Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification

Multi-Label Remote Sensing Image Classification (MLRSIC) has received increasing research interest. Taking the cooccurrence relationship of multiple labels as additional information helps to improve the performance of this task. Current methods focus on using it to constrain the final feature output of a Convolutional Neural Network (CNN). On the one hand, these methods do not make full use of label correlation to form feature representation. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called Semantic Interleaving Global Channel Attention (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the data set. The label co-occurrence graph is used as the input of the Graph Neural Network (GNN) to generate optimal feature representations. Then, the semantic features and visual features are interleaved, to guide the feature expression of the image from the original feature space to the semantic feature space with embedded label relations. SIGNA triggers global attention of feature maps channels in a new semantic feature space to extract more important visual features. Multihead SIGNA based feature adaptive weighting networks are proposed to act on any layer of CNN in a plug-and-play manner. For remote sensing images, better classification performance can be achieved by inserting CNN into the shallow layer. We conduct extensive experimental comparisons on three data sets: UCM data set, AID data set, and DFC15 data set. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. It is worth mentioning that the codes of this paper will be open to the community for reproducibility research. Our codes are available at https://github.com/kyle-one/SIGNA.Comment: 14 pages, 13 figure

FMMRec: Fairness-aware Multimodal Recommendation

Recently, multimodal recommendations have gained increasing attention for effectively addressing the data sparsity problem by incorporating modality-based representations. Although multimodal recommendations excel in accuracy, the introduction of different modalities (e.g., images, text, and audio) may expose more users' sensitive information (e.g., gender and age) to recommender systems, resulting in potentially more serious unfairness issues. Despite many efforts on fairness, existing fairness-aware methods are either incompatible with multimodal scenarios, or lead to suboptimal fairness performance due to neglecting sensitive information of multimodal content. To achieve counterfactual fairness in multimodal recommendations, we propose a novel fairness-aware multimodal recommendation approach (dubbed as FMMRec) to disentangle the sensitive and non-sensitive information from modal representations and leverage the disentangled modal representations to guide fairer representation learning. Specifically, we first disentangle biased and filtered modal representations by maximizing and minimizing their sensitive attribute prediction ability respectively. With the disentangled modal representations, we mine the modality-based unfair and fair (corresponding to biased and filtered) user-user structures for enhancing explicit user representation with the biased and filtered neighbors from the corresponding structures, followed by adversarially filtering out sensitive information. Experiments on two real-world public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://anonymous.4open.science/r/FMMRec

FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly

While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation. In response, we introduce FocalDreamer, a framework that merges base shape with editable parts according to text prompts for fine-grained editing within desired regions. Specifically, equipped with geometry union and dual-path rendering, FocalDreamer assembles independent 3D parts into a complete object, tailored for convenient instance reuse and part-wise control. We propose geometric focal loss and style consistency regularization, which encourage focal fusion and congruent overall appearance. Furthermore, FocalDreamer generates high-fidelity geometry and PBR textures which are compatible with widely-used graphics engines. Extensive experiments have highlighted the superior editing capabilities of FocalDreamer in both quantitative and qualitative evaluations.Comment: Project website: https://focaldreamer.github.i

A van der Waals pn heterojunction with organic/inorganic semiconductors

van der Waals (vdW) heterojunctions formed by two-dimensional (2D) materials have attracted tremendous attention due to their excellent electrical/optical properties and device applications. However, current 2D heterojunctions are largely limited to atomic crystals, and hybrid organic/inorganic structures are rarely explored. Here, we fabricate hybrid 2D heterostructures with p-type dioctylbenzothienobenzothiophene (C8-BTBT) and n-type MoS2. We find that few-layer C8-BTBT molecular crystals can be grown on monolayer MoS2 by vdW epitaxy, with pristine interface and controllable thickness down to monolayer. The operation of the C8-BTBT/MoS2 vertical heterojunction devices is highly tunable by bias and gate voltages between three different regimes: interfacial recombination, tunneling and blocking. The pn junction shows diode-like behavior with rectifying ratio up to 105 at the room temperature. Our devices also exhibit photovoltaic responses with power conversion efficiency of 0.31% and photoresponsivity of 22mA/W. With wide material combinations, such hybrid 2D structures will offer possibilities for opto-electronic devices that are not possible from individual constituents.Comment: 16 pages, 4 figure

