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A long-standing topic in artificial intelligence is the effective recognition of patterns from noisy images. In this regard, the recent data-driven paradigm considers 1) improving the representation robustness by adding noisy samples in training phase (i.e., data augmentation) or 2) pre-processing the noisy image by learning to solve the inverse problem (i.e., image denoising). However, such methods generally exhibit inefficient process and unstable result, limiting their practical applications. In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing. Here, the noise-robust representation is designed as Fractional-order Moments in Radon space (FMR), with also beneficial properties of orthogonality and rotation invariance. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases, and the introduced fractional-order parameter offers time-frequency analysis capability that is not available in classical methods. Formally, both implicit and explicit paths for constructing the FMR are discussed in detail. Extensive simulation experiments and robust visual applications are provided to demonstrate the uniqueness and usefulness of our FMR, especially for noise robustness, rotation invariance, and time-frequency discriminability.

Group-based content push can be widely applied in integrated networks, where group key management is crucial for the push's security. Existing group key management methods mainly include symmetric group key agreement, broadcast encryption, asymmetric group key agreement, and attribute-based encryption. However, most of them do not consider user equipment (UE) identity privacy and unlinkability, cannot support flexibility and efficiency due to each UE maintaining group keys, and lack the trustworthiness of UE and group key management, which hinders the widespread adoption of group-based content push in trustless environments like integrated networks. In this paper, we investigate a novel decentralized group key management (DeGKM) scheme for group-based content push in integrated networks, where different operators manage pseudonyms and group keys across domains in a decentralized manner. In particular, our scheme adopts verifiable shuffling to establish a unified and trustworthy inter-domain pseudonym management approach that can preserve UE identity privacy and pseudonym unlinkability without relying on a trusted third party, and introduces a unified inter-domain group key management method based on Chinese remainder theorem and blockchain that significantly guarantees the flexibility, efficiency and trustworthiness. We formally prove the security of DeGKM and show its efficiency through simulations and comparisons with related works.

The increasing privacy concerns associated with cloud-assisted image retrieval have captured the attention of researchers. However, a significant number of current research endeavors encounter limitations, including suboptimal accuracy, inefficient retrieval, and a lack of effective result verification mechanisms. To address these limitations, we propose an adaptive verifiable privacy-preserving medical image retrieval (AVPMIR) scheme in the outsourced cloud. Specifically, we utilize the convolutional neural network (CNN) ResNet50 model to extract the feature of each medical image within the dataset of the medical institution, aiming to enhance retrieval accuracy. To enhance retrieval efficiency, we build an encryption searchable index based on a mini-batch $k$k-means clustering algorithm. Furthermore, we present an index merging method in which multi-data owners build a different index tree according to different standards. To check the correctness of the returned results from the cloud server, we construct an adaptive verification framework for the obtained results based on chameleon hash and BLS signature. To provide strong security for the medical image datasets, we design an improved logistic chaotic mapping algorithm. The security analysis demonstrates that AVPMIR can defend various threat models. The experiment analysis further indicates that the AVPMIR can improve retrieval efficiency and demonstrate its practicability.

Point cloud registration is a basic task in computer vision and computer graphics. Recently, deep learning-based end-to-end methods have made great progress in this field. One of the challenges of these methods is to deal with partial-to-partial registration tasks. In this work, we propose a novel end-to-end framework called MCLNet that makes full use of multi-level consistency for point cloud registration. First, the point-level consistency is exploited to prune points located outside overlapping regions. Second, we propose a multi-scale attention module to perform consistency learning at the correspondence-level for obtaining reliable correspondences. To further improve the accuracy of our method, we propose a novel scheme to estimate the transformation based on geometric consistency between correspondences. Compared to baseline methods, experimental results show that our method performs well on smaller-scale data, especially with exact matches. The reference time and memory footprint of our method are relatively balanced, which is more beneficial for practical applications.

First-person view attention has been widely studied in computer science domain since 1990s while third-person view attention in natural scenarios begins to gain the intensive interest until 2015. This paper focuses on the problem of third-person view attention prediction in natural scenarios where a human freely performs daily activities without constraints. To handle the two insuffiencies of existing methods: 1) assuming some extra information (except for input images) are given in advance; and 2) ignoring the importance of human-scene interaction, this paper proposes a model with weak information dependency, which helps to alleviate annotation costs. In addition, a transformer-based human-scene interaction mechanism is proposed to explore the global and long-dependency contexts between the human and scene. The pipeline of the proposed model is firstly extracting human and scene features, then inferring human attention probability map by fusing human and scene features via a transformer-based network, and finally predicting human attention object based on human attention probability map and object detection. The experiments on two public datasets validate the effectiveness of our model.

