Surface, Interface and Structure Optimization of Metal-Organic Frameworks: Towards Efficient Resourceful Conversion of Industrial Waste Gases.

Industrial waste gas emissions from fossil fuel over-exploitation have aroused great attention in modern society. Recently, metal-organic frameworks (MOFs) have been developed in the capture and catalytic conversion of industrial exhaust gases such as SO2 , H2 S, NOx , CO2 , CO, etc. Based on these resourceful conversion applications, in this review, we summarize the crucial role of the surface, interface, and structure optimization of MOFs for performance enhancement. The main points include (1) adsorption enhancement of target molecules by surface functional modification, (2) promotion of catalytic reaction kinetics through enhanced coupling in interfaces, and (3) adaptive matching of guest molecules by structural and pore size modulation. We expect that this review will provide valuable references and illumination for the design and development of MOF and related materials with excellent exhaust gas treatment performance.

VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers.

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX.

Gas-Phase Photoelectrocatalytic Oxidation of NO via TiO2 Nanorod Array/FTO Photoanodes.

Most photoelectrocatalytic (PEC) reactions are performed in the liquid phase for convenient electron transfer in an electrolyte solution. Herein, a novel PEC reactor involving a tandem combination of TiO2 nanorod array/fluorine-doped tin oxide (TiO2-NR/FTO) working electrodes and an electrochemical auxiliary cell was constructed to drive the highly efficient PEC oxidation of indoor gas (NOx). With the aid of a low bias voltage (0.3 V), the as-formed PEC reactor exhibited an 80% removal rate for oxidizing NO (500 ppb) under light irradiation, which is much higher than that of the traditional photocatalytic (PC) process. Upon being irradiated by light, the photogenerated electrons are quickly separated from the holes and transferred to the counter electrode (Pt) owing to the applied bias voltage, leaving photogenerated holes in the TiO2-NR/FTO electrode for oxidizing NO molecules. Moreover, both dry and humid NO could be effectively removed by the tandem TiO2-NR/FTO-based gas-phase PEC reactor, indicating that the NO molecules could also be directly oxidized by photogenerated holes in addition to hydroxyl radicals. The presence of trace amounts of water could promote the PEC oxidation of NO owing to the formation of hydroxyl radicals induced by reactions between the water and holes, which could further oxidize NO. This PEC reactor offers an energy-saving, environmentally friendly, and efficient route to treat air polluted with low concentrations of gases (NOx and SOx).

Correction: Recent advances in understanding of the mechanism and control of Li2O2 formation in aprotic Li-O2 batteries.

Correction for 'Recent advances in understanding of the mechanism and control of Li2O2 formation in aprotic Li-O2 batteries' by Zhiyang Lyu et al., Chem. Soc. Rev., 2017, DOI: 10.1039/c7cs00255f.

Recent advances in understanding of the mechanism and control of Li2O2 formation in aprotic Li-O2 batteries.

Aprotic Li-O2 batteries represent promising alternative devices for electrical energy storage owing to their extremely high energy densities. Upon discharge, insulating solid Li2O2 forms on cathode surfaces, which is usually governed by two growth models, namely the solution model and the surface model. These Li2O2 growth models can largely determine the battery performances such as the discharge capacity, round-trip efficiency and cycling stability. Understanding the Li2O2 formation mechanism and controlling its growth are essential to fully realize the technological potential of Li-O2 batteries. In this review, we overview the recent advances in understanding the electrochemical and chemical processes that occur during the Li2O2 formation. In the beginning, the oxygen reduction mechanisms, the identification of O2-/LiO2 intermediates, and their influence on the Li2O2 morphology have been discussed. The effects of the discharge current density and potential on the Li2O2 growth model have been subsequently reviewed. Special focus is then given to the prominent strategies, including the electrolyte-mediated strategy and the cathode-catalyst-tailoring strategy, for controlling the Li2O2 growth pathways. Finally, we conclude by discussing the profound implications of controlling Li2O2 formation for further development in Li-O2 batteries.

HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS.

MOTIVATION: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. RESULTS: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. AVAILABILITY AND IMPLEMENTATION: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Increase in contralateral prophylactic mastectomy conversation online unrelated to decision-making.

BACKGROUND: The increased uptake of contralateral prophylactic mastectomy (CPM) among breast cancer patients remains poorly understood. We hypothesized that the increased rate of CPM is represented in conversations on an online breast cancer community and may contribute to patients choosing this operation. METHODS: We downloaded 328,763 posts and their dates of creation from an online breast cancer community from August 1, 2000, to May 22, 2016. We then performed a keyword search to identify posts which mentioned breast cancer surgeries: contralateral prophylactic mastectomy (n = 7095), mastectomy (n = 10,889), and lumpectomy (n = 9694). We graphed the percentage of CPM-related, lumpectomy-related, and mastectomy-related conversations over time. We also graphed the frequency of posts which mentioned multiple operations over time. Finally, we performed a qualitative study to identify factors influencing the observed trends. RESULTS: Surgically related posts (e.g., mentioning at least one operation) made up a small percentage (n = 27,678; 8.4%) of all posts on this community. The percentage of surgically related posts mentioning CPM was found to increase over time, whereas the percentage of surgically related posts mentioning mastectomy decreased over time. Among posts that mentioned more than one operation, mastectomy and lumpectomy were the procedures most commonly mentioned together, followed by mastectomy and CPM. There was no change over time in the frequency of posts that mentioned more than one operation. Our qualitative review found that most posts mentioning a single operation were unrelated to surgical decision-making; rather the operation was mentioned only in the context of the patient's cancer history. Conversely, the most posts mentioning multiple operations centered around the patients' surgical decision-making process. CONCLUSIONS: CPM-related conversation is increasing on this online breast cancer community, whereas mastectomy-related conversation is decreasing. These results appear to be primarily informed by patients reporting the types of operations they have undergone, and thus appear to correspond to the known increased uptake of CPM.

Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE).

BACKGROUND: In biomedical research, data sharing and information exchange are very important for improving quality of care, accelerating discovery, and promoting the meaningful secondary use of clinical data. A big concern in biomedical data sharing is the protection of patient privacy because inappropriate information leakage can put patient privacy at risk. METHODS: In this study, we deployed a grid logistic regression framework based on Secure Multi-party Computation (SMAC-GLORE). Unlike our previous work in GLORE, SMAC-GLORE protects not only patient-level data, but also all the intermediary information exchanged during the model-learning phase. RESULTS: The experimental results demonstrate the feasibility of secure distributed logistic regression across multiple institutions without sharing patient-level data. CONCLUSIONS: In this study, we developed a circuit-based SMAC-GLORE framework. The proposed framework provides a practical solution for secure distributed logistic regression model learning.

Highly efficient and stable Au/CeO2-TiO2 photocatalyst for nitric oxide abatement: potential application in flue gas treatment.

In the present work, highly efficient and stable Au/CeO2-TiO2 photocatalysts were prepared by a microwave-assisted solution approach. The Au/CeO2-TiO2 composites with optimal molar ratio of Au/Ce/Ti of 0.004:0.1:1 delivered a remarkably high and stable NO conversion rate of 85% in a continuous flow reactor system under simulated solar light irradiation, which far exceeded the rate of 48% over pure TiO2. The tiny Au nanocrystals (∼1.1 nm) were well stabilized by CeO2 via strong metal-support bonding even it was subjected to calcinations at 550 °C for 6 h. These Au nanocrystals served as the very active sites for activating the molecule of nitric oxide and reducing the transmission time of the photogenerated electrons to accelerate O2 transforming to reactive oxygen species. Moreover, the Au-Ce(3+) interface formed and served as an anchoring site of O2 molecule. Then more adsorbed oxygen could react with photogenerated electrons on TiO2 surfaces to produce more superoxide radicals for NO oxidation, resulting in the improved efficiency. Meanwhile, O2 was also captured at the Au/TiO2 perimeter site and the NO molecules on TiO2 sites were initially delivered to the active perimeter site via diffusion on the TiO2 surface, where they assisted O-O bond dissociation and reacted with oxygen at these perimeter sites. Therefore, these finite Au nanocrystals can consecutively expose active sites for oxidizing NO. These synergistic effects created an efficient and stable system for breaking down NO pollutants. Furthermore, the excellent antisintering property of the catalyst will allow them for the potential application in photocatalytic treatment of high-temperature flue gas from power plant.

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption.

BACKGROUND: The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment. METHODS: We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics. RESULTS: The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets. CONCLUSIONS: Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics.

3-D Point Cloud Attribute Compression With -Laplacian Embedding Graph Dictionary Learning.

3-D point clouds facilitate 3-D visual applications with detailed information of objects and scenes but bring about enormous challenges to design efficient compression technologies. The irregular signal statistics and high-order geometric structures of 3-D point clouds cannot be fully exploited by existing sparse representation and deep learning based point cloud attribute compression schemes and graph dictionary learning paradigms. In this paper, we propose a novel p-Laplacian embedding graph dictionary learning framework that jointly exploits the varying signal statistics and high-order geometric structures for 3-D point cloud attribute compression. The proposed framework formulates a nonconvex minimization constrained by p-Laplacian embedding regularization to learn a graph dictionary varying smoothly along the high-order geometric structures. An efficient alternating optimization paradigm is developed by harnessing ADMM to solve the nonconvex minimization. To our best knowledge, this paper proposes the first graph dictionary learning framework for point cloud compression. Furthermore, we devise an efficient layered compression scheme that integrates the proposed framework to exploit the correlations of 3-D point clouds in a structured fashion. Experimental results demonstrate that the proposed framework is superior to state-of-the-art transform-based methods in M-term approximation and point cloud attribute compression and outperforms recent MPEG G-PCC reference software.

Variance Reduced Domain Randomization for Reinforcement Learning With Policy Gradient.

By introducing randomness on the environments, domain randomization (DR) imposes diversity to the policy training of deep reinforcement learning, and thus improves its capability of generalization. The randomization of environments, however, introduces another source of variability for the estimate of policy gradients, in addition to the already high variance incurred by trajectory sampling. Therefore, with standard state-dependent baselines, the policy gradient methods may still suffer high variance, causing a low sample efficiency during the training of DR. In this paper, we theoretically derive a bias-free and state/environment-dependent optimal baseline for DR, and analytically show its ability to achieve further variance reduction over the standard constant and state-dependent baselines for DR. Based on our theory, we further propose a variance reduced domain randomization (VRDR) approach for policy gradient methods, to strike a tradeoff between the variance reduction and computational complexity for the practical implementation. By dividing the entire space of environments into some subspaces and then estimating the state/subspace-dependent baseline, VRDR enjoys a theoretical guarantee of variance reduction and faster convergence than the state-dependent baselines. Empirical evaluations on six robot control tasks with randomized dynamics demonstrate that VRDR not only accelerates the convergence of policy training, but can consistently achieve a better eventual policy with improved training stability.

Source-Free Domain Adaptation With Domain Generalized Pretraining for Face Anti-Spoofing.

Source-free domain adaptation (SFDA) shows the potential to improve the generalizability of deep learning-based face anti-spoofing (FAS) while preserving the privacy and security of sensitive human faces. However, existing SFDA methods are significantly degraded without accessing source data due to the inability to mitigate domain and identity bias in FAS. In this paper, we propose a novel Source-free Domain Adaptation framework for FAS (SDA-FAS) that systematically addresses the challenges of source model pre-training, source knowledge adaptation, and target data exploration under the source-free setting. Specifically, we develop a generalized method for source model pre-training that leverages a causality-inspired PatchMix data augmentation to diminish domain bias and designs the patch-wise contrastive loss to alleviate identity bias. For source knowledge adaptation, we propose a contrastive domain alignment module to align conditional distribution across domains with a theoretical equivalence to adaptation based on source data. Furthermore, target data exploration is achieved via self-supervised learning with patch shuffle augmentation to identify unseen attack types, which is ignored in existing SFDA methods. To our best knowledge, this paper provides the first full-stack privacy-preserving framework to address the generalization problem in FAS. Extensive experiments on nineteen cross-dataset scenarios show our framework considerably outperforms state-of-the-art methods.

Combination of facial and nose features of Amur tigers to determine age.

We found that the area of black round or irregular-shaped spots on the tiger's nose increased with age, indicating a positive relationship between age and nose features. We used the deep learning model to train the facial and nose image features to identify the age of Amur tigers, using a combination of classification and prediction methods to achieve age determination with an accuracy of 87.81%.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding.

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.

Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks.

It is promising to solve linear inverse problems by unfolding iterative algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with learnable parameters. However, existing ISTA-based unfolded algorithms restrict the network architectures for iterative updates with the partial weight coupling structure to guarantee convergence. In this paper, we propose hybrid ISTA to unfold ISTA with both pre-computed and learned parameters by incorporating free-form DNNs (i.e., DNNs with arbitrary feasible and reasonable network architectures), while ensuring theoretical convergence. We first develop HCISTA to improve the efficiency and flexibility of classical ISTA (with pre-computed parameters) without compromising the convergence rate in theory. Furthermore, the DNN-based hybrid algorithm is generalized to popular variants of learned ISTA, dubbed HLISTA, to enable a free architecture of learned parameters with a guarantee of linear convergence. To our best knowledge, this paper is the first to provide a convergence-provable framework that enables free-form DNNs in ISTA-based unfolded algorithms. This framework is general to endow arbitrary DNNs for solving linear inverse problems with convergence guarantees. Extensive experiments demonstrate that hybrid ISTA can reduce the reconstruction error with an improved convergence rate in the tasks of sparse recovery and compressive sensing.

Automatic Representative Frame Selection and Intrathoracic Lymph Node Diagnosis With Endobronchial Ultrasound Elastography Videos.

