Unsupervised machine learning for identifying phase transition using two-times clustering

In recent years, developing unsupervised machine learning for identifying phase transition is a research direction. In this paper, we introduce a two-times clustering method that can help select perfect configurations from a set of degenerate samples and assign the configuration with labels in a manner of unsupervised machine learning. These perfect configurations can then be used to train a neural network to classify phases. The derivatives of the predicted classification in the phase diagram, show peaks at the phase transition points. The effectiveness of our method is tested for the Ising, Potts, and Blume-Capel models. By using the ordered configuration from two-times clustering, our method can provide a useful way to obtain phase diagrams.Comment: 8 pages, 7 figure

Exploring the ex-situ components within Gaia DR3

The presence of Gaia DR3 provides a large sample of stars with complete 6D information, offering a fertile ground for the exploration of stellar objects that were accreted to the Milky Way through ancient merger events. In this study, we developed a deep learning methodology to identify ex-situ stars within the Gaia DR3 catalogue. After two phases of training, our neural network (NN) model was capable of performing binary classification of stars based on input data consisting of 3D position and velocity, as well as actions. From the target sample of 27 085 748 stars, our NN model managed to identify 160 146 ex-situ stars. The metallicity distribution suggests that this ex-situ sample comprises multiple components but appears to be predominated by the Gaia-Sausage-Enceladus. We identified member stars of the Magellanic Clouds, Sagittarius, and 20 globular clusters throughout our examination. Furthermore, an extensive group of member stars from Gaia-Sausage-Enceladus, Thamnos, Sequoia, Helmi streams, Wukong, and Pontus were meticulously selected, constituting an ideal sample for the comprehensive study of substructures. Finally, we conducted a preliminary estimation to determine the proportions of ex-situ stars in the thin disc, thick disc, and halo, which resulted in percentages of 0.1%, 1.6%, and 63.2%, respectively. As the vertical height from the Galactic disc and distance from the Galactic centre increased, there was a corresponding upward trend in the ex-situ fraction of the target sample

Judging LLM-as-a-judge with MT-Bench and Chatbot Arena

Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, such as position and verbosity biases and limited reasoning ability, and propose solutions to migrate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80\% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Additionally, we show our benchmark and traditional benchmarks complement each other by evaluating several variants of LLaMA/Vicuna. We will publicly release 80 MT-bench questions, 3K expert votes, and 30K conversations with human preferences from Chatbot Arena

Erratum: A multi-objective optimization-based layer-by-layer blade-coating approach for organic solar cells: Rational control of vertical stratification for high performance (Energy and Environmental Science (2019) 12 (3118-3132) DOI: 10.1039/C9EE02295C)

The Acknowledgements section should have included the following sentence: "This work was performed in part on the SAXS/ WAXS beamline at the Australian Synchrotron, part of ANSTO". The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers

A multi-objective optimization-based layer-by-layer blade-coating approach for organic solar cells:Rational control of vertical stratification for high performance

A major breakthrough in organic solar cells (OSCs) in the last thirty years was the development of the bulk heterojunction (BHJ) solution processing strategy, which effectively provided a nanoscale phase-separated morphology, aiding in the separation of Coulombically bound excitons and facilitating charge transport and extraction. Compared with the application of the layer-by-layer (LbL) approach proposed in the same period, the BHJ spin-coating technology shows overwhelming advantages for evaluating the performance of photovoltaic materials and achieving more-efficient photoelectric conversion. Thus, in this study, we have further compared the BHJ and LbL processing strategies via the doctor-blade coating technology because it is a roll-to-roll compatible high-throughput thin film fabrication route. We systematically evaluated multiple target parameters, including morphological characteristics, optical simulation, physical kinetics, device efficiency, and blend stability issues. It is worth emphasizing that our findings disprove the old stereotypes such as the BHJ processing method is superior to the LbL technology for the preparation of high-performance OSCs and the LbL approach requires an orthogonal solvent and donor/acceptor materials with special solubility. Our studies demonstrate that the LbL blade-coating approach is a promising strategy to effectively reduce the efficiency-stability gap of OSCs and even a superior alternative to the BHJ method in commercial applications

Essays on Vertical Cooperation, Intellectual Property Protection, and the International Development and Diffusion of New Technologies

