Fooling an Unbounded Adversary with a Short Key, Repeatedly: The Honey Encryption Perspective

This article is motivated by the classical results from Shannon that put the simple and elegant one-time pad away from practice: key length has to be as large as message length and the same key could not be used more than once. In particular, we consider encryption algorithm to be defined relative to specific message distributions in order to trade for unconditional security. Such a notion named honey encryption (HE) was originally proposed for achieving best possible security for password based encryption where secrete key may have very small amount of entropy. Exploring message distributions as in HE indeed helps circumvent the classical restrictions on secret keys.We give a new and very simple honey encryption scheme satisfying the unconditional semantic security (for the targeted message distribution) in the standard model (all previous constructions are in the random oracle model, even for message recovery security only). Our new construction can be paired with an extremely simple yet "tighter" analysis, while all previous analyses (even for message recovery security only) were fairly complicated and require stronger assumptions. We also show a concrete instantiation further enables the secret key to be used for encrypting multiple messages

Occurrences and phenotypes of RIPK3-positive gastric cells in Helicobacter pylori infected gastritis and atrophic lesions

Author's accepted version (postprint).This is an Accepted Manuscript of an article published by Elsevier in Digestive and Liver Disease on 02/05/2022.Available online: https://www.dldjournalonline.com/article/S1590-8658(22)00258-4/fulltextacceptedVersio

Dynamic stromal cellular reaction throughout human colorectal adenoma-carcinoma sequence : A role of TH17/IL-17A

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The expression of RIPK3 is associated with cell turnover of gastric mucosa in the mouse and human stomach

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Water, rather than temperature, dominantly impacts how soil fauna affect dissolved carbon and nitrogen release from fresh litter during early litter decomposition

Longstanding observations suggest that dissolved materials are lost from fresh litter through leaching, but the role of soil fauna in controlling this process has been poorly documented. In this study, a litterbag experiment employing litterbags with different mesh sizes (3 mm to permit soil fauna access and 0.04 mm to exclude fauna access) was conducted in three habitats (arid valley, ecotone and subalpine forest) with changes in climate and vegetation types to evaluate the effects of soil fauna on the concentrations of dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) during the first year of decomposition. The results showed that the individual density and community abundance of soil fauna greatly varied among these habitats, but Prostigmata, Isotomidae and Oribatida were the dominant soil invertebrates. At the end of the experiment, the mass remaining of foliar litter ranged from 58% for shrub litter to 77% for birch litter, and the DOC and TDN concentrations decreased to 54%-85% and increased to 34%-269%, respectively, when soil fauna were not present. The effects of soil fauna on the concentrations of both DOC and TDN in foliar litter were greater in the subalpine forest (wetter but colder) during the winter and in the arid valley (warmer but drier) during the growing season, and this effect was positively correlated with water content. Moreover, the effects of fauna on DOC and TDN concentrations were greater for high-quality litter and were related to the C/N ratio. These results suggest that water, rather than temperature, dominates how fauna affect the release of dissolved substances from fresh litter

Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure

This paper presents adaptive neural tracking control for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine purefeedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem (MVT) is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularityfree adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SGUUB). Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this work

Evaluation of anti-TNF therapeutic response in patients with inflammatory bowel disease : Current and novel biomarkers

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Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network

Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into pre-defined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment. In this paper, we propose a method for DAC from audio recordings using a multi-scale dilated depthwise separable convolutional network (DSCN). The DSCN is a lightweight neural network with small size of parameters and thus suitable to be deployed in portable terminals with limited computing resources. To expand the receptive field with the same size of DSCN's parameters, dilated convolution, instead of normal convolution, is used in the DSCN for further improving the DSCN's performance. In addition, the embeddings of various scales learned by the dilated DSCN are concatenated as a multi-scale embedding for representing property differences among various classes of domestic activities. Evaluated on a public dataset of the Task 5 of the 2018 challenge on Detection and Classification of Acoustic Scenes and Events (DCASE-2018), the results show that: both dilated convolution and multi-scale embedding contribute to the performance improvement of the proposed method; and the proposed method outperforms the methods based on state-of-the-art lightweight network in terms of classification accuracy.Comment: 5 pages, 2 figures, 4 tables. Accepted for publication in IEEE MMSP202

Temporal and spatial changes of cells positive for stem-like markers in different compartments and stages of human colorectal adenoma-carcinoma sequence

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