Neutrino mu-tau reflection symmetry and its breaking in the minimal seesaw

In this paper, we attempt to implement the neutrino $\mu$-$\tau$ reflection symmetry (which predicts $\theta^{}_{23} = \pi/4$ and $\delta = \pm \pi/2$ as well as trivial Majorana phases) in the minimal seesaw (which enables us to fix the neutrino masses). For some direct (the preliminary experimental hints towards $\theta^{}_{23} \neq \pi/4$ and $\delta \neq - \pi/2$) and indirect (inclusion of the renormalization group equation effect and implementation of the leptogenesis mechanism) reasons, we particularly study the breakings of this symmetry and their phenomenological consequences.Comment: 20 pages, 7 figures, accepted for publication in JHE

Selective Refinement Network for High Performance Face Detection

High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.Comment: The first two authors have equal contributions. Corresponding author: Shifeng Zhang ([email protected]

Relational Learning for Joint Head and Human Detection

Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without considering their inherent correlation, leading to that 1) head detection is often trapped in more false positives, and 2) the performance of human detector frequently drops dramatically in crowd scenes. To handle these two issues, we present a novel joint head and human detection network, namely JointDet, which effectively detects head and human body simultaneously. Moreover, we design a head-body relationship discriminating module to perform relational learning between heads and human bodies, and leverage this learned relationship to regain the suppressed human detections and reduce head false positives. To verify the effectiveness of the proposed method, we annotate head bounding boxes of the CityPersons and Caltech-USA datasets, and conduct extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets. As a consequence, the proposed JointDet detector achieves state-of-the-art performance on these three benchmarks. To facilitate further studies on the head and human detection problem, all new annotations, source codes and trained models will be public

catena-Poly[[triaqua­(pyridine-κN)nickel(II)]-μ-sulfato-κ2 O:O′]

The title compound, [Ni(SO4)(C5H5N)(H2O)3]n, was synthesized by the hydro­thermal reaction of NiSO4·6H2O, pyridine and water. The central NiII atom is coordinated in a distorted octa­hedral environment by a pyridine N atom, three aqua O atoms and two O atoms of bridging sulfate anions, yielding a zigzag chain. A three-dimensional network is generated via complex hydrogen bonds involving the sulfate and aqua ligands and a pyridine C—H group

Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation

Recently, an audio-visual segmentation (AVS) task has been introduced, aiming to group pixels with sounding objects within a given video. This task necessitates a first-ever audio-driven pixel-level understanding of the scene, posing significant challenges. In this paper, we propose an innovative audio-visual transformer framework, termed COMBO, an acronym for COoperation of Multi-order Bilateral relatiOns. For the first time, our framework explores three types of bilateral entanglements within AVS: pixel entanglement, modality entanglement, and temporal entanglement. Regarding pixel entanglement, we employ a Siam-Encoder Module (SEM) that leverages prior knowledge to generate more precise visual features from the foundational model. For modality entanglement, we design a Bilateral-Fusion Module (BFM), enabling COMBO to align corresponding visual and auditory signals bi-directionally. As for temporal entanglement, we introduce an innovative adaptive inter-frame consistency loss according to the inherent rules of temporal. Comprehensive experiments and ablation studies on AVSBench-object (84.7 mIoU on S4, 59.2 mIou on MS3) and AVSBench-semantic (42.1 mIoU on AVSS) datasets demonstrate that COMBO surpasses previous state-of-the-art methods. Code and more results will be publicly available at https://yannqi.github.io/AVS-COMBO/.Comment: CVPR 2024 Highlight. 13 pages, 10 figure

MicroRNA-148b is frequently down-regulated in gastric cancer and acts as a tumor suppressor by inhibiting cell proliferation

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are involved in cancer development and progression, acting as tumor suppressors or oncogenes. Our previous studies have revealed that miR-148a and miR-152 are significantly down-regulated in gastrointestinal cancers. Interestingly, miR-148b has the same "seed sequences" as miR-148a and miR-152. Although aberrant expression of miR-148b has been observed in several types of cancer, its pathophysiologic role and relevance to tumorigenesis are still largely unknown. The purpose of this study was to elucidate the molecular mechanisms by which miR-148b acts as a tumor suppressor in gastric cancer.</p> <p>Results</p> <p>We showed significant down-regulation of miR-148b in 106 gastric cancer tissues and four gastric cancer cell lines, compared with their non-tumor counterparts by real-time RT-PCR. <it>In situ </it>hybridization of ten cases confirmed an overt decrease in the level of miR-148b in gastric cancer tissues. Moreover, the expression of miR-148b was demonstrated to be associated with tumor size (P = 0.027) by a Mann-Whitney U test. We also found that miR-148b could inhibit cell proliferation <it>in vitro </it>by MTT assay, growth curves and an anchorage-independent growth assay in MGC-803, SGC-7901, BGC-823 and AGS cells. An experiment in nude mice revealed that miR-148b could suppress tumorigenicity <it>in vivo</it>. Using a luciferase activity assay and western blot, CCKBR was identified as a target of miR-148b in cells. Moreover, an obvious inverse correlation was observed between the expression of CCKBR protein and miR-148b in 49 pairs of tissues (P = 0.002, Spearman's correlation).</p> <p>Conclusions</p> <p>These findings provide important evidence that miR-148b targets CCKBR and is significant in suppressing gastric cancer cell growth. Maybe miR-148b would become a potential biomarker and therapeutic target against gastric cancer.</p