Alpha-decay half-lives and Q_alpha values of superheavy nuclei

The alpha-decay half-lives of recently synthesized superheavy nuclei (SHN) are investigated based on a unified fission model (UFM) where a new method to calculate the assault frequency of alpha-emission is used. The excellent agreement with the experimental data indicates the UFM is a useful tool to investigate these alpha-decays. It is found that the half-lives become more and more insensitive to the Q_alpha values as the atomic number increases on the whole, which is favorable for us to predict the half-lives of SHN. In addition, a formula is suggested to compute the Q_alpha values for the nuclei with Z > 92 and N > 140 with a good accuracy, according to which the long-lived SHN should be neutron rich. With Q_alpha values from this formula as inputs, we predict the half-lives of isotopes of Z = 117, which may be useful for experimental identication in the future.Comment: 7 pages, 3 figure

Intertwined charge and pair density orders in a monolayer high-Tc iron-based superconductor

Symmetry-breaking electronic phase in unconventional high-temperature (high-Tc) superconductors is a fascinating issue in condensed-matter physics, among which the most attractive phases are charge density wave (CDW) phase with four unit-cell periodicity in cuprates and nematic phase breaking the C4 rotational symmetry in iron-based superconductors (FeSCs). Recently, pair density wave (PDW), an exotic superconducting phase with non-zero momentum Cooper pairs, has been observed in high-Tc cuprates and the monolayer FeSC. However, the interplay between the CDW, PDW and nematic phase remains to be explored. Here, using scanning tunneling microscopy/spectroscopy, we detected commensurate CDW and CDW-induced PDW orders with the same period of lambda = 4aFe (aFe is the distance between neighboring Fe atoms) in a monolayer high-Tc Fe(Te,Se) film grown on SrTiO3(001) substrate. Further analyses demonstrate the observed CDW is a smectic order, which breaks both translation and C4 rotational symmetry. Moreover, the smecticity of the CDW order is strongest near the superconducting gap but weakens near defects and in an applied magnetic field, indicating the interplay between the smectic CDW and PDW orders. Our works provide a new platform to study the intertwined orders and their interactions in high-Tc superconductors

La recherche et la pratique du management interculturel à la centrale nucléaire de Daya Bay

En nous référant à la théorie traditionnelle du management interculturel qui étudie la différence culturelle des nations, nous tenterons de construire un modèle structurant du management interculturel et d’analyser en recourant à ce modèle des actions du management interculturel sur le centrale de Daya Bay. Nous nous proposons par cette recherche de formaliser une nouvelle méthode de management interculturel qui pourrait être une référence pour la construction et le management des futures centrales nucléaires en Chine.Referring to the traditional theory of the intercultural management that studies the cultural difference of the nations, we are tempted to establish a frame model of the intercultural management and resorting to this model analyzer the actions of intercultural management in Daya Bay Station. By this research, we intend to formalize a new model of intercultural management that may be a reference for the construction and management of the futures nuclear power plants in Chine

Hierarchical Pruning of Deep Ensembles with Focal Diversity

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble, which can guide ensemble pruning. Second, we design a focal diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better generalizability while being more time and space efficient in ensemble decision making.Comment: To appear on ACM Transactions on Intelligent Systems and Technolog

Rethinking Learning Rate Tuning in the Era of Large Language Models

Large Language Models (LLMs) represent the recent success of deep learning in achieving remarkable human-like predictive performance. It has become a mainstream strategy to leverage fine-tuning to adapt LLMs for various real-world applications due to the prohibitive expenses associated with LLM training. The learning rate is one of the most important hyperparameters in LLM fine-tuning with direct impacts on both fine-tuning efficiency and fine-tuned LLM quality. Existing learning rate policies are primarily designed for training traditional deep neural networks (DNNs), which may not work well for LLM fine-tuning. We reassess the research challenges and opportunities of learning rate tuning in the coming era of Large Language Models. This paper makes three original contributions. First, we revisit existing learning rate policies to analyze the critical challenges of learning rate tuning in the era of LLMs. Second, we present LRBench++ to benchmark learning rate policies and facilitate learning rate tuning for both traditional DNNs and LLMs. Third, our experimental analysis with LRBench++ demonstrates the key differences between LLM fine-tuning and traditional DNN training and validates our analysis

Discovery of a pair density wave state in a monolayer high-Tc iron-based superconductor

The pair density wave (PDW) is an extraordinary superconducting state where Cooper pairs carry nonzero momentum. It can emerge when the full condensation of zero momentum Cooper pairs is frustrated. Evidence for the existence of intrinsic PDW order in high-temperature (high-Tc) cuprate superconductors and kagome superconductors has emerged recently. However, the PDW order in iron-based high-Tc superconductors has not been observed experimentally. Here, using scanning tunneling microscopy/spectroscopy, we report the discovery of the PDW state in monolayer iron-based high-Tc Fe(Te,Se) films grown on SrTiO3(001) substrates. The PDW state with a period of {\lambda}~3.6a_Fe (a_Fe is the distance between neighboring Fe atoms) is observed at the domain walls by the spatial electronic modulations of the local density of states, superconducting gap, and the {\pi}-phase shift boundaries of the PDW around the dislocations of the intertwined charge density wave order. The discovery of the PDW state in the monolayer Fe(Te,Se) film provides a low-dimensional platform to study the interplay between the correlated electronic states and unconventional Cooper pairing in high-Tc superconductors

Invisible Watermarking for Audio Generation Diffusion Models

Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains. In the rapidly evolving field of audio-based machine learning, safeguarding model integrity and establishing data copyright are of paramount importance. This paper presents the first watermarking technique applied to audio diffusion models trained on mel-spectrograms. This offers a novel approach to the aforementioned challenges. Our model excels not only in benign audio generation, but also incorporates an invisible watermarking trigger mechanism for model verification. This watermark trigger serves as a protective layer, enabling the identification of model ownership and ensuring its integrity. Through extensive experiments, we demonstrate that invisible watermark triggers can effectively protect against unauthorized modifications while maintaining high utility in benign audio generation tasks.Comment: This is an invited paper for IEEE TPS, part of the IEEE CIC/CogMI/TPS 2023 conferenc