Using collocation segmentation to augment the phrase table

This paper describes the 2010 phrase-based statistical machine translation system developed at the TALP Research Center of the UPC1 in cooperation with BMIC2 and VMU3. In phrase-based SMT, the phrase table is the main tool in translation. It is created extracting phrases from an aligned parallel corpus and then computing translation model scores with them. Performing a collocation segmentation over the source and target corpus before the alignment causes that di erent and larger phrases are extracted from the same original documents. We performed this segmentation and used the union of this phrase set with the phrase set extracted from the nonsegmented corpus to compute the phrase table. We present the con gurations considered and also report results obtained with internal and o cial test sets.Postprint (published version

UPC-BMIC-VDU system description for the IWSLT 2010: testing several collocation segmentations in a phrase-based SMT system

This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT system is a standard phrase-based enriched with novel segmentations. These novel segmentations are computed using statistical measures such as Log-likelihood, T-score, Chi-squared, Dice, Mutual Information or Gravity-Counts. The analysis of translation results allows to divide measures into three groups. First, Log-likelihood, Chi-squared and T-score tend to combine high frequency words and collocation segments are very short. They improve the SMT system by adding new translation units. Second, Mutual Information and Dice tend to combine low frequency words and collocation segments are short. They improve the SMT system by smoothing the translation units. And third, Gravity- Counts tends to combine high and low frequency words and collocation segments are long. However, in this case, the SMT system is not improved. Thus, the road-map for translation system improvement is to introduce new phrases with either low frequency or high frequency words. It is hard to introduce new phrases with low and high frequency words in order to improve translation quality. Experimental results are reported in the Frenchto- English IWSLT 2010 evaluation where our system was ranked 3rd out of nine systems.Postprint (published version

Security analysis of wireless mesh backhauls for mobile networks

Radio links are used to provide backhaul connectivity for base stations of mobile networks, in cases in which cable-based alternatives are not available and cannot be deployed in an economic or timely manner. While such wireless backhauls have been predominantly used in redundant tree and ring topologies in the past, mobile network operators have become increasingly interested in meshed topologies for carrier-grade wireless backhauls. However, wireless mesh backhauls are potentially more susceptible to security vulnerabilities, given that radio links are more exposed to tampering and given their higher system complexity. This article extends prior security threat analyses of 3rd generation mobile network architectures for the case of wireless mesh backhauls. It presents a description of the security model for the considered architecture and provides a list of the basic assumptions, security objectives, assets to be protected and actors of the analysis. On this foundation, potential security threats are analyzed and discussed and then assessed for their corresponding risk. The result of this risk assessment is then used to define a set of security requirements. Finally, we give some recommendations for wireless mesh backhaul designs and implementations following these requirements.European Community's Seventh Framework ProgramPublicad

Resource-on-demand schemes in 802.11 WLANs with non-zero start-up times

Increasing the density of access points is one of the most effective mechanisms to cope with the growing traffic demand in wireless networks. To prevent energy wastage at low loads, a resource-on-demand (RoD) scheme is required to opportunistically (de)activate access points as network traffic varies. While previous publications have analytically modeled these schemes in the past, they have assumed that resources are immediately available when activated, an assumption that leads to inaccurate results and might result in inappropriate configurations of the RoD scheme. In this paper, we analyze a general RoD scenario with N access points and non-zero start-up times. We first present an exact analytical model that accurately predicts performance but has a high computational complexity, and then derive a simplified analysis that sacrifices some accuracy in exchange for a much lower computational cost. To illustrate the practicality of this model, we present the design of a simple configuration algorithm for RoD. Simulation results confirm the validity of the analyses, and the effectiveness of the configuration algorithm

