The Las Vegas Fremont Street experience: A new tourist destination to revitalize a declining downtown

Downtown Las Vegas, the old city center, represents the history, culture, and heritage of Las Vegas. Casino Core is a district whose function as a planned casino, hotel and entertainment district in the heart of Downtown Las Vegas. With the development of Las Vegas as an international entertainment center, the old city center declined. In order to revitalize the declined downtown, the Fremont Street Experience, a project of public-private partnership between City of Las Vegas and the Fremont Street Experience Company, was implemented to try to attract tourists down to downtown area and compete with casinos along the Las Vegas Boulevard. With the addition of the Fremont Street Experience (described below), this area came together as a compact, interactive and intense pedestrian oriented casino, hotel and entertainment environment, incorporating restaurant, special gift shop and supporting entertainment uses along Fremont Street, with continuous active frontage from one end to the other. The design of the project transformed the idea of pedestrian shopping mall into an urban theater and made downtown a complete resort destination and a must-see attraction; This paper verifies the success of the Fremont Street Experience as an tourist destination project and an initial project to revitalize the old downtown Las Vegas, and at the same time, evaluates the influence of technical show in making the attraction for the revitalization of the declined downtown

Mining urban perceptions from social media data

This vision paper summaries the methods of using social media data (SMD) to measure urban perceptions. We highlight two major types of data sources (i.e., texts and imagery) and two corresponding techniques (i.e., natural language processing and computer vision). Recognizing the data quality issues of SMD, we propose three criteria for improving the reliability of SMD-based studies. In addition, integrating multi-source data is a promising approach to mitigating the data quality problems

A study of conceptual language similarity: comparison and evaluation

An interesting line of research in natural language processing (NLP) aims to incorporate linguistic typology to bridge linguistic diversity and assist the research of low-resource languages. While most works construct linguistic similarity measures based on lexical or typological features, such as word order and verbal inflection, recent work has introduced a novel approach to defining language similarity based on how they represent basic concepts, which is complementary to existing similarity measures. In this work, we study the conceptual similarity in detail and evaluate it extensively on a binary classification task

Multi-user beamforming on intelligent reflecting surface and networks

Full abstract available: pages i-iii. A new type of retrofitted low-cost material is recently proposed and made it possible to control (or program) part of the wireless channel around us. It is called the intelligent reflecting surface (IRS), also named reconfigurable intelligent surface (RIS) or metasurface. The metamaterial composed surface can realize selective EM properties by integrating artificially designed electronic elements that can be controlled by processors (e.g. field programmable gate array (FPGA)). Therefore, the wireless channel is controllable with such IRS posting on the ceiling and wall. Specifically, this functionality is realized by controlling the excitation and phase of each electronic element on the surface. The phase and amplitude of the EM wave impinging on the surface can be reflected in a designed manner of EM wave’s superposition. From this view, the wireless transmission can be enhanced by focusing the signal power while mitigating the interference power

On the Copying Problem of Unsupervised NMT: A Training Schedule with a Language Discriminator Loss

Although unsupervised neural machine translation (UNMT) has achieved success in many language pairs, the copying problem, i.e., directly copying some parts of the input sentence as the translation, is common among distant language pairs, especially when low-resource languages are involved. We find this issue is closely related to an unexpected copying behavior during online back-translation (BT). In this work, we propose a simple but effective training schedule that incorporates a language discriminator loss. The loss imposes constraints on the intermediate translation so that the translation is in the desired language. By conducting extensive experiments on different language pairs, including similar and distant, high and low-resource languages, we find that our method alleviates the copying problem, thus improving the translation performance on low-resource languages.Comment: IWSLT 202

OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining

Pretraining multilingual language models from scratch requires considerable computational resources and substantial training data. Therefore, a more efficient method is to adapt existing pretrained language models (PLMs) to new languages via vocabulary extension and continued pretraining. However, this method usually randomly initializes the embeddings of new subwords and introduces substantially more embedding parameters to the language model, thus weakening the efficiency. To address these issues, we propose a novel framework: \textbf{O}ne \textbf{F}or \textbf{A}ll (\textbf{\textsc{Ofa}}), which wisely initializes the embeddings of unseen subwords from target languages and thus can adapt a PLM to multiple languages efficiently and effectively. \textsc{Ofa} takes advantage of external well-aligned multilingual word embeddings and injects the alignment knowledge into the new embeddings. In addition, \textsc{Ofa} applies matrix factorization and replaces the cumbersome embeddings with two lower-dimensional matrices, which significantly reduces the number of parameters while not sacrificing the performance. Through extensive experiments, we show models initialized by \textsc{Ofa} are efficient and outperform several baselines. \textsc{Ofa} not only accelerates the convergence of continued pretraining, which is friendly to a limited computation budget, but also improves the zero-shot crosslingual transfer on a wide range of downstream tasks. We make our code and models publicly available

A Non-Line-of-Sight Mitigation Method For Indoor Ultra-Wideband Localization With Multiple Walls

Ultra-wideband (UWB) ranging techniques can provide accurate distance measurement under line-of-sight (LOS) conditions. However, various walls and obstacles in indoor non-LOS (NLOS) environments, which obstruct the direct propagation of UWB signals, can generate significant ranging errors. Due to the complex through-wall UWB signal propagation, most conventional studies simplify the ranging error model by assuming that the incidence angle is zero or the relative permittivity\u27s for different walls are the same to improve the through-wall UWB localization performance. Considering walls are different in realistic settings, this article presents a through-multiple-wall NLOS mitigation method for UWB indoor positioning. First, spatial geometric equilibrium equations of UWB through-wall propagation and a numerical method are developed for the precise modeling of UWB through-wall ranging errors. Then, calculated error maps are determined numerically without field measurements. Finally, the determined error maps are combined with a gray wolf optimization algorithm for localization. The proposed method is evaluated via field experiments with four rooms, three walls, and six penetration cases. The results demonstrate that the method can strongly mitigate the multi-wall. NLOS effects on the performance of UWB positioning systems. This solution can reduce project costs and number of power supplies for UWB indoor positioning applications