Role and potential therapeutic value of histone methyltransferases in drug resistance mechanisms in lung cancer

Lung cancer, ranking second globally in both incidence and high mortality among common malignant tumors, presents a significant challenge with frequent occurrences of drug resistance despite the continuous emergence of novel therapeutic agents. This exacerbates disease progression, tumor recurrence, and ultimately leads to poor prognosis. Beyond acquired resistance due to genetic mutations, mounting evidence suggests a critical role of epigenetic mechanisms in this process. Numerous studies have indicated abnormal expression of Histone Methyltransferases (HMTs) in lung cancer, with the abnormal activation of certain HMTs closely linked to drug resistance. HMTs mediate drug tolerance in lung cancer through pathways involving alterations in cellular metabolism, upregulation of cancer stem cell-related genes, promotion of epithelial-mesenchymal transition, and enhanced migratory capabilities. The use of HMT inhibitors also opens new avenues for lung cancer treatment, and targeting HMTs may contribute to reversing drug resistance. This comprehensive review delves into the pivotal roles and molecular mechanisms of HMTs in drug resistance in lung cancer, offering a fresh perspective on therapeutic strategies. By thoroughly examining treatment approaches, it provides new insights into understanding drug resistance in lung cancer, supporting personalized treatment, fostering drug development, and propelling lung cancer therapy into novel territories

Physicochemical properties, antioxidant activities and hypoglycemic effects of soluble dietary fibers purified from Lentinula edodes

Abstract Lentinula edodes (L. edodes), which imparts various health benefits to humans, is considered a novel source of soluble dietary fiber (SDF). In this study, ultrasonic-assisted hot-water method was used to extract SDF (U-SDF) from L. edodes, and physicochemical, antioxidant and hypoglycemic properties of the U-SDF were investigated. Physicochemical properties of U-SDF showed that water solubility, water holding capacity, swelling capacity, and oil holding capacity were higher than that the SDF extracted using hot water method without ultrasonication. The DPPH, •OH, and •O2- radical clearance rates indicated that U-SDF exhibited better antioxidant activities. U-SDF also exhibited notable α-amylase and α-glucosidase inhibition activities. Treatment with U-SDF alleviated glucose and peroxidation metabolism disorders in vivo. Histological analysis indicated that U-SDF improved the oxidative tissue damage in diabetic mice. These results provided a theoretical basis for the development and utilization of SDF derived from L. edodes

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Experimental of natural ventilation in a semi-transparent photovoltaic double skin façade in summer

Semi-transparent photovoltaic double skin façade (STPV-DSF) is a novel structure which integrates photoelectric, photothermal, ventilation and energy-saving features, which proves to be extremely attractive and promising. In this study, a full-scale experimental system was built, airflow and heat transfer in a rectangular cavity with different transmittance (τ) and different ventilation modes in summer that studies a STPV-DSF and includes natural ventilation were examined experimentally. The Rayleigh number and Nusselt number of STPV-DSF is significantly higher than that of traditional DSF. This also means stronger intense flow. And the maximum temperature difference at night between mode 1 and mode 2 can reach 7.3°C. When the external air circulation mode is switched to the external and internal mode, the indoor temperature drops by 2.88°C in ten minutes. Therefore, making fully use of natural ventilation can effectively reduce the cooling load of air conditioning in summer. The solar radiation intensity is proved to have the greatest influence on the cavity temperature, followed by the transmittance, and the the ventilation mode least influence. Applying naturally ventilated STPV-DSF would be a new efficient way for the curtain wall buildings to meet the task of sustainable building design