Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

Group data communication with M2MI

The Anhinga Project is developing an infrastructure that supports board range of collaborative systems running on small proximal wireless devices in ad-hoc networks. The core of Anhinga Infrastructure is a new method invocation technology called the Many-to-Many Invocation (M2MI). In this technology, every method invocation is broadcasted through the network and all the objects that implement the same method execute it. M2MI is layered on a new network protocol, Many-to-Many Protocol (M2MP), which is designed for broadcasting messages within small wireless devices in Ad hoc network. In this project, I will provide three different design patterns of M2MI-based collaborative systems, implement and simulate those designs in LAN environment, and compare the advantages and disadvantages of the M2MI-based solutions with RMI-based solutions of those three different problems, collaborative groupware, multiple participants chat system, and the distributed solution of shared resource allocation. This project has the following research concepts: a) Investigate the design pattern and model design of collaborative groupware; b) Investigate the JAVA design and implementation of the collaborative groupware; c) Investigate M2MI mechanism using in the three different problems in ad hoc environment; d) Investigate the architecture, mechanism and performance of the designs of the three problems and compare them with RMI based solution. Test will be performed while using varieties of M2MP packet

SCOOTER: A compact and scalable dynamic labeling scheme for XML updates

Although dynamic labeling schemes for XML have been the focus of recent research activity, there are significant challenges still to be overcome. In particular, though there are labeling schemes that ensure a compact label representation when creating an XML document, when the document is subject to repeated and arbitrary deletions and insertions, the labels grow rapidly and consequently have a significant impact on query and update performance. We review the outstanding issues todate and in this paper we propose SCOOTER - a new dynamic labeling scheme for XML. The new labeling scheme can completely avoid relabeling existing labels. In particular, SCOOTER can handle frequently skewed insertions gracefully. Theoretical analysis and experimental results confirm the scalability, compact representation, efficient growth rate and performance of SCOOTER in comparison to existing dynamic labeling schemes

EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

During the past decades, evolutionary computation (EC) has demonstrated promising potential in solving various complex optimization problems of relatively small scales. Nowadays, however, ongoing developments in modern science and engineering are bringing increasingly grave challenges to the conventional EC paradigm in terms of scalability. As problem scales increase, on the one hand, the encoding spaces (i.e., dimensions of the decision vectors) are intrinsically larger; on the other hand, EC algorithms often require growing numbers of function evaluations (and probably larger population sizes as well) to work properly. To meet such emerging challenges, not only does it require delicate algorithm designs, but more importantly, a high-performance computing framework is indispensable. Hence, we develop a distributed GPU-accelerated algorithm library -- EvoX. First, we propose a generalized workflow for implementing general EC algorithms. Second, we design a scalable computing framework for running EC algorithms on distributed GPU devices. Third, we provide user-friendly interfaces to both researchers and practitioners for benchmark studies as well as extended real-world applications. To comprehensively assess the performance of EvoX, we conduct a series of experiments, including: (i) scalability test via numerical optimization benchmarks with problem dimensions/population sizes up to millions; (ii) acceleration test via a neuroevolution task with multiple GPU nodes; (iii) extensibility demonstration via the application to reinforcement learning tasks on the OpenAI Gym. The code of EvoX is available at https://github.com/EMI-Group/EvoX

Regional association of pCASL-MRI with FDG-PET and PiB-PET in people at risk for autosomal dominant Alzheimer's disease.

Autosomal dominant Alzheimer's disease (ADAD) is a small subset of Alzheimer's disease that is genetically determined with 100% penetrance. It provides a valuable window into studying the course of pathologic processes that leads to dementia. Arterial spin labeling (ASL) MRI is a potential AD imaging marker that non-invasively measures cerebral perfusion. In this study, we investigated the relationship of cerebral blood flow measured by pseudo-continuous ASL (pCASL) MRI with measures of cerebral metabolism (FDG PET) and amyloid deposition (Pittsburgh Compound B (PiB) PET). Thirty-one participants at risk for ADAD (age 39 ± 13 years, 19 females) were recruited into this study, and 21 of them received both MRI and FDG and PiB PET scans. Considerable variability was observed in regional correlations between ASL-CBF and FDG across subjects. Both regional hypo-perfusion and hypo-metabolism were associated with amyloid deposition. Cross-sectional analyses of each biomarker as a function of the estimated years to expected dementia diagnosis indicated an inverse relationship of both perfusion and glucose metabolism with amyloid deposition during AD development. These findings indicate that neurovascular dysfunction is associated with amyloid pathology, and also indicate that ASL CBF may serve as a sensitive early biomarker for AD. The direct comparison among the three biomarkers provides complementary information for understanding the pathophysiological process of AD

