PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition

Multi-label image recognition is a fundamental task in computer vision. Recently, vision-language models have made notable advancements in this area. However, previous methods often failed to effectively leverage the rich knowledge within language models and instead incorporated label semantics into visual features in a unidirectional manner. In this paper, we propose a Prompt-driven Visual-Linguistic Representation Learning (PVLR) framework to better leverage the capabilities of the linguistic modality. In PVLR, we first introduce a dual-prompting strategy comprising Knowledge-Aware Prompting (KAP) and Context-Aware Prompting (CAP). KAP utilizes fixed prompts to capture the intrinsic semantic knowledge and relationships across all labels, while CAP employs learnable prompts to capture context-aware label semantics and relationships. Later, we propose an Interaction and Fusion Module (IFM) to interact and fuse the representations obtained from KAP and CAP. In contrast to the unidirectional fusion in previous works, we introduce a Dual-Modal Attention (DMA) that enables bidirectional interaction between textual and visual features, yielding context-aware label representations and semantic-related visual representations, which are subsequently used to calculate similarities and generate final predictions for all labels. Extensive experiments on three popular datasets including MS-COCO, Pascal VOC 2007, and NUS-WIDE demonstrate the superiority of PVLR.Comment: 15 pages, 8 figure

Decorrelation of Neutral Vector Variables: Theory and Applications

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations

Surface Chern-Simons theory for third-order topological insulators and superconductors

Three-dimensional 3rd-order topological insulators (TOTIs) and superconductors (TOTSCs), as the highestorder topological phases hosting zero corner modes in physical dimension, has sparked extensive research interest. However, such topological states have not been discovered in reality due to the lack of experimental schemes of realization. Here, we propose a novel surface Chern-Simons (CS) theory for 3rd-order topological phases, and show that the theory enables a feasible and systematic design of TOTIs and TOTSCs. We show that the emergence of zero Dirac (Majorana) corner modes is entirely captured by an emergent $\mathbb{Z}_{2}$ CS term that can be further characterized by a novel two-particle Wess-Zumino (WZ) term uncovered here in the surfaces of three-dimensional topological materials. Importantly, our proposed CS term characterization and two-particle WZ term mechanism provide a unique perspective to design TOTIs (TOTSCs) in terms of minimal ingredients, feasibly guiding the search for underlying materials, with promising candidates being discussed. This work shall advance both the theoretical and experimental research for highest-order topological matters.Comment: 5+11 pages, 4+5 figure

Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method

Training-free guided sampling in diffusion models leverages off-the-shelf pre-trained networks, such as an aesthetic evaluation model, to guide the generation process. Current training-free guided sampling algorithms obtain the guidance energy function based on a one-step estimate of the clean image. However, since the off-the-shelf pre-trained networks are trained on clean images, the one-step estimation procedure of the clean image may be inaccurate, especially in the early stages of the generation process in diffusion models. This causes the guidance in the early time steps to be inaccurate. To overcome this problem, we propose Symplectic Adjoint Guidance (SAG), which calculates the gradient guidance in two inner stages. Firstly, SAG estimates the clean image via $n$ function calls, where $n$ serves as a flexible hyperparameter that can be tailored to meet specific image quality requirements. Secondly, SAG uses the symplectic adjoint method to obtain the gradients accurately and efficiently in terms of the memory requirements. Extensive experiments demonstrate that SAG generates images with higher qualities compared to the baselines in both guided image and video generation tasks

The Impact of Sustained Drought on Vegetation Ecosystem in Southwest China Based on Remote Sensing

