Numerical Study of the Spin-Flop Transition in Anisotropic Spin-1/2 Antiferromagnets

Magnetization processes of the spin-1/2 antiferromagnetic $XXZ$ model in two and three spatial dimensions are studied using quantum Monte Carlo method based on stochastic series expansions. Recently developed operator-loop algorithm enables us to show a clear evidence of the first-order phase transition in the presence of an external magnetic field. Phase diagrams of closely related systems, hard core bosons with nearest-neighbor repulsions, are also discussed focusing on possibilities of phase-separated and supersolid phases.Comment: 4 pages, Revtex version 4, with 4 figures embedded, To appear in Phys. Rev.

Directed geometrical worm algorithm applied to the quantum rotor model

We discuss the implementation of a directed geometrical worm algorithm for the study of quantum link-current models. In this algorithm Monte Carlo updates are made through the biased reptation of a worm through the lattice. A directed algorithm is an algorithm where, during the construction of the worm, the probability for erasing the immediately preceding part of the worm, when adding a new part,is minimal. We introduce a simple numerical procedure for minimizing this probability. The procedure only depends on appropriately defined local probabilities and should be generally applicable. Furthermore we show how correlation functions, C(r,tau) can be straightforwardly obtained from the probability of a worm to reach a site (r,tau) away from its starting point independent of whether or not a directed version of the algorithm is used. Detailed analytical proofs of the validity of the Monte Carlo algorithms are presented for both the directed and un-directed geometrical worm algorithms. Results for auto-correlation times and Green functions are presented for the quantum rotor model.Comment: 11 pages, 9 figures, v2 : Additional results and data calculated at an incorrect chemical potential replaced. Conclusions unchange

Destruction of diagonal and off-diagonal long range order by disorder in two-dimensional hard core boson systems

We use quantum Monte Carlo simulations to study the effect of disorder, in the form of a disordered chemical potential, on the phase diagram of the hard core bosonic Hubbard model in two dimensions. We find numerical evidence that in two dimensions, no matter how weak the disorder, it will always destroy the long range density wave order (checkerboard solid) present at half filling and strong nearest neighbor repulsion and replace it with a bose glass phase. We study the properties of this glassy phase including the superfluid density, energy gaps and the full Green's function. We also study the possibility of other localized phases at weak nearest neighbor repulsion, i.e. Anderson localization. We find that such a phase does not truly exist: The disorder must exceed a threshold before the bosons (at weak nn repulsion) are localized. The phase diagram for hard core bosons with disorder cannot be obtained easily from the soft core phase diagram discussed in the literature.Comment: 7 pages, 10 eps figures include

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA