She is currently working on domain dynamics, 2D ferroelectrics, and nonvolatile memories based on piezoresponse force microscopy. Ni Zhong received her B.S. degree from Shanghai Institute of Ceramics, Chinese Academy of Sciences, and her Ph.D. from NARA Institute of Science and Technology (NAIST), Japan. In 2012, she joined the Key Laboratory of Polar Materials and Devices, Ministry of Education, East China Normal University as an associate professor. She is currently focusing on ferroelectric thin films/2D ferroelectrics/strongly correlated material and novel devices for next-generation computing systems.

Ferroelectric tunnel junctions, in which ferroelectric polarization and quantum tunneling are closely coupled to induce the tunneling electroresistance (TER) effect, have attracted considerable interest due to their potential in non-volatile and low-power consumption memory devices.The ferroelectric size effect, however, has hindered ferroelectric tunnel junctions from exhibiting robust TER effect. Here, our study proposes doping engineering in a two-dimensional in-plane ferroelectric semiconductor as an effective strategy to design a two-dimensional ferroelectric tunnel junction composed of homostructural p-type semiconductor/ferroelectric/n-type semiconductor.Since the in-plane polarization persists in the monolayer ferroelectric barrier, the vertical thickness of two-dimensional ferroelectric tunnel junction can be as thin as monolayer. We show that the monolayer In:SnSe/SnSe/Sb:SnSe junction provides an embodiment of this strategy. Combining density functional theory calculations with non-equilibrium Green's function formalism, we investigate the electron transport properties of In:SnSe/SnSe/Sb:SnSe and reveal a giant TER effect of 1460%. The dynamical modulation of both barrier width and barrier height during the ferroelectric switching are responsible for this giant TER effect. These findings provide an important insight towards the understanding of the quantum behaviors of electrons in materials at the twodimensional limit, and enable new possibilities for next-generation non-volatile memory devices based on flexible two-dimensional lateral ferroelectric tunnel junctions. * Electronic address:

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We consider the problem of makespan minimization: i.e., scheduling jobs on machines to minimize the maximum load. For the deterministic case, good approximations are known even when the machines are unrelated. However, the problem is not well-understood when there is uncertainty in the job sizes. In our setting the job sizes are stochastic, i.e., the size of a job j on machine i is a random variable X ij , whose distribution is known. (Sizes of different jobs are independent of each other.) The goal is to find a fixed assignment of jobs to machines, to minimize the expected makespan-i.e., the expected value of the maximum load over the m machines. For the identical machines special case when the size of a job is the same across all machines, a constant-factor approximation algorithm has long been known. However, the problem has remained open even for the next-harder related machines case. Our main result is a constant-factor approximation for the most general case of unrelated machines. The main technical challenge we overcome is obtaining an efficiently computable lower bound for the optimal solution. We give an exponential-sized LP that we argue gives a strong lower bound. Then we show how to round any fractional solution to satisfy only a small subset of the constraints, which are enough to bound the expected makespan of our solution. We then consider two generalizations. The first is the budgeted makespan minimization problem, where the goal is to minimize the makespan subject to scheduling any subset of jobs whose reward is at least some target reward R. We extend our above result to a constant-factor approximation here using polyhedral properties of the bipartite matching polytope. The second problem is the q-norm minimization problem, where we want to minimize the expected q -norm of the load vectors. Here we give an O(q/ log q)-approximation algorithm using a reduction to the deterministic q-norm problem with side constraints.

Lung cancer patients treated with tyrosine kinase inhibitors (TKIs) often develop resistance. More effective and safe therapeutic agents are urgently needed to overcome TKI resistance. Here, we propose a medical genetics–based approach to identify indications for over 1,000 US Food and Drug Administration–approved (FDA-approved) drugs with high accuracy. We identified a potentially novel indication for an approved antidepressant drug, sertraline, for the treatment of non–small cell lung cancer (NSCLC). We found that sertraline inhibits the viability of NSCLC cells and shows a synergy with erlotinib. Specifically, the cotreatment of sertraline and erlotinib effectively promotes autophagic flux in cells, as indicated by LC3-II accumulation and autolysosome formation. Mechanistic studies further reveal that dual treatment of sertraline and erlotinib reciprocally regulates the AMPK/mTOR pathway in NSCLC cells. The blockade of AMPK activation decreases the anticancer efficacy of either sertraline alone or the combination. Efficacy of this combination regimen is decreased by pharmacological inhibition of autophagy or genetic knockdown of ATG5 or Beclin 1. Importantly, our results suggest that sertraline and erlotinib combination suppress tumor growth and prolong mouse survival in an orthotopic NSCLC mouse model (P = 0.0005). In summary, our medical genetics–based approach facilitates discovery of new anticancer indications for FDA-approved drugs for the treatment of NSCLC.