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1. Observation of an axial-vector state in the study of $\psi(3686) \to \phi \eta \eta'$ decay

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fischer, K., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guan, Z. L., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hölzken, F., Hüsken, N, der Wiesche, N. in, Irshad, M., Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. J., Li, Z. X., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, S. Q., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J, Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, R. S., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Yifan, Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y, Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Yao, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using (2712.4 $\pm$ 14.3)$\times 10^{6}$ $\psi(3686)$ events collected with the BESIII detector at BEPCII, a partial wave analysis of the decay $\psi(3686) \to \phi \eta \eta' $ is performed with the covariant tensor approach. An axial-vector state with a mass near 2.3 $\rm GeV/c^2$ is observed for the first time. Its mass and width are measured to be 2316 $\pm 9_{\mathrm{stat}} \pm 30_{\mathrm{syst}}\,\rm MeV/c^2$ and 89 $\pm 15_{\mathrm{stat}} \pm 26_{\mathrm{syst}}\,\rm MeV$, respectively. The product branching fractions of $\mathcal{B}(\psi(3686) \to X(2300) \eta') \mathcal{B}(X(2300)\to \phi \eta)$ and $\mathcal{B}(\psi(3686) \to X(2300) \eta)\mathcal{B}(X(2300)\to \phi \eta')$ are determined to be (4.8 $\pm 1.3_{\mathrm{stat}} \pm 0.7_{\mathrm{syst}})\times 10^{-6}$ and (2.2 $\pm 0.7_{\mathrm{stat}} \pm 0.7_{\mathrm{syst}})\times 10^{-6}$, respectively. The branching fraction $\mathcal{B}(\psi(3686) \to \phi \eta \eta')$ is measured for the first time to be (3.14$\pm0.17_{\mathrm{stat}}\pm0.24_{\mathrm{syst}})\times10^{-5}$. The first uncertainties are statistical and the second are systematic.
Published: 2024

2. Quantum Rewinding for IOP-Based Succinct Arguments

Author: Chiesa, Alessandro, Agnol, Marcel Dall, Di, Zijing, Guan, Ziyi, and Spooner, Nicholas
Subjects: Computer Science - Cryptography and Security, Quantum Physics
Abstract: We analyze the post-quantum security of succinct interactive arguments constructed from interactive oracle proofs (IOPs) and vector commitment schemes. We prove that an interactive variant of the BCS transformation is secure in the standard model against quantum adversaries when the vector commitment scheme is collapsing. Our proof builds on and extends prior work on the post-quantum security of Kilians succinct interactive argument, which is instead based on probabilistically checkable proofs (PCPs). We introduce a new quantum rewinding strategy that works across any number of rounds. As a consequence of our results, we obtain standard-model post-quantum secure succinct arguments with the best asymptotic complexity known.
Published: 2024

3. A Guide to Misinformation Detection Datasets

Author: Thibault, Camille, Peloquin-Skulski, Gabrielle, Tian, Jacob-Junqi, Laflamme, Florence, Guan, Yuxiang, Rabbany, Reihaneh, Godbout, Jean-François, and Pelrine, Kellin
Subjects: Computer Science - Social and Information Networks, Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract: Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this problem, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations, or political bias. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.
Published: 2024

4. Topological Singularities in Metasurface Scattering Matrices: From Nodal Lines to Exceptional Lines

Author: Chen, Jingguang, Liu, Wenzhe, Wang, Jiajun, Zhang, Ruo-Yang, Cui, Xiaohan, Guan, Fang, Shi, Lei, Zi, Jian, and Chan, C. T.
Subjects: Physics - Optics
Abstract: Topological properties of photonic structures described by Hamiltonian matrices have been extensively studied in recent years. Photonic systems are often open systems, and their coupling with the environment is characterized by scattering matrices, which can exhibit topological features as well. In this work, we uncover that topological singularities can be manifested in the scattering matrices of two-dimensional periodic photonic systems with open boundaries in the third dimension, introducing a new topological approach to describe scattering. We elaborate the importance of symmetry and demonstrate that mirror symmetry gives rise to the formation of diabolic points and nodal lines in the three-dimensional frequency-momentum space, which transform into exceptional points and lines in the presence of material loss. These topological features in the eigenvalue structure of the scattering matrix manifest as vortex lines in the cross-polarization scattering phase, providing a direct link between the eigen-problem and observable scattering phenomena in the frequency-momentum space. We demonstrate these phenomena numerically and experimentally using a reflective non-local metasurface. These findings extend the concept of topological singularities to scattering matrices and pave the way for novel photonic devices and wavefront engineering techniques., Comment: 19 pages, 4 figures
Published: 2024

5. How Transformers Solve Propositional Logic Problems: A Mechanistic Analysis

Author: Hong, Guan Zhe, Dikkala, Nishanth, Luo, Enming, Rashtchian, Cyrus, Wang, Xin, and Panigrahy, Rina
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Large language models (LLMs) have shown amazing performance on tasks that require planning and reasoning. Motivated by this, we investigate the internal mechanisms that underpin a network's ability to perform complex logical reasoning. We first construct a synthetic propositional logic problem that serves as a concrete test-bed for network training and evaluation. Crucially, this problem demands nontrivial planning to solve, but we can train a small transformer to achieve perfect accuracy. Building on our set-up, we then pursue an understanding of precisely how a three-layer transformer, trained from scratch, solves this problem. We are able to identify certain "planning" and "reasoning" circuits in the network that necessitate cooperation between the attention blocks to implement the desired logic. To expand our findings, we then study a larger model, Mistral 7B. Using activation patching, we characterize internal components that are critical in solving our logic problem. Overall, our work systemically uncovers novel aspects of small and large transformers, and continues the study of how they plan and reason.
Published: 2024

6. Where postdoctoral journeys lead

Author: Duan, Yueran, Memon, Shahan Ali, AlShebli, Bedoor, Guan, Qing, Holme, Petter, and Rahwan, Talal
Subjects: Computer Science - Computers and Society, Physics - Physics and Society
Abstract: Postdoctoral training is a career stage often described as a demanding and anxiety-laden time when many promising PhDs see their academic dreams slip away due to circumstances beyond their control. We use a unique data set of academic publishing and careers to chart the more or less successful postdoctoral paths. We build a measure of academic success on the citation patterns two to five years into a faculty career. Then, we monitor how students' postdoc positions -- in terms of relocation, change of topic, and early well-cited papers -- relate to their early-career success. One key finding is that the postdoc period seems more important than the doctoral training to achieve this form of success. This is especially interesting in light of the many studies of academic faculty hiring that link Ph.D. granting institutions and hires, omitting the postdoc stage. Another group of findings can be summarized as a Goldilocks principle: it seems beneficial to change one's direction, but not too much.
Published: 2024

7. DEIO: Deep Event Inertial Odometry

Author: Guan, Weipeng, Lin, Fuling, Chen, Peiyu, and Lu, Peng
Subjects: Computer Science - Robotics
Abstract: Event cameras are bio-inspired, motion-activated sensors that demonstrate impressive potential in handling challenging situations, such as motion blur and high-dynamic range. Despite their promise, existing event-based simultaneous localization and mapping (SLAM) approaches exhibit limited performance in real-world applications. On the other hand, state-of-the-art SLAM approaches that incorporate deep neural networks for better robustness and applicability. However, these is a lack of research in fusing learning-based event SLAM methods with IMU, which could be indispensable to push the event-based SLAM to large-scale, low-texture or complex scenarios. In this paper, we propose DEIO, the first monocular deep event-inertial odometry framework that combines learning-based method with traditional nonlinear graph-based optimization. Specifically, we tightly integrate a trainable event-based differentiable bundle adjustment (e-DBA) with the IMU pre-integration in a factor graph which employs keyframe-based sliding window optimization. Numerical Experiments in nine public challenge datasets show that our method can achieve superior performance compared with the image-based and event-based benchmarks. The source code is available at: https://github.com/arclab-hku/DEIO.
Published: 2024

8. Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction

Author: Guan, Yu, Cai, Qinrong, Li, Wei, Fan, Qiuyun, Liang, Dong, and Liu, Qiegen
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming convergence. This phenomenon is partic-ularly notable when directly apply conventional diffusion process to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. To tackle these challenges, we introduce subspace diffusion model with orthogonal decomposition, a method (referred to as Sub-DM) that restrict the diffusion process via projections onto subspace as the k-space data distribution evolves toward noise. Particularly, the subspace diffusion model circumvents the inference challenges posed by the com-plex and high-dimensional characteristics of k-space data, so the highly compact subspace ensures that diffusion process requires only a few simple iterations to produce accurate prior information. Furthermore, the orthogonal decomposition strategy based on wavelet transform hin-ders the information loss during the migration of the vanilla diffusion process to the subspace. Considering the strate-gy is approximately reversible, such that the entire pro-cess can be reversed. As a result, it allows the diffusion processes in different spaces to refine models through a mutual feedback mechanism, enabling the learning of ac-curate prior even when dealing with complex k-space data. Comprehensive experiments on different datasets clearly demonstrate that the superiority of Sub-DM against state of-the-art methods in terms of reconstruction speed and quality., Comment: 10 pages, 11 figures
Published: 2024

9. Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model

Author: Guan, Yu, Zhang, Kunlong, Qi, Qi, Wang, Dong, Ke, Ziwen, Wang, Shaoyu, Liang, Dong, and Liu, Qiegen
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great potential in handling unsampled data and reducing noise, highlighting their promise as generative models. However, their application in dynamic MRI remains relatively underexplored. This is primarily due to the substantial amount of fully-sampled data typically required for training, which is difficult to obtain in dynamic MRI due to its spatio-temporal complexity and high acquisition costs. To address this challenge, we propose a dynamic MRI reconstruction method based on a time-interleaved acquisition scheme, termed the Glob-al-to-local Diffusion Model. Specifically, fully encoded full-resolution reference data are constructed by merging under-sampled k-space data from adjacent time frames, generating two distinct bulk training datasets for global and local models. The global-to-local diffusion framework alternately optimizes global information and local image details, enabling zero-shot reconstruction. Extensive experiments demonstrate that the proposed method performs well in terms of noise reduction and detail preservation, achieving reconstruction quality comparable to that of supervised approaches., Comment: 11 pages, 9 figures
Published: 2024

10. Temporal-Difference Learning Using Distributed Error Signals

Author: Guan, Jonas, Verch, Shon Eduard, Voelcker, Claas, Jackson, Ethan C., Papernot, Nicolas, and Cunningham, William A.
Subjects: Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract: A computational problem in biological reward-based learning is how credit assignment is performed in the nucleus accumbens (NAc). Much research suggests that NAc dopamine encodes temporal-difference (TD) errors for learning value predictions. However, dopamine is synchronously distributed in regionally homogeneous concentrations, which does not support explicit credit assignment (like used by backpropagation). It is unclear whether distributed errors alone are sufficient for synapses to make coordinated updates to learn complex, nonlinear reward-based learning tasks. We design a new deep Q-learning algorithm, Artificial Dopamine, to computationally demonstrate that synchronously distributed, per-layer TD errors may be sufficient to learn surprisingly complex RL tasks. We empirically evaluate our algorithm on MinAtar, the DeepMind Control Suite, and classic control tasks, and show it often achieves comparable performance to deep RL algorithms that use backpropagation., Comment: 10 pages, to be published at NeurIPS 2024
Published: 2024

11. LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior

Author: Tang, Xingjian, Guan, Jingwei, Li, Linge, Zhang, Youmei, Lyu, Mengye, and Yan, Li
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Diffusion model, as a powerful generative model, has found a wide range of applications including MRI reconstruction. However, most existing diffusion model-based MRI reconstruction methods operate directly in pixel space, which makes their optimization and inference computationally expensive. Latent diffusion models were introduced to address this problem in natural image processing, but directly applying them to MRI reconstruction still faces many challenges, including the lack of control over the generated results, the adaptability of Variational AutoEncoder (VAE) to MRI, and the exploration of applicable data consistency in latent space. To address these challenges, a Latent Diffusion Prior based undersampled MRI reconstruction (LDPM) method is proposed. A sketcher module is utilized to provide appropriate control and balance the quality and fidelity of the reconstructed MR images. A VAE adapted for MRI tasks (MR-VAE) is explored, which can serve as the backbone for future MR-related tasks. Furthermore, a variation of the DDIM sampler, called the Dual-Stage Sampler, is proposed to achieve high-fidelity reconstruction in the latent space. The proposed method achieves competitive results on fastMRI datasets, and the effectiveness of each module is demonstrated in ablation experiments.
Published: 2024

12. Multimodal Trustworthy Semantic Communication for Audio-Visual Event Localization

Author: Li, Yuandi, Xiang, Zhe, Yu, Fei, Guan, Zhangshuang, Ji, Hui, Wan, Zhiguo, and Feng, Cheng
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: The exponential growth in wireless data traffic, driven by the proliferation of mobile devices and smart applications, poses significant challenges for modern communication systems. Ensuring the secure and reliable transmission of multimodal semantic information is increasingly critical, particularly for tasks like Audio-Visual Event (AVE) localization. This letter introduces MMTrustSC, a novel framework designed to address these challenges by enhancing the security and reliability of multimodal communication. MMTrustSC incorporates advanced semantic encoding techniques to safeguard data integrity and privacy. It features a two-level coding scheme that combines error-correcting codes with conventional encoders to improve the accuracy and reliability of multimodal data transmission. Additionally, MMTrustSC employs hybrid encryption, integrating both asymmetric and symmetric encryption methods, to secure semantic information and ensure its confidentiality and integrity across potentially hostile networks. Simulation results validate MMTrustSC's effectiveness, demonstrating substantial improvements in data transmission accuracy and reliability for AVE localization tasks. This framework represents a significant advancement in managing intermodal information complementarity and mitigating physical noise, thus enhancing overall system performance.
Published: 2024

13. Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback

Author: Lin, Guan-Ting, Shivakumar, Prashanth Gurunath, Gourav, Aditya, Gu, Yile, Gandhe, Ankur, Lee, Hung-yi, and Bulyko, Ivan
Subjects: Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: While textless Spoken Language Models (SLMs) have shown potential in end-to-end speech-to-speech modeling, they still lag behind text-based Large Language Models (LLMs) in terms of semantic coherence and relevance. This work introduces the Align-SLM framework, which leverages preference optimization inspired by Reinforcement Learning with AI Feedback (RLAIF) to enhance the semantic understanding of SLMs. Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO). We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation. Experimental results show that our method achieves state-of-the-art performance for SLMs on most benchmarks, highlighting the importance of preference optimization to improve the semantics of SLMs.
Published: 2024

14. LVI-GS: Tightly-coupled LiDAR-Visual-Inertial SLAM using 3D Gaussian Splatting

Author: Zhao, Huibin, Guan, Weipeng, and Lu, Peng
Subjects: Computer Science - Robotics
Abstract: 3D Gaussian Splatting (3DGS) has shown its ability in rapid rendering and high-fidelity mapping. In this paper, we introduce LVI-GS, a tightly-coupled LiDAR-Visual-Inertial mapping framework with 3DGS, which leverages the complementary characteristics of LiDAR and image sensors to capture both geometric structures and visual details of 3D scenes. To this end, the 3D Gaussians are initialized from colourized LiDAR points and optimized using differentiable rendering. In order to achieve high-fidelity mapping, we introduce a pyramid-based training approach to effectively learn multi-level features and incorporate depth loss derived from LiDAR measurements to improve geometric feature perception. Through well-designed strategies for Gaussian-Map expansion, keyframe selection, thread management, and custom CUDA acceleration, our framework achieves real-time photo-realistic mapping. Numerical experiments are performed to evaluate the superior performance of our method compared to state-of-the-art 3D reconstruction systems.
Published: 2024

15. VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization

Author: Zhang, Yiwei, Gao, Jin, Ge, Fudong, Luo, Guan, Li, Bing, Zhang, Zhaoxiang, Ling, Haibin, and Hu, Weiming
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Bird's-eye-view (BEV) map layout estimation requires an accurate and full understanding of the semantics for the environmental elements around the ego car to make the results coherent and realistic. Due to the challenges posed by occlusion, unfavourable imaging conditions and low resolution, \emph{generating} the BEV semantic maps corresponding to corrupted or invalid areas in the perspective view (PV) is appealing very recently. \emph{The question is how to align the PV features with the generative models to facilitate the map estimation}. In this paper, we propose to utilize a generative model similar to the Vector Quantized-Variational AutoEncoder (VQ-VAE) to acquire prior knowledge for the high-level BEV semantics in the tokenized discrete space. Thanks to the obtained BEV tokens accompanied with a codebook embedding encapsulating the semantics for different BEV elements in the groundtruth maps, we are able to directly align the sparse backbone image features with the obtained BEV tokens from the discrete representation learning based on a specialized token decoder module, and finally generate high-quality BEV maps with the BEV codebook embedding serving as a bridge between PV and BEV. We evaluate the BEV map layout estimation performance of our model, termed VQ-Map, on both the nuScenes and Argoverse benchmarks, achieving 62.2/47.6 mean IoU for surround-view/monocular evaluation on nuScenes, as well as 73.4 IoU for monocular evaluation on Argoverse, which all set a new record for this map layout estimation task. The code and models are available on \url{https://github.com/Z1zyw/VQ-Map}.
Published: 2024

16. Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces

Author: Chen, Siqi, Wang, Zhiqiang, Deng, Xianqi, Shen, Yili, Ju, Cheng-Wei, Yi, Jun, Xiong, Lin, Ling, Guo, Alhmoud, Dieaa, Guan, Hui, and Lin, Zhou
Subjects: Condensed Matter - Materials Science, Computer Science - Neural and Evolutionary Computing, Physics - Chemical Physics
Abstract: Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum mechanical (QM) modeling became infeasible as the size of a material grew beyond hundreds of atoms. In this study, we developed a new computational tool integrating fragment-based graph neural networks (FBGNN) into the fragment-based many-body expansion (MBE) theory, referred to as FBGNN-MBE, and demonstrated its capacity to reproduce full-dimensional potential energy surfaces (FD-PES) for hierarchic chemical systems with manageable accuracy, complexity, and interpretability. In particular, we divided the entire system into basic building blocks (fragments), evaluated their single-fragment energies using a first-principles QM model and attacked many-fragment interactions using the structure-property relationships trained by FBGNNs. Our development of FBGNN-MBE demonstrated the potential of a new framework integrating deep learning models into fragment-based QM methods, and marked a significant step towards computationally aided design of large functional materials., Comment: Accepted as a Spotlight paper to NeurIPS 2024 AI4Mat Workshop. See https://openreview.net/forum?id=ra3CxVuhUf
Published: 2024

17. Shannon entropy of optimized proton-neutron pair condensates

Author: Liang, Shu-Yuan, Lu, Yi, Lei, Yang, Johnson, Calvin W., Fu, Guan-Jian, and Shen, Jia Jie
Subjects: Nuclear Theory
Abstract: Proton-neutron pairing and like-nucleon pairing are two different facets of atomic nuclear configurations. While like-nucleon pair condensates manifest their superfluidic nature in semi magic nuclei, it is not absolutely clear if there exists a T=0 proton-neutron pair condensate phase in $N=Z$ nuclei. With an explicit formalism of general pair condensates with good particle numbers, we optimize proton-neutron pair condensates for all $N=Z$ nuclei between $^{16}$O and $^{100}$Sn, given shell model effective interactions. As comparison, we also optimize like-nucleon pair condensates for their semi-magic isotones. Shannon entanglement entropy is a measurement of mixing among pair configurations, and can signal intrinsic phase transition. It turns out the like-nucleon pair condensates for semi-magic nuclei have large entropies signaling an entangled phase, but the proton-neutron pair condensates end up not far from a Hartree-Fock solution, with small entropy. With artificial pairing interaction strengths, we show that the general proton-neutron pair condensate can transit from an entangled T=1 phase to an entangled T=0 phase, i.e. pairing phase transition driven by external parameters. In the T=0 limit, the proton-neutron pair condensate turns out to be a purely P pair condensate with large entanglement entropy, although such cases may occur in cold atom systems, unlikely in atomic nuclei.
Published: 2024

