A Study of Digital Architecture Talent Development Based on the Workcamp Model

Digital architecture is a trend that can not be ignored in today's construction industry, and the development of digital technology has had a far-reaching impact on the traditional construction industry. The cultivation of digital architecture talents has become an important issue in the field of architecture education. In this paper, we take the demand for talent in the construction industry and the development trend of digital architecture as the background, explore the teaching mode and characteristics of the work camp, and put forward a set of digital architecture talent cultivation strategies based on the work camp mode. Through the combination of theoretical analysis and practical operation, it provides certain reference significance for the cultivation of digital architecture talents

Using Experience Classification for Training Non-Markovian Tasks

Unlike the standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, whose rewards are predicated on state history rather than solely on the current state. Solving a non-Markovian task, frequently applied in practical applications such as autonomous driving, financial trading, and medical diagnosis, can be quite challenging. We propose a novel RL approach to achieve non-Markovian rewards expressed in temporal logic LTL$_f$ (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTL$_f$ into MDPs (Markov Decision Processes) is introduced to take advantage of advanced RL algorithms. Then, a prioritized experience replay technique based on the automata structure (semantics equivalent to LTL$_f$ specification) is utilized to improve the training process. We empirically evaluate several benchmark problems augmented with non-Markovian tasks to demonstrate the feasibility and effectiveness of our approach

Stacking Dependent Optical Conductivity of Bilayer Graphene

The optical conductivities of graphene layers are strongly dependent on their stacking orders. Our first-principle calculations show that while the optical conductivities of single layer graphene (SLG) and bilayer graphene (BLG) with Bernal stacking are almost frequency independent in the visible region, the optical conductivity of twisted bilayer graphene (TBG) is frequency dependent, giving rise to additional absorption features due to the band folding effect. Experimentally, we obtain from contrast spectra the optical conductivity profiles of BLG with different stacking geometries. Some TBG samples show additional features in their conductivity spectra in full agreement with our calculation results, while a few samples give universal conductivity values similar to that of SLG. We propose those variations of optical conductivity spectra of TBG samples originate from the difference between the commensurate and incommensurate stackings. Our results reveal that the optical conductivity measurements of graphene layers indeed provide an efficient way to select graphene films with desirable electronic and optical properties, which would great help the future application of those large scale misoriented graphene films in photonic devices.Comment: 20 pages, 5 figures, accepted by ACS Nan