Inharmonious Region Localization by Magnifying Domain Discrepancy

Inharmonious region localization aims to localize the region in a synthetic image which is incompatible with surrounding background. The inharmony issue is mainly attributed to the color and illumination inconsistency produced by image editing techniques. In this work, we tend to transform the input image to another color space to magnify the domain discrepancy between inharmonious region and background, so that the model can identify the inharmonious region more easily. To this end, we present a novel framework consisting of a color mapping module and an inharmonious region localization network, in which the former is equipped with a novel domain discrepancy magnification loss and the latter could be an arbitrary localization network. Extensive experiments on image harmonization dataset show the superiority of our designed framework. Our code is available at https://github.com/bcmi/MadisNet-Inharmonious-Region-Localization

Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis

This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of sentiment analysis and how deep learning methods are utilized in this domain. Subsequently, it delves into the architecture and characteristics of BERT models. Through detailed explanation, it elucidates the application effects and optimization strategies of BERT models in sentiment analysis, supported by experimental validation. The experimental findings indicate that BERT models exhibit robust performance in sentiment analysis tasks, with notable enhancements post fine-tuning. Lastly, the paper concludes by summarizing the potential applications of BERT models in sentiment analysis and suggests directions for future research and practical implementations

Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations

With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences. Although deep neural networks (DNNs) have made significant progress in improving recommendation systems by simulating the interaction between users and items and incorporating their textual information, these DNN-based approaches still have some limitations, such as the difficulty of effectively understanding users' interests and capturing textual information. It is not possible to generalize to different seen/unseen recommendation scenarios and reason about their predictions. At the same time, the emergence of large language models (LLMs), represented by ChatGPT and GPT-4, has revolutionized the fields of natural language processing (NLP) and artificial intelligence (AI) due to their superior capabilities in the basic tasks of language understanding and generation, and their impressive generalization and reasoning capabilities. As a result, recent research has sought to harness the power of LLM to improve recommendation systems. Given the rapid development of this research direction in the field of recommendation systems, there is an urgent need for a systematic review of existing LLM-driven recommendation systems for researchers and practitioners in related fields to gain insight into. More specifically, we first introduced a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems from the three paradigms of pre-training, fine-tuning, and prompting. Finally, we had a comprehensive discussion on the future direction of this emerging field

Preoperative paclitaxel weekly combined with radical gastrectomy for gastric cancer: randomized controlled study

Objective To explore the clinical efficacy of preoperative paclitaxel weekly therapy as neoadjuvant chemotherapy combined with radical surgery for gastric cancer. Methods Using a randomized controlled study method, 120 gastric cancer patients admitted to Nanyang Nanshi Hospital from June 2022 to June 2023 were randomly divided into control group and study group, with 60 cases in each group. The patients in control group were treated with gastric cancer radical surgery, while the study group patients received neoadjuvant chemotherapy 2 weeks before surgery, using a paclitaxel weekly therapy mode with a dose of 175 mg/m2 for 2 consecutive weeks, followed by gastric cancer radical surgery. The Barthel index, self-rating anxiety scale (SAS), self-rating depression scale (SDS), visual analog scale (VAS), carcinoembryonic antigen, carbohydrate antigen199 and total adverse reaction rate were compared between two groups. Results After treatment, the Barthel index was higher, while the SAS and SDS scores were lower of patients in the study group than those before treatment and control group (P＜0.05). VAS score, carcinoembryonic antigen, and carbohydrate antigen 199 in the study group were lower than those before treatment and the control group (P＜0.05). There was no significant difference in the incidence of adverse reactions between the study group and the control group (8.33% vs 6.67%, P>0.05). Conclusion Preoperative paclitaxel weekly therapy as neoadjuvant chemotherapy combined with radical surgery for gastric cancer can improve the anxiety and depression status of patients and increase the overall effective rate

Stencil Computation with Vector Outer Product

Matrix computation units have been equipped in current architectures to accelerate AI and high performance computing applications. The matrix multiplication and vector outer product are two basic instruction types. The latter one is lighter since the inputs are vectors. Thus it provides more opportunities to develop flexible algorithms for problems other than dense linear algebra computing and more possibilities to optimize the implementation. Stencil computations represent a common class of nested loops in scientific and engineering applications. This paper proposes a novel stencil algorithm using vector outer products. Unlike previous work, the new algorithm arises from the stencil definition in the scatter mode and is initially expressed with formulas of vector outer products. The implementation incorporates a set of optimizations to improve the memory reference pattern, execution pipeline and data reuse by considering various algorithmic options and the data sharing between input vectors. Evaluation on a simulator shows that our design achieves a substantial speedup compared with vectorized stencil algorithm

