How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three “V” or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL

Casimir probe based upon metallized high Q SiN nanomembrane resonator

We present the instrumentation and measurement scheme of a new Casimir force probe that bridges Casimir force measurements at microscale and macroscale. A metallized high Q silicon nitride nanomembrane resonator is employed as a sensitive force probe. The high tensile stress present in the nanomembrane not only enhances the quality factor but also maintains high flatness over large area serving as the bottom electrode in a sphere-plane configuration. A fiber interferometer is used to readout the oscillation of the nanomembrane and a phase-locked loop scheme is applied to track the change of the resonance frequency. Because of the high quality factor of the nanomembrane and the high stability of the setup, a frequency resolution down to $2\times10^{-9}$ and a corresponding force gradient resolution of 3 $\mu$N/m is achieved. Besides sensitive measurement of Casimir force, our measurement technique simultaneously offers Kelvin probe measurement capability that allows in situ imaging of the surface potentials

From swarm intelligence to metaheuristics: nature-inspired optimization algorithms

Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges

Neoadjuvant sipuleucel-T induces both Th1 activation and immune regulation in localized prostate cancer.

Sipuleucel-T is the only FDA-approved immunotherapy for metastatic castration-resistant prostate cancer. The mechanism by which this treatment improves survival is not fully understood. We have previously shown that this treatment can induce the recruitment of CD4 and CD8 T cells to the tumor microenvironment. In this study, we examined the functional state of these T cells through gene expression profiling. We found that the magnitude of T cell signatures correlated with the frequency of T cells as measured by immunohistochemistry. Sipuleucel-T treatment was associated with increased expression of Th1-associated genes, but not Th2-, Th17 - or Treg-associated genes. Post-treatment tumor tissues with high CD8+T cell infiltration was associated with high levels of CXCL10 expression. On in situ hybridization, CXCL10+ cells colocalized with CD8+T cells in post-treatment prostatectomy tumor tissue. Neoadjuvant sipuleucel-T was also associated with upregulation of immune inhibitory checkpoints, including CTLA4 and TIGIT, and downregulation of the immune activation marker, dipeptidylpeptidase, DPP4. Treatment-associated declines in serum PSA were correlated with induction of Th1 response. In contrast, rises in serum PSA while on treatment were associated with the induction of multiple immune checkpoints, including CTLA4, CEACAM6 and TIGIT. This could represent adaptive immune resistance mechanisms induced by treatment. Taken together, neoadjuvant sipuleucel-T can induce both a Th1 response and negative immune regulation in the prostate cancer microenvironment