Introducing dynamical constraints into representation learning

While representation learning has been central to the rise of machine learning and artificial intelligence, a key problem remains in making the learnt representations meaningful. For this the typical approach is to regularize the learned representation through prior probability distributions. However such priors are usually unavailable or ad hoc. To deal with this, we propose a dynamics-constrained representation learning framework. Instead of using predefined probabilities, we restrict the latent representation to follow specific dynamics, which is a more natural constraint for representation learning in dynamical systems. Our belief stems from a fundamental observation in physics that though different systems can have different marginalized probability distributions, they typically obey the same dynamics, such as Newton's and Schrodinger's equations. We validate our framework for different systems including a real-world fluorescent DNA movie dataset. We show that our algorithm can uniquely identify an uncorrelated, isometric and meaningful latent representation

Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.Comment: 6 pages, 5 figure

Effects of Financial and Business Cycles and CEO Characteristics on Firm Risk and Performance

Using a sample of Chinese A share firms from 1991 to 2019, this thesis extends the literature on how business cycles and financial cycle wavelets relate to firm risks and performance after accounting for CEO demographic characteristics. The thesis documents that higher (lower) firm risks are associated economically and statistically with increases (decreases) during business or financial expansions (recessions). These associations also are more pronounced for business recession periods and financial expansion periods. The findings suggest that firms headed by female CEOs are less risk seeking throughout cycle wavelets compared to firms headed by their male counterparts. However, firm risk is higher for firms headed by a female CEO that has obtained a postgraduate degree. The relation between firm risk and performance during changing macro events is less conclusive. These findings potentially provide important implications for understanding business cycles and financial cycles and their effects on the corporate sector in China

A critical review of capillary pressure behavior and characterization in fractional-wet reservoirs

Fractional wettability is common in oil and gas reservoirs, resulting in complex fluid distribution and transport phenomena. A precise understanding of capillary pressure behavior and characterization in fractional-wet reservoirs, including the two-phase flow mechanisms within pores and relationship between capillary pressure and saturation in porous media, is significant to enhanced oil recovery strategies. In this paper, an in-depth review of the two-phase flow mechanisms in fractional-wet pores and capillary entry pressures in various displacement processes was conducted. Furthermore, the effects of oil-wet proportion and contact angle on capillary pressure characterization were summarized, highlighting the emergence of similar capillary pressure curves under conditions of low oil-wet proportions. The prediction models for capillary pressure, containing empirical equations and physics-based models were discussed, with the aim of clarifying the most effective prediction methodologies. Finally, the review was finalized by outlining key findings and future directions for both experimental and theoretical studies in the realm of capillary pressure behavior and characterization.Document Type: Invited reviewCited as: Xiao, Y., You, Z., Wang, L., Du, Z. A critical review of capillary pressure behavior and characterization in fractional-wet reservoirs. Capillarity, 2024, 10(1): 12-21. https://doi.org/10.46690/capi.2024.01.0