1,661 research outputs found
Searching for heavy neutral lepton and lepton number violation through VBS at high-energy muon colliders
High-energy muon collider can play as an emitter of electroweak gauge bosons
and thus leads to substantial vector boson scattering (VBS) processes. In this
work, we investigate the production of heavy neutral lepton (HNL) and
lepton number violation (LNV) signature through VBS at high-energy muon
colliders. VBS induces LNV processes with an on-shell HNL at
colliders. In analogy to neutrinoless double-beta decay with the
HNL in t-channel, the LNV signature can also happen
via VBS at same-sign muon collider. They provide clean and robust LNV
signatures to tell the nature of Majorana HNLs and thus have more advantageous
benefits than direct annihilation. We analyze the potential of
searching for Majorana HNL and obtain the exclusion limits on mixing . Based on this same-sign lepton signature, we also obtain the sensitivity
of muon collider to the Weinberg operator.Comment: 24 pages, 8 figures, 2 tables. Accepted for publication in JHE
An \textit{ab initio} study of magnetic structure transitions of FePS under high pressure
Recent experimental work shows that FePS undergoes phase transitions from
() to () at GPa
and then to metallic at GPa, with the magnetic ordering wave
vector turning from to at GPa and to
short-range magnetic order accompanying the insulator-metal transition. By
preserving the magnetic point groups in calculations we report
the following: (1) We successfully reproduce the first magnetic structure
transition at GPa and briefly discuss the influence of the Hubbard U
parameter on this transition. This isostructural transition causes a change of
the Brillouin zone from base-centered monoclinic to primitive monoclinic, and
an indrect band gap to direct band gap transition. (2) There is a rotation of
the Fe-S octahedron about through the axis before the
neighboring layers shift. (3) The shift between neighboring layers is predicted
to occur at GPa and reverses the energy order between and
. (4) A sudden decrease of Fe-S bond length to \AA \ accompanies
the vanishing of magnetic moment in the insulator-metal transition. Our work
shows the importance of symmetries of magnetic structures in pressure-induced
phase transition of magnetic systems
分析林夕歌詞中的佛家思想及其創作風格
林夕,文字工作者,亦為當代香港著名填詞人,填詞作品超過三千首,填有不少膾炙人口的作品。林夕信佛,在其歌詞中不難發現有佛理的蹤影。一直以來學界對於歌詞的研究不多,關於林夕詞作的研究,尤其針對其佛理歌詞作研究更是寥寥可數。因此本文以林夕歌詞為主題探討其中的佛家思想及創作風格。
本文第一章為緒論,交代研究緣起、有關林夕的研究狀況以及本文研究方法。此章簡單交代歌詞的文學性,香港流行曲歌詞以及林夕的背景,用以論述研究林夕佛理歌詞的意義。第二章以香港其他著名填詞人和林夕比對,分析當中異同,從而推論林夕歌詞的創作風格,尤其佛理風格的形成。第三章承接上章,主要分析林夕三個時期(「棉裡藏針」、「大張旗鼓」、「大象無形」)的佛理歌詞帶有什麼的佛家思想內涵以及風格如何轉變。第四章是以互文性的角度論林夕如何在歌詞帶出佛家思想,並加以印證其風格層次及變化。第五章為結論,整合全文總結
Charged lepton flavor violation in light of the muon magnetic moment anomaly and colliders
Any observation of charged lepton flavor violation (CLFV) implies the
existence of new physics beyond the SM in charged lepton sector. CLFV
interactions may also contribute to the muon magnetic moment and explain the
discrepancy between the SM prediction and the recent muon precision
measurement at Fermilab. We consider the most general SM gauge invariant
Lagrangian of bileptons with CLFV couplings and investigate the
interplay of low-energy precision experiments and colliders in light of the
muon magnetic moment anomaly. We go beyond previous work by demonstrating the
sensitivity of the LHC, the MACE experiment, a proposed muonium-antimuonium
conversion experiment, and a muon collider. Currently-available LHC data is
already able to probe unexplored parameter space via the CLFV process
.Comment: 21 pages, 4 figures, 4 tables. version accepted by EPJ
Distributed human computation framework for linked data co-reference resolution
Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud
Quantification of substance P mRNA expression in the midbrain of ovariectomized migraine rats with SYBR green I real-time polymerase chain reaction