AbstractSouthwest China is an important ecological shelter and ecologically vulnerable area. Since last winter and this spring, southwest china have suffered from sustained drought that rarely happened in the same season of past years, severely threatening the health of vegetation ecosystem. Annually contemporaneous difference of NDVI is used as an evaluation indicator in this analysis, in which vegetations are monitored and analyzed in a macro-scale. The results indicate that from August 2009 through March 2010: 1) vegetation in southwest China was remarkably impacted by sustained drought, leading to the ascendant trend of threatening degree. 2) the area of vegetation ecosystem that suffered from this disaster in Yunnan, Guangxi and Guizhou accounts for more than 80% of the total area of the vegetation ecosystem in these three administrative regions. 3) farmland vegetation was seriously damaged, resulting in large areas of crops dying off and failing and reservoirs and ponds drying up; 4) The effect on natural vegetation was obvious and the growth was apparently suppressed. Large areas of vegetations in dry-hot valley and Karst area degenerated, threatening the local biodiversity. Verification showed that study result is consistent with the result of practical monitoring, indicating that annually contemporaneous difference of NDVI responds strongly to the spatial and temporal sustained drought, which could precisely represent the occurrence and progress of drought and detailed spatial distribution

What is the impact of Work-From-Home (WFH) Arrangements on the Quality of Life (QoL)?

The ongoing pandemic has forced countries’ education systems to continue to operate in a fragile and uncertain environment. Given the limited existing literature regarding the pandemic’s impact on the Quality of life (QoL) for teachers, this study aims to bridge the gap and provide a detailed analysis of how the extent of providing online courses and time to transition online during the pandemic could impact a tertiary educator’s QoL. The factors defining the dependent variable, QoL, were derived from past studies and made applicable within the confines of our research. The independent variables are the amount of time spent working online, notice to transition online, and various control variables. The study will utilize cross-sectional data collated by conducting convenience and volunteer sampling surveys with Embry-Riddle Aeronautical University (ERAU) faculty in campuses around the world. The data will be analyzed through regression analysis and ANOVA test. The findings of the study will aid in the development of government and educational policies to ensure the future sustainability of the education workforce in the unknown endemic landscape

Analysis of peripheral blood of ovarian cancer patients indicates higher sub-populations of natural killer and B cells compared to healthy volunteers

Ovarian cancer is a challenging disease to treat, and one of the potential treatments is by immunotherapy. NK cells have been shown to play a role in slowing tumour progression and cancer development. This study aims to investigate the numbers of NK cells and other lymphocyte sub-populations in ovarian cancer and their impact on ovarian cancer clinical outcome. This project aims to study the significance of different lymphocyte populations, particularly NK cells, involved in the peripheral blood of ovarian cancer patients. Venal blood was drawn from ovarian cancer patients before chemotherapy. PBMCs were isolated from 13 ovarian cancer patients and 11 age-matched healthy volunteers. Immunophenotyping was performed using a commercial kit to quantify the lymphocyte populations and RNA isolation performed to examine the expression of KIR genes using reverse transcription polymerase chain reaction. Immunophenotyping of PBMC was successfully performed on 13 ovarian cancer patients and 11 healthy controls. Significant increases in the mean of peripheral NK cells and B cells were found in ovarian cancer patients as compared to the healthy controls (P=0.0559). No other significant results were obtained for C D4 and CD8 lymphocytes. There was significant increase in numbers of NK cells and B cells in ovarian cancer patients as compared to the healthy volunteers. These results should be pursued with a larger sample size with the hopes of finding a significant difference between the two groups and to provide a keener insight into are promising preliminary results the immune defence against ovarian cancer

Separate density and viscosity measurements of unknown liquid using quartz crystal microbalance

Aqueous liquids have a wide range of applications in many fields. Basic physical properties like the density and the viscosity have great impacts on the functionalities of a given ionic liquid. For the millions kinds of existing liquids, only a few have been systematically measured with the density and the viscosity using traditional methods. However, these methods are limited to measure the density and the viscosity of an ionic liquid simultaneously especially in processing micro sample volumes. To meet this challenge, we present a new theoretical model and a novel method to separate density and viscosity measurements with single quartz crystal microbalance (QCM) in this work. The agreement of experimental results and theocratical calculations shows that the QCM is capable to measure the density and the viscosity of ionic liquid