The role of incommensurate (IC) modulation in the evolution of the pinched polarization–electric field (P–E) hysteresis loops has been investigated and discussed based on the structure and polarization evolution in Ba4(Sm1–xLax)2Ti4Nb6O30tetragonal tungsten bronzes. The relaxor behavior in the La-rich compound is accompanied by an IC modulation structure. Introduction of smaller Sm in the system increases the driving force for the transition from an IC modulation structure to a commensurate superstructure, which coupled with the ferroelectric transition in the middle composition with x= 0.5. In the Sm-rich compounds, the IC modulation structure reappears as a metastable state to balance the structural instability caused by the too small average ionic radius of the rare-earth ion; meanwhile, the field-induced transition from the IC modulation structure to the commensurate superstructure is confirmed by selected area electron diffraction using an in situ bias technique as the structural origin for the pinched P–Eloops. A phase diagram has been established by combining the ferroelectric phase transition and the modulation structure transition, and a new region with both very small A-site size (A1 + A2)/2 and A1-site tolerance factor (tA1) related to the ferroelectric compounds with pinched P–Eloops (pinched FE) was added into the previously reported crystal-chemical framework (Chem. Mater.2015,27,3250–3261). The present work expands the composition–structure–property relationships in tungsten bronze ferroelectrics by including the recently reported “pinched FE” and meanwhile extends the composition manipulation ranges from the crossover between relaxor and normal ferroelectrics to ferroelectrics with pinched P–Eloops.

Hydrogel bioelectronics has been widely used in wearable sensors, electronic skin, human-machine interfaces, and implantable tissue-electrode interfaces, providing great convenience for human health, safety, and education. The generation of electronic waste from bioelectronic devices jeopardizes human health and the natural environment. The development of degradable and recyclable hydrogels is recognized as a paradigm for realizing the next generation of environmentally friendly and sustainable bioelectronics. This review first summarizes the wide range of applications for bioelectronics, including wearable and implantable devices. Then, the employment of natural and synthetic polymers in hydrogel bioelectronics is discussed in terms of degradability and recyclability. Finally, this work provides constructive thoughts and perspectives on the current challenges toward hydrogel bioelectronics, providing valuable insights and guidance for the future evolution of sustainable hydrogel bioelectronics.

Alternating shear stress is a critical factor in the accumulation of damage during rolling contact fatigue, severely limiting the service life of bearings. However, the specific mechanisms responsible for the cyclic shear fatigue damage in bearing steel have not been fully understood. Here the mechanical response and microstructural evolution of a model GGr15 bearing steel under cyclic shear loading are investigated through the implementation of molecular dynamics simulations. The samples undergo 30 cycles under three different loading conditions with strains of 6.2%, 9.2%, and 12.2%, respectively. The findings indicate that severe cyclic shear deformation results in early cyclic softening and significant accumulation of plastic damage in the bearing steel. Besides, samples subjected to higher strain-controlled loading exhibit higher plastic strain energy and shorter fatigue life. Additionally, strain localization is identified as the predominant damage mechanism in cyclic shear fatigue of the bearing steel, which accumulates and ultimately results in fatigue failure. Furthermore, simulation results also revealed the microstructural reasons for the strain localization (e.g., BCC phase transformation into FCC and HCP phase), which well explained the formation of white etching areas. This study provides fresh atomic-scale insights into the mechanisms of cyclic shear fatigue damage in bearing steels.

The conventional melting methods were used to obtain in situ TiC particle-reinforced dual-phase steel, followed by hot rolling and heat treatment processes. The aim was to investigate the effect of TiC particles on the fracture behavior of dual-phase steel at different annealing temperatures, by analyzing the microstructure and tensile behavior of the multiscale TiC particle-reinforced dual-phase steel. The results showed that TiC particles precipitated in the as-cast microstructure of dual-phase steel were distributed along the grain boundaries. During hot rolling, the grain boundary-like morphology of the micron-sized TiC particles was disrupted, and the particles became more refined and evenly distributed in the matrix. The tensile tests revealed that the strength of the TiC particle-reinforced dual-phase steel increased with increasing martensite content, while the elongation decreased. These results were similar to those of conventional steel. The addition of 1 vol.% multiscale TiC particles improved the strength of the dual-phase steel but did not affect elongation of the steel. Cracks and holes were primarily concentrated around the TiC particles rather than at the interface of martensite and ferrite. The main causes of crack sprouting were TiC particle interface cracking and TiC particle internal fragmentation. Overall, the study demonstrated the potential of multiscale TiC particle-reinforced dual-phase steel as a strong and tough material. The refined distribution of TiC particles in the matrix improved the strength of the material without compromising its elongation. The results also highlighted the importance of careful selection of reinforcement particles to avoid detrimental effects on the fracture behavior of the material.

