This paper presents the result of three years of experience in research, design, and deployment of a complete architecture aimed at automatically identifying service disruptions in large BGP/MPLS VPN networks. We present the main components of a comprehensive architecture that can be operated in production environments, highlighting the requirements that led to their design. We describe the data that are collected from the network using IETF standard protocols, the processing that is performed onto them to detect anomalies, and the scaling aspects that need to be considered when ingesting the large amounts of data that is necessary for the purpose at hand. We report on two and a half years of deployment experience on the Swisscom BGP/MPLS VPN Network services, by analyzing the behavior of our system in the face of actual network incidents. After each incident, we systematically performed postmortem analyzes. These investigations led us to conclude that the rule-based approaches that are currently used in deployment, supported by a profiling of the VPN customers to fine-tune rule parameters, enables the detection of service disruptions with the required accuracy.

E-bikes are recognized for their sustainable transportation benefits. However, the higher speeds associated with e-bikes pose an increased risk of potential accidents and hinder fluid riding in swarms with conventional bicycles. In this paper, we analyze the accuracy of maintaining an unknown speed, assess the associated workload, and investigate the self-reported speeds of e-bike cyclists in order to adapt the electric assistance to dynamic speed limits based on the surrounding traffic conditions. Our results from a pilot study with 15 participants show that the accuracy of maintaining a speed limit through active motor control and the associated workload are influenced by factors such as the level of electrical assistance and the perception of motor disengagement. E-bike cyclists using higher levels of electrical assistance demonstrated more accurate target speed maintenance. On average, participants consistently underestimated adapted speed limits, which were also influenced by the level of electrical support.

This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The focus is on addressing challenges like transmission efficiency, localization errors, communication disruptions, and heterogeneity. Moreover, we explore strategies to counter adversarial attacks and defenses, as well as approaches to adapt to domain shifts. The objective is to present an overview of how intermediate fusion methods effectively meet these diverse challenges, highlighting their role in advancing the field of collaborative perception in autonomous driving.

Identifying anomalies in a network is a crutial endeavor for Internet Service Providers (ISPs). Anomalies that impact the traffic of the ISP customers can lead to a degradation in the reputation of the company. Moreover, silent anomalies that do not break connectivity can impact the revenue and business of ISPs. Therefore, monitoring and anomaly detection has become essential for ISPs. In this paper, we present an ongoing research project aimed at identifying anomalies in Internet services provided by an ISP. We aim at detecting anomalies within the domain managed by the ISP that impact the customers and the business of the ISP. We propose a rule-based approach designed to promptly detect and provide reporting for such anomalies in near real time, giving information that allows the operator to identify whether a solution can be brought. In this paper, we describe the collected network telemetry metrics and illustrate how they are processed using open-source solutions. We introduce a set of use cases showing that an ISP can monitor Internet services using IETF standard metrics.

MOTIVATION: The compartmentalization of biochemical reactions, involved in the activation of gene expression in the eukaryotic nucleus, leads to the formation of membraneless bodies through liquid-liquid phase separation. These formations, called transcriptional condensates, appear to play important roles in gene regulation as they are assembled through the association of multiple enhancer regions in 3D genomic space. To date, we are still lacking efficient computational methodologies to identify the regions responsible for the formation of such condensates, based on genomic and conformational data. RESULTS: In this work, we present SEGCOND, a computational framework aiming to highlight genomic regions involved in the formation of transcriptional condensates. SEGCOND is flexible in combining multiple genomic datasets related to enhancer activity and chromatin accessibility, to perform a genome segmentation. It then uses this segmentation for the detection of highly transcriptionally active regions of the genome. At a final step, and through the integration of Hi-C data, it identifies regions of putative transcriptional condensates (PTCs) as genomic domains where multiple enhancer elements coalesce in 3D space. SEGCOND identifies a subset of enhancer segments with increased transcriptional activity. PTCs are also found to significantly overlap highly interconnected enhancer elements and super enhancers obtained through two independent approaches. Application of SEGCOND on data from a well-defined system of B-cell to macrophage transdifferentiation leads to the identification of previously unreported genes with a likely role in the process. AVAILABILITY AND IMPLEMENTATION: Source code and details for the implementation of SEGCOND is available at https://github.com/AntonisK95/SEGCOND. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This contribution focuses on designing a disturbance feedforward control for optically addressed deformable mirrors (OADMs). These mirrors can be used as intra-cavity actuators in high-power lasers to compensate for wavefront perturbations and for beam shaping within the framework of adaptive optics. For achieving the required deformations with high precision, the thermoelastic transfer behavior of the control intensity as well as disturbances due to the primary intensity have to be taken into account. The proposed control approach aims at minimizing the control effort in a spatial sense. Moreover, input constraints are considered explicitly, as the control intensity acts as a volumetric heat source which cannot cool the mirror. Furthermore, the 3D heat transfer in the cylindrical mirror is captured by a modal decomposition in the plate-direction in conjunction with a closed-form solution in the normal direction. By applying a similar approach to the mechanical equations, we are in the position to compute the equilibrium solution of the overall system in an efficient way. Based on the optimal solution, we subsequently derive an approximate solution by virtue of heuristics towards an online-capable algorithm. Finally, the performance is illustrated by means of simulations and a benchmark optimization.

At the 2011 American College of Medical Informatics (ACMI) Winter Symposium we studied the overlap between health IT and economics and what leading healthcare delivery organizations are achieving today using IT that might offer paths for the nation to follow for using health IT in healthcare reform. We recognized that health IT by itself can improve health value, but its main contribution to health value may be that it can make possible new care delivery models to achieve much larger value. Health IT is a critically important enabler to fundamental healthcare system changes that may be a way out of our current, severe problem of rising costs and national deficit. We review the current state of healthcare costs, federal health IT stimulus programs, and experiences of several leading organizations, and offer a model for how health IT fits into our health economic future.

This paper investigates the origins of the spamming process, specifically concerning address harvesting on the web, by relying on an extensive measurement data set spanning over three years. Concretely, we embedded more than 23 million unique spamtrap addresses in web pages. 0.5% of the embedded trap addresses received a total of 620,000 spam messages. Besides the scale of the experiment, the critical aspect of our methodology is the uniqueness of the issued spamtrap addresses, which enables the mapping of crawling activities to the actual spamming process.

Given a set S of n points in R/sup D/, D/spl ges/2. Each point p/spl isin/S is assigned a color c(p) chosen from a fixed color set. The All-Nearest-Foreign-Neighbors Problem (ANFNP) is to find for each point p/spl isin/S its nearest foreign neighbors, i.e. the set of all points in S/{p} that are closest to p among the points in S with a color different from c(p). We introduce the Well Separated Color Decomposition (WSCD) which gives an optimal O(log n) parallel algorithm to solve the AMFNP, for fixed dimension D/spl ges/2 and fixed L/sup t/-metric d/sub t/, 1/spl les/t/spl les//spl infin/. The WSCD is based upon the Well Separated Pair Decomposition (Callahan et al., 1992). The ANFNP finds extensive applications in VLSI design and verification for two dimensions, and in traffic-control systems and Geographic Information Systems for D>2 dimensions. To the best of our knowledge, this is the only known optimal parallel algorithm for the ANFNP.