Synthetic Biology: A Bridge between Artificial and Natural Cells.

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications

Software for Implementing the Sequential Elimination of Level Combinations Algorithm

Genetic algorithms (GAs) are a popular technology to search for an optimum in a large search space. Using new concepts of forbidden array and weighted mutation, Mandal, Wu, and Johnson (2006) used elements of GAs to introduce a new global optimization technique called sequential elimination of level combinations (SELC), that efficiently finds optimums. A SAS macro, and MATLAB and R functions are developed to implement the SELC algorithm.

Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

Sympathy and Punishment: Evolution of Cooperation in Public Goods Game

An important way to maintain human cooperation is punishing defection. However, since punishment is costly, how can it arise and evolve given that individuals who contribute but do not punish fare better than the punishers? This leads to a violation of causality, since the evolution of punishment is prior to the one of cooperation behaviour in evolutionary dynamics. Our public goods game computer simulations based on generalized Moran Process, show that, if there exists a \'behaviour-based sympathy\' that compensates those who punish at a personal cost, the way for the emergence and establishment of punishing behaviour is paved. In this way, the causality violation dissipates. Among humans sympathy can be expressed in many ways such as care, praise, solace, ethical support, admiration, and sometimes even adoration; in our computer simulations, we use a small amount of transfer payment to express \'behaviour-based sympathy\'. Our conclusions indicate that, there exists co-evolution of sympathy, punishment and cooperation. According to classical philosophy literature, sympathy is a key factor in morality and justice is embodied by punishment; in modern societies, both the moral norms and the judicial system, the representations of sympathy and punishment, play an essential role in stable social cooperation.Public Goods Game, Cooperation, Social Dilemma, Co-Evolution, Sympathy, Punishment

Generalized variational inequalities and generalized quasi-variational inequalities

AbstractA very general minimax inequality is first established. Three generalized variational inequalities are then derived, which improve those obtained by Tan and Browder. By applying a fixed point theorem of Himmelberg, two generalized quasi-variational inequalities are also proved, one of which generalizes those of Shih-Tan to the non-compact case with much weaker hypotheses and in a more general setting