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We study dark gauge-mediated supersymmetry breaking (dark GMSB) in a theory with a new unbroken $U(1)_{D}$ local symmetry and massless dark photon. Messenger fields charged under both Standard Model and dark gauge symmetries produce new soft supersymmetry-breaking terms due to gauge kinetic mixing between $U(1)_Y$ hypercharge and $U(1)_D$. We show that large kinetic mixing induces significant distortions to the superpartner spectra relative to conventional GMSB. Notably, shifts in the Higgs soft masses impact the conditions for electroweak symmetry breaking, lowering the $\mu$ parameter and yielding a relatively light Higgsino that may be accessible at the LHC. Furthermore, for very simple messenger representations, a very light bino-dark photino mixed state is present in the spectrum, which may be probed through exotic Higgs boson decays at future Higgs factories. We also examine the cosmological and phenomenological consequences of the messengers, the lightest of which is absolutely stable and carries fractional electric charge., Comment: 45 pages, 20 figures

Data augmentation has shown significant advancements in computer vision to improve model performance over the years, particularly in scenarios with limited and insufficient data. Currently, most studies focus on adjusting the image or its features to expand the size, quality, and variety of samples during training in various tasks including object detection. However, we argue that it is necessary to investigate bounding box transformations as a data augmentation technique rather than image-level transformations, especially in aerial imagery due to potentially inconsistent bounding box annotations. Hence, this letter presents a thorough investigation of bounding box transformation in terms of scaling, rotation, and translation for remote sensing object detection. We call this augmentation strategy NBBOX (Noise Injection into Bounding Box). We conduct extensive experiments on DOTA and DIOR-R, both well-known datasets that include a variety of rotated generic objects in aerial images. Experimental results show that our approach significantly improves remote sensing object detection without whistles and bells and it is more time-efficient than other state-of-the-art augmentation strategies., Comment: Accepted to IEEE Geoscience and Remote Sensing Letters

Recently, numerous methods have achieved impressive performance in remote sensing object detection, relying on convolution or transformer architectures. Such detectors typically have a feature backbone to extract useful features from raw input images. For the remote sensing domain, a common practice among current detectors is to initialize the backbone with pre-training on ImageNet consisting of natural scenes. Fine-tuning the backbone is then typically required to generate features suitable for remote-sensing images. However, this could hinder the extraction of basic visual features in long-term training, thus restricting performance improvement. To mitigate this issue, we propose a novel method named DBF (Dynamic Backbone Freezing) for feature backbone fine-tuning on remote sensing object detection. Our method aims to handle the dilemma of whether the backbone should extract low-level generic features or possess specific knowledge of the remote sensing domain, by introducing a module called 'Freezing Scheduler' to dynamically manage the update of backbone features during training. Extensive experiments on DOTA and DIOR-R show that our approach enables more accurate model learning while substantially reducing computational costs. Our method can be seamlessly adopted without additional effort due to its straightforward design., Comment: Under Review

The temperature-dependent mass of the heavy neutrino can lead to the second leptogenesis occurring below the electroweak scale, potentially explaining the large discrepancy between baryon and lepton asymmetries. We investigate this scenario further, exploring the intricate interplay of the weak interaction processes within this framework. It includes notable shifts in the dominant decay channels of heavy neutrinos around the electroweak symmetry breaking, along with the resonance behavior of the scattering processes near the $W/Z$ mass. The $CP$ asymmetry can also vary over cosmic history due to the temperature-dependent mass, allowing the $B-L$ asymmetry generation to be amplified in the late epoch. These findings elucidate how such alterations in the dynamics of second leptogenesis contribute to addressing the observed discrepancies in baryon-lepton asymmetry., Comment: 29 pages, 4 figures

We propose a novel scenario to explain the matter-antimatter asymmetry by twofold leptogenesis, wherein heavy Majorana neutrinos exhibit temperature-dependent masses and engage in $CP$-violating decays. This scenario envisages two distinct phases of leptogenesis: one occurring above the electroweak scale and the other below it. The sphaleron process converts the first lepton asymmetry to baryon asymmetry, but not the second one due to its decoupling. This mechanism potentially explains the significant discrepancy between baryon and lepton asymmetries, as suggested by recent observations of Helium-4. Furthermore, our model implies that the present masses of Majorana neutrinos are lighter than the electroweak scale, offering a tangible avenue for experimental verification in various terrestrial settings., Comment: 16 pages, 4 figures, 1 table, version accepted by JHEP

Some properties of a neutrino may differ significantly depending on whether it is Dirac or Majorana type. The type is determined by the relative size of Dirac and Majorana masses, which may vary if they arise from an oscillating scalar dark matter. We show that the change can be significant enough to convert the neutrino type between Dirac and Majorana periodically while satisfying constraints on the dark matter. This neutrino type oscillation predicts periodic modulations in the event rates in various neutrino phenomena including the neutrinoless double beta decay. As the energy density and, thus, the oscillation amplitude of the dark matter evolves in the cosmic time scale, the neutrino masses change accordingly, which provides an interesting link between the present-time neutrino physics to the early universe cosmology including the leptogenesis., Comment: Version matching the publication

Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets. However, imbalanced class distributions still limit the wide applicability of these models due to degradation in performance. To solve this problem, in this paper, we concentrate on the study of cross entropy which mostly ignores output scores on incorrect classes. This work discovers that neutralizing predicted probabilities on incorrect classes improves the prediction accuracy for imbalanced image classification. This paper proposes a simple but effective loss named complement cross entropy based on this finding. The proposed loss makes the ground truth class overwhelm the other classes in terms of softmax probability, by neutralizing probabilities of incorrect classes, without additional training procedures. Along with it, this loss facilitates the models to learn key information especially from samples on minority classes. It ensures more accurate and robust classification results on imbalanced distributions. Extensive experiments on imbalanced datasets demonstrate the effectiveness of the proposed method., Comment: 8 pages, Accepted to Pattern Recognition Letters (PRL), August 2021