A NEURAL NET WITH SELF-INHIBITING UNITS FOR THE N-QUEENS PROBLEM

1992 ◽  
Vol 03 (03) ◽  
pp. 249-252 ◽  
Author(s):  
ORON SHAGRIR
Keyword(s):  
Polynomial Time ◽  
Global Minimum ◽  
Theoretical Explanation ◽  
The Self ◽  
Experimental Results ◽  
Level Energy ◽  
Neural Net ◽  
Local Minima ◽  
Low Level

Suggested here is a neural net algorithm for the n-queens problem. The net is basically a Hopfield net but with one major difference: every unit is allowed to inhibit itself. This distinctive characteristic enables the net to escape efficiently from all local minima. The net’s dynamics then can be described as a travel in paths of low-level energy spaces until it finds a solution (global minimum). The paper explains why standard Hopfield nets have failed to solve the queens problem and proofs that the self-inhibiting net (NQ2 algorithm in the text) never stabilizes in local minima and relaxes when it falls into a global minimum are provided. The experimental results supported by theoretical explanation indicate that the net never continually oscillates but relaxes into a solution in polynomial time. In addition, it appears that the net solves the queens problem regardless of the dimension n or the initialized values. The net uses only few parameters to fix the weights; all globally determined as a function of n.

Lateral Tire Forces on Wet Roads

1977 ◽  
Vol 5 (2) ◽  
pp. 75-82 ◽  
Author(s):  
A. Schallamach
Keyword(s):  
Normal Load ◽  
The Self ◽  
Experimental Results ◽  
Slip Angle ◽  
Tire Model ◽  
Side Force ◽  
Tire Forces

Abstract Expressions are derived for side force and self-aligning torque of a simple tire model on wet roads with velocity-dependent friction. The results agree qualitatively with experimental results at moderate speeds. In particular, the theory correctly predicts that the self-aligning torque can become negative under easily realizable circumstances. The slip angle at which the torque reverses sign should increase with the normal load.

RAMAN SPECTRA OF THE H2SO4–SO3 AND D2SO4–SO3 SYSTEMS: EVIDENCE FOR THE OCCURRENCE OF POLYSULPHURIC ACIDS

1962 ◽  
Vol 40 (4) ◽  
pp. 658-674 ◽  
Author(s):  
R. J. Gillespie ◽  
E. A. Robinson
Keyword(s):  
Sulphuric Acid ◽  
Raman Spectra ◽  
Ionic Species ◽  
The Self ◽  
Experimental Results ◽  
Similar Work ◽  
Stretching Vibrations ◽  
Sulphur Trioxide ◽  
High Concentrations

The Raman spectra of oleums, i.e. mixtures of sulphur trioxide and sulphuric acid, have been re-examined. Similar measurements on the sulphur trioxide – deuterosulphuric acid (D2SO4) system are also reported. The experimental results and conclusions of previous similar work on oleums are discussed. By comparison of the spectra of oleums with those of the polysulphuryl halides it is shown that the polysulphuric acids H2S2O7 and H2S3O10 are present in this system. The increase in the frequency of the SO2 stretching vibrations with increasing concentration of sulphur trioxide gives evidence for the existence of higher polysulphuric acids such as H2S4O13 at high concentrations of sulphur trioxide. In relatively concentrated oleum, sulphur trioxide monomer and trimer are also present. It is shown that the self-dissociation of liquid H2S2O7 gives mainly molecular H2S2O10 and H2SO4 and not ionic species. The conclusions reached from the interpretation of the Raman spectra of the D2SO4–SO3 system are similar to those arrived at for sulphuric acid oleums. The spectra of solutions of NaHSO4 in oleums were also examined, and are discussed.

scholarly journals A Self-Supervised Tibetan-Chinese Vocabulary Alignment Method based on Adversarial Learning

