Change Point Modeling of Covid-19 Data in the United States

To simultaneously model the change point and the possibly nonlinear relationship in the Covid-19 data of the US, a continuous second-order free knot spline model was proposed. Using the least squares method, the change point of the daily new cases against the total confirmed cases up to the previous day was estimated to be 04 April 2020. Before the point, the daily new cases were proportional to the total cases with a ratio of 0.287, suggesting that each patient had 28.7% chance to infect another person every day. After the point, however, such ratio was no longer maintained and the daily new cases were decreasing slowly. At the individual state level, it was found that most states had change points. Before its change point for each state, the daily new cases were still proportional to the total cases. And all the ratios were about the same except for New York State in which the ratio was much higher (probably due to its high population density and heavy usage of public transportation). But after the points, different states had different patterns. One interesting observation was that the change point of one state was about 3 weeks lagged behind the state declaration of emergency. This might suggest that there was a lag period, which could help identify possible causes for the second wave. In the end, consistency and asymptotic normality of the estimates were briefly discussed where the criterion functions are continuous but not differentiable (irregular)

Coded Speech Quality Measurement by a Non-Intrusive PESQ-DNN

Wideband codecs such as AMR-WB or EVS are widely used in (mobile) speech communication. Evaluation of coded speech quality is often performed subjectively by an absolute category rating (ACR) listening test. However, the ACR test is impractical for online monitoring of speech communication networks. Perceptual evaluation of speech quality (PESQ) is one of the widely used metrics instrumentally predicting the results of an ACR test. However, the PESQ algorithm requires an original reference signal, which is usually unavailable in network monitoring, thus limiting its applicability. NISQA is a new non-intrusive neural-network-based speech quality measure, focusing on super-wideband speech signals. In this work, however, we aim at predicting the well-known PESQ metric using a non-intrusive PESQ-DNN model. We illustrate the potential of this model by predicting the PESQ scores of wideband-coded speech obtained from AMR-WB or EVS codecs operating at different bitrates in noisy, tandeming, and error-prone transmission conditions. We compare our methods with the state-of-the-art network topologies of QualityNet, WaweNet, and DNSMOS -- all applied to PESQ prediction -- by measuring the mean absolute error (MAE) and the linear correlation coefficient (LCC). The proposed PESQ-DNN offers the best total MAE and LCC of 0.11 and 0.92, respectively, in conditions without frame loss, and still is best when including frame loss. Note that our model could be similarly used to non-intrusively predict POLQA or other (intrusive) metrics. Upon article acceptance, code will be provided at GitHub

FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning

Recently, the success of large models has demonstrated the importance of scaling up model size. This has spurred interest in exploring collaborative training of large-scale models from federated learning perspective. Due to computational constraints, many institutions struggle to train a large-scale model locally. Thus, training a larger global model using only smaller local models has become an important scenario (i.e., the \textbf{small-to-large scenario}). Although recent device-heterogeneity federated learning approaches have started to explore this area, they face limitations in fully covering the parameter space of the global model. In this paper, we propose a method called \textbf{FedBRB} (\underline{B}lock-wise \underline{R}olling and weighted \underline{B}roadcast) based on the block concept. FedBRB can uses small local models to train all blocks of the large global model, and broadcasts the trained parameters to the entire space for faster information interaction. Experiments demonstrate FedBRB yields substantial performance gains, achieving state-of-the-art results in this scenario. Moreover, FedBRB using only minimal local models can even surpass baselines using larger local models