134 research outputs found
Adversarial Lagrangian Integrated Contrastive Embedding for Limited Size Datasets
Certain datasets contain a limited number of samples with highly various
styles and complex structures. This study presents a novel adversarial
Lagrangian integrated contrastive embedding (ALICE) method for small-sized
datasets. First, the accuracy improvement and training convergence of the
proposed pre-trained adversarial transfer are shown on various subsets of
datasets with few samples. Second, a novel adversarial integrated contrastive
model using various augmentation techniques is investigated. The proposed
structure considers the input samples with different appearances and generates
a superior representation with adversarial transfer contrastive training.
Finally, multi-objective augmented Lagrangian multipliers encourage the
low-rank and sparsity of the presented adversarial contrastive embedding to
adaptively estimate the coefficients of the regularizers automatically to the
optimum weights. The sparsity constraint suppresses less representative
elements in the feature space. The low-rank constraint eliminates trivial and
redundant components and enables superior generalization. The performance of
the proposed model is verified by conducting ablation studies by using
benchmark datasets for scenarios with small data samples.Comment: Submitted to Neural Networks Journal: 36 pages, 6 figure
Temperature-dependent development of the two-spotted ladybeetle, Adalia bipunctata, on the green peach aphid, Myzus persicae, and a factitious food under constant temperatures
The ability of a natural enemy to tolerate a wide temperature range is a critical factor in the evaluation of its suitability as a biological control agent. In the current study, temperature-dependent development of the two-spotted ladybeetle A. bipunctata L. (Coleoptera: Coccinellidae) was evaluated on Myzus persicae (Sulzer) (Hemiptera: Aphididae) and a factitious food consisting of moist bee pollen and Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) eggs under six constant temperatures ranging from 15 to 35 degrees C. On both diets, the developmental rate of A. bipunctata showed a positive linear relationship with temperature in the range of 15-30 degrees C, but the ladybird failed to develop to the adult stage at 35 degrees C. Total immature mortality in the temperature range of 15-30 degrees C ranged from 24.30-69.40% and 40.47-76.15% on the aphid prey and factitious food, respectively. One linear and two nonlinear models were fitted to the data. The linear model successfully predicted the lower developmental thresholds and thermal constants of the predator. The non-linear models of Lactin and Briere overestimated the upper developmental thresholds of A. bipunctata on both diets. Furthermore, in some cases, there were marked differences among models in estimates of the lower developmental threshold (t(min)). Depending on the model, t(min) values for total development ranged from 10.06 to 10.47 degrees C and from 9.39 to 11.31 degrees C on M. persicae and factitious food, respectively. Similar thermal constants of 267.9DD (on the aphid diet) and 266.3DD (on the factitious food) were calculated for the total development of A. bipunctata, indicating the nutritional value of the factitious food
Simultaneously Structured Models with Application to Sparse and Low-rank Matrices
The topic of recovery of a structured model given a small number of linear
observations has been well-studied in recent years. Examples include recovering
sparse or group-sparse vectors, low-rank matrices, and the sum of sparse and
low-rank matrices, among others. In various applications in signal processing
and machine learning, the model of interest is known to be structured in
several ways at the same time, for example, a matrix that is simultaneously
sparse and low-rank.
Often norms that promote each individual structure are known, and allow for
recovery using an order-wise optimal number of measurements (e.g.,
norm for sparsity, nuclear norm for matrix rank). Hence, it is reasonable to
minimize a combination of such norms. We show that, surprisingly, if we use
multi-objective optimization with these norms, then we can do no better,
order-wise, than an algorithm that exploits only one of the present structures.
This result suggests that to fully exploit the multiple structures, we need an
entirely new convex relaxation, i.e. not one that is a function of the convex
relaxations used for each structure. We then specialize our results to the case
of sparse and low-rank matrices. We show that a nonconvex formulation of the
problem can recover the model from very few measurements, which is on the order
of the degrees of freedom of the matrix, whereas the convex problem obtained
from a combination of the and nuclear norms requires many more
measurements. This proves an order-wise gap between the performance of the
convex and nonconvex recovery problems in this case. Our framework applies to
arbitrary structure-inducing norms as well as to a wide range of measurement
ensembles. This allows us to give performance bounds for problems such as
sparse phase retrieval and low-rank tensor completion.Comment: 38 pages, 9 figure
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