arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 325 records · Page 18Linked to original sources

arXiv2019

Pulse-width-induced polarization enhancement of optically-pumped N-V electron spin in diamond

The nitrogen-vacancy (N-V) center in diamond is a widely-used platform for quantum information processing and metrology. The electron-spin state of N-V center could be initialized and readout optically, and manipulated by resonate microwave fields. In this work, we analyze the dependence of electron-spin initialization on widths of laser pulses. We build a numerical model to simulate this process and verify the simulation results in experiment. Both simulations and experiments reveal a fact that shorter laser pulses are helpful to the electron-spin polarization. We therefore propose to use extremely-short laser pulses for electron-spin initialization. In this new scheme, the spin-state contrast could be improved about 10% in experiment by using laser pulses as short as 4 ns in width. Furthermore, we provide a mechanism to explain this effect which is due to the occupation time in the meta-stable spin-singlet states of N-V center. Our new scheme is applicable in a broad range of NV-based applications in the future.

quant-ph↗

arXiv2019

A topological charge of black holes

The topological charge of a maximally symmetric black hole naturally arises in holography, which can be viewed as the last charge of the black hole in the sense that it together with all other known charges satisfies the holographic Gibbs-Duhem-like relation as a completeness relation. It is then observed that this topological charge, which we have missed before, fits perfectly into the black hole thermodynamics from two different approaches, not only in Einstein's gravity, but also in the general Lovelock-Maxwell theory.

gr-qc↗

arXiv2019

Rethinking Kernel Methods for Node Representation Learning on Graphs

Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which is a related problem to graph representation learning, is still ill-posed and the state-of-the-art methods are heavily based on heuristics. Here, we present a novel theoretical kernel-based framework for node classification that can bridge the gap between these two representation learning problems on graphs. Our approach is motivated by graph kernel methodology but extended to learn the node representations capturing the structural information in a graph. We theoretically show that our formulation is as powerful as any positive semidefinite kernels. To efficiently learn the kernel, we propose a novel mechanism for node feature aggregation and a data-driven similarity metric employed during the training phase. More importantly, our framework is flexible and complementary to other graph-based deep learning models, e.g., Graph Convolutional Networks (GCNs). We empirically evaluate our approach on a number of standard node classification benchmarks, and demonstrate that our model sets the new state of the art.

cs.LG↗

arXiv2019

Vortex Lattice in a Rotating Holographic Superfluid

By utilizing the AdS/CFT correspondence, we explore the dynamics of strongly coupled superfluid vortices in a disk with constant angular velocity. Each vortex in the vortex lattice is quantized with vorticity one from the direct inspection of their phases. As the angular velocity of the disk is greater than a critical value, the first vortex will be excited as expected from theoretical predictions. The subsequent two and more vortices are also generated by increasingly rotating the disk, resulting in the remarkable step transitions for the angular velocity to excite each individual vortex. When the vortex number is large enough, the density of vortices is found to be linearly proportional to the angular velocity, which matches the Feynman relation very well.

hep-th↗

arXiv2019

Dynamically Polarizing Spin Register of NV Centers in Diamond using Chopped Laser Pulses

Nuclear spins nearby nitrogen-vacancy (NV) centers in diamond are excellent quantum memory for quantum computing and quantum sensing, but are difficult to be initialized due to their weak interactions with the environment. Here we propose and demonstrate a magnetic-field-independent, deterministic and highly efficient polarization scheme by introducing chopped laser pulses into the double-resonance initialization method. With this method, we demonstrate initialization of single-nuclear-spin approaching $98.1\%$ and a $^{14}N$-$^{13}C$ double-nuclear-spin system approaching $96.8\%$ at room temperature. The initialization is limited by a nuclear-spin depolarization effect due to chopped laser excitation. Our approach could be extended to NV systems with more nuclear spins and would be a useful tool in future applications such as nano-MRI and single-cell NMR.

quant-ph↗

arXiv2019

Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach

We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and transient liquidity costs, as well as multiple endogenous state variables, namely the portfolio value and the asset prices subject to permanent market impacts. To do so, we improve the accuracy of the control randomization approach in the case of discrete controls, and propose a global iteration procedure to further improve the allocation estimates. We validate our numerical method by solving a realistic cash-and-stock portfolio with a power-law liquidity model. We quantify the certainty equivalent losses associated with ignoring liquidity effects, and illustrate how our dynamic allocation protects the investor's capital under illiquid market conditions. Lastly, we analyze, under different liquidity conditions, the sensitivities of certainty equivalent returns and optimal allocations with respect to trading volume, stock price volatility, initial investment amount, risk-aversion level and investment horizon.

