7,328 research outputs found
Poly[bisÂ[μ2-2-(1H-1,2,4-triazol-1-yl)acetato]zinc(II)]
In the title compound, [Zn(C4H4N3O2)2]n, the ZnII atom is coordinated by two O atoms [Zn—O = 1.969 (2) and 1.997 (2) Å] and two N atoms [Zn—N = 2.046 (2) and 2.001 (2) Å] in a distorted tetraÂhedral geometry. Non-classical interÂmolecular C—H⋯O hydrogen bonds link the complex into a three-dimensional supraÂmolecular framework
Dispersion and Property Manipulation of Carbon Nanotubes by Self-Assemibles of Amphiphilic Molecules
Two classes of minimal generic fundamental invariants for tensors
Motivated by the problems raised by B\"{u}rgisser and Ikenmeyer, we discuss
two classes of minimal generic fundamental invariants for tensors of order 3.
The first one is defined on , where . We study
its construction by obstruction design introduced by B\"{u}rgisser and
Ikenmeyer, which partially answers one problem raised by them. The second one
is defined on . We study its evaluation on the matrix multiplication
tensor and unit tensor when
. The evaluation on the unit tensor leads to the definition of Latin
cube and 3-dimensional Alon-Tarsi problem. We generalize some results on Latin
square to Latin cube, which enrich the understanding of 3-dimensional
Alon-Tarsi problem. It is also natural to generalize the constructions to
tensors of other orders. We illustrate the distinction between even and odd
dimensional generalizations by concrete examples. Finally, some open problems
in related fields are raised.Comment: Some typos were changed.New publication information has been update
Simplifying Deep-Learning-Based Model for Code Search
To accelerate software development, developers frequently search and reuse
existing code snippets from a large-scale codebase, e.g., GitHub. Over the
years, researchers proposed many information retrieval (IR) based models for
code search, which match keywords in query with code text. But they fail to
connect the semantic gap between query and code. To conquer this challenge, Gu
et al. proposed a deep-learning-based model named DeepCS. It jointly embeds
method code and natural language description into a shared vector space, where
methods related to a natural language query are retrieved according to their
vector similarities. However, DeepCS' working process is complicated and
time-consuming. To overcome this issue, we proposed a simplified model
CodeMatcher that leverages the IR technique but maintains many features in
DeepCS. Generally, CodeMatcher combines query keywords with the original order,
performs a fuzzy search on name and body strings of methods, and returned the
best-matched methods with the longer sequence of used keywords. We verified its
effectiveness on a large-scale codebase with about 41k repositories.
Experimental results showed the simplified model CodeMatcher outperforms DeepCS
by 97% in terms of MRR (a widely used accuracy measure for code search), and it
is over 66 times faster than DeepCS. Besides, comparing with the
state-of-the-art IR-based model CodeHow, CodeMatcher also improves the MRR by
73%. We also observed that: fusing the advantages of IR-based and
deep-learning-based models is promising because they compensate with each other
by nature; improving the quality of method naming helps code search, since
method name plays an important role in connecting query and code
Large-step neural network for learning the symplectic evolution from partitioned data
In this study, we focus on learning Hamiltonian systems, which involves
predicting the coordinate (q) and momentum (p) variables generated by a
symplectic mapping. Based on Chen & Tao (2021), the symplectic mapping is
represented by a generating function. To extend the prediction time period, we
develop a new learning scheme by splitting the time series (q_i, p_i) into
several partitions. We then train a large-step neural network (LSNN) to
approximate the generating function between the first partition (i.e. the
initial condition) and each one of the remaining partitions. This partition
approach makes our LSNN effectively suppress the accumulative error when
predicting the system evolution. Then we train the LSNN to learn the motions of
the 2:3 resonant Kuiper belt objects for a long time period of 25000 yr. The
results show that there are two significant improvements over the neural
network constructed in our previous work (Li et al. 2022): (1) the conservation
of the Jacobi integral, and (2) the highly accurate predictions of the orbital
evolution. Overall, we propose that the designed LSNN has the potential to
considerably improve predictions of the long-term evolution of more general
Hamiltonian systems.Comment: 13 pages, 7 figures, accepted for publication in MNRA
A study of particles looseness in screening process of a linear vibrating screen
We investigated the movement of particles in screening process over the sieve plate of a linear vibrating screen using the Discrete Element Method (DEM). The behavior of particles which is affected by a series of vibrational parameters including amplitude, frequency and vibration direction angle determining screening performance. This paper centers on particles looseness by analyzing the looseness coefficient and looseness rate. The relationships between the looseness coefficient, looseness rate and vibration parameters were profoundly discussed. Mathematical models relating looseness coefficient to time were established using the least squares method. An experimental platform which combines high-speed camera system with experimental prototype of vibrating screen was designed. The research made a more in-depth investigation of particles’ movements and analysis of particle looseness. Physical experiments were used to verify the reliability of simulation results. Finally, we would come into the following conclusions: high frequency and large amplitude make particles obtain more energy to be active and the average distances among particles get larger slowly. On the contrary, at low frequency and amplitude, the looseness coefficient and looseness rate were relatively low. When the amplitude approaches 2.7 mm, the frequency is about 34 Hz and the vibration angle is around 42 degrees, the looseness ratio produces better performance. The paper offered insights to the design and manufacturing of vibrating screen
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