Superadditivity in Trade-Off Capacities of Quantum Channels

© 1963-2012 IEEE. In this paper, we investigate the additivity phenomenon in the quantum dynamic capacity region of a quantum channel for trading the resources of classical communication, quantum communication, and entanglement. Understanding such an additivity property is important if we want to optimally use a quantum channel for general communication purposes. However, in a lot of cases, the channel one will be using only has an additive single or double resource capacity region, and it is largely unknown if this could lead to a strictly superadditive double or triple resource capacity region, respectively. For example, if a channel has additive classical and quantum capacities, can the classical-quantum capacity region be strictly superadditive? In this paper, we answer such questions affirmatively. We give proof-of-principle requirements for these channels to exist. In most cases, we can provide an explicit construction of these quantum channels. The existence of these superadditive phenomena is surprising in contrast to the result that the additivity of both classical-entanglement and classical-quantum capacity regions imply the additivity of the triple resource capacity region for a given channel

Handling oversampling in dynamic networks using link prediction

Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many important algorithmic problems on dynamic networks, including link prediction. Link prediction seeks to predict edges that will be added to the network given previous snapshots. We show that not only does oversampling affect the quality of link prediction, but that we can use link prediction to recover from the effects of oversampling. We also introduce a novel generative model of noise in dynamic networks that represents oversampling. We demonstrate the results of our approach on both synthetic and real-world data.Comment: ECML/PKDD 201

Anisotropic step-flow growth and island growth of GaN(0001) by molecular beam epitaxy

GaN(0001) thin films are grown using radio frequency plasma assisted molecular beam epitaxy. By changing the growth temperature, anisotropic growth rate behavior is observed in both the step-flow growth mode and the 2D island growth mode. Tunneling scanning microscopy reveals, in the step-flow growth mode, strong influences from the growth anisotropy on the shape of the terrace edges, resulting in striking differences between hexagonal and cubic films. In the 2D nucleation growth mode, triangularly shaped islands are formed. The significance of growth anisotropy to growing high quality GaN films is discussed.published_or_final_versio

Primary Care Workforce And Continuous Medical Education In China: Lessons To Learn From A Nationwide Cross-sectional Survey

published_or_final_versio

Determining Principal Component Cardinality through the Principle of Minimum Description Length

PCA (Principal Component Analysis) and its variants areubiquitous techniques for matrix dimension reduction and reduced-dimensionlatent-factor extraction. One significant challenge in using PCA, is thechoice of the number of principal components. The information-theoreticMDL (Minimum Description Length) principle gives objective compression-based criteria for model selection, but it is difficult to analytically applyits modern definition - NML (Normalized Maximum Likelihood) - to theproblem of PCA. This work shows a general reduction of NML prob-lems to lower-dimension problems. Applying this reduction, it boundsthe NML of PCA, by terms of the NML of linear regression, which areknown.Comment: LOD 201

Broadband gradient impedance matching using an acoustic metamaterial for ultrasonic transducers

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

Bag of Deep Features for Instructor Activity Recognition in Lecture Room

This paper has been presented at : 25th International Conference on MultiMedia Modeling (MMM2019)This research aims to explore contextual visual information in the lecture room, to assist an instructor to articulate the effectiveness of the delivered lecture. The objective is to enable a self-evaluation mechanism for the instructor to improve lecture productivity by understanding their activities. Teacher’s effectiveness has a remarkable impact on uplifting students performance to make them succeed academically and professionally. Therefore, the process of lecture evaluation can significantly contribute to improve academic quality and governance. In this paper, we propose a vision-based framework to recognize the activities of the instructor for self-evaluation of the delivered lectures. The proposed approach uses motion templates of instructor activities and describes them through a Bag-of-Deep features (BoDF) representation. Deep spatio-temporal features extracted from motion templates are utilized to compile a visual vocabulary. The visual vocabulary for instructor activity recognition is quantized to optimize the learning model. A Support Vector Machine classifier is used to generate the model and predict the instructor activities. We evaluated the proposed scheme on a self-captured lecture room dataset, IAVID-1. Eight instructor activities: pointing towards the student, pointing towards board or screen, idle, interacting, sitting, walking, using a mobile phone and using a laptop, are recognized with an 85.41% accuracy. As a result, the proposed framework enables instructor activity recognition without human intervention.Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2014-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander

SMAD3 prevents binding of NKX2.1 and FOXA1 to the SpB promoter through its MH1 and MH2 domains

Mechanisms of gene repression by transforming growth factor-beta (TGF-beta) are not well understood. TGF-beta represses transcription of pulmonary surfactant protein-B gene in lung epithelial cells. Repression is mediated by SMAD3 through interactions with NKX2.1 and FOXA1, two key transcription factors that are positive regulators of SpB transcription. In this study, we found that SMAD3 interacts through its MAD domains, MH1 and MH2 with NKX2.1 and FOXA1 proteins. The sites of interaction on NKX2.1 are located within the NH2 and COOH domains, known to be involved in transactivation function. In comparison, weaker interaction of FOXA1 winged helix, and the NH2-terminal domains was documented with SMAD3. Both in vitro studies and in vivo ChIP assays show that interaction of SMAD3 MH1 and MH2 domains with NKX2.1 and FOXA1 results in reduced binding of NKX2.1 and FOXA1 to their cognate DNA-binding sites, and diminished promoter occupancy within the SpB promoter. Thus, these studies reveal for the first time a mechanism of TGF-beta-induced SpB gene repression that involves interactions between specific SMAD3 domains and the corresponding functional sites on NKX2.1 and FOXA1 transcription factors