Survey of Meta-Heuristic Algorithms for Deep Learning Training

Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchical layers. DL is implemented by deep neural network (DNN) which has multi-hidden layers. DNN is developed from traditional artificial neural network (ANN). However, in the training process of DL, it has certain inefficiency due to very long training time required. Meta-heuristic aims to find good or near-optimal solutions at a reasonable computational cost. In this article, meta-heuristic algorithms are reviewed, such as genetic algorithm (GA) and particle swarm optimization (PSO), for traditional neural network’s training and parameter optimization. Thereafter the possibilities of applying meta-heuristic algorithms on DL training and parameter optimization are discussed

Using Medical History Embedded in Biometrics Medical Card for User Identity Authentication: Data Representation by AVT Hierarchical Data Tree

User authentication has been widely used by biometric applications that work on unique bodily features, such as fingerprints, retina scan, and palm vessels recognition. This paper proposes a novel concept of biometric authentication by exploiting a user's medical history. Although medical history may not be absolutely unique to every individual person, the chances of having two persons who share an exactly identical trail of medical and prognosis history are slim. Therefore, in addition to common biometric identification methods, medical history can be used as ingredients for generating Q&A challenges upon user authentication. This concept is motivated by a recent advancement on smart-card technology that future identity cards are able to carry patents' medical history like a mobile database. Privacy, however, may be a concern when medical history is used for authentication. Therefore in this paper, a new method is proposed for abstracting the medical data by using attribute value taxonomies, into a hierarchical data tree (h-Data). Questions can be abstracted to various level of resolution (hence sensitivity of private data) for use in the authentication process. The method is described and a case study is given in this paper

Gz, a guanine nucleotide-binding protein with unique biochemical properties

Cloning of a complementary DNA (cDNA) for Gz alpha, a newly appreciated member of the family of guanine nucleotide-binding regulatory proteins (G proteins), has allowed preparation of specific antisera to identify the protein in tissues and to assay it during purification from bovine brain. Additionally, expression of the cDNA in Escherichia coli has resulted in the production and purification of the recombinant protein. Purification of Gz from bovine brain is tedious, and only small quantities of protein have been obtained. The protein copurifies with the beta gamma subunit complex common to other G proteins; another 26- kDa GTP-binding protein is also present in these preparations. The purified protein could not serve as a substrate for NAD-dependent ADP- ribosylation catalyzed by either pertussis toxin or cholera toxin. Purification of recombinant Gz alpha (rGz alpha) from E. coli is simple, and quantities of homogeneous protein sufficient for biochemical analysis are obtained. Purified rGz alpha has several properties that distinguish it from other G protein alpha subunit polypeptides. These include a very slow rate of guanine nucleotide exchange (k = 0.02 min^-1), which is reduced greater than 20-fold in the presence of mM concentrations of Mg2+. In addition, the rate of the intrinsic GTPase activity of Gz alpha is extremely slow. The hydrolysis rate (kcat) for rGz alpha at 30 degrees C is 0.05 min^-1, or 200-fold slower than that determined for other G protein alpha subunits. rGz alpha can interact with bovine brain beta gamma but does not serve as a substrate for ADP-ribosylation catalyzed by either pertussis toxin or cholera toxin. These studies suggest that Gz may play a role in signal transduction pathways that are mechanistically distinct from those controlled by the other members of the G protein family

A Framework for Population-Based Stochastic Optimization on Abstract Riemannian Manifolds

We present Extended Riemannian Stochastic Derivative-Free Optimization (Extended RSDFO), a novel population-based stochastic optimization algorithm on Riemannian manifolds that addresses the locality and implicit assumptions of manifold optimization in the literature. We begin by investigating the Information Geometrical structure of statistical model over Riemannian manifolds. This establishes a geometrical framework of Extended RSDFO using both the statistical geometry of the decision space and the Riemannian geometry of the search space. We construct locally inherited probability distribution via an orientation-preserving diffeomorphic bundle morphism, and then extend the information geometrical structure to mixture densities over totally bounded subsets of manifolds. The former relates the information geometry of the decision space and the local point estimations on the search space manifold. The latter overcomes the locality of parametric probability distributions on Riemannian manifolds. We then construct Extended RSDFO and study its structure and properties from a geometrical perspective. We show that Extended RSDFO's expected fitness improves monotonically and it's global eventual convergence in finitely many steps on connected compact Riemannian manifolds. Extended RSDFO is compared to state-of-the-art manifold optimization algorithms on multi-modal optimization problems over a variety of manifolds. In particular, we perform a novel synthetic experiment on Jacob's ladder to motivate and necessitate manifold optimization. Jacob's ladder is a non-compact manifold of countably infinite genus, which cannot be expressed as polynomial constraints and does not have a global representation in an ambient Euclidean space. Optimization problems on Jacob's ladder thus cannot be addressed by traditional (constraint) optimization methods on Euclidean spaces.Comment: The present abstract is slightly altered from the PDF version due to the limitation "The abstract field cannot be longer than 1,920 characters

Distinct forms of the ß subunit of GTP-binding regulatory proteins identified by molecular cloning

Two distinct β subunits of guanine nucleotide-binding regulatory proteins have been identified by cDNA cloning and are referred to as β 1 and β 2 subunits. The bovine transducin β subunit (β 1) has been cloned previously. We have now isolated and analyzed cDNA clones that encode the β 2 subunit from bovine adrenal, bovine brain, and a human myeloid leukemia cell line, HL-60. The 340-residue Mr 37,329 β 2 protein is 90% identical with β 1 in predicted amino acid sequence, and it is also organized as a series of repetitive homologous segments. The major mRNA that encodes the bovine β 2 subunit is 1.7 kilobases in length. It is expreβed at lower levels than β 1 subunit mRNA in all tiβues examined. The β 1 and β 2 meβages are expreβed in cloned human cell lines. Hybridization of cDNA probes to bovine DNA showed that β 1 and β 2 are encoded by separate genes. The amino acid sequences for the bovine and human β 2 subunit are identical, as are the amino acid sequences for the bovine and human β 1 subunit. This evolutionary conservation suggests that the two β subunits have different roles in the signal transduction process

Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal–spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min–max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.University of Macau MYRG2016-00069-FSTFDCT Macau FDCT/126/2014/A32018 Guangzhou Science and Technology Innovation and Development of Special Funds201907010001EF003/FST-FSJ/2019/GSTI

Metaheuristics and Chaos Theory

Chaos theory is a novelty approach that has been widely used into various applications. One of the famous applications is the introduction of chaos theory into optimization. Note that chaos theory is highly sensitive to initial condition and has the feature of randomness. As chaos theory has the feature of randomness and dynamical properties, it is easy to accelerate the optimization algorithm convergence and enhance the capability of diversity. In this work, we integrated 10 chaotic maps into several metaheuristic algorithms in order to extensively investigate the effectiveness of chaos theory for improving the search capability. Extensive experiments have been carried out and the results have shown that chaotic optimization can be a very promising tool for solving optimization algorithms