Flexible pressure and strain sensors have great potential for applications in wearable and implantable devices, soft robotics and artificial skin. Compared to flexible sensors based on ...filler/elastomer composites, conductive hydrogels are advantageous due to their biomimetic structures and properties, as well as biocompatibility. Numerous chemical and structural designs provide unlimited opportunities to tune the properties and performance of conductive hydrogels to match various demands for practical applications. Many electronically and ionically conductive hydrogels have been developed to fabricate pressure and strain sensors with different configurations, including resistance type and capacitance type. The sensitivity, reliability and stability of hydrogel sensors are dependent on their network structures and mechanical properties. This review focuses on tough conductive hydrogels for flexible sensors. Representative strategies to prepare stretchable, strong, tough and self-healing hydrogels are briefly reviewed since these strategies are illuminating for the development of tough conductive hydrogels. Then, a general account on various conductive hydrogels is presented and discussed. Recent advances in tough conductive hydrogels with well designed network structures and their sensory performance are discussed in detail. A series of conductive hydrogel sensors and their application in wearable devices are reviewed. Some perspectives on flexible conductive hydrogel sensors and their applications are presented at the end.
Robots are often required to generalize the skills learned from human demonstrations to fulfil new task requirements. However, skill generalization will be difficult to realize when facing with the ...following situations: the skill for a complex multistep task includes a number of features; some special constraints are imposed on the robots during the process of task reproduction; and a completely new situation quite different with the one in which demonstrations are given to the robot. This work proposes a new framework to facilitate robot skill generalization. The basic idea lies in that the learned skills are first segmented into a sequence of subskills automatically, then each individual subskill is encoded and regulated accordingly. Specifically, we adapt each set of the segmented movement trajectories individually instead of the whole movement profiles, thus, making it more convenient for the realization of skill generalization. In addition, human limb stiffness estimated from surface electromyographic signals is considered in the framework for the realization of human-to-robot variable impedance control skill transfer, as well as the generalization of both movement trajectories and stiffness profiles. Experimental study has been performed to verify the effectiveness of the proposed framework.
Federated machine learning which enables resource-constrained node devices (e.g., Internet of Things (IoT) devices and smartphones) to establish a knowledge-shared model while keeping the raw data ...local, could provide privacy preservation, and economic benefit by designing an effective communication protocol. However, this communication protocol can be adopted by attackers to launch data poisoning attacks for different nodes, which has been shown as a big threat to most machine learning models. Therefore, we in this article intend to study the model vulnerability of federated machine learning, and even on IoT systems. To be specific, we here attempt to attacking a popular federated multitask learning framework, which uses a general multitask learning framework to handle statistical challenges in the federated learning setting. The problem of calculating optimal poisoning attacks on federated multitask learning is formulated as a bilevel program, which is adaptive to the arbitrary selection of target nodes and source attacking nodes. We then propose a novel systems-aware optimization method, called as attack on federated learning (AT 2 FL), to efficiently derive the implicit gradients for poisoned data, and further attain optimal attack strategies in the federated machine learning. This is an earlier work, to our knowledge, that explores attacking federated machine learning via data poisoning. Finally, experiments on several real-world data sets demonstrate that when the attackers directly poison the target nodes or indirectly poison the related nodes via using the communication protocol, the federated multitask learning model is sensitive to both poisoning attacks.
As of June 8, 2020, the global reported number of COVID-19 cases had reached more than 7 million with over 400 000 deaths. The household transmissibility of the causative pathogen, severe acute ...respiratory syndrome coronavirus 2 (SARS-CoV-2), remains unclear. We aimed to estimate the secondary attack rate of SARS-CoV-2 among household and non-household close contacts in Guangzhou, China, using a statistical transmission model.
In this retrospective cohort study, we used a comprehensive contact tracing dataset from the Guangzhou Center for Disease Control and Prevention to estimate the secondary attack rate of COVID-19 (defined as the probability that an infected individual will transmit the disease to a susceptible individual) among household and non-household contacts, using a statistical transmission model. We considered two alternative definitions of household contacts in the analysis: individuals who were either family members or close relatives, such as parents and parents-in-law, regardless of residential address, and individuals living at the same address regardless of relationship. We assessed the demographic determinants of transmissibility and the infectivity of COVID-19 cases during their incubation period.
