Media coverage of stand your ground laws deters crime in some cities, but not in others

So-called ‘stand your ground laws’ – which give people the right to use deadly force to defend themselves – have now been in place for a decade. In new research which uses a Texas shooting incident as a case study, Ling Ren, Yan Zhang, and Jihong “Solomon” Zhao examine whether or not the publicity over shooting incidents where the law is invoked helps to deter crime – specifically residential and business burglaries. They find that such media coverage of high-profile incidents does have a deterrent effect in some nearby cities, but not in others

Nodal-link semimetals

In topological semimetals, the valence band and conduction band meet at zero-dimensional nodal points or one-dimensional nodal rings, which are protected by band topology and symmetries. In this Rapid Communication, we introduce "nodal-link semimetals", which host linked nodal rings in the Brillouin zone. We put forward a general recipe based on the Hopf map for constructing models of nodal-link semimetal. The consequences of nodal ring linking in the Landau levels and Floquet properties are investigated.Comment: 12 pages, 5 figures, including supplemental material. Published versio

6-Chloro-8-methyl-4H-3,1-benzoxazine-2,4(1H)-dione

The two mol­ecules in the asymmetric unit of the title compound, C9H6ClNO3, are nearly planar, with r.m.s. deviations of 0.034 and 0.037 Å. The crystal structure is stabilized by two weak inter­molecular N—H⋯O inter­actions

DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference.Comment: Accepted by SIGKDD 2022 Applied Data Science Trac

Estimation of surface shortwave radiation components under all sky conditions: Modeling and sensitivity analysis

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