Operational networks are increasingly using machine learning models for a variety of tasks, including detecting anomalies, inferring application performance, and forecasting demand. Accurate models are important, yet accuracy can degrade over time du
e to concept drift, whereby either the characteristics of the data change over time (data drift) or the relationship between the features and the target predictor change over time (model drift). Drift is important to detect because changes in properties of the underlying data or relationships to the target prediction can require model retraining, which can be time-consuming and expensive. Concept drift occurs in operational networks for a variety of reasons, ranging from software upgrades to seasonality to changes in user behavior. Yet, despite the prevalence of drift in networks, its extent and effects on prediction accuracy have not been extensively studied. This paper presents an initial exploration into concept drift in a large cellular network in the United States for a major metropolitan area in the context of demand forecasting. We find that concept drift arises largely due to data drift, and it appears across different key performance indicators (KPIs), models, training set sizes, and time intervals. We identify the sources of concept drift for the particular problem of forecasting downlink volume. Weekly and seasonal patterns introduce both high and low-frequency model drift, while disasters and upgrades result in sudden drift due to exogenous shocks. Regions with high population density, lower traffic volumes, and higher speeds also tend to correlate with more concept drift. The features that contribute most significantly to concept drift are User Equipment (UE) downlink packets, UE uplink packets, and Real-time Transport Protocol (RTP) total received packets.
The COVID-19 pandemic has resulted in dramatic changes to the daily habits of billions of people. Users increasingly have to rely on home broadband Internet access for work, education, and other activities. These changes have resulted in correspondin
g changes to Internet traffic patterns. This paper aims to characterize the effects of these changes with respect to Internet service providers in the United States. We study three questions: (1)How did traffic demands change in the United States as a result of the COVID-19 pandemic?; (2)What effects have these changes had on Internet performance?; (3)How did service providers respond to these changes? We study these questions using data from a diverse collection of sources. Our analysis of interconnection data for two large ISPs in the United States shows a 30-60% increase in peak traffic rates in the first quarter of 2020. In particular, we observe traffic downstream peak volumes for a major ISP increase of 13-20% while upstream peaks increased by more than 30%. Further, we observe significant variation in performance across ISPs in conjunction with the traffic volume shifts, with evident latency increases after stay-at-home orders were issued, followed by a stabilization of traffic after April. Finally, we observe that in response to changes in usage, ISPs have aggressively augmented capacity at interconnects, at more than twice the rate of normal capacity augmentation. Similarly, video conferencing applications have increased their network footprint, more than doubling their advertised IP address space.
The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the research community. In this paper, we perform an in-depth investigatio
n and observe that the efficacy of pseudo-LiDAR representation comes from the coordinate transformation, instead of data representation itself. Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our PatchNet is organized as the image representation, which means existing 2D CNN designs can be easily utilized for extracting deep features from input data and boosting 3D detection performance. We conduct extensive experiments on the challenging KITTI dataset, where the proposed PatchNet outperforms all existing pseudo-LiDAR based counterparts. Code has been made available at: https://github.com/xinzhuma/patchnet.
We construct a local model for Hilbert-Siegel moduli schemes with $Gamma_1(p)$-level bad reduction over $text{Spec }mathbb{Z}_{q}$, where $p$ is a prime unramified in the totally real field and $q$ is the residue cardinality over $p$. Our main tool i
s a variant over the small Zariski site of the ring-equivariant Lie complex $_Aunderline{ell}_G^{vee}$ defined by Illusie in his thesis, where $A$ is a commutative ring and $G$ is a scheme of $A$-modules. We use it to calculate the $mathbb{F}_{q}$-equivariant Lie complex of a Raynaud group scheme, then relate the integral model and the local model.