Between Jan 7, 2020, and Feb 18, 2020, we traced 195 unrelated close contact groups (215 primary cases, 134 secondary or tertiary cases, and 1964 uninfected close contacts). By identifying households from these groups, assuming a mean incubation period of 5 days, a maximum infectious period of 13 days, and no case isolation, the estimated secondary attack rate among household contacts was 12·4% (95% CI 9·8–15·4) when household contacts were defined on the basis of close relatives and 17·1% (13·3–21·8) when household contacts were defined on the basis of residential address. Compared with the oldest age group (≥60 years), the risk of household infection was lower in the youngest age group (<20 years; odds ratio OR 0·23 95% CI 0·11–0·46) and among adults aged 20–59 years (OR 0·64 95% CI 0·43–0·97). Our results suggest greater infectivity during the incubation period than during the symptomatic period, although differences were not statistically significant (OR 0·61 95% CI 0·27–1·38). The estimated local reproductive number (R) based on observed contact frequencies of primary cases was 0·5 (95% CI 0·41–0·62) in Guangzhou. The projected local R, had there been no isolation of cases or quarantine of their contacts, was 0·6 (95% CI 0·49–0·74) when household was defined on the basis of close relatives.
SARS-CoV-2 is more transmissible in households than SARS-CoV and Middle East respiratory syndrome coronavirus. Older individuals (aged ≥60 years) are the most susceptible to household transmission of SARS-CoV-2. In addition to case finding and isolation, timely tracing and quarantine of close contacts should be implemented to prevent onward transmission during the viral incubation period.
US National Institutes of Health, Science and Technology Plan Project of Guangzhou, Project for Key Medicine Discipline Construction of Guangzhou Municipality, Key Research and Development Program of China.
Adhesive hydrogels have broad applications in tissue adhesives, hemostatic agents, and biomedical sensors. Various bio‐inspired glues and synthetic adhesives are clinically used as conventional ...hemostatic agents and auxiliary tools for wound closure. Medical adhesives are needed to effectively and quickly control bleeding, thereby reducing the risk of complications caused by severe blood loss. Medical sensors need to have excellent skin compliance, mechanical properties, sensitivity, and biological safety. This review focuses on recent progress in adhesive hydrogel systems, their structures, adhesion mechanisms, construction strategies, and emerging applications in the biomedical field.
Biocompatible hydrogel inks with shear‐thinning, appropriate yield strength, and fast self‐healing are desired for 3D bioprinting. However, the lack of ideal 3D bioprinting inks with outstanding ...printability and high structural fidelity, as well as cell‐compatibility, has hindered the progress of extrusion‐based 3D bioprinting for tissue engineering. In this study, novel self‐healable pre‐cross‐linked hydrogel microparticles (pcHμPs) of chitosan methacrylate (CHMA) and polyvinyl alcohol (PVA) hybrid hydrogels are developed and used as bioinks for extrusion‐based 3D printing of scaffolds with high fidelity and biocompatibility. The pcHμPs display excellent shear thinning when injected through a syringe and subsequently self‐heal into gels as shear forces are removed. Numerical simulations indicate that the pcHμPs experience a plug flow in the nozzle with minimal disturbance, which favors a steady and continuous printing. Moreover, the pcHμPs show a self‐supportive yield strength (540 Pa), which is critical for the fidelity of printed constructs. A series of biomimetic constructs with very high aspect ratio and delicate fine structures are directly printed by using the pcHμP ink. The 3D printed scaffolds support the growth of bone‐marrow‐derived mesenchymal stem cells and formation of cell spheroids, which are most important for tissue engineering.
Self‐healable pre‐cross‐linked hydrogel microparticle inks from chitosan methacrylate and polyvinyl alcohol composite hydrogels are prepared for extrusion‐based 3D bioprinting. Complex biomimetic structures with high fidelity are achieved without any support agents. The scaffold displays prominent cytocompatibility, and especially supports in situ stem cell spheroid formation. This biomaterial can be used as a cell responsive platform for tissue engineering.