2021 ◽  
Author(s):  
Enshuai Hou ◽  
Jie zhu
Keyword(s):  
The Self ◽  
Experimental Results ◽  
Chinese Characters ◽  
Training Method ◽  
Alignment Method ◽  
Adversarial Learning ◽  
Semantic Correlation ◽  
Data Alignment ◽  
Similarity Calculation ◽  
Adversarial Training

Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved self-supervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. First, the experimental results of Tibetan syllables Chinese characters are not good, which reflects the weak semantic correlation between Tibetan syllables and Chinese characters; second, the seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.

scholarly journals Is the Skip Connection Provable to Reform the Neural Network Loss Landscape?

2020 ◽  
Author(s):  
Lifu Wang ◽  
Bo Shen ◽  
Ning Zhao ◽  
Zhiyuan Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Level Sets ◽  
Theoretical Explanation ◽  
Learning Ability ◽  
Local Minima ◽  
Global Minima ◽  
Residual Network ◽  
The Neural Network ◽  
Hidden Nodes

The residual network is now one of the most effective structures in deep learning, which utilizes the skip connections to “guarantee" the performance will not get worse. However, the non-convexity of the neural network makes it unclear whether the skip connections do provably improve the learning ability since the nonlinearity may create many local minima. In some previous works [Freeman and Bruna, 2016], it is shown that despite the non-convexity, the loss landscape of the two-layer ReLU network has good properties when the number m of hidden nodes is very large. In this paper, we follow this line to study the topology (sub-level sets) of the loss landscape of deep ReLU neural networks with a skip connection and theoretically prove that the skip connection network inherits the good properties of the two-layer network and skip connections can help to control the connectedness of the sub-level sets, such that any local minima worse than the global minima of some two-layer ReLU network will be very “shallow". The “depth" of these local minima are at most O(m^(η-1)/n), where n is the input dimension, η<1. This provides a theoretical explanation for the effectiveness of the skip connection in deep learning.

scholarly journals Experimental Study and Extended Finite Element Simulation of Fracture of Self-Compacting Rubberized Concrete

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xinquan Wang ◽  
Hongguo Diao ◽  
Yunliang Cui ◽  
Changguang Qi ◽  
Shangyu Han
Keyword(s):  
Finite Element ◽  
Loading Rate ◽  
High Performance Concrete ◽  
The Self ◽  
Experimental Results ◽  
Crack Mouth Opening Displacement ◽  
Displacement Curve ◽  
Rubberized Concrete ◽  
Extended Finite Element ◽  
Self Compacting Concrete

Self-compacting rubberized concrete (SCRC) is a high-performance concrete that can achieve compacting effect by self-gravity without vibration during pouring. Because of its excellent fluidity, homogeneity, and stability, the application of self-compacting concrete in engineering can improve work efficiency and reduce project cost. The effects of loading rate on the fracture behavior of self-compacting concrete were studied in this paper. Three-point bend (TPB) tests were carried out at five loading rates of 1, 0.1, 0.001, 0.0001, and 0.00001 mm/s. The dimensions of the specimens were 100  mm × 100 mm × 400 mm. A precast crack was set in the middle of the specimen with a notch-depth ratio of 0.4. The experimental results show that the peak load on the load-CMOD (crack mouth opening displacement) curve gradually increases with the increase of the loading rate. Although the fracture energy a presented greater dispersion under the loading rate of 1 mm/s, the overall changes were still rising with the increase of the loading rate. Besides studying the softening characteristics of the self-compacting concrete, the constitutive softening curve of the self-compacting concrete was obtained using the bilinear model. Finally, curved three-point bending beams were simulated by using the extended finite element method based on ABAQUS. The fracture process of the self-compacting concrete under different loading conditions was analyzed more intuitively. The simulation results were compared with the experimental results, and the same conclusions were obtained.

scholarly journals Multivariate Self-Dual Morphological Operators Based on Extremum Constraint

