Bond distortion effects and electric orders in spiral multiferroic magnets

We study in this paper bond distortion effect on electric polarization in spiral multiferroic magnets based on cluster and chain models. The bond distortion break inversion symmetry and modify the $d$-$p$ hybridization. Consequently, it will affect electric polarization which can be divided into spin-current part and lattice-mediated part. The spin-current polarization can be written in terms of $\vec{e}_{i,j}\times(\vec{e}_{i}\times\vec{e}_{j})$ and the lattice-mediated polarization exists only when the M-O-M bond is distorted. The electric polarization for three-atom M-O-M and four-atom M-O$_{2}$-M clusters is calculated. We also study possible electric ordering in three kinds of chains made of different clusters. We apply our theory to multiferroics cuprates and find that the results are in agreement with experimental observations.Comment: 14 pages, 11 figure

Mitigating sign problem by automatic differentiation

As an intrinsically-unbiased method, quantum Monte Carlo (QMC) is of unique importance in simulating interacting quantum systems. Unfortunately, QMC often suffers from the notorious sign problem. Although generically curing sign problem is shown to be hard (NP-hard), sign problem of a given quantum model may be mitigated (sometimes even cured) by finding better choices of simulation scheme. A universal framework in identifying optimal QMC schemes has been desired. Here, we propose a general framework using automatic differentiation (AD) to automatically search for the best continuously-parameterized QMC scheme, which we call "automatic differentiable sign mitigation" (ADSM). We further apply the ADSM framework to the honeycomb lattice Hubbard model with Rashba spin-orbit coupling and demonstrate ADSM's effectiveness in mitigating its sign problem. For the model under study, ADSM leads a significant power-law acceleration in computation time (the computation time is reduced from $M$ to the order of $M^{\nu}$ with $\nu\approx 2/3$).Comment: 4.1 pages + supplemental materials, 4 figure

Asymptotic sign free in interacting fermion models

As an intrinsically-unbiased approach, quantum Monte Carlo (QMC) is of vital importance in understanding correlated phases of matter. Unfortunately, it often suffers notorious sign problem when simulating interacting fermion models. Here, we show for the first time that there exist interacting fermion models whose sign problem becomes less severe for larger system sizes and eventually disappears in the thermodynamic limit, which we dub as "asymptotic sign free". We demonstrate asymptotically-free sign in determinant QMC for various interacting models. Moreover, based on renormalization-group-like ideas we propose a heuristic understanding of the feature of asymptotic sign free. We believe that asymptotic sign free behavior could shed new lights to deepening our understanding of sign problem. More importantly, it can provide a promising way to decipher intriguing physics in correlated models which were conventionally thought not accessible by QMC.Comment: 4.5 pages plus supplemental material, 5 figure

Automatic Differentiable Monte Carlo: Theory and Application

Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored. Here we present the general theory enabling infinite-order automatic differentiation on expectations computed by Monte Carlo with unnormalized probability distributions, which we call "automatic differentiable Monte Carlo" (ADMC). By implementing ADMC algorithms on computational graphs, one can also leverage state-of-the-art machine learning frameworks and techniques to traditional Monte Carlo applications in statistics and physics. We illustrate the versatility of ADMC by showing some applications: fast search of phase transitions and accurately finding ground states of interacting many-body models in two dimensions. ADMC paves a promising way to innovate Monte Carlo in various aspects to achieve higher accuracy and efficiency, e.g. easing or solving the sign problem of quantum many-body models through ADMC.Comment: 11.5 pages + supplemental materials, 4 figure

NGF-Induced Axon Growth Is Mediated by Localized Inactivation of GSK-3β and Functions of the Microtubule Plus End Binding Protein APC

Little is known about how nerve growth factor (NGF) signaling controls the regulated assembly of microtubules that underlies axon growth. Here we demonstrate that a tightly regulated and localized activation of phosphatidylinositol 3-kinase (PI3K) at the growth cone is essential for rapid axon growth induced by NGF. This spatially activated PI3K signaling is conveyed downstream through a localized inactivation of glycogen synthase kinase 3β (GSK-3β). These two spatially coupled kinases control axon growth via regulation of a microtubule plus end binding protein, adenomatous polyposis coli (APC). Our results demonstrate that NGF signals are transduced to the axon cytoskeleton via activation of a conserved cell polarity signaling pathway

Ground state fidelity in bond-alternative Ising chains with Dzyaloshinskii-Moriya interactions

A systematic analysis is performed for quantum phase transitions in a bond-alternative one-dimensional Ising model with a Dzyaloshinskii-Moriya (DM) interaction by using the fidelity of ground state wave functions based on the infinite matrix product states algorithm. For an antiferromagnetic phase, the fidelity per lattice site exhibits a bifurcation, which shows spontaneous symmetry breaking in the system. A critical DM interaction is inversely proportional to an alternating exchange coupling strength for a quantum phase transition. Further, a finite-entanglement scaling of von Neumann entropy with respect to truncation dimensions gives a central charge c = 0.5 at the critical point.Comment: 6 pages, 4 figure

Global Optimization of Minority Game by Smart Agents

We propose a new model of minority game with so-called smart agents such that the standard deviation and the total loss in this model reach the theoretical minimum values in the limit of long time. The smart agents use trail and error method to make a choice but bring global optimization to the system, which suggests that the economic systems may have the ability to self-organize into a highly optimized state by agents who are forced to make decisions based on inductive thinking for their limited knowledge and capabilities. When other kinds of agents are also present, the experimental results and analyses show that the smart agent can gain profits from producers and are much more competent than the noise traders and conventional agents in original minority game.Comment: 5 pages, 5 figure

Bis[N-(2-furylmeth­yl)ethane-1,2-di­amine]­bis­(perchlorato)copper(II)

In the title complex, [Cu(ClO4)2(C7H12N2O)2], the Cu(II) ion lies on a crystallographic inversion centre. The coordination sphere around Cu(II) ion can be described as tetragonally distorted octa­hedral with two perchlorate O atoms occupying the apical positions and four N atoms from two N 1-(2-furyl­methyl)ethane-1,2-diamine ligands in the basal plane