To address the current issues of brittleness and lower mechanical performance for Zn alloys, the Zn–Mn–Li system that has significant potential for enhancing strength and toughness is selected for the design, fabrication, and investigation. Li and Mn are selected as alloying elements, and a series of Zn-0.4Mn-xLi alloys are prepared. A comprehensive study of as-cast alloys is conducted on the microstructure, mechanical properties, and corrosion behavior. The results reveal that the initially precipitated β-LiZn4phase in the as-cast Zn-0.4Mn-xLi alloys gradually transforms into a β-LiZn4/Zn lamellar structure with the increase of Li content. The β-LiZn4/Zn lamellar structure significantly contributes to the strength of the alloy. The as-cast Zn-0.4Mn-0.8Li alloy exhibits optimal mechanical strength, with yield strength and ultimate tensile strength of 203.2 ± 8.9 MPa and 271.5 ± 20.0 MPa, respectively. Electrochemical and immersion experiments indicate a dense Li-rich corrosion product layer formed on the alloy surface suppresses the generation of pitting corrosion and enhances the corrosion resistance of the alloy. These results manifest that the Li content of the Zn–Mn–Li alloy has a significant influence on the performance, such as the microstructure, mechanical properties, and corrosion behavior. According to the results of mechanical properties and corrosion behavior, Zn-0.4Mn-0.8Li alloy exhibits an optimal comprehensive performance, which provides valuable insight for the development of superior Zn–Mn–Li alloys.

Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.

Perceptual image encryption degrades image quality by selectively encrypting some key information of the plain images. The encrypted images are partially perceptible according to the security or quality requirements. Although several types of attacks have tried to infer privacy information from the encrypted images, they can only either extract statistical information or enhance image sketch. In this paper, we take one step further and fully recover the plain images from perceptually encrypted counterparts by designing a non-local attack network (NL-ANet). NL-ANet is composed of densely cascaded multiscale non-local modules (MSNL) and a hierarchical attention fusion module (HAFM). In particular, to better reconstruct encryption distortion, we introduce MSNL to capture powerful hierarchical features from different scales, and propose HAFM to adaptively aggregate and enhance informative hierarchical features for reconstruction. We also propose a new instantiation of the multi-head non-local block with channel attention (MHCA) to explore the long-range dependencies of global contextual information. Extensive experiments show that NL-ANet is encryption-agnostic and superior on different perceptual encryption schemes under different encryption strengths. NL-ANet also achieves better performance than state-of-the-art image restoration methods.

In cloud storage systems, secure deduplication plays a critical role in saving storage costs for the cloud server and ensuring data confidentiality for cloud users. Traditional secure deduplication schemes require users to encrypt their outsourced files using specific encryption algorithms that cannot provide semantic security. However, users are unable to directly benefit from the storage savings, as the relation between the actual storage cost and the offered prices remains not transparent. As a result, users may be unwilling to cooperate with the cloud by encrypting their data using semantically secure algorithms. Moreover, data integrity is a significant concern for cloud storage users. To address these issues, this paper proposes a novel transparent and secure deduplication scheme that supports integrity auditing. Compared to previous works, our design can verify the number of file owners and the integrity through one-time proof verification. It also protects the private contents of files and the privacy of file ownership from malicious users. Moreover, our scheme includes a batch auditing method to simultaneously verify the numbers of file owners and the integrity of multiple files. Theoretical analysis confirms the correctness and security of our scheme. Comparison results demonstrate its competing performance over previous solutions.

Data aggregation plays a crucial role in smart grid communication as it enables the collection of data in an energy-efficient manner. However, the widespread deployment of smart meters has raised significant concerns regarding the privacy of users’ personal data. Therefore, in this paper, we present an efficient and privacy-preserving data aggregation and trust management scheme (PATM) for an IoT-enabled smart grid based on smart contract. First, we propose a five-layer architecture for smart grid communication to support secure and efficient data aggregation and management. Under the architecture, the Boneh-Goh-Nissim cryptosystem with blind factor is improved to facilitate privacy protection. In addition, the tamper-evident nature of blockchain is utilized for effective data management. Our designs also enhance the resistance to differential attack and prevent privacy breaches during the aggregation process. Detailed security proof and theoretical analysis confirm that our PATM can satisfies the necessary security and privacy requirements while maintaining the required efficiency for smart grid operations. Furthermore, comparative experiments demonstrate that PATM outperforms other proposed work in terms of storage cost, computational complexity, and utility of differential privacy.

Blockchain is extensively applied to many fields as an immutable distributed ledger. However, the immutability contradicts regulations such as the GDPR ruling “the right to be forgotten” of data. Besides, numerous emerging blockchain-based applications call for elastic data management. To erase some data, redactable blockchains are proposed for breaking the immutability in a controlled way. Unfortunately, the prior solutions may suffer from poor security and centralized control of the redaction privilege. They cannot support dynamic nodes, where the departure of participators will result in a single point of failure. This article proposes a novel dynamic and decentralized attribute-based chameleon hash (DACH) to make blockchain history mutable, achieving a securely and dynamically redactable blockchain (SDR-chain) in a decentralized setting. We first propose the formal definition, security models, and concrete construction of our DACH. Meanwhile, we design a delegation algorithm of DACH to support a dynamically changing committee, where participators can freely and securely leave and join the network. Then, the transactions of the SDR-chain are redacted by computing DACH collisions. The security is analyzed in the random oracle model. Finally, theoretical analysis and experimental evaluation demonstrate that our SDR-chain is superior to the prior solutions in terms of security and functionality.