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower prediction error. Recently, the emerging application scenarios of deep learning have raised higher demands for network architectures considering multiple design criteria: number of parameters/floating-point operations, and inference latency, among others. From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them. Nonetheless, there is still a clear gap confining the related research along this pathway: on the one hand, there is a lack of a general problem formulation of NAS tasks from an optimization point of view; on the other hand, there are challenges in conducting benchmark assessments of EMO algorithms on NAS tasks. To bridge the gap: (i) we formulate NAS tasks into general multi-objective optimization problems and analyze the complex characteristics from an optimization point of view; (ii) we present an end-to-end pipeline, dubbed $\texttt{EvoXBench}$, to generate benchmark test problems for EMO algorithms to run efficiently -- without the requirement of GPUs or Pytorch/Tensorflow; (iii) we instantiate two test suites comprehensively covering two datasets, seven search spaces, and three hardware devices, involving up to eight objectives. Based on the above, we validate the proposed test suites using six representative EMO algorithms and provide some empirical analyses. The code of $\texttt{EvoXBench}$ is available from $\href{https://github.com/EMI-Group/EvoXBench}{\rm{here}}$

The uses and impact of social and emerging media on public relations practices in Malaysia

Research has shown that social media has been widely discussed among public relations practitioners and scholars in relation to how it has changed public relations practices. A study by Wright and Hinson (2017) revealed that public relations practitioners continue to strongly agree that social and other emerging media technologies have brought dramatic changes to how public relations is practiced in the United States of America. In the Malaysian context, the explosion in social media, especially social networking site such as Facebook, has caused many public relations practitioners to recognise the need to embrace these new media for effective communication with the internal and external audiences. Drawing on Wright and Hinson’s (2016) survey instrument, this study measured the actual use of social and other emerging media by public relations practitioners in Malaysia, and explored its impact on public relations practices. Through a web-based survey, this study found evidence that public relations practitioners in Malaysia have frequently used social media especially Facebook, Instagram and LinkedIn. On average, they spent approximately 26% to 50% of their working time using social and emerging media for public relations and communications activities. The results of this study provide useful insights for academics, researchers and public relations practitioners on how social and emerging media technologies are used in the Malaysian public relations industry

A targeted gene panel that covers coding, non-coding and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases

Genetic testing for neurodegenerative diseases (NDs) is highly challenging because of genetic heterogeneity and overlapping manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington\u27s disease and spinocerebellar ataxias type 3 (SCA3). Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known or likely pathogenic variation was found in 54 patients; 27 patients demonstrated clinical profiles that matched the variants; and 16 patients whose original diagnosis were refined. A high concordance of variant calling were observed when comparing the results from TGP and whole-exome sequencing of four patients. Our in-house STR detection algorithm has reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combined comprehensive TGPs-bioinformatics pipeline can improve the clinical diagnosis of NDs

Radius Stabilization by Two-Loop Casimir Energy

It is well known that the Casimir energy of bulk fields induces a non-trivial potential for the compactification radius of higher-dimensional field theories. On dimensional grounds, the 1-loop potential is ~ 1/R^4. Since the 5d gauge coupling constant g^2 has the dimension of length, the two-loop correction is ~ g^2/R^5. The interplay of these two terms leads, under very general circumstances (including other interacting theories and more compact dimensions), to a stabilization at finite radius. Perturbative control or, equivalently, a parametrically large compact radius is ensured if the 1-loop coefficient is small because of an approximate fermion-boson cancellation. This is similar to the perturbativity argument underlying the Banks-Zaks fixed point proposal. Our analysis includes a scalar toy model, 5d Yang-Mills theory with charged matter, the examination of S^1 and S^1/Z_2 geometries, as well as a brief discussion of the supersymmetric case with Scherk-Schwarz SUSY breaking. 2-Loop calculability in the S^1/Z_2 case relies on the log-enhancement of boundary kinetic terms at the 1-loop level.Comment: 18 pages, 2 figures, uses axodraw, references adde