The development of new nonlinear optical (NLO) materials for deep-ultraviolet (DUV) applications is in great demand. However, the synthesis of an ideal DUV NLO crystal is a serious challenge. Herein, three new alkali-metal fluorooxoborates, AB O F (A=K, Rb, and Cs, and a mixed cation between two of them), were successfully synthesized by cation regulation. It is found that all reported compounds exhibit short UV absorption edges (<190 nm), and show second harmonic generation (SHG) responses ranging from 0.8 to 1.9 KH PO (KDP). Interestingly, by judicious selection of the A-site alkali-metal cations, the arrangement of NLO-active structural units is fine-tuned to an optimal configuration, which contributes to large SHG responses.
The simple binary fluoride α‐SnF2 is shown to be an excellent birefringent material with outstanding birefringence, about 14 times that of MgF2. Furthermore, it exhibits a shorter UV cutoff edge and easy crystal growth at ambient temperature compared to YVO4 and TiO2. A novel theoretical calculation mode was established to analyze the stereochemical‐activity lone‐pair (SCALP) contribution to the birefringence (SCB) based on the SCALP′s strength and arrangement, and it was found that the large birefringence of α‐SnF2 mainly benefits from well‐aligned [SnF5] polyhedra with a strong SCALP. The exploration of the α‐SnF2 birefringent crystal points out the direction of the future search for excellent birefringent materials.
Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one field often interact differently with features from different other fields. Recently, Fieldaware Factorization Machines (FFMs) have been among the best performing models for CTR prediction by explicitly modeling such difference. However, the number of parameters in FFMs is in the order of feature number times field number, which is unacceptable in the real-world production systems. In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way. Our experimental evaluations show that FwFMs can achieve competitive prediction performance with only as few as 4% parameters of FFMs. When using the same number of parameters, FwFMs can bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.
Motivated by mass-spectrometry protein sequencing, we consider a simply-stated problem of reconstructing a string from the multiset of its substring compositions. We show that all strings of length 7, one less than a prime, or one less than twice a prime, can be reconstructed uniquely up to reversal. For all other lengths we show that reconstruction is not always possible and provide sometimes-tight bounds on the largest number of strings with given substring compositions. The lower bounds are derived by combinatorial arguments and the upper bounds by algebraic considerations that precisely characterize the set of strings with the same substring compositions in terms of the factorization of bivariate polynomials. The problem can be viewed as a combinatorial simplification of the turnpike problem, and its solution may shed light on this long-standing problem as well. Using well known results on transience of multi-dimensional random walks, we also provide a reconstruction algorithm that reconstructs random strings over alphabets of size ≥ 4 in optimal near-quadratic time. * UCSD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.