A Comprehensive Four-Quark Interpretation of D_s(2317), D_s(2457) and D_s(2632)

The recently observed new member of the charm-strange family D_s(2632) which has a surprisingly narrow width is challenging our theory. D_s(2317) and D_s(2457) which were observed earlier have similar behaviors and receive various theoretical explanations. Some authors use the heavy hadron chiral effective theory to evaluate heavy-light quark systems and obtain a reasonable evaluation on the masses of D_s(2317) and D_s(2457). An alternative picture is to interpret them as four-quark or molecular states. In this work, we are following the later and propose a unitive description for all the three new members D_s(2632), D_s(2317) and D_s(2457) and at least, so far our picture is consistent with the data.Comment: 6 page

Data on Breastfeeding and State Policies in the United States

Breastfeeding is critically important to maternal and child health in the United States. Examining the relationship between breastfeeding outcomes and state policies requires multidisciplinary efforts to link data from various sources. This article describes an integrated dataset that was used to understand the relationship between participation in a nutrition assistance program and low-income children\u27s breastfeeding outcomes [1]. This dataset merged public health information from the National Immunization Surveys Data from 2006 to 2016 and matching state policy data from the Correlates of State Policy Project (CSPP), the U.S. Department of Agriculture/Economic Research Services (USDA/ERS) Supplemental Nutrition Assistance Program (SNAP) Policy Index, the U.S. Bureau of Labor Statistics (BLS), Centers for Medicare & Medicaid Services (CMS), and the Census Bureau. The integrated dataset compiles variables in breastfeeding outcome, child\u27s and mother\u27s socio-demographic characteristics, and state-level policy measures, including SNAP participation rates, SNAP policy indices, unemployment rates, and Children\u27s Health Insurance Program (CHIP) enrollment rates. This multidisciplinary dataset included information on a total of 219,904 children with 98 variables

Topological quantum memory interfacing atomic and superconducting qubits

We propose a scheme to manipulate a topological spin qubit which is realized with cold atoms in a one-dimensional optical lattice. In particular, by introducing a quantum opto-electro-mechanical interface, we are able to first transfer a superconducting qubit state to an atomic qubit state and then to store it into the topological spin qubit. In this way, an efficient topological quantum memory could be constructed for the superconducting qubit. Therefore, we can consolidate the advantages of both the noise resistance of the topological qubits and the scalability of the superconducting qubits in this hybrid architecture.Comment: v2: Accepted for publication in Science China-Physics, Mechanics & Astronom

Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning

In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained under them. Specifically, we propose a new meta-learning framework for learning model-agnostic loss functions via a hybrid neuro-symbolic search approach. The framework first uses evolution-based methods to search the space of primitive mathematical operations to find a set of symbolic loss functions. Second, the set of learned loss functions are subsequently parameterized and optimized via an end-to-end gradient-based training procedure. The versatility of the proposed framework is empirically validated on a diverse set of supervised learning tasks. Results show that the meta-learned loss functions discovered by the newly proposed method outperform both the cross-entropy loss and state-of-the-art loss function learning methods on a diverse range of neural network architectures and datasets