Testing leptogenesis at the LHC and future muon colliders: a Z′Z' scenario

If the masses of at least two generations of right-handed neutrinos (RHNs) are near-degenerate, the scale of leptogenesis can be as low as $\sim$ 100 GeV. In this work, we study probing such resonant leptogenesis in the $B-L$ model at the LHC and future multi-TeV muon colliders via the process $Z'\to NN\to\ell^\pm\ell^\pm+{\rm jets}$, with $Z'$ the $U(1)_{B-L}$ gauge boson and $N$ the RHN. The same-sign dilepton feature of the signal makes it almost background-free, while the event number difference between positive and negative leptons is a hint for $CP$ violation, which is a key ingredient of leptogenesis. We found that resonant leptogenesis can be tested at the HL-LHC for $M_{Z'}$ up to 12 TeV, while at a 10 (30) TeV muon collider the reach can be up to $M_{Z'}\sim28~(100)$ TeV via the off-shell production of $Z'$.Comment: 11 pages + references, 4 figures, 2 tables. To match the PRD versio

An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks

The activated scaling behavior of quantum Griffiths singularity in two-dimensional superconductors

Quantum Griffiths singularity is characterized by the divergence of the dynamical critical exponent with the activated scaling law and has been widely observed in various two-dimensional superconductors. Recently, the direct activated scaling analysis with the irrelevant correction has been proposed and successfully used to analyze the experimental data of crystalline PdTe2 and polycrystalline \b{eta}-W films, which provides new evidence of quantum Griffiths singularity. Here we show that the direct activated scaling analysis is applicable to the experimental data in different superconducting films, including tri-layer Ga films and LaAlO3/SrTiO3 interface superconductor. When taking the irrelevant correction into account, we calculate the corrected sheet resistance at ultralow temperatures. The scaling behavior of the corrected resistance in a comparably large temperature regime and the theoretical fitting of the phase boundary give unambiguous evidence of quantum Griffiths singularity. Compared to the previous method based on the finite size scaling, the direct activated scaling analysis represents a more direct and precise way to analyze the experimental data of quantum Griffiths singularity in diverse two-dimensional superconductors

A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔT$_{sb}$) on the daily mean LST (T$_{dm}$) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the T$_{dm}$. Several methods have been proposed for the estimation of the T$_{dm}$, yet they are becoming less capable of generating spatiotemporally seamless T$_{dm}$ across the globe. Based on MODIS and reanalysis data, here we propose an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless T$_{dm}$ products ranging from 2003 to 2019 (named the GADTC products). The validations show that the IADTC framework reduces the systematic ΔT$_{sb}$ significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and FLUXNET data, respectively, and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are −1.6 and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the T$_{dm}$ estimated with the traditional averaging method yields a positive systematic ΔT$_{sb}$ of greater than 2.0 K in low-latitude and midlatitude regions while of a relatively small value in high-latitude regions. Although the global-mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K yr$^{–1}$), regional discrepancies in LST trend do occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr$^{–1}$. We consider the IADTC framework can guide the further optimization of T$_{dm}$ estimation across the globe, and the generated GADTC products should be valuable in various applications such as global and regional warming analysis

Supervised Learning in Spiking Neural Networks with Synaptic Delay-Weight Plasticity

Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways

Engaging stakeholders to level up COPD care in LMICs:lessons learned from the "Breathe Well" programme in Brazil, China, Georgia, and North Macedonia

BACKGROUND: Effective stakeholder engagement in health research is increasingly being recognised and promoted as an important pathway to closing the gap between knowledge production and its use in health systems. However, little is known about its process and impacts, particularly in low-and middle-income countries. This opinion piece draws on the stakeholder engagement experiences from a global health research programme on Chronic Obstructive Pulmonary Disease (COPD) led by clinician researchers in Brazil, China, Georgia and North Macedonia, and presents the process, outcomes and lessons learned.MAIN BODY: Each country team was supported with an overarching engagement protocol and mentored to develop a tailored plan. Patient involvement in research was previously limited in all countries, requiring intensive efforts through personal communication, meetings, advisory groups and social media. Accredited training programmes were effective incentives for participation from healthcare providers; and aligning research findings with competing policy priorities enabled interest and dialogue with decision-makers. The COVID-19 pandemic severely limited possibilities for planned engagement, although remote methods were used where possible. Planned and persistent engagement contributed to shared knowledge and commitment to change, including raised patient and public awareness about COPD, improved skills and practice of healthcare providers, increased interest and support from clinical leaders, and dialogue for integrating COPD services into national policy and practice.CONCLUSION: Stakeholder engagement enabled relevant local actors to produce and utilise knowledge for small wins such as improving day-to-day practice and for long-term goals of equitable access to COPD care. For it to be successful and sustained, stakeholder engagement needs to be valued and integrated throughout the research and knowledge generation process, complete with dedicated resources, contextualised and flexible planning, and commitment.</p