This study was designed to develop a SYBR green I-based real-time polymerase chain reaction (RTPCR) for quantitative detection of substance P (SP) mRNA in the midbrain of ovariectomized migraine rats and to evaluate the effects of estradiol on the mRNA expression of SP in order to shed light on the mechanisms underlying the pathogenesis of migraine and estrogen-conferred protection against migraine. 24 female rats were randomly assigned to the following groups: A: non-migraine controls; B: migraines; C, migraine rats receiving low estradiol; D, migraine rats receiving high estradiol. One week following ovariectomy, migraine was induced in groups B, C and D by nitroglycerin (i.p.). Behavior changes before and after migraine was examined. A SYBR green I-based RT-PCR assay was established to measure the absolute levels of SP mRNA in the midbrain. Behavioral changes in group D were significantly mitigated when compared with those in group B, whereas no marked behavioral changes were noted in groups C and B. In addition, mRNA copies of SP in group B were remarkably lower than group A, while the level of SP mRNA in both groups C and D was higher than group B, although no significance was reached (P > 0.05). SP mRNA expression decreased in the midbrain of migraine rats when compared with the non-migraine controls. High doses of estrogen partially restored SP expression in migraine rats and reduce migraine attack. Our study validated the SYBR green I-based RT-PCR technique for quantitative detection of SP mRNA.Keywords: Substance P, migraine, estrogen, midbrain, real-time quantitative polymerase chain reaction, ratsAfrican Journal of Biotechnology Vol. 9(34), pp. 5481-5487, 23 August, 201
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network
Event cameras are considered to have great potential for computer vision and
robotics applications because of their high temporal resolution and low power
consumption characteristics. However, the event stream output from event
cameras has asynchronous, sparse characteristics that existing computer vision
algorithms cannot handle. Spiking neural network is a novel event-based
computational paradigm that is considered to be well suited for processing
event camera tasks. However, direct training of deep SNNs suffers from
degradation problems. This work addresses these problems by proposing a spiking
neural network architecture with a novel residual block designed and
multi-dimension attention modules combined, focusing on the problem of depth
prediction. In addition, a novel event stream representation method is
explicitly proposed for SNNs. This model outperforms previous ANN networks of
the same size on the MVSEC dataset and shows great computational efficiency
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by
knowledge transferred from the seen domain, relying on the intrinsic
interactions between visual and semantic information. Prior works mainly
localize regions corresponding to the sharing attributes. When various visual
appearances correspond to the same attribute, the sharing attributes inevitably
introduce semantic ambiguity, hampering the exploration of accurate
semantic-visual interactions. In this paper, we deploy the dual semantic-visual
transformer module (DSVTM) to progressively model the correspondences between
attribute prototypes and visual features, constituting a progressive
semantic-visual mutual adaption (PSVMA) network for semantic disambiguation and
knowledge transferability improvement. Specifically, DSVTM devises an
instance-motivated semantic encoder that learns instance-centric prototypes to
adapt to different images, enabling the recast of the unmatched semantic-visual
pair into the matched one. Then, a semantic-motivated instance decoder
strengthens accurate cross-domain interactions between the matched pair for
semantic-related instance adaption, encouraging the generation of unambiguous
visual representations. Moreover, to mitigate the bias towards seen classes in
GZSL, a debiasing loss is proposed to pursue response consistency between seen
and unseen predictions. The PSVMA consistently yields superior performances
against other state-of-the-art methods. Code will be available at:
https://github.com/ManLiuCoder/PSVMA.Comment: Accepted by CVPR202
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