unit,
respectively. As the tensile strain increases, the magnetic moments of MXenes
are greatly enhanced and a transition from nonmagnetism to ferromagnetism is
observed for those nonmagnetic MXenes at zero strains. The most distinct
transition is found in HfC, in which the magnetic moment is elevated to 1.5
/unit at a strain of 15%. We further show that the magnetic properties
of HfC are attributed to the band shift mainly composed of Hf(5) states.
This strain-tunable magnetism can be utilized to design future spintronics
based on MXenes
Graphs with 3-rainbow index and
Let be a nontrivial connected graph with an edge-coloring
, where adjacent edges
may be colored the same. A tree in is a if no two edges
of receive the same color. For a vertex set , the tree
connecting in is called an -tree. The minimum number of colors that
are needed in an edge-coloring of such that there is a rainbow -tree for
each -set of is called the -rainbow index of , denoted by
. In \cite{Zhang}, they got that the -rainbow index of a tree is
and the -rainbow index of a unicyclic graph is or . So
there is an intriguing problem: Characterize graphs with the -rainbow index
and . In this paper, we focus on , and characterize the graphs
whose 3-rainbow index is and , respectively.Comment: 14 page
Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Many similarity-based clustering methods work in two separate steps including
similarity matrix computation and subsequent spectral clustering. However,
similarity measurement is challenging because it is usually impacted by many
factors, e.g., the choice of similarity metric, neighborhood size, scale of
data, noise and outliers. Thus the learned similarity matrix is often not
suitable, let alone optimal, for the subsequent clustering. In addition,
nonlinear similarity often exists in many real world data which, however, has
not been effectively considered by most existing methods. To tackle these two
challenges, we propose a model to simultaneously learn cluster indicator matrix
and similarity information in kernel spaces in a principled way. We show
theoretical relationships to kernel k-means, k-means, and spectral clustering
methods. Then, to address the practical issue of how to select the most
suitable kernel for a particular clustering task, we further extend our model
with a multiple kernel learning ability. With this joint model, we can
automatically accomplish three subtasks of finding the best cluster indicator
matrix, the most accurate similarity relations and the optimal combination of
multiple kernels. By leveraging the interactions between these three subtasks
in a joint framework, each subtask can be iteratively boosted by using the
results of the others towards an overall optimal solution. Extensive
experiments are performed to demonstrate the effectiveness of our method.Comment: Published in AAAI 201
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