18. PDBBind Optimization to Create a High-Quality Protein-Ligand Binding Dataset for Binding Affinity Prediction

Author: Wang, Yingze, Sun, Kunyang, Li, Jie, Guan, Xingyi, Zhang, Oufan, Bagni, Dorian, and Head-Gordon, Teresa
Subjects: Physics - Biological Physics
Abstract: Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data, and often relies on the PDBBind dataset for training and testing their parameters. In this work we show that PDBBind suffers from several common structural artifacts of both proteins and ligands and non-uniform reporting of binding energies of its derived training and tests, which may compromise the accuracy, reliability and generalizability of the resulting SFs. Therefore we have developed a series of algorithms organized in an automated workflow, PDBBind-Opt, that curates non-covalent protein-ligand datasets to fix common problems observed in the general, refined, and core sets of PDBBind. We also use PDBBind-Opt to create an independent data set by matching binding free energies from BioLiP2 with co-crystalized ligand-protein complexes from the PDB. The resulting PDBBind-Opt workflow and BioLiP2-Opt dataset are designed to ensure reproducibility and to minimize human intervention, while also being open-source to foster transparency in the improvements made to this important resource for the biology and drug discovery communities.
Published: 2024

19. Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials

Author: Nasir, Jamal Abdul, Guan, Jingcheng, Jee, Woongkyu, Woodley, Scott M., Sokol, Alexey A., Catlow, C. Richard A., and Elena, Alin Marin
Subjects: Condensed Matter - Materials Science
Abstract: Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics, thermodynamic and mechanical stability under varying conditions and due to their geological importance. Computational modelling has played a crucial role in understanding the relationship between the structure and functionality of silicas and silicates including zeolites. In this study, we apply the MACE-MP-0 machine learnt interatomic potentials (ML-IP) to model the framework energies of siliceous zeolites and examine the phase transitions of silica and ZSM-5 polymorphs under high-pressure conditions. The results reproduce the known metastability of siliceous zeolites relative to {\alpha}-quartz, with energy differences between microporous and dense phases calculated by MACE-MP-0 medium ML-IP and density functional theory (DFT) methods closely aligning with experimental calorimetric data. The high-pressure simulations reveal distinct compression behaviour in the quartz, coesite, and stishovite polymorphs of silica, with coesite and stishovite showing increased stability at elevated pressures in line with experimental data. The calculated phase transition pressures from quartz to coesite (~3.5 GPa) and coesite to stishovite (~9 GPa) are close to experimental findings, demonstrating the reliability of MACE ML-IP in modelling the structural and energetic properties of silica polymorphs., Comment: 21 pages, 7 figures
Published: 2024

20. Measurement of the time-integrated CP asymmetry in $D^{0}\rightarrow K^{0}_{S}K^{0}_{S}$ decays using Belle and Belle II data

Author: Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Beaubien, A., Becker, J., Bennett, J. V., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Das, S., De La Cruz-Burelo, E., De La Motte, S. A., De Pietro, G., de Sangro, R., Destefanis, M., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dossett, D., Dujany, G., Ecker, P., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaliyar, A. B., Kandra, J., Karyan, G., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y. B., Liao, Y. P., Libby, J., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Oxford, E. R., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Raiz, S., Rauls, N., Rehman, J. U., Reif, M., Reiter, S., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sumihama, M., Sumisawa, K., Svidras, H., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, M. -Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Yabsley, B. D., Yamada, S., Yan, W., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, J., Zani, L., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: We measure the time-integrated CP asymmetry in $D^{0} \rightarrow K^{0}_{S}K^{0}_{S}$ decays reconstructed in $e^{+}e^{-} \rightarrow c\overline{c}$ events collected by the Belle and Belle II experiments. The corresponding data samples have integrated luminosities of 980 fb$^{-1}$ and 428 fb$^{-1}$, respectively. The $D^{0}$ decays are required to originate from the $D^{*+} \rightarrow D^{0}\pi^{+}$ decay, which determines the charm flavor at production time. A control sample of $D^{0} \rightarrow K^{+}K^{-}$ decays is used to correct for production and detection asymmetries. The result, $(-1.4\pm1.3{\rm(stat)}\pm0.1{\rm (syst)})\%$, is consistent with previous determinations and with CP symmetry., Comment: 10 pages, 3 figures. arXiv admin note: text overlap with arXiv:2410.22961
Published: 2024

21. Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification

Author: Wei, Shengxun, Gao, Zan, Zhao, Yibo, and Guan, Weili
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The primary challenge in this field is to overcome the complex interplay between intra-class and inter-class variations and to identify features that remain unaffected by changes in appearance. Sufficient data collection for model training would significantly aid in addressing this problem. However, it is challenging to gather diverse datasets in practice. Current methods focus on implicitly learning identity information from the original image or introducing additional auxiliary models, which are largely limited by the quality of the image and the performance of the additional model. To address these issues, inspired by prompt learning, we propose a novel multiple information prompt learning (MIPL) scheme for cloth-changing person ReID, which learns identity robust features through the common prompt guidance of multiple messages. Specifically, the clothing information stripping (CIS) module is designed to decouple the clothing information from the original RGB image features to counteract the influence of clothing appearance. The Bio-guided attention (BGA) module is proposed to increase the learning intensity of the model for key information. A dual-length hybrid patch (DHP) module is employed to make the features have diverse coverage to minimize the impact of feature bias. Extensive experiments demonstrate that the proposed method outperforms all state-of-the-art methods on the LTCC, Celeb-reID, Celeb-reID-light, and CSCC datasets, achieving rank-1 scores of 74.8%, 73.3%, 66.0%, and 88.1%, respectively. When compared to AIM (CVPR23), ACID (TIP23), and SCNet (MM23), MIPL achieves rank-1 improvements of 11.3%, 13.8%, and 7.9%, respectively, on the PRCC dataset.
Published: 2024

22. Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction

Author: Huang, Guan-Hua, Lai, Wan-Chen, Chen, Tai-Been, Hsu, Chien-Chin, Chen, Huei-Yung, Wu, Yi-Chen, and Yeh, Li-Ren
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures. Initially, we treated the 3D images as sequences of two-dimensional (2D) slices and fed them sequentially into 2D convolutional neural network (CNN) models pretrained on ImageNet, averaging the outputs to obtain the final predicted stage. We also applied 3D CNN models pretrained on Kinetics-400. Additionally, we incorporated an attention mechanism to account for the varying importance of different slices in the prediction process. To further enhance model efficacy and robustness, we simultaneously trained the two data sets using weight sharing, a technique known as cotraining. Our results demonstrated that 2D models pretrained on ImageNet outperformed 3D models pretrained on Kinetics-400, and models utilizing the attention mechanism outperformed both 2D and 3D models. The cotraining technique proved effective in improving model performance when the cotraining data sets were sufficiently large., Comment: 34 pages, 7 figures, and 4 tables
Published: 2024

23. Why Fine-grained Labels in Pretraining Benefit Generalization?

Author: Hong, Guan Zhe, Cui, Yin, Fuxman, Ariel, Chan, Stanely, and Luo, Enming
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract: Recent studies show that pretraining a deep neural network with fine-grained labeled data, followed by fine-tuning on coarse-labeled data for downstream tasks, often yields better generalization than pretraining with coarse-labeled data. While there is ample empirical evidence supporting this, the theoretical justification remains an open problem. This paper addresses this gap by introducing a "hierarchical multi-view" structure to confine the input data distribution. Under this framework, we prove that: 1) coarse-grained pretraining only allows a neural network to learn the common features well, while 2) fine-grained pretraining helps the network learn the rare features in addition to the common ones, leading to improved accuracy on hard downstream test samples., Comment: arXiv admin note: substantial text overlap with arXiv:2303.16887
Published: 2024

24. Dispersion kinks from electronic correlations in an unconventional iron-based superconductor

Author: Chang, Ming-Hua, Backes, Steffen, Lu, Donghui, Gauthier, Nicolas, Hashimoto, Makoto, Chen, Guan-Yu, Wen, Hai-Hu, Mo, Sung-Kwan, Valenti, Roser, and Pfau, Heike
Subjects: Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Superconductivity
Abstract: The attractive interaction in conventional BCS superconductors is provided by a bosonic mode. However, the pairing glue of most unconventional superconductors is unknown. The effect of electron-boson coupling is therefore extensively studied in these materials. A key signature are dispersion kinks that can be observed in the spectral function as abrupt changes in velocity and lifetime of quasiparticles. Here, we show the existence of two kinks in the unconventional iron-based superconductor RbFe$_2$As$_2$ using angle-resolved photoemission spectroscopy (ARPES) and dynamical mean field theory (DMFT). In addition, we observe the formation of a Hubbard band multiplet due to the combination of Coulomb interaction and Hund's rule coupling in this multiorbital systems. We demonstrate that the two dispersion kinks are a consequence of these strong many-body interactions. This interpretation is in line with a growing number of theoretical predictions for kinks in various general models of correlated materials. Our results provide a unifying link between iron-based superconductors and different classes of correlated, unconventional superconductors such as cuprates and heavy-fermion materials.
Published: 2024

25. Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration

Author: Guan, Yanchu, Wang, Dong, Wang, Yan, Wang, Haiqing, Sun, Renen, Zhuang, Chenyi, Gu, Jinjie, and Chu, Zhixuan
Subjects: Computer Science - Computation and Language
Abstract: Autonomous mobile app interaction has become increasingly important with growing complexity of mobile applications. Developing intelligent agents that can effectively navigate and interact with mobile apps remains a significant challenge. In this paper, we propose an Explainable Behavior Cloning LLM Agent (EBC-LLMAgent), a novel approach that combines large language models (LLMs) with behavior cloning by learning demonstrations to create intelligent and explainable agents for autonomous mobile app interaction. EBC-LLMAgent consists of three core modules: Demonstration Encoding, Code Generation, and UI Mapping, which work synergistically to capture user demonstrations, generate executable codes, and establish accurate correspondence between code and UI elements. We introduce the Behavior Cloning Chain Fusion technique to enhance the generalization capabilities of the agent. Extensive experiments on five popular mobile applications from diverse domains demonstrate the superior performance of EBC-LLMAgent, achieving high success rates in task completion, efficient generalization to unseen scenarios, and the generation of meaningful explanations., Comment: 20 pages
Published: 2024

26. Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots

Author: Guan, Vincent, Janssen, Joseph, Rahmani, Hossein, Warren, Andrew, Zhang, Stephen, Robeva, Elina, and Schiebinger, Geoffrey
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory
Abstract: Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets, and image generation. However, learning the underlying SDE from observational data is a challenging task, especially when individual trajectories are not observable. Motivated by burgeoning research in single-cell datasets, we present the first comprehensive approach for jointly estimating the drift and diffusion of an SDE from its temporal marginals. Assuming linear drift and additive diffusion, we prove that these parameters are identifiable from marginals if and only if the initial distribution is not invariant to a class of generalized rotations, a condition that is satisfied by most distributions. We further prove that the causal graph of any SDE with additive diffusion can be recovered from the SDE parameters. To complement this theory, we adapt entropy-regularized optimal transport to handle anisotropic diffusion, and introduce APPEX (Alternating Projection Parameter Estimation from $X_0$), an iterative algorithm designed to estimate the drift, diffusion, and causal graph of an additive noise SDE, solely from temporal marginals. We show that each of these steps are asymptotically optimal with respect to the Kullback-Leibler divergence, and demonstrate APPEX's effectiveness on simulated data from linear additive noise SDEs.
Published: 2024

27. Automated Vulnerability Detection Using Deep Learning Technique

Author: Yang, Guan-Yan, Ko, Yi-Heng, Wang, Farn, Yeh, Kuo-Hui, Chang, Haw-Shiang, and Chen, Hsueh-Yi
Subjects: Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Software Engineering, D.2.4, D.2.5
Abstract: Our work explores the utilization of deep learning, specifically leveraging the CodeBERT model, to enhance code security testing for Python applications by detecting SQL injection vulnerabilities. Unlike traditional security testing methods that may be slow and error-prone, our approach transforms source code into vector representations and trains a Long Short-Term Memory (LSTM) model to identify vulnerable patterns. When compared with existing static application security testing (SAST) tools, our model displays superior performance, achieving higher precision, recall, and F1-score. The study demonstrates that deep learning techniques, particularly with CodeBERT's advanced contextual understanding, can significantly improve vulnerability detection, presenting a scalable methodology applicable to various programming languages and vulnerability types., Comment: 4 pages, 1 figures; Presented at The 30st International Conference on Computational & Experimental Engineering and Sciences (ICCES2024)
Published: 2024

28. Search for $\Lambda$-$\bar{\Lambda} $ oscillation in $J/\psi\rightarrow\Lambda\bar{\Lambda}$ decay

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using $(10087\pm44)\times 10^{6}$ $J/\psi$ decays collected by the BESIII detector at the BEPCII collider, we search for baryon number violation via $\Lambda-\bar{\Lambda}$ oscillation in the decay $J/\psi \to \Lambda \bar{\Lambda}$. No evidence for $\Lambda-\bar\Lambda$ oscillation is observed. The upper limit on the time-integrated probability of $\Lambda-\bar{\Lambda}$ oscillation is estimated to be $1.4\times 10^{-6}$, corresponding to an oscillation parameter less than $2.1\times 10^{-18}~\mathrm{GeV}$ at $90\%$ confidence level., Comment: 8 pages, 2 figures
Published: 2024

29. Topological surface state dominated nonlinear transverse response and microwave rectification at room temperature

Author: Shen, Qia, Chen, Jiaxin, Rong, Bin, Rong, Yaqi, Chen, Hongliang, Zhao, Tieyang, Duan, Xianfa, Guan, Dandan, Wang, Shiyong, Li, Yaoyi, Zheng, Hao, Liu, Xiaoxue, Qiu, Xuepeng, Chen, Jingsheng, Cong, Longqing, Li, Tingxin, Zhong, Ruidan, Liu, Canhua, Yang, Yumeng, Liu, Liang, and Jia, Jinfeng
Subjects: Condensed Matter - Materials Science
Abstract: Nonlinear Hall effect (NLHE) offers a novel means of uncovering symmetry and topological properties in quantum materials, holding promise for exotic (opto)electronic applications such as microwave rectification and THz detection. The BCD-independent NLHE could exhibit a robust response even at room temperature, which is highly desirable for practical applications. However, in materials with bulk inversion symmetry, the coexistence of bulk and surface conducting channels often leads to a suppressed NLHE and complex thickness-dependent behavior. Here, we report the observation of room-temperature nonlinear transverse response in 3D topological insulator Bi2Te3 thin films, whose electrical transport properties are dominated by topological surface state (TSS). By varying the thickness of Bi2Te3 epitaxial films from 7 nm to 50 nm, we found that the nonlinear transverse response increases with thickness from 7 nm to 25 nm and remains almost constant above 25 nm. This is consistent with the thickness-dependent basic transport properties, including conductance, carrier density, and mobility, indicating a pure and robust TSS-dominated linear and nonlinear transport in thick (>25 nm) Bi2Te3 films. The weaker nonlinear transverse response in Bi2Te3 below 25 nm was attributed to Te deficiency and poorer crystallinity. By utilizing the TSS-dominated electrical second harmonic generation, we successfully achieved the microwave rectification from 0.01 to 16.6 GHz in 30 nm and bulk Bi2Te3. Our work demonstrated the room temperature nonlinear transverse response in a paradigm topological insulator, addressing the tunability of the topological second harmonic response by thickness engineering.
Published: 2024

30. Einstein Probe discovery of EP240408a: a peculiar X-ray transient with an intermediate timescale

Author: Zhang, Wenda, Yuan, Weimin, Ling, Zhixing, Chen, Yong, Rea, Nanda, Rau, Arne, Cai, Zhiming, Cheng, Huaqing, Zelati, Francesco Coti, Dai, Lixin, Hu, Jingwei, Jia, Shumei, Jin, Chichuan, Li, Dongyue, O'Brien, Paul, Shen, Rongfeng, Shu, Xinwen, Sun, Shengli, Sun, Xiaojin, Wang, Xiaofeng, Yang, Lei, Zhang, Bing, Zhang, Chen, Zhang, Shuang-Nan, Zhang, Yonghe, An, Jie, Buckley, David, Coleiro, Alexis, Cordier, Bertrand, Dou, Liming, Eyles-Ferris, Rob, Fan, Zhou, Feng, Hua, Fu, Shaoyu, Fynbo, Johan P. U., Galbany, Lluis, Jha, Saurabh W., Jiang, Shuaiqing, Kong, Albert, Kuulkers, Erik, Lei, Weihua, Li, Wenxiong, Liu, Bifang, Liu, Mingjun, Liu, Xing, Liu, Yuan, Liu, Zhu, Maitra, Chandreyee, Marino, Alessio, Monageng, Itumeleng, Nandra, Kirpal, Sanders, Jeremy, Soria, Roberto, Tao, Lian, Wang, Junfeng, Wang, Song, Wang, Tinggui, Wang, Zhongxiang, Wu, Qingwen, Wu, Xuefeng, Xu, Dong, Xu, Yanjun, Xue, Suijian, Xue, Yongquan, Zhang, Zijian, Zhu, Zipei, Zou, Hu, Bao, Congying, Chen, Fansheng, Chen, Houlei, Chen, Tianxiang, Chen, Wei, Chen, Yehai, Chen, Yifan, Cui, Chenzhou, Cui, Weiwei, Dai, Yanfeng, Fan, Dongwei, Guan, Ju, Han, Dawei, Hou, Dongjie, Hu, Haibo, Huang, Maohai, Huo, Jia, Jia, Zhenqing, Jiang, Bowen, Jin, Ge, Li, Chengkui, Li, Junfei, Li, Longhui, Li, Maoshun, Li, Wei, Li, Zhengda, Lian, Tianying, Liu, Congzhan, Liu, Heyang, Liu, Huaqiu, Lu, Fangjun, Luo, Laidan, Ma, Jia, Mao, Xuan, Pan, Haiwu, Pan, Xin, Song, Liming, Sun, Hui, Tan, Yunyin, Tang, Qingjun, Tao, Yihan, Wang, Hao, Wang, Juan, Wang, Lei, Wang, Wenxin, Wang, Yilong, Wang, Yusa, Wu, Qinyu, Xu, Haitao, Xu, Jingjing, Xu, Xinpeng, Xu, Yunfei, Xu, Zhao, Xue, Changbin, Xue, Yulong, Yan, Ailiang, Yang, Haonan, Yang, Xiongtao, Yang, Yanji, Zhang, Juan, Zhang, Mo, Zhang, Wenjie, Zhang, Zhen, Zhang, Ziliang, Zhao, Donghua, Zhao, Haisheng, Zhao, Xiaofan, Zhao, Zijian, Zhou, Hongyan, Zhou, Yilin, Zhu, Yuxuan, and Zhu, Zhencai
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures
Published: 2024
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31. Approaches to Simultaneously Solving Variational Quantum Eigensolver Problems

