6 research outputs found
Flowtree: Enabling Distributed Flow Summarization at Scale
© ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGCOMM 2018, http://dx.doi.org/10.1145/3234200.3234225.NetFlow and IPFIX raw ow captures are insightful yet, due to their large volume, challenging to timely analyze and query. In particular, if these captures span long time periods or are collected at remote locations, storing or transferring them for analysis becomes increasingly expensive.
Enabling efficient execution of a large range of queries over ow captures while reducing storage and transfer volume requires working with mergeable succinct summaries that capture the most essential features of flows dynamically. However, the problem of building such structures is yet unmet. In this work, we introduce a self-adjusting data structure of generalized flows, called Flowtree, that (1) re- duces the storage requirements by more than 95% while providing highly accurate answers for popular hierarchical flows, (2) minimizes transfer cost of ow summaries, and (3) supports several operators with distributed execution and summarization across time and multiple sites. The evaluation of our solution on different network traces confirms that Flowtree can accurately and promptly answer questions about flows using different feature sets.EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNetDFG, FE 570/4-1, Gottfried Wilhelm Leibniz-Preis 2011BMBF, 01IS14013A, BBDC - Berliner Kompetenzzentrum für Big Dat
Exploring Network-Wide Flow Data with Flowyager
Many network operations, ranging from attack investigation and mitigation to traffic management, require answering network-wide flow queries in seconds. Although flow records are collected at each router, using available traffic capture utilities, querying the resulting datasets from hundreds of routers across sites and over time, remains a significant challenge due to the sheer traffic volume and distributed nature of flow records.
In this paper, we investigate how to improve the response time for a priori unknown network-wide queries. We present Flowyager, a system that is built on top of existing traffic capture utilities. Flowyager generates and analyzes tree data structures, that we call Flowtrees, which are succinct summaries of the raw flow data available by capture utilities. Flowtrees are self-adjusted data structures that drastically reduce space and transfer requirements, by 75% to 95%, compared to raw flow records. Flowyager manages the storage and transfers of Flowtrees, supports Flowtree operators, and provides a structured query language for answering flow queries across sites and time periods. By deploying a Flowyager prototype at both a large Internet Exchange Point and a Tier-1 Internet Service Provider, we showcase its capabilities for networks with hundreds of router interfaces. Our results show that the query response time can be reduced by an order of magnitude when compared with alternative data analytics platforms. Thus, Flowyager enables interactive network-wide queries and offers unprecedented drill-down capabilities to, e.g., identify DDoS culprits, pinpoint the involved sites, and determine the length of the attack.EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNetBMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and DataBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Dat
Bias mitigation for large language models using adversarial learning
Commercial applications increasingly build on large language models (LLMs). Given the inherent biases of LLMs, advancements in fairness research are urgent. Prior methods for mitigating biases in LLMs only address fairness in either language generation tasks or downstream tasks. Additionally, they often incur substantial computational costs by training from scratch. We propose a novel debiasing method that employs adversarial learning during model pre training. Without hyperparameter optimization our comparably computationally efficient method demonstrates increased fairness on a natural language generation task while maintaining performance. In addition, we show that our fairness gains transfer to a downstream task, at a performance cost. We explore a fairness approach which holds a significant potential for redefining the landscape of fairness of LLMs: By learning a single debiased model which can be applied to a variety of tasks, this approach eliminates the need for additional or task-specific debiasing steps. Hence, it facilitates the development of fair commercial applications and constitutes a step towards the broader goal of fairness in societies at large
A mathematical model of the coupled mechanisms of cell adhesion, contraction and spreading
Amantadine use in the French prospective NS-Park cohort
International audienceObjective: To assess amantadine use and associated factors in the patients with Parkinson's disease (PD).Background: Immediate-release amantadine is approved for the treatment of PD and is largely used in clinical practice to treat "levodopa-induced dyskinesia (LIDs). Its use varies according to countries and PD stages. The prospective NS-Park cohort collects features of PD patients followed by 26 French PD Expert Centres.Methods: Variables used for the analyses included demographics, motor and non-motor PD symptoms and motor complications [motor fluctuations (MFs), LIDs)], antiparkinsonian pharmacological classes and levodopa equivalent daily dose (LEDD). We evaluated: (i) prevalence of amantadine use and compared clinical features of amantadine users vs. non-users (cross-sectional analysis); (ii) factors associated with amantadine initiation (longitudinal analysis); (iii) amantadine effect on LIDs, MFs, apathy, impulse control disorders and freezing of gait (Fog) (longitudinal analysis).Results: Amantadine use prevalence was 12.6% (1,585/12,542, median dose = 200 mg). Amantadine users were significantly younger, with longer and more severe PD symptoms, greater LEDD and more frequent use of device-aided/surgical treatment. Factors independently associated with amantadine initiation were younger age, longer PD duration, more frequent LIDs, MFs and FoG, higher LEDD and better cognitive function. 9 of the 658 patients on amantadine had stopped it at the following visit, after 12-18 months (1.3%). New users of amantadine presented a higher improvement in LIDs and MF compared to amantadine never users.Conclusions: About 12% of PD patients within the French NS-Park cohort used amantadine, mostly those with younger age and more severe PD. Amantadine initiation was associated with a subsequent reduction in LIDs and MFs