Phases of the infinite U Hubbard model

We apply the density matrix renormalization group (DMRG) to study the phase diagram of the infinite U Hubbard model on 2-, 4-, and 6-leg ladders. Where the results are largely insensitive to the ladder width, we consider the results representative of the 2D square lattice model. We find a fully polarized ferromagnetic Fermi liquid phase when n, the density of electrons per site, is in the range 1>n>n_F ~ 4/5. For n=3/4 we find an unexpected commensurate insulating "checkerboard" phase with coexisting bond density order with 4 sites per unit cell and block spin antiferromagnetic order with 8 sites per unit cell. For 3/4 > n, the wider ladders have unpolarized groundstates, which is suggestive that the same is true in 2D

IN-SYNC. VIII. Primordial Disk Frequencies in NGC 1333, IC 348, and the Orion A Molecular Cloud

In this paper, we address two issues related to primordial disk evolution in three clusters (NGC 1333, IC 348, and Orion A) observed by the INfrared Spectra of Young Nebulous Clusters (IN-SYNC) project. First, in each cluster, averaged over the spread of age, we investigate how disk lifetime is dependent on stellar mass. The general relation in IC 348 and Orion A is that primordial disks around intermediate mass stars (2--5$M_{\odot}$) evolve faster than those around loss mass stars (0.1--1$M_{\odot}$), which is consistent with previous results. However, considering only low mass stars, we do not find a significant dependence of disk frequency on stellar mass. These results can help to better constrain theories on gas giant planet formation timescales. Secondly, in the Orion A molecular cloud, in the mass range of 0.35--0.7$M_{\odot}$, we provide the most robust evidence to date for disk evolution within a single cluster exhibiting modest age spread. By using surface gravity as an age indicator and employing 4.5 $\mu m$ excess as a primordial disk diagnostic, we observe a trend of decreasing disk frequency for older stars. The detection of intra-cluster disk evolution in NGC 1333 and IC 348 is tentative, since the slight decrease of disk frequency for older stars is a less than 1-$\sigma$ effect.Comment: 25 pages, 26 figures; submitted for publication (ApJ

Running mass of the b-quark in QCD and SUSY QCD

The running mass of the b-quark defined in DRbar-scheme is one of the important parameters of SUSY QCD. To find its value it should be related to some known experimental input. In this paper the b-quark running mass defined in nonsupersymmetric QCD is chosen for determination of corresponding parameter in SUSY QCD. The relation between these two quantities is found by considering five-flavor QCD as an effective theory obtained from its supersymmetric extension. A numerical analysis of the calculated two-loop relation and its impact on the MSSM spectrum is discussed. Since for nonsupersymmetric models MSbar-scheme is more natural than DRbar, we also propose a new procedure that allows one to calculate relations between MSbar- and DRbar-parameters. Unphysical epsilon-scalars that give rise to the difference between mentioned schemes are assumed to be heavy and decoupled in the same way as physical degrees of freedom. By means of this method it is possible to ``catch two rabbits'', i.e., decouple heavy particles and turn from DRbar to MSbar, at the same time. Explicit two-loop example of DRbar -> MSbar transition is given in the context of QCD. The advantages and disadvantages of the method are briefly discussed.Comment: 33 pages, 6 figures, 1 table, typos corrected, added references

Loss Guided Activation for Action Recognition in Still Images

One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\% relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.Comment: Accepted to appear in ACCV 201