Primal Dual Alternating Proximal Gradient Algorithms for Nonsmooth Nonconvex Minimax Problems with Coupled Linear Constraints

Nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose a primal dual alternating proximal gradient (PDAPG) algorithm and a primal dual proximal gradient (PDPG-L) algorithm for solving nonsmooth nonconvex-strongly concave and nonconvex-linear minimax problems with coupled linear constraints, respectively. The corresponding iteration complexity of the two algorithms are proved to be $\mathcal{O}\left( \varepsilon ^{-2} \right)$ and $\mathcal{O}\left( \varepsilon ^{-3} \right)$ to reach an $\varepsilon$-stationary point, respectively. To our knowledge, they are the first two algorithms with iteration complexity guarantee for solving the two classes of minimax problems

Auricle shaping using 3D printing and autologous diced cartilage.

ObjectiveTo reconstruct the auricle using a porous, hollow, three-dimensional (3D)-printed mold and autologous diced cartilage mixed with platelet-rich plasma (PRP).MethodsMaterialise Magics v20.03 was used to design a 3D, porous, hollow auricle mold. Ten molds were printed by selective laser sintering with polyamide. Cartilage grafts were harvested from one ear of a New Zealand rabbit, and PRP was prepared using 10 mL of auricular blood from the same animal. Ear cartilage was diced into 0.5- to 2.0-mm pieces, weighed, mixed with PRP, and then placed inside the hollow mold. Composite grafts were then implanted into the backs of respective rabbits (n = 10) for 4 months. The shape and composition of the diced cartilage were assessed histologically, and biomechanical testing was used to determine stiffness.ResultsThe 3D-printed auricle molds were 0.6-mm thick and showed connectivity between the internal and external surfaces, with round pores of 0.1 to 0.3 cm. After 4 months, the diced cartilage pieces had fused into an auricular shape with high fidelity to the anthropotomy. The weight of the diced cartilage was 5.157 ± 0.230 g (P > 0.05, compared with preoperative). Histological staining showed high chondrocyte viability and the production of collagen II, glycosaminoglycans, and other cartilaginous matrix components. In unrestricted compression tests, auricle stiffness was 0.158 ± 0.187 N/mm, similar to that in humans.ConclusionAuricle grafts were constructed successfully through packing a 3D-printed, porous, hollow auricle mold with diced cartilage mixed with PRP. The auricle cartilage contained viable chondrocytes, appropriate extracellular matrix components, and good mechanical properties.Levels of evidenceNA. Laryngoscope, 129:2467-2474, 2019

Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision

In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, and aligns the group tokens to the corresponding caption embedding. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given the group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, which encourages the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on three benchmark datasets, PASCAL VOC 2012, PASCAL Context, and COCO Object. Our model achieves superior segmentation results over the state-of-the-art method by using only 3\% data (4M vs 134M) for pre-training. Code and pre-trained models will be released for future research

Harnessing high-dimensional hyperentanglement through a biphoton frequency comb

Quantum entanglement is a fundamental resource for secure information processing and communications, where hyperentanglement or high-dimensional entanglement has been separately proposed towards high data capacity and error resilience. The continuous-variable nature of the energy-time entanglement makes it an ideal candidate for efficient high-dimensional coding with minimal limitations. Here we demonstrate the first simultaneous high-dimensional hyperentanglement using a biphoton frequency comb to harness the full potential in both energy and time domain. The long-postulated Hong-Ou-Mandel quantum revival is exhibited, with up to 19 time-bins, 96.5% visibilities. We further witness the high-dimensional energy-time entanglement through Franson revivals, which is observed periodically at integer time-bins, with 97.8% visibility. This qudit state is observed to simultaneously violate the generalized Bell inequality by up to 10.95 deviations while observing recurrent Clauser-Horne-Shimony-Holt S-parameters up to 2.76. Our biphoton frequency comb provides a platform in photon-efficient quantum communications towards the ultimate channel capacity through energy-time-polarization high-dimensional encoding

Near-infrared Hong-Ou-Mandel interference on a silicon quantum photonic circuit

Near-infrared Hong-Ou-Mandel quantum interference is observed in silicon nanophotonic directional couplers with raw visibilities on-chip at 90.5%. Spectrally-bright 1557-nm two-photon states are generated in a periodically-poled KTiOPO4 waveguide chip, serving as the entangled photon source and pumped with a self-injection locked laser, for the photon statistical measurements. Efficient four-port coupling in the communications C-band and in the high-index-contrast silicon photonics platform is demonstrated, with matching theoretical predictions of the quantum interference visibility. Constituents for the residual quantum visibility imperfection are examined, supported with theoretical analysis of the sequentially-triggered multipair biphoton contribution and techniques for visibility compensation, towards scalable high-bitrate quantum information processing and communications.Comment: 15 pages, 6 figure