q-fin.PM↗

arXiv2019

Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method

In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investment goal. This strategy favorably shapes the entire probability distribution of returns, as it simultaneously seeks a desired expected return, cuts off downside risk and implicitly caps volatility and higher moments. To illustrate the effectiveness of this investment strategy, we study a multiperiod portfolio optimization problem with transaction costs and develop a two-stage regression approach that improves the classical least squares Monte Carlo (LSMC) algorithm when dealing with difficult payoffs, such as highly concave, abruptly changing or discontinuous functions. Our numerical results show substantial improvements over the classical LSMC algorithm for both the constant relative risk-aversion (CRRA) utility approach and the proposed skewed target range strategy (STRS). Our numerical results illustrate the ability of the STRS to contain the portfolio value within the targeted range. When compared with the CRRA utility approach, the STRS achieves a similar mean-variance efficient frontier while delivering a better downside risk-return trade-off.

q-fin.PM↗

arXiv2019

Black and gray solitons in holographic superfluids at zero temperature

We construct gray soliton configurations, which move at constant speeds, in holographic superfluids for the first time. Since there should be no dissipation for a moving soliton to exist, we use the simplest holographic superfluid model at zero temperature, considering both the standard and alternative quantizations. For comparison purpose, we first investigate black solitons in the zero temperature holographic superfluids, which are static configurations. Then we focus on the numerical construction of gray solitons under both quantizations, which interpolate between the (static) black solitons and sound waves (moving at the speed of sound). Interestingly, under the standard quantization, a peculiar oscillation of the soliton configurations is observed, very much resembling the Friedel oscillation in fermionic superfluids at the BCS regime. Some implications and other aspects of the soliton configurations are also discussed.

hep-th↗

arXiv2019

Raman spectroscopy of sodium chloride under high-pressure and high-temperature

The high-pressure and high-temperature behaviors of sodium chloride (NaCl) have the fundamental and application significance to the high pressure physics and chemistry. To explore the reactivity of NaCl at high pressure and high temperature, we compressed the NaCl samples to pressures up to 85 GPa and heated them to a temperature of 1800 by using the diamond anvil cell and the double-sided laser heating technology. Our investigation of Raman spectroscopy shows that NaCl decomposes to NaCl3 with orthorhombic Pnma structure plus possible Na3Cl with tetragonal P4/mmm structure above 31 GPa. It shows that the competition of the unusual Cl-Cl covalent bond and Na-Na metallic bond with the Na-Cl ionic bond at high pressure and high temperature may prefer the occurrence of the NaCl3 and Na3Cl combination. We speculate that the large temperature gradient in the NaCl sample layers in the diamond anvil cell experiments, which indicate a non-equilibrium state, is critical to the decomposition reaction of NaCl observed in this study. The catalytic effect of the used laser absorbing materials of transition metal oxides could be another factor to promote the chemical reaction of NaCl. The results of this investigation provide a new pathway to investigate the distinctive non-1:1 stoichiometric alkali metal-halogen compounds that may have exotic properties.

cond-mat.mtrl-sci↗

arXiv2019

Acoustic black holes in curved spacetime and emergence of analogue Minkowski metric

Gravity is not only able to be mimicked in flat spacetimes, but also in curved spacetimes. We study analogue gravity models in curved spacetime by considering the relativistic Gross-Pitaevskii theory and Yang-Mills theory in the fixed background spacetime geometry. The results show that acoustic metrics can be emergent from curved spacetimes yielding a Hadamard product of a real metric-tensor and an analogue metric-tensor. Taking \emph{quantum vortices} as \emph{test particles}, we evaluate their released energy ratio during the "gravitational binding". The $2+1$-dimensional flat Minkowski metric is derived from the $3+1$-dimensional Anti-de Sitter space by considering perturbations of the Yang-Mills field, which implies that Minkowski spacetime can be also simulated and the derivations presented here have some deep connections with the holographic principle.

hep-th↗

arXiv2019

Raman spectra of Nontraditional Compound KO4 and Novel Chemical Reaction of KCl-O2

The non-traditional compound KO4 still existing at ambient conditions was synthesized by the reaction of KCl-O2 under high pressure and high temperature (HPHT) using the diamond anvil cell and the laser heating technology. The KCl-O2 sample was heated at 37 GPa (1800 K) and then the products were measured by Raman technology at room temperature. The acquired 1386 cm-1 characteristic vibration band of KO4 reflects the O-O pair with fractional negative charge. The other reaction products include another non-traditional compound KCl3, a bit KClO4 and intermediate product Cl2. The KCl3 can distinguish 11 Raman peaks and decompose into KCl and Cl2 below 10 GPa during decompression. These novel products show that high pressure promotes oxygen and chlorine forming unconventional pair-anions and polyanions, the novel chemical reaction provides new perspectives for developing new batteries materials and studying batteries reactions.

