Traffic Engineering with Segment Routing: SDN-based Architectural Design and Open Source Implementation

Traffic Engineering (TE) in IP carrier networks is one of the functions that can benefit from the Software Defined Networking paradigm. By logically centralizing the control of the network, it is possible to "program" per-flow routing based on TE goals. Traditional per-flow routing requires a direct interaction between the SDN controller and each node that is involved in the traffic paths. Depending on the granularity and on the temporal properties of the flows, this can lead to scalability issues for the amount of routing state that needs to be maintained in core network nodes and for the required configuration traffic. On the other hand, Segment Routing (SR) is an emerging approach to routing that may simplify the route enforcement delegating all the configuration and per-flow state at the border of the network. In this work we propose an architecture that integrates the SDN paradigm with SR-based TE, for which we have provided an open source reference implementation. We have designed and implemented a simple TE/SR heuristic for flow allocation and we show and discuss experimental results.Comment: Extended version of poster paper accepted for EWSDN 2015 (version v4 - December 2015

Temperature independent band structure of WTe2 as observed from ARPES

Extremely large magnetoresistance (XMR), observed in transition metal dichalcogendies, WTe$_2$, has attracted recently a great deal of research interests as it shows no sign of saturation up to the magnetic field as high as 60 T, in addition to the presence of type-II Weyl fermions. Currently, there has been a lot of discussion on the role of band structure changes on the temperature dependent XMR in this compound. In this contribution, we study the band structure of WTe$_2$ using angle-resolved photoemission spectroscopy (ARPES) and first-principle calculations to demonstrate that the temperature dependent band structure has no substantial effect on the temperature dependent XMR as our measurements do not show band structure changes on increasing the sample temperature between 20 and 130 K. We further observe an electronlike surface state, dispersing in such a way that it connects the top of bulk holelike band to the bottom of bulk electronlike band. Interestingly, similar to bulk states, the surface state is also mostly intact with the sample temperature. Our results provide invaluable information in shaping the mechanism of temperature dependent XMR in WTe$_2$.Comment: 7 pages, 3 figures. arXiv admin note: text overlap with arXiv:1705.0721

CompHEP 4.4 - Automatic Computations from Lagrangians to Events

We present a new version of the CompHEP program (version 4.4). We describe shortly new issues implemented in this version, namely, simplification of quark flavor combinatorics for the evaluation of hadronic processes, Les Houches Accord based CompHEP-PYTHIA interface, processing the color configurations of events, implementation of MSSM, symbolical and numerical batch modes, etc. We discuss how the CompHEP program is used for preparing event generators for various physical processes. We mention a few concrete physics examples for CompHEP based generators prepared for the LHC and Tevatron.Comment: The paper has been presented on IX International Workshop on Advanced Computing and Analysis Techniques in Physics Research December 1-5, 2003. KEK, Japan. 10 pages, 2 figure

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics. In this paper, we focus on 3D deformable shapes that share a common topological structure, such as human faces and bodies. Morphable Models and their variants, despite their linear formulation, have been widely used for shape representation, while most of the recently proposed nonlinear approaches resort to intermediate representations, such as 3D voxel grids or 2D views. In this work, we introduce a novel graph convolutional operator, acting directly on the 3D mesh, that explicitly models the inductive bias of the fixed underlying graph. This is achieved by enforcing consistent local orderings of the vertices of the graph, through the spiral operator, thus breaking the permutation invariance property that is adopted by all the prior work on Graph Neural Networks. Our operator comes by construction with desirable properties (anisotropic, topology-aware, lightweight, easy-to-optimise), and by using it as a building block for traditional deep generative architectures, we demonstrate state-of-the-art results on a variety of 3D shape datasets compared to the linear Morphable Model and other graph convolutional operators.Comment: to appear at ICCV 201