Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach

With the growth of neural network size, model compression has attracted increasing interest in recent research. As one of the most common techniques, pruning has been studied for a long time. By exploiting the structured sparsity of the neural network, existing methods can prune neurons instead of individual weights. However, in most existing pruning methods, surviving neurons are randomly connected in the neural network without any structure, and the non-zero weights within each neuron are also randomly distributed. Such irregular sparse structure can cause very high control overhead and irregular memory access for the hardware and even increase the neural network computational complexity. In this paper, we propose a three-layer hierarchical prior to promote a more regular sparse structure during pruning. The proposed three-layer hierarchical prior can achieve per-neuron weight-level structured sparsity and neuron-level structured sparsity. We derive an efficient Turbo-variational Bayesian inferencing (Turbo-VBI) algorithm to solve the resulting model compression problem with the proposed prior. The proposed Turbo-VBI algorithm has low complexity and can support more general priors than existing model compression algorithms. Simulation results show that our proposed algorithm can promote a more regular structure in the pruned neural networks while achieving even better performance in terms of compression rate and inferencing accuracy compared with the baselines

Effect of isospin dependent cross-section on fragment production in the collision of charge asymmetric nuclei

To understand the role of isospin effects on fragmentation due to the collisions of charge asymmetric nuclei, we have performed a complete systematical study using isospin dependent quantum molecular dynamics model. Here simulations have been carried out for $^{124}X_{n}+ ^{124}X_{n}$, where n varies from 47 to 59 and for $^{40}Y_{m}+ ^{40}Y_{m}$, where m varies from 14 to 23. Our study shows that isospin dependent cross-section shows its influence on fragmentation in the collision of neutron rich nuclei

Discrimination between two mechanisms of surface-scattering in a single-mode waveguide

Transport properties of a single-mode waveguide with rough boundary are studied by discrimination between two mechanisms of surface scattering, the amplitude and square-gradient ones. Although these mechanisms are generically mixed, we show that for some profiles they can separately operate within non-overlapping intervals of wave numbers of scattering waves. This effect may be important in realistic situations due to inevitable long-range correlations in scattering profiles.Comment: 5 pages, 3 figure

Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool

In this paper we present a new version of a GP financial forecasting tool, called EDDIE 8. The novelty of this version is that it allows the GP to search in the space of indicators, instead of using pre-specified ones. We compare EDDIE 8 with its predecessor, EDDIE 7, and find that new and improved solutions can be found. Analysis also shows that, on average, EDDIE 8's best tree performs better than the one of EDDIE 7. The above allows us to characterize EDDIE 8 as a valuable forecasting tool

CD4+ T-cell responses to Epstein-Barr virus nuclear antigen EBNA1 in Chinese populations are highly focused on novel C-terminal domain-derived epitopes

Epstein-Barr virus nuclear antigen EBNA1, the one viral protein uniformly expressed in nasopharyngeal carcinoma (NPC), represents a prime target for T-cell-based immunotherapy. However, little is known about the EBNA1 epitopes, particularly CD4 epitopes, presented by HLA alleles in Chinese people, the group at highest risk for NPC. We analyzed the CD4$^+$ T-cell responses to EBNA1 in 78 healthy Chinese donors and found marked focusing on a small number of epitopes in the EBNA1 C-terminal region, including a DP5- restricted epitope that was recognized by almost half of the donors tested and elicited responses able to recognize EBNA1-expressing, DP5-positive target cells

Berberine induces autophagic cell death and mitochondrial apoptosis in liver cancer cells: The cellular mechanism

Extensive studies have revealed that berberine, a small molecule derived from Coptidis rhizoma (Huanglian in Chinese) and many other plants, has strong anti-tumor properties. To better understand berberine-induced cell death and its underlying mechanisms in cancer, we examined autophagy and apoptosis in the human hepatic carcinoma cell lines HepG2 and MHCC97-L. The results of this study indicate that berberine can induce both autophagy and apoptosis in hepatocellular carcinoma cells. Berberine-induced cell death in human hepatic carcinoma cells was diminished in the presence of the cell death inhibitor 3-methyladenine, or following interference with the essential autophagy gene Atg5. Mechanistic studies showed that berberine may activate mitochondrial apoptosis in HepG2 and MHCC97-L cells by increasing Bax expression, the formation of permeable transition pores, cytochrome C release to cytosol, and subsequent activation of the caspases 3 and 9 execution pathway. Berberine may also induce autophagic cell death in HepG2 and MHCC97-L cells through activation of Beclin-1 and inhibition of the mTOR-signaling pathway by suppressing the activity of Akt and up-regulating P38 MAPK signaling. This is the first study to describe the role of Beclin-1 activation and mTOR inhibition in berberine-induced autophagic cell death. These results further demonstrate the potential of berberine as a therapeutic agent in the emerging list of cancer therapies with novel mechanisms. © 2010 Wiley-Liss, Inc.postprin

The Microscopic Approach to Nuclear Matter and Neutron Star Matter

We review a variety of theoretical and experimental investigations aimed at improving our knowledge of the nuclear matter equation of state. Of particular interest are nuclear matter extreme states in terms of density and/or isospin asymmetry. The equation of state of matter with unequal concentrations of protons and neutrons has numerous applications. These include heavy-ion collisions, the physics of rare, short-lived nuclei and, on a dramatically different scale, the physics of neutron stars. The "common denominator" among these (seemingly) very different systems is the symmetry energy, which plays a crucial role in both the formation of the neutron skin in neutron-rich nuclei and the radius of a neutron star (a system 18 orders of magnitude larger and 55 orders of magnitude heavier). The details of the density dependence of the symmetry energy are not yet sufficiently constrained. Throughout this article, our emphasis will be on the importance of adopting a microscopic approach to the many-body problem, which we believe to be the one with true predictive power.Comment: 56 pages, review article to appear in the International Journal of Modern Physics

Generalization and induction: Misconceptions, clarifications and a classification of induction

In “Generalizing Generalizability in Information Systems Research,” Lee and Baskerville (2003) try to clarify generalization and classify it into four types. Unfortunately, their account is problematic. We propose repairs. Central among these is our balance-of-evidence argument that we should adopt the view that Hume’s problem of induction has a solution, even if we do not know what it is. We build upon this by proposing an alternative classification of induction. There are five types of generalization: (1) theoretical, (2) within-population, (3) cross-population, (4) contextual, and (5) temporal, with theoretical generalization being across the empirical and theoretical levels and the rest within the empirical level. Our classification also includes two kinds of inductive reasoning that do not belong to the domain of generalization. We then discuss the implications of our classification for information systems research