Introduction to Two Challenging

Although great successes have been obtained, learning the RS images’ representative features and enhancing the retrieval efficiency for the large-scale CBRSIR tasks are still two challenging problems.

Although much progress has been made in multi-target regression, there are still two challenging issues, that is, how to model the underlying relationships between input features and output targets, and how to explore inter-target dependencies.

We present a framework based on Software Defined Networking (SDN) and BlockChain (BC) to address two challenging issues of EV-aided SG ecosystems, namely, privacy assurance and power security.

To capture the highly abstract art form inherent in APDrawings, we address two challenging operations—(1) coping with lines with small misalignments while penalizing large discrepancy and (2) generating more continuous lines—by introducing two novel loss terms: one is a novel distance transform loss with nonlinear mapping and the other is a novel line continuity loss, both of which improve the line quality.

O3ERS also addresses two challenging problems in real scenarios, namely, the sparsity and delay of online explanations’ feedback as well as the partialness and insufficiency of online recommendations’ feedback.

SDDNet mainly addresses two challenging issues—large texture variation and small size of defects—by introducing two modules: feature retaining block (FRB) and skip densely connected module (SDCM).

The proposed model requires solving two challenging problems.

Extensive computational experiments in solving two challenging robust-hard problems indicate that these procedures can highly improve the quality of the bounds obtained by the Benders’ framework within a limited execution time.

The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs).

Despite the progress in developing 3D AFRSs, achieving high-quality real-time reconstruction and high-accuracy recognition of 3D fingerprints remain two challenging issues.

In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and a image collection (untrained).

In this article, we study two challenging problems in semisupervised cross-view learning.

Experiments on two challenging datasets of remote sensing imagery show that the proposed method performs better than other models and can extract road information from complex scenes.

Extensive experiments are conducted to demonstrate SRENet on two challenging datasets obtaining high resolution images.

Although great successes have been obtained, learning the RS images’ representative features and enhancing the retrieval efficiency for the large-scale CBRSIR tasks are still two challenging problems.

O3ERS also addresses two challenging problems in real scenarios, namely, the sparsity and delay of online explanations’ feedback as well as the partialness and insufficiency of online recommendations’ feedback.

Extensive experiments on two challenging benchmark datasets show that our method has achieved state-of-the-art performance.

We evaluate the proposed method on two challenging benchmark datasets (the IBSR dataset and the MALC dataset).

We present a framework based on Software Defined Networking (SDN) and BlockChain (BC) to address two challenging issues of EV-aided SG ecosystems, namely, privacy assurance and power security.

Although much progress has been made in multi-target regression, there are still two challenging issues, that is, how to model the underlying relationships between input features and output targets, and how to explore inter-target dependencies.

In the proposed architecture, to enhance the accuracy with low computational costs we focus on two challenging tasks of shelf life (remaining useful life) prediction: 1) detecting the intrinsic features like internal defects, bruises, texture, and color of the fruits; and 2) classification of fruits according to their remaining useful life.

Comparison with state-of-the-art adaptation methods reveals considerably better performance of our model on two challenging tasks.

We extensively evaluate the proposed semi-supervised deep learning methods on two challenging medical image classification tasks: breast cancer screening from ultrasound images and multi-class ophthalmic disease classification from optical coherence tomography B-scan images.

We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks.

We discuss the statistical properties of HAIS and show its high performance in two challenging examples.

Its performance is illustrated with two challenging examples from nonlinear multiparticle beam dynamics, including the first electron cooling simulations based on first principles.

Our proposed approach is validated on two challenging video datasets (FAIR-Play and YT-MUSIC), and achieves new state-of-the-art performance.

Our method achieves new state-of-the-art performances on two challenging video semantic segmentation datasets, particularly 80.

Second, to further bolster our confidence in empirical evaluation, we report experiments on two challenging data slices, and observe a consistent 12 − 55% drop in accuracy.

The new model was tested against two challenging data sets collected from two stations located in Europe and the United States.

Two challenging scenarios with static and moving obstacles are designed, and the simulation results verify the effectiveness of the proposed method.

Using two challenging scenarios, we verify the performance of our procedure in the presence of severe grid abnormalities, including voltage unbalance, harmonics, frequency jump, and dc offset.

This is proved from the higher scores of accuracy, F1-score and AUC metrics in our experiments on two challenging multi-class datasets: Graz-02 and TF-Flowers, as compared to the state-of-the-art methods.

We demonstrate our Semantic-rank-and-Knowledge-Revise-based Question Answering (SKR-QA) approach on two challenging multi-choice question and answering tasks: ARC–Challenge and OpenbookQA.

The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardware.

Researchers often gather data on many variables of interest and then are faced with two challenging goals: building an accurate model with few predictors, and making probabilistic statements (inference) about this model.

We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering.

Experimental results on two challenging cross-lingual entity linking datasets spanning over 11 languages show that the pivot-based approach outperforms both the lexicon-based and semantic-based approach by a large margin.

The chapter aims to summarize current evidence regarding differential diagnosis and optimal management of these two challenging clinical entities.

However, given two challenging clinical scenarios, 66% and 90%, respectively endorsed treatment decisions that were unacceptable to pain experts.

Two challenging experimental test-cases are solved and prove the method effectiveness: the linearized multibody model of a flexible manipulator and a structure made by a cantilever beam plus a lumped spring-mass system.

The approach is successfully demonstrated and validated via a number of simulated case studies of common Single-Degree-of-Freedom (SDOF) nonlinear dynamic systems, and on two challenging experimental data sets.

We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net.

The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches.

To test our algorithms, we built two challenging environments with sparse reward functions.

We evaluated the performance of a large variety of these Zn2+ models in two challenging environments for which little is known about the performance of these methods, namely in a monometallic (Carbonic Anhydrase II) and a bimetallic ligand binding site (metallo-β-lactamase VIM-2).