Fast Adaptation Research Articles

Few-shot time series forecasting in a meta-learning framework

Time series forecasting has a wide range of applications in various fields. To eliminate the need for time series data volume, a meta-learning-based few-shot time series forecasting method is proposed. This method uses a residual stack module as its backbone and connects the residuals forward and backward through a multilayer fully connected network so that the model and the meta-learning framework can be seamlessly combined. The Empirical knowledge of different time-sequence tasks is obtained through meta-training. To enable fast adaptation to new prediction tasks, a small meta-network is introduced to adaptively and dynamically generate the learning rate and weight decay coefficient of each step in the network. This method can use sequences of different data distribution characteristics for cross-task learning, and each training task only needs a small number of time series to achieve sequence prediction for the target task. The results show that compared with the two baselines, the proposed method has improved performance on 67.07% and 58.53% of the evaluated tasks. Thus, this method can effectively alleviate the problems caused by insufficient data during training and has broad application prospects in the field of time series.

Bayesian Meta-Learning: Toward Fast Adaptation in Neural Network Positioning Techniques

Bayesian Meta-Learning: Toward Fast Adaptation in Neural Network Positioning Techniques

Utilizing a High-Performance Piezoelectric Nanocomposite as a Self-Activating Component in Piezotronic Artificial Mechanoreceptors.

Challenges such as poor dispersion and insufficient polarization of BaTiO3 (BTO) nanoparticles (NPs) within poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) composites have hindered their piezoelectricity, limiting their uses in pressure sensors, nanogenerators, and artificial sensory synapses. Here, we introduce a high-performance piezoelectric nanocomposite material consisting of P(VDF-TrFE)/modified-BTO (mBTO) NPs for use as a self-activating component in a piezotronic artificial mechanoreceptor. To generate high-performance piezoelectric nanocomposite materials, the surface of BTO is hydroxylated, followed by the covalent attachment of (3-aminopropyl)triethoxysilane to improve the dispersibility of mBTO NPs within the P(VDF-TrFE) matrix. We also aim to enhance the crystallization degree of P(VDF-TrFE), the efficiency characteristics of mBTO, and the poling efficiency, even when incorporating small amounts of mBTO NPs. The piezoelectric potential mechanically induced from the P(VDF-TrFE)/mBTO NPs nanocomposite was three times greater than that from P(VDF-TrFE) and twice as high as that from the P(VDF-TrFE)/BTO NPs nanocomposite. The piezoelectric potential generated by mechanical stimuli on the piezoelectric nanocomposite was utilized to activate the synaptic ionogel-gated field-effect transistor for the development of self-powered piezotronics artificial mechanoreceptors on a polyimide substrate. The device successfully emulated fast-adapting (FA) functions found in biological FA mechanoreceptors. This approach has great potential for applications to future intelligent tactile perception technology.

Continuous optical zoom telescopic system based on liquid lenses

Telescopes play an essential important role in the fields of astronomical observation, emergency rescue, etc. The traditional telescopes achieve zoom function through the mechanical movement of the solid lenses, usually requiring refocusing after magnification adjustment. Therefore, the traditional telescopes lack adaptability, port-ability and real-time capability. In this paper, a continuous optical zoom telescopic system based on liquid lenses is proposed. The main components of the system consist of an objective lens, an eyepiece, and a zoom group composed of six pieces of liquid lenses. By adjusting the external voltages on the liquid lenses, the zoom telescopic system can achieve continuous optical zoom from ∼1.0× to ∼4.0× operating with an angular resolution from 28.648" to 19.098", and the magnification switching time is ∼50ms. The optical structure of the zoom telescopic system with excellent performance is given, and its feasibility is demonstrated by simulations and experiments. The proposed system with fast response, portability and high adaptability is expected to be applied to astronomical observation, emergency rescue and so on.

Cellulose nanocrystals intercalated clay biocomposite for rapid Cr(VI) removal.

The application of bentonite (Bt) as an adsorbent for heavy metals has been limited due to its hydrophobicity and insufficient surface area. Herein, we present cellulose nanocrystal (CNC) modified Bt composite (CNC@Bt) with enhanced efficiency for Cr(VI) removal. CNC@Bt exhibited an increased specific surface area and a porous structure, while maintaining the original crystal structure of Bt. This was achieved through a synergistic function of ion exchange, hydrogen bonding, electrostatic interactions, and steric hindrance. The adsorption of Cr(VI) by CNC@Bt followed the pseudo-second-order kinetic and Langmuir isotherm adsorption model. Moreover, the process was endothermic and spontaneous. At an initial Cr(VI) concentration of 20mg/L and pH = 4.0, 10g/L CNC@Bt achieved a removal rate of 92.7%, and the adsorption capacity was 1.85mg/g, significantly higher than bare Bt (37.9% and 0.76mg/g). The removal efficiency remained consistently above 80% over a wide pH range, indicating the potential practical applicability of CNC@Bt. With its fast adsorption rate, pH adaptability, and stable performance, CNC@Bt presents promising prospects for the rapid treatment of Cr-contaminated wastewater.

