Group Divisible Codes and Their Application in the Construction of Optimal Constant-Composition Codes of Weight Three

The concept of group divisible codes, a generalization of group divisible designs with constant block size, is introduced in this paper. This new class of codes is shown to be useful in recursive constructions for constant-weight and constant-composition codes. Large classes of group divisible codes are constructed which enabled the determination of the sizes of optimal constant-composition codes of weight three (and specified distance), leaving only four cases undetermined. Previously, the sizes of constant-composition codes of weight three were known only for those of sufficiently large length.Comment: 13 pages, 1 figure, 4 table

List Decodability at Small Radii

$A'(n,d,e)$, the smallest $\ell$ for which every binary error-correcting code of length $n$ and minimum distance $d$ is decodable with a list of size $\ell$ up to radius $e$, is determined for all $d\geq 2e-3$. As a result, $A'(n,d,e)$ is determined for all $e\leq 4$, except for 42 values of $n$.Comment: to appear in Designs, Codes, and Cryptography (accepted October 2010

Topological phase transition and interface states in hybrid plasmonic-photonic systems

The geometric phase and topological property for one-dimensional hybrid plasmonic-photonic crystals consisting of a simple lattice of graphene sheets are investigated systematically. For transverse magnetic waves, both plasmonic and photonic modes exist in the momentum space. The accidental degeneracy point of these two kinds of modes is identified to be a diabolic point accompanied with a topological phase transition. For a closed loop around this degeneracy point, the Berry phase is Pi as a consequence of the discontinuous jump of the geometric Zak phase. The wave impedance is calculated analytically for the semi-infinite system, and the corresponding topological interface states either start from or terminate at the degeneracy point. This type of localized interface states may find potential applications in photonics and plasmonics.Comment: 5 pages, 4 figure

Prompting Fab Yeast Surface Display Efficiency by ER Retention and Molecular Chaperon Co-expression.

For antibody discovery and engineering, yeast surface display (YSD) of antigen-binding fragments (Fabs) and coupled fluorescence activated cell sorting (FACS) provide intact paratopic conformations and quantitative analysis at the monoclonal level, and thus holding great promises for numerous applications. Using anti-TNFα mAbs Infliximab, Adalimumab, and its variants as model Fabs, this study systematically characterized complementary approaches for the optimization of Fab YSD. Results suggested that by using divergent promoter GAL1-GAL10 and endoplasmic reticulum (ER) signal peptides for co-expression of light chain and heavy chain-Aga2 fusion, assembled Fabs were functionally displayed on yeast cell surface with sigmoidal binding responses toward TNFα. Co-expression of a Hsp70 family molecular chaperone Kar2p and/or protein-disulfide isomerase (Pdi1p) significantly improved efficiency of functional display (defined as the ratio of cells displaying functional Fab over cells displaying assembled Fab). Moreover, fusing ER retention sequences (ERSs) with light chain also enhanced Fab display quality at the expense of display quantity, and the degree of improvements was correlated with the strength of ERSs and was more significant for Infliximab than Adalimumab. The feasibility of affinity maturation was further demonstrated by isolating a high affinity Fab clone from 1:103 or 1:105 spiked libraries

How Transferable are Neural Networks in NLP Applications?

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.Comment: Accepted by EMNLP-1

Mapping the managerial areas of Building Information Modeling (BIM) using scientometric analysis

The successful adoption of Building Information Modeling (BIM) leads to the subsequent need for improving management practices and stakeholders' relationships. Previous studies have attempted to explore solutions for non-technical issues; however, a systematic and quantitative review of the details of non-technical field, namely, the managerial areas of BIM (MA–BIM), seems to be missing. Hence, a scientometric approach is used to construct knowledge maps in MA–BIM, thereby allowing bibliometric data to provide an objective and accurate perspective in the field as a whole. Through keyword and abstract term analysis of 126 related papers published from 2007 to 2015, an integrated conceptual framework is proposed to summarize current status and structure future directions of MA–BIM based on five principal research areas. This study shows the transformation of MA–BIM from an individual approach to a wide-ranging organizational strategy. It provides new insights into managing BIM projects by referring to the accurate representation and analysis of previous research efforts

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications