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Optical remote sensing and Synthetic Aperture Radar(SAR) remote sensing are crucial for earth observation, offering complementary capabilities. While optical sensors provide high-quality images, they are limited by weather and lighting conditions. In contrast, SAR sensors can operate effectively under adverse conditions. This letter proposes a GAN-based SAR-to-optical image translation method named Seg-CycleGAN, designed to enhance the accuracy of ship target translation by leveraging semantic information from a pre-trained semantic segmentation model. Our method utilizes the downstream task of ship target semantic segmentation to guide the training of image translation network, improving the quality of output Optical-styled images. The potential of foundation-model-annotated datasets in SAR-to-optical translation tasks is revealed. This work suggests broader research and applications for downstream-task-guided frameworks. The code will be available at https://github.com/NPULHH/, Comment: 8 pages, 5 figures

Recent years have witnessed the success of the deep learning-based technique in research of no-reference point cloud quality assessment (NR-PCQA). For a more accurate quality prediction, many previous studies have attempted to capture global and local feature in a bottom-up manner, but ignored the interaction and promotion between them. To solve this problem, we propose a novel asynchronous feedback network (AFNet). Motivated by human visual perception mechanisms, AFNet employs a dual-branch structure to deal with global and local feature, simulating the left and right hemispheres of the human brain, and constructs a feedback module between them. Specifically, the input point clouds are first fed into a transformer-based global encoder to generate the attention maps that highlight these semantically rich regions, followed by being merged into the global feature. Then, we utilize the generated attention maps to perform dynamic convolution for different semantic regions and obtain the local feature. Finally, a coarse-to-fine strategy is adopted to merge the two features into the final quality score. We conduct comprehensive experiments on three datasets and achieve superior performance over the state-of-the-art approaches on all of these datasets. The code will be available at https://github.com/zhangyujie-1998/AFNet.

Full-reference point cloud quality assessment (FR-PCQA) aims to infer the quality of distorted point clouds with available references. Most of the existing FR-PCQA metrics ignore the fact that the human visual system (HVS) dynamically tackles visual information according to different distortion levels (i.e., distortion detection for high-quality samples and appearance perception for low-quality samples) and measure point cloud quality using unified features. To bridge the gap, in this paper, we propose a perception-guided hybrid metric (PHM) that adaptively leverages two visual strategies with respect to distortion degree to predict point cloud quality: to measure visible difference in high-quality samples, PHM takes into account the masking effect and employs texture complexity as an effective compensatory factor for absolute difference; on the other hand, PHM leverages spectral graph theory to evaluate appearance degradation in low-quality samples. Variations in geometric signals on graphs and changes in the spectral graph wavelet coefficients are utilized to characterize geometry and texture appearance degradation, respectively. Finally, the results obtained from the two components are combined in a non-linear method to produce an overall quality score of the tested point cloud. The results of the experiment on five independent databases show that PHM achieves state-of-the-art (SOTA) performance and offers significant performance improvement in multiple distortion environments. The code is publicly available at https://github.com/zhangyujie-1998/PHM.

A novel dual-band reconfigurable intelligent surface (DBI-RIS) design that combines the functionalities of millimeter-wave (mmWave) and sub-6 GHz bands within a single aperture is proposed. This design aims to bridge the gap between current single-band reconfigurable intelligent surfaces (RISs) and wireless systems utilizing sub-6 GHz and mmWave bands that require RIS with independently reconfigurable dual-band operation. The mmWave element is realized by a double-layer patch antenna loaded with 1-bit phase shifters, providing two reconfigurable states. An 8x8 mmWave element array is selectively interconnected using three RF switches to form a reconfigurable sub-6 GHz element at 3.5 GHz. A suspended electromagnetic band gap (EBG) structure is proposed to suppress surface waves and ensure sufficient geometric space for the phase shifter and control networks in the mmWave element. A low-cost planar spiral inductor (PSI) is carefully optimized to connect mmWave elements, enabling the sub-6 GHz function without affecting mmWave operation. Finally, prototypes of the DBI-RIS are fabricated, and experimental verification is conducted using two separate measurement testbeds. The fabricated sub-6 GHz RIS successfully achieves beam steering within the range of -35 to 35 degrees for DBI-RIS with 4x4 sub-6 GHz elements, while the mmWave RIS demonstrates beam steering between -30 to 30 degrees for DBI-RIS with 8x8 mmWave elements, and have good agreement with simulation results.

Federated Learning (FL) is a decentralized machine learning method that enables participants to collaboratively train a model without sharing their private data. Despite its privacy and scalability benefits, FL is susceptible to backdoor attacks, where adversaries poison the local training data of a subset of clients using a backdoor trigger, aiming to make the aggregated model produce malicious results when the same backdoor condition is met by an inference-time input. Existing backdoor attacks in FL suffer from common deficiencies: fixed trigger patterns and reliance on the assistance of model poisoning. State-of-the-art defenses based on analyzing clients' model updates exhibit a good defense performance on these attacks because of the significant divergence between malicious and benign client model updates. To effectively conceal malicious model updates among benign ones, we propose DPOT, a backdoor attack strategy in FL that dynamically constructs backdoor objectives by optimizing a backdoor trigger, making backdoor data have minimal effect on model updates. We provide theoretical justifications for DPOT's attacking principle and display experimental results showing that DPOT, via only a data-poisoning attack, effectively undermines state-of-the-art defenses and outperforms existing backdoor attack techniques on various datasets.

No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference, which have achieved tremendous improvements due to the utilization of deep neural networks. However, learning-based NR-PCQA methods suffer from the scarcity of labeled data and usually perform suboptimally in terms of generalization. To solve the problem, we propose a novel contrastive pre-training framework tailored for PCQA (CoPA), which enables the pre-trained model to learn quality-aware representations from unlabeled data. To obtain anchors in the representation space, we project point clouds with different distortions into images and randomly mix their local patches to form mixed images with multiple distortions. Utilizing the generated anchors, we constrain the pre-training process via a quality-aware contrastive loss following the philosophy that perceptual quality is closely related to both content and distortion. Furthermore, in the model fine-tuning stage, we propose a semantic-guided multi-view fusion module to effectively integrate the features of projected images from multiple perspectives. Extensive experiments show that our method outperforms the state-of-the-art PCQA methods on popular benchmarks. Further investigations demonstrate that CoPA can also benefit existing learning-based PCQA models.

No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference, which has achieved remarkable performance due to the utilization of deep learning-based models. However, these data-driven models suffer from the scarcity of labeled data and perform unsatisfactorily in cross-dataset evaluations. To address this problem, we propose a self-supervised pre-training framework using masked autoencoders (PAME) to help the model learn useful representations without labels. Specifically, after projecting point clouds into images, our PAME employs dual-branch autoencoders, reconstructing masked patches from distorted images into the original patches within reference and distorted images. In this manner, the two branches can separately learn content-aware features and distortion-aware features from the projected images. Furthermore, in the model fine-tuning stage, the learned content-aware features serve as a guide to fuse the point cloud quality features extracted from different perspectives. Extensive experiments show that our method outperforms the state-of-the-art NR-PCQA methods on popular benchmarks in terms of prediction accuracy and generalizability.

Counter-rotating mechanical metamaterials have previously been found to have anomalous characteristics or functions such as auxetics effects, shape changers, and soliton transports, which are all under monostable conditions. The properties of counter-rotating mechanical metamaterials under bistable conditions have not yet been explored. Here, we found that for a bistable counter-rotating metamaterial chain, the effective stiffnesses of the two steady states are different in the chain with even-numbered nodes. For the chain with odd-numbered nodes, the effective stiffnesses corresponding to the two steady states are exactly the same. This special property is not characterized by the characteristic attenuation lengths of the underlying mechanism, but depends on the different symmetries of the underlying mechanism of the chains with odd and even nodes. In addition, the relationship between the abnormal non-monotonic size effect and equilibrium angle are clarified. More interestingly, for one-dimensional chains with even-numbered nodes, the size effect of effective stiffness bifurcates at a specific equilibrium angle, and the according mechanisms are revealed.

