Efficient 3D Face Recognition with Gabor Patched Spectral Regression

In this paper, we utilize a novel framework for 3D face recognition, called 3D Gabor Patched Spectral Regression (3D GPSR), which can overcome some of the continuing challenges encountered with 2D or 3D facial images. In this active field, some obstacles, like expression variations, pose correction and data noise deteriorate the performance significantly. Our proposed system addresses these problems by first extracting the main facial area to remove irrelevant information corresponding to shoulders and necks. Pose correction is used to minimize the influence of large pose variations and then the normalized depth and gray images can be obtained. Due to better time-frequency characteristics and a distinctive biological background, the Gabor feature is extracted on depth images, known as 3D Gabor faces. Data noise is mainly caused by distorted meshes, varieties of subordinates and misalignment. To solve these problems, we introduce a Patched Spectral Regression strategy, which can make good use of the robustness and efficiency of accurate patched discriminant low-dimension features and minimize the effect of noise term. Computational analysis shows that spectral regression is much faster than the traditional approaches. Our experiments are based on the CASIA and FRGC 3D face databases which contain a huge number of challenging data. Experimental results show that our framework consistently outperforms the other existing methods with the distinctive characteristics of efficiency, robustness and generality

5-Hydroxymethylfurfural synthesis from fructose over deep eutectic solvents in batch reactors and continuous flow microreactors

In this work, a deep eutectic solvent (DES) composed of choline chloride (ChCl) and ethylene glycol (EG) was prepared and applied for the conversion of fructose to 5-hydroxymethylfurfural (HMF), catalyzed by HCl in both laboratory batch reactors and continuous flow microreactors. The effects of reaction temperature, batch time, catalyst loading and molar ratio of ChCl to EG on the fructose conversion and HMF yield were first investigated in the monophasic batch system of ChCl/EG DES. To inhibit HMF-involved side reactions (e.g., its polymerization to humins), methyl isobutyl ketone (MIBK) was used as the extraction agent to form a biphasic system with DES in batch reactors. As a result, the maximum HMF yield could be enhanced at an MIBK to DES volume ratio of 3:1, e.g., increased from 48% in the monophasic DES (with a molar ratio ChCl to EG at 1:3) to 63% in the biphasic system at 80°C and 5 mol% of HCl loading. Based on the optimized results in batch reactors, biphasic experiments were conducted in capillary microreactors under slug flow operation, where a maximum HMF yield of ca. 61% could be obtained in 13 min, which is similar to that in batch under otherwise the same conditions. The slight mass transfer limitation in microreactors was confirmed by performing experiments with microreactors of varying length, and comparing the characteristic mass transfer time and reaction time, indicating further room for improvement

How to Describe Images in a More Funny Way? Towards a Modular Approach to Cross-Modal Sarcasm Generation

Sarcasm generation has been investigated in previous studies by considering it as a text-to-text generation problem, i.e., generating a sarcastic sentence for an input sentence. In this paper, we study a new problem of cross-modal sarcasm generation (CMSG), i.e., generating a sarcastic description for a given image. CMSG is challenging as models need to satisfy the characteristics of sarcasm, as well as the correlation between different modalities. In addition, there should be some inconsistency between the two modalities, which requires imagination. Moreover, high-quality training data is insufficient. To address these problems, we take a step toward generating sarcastic descriptions from images without paired training data and propose an Extraction-Generation-Ranking based Modular method (EGRM) for cross-model sarcasm generation. Specifically, EGRM first extracts diverse information from an image at different levels and uses the obtained image tags, sentimental descriptive caption, and commonsense-based consequence to generate candidate sarcastic texts. Then, a comprehensive ranking algorithm, which considers image-text relation, sarcasticness, and grammaticality, is proposed to select a final text from the candidate texts. Human evaluation at five criteria on a total of 1200 generated image-text pairs from eight systems and auxiliary automatic evaluation show the superiority of our method

Modelling endogenous insulin concentration in type 2 diabetes during closed loop insulin delivery

This is the final published version. It first appeared at http://www.biomedical-engineering-online.com/content/14/1/19.Background: Closed-loop insulin delivery is an emerging treatment for type 1 diabetes (T1D) evaluated clinically and using computer simulations during pre-clinical testing. Efforts to make closed-loop systems available to people with type 2 diabetes (T2D) calls for the development of a new type of simulators to accommodate differences between T1D and T2D. Presented here is the development of a model of posthepatic endogenous insulin concentration, a component omitted in T1D simulators but key for simulating T2D physiology. Methods: We evaluated six competing models to describe the time course of endogenous insulin concentration as a function of the plasma glucose concentration and time. The models were fitted to data collected in insulin-naive subjects with T2D who underwent two 24-h visits and were treated, in a random order, by either closed-loop insulin delivery or glucose-lowering oral agents. The model parameters were estimated using a Bayesian approach, as implemented in the WinBUGS software. Model selection criteria were used to identify the best model describing our clinical data. Results: The selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates. Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05). The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05). Conclusions: A model to describe endogenous insulin concentration in T2D including components of posthepatic glucose-dependent and glucose-independent insulin secretion was identified and validated. The model is suitable to be incorporated in a simulation environment for evaluating closed-loop insulin delivery in T2D.This work was funded in part by a National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre Grant, Diabetes UK (BDA07/0003549), and Wellcome Strategic Award (100574/Z/12/Z). The research was conducted with support from Addenbrooke’s Clinical Research Facility (Cambridge, UK). We gratefully acknowledge laboratory support from Angie Watts (University of Cambridge, Cambridge UK), Dr Stephen Luzio and Mr Gareth Dunseath (University of Swansea, Swansea, UK), and Dr Keith Burling (University of Cambridge, UK)

Research on CRO's Dilemma In Sapiens Chain: A Game Theory Method

In recent years, blockchain-based techniques have been widely used in cybersecurity, owing to the decentralization, anonymity, credibility and not be tampered properties of the blockchain. As one of the decentralized framework, Sapiens Chain was proposed to protect cybersecurity by scheduling the computational resources dynamically, which were owned by Computational Resources Owners (CROs). However, when CROs in the same pool attack each other, all CROs will earn less. In this paper, we tackle the problem of prisoner's dilemma from the perspective of CROs. We first define a game that a CRO infiltrates another pool and perform an attack. In such game, the honest CRO can control the payoffs and increase its revenue. By simulating this game, we propose to apply Zero Determinant (ZD) strategy on strategy decision, which can be categorized into cooperation and defecting. Our experimental results demonstrate the effectiveness of the proposed strategy decision method