Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

Gene loss and co-option of toll-like receptors facilitate paternal immunological adaptation in the brood pouch of pregnant male seahorses

Male pregnancy in syngnathids (seahorses, pipefishes, and sea dragons) is an evolutionary innovation in the animal kingdom. Paternal immune resistance to the fetus is a critical challenge, particularly in seahorses with fully enclosed brood pouches and sophisticated placentas. In this study, comparative genomic analysis revealed that all syngnathid species lost three vertebrate-conserved Toll-like receptors (TLR1, TLR2, and TLR9), of which all play essential roles in immune protection and immune tolerance in the uterus and placenta. Quantitative real-time PCR (qRT-PCR) analysis showed that the TLR paralog genes including TLR18, TLR25, and TLR21 were highly expressed in the placenta inside the seahorse brood pouch and changed dynamically during the breeding cycle, suggesting the potentially important role of the TLRs during male pregnancy. Furthermore, the immune challenge test in vitro showed a remarkable expression response from all three TLR genes to specific pathogenic antigens, confirming their immune function in seahorse brood pouches. Notably, the altered antigen recognition spectrum of these genes appeared to functionally compensate in part for the lost TLRs, in contrast to that observed in other species. Therefore, we suggest that gene loss and co-option of TLRs may be a typical evolutionary strategy for facilitating paternal immunological adaptation during male pregnancy

XTSFormer: Cross-Temporal-Scale Transformer for Irregular Time Event Prediction

Event prediction aims to forecast the time and type of a future event based on a historical event sequence. Despite its significance, several challenges exist, including the irregularity of time intervals between consecutive events, the existence of cycles, periodicity, and multi-scale event interactions, as well as the high computational costs for long event sequences. Existing neural temporal point processes (TPPs) methods do not capture the multi-scale nature of event interactions, which is common in many real-world applications such as clinical event data. To address these issues, we propose the cross-temporal-scale transformer (XTSFormer), designed specifically for irregularly timed event data. Our model comprises two vital components: a novel Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly captures the cyclical nature of time, and a hierarchical multi-scale temporal attention mechanism. These scales are determined by a bottom-up clustering algorithm. Extensive experiments on several real-world datasets show that our XTSFormer outperforms several baseline methods in prediction performance

Experimental study on pressure relief blasting in rock-blast tunnel

Rock burst is a difficult and urgent problem during the construction process of underground engineering in a zone with high surrounding rock pressure. Pressure relief blasting is one of the most effective methods for solving the problem. An in-situ blasting test was carried out in a tunnel under excavation, and the geology stress (geo-stress) of the surrounding rock was tested before and after the pressure relief blasting. The experimental results will be effective for further study of the law of pressure relief blasting and rock-burst prediction. An explicit stress analytic function is also employed to analyze the stress distribution of the tunnel surrounding rock after pressure relief blasting based on the propagation of the blasting stress wave. Furthermore, the stress attenuation coefficient and the variation of the geo-stress distribution of surrounding rock under the pressure relief blasting were discussed in detail with the in-situ blasting test data. Highlights. The main contribution of this paper is focused on: 1. This study measures the in-situ geo-stress of surrounding rock in the ‘Sang zhu ling’ tunnel, both before and after the pressure relief blasting. This data is crucial for ensuring the safety resilience of urban infrastructure. 2. A two-dimensional explicit equation of stress attenuation was derived based on the characteristics of stress propagation, providing a more accurate analytical tool for engineers and researchers, 3. The traditionally obtained stress attenuation coefficient is shown to require modification through further in-situ experiments. Future studies will refine this coefficient, contributing to the development of more reliable methods for safeguarding urban lifelines. These contributions align with the journal’s focus on advancing urban science and engineering by promoting innovative, data-driven solutions for urban safety.This article is published as Chen, ZhenLin, Wenjing Cai, Meng Wei, Cassandra J. Rutherford, Xiao Zhu, Zelin Wang, and Tao Su. "Experimental study on pressure relief blasting in rock-blast tunnel." Urban Lifeline 3, no. 1 (2025): 3. doi: https://doi.org/10.1007/s44285-024-00036-x

JUNO Sensitivity to Invisible Decay Modes of Neutrons

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $n \rightarrow { inv}$ and $nn \rightarrow { inv}$. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$.Comment: 28 pages, 7 figures, 4 table

A ‘Third Culture’ in Economics? An Essay on Smith, Confucius and the Rise of China

China's rise drives a growing impact of China on economics. So far, this mainly works via the force of example, but there is also an emerging role of Chinese thinking in economics. This paper raises the question how far Chinese perspectives can affect certain foundational principles in economics, such as the assumptions on individualism and self-interest allegedly originating in Adam Smith. I embark on sketching a 'third culture' in economics, employing a notion from cross-cultural communication theory, which starts out from the observation that the Chinese model was already influential during the European enlightenment, especially on physiocracy, suggesting a particular conceptualization of the relation between good government and a liberal market economy. I relate this observation with the current revisionist view on China's economic history which has revealed the strong role of markets in the context of informal institutions, and thereby explains the strong performance of the Chinese economy in pre-industrial times. I sketch the cultural legacy of this pattern for traditional Chinese conceptions of social interaction and behavior, which are still strong in rural society until today. These different strands of argument are woven together in a comparison between Confucian thinking and Adam Smith, especially with regard to the 'Theory of Moral Sentiments', which ends up in identifying a number of conceptual family resemblances between the two. I conclude with sketching a 'third culture' in economics in which moral aspects of economic action loom large, as well as contextualized thinking in economic policies