Semantically Consistent Regularization for Zero-Shot Recognition

The role of semantics in zero-shot learning is considered. The effectiveness of previous approaches is analyzed according to the form of supervision provided. While some learn semantics independently, others only supervise the semantic subspace explained by training classes. Thus, the former is able to constrain the whole space but lacks the ability to model semantic correlations. The latter addresses this issue but leaves part of the semantic space unsupervised. This complementarity is exploited in a new convolutional neural network (CNN) framework, which proposes the use of semantics as constraints for recognition.Although a CNN trained for classification has no transfer ability, this can be encouraged by learning an hidden semantic layer together with a semantic code for classification. Two forms of semantic constraints are then introduced. The first is a loss-based regularizer that introduces a generalization constraint on each semantic predictor. The second is a codeword regularizer that favors semantic-to-class mappings consistent with prior semantic knowledge while allowing these to be learned from data. Significant improvements over the state-of-the-art are achieved on several datasets.Comment: Accepted to CVPR 201

Violência contra as mulheres: tempo de actuar

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Editorial: The impact of stress on cognition and motivation

This work was supported by FEDER funds, through the Competitiveness Factors Operational Programme (COMPETE), and by National funds, through the Foundation for Science and Technology (FCT), under the scope of the project POCI-01- 0145-FEDER-007038. This article has been developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER), and the BIAL foundation, Porto, Portugal (grant number PT/FB/BL-2016-206)

A Closer Look at Weakly-Supervised Audio-Visual Source Localization

Audio-visual source localization is a challenging task that aims to predict the location of visual sound sources in a video. Since collecting ground-truth annotations of sounding objects can be costly, a plethora of weakly-supervised localization methods that can learn from datasets with no bounding-box annotations have been proposed in recent years, by leveraging the natural co-occurrence of audio and visual signals. Despite significant interest, popular evaluation protocols have two major flaws. First, they allow for the use of a fully annotated dataset to perform early stopping, thus significantly increasing the annotation effort required for training. Second, current evaluation metrics assume the presence of sound sources at all times. This is of course an unrealistic assumption, and thus better metrics are necessary to capture the model's performance on (negative) samples with no visible sound sources. To accomplish this, we extend the test set of popular benchmarks, Flickr SoundNet and VGG-Sound Sources, in order to include negative samples, and measure performance using metrics that balance localization accuracy and recall. Using the new protocol, we conducted an extensive evaluation of prior methods, and found that most prior works are not capable of identifying negatives and suffer from significant overfitting problems (rely heavily on early stopping for best results). We also propose a new approach for visual sound source localization that addresses both these problems. In particular, we found that, through extreme visual dropout and the use of momentum encoders, the proposed approach combats overfitting effectively, and establishes a new state-of-the-art performance on both Flickr SoundNet and VGG-Sound Source. Code and pre-trained models are available at https://github.com/stoneMo/SLAVC

A Systematic Review of Teacher-Facing Dashboards for Collaborative Learning Activities and Tools in Online Higher Education

Dashboard for online higher education support monitoring and evaluation of students’ interactions, but mostly limited to interaction occurring within learning management systems. In this study, we sought to find which collaborative learning activities and tools in online higher education are included in teaching dashboards. By following Kitchenham’s procedure for systematic reviews, 36 papers were identified according to this focus and analysed. The results identify dashboards supporting collaborative tools, both synchronous and asynchronous, along categories such as learning management systems, communication tools, social media, computer programming code management platforms, project management platforms, and collaborative writing tools. Dashboard support was also found for collaborative activities, grouped under four categories of forum discussion activities, three categories of communication activities and four categories of collaborative editing/sharing activities, though most of the analysed dashboards only provide support for no more than two or three collaborative tools. This represents a need for further research on how to develop dashboards that combine data from a more diverse set of collaborative activities and tools.This work was supported by the TRIO project funded by the European Union’s Erasmus+ KA220-ADU – Cooperation partnerships in adult education programme under grant agreement no. KA220-ADU-1B9975F8.info:eu-repo/semantics/publishedVersio

Design and testing of a rear wing for a Formula Student car

Tese de mestrado integrado, Engenharia Física, 2022, Universidade de Lisboa, Faculdade de CiênciasFormula Student teams go to extreme lengths to develop their aerodynamic packages, as it is a key factor to enhance car performance. The yearly objectives for the aerodynamic department are usually supplied by the vehicle dynamics department through lap time simulations. However, these usually are not capable of relating car attitude to aerodynamic performance and do not assume any relation between the aerodynamic coefficients. A simple aerodynamic model relating the lift coefficient to the drag coefficient and mass was added to the point mass simulator from the FST Lisboa vehicle dynamics department, to estimate the ideal aerodynamic coefficients and maximize vehicle performance for the current car design. By applying the results from the upgraded point mass simulator, a maximum theoretical lift, drag and mass were obtained. Through these results, a new rear wing concept, based on using airfoils as endplates was adopted, in order to create a design that would suit the new aerodynamic targets. Initially a low drag design was tested, however, preliminary results showed that due to high car mass it was not a viable design choice to follow. The final choice was to develop a high downforce rear wing. The resulting design was then validated using IST’s aeroacoustic wind tunnel, to assess its on-track performance. During this test, the aerodynamic forces applied on the whole vehicle were measured. A qualitative analysis of the results showed that the numerical simulations captured the experimental trends. Wool tufts were used as a flow visualization technique, these showed some discrepancies between the CFD simulations and experimental results, which were attributed to the simplifications made in both the numerical and experimental models. The implementation of the new aerodynamic model proved effective, as a design which yielded increase performance was obtained. Correlation between the CFD and on-track results is still limited due to modelling limitations in both experimental and numerical domains

Coprophagia and Entomophagia in a Patient with Alcohol Related Dementia

Coprophagia and entomophagia are two phenomena not commonly reported in the medical literature and their occurrence is usually associated with mental disorders. We present the case of a 59-year-old man with a history of alcohol abuse who was evaluated due to cognitive deterioration and disturbed eating habits including feces and living insects. Organic causes were ruled out and an important cognitive impairment became evident on neuropsychological formal test. The behavior remitted after antipsychotic pharmacologic therapy and alcohol detoxification, leaving the diagnostic impression of alcohol related dementia. This report shows a rare association of these two conditions in a patient with dementia.info:eu-repo/semantics/publishedVersio