Domain Conditioned Adaptation Network

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.Comment: Accepted by AAAI 202

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

We investigate the challenging task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal relationships among both latent and observed variables. To address this, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables. Specifically, for two observed random vectors $\bf{Y}$ and $\bf{Z}$, GIN holds if and only if $\omega^{\intercal}\mathbf{Y}$ and $\mathbf{Z}$ are independent, where $\omega$ is a non-zero parameter vector determined by the cross-covariance between $\mathbf{Y}$ and $\mathbf{Z}$. We then give necessary and sufficient graphical criteria of the GIN condition in linear non-Gaussian acyclic causal models. Roughly speaking, GIN implies the existence of an exogenous set $\mathcal{S}$ relative to the parent set of $\mathbf{Y}$ (w.r.t. the causal ordering), such that $\mathcal{S}$ d-separates $\mathbf{Y}$ from $\mathbf{Z}$. Interestingly, we find that the independent noise condition (i.e., if there is no confounder, causes are independent of the residual derived from regressing the effect on the causes) can be seen as a special case of GIN. With such a connection between GIN and latent causal structures, we further leverage the proposed GIN condition, together with a well-designed search procedure, to efficiently estimate Linear, Non-Gaussian Latent Hierarchical Models (LiNGLaHs), where latent confounders may also be causally related and may even follow a hierarchical structure. We show that the underlying causal structure of a LiNGLaH is identifiable in light of GIN conditions under mild assumptions. Experimental results show the effectiveness of the proposed approach

A Fast Alternating Minimization Algorithm for Nonlocal Vectorial Total Variational Multichannel Image Denoising

The variational models with nonlocal regularization offer superior image restoration quality over traditional method. But the processing speed remains a bottleneck due to the calculation quantity brought by the recent iterative algorithms. In this paper, a fast algorithm is proposed to restore the multichannel image in the presence of additive Gaussian noise by minimizing an energy function consisting of an l2-norm fidelity term and a nonlocal vectorial total variational regularization term. This algorithm is based on the variable splitting and penalty techniques in optimization. Following our previous work on the proof of the existence and the uniqueness of the solution of the model, we establish and prove the convergence properties of this algorithm, which are the finite convergence for some variables and the q-linear convergence for the rest. Experiments show that this model has a fabulous texture-preserving property in restoring color images. Both the theoretical derivation of the computation complexity analysis and the experimental results show that the proposed algorithm performs favorably in comparison to the widely used fixed point algorithm

β-Diketone-Driven Deep Eutectic Solvent for Ultra-Efficient Natural Stable Lithium-7 Isotope Separation

6Li and 7Li are strategic resources. Because Li+ ions have no outermost electrons and the radii of 6Li and 7Li differ by only one neutron, the separation of the naturally stable isotopes of Li, especially by solvent extraction, is recognized as a difficult problem worldwide. Therefore, in this paper, an advanced β-diketone-driven deep eutectic solvent (DES) extraction system containing 2-thenoyltrifluoroacetone (HTTA) and tri-n-octyl phosphine oxide (TOPO) is introduced to the extraction and separation of 6Li+ and 7Li+ ions. Compared with those of reported HTTA extraction systems and crown ether extraction systems, the separation coefficient (β7Li/6Li) of the β-diketone-driven DES extraction system can reach the best value of 1.068, which is now the highest known β-value reported in the extraction system. From the intramolecular hydrogen bond of HTTA to the intermolecular hydrogen bond of DES, the bond energy increases by 47.8%. Because the active site of the proton in DES provides a higher energy barrier for the separation of 7Li, the β7Li/6Li is significantly increased. The extractions were characterized by spectrum, using 1H nuclear magnetic resonance (NMR) spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS). The mechanism was determined on the basis of the reaction kinetics and density functional theory (DFT). The DES extractant shows excellent cycle performance with regard to stripping and reusability. In conclusion, the highly efficient, economical, and stable β-diketone-driven DES extraction system can be used for the separation of naturally stable Li isotopes, which provides good industrial application prospects

A Collaborative Neighbor Representation Based Face Recognition Algorithm

We propose a new collaborative neighbor representation algorithm for face recognition based on a revised regularized reconstruction error (RRRE), called the two-phase collaborative neighbor representation algorithm (TCNR). Specifically, the RRRE is the division of  l2-norm of reconstruction error of each class into a linear combination of  l2-norm of reconstruction coefficients of each class, which can be used to increase the discrimination information for classification. The algorithm is as follows: in the first phase, the test sample is represented as a linear combination of all the training samples by incorporating the neighbor information into the objective function. In the second phase, we use the k classes to represent the test sample and calculate the collaborative neighbor representation coefficients. TCNR not only can preserve locality and similarity information of sparse coding but also can eliminate the side effect on the classification decision of the class that is far from the test sample. Moreover, the rationale and alternative scheme of TCNR are given. The experimental results show that TCNR algorithm achieves better performance than seven previous algorithms

Integrating Globality and Locality for Robust Representation Based Classification

The representation based classification method (RBCM) has shown huge potential for face recognition since it first emerged. Linear regression classification (LRC) method and collaborative representation classification (CRC) method are two well-known RBCMs. LRC and CRC exploit training samples of each class and all the training samples to represent the testing sample, respectively, and subsequently conduct classification on the basis of the representation residual. LRC method can be viewed as a “locality representation” method because it just uses the training samples of each class to represent the testing sample and it cannot embody the effectiveness of the “globality representation.” On the contrary, it seems that CRC method cannot own the benefit of locality of the general RBCM. Thus we propose to integrate CRC and LRC to perform more robust representation based classification. The experimental results on benchmark face databases substantially demonstrate that the proposed method achieves high classification accuracy

Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models

This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method