Integrated all-optical logic discriminators based on plasmonic bandgap engineering

Optical computing uses photons as information carriers, opening up the possibility for ultrahigh-speed and ultrawide-band information processing. Integrated all-optical logic devices are indispensible core components of optical computing systems. However, up to now, little experimental progress has been made in nanoscale all-optical logic discriminators, which have the function of discriminating and encoding incident light signals according to wavelength. Here, we report a strategy to realize a nanoscale all-optical logic discriminator based on plasmonic bandgap engineering in a planar plasmonic microstructure. Light signals falling within different operating wavelength ranges are differentiated and endowed with different logic state encodings. Compared with values previously reported, the operating bandwidth is enlarged by one order of magnitude. Also the SPP light source is integrated with the logic device while retaining its ultracompact size. This opens up a way to construct on-chip all-optical information processors and artificial intelligence systems.Comment: 4 figures 201

Ultrawide-band Unidirectional Surface Plasmon Polariton Launchers

Plasmonic devices and circuits, bridging the gap between integrated photonic and microelectronic technology, are promising candidates to realize on-chip ultrawide-band and ultrahigh-speed information processing. Unfortunately, the wideband surface plasmon source, one of the most important core components of integrated plasmonic circuits, is still unavailable up to now. This has seriously restricted the practical applications of plasmonic circuits. Here, we report an ultrawide-band unidirectional surface plasmon polariton launcher with high launching efficiency ratio and large extinction ratio, realized by combining plasmonic bandgap engineering and linear interference effect. This device offers excellent performances over an ultrabroad wavelength range from 690 to 900 nm, together with a high average launching efficiency ratio of 1.25, large average extinction ratio of 30 dB, and ultracompact lateral dimension of less than 4 um. Compared with previous reports, the operating bandwidth is enlarged 210 folds, while the largest launching efficiency ratio, largest extinction ratio, and small feature size are maintained simultaneously. This provides a strategy for constructing on-chip surface plasmon source, and also paving the way for the study of integrated plasmonic circuits.Comment: 4 figure

Scene-Aware Feature Matching

Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and illumination changes. To tackle this problem, we propose a novel model named SAM, which applies attentional grouping to guide Scene-Aware feature Matching. SAM handles multi-level features, i.e., image tokens and group tokens, with attention layers, and groups the image tokens with the proposed token grouping module. Our model can be trained by ground-truth matches only and produce reasonable grouping results. With the sense-aware grouping guidance, SAM is not only more accurate and robust but also more interpretable than conventional feature matching models. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that our model achieves state-of-the-art performance.Comment: Accepted to ICCV 202

ParaFormer: Parallel Attention Transformer for Efficient Feature Matching

Heavy computation is a bottleneck limiting deep-learningbased feature matching algorithms to be applied in many realtime applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are expected to be matched. This paper tackles this problem and proposes two concepts: 1) a novel parallel attention model entitled ParaFormer and 2) a graph based U-Net architecture with attentional pooling. First, ParaFormer fuses features and keypoint positions through the concept of amplitude and phase, and integrates self- and cross-attention in a parallel manner which achieves a win-win performance in terms of accuracy and efficiency. Second, with U-Net architecture and proposed attentional pooling, the ParaFormer-U variant significantly reduces computational complexity, and minimize performance loss caused by downsampling. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that ParaFormer achieves state-of-the-art performance while maintaining high efficiency. The efficient ParaFormer-U variant achieves comparable performance with less than 50% FLOPs of the existing attention-based models.Comment: Have been accepted by AAAI 202

An Efficient Built-in Temporal Support in MVCC-based Graph Databases

Real-world graphs are often dynamic and evolve over time. To trace the evolving properties of graphs, it is necessary to maintain every change of both vertices and edges in graph databases with the support of temporal features. Existing works either maintain all changes in a single graph or periodically materialize snapshots to maintain the historical states of each vertex and edge and process queries over proper snapshots. The former approach presents poor query performance due to the ever-growing graph size as time goes by, while the latter one suffers from prohibitively high storage overheads due to large redundant copies of graph data across different snapshots. In this paper, we propose a hybrid data storage engine, which is based on the MVCC mechanism, to separately manage current and historical data, which keeps the current graph as small as possible. In our design, changes in each vertex or edge are stored once. To further reduce the storage overhead, we simply store the changes as opposed to storing the complete snapshot. To boost the query performance, we place a few anchors as snapshots to avoid deep historical version traversals. Based on the storage engine, a temporal query engine is proposed to reconstruct subgraphs as needed on the fly. Therefore, our alternative approach can provide fast querying capabilities over subgraphs at a past time point or range with small storage overheads. To provide native support of temporal features, we integrate our approach into Memgraph, and call the extended database system TGDB(Temporal Graph Database). Extensive experiments are conducted on four real and synthetic datasets. The results show TGDB performs better in terms of both storage and performance against state-of-the-art methods and has almost no performance overheads by introducing the temporal features

Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning

Catastrophic forgetting remains a critical challenge in the field of continual learning, where neural networks struggle to retain prior knowledge while assimilating new information. Most existing studies emphasize mitigating this issue only when encountering new tasks, overlooking the significance of the pre-task phase. Therefore, we shift the attention to the current task learning stage, presenting a novel framework, C&F (Create and Find Flatness), which builds a flat training space for each task in advance. Specifically, during the learning of the current task, our framework adaptively creates a flat region around the minimum in the loss landscape. Subsequently, it finds the parameters' importance to the current task based on their flatness degrees. When adapting the model to a new task, constraints are applied according to the flatness and a flat space is simultaneously prepared for the impending task. We theoretically demonstrate the consistency between the created and found flatness. In this manner, our framework not only accommodates ample parameter space for learning new tasks but also preserves the preceding knowledge of earlier tasks. Experimental results exhibit C&F's state-of-the-art performance as a standalone continual learning approach and its efficacy as a framework incorporating other methods. Our work is available at https://github.com/Eric8932/Create-and-Find-Flatness.Comment: 10pages, ECAI2023 conferenc

Application of 25 MHz B-Scan Ultrasonography to Determine the Integrity of the Posterior Capsule in Posterior Polar Cataract

Purpose. To report the application of 25 MHz B-scan ultrasonography (MHzB) to determine the integrity of the posterior capsule (PC) in posterior polar cataract (PPC). Methods. Patients with whom PPC was clinically diagnosed using slit lamp microscopy who underwent 25 MHzB before phacoemulsification were retrospectively reviewed. The status of the PC was determined by 25 MHzB before phacoemulsification and confirmed during cataract surgery. Results. In total, 21 eyes in 14 clinically diagnosed PPC patients were enrolled in this study. Out of 25 MHzB images, 19 PCs were found to be intact, while 2 showed dehiscence before cataract surgery. During phacoemulsification, 17 PCs were observed to be intact, while 4 PCs showed posterior capsule rupture (PCR). These 4 PCR cases included the above 2 eyes, in which preexisting dehiscence was detected by 25 MHzB. The other 2 PCR cases showed high reflectivity between high echoes in posterior opacities and the PC, indicating synechia between the PPC and PC. Conclusion. This is the first report to show that 25 MHzB can be used to clearly visualize the status of the PC in PPC. These results, in turn, could be used to select the appropriate treatment and to thereby avoid further complications during PPC surgery