XPrompt: Exploring the Extreme of Prompt Tuning

Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner. While prompt tuning has gradually reached the performance level of fine-tuning as the model scale increases, there is still a large performance gap between prompt tuning and fine-tuning for models of moderate and small scales (typically less than 11B parameters). In this paper, we empirically show that the trained prompt tokens can have a negative impact on a downstream task and thus degrade its performance. To bridge the gap, we propose a novel Prompt tuning model with an eXtremely small scale (XPrompt) under the regime of lottery tickets hypothesis. Specifically, XPrompt eliminates the negative prompt tokens at different granularity levels through a hierarchical structured pruning, yielding a more parameter-efficient prompt yet with a competitive performance. Comprehensive experiments are carried out on SuperGLUE tasks, and the extensive results indicate that XPrompt is able to close the performance gap at smaller model scales.Comment: 15 pages, accepted to EMNLP 2022 main conferenc

Experimental characterisation of dynamic properties of an all-FRP truss bridge

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordFibre Reinforced Polymers (FRPs) have increasingly been utilised for construction of pedestrian bridges due to high strength- and stiffness-to-weight ratios, low maintenance costs and quick installation. Their relatively low mass and stiffness make these bridges potentially susceptible to vibration serviceability problems, which increasingly govern the design. Currently, the wider application of FRPs in civil engineering is hindered by the lack of experimental insight in dynamic performance of as-built structures. This paper presents an experimental investigation on a 25, m long glass-FRP truss footbridge in Italy. Ambient vibration tests were conducted to identify the dynamic properties. The peak-picking method and stochastic subspace identification approach were employed for modal parameter identification. The two methods produced very consistent results. Eight vibration modes were identified in the frequency range up to 10, Hz. Two lateral flexural vibration modes having natural frequencies of 5.8 and 9.6, Hz were identified, as well as two vertical flexural modes (at 7.5 and 8.1, Hz) and four torsional modes (at 2.1, 2.7, 4.8 and 9.3, Hz). Damping ratios for all modes up to 10, Hz except the eighth mode were above 1.2%.Engineering and Physical Sciences Research Council (EPSRC

On the energy transfer from nanocrystalline ZnS to Tb 3+ ions confined in reverse micelles

Abstract ZnS nanoparticles with different sizes are synthesized using a reverse micelles method in the presence of Tb 3+ ions. The photoluminescence, excitation and absorption spectra of ZnS/Tb 3+ NPs are studied to elucidate remarkable energy transfer from both ZnS host and the surfactant, i.e., AOT, to Tb 3+ ions. When Tb 3+ ions are introduced merely on the outside of ZnS nanoparticles in reverse micelles, obvious energy transfer from ZnS to the Tb 3+ ions is also observed, indicating the important role of spatial confinement on the performance of energy transfer between host and luminescent centers which are even not doped into the host lattices

Understanding Arctic Sea Ice Thickness Predictability by a Markov Model

The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model- assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7–0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model’s skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper-ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. Sea ice concentration (SIC) is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions

Bipolar Magnetic Semiconductors: A New Class of Spintronics Materials

Electrical control of spin polarization is very desirable in spintronics, since electric field can be easily applied locally in contrast with magnetic field. Here, we propose a new concept of bipolar magnetic semiconductor (BMS) in which completely spin-polarized currents with reversible spin polarization can be created and controlled simply by applying a gate voltage. This is a result of the unique electronic structure of BMS, where the valence and conduction bands possess opposite spin polarization when approaching the Fermi level. Our band structure and spin-polarized electronic transport calculations on semi-hydrogenated single-walled carbon nanotubes confirm the existence of BMS materials and demonstrate the electrical control of spin-polarization in them.Comment: 20 pages, 6 figures, accepted by Nanoscal