Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality     

Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the Psi Sigma Neural Network (PSI) and the Gene Expression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USD exchange rate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naïve strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models.     

The effect of two types of C-S-H on the elasticity of cement-based materials: Results from nanoindentation and micromechanical modeling

It has long been recognized, in cement chemistry, that two types of calcium-silicate-hydrate (C-S-H) exist in cement-based materials, but less is known about how the two types of C-S-H affect the mechanical properties. By means of nanoindentation tests on nondegraded and calcium leached cement paste, the paper confirms the existence of two types of C-S-H, and investigates the distinct role played by the two phases on the elastic properties of cement-based materials. It is found that (1) high-density C-S-H are mechanically less affected by calcium leaching than low density C-S-H, and (2) the volume fractions occupied by the two phases in the C-S-H matrix are not affected by calcium leaching. The nanoindentation results also provide quantitative evidence, suggesting that the elastic properties of the C-S-H phase are intrinsic material properties that do not depend on mix proportions of cement-based materials. The material properties and volume fractions are used in a novel two-step homogenization model, that predicts the macroscopic elastic properties of cement pastes with high accuracy. Combined with advanced physical chemistry models that allow, for a given w/c ratio, determination of the volume fractions of the two types of C-S-H, the model can be applied to any cement paste, with or without Portlandite, Clinker, and so on. In particular, from an application of the model to decalcified cement pastes, it is shown that that the decalcification of the C-S-H phase is the primary source of the macroscopic elastic modulus degradation, that dominates over the effect of the dissolution of Portlandite in cement-based material systems.     

Crack and flaw identification in elastodynamics using Kalman filter techniques

Filter-driven optimization based on the extended Kalman filter concept is used here for the numerical solution of crack and flaw identification problems in elastodynamics. The mechanical modeling of the studied two-dimensional problem, which includes the effect of unilateral contact along the sides of the crack, is done with the help of the boundary element method. The effect of various dynamical test loads and the applicability of this method for crack and defect identification in disks are investigated. The work has been supported by the German Research Foundation (DFG). Partial support has been provided by a Greek-German Research Cooperation Grant (IKYDA2001). This support is greatfully acknowledged.     

Defect identification in 3-D elastostatics using a genetic algorithm

An inverse problem in engineering mechanics is considered where the position and the geometry of three-dimensional, ellipsoidal defects are identified by using measurements of the mechanical response under static loading on the external surface of the structure. The problem is solved by appropriate combination of genetic optimization (GO) and boundary element method (BEM) and following previously published two-dimensional problems. The three-dimensional case presents some additional difficulties. Furthermore, the function of several genetic operators and the effect of the parameters of genetic optimization on the efficiency of the solution has been numerically examined.     

A heuristic algorithm for the just-in-time single machine scheduling problem with setups: a comparison with simulated annealing

This paper addresses the single machine early/tardy problem with unrestricted common due date and sequence-dependent setup times. Two algorithms are introduced to reach near-optimum solutions: the SAPT, a heuristic tailored for the problem, and a simulated annealing (SA) algorithm. It will be shown that SA provides solutions with slightly better quality; however, SAPT requires much less computational time. SAPT-SA is a hybrid heuristic that combines both approaches to obtain high quality solutions with low computational cost. Solutions provided by the three algorithms were compared to optimal solutions for problems with up to 25 jobs and to each other for larger problems.     

手动式电磁定位及图像导航的短径癌症放射治疗系统

提出一个用于高剂量短径癌症放射治疗的手动式电磁定们及图像导航系统，该系统主要由4个模块组成：预处理、术前规划、术内导航和术后计划，它们分别对应高剂量短径癌症放射治疗的4个流程：在术前规划阶段，用户可交互地设计探针放置位置以及三维重建病灶轮廓；在手术进行过程中，六自由度的电磁式定位技术实时而精确地跟踪探针位置，高质量的可视化技术指引医生完成手术探针放置任务；手术探针全部放置完毕后，术后计划模板计算剂量分布并利用后装机设备进行放射治疗。初步实验表明，计算机辅助的探针定位比手工定位精度提高2-3倍，手术时间减少5倍以上，同时也减轻了病人所承受的放射剂量。     

The Gestational Effects of Maternal Bone Marker Molecules on Fetal Growth,Metabolism and Long-Term Metabolic Health: A Systematic Review

Fetal exposure in adverse environmental factors during intrauterine life can lead to various biological adjustments, affecting not only in utero development of the conceptus, but also its later metabolic and endocrine wellbeing. During human gestation, maternal bone turnover increases, as reflected by molecules involved in bone metabolism, such as vitamin D, osteocalcin, sclerostin, sRANKL, and osteoprotegerin; however, recent studies support their emerging role in endocrine functions and glucose homeostasis regulation. Herein, we sought to systematically review current knowledge on the effects of aforementioned maternal bone biomarkers during pregnancy on fetal intrauterine growth and metabolism, neonatal anthropometric measures at birth, as well as on future endocrine and metabolic wellbeing of the offspring. A growing body of literature converges on the view that maternal bone turnover is likely implicated in fetal growth, and at least to some extent, in neonatal and childhood body composition and metabolic wellbeing. Maternal sclerostin and sRANKL are positively linked with fetal abdominal circumference and subcutaneous fat deposition, contributing to greater birthweights. Vitamin D deficiency correlates with lower birthweights, while research is still needed on intrauterine fetal metabolism, as well as on vitamin D dosing supplementation during pregnancy, to diminish the risks of low birthweight or SGA neonates in high-risk populations.     

Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens.