Decoupling Crossover in Asymmetric Broadside Coupled Split Ring Resonators at Terahertz Frequencies

We investigate the electromagnetic response of asymmetric broadside coupled split ring resonators (ABC-SRRs) as a function of the relative in-plane displacement between the two component SRRs. The asymmetry is defined as the difference in the capacitive gap widths (\Delta g) between the two resonators comprising a coupled unit. We characterize the response of ABC-SRRs both numerically and experimentally via terahertz time-domain spectroscopy. As with symmetric BC-SRRs (\Delta g=0 \mu m), a large redshift in the LC resonance is observed with increasing displacement, resulting from changes in the capacitive and inductive coupling. However, for ABC-SRRs, in-plane shifting between the two resonators by more than 0.375Lo (Lo=SRR sidelength) results in a transition to a response with two resonant modes, associated with decoupling in the ABC-SRRs. For increasing \Delta g, the decoupling transition begins at the same relative shift (0.375Lo), though with an increase in the oscillator strength of the new mode. This strongly contrasts with symmetric BC-SRRs which present only one resonance for shifts up to 0.75Lo. Since all BC-SRRs are effectively asymmetric when placed on a substrate, an understanding of ABC-SRR behavior is essential for a complete understanding of BC-SRR based metamaterials

Soil Quality Analysis for Sustainability of Forest Ecosystem: The Case of Chilimo-Gaji Forest, West Shewa Zone, Ethiopia

The study was conducted to assess the soil quality with respect to the sustainability of Chilimo-Gaji Forest ecosystem using selected soils physicochemical parameters. Soil samples were taken through random sampling from the natural forest land under three different forest user groups (FUGs) in order to determine selected soils physicochemical properties. The result of the present study indicated that total N, available P and K, and % C were higher on the surface soil (0-20cm) than in the subsoil (20-30cm) depth indicating more nutrients are concentrated in the surface soil. The result of the study also revealed that presence of low bulk density ranges from 0.4 to 1.029 and high moisture content of soil ranging 4.89%-7.60%. The result also indicated that there is a higher per cent of carbon and organic matter across the three FUGs with Galessa recording the highest % carbon (7.69) and organic matter (13.25), followed by Gaji and Chilimo FUGs. The study also revealed that forest soil of the study area was very fertile and sustainable as the parameters analyzed indicating the forest ecosystem in the study area is sustainably managed under the new paradigm of participatory forest management. Scaling up participatory forest management to other protected forests in Ethiopia is crucial and plays a key role in the sustainability of healthy forest ecosystem. Keywords: Ethiopia; Forest user groups; physicochemical parameters; Soil organic carbon; Soil quality DOI: 10.7176/JEES/9-3-01 Publication date:March 31st 201

Three-dimensional broadband tunable terahertz metamaterials

We present optically tunable magnetic 3D metamaterials at terahertz (THz) frequencies which exhibit a tuning range of ~30% of the resonance frequency. This is accomplished by fabricating 3D array structures consisting of double-split-ring resonators (DSRRs) on silicon-on-sapphire, fabricated using multilayer electroplating. Photoexcitation of free carriers in the silicon within the capacitive region of the DSRR results in a red-shift of the resonant frequency from 1.74 THz to 1.16 THz. The observed frequency shift leads to a transition from a magnetic-to-bianisotropic response as verified through electromagnetic simulations and parameter retrieval. Our approach extends dynamic metamaterial tuning to magnetic control, and may find applications in switching and modulation, polarization control, or tunable perfect absorbers.Comment: 5page

FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration

To accomplish punctuation restoration, most existing methods focus on introducing extra information (e.g., part-of-speech) or addressing the class imbalance problem. Recently, large-scale transformer-based pre-trained language models (PLMS) have been utilized widely and obtained remarkable success. However, the PLMS are trained on the large dataset with marks, which may not fit well with the small dataset without marks, causing the convergence to be not ideal. In this study, we propose a Feature Fusion two-stream framework (FF2) to bridge the gap. Specifically, one stream leverages a pre-trained language model to capture the semantic feature, while another auxiliary module captures the feature at hand. We also modify the computation of multi-head attention to encourage communication among heads. Then, two features with different perspectives are aggregated to fuse information and enhance context awareness. Without additional data, the experimental results on the popular benchmark IWSLT demonstrate that FF2 achieves new SOTA performance, which verifies that our approach is effective.Comment: 5pages. arXiv admin note: substantial text overlap with arXiv:2203.1248

Nonlinear terahertz metamaterials via field-enhanced carrier dynamics in GaAs

We demonstrate nonlinear metamaterial split ring resonators (SRRs) on GaAs at terahertz frequencies. For SRRs on doped GaAs films, incident terahertz radiation with peak fields of ~20 - 160 kV/cm drives intervalley scattering. This reduces the carrier mobility and enhances the SRR LC response due to a conductivity decrease in the doped thin film. Above ~160 kV/cm, electric field enhancement within the SRR gaps leads to efficient impact ionization, increasing the carrier density and the conductivity which, in turn, suppresses the SRR resonance. We demonstrate an increase of up to 10 orders of magnitude in the carrier density in the SRR gaps on semi-insulating GaAs substrate. Furthermore, we show that the effective permittivity can be swept from negative to positive values with increasing terahertz field strength in the impact ionization regime, enabling new possibilities for nonlinear metamaterials.Comment: 5 pages, 4 figure

TLM: Token-Level Masking for Transformers

Structured dropout approaches, such as attention dropout and DropHead, have been investigated to regularize the multi-head attention mechanism in Transformers. In this paper, we propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting. Specifically, we devise a novel Token-Level Masking (TLM) training strategy for Transformers to regularize the connections of self-attention, which consists of two masking techniques that are effective and easy to implement. The underlying idea is to manipulate the connections between tokens in the multi-head attention via masking, where the networks are forced to exploit partial neighbors' information to produce a meaningful representation. The generality and effectiveness of TLM are thoroughly evaluated via extensive experiments on 4 diversified NLP tasks across 18 datasets, including natural language understanding benchmark GLUE, ChineseGLUE, Chinese Grammatical Error Correction, and data-to-text generation. The results indicate that TLM can consistently outperform attention dropout and DropHead, e.g., it increases by 0.5 points relative to DropHead with BERT-large on GLUE. Moreover, TLM can establish a new record on the data-to-text benchmark Rotowire (18.93 BLEU). Our code will be publicly available at https://github.com/Young1993/tlm.Comment: 13 pages. Accepted by EMNLP2023 main conferenc