Measurement of the squeezed vacuum state by a bichromatic local oscillator

We present the experimental measurement of a squeezed vacuum state by means of a bichromatic local oscillator (BLO). A pair of local oscillators at $\pm$5 MHz around the central frequency $\omega_{0}$ of the fundamental field with equal power are generated by three acousto-optic modulators and phase-locked, which are used as a BLO. The squeezed vacuum light are detected by a phase-sensitive balanced-homodyne detection with a BLO. The baseband signal around $\omega_{0}$ combined with a broad squeezed field can be detected with the sensitivity below the shot-noise limit, in which the baseband signal is shifted to the vicinity of 5 MHz (the half of the BLO separation). This work has the important applications in quantum state measurement and quantum informatio

Biological Lasers for Biomedical Applications

A biolaser utilizes biological materials as part of its gain medium and/or part of its cavity. It can also be a micro- or nanosized laser embedded/integrated within biological materials. The biolaser employs lasing emission rather than regular fluorescence as the sensing signal and therefore has a number of unique advantages that can be explored for broad applications in biosensing, labeling, tracking, contrast agent development, and bioimaging. This article reports on the progress in biolasers with focus on the work done in the past five years. In the end, the possible future directions of the biolaser are discussed.Biolasers and their applications in biology and biomedicine are reviewed in this progress report. The biolaser employs lasing emission rather than regular fluorescence as the sensing signal and therefore has a number of unique advantages that can be explored for broad applications in biosensing, labeling, tracking, contrast agent development, and bioimaging.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151258/1/adom201900377.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151258/2/adom201900377_am.pd

Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters

Convolution is the main building block of convolutional neural networks (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels increases with depth, reducing the expressive power of feature representations. We propose Tied Block Convolution (TBC) that shares the same thinner filters over equal blocks of channels and produces multiple responses with a single filter. The concept of TBC can also be extended to group convolution and fully connected layers, and can be applied to various backbone networks and attention modules. Our extensive experimentation on classification, detection, instance segmentation, and attention demonstrates TBC's significant across-the-board gain over standard convolution and group convolution. The proposed TiedSE attention module can even use 64 times fewer parameters than the SE module to achieve comparable performance. In particular, standard CNNs often fail to accurately aggregate information in the presence of occlusion and result in multiple redundant partial object proposals. By sharing filters across channels, TBC reduces correlation and can effectively handle highly overlapping instances. TBC increases the average precision for object detection on MS-COCO by 6% when the occlusion ratio is 80%. Our code will be released.Comment: 13 page

A dynamic programming approach for generalized nearly isotonic optimization

Shape restricted statistical estimation problems have been extensively studied, with many important practical applications in signal processing, bioinformatics, and machine learning. In this paper, we propose and study a generalized nearly isotonic optimization (GNIO) model, which recovers, as special cases, many classic problems in shape constrained statistical regression, such as isotonic regression, nearly isotonic regression and unimodal regression problems. We develop an efficient and easy-to-implement dynamic programming algorithm for solving the proposed model whose recursion nature is carefully uncovered and exploited. For special $\ell_2$-GNIO problems, implementation details and the optimal ${\cal O}(n)$ running time analysis of our algorithm are discussed. Numerical experiments, including the comparison between our approach and the powerful commercial solver Gurobi for solving $\ell_1$-GNIO and $\ell_2$-GNIO problems, on both simulated and real data sets are presented to demonstrate the high efficiency and robustness of our proposed algorithm in solving large scale GNIO problems

Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms

We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules. Such a model captures the diversity of real individual interactions. We propose a learning algorithm based on multi-armed bandit algorithms to address the problem. The goal of the algorithm is to find each agent's update rule from several candidate rules and to learn the underlying network. At each iteration, the algorithm assumes that each agent has one of the updated rules and then modifies network estimates to reduce validation error. Numerical experiments show that the proposed algorithm improves initial estimates of the network and update rules, decreases prediction error, and performs better than other methods such as sparse linear regression and Gaussian process regression