Phenotypic spectrum of MFN2 mutations in the Spanish population

INTRODUCTION: The most common form of axonal Charcot-Marie-Tooth (CMT) disease is type 2A, caused by mutations in the mitochondrial GTPase mitofusin 2 (MFN2). OBJECTIVE: The objective of our study is to establish the incidence of MFN2 mutations in a cohort of Spanish patients with axonal CMT neuropathy. MATERIAL AND METHODS: Eighty-five families with suspected axonal CMT were studied. All MFN2 exons were studied through direct sequencing. A bioenergetics study in fibroblasts was conducted using a skin biopsy taken from a patient with an Arg468His mutation. RESULTS: Twenty-four patients from 14 different families were identified with nine different MFN2 mutations (Arg94Trp, Arg94Gln, Ile203Met, Asn252Lys, Gln276His, Gly296Arg, Met376Val, Arg364Gln and Arg468His). All mutations were found in the heterozygous state and four of these mutations had not been described previously. MFN2 mutations were responsible for CMT2 in 16% +/- 7% of the families studied and in 30.8 +/- 14.2% (12/39) of families with known dominant inheritance. The bioenergetic studies in fibroblasts show typical results of MFN2 patients with a mitochondrial coupling defect (ATP/O) and an increase of the respiration rate linked to complex II. CONCLUSION: It is concluded that mutations in MFN2 are the most frequent cause of CMT2 in this region. The Arg468His mutation was the most prevalent (6/14 families), and our study confirms that it is pathological, presenting as a neuropathy in a mild to moderate degree. This study also demonstrates the value of MFN2 studies in cases of congenital axonal neuropathy, especially in cases of dominant inheritance, severe clinical symptoms or additional symptoms such as optic atrophy

An adaptive 5G multiservice and multitenant radio access network architecture

This article provides an overview on objectives and first results of the Horizon 2020 project 5G NOvel Radio Multiservice adaptive network Architecture (5GNORMA). With 5G NORMA, leading players in the mobile ecosystem aim to underpin Europe's leadership position in 5G. The key objective of 5G NORMA is to develop a conceptually novel, adaptive and future-proof 5G mobile network architecture. This architecture will allow for adapting the network to a wide range of service specific requirements, resulting in novel service-aware and context-aware end-to-end function chaining. The technical approach is based on an innovative concept of adaptive (de)composition and allocation of mobile network functions based on end-user requirements and infrastructure capabilities. At the same time, cost savings and faster time to market are to be expected by joint deployment of logically separated multiservice and multitenant networks on common hardware and other physical resources making use of traffic multiplexing gains. In this context architectural enablers such as network function virtualization and software-defined mobile networking will play a key role for introducing the needed flexible resource assignment to logical networks and specific virtual network functions.This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA

vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

In Press / En PrensaThe virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complexrelationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resourceorchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data(traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithmbased on an actor-critic neural network structure and a classifier to map contexts into resource control decisions.We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over aproduction RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods;(ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning,vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To ourknowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-freesolution that does not need to assume any particular platform or context.This work was partially supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 856950 (5G-TOURS); by Science Foundation Ireland (SFI) through Grant No. 17/CDA/4760; and AEI/FEDER through project AIM under Grant No. TEC2016-76465-C2-1-R. Furthermore, the work is closely related to the EU project DAEMON (Grant No. 101017109)

Demo: vrAIn proof-of-concept: a deep learning approach for virtualized RAN resource control

Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.While the application of the NFV paradigm into the network is proceeding full steam ahead, there is still one last milestone to be achieved in this context: the virtualization of the radio access network (vRAN). Due to the very complex dependency between the radio conditions and the computing resources needed to provide the baseband processing functionality, attaining an efficient resource control is particularly challenging. In this demonstration, we will showcase vrAIn, a vRAN dynamic resource controller that employs deep reinforcement learning to perform resource assignment decisions. vrAIn, which is implemented using an open-source LTE stack over a Linux platform, can achieve substantial savings in the used CPU resources while maintaining the target QoS for the attached terminals and maximizing throughput when there is a deficit of computational capacity.The work of University Carlos III of Madrid was supported by H2020 5G-MoNArch project (grant agreement no. 761445) and H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agreement no. 761536) and 5GROWTH project (grant agreement no. 856709). The work of University of Cartagena was supported by Grant AEI/FEDER TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701