Towards Efficient Visual Adaption via Structural Re-parameterization

Parameter-efficient transfer learning (PETL) is an emerging research spot aimed at inexpensively adapting large-scale pre-trained models to downstream tasks. Recent advances have achieved great success in saving storage costs for various vision tasks by updating or injecting a small number of parameters instead of full fine-tuning. However, we notice that most existing PETL methods still incur non-negligible latency during inference. In this paper, we propose a parameter-efficient and computationally friendly adapter for giant vision models, called RepAdapter. Specifically, we prove that the adaption modules, even with a complex structure, can be seamlessly integrated into most giant vision models via structural re-parameterization. This property makes RepAdapter zero-cost during inference. In addition to computation efficiency, RepAdapter is more effective and lightweight than existing PETL methods due to its sparse structure and our careful deployment. To validate RepAdapter, we conduct extensive experiments on 27 benchmark datasets of three vision tasks, i.e., image and video classifications and semantic segmentation. Experimental results show the superior performance and efficiency of RepAdapter than the state-of-the-art PETL methods. For instance, by updating only 0.6% parameters, we can improve the performance of ViT from 38.8 to 55.1 on Sun397. Its generalizability is also well validated by a bunch of vision models, i.e., ViT, CLIP, Swin-Transformer and ConvNeXt. Our source code is released at https://github.com/luogen1996/RepAdapter

Involvement of the Apoptotic Mechanism in Pemphigus Foliaceus Autoimmune Injury of the Skin

Pemphigus foliaceus (PF) is an organ-specific autoimmune skin disease characterized by subcorneal epidermal cell detachment (acantholysis) and pathogenic autoantibodies against desmoglein 1. The mechanism responsible for pemphigus autoantibody-induced epidermal injury is not fully understood. In this study we used the IgG passive transfer mouse model of PF to investigate the relevance of the apoptotic mechanisms in pemphigus pathogenesis. TUNEL-positive epidermal cells and increased oligonucleosomes in the epidermal cytosolic fractions were detected in the diseased mice. Time course study reveals that TUNEL-positive epidermal cells appear prior to intraepidermal blisters. Moreover, the pro-apoptotic factor Bax was up-regulated at the earlier time points (2 and 4 h) while the anti-apoptotic factor Bcl-xl was down-regulated at the later time points (6, 8, and 20 h) post PF IgG injection by Western blot analysis. The active forms of caspase-3 and -6 were detected at the later time period (6, 8, and 20 h). Administration of Ac-DEVD-cmk, a peptide-based caspase-3/7 inhibitor, protected mice from developing intraepidermal blisters and clinical disease induced by PF IgG. The same protective effect was also observed using a broad-spectrum caspase inhibitor, Bok-D-fmk. Collectively, these findings show that biochemical events of apoptosis are provoked in the epidermis of mice injected with PF autoantibodies. Caspase activation may contribute to acantholytic blister formation in PF

Microbial-environmental interactions reveal the evaluation of fermentation time on the nutrient properties of soybean meal

Microbial fermentation techniques are often used to improve their quality, where the keys are fermentation strains and fermentation time. This study studied the interaction between microbiota and environmental (or nutritional) factors and microbiota at different fermentation times to determine the most appropriate time, using lactic acid bacteria as fermentation strains. It can be concluded that fermentation improved the nutritional value of soybean meals. In the early stages of fermentation, debris in soybean meal highly proliferated and destabilized the microbial community, while pH and nutritional conditions played an important role in helping its stabilization. In addition, we must pay attention to the interspecific interactions of microorganisms, which makes it easy to understand how the microbial community maintains community stability. A 4-day fermentation of soybean meal with Lactobacillus is recommended

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method’s high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method’s good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form

Effect of molecular conformations on the electronic transport in oxygen-substituted alkanethiol molecular junctions

The relationship between the molecular structure and the electronic transport properties of molecular junctions based on thiol-terminated oligoethers, which are obtained by replacing every third methylene unit in the corresponding alkanethiols with an oxygen atom, is investigated by employing the non-equilibrium Green?s function formalism combined with density functional theory. Our calculations show that the low-bias conductance depends strongly on the conformation of the oligoethers in the junction. Specifically, in the cases of trans-extended conformation, the oxygen-dominated transmission peaks are very sharp and well below the Fermi energy, EF, thus hardly affect the transmission around EF; the Au?S interface hybrid states couple with ?-bonds in the molecular backbone forming the conduction channel at EF, resulting in a conductance decay against the molecular length close to that for alkanethiols. By contrast, for junctions with oligoethers in helical conformations, some ?-type oxygen orbitals coupling with the Au?S interface hybrid states contribute to the transmission around EF. The molecule-electrode electronic coupling is also enhanced at the non-thiol side due to the specific spatial orientation introduced by the twist of the molecular backbone. This leads to a much smaller conductance decay constant. Our findings highlight the important role of the molecular conformation of oligoethers in their electronic transport properties and are also helpful for the design of molecular wires with heteroatom-substituted alkanethiols

Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting

Electricity inspection is important to support sustainable development and is core to the marketing of electric power. In addition, it contributes to the effective management of power companies and to their financial performance. Continuous improvement in the penetration rate of new energy generation can improve environmental standards and promote sustainable development, but creates challenges for electricity inspection. Traditional electricity inspection methods are time-consuming and quite inefficient, which hinders the sustainable development of power firms. In this paper, a load-forecasting model based on an improved moth-flame-algorithm-optimized extreme learning machine (IMFO-ELM) is proposed for use in electricity inspection. A chaotic map and improved linear decreasing weight are introduced to improve the convergence ability of the traditional moth-flame algorithm to obtain optimal parameters for the ELM. Abnormal data points are screened out to determine the causes of abnormal occurrences by analyzing the model prediction results and the user’s actual power consumption. The results show that, compared with existing PSO-ELM and MFO-ELM models, the root mean square error of the proposed model is reduced by at least 1.92% under the same conditions, which supports application of the IMFO-ELM model in electricity inspection. The proposed power-load-forecasting-based abnormal data detection method can improve the efficiency of electricity inspection, enhance user experience, contribute to the intelligence level of power firms and promote their sustainable development