Maolin Zheng

Zehui Zhao, Yuxuan Zhao, Gaojing Zhang et al.

Human demonstrations, which can be collected at scale and naturally capture active hand-eye coordination, are a promising data source for learning humanoid loco-manipulation. However, directly transferring human demonstrations to humanoids requires a precise world-frame tracking controller, which is often brittle under Out-of-Distribution(OOD) targets, while human-to-humanoid gaps persist in both egocentric observation and action execution. To address these challenges, we present HALOMI, a scalable framework for learning humanoid loco-manipulation with active perception from human demonstrations. HALOMI extends Universal Manipulation Interface (UMI) with egocentric sensing to collect ego-view and wrist-view observations along with head-hand trajectories at scale. We further propose a manifold-constrained controller that plans in a learned latent behavior manifold to enable precise and robust head-hand tracking in the world frame. To bridge the human-to-humanoid gap, we perform ego-view alignment and introduce a controller-aware reference trajectory adaptation to reduce mismatch in both observation and action execution. We validate HALOMI on a Unitree G1 humanoid robot with an actuated neck across five real-world tasks involving navigation, grasping, bimanual manipulation, whole-body coordination, and dynamic behaviors. Across the three quantitatively evaluated tasks, HALOMI achieves an average success rate of 85\%, while additional qualitative demonstrations show its ability to support dynamic tossing and deep-squat grasping.

Luke Zeng, Shawn Xin, Avadesian Xu et al.

In this paper, we will present a new PoW consensus algorithm used in Seele's main-net, MPoW (Matrix-Proof-of-Work). Compared to Bitcoin's PoW consensus algorithm, MPoW requires miners to compute the determinants of submatrices from a matrix constructed with n hashes other than brute-force-hashing using a hash function to find the target. This paper will evaluate this algorithm's compatibility with difficulty adjustment. Then we will discuss its efficiency in countering machines with hashrate advantage, and its feasibility to personal computers. We believe more innovative consensus protocols can be developed based on this algorithm.

Wei Bi, Xiaoyun Jia, Maolin Zheng

Most cryptocurrency systems or systems based on blockchain technology are currently using the elliptic curves digital signature algorithm (ECDSA) on the secp256k1 curve, which is susceptible to backdoors implemented by the curve creator (secp256k1). The paper proposes a multiple elliptic curves digi-tal signature algorithm (MECDSA), which allows not only for setting the number of elliptic curves according to practical security requirements, but also for editing the parameters of each elliptic curve. The performance analy-sis proves that the scheme is secure and efficient, and can avoid any back-doors implemented by curve creators. We suggest that the systems based on blockchain should operate in two elliptic curves considering the contradic-tion between security and efficiency.