RGB-T salient object detection via fusing multi-level CNN features

RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast

Type-II Dirac points and Dirac nodal loops on the magnons of square-hexagon-octagon lattice

We study topological magnons on an anisotropic square-hexagon-octagon (SHO) lattice which has been found by a two-dimensional Biphenylene network (BPN). We propose the concepts of type-II Dirac magnonic states where new schemes to achieve topological magnons are unfolded without requiring the Dzyaloshinsky-Moriya interactions (DMIs). In the ferromagnetic states, the topological distinctions at the type-II Dirac points along with one-dimensional (1D) closed lines of Dirac magnon nodes are characterized by the $\mathbb{Z}_2$ invariant. We find pair annihilation of the Dirac magnons and use the Wilson loop method to depict the topological protection of the band-degeneracy. The Green's function approach is used to calculte chiral edge modes and magnon density of states (DOS). We introduce the DMIs to gap the type-II Dirac magnon points and demonstrate the Dirac nodal loops (DNLs) are robust against the DMIs within a certain parameter range. The topological phase diagram of magnon bands is given via calculating the Berry curvature and Chern number. We find that the anomalous thermal Hall conductivity gives connection to the magnon edge current. Furthermore, we derive the differential gyromagnetic ratio to exhibit the Einstein-de Haas effect (EdH) of magnons with topological features.Comment: arXiv admin note: text overlap with arXiv:2207.0288

Masking: A New Perspective of Noisy Supervision

It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels. However, such estimation is practically difficult, due to either the indirect nature of two-step approaches, or not big enough data to afford end-to-end approaches. In this paper, we propose a human-assisted approach called Masking that conveys human cognition of invalid class transitions and naturally speculates the structure of the noise transition matrix. To this end, we derive a structure-aware probabilistic model incorporating a structure prior, and solve the challenges from structure extraction and structure alignment. Thanks to Masking, we only estimate unmasked noise transition probabilities and the burden of estimation is tremendously reduced. We conduct extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as well as the industrial-level Clothing1M with agnostic noise structure, and the results show that Masking can improve the robustness of classifiers significantly.Comment: NIPS 2018 camera-ready versio

A Novel Model-Independent Approach to Explore New Physics in Five-body Semileptonic Decays

We derive three parameters associated with the angular distribution of semileptonic five-body decays $D_{(s)}/B_{(s)}\to V\ell^+\nu_{\ell}\,(\ell=e,\mu,\tau)$ with $V\to PPP$, where $V$ and $P$ denote vector and pseudo-scalar particles. These parameters, expected to be unity in the Standard Model, may deviate if new physics is involved. Our model-independent approach involves deriving the specific form of the angular distribution under the most general form of the decay matrix element $D_{(s)}/B_{(s)}\to V$. The outcomes have potential applications in precisely testing the Standard Model and exploring new physics. Relevant measurements can be carried out using data obtained from BESIII, Belle~II, and LHCb.Comment: arXiv admin note: text overlap with arXiv:2404.0481

Yield strength measurement of ferromagnetic materials based on the inverse magnetostrictive effect

Ferromagnetic materials are widely used in industry and risking the hazards of aging and degradation of their mechanical properties. This paper proposed a non-destructive method for the measurement of the yield strength of ferromagnetic materials imprinted by the materials’ microstructure as the microstructure influences the pattern of the inverse magnetostrictive effect of ferromagnetic materials. For experimental verification, yield strengths of ferromagnetic specimens were measured on an electromagnetic ultrasonic transducer (EMAT) detection system. The relationship between electromagnetic acoustic transducer signals and the static magnetic field strength was obtained, from which we extracted the pattern parameters related to the yield strength. The regression model of the pattern parameters versus the yield strength was established and then verified with trial on a specimen processed in the same batch with a maximum prediction accuracy of 12.78%