As social media communication develops, reliable multimedia quality evaluation indicators have become a prerequisite for enriching user experience services. In this paper, we propose a multiscale spatiotemporal pyramid attention (SPA) block for constructing a blind video quality assessment (VQA) method to evaluate the perceptual quality of videos. First, we extract motion information from the video frames at different temporal scales to form a feature pyramid, which provides a feature representation with multiple visual perceptions. Second, an SPA module, which can effectively extract multiscale spatiotemporal information at various temporal scales and develop a cross-scale dependency relationship, is proposed. Finally, the quality estimation process is completed by passing the extracted features obtained from a network of multiple stacked spatiotemporal pyramid blocks through a regression network to determine the perceived quality. The experimental results demonstrate that our method is on par with the state-of-the-art approaches. The source code necessary for conducting groundbreaking scientific research is accessible online https://github.com/Land5cape/SPBVQA.

Objective: SUMO-specific protease 3 (SENP3), a member of the SUMO-specific protease family, reverses the SUMOylation of SUMO-2/3 conjugates. Dysregulation of SENP3 has been proven to be involved in the development of various tumors. However, its role in mantle cell lymphoma (MCL), a highly aggressive lymphoma, remains unclear. This study was aimed to elucidate the effect of SENP3 in MCL. Methods: The expression of SENP3 in MCL cells and tissue samples was detected by RT-qPCR, Western blotting or immunohistochemistry. MCL cells with stable SENP3 knockdown were constructed using short hairpin RNAs. Cell proliferation was assessed by CCK-8 assay, and cell apoptosis was determined by flow cytometry. mRNA sequencing (mRNA-seq) was used to investigate the underlying mechanism of SENP3 knockdown on MCL development. A xenograft nude mouse model was established to evaluate the effect of SENP3 on MCL growth in vivo. Results: SENP3 was upregulated in MCL patient samples and cells. Knockdown of SENP3 in MCL cells inhibited cell proliferation and promoted cell apoptosis. Meanwhile, the canonical Wnt signaling pathway and the expression of Wnt10a were suppressed after SENP3 knockdown. Furthermore, the growth of MCL cells in vivowas significantly inhibited after SENP3 knockdown in a xenograft nude mouse model. Conclusion: SENP3 participants in the development of MCL and may serve as a therapeutic target for MCL.

Porous Ti–Zr–Cu–Pd–Sn bulk metallic glass (BMG) produced by Spark Plasma Sintering (SPS) in our previous work demonstrates bone-like mechanical properties, effectively mitigating the issue of stress shielding within the implant. Nevertheless, concerns persist regarding the BMG's brittleness and its lack of bioactivity, both of which pose concealed risks in practical applications. In light of these challenges, a semi-degradable biomaterial, the MG-Fe composites, has been meticulously crafted via SPS in this work. The incorporation of ductile Fe phase in the MG matrix significantly enhances its plasticity. Moreover, the degradation of Fe results in the deposition of Ca–P compounds, imbuing the MG-Fe composites with a degree of bioactivity. Furthermore, by introducing a gradient porous structure, researchers have managed to fine-tune the mechanical properties of the MG-Fe composites. This innovative design imparts plastic and ductile compression deformation behavior to the gradient porous MG-Fe composites, offering a potential solution to the issue of brittle fracture behavior observed in conventional brittle BMGs. In addition, the introduction of the gradient porous structure serves to further accelerate the degradation rate of Fe. This advancement holds the potential to strike a dynamic balance with the growth rate of human bone, further elevating the bioactivity of the MG-Fe composites.

• Porous TiZrNbTa high-entropy alloys/Ti composite with high strength and low Young's modulus was fabricated. • A novel MgO space holder was excavated and successfully applied. • MgO particles can be used in high temperature and sintering pressure conditions. Stress shielding is caused by the mismatch of stiffness between bone and implant materials, which may give rise to bone resorption and loosening, thereby causing implantation failure. There is a huge gap between Young's modulus of human bone and low Young's modulus β Ti alloys. A porous structure design can achieve the target of low Young's modulus, and thus achieve the matching between human bone and implant materials. However, a suitable space holder (SH) that can be applied at high temperatures and sintering pressure has not been reported. In this study, the TiZrNbTa/Ti titanium matrix composite (TMC) with high strength and large ductility was used as scaffold materials and combined the SH technique with the spark plasma sintering (SPS) technique to obtain a porous structure. A novel space holder, i.e., MgO particles was adopted, which can withstand high-temperature sintering accompanied by a sintering pressure. The porous TiZrNbTa/Ti with 40 vol.% MgO added exhibits a maximum strength of 345.9 ± 10.4 MPa and Young's modulus of 24.72 ± 0.20 GPa, respectively. It possesses higher strength compared with human bone and matches Young's modulus of human bone, which exhibits great potential for clinical application. [Display omitted] [ABSTRACT FROM AUTHOR]

