We introduce MapAnything, a unified transformer-based feed-forward model that ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, depth, or partial reconstructions, and directly regresses the metric 3D scene geometry and cameras. MapAnything lever-ages a factored representation of multi-view scene geome-try, i.e., a collection of depth maps, local raymaps, camera poses, and a metric scale factor that effectively upgrades local reconstructions into a globally consistent metric frame. Standardizing the supervision and training across diverse datasets, along with flexible input augmentation, enables MapAnything to address a broad range of 3D vision tasks in a single feed-forward pass, including uncalibrated structure-from-motion, calibrated multi-view stereo, monocular depth estimation, camera localization, depth completion, and more. We provide extensive experimental analyses and model ab-lations demonstrating that MapAnything We provide extensive experimental analyses and model ab-lations demonstrating that MapAnything outperforms or matches specialist feed-forward models while offering more efficient joint training behavior, thus paving the way toward a universal 3D reconstruction backbone.

In this paper, we revisit the estimation of the size distribution of packet flows in Internet traffic through an inversion approach for several packet sampling schemes which are based on probabilistic sampling (PS). We first study the statistical properties of the previously introduced inversion estimator in its general form and make connections to the singular value decomposition. This motivates the use of a regularization technique in the estimation of the flow size distribution. More specifically, a penalized weighted least square approach is proposed. We compare theoretically the penalized estimator under simplified assumptions against the (non-penalized) inversion approach in order to explain differences in their statistical behaviors. A data study with two real traces shows that the proposed penalized estimator outperforms the inversion estimator for all sampling schemes, corroborating the theoretical analysis. This work reveals that the simplest sampling schemes based on PS, that do not work with small sampling probabilities under the inversion approach, can be used with the penalized approach. Furthermore, the penalized approach allows considering smaller packet sampling rates for all the other sampling schemes.

Traffic monitoring and estimation of flow characteristics, such as the size and duration distributions, can be problematic when the length of an observation window is constrained (e.g., due to hard network resources). Indeed, as shown in this work, sampled flows are usually affected by censoring in an observation window, which leads to biased estimators. To account for censoring, a mathematical framework that describes sampling of flows in a time window is developed. Using censoring analysis, we provide nonparametric maximum likelihood estimators for the flow duration and size distributions. The estimators are computed using the EM algorithm. Finally, the estimators are applied to an actual traffic trace, and are found to perform very well.

Passive optical networks are increasingly used for access to the Internet and it is important to understand the performance of future long-reach, multi-channel variants. In this paper we discuss requirements on upstream dynamic wavelength and bandwidth allocation (DWBA) and propose a simple novel algorithm that is considerably more efficient than classical approaches. We demonstrate that the algorithm emulates a multi-server polling system and deduce capacity formulas that are valid for general traffic processes.

Due to complexity and intractability reasons, most of the analytical studies on the reliability of communication paths in mobile ad hoc networks are based on the assumption of link independence. In this paper, an analytical framework is developed to characterize the random behavior of a multihop path and derive path metrics to characterize the reliability of paths. This is achieved through the modeling of a multihop path as a PDMP (piecewise deterministic Markov process). Two path based metrics are obtained as expectations of functionals of the process: the mean path duration and the path persistence. We show that these metrics are the unique solution of a set of integro-differential equations and provide a recursive scheme for their computation. Finally, numerical results illustrate the computation of the metrics; these results are compared with independent link approximation results.

In this paper, it is shown that the coexistence of a variety of different traffics in third generation cellular networks may lead to a very undesirable behavior of the whole network: a metastability property. When this property holds, the state of the network fluctuates on a very long time scale between different set of states. These long oscillations of the network make impossible to predict the average performances of some of the key characteristics of the connections, such as the handoff blocking rate or the probability of call blocking. As a consequence, the quality of service provided by such a network can be guaranteed only by, sometimes poor, lower bounds. Experiments of a UMTS network with this behavior are presented and the analysis of a corresponding simplified mathematical model is developed. The practical implications in the design of radio resource management for CDMA cellular networks are discussed.

On the basis of a reference model for ADSL traffic on an IP backbone link, established in an earlier study, we show that it is possible to infer the characteristics of long flows by performing a deterministic 1/N packet sampling. By using the fact that the number of active long flows can be represented by means of the number of customers in an M/G//spl infin/ queue with Weibullian service times, we derive some probabilistic properties of the sampled data. These properties are then used to infer the characteristics of the original flows. The method is illustrated by considering an actual traffic trace captured in the France Telecom IP backbone network. Experimental data show that the method proves quite efficient.