Adaptive Semantic Consistency for Cross-domain Few-shot Classification

Cross-domain few-shot classification (CD-FSC) aims to identify novel target classes with a few samples, assuming that there exists a domain shift between source and target domains. Existing state-of-the-art practices typically pre-train on source domain and then finetune on the few-shot target data to yield task-adaptive representations. Despite promising progress, these methods are prone to overfitting the limited target distribution since data-scarcity and ignore the transferable knowledge learned in the source domain. To alleviate this problem, we propose a simple plug-and-play Adaptive Semantic Consistency (ASC) framework, which improves cross-domain robustness by preserving source transfer capability during the finetuning stage. Concretely, we reuse the source images in the pretraining phase and design an adaptive weight assignment strategy to highlight the samples similar to target domain, aiming to aggregate informative target-related knowledge from source domain. Subsequently, a semantic consistency regularization is applied to constrain the consistency between the semantic features of the source images output by the source model and target model. In this way, the proposed ASC enables explicit transfer of source domain knowledge to prevent the model from overfitting the target domain. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed ASC, and ASC provides consistent improvements over the baselines. The source code will be released

Towards General Low-Light Raw Noise Synthesis and Modeling

Modeling and synthesizing low-light raw noise is a fundamental problem for computational photography and image processing applications. Although most recent works have adopted physics-based models to synthesize noise, the signal-independent noise in low-light conditions is far more complicated and varies dramatically across camera sensors, which is beyond the description of these models. To address this issue, we introduce a new perspective to synthesize the signal-independent noise by a generative model. Specifically, we synthesize the signal-dependent and signal-independent noise in a physics- and learning-based manner, respectively. In this way, our method can be considered as a general model, that is, it can simultaneously learn different noise characteristics for different ISO levels and generalize to various sensors. Subsequently, we present an effective multi-scale discriminator termed Fourier transformer discriminator (FTD) to distinguish the noise distribution accurately. Additionally, we collect a new low-light raw denoising (LRD) dataset for training and benchmarking. Qualitative validation shows that the noise generated by our proposed noise model can be highly similar to the real noise in terms of distribution. Furthermore, extensive denoising experiments demonstrate that our method performs favorably against state-of-the-art methods on different sensors.Comment: 11 pages, 7 figures. Accepted by ICCV 202

Activation of STING Based on Its Structural Features

The cGAS-cGAMP-STING pathway is an important innate immune signaling cascade responsible for the sensing of abnormal cytosolic double-stranded DNA (dsDNA), which is a hallmark of infection or cancers. Recently, tremendous progress has been made in the understanding of the STING activation mechanism from various aspects. In this review, the molecular mechanism of activation of STING protein based on its structural features is briefly discussed. The underlying molecular mechanism of STING activation will enable us to develop novel therapeutics to treat STING-associated diseases and understand how STING has evolved to eliminate infection and maintain immune homeostasis in innate immunity

Techniques and graft materials for repairing peripheral nerve defects

Peripheral nerve defects refer to damage or destruction occurring in the peripheral nervous system, typically affecting the limbs and face. The current primary approaches to address peripheral nerve defects involve the utilization of autologous nerve transplants or the transplantation of artificial material. Nevertheless, these methods possess certain limitations, such as inadequate availability of donor nerve or unsatisfactory regenerative outcomes post-transplantation. Biomaterials have been extensively studied as an alternative approach to promote the repair of peripheral neve defects. These biomaterials include both natural and synthetic materials. Natural materials consist of collagen, chitosan, and silk, while synthetic materials consist of polyurethane, polylactic acid, and polycaprolactone. Recently, several new neural repair technologies have also been developed, such as nerve regeneration bridging technology, electrical stimulation technology, and stem cell therapy technology. Overall, biomaterials and new neural repair technologies provide new methods and opportunities for repairing peripheral nerve defects. However, these methods still require further research and development to enhance their effectiveness and feasibility

A heterozygous moth genome provides insights into herbivory and detoxification

How an insect evolves to become a successful herbivore is of profound biological and practical importance. Herbivores are often adapted to feed on a specific group of evolutionarily and biochemically related host plants1, but the genetic and molecular bases for adaptation to plant defense compounds remain poorly understood2. We report the first whole-genome sequence of a basal lepidopteran species, Plutella xylostella, which contains 18,071 protein-coding and 1,412 unique genes with an expansion of gene families associated with perception and the detoxification of plant defense compounds. A recent expansion of retrotransposons near detoxification-related genes and a wider system used in the metabolism of plant defense compounds are shown to also be involved in the development of insecticide resistance. This work shows the genetic and molecular bases for the evolutionary success of this worldwide herbivore and offers wider insights into insect adaptation to plant feeding, as well as opening avenues for more sustainable pest management.Minsheng You … Simon W Baxter … et al

A Novel Axial-Flux Dual-Stator Toothless Permanent Magnet Machine for Flywheel Energy Storage

This paper presents an alternative system called the axial-flux dual-stator toothless permanent magnet machine (AFDSTPMM) system for flywheel energy storage. This system lowers self-dissipation by producing less core loss than existing structures; a permanent magnet (PM) array is put forward to enhance the air–gap flux density of the symmetrical air gap on both sides. Moreover, its vertical stability is strengthened through the adoption of an axial-flux machine, so expensive active magnetic bearings can be replaced. The symmetry configuration of the AFDSTPMM system is shown in this paper. Then, several parts of the AFDSTPMM system are optimized thoroughly, including stator windings, number of pole pairs and the PM parameters. Further, the performance of the proposed PM array, including back-EMFs, air–gap flux density, average torque, torque ripple and over-load capacity, are compared with the Halbach PM array and spoke PM array, showing the superiority of proposed configuration. Finally, 3D simulations were made to testify for the 2D analyses

Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural Network

Lu J, Ding J, Liu C, Jin Y. Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural Network. IFAC-PapersOnLine. 2018;51(18):655-660.Prediction of physical properties of crude oil plays a key role in the petroleum refining industry, therefore, it is of great significance to establish the prediction model of physical properties of crude oil. In this paper, we propose an ensemble random weights neural network based prediction model whose inputs are nuclear magnetic resonance (NMR) spectra and outputs are carbon residual and asphaltene of crude oil. The model uses random vector functional link (RVFL) networks as the basic components and employs the regularized negative correlation learning strategy to build neural network ensemble and the online method to learn the new data. The experiment using the practical data collected from a refinery is carried out and compared with the decorrelated neural network ensembles with random weights (DNNE), least squares support vector machine (LS-SVM), partial least squares regression (PLS) and multiple linear regression (MLR). The results indicate the effectiveness of the proposed approach

Research on Equalization Strategy of Lithium Battery Pack Based on Multi-Layer Circuit

Effective balanced management of battery packs can not only increase the available capacity of a battery pack but reduce attenuation and capacity loss caused by cell inconsistencies and remove safety hazards caused by abnormal use such as overcharge and over-discharge. This research considers both the equilibration period and the battery operating current. The State of Charge (SOC), current, and equalization current of batteries are all limited. Based on the existing multi-layer equalization model, the equalization current of the equalizer was tuned with restrictions. It can equalize multiple batteries simultaneously and ensure the normal operation of the batteries. A layered control strategy was then found to solve the optimal equalization current of the equalizer layer by layer. The proposed control method reduces computation time and guarantees that the equalization approach can be employed in practice. Finally, through MATLAB simulation analysis, this technique can limit the cell current to (−3 A, 3 A), which improves the balancing efficiency by 23.55% compared with the balancing of adjacent cells