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We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and inverse uncertainty propagation. Existing surrogate modeling approaches usually focus on approximating the deterministic component of physical model. However, this strategy necessitates knowledge of noise and resorts to auxiliary sampling methods for quantifying inverse uncertainty propagation. In this work, we develop the conditional pseudo-reversible normalizing flow model to directly learn and efficiently generate samples from the conditional probability density functions. The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Our model, once trained, can generate samples from any conditional probability density functions whose high probability regions are covered by the training set. Moreover, the pseudo-reversibility feature allows for the use of fully-connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional pseudo-reversible normalizing flow model, showing its ability to converge to the target conditional probability density function using the Kullback-Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

We present a pseudo-reversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with different initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDE, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE's final state without simulating trajectories. Existing normalizing flows for SDEs depend on the initial distribution, meaning the model needs to be re-trained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. We provide a rigorous convergence analysis of the pseudo-reversible normalizing flow model to the target probability density function in the Kullback-Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model.

Inspired by the method that can deterministically generated the massive entanglement through phase transitions, we study the ground state properties of a spin-1 condensate mixture, under the premise that the heteronuclear spin-exchange collision is taken into account. We developed a effective model to analyze the binary-coupled two-level system and studied the ground state phase transitions. Three representative quantum states with the same number distribution are studied and distinguished through the number fluctuations. We demonstrate that there will be the GreenbergerHorne-Zeilinger (GHZ) state in the mixture if the the extra magnetic field is specifically selected or adiabatically adjusted. One advantage of preparing entangled states in mixtures is that we only need to adjust the external magnetic field, instead of considering the microwaves-magnetic cooperation. Finally we estimate the feasibility of experimentally generating the heteronuclear many-body entanglement in the alkali-metal atomic mixture., Comment: 9 pages, 7 figures

Recent studies demonstrate that deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition to the much more expensive training costs than standard training. In this paper, we investigate this intriguing problem from a new perspective, i.e., injecting appropriate forms of sparsity during adversarial training. We introduce two alternatives for sparse adversarial training: (i) static sparsity, by leveraging recent results from the lottery ticket hypothesis to identify critical sparse subnetworks arising from the early training; (ii) dynamic sparsity, by allowing the sparse subnetwork to adaptively adjust its connectivity pattern (while sticking to the same sparsity ratio) throughout training. We find both static and dynamic sparse methods to yield win-win: substantially shrinking the robust generalization gap and alleviating the robust overfitting, meanwhile significantly saving training and inference FLOPs. Extensive experiments validate our proposals with multiple network architectures on diverse datasets, including CIFAR-10/100 and Tiny-ImageNet. For example, our methods reduce robust generalization gap and overfitting by 34.44% and 4.02%, with comparable robust/standard accuracy boosts and 87.83%/87.82% training/inference FLOPs savings on CIFAR-100 with ResNet-18. Besides, our approaches can be organically combined with existing regularizers, establishing new state-of-the-art results in AT. Codes are available in https://github.com/VITA-Group/Sparsity-Win-Robust-Generalization.

Observation of strong correlations and superconductivity in twisted-bilayer-graphene have stimulated tremendous interest in fundamental and applied physics. In this system, the superposition of two twisted honeycomb lattices, generating a Moir$\acute{\mathrm{e}}$ pattern, is the key to the observed flat electronic bands, slow electron velocity and large density of states. Despite these observations, a full understanding of the emerging superconductivity from the coupled insulating layers and the appearance of a small magic angle remain a hot topic of research. Here, we demonstrate a quantum simulation platform to study superfluids in twisted bilayer lattices based on Bose-Einstein condensates loaded into spin-dependent optical lattices. The lattices are made of two sets of laser beams that independently address atoms in different spin states, which form the synthetic dimension of the two layers. The twisted angle of the two lattices is controlled by the relative angle of the laser beams. We show that atoms in each spin state only feel one set of the lattice and the interlayer coupling can be controlled by microwave coupling between the spin states. Our system allows for flexible control of both the inter- and intralayer couplings. Furthermore we directly observe the spatial Moir$\acute{\mathrm{e}}$ pattern and the momentum diffraction, which confirm the presence of atomic superfluid in the bilayer lattices. Our system constitutes a powerful platform to investigate the physics underlying the superconductivity in twisted-bilayer-graphene and to explore other novel quantum phenomena difficult to realize in materials., Comment: 6 pages, 5 figures

