A Framework of Cloud and AI based Intelligent Hotel

In the past 10 years, the hospitality industry\u27s discussions on cloud technology have been ongoing to rise, but they had been stuck in the stage of paper talks, and there were no substantive action. In view of the huge potential of the intelligent hospitality industry market, and the related academic research literatures about the virtualized cloud architecture, AI technology application and intelligent virtual housekeeper system among in this industry is still relatively few. Therefore, I selected the industry success case led by Taiwan\u27s Ministry of Science and Technology as a case study for this research, besides, quantified the system\u27s trial operation phase, relevant operational performance results, as the focus of this intelligent hotel case implementation effectiveness evaluation

Constructing the Smart Hotel Architecture – A Case Study in Taiwan

A smart hotel is an intelligent hotel with a range of information technologies working together to let the customers have an honorable and convenient vacation environment. It allows customers to have a profound image about not only the hotel, but also the city and the country. Furthermore, it can increase the customer loyalty and repurchase rate as well. Thus, developing a smart hotel is critical for the hospitality industry in practice. This research presents a case study of a new five star intelligent hotel- Fleur de Chine Hotel at Sun Moon Lake in Taiwan. It describes how they built their digital hotel and virtual housekeeper service platform. Through the networked facilities and integrated information systems, people could get relevant information easily and efficiently. In addition, this smart system could provide hotel customers with the housekeeper service similar to a very important person (VIP) room or a presidential suite

Stationary Light Pulses in Cold Atomic Media

Stationary light pulses (SLPs), i.e., light pulses without motion, are formed via the retrieval of stored probe pulses with two counter-propagating coupling fields. We show that there exist non-negligible hybrid Raman excitations in media of cold atoms that prohibit the SLP formation. We experimentally demonstrate a method to suppress these Raman excitations and realize SLPs in laser-cooled atoms. Our work opens the way to SLP studies in cold as well as in stationary atoms and provides a new avenue to low-light-level nonlinear optics.Comment: 4 pages, 4 figure

Controlled Heterogeneous Nucleation and Growth of Germanium Quantum Dots on Nanopatterned Silicon Dioxide and Silicon Nitride Substrates

Controlled heterogeneous nucleation and growth of Ge quantum dots (QDs) are demonstrated on SiO_2/Si_3N_4 substrates by means of a novel fabrication process of thermally oxidizing nanopatterned SiGe layers. The otherwise random self-assembly process for QDs is shown to be strongly influenced by the nanopatterning in determining both the location and size of the QDs. Ostwald ripening processes are observed under further annealing at the oxidation temperature. Both nanopattern oxidation and Ostwald ripening offer additional mechanisms for lithography for controlling the size and placement of the QDs

High-Mobility Pentacene-Based Thin-Film Transistors With a Solution-Processed Barium Titanate Insulator

Abstract—Pentacene-based organic thin-film transistors (OTFTs) with solution-processed barium titanate (Ba1.2Ti0.8O3) as a gate insulator are demonstrated. The electrical properties of pentacene-based TFTs show a high field-effect mobility of 8.85 cm2 · V−1 · s−1, a low threshold voltage of −1.89 V, and a low subthreshold slope swing of 310 mV/decade. The chemical composition and binding energy of solution-processed barium titanate thin films are analyzed through X-ray photoelectron spectroscopy. The matching surface energy on the surface of the barium titanate thin film is 43.12 mJ · m−2, which leads to Stranski–Krastanov mode growth, and thus, high mobility is exhibited in pentacene-based TFTs. Index Terms—Barium titanate, high field-effect mobility, high permittivity, organic thin-filmtransistor (OTFT), solution process

SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape

Neural Architecture Search (NAS) has become a widely used tool for automating neural network design. While one-shot NAS methods have successfully reduced computational requirements, they often require extensive training. On the other hand, zero-shot NAS utilizes training-free proxies to evaluate a candidate architecture's test performance but has two limitations: (1) inability to use the information gained as a network improves with training and (2) unreliable performance, particularly in complex domains like RecSys, due to the multi-modal data inputs and complex architecture configurations. To synthesize the benefits of both methods, we introduce a "sub-one-shot" paradigm that serves as a bridge between zero-shot and one-shot NAS. In sub-one-shot NAS, the supernet is trained using only a small subset of the training data, a phase we refer to as "warm-up." Within this framework, we present SiGeo, a proxy founded on a novel theoretical framework that connects the supernet warm-up with the efficacy of the proxy. Extensive experiments have shown that SiGeo, with the benefit of warm-up, consistently outperforms state-of-the-art NAS proxies on various established NAS benchmarks. When a supernet is warmed up, it can achieve comparable performance to weight-sharing one-shot NAS methods, but with a significant reduction ($\sim 60$\%) in computational costs.Comment: 24 pages, 7 figure