A highly conserved tRNA modification contributes to C. albicans filamentation and virulence.

tRNA modifications play important roles in maintaining translation accuracy in all domains of life. Disruptions in the tRNA modification machinery, especially of the anticodon stem loop, can be lethal for many bacteria and lead to a broad range of phenotypes in baker's yeast. Very little is known about the function of tRNA modifications in host-pathogen interactions, where rapidly changing environments and stresses require fast adaptations. We found that two closely related fungal pathogens of humans, the highly pathogenic Candida albicans and its much less pathogenic sister species, Candida dubliniensis, differ in the function of a tRNA-modifying enzyme. This enzyme, Hma1, exhibits species-specific effects on the ability of the two fungi to grow in the hypha morphology, which is central to their virulence potential. We show that Hma1 has tRNA-threonylcarbamoyladenosine dehydratase activity, and its deletion alters ribosome occupancy, especially at 37°C-the body temperature of the human host. A C. albicans HMA1 deletion mutant also shows defects in adhesion to and invasion into human epithelial cells and shows reduced virulence in a fungal infection model. This links tRNA modifications to host-induced filamentation and virulence of one of the most important fungal pathogens of humans.IMPORTANCEFungal infections are on the rise worldwide, and their global burden on human life and health is frequently underestimated. Among them, the human commensal and opportunistic pathogen, Candida albicans, is one of the major causative agents of severe infections. Its virulence is closely linked to its ability to change morphologies from yeasts to hyphae. Here, this ability is linked-to our knowledge for the first time-to modifications of tRNA and translational efficiency. One tRNA-modifying enzyme, Hma1, plays a specific role in C. albicans and its ability to invade the host. This adds a so-far unknown layer of regulation to the fungal virulence program and offers new potential therapeutic targets to fight fungal infections.

Treatment planning evaluation and experimental validation of the magnetic resonance-based intrafraction drift correction

Background and purposeMRI-guided online adaptive treatments can account for interfractional variations, however intrafraction motion reduces treatment accuracy. Intrafraction plan adaptation methods, such as the Intrafraction Drift Correction (IDC) or sub-fractionation, are needed. IDC uses real-time automatic monitoring of the tumor position to initiate plan adaptations by repositioning segments. IDC is a fast adaptation method that occurs only when necessary and this method could enable margin reduction. This research provides a treatment planning evaluation and experimental validation of the IDC. Materials and methodsAn in silico treatment planning evaluation was performed for 13 prostate patients mid-treatment without and with intrafraction plan adaptation (IDC and sub-fractionation). The adaptation methods were evaluated using dose volume histogram (DVH) metrics. To experimentally verify IDC a treatment was mimicked whereby a motion phantom containing an EBT3 film moved mid-treatment, followed by repositioning of segments. In addition, the delivered treatment was irradiated on a diode array phantom for plan quality assurance purposes. ResultsThe planning study showed benefits for using intrafraction adaptation methods relative to no adaptation, where the IDC and sub-fractionation showed consistently improved target coverage with median target coverages of 100.0%. The experimental results verified the IDC with high minimum gamma passing rates of 99.1% and small mean dose deviations of maximum 0.3%. ConclusionThe straightforward and fast IDC technique showed DVH metrics consistent with the sub-fractionation method using segment weight re-optimization for prostate patients. The dosimetric and geometric accuracy was shown for a full IDC workflow using film and diode array dosimetry.

Few-shot cow identification via meta-learning

Cow identification is a prerequisite for precision livestock farming. Biometric-based methods have made significant progress in cow identification. However, substantial labelling costs and frequent identification task changes are still hamper model application. In this work, a novel method called “MFCI” was proposed to achieve accurate cow identification under few-shot and task-changing conditions. Specifically, the proposed method comprises two components: cow location and cow identification. First, an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images. Then, the Model-Agnostic Meta-Learning (MAML) framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows. Moreover, an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches. The experimental results showed that the proposed cow location model achieved a mAP of 99.5 %. The proposed cow identification model attained an accuracy of 90.43 % with only five samples per cow for 20 cows, outperforming other state-of-the-art methods. The results demonstrate the broad applicability and significant value of the proposed method.

Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning

Driver-preferred route planning often evaluates the quality of a planned route based on how closely it is followed by the driver. Despite decades of research in this area, there still exist nonnegligible deviations from planned routes. Recently, with the prevalence of GPS data, Inverse Reinforcement Learning (IRL) has attracted much interest due to its ability to directly learn routing patterns from GPS trajectories. However, existing IRL methods are limited in that: (1) They rely on numerical approximations to calculate the expected state visitation frequencies (SVFs), which are inaccurate and time-consuming; and (2) They ignore the fact that the coverage of GPS trajectories is skewed toward popular road segments, causing difficulties in learning from sparsely covered ones. To overcome these challenges, we propose a recursive logit-based meta-IRL approach, where (1) We use the recursive logit model to capture drivers’ route choice behavior so that the expected SVFs can be analytically derived, which substantially reduces the computational efforts; and (2) We introduce meta-parameters and employ meta-learning techniques so that the learning on sparsely covered road segments can benefit from that on popular ones. When training our IRL model, we update the rewards of road segments with the expected SVFs by solving several systems of linear equations and update the meta-parameters through a two-level optimization structure to ensure its fast adaption and versatility. We validate our approach using real GPS data in Chengdu, China. Results show that our planned routes better match actual routes compared with state-of-the-art methods including the recursive logit model, Deep-IRL and Dij-IRL: the F1-Score increases by 4.17% with the introduction of the recursive logit model and further increases to 5.19% after meta-learning is employed. Moreover, we can reduce training time by over 95%.

Growth, survival and reproductive traits of two genetically improved allotriploid oysters derived from Crassostrea gigas and C. angulata

Allotriploid oysters between Crassostrea gigas and C. angulata have been utilized in commercial culture in China. The prevalence of this allotriploids is attributed to fast growth and extensive temperature adaptability, however its drawbacks are low meat ratio and slightly restored fecundity. To tackle these problems, two types of allotetraploids were produced in a previous study. Here, a comparative assessment of the growth, survival and reproductive traits was made for genetically improved allotriploids (based on allotetraploids), traditional allotriploids, and autotriploids (C. gigas and C. angulata). The results showed that the survival rates of larvae for all crosses were >80% on day 15. Genetically improved allotriploids survived better than autotriploids during juvenile and adult stages. The whole weights, wet meat weights, and meat ratios of genetically improved allotriploids were larger than traditional allotriploids on day 390 and day 510. The condition indexes, infertile percentages and fecundity of genetically improved allotriploids were lower than C. gigas autotriploids but higher than that of traditional allotriploids. At the end of the experiment, genetically improved allotriploids (GIT1) had the largest yield (56.79 kg) among all the six crosses. Overall, this study demonstrated that GIT1 exhibited superior growth and survival characteristics, as well as higher sterility compared with traditional allotriploids, which could be used as a potential variety in northern China.

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As the utilization of stratospheric airships becomes more prevalent, ensuring their safe operation becomes crucial. This paper explores the ability of an L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {L}}_{1}$$\\end{document} adaptive controller to maintain fault tolerance in the actuators of a stratospheric airship. L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {L}}_{1}$$\\end{document} adaptive control offers fast adaptation while separating adaptation and robustness. This makes the approach a suitable candidate for fault-tolerant control. The performance of the proposed design is compared to the Linear Quadratic Integral and Adaptive Sliding Mode Backstepping controllers. Simulation results show that the robustness of the airship model against faults is improved with the use of the L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {L}}{}_{1}$$\\end{document} adaptive controller.

Task-Adaptive Prompted Transformer for Cross-Domain Few-Shot Learning

Cross-Domain Few-Shot Learning (CD-FSL) aims at recognizing samples in novel classes from unseen domains that are vastly different from training classes, with few labeled samples. However, the large domain gap between training and novel classes makes previous FSL methods perform poorly. To address this issue, we propose MetaPrompt, a Task-adaptive Prompted Transformer model for CD-FSL, by jointly exploiting prompt learning and the parameter generation framework. The proposed MetaPrompt enjoys several merits. First, a task-conditioned prompt generator is established upon attention mechanisms. It can flexibly produce a task-adaptive prompt with arbitrary length for unseen tasks, by selectively gathering task characteristics from the contextualized support embeddings. Second, the task-adaptive prompt is attached to Vision Transformer to facilitate fast task adaptation, steering the task-agnostic representation to incorporate task knowledge. To our best knowledge, this is the first work to exploit a prompt-based parameter generation mechanism for CD-FSL. Extensive experimental results on the Meta-Dataset benchmark demonstrate that our method achieves superior results against state-of-the-art methods.

Referee-Meta-Learning for Fast Adaptation of Locational Fairness

When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases that sabotage the spatial fairness of the algorithm. This unfairness can easily introduce biases in subsequent decision-making given broad adoptions of learning-based solutions in practice. However, locational biases in AI are largely understudied. To mitigate biases over locations, we propose a locational meta-referee (Meta-Ref) to oversee the few-shot meta-training and meta-testing of a deep neural network. Meta-Ref dynamically adjusts the learning rates for training samples of given locations to advocate a fair performance across locations, through an explicit consideration of locational biases and the characteristics of input data. We present a three-phase training framework to learn both a meta-learning-based predictor and an integrated Meta-Ref that governs the fairness of the model. Once trained with a distribution of spatial tasks, Meta-Ref is applied to samples from new spatial tasks (i.e., regions outside the training area) to promote fairness during the fine-tune step. We carried out experiments with two case studies on crop monitoring and transportation safety, which show Meta-Ref can improve locational fairness while keeping the overall prediction quality at a similar level.

Status, Gaps and Perspectives of Powdery Mildew Resistance Research and Breeding in Cucurbits

Cucurbitaceae, the gourd family of flowering plants, is a very large and diverse family, the order Cucurbitales, contains 95 genera and 950–980 species of food and ornamental plants, and wild and weedy species mostly with high genetic diversity. This review is focused on the most important cucurbit crops (Cucumis sativus, Cucumis melo, Cucurbita spp., Citrullus lanatus, Momordica charantia, Lagenaria siceraria, and Luffa acutangula) and some their wild relatives as hosts of cucurbit powdery mildews (CPM). Powdery mildews (PM) (Ascomycota, Erysiphales) are one of the most frequently encountered and easily visible groups of plant pathogenic fungi with > 900 species. They are obligate biotrophs, they colonize above-ground plant tissues, mostly leaves, though they may also colonize stems, petioles, flowers and fruits, and they are usually debilitators, not killers. These parasitic fungi have been problematic on cucurbits for a long time world-wide, causing serious economic losses in yield and quality. All economically important cucurbit crops host CPM. Seven PM species with different taxonomic positions, host ranges, geographic distributions and ecological requirements are known on cucurbits. CPM species taxonomy and denomination rapidly changed during last few decades through detailed analyses and clarifications. At least three PM species frequently parasitize cucurbits: the endoparasite Leveillula taurica (Lt) with marginal economic importance; and two ectoparasitic species, Golovinomyces orontii (Go), and Podosphaera xanthii (Px), which are economically important world-wide. The two pathogens differ in ecological requirements and distribution, though they may occur together in mixed infections. They are highly variable at the population level for virulence, race identities, and fast adaptation of pathogens. Cucurbit-CPM species interactions are diverse and complicated, and differ between hosts and their respective pathogen genera and species. Here we present a critical overview of obstacles, gaps and recent progress in these matters for six cucurbit genera with respect to resistance resources, genetics of resistance, genetic mapping and development of molecular markers, physiology and mechanisms of resistance, developments in mlo-mediated resistance, patents, and CPM resistance breeding.

Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning.

The objective of few-shot learning is to design a system that can adapt to a given task with only few examples while achieving generalization. Model-agnostic meta-learning (MAML), which has recently gained the popularity for its simplicity and flexibility, learns a good initialization for fast adaptation to a task under few-data regime. However, its performance has been relatively limited especially when novel tasks are different from tasks previously seen during training. In this work, instead of searching for a better initialization, we focus on designing a better fast adaptation process. Consequently, we propose a new task-adaptive weight update rule that greatly enhances the fast adaptation process. Specifically, we introduce a small meta-network that can generate per-step hyperparameters for each given task: learning rate and weight decay coefficients. The experimental results validate that learning a good weight update rule for fast adaptation is the equally important component that has drawn relatively less attention in the recent few-shot learning approaches. Surprisingly, fast adaptation from random initialization with ALFA can already outperform MAML. Furthermore, the proposed weight-update rule is shown to consistently improve the task-adaptation capability of MAML across diverse problem domains: few-shot classification, cross-domain few-shot classification, regression, visual tracking, and video frame interpolation.

MetaKernel: Learning Variational Random Features With Limited Labels.

Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-shot learning is to extract prior knowledge from related tasks to enable fast adaptation to a new task with a limited amount of data. In this paper, we propose meta-learning kernels with random Fourier features for few-shot learning, we call MetaKernel. Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting. We treat the random feature basis as the latent variable, which is estimated by variational inference. The shared knowledge from related tasks is incorporated into a context inference of the posterior, which we achieve via a long-short term memory module. To establish more expressive kernels, we deploy conditional normalizing flows based on coupling layers to achieve a richer posterior distribution over random Fourier bases. The resultant kernels are more informative and discriminative, which further improves the few-shot learning. To evaluate our method, we conduct extensive experiments on both few-shot image classification and regression tasks. A thorough ablation study demonstrates that the effectiveness of each introduced component in our method. The benchmark results on fourteen datasets demonstrate MetaKernel consistently delivers at least comparable and often better performance than state-of-the-art alternatives.

MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning.

As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge. In this paper, we concentrate on this "slow versus fast" (SvF) dilemma to determine which knowledge components to be updated in a slow fashion or a fast fashion, and thereby balance old-knowledge preservation and new-knowledge adaptation. We propose a multi-grained SvF learning strategy to cope with the SvF dilemma from two different grains: intra-space (within the same feature space) and inter-space (between two different feature spaces). The proposed strategy designs a novel frequency-aware regularization to boost the intra-space SvF capability, and meanwhile develops a new feature space composition operation to enhance the inter-space SvF learning performance. With the multi-grained SvF learning strategy, our method outperforms the state-of-the-art approaches by a large margin.

Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking.

Tracking visual objects from a single initial exemplar in the testing phase has been broadly cast as a one-/few-shot problem, i.e., one-shot learning for initial adaptation and few-shot learning for online adaptation. The recent few-shot online adaptation methods incorporate the prior knowledge from large amounts of annotated training data via complex meta-learning optimization in the offline phase. This helps the online deep trackers to achieve fast adaptation and reduce overfitting risk in tracking. In this paper, we propose a simple yet effective recursive least-squares estimator-aided online learning approach for few-shot online adaptation without requiring offline training. It allows an in-built memory retention mechanism for the model to remember the knowledge about the object seen before, and thus the seen data can be safely removed from training. This also bears certain similarities to the emerging continual learning field in preventing catastrophic forgetting. This mechanism enables us to unveil the power of modern online deep trackers without incurring too much extra computational cost. We evaluate our approach based on two networks in the online learning families for tracking, i.e., multi-layer perceptrons in RT-MDNet and convolutional neural networks in DiMP. The consistent improvements on several challenging tracking benchmarks demonstrate its effectiveness and efficiency.

Multimodal meta-learning through meta-learned task representations

Few-shot meta-learning involves training a model on multiple tasks to enable it to efficiently adapt to new, previously unseen tasks with only a limited number of samples. However, current meta-learning methods assume that all tasks are closely related and belong to a common domain, whereas in practice, tasks can be highly diverse and originate from multiple domains, resulting in a multimodal task distribution. This poses a challenge for existing methods as they struggle to learn a shared representation that can be easily adapted to all tasks within the distribution. To address this challenge, we propose a meta-learning framework that can handle multimodal task distributions by conditioning the model on the current task, resulting in a faster adaptation. Our proposed method learns to encode each task and generate task embeddings that modulate the model’s activations. The resulting modulated model become specialized for the current task and leads to more effective adaptation. Our framework is designed to work in a realistic setting where the mode from which a task is sampled is unknown. Nonetheless, we also explore the possibility of incorporating auxiliary information, such as the task-mode-label, to further enhance the performance of our method if such information is available. We evaluate our proposed framework on various few-shot regression and image classification tasks, demonstrating its superiority over other state-of-the-art meta-learning methods. The results highlight the benefits of learning to embed task-specific information in the model to guide the adaptation when tasks are sampled from a multimodal distribution.

Biomimetic dual sensing polymer nanocomposite for biomedical applications.

There is a growing need for sensing materials that can provide multiple sensing capabilities for wearable devices, implantable sensors, and diagnostics tools. As complex human physiology requires materials that can simultaneously detect and respond to slow and fast pressure fluctuations. Mimicking the slow adaptive (SA) and fast adaptive (FA) mechanoreceptors in skin can lead to the development of dual sensing electrospun polymer nanocomposites for biomedical applications. These dual sensing nanocomposites can provide simultaneous sensing of both slow and fast pressure fluctuations, making them ideal for applications such as monitoring vital signs, detecting a wider range of movements and pressures. Here we develop a novel dual sensing PVDF-HFP-based nanocomposite that combines the advantages of capacitive and piezoelectric properties through controling electrospinning environment and processing parameters, polymer solution composition, and addition of nucleating agents such as Carbon Black (CB) to enhance the crystalline development of β-phase, fibre thickness, and morphology. The developed PVDF-HFP/CB nanocomposite presents and response to both slow and fast pressure fluctuations with high capacitance (5.37nF) and output voltage (1.51V) allowing for accurate and reliable measurements.