Camouflaged Object Detection with Feature Grafting and Distractor Aware

The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detection as the texture between the target and background is visually indistinguishable. In this paper, we proposed a novel Feature Grafting and Distractor Aware network (FDNet) to handle the COD task. Specifically, we use CNN and Transformer to encode multi-scale images in parallel. In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module. A Distractor Aware Module is designed to explicitly model the two possible distractors in the COD task to refine the coarse camouflage map. We also proposed the largest artificial camouflaged object dataset which contains 2000 images with annotations, named ACOD2K. We conducted extensive experiments on four widely used benchmark datasets and the ACOD2K dataset. The results show that our method significantly outperforms other state-of-the-art methods. The code and the ACOD2K will be available at https://github.com/syxvision/FDNet.Comment: ICME2023 pape

Tislelizumab versus sorafenib as first-line treatment for advanced hepatocellular carcinoma in China: a cost-effectiveness analysis

ObjectiveThe goal of this study is to compare the cost-effectiveness of tislelizumab and sorafenib as first-line treatment for advanced hepatocellular carcinoma in China.MethodsA comprehensive cost-effectiveness analysis was undertaken within the framework of a partitioned survival model to accurately gage the incremental cost-effectiveness ratio (ICER) of tislelizumab compared to sorafenib. The model incorporated relevant clinical data and all survival rates were from RATIONALE-301 trials. The stability of the partitioned survival model was assessed by performing one-way and two-way sensitivity analyses.ResultsThe total cost incurred for the tislelizumab treatment was $16181.24, whereas the sorafenib was$14306.87. The tislelizumab regimen resulted in a significant increase of 0.18 quality-adjusted life years (QALYs) and an extra cost of $1874.37 as compared to chemotherapy. The ICER was$10413.17 per QALY, which was found to be below the willingness-to-pay (WTP) threshold of $37304.34/QALY. The results of the sensitivity analysis found that no fluctuations in any of the factors affected our results, even when these parameters fluctuated.ConclusionTislelizumab appears to be a cost-effective first-line treatment for advanced hepatocellular carcinoma when compared to sorafenib in China. These findings can inform decision-making processes regarding the selection of the most cost-effective treatment option for advanced hepatocellular carcinoma

Optimal ultra-miniature polarimeters in silicon photonic integrated circuits

Measurement of the state of polarization of light is essential in a vast number of applications, such as quantum and classical communications, remote sensing, astronomy, and biomedical diagnostics. Nanophotonic structures and integrated photonic circuits can, in many circumstances, replace conventional discrete optical components for miniature polarimeters and chip-scale polarimetry systems and thus significantly improve robustness while minimizing the footprint and cost. We propose and experimentally demonstrate two silicon photonic four-photodetector (PD) division-of-amplitude polarimeters (4PD-DOAPs) using a complementary metal–oxide–semiconductor-compatible photonic fabrication process. The first design targets minimizing the number of optical components. The second design makes use of a slightly more complex circuit design to achieve an optimal frame for measurements; this measurement frame minimizes and equalizes estimation variances in the presence of the additive white Gaussian noise and the signal dependent shot noise. Further theoretical examination reveals that within the optimal measurement frames for Stokes polarimeters, the DOAP with four PDs has the minimal equally weighted variance compared to those with a greater number of PDs

A Chip-scale, Full-Stokes Polarimeter

The polarization of light conveys unique information that can be exploited by crucial applications. The bulky and costly discrete optical components used in conventional polarimeters limit their broad adoption. A compact, low-cost polarimeter would bring this functionality into a myriad of new scenarios and revolutionize its exploitation. Here we present a high-performance, full-Stokes polarimeter on a silicon chip. A surface polarization splitter and on-chip optical interferometer circuit produce the analysis matrix of an optimally conditioned polarimeter. This solid-state polarimeter is a system-on-a-chip with exceptional compactness, stability, and speed that could be used singly or in integrated arrays. Large arrays can increase the speed and resolution of full-Stokes imaging; therefore, our design provides a scalable polarimeter solution.Comment: 5 figure

On-chip circular polarization splitter using silicon photonic nanoantenna array

Control and sorting of quantum states of photons through the manipulation of polarization and spatial modes of light in integrated photonic circuits contributes important applications in optical communications and quantum-optical systems. We design and demonstrate a novel structure for a silicon nanoantenna array that can split the circular polarization states and couple them to separate single-mode silicon waveguides. Implemented using a CMOS-compatible photonic fabrication process, the array can be monolithically integrated with other photonic components for chip-scale optical signal processing. We also show that the polarization sorting property of the nanoantenna array can be flexibly controlled (by adjusting design parameters at subwavelength scale) to split any two arbitrary orthogonal polarization states

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

Multimodal Emotion Recognition Model using Physiological Signals

As an important field of research in Human-Machine Interactions, emotion recognition based on physiological signals has become research hotspots. Motivated by the outstanding performance of deep learning approaches in recognition tasks, we proposed a Multimodal Emotion Recognition Model that consists of a 3D convolutional neural network model, a 1D convolutional neural network model and a biologically inspired multimodal fusion model which integrates multimodal information on the decision level for emotion recognition. We use this model to classify four emotional regions from the arousal valence plane, i.e., low arousal and low valence (LALV), high arousal and low valence (HALV), low arousal and high valence (LAHV) and high arousal and high valence (HAHV) in the DEAP and AMIGOS dataset. The 3D CNN model and 1D CNN model are used for emotion recognition based on electroencephalogram (EEG) signals and peripheral physiological signals respectively, and get the accuracy of 93.53% and 95.86% with the original EEG signals in these two datasets. Compared with the single-modal recognition, the multimodal fusion model improves the accuracy of emotion recognition by 5% ~ 25%, and the fusion result of EEG signals (decomposed into four frequency bands) and peripheral physiological signals get the accuracy of 95.77%, 97.27% and 91.07%, 99.74% in these two datasets respectively. Integrated EEG signals and peripheral physiological signals, this model could reach the highest accuracy about 99% in both datasets which shows that our proposed method demonstrates certain advantages in solving the emotion recognition tasks.Comment: 10 pages, 10 figures, 6 table