cond-mat.mtrl-sci↗

arXiv2018

Sonic velocity in holographic fluids and its applications

Gravity/fluid correspondence becomes an important tool to investigate the strongly correlated fluids. We carefully investigate the holographic fluids at the finite cutoff surface by considering different boundary conditions in the scenario of gravity/fluid correspondence. We find that the sonic velocity of the boundary fluids at the finite cutoff surface is critical to clarify the superficial similarity between bulk viscosity and perturbation of the pressure for the holographic fluid, where we set a special boundary condition at the finite cutoff surface to explicitly express this superficial similarity. Moreover, we further take the sonic velocity into account to investigate a case with more general boundary condition. In this more general case, two parameters in the first order stress tensor of holographic fluid cannot be fixed, one can still extract the information of transport coefficients by considering the sonic velocity seriously.

hep-th↗

arXiv2018

Strong cosmic censorship for the massless charged scalar field in the Reissner-Nordstrom-de Sitter spacetime

It has recently been shown that the strong cosmic censorship conjecture can be violated by the massless neutral scalar field in the nearly extremal Reissner-Nordstrom-de Sitter black hole. However, the formation of such a black hole by gravitational collapse necessitates the presence of the charged sector on top of the Einstein-Maxwell system. Thus we numerically calculate the quasi-normal modes for a massless charged scalar field in the Reissner-Nordstrom-de Sitter spacetime by generalizing the characteristic formulation to the charged case. As a result, the strong cosmic censorship turns out to be recovered by our massless charged scalar field except in the highly extremal limit $Q\rightarrow Q_m$, where the violation still occurs when the scalar field is appropriately charged.

gr-qc↗

arXiv2018

Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization

The least squares Monte Carlo algorithm has become popular for solving portfolio optimization problems. A simple approach is to approximate the value functions on a discrete grid of portfolio weights, then use control regression to generalize the discrete estimates. However, the classical global control regression can be expensive and inaccurate. To overcome this difficulty, we introduce a local control regression technique, combined with adaptive grids. We show that choosing a coarse grid for local regression can produce sufficiently accurate results.

q-fin.PM↗

arXiv2018

Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object. Recent methods for such problems typically train transformation networks to generate future frames conditioned on the structure sequence. Parallel work has shown that short high-quality motions can be generated by spatiotemporal generative networks that leverage temporal knowledge from the training data. We combine the benefits of both approaches and propose a two-stage generation framework where videos are generated from structures and then refined by temporal signals. To model motions more efficiently, we train networks to learn residual motion between the current and future frames, which avoids learning motion-irrelevant details. We conduct extensive experiments on two image-to-video translation tasks: facial expression retargeting and human pose forecasting. Superior results over the state-of-the-art methods on both tasks demonstrate the effectiveness of our approach.

cs.CV↗

arXiv2018

CR-GAN: Learning Complete Representations for Multi-view Generation

Generating multi-view images from a single-view input is an essential yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn "incomplete" representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network. We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning pathways collaborate and compete in a parameter-sharing manner, yielding considerably improved generalization ability to "unseen" dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. The experimental results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from "unseen" inputs in wild conditions.

cs.CV↗

arXiv2018

Collective diffusion and quantum chaos in holography

We define a particular combination of charge and heat currents that is decoupled with the heat current. This `heat-decoupled' (HD) current can be transported by diffusion at long distances, when some thermo-electric conductivities and susceptibilities satisfy a simple condition. Using the diffusion condition together with the Kelvin formula, we show that the HD diffusivity can be same as the charge diffusivity and also the heat diffusivity. We illustrate that such mechanism is implemented in a strongly coupled field theory, which is dual to a Lifshitz gravity with the dynamical critical index z=2. In particular, it is exhibited that both charge and heat diffusivities build the relationship to the quantum chaos. Moreover, we study the HD diffusivity without imposing the diffusion condition. In some homogeneous holographic lattices, it is found that the diffusivity/chaos relation holds independently of any parameters, including the strength of momentum relaxation, chemical potential, or temperature. We also show a counter example of the relation and discuss its limited universality.

hep-th↗

arXiv2018

Holographic Turbulence in Einstein-Gauss-Bonnet Gravity at Large $D$

We study the holographic hydrodynamics in the Einstein-Gauss-Bonnet(EGB) gravity in the framework of the large $D$ expansion. We find that the large $D$ EGB equations can be interpreted as the hydrodynamic equations describing the conformal fluid. These fluid equations are truncated at the second order of the derivative expansion, similar to the Einstein gravity at large $D$. From the analysis of the fluid flows, we find that the fluid equations can be taken as a variant of the compressible version of the non-relativistic Navier-Stokes equations. Particularly, in the limit of small Mach number, these equations could be cast into the form of the incompressible Navier-Stokes equations with redefined Reynolds number and Mach number. By using numerical simulation, we find that the EGB holographic turbulence shares similar qualitative feature as the turbulence from the Einstein gravity, despite the presence of two extra terms in the equations of motion. We analyze the effect of the GB term on the holographic turbulence in detail.

hep-th↗