Quantum filtering for multiple measurements driven by fields in single-photon states

In this paper, we derive the stochastic master equations for quantum systems driven by a single-photon input state which is contaminated by quantum vacuum noise. To improve estimation performance, quantum filters based on multiple-channel measurements are designed. Two cases, namely diffusive plus Poissonian measurements and two diffusive measurements, are considered.Comment: 8 pages, 6 figures, submitted for publication. Comments are welcome

Scalable Image Retrieval by Sparse Product Quantization

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.Comment: 12 page

Structured Kernel Estimation for Photon-Limited Deconvolution

Images taken in a low light condition with the presence of camera shake suffer from motion blur and photon shot noise. While state-of-the-art image restoration networks show promising results, they are largely limited to well-illuminated scenes and their performance drops significantly when photon shot noise is strong. In this paper, we propose a new blur estimation technique customized for photon-limited conditions. The proposed method employs a gradient-based backpropagation method to estimate the blur kernel. By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem. When plugged into an iterative framework, our novel low-dimensional representation provides improved kernel estimates and hence significantly better deconvolution performance when compared to end-to-end trained neural networks. The source code and pretrained models are available at \url{https://github.com/sanghviyashiitb/structured-kernel-cvpr23}Comment: main document and supplementary; accepted at CVPR202

Multi-material topology optimization of adhesive backing layers via J-integral and strain energy minimizations

Strong adhesives rely on reduced stress concentrations, often obtained via specific geometry or composition of materials. In many examples in nature and engineering prototypes, the adhesive performance relies on structural rigidity being placed in specific locations. A few design principles have been formulated, based on parametric optimization, while a general design tool is still missing. We propose to use topology optimization to achieve optimal stiffness distribution in a multi-material adhesive backing layer, reducing stress concentration at specified locations. The method involves the minimization of a linear combination of J-integral and strain energy. While the J-integral minimization is aimed at reducing stress concentration, we observe that the combination of these two objectives ultimately provides the best results. We analyze three cases in plane strain conditions, namely (i) double-edged crack and (ii) center crack in tension and (iii) edge crack under shear. Each case evidences a different optimal topology with (i) and (ii) providing similar results. The optimal topology allocates stiffness in regions that are far away from the crack tip, intuitively, but the allocation of softer materials over stiffer ones can be non-trivial. To test our solutions, we plot the contact stress distribution across the interface. In all observed cases, we eliminate the stress singularity at the crack tip. Stress concentrations might arise in locations far away from the crack tip, but the final results are independent of crack size. Our method ultimately provides optimal, flaw tolerant, adhesives where the crack location is known

Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model

Image restoration algorithms for atmospheric turbulence are known to be much more challenging to design than traditional ones such as blur or noise because the distortion caused by the turbulence is an entanglement of spatially varying blur, geometric distortion, and sensor noise. Existing CNN-based restoration methods built upon convolutional kernels with static weights are insufficient to handle the spatially dynamical atmospheric turbulence effect. To address this problem, in this paper, we propose a physics-inspired transformer model for imaging through atmospheric turbulence. The proposed network utilizes the power of transformer blocks to jointly extract a dynamical turbulence distortion map and restore a turbulence-free image. In addition, recognizing the lack of a comprehensive dataset, we collect and present two new real-world turbulence datasets that allow for evaluation with both classical objective metrics (e.g., PSNR and SSIM) and a new task-driven metric using text recognition accuracy. Both real testing sets and all related code will be made publicly available.Comment: This paper is accepted as a poster at ECCV 202

Planar Manipulation via Learning Regrasping

Regrasping is important for robots to reorient objects in planar manipulation tasks. Different placements of objects can provide robots with alternative grasp configurations, which are used in complex planar manipulation tasks that require multiple pick-rotate-and-place steps due to the constraints of the environment and robot kinematics. In this work, our goal is to generate diverse placements of objects on the plane using deep neural networks. We propose a pipeline with the stages of orientation generation, position refinement, and placement discrimination to obtain accurate and diverse stable placements based on the perception of point clouds. A large-scale dataset is created for training, including simulated placements and contact information between objects and the plane. The simulation results show that our pipeline outperforms the start-of-the-art, achieving an accuracy rate of 90.4% and a diversity rate of 81.3% in simulation on generated placements. Our pipeline is also validated in real-robot experiments. With the generated placements, sequential pick-rotate-and-place steps are calculated for the robot to reorient objects to goal poses that are not reachable within one step. Videos and dataset are available at https://sites.google.com/view/pmvlr2022/

Small-Sample Inferred Adaptive Recoding for Batched Network Coding

Batched network coding is a low-complexity network coding solution to feedbackless multi-hop wireless packet network transmission with packet loss. The data to be transmitted is encoded into batches where each of which consists of a few coded packets. Unlike the traditional forwarding strategy, the intermediate network nodes have to perform recoding, which generates recoded packets by network coding operations restricted within the same batch. Adaptive recoding is a technique to adapt the fluctuation of packet loss by optimizing the number of recoded packets per batch to enhance the throughput. The input rank distribution, which is a piece of information regarding the batches arriving at the node, is required to apply adaptive recoding. However, this distribution is not known in advance in practice as the incoming link's channel condition may change from time to time. On the other hand, to fully utilize the potential of adaptive recoding, we need to have a good estimation of this distribution. In other words, we need to guess this distribution from a few samples so that we can apply adaptive recoding as soon as possible. In this paper, we propose a distributionally robust optimization for adaptive recoding with a small-sample inferred prediction of the input rank distribution. We develop an algorithm to efficiently solve this optimization with the support of theoretical guarantees that our optimization's performance would constitute as a confidence lower bound of the optimal throughput with high probability.Comment: 7 pages, 2 figures, accepted in ISIT-21, appendix adde

Ultrathin MgB2 films fabricated on Al2O3 substrate by hybrid physical-chemical vapor deposition with high Tc and Jc

Ultrathin MgB2 superconducting films with a thickness down to 7.5 nm are epitaxially grown on (0001) Al2O3 substrate by hybrid physical-chemical vapor deposition method. The films are phase-pure, oxidation-free and continuous. The 7.5 nm thin film shows a Tc(0) of 34 K, which is so far the highest Tc(0) reported in MgB2 with the same thickness. The critical current density of ultrathin MgB2 films below 10 nm is demonstrated for the first time as Jc ~ 10^6 A cm^{-2} for the above 7.5 nm sample at 16 K. Our results reveal the excellent superconducting properties of ultrathin MgB2 films with thicknesses between 7.5 and 40 nm on Al2O3 substrate.Comment: 7 pages, 4 figures, 2 table