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 $A$ is an antisymmetric $N\times N$ constant matrix, $L\in C(\mathbb{R},\mathbb{R}^{N\times N})$ may not be uniformly positive definite for all $t\in\mathbb{R}$, and $W(t,u)$ is allowed to be sign-changing and local superquadratic. Under some simple assumptions on $A$, $L$ and $W$, 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

Graph Attention Based Spatial Temporal Network for EEG Signal Representation

Graph attention networks (GATs) based architectures have proved to be powerful at implicitly learning relationships between adjacent nodes in a graph. For electroencephalogram (EEG) signals, however, it is also essential to highlight electrode locations or underlying brain regions which are active when a particular event related potential (ERP) is evoked. Moreover, it is often im-portant to identify corresponding EEG signal time segments within which the ERP is activated. We introduce a GAT Inspired Spatial Temporal (GIST) net-work that uses multilayer GAT as its base for three attention blocks: edge atten-tions, followed by node attention and temporal attention layers, which focus on relevant brain regions and time windows for better EEG signal classification performance, and interpretability. We assess the capability of the architecture by using publicly available Transcranial Electrical Stimulation (TES), neonatal pain (NP) and DREAMER EEG datasets. With these datasets, the model achieves competitive performance. Most importantly, the paper presents atten-tion visualisation and suggests ways of interpreting them for EEG signal under-standing

Dynamics and reliability of access system of high density magnetic recording

Ph.DDOCTOR OF PHILOSOPH

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

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

Multi-objective Network Opportunistic Access for Group Mobility in Mobile Internet

The integration of existing and emerging heterogeneous wireless networks in mobile Internet is a combination of diverse but complementary wireless access technologies. Satisfying a set of imperative constrains and optimization objectives, access network selection (ANS) for mobile node (MN) is an inherent procedure in mobility management that needs to be solved in a reasonable manner for the whole system to operate in an optimal fashion. However, ANS remains a significant challenge. Because many MNs with distinctive call characteristics are likely to have correlated mobility and may need to perform mobility management at the same time, this paper, with the goal of investigating group mobility solutions, proposes a network opportunistic access for group mobility (NOA-GM) scheme. By analyzing the directional patterns of moving MNs and introducing the idea of opportunistic access, this scheme first identifies underloaded access networks as candidates. Then, the candidates are evaluated using normalized models of objective and subjective metrics. On this basis, the ANS problem for group mobility can be conducted as a multiobjective combination optimization and then transferred to a signal-objective model by considering the optimization of the performance of the whole system as a global goal while still achieving each MN\u27s performance request. Using an improved genetic algorithm with newly designed evolutionary operators to solve the signal-objective model, an optimal result option for ANS for group mobility is achieved. Simulations conducted on the NS-2 platform show that NOA-GM outperforms the compared schemes in several critical performance metrics