Associated production of the heavy charged gauge boson WH{W_{H}} and a top quark at LHC

In the context of topflavor seesaw model, we study the production of the heavy charged gauge boson ${W_{H}}$ associated with a top quark at the LHC. Focusing on the searching channel $pp\rightarrow tW_H\rightarrow t\bar{t}b \rightarrow l\nu jjbbb$, we carry out a full simulation of the signal and the relevant standard model backgrounds. The kinematical distributions of final states are presented. It is found that the backgrounds can be significantly suppressed by sets of kinematic cuts, and the signal of the heavy charged boson might be detected at the LHC with $\sqrt{s}=14$ TeV. With a integrated luminosity of \LL= 100 $fb^{-1}$, a $8.3 \sigma$ signal significance can be achieved for $m_{W_H}=1.6$ TeV.Comment: 16 pages, 6 figure

Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species. Previous studies always implicitly assume that the training and test data have the same underlying distributions, and that features extracted by modern backbone architectures remain discriminative and generalize well to unseen test data. However, we empirically justify that these conditions are not always true on benchmark datasets. To this end, we combine the merits of invariant risk minimization (IRM) and information bottleneck (IB) principle to learn invariant and minimum sufficient (IMS) representations for FGVC, such that the overall model can always discover the most succinct and consistent fine-grained features. We apply the matrix-based R{\'e}nyi's $\alpha$-order entropy to simplify and stabilize the training of IB; we also design a ``soft" environment partition scheme to make IRM applicable to FGVC task. To the best of our knowledge, we are the first to address the problem of FGVC from a generalization perspective and develop a new information-theoretic solution accordingly. Extensive experiments demonstrate the consistent performance gain offered by our IMS.Comment: Manuscript accepted by CVIU, code is available at Githu

CAFE Learning to Condense Dataset by Aligning Features

Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one. State-of-the-art approaches largely rely on learning the synthetic data by matching the gradients between the real and synthetic data batches. Despite the intuitive motivation and promising results, such gradient-based methods, by nature, easily overfit to a biased set of samples that produce dominant gradients, and thus lack global supervision of data distribution. In this paper, we propose a novel scheme to Condense dataset by Aligning FEatures (CAFE), which explicitly attempts to preserve the real-feature distribution as well as the discriminant power of the resulting synthetic set, lending itself to strong generalization capability to various architectures. At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales, while accounting for the classification of real samples. Our scheme is further backed up by a novel dynamic bi-level optimization, which adaptively adjusts parameter updates to prevent over-/under-fitting. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art: on the SVHN dataset, for example, the performance gain is up to 11%. Extensive experiments and analyses verify the effectiveness and necessity of proposed designs.Comment: The manuscript has been accepted by CVPR-2022

CDLT: A Dataset with Concept Drift and Long-Tailed Distribution for Fine-Grained Visual Categorization

Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC). In the existing FGVC datasets used in computer vision, it is generally assumed that each collected instance has fixed characteristics and the distribution of different categories is relatively balanced. In contrast, the real world scenario reveals the fact that the characteristics of instances tend to vary with time and exhibit a long-tailed distribution. Hence, the collected datasets may mislead the optimization of the fine-grained classifiers, resulting in unpleasant performance in real applications. Starting from the real-world conditions and to promote the practical progress of fine-grained visual categorization, we present a Concept Drift and Long-Tailed Distribution dataset. Specifically, the dataset is collected by gathering 11195 images of 250 instances in different species for 47 consecutive months in their natural contexts. The collection process involves dozens of crowd workers for photographing and domain experts for labelling. Extensive baseline experiments using the state-of-the-art fine-grained classification models demonstrate the issues of concept drift and long-tailed distribution existed in the dataset, which require the attention of future researches

FMRFamide-Like Peptide 22 Influences the Head Movement, Host Finding, and Infection of Heterodera glycines

The FMRFamide-like peptides (FLPs) represent the largest family of nematode neuropeptides and are involved in multiple parasitic activities. The immunoreactivity to FMRFamide within the nervous system of Heterodera glycines, the most economically damaging parasite of soybean [Glycine max L. (Merr)], has been reported in previous research. However, the family of genes encoding FLPs of H. glycines were not identified and functionally characterized. In this study, an FLP encoding gene Hg-flp-22 was cloned from H. glycines, and its functional characterization was uncovered by using in vitro RNA interference and application of synthetic peptides. Bioinformatics analysis showed that flp-22 is widely expressed in multiple nematode species, where they encode the highly conserved KWMRFamide motifs. Quantitative real-time (qRT)-PCR results revealed that Hg-flp-22 was highly expressed in the infective second-stage juveniles (J2s) and adult males. Silencing of Hg-flp-22 resulted in the reduced movement of J2s to the host root and reduced penetration ability, as well as a reduction in their subsequent number of females. Behavior and infection assays demonstrated that application of synthetic peptides Hg-FLP-22b (TPQGKWMRFa) and Hg-FLP-22c (KMAIEGGKWVRFa) significantly increased the head movement frequency and host invasion abilities in H. glycines but not in Meloidogyne incognita. In addition, the number of H. glycines females on the host roots was found to be significantly higher in Hg-FLP-22b treated nematodes than the ddH2O-treated control J2s. These results presented in this study elucidated that Hg-flp-22 plays a role in regulating locomotion and infection of H. glycines. This suggests the potential of FLP signaling as putative control targets for H. glycines in soybean production