BPS D-branes from an Unstable D-brane

We search for exact tachyon kink solutions of DBI type effective action describing an unstable D-brane with worldvolume gauge field turned in both the flat and a curved background. There are various kinds of solutions in the presence of electromagnetic fields in the flat space, such as periodic arrays, topological tachyon kinks, half kinks, and bounces. We identify a BPS object, D($p$-1)F1 bound state, which describes a thick brane with string flux density. The curved background of interest is the ten-dimensional lift of the Salam-Sezgin vacuum and, in the asymptotic limit, it approaches ${\rm R}^{1,4}\times {\rm T}^2\times {\rm S}^3$. The solutions in the curved background are identified as composites of lower-dimensional D-branes and fundamental strings, and, in the BPS limit, they become a D4D2F1 composite wrapped on ${\rm R}^{1,2}\times {\rm T}^2$ where ${\rm T}^2$ is inside ${\rm S}^3$.Comment: 4 pages, to appear in the proceeding of PASCOS 2005, Gyeongju, Korea, May 30-June 4, 200

Analysis of adaptive algorithms for an integrated communication network

Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes

A serpentine laminating micromixer combining splitting/recombination and advection

Mixing enhancement has drawn great attention from designers of micromixers, since the flow in a microchannel is usually characterized by a low Reynolds number ( Re) which makes the mixing quite a difficult task to accomplish. In this paper, a novel integrated efficient micromixer named serpentine laminating micromixer (SLM) has been designed, simulated, fabricated and fully characterized. In the SLM, a high level of efficient mixing can be achieved by combining two general chaotic mixing mechanisms: splitting/recombination and chaotic advection. The splitting and recombination ( in other terms, lamination) mechanism is obtained by the successive arrangement of "F''-shape mixing units in two layers. The advection is induced by the overall three-dimensional serpentine path of the microchannel. The SLM was realized by SU-8 photolithography, nickel electroplating, injection molding and thermal bonding. Mixing performance of the SLM was fully characterized numerically and experimentally. The numerical mixing simulations show that the advection acts favorably to realize the ideal vertical lamination of fluid flow. The mixing experiments based on an average mixing color intensity change of phenolphthalein show a high level of mixing performance was obtained with the SLM. Numerical and experimental results confirm that efficient mixing is successfully achieved from the SLM over the wide range of Re. Due to the simple and mass producible geometry of the efficient micromixer, SLM proposed in this study, the SLM can be easily applied to integrated microfluidic systems, such as micro-total-analysis-systems or lab-on-a-chip systems.X11159165sciescopu

Disposable Integrated Microfluidic Biochip for Blood Typing by Plastic Microinjection Moulding

Blood typing is the most important test for both transfusion recipients and blood donors. In this paper, a low cost disposable blood typing integrated microfluidic biochip has been designed, fabricated and characterized. In the biochip, flow splitting microchannels, chaotic micromixers, reaction microchambers and detection microfilters are fully integrated. The loaded sample blood can be divided by 2 or 4 equal volumes through the flow splitting microchannel so that one can perform 2 or 4 blood agglutination tests in parallel. For the purpose of obtaining efficient reaction of agglutinogens on red blood cells (RBCs) and agglutinins in serum, we incorporated a serpentine laminating micromixer into the biochip, which combines two chaotic mixing mechanisms of splitting/recombination and chaotic advection. Relatively large area reaction microchambers were also introduced for the sake of keeping the mixture of the sample blood and serum during the reaction time before filtering. The gradually decreasing multi-step detection microfilters were designed in order to effectively filter the reacted agglutinated RBCs, which show the corresponding blood group. To achieve the cost-effectiveness of the microfluidic biochip for disposability, the biochip was realized by the microinjection moulding of COC (cyclic olefin copolymer) and thermal bonding of two injection moulded COC substrates in mass production with a total fabrication time of less than 20 min. Mould inserts of the biochip for the microinjection moulding were fabricated by SU-8 photolithography and the subsequent nickel electroplating process. Human blood groups of A, B and AB have been successfully determined with the naked eye, with 3 mu l of the whole sample bloods, by means of the fabricated biochip within 3 min.X11100104sciescopu

Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification

Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on finding appropriate graph filters (e.g. aggregation methods) to perform well on both homophilic and heterophilic graphs. While these methods are effective, they can still suffer from the sparsity of node features, where the initial data contain few non-zero elements. This can lead to overfitting in certain dimensions in the first projection matrix, as training samples may not cover the entire range of graph filters (hyperplanes). To address this, we propose a novel data augmentation strategy. Specifically, by flipping both the initial features and hyperplane, we create additional space for training, which leads to more precise updates of the learnable parameters and improved robustness for unseen features during inference. To the best of our knowledge, this is the first attempt to mitigate the overfitting caused by the initial features. Extensive experiments on real-world datasets show that our proposed technique increases node classification accuracy by up to 46.5% relatively

Is Signed Message Essential for Graph Neural Networks?

Message-passing Graph Neural Networks (GNNs), which collect information from adjacent nodes, achieve satisfying results on homophilic graphs. However, their performances are dismal in heterophilous graphs, and many researchers have proposed a plethora of schemes to solve this problem. Especially, flipping the sign of edges is rooted in a strong theoretical foundation, and attains significant performance enhancements. Nonetheless, previous analyses assume a binary class scenario and they may suffer from confined applicability. This paper extends the prior understandings to multi-class scenarios and points out two drawbacks: (1) the sign of multi-hop neighbors depends on the message propagation paths and may incur inconsistency, (2) it also increases the prediction uncertainty (e.g., conflict evidence) which can impede the stability of the algorithm. Based on the theoretical understanding, we introduce a novel strategy that is applicable to multi-class graphs. The proposed scheme combines confidence calibration to secure robustness while reducing uncertainty. We show the efficacy of our theorem through extensive experiments on six benchmark graph datasets

Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation

A cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Despite such progress, existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they fail to generalize well without such requirements. To deal with these problems, we suggest utilizing review texts that are general to most e-commerce systems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning. Here, we suggest a novel optimization strategy that can enhance the quality of domain disentanglement, and also debilitates detrimental information of a source domain. Also, we extend the encoding network from a single to multiple domains, which has proven to be powerful for review-based recommender systems. Extensive experiments and ablation studies demonstrate that our method is efficient, robust, and scalable compared to the state-of-the-art single and cross-domain recommendation methods