In high-temperature cuprate superconductors, the superconducting transition temperature (Tc) depends on the number of CuO2planes in the structural unit and the maximum Tcis realized in the trilayer system. Trilayer superconductors also exhibit an unusual phase diagram where Tcis roughly constant in the overdoped region, which is in contrast to the decrease usually found in other cuprate superconductors. The mechanism for these two effects remains unclear. Here we report features in the electronic structure of Bi2Sr2Ca2Cu3O10+δsuperconductor that helps to explain this issue. Our angle-resolved photoemission spectroscopy measurements show the splitting of bands from the three layers, and this allows us to parameterize a three-layer interaction model that effectively describes the data. This, in turn, demonstrates the electronic origin of the maximum Tcand its persistence in the overdoped region. These results are qualitatively consistent with a composite picture where a high Tcis realized in an array of coupled planes with different doping levels such that a high pairing strength is derived from the underdoped planes, whereas a large phase stiffness comes from the optimally or overdoped ones.

Privacy-preserving machine learning as a service (PP-MLaaS) can achieve the secure model computation towards the client’s private input through a series of privacy-preserving operations. However, existing PP-MLaaS schemes are suffering from low computational efficiency thwarting their application in real-world scenarios. To mitigate this issue, this article seeks to identify the main algorithmic problems biting computation efficiency and present possible enablers to accelerate the process of secure model computation. The investigation consists of four hierarchical parts. In the first part, existing PP-MLaaS frameworks are reviewed, and the problem statement is discussed in the next part, in which the possible issues that lead to inefficient computation are illustrated from computational logic and computational model, respectively. In the third part, we investigate two potential strategies to improve the calculation efficiency of privacy-preserving neural computing, involving the optimization of cost hierarchy in the calculation process and the crypto-friendly pruning in the neural computation model. Research directions and open topics for efficient PP-MLaaS are discussed in the last part, including rotation-free, neural architecture searching, hardware-aware, and nonlinearity-efficient PPMLaaS.

In multiple-instance learning (MIL), each training example is represented by a bag of instances. A training bag is either negative if it contains no positive instances or positive if it has at least one positive instance. Previous MIL methods generally assume that training bags are fully labeled. However, the exact labels of training examples may not be accessible, due to security, confidentiality, and privacy concerns. Fortunately, it could be easier for us to access the pairwise similarity between two bags (indicating whether two bags share the same label or not) and unlabeled bags, as we do not need to know the underlying label of each bag. In this paper, we provide the first attempt to investigate MIL from only similar-dissimilar-unlabeled bags. To solve this new MIL problem, we first propose a strong baseline method that trains an instance-level classifier by employing an unlabeled-unlabeled learning strategy. Then, we also propose to train a bag-level classifier based on a convex formulation and theoretically derive a generalization error bound for this method. Comprehensive experimental results show that our instance-level classifier works well, while our bag-level classifier even has better performance.

Distributed fog computing has received increasing attention recently and fog-assisted cloud storage can provide a real-time service to collect and manage large-scale data for the applications of Internet of Things. Encrypted data deduplication over cloud storage can significantly save storage space of the cloud server while protecting the confidentiality of the outsourced data. Previous encrypted data deduplication schemes are mostly designed for traditional cloud storage with a two-layer architecture and cannot be applied to the emerging fog-assisted cloud storage that has a more complex three-layer architecture (i.e., cloud server, fog node and endpoint device). In this paper, we design, analyze and implement FCDedup, a new encrypted data deduplication scheme for fog-assisted cloud storage. FCDedup is a two-level deduplication system that enables each fog node to detect duplicated encrypted data uploaded by different endpoint devices, as well as enables cloud server to detect duplicated encrypted data from different fog nodes. By doing so, FCDedup can achieve both intra-deduplication within a single data owner and inter-deduplication across different data owners. FCDedup is also designed to prevent cloud server and fog nodes launching the brute-force attacks, and to guarantee the reliability of files downloaded from the cloud. Formal analysis is provided to justify its deduplication correctness and security. Besides, we implement a prototype of FCDedup using Alibaba Cloud as backend storage. Our evaluations demonstrate that FCDedup is completely compatible with existing cloud storage systems and achieves modest performance overhead.

In this paper, a framework based on two feature extraction networks and a multilevel feature fusion (MFF) network is proposed. Multilevel degradation features can be obtained through this method, and combined with the human visual perception system, the local and global feature information contained in these features can be captured, which is conducive to the prediction of distorted images. First, a restored image approximating a reference image is generated by a restorative generative adversarial network (GAN). Furthermore, the multilevel degradation features of distorted images and the restored image features are extracted by EfficientNet. Second, the features extracted by EfficientNet are input into the MFF network and are fully expressed by the top-down, bottom-up and third edge joining methods. Moreover, the features provide more low-level details and high-level semantic features for the prediction of image quality scores. In addition, after the MFF stage, the framework calculates the score of each branch feature and obtains the average quality score. Experimental results show that our method achieves greatly improved prediction accuracy and performance on five standard databases.

