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) $N$ and lepton number violation (LNV) signature through VBS at high-energy muon colliders. VBS induces LNV processes $W^\pm Z/\gamma\to \ell^\pm N \to \ell^\pm \ell^\pm W^\mp\to \ell^\pm \ell^\pm q\bar{q}'$ with an on-shell HNL $N$ at $\mu^+\mu^-$ colliders. In analogy to neutrinoless double-beta decay with the HNL in t-channel, the LNV signature $W^+W^+\to \ell^+\ell^+$ 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 $\mu\mu$ annihilation. We analyze the potential of searching for Majorana HNL and obtain the exclusion limits on mixing $V_{\ell N}$. 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 FePS3_3 under high pressure

Recent experimental work shows that FePS$_3$ undergoes phase transitions from $C2/m$ ($\beta\sim107^{\circ}$) to $C2/m$ ($\beta\sim90^{\circ}$) at $6$ GPa and then to metallic $P\bar{3}1m$ at $14$ GPa, with the magnetic ordering wave vector turning from $k=(01\frac{1}{2})$ to $k=(010)$ at $2$ GPa and to short-range magnetic order accompanying the insulator-metal transition. By preserving the magnetic point groups in $ab \ initio$ calculations we report the following: (1) We successfully reproduce the first magnetic structure transition at $1.2$ 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 $0.5^\circ$ through the $[001]$ axis before the neighboring layers shift. (3) The shift between neighboring layers is predicted to occur at $10.0$ GPa and reverses the energy order between $d_{x^2-y^2}$ and $d_{xy}$. (4) A sudden decrease of Fe-S bond length to $2.20$ \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

Training a General Spiking Neural Network with Improved Efficiency and Minimum Latency

Spiking Neural Networks (SNNs) that operate in an event-driven manner and employ binary spike representation have recently emerged as promising candidates for energy-efficient computing. However, a cost bottleneck arises in obtaining high-performance SNNs: training a SNN model requires a large number of time steps in addition to the usual learning iterations, hence this limits their energy efficiency. This paper proposes a general training framework that enhances feature learning and activation efficiency within a limited time step, providing a new solution for more energy-efficient SNNs. Our framework allows SNN neurons to learn robust spike feature from different receptive fields and update neuron states by utilizing both current stimuli and recurrence information transmitted from other neurons. This setting continuously complements information within a single time step. Additionally, we propose a projection function to merge these two stimuli to smoothly optimize neuron weights (spike firing threshold and activation). We evaluate the proposal for both convolution and recurrent models. Our experimental results indicate state-of-the-art visual classification tasks, including CIFAR10, CIFAR100, and TinyImageNet.Our framework achieves 72.41% and 72.31% top-1 accuracy with only 1 time step on CIFAR100 for CNNs and RNNs, respectively. Our method reduces 10x and 3x joule energy than a standard ANN and SNN, respectively, on CIFAR10, without additional time steps.Comment: Accepted by ACML 202

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 $g-2$ precision measurement at Fermilab. We consider the most general SM gauge invariant Lagrangian of $\Delta L=0$ 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 $pp\to\gamma^*/Z^*\to \ell_1^\pm \ell_1^\pm \ell_2^\mp \ell_2^\mp$.Comment: 21 pages, 4 figures, 4 tables. version accepted by EPJ

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

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

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