Endobronchial ultrasound (EBUS) elastography videos have shown great potential to supplement intrathoracic lymph node diagnosis. However, it is laborious and subjective for the specialists to select the representative frames from the tedious videos and make a diagnosis, and there lacks a framework for automatic representative frame selection and diagnosis. To this end, we propose a novel deep learning framework that achieves reliable diagnosis by explicitly selecting sparse representative frames and guaranteeing the invariance of diagnostic results to the permutations of video frames. Specifically, we develop a differentiable sparse graph attention mechanism that jointly considers frame-level features and the interactions across frames to select sparse representative frames and exclude disturbed frames. Furthermore, instead of adopting deep learning-based frame-level features, we introduce the normalized color histogram that considers the domain knowledge of EBUS elastography images and achieves superior performance. To our best knowledge, the proposed framework is the first to simultaneously achieve automatic representative frame selection and diagnosis with EBUS elastography videos. Experimental results demonstrate that it achieves an average accuracy of 81.29% and area under the receiver operating characteristic curve (AUC) of 0.8749 on the collected dataset of 727 EBUS elastography videos, which is comparable to the performance of the expert-based clinical methods based on manually-selected representative frames.

Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning.

Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations as an individual class and tries to distinguish them from all other images, has been verified effective for representation learning. However, conventional contrastive learning does not model the relation between semantically similar samples explicitly. In this paper, we propose a general module that considers the semantic similarity among images. This is achieved by expanding the views generated by a single image to Cross-Samples and Multi-Levels, and modeling the invariance to semantically similar images in a hierarchical way. Specifically, the cross-samples are generated by a data mixing operation, which is constrained within samples that are semantically similar, while the multi-level samples are expanded at the intermediate layers of a network. In this way, the contrastive loss is extended to allow for multiple positives per anchor, and explicitly pulling semantically similar images together at different layers of the network. Our method, termed as CSML, has the ability to integrate multi-level representations across samples in a robust way. CSML is applicable to current contrastive based methods and consistently improves the performance. Notably, using MoCo v2 as an instantiation, CSML achieves 76.6% top-1 accuracy with linear evaluation using ResNet-50 as backbone, 66.7% and 75.1% top-1 accuracy with only 1% and 10% labels, respectively. All these numbers set the new state-of-the-art. The code is available at https://github.com/haohang96/CSML.

BNET: Batch Normalization With Enhanced Linear Transformation.

Batch normalization (BN) is a fundamental unit in modern deep neural networks. However, BN and its variants focus on normalization statistics but neglect the recovery step that uses linear transformation to improve the capacity of fitting complex data distributions. In this paper, we demonstrate that the recovery step can be improved by aggregating the neighborhood of each neuron rather than just considering a single neuron. Specifically, we propose a simple yet effective method named batch normalization with enhanced linear transformation (BNET) to embed spatial contextual information and improve representation ability. BNET can be easily implemented using the depth-wise convolution and seamlessly transplanted into existing architectures with BN. To our best knowledge, BNET is the first attempt to enhance the recovery step for BN. Furthermore, BN is interpreted as a special case of BNET from both spatial and spectral views. Experimental results demonstrate that BNET achieves consistent performance gains based on various backbones in a wide range of visual tasks. Moreover, BNET can accelerate the convergence of network training and enhance spatial information by assigning important neurons with large weights accordingly.

NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification.

Message passing has evolved as an effective tool for designing graph neural networks (GNNs). However, most existing methods for message passing simply sum or average all the neighboring features to update node representations. They are restricted by two problems: 1) lack of interpretability to identify node features significant to the prediction of GNNs and 2) feature overmixing that leads to the oversmoothing issue in capturing long-range dependencies and inability to handle graphs under heterophily or low homophily. In this article, we propose a node-level capsule graph neural network (NCGNN) to address these problems with an improved message passing scheme. Specifically, NCGNN represents nodes as groups of node-level capsules, in which each capsule extracts distinctive features of its corresponding node. For each node-level capsule, a novel dynamic routing procedure is developed to adaptively select appropriate capsules for aggregation from a subgraph identified by the designed graph filter. NCGNN aggregates only the advantageous capsules and restrains irrelevant messages to avoid overmixing features of interacting nodes. Therefore, it can relieve the oversmoothing issue and learn effective node representations over graphs with homophily or heterophily. Furthermore, our proposed message passing scheme is inherently interpretable and exempt from complex post hoc explanations, as the graph filter and the dynamic routing procedure identify a subset of node features that are most significant to the model prediction from the extracted subgraph. Extensive experiments on synthetic as well as real-world graphs demonstrate that NCGNN can well address the oversmoothing issue and produce better node representations for semisupervised node classification. It outperforms the state of the arts under both homophily and heterophily.