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One unexpected technique that emerged in recent years consists in training a Deep Network (DN) with a Self-Supervised Learning (SSL) method,… (voir plus) and using this network on downstream tasks but with its last few projector layers entirely removed. This trick of throwing away the projector is actually critical for SSL methods to display competitive performances on ImageNet for which more than 30 percentage points can be gained that way. This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last projector layer) should be the one to use for best generalization performance downstream. But it seems not to be, and this study sheds some light on why. This trick, which we name Guillotine Regularization (GR), is in fact a generically applicable method that has been used to improve generalization performance in transfer learning scenarios. In this work, we identify the underlying reasons behind its success and show that the optimal layer to use might change significantly depending on the training setup, the data or the downstream task. Lastly, we give some insights on how to reduce the need for a projector in SSL by aligning the pretext SSL task and the downstream task.
Greenhouses are a key component of modernised agriculture, aiming for producing high-quality crops and plants. Furthermore, a network of gre… (voir plus)enhouses has enormous potential as part of demand response programs. Saving energy during off-peak time, reducing power consumption and delaying the start time of subsystems during on-peak time are some strategies that can be used to limit power exchanged with the main grid. In this work, a hierarchical distributed alternating direction method of multipliers-based model predictive control framework is proposed that has two main objectives: 1) providing appropriate conditions for greenhouses' crops and plants to grow, and 2) limiting the total power exchanged with the main grid. At each time step in the framework, an aggregator coordinates the greenhouses to reach a consensus and limit the total electric power exchanged while managing shared resources, e.g., reservoir water. The proposed framework's performance is investigated through a case study.
This paper is concerned with the identification in the limit from positive data of sub-stitutable context-free languages cfl s) over infinit… (voir plus)e alphabets. Clark and Eyraud (2007) showed that substitutable cfl s over finite alphabets are learnable in this learning paradigm. We show that substitutable cfl s generated by grammars whose production rules may have predicates that represent sets of potentially infinitely many terminal symbols in a compact manner are learnable if the terminal symbol sets represented by those predicates are learnable, under a certain condition. This can be seen as a result parallel to Argyros and D’Antoni’s work (2018) that amplifies the query learnability of predicate classes to that of symbolic automata classes. Our result is the first that shows such amplification is possible for identifying some cfl s in the limit from positive data.
The restrictions imposed by the COVID-19 pandemic required software development teams to adapt, being forced to work remotely and adjust the… (voir plus) software engineering activities accordingly. In the studies evaluating these effects, a few have assessed the impact on software engineering activities from a broader perspective and after a period of time when teams had time to adjust to the changes. No studies have been found comparing software startups and established companies either. This paper aims to investigate the impacts of COVID-19 on software development activities after one year of the pandemic restrictions, comparing the results between startups and established companies. Our approach was to design a cross-sectional survey and distribute it online among software development companies worldwide. The participants were asked about their perception of COVID-19’s pandemic impact on different software engineering activities: requirements engineering, software architecture, user experience design, software implementation, and software quality assurance. The survey received 170 valid answers from 29 countries, and for all the software engineering activities, we found that most respondents did not observe a significant impact. The results also showed that software startups and established companies were affected differently since, in some activities, we found a negative impact in the former and a positive impact in the latter. Regarding the time spent on each software engineering activity, most of the answers reported no change, but on those that did, the result points to an increase in time. Thus, we cannot find any relation between the change in time of effort and the reported positive or negative impact.
