Enhancing Hydrogen Generation Through Nanoconfinement of Sensitizers and Catalysts in a Homogeneous Supramolecular Organic Framework.

Enrichment of molecular photosensitizers and catalysts in a confined nanospace is conducive for photocatalytic reactions due to improved photoexcited electron transfer from photosensitizers to catalysts. Herein, the self-assembly of a highly stable 3D supramolecular organic framework from a rigid bipyridine-derived tetrahedral monomer and cucurbit[8]uril in water, and its efficient and simultaneous intake of both [Ru(bpy)3 ]2+ -based photosensitizers and various polyoxometalates, that can take place at very low loading, are reported. The enrichment substantially increases the apparent concentration of both photosensitizer and catalyst in the interior of the framework, which leads to a recyclable, homogeneous, visible light-driven photocatalytic system with 110-fold increase of the turnover number for the hydrogen evolution reaction

Network Binarization via Contrastive Learning

Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit. However, there is still a huge performance gap between Binary Neural Networks (BNNs) and their full-precision (FP) counterparts. As the quantization error caused by weights binarization has been reduced in earlier works, the activations binarization becomes the major obstacle for further improvement of the accuracy. BNN characterises a unique and interesting structure, where the binary and latent FP activations exist in the same forward pass (i.e., $\text{Binarize}(\mathbf{a}_F) = \mathbf{a}_B$). To mitigate the information degradation caused by the binarization operation from FP to binary activations, we establish a novel contrastive learning framework while training BNNs through the lens of Mutual Information (MI) maximization. MI is introduced as the metric to measure the information shared between binary and FP activations, which assists binarization with contrastive learning. Specifically, the representation ability of the BNNs is greatly strengthened via pulling the positive pairs with binary and FP activations from the same input samples, as well as pushing negative pairs from different samples (the number of negative pairs can be exponentially large). This benefits the downstream tasks, not only classification but also segmentation and depth estimation, etc. The experimental results show that our method can be implemented as a pile-up module on existing state-of-the-art binarization methods and can remarkably improve the performance over them on CIFAR-10/100 and ImageNet, in addition to the great generalization ability on NYUD-v2.Comment: Accepted to ECCV 202

Complete Solutions to General Box-Constrained Global Optimization Problems

This paper presents a global optimization method for solving general nonlinear programming problems subjected to box constraints. Regardless of convexity or nonconvexity, by introducing a differential flow on the dual feasible space, a set of complete solutions to the original problem is obtained, and criteria for global optimality and existence of solutions are given. Our theorems improve and generalize recent known results in the canonical duality theory. Applications to a class of constrained optimal control problems are discussed. Particularly, an analytical form of the optimal control is expressed. Some examples are included to illustrate this new approach

Preparation of planar and hydrophobic benzocyclobutene‐based dielectric material from biorenewable rosin

A rosin‐based monomer with thermally crosslinkable benzocyclobutene groups was synthesized in this study. The structure of the monomer was examined using mass spectroscopy, Fourier transform infrared spectroscopy, and nuclear magnetic resonance spectroscopy. An amorphous crosslinked network with dielectric constant of 2.71 and dielectric loss of 0.0012 at 30 MHz was formed when the monomer was polymerized at high temperature (> 200 °C). The polymer film exhibits surface roughness (Ra) of 0.337 nm in a 5.0 × 5.0 μm2 area and the water contact angle of 110°. In addition, results from thermogravimetric analysis indicate that the polymer has T5% = 402 °C, and differential scanning calorimetry measurements show that the glass transition temperature is at least 350 °C. Results from nanoindentation tests show that the hardness and Young’s modulus of the polymer are 0.418 and 4.728 GPa, respectively. These data suggest that this new polymer may have potential applications in electronics and microelectronics. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48831.A new rosin‐based monomer containing bibenzocyclobutene groups was synthesized using dehydroabietic acid as the raw material. The monomer could be converted to crosslinked network via thermally ring‐opening polymerization which exhibited excellent planarity and dielectric properties. These results indicate that the polymer is suitable as encapsulation resin or dielectric material in the field of electronics and microelectronics.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154646/1/app48831.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154646/2/app48831_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154646/3/app48831-sup-0001-supinfo.pd

Lipschitz Continuity Retained Binary Neural Network

Relying on the premise that the performance of a binary neural network can be largely restored with eliminated quantization error between full-precision weight vectors and their corresponding binary vectors, existing works of network binarization frequently adopt the idea of model robustness to reach the aforementioned objective. However, robustness remains to be an ill-defined concept without solid theoretical support. In this work, we introduce the Lipschitz continuity, a well-defined functional property, as the rigorous criteria to define the model robustness for BNN. We then propose to retain the Lipschitz continuity as a regularization term to improve the model robustness. Particularly, while the popular Lipschitz-involved regularization methods often collapse in BNN due to its extreme sparsity, we design the Retention Matrices to approximate spectral norms of the targeted weight matrices, which can be deployed as the approximation for the Lipschitz constant of BNNs without the exact Lipschitz constant computation (NP-hard). Our experiments prove that our BNN-specific regularization method can effectively strengthen the robustness of BNN (testified on ImageNet-C), achieving state-of-the-art performance on CIFAR and ImageNet.Comment: Paper accepted to ECCV 202