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Tao Lei ◽  
Yi Wang ◽  
Weiwei Luo
Keyword(s):  
Noise Removal ◽  
Complete Characterization ◽  
The Self ◽  
Experimental Results ◽  
Watershed Transformation ◽  
Morphological Operators ◽  
Self Duality ◽  
Multichannel Images ◽  
Duality Property ◽  
Image Details

Self-dual morphological operators (SDMO) do not rely on whether one starts the sequence with erosion or dilation; they treat the image foreground and background identically. However, it is difficult to extend SDMO to multichannel images. Based on the self-duality property of traditional morphological operators and the theory of extremum constraint, this paper gives a complete characterization for the construction of multivariate SDMO. We introduce a pair of symmetric vector orderings (SVO) to construct multivariate dual morphological operators. Furthermore, utilizing extremum constraint to optimize multivariate morphological operators, we construct multivariate SDMO. Finally, we illustrate the importance and effectiveness of the multivariate SDMO by applications of noise removal and segmentation performance. The experimental results show that the proposed multivariate SDMO achieves better results, and they suppress noises more efficiently without losing image details compared with other filtering methods. Moreover, the proposed multivariate SDMO is also shown to have the best segmentation performance after the filtered images via watershed transformation.

Experimental results on wavefront correction using the self-coherent camera

2012 ◽  
Author(s):  
M. Mas ◽  
P. Baudoz ◽  
J. Mazoyer ◽  
R. Galicher ◽  
G. Rousset
Keyword(s):  
The Self ◽  
Experimental Results ◽  
Wavefront Correction

A Visual Saliency Detection Approach by Fusing Low-Level Priors With High-Level Priors

2019 ◽  
Vol 9 (3) ◽  
pp. 23-37
Author(s):  
Monika Singh ◽  
Anand Singh Singh Jalal ◽  
Ruchira Manke ◽  
Aamir Khan
Keyword(s):  
Minimum Distance ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Research Area ◽  
Experimental Results ◽  
Low Level ◽  
Detection Approach ◽  
Art Methods ◽  
High Level ◽  
Visual Saliency Detection

Saliency detection has always been a challenging and interesting research area for researchers. The existing methodologies either focus on foreground regions or background regions of an image by computing low-level features. However, considering only low-level features did not produce worthy results. In this paper, low-level features, which are extracted using super pixels, are embodied with high-level priors. The background features are assumed as the low-level prior due to the similarity in the background areas and boundary of an image which are interconnected and have minimum distance in between them. High-level priors such as location, color, and semantic prior are incorporated with low-level prior to spotlight the salient area in the image. The experimental results illustrate that the proposed approach outperform the sate-of-the-art methods.

scholarly journals Universal Word Segmentation: Implementation and Interpretation

2018 ◽  
Vol 6 ◽  
pp. 421-435 ◽  
Author(s):  
Yan Shao ◽  
Christian Hardmeier ◽  
Joakim Nivre
Keyword(s):  
State Of The Art ◽  
Word Segmentation ◽  
Experimental Results ◽  
Word Boundary ◽  
Writing Systems ◽  
Low Level ◽  
Wide Range ◽  
Segmentation Accuracy ◽  
Small Set

Word segmentation is a low-level NLP task that is non-trivial for a considerable number of languages. In this paper, we present a sequence tagging framework and apply it to word segmentation for a wide range of languages with different writing systems and typological characteristics. Additionally, we investigate the correlations between various typological factors and word segmentation accuracy. The experimental results indicate that segmentation accuracy is positively related to word boundary markers and negatively to the number of unique non-segmental terms. Based on the analysis, we design a small set of language-specific settings and extensively evaluate the segmentation system on the Universal Dependencies datasets. Our model obtains state-of-the-art accuracies on all the UD languages. It performs substantially better on languages that are non-trivial to segment, such as Chinese, Japanese, Arabic and Hebrew, when compared to previous work.

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