SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing

This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021. The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints. Specifically, we consider the scenario where annotations exist for a domain but cannot be shared. Instead, participants are provided with models trained on that (source) data. Participants also receive some labeled data from a new (development) domain on which to explore domain adaptation algorithms. Participants are then tested on data representing a new (target) domain. We explored this scenario with two different semantic tasks: negation detection (a text classification task) and time expression recognition (a sequence tagging task). © 2021 Association for Computational Linguistics.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Inlet effects on the aerodynamics and acoustics of a centrifugal blower

SIGLEAvailable from British Library Document Supply Centre-DSC:8715.888(no 296) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Transient transformation of sunflower leaf discs via an Agrobacterium-mediated method: applications for gene expression and silencing studies

The sunflower belongs to the Compositae family and is an economically important crop because of the quality of its oil. Unfortunately, molecular analyses are limited due to the lack of genomic information, mutant libraries and efficient and rapid transformation protocols. In a wide variety of species, Agrobacterium-mediated transient transformation is a useful tool that can provide valuable insight into many biological processes. However, this technology has not been routinely applied to the sunflower because of difficulties with infiltration. Here, we present an optimized protocol for Agrobacterium-mediated transient transformation of leaf discs. Using this procedure, we were able to quickly overexpress or silence a given gene, enabling us to study several biochemical processes and characterize sunflower regulatory sequences. One of the major advantages of this approach is that in only 1 work-week it is possible to acquire considerable molecular information while avoiding the use of controversial heterologous systems. Transforming heterologous species is frequently unacceptable, as the conservation of molecular events in many cases is not well documented.Fil: Manavella, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Chan, Raquel Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentin

Nonnegative matrix factorization-based blind source separation for full-field and high-resolution modal identification from video

Traditional modal analysis requires physically attached sensors for data acquisition and vibration-based monitoring. Although traditional modal analysis presents well-established techniques for dynamics analysis, they can impose mass-loading effects on lightweight structures and increase budgetary demands on the maintenance of such data acquisition systems. Recently video-based techniques have become of increasing interest in the identification of the dynamic properties of infrastructures with arbitrary complexity. However, most applications rely on frame by frame tracking of fixed speckle targets to derive time-varying physical parameters. This imposes serious limitations for real-world applications, especially in scenarios where the structure is out of reach. Therefore, to address these issues, we propose a novel output-only operational modal analysis method based on vision-based blind source separation scheme. The proposed algorithm makes use of each pixel as a potential measurement point. This enables an increase in the spatial density of sensors conventionally used on a structure by orders of magnitude. This simultaneous processing of all pixel time-series derives full-field high-resolution mode shapes instead of low spatial resolution mode shapes achieved when measuring a limited number of discrete locations with typical sensors. Compared to other approaches, we propose a blind source separation scheme simpler than the ones based on phase extraction and complex steerable pyramids that still capable of disentangling local structural vibration from video measurement only. Moreover, a simple method to magnify and visualize independent vibration modes is introduced using the extracted modal information only. We validate our method by laboratory experiments on a bench-scale building structure and a cantilever beam. The results demonstrate that the proposed technique can decompose high-resolution modal parameters, visualize and reconstruct even those weakly-excited vibration modes