Vision-based practical applications, such as consumer photography and automated driving systems, greatly rely on enhancing the visibility of images captured in night-time environments. For this reason, various image enhancement algorithms (EHAs) have been proposed. However, little attention has been given to the quality evaluation of enhanced night-time images. In this paper, we conduct the first dedicated exploration of the subjective and objective quality evaluation of enhanced night-time images. First, we build an enhanced night-time image quality (EHNQ) database, which is the largest of its kind so far. It includes 1,500 enhanced images generated from 100 real night-time images using 15 different EHAs. Subsequently, we perform a subjective quality evaluation and obtain subjective quality scores on the EHNQ database. Thereafter, we present an objective blind quality index for enhanced night-time images (BEHN). Enhanced night-time images usually suffer from inappropriate brightness and contrast, deformed structure, and unnatural colorfulness. In BEHN, we capture perceptual features that are highly relevant to these three types of corruptions, and we design an ensemble training strategy to map the extracted features into the quality score. Finally, we conduct extensive experiments on EHNQ and EAQA databases. The experimental and analysis results validate the performance of the proposed BEHN compared with the state-of-the-art approaches. Our EHNQ database is publicly available for download at https://sites.google.com/site/xiangtaooo/.

In early life, sialic acid (SA) plays a crucial role in neurodevelopment and neuronal function. However, it remains unclear whether and how SA supplementation in early life promotes behavioral response to stress in adolescence.

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired. In this paper, we systematically analyze existing reconstruction attacks and propose to leverage data augmentation to defeat these attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract training samples from the corresponding gradients. We first design two new metrics to quantify the impacts of transformations on data privacy and model usability. With the two metrics, we design a novel search method to automatically discover qualified policies from a given data augmentation library. Our defense method can be further combined with existing collaborative training systems without modifying the training protocols. We conduct comprehensive experiments on various system settings. Evaluation results demonstrate that the policies discovered by our method can defeat state-of-the-art reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.

• The multi-scale structured Zn-0.4Mn-0.8Li alloy was fabricated by hot rolling. • The cooperation of coarse grains and fine grains was found to improve the strength and guarantee the ductility of Zn-0.4Mn-0.8Li alloy significantly. • The activation of {10-12} tensile twinning further promoted the enhancement of the mechanical strength of Zn-0.4Mn-0.8Li alloy. Zinc-based degradable metals are considered one of the most promising biodegradable materials due to their moderate corrosion rate, excellent mechanical properties, and good biocompatibility. In this work, biodegradable Zn-0.4Mn-0.8Li alloy was fabricated and rolled in multiple passes at different temperatures. As the hot rolling temperature increases, the grain size of Zn-0.4Mn-0.8Li alloy was found to increase correspondingly. Further, a multi-scale structure with the coexistence of coarse grains and fine grains was obtained. The results demonstrated that the mechanical strength and corrosion resistance were improved by increasing the rolled temperature. It was observed that Zn-0.4Mn-0.8Li alloy with a total reduction of 90% after hot rolling at 325 °C exhibited excellent mechanical and corrosion properties. The cooperation of multi-scale microstructure and twinning was found to improve the strength and guarantee the ductility of Zn-0.4Mn-0.8Li alloy significantly so that the 325 °C hot-rolled Zn-0.4Mn-0.8Li alloy has optimal comprehensive properties. Further, yield strength, ultimate tensile strength, and elongation were found to be 449.7 ± 5.3 MPa, 505.1 ± 6.5 MPa, and 40.5% ± 7.5%, respectively. Meanwhile, Zn-0.4Mn-0.8Li alloy via 325 °C hot-rolled processes also exhibited excellent corrosion resistance. The corrosion current density and corrosion potential were found to be 8.8 × 10–5 mA cm–2 and −0.929 V, respectively. The preliminary study indicates that Zn-0.4Mn-0.8Li alloy is a promising candidate material for medical device applications. [Display omitted] [ABSTRACT FROM AUTHOR]

In contrast to conventional Fermi liquids where resistivity scales quadratically with temperature, unconventional superconductors usually exhibit a normal-state resistivity that varies as a linear function of temperature (T-linear) in the low-temperature limit. This phenomenon, termed as the strange metal, has been extensively studied in cuprates and is thought to be intimately linked to superconductivity. A quantitative description of its relationship with superconductivity is, therefore, of great importance for developing further theoretical understanding; however, so far, comprehensive studies are scarce due to the difficulties in realizing a systematic control of the superconducting state. Here we report the observation of a typical strange-metal behaviour in FeSe, namely, T-linear resistivity, linear-in-field magnetoresistance and universal scaling of magnetoresistance. More importantly, when we tune the superconductivity by ionic-liquid gating, the superconducting transition temperature increases from approximately 10 to 45 K, and the T-linear resistivity coefficient exhibits a quadratic dependence on the critical temperature. This is a ubiquitous feature that describes the relation between these parameters in various systems including overdoped cuprates and Bechgaard salts. This suggests that there may be a universal mechanism underlying the T-linear resistivity and unconventional superconductivity.

