PVP: Pre-trained Visual Parameter-Efficient Tuning

Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have significantly reduced the computation and storage cost by inserting lightweight prompt modules into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone frozen. Although only a few parameters need to be adjusted, most PETuning methods still require a significant amount of downstream task training data to achieve good results. The performance is inadequate on low-data regimes, especially when there are only one or two examples per class. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Pre-trained Visual Parameter-efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform parameter-efficient tuning on downstream tasks. Experiment results on five Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that our proposed method significantly outperforms state-of-the-art PETuning methods

Study of mercury transport and transformation in mangrove forests using stable mercury isotopes.

Mangrove forests are important wetland ecosystems that are a sink for mercury from tides, rivers and precipitation, and can also be sources of mercury production and export. Natural abundance mercury stable isotope ratios have been proven to be a useful tool to investigate mercury behavior in various ecosystems. In this study, mercury isotopic data were collected from seawater, sediments, air, and plant tissues in two mangrove forests in Guangxi and Fujian provinces, China, to study the transport and transformation of mercury in mangrove sediments. The mangroves were primarily subject to mercury inputs from external sources, such as anthropogenic activities, atmospheric deposition, and the surrounding seawater. An isotope mixing model based on mass independent fractionation (MIF) estimated that the mangrove wetland ecosystems accounted for <40% of the mercury in the surrounding seawater. The mercury in plant root tissues was derived mainly from sediments and enriched with light mercury isotopes. The exogenous mercury inputs from the fallen leaves were diluted by seawater, leading to a positive Δ199Hg offset between the fallen leaves and sediments. Unlike river and lake ecosystems, mangrove ecosystems are affected by tidal action, and the δ202Hg and Δ199Hg values of sediments were more negative than that of the surrounding seawater. The isotopic signature differences between these environmental samples were partially due to isotope fractionation driven by various physical and chemical processes (e.g., sorption, photoreduction, deposition, and absorption). These results contribute to a better understanding of the biogeochemical cycling of mercury in mangrove wetland ecosystems

A medium-entropy transition metal oxide cathode for high-capacity lithium metal batteries

The limited capacity of the positive electrode active material in non-aqueous rechargeable lithium-based batteries acts as a stumbling block for developing high-energy storage devices. Although lithium transition metal oxides are high-capacity electrochemical active materials, the structural instability at high cell voltages (e.g., >4.3 V) detrimentally affects the battery performance. Here, to circumvent this issue, we propose a Li1.46Ni0.32Mn1.2O4-x (0 < x < 4) material capable of forming a medium-entropy state spinel phase with partial cation disordering after initial delithiation. Via physicochemical measurements and theoretical calculations, we demonstrate the structural disorder in delithiated Li1.46Ni0.32Mn1.2O4-x, the direct shuttling of Li ions from octahedral sites to the spinel structure and the charge-compensation Mn3+/Mn4+ cationic redox mechanism after the initial delithiation. When tested in a coin cell configuration in combination with a Li metal anode and a LiPF6-based non-aqueous electrolyte, the Li1.46Ni0.32Mn1.2O4-x-based positive electrode enables a discharge capacity of 314.1 mA h g−1 at 100 mA g−1 with an average cell discharge voltage of about 3.2 V at 25 ± 5 °C, which results in a calculated initial specific energy of 999.3 Wh kg−1 (based on mass of positive electrode’s active material)

Role of Electronic Excited State in Kinetics of the CH2OO + SO2 ! HCHO + SO3 Reaction

In this work, kinetics of the CH2OO + SO2 ! HCHO + SO3 reaction was studied by ring-polymer molecular dynamics (RPMD). To perform RPMD calculations, multi-reference configuration interaction (MRCI) was first carried out to compute data for constructing potential energy surface (PES) through a kernel regression method. On the basis of the present MRCI calculations, the statics multi-state mechanism involving the lowest-lying singlet excited state (denoted by S 1) was proposed, which is di?erent from the previously proposed mechanism with the lowest-lying triplet state (denoted by T1). Moreover, the present RPMD calculations predicted the rate coe?cient of 3:95?1011cm3 molecule1s1 at the room temperature (namely 298 K), agreeing with the previously reported experimental values. Finally, based on the present calculations, a probable dynamics mechanism was discussed, where the produced HCHO molecule was proposed to be in a vibrationally excited state. This needs further experimental and theoretical observation in the future.<br /

Multi-Component Graph Convolutional Collaborative Filtering

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motivations, such as high cost performance or eye-catching appearance, which are indistinguishably represented by the edges. The existing approaches still remain the differences between various purchasing motivations unexplored, rendering the inability to capture fine-grained user preference. Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. Specifically, there are two elaborately designed modules, decomposer and combiner, inside MCCF. The former first decomposes the edges in user-item graph to identify the latent components that may cause the purchasing relationship; the latter then recombines these latent components automatically to obtain unified embeddings for prediction. Furthermore, the sparse regularizer and weighted random sample strategy are utilized to alleviate the overfitting problem and accelerate the optimization. Empirical results on three real datasets and a synthetic dataset not only show the significant performance gains of MCCF, but also well demonstrate the necessity of considering multiple components

Explainable prediction of loan default based on machine learning models

Owing to the convenience of online loans, an increasing number of people are borrowing money on online platforms. With the emergence of machine learning technology, predicting loan defaults has become a popular topic. However, machine learning models have a black-box problem that cannot be disregarded. To make the prediction model rules more understandable and thereby increase the user’s faith in the model, an explanatory model must be used. Logistic regression, decision tree, XGBoost, and LightGBM models are employed to predict a loan default. The prediction results show that LightGBM and XGBoost outperform logistic regression and decision tree models in terms of the predictive ability. The area under curve for LightGBM is 0.7213. The accuracies of LightGBM and XGBoost exceed 0.8. The precisions of LightGBM and XGBoost exceed 0.55. Simultaneously, we employed the local interpretable model-agnostic explanations approach to undertake an explainable analysis of the prediction findings. The results show that factors such as the loan term, loan grade, credit rating, and loan amount affect the predicted outcomes