2,292 research outputs found
SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction
Packaged fresh-cut lettuce is widely consumed as a major component of
vegetable salad owing to its high nutrition, freshness, and convenience.
However, enzymatic browning discoloration on lettuce cut edges significantly
reduces product quality and shelf life. While there are many research and
breeding efforts underway to minimize browning, the progress is hindered by the
lack of a rapid and reliable methodology to evaluate browning. Current methods
to identify and quantify browning are either too subjective, labor intensive,
or inaccurate. In this paper, we report a deep learning model for lettuce
browning prediction. To the best of our knowledge, it is the first-of-its-kind
on deep learning for lettuce browning prediction using a pretrained Siamese
Quadratic Swin (SQ-Swin) transformer with several highlights. First, our model
includes quadratic features in the transformer model which is more powerful to
incorporate real-world representations than the linear transformer. Second, a
multi-scale training strategy is proposed to augment the data and explore more
of the inherent self-similarity of the lettuce images. Third, the proposed
model uses a siamese architecture which learns the inter-relations among the
limited training samples. Fourth, the model is pretrained on the ImageNet and
then trained with the reptile meta-learning algorithm to learn higher-order
gradients than a regular one. Experiment results on the fresh-cut lettuce
datasets show that the proposed SQ-Swin outperforms the traditional methods and
other deep learning-based backbones
Gradient matching for domain generalization
Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their ability to generalize to unseen domains. Here, we propose an inter-domain gradient matching objective that targets domain generalization by maximizing the inner product between gradients from different domains. Since direct optimization of the gradient inner product can be computationally prohibitive - it requires computation of second-order derivatives - we derive a simpler first-order algorithm named Fish that approximates its optimization. We perform experiments on the WILDS benchmark, which captures distribution shift in the real world, as well as the DOMAINBED benchmark that focuses more on synthetic-to-real transfer. Our method produces competitive results on both benchmarks, demonstrating its effectiveness across a wide range of domain generalization tasks. Code is available at https://github.com/YugeTen/fish
Latitudinal Variation in Seasonal Activity and Mortality in Ratsnakes (Elaphe obsoleta)
The ecology of ectotherms should be particularly affected by latitude because so much of their biology is temperature dependent. Current latitudinal patterns should also be informative about how ectotherms will have to modify their behavior in response to climate change. We used data from a total of 175 adult black ratsnakes (Elaphe obsoleta) radio tracked in Ontario, Illinois, and Texas, a latitudinal distance of \u3e 1500 km, to test predictions about how seasonal patterns of activity and mortality should vary with latitude. Despite pronounced differences in temperatures among study locations, and despite ratsnakes in Texas not hibernating and switching from diurnal to nocturnal activity in the summer, seasonal patterns of snake activity were remarkably similar during the months that snakes in all populations were active. Rather than being a function of temperature, activity may be driven by the timing of reproduction, which appears similar among populations. Contrary to the prediction that mortality should be highest in the most active population, overall mortality did not follow a clinal pattern. Winter mortality did increase with latitude, however, consistent with temperature limiting the northern distribution of ratsnakes. This result was opposite that found in the only previous study of latitudinal variation in winter mortality in reptiles, which may be a consequence of whether or not the animals exhibit true hibernation. Collectively, these results suggest that, at least in the northern part of their range, ratsnakes should be able to adjust easily to, and may benefit from, a warmer climate, although climate-based changes to the snakes\u27 prey or habitat, for example, could alter that prediction
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
Sex-specific Habitat Use and Responses to Fragmentation in an Endemic Chameleon Fauna
Chameleons are an understudied taxon facing many threats, including collection for the international pet trade and habitat loss and fragmentation. A recent field study reports a highly female-biased sex ratio in the Eastern Arc Endemic Usambara three horned chameleon, Trioceros deremensis, a large, sexually dimorphic species. This species is collected for the pet trade, and local collectors report males bring a higher price because only this sex has horns. Thus, sex ratios may vary due to differential rates of survival or harvesting. Alternatively, they may simply appear to be skewed if differences in habitat use biases detection of the sexes. Another threat facing chameleons is that of habitat loss and fragmentation. Despite enormous amounts of research, the factors of fragmentation that different species respond to is still under debate. Understanding these responses is important for current mitigation efforts as well as predicting how species will respond to future habitat alteration and climate change. My study suggests that differences in survival and detection may explain much of the observed seasonal sex skew in adult T. deremensis. Within fragmented habitat chameleons consistently responded more to edge effects and vegetative characteristics associated with fragmentation than to area or isolation effects. This may bode poorly for chameleon populations in the coming decades as climate change further alters vegetative communities and exacerbates edge effects
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