Neovascularization is critical to improve the diabetic microenvironment, deliver abundant nutrients to the wound and promote wound closure. However, the excess of oxidative stress impedes the healing process. Herein, a self-adaptive multifunctional hydrogel with self-healing property and injectability is fabricated through a boronic ester-based reaction between the phenylboronic acid groups of the 3-carboxyl-4-fluorophenylboronic acid -grafted quaternized chitosan and the hydroxyl groups of the polyvinyl alcohol, in which pro-angiogenic drug of desferrioxamine (DFO) is loaded in the form of gelatin microspheres (DFO@G). The boronic ester bonds of the hydrogel can self-adaptively react with hyperglycemic and hydrogen peroxide to alleviate oxidative stress and release DFO@G in the early phase of wound healing. A sustained release of DFO is then realized by responding to overexpressed matrix metalloproteinases. In a full-thickness diabetic wound model, the DFO@G loaded hydrogel accelerates angiogenesis by upregulating expression of hypoxia-inducible factor-1 and angiogenic growth factors, resulting in collagen deposition and rapid wound closure. This multifunctional hydrogel can not only self-adaptively change the microenvironment to a pro-healing state by decreasing oxidative stress, but also respond to matrix metalloproteinases to release DFO. The self-adaptive multifunctional hydrogel has a potential for treating diabetic wounds.

Internet of Vehicles (IoV) realizes information interaction between vehicles and all networked entities, and the heterogeneous, openness, and diversity make it suffer from various security challenges such as authentication and access control problems. Attribute-based signature (ABS) is a promising cryptographic tool for addressing the above concerns, however, some existing ABS schemes take heavy computation overheads for signature verification. Though some server-aided ABS schemes are proposed to enhance performance, those schemes suffer from devastating collusion attacks. In addition, current signature-policy ABS schemes only support the single threshold access policy, which fails to meet various access control requirements of IoV. To address the above problems, we propose an efficient server-aided ABS with collusion resistance (SAABS-CR) for IoV. It utilizes server-aided computation technology to mitigate the computation burden of verifiers, while resisting perfectly the collusion attacks that include between signers and between the signer and the aided server. Moreover, SAABS-CR supports flexible expressions of access policy under the signature-policy framework by using the linear secret-sharing scheme (LSSS), and it is proved to be secure in the security model of strong unforgeability against chosen-message attacks (SU-CMA). Theoretical analyses and experimental evaluations show that SAABS-CR improves efficiency by reducing the time cost of signature generation at least 34.8% and user verification at least 15%.

In this paper, an end-to-end blind image quality assessment (BIQA) model based on feature fusion with an attention mechanism is proposed. We extracted the multilayer features of the image and fused them based on the attention mechanism; the fused features are then mapped into score, and the image quality assessment without reference is realized. First, because the human visual perception system hierarchically approaches the input information from local to global, we used three different neural networks to extract physically meaningful image features, and we use modified VGG19 and modified VGG16 to extract the substrate texture information and the local information of the edges, respectively. Meanwhile, we use the resNet50 to extract high-level global semantic information. Second, to take full advantage of multilevel features and avoid monotonic addition in hierarchical feature fusion, we adopt an attention-based feature fusion mechanism that combines the global and local contexts of the features and assigns different weights to the features to be fused, so that the model can perceive richer types of distortion. Experimental findings on six standard databases show that our approach yields improved performance.

In this paper, we propose a pyramidal spatiotemporal feature hierarchy (PSFH)-based no-reference (NR) video quality assessment (VQA) method using transfer learning. First, we generate simulated videos by a generative adversarial network (GAN)-based image restoration model. The residual maps between the distorted frames and simulated frames, which can capture rich information, are utilized as one input of the quality regression network. Second, we use 3D convolution operations to construct a PSFH network with five stages. The spatiotemporal features incorporating the shared features transferred from the pretrained image restoration model are fused stage by stage. Third, with the guidance of the transferred knowledge, each stage generates multiple feature mapping layers that encode different semantics and degradation information using 3D convolution layers and gated recurrent units (GRUs). Finally, five approximate perceptual quality scores and a precise prediction score are obtained by fully connected (FC) networks. The whole model is trained under a finely designed loss function that combines pseudo-Huber loss and Pearson linear correlation coefficient (PLCC) loss to improve the robustness and prediction accuracy. According to the extensive experiments, outstanding results can be obtained compared with other state-of-the-art methods. Both the source code and models are available online.1

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce. This is because most learning algorithms strongly rely on the i.i.d. assumption on source/target data, which is often violated in practice due to domain shift. Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning. Over the last ten years, research in DG has made great progress, leading to a broad spectrum of methodologies, e.g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, to name a few; DG has also been studied in various application areas including computer vision, speech recognition, natural language processing, medical imaging, and reinforcement learning. In this paper, for the first time a comprehensive literature review in DG is provided to summarize the developments over the past decade. Specifically, we first cover the background by formally defining DG and relating it to other relevant fields like domain adaptation and transfer learning. Then, we conduct a thorough review into existing methods and theories. Finally, we conclude this survey with insights and discussions on future research directions.