We study the periodic potential of one-dimensional optical lattice originated from scalar shift and vector shift by manipulating the lattice polarizations. The ac Stark shift of optical lattice is measured by Kapitza-Dirac scattering of $^{87}$Rb Bose-Einstein condensate and the characteristics of spin-dependent optical lattice are presented by scanning the lattice wavelength between the D1 and D2 lines. At the same time, tune-out wavelengths that ac Stark shift cancels can be probed by optical lattice. We give the tune-out wavelengths in more general cases of balancing the contributions of both the scalar and vector shift. Our results provide a clear interpretation for spin-dependent optical lattice and tune-out wavelengths, and help to design it by choosing the appropriate lattice wavelength.

We report the high resolution photoassociation (PA) spectroscopy of a $^{87}Rb$ Bose-Einstein condensate (BEC) to excited molecular states near the dissociation limit of $5P_{1/2} +5S_{1/2}$ by optical Bragg scattering. Since the detection of optical Bragg scattering in BEC has a high signal-noise ratio, we obtain the high resolution PA spectrum of excited molecular states in the range of $\pm1$ GHz near the dissociation limit of $5P_{1/2} +5S_{1/2}$. We compare the results with the conventional method of trap loss and show that the results agree each other very well. Many interesting phenomena of excited molecular states are observed, such as light-induced frequency shift and the anomalous strong bound molecular lines at the atomic transition from $|F=1\rangle$ to $|F^{\prime}=2\rangle$. The observed excited molecular states in the range of $\pm1$ GHz near the dissociation limit of $5P_{1/2} +5S_{1/2}$ are never reported before, which will help to further improve the long range bound state models near the dissociation limit., Comment: 7 pages, 5 figures

We experimentally realize the spin-tensor momentum coupling (STMC) using the three ground Zeeman states coupled by three Raman laser beams in ultracold atomic system of $^{40}$K Fermi atoms. This new type of STMC consists of two bright-state bands as a regular spin-orbit coupled spin-1/2 system and one dark-state middle band. Using radio-frequency spin-injection spectroscopy, we investigate the energy band of STMC. It is demonstrated that the middle state is a dark state in the STMC system. The realized energy band of STMC may open the door for further exploring exotic quantum matters., Comment: 6 pages, 4 figures

We report an experimental demonstration of engineering phase wraps for sub-wavelength structure in a Bose-Einstein condensate (BEC) with two-dimensional optical lattices. A short lattice pulse is applied on BEC working in the Kapitza-Dirac (or Raman-Nath) regime, which corresponds to phase modulation imprint on matter wave. When the phase modulation on matter wave is larger than $2\pi$ in a lattice cell, there appears phase wraps with multiple $2\pi$ jumps, generating the sub-wavelength phase structure. The phase wraps for sub-wavelength structure are measured in momentum space via the time-of-flight absorption image, which corresponds to converting phase information into amplitude. %Two different kinds of two-dimensional optical lattice are studied, which show the different subwavelength phase structure. Moreover, we identify an additional condition for the validity of Kapitza-Dirac regime, which relies crucially on the lattice configurations. This scheme can be used for studying the property of optical lattices and topological defects in matter wave., Comment: 5 pages, 5 figures

We measure the superradiant emission in a one-dimensional (1D) superradiance lattice (SL) in ultracold atoms. Resonantly excited to a superradiant state, the atoms are further coupled to other collectively excited states, which form a 1D SL. The directional emission of one of the superradiant excited states in the 1D SL is measured. The emission spectra depend on the band structure, which can be controlled by the frequency and intensity of the coupling laser fields. This work provides a platform for investigating the collective Lamb shift of resonantly excited superradiant states in Bose-Einstein condensates and paves the way for realizing higher dimensional superradiance lattices., Comment: 5 pages, 4 figures

Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN), develops rapidly and is applied to many tasks, such as image classification, face recognition, image segmentation, and human detection. Due to its superior performance, DL-based models have a wide range of application in many areas, some of which are extremely safety-critical, e.g. intelligent surveillance and autonomous driving. Due to the latency and privacy problem of cloud computing, embedded accelerators are popular in these safety-critical areas. However, the robustness of the embedded DL system might be harmed by inserting hardware/software Trojans into the accelerator and the neural network model, since the accelerator and deploy tool (or neural network model) are usually provided by third-party companies. Fortunately, inserting hardware Trojans can only achieve inflexible attack, which means that hardware Trojans can easily break down the whole system or exchange two outputs, but can't make CNN recognize unknown pictures as targets. Though inserting software Trojans has more freedom of attack, it often requires tampering input images, which is not easy for attackers. So, in this paper, we propose a hardware-software collaborative attack framework to inject hidden neural network Trojans, which works as a back-door without requiring manipulating input images and is flexible for different scenarios. We test our attack framework for image classification and face recognition tasks, and get attack success rate of 92.6% and 100% on CIFAR10 and YouTube Faces, respectively, while keeping almost the same accuracy as the unattacked model in the normal mode. In addition, we show a specific attack scenario in which a face recognition system is attacked and gives a specific wrong answer., Comment: 6 pages, 8 figures, 5 tables, accepted to ISVLSI 2018

We report on the efficient gray molasses cooling of sodium atoms using the $D_{2}$ optical transition at 589.1 nm. Thanks to the hyperfine split about 6$\Gamma$ between the $|F'=2\rangle$ and $|F'=3\rangle$ in the excited state 3$^{2}P_{3/2}$, this atomic transition is effective for the gray molasses cooling mechanism. Using this cooling technique, the atomic sample in $F = 2$ ground manifold is cooled from 700 $\upmu$K to 56 $\upmu$K in 3.5 ms. We observe that the loading efficiency into magnetic trap is increased due to the lower temperature and high phase space density of atomic cloud after gray molasses. This technique offers a promising route for the fast cooling of the sodium atoms in the $F=2$ state., Comment: 5 pages, 4 figures

In this Letter we report the experimental results on optical control of a p-wave Feshbach resonance, by utilizing a laser driven bound-to-bound transition to shift the energy of closed channel molecule. The magnetic field location for p-wave resonance as a function of laser detuning can be captured by a simple formula with essentially one parameter, which describes how sensitive the resonance depends on the laser detuning. The key result of this work is to demonstrate, both experimentally and theoretically, that the ratio between this parameter for $m=0$ resonance and that for $m=\pm 1$ resonance, to large extent, is universal. We also show that this optical control can create intriguing situations where interesting few- and many-body physics can occurs, such as a p-wave resonance overlapping with an s-wave resonance or three p-wave resonances being degenerate., Comment: 5 pages, 3 figures

Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs). With the proliferation of large-scale and long-term WSNs, it is becoming more important to calibrate sensors when the ground truth is unavailable. This problem is called "blind calibration". In this paper, we propose a novel deep learning method named projection-recovery network (PRNet) to blindly calibrate sensor measurements online. The PRNet first projects the drifted data to a feature space, and uses a powerful deep convolutional neural network to recover the estimated drift-free measurements. We deploy a 24-sensor testbed and provide comprehensive empirical evidence showing that the proposed method significantly improves the sensing accuracy and drifted sensor detection. Compared with previous methods, PRNet can calibrate 2x of drifted sensors at the recovery rate of 80% under the same level of accuracy requirement. We also provide helpful insights for designing deep neural networks for sensor calibration. We hope our proposed simple and effective approach will serve as a solid baseline in blind drift calibration of sensor networks.

Periodic driving of a quantum system can significantly alter its energy bands and even change the band topology, opening a completely new avenue for engineering novel quantum matter. Although important progress has been made recently in measuring topological properties of Floquet bands in different systems, direct experimental measurement of Floquet band dispersions and their topology change is still demanding. Here we directly measure Floquet band dispersions in a periodically driven spin-orbit coupled ultracold Fermi gas. Using spin injection radio-frequency spectroscopy, we observe that the Dirac point originating from two dimensional spin-orbit coupling can be manipulated to emerge at the lowest or highest two dressed bands by fast modulating Raman laser frequencies, demonstrating topological change of Floquet bands. Our work will provide a powerful tool for understanding fundamental Floquet physics as well as engineering exotic topological quantum matter., Comment: 6 pages, 5 figures