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)

Phosphine-Catalyzed Sequential [2 +3] and [3 + 2] Annulation Domino Reaction of γ‑Benzyl-Substituted Allenoates with α,β-Unsaturated Ketimines To Construct aza-Bicyclo[3,3,0]octane Derivatives

A novel phosphine-catalyzed sequential [2 + 3] and [3 + 2] annulation domino reaction of γ-benzyl-substituted allenoates has been developed. The reaction can proceed smoothly to produce the corresponding aza-bicyclo­[3,3,0]­octane derivatives in good yields and excellent diastereoselectivity (only one isomer)

Did an Ultra-Low Emissions Policy on Coal-Fueled Thermal Power Reduce the Harmful Emissions? Evidence from Three Typical Air Pollutants Abatement in China

Thermal power generation based on coal-fired power plants has the advantages of stability and controllability and has been the largest source of electricity supply in China. Coal-fired power plants, however, are also accompanied by high carbon emissions and the release of harmful substances (mainly including sulfur dioxide, nitrogen oxides, and smoke dust), and are even regarded as the “chief criminal” in terms of air pollution. However, thermal power is also a pioneering industry involved in several environmental regulations and cleaner production techniques before other industries. Evidence of this is China’s ultra-low emissions (ULE) policy on coal-fired power plants, implemented in 2015. To verify this policy’s effect, this study treats ULE as an exogenous impact variable, examining its emissions reduction effect on SO2, NOx, and smoke dust in Eastern and Central China using the difference-in-difference method (DID). The results show that the total emissions of the three pollutants were abated by 0.133%, 0.057% and 0.036% in Eastern, and by 0.120%, 0.035% and 0.043% in Central China at every 1% rise of thermal power generated after ULE. In addition, several other factors can also argue for the promotion of thermal power. Other industries, such as steel or chemical, have proven that they can contribute significant SO2 and NOx emissions. Based on these results, we provide suggestions on synergistic emissions reduction among multiple industries, as well as a discussion on the necessity of implementing ULE in Western China

Molecular characterization of multidrug resistant strains of Acinetobacter baumannii isolated from pediatric intensive care unit in a Chinese tertiary hospital

Abstract Background Acinetobacter baumannii is a nosocomial pathogen which is reported as a major cause of morbidity and mortality in intensive care units (ICUs). However, there is a lack of analysis focused on multidrug-resistant Acinetobacter baumannii (MDRAB) infection among patients from pediatric intensive care unit (PICU) in China. The aim of this study was to investigate the molecular characterization of MDRAB isolated from PICU. Methods In this study, 86 isolates of MDRAB were collected from PICU patients, from the First Affiliated Hospital of Sun Yat-sen University. The minimal inhibitory concentrations (MICs) of the isolates against common antibiotics were determined. The carbapenemase-encoding resistance genes and AdeABC-AdeRS efflux system genes of these isolates were detected by PCR. Real-time PCR was performed to determine the relative expression of the relevant efflux pumps. Results Among 86 strains of MDRAB, 76.7% (66/86) were carbapenem-resistant A. baumannii (CRAB). All 86 clinical isolates possessed the bla OXA-51 gene. Bla OXA-23 was detected as the second most frequent (90.7%) carbapenemase. Harboring AdeABC efflux pump genes was prevalent among the majority of the MDR isolates. Specially, the distributions of AdeABC-AdeRS efflux system genes in CRAB strains reached up to 90.0%. Compared with those of the CSAB strains, there was a statistically significant increasing distribution of the regulator AdeR and AdeS genes(p < 0.05). Moreover, CRAB strains showed significantly increased expression of AdeB(12.3- fold), but decreased expression of AdeR (3.3- fold)(p < 0.05). Conclusion The present study showed a high distribution of multiple genes, mainly the genes of bla OXA-23 /bla OXA-51 carbapenemase and AdeABC efflux pump, is responsible to distinct drug-resistance in PICU. It is urgent to strengthen the molecular epidemiological surveillance of pediatric MDRAB isolates to prevent further outbreaks. This study is of significant help for the clinicians to make therapeutic decisions and manage infection control in PICU

Highly reversible oxygen redox in layered compounds enabled by surface polyanions

Oxygen-anion redox in lithium-rich layered oxides can boost the capacity of lithium-ion battery cathodes. Here, the authors investigate the mechanism of surface degradation caused by oxygen oxidation and the kinetics of surface reconstruction