CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation

This paper describes the submissions of the team of the Department of Computational Linguistics, University of Zurich, to the SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation. Our submissions use a character-level neural transducer that operates over traditional edit actions. While this model has been found particularly wellsuited for low-resource settings, using it with large data quantities has been difficult. Existing implementations could not fully profit from GPU acceleration and did not efficiently implement mini-batch training, which could be tricky for a transition-based system. For this year’s submission, we have ported the neural transducer to PyTorch and implemented true mini-batch training. This has allowed us to successfully scale the approach to large data quantities and conduct extensive experimentation. We report competitive results for morpheme segmentation (including sharing first place in part 2 of the challenge). We also demonstrate that reducing sentence-level morpheme segmentation to a word-level problem is a simple yet effective strategy. Additionally, we report strong results in inflection generation (the overall best result for large training sets in part 1, the best results in low-resource learning trajectories in part 2). Our code is publicly available

Low-Power Photothermal Probing of Single Plasmonic Nanostructures with Nanomechanical String Resonators

We demonstrate the direct photothermal probing and mapping of single plasmonic nanostructures via the temperature induced detuning of nanomechanical string resonators. Single Au nanoslits are illuminated with a low-power polarized focused laser beam ({\lambda} = 633 nm). Polarization dependent heat generation in gold nanoslits is then imaged with high sensitivity. A sensitivity of -4.1 ppm/nW with respect to the illuminated light (beam diameter 5.0pm0.8 {\mu}m) is determined for a single nanoslit (1 {\mu}m long and 53 nm wide), which equals to a total light absorption of 16%. This results in a heating of 0.5 K for an illuminance of 8 nW/{\mu}m2. Our results show that nanomechanical resonators are a unique and robust analysis tool for the low-power investigation of thermoplasmonic effects in plasmonic hot spots

Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their ability for prediction in ungauged catchments (PUB) with high efficiency. Catchment characteristics, the number of gauged catchments, and their level of hydroclimatic heterogeneity in the training dataset used for model regionalization can directly affect the model’s performance. Here, we study the generalization ability of a DL model to these factors by applying an Encoder-Decoder Long Short-Term Memory neural network for a 6-hour lead-time runoff prediction in 35 mountainous catchments in China. By varying the available number of catchments and model settings with different training datasets, namely local, regional, and PUB models, we evaluated the generalization ability of our model. We found that both quantity (i.e. number of gauged catchments available) and heterogeneity of the training dataset used for the DL model are important for improving model performance in the PUB context, due to a data synergy effect. The assessment of the sensitivity to catchment characteristics showed that the model performance is mainly correlated to the local hydro-climatic conditions; the more arid the region, the more likely it is to have a poor model performance for prediction in ungauged catchments. The results suggest that the regional ED-LSTM model is a promising method to predict streamflow from rainfall inputs in PUB, and outline the need for preparing a representative training dataset

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI.

OBJECTIVES To differentiate prostate cancer lesions with high and with low Gleason score by diffusion-weighted-MRI (DW-MRI). METHODS This prospective study was approved by the responsible ethics committee. DW-MRI of 84 consenting prostate and/or bladder cancer patients scheduled for radical prostatectomy were acquired and used to compute apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM: the pure diffusion coefficient D t, the pseudo-diffusion fraction F p and the pseudo-diffusion coefficient D p), and high b value (as acquired and Hessian filtered) parameters within the index lesion. These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on whether whole-prostate histopathological analysis after prostatectomy determined a high (≥7) or low (6) Gleason score. RESULTS Mean ADC and D t differed significantly (p of independent two-sample t test < 0.01) between high- and low-grade lesions. The highest classification accuracy was achieved by the mean ADC (AUC 0.74) and D t (AUC 0.70). A logistic regression model based on mean ADC, mean F p and mean high b value image led to an AUC of 0.74 following leave-one-out cross-validation. CONCLUSIONS Classification by IVIM parameters was not superior to classification by ADC. DW-MRI parameters correlated with Gleason score but did not provide sufficient information to classify individual patients. KEY POINTS • Mean ADC and diffusion coefficient differ between high- and low-grade prostatic lesions. • Accuracy of trivariate logistic regression is not superior to using ADC alone. • DW-MRI is not a valid substitute for biopsies in clinical routine yet

Nanomechanical Pyrolytic Carbon Resonators: Novel Fabrication Method and Characterization of Mechanical Properties

Micro- and nanomechanical string resonators, which essentially are highly stressed bridges, are of particular interest for micro- and nanomechanical sensing because they exhibit resonant behavior with exceptionally high quality factors. Here, we fabricated and characterized nanomechanical pyrolytic carbon resonators (strings and cantilevers) obtained through pyrolysis of photoresist precursors. The developed fabrication process consists of only three processing steps: photolithography, dry etching and pyrolysis. Two different fabrication strategies with two different photoresists, namely SU-8 2005 (negative) and AZ 5214e (positive), were compared. The resonant behavior of the pyrolytic resonators was characterized at room temperature and in high vacuum using a laser Doppler vibrometer. The experimental data was used to estimate the Young’s modulus of pyrolytic carbon and the tensile stress in the string resonators. The Young’s moduli were calculated to be 74 ± 8 GPa with SU-8 and 115 ± 8 GPa with AZ 5214e as the precursor. The tensile stress in the string resonators was 33 ± 7 MPa with AZ 5214e as the precursor. The string resonators displayed maximal quality factor values of up to 3000 for 525-µm-long structures