Downlink resource auction in a tree topology structured wireless mesh network

We analyze the problem of downlink resource allocation in a non-cooperative multi-level tree topology structured wireless mesh network in which a selfish mesh router (MR) may refuse to relay other MRs' traffic so as to improve its own performance at the cost of overall system performance. Based on game theory, we propose an auction framework, where the parent MR serves as the auctioneer while its children MRs act as bidders and compete for time-slots. We derive a payment function from radio resource used for relaying traffic instead of money, so as to simplify the implementation and avoid the possible security problems from monetary payment. We prove the existence and uniqueness of Nash Equilibrium and propose a stochastic best response updating algorithm to allow the bids to iteratively converge to NE in a practical distributed fashion. Simulation results show the proposed auction algorithm greatly outperforms traditional algorithms in non-cooperative environments. © 2010 IEEE.published_or_final_versio

Discovering multiple resource holders in query-incentive networks

Session - Content Distribution and Peer-to-Peer NetworksIn this paper, we study the problem of discovering multiple resource holders and how to evaluate a node's satisfaction in query incentive networks. Utilizing an acyclic tree, we show that query propagation has a nature of exponential start, polynomial growth, and eventually becoming a constant. We model the query propagation as an extensive game, obtain nodes' greedy behaviors from Nash equilibrium analysis, and show the impairment of greedy behaviors via a repeated Prisoner's Dilemma. We demonstrate that cooperation enforcement is required to achieve the optimal state of resource discovery. © 2011 IEEE.published_or_final_versionThe 8th IEEE Consumer Communications and Networking Conference (CCNC 2011), Las Vegas, NV., 9-12 January 2011. In Proceedings of the 8th CCNC, 2011, p. 1000-100

Zn-impurity effect and interplay of s± and s++ pairings in iron-based superconductors

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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201

Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach

A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on demand. Metasurfaces are versatile, novel platforms for manipulating the scattering, color, phase, or intensity of light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters, such as various materials’ properties and coupling effects, as well as the geometrical parameters. Ideally, this would require multidimensional space optimization through direct numerical simulations. Recently, an alternative, popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted method. We utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude, and spectral position. We exploit such high-Q resonances for enhanced light–matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation; at the same time, they also contribute to 100-fold enhancement of the amplitude of optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses

Folate cycle enzyme MTHFD1L confers metabolic advantages in hepatocellular carcinoma

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An analytical model for the identification of the threshold of stress intensity factor range for crack growth

The value of the stress intensity factor (SIF) range threshold (∆K_th ) for fatigue crack growth (FCG) depends highly on its experimental identification. The identification and application of ∆K_(th )are not well established as its determination depends on various factors including experimental, numerical or analytical techniques used. A new analytical model which can fit the raw FCG experimental data is proposed. The analytical model proposed is suitable to fit with a high accuracy the experimental data and capable to estimate the threshold SIF range. The comparison between the threshold SIF range identified with the model proposed and those found in literature is also discussed. The ∆K_th identified is found to be quite accurate and consistent when compared to the literature with a maximum deviation of 5.61%. The accuracy with which the analytical model is able to fit the raw data is also briefly discussed

Effects of thermal stress on morality in the older population of Hong Kong

BACKGROUND AND AIMS: A wide body of epidemiological evidence demonstrated consistent associations between temperature and daily mortality mainly from ecological time series studies. But few studies have examined these associations in a cohort. METHODS: We used a matched case-control design with time-dependent covariates to assess short-term effects of apparent temperature (AT) on mortality in a cohort of 66,820 persons aged 65 years or older, with a total of 14,446 deaths after about 10 years of follow up. The cases and controls were matched by duration of exposure with adjustment for particulate matter of aero-diameter …postprin

Mechanistic Investigation of the Specific Anticancer Property of Artemisinin and Its Combination with Aminolevulinic Acid for Enhanced Anticolorectal Cancer Activity.

The antimalarial artemisinin (ART) possesses anticancer activity, but its underlying mechanism remains largely unclear. Using a chemical proteomics approach with artemisinin-based activity probes, we identified over 300 specific ART targets. This reveals an anticancer mechanism whereby ART promiscuously targets multiple critical biological pathways and leads to cancer cell death. The specific cytotoxicity of ART against colorectal cancer (CRC) cells rather than normal colon epithelial cells is due to the elevated capacity of heme synthesis in the cancer cells. Guided by this mechanism, the specific cytotoxicity of ART toward CRC cells can be dramatically enhanced with the addition of aminolevulinic acid (ALA), a clinically used heme synthesis precursor, to increase heme levels. Importantly, this novel ART/ALA combination therapy proves to be more effective than an ART monotherapy in a mouse xenograft CRC model. Thus, ART can be repurposed and potentiated by exploitation of its mechanism of action and the metabolic features of the CRC cells