Spectral Representation Learning for Conditional Moment Models

Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of conditional moment restrictions. For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hypothesis space, which are hard to validate when flexible models are used. In this work, we address this issue by proposing a procedure that automatically learns representations with controlled measures of ill-posedness. Our method approximates a linear representation defined by the spectral decomposition of a conditional expectation operator, which can be used for kernelized estimators and is known to facilitate minimax optimal estimation in certain settings. We show this representation can be efficiently estimated from data, and establish L2 consistency for the resulting estimator. We evaluate the proposed method on proximal causal inference tasks, exhibiting promising performance on high-dimensional, semi-synthetic data

Relational structure-aware knowledge graph representation in complex space

Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Characterization and Expression of Glutamate Dehydrogenase in Response to Acute Salinity Stress in the Chinese Mitten Crab, Eriocheir sinensis

Glutamate dehydrogenase (GDH) is a key enzyme for the synthesis and catabolism of glutamic acid, proline and alanine, which are important osmolytes in aquatic animals. However, the response of GDH gene expression to salinity alterations has not yet been determined in macro-crustacean species.GDH cDNA was isolated from Eriocheir sinensis. Then, GDH gene expression was analyzed in different tissues from normal crabs and the muscle of crabs following transfer from freshwater (control) directly to water with salinities of 16‰ and 30‰, respectively. Full-length GDH cDNA is 2,349 bp, consisting of a 76 bp 5'- untranslated region, a 1,695 bp open reading frame encoding 564 amino acids and a 578 bp 3'- untranslated region. E. sinensis GDH showed 64-90% identity with protein sequences of mammalian and crustacean species. Muscle was the dominant expression source among all tissues tested. Compared with the control, GDH expression significantly increased at 6 h in crabs transferred to 16‰ and 30‰ salinity, and GDH expression peaked at 48 h and 12 h, respectively, with levels approximately 7.9 and 8.5 fold higher than the control. The free amino acid (FAA) changes in muscle, under acute salinity stress (16‰ and 30‰ salinities), correlated with GDH expression levels. Total FAA content in the muscle, which was based on specific changes in arginine, proline, glycine, alanine, taurine, serine and glutamic acid, tended to increase in crabs following transfer to salt water. Among these, arginine, proline and alanine increased significantly during salinity acclimation and accounted for the highest proportion of total FAA.E. sinensis GDH is a conserved protein that serves important functions in controlling osmoregulation. We observed that higher GDH expression after ambient salinity increase led to higher FAA metabolism, especially the synthesis of glutamic acid, which increased the synthesis of proline and alanine to meet the demand of osmoregulation at hyperosmotic conditions

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods—Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses