A Preliminary Study on Using Multi-Nozzle Polymer Deposition System to Fabricate Composite Alginate/Carbon Nanotube Tissue Scaffolds

Three-dimensional composite alginate/single wall carbon nanotube (SWCNT) scaffolds encapsulated with endothelial cells were fabricated by a multi-nozzle biopolymer freeform deposition system. This system enables the converting of CAD designed scaffold pattern into process toolpaths and the use of computer control program to guide the nozzle deposition at spatial position for layered fabrication of 3D tissue scaffolds. The morphological, mechanical, structural and biological properties of as-fabricated scaffolds were characterized by optical microscope, SEM, Microtensile testing machine, Alamar Blue Assay, and Live-Dead Assay, respectively. The multi-nozzle deposition system demonstrated a highly efficient and effective process to build tissue scaffold or cell embedded constructs. Characterization results showed that the incorporation of SWCNT into alginate not only enhanced the mechanical strength of the scaffolds but also improved the cell affinity and the interaction with substrate. Further cell culture experimental results also showed that the incorporation of SWCNT in alginate enhanced endothelial cell proliferation compared with pure alginate scaffold.Mechanical Engineerin

Spin relaxation and decoherence of two-level systems

We revisit the concepts of spin relaxation and spin decoherence of two level (spin-1/2) systems. From two toy-models, we clarify two issues related to the spin relaxation and decoherence: 1) For an ensemble of two-level particles each subjected to a different environmental field, there exists an ensemble relaxation time $T_1^*$ which is fundamentally different from $T_1$. When the off-diagonal coupling of each particle is in a single mode with the same frequency but a random coupling strength, we show that $T_1^*$ is finite while the spin relaxation time of a single spin $T_1$ and the usual ensemble decoherence time $T_2^*$ are infinite. 2) For a two-level particle under only a random diagonal coupling, its relaxation time $T_1$ shall be infinite but its decoherence time $T_2$ is finite.Comment: 5 pages, 2 figure

Universal Tomonaga-Luttinger liquid phases in one-dimensional strongly attractive SU(N) fermionic cold atoms

A simple set of algebraic equations is derived for the exact low-temperature thermodynamics of one-dimensional multi-component strongly attractive fermionic atoms with enlarged SU(N) spin symmetry and Zeeman splitting. Universal multi-component Tomonaga-Luttinger liquid (TLL) phases are thus determined. For linear Zeeman splitting, the physics of the gapless phase at low temperatures belongs to the universality class of a two-component asymmetric TLL corresponding to spin-neutral N-atom composites and spin-(N-1)/2 single atoms. The equation of states is also obtained to open up the study of multi-component TLL phases in 1D systems of N-component Fermi gases with population imbalance.Comment: 12 pages, 3 figure

Decoding of 1/2-rate (24,12) Golay codes

A decoding method for a (23,12) Golay code is extended to the important 1/2-rate (24,12) Golay code so that three errors can be corrected and four errors can be detected. It is shown that the method can be extended to any decoding method which can correct three errors in the (23,12) Golay code

Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets