Collapse and revival oscillations as a probe for the tunneling amplitude in an ultra-cold Bose gas

We present a theoretical study of the quantum corrections to the revival time due to finite tunneling in the collapse and revival of matter wave interference after a quantum quench. We study hard-core bosons in a superlattice potential and the Bose-Hubbard model by means of exact numerical approaches and mean-field theory. We consider systems without and with a trapping potential present. We show that the quantum corrections to the revival time can be used to accurately determine the value of the hopping parameter in experiments with ultracold bosons in optical lattices.Comment: 10 pages, 12 figures, typos in section 3A correcte

Spontaneous Breaking of Rotational Symmetry in Rotating Solitons - a Toy Model of Excited Nucleons with High Angular Momentum

We study the phenomenon of spontaneous breaking of rotational symmetry (SBRS) in the rotating solutions of two types of baby Skyrme models. In the first the domain is a two-sphere and in the other, the Skyrmions are confined to the interior of a unit disk. Numerical full-field results show that when the angular momentum of the Skyrmions increases above a certain critical value, the rotational symmetry of the solutions is broken and the minimal energy configurations become less symmetric. We propose a possible mechanism as to why SBRS is present in the rotating solutions of these models, while it is not observed in the `usual' baby Skyrme model. Our results might be relevant for a qualitative understanding of the non-spherical deformation of excited nucleons with high orbital angular momentum.Comment: RevTex, 9 pages, 9 figures. Added conten

Hexagonal Structure of Baby Skyrmion Lattices

We study the zero-temperature crystalline structure of baby Skyrmions by applying a full-field numerical minimization algorithm to baby Skyrmions placed inside different parallelogramic unit-cells and imposing periodic boundary conditions. We find that within this setup, the minimal energy is obtained for the hexagonal lattice, and that in the resulting configuration the Skyrmion splits into quarter-Skyrmions. In particular, we find that the energy in the hexagonal case is lower than the one obtained on the well-studied rectangular lattice, in which splitting into half-Skyrmions is observed.Comment: RevTeX, 7 pages, 6 figure

DeepMimic: Mentor-Student Unlabeled Data Based Training

In this paper, we present a deep neural network (DNN) training approach called the "DeepMimic" training method. Enormous amounts of data are available nowadays for training usage. Yet, only a tiny portion of these data is manually labeled, whereas almost all of the data are unlabeled. The training approach presented utilizes, in a most simplified manner, the unlabeled data to the fullest, in order to achieve remarkable (classification) results. Our DeepMimic method uses a small portion of labeled data and a large amount of unlabeled data for the training process, as expected in a real-world scenario. It consists of a mentor model and a student model. Employing a mentor model trained on a small portion of the labeled data and then feeding it only with unlabeled data, we show how to obtain a (simplified) student model that reaches the same accuracy and loss as the mentor model, on the same test set, without using any of the original data labels in the training of the student model. Our experiments demonstrate that even on challenging classification tasks the student network architecture can be simplified significantly with a minor influence on the performance, i.e., we need not even know the original network architecture of the mentor. In addition, the time required for training the student model to reach the mentor's performance level is shorter, as a result of a simplified architecture and more available data. The proposed method highlights the disadvantages of regular supervised training and demonstrates the benefits of a less traditional training approach

Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data

As state-of-the-art deep neural networks are deployed at the core of more advanced Al-based products and services, the incentive for copying them (i.e., their intellectual properties) by rival adversaries is expected to increase considerably over time. The best way to extract or steal knowledge from such networks is by querying them using a large dataset of random samples and recording their output, followed by training a student network to mimic these outputs, without making any assumption about the original networks. The most effective way to protect against such a mimicking attack is to provide only the classification result, without confidence values associated with the softmax layer.In this paper, we present a novel method for generating composite images for attacking a mentor neural network using a student model. Our method assumes no information regarding the mentor's training dataset, architecture, or weights. Further assuming no information regarding the mentor's softmax output values, our method successfully mimics the given neural network and steals all of its knowledge. We also demonstrate that our student network (which copies the mentor) is impervious to watermarking protection methods, and thus would not be detected as a stolen model.Our results imply, essentially, that all current neural networks are vulnerable to mimicking attacks, even if they do not divulge anything but the most basic required output, and that the student model which mimics them cannot be easily detected and singled out as a stolen copy using currently available techniques

A T-odd observable sensitive to CP violating phases in squark decay

We present a new observable sensitive to a certain combination of CP violating phases in supersymmetric extensions of the Standard Model, viz. a triple product of momenta in the cascade decay of a heavy squark via an on-shell neutralino and off-shell slepton. We investigate the regions of parameter space in which the signal is strong enough to be detectable at the LHC with $\sim \bigl(10^2-10^3\bigr)/\sin^2(2\Delta\phi)$ identified events, where $\Delta\phi$ is a certain combination of phases in the MSSM presented in the text.Comment: Several references adde

Energetic cost of superadiabatic quantum computation

We discuss the energetic cost of superadiabatic models of quantum computation. Specifically, we investigate the energy-time complementarity in general transitionless controlled evolutions and in shortcuts to the adiabatic quantum search over an unstructured list. We show that the additional energy resources required by superadiabaticity for arbitrary controlled evolutions can be minimized by using probabilistic dynamics, so that the optimal success probability is fixed by the choice of the evolution time. In the case of analog quantum search, we show that the superadiabatic approach induces a non-oracular counter-diabatic Hamiltonian, with the same energy-time complexity as equivalent adiabatic implementations.Comment: v2: 14 pages, 1 figure, 1 table. Published versio

Enhancement of the superconducting transition temperature in La2-xSrxCuO4 bilayers: Role of pairing and phase stiffness

The superconducting transition temperature, Tc, of bilayers comprising underdoped La2-xSrxCuO4 films capped by a thin heavily overdoped metallic La1.65Sr0.35CuO4 layer, is found to increase with respect to Tc of the bare underdoped films. The highest Tc is achieved for x = 0.12, close to the 'anomalous' 1/8 doping level, and exceeds that of the optimally-doped bare film. Our data suggest that the enhanced superconductivity is confined to the interface between the layers. We attribute the effect to a combination of the high pairing scale in the underdoped layer with an enhanced phase stiffness induced by the overdoped film.Comment: Published versio