Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc

Bayesian Speaker Adaptation Based on a New Hierarchical Probabilistic Model

In this paper, a new hierarchical Bayesian speaker adaptation method called HMAP is proposed that combines the advantages of three conventional algorithms, maximum a posteriori (MAP), maximum-likelihood linear regression (MLLR), and eigenvoice, resulting in excellent performance across a wide range of adaptation conditions. The new method efficiently utilizes intra-speaker and inter-speaker correlation information through modeling phone and speaker subspaces in a consistent hierarchical Bayesian way. The phone variations for a specific speaker are assumed to be located in a low-dimensional subspace. The phone coordinate, which is shared among different speakers, implicitly contains the intra-speaker correlation information. For a specific speaker, the phone variation, represented by speaker-dependent eigenphones, are concatenated into a supervector. The eigenphone supervector space is also a low dimensional speaker subspace, which contains inter-speaker correlation information. Using principal component analysis (PCA), a new hierarchical probabilistic model for the generation of the speech observations is obtained. Speaker adaptation based on the new hierarchical model is derived using the maximum a posteriori criterion in a top-down manner. Both batch adaptation and online adaptation schemes are proposed. With tuned parameters, the new method can handle varying amounts of adaptation data automatically and efficiently. Experimental results on a Mandarin Chinese continuous speech recognition task show good performance under all testing conditions

Influence of key polymer attributes, manufacturing conditions and sintering on abuse deterrence of physical barrier type abuse deterrent formulations (ADF)

When sintering us used to treat tablet formulations containing polyethylene oxide (PEO), the polymer particles are able to form stronger bonds thereby increase tablet tensile strength. This increase in strength can make it more difficult for an abuser to break, chew, or grind opioid tablets. A mechanistic study was implemented to understand the key sintering factors that influence tensile strength

Experience, tenure and gender wage difference: evidence from China

This paper studies the returns to general labour market experience and firm-specific tenure, using data from China. Specifically, it focuses on explaining the gender wage difference from the perspective of general human capital and specific human capital. It applies the Heckman maximum likelihood estimator and Topel two-step estimation methodology to correct sample selection bias and individual heterogeneity. After correcting the errors, the authors find that returns to experience are higher for men than women, especially for married men and women. Furthermore, the return to tenure is higher than that to general experience. For men, the former is about 6% higher than the latter. But for women, tenure contributes 7–8% more to the wage growth than experience. The return of general experience mainly contributes to gender wage difference in China. Empirical results also show that the cross section analysis downward biases the returns to potential experience and a simple Topel-2S estimation in the panel study upward biases the return