Author: Hutchings, Adam, Yarnot, Eric, Li, Xinpeng, Guan, Qiang, Xie, Ning, Xu, Shuai, and Chaudhary, Vipin
Subjects: Quantum Physics, Computer Science - Emerging Technologies
Abstract: The variational quantum eigensolver (VQE), a type of variational quantum algorithm, is a hybrid quantum-classical algorithm to find the lowest-energy eigenstate of a particular Hamiltonian. We investigate ways to optimize the VQE solving process on multiple instances of the same problem, by observing the process on one instance of the problem to inform initialization for other processes. We aim to take advantage of the VQE solution process to obtain useful information while disregarding information which we can predict to not be very useful. In particular, we find that the solution process produces lots of data with very little new information. Therefore, we can safely disregard much of this repetitive information with little effect on the outcome of the solution process., Comment: 4 pages, 5 figures, QCCC-24 conference
Published: 2024

32. Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design

Author: Zhou, Xiangxin, Guan, Jiaqi, Zhang, Yijia, Peng, Xingang, Wang, Liang, and Ma, Jianzhu
Subjects: Computer Science - Machine Learning, Quantitative Biology - Biomolecules
Abstract: Dual-target therapeutic strategies have become a compelling approach and attracted significant attention due to various benefits, such as their potential in overcoming drug resistance in cancer therapy. Considering the tremendous success that deep generative models have achieved in structure-based drug design in recent years, we formulate dual-target drug design as a generative task and curate a novel dataset of potential target pairs based on synergistic drug combinations. We propose to design dual-target drugs with diffusion models that are trained on single-target protein-ligand complex pairs. Specifically, we align two pockets in 3D space with protein-ligand binding priors and build two complex graphs with shared ligand nodes for SE(3)-equivariant composed message passing, based on which we derive a composed drift in both 3D and categorical probability space in the generative process. Our algorithm can well transfer the knowledge gained in single-target pretraining to dual-target scenarios in a zero-shot manner. We also repurpose linker design methods as strong baselines for this task. Extensive experiments demonstrate the effectiveness of our method compared with various baselines., Comment: Accepted to NeurIPS 2024
Published: 2024

33. Optimal Hardening Strategy for Electricity-Hydrogen Networks with Hydrogen Leakage Risk Control against Extreme Weather

Author: Liu, Sicheng, Yang, Bo, Li, Xin, Yang, Xu, Wang, Zhaojian, Zhu, Dafeng, and Guan, Xinping
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: Defense hardening can effectively enhance the resilience of distribution networks against extreme weather disasters. Currently, most existing hardening strategies focus on reducing load shedding. However, for electricity-hydrogen distribution networks (EHDNs), the leakage risk of hydrogen should be controlled to avoid severe incidents such as explosions. To this end, this paper proposes an optimal hardening strategy for EHDNs under extreme weather, aiming to minimize load shedding while limiting the leakage risk of hydrogen pipelines. Specifically, modified failure uncertainty models for power lines and hydrogen pipelines are developed. These models characterize not only the effect of hardening, referred to as decision-dependent uncertainties (DDUs), but also the influence of disaster intensity correlations on failure probability distributions. Subsequently, a hardening decision framework is established, based on the two-stage distributionally robust optimization incorporating a hydrogen leakage chance constraint (HLCC). To enhance the computational efficiency of HLCC under discrete DDUs, an efficient second-order-cone transformation is introduced. Moreover, to address the intractable inverse of the second-order moment under DDUs, lifted variables are adopted to refine the main-cross moments. These reformulate the hardening problem as a two-stage mixed-integer second-order-cone programming, and finally solved by the column-and-constraint generation algorithm. Case studies demonstrate the effectiveness and superiority of the proposed method.
Published: 2024

34. Efficient Circuit Wire Cutting Based on Commuting Groups

Author: Li, Xinpeng, Kulkarni, Vinooth, Chen, Daniel T., Guan, Qiang, Jiang, Weiwen, Xie, Ning, Xu, Shuai, and Chaudhary, Vipin
Subjects: Quantum Physics, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Current quantum devices face challenges when dealing with large circuits due to error rates as circuit size and the number of qubits increase. The circuit wire-cutting technique addresses this issue by breaking down a large circuit into smaller, more manageable subcircuits. However, the exponential increase in the number of subcircuits and the complexity of reconstruction as more cuts are made poses a great practical challenge. Inspired by ancilla-assisted quantum process tomography and the MUBs-based grouping technique for simultaneous measurement, we propose a new approach that can reduce subcircuit running overhead. The approach first uses ancillary qubits to transform all quantum input initializations into quantum output measurements. These output measurements are then organized into commuting groups for the purpose of simultaneous measurement, based on MUBs-based grouping. This approach significantly reduces the number of necessary subcircuits as well as the total number of shots. Lastly, we provide numerical experiments to demonstrate the complexity reduction., Comment: Accepted in IEEE International Conference on Quantum Computing and Engineering - QCE24
Published: 2024

35. Measurement of the branching fraction of $D^+ \to \tau^+\nu_{\tau}$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Tian, J. Y., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: By analyzing $e^{+}e^{-}$ collision data with an integrated luminosity of 7.9~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV, the branching fraction of $D^+\to\tau^+\nu_{\tau}$ is determined as $\mathcal{B}=(9.9\pm 1.1_\mathrm{stat}\pm 0.5_\mathrm{syst})\times10^{-4}$. Taking the most precise result $\mathcal{B}(D^+\to\mu^+\nu_{\mu})=(3.981\pm 0.079_\mathrm{stat}\pm0.040_\mathrm{syst})\times10^{-4}$, we determine $R_{\tau/\mu} = \Gamma(D^+\to\tau^+\nu_{\tau})/\Gamma(D^+\to\mu^+\nu_{\mu})= 2.49\pm0.31$, achieving a factor of two improvement in precision compared to the previous BESIII result. This measurement is in agreement with the standard model prediction of lepton flavor universality within one standard deviation.
Published: 2024

36. Tunable topological edge states in black phosphorus-like Bi(110)

Author: Liu, Chen, Tao, Shengdan, Wang, Guanyong, Chen, Hongyuan, Xia, Bing, Yang, Hao, Liu, Xiaoxue, Liu, Liang, Li, Yaoyi, Wang, Shiyong, Zheng, Hao, Liu, Canhua, Guan, Dandan, Lu, Yunhao, and Jia, Jin-feng
Subjects: Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: We have investigated the structures and electronic properties of ultra-thin Bi(110) films grown on an s-wave superconductor substrate using low-temperature scanning tunneling microscopy and spectroscopy. Remarkably, our experimental results validate the theoretical predictions that the manipulation of Bi(110) surface atom buckling can control the topological phase transition. Notably, we have observed robust unreconstructed edge states at the edges of both 3-bilayer (BL) and 4-BL Bi(110) films, with the 4-BL film displaying stronger edge state intensity and a smaller degree of atomic buckling. First-principle calculations further substantiate these findings, demonstrating a gradual reduction in buckling as the film thickness increases, with average height differences between two Bi atoms of approximately 0.19 {\AA}, 0.10 {\AA}, 0.05 {\AA}, and 0.00 {\AA} for the 1-BL, 2-BL, 3-BL, and 4-BL Bi(110) films, respectively. When Bi films are larger than 2 layers, the system changes from a trivial to a non-trivial phase. This research sets the stage for the controlled realization of topological superconductors through the superconducting proximity effect, providing a significant platform for investigating Majorana zero modes and fabricating quantum devices.
Published: 2024

37. Active Target Tracking Using Bearing-only Measurements With Gaussian Process Learning

Author: Fu, Yingbo, Yang, Ziwen, Zhu, Shanying, Chen, Cailian, and Guan, Xinping
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: This paper studies the tracking problem of target with the partially unknown motion model by an active agent with bearing-only measurements using Gaussian process learning. To address this problem, a learning-planning-control framework is proposed. First, to learn and predict the target motion under mild assumptions, a Gaussian-process-based scheme is proposed, and a probabilistic uniform prediction error bound can be rigorously proved. Second, by analyzing the data dependence of the posterior covariance, we obtain an optimal relative trajectory to achieve efficient sampling. Third, to realize efficient learning, a controller to track the planned path is proposed based on the learned target motion, which can provide guaranteed tracking performance. Theoretical analysis is conducted to prove the the given probabilistic error bounds. Numerical examples and comparison with other typical methods verify the feasibility and superior performance of our proposed framework.
Published: 2024

38. Search for $\eta_c(2S)\to p\bar{p}$ and branching fraction measurements of $\chi_{cJ} \to p\bar{p}$ via $\psi(2S)$ radiative decays

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De~Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using $(27.12\pm0.14) \times 10^{8}$ $\psi(2S)$ events collected by the BESIII detector operating at BEPCII, we search for the decay $\eta_c(2S)\to p\bar{p}$ via the process $\psi(2S)\to \gamma\eta_c(2S)$, and only find a signal with a significance of $1.7\,\sigma$. The upper limit of the product branching fraction at the 90% confidence level is determined to be $\mathcal{B}(\psi(2S)\to \gamma\eta_c(2S))\times \mathcal{B}(\eta_c(2S)\to p\bar{p})<2.4\times 10^{-7}$. The branching fractions of $\chi_{cJ}\to p\bar{p}~(J=0,1,2)$ are also measured to be $\mathcal{B}(\chi_{c0}\to p\bar{p})=(2.51\pm0.02\pm0.08)\times 10^{-4}$, $\mathcal{B}(\chi_{c1}\to p\bar{p})=(8.16\pm0.09\pm0.25)\times 10^{-4}$, and $\mathcal{B}(\chi_{c2}\to p\bar{p})=(8.33\pm0.09\pm0.22)\times 10^{-4}$, where the first uncertainty is statistical and the second systematic.
Published: 2024

39. The Atacama Cosmology Telescope: A measurement of galaxy cluster temperatures through relativistic corrections to the thermal Sunyaev-Zeldovich effect

Author: Coulton, William R., Duivenvoorden, Adriaan J., Atkins, Zachary, Battaglia, Nicholas, Battistelli, Elia Stefano, Bond, J Richard, Cai, Hongbo, Calabrese, Erminia, Choi, Steve K., Crowley, Kevin T., Devlin, Mark J., Dunkley, Jo, Ferraro, Simone, Guan, Yilun, Hervías-Caimapo, Carlos, Hill, J. Colin, Hilton, Matt, Hincks, Adam D., Kosowsky, Arthur, Madhavacheril, Mathew S., van Marrewijk, Joshiwa, McCarthy, Fiona, Moodley, Kavilan, Mroczkowski, Tony, Niemack, Michael D., Page, Lyman A., Partridge, Bruce, Schaan, Emmanuel, Sehgal, Neelima, Sherwin, Blake, Sifón, Cristóbal, Spergel, David N., Staggs, Suzanne T., Vavagiakis, Eve M., and Wollack, Edward J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The high electron temperature in galaxy clusters ($>1\,$keV or $>10^7\,$K) leads to corrections at the level of a few percent in their thermal Sunyaev-Zeldovich effect signatures. Both the size and frequency dependence of these corrections, which are known as relativistic temperature corrections, depend upon the temperature of the objects. In this work we exploit this effect to measure the average temperature of a stack of Compton-$y$ selected clusters. Specifically, we apply the "spectroscopic method" and search for the temperature that best fits the clusters' signal measured at frequencies from 30 to 545 GHz by the Atacama Cosmology Telescope and Planck satellite. We measure the average temperature of clusters detected in the ACT maps to be $8.5\pm 2.4\,$keV, with an additional systematic error of comparable amplitude dominated by passband uncertainty. Upcoming surveys, such as the Simons Observatory and CMB-S4, have the potential to dramatically improve upon these measurements and thereby enable precision studies of cluster temperatures with millimeter observations. The key challenge for future observations will be mitigating instrumental systematic effects, which already limit this analysis., Comment: 21 pages with 17 figures
Published: 2024

40. Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing

Author: Guo, Dongliang, Hu, Mengxuan, Guan, Zihan, Guo, Junfeng, Hartvigsen, Thomas, and Li, Sheng
Subjects: Computer Science - Artificial Intelligence
Abstract: Large pre-trained models have achieved notable success across a range of downstream tasks. However, recent research shows that a type of adversarial attack ($\textit{i.e.,}$ backdoor attack) can manipulate the behavior of machine learning models through contaminating their training dataset, posing significant threat in the real-world application of large pre-trained model, especially for those customized models. Therefore, addressing the unique challenges for exploring vulnerability of pre-trained models is of paramount importance. Through empirical studies on the capability for performing backdoor attack in large pre-trained models ($\textit{e.g.,}$ ViT), we find the following unique challenges of attacking large pre-trained models: 1) the inability to manipulate or even access large training datasets, and 2) the substantial computational resources required for training or fine-tuning these models. To address these challenges, we establish new standards for an effective and feasible backdoor attack in the context of large pre-trained models. In line with these standards, we introduce our EDT model, an \textbf{E}fficient, \textbf{D}ata-free, \textbf{T}raining-free backdoor attack method. Inspired by model editing techniques, EDT injects an editing-based lightweight codebook into the backdoor of large pre-trained models, which replaces the embedding of the poisoned image with the target image without poisoning the training dataset or training the victim model. Our experiments, conducted across various pre-trained models such as ViT, CLIP, BLIP, and stable diffusion, and on downstream tasks including image classification, image captioning, and image generation, demonstrate the effectiveness of our method. Our code is available in the supplementary material.
Published: 2024

41. Neutrinoless Double Beta Decay Sensitivity of the XLZD Rare Event Observatory

Author: XLZD Collaboration, Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Akerib, D. S., Musalhi, A. K. Al, Alder, F., Althueser, L., Amaral, D. W. P., Amarasinghe, C. S., Ames, A., Andrieu, B., Angelides, N., Angelino, E., Antunovic, B., Aprile, E., Araújo, H. M., Armstrong, J. E., Arthurs, M., Babicz, M., Bajpai, D., Baker, A., Balzer, M., Bang, J., Barberio, E., Bargemann, J. W., Barillier, E., Basharina-Freshville, A., Baudis, L., Bauer, D., Bazyk, M., Beattie, K., Beaupere, N., Bell, N. F., Bellagamba, L., Benson, T., Bhatti, A., Biesiadzinski, T. P., Biondi, R., Biondi, Y., Birch, H. J., Bishop, E., Bismark, A., Boehm, C., Boese, K., Bolotnikov, A., Brás, P., Braun, R., Breskin, A., Brew, C. A. J., Brommer, S., Brown, A., Bruni, G., Budnik, R., Burdin, S., Cai, C., Capelli, C., Carini, G., Carmona-Benitez, M. C., Carter, M., Chauvin, A., Chawla, A., Chen, H., Cherwinka, J. J., Chin, Y. T., Chott, N. I., Chavez, A. P. Cimental, Clark, K., Colijn, A. P., Colling, D. J., Conrad, J., Converse, M. V., Coronel, R., Costanzo, D., Cottle, A., Cox, G., Cuenca-García, J. J., Curran, D., Cussans, D., D'Andrea, V., Garcia, L. C. Daniel, Darlington, I., Dave, S., David, A., Davies, G. J., Decowski, M. P., Deisting, A., Delgaudio, J., Dey, S., Di Donato, C., Di Felice, L., Di Gangi, P., Diglio, S., Ding, C., Dobson, J. E. Y., Doerenkamp, M., Drexlin, G., Druszkiewicz, E., Dunbar, C. L., Eitel, K., Elykov, A., Engel, R., Eriksen, S. R., Fayer, S., Fearon, N. M., Ferella, A. D., Ferrari, C., Fieldhouse, N., Fischer, H., Flaecher, H., Flehmke, T., Flierman, M., Fraser, E. D., Fruth, T. M. A., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Gaitskell, R. J., Gallice, N., Galloway, M., Gao, F., Garroum, N., Geffre, A., Genovesi, J., Ghag, C., Ghosh, S., Giacomobono, R., Gibbons, R., Girard, F., Glade-Beucke, R., Glück, F., Gokhale, S., Grandi, L., Green, J., Grigat, J., van der Grinten, M. G. D., Größle, R., Guan, H., Guida, M., Gyorgy, P., Haiston, J. J., Hall, C. R., Hall, T., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Hartigan-O'Connor, E., Haselschwardt, S. J., Hernandez, M., Hertel, S. A., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Homenides, G. J., Hood, N. F., Horn, M., Huang, D. Q., Hughes, S., Hunt, D., Iacovacci, M., Itow, Y., Jacquet, E., Jakob, J., James, R. S., Joerg, F., Jones, S., Kaboth, A. C., Kahlert, F., Kamaha, A. C., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kemp-Russell, P., Khaitan, D., Kharbanda, P., Kilminster, B., Kim, J., Kirk, R., Kleifges, M., Klute, M., Kobayashi, M., Kodroff, D., Koke, D., Kopec, A., Korolkova, E. V., Kraus, H., Kravitz, S., Kreczko, L., von Krosigk, B., Kudryavtsev, V. A., Kuger, F., Kurita, N., Landsman, H., Lang, R. F., Lawes, C., Lee, J., Lehnert, B., Leonard, D. S., Lesko, K. T., Levinson, L., Li, A., Li, I., Li, S., Liang, S., Liang, Z., Lin, J., Lin, Y. -T., Lindemann, S., Linden, S., Lindner, M., Lindote, A., Lippincott, W. H., Liu, K., Loizeau, J., Lombardi, F., Lopes, J. A. M., Lopes, M. I., Lorenzon, W., Loutit, M., Lu, C., Lucchetti, G. M., Luce, T., Luitz, S., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Majewski, P. A., Manalaysay, A., Mancuso, A., Manenti, L., Mannino, R. L., Marignetti, F., Marley, T., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Maupin, C., McCabe, C., McCarthy, M. E., McKinsey, D. N., McLaughlin, J. B., Melchiorre, A., Menéndez, J., Messina, M., Miller, E. H., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Mizrachi, E., Molinario, A., Monteiro, C. M. B., Monzani, M. E., Morå, K., Moriyama, S., Morrison, E., Morteau, E., Mosbacher, Y., Mount, B. J., Müller, J., Murdy, M., Murphy, A. St. J., Murra, M., Naylor, A., Nelson, H. N., Neves, F., Newstead, J. L., Nguyen, A., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Olcina, I., Oliver-Mallory, K. C., Gann, G. D. Orebi, Orpwood, J., Ostrowskiy, I., Ouahada, S., Oyulmaz, K., Paetsch, B., Palladino, K. J., Palmer, J., Pan, Y., Pandurovic, M., Pannifer, N. J., Paramesvaran, S., Patton, S. J., Pellegrini, Q., Penning, B., Pereira, G., Peres, R., Perry, E., Pershing, T., Piastra, F., Pienaar, J., Piepke, A., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qie, Y., Qin, J., Radeka, S., Radeka, V., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Reichenbacher, J., Rhyne, C. A., Richards, A., Rischbieter, G. R. C., Riyat, H. S., Rosero, R., Roy, A., Rushton, T., Rynders, D., Saakyan, R., Sanchez, L., Sanchez-Lucas, P., Santone, D., Santos, J. M. F. dos, Sartorelli, G., Sazzad, A. B. M. R., Scaffidi, A., Schnee, R. W., Schreiner, J., Schulte, P., Schulze, H., Eißing, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shaw, S., Shen, W., Sherman, L., Shi, S., Shi, S. Y., Shimada, T., Shutt, T., Silk, J. J., Silva, C., Simgen, H., Sinev, G., Singh, R., Siniscalco, J., Solmaz, M., Solovov, V. N., Song, Z., Sorensen, P., Soria, J., Stanley, O., Steidl, M., Stenhouse, T., Stevens, A., Stifter, K., Sumner, T. J., Takeda, A., Tan, P. -L., Taylor, D. J., Taylor, W. C., Thers, D., Thümmler, T., Tiedt, D. R., Tönnies, F., Tong, Z., Toschi, F., Tovey, D. R., Tranter, J., Trask, M., Trinchero, G., Tripathi, M., Tronstad, D. R., Trotta, R., Tunnell, C. D., Urquijo, P., Usón, A., Utoyama, M., Vaitkus, A. C., Valentino, O., Valerius, K., Vecchi, S., Velan, V., Vetter, S., de Viveiros, L., Volta, G., Vorkapic, D., Wang, A., Wang, J. J., Wang, W., Wang, Y., Waters, D., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Whitis, T. J., Wild, K., Williams, M., Wilson, M., Wilson, S. T., Wittweg, C., Wolf, J., Wolfs, F. L. H., Woodford, S., Woodward, D., Worcester, M., Wright, C. J., Wu, V. H. S., üstling, S. W, Wurm, M., Xia, Q., Xing, Y., Xu, D., Xu, J., Xu, Y., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yeh, M., Yu, B., Zavattini, G., Zha, W., Zhong, M., and Zuber, K.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Nuclear Experiment
Abstract: The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures
Published: 2024

42. DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR

Author: Contreras, Miguel, Kapoor, Sumit, Zhang, Jiaqing, Davidson, Andrea, Ren, Yuanfang, Guan, Ziyuan, Ozrazgat-Baslanti, Tezcan, Nerella, Subhash, Bihorac, Azra, and Rashidi, Parisa
Subjects: Computer Science - Artificial Intelligence
Abstract: Delirium is an acute confusional state that has been shown to affect up to 31% of patients in the intensive care unit (ICU). Early detection of this condition could lead to more timely interventions and improved health outcomes. While artificial intelligence (AI) models have shown great potential for ICU delirium prediction using structured electronic health records (EHR), most of them have not explored the use of state-of-the-art AI models, have been limited to single hospitals, or have been developed and validated on small cohorts. The use of large language models (LLM), models with hundreds of millions to billions of parameters, with structured EHR data could potentially lead to improved predictive performance. In this study, we propose DeLLiriuM, a novel LLM-based delirium prediction model using EHR data available in the first 24 hours of ICU admission to predict the probability of a patient developing delirium during the rest of their ICU admission. We develop and validate DeLLiriuM on ICU admissions from 104,303 patients pertaining to 195 hospitals across three large databases: the eICU Collaborative Research Database, the Medical Information Mart for Intensive Care (MIMIC)-IV, and the University of Florida Health's Integrated Data Repository. The performance measured by the area under the receiver operating characteristic curve (AUROC) showed that DeLLiriuM outperformed all baselines in two external validation sets, with 0.77 (95% confidence interval 0.76-0.78) and 0.84 (95% confidence interval 0.83-0.85) across 77,543 patients spanning 194 hospitals. To the best of our knowledge, DeLLiriuM is the first LLM-based delirium prediction tool for the ICU based on structured EHR data, outperforming deep learning baselines which employ structured features and can provide helpful information to clinicians for timely interventions.
Published: 2024

43. The XLZD Design Book: Towards the Next-Generation Liquid Xenon Observatory for Dark Matter and Neutrino Physics

Author: XLZD Collaboration, Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Akerib, D. S., Musalhi, A. K. Al, Alder, F., Althueser, L., Amaral, D. W. P., Amarasinghe, C. S., Ames, A., Andrieu, B., Angelides, N., Angelino, E., Antunovic, B., Aprile, E., Araújo, H. M., Armstrong, J. E., Arthurs, M., Babicz, M., Bajpai, D., Baker, A., Balzer, M., Bang, J., Barberio, E., Bargemann, J. W., Barillier, E., Basharina-Freshville, A., Baudis, L., Bauer, D., Bazyk, M., Beattie, K., Beaupere, N., Bell, N. F., Bellagamba, L., Benson, T., Bhatti, A., Biesiadzinski, T. P., Biondi, R., Biondi, Y., Birch, H. J., Bishop, E., Bismark, A., Boehm, C., Boese, K., Bolotnikov, A., Brás, P., Braun, R., Breskin, A., Brew, C. A. J., Brommer, S., Brown, A., Bruni, G., Budnik, R., Burdin, S., Cai, C., Capelli, C., Carini, G., Carmona-Benitez, M. C., Carter, M., Chauvin, A., Chawla, A., Chen, H., Cherwinka, J. J., Chin, Y. T., Chott, N. I., Chavez, A. P. Cimental, Clark, K., Colijn, A. P., Colling, D. J., Conrad, J., Converse, M. V., Coronel, R., Costanzo, D., Cottle, A., Cox, G., Cuenca-García, J. J., Curran, D., Cussans, D., D'Andrea, V., Garcia, L. C. Daniel, Darlington, I., Dave, S., David, A., Davies, G. J., Decowski, M. P., Deisting, A., Delgaudio, J., Dey, S., Di Donato, C., Di Felice, L., Di Gangi, P., Diglio, S., Ding, C., Dobson, J. E. Y., Doerenkamp, M., Drexlin, G., Druszkiewicz, E., Dunbar, C. L., Eitel, K., Elykov, A., Engel, R., Eriksen, S. R., Fayer, S., Fearon, N. M., Ferella, A. D., Ferrari, C., Fieldhouse, N., Fischer, H., Flaecher, H., Flehmke, T., Flierman, M., Fraser, E. D., Fruth, T. M. A., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Gaitskell, R. J., Gallice, N., Galloway, M., Gao, F., Garroum, N., Geffre, A., Genovesi, J., Ghag, C., Ghosh, S., Giacomobono, R., Gibbons, R., Girard, F., Glade-Beucke, R., Glück, F., Gokhale, S., Grandi, L., Green, J., Grigat, J., van der Grinten, M. G. D., Größle, R., Guan, H., Guida, M., Gyorgy, P., Haiston, J. J., Hall, C. R., Hall, T., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Hartigan-O'Connor, E., Haselschwardt, S. J., Hernandez, M., Hertel, S. A., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Homenides, G. J., Hood, N. F., Horn, M., Huang, D. Q., Hughes, S., Hunt, D., Iacovacci, M., Itow, Y., Jacquet, E., Jakob, J., James, R. S., Joerg, F., Jones, S., Kaboth, A. C., Kahlert, F., Kamaha, A. C., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kemp-Russell, P., Khaitan, D., Kharbanda, P., Kilminster, B., Kim, J., Kirk, R., Kleifges, M., Klute, M., Kobayashi, M., Kodroff, D., Koke, D., Kopec, A., Korolkova, E. V., Kraus, H., Kravitz, S., Kreczko, L., von Krosigk, B., Kudryavtsev, V. A., Kuger, F., Kurita, N., Landsman, H., Lang, R. F., Lawes, C., Lee, J., Lehnert, B., Leonard, D. S., Lesko, K. T., Levinson, L., Li, A., Li, I., Li, S., Liang, S., Liang, Z., Lin, J., Lin, Y. -T., Lindemann, S., Linden, S., Lindner, M., Lindote, A., Lippincott, W. H., Liu, K., Loizeau, J., Lombardi, F., Lopes, J. A. M., Lopes, M. I., Lorenzon, W., Loutit, M., Lu, C., Lucchetti, G. M., Luce, T., Luitz, S., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Majewski, P. A., Manalaysay, A., Mancuso, A., Manenti, L., Mannino, R. L., Marignetti, F., Marley, T., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Maupin, C., McCabe, C., McCarthy, M. E., McKinsey, D. N., McLaughlin, J. B., Melchiorre, A., Menéndez, J., Messina, M., Miller, E. H., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Mizrachi, E., Molinario, A., Monteiro, C. M. B., Monzani, M. E., Morå, K., Moriyama, S., Morrison, E., Morteau, E., Mosbacher, Y., Mount, B. J., Müller, J., Murdy, M., Murphy, A. St. J., Murra, M., Naylor, A., Nelson, H. N., Neves, F., Newstead, J. L., Nguyen, A., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Olcina, I., Oliver-Mallory, K. C., Gann, G. D. Orebi, Orpwood, J., Ostrowskiy, I., Ouahada, S., Oyulmaz, K., Paetsch, B., Palladino, K. J., Palmer, J., Pan, Y., Pandurovic, M., Pannifer, N. J., Paramesvaran, S., Patton, S. J., Pellegrini, Q., Penning, B., Pereira, G., Peres, R., Perry, E., Pershing, T., Piastra, F., Pienaar, J., Piepke, A., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qie, Y., Qin, J., Radeka, S., Radeka, V., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Reichenbacher, J., Rhyne, C. A., Richards, A., Rischbieter, G. R. C., Riyat, H. S., Rosero, R., Roy, A., Rushton, T., Rynders, D., Saakyan, R., Sanchez, L., Sanchez-Lucas, P., Santone, D., Santos, J. M. F. dos, Sartorelli, G., Sazzad, A. B. M. R., Scaffidi, A., Schnee, R. W., Schreiner, J., Schulte, P., Schulze, H., Eißing, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shaw, S., Shen, W., Sherman, L., Shi, S., Shi, S. Y., Shimada, T., Shutt, T., Silk, J. J., Silva, C., Simgen, H., Sinev, G., Singh, R., Siniscalco, J., Solmaz, M., Solovov, V. N., Song, Z., Sorensen, P., Soria, J., Stanley, O., Steidl, M., Stenhouse, T., Stevens, A., Stifter, K., Sumner, T. J., Takeda, A., Tan, P. -L., Taylor, D. J., Taylor, W. C., Thers, D., Thümmler, T., Tiedt, D. R., Tönnies, F., Tong, Z., Toschi, F., Tovey, D. R., Tranter, J., Trask, M., Trinchero, G., Tripathi, M., Tronstad, D. R., Trotta, R., Tunnell, C. D., Urquijo, P., Usón, A., Utoyama, M., Vaitkus, A. C., Valentino, O., Valerius, K., Vecchi, S., Velan, V., Vetter, S., de Viveiros, L., Volta, G., Vorkapic, D., Wang, A., Wang, J. J., Wang, W., Wang, Y., Waters, D., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Whitis, T. J., Wild, K., Williams, M., Wilson, M., Wilson, S. T., Wittweg, C., Wolf, J., Wolfs, F. L. H., Woodford, S., Woodward, D., Worcester, M., Wright, C. J., Wu, V. H. S., üstling, S. W, Wurm, M., Xia, Q., Xing, Y., Xu, D., Xu, J., Xu, Y., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yeh, M., Yu, B., Zavattini, G., Zha, W., Zhong, M., and Zuber, K.
Subjects: High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract: This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures
Published: 2024

