On the singular hyperbolicity of star flows

We prove for a generic star vector field $X$ that, if for every chain recurrent class $C$ of $X$ all singularities in $C$ have the same index, then the chain recurrent set of $X$ is singular hyperbolic. We also prove that every Lyapunov stable chain recurrent class of $X$ is singular hyperbolic. As a corollary, we prove that the chain recurrent set of a generic 4-dimensional star flow is singular hyperbolic.Comment: 29 pages, version to appear in J. Mod. Dy

Synthesis and Characterization of a Magnetically Responsive Polymeric Drug Delivery System

A magnetic target drug delivery system consisting of biodegradable polymeric microspheres (poly D, L-lactic acid) loaded with magnetite nanoparticles (10-100 nm) and anticancer drug (paclitaxel) was studied. The magnetite nanoparticles were synthesized by chemical precipitation. The as-synthesized magnetite nanoparticles were subsequently introduced into a mixture of polymer magnetic polymeric composite particles were investigated and further correlated with the reaction parameters. It was found that the size and characteristics of the polymeric composite particles depended on the viscosity of the polymer solution. Preliminary drug release experiments showed that the loaded drug was released with the degradation of the polymer. The release rates could be enhanced by an oscillating external magnetic field.Singapore-MIT Alliance (SMA

Preparation of Polymer-Coated Functionalized Ferrimagnetic Iron Oxide Nanoparticles*

A simple chemical method to synthesize PMAA coated maghemite nanoparticles is described. Monomer methacrylic acid molecules were absorbed onto the synthesized ferrimagnetic nanoparticles followed by polymerization. The carboxylic group of PMAA coating allowed surface immobilization of foreign molecules. An anti-cancer drug was successfully adsorbed onto the PMAA coated maghemite nanoparticles for potential targeted drug delivery.Singapore-MIT Alliance (SMA

A New Look at Physical Layer Security, Caching, and Wireless Energy Harvesting for Heterogeneous Ultra-dense Networks

Heterogeneous ultra-dense networks enable ultra-high data rates and ultra-low latency through the use of dense sub-6 GHz and millimeter wave (mmWave) small cells with different antenna configurations. Existing work has widely studied spectral and energy efficiency in such networks and shown that high spectral and energy efficiency can be achieved. This article investigates the benefits of heterogeneous ultra-dense network architecture from the perspectives of three promising technologies, i.e., physical layer security, caching, and wireless energy harvesting, and provides enthusiastic outlook towards application of these technologies in heterogeneous ultra-dense networks. Based on the rationale of each technology, opportunities and challenges are identified to advance the research in this emerging network.Comment: Accepted to appear in IEEE Communications Magazin

Cloning and Characterization of the ζ-Carotene Desaturase Gene from Chlorella protothecoides CS-41

To elucidate the lutein biosynthesis pathway in the lutein-producing alga, Chlorella protothecoides CS-41, the ζ-carotene desaturase gene (zds) was isolated from Chlorella protothecoides using the approach of rapid amplification of cDNA ends. The full-length cDNA sequence was 2031 bp and contained 1755 bp putative open reading frame which encodes a 584 amino acid deduced polypeptide whose computed molecular weight was 63.7 kDa. Sequence homology research indicated that the nucleotide and putative protein had sequence identities of 72.5% and 69.5% with those of the green alga Chlamydomonas reinhardtii, respectively. Phylogenetic analysis demonstrated that the ZDS from C. protothecoides CS-41 had a closer relationship with those of chlorophyta and higher plants than with those of other species. In addition, we also found that the zds gene expression was upregulated in response to light

A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness

Jaeger: A Concatenation-Based Multi-Transformer VQA Model

Document-based Visual Question Answering poses a challenging task between linguistic sense disambiguation and fine-grained multimodal retrieval. Although there has been encouraging progress in document-based question answering due to the utilization of large language and open-world prior models\cite{1}, several challenges persist, including prolonged response times, extended inference durations, and imprecision in matching. In order to overcome these challenges, we propose Jaegar, a concatenation-based multi-transformer VQA model. To derive question features, we leverage the exceptional capabilities of RoBERTa large\cite{2} and GPT2-xl\cite{3} as feature extractors. Subsequently, we subject the outputs from both models to a concatenation process. This operation allows the model to consider information from diverse sources concurrently, strengthening its representational capability. By leveraging pre-trained models for feature extraction, our approach has the potential to amplify the performance of these models through concatenation. After concatenation, we apply dimensionality reduction to the output features, reducing the model's computational effectiveness and inference time. Empirical results demonstrate that our proposed model achieves competitive performance on Task C of the PDF-VQA Dataset. If the user adds any new data, they should make sure to style it as per the instructions provided in previous sections.Comment: This paper is the technical research paper of CIKM 2023 DocIU challenges. The authors received the CIKM 2023 DocIU Winner Award, sponsored by Google, Microsoft, and the Centre for data-driven geoscienc

Centralizers of derived-from-Anosov systems on T3\mathbb{T}^3: rigidity versus triviality

In this paper, we study the centralizer of a partially hyperbolic diffeomorphism on $\mathbb{T}^3$ which is homotopic to an Anosov automorphism, and we show that either its centralizer is virtually trivial or such diffeomorphism is smoothly conjugate to its linear part.Comment: 24 page