Wavelet Integrated CNNs for Noise-Robust Image Classification

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.Comment: CVPR accepted pape

Supervised Transfer Learning for Product Information Question Answering

Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily. In this paper, we show that the large volume of existing community question answering data can be beneficial when building a system for answering questions related to product facts and specifications. Our experimental results demonstrate that the performance of a model for answering questions related to products listed in the Home Depot website can be improved by a large margin via a simple transfer learning technique from an existing large-scale Amazon community question answering dataset. Transfer learning can result in an increase of about 10% in accuracy in the experimental setting where we restrict the size of the data of the target task used for training. As an application of this work, we integrate the best performing model trained in this work into a mobile-based shopping assistant and show its usefulness.Comment: 2018 17th IEEE International Conference on Machine Learning and Application

Semi-supervised Local Cluster Extraction by Compressive Sensing

Local clustering problem aims at extracting a small local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we propose a new semi-supervised local cluster extraction approach by applying the idea of compressive sensing based on two pioneering works under the same framework. Our approves improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of existing works, which is the low quality of initial cut. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our approach

Top Quark Loop Corrections to the Neutral Higgs Boson Production at the Fermilab Tevatron

We calculate the $O(\alpha m_t^2/m^2_W)$ corrections arising from diagrams involving the top-quark loops to the light neutral Higgs boson production via $q\bar q'\to WH$ at the Fermilab Tevatron in both the standard model and the minimal supersymmetric model. In contrast to the QCD correction which increases the tree-level cross section, the corrections imply a few percent reduction in the production cross section relative to the tree-leve results.Comment: Some misprints are corrected, numerical results and conclusions are unchanged. To appear in PL

Gold as hydrogen: Structural and electronic properties and chemical bonding in Si3Au3+/0/- and comparisons to Si3H3+/0/-

A single Au atom has been shown to behave like H in its bonding to Si in several mono- and disilicon gold clusters. In the current work, we investigate the Au∕H analogy in trisilicon gold clusters, Si3Au+∕0∕−3. Photoelectron spectroscopy and density functional calculations are combined to examine the geometric and electronic structure of Si3Au−3. We find that there are three isomers competing for the ground state of Si3Au−3 as is the case for Si3H−3. Extensive structural searches show that the potential energy surfaces of the trisilicon gold clusters (Si3Au−3, Si3Au3, and Si3Au+3) are similar to those of the corresponding silicon hydrides. The lowest energy isomers for Si3Au−3 and Si3Au3 are structurally similar to a Si3Au four-membered ring serving as a common structural motif. For Si3Au+3, the 2π aromatic cyclotrisilenylium auride ion, analogous to the aromatic cyclotrisilenylium ion (Si3H+3), is the most stable species. Comparison of the structures and chemical bonding between Si3Au+∕0∕−3 and the corresponding silicon hydrides further extends the isolobal analogy between Au and H

Analytical approximations to charged black hole solutions in Einstein-Maxwell-Weyl gravity

The Homotopy Analysis Method (HAM) is a useful method to derive analytical approximate solutions of black holes in modified gravity theories. In this paper, we study the Einstein-Weyl gravity coupled with Maxwell field, and obtain analytical approximation solutions for charged black holes by using the HAM. It is found that the analytical approximate solutions are sufficiently accurate in the entire spacetime outside the black hole's event horizon, and also consistent with numerical ones for charged black holes in the Einstein-Maxwell-Weyl gravity.Comment: 17 pages, 6 figures. arXiv admin note: text overlap with arXiv:2308.0350

Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification

Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsupervised Re-ID is clustering-based, which generates pseudo labels by clustering and uses the pseudo labels to train a Re-ID model iteratively. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the intra-cluster variance mainly caused by the change of cameras. To address this issue, we propose to split each single cluster into multiple proxies according to camera views. The camera-aware proxies explicitly capture local structures within clusters, by which the intra-ID variance and inter-ID similarity can be better tackled. Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online association results. The offline association directly associates proxies according to the clustering and splitting results, while the online strategy dynamically associates proxies in terms of up-to-date features to reduce the noise caused by the delayed update of pseudo labels. The combination of two losses enables us to train a desirable Re-ID model. Extensive experiments on three person Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach demonstrates competitive performance with state-of-the-art methods. Code will be available at: https://github.com/Terminator8758/O2CAP.Comment: Accepted to TI

β-Ga2O3 solar-blind deep-ultraviolet photodetector based on a four-terminal structure with or without Zener diodes

A four-terminal photodetector was fabricated on the (2⎯⎯⎯012¯01)-dominant β-Ga2O3 thin film which was deposited in a plasma-assisted molecular beam epitaxy system. The suitability of this film for solar-blind DUV detection was proved by its transmission spectra. Moreover, the device operating in a specific voltage-current mode can accurately detect the DUV radiation both qualitatively and quantitatively. Accordingly, a dark/photo voltage ratio of 15 was achieved, which is comparable to that of previously-reported β-Ga2O3 interdigital metal-semiconductor-metal photoconductor. More importantly, the aperture ratio of our proposed device exceeds 80%, nearly doubling that of the conventional interdigital metal-semiconductor-metal devices including photoconductor and Schottky-type photodiode, which can intensively benefit the detection efficiency. Furthermore, it was found the dark/photo voltage ratio was nearly trebled with the assistance of two Zener diodes, and further enhancement can be expected by increasing the operating current and/or adopting Zener diodes with smaller Zener voltage. Therefore, this work provides a promising alternative for solar-blind DUV detection.published_or_final_versio

Retraction and Generalized Extension of Computing with Words

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy System

Study on Traffic Status Threshold Based on Floating Taxi

AbstractThe applications of floating car in road traffic condition identification are taken seriously and gradually developed. The paper studies the variables threshold in traffic condition information based on the floating taxi: section traffic information update cycle, data sampling interval, section covering ratio, floating taxi sample size. The optimization idea of floating taxi sample size is given. The traffic condition identification algorithm based on the floating taxi is put forward. The practice in two road sections shows that the algorithm is feasible which can offer useful reference for urban traffic management and resident trips decision