44. Competition between ponderomotive acceleration and attosecond chirps in optimizing attosecond pulse generation in solids

Author: Zhang, Xinyuan, Hu, Shiqi, Guan, Mengxue, and Meng, Sheng
Subjects: Physics - Optics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: A practical approach is proposed for efficiently generating ultrashort attosecond pulses (APs) from realistic solid-state materials, aiming to achieve pulse widths comparable to those generated in gases. This approach focuses on modulating the plateau region of the high harmonic spectrum by adjusting the peak vector potential of laser fields through changing photon energy, while maintaining a constant peak electric field. It initiates a competition between heightened ponderomotive acceleration and the attosecond chirp effect, leading to a non-monotonic variation in both intensity and duration of the generated APs. Using hexagonal boron nitride as a prototypical material, we demonstrate the generation of optimal ultrashort pulses with a full width at half maximum (FWHM) of 143 attoseconds, marking an updated record for the shortest pulse duration from solid-state sources. This strategy shows promise for application across a broad range of materials, offering new pathways to promote high harmonic performance., Comment: 6 pages, 4 figures and supplemental materials
Published: 2024

45. GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks

Author: Hou, Shuyang, Shen, Zhangxiao, Zhao, Anqi, Liang, Jianyuan, Gui, Zhipeng, Guan, Xuefeng, Li, Rui, and Wu, Huayi
Subjects: Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract: The increasing demand for spatiotemporal data and modeling tasks in geosciences has made geospatial code generation technology a critical factor in enhancing productivity. Although large language models (LLMs) have demonstrated potential in code generation tasks, they often encounter issues such as refusal to code or hallucination in geospatial code generation due to a lack of domain-specific knowledge and code corpora. To address these challenges, this paper presents and open-sources the GeoCode-PT and GeoCode-SFT corpora, along with the GeoCode-Eval evaluation dataset. Additionally, by leveraging QLoRA and LoRA for pretraining and fine-tuning, we introduce GeoCode-GPT-7B, the first LLM focused on geospatial code generation, fine-tuned from Code Llama-7B. Furthermore, we establish a comprehensive geospatial code evaluation framework, incorporating option matching, expert validation, and prompt engineering scoring for LLMs, and systematically evaluate GeoCode-GPT-7B using the GeoCode-Eval dataset. Experimental results show that GeoCode-GPT outperforms other models in multiple-choice accuracy by 9.1% to 32.1%, in code summarization ability by 1.7% to 25.4%, and in code generation capability by 1.2% to 25.1%. This paper provides a solution and empirical validation for enhancing LLMs' performance in geospatial code generation, extends the boundaries of domain-specific model applications, and offers valuable insights into unlocking their potential in geospatial code generation.
Published: 2024

46. Measurement of the branching fractions of the decays $\Lambda_{c}^{+}\rightarrow\Lambda K_{S}^{0}K^{+}$, $\Lambda_{c}^{+}\rightarrow\Lambda K_{S}^{0}\pi^{+}$ and $\Lambda_{c}^{+}\rightarrow\Lambda K^{*+}$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Studies are performed of the Cabibbo-favored decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}K^+$ and the singly Cabibbo-suppressed decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$, based on a sample of $e^{+}e^{-}$ collision data, corresponding to an integrated luminosity of 4.5 fb$^{-1}$, accumulated at center-of-mass energies between $4599.53$ MeV and $4698.82$ MeV with the BESIII detector. The decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ is observed for the first time. The branching fractions of $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}K^+$ and $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ are measured to be $(3.04\pm0.30\pm0.16)\times 10^{-3}$ and $(1.73\pm0.27\pm0.10)\times 10^{-3}$, respectively, where the first uncertainties are statistical and the second are systematic. These results correspond to the most precise measurement of these quantities for both decays. Evidence of a $K^{*+}$ contribution in the $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ decay is found with a statistical significance of $4.7\sigma$. The branching fraction of $\Lambda_{c}^{+}\to\Lambda K^{*+}$ is calculated under three possible interference scenarios.
Published: 2024

47. Automatic Extraction and Compensation of P-Bit Device Variations in Large Array Utilizing Boltzmann Machine Training

Author: Zhang, Bolin, Liu, Yu, Gao, Tianqi, Yin, Jialiang, Guan, Zhenyu, Zhang, Deming, and Zeng, Lang
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics
Abstract: Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly limiting the practical usage of Ising computation. In this work, a behavioral model which attributes P-Bit variations to two parameters {\alpha} and {\Delta}V is proposed. Then the weight compensation method is introduced, which can mitigate {\alpha} and {\Delta}V of P-Bits device variations by rederiving the weight matrix, enabling them to compute as ideal identical PBits without the need for weights retraining. Accurately extracting the {\alpha} and {\Delta}V simultaneously from a large P-Bit array which is prerequisite for the weight compensation method is a crucial and challenging task. To solve this obstacle, we present the novel automatic variation extraction algorithm which can extract device variations of each P-Bit in a large array based on Boltzmann machine learning. In order for the accurate extraction of variations from an extendable P-Bit array, an Ising Hamiltonian based on 3D ferromagnetic model is constructed, achieving precise and scalable array variation extraction. The proposed Automatic Extraction and Compensation algorithm is utilized to solve both 16-city traveling salesman problem(TSP) and 21-bit integer factorization on a large P-Bit array with variation, demonstrating its accuracy, transferability, and scalability., Comment: 15 pages, 17 figures
Published: 2024

48. A method to detect the VUV photons from cooled $^{229}$Th:CaF$_2$ crystals

Author: Guan, Ming, Bartokos, Michael, Beeks, Kjeld, Fukunaga, Yuta, Hiraki, Takahiro, Masuda, Takahiko, Miyamoto, Yuki, Okage, Ryoichiro, Okai, Koichi, Sasao, Noboru, Schaden, Fabian, Schumm, Thorsten, Shimizu, Koutaro, Takatori, Sayuri, Yoshimi, Akihiro, and Yoshimura, Koji
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract: Thorium-229, with its exceptionally low-energy nuclear excited state, is a key candidate for developing nuclear clocks. $^{229}$Th-doped CaF$_2$ crystals, benefiting from calcium fluoride's wide band gap, show great promise as solid-state nuclear clock materials. These crystals are excited by vacuum ultraviolet (VUV) lasers, which over time cause radiation damage. Cooling the crystals can mitigate this damage but introduces a challenge: photoabsorption. This occurs when residual gas molecules condense on the crystal surface, absorbing VUV photons and deteriorating detection efficiency. To solve this, we developed a cooling technique using a copper shield to surround the crystal, acting as a cold trap. This prevents ice-layer formation, even at temperatures below $-100\,^\circ$C, preserving high VUV photon detection efficiency. Our study detailed the experimental cooling setup and demonstrated the effectiveness of the copper shield in maintaining crystal performance, a critical improvement for future solid-state nuclear clocks operating at cryogenic temperatures., Comment: 5 figures
Published: 2024

49. Equilibrium portfolio selection under beliefs-dependent utilities

Author: Chen, Xiaochen, Guan, Guohui, and Liang, Zongxia
Subjects: Mathematics - Optimization and Control
Abstract: This paper investigates portfolio selection within a continuous-time financial market with regime-switching and beliefs-dependent utilities. The market coefficients and the investor's utility function both depend on the market regime, which is modeled by an observable finite-state continuous-time Markov chain. The optimization problem is formulated by aggregating expected certainty equivalents under different regimes, leading to time-inconsistency. Utilizing the equilibrium strategy, we derive the associated extended Hamilton-Jacobi-Bellman (HJB) equations and establish a rigorous verification theorem. As a special case, we analyze equilibrium portfolio selection in a beliefs-dependent risk aversion model. In a bull regime, the excess asset returns, volatility, and risk aversion are all low, while the opposite holds in a bear regime. Closed-form solutions in the CRRA preference regime model of bull and bear markets are obtained, which is expressed by a solution to four-dimensional non-linear ODEs. The global existence of the ODEs is proven and we verify the equilibrium solution rigorously. We show that the equilibrium investment strategy lies between two constant Merton's fractions. Additionally, in our numerical experiment, the equilibrium proportion allocated in the risky asset is greater in a bull regime than in a bear regime and the equilibrium proportion increases with time in a bull regime while decreasing in a bear regime.
Published: 2024

50. Decentralized Hybrid Precoding for Massive MU-MIMO ISAC

Author: Zhu, Jun, Xu, Yin, He, Dazhi, Li, Haoyang, Guan, YunFeng, and Zhang, Wenjun
Subjects: Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract: Integrated sensing and communication (ISAC) is a very promising technology designed to provide both high rate communication capabilities and sensing capabilities. However, in Massive Multi User Multiple-Input Multiple-Output (Massive MU MIMO-ISAC) systems, the dense user access creates a serious multi-user interference (MUI) problem, leading to degradation of communication performance. To alleviate this problem, we propose a decentralized baseband processing (DBP) precoding method. We first model the MUI of dense user scenarios with minimizing Cramer-Rao bound (CRB) as an objective function.Hybrid precoding is an attractive ISAC technique, and hybrid precoding using Partially Connected Structures (PCS) can effectively reduce hardware cost and power consumption. We mitigate the MUI between dense users based on ThomlinsonHarashima Precoding (THP). We demonstrate the effectiveness of the proposed method through simulation experiments. Compared with the existing methods, it can effectively improve the communication data rates and energy efficiency in dense user access scenario, and reduce the hardware complexity of Massive MU MIMO-ISAC systems. The experimental results demonstrate the usefulness of our method for improving the MUI problem in ISAC systems for dense user access scenarios.
Published: 2024

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