792 research outputs found
On the existence of positive solutions of p-Laplacian difference equations
AbstractIn this paper, by means of fixed point theorem in a cone, the existence of positive solutions of p-Laplacian difference equations is considered
New existence and multiplicity of homoclinic solutions for second order non-autonomous systems
In this paper, we study the second order non-autonomous system
\begin{eqnarray*}
\ddot{u}(t)+A\dot{u}(t)-L(t)u(t)+\nabla W(t,u(t))=0, \ \ \forall t\in\mathbb{R},
\end{eqnarray*}
where is an antisymmetric constant matrix, may not be uniformly positive definite for all , and is allowed to be sign-changing and local superquadratic. Under some simple assumptions on , and , we establish some existence criteria to guarantee that the above system has at least one homoclinic solution or infinitely many homoclinic solutions by using mountain pass theorem or fountain theorem, respectively.
Recent results in the literature are generalized and significantly improved
Multiple homoclinic orbits for second order discrete Hamiltonian systems without symmetric condition
The Principles in Xiangji Operation: The Theoretical Crystallization of Chinese Economic Anthropology
The Chinese nation is a large family made up of multi-ethnic integration. In the process of adapting to its natural ecosystem and sociocultural background, all ethnic groups have formed their own unique ethnic cultures. In cross-cultural and cross-regional economic activities, the influence of many non-economic factors such as ethnic cultural values cannot be ignored. It is on this premise that the book “The Principles in Xiangji Operation” combines western economic anthropology theory with China's reality through field survey data from Guizhou, and puts forward the theory of xiangji (interphase) management. This way, it has successfully introduced a theoretical system of economic anthropology with Chinese characteristics
Dynamics and reliability of access system of high density magnetic recording
Ph.DDOCTOR OF PHILOSOPH
A study of conditioned inhibition procedures in relation to individual differences and disorder
Classical conditioning and conditioned inhibition are fundamental for cognitive processes in both animals and humans. Conditioned inhibition is involved in a wide range of normal behaviour – and its disruption could produce a wide range of behavioural deficits. For example, lack of inhibitory control has been argued to lie at the core of impulsivity (Buss & Plomin, 1975). Impulsivity is one of the core features in some of the clinical groups, such as schizophrenic patients and patients with cluster B personality disorders (PD), especially patients with PD within forensic populations (Hare et al., 1991; Munro et al., 2007). Previous research studied impulsivity by using some laboratory behaviour learning tasks (e.g. Go-NoGo tasks). People with higher impulsivity have difficulty withholding responding which is demonstrated by poor performances in these tasks. Such tasks measured participants’ ability to inhibit pre-potent motor responses, and these tasks are usually thought to involve inhibition of stimulus-response (S-R) association. To date, little research has explored the inhibition of stimulus-stimulus (S-S) associations (formally ‘conditioned inhibition’, CI) in relation to individual differences, and no research has explicitly examined CI learning in any clinical groups.
The present study developed a suitable procedure to examine human participants’ conditioned inhibition in a summation test and explored CI learning performance in relation to individual differences and disorders. Two hundred and thirty-seven participants in the University of Nottingham completed a set of questionnaires [BIS/BAS, UPPS, EPQ-RS, O-LIFE (short) and STB] to assess their individual differences and a computer-based experiment to test their excitatory and conditioned inhibitory learning. The results suggested various correlations between the scores of questionnaires and the measures of excitatory and inhibitory learning, which confirmed that the higher impulsivity, neuroticism and schizotypy levels, the less evidence of the excitatory learning. At the same time, the higher anxiety, neuroticism and schizotypy levels, the less evidence of the conditioned inhibition.
Twenty-five schizophrenic patients in community-based and 24 patients with PD in forensic settings were also tested using the CI learning task. The results suggested that schizophrenic patients showed a clear reduction in their excitatory and inhibitory learning performance. Moreover, schizophrenic patients with higher negative scores on PANSS, perform worse on the CI learning task. For PD patients at Rampton hospital, the CI effect was abolished in the samples. There was also a significant difference in the CI effect between patients in the PD and the DSPD units. Specifically participants in the DSPD unit showed significantly less CI. Within the clinical samples used in the present study, it was unable to demonstrate any relationship between the levels of CI and medication. Implications of these findings for personality dimensions affect learning in normal populations and clinical groups would be discussed, and further research would be suggested in this thesis
Dual Long Short-Term Memory Networks for Sub-Character Representation Learning
Characters have commonly been regarded as the minimal processing unit in
Natural Language Processing (NLP). But many non-latin languages have
hieroglyphic writing systems, involving a big alphabet with thousands or
millions of characters. Each character is composed of even smaller parts, which
are often ignored by the previous work. In this paper, we propose a novel
architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to
learn sub-character level representation and capture deeper level of semantic
meanings. To build a concrete study and substantiate the efficiency of our
neural architecture, we take Chinese Word Segmentation as a research case
example. Among those languages, Chinese is a typical case, for which every
character contains several components called radicals. Our networks employ a
shared radical level embedding to solve both Simplified and Traditional Chinese
Word Segmentation, without extra Traditional to Simplified Chinese conversion,
in such a highly end-to-end way the word segmentation can be significantly
simplified compared to the previous work. Radical level embeddings can also
capture deeper semantic meaning below character level and improve the system
performance of learning. By tying radical and character embeddings together,
the parameter count is reduced whereas semantic knowledge is shared and
transferred between two levels, boosting the performance largely. On 3 out of 4
Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to
0.4%. Our results are reproducible, source codes and corpora are available on
GitHub.Comment: Accepted & forthcoming at ITNG-201
- …