Generally, 360-degree images have absolute senses of reality and three-dimensionality, providing a wide range of immersive interactions. Due to the novel rendering and display technology of 360-degree images, they have more complex perceptual characteristics than other images. It is challenging to perform comprehensive image quality assessment (IQA) learning by simply stacking multichannel neural network architectures for pre/postprocessing, compression, and rendering tasks. To thoroughly learn the global and local features in 360-degree images, reduce the complexity of multichannel neural network models and simplify the training process, this paper proposes a joint architecture with user perception and an efficient transformer dedicated to 360-degree no-reference (NR) IQA. The input of the proposed method is a 360-degree cube map projection (CMP) image. Furthermore, the proposed 360-degree NRIQA method includes a saliency map-based non-overlapping self-attention selection module and a U-shaped transformer (U-former)-based feature extraction module to account for perceptual region importance and projection distortion. The transformer-based architecture and the weighted average technique are jointly utilized for predicting local perceptual quality. Experimental results obtained on widely used databases show that the proposed model outperforms other state-of-the-art methods in NR 360-degree image quality evaluation cases. Furthermore, a cross-database evaluation and an ablation study also demonstrate the inherent robustness and generalization ability of the proposed model.

In this paper, we propose a blind image quality assessment (BIQA) method using self-attention and a recurrent neural network (RNN); this approach can effectively capture both local and global information from an input image. The implementation of our constructed deep no-reference (NR) assessment framework does not rely on any convolutional operations. First, the capture step for obtaining locally significant information is performed by a self-attention operation inside a divided window. Second, we design a serialized feature input memory subnetwork to fuse the global features of the image. Finally, all the integrated features are uniformly mapped to the target score. The experimental results obtained on publicly available benchmark IQA databases show that our approach outperforms other state-of-the-art algorithms.

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.

The rapid development of IoT (Internet of Things) brings great convenience to people through the utilization of IoT applications, but also brings huge security challenges. Existing IoT security breaches show that many IoT devices have authentication flaws. Although many IoT authentication schemes were proposed, they are not fit for recent smart IoT applications covering IoT device, back-end sever, and user-end mobile applications. To build the first line of defense for smart IoT systems, this paper proposes a new authentication scheme. The proposed scheme first models the entire lifecycle of the IoT device authentication for real-world scenarios of smart IoT systems that contains factory manufacturing, daily usage, and system resetting. For each stage in the lifecycle, the proposed scheme employs efficient symmetric key mechanisms to achieve the authentication between IoT device, back-end server, and mobile application. The proposed scheme supports both server-free local area network communication and sever-involved remote public area communication. Formal security verification shows that the proposed scheme resists existing attacks. The open-source experimental evaluations also show that the proposed scheme is efficient and promising for practical usage.

The corrosion mechanism of Cu-based crystalline/amorphous composites in NaCl solution is investigated. xCuCrZr/(1–x)CuZrAl (x = 10, 50, 90 wt%) crystalline/amorphous composites with excellent interfacial bonding are fabricated by ball milling and spark plasma sintering (SPS) processes. According to the findings of electrochemical testing and immersion experiments, composites containing 50% amorphous CuZrAl exhibit the lowest corrosion rate. In-depth behavioral analysis reveals that uniform corrosion dominates the crystalline CuCrZr phase, while pitting corrosion dominates the amorphous CuZrAl phase. The corrosion model of the composites is constructed to disclose corrosion processes at various phases as well as the kinetic mechanisms that govern individuals.

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this article, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.

Oxygen solid solution-strengthened titanium-oxygen (Ti–O) alloys with ultrahigh yield strength and large ductility were designed and synthesized via high-energy ball milling (BM) combined with spark plasma sintering (SPS) in compression. The morphology, dispersion state, and solid solution microstructure of the Ti–O alloys were optimized by regulating the mixing method, sintering temperature, and added oxygen content. Outstanding properties that include a compressive yield strength of 1220 MPa and large ductility of 36.3% were achieved after 1 h high-energy BM and 1000 °C SPS with the addition of about 1.8 at% oxygen. The grain refinement and oxygen solid solution strengthening indicate that the dense morphology, refined grain size, and oxygen solid solute atoms are responsible for the balance between the superior strength and ductility of the titanium alloys.