Fusion of Heterogeneous Social Networks for Synergistic Knowledge Discovery

In this thesis, we will focus on introducing the information fusion learning works done based on online social media data. To enjoy more social network services, people are usually involved in multiple online social networks simultaneously, such as Facebook, Twitter and Foursquare. Our work in this thesis covers five strongly correlated research directions in the study of information networks fusion and mining, including network alignment, link prediction, community detection, information diffusion, and network embedding. These application tasks are fundamental problems in social network studies, which together with the network alignment problem will form the backbone of the multiple social network fusion learning ecosystem

Table_1_Effects of non-invasive neurostimulation on autism spectrum disorder: A systematic review.docx

Non-invasive neurostimulation techniques (NIBS) have shown benefits in psychiatric conditions. While in ASD patients, no guideline has so-far been recommended on these techniques due to a lack of high-quality synthetic evidence. Here, a comprehensive search from database inception onward was conducted in PubMed, EMBASE, and Cochrane library. Sham-controlled studies assessing the effects of NIBS in ASD patients were identified. After screening, twenty-two studies were included. A total of 552 patients were involved, and the sample size ranged from 5 to 78 patients. Although an iteration from exploratory attempts to more strictly designed trials has been seen to evaluate the efficacy of NIBS on ASD, further trials should also be needed to enable the clinicians and researchers to reach any consensus.Systematic review registration[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021292434], identifier [CRD42021292434].</p

Meta-analysis of studies on the association of rs10757278 on chromosome 9p21 with ischemic stroke.

<p>Meta-analysis of studies on the association of rs10757278 on chromosome 9p21 with ischemic stroke.</p

Forest plot for the association between rs10757278 and ischemic stroke risk by stroke subtype status.

<p>Forest plot for the association between rs10757278 and ischemic stroke risk by stroke subtype status.</p

Study selection process.

<p>Study selection process.</p

Characteristics of the studies included in the meta-analysis.

<p>Characteristics of the studies included in the meta-analysis.</p

Meta-analysis of the chromosome 9p21 genetic marker on ischemic stroke risk.

a<p>Cochran’s chi-square Q statistic test used to assess the heterogeneity in subgroups.</p>b<p>Cochran’s chi-square Q statistic test used to assess the heterogeneity between subgroups.</p><p>Allele contrast (effect of each additional risk allele).</p><p>Dominant model (presence vs. absence of the risk allele).</p><p>Recessive model (presence vs. absence of two copies of the risk allele).</p

Realization of bound states in the continuum in anti-PT-symmetric optical systems

Novel physical concepts that originate from quantum mechanics, such as non-Hermitian systems (dealing mostly with PT and anti-PT symmetry) and bound states in the continuum (BICs), have attracted great interest in the optics and photonics community. To date, BICs and anti-PT symmetry seem to be two independent topics. Here, we for the first time propose a parallel cascaded-resonator system to achieve BICs and anti-PT symmetry simultaneously. We found that the requirements for the Fabry-P\'erot BIC and anti-PT symmetry can both be satisfied when the phase shift between any two adjacent resonators is an integer multiple of {\pi}. We further analyzed the cascaded-resonator systems which consist of different numbers of resonators and demonstrated their robustness to fabrication imperfections. The proposed structure can readily be realized on an integrated photonic platform, which can have many applications that benefit from the advantages of both BICs and anti-PT symmetry, such as ultralow-linewidth lasing, enhanced optical sensing, and optical signal processing

Scalable optical neural networks based on temporal computing

The optical neural network (ONN) has been considered as a promising candidate for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption, with significant potential to release unprecedented computational capability. Large-scale ONNs could process more neural information and improve the prediction performance. However, previous ONN architectures based on matrix multiplication are difficult to scale up due to manufacturing limitations, resulting in limited scalability and small input data volumes. To address this challenge, we propose a compact and scalable photonic computing architecture based on temporal photoelectric multiplication and accumulate (MAC) operations, allowing direct processing of large-scale matrix computations in the time domain. By employing a temporal computing unit composed of cascaded modulators and time-integrator, we conduct a series of proof-of-principle experiments including image edge detection, optical neural networks-based recognition tasks, and sliding-window method-based multi-target detection. Thanks to its intrinsic scalability, the demonstrated photonic computing architecture could be easily integrated on a single chip toward large-scale photonic neural networks with ultrahigh computation throughputs