Knowledge based cloud FE simulation of sheet metal forming processes

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques. This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise representations of static images. The obvious approach of training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, competitive results for self-supervision in general, and is overall state of the art in self-supervised pretraining for semantic image segmentation, as demonstrated on standard benchmarks

Higgs Physics at the Large Hadron Collider

In this talk I will begin by summarising the importance of the Higgs physics studies at the LHC. I will then give a short description of the pre-LHC constraints on the Higgs mass and the theoretical predictions for the LHC along with a discussion of the current experimental results, ending with prospects in the near future at the LHC. In addition to the material covered in the presented talk, I have included in the writeup, a critical appraisal of the theoretical uncertainties in the Higgs cross-sections at the Tevatron as well as a discussion of the recent experimental results from the LHC which have become available since the time of the workshop.Comment: LateX, 12 figures, 15 pages, Presented at the XIth Workshop on High Energy Physics Phenomenology, 2010, Ahmedabad, Indi

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201

Proximity effect model for x-ray transition edge sensors

Transition Edge Sensors are ultra-sensitive superconducting detectors with applications in many areas of research, including astrophysics. The device consists of a superconducting thin film, often with additional normal metal features, held close to its transition temperature and connected to two superconducting leads of a higher transition temperature. There is currently no way to reliably assess the performance of a particular device geometry or material composition without making and testing the device. We have developed a proximity effect model based on the Usadel equations to predict the effects of device geometry and material composition on sensor performance. The model is successful in reproducing I-V curves for two devices currently under study. We use the model to suggest the optimal size and geometry for TESs, considering how small the devices can be made before their performance is compromised. In the future, device modelling prior to manufacture will reduce the need for time-consuming and expensive testing.This work was partly supported by ESA CTP contract with No. 4000114932/15/NL/BW and EU H2020 AHEAD program

(Non)-Renormalization of the Chiral Vortical Effect Coefficient

We show using diagramtic arguments that in some (but not all) cases, the temperature dependent part of the chiral vortical effect coefficient is independent of the coupling constant. An interpretation of this result in terms of quantization in the effective 3 dimensional Chern-Simons theory is also given. In the language of 3D dimensionally reduced theory, the value of the chiral vortical coefficient is related to the formula $\sum_{n=1}^\infty n=-1/12$. We also show that in the presence of dynamical gauge fields, the CVE coefficient is not protected from renormalization, even in the large $N$ limit.Comment: 11 pages, 3 figures. Version 2 corrects an error and calculates leading radiative correctio

Resistive switching and threshold switching behaviors in La 0.1Bi 0.9Fe 1-xCo xO 3 ceramics

The effects of cobalt doping on the electrical conductivity of La 0.1Bi 0.9Fe 1-xCo xO 3 (LBFCO, x=0, 0.01, 0.03) ceramics were investigated. It is found that the leakage current increases with cobalt dopant concentration in LBFCO. On the application of bias voltage LBFCO ceramics with cobalt doping exhibits resistive switching effects at room temperature and threshold switching effects at elevated temperatures (50°C and 80°C). X-ray photoelectron spectroscopy of LBFCO ceramics show that cobalt dopant is bivalent as an acceptor, which induces an enhancement of oxygen vacancy concentration in LBFCO ceramics. Possible mechanisms for both resistive switching and threshold switching effects are discussed on the basis of the interplay of bound ferroelectric charges and mobile charged defects. © 2012 American Institute of Physics.published_or_final_versio

Electrical reliability and leakage mechanisms in highly resistive multiferroic La0.1Bi0.9FeO3 ceramics

Multiferroic La0.1 Bi0.9 FeO3 (LBFO) ceramics with high resistivity were synthesized by using a modified rapid thermal process. The LBFO ceramics show very low leakage and low dielectric loss. Well saturated ferroelectric hysteresis loops and polarization switching currents have been observed. For a maximum applied electric field of 145 kV/cm, the remanent polarization is 17.8 μC/ cm2 and the coercive filed is 75 kV/cm. The dominant conduction mechanism in the LBFO ceramics has been found to be the space-charge-limited current mechanism rather than the thermal excitation mechanism. Electrical reliability related to the fatigue and polarization retention of the LBFO ceramics has also been discussed with the leakage mechanisms. © 2011 American Institute of Physics.published_or_final_versio

Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer

Video style transfer techniques inspire many exciting applications on mobile devices. However, their efficiency and stability are still far from satisfactory. To boost the transfer stability across frames, optical flow is widely adopted, despite its high computational complexity, e.g. occupying over 97% inference time. This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm. We adopt two teacher networks, one of which takes optical flow during inference while the other does not. The output difference between these two teacher networks highlights the improvements made by optical flow, which is then adopted to distill the target student network. Furthermore, a low-rank distillation loss is employed to stabilize the output of student network by mimicking the rank of input videos. Extensive experiments demonstrate that our student network without an optical flow module is still able to generate stable video and runs much faster than the teacher network

Random Planar Lattices and Integrated SuperBrownian Excursion

In this paper, a surprising connection is described between a specific brand of random lattices, namely planar quadrangulations, and Aldous' Integrated SuperBrownian Excursion (ISE). As a consequence, the radius r_n of a random quadrangulation with n faces is shown to converge, up to scaling, to the width r=R-L of the support of the one-dimensional ISE. More generally the distribution of distances to a random vertex in a random quadrangulation is described in its scaled limit by the random measure ISE shifted to set the minimum of its support in zero. The first combinatorial ingredient is an encoding of quadrangulations by trees embedded in the positive half-line, reminiscent of Cori and Vauquelin's well labelled trees. The second step relates these trees to embedded (discrete) trees in the sense of Aldous, via the conjugation of tree principle, an analogue for trees of Vervaat's construction of the Brownian excursion from the bridge. From probability theory, we need a new result of independent interest: the weak convergence of the encoding of a random embedded plane tree by two contour walks to the Brownian snake description of ISE. Our results suggest the existence of a Continuum Random Map describing in term of ISE the scaled limit of the dynamical triangulations considered in two-dimensional pure quantum gravity.Comment: 44 pages, 22 figures. Slides and extended abstract version are available at http://www.loria.fr/~schaeffe/Pub/Diameter/ and http://www.iecn.u-nancy.fr/~chassain