Second, DRAE extracts local representations of instances sharing the same label in both domains to maintain class-discriminative information in each class. Finally, DRAE constructs dual representations by aligning the global and local representations with different weights. Using three text and two image datasets and 12 state-of-the-art domain adaptation methods, the extensive experiments have demonstrated the effectiveness of DRAE.We show that any characteristic function game (CFG) G can be always turned into an approximately equivalent game represented using the induced subgraph game (ISG) representation. Such a transformation incurs obvious benefits in terms of tractability of computing solution concepts for G. Our transformation approach, namely, AE-ISG, is based on the solution of a norm approximation problem. We then propose a novel coalition structure generation (CSG) approach for ISGs that is based on graph clustering, which outperforms existing CSG approaches for ISGs by using off-the-shelf optimization solvers. Finally, we provide theoretical guarantees on the value of the optimal CSG solution of G with respect to the optimal CSG solution of the approximately equivalent ISG. As a consequence, our approach allows one to compute approximate CSG solutions with quality guarantees for any CFG. Results on a real-world application domain show that our approach outperforms a domain-specific CSG algorithm, both in terms of quality of the solutions and theoretical quality guarantees.This article studies the decentralized event-triggered control problem for a class of constrained nonlinear interconnected systems. By assigning a specific cost function for each constrained auxiliary subsystem, the original control problem is equivalently transformed into finding a series of optimal control policies updating in an aperiodic manner, and these optimal event-triggered control laws together constitute the desired decentralized controller. It is strictly proven that the system under consideration is stable in the sense of uniformly ultimate boundedness provided by the solutions of event-triggered Hamilton-Jacobi-Bellman equations. Different from the traditional adaptive critic design methods, we present an identifier-critic network architecture to relax the restrictions posed on the system dynamics, and the actor network commonly used to approximate the optimal control law is circumvented. The weights in the critic network are tuned on the basis of the gradient descent approach as well as the historical data, such that the persistence of excitation condition is no longer needed. The validity of our control scheme is demonstrated through a simulation example.Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. Selleck Proteasome inhibitor In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using CRF and TTA. We have performed extensive evaluations and validated the improvements using six publicly available datasets Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib Polyp DB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-e in clinical practice.Based on the current researches of EEG emotion recognition, there are some limitations, such as hand-engineered features, redundant and meaningless signal frames and the loss of frame-to-frame correlation. In this paper, a novel deep learning framework is proposed, named Frame Level Distilling Neural Network (FLDNet), to learn distilled features from correlation of different frames. A layer named frame gate is designed to integrate weighted semantic information on multiple frames for removing redundant and meaningless signal frames. Triple-net structure is introduced to distill the learned features net by net for replacing the hand-engineered features with professional knowledge. To be specific, one neural network will be normally trained for several epoches. Then, a second network of the same structure will be initialized again to learn the extracted features from the frame gate of the first neural network based on the output of the first net. Similarly, the third net improves the features based on the frame gate of the second network. To utilize the representation ability of the triple neural network, an ensemble layer is conducted to integrate the discriminative ability of proposed framework for final decision. Consequently, the proposed FLDNet provides an effective way to capture the correlation between different frames and automatically learn distilled high-level features for emotion recognition. The experiments are carried out, in a subject independent emotion recognition task, on public emotion datasets of DEAP and DREAMER benchmarks, which have demonstrated the effectiveness and robustness of the proposed FLDNet.By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not require between-module backpropagation. This modular approach brings new insights into the label requirement of deep learning (DL). It leverages only implicit pairwise labels (weak supervision) when learning the hidden modules. When training the output module, on the other hand, it requires full supervision but achieves high label efficiency, needing as few as ten randomly selected labeled examples (one from each class) to achieve 94.88% accuracy on CIFAR-10 using a ResNet-18 backbone. Moreover, modular training enables fully modularized DL workflows, which then simplify the design and implementation of pipelines and improve the maintainability and reusability of models.