Information Losses in Neural Classifiers from Sampling

This paper considers the subject of information losses arising from the finite datasets used in the training of neural classifiers. It proves a relationship between such losses as the product of the expected total variation of the estimated neural model with the information about the feature space contained in the hidden representation of that model. It then bounds this expected total variation as a function of the size of randomly sampled datasets in a fairly general setting, and without bringing in any additional dependence on model complexity. It ultimately obtains bounds on information losses that are less sensitive to input compression and in general much smaller than existing bounds. The paper then uses these bounds to explain some recent experimental findings of information compression in neural networks which cannot be explained by previous work. Finally, the paper shows that not only are these bounds much smaller than existing ones, but that they also correspond well with experiments.Comment: To be published in IEEE TNNL

Simulating the Seismic Performance of Cold-Formed Steel Framed Buildings using Corrugated Sheet Shear Walls

Cold-formed steel framed shear wall sheathed with corrugated steel sheets is a promising shear wall system for low- and mid-rise constructions at high wind and seismic zones due to its advantages of non-combustibility, high shear strength, and high shear stiffness. A lot of work has been done on this subject. However, all the previous work is focused on the wall panel levels and more research work is needed on the entire building systems. The objective of this paper is to investigate the response of a cold-formed steel framed building with corrugated sheet sheathing subjected to earthquake excitation primarily through nonlinear time history analysis employing the incremental dynamic analysis (IDA) framework. High fidelity models were simulated in OpenSees program. The detailed modeling information and system assessment are presented in this paper

2-Methyl-1H-benzimidazol-3-ium hydrogen phthalate

The asymmetric unit of the title compound, C8H9N2 +·C8H5O4 −, contains two independent ion pairs. In each 2-methyl-1H-benzimidazolium ion, an intra­molecular O—H⋯O bond forms an S(7) graph-set motif. In the crystal, the components are linked by N—H⋯O hydrogen bonds, forming chains along [210]. Further stabilization is provided by weak C—H⋯O hydrogen bonds

SKFlow: Learning Optical Flow with Super Kernels

Optical flow estimation is a classical yet challenging task in computer vision. One of the essential factors in accurately predicting optical flow is to alleviate occlusions between frames. However, it is still a thorny problem for current top-performing optical flow estimation methods due to insufficient local evidence to model occluded areas. In this paper, we propose the Super Kernel Flow Network (SKFlow), a CNN architecture to ameliorate the impacts of occlusions on optical flow estimation. SKFlow benefits from the super kernels which bring enlarged receptive fields to complement the absent matching information and recover the occluded motions. We present efficient super kernel designs by utilizing conical connections and hybrid depth-wise convolutions. Extensive experiments demonstrate the effectiveness of SKFlow on multiple benchmarks, especially in the occluded areas. Without pre-trained backbones on ImageNet and with a modest increase in computation, SKFlow achieves compelling performance and ranks $\textbf{1st}$ among currently published methods on the Sintel benchmark. On the challenging Sintel clean and final passes (test), SKFlow surpasses the best-published result in the unmatched areas ($7.96$ and $12.50$) by $9.09\%$ and $7.92\%$. The code is available at \href{https://github.com/littlespray/SKFlow}{https://github.com/littlespray/SKFlow}.Comment: Accepted to NeurIPS 202