IOSL: Incremental Open Set Learning Ma, Bingtao; Cong, Yang; Ren, Yu
IEEE transactions on circuits and systems for video technology,
04/2024, Volume:
34, Issue:
4
Journal Article
Peer reviewed
Class incremental learning (CIL) has drawn wide attention in academic researches. However, most existing methods cannot be applied to some practical scenarios in which unknown classes occur during ...the inference stage. To solve this problem, we target a more challenging and realistic setting: Incremental Open Set Learning (IOSL), which needs to reject unknown classes from test data while incrementally learning new classes. IOSL has two coupled key challenges: 1) overcoming the catastrophic forgetting of old classes when learning new classes incrementally due to the rarity of old training samples; and 2) minimizing the empirical classification risk on known classes and the open space risk on unknown classes. To address these challenges, we propose an incremental open-set learning method with a "future-look" ability. This ability reserves embedding space for incrementally arriving new classes and potential unknown classes simultaneously to alleviate the catastrophic forgetting indirectly and recognize unknown classes well. Specifically, a normalized prototype learning strategy is designed to minimize the empirical classification risk and implicitly reserve some space. Moreover, we design an extra classes synthesizing module to explicitly reserve more suitable space. This further minimizes the empirical classification risk while reducing the open space risk. Furthermore, we develop an adaptive metric learning loss to mitigate the class imbalance between old and new classes, which focuses on exploiting exemplars fully and selects an adaptive margin for pairs of old and new classes. Extensive experiments on representative classification datasets validate the superiority of our method.
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require ...effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the underlying semantic dependencies among weakly-labeled target enteroscopy diseases and fully-annotated source gastroscope lesions, while forcefully utilizing untransferable dependencies leads to the negative performance. To tackle above issues, we propose a new weakly-supervised lesions transfer framework, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent the negative transfer of untransferable representations. Specifically, a Wasserstein quantified transferability framework is developed to highlight wide-range transferable contextual dependencies, while neglecting the irrelevant semantic characterizations. Moreover, a novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples. It inhibits the enormous deviation of false pseudo pixel labels under the self-supervision manner. Afterwards, dynamically-searched feature centroids are aligned to narrow category-wise distribution shift. Comprehensive theoretical analysis and experiments show the superiority of our model on the endoscopic dataset and several public datasets.
Traditional 3D point cloud classification tasks focus on training a classifier in the closed-set scenario, where training and test data have the same label set and the same data distribution. In this ...work, we focus on a more challenging and realistic scenario in 3D point cloud classification task: universal domain adaptation (UniDA), where 1) data distributions for training and test data are different; and 2) for given label sets of training data and test data, they may have the shared classes and keep the private classes respectively, introducing an extra label set discrepancy. To solve UniDA problem, researchers have designed many methods based on 2D image datasets. However, due to the difficulty in capturing discriminative local geometric structures brought by the unordered and irregular 3D point cloud data, we cannot directly deploy the existing methods based on 2D image datasets to the 3D scenarios. To address UniDA in 3D scenarios, we develop a 3D universal domain adaptation framework, which consists of three modules: Self-Constructed Geometric (SCG) module, Local-to-Global Hypersphere Reasoning (LGHR) module and Self-Supervised Boundary Adaptation (SBA) module. SCG and LGHR generate the discriminative representation, which is used to acquire domain-invariant knowledge for training and test data. SBA is designed to automatically recognize whether a given label is from the shared label set or private label set, and adapts training and test data from the shared label set. To our best knowledge, this work is the first exploration of UniDA for 3D scenarios. Extensive experiments on public 3D point cloud datasets verify that the proposed method outperforms the existing UniDA methods.
In the past year, the global epidemic situation is still not optimistic, showing a trend of continuous expansion. With the research and application of vaccines, there is an urgent need to develop ...some optimal vaccination strategies. How to make a reasonable vaccination strategy to determine the priority of vaccination under the limited vaccine resources to control the epidemic and reduce human casualties? We build a dynamic model with vaccination which is extended the classical SEIR model. By fitting the epidemic data of three countries-China, Brazil, Indonesia, we have evaluated age-specific vaccination strategy for the number of infections and deaths. Furthermore, we have evaluated the impact of age-specific vaccination strategies on the number of the basic reproduction number. At last, we also have evaluated the different age structure of the vaccination priority. It shows that giving priority to vaccination of young people can control the number of infections, while giving priority to vaccination of the elderly can greatly reduce the number of deaths in most cases. Furthermore, we have found that young people should be mainly vaccinated to reduce the number of infections. When the emphasis is on reducing the number of deaths, it is important to focus vaccination on the elderly. Simulations suggest that appropriate age-specific vaccination strategies can effectively control the epidemic, both in terms of the number of infections and deaths.
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