In the first essay, we develop a theoretical model, to analyse the trade-off between two modes, vertical partnership and vertical merger, of the cooperation between a high-tech northern firm and a southern firm that has low-labour-cost advantage. We conclude that if there is high “importance/degree” of asymmetric information on the quality of the northern firm’s technology, the vertical partnership mode making it possible to screen out low-quality technologies, tends to arise as the equilibrium cooperation mode, rather than the vertical merger mode achieving higher overall cost efficiency. In the second essay, we examine empirically how two legal regimes of intellectual property protection in a country, patent protection and trade secret protection, affect the foreign-sourced R&D investment into the country. We find that both patent and trade secret protection may have positive or negative effects on the foreign-sourced R&D investment, but mostly, the dominant effects of both regimes on the foreign-sourced R&D investment are their positive effects that stem from the “appropriability” channel: both patent and trade secret protection can increase the appropriability of R&D achievements. Also, when patent and trade secret protection work for boosting the foreign-sourced R&D investment, the two regimes complement each other. In the third essay, we examine empirically how the manufacturing R&D investment and service R&D investment in a country, respectively, are affected by the patent protection and trade secret protection regimes in the country. We find that on the one hand, patent protection positively affects both the levels of R&D investment in manufacturing and in services. On the other hand, trade secret protection has no significant effect on the R&D investment in manufacturing, while our results weakly indicate a U-shaped effect of trade secret protection on the R&D investment in services

A Comparison of Etiology, Pathogenesis, Vaccinal and Antiviral Drug Development between Influenza and COVID-19

Influenza virus and coronavirus, two kinds of pathogens that exist widely in nature, are common emerging pathogens that cause respiratory tract infections in humans. In December 2019, a novel coronavirus SARS-CoV-2 emerged, causing a severe respiratory infection named COVID-19 in humans, and raising a global pandemic which has persisted in the world for almost three years. Influenza virus, a seasonally circulating respiratory pathogen, has caused four global pandemics in humans since 1918 by the emergence of novel variants. Studies have shown that there are certain similarities in transmission mode and pathogenesis between influenza and COVID-19, and vaccination and antiviral drugs are considered to have positive roles as well as several limitations in the prevention and control of both diseases. Comparative understandings would be helpful to the prevention and control of these diseases. Here, we review the study progress in the etiology, pathogenesis, vaccine and antiviral drug development for the two diseases

Image_1_Deficiency of C-reactive protein or human C-reactive protein transgenic treatment aggravates influenza A infection in mice.jpeg

C-reactive protein (CRP) has been shown to be a potential candidate target in the immunotherapy of severe influenza A infection. However, it is unclear on the pathogenesis associated with CRP in influenza infections. Here, we used influenza A H1N1 CA04 to infect human CRP transgenic mice (KI), CRP knockout mice (KO), and wild-type mice (WT), respectively, and compared the viral pathogenicity and associated immune response in those mice. The results showed that CA04 infection resulted in 100%, 80%, and 60% death in KO, KI, and WT mice, respectively. Compared to WT mice, CA04 infection resulted in higher TCID50 in lungs on day 3 after infection but lowered HI antibody titers in sera of survivors on day 21 after infection in KI mice. ELISA assay showed that IFN-γ concentration was significantly increased in sera of WT, KI, or KO mice on day 7 after infection, and IL-17 was remarkably increased in sera of WT mice but decreased in sera of KI mice while no significant change in sera of KO mice on day 3 or 7 after infection. Quantitative RT-PCR showed that the relative expression levels of immune checkpoint CTLA-4, LAIR-1, GITR, BTLA, TIM-3, or PD-1 mRNA in the lung presented decreased levels on day 3 or 7 after infection in KI or KO mice. The correlation analysis showed that mRNA expression levels of the 6 molecules positively correlated with viral TICD50 in WT mice but negatively correlated with viral TCID50 in KI or KO mice. However, only LAIR-1 presented a significant correlation in each lung tissue of WT, KI, or KO mice with CA07 infection statistically. IHC results showed that LAIR-1 positive cells could be found in WT, KO, or KI mice lung tissues with CA04 infection, and the positive cells were mainly distributed in an inflammatory dense area. Our results suggested that deficiency of CRP or human CRP transgenic treatment aggravates influenza A virus infection in mice. CRP is a double sword in immune regulation of influenza infection in which IL-17 and immune checkpoint may be involved.</p