A Z2_2 spin-orbital liquid state in the square lattice Kugel-Khomskii model

We argue for the existence of a liquid ground state in a class of square lattice models of orbitally degenerate insulators. Starting with the SU(4) symmetric Kugel-Khomskii model, we utilize a Majorana Fermion representation of spin-orbital operators to access novel phases. Variational wavefunctions of candidate liquid phases are thus obtained, whose properties are evaluated using Variational Monte Carlo. These states are disordered, and are found to have excellent energetics and ground state overlap ($>40$) when compared with exact diagonalization on 16 site clusters. We conclude that these are spin-orbital liquid ground states with emergent nodal fermions and Z$_2$ gauge fields. Connections to spin 3/2 cold atom systems and properties in the absence of SU(4) symmetry are briefly discussed.Comment: 9 pages, 4 figures, 3 tables, published versio

Noise Correlations in one-dimensional systems of ultra-cold fermions

Time of flight images reflect the momentum distribution of the atoms in the trap, but the spatial noise in the image holds information on more subtle correlations. Using Bosonization, we study such noise correlations in generic one dimensional systems of ultra cold fermions. Specifically, we show how pairing as well as spin and charge density wave correlations may be identified and extracted from the time of flight images. These incipient orders manifest themselves as power law singularities in the noise correlations, that depend on the Luttinger parameters, which suggests a general experimental technique to obtain them.Comment: 5 pages, 3 figures. Added discussion on the visibility of noise correlation features for realistic condition

Dynamic projection on Feshbach molecules: a probe of pairing and phase fluctuations

We describe and justify a simple model for the dynamics associated with rapid sweeps across a Feshbach resonance, from the atomic to the molecular side, in an ultra cold Fermi system. The model allows us to relate the observed molecule momentum distribution, including its dependence on the sweep rate, to equilibrium properties of the initial state. For initial state near resonance, we find that phase fluctuations sharply reduce the observed condensate fraction. Moreover, for very fast sweeps and low temperatures, we predict a surprising nonmonotonic dependence of the molecule condensate fraction on detuning, that is a direct signature of quantum phase fluctuations. The dependence of the total molecule number on sweep rate is found to be a sensitive probe of pairing in the initial state, whether condensed or not. Hence it can be utilized to establish the presence of a phase fluctuation induced `psuedogap' phase in these systems.Comment: Added reference

GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

Facial landmarks constitute the most compressed representation of faces and are known to preserve information such as pose, gender and facial structure present in the faces. Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks. In contrast, in this work, an attempt is made to tackle the inverse problem of synthesizing faces from their respective landmarks. The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces. Though the problem is particularly challenging due to its ill-posed nature, we believe that successful synthesis will enable several applications such as boosting performance of high-level face related tasks using landmark points and performing dataset augmentation. To this end, a novel face-synthesis method known as Gender Preserving Generative Adversarial Network (GP-GAN) that is guided by adversarial loss, perceptual loss and a gender preserving loss is presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN that leverages advantages of U-Net and DenseNet architectures. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International Conference on Pattern Recognition (ICPR2018

High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks

Synthesizing face sketches from real photos and its inverse have many applications. However, photo/sketch synthesis remains a challenging problem due to the fact that photo and sketch have different characteristics. In this work, we consider this task as an image-to-image translation problem and explore the recently popular generative models (GANs) to generate high-quality realistic photos from sketches and sketches from photos. Recent GAN-based methods have shown promising results on image-to-image translation problems and photo-to-sketch synthesis in particular, however, they are known to have limited abilities in generating high-resolution realistic images. To this end, we propose a novel synthesis framework called Photo-Sketch Synthesis using Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution to high resolution images in an adversarial way. The hidden layers of the generator are supervised to first generate lower resolution images followed by implicit refinement in the network to generate higher resolution images. Furthermore, since photo-sketch synthesis is a coupled/paired translation problem, we leverage the pair information using CycleGAN framework. Both Image Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to demonstrate the superior performance of our framework in comparison to existing state-of-the-art solutions. Code available at: https://github.com/lidan1/PhotoSketchMAN.Comment: Accepted by 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)(Oral