335 research outputs found
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:Â 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
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