Full counting statistics of renormalized dynamics in open quantum transport system

The internal dynamics of a double quantum dot system is renormalized due to coupling respectively with transport electrodes and a dissipative heat bath. Their essential differences are identified unambiguously in the context of full counting statistics. The electrode coupling caused level detuning renormalization gives rise to a fast-to-slow transport mechanism, which is not resolved at all in the average current, but revealed uniquely by pronounced super-Poissonian shot noise and skewness. The heat bath coupling introduces an interdot coupling renormalization, which results in asymmetric Fano factor and an intriguing change of line shape in the skewness.Comment: 9 pages, 5 figure

Dynamically weighted clustering with noise set

Motivation: Various clustering methods have been applied to microarray gene expression data for identifying genes with similar expression profiles. As the biological annotation data accumulated, more and more genes have been organized into functional categories. Functionally related genes may be regulated by common cellular signals, thus likely to be co-expressed. Consequently, utilizing the rapidly increasing functional annotation resources such as Gene Ontology (GO) to improve the performance of clustering methods is of great interest. On the opposite side of clustering, there are genes that have distinct expression profiles and do not co-express with other genes. Identification of these scattered genes could enhance the performance of clustering methods.Results: We developed a new clustering algorithm, Dynamically Weighted Clustering with Noise set (DWCN), which makes use of gene annotation information and allows for a set of scattered genes, the noise set, to be left out of the main clusters. We tested the DWCN method and contrasted its results with those obtained using several common clustering techniques on a simulated dataset as well as on two public datasets: the Stanford yeast cell-cycle gene expression data, and a gene expression dataset for a group of genetically different yeast segregants.Conclusion: Our method produces clusters with more consistent functional annotations and more coherent expression patterns than existing clustering techniques.Contact: [email protected] information: Supplementary data are available at Bioinformatics online

A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse

As the Metaverse continues to grow, the need for efficient communication and intelligent content generation becomes increasingly important. Semantic communication focuses on conveying meaning and understanding from user inputs, while AI-Generated Content utilizes artificial intelligence to create digital content and experiences. Integrated Semantic Communication and AI-Generated Content (ISGC) has attracted a lot of attentions recently, which transfers semantic information from user inputs, generates digital content, and renders graphics for Metaverse. In this paper, we introduce a unified framework that captures ISGC two primary benefits, including integration gain for optimized resource allocation and coordination gain for goal-oriented high-quality content generation to improve immersion from both communication and content perspectives. We also classify existing ISGC solutions, analyze the major components of ISGC, and present several use cases. We then construct a case study based on the diffusion model to identify an optimal resource allocation strategy for performing semantic extraction, content generation, and graphic rendering in the Metaverse. Finally, we discuss several open research issues, encouraging further exploring the potential of ISGC and its related applications in the Metaverse.Comment: 8 pages, 6 figure

A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust Oracle settings according to varied semantic demands.Comment: 8 pages, 5 figures, 1 tabl

Large-deviation analysis for counting statistics in mesoscopic transports

We present an efficient approach, based on a number-conditioned master equation, for large-deviation analysis in mesoscopic transports. Beyond the conventional full-counting-statistics study, the large-deviation approach encodes complete information of both the typical trajectories and the rare ones, in terms of revealing a continuous change of the dynamical phase in trajectory space. The approach is illustrated with two examples: (i) transport through a single quantum dot, where we reveal the inhomogeneous distribution of trajectories in general case and find a particular scale invariance point in trajectory statistics; and (ii) transport through a double dots, where we find a dynamical phase transition between two distinct phases induced by the Coulomb correlation and quantum interference.Comment: 8 pages, 3 figure

Automation-aided high-throughput technologies for synthetic biology

Synthetic biology is a research discipline which harnesses technological progress in de novo DNA synthesis as well as combining expertise of biological sciences and engineering research fields to facilitate construction of novel artificial biological systems. Since the past two decades, application of its methodologies has led to significant advances in metabolic engineering, providing alternative biochemical routes for the production of therapeutic products, cosmetics and biofuels. However, several challenges remain to be addressed to support development of synthetic biology applications, notably the demand for faster, cheaper and more reliable DNA manufacturing as well as efficient methods for genome-scale engineering of living organisms. This doctoral thesis proposes new interdisciplinary approaches to these problems, taking advantage of the latest laboratory automation technologies to improve efficiency of modern DNA assembly and genome editing methods. The first results chapter proposes application of a robotic platform for an acoustic liquid transfer for miniaturisation of DNA fabrication. This research, published in 2016, demonstrates the possibility to cost-efficiently assemble DNA in sub-microlitre assembly reactions. The second results chapter presents efforts to develop a method for genome-scale engineering of a model eukaryote, the budding yeast. This work capitalises on the recent progress in on-chip DNA synthesis and the next-generation sequencing (NGS) technology. Finally, the last results chapter demonstrates computational studies to predict and accelerate turnaround times of a commercial DNA supply chain using probabilistic simulations. The developed software is used to estimate sequence-specific DNA manufacturing turnaround times in order to help plan DNA manufacturing and guide decisions regarding further automation of different experimental procedures

Phonon Polaritons in Monolayers of Hexagonal Boron Nitride.

Phonon polaritons in van der Waals materials reveal significant confinement accompanied with long propagation length: important virtues for tasks pertaining to the control of light and energy flow at the nanoscale. While previous studies of phonon polaritons have relied on relatively thick samples, here reported is the first observation of surface phonon polaritons in single atomic layers and bilayers of hexagonal boron nitride (hBN). Using antenna-based near-field microscopy, propagating surface phonon polaritons in mono- and bilayer hBN microcrystals are imaged. Phonon polaritons in monolayer hBN are confined in a volume about one million times smaller than the free-space photons. Both the polariton dispersion and their wavelength-thickness scaling law are altered compared to those of hBN bulk counterparts. These changes are attributed to phonon hardening in monolayer-thick crystals. The data reported here have bearing on applications of polaritons in metasurfaces and ultrathin optical elements

Total and horizontal distances of the foveal stereotaxic displacement can be prognostic indicators for patients with idiopathic epiretinal membrane

IntroductionThis study aimed to examine the foveal stereo deviations in the different ectopic inner foveal layer (EIFL) stages of idiopathic epiretinal membrane (iERM) and assess its predictive utility for the baseline and postoperative best-corrected visual acuity (BCVA).MethodsBased on the calculational combination of foveal displacements in the horizontal and vertical axial optical coherence tomography (OCT) images, the foveal stereotaxic displacement was estimated through the total distance (TD, the distance from the foveal bottom to the inner edge of displaced central foveal) and horizontal distance (HD, projection of the TD in the retinal plane). The preoperative TD, HD, and other OCT- and OCT angiography (OCTA)-related indicators were obtained. The correlations between structural parameters and baseline and postoperative BCVA were evaluated through correlation and multiple linear regression analyses.ResultsIn patients with advanced EIFL stage, there was a significant increase in the HD, TD, baseline log of the minimum angle of resolution unit for BCVA, central macular thickness (CMT), acircularity index, and incidence of microcystic macular edema (MME; p < 0.05). Further, they showed a decreased foveal avascular zone (FAZ) area and perimeter (p < 0.001). HD, TD, CMT, MME, FAZ area, and FAZ perimeter were significantly correlated with the baseline and postoperative BCVA (p < 0.05). TD had the highest correlation indexic and was an individual predictor of the baseline and postoperative BCVA. Moreover, FD-300 and MME were individual predictors of postoperative BCVA.DiscussionStereoscopic foveal deviations significantly correlated with the baseline and postoperative visual acuity. TD may be used as an independent prognostic factor for BCVA