Is the Market Smart Enough to Identify Superior Analysts and Follow their Recommendations?

In this article we investigate whether there is persistency in analysts forecasting ability and if it exists, whether the market has the ability to identify these differences in abilities. Our results reveal that the forecasting ability of analysts is persistent. We then investigate whether investors identify superior analysts’ ability by analyzing market reaction to their recommendations compared to the reaction to other analysts. Our findings suggest that the twoday returns after the analysts’ reports’ are strongly positively correlated with analysts’ recommendations and there is a significant difference in reaction between high and low quality analysts. We conclude that the market is smart enough to identify different types of analysts and follow their recommendations respectively

Phases of the infinite U Hubbard model

We apply the density matrix renormalization group (DMRG) to study the phase diagram of the infinite U Hubbard model on 2-, 4-, and 6-leg ladders. Where the results are largely insensitive to the ladder width, we consider the results representative of the 2D square lattice model. We find a fully polarized ferromagnetic Fermi liquid phase when n, the density of electrons per site, is in the range 1>n>n_F ~ 4/5. For n=3/4 we find an unexpected commensurate insulating "checkerboard" phase with coexisting bond density order with 4 sites per unit cell and block spin antiferromagnetic order with 8 sites per unit cell. For 3/4 > n, the wider ladders have unpolarized groundstates, which is suggestive that the same is true in 2D

Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

As a fundamental aspect of human life, two-person interactions contain meaningful information about people's activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this paper, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches across all standard evaluation settings