Evidence-based factual error correction

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence. We demonstrate that it is feasible to train factual error correction systems from existing fact checking datasets which only contain labeled claims accompanied by evidence, but not the correction. We achieve this by employing a two-stage distant supervision approach that incorporates evidence into masked claims when generating corrections. Our approach, based on the T5 transformer and using retrieved evidence, achieved better results than existing work which used a pointer copy network and gold evidence, producing accurate factual error corrections for 5x more instances in human evaluation and a.125 increase in SARI score. The evaluation is conducted on a dataset of 65,000 instances based on a recent fact verification shared task and we release it to enable further work on the task

Inverted neutrino mass hierarchies from U(1) symmetries

Motivated by effective low energy models of string origin, we discuss the neutrino masses and mixing within the context of the Minimal Supersymmetric Standard Model supplemented by a U(1) anomalous family symmetry and additional Higgs singlet fields charged under this extra U(1). In particular, we interpret the solar and atmospheric neutrino data assuming that there are only three left-handed neutrinos which acquire Majorana masses via a lepton number violating dimension-five operator. We derive the general form of the charged lepton and neutrino mass matrices when two different pairs of singlet Higgs fields develop non--zero vacuum expectation values and show how the resulting neutrino textures are related to approximate lepton flavor symmetries. We perform a numerical analysis for one particular case and obtain solutions for masses and mixing angles, consistent with experimental data.Comment: 15 pages, 4 figure

High-accuracy sub-pixel motion estimation from noisy images in Fourier domain

In this paper, we propose a new method for estimating subpixel motion via exploiting the principle of phase correlation in the Fourier domain. The method is based on linear weighting of the height of the main peak on the one hand and the difference between its two neighboring side-peaks on the other. Using both synthetic and real data we show that the proposed method outperforms many established approaches and achieves improved accuracy even in the presence of noisy samples

Radio-frequency chain selection for energy and spectral efficiency maximization in hybrid beamforming under hardware imperfections

The next-generation wireless communications require reduced energy consumption, increased data rates and better signal coverage. The millimetre-wave frequency spectrum above 30 GHz can help fulfil the performance requirements of the next-generation mobile broadband systems. Multiple-input multiple-output technology can provide performance gains to help mitigate the increased path loss experienced at millimetre-wave frequencies compared with microwave bands. Emerging hybrid beamforming architectures can reduce the energy consumption and hardware complexity with the use of fewer radio-frequency (RF) chains. Energy efficiency is identified as a key fifth-generation metric and will have a major impact on the hybrid beamforming system design. In terms of transceiver power consumption, deactivating parts of the beamformer structure to reduce power typically leads to significant loss of spectral efficiency. Our aim is to achieve the highest energy efficiency for the millimetre-wave communications system while mitigating the resulting loss in spectral efficiency. To achieve this, we propose an optimal selection framework which activates specific RF chains that amplify the digitally beamformed signals with the analogue beamforming network. Practical precoding is considered by including the effects of user interference, noise and hardware impairments in the system modelling

Evaluating adversarial attacks against multiple fact verification systems

© 2019 Association for Computational Linguistics Automated fact verification has been progressing owing to advancements in modeling and availability of large datasets. Due to the nature of the task, it is critical to understand the vulnerabilities of these systems against adversarial instances designed to make them predict incorrectly. We introduce two novel scoring metrics, attack potency and system resilience which take into account the correctness of the adversarial instances, an aspect often ignored in adversarial evaluations. We consider six fact verification systems from the recent Fact Extraction and VERification (FEVER) challenge: the four best-scoring ones and two baselines. We evaluate adversarial instances generated by a recently proposed state-of-the-art method, a paraphrasing method, and rule-based attacks devised for fact verification. We find that our rule-based attacks have higher potency, and that while the rankings among the top systems changed, they exhibited higher resilience than the baselines