We investigate the relationship between projective measurements and positive operator-valued measures (POVMs) in the task of quantum steering. A longstanding open problem in the field has been whether POVMs are more powerful than projective measurements for the steerability of noisy singlet states, which are known as Werner states. We resolve this problem for two-qubit systems and show that the two are equally powerful, thereby closing the so-called Werner gap. Using the incompatible criteria for noisy POVMs and the connection between quantum steering and measurement incompatibility, we construct a local hidden state model for Werner states with Bloch sphere radius $r\leq 1/2$ under general POVMs. This construction also provides a local hidden variable model for a larger range of Werner states than previously known. {In contrast, we also show that projective measurements and POVMs can have inequivalent noise tolerances when using a fixed state ensemble to build different local hidden state models.} These results help clarify the relationship between projective measurements and POVMs for the tasks of quantum steering and nonlocal information processing.

We propose a method to build an astronomical interferometer using continuous variable quantum teleportation to overcome the transmission loss between distant telescopes. The scheme relies on two-mode squeezed states shared by distant telescopes as entanglement resources, which are distributed using continuous variable quantum repeaters. We find the optimal measurement on the teleported states, which uses beam-splitters and photon-number-resolved detection. Compared to prior proposals relying on discrete states, our scheme has the advantages of using linear optics to implement the scheme without wasting stellar photons and making use of multiphoton events, which are regarded as noise in previous discrete schemes., Comment: 15 pages, 7 figures

The performance of antennas can severely deteriorate in the presence of adjacent electrically-large scatterers. In this work, we use a conducting hollow cylinder to shield the scatterer. The cylinder is shelled with single layer dielectric and electromagnetic metasurface. The scattering field analysis with respect to the surface impedance is derived. By optimizing the anisotropic impedance distribution, the scattering cross-section can be effectively reduced. The proposed method is valid for both TMz, TEz and non-TM/TE incident field. The accuracy and effectiveness of the method are verified by four cloaking scenarios in microwave regime. We demonstrate that with the surface impedance obtained by our method, a metasurface is designed with physical subwavelength structure. We also show a cloaking scenario under magnetic dipole radiation, which is closer to the case of a realistic antenna. This method can be further applied to cloaking tasks in terahertz and optical regimes., Comment: 16 pages, 10 figures

Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years. However, as reference point clouds are not available in many cases, no-reference (NR) metrics have become a research hotspot. Existing NR methods suffer from poor generalization performance. To address this shortcoming, we propose a novel NR-PCQA method, Point Cloud Quality Assessment via Domain-relevance Degradation Description (D$^3$-PCQA). First, we demonstrate our model's interpretability by deriving the function of each module using a kernelized ridge regression model. Specifically, quality assessment can be characterized as a leap from the scattered perceptual domain (reflecting subjective perception) to the ordered quality domain (reflecting mean opinion score). Second, to reduce the significant domain discrepancy, we establish an intermediate domain, the description domain, based on insights from subjective experiments, by considering the domain relevance among samples located in the perception domain and learning a structured latent space. The anchor features derived from the learned latent space are generated as cross-domain auxiliary information to promote domain transformation. Furthermore, the newly established description domain decomposes the NR-PCQA problem into two relevant stages. These stages include a classification stage that gives the degradation descriptions to point clouds and a regression stage to determine the confidence degrees of descriptions, providing a semantic explanation for the predicted quality scores. Experimental results demonstrate that D$^3$-PCQA exhibits robust performance and outstanding generalization ability on several publicly available datasets. The code in this work will be publicly available at https://smt.sjtu.edu.cn.

Objective: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a UMLS-colloquial symptom dictionary from COVID-19-related tweets as proof of concept. Methods: COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity sample were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research. Results: We identified 498,480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18,226. The final dictionary contains 38,175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons. Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes., Comment: Updated full paper. Abstract presented at IEEE ICHI 2023 and AMIA Annual Symposium 2023