An equivalent expression of Z2 Topological Invariant for band insulators using Non-Abelian Berry's connection

We introduce a new expression for the Z2 topological invariant of band insulators using non- Abelian Berry's connection. Our expression can identify the topological nature of a general band insulator without any of the gauge fixing problems that plague the concrete implementation of previous invariants. The new expression can be derived from the "partner switching" of the Wannier function center during time reversal pumping and is thus equivalent to the Z2 topological invariant proposed by Kane and Mele.Comment: 14 pages, 8 figure

Two halosesquiterpenes from Laurencia composita

Two new chamigrane sesquiterpenes, yicterpenes A (1) and B (2) each with an unprecedented halogenation at C-9 and a hemiketal unit at C-10, were isolated from the marine red algaLaurencia composita. The structures and absolute configurations of them were identified by NMR, ECD, and mass spectrometric methods as well as quantum chemical calculations. Compound 1 with a chlorination at C-9 was more active against some tested marine-derived organisms than 2 with a bromination at C-9.  Two new chamigrane sesquiterpenes, yicterpenes A (1) and B (2) each with an unprecedented halogenation at C-9 and a hemiketal unit at C-10, were isolated from the marine red alga Laurencia composita. The structures and absolute configurations of them were identified by NMR, ECD, and mass spectrometric methods as well as quantum chemical calculations. Compound 1 with a chlorination at C-9 was more active against some tested marine-derived organisms than 2 with a bromination at C-9

An FBG staged monitoring method for carbon fiber reinforced plastics composite fracture status based on modulus/strain wave coupling property

From the sensitivity of the FBG center wavelength changing with the macro-elastic modulus and the instantaneous fracture strain wave on the surface of carbon fiber reinforced plastics (CFRP) composite, we investigate the correlation between the macro-elastic modulus (the changing rate of the FBG center wavelength during the stretching process) and the fracture status of CFRP specimen. An FBG staged monitoring method based on modulus/strain wave coupling properties designed to monitor tensile fracture state of composite has been proposed. By monitoring the change of macro-elastic modulus during the stretching process, the damage state of composite in a macro perspective is obtained; when the internal damage reaches a critical state, the fracture distribution status of CFRP specimen is captured by monitoring the strain wave response induced by stress relaxation in different locations. Simulated analysis and experimental results in this paper show that the proposed FBG staged monitoring method can achieve the identification of the damage state and the breakage position of CFRP composite effectively, with a good prospect

HTP: Exploiting Holistic Temporal Patterns for Sequential Recommendation

Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals. We are the first to explore item-oriented absolute time patterns. While existing models consider only one or two of these three patterns, we propose a novel holistic temporal pattern based neural network, named HTP, to fully leverage all these three patterns. In particular, we introduce novel components to address the subtle correlations between relative item time intervals and relative recommendation time intervals, which render a major technical challenge. Extensive experiments on three real-world benchmark datasets show that our HTP model consistently and substantially outperforms many state-of-the-art models. Our code is publically available at https://github.com/623851394/HTP/tree/main/HTP-mai