Long Short-Term Memory Spatial Transformer Network

Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially. In this paper, we propose a combined spatial transformer network (STN) and a Long Short-Term Memory network (LSTM) to classify digits in sequences formed by MINST elements. This LSTM-STN model has a top-down attention mechanism profit from LSTM layer, so that the STN layer can perform short-term independent elements for the statement in the process of spatial transformation, thus avoiding the distortion that may be caused when the entire sequence is spatially transformed. It also avoids the influence of this distortion on the subsequent classification process using convolutional neural networks and achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional Neural Network with STN layer

An Empirical Study on use of Social Media in the Hotel Industry in China: A Study of Customers’ Preferences and Attitudes

With the development of the technology, business corporations start to use social media to promote their businesses. This study focuses on the preferences and attitudes of travelers’ in China with the relationship between usage of social media (based on WeChat and Sina Weibo) with the option of hotel choice in the hospitality industry. There are two instruments used in this study: (1) sending out online surveys and (2) scheduled interviews with people who are working in the hotel industry. From our survey data (N=245) were participants who completed questionnaires located all over China. However, the results of the study indicate that participating hotels and online tickets firms prefer to use social media to attract guests and even potential consumers. The top about three elements that influence decision making of hotel choice are: price (71.84%); location (68.16%); and online rating (33.06%)

Impact of SZ cluster residuals in CMB maps and CMB-LSS cross-correlations

Residual foreground contamination in cosmic microwave background (CMB) maps, such as the residual contamination from thermal Sunyaev-Zeldovich (SZ) effect in the direction of galaxy clusters, can bias the cross-correlation measurements between CMB and large-scale structure optical surveys. It is thus essential to quantify those residuals and, if possible, to null out SZ cluster residuals in CMB maps. We quantify for the first time the amount of SZ cluster contamination in the released Planck 2015 CMB maps through (i) the stacking of CMB maps in the direction of the clusters, and (ii) the computation of cross-correlation power spectra between CMB maps and the SDSS-IV large-scale structure data. Our cross-power spectrum analysis yields a $30\sigma$ detection at the cluster scale ($\ell=1500-2500$) and a $39\sigma$ detection on larger scales ($\ell=500-1500$) due to clustering of SZ clusters, giving an overall $54\sigma$ detection of SZ cluster residuals in the Planck CMB maps. The Planck 2015 NILC CMB map is shown to have $44\pm4\%$ of thermal SZ foreground emission left in it. Using the 'Constrained ILC' component separation technique, we construct an alternative Planck CMB map, the 2D-ILC map, which is shown to have negligible SZ contamination, at the cost of being slightly more contaminated by Galactic foregrounds and noise. We also discuss the impact of the SZ residuals in CMB maps on the measurement of the ISW effect, which is shown to be negligible based on our analysis.Comment: 14 pages, 11 figures, 1 table, accepted by MNRAS, close to the published versio

Impact of Simulated 1/f Noise for HI Intensity Mapping Experiments

Cosmology has entered an era where the experimental limitations are not due to instrumental sensitivity but instead due to inherent systematic uncertainties in the instrumentation and data analysis methods. The field of HI intensity mapping (IM) is still maturing, however early attempts are already systematics limited. One such systematic limitation is 1/f noise, which largely originates within the instrumentation and manifests as multiplicative gain fluctuations. To date there has been little discussion about the possible impact of 1/f noise on upcoming single-dish HI IM experiments such as BINGO, FAST or SKA. Presented in this work are Monte-Carlo end-to-end simulations of a 30 day HI IM survey using the SKA-MID array covering a bandwidth of 950 and 1410 MHz. These simulations extend 1/f noise models to include not just temporal fluctuations but also correlated gain fluctuations across the receiver bandpass. The power spectral density of the spectral gain fluctuations are modelled as a power-law, and characterised by a parameter $\beta$. It is found that the degree of 1/f noise frequency correlation will be critical to the success of HI IM experiments. Small values of $\beta$ ($\beta$ < 0.25) or high correlation is preferred as this is more easily removed using current component separation techniques. The spectral index of temporal fluctuations ($\alpha$) is also found to have a large impact on signal-to-noise. Telescope slew speed has a smaller impact, and a scan speed of 1 deg s$^{-1}$ should be sufficient for a HI IM survey with the SKA.Comment: 22 pages, 15 figures, 2 table

MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation

Having been studied for more than a decade, Wi-Fi human sensing still faces a major challenge in the presence of multiple persons, simply because the limited bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically separate multiple subjects. Existing solutions mostly avoid this challenge by switching to radars with GHz bandwidth, at the cost of cumbersome deployments. Therefore, could Wi-Fi human sensing handle multiple subjects remains an open question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing system with physical separability. The principle behind MUSE-Fi is that, given a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field channel variation caused by the subject significantly overwhelms variations caused by other distant subjects. Consequently, focusing on the channel state information (CSI) carried by the traffic in and out of this device naturally allows for physically separating multiple subjects. Based on this principle, we propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI, and iii) downlink beamforming feedback, where we specifically tackle signal recovery from sparse (per-user) traffic under realistic multi-user communication scenarios. Our extensive evaluations clearly demonstrate that MUSE-Fi is able to successfully handle multi-person sensing with respect to three typical applications: respiration monitoring, gesture detection, and activity recognition.Comment: 15 pages. Accepted by ACM MobiCom 202

HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning

Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. Recently, BSs adopting reconfigurable holographic surfaces (RHSs) have been advocated for positioning as RHSs' large number of antenna elements enable generation of arbitrary and highly-focused signal beam patterns. However, existing designs face two major challenges: i) RHSs only have limited operating bandwidth, and ii) the positioning methods cannot adapt to the diverse environments encountered in practice. To overcome these challenges, we present HoloFed, a system providing high-precision environment-adaptive user positioning services by exploiting multi-band(MB)-RHS and federated learning (FL). For improving the positioning performance, a lower bound on the error variance is obtained and utilized for guiding MB-RHS's digital and analog beamforming design. For better adaptability while preserving privacy, an FL framework is proposed for users to collaboratively train a position estimator, where we exploit the transfer learning technique to handle the lack of position labels of the users. Moreover, a scheduling algorithm for the BS to select which users train the position estimator is designed, jointly considering the convergence and efficiency of FL. Our simulation results confirm that HoloFed achieves a 57% lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments