科研成果 by Type: Conference Paper

This paper examines a Sinopean victory list of the boxer Marcus Iutius Marcianus Rufus (French 2004: 76-77 no. 105) and the implications of counting the number of victories he won. Inscribed and set up by the Sinopean boule, the list represents an official recognition of the athlete's successful boxing career, which not only included victories in the four periodoi of mainland Greece, but also the Capitoline and Neapolitan games in Italy. The text has been studied by Theodoré Reinach (1916), George Bean (1953), and David French (2004), and resulting in different ways to count Rufus' victories.The three epigraphists encountered several issues with counting Rufus' victories. How to differentiate between a Bithynian koinon event from a metropolitan event held by Nicaea and Nicomedia is one issue, and whether to count the half-talent victories with the iselastic victories so to fit an ideal number of total victories that Rufus won is another, with the three epigraphists producing different solutions. Perhaps more perplexing of all, however, is how to interpret the Greek letters ΡΝ placed at the end of the victory list. Reinach interpreted them as the remaining letters of ἀνδριατί or "jeux mineurs" (Reinach 1916: 358). Bean and French saw them as Greek numerals, indicating the total tally of all listed victories. While the total tally seems a convincing interpretation on formulaic grounds, the arithmetic does not add up. On the one hand, tabulation indicates that Bean's count of total victories yields 159, with 110 half-talent victories and 49 iselastic victories. He reconciled the number by claiming to have seen signs of reinscribing in the squeeze, and suggested that Rufus initially won 101 half-talent victories, only to have achieved 110 at a later time, upon which occasion an update was applied to his monument (Bean 1953: 176). On the other hand, while French counted the half-talent victories as 110, and his total number of iselastic victories amount to 48, he still maintained that ΡΝ stands for "(In all) 150 victories," leaving the arithmetic issue open for further examination (French 2004: 77).This paper surveys other victory lists to study how koinon and metropolitan victories were differentiated and counted, and how chronographic features were positioned and identified. This paper also proposes to disassociate the number 150 from the total count of victories, and reconsider what was signified by this number. One possibility is the era: the 150th year of the era of Sinope. It has been demonstrated that Sinopean coinage during the imperial period used first the colonial era from 45 BCE, then the so-called Lucullan era of 70 BCE (Leschhorn 1993: 161-162). While era-based chronography is not found on extant imperial period inscriptions from Sinope, Rufus' victory list may be the first surviving example.

In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and cognitive consistency theory, we propose a decentralized controller without the knowledge of the dynamics of the fish-like robots. The proposed controller can be transferred from simulation to reality. It is only trained in our established simulation environment, and the trained controller can be deployed to real robots without any manual tuning. Simulation results confirm that the proposed model-free robust formation control method is scalable with respect to the group size of the robots and outperforms other representative RL algorithms. Several experiments in the real world verify the effectiveness of our RL-based approach for circle formation control.

Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Considering the huge success of ResNet in deep learning, it would be natural to train deep SNNs with residual learning. Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning. In this paper, we propose the spike-element-wise (SEW) ResNet to realize residual learning in deep SNNs. We prove that the SEW ResNet can easily implement identity mapping and overcome the vanishing/exploding gradient problems of Spiking ResNet. We evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in both accuracy and time-steps. Moreover, SEW ResNet can achieve higher performance by simply adding more layers, providing a simple method to train deep SNNs. To our best knowledge, this is the first time that directly training deep SNNs with more than 100 layers becomes possible. Our codes are available at https://github.com/fangwei123456/Spike-Element-Wise-ResNet.

With frame-based cameras, capturing fast-moving scenes without suffering from blur often comes at the cost of low SNR and low contrast. Worse still, the photometric constancy that enhancement techniques heavily relied on is fragile for frames with short exposure. Event cameras can record brightness changes at an extremely high temporal resolution. For low-light videos, event data are not only suitable to help capture temporal correspondences but also provide alternative observations in the form of intensity ratios between consecutive frames and exposure-invariant information. Motivated by this, we propose a low-light video enhancement method with hybrid inputs of events and frames. Specifically, a neural network is trained to establish spatiotemporal coherence between visual signals with different modalities and resolutions by constructing correlation volume across space and time. Experimental results on synthetic and real data demonstrate the superiority of the proposed method compared to the state-of-the-art methods.