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"Liu, Yang"
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Higgs inflation and its extensions and the further refining dS swampland conjecture
On the one hand, Andriot and Roupec (Fortsch Phys, 1800105, 2019) proposed an alternative refined de Sitter conjecture, which gives a natural condition on a combination of the first and second derivatives of the scalar potential (Andriot and Roupec 2019). On the other hand, in our previous article (Liu in Eur Phys J Plus 136:901, 2021) , we have found that Palatini Higgs inflation model is in strong tension with the refined de Sitter swampland conjecture (Liu 2021). Therefore, following our previous research, in this article we examine if Higgs inflation model and its two variations: Palatini Higgs inflation and Higgs-Dilaton model (Rubio in Front Astron Space Sci, https://doi.org/10.3389/fspas.2018.00050, 2019) can satisfy the “further refining de Sitter swampland conjecture” or not. Based on observational data (Ade et al., Phys Rev Lett 121:221301, 2018; Akrami et al., Planck 2018 results. X. Constraints on inflation, arXiv:1807.06211 [astro-ph.CO], 2018; Aghanim et al., Planck 2018 results: VI. Cosmological parameters, arXiv:1807.06209 [astro-ph.CO], 2018), we find that these three inflationary models can always satisfy this new swampland conjecture if only we adjust the relevant parameters a, b=1-a and q. Therefore, if the “further refining de Sitter swampland conjecture” does indeed hold, then the three inflationary models might all be in “landscape”.
Journal Article
Deep learning in natural language processing
Deep learning has revolutionized a number of applications in artificial intelligence, including speech, vision, natural language, game playing, healthcare, and robotics. In particular, the recent striking success of deep learning in a wide variety of Natural Language Processing (NLP) application areas has been taken as a landmark of deep learning in one of the most important tasks in Artificial Intelligence. The book presents the state-of-the-art of deep learning research, and its applications in major NLP tasks including speech recognition, lexical analysis, parsing, knowledge graph, machine translation, information retrieval, question answering, sentiment analysis, social computing, spoken language understanding, and dialogue systems. The self-contained, comprehensive chapters have been written by leading researchers in the field. It appeals undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers and anyone interested in deep learning and natural language processing.
Book
Higgs inflation and scalar weak gravity conjecture
In this article, we intend to find a specific model which can satisfy the further refining dS swampland conjecture and scalar weak gravity conjecture (SWGC) simultaneously, in particular, Higgs inflation model and its two extensions: Higgs-dilaton model and Palatini Higgs inflation. We determine the conditions if the three inflation models satisfy scalar weak gravity conjecture (SWGC) and strong scalar weak gravity conjecture (SSWGC).
Journal Article
Emerging evidence and treatment paradigm of non-small cell lung cancer
Research on biomarker-driven therapy and immune check-point blockade in non-small cell lung cancer (NSCLC) is rapidly evolving. The width and depth of clinical trials have also dramatically improved in an unprecedented speed. The personalized treatment paradigm evolved every year. In this review, we summarize the promising agents that have shifted the treatment paradigm for NSCLC patients across all stages, including targeted therapy and immunotherapy using checkpoint inhibitors. Based on recent evidence, we propose treatment algorithms for NSCLC and propose several unsolved clinical issues, which are being explored in ongoing clinical trials. The results of these trials are likely to impact future clinical practice.
Journal Article
Jie neng jian zhu = Sustainable & green building
Ben shu jiang shu le jie neng jian zhu zai ban gong shang ye jie de ti xian he yun yong, Bing li ju ma de li ke kou ke le gong si zong bu, Ha xi xin qu fa zhan da sha, Rui shi lian bang shui zi yuan yan jiu zhong xin ban gong da lou, Du lin qu di fang fa yuan deng jian zhu shi li jin xing fen xi.
Book
Fine-tuning problems in type IIA string theory
A
bstract
We demonstrate a unified resolution to the strong CP, hierarchy, and cosmological constant problems in type IIA flux compactifications, via 4-form fluxes and KL stabilization. We show that the strong CP problem can be effectively “solved” in type IIA orientifold constructions, particularly in the type IIA
T
6
/(
ℤ
2
×
ℤ
2
) model. Building on this, we explore whether the remaining two fine-tuning problems can also be resolved within the same setup. To obtain a small cosmological constant, we adopt the KL scenario and find that, in order to avoid conflicts with the swampland distance conjecture and to eliminate the need for fine-tuning, the perturbative superpotential ∆
W
must take the form
f
0
U
3
. Additionally, we compute the gravitino mass. This allows for a resolution of the hierarchy problem without introducing fine-tuning if gravitino mass lies below 100 TeV. Taken together, these results suggest that the type IIA
T
6
/(
ℤ
2
×
ℤ
2
) orientifold model provides a promising framework in which all three fine-tuning problems may be addressed simultaneously.
Journal Article
Application of artificial intelligence CNN model in emotional recognition of instrumental music
To enhance the accuracy and expressiveness of emotional recognition in instrumental music, this study proposes a multi-layer music emotion recognition model. The model integrates Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention Mechanism, aiming to accurately capture complex emotional information in audio. First, the CNN is used to extract local emotional features of the audio. Then, the BiGRU is employed to model the contextual information of time series, strengthening the temporal continuity of emotional expression. Finally, the attention mechanism is introduced to dynamically focus on key emotional segments. To achieve multi-scale feature fusion, the model combines low-level audio features and high-level semantic features through weighted summation during the feature extraction stage. The experimental section is validated using three music emotion datasets, including two publicly available datasets Instrument Recognition in Musical Audio Signals (IRMAS) and Multitrack Dataset for Musical Audio (MedleyDB), as well as a large-scale dataset Database for Emotional Analysis of Music (DEAM), to comprehensively evaluate the performance, generalization ability, and robustness of the model. These datasets cover a large number of multi-category instrumental audio samples. The model is evaluated on three continuous emotional dimensions: Valence, Arousal, and Dominance. The experimental results show that the proposed model achieves Pearson correlation coefficients of 0.871, 0.832, and 0.784, respectively, which are better than those of the comparative models. In terms of Mean Squared Error (MSE), the values are 0.0187, 0.0208, and 0.0243, respectively, indicating higher prediction accuracy. In conclusion, the proposed fusion deep neural network model significantly improves the accuracy and generalization ability of emotional recognition in instrumental music. This study provides an effective method and practical inspiration for emotional modeling in complex musical environments.
Journal Article