Greening Your Way to Profits: Green Strategies and Green Revenues

We examine hot-debated but underexplored questions of whether and how green strategies affect corporate green revenues. Using a generalized Difference-in-Differences (DiD) framework, we find that green strategies significantly enhance corporate green revenues in the presence of China's Emission Trading Scheme (ETS) pilot. This is consistent with the Porter Hypothesis. Our mechanism analyses document that green strategies increase green revenues by improving green quality and catalyzing environmentally friendly transformation. This study has important implications for policymakers and practitioners, offering new insights into the intended consequences and real outcomes of environmental regulations

Generalizing Graph ODE for Learning Complex System Dynamics across Environments

Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built to learn single system dynamics from observed historical data and predict the future trajectory. In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity. One simple solution is to learn multiple environment-specific models, but it fails to exploit the potential commonalities among the dynamics across environments and offers poor prediction results where per-environment data is sparse or limited. Here, we present GG-ODE (Generalized Graph Ordinary Differential Equations), a machine learning framework for learning continuous multi-agent system dynamics across environments. Our model learns system dynamics using neural ordinary differential equations (ODE) parameterized by Graph Neural Networks (GNNs) to capture the continuous interaction among agents. We achieve the model generalization by assuming the dynamics across different environments are governed by common physics laws that can be captured via learning a shared ODE function. The distinct latent exogenous factors learned for each environment are incorporated into the ODE function to account for their differences. To improve model performance, we additionally design two regularization losses to (1) enforce the orthogonality between the learned initial states and exogenous factors via mutual information minimization; and (2) reduce the temporal variance of learned exogenous factors within the same system via contrastive learning. Experiments over various physical simulations show that our model can accurately predict system dynamics, especially in the long range, and can generalize well to new systems with few observations

MedChatZH: a Better Medical Adviser Learns from Better Instructions

Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these models may perform unsatisfactorily without fine-tuning on domain-specific datasets. To address this, we introduce MedChatZH, a dialogue model designed specifically for traditional Chinese medical QA. Our model is pre-trained on Chinese traditional medical books and fine-tuned with a carefully curated medical instruction dataset. It outperforms several solid baselines on a real-world medical dialogue dataset. We release our model, code, and dataset on https://github.com/tyang816/MedChatZH to facilitate further research in the domain of traditional Chinese medicine and LLMs.Comment: 7 pages, 3 figure

Determination of chlorantraniliprole and penoxsulam residues in rice by QuEChERS-UPLC-MS/MS

Objective: To establish a new QuEChERS-ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the detection of chlorantraniliprole and penoxsulam residues in rice. Methods: After the sample was extracted with 0.2% formic acid-acetonitrile, purified with N-propylethylenediamine (PSA) and graphitized carbon black (GCB) packing, 0.2% formic acid water and acetonitrile were used as mobile phases for gradient elution, and then subjected to C18 chromatography. UPLC-MS/MS was used to column separation and determination. Results: The limits of quantification (LOQ) of chlorantraniliprole and penoxsulam were both 0.004 mg/kg, and the method detection limits (LOD) were both 0.001 mg/kg. Chlorantraniliprole and penoxsulam had a good linear relationship in the range of 0.002~0.5 mg/L, and their coefficient was greater than 0.993. At the addition levels of 0.05, 0.1, and 0.5 mg/kg, the average recovery rates of chlorantraniliprole and penoxsulam were 86% to 110% with the relative standard deviations (RSD) of 1.5% to 6.1%. Conclusion: This method is efficient and simple, has good stability and high sensitivity, and is suitable for the detection of chlorantraniliprole and penoxsulam in rice

Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks

Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by collaborative filtering approaches, i.e. if users co-engage with a pair of entities frequently enough, the embeddings should be similar. However, relying on co-engagement data alone can result in lower-quality embeddings for new and unpopular entities. We study this problem in the context recommender systems at Netflix. We observe that there is abundant semantic information such as genre, content maturity level, themes, etc. that complements co-engagement signals and provides interpretability in similarity models. To learn entity similarities from both data sources holistically, we propose a novel graph-based approach called SemanticGNN. SemanticGNN models entities, semantic concepts, collaborative edges, and semantic edges within a large-scale knowledge graph and conducts representation learning over it. Our key technical contributions are twofold: (1) we develop a novel relation-aware attention graph neural network (GNN) to handle the imbalanced distribution of relation types in our graph; (2) to handle web-scale graph data that has millions of nodes and billions of edges, we develop a novel distributed graph training paradigm. The proposed model is successfully deployed within Netflix and empirical experiments indicate it yields up to 35% improvement in performance on similarity judgment tasks

Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems

Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. For example, the COVID-19 transmission in the U.S. can be viewed as a multi-agent system, where states act as agents and daily population movements between them are interactions. Estimating the counterfactual outcomes in such systems enables accurate future predictions and effective decision-making, such as formulating COVID-19 policies. However, existing methods fail to model the continuous dynamic effects of treatments on the outcome, especially when multiple treatments (e.g., "stay-at-home" and "get-vaccine" policies) are applied simultaneously. To tackle this challenge, we propose Causal Graph Ordinary Differential Equations (CAG-ODE), a novel model that captures the continuous interaction among agents using a Graph Neural Network (GNN) as the ODE function. The key innovation of our model is to learn time-dependent representations of treatments and incorporate them into the ODE function, enabling precise predictions of potential outcomes. To mitigate confounding bias, we further propose two domain adversarial learning-based objectives, which enable our model to learn balanced continuous representations that are not affected by treatments or interference. Experiments on two datasets (i.e., COVID-19 and tumor growth) demonstrate the superior performance of our proposed model

TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems

Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling. Extended from purely data-driven approaches, existing physics-informed approaches such as Hamiltonian Neural Network strictly follow energy conservation law to introduce inductive bias, making their learning more sample efficiently. However, many real-world systems do not strictly conserve energy, such as spring systems with frictions. Recognizing this, we turn our attention to a broader physical principle: Time-Reversal Symmetry, which depicts that the dynamics of a system shall remain invariant when traversed back over time. It still helps to preserve energies for conservative systems and in the meanwhile, serves as a strong inductive bias for non-conservative, reversible systems. To inject such inductive bias, in this paper, we propose a simple-yet-effective self-supervised regularization term as a soft constraint that aligns the forward and backward trajectories predicted by a continuous graph neural network-based ordinary differential equation (GraphODE). It effectively imposes time-reversal symmetry to enable more accurate model predictions across a wider range of dynamical systems under classical mechanics. In addition, we further provide theoretical analysis to show that our regularization essentially minimizes higher-order Taylor expansion terms during the ODE integration steps, which enables our model to be more noise-tolerant and even applicable to irreversible systems. Experimental results on a variety of physical systems demonstrate the effectiveness of our proposed method. Particularly, it achieves an MSE improvement of 11.5 % on a challenging chaotic triple-pendulum systems

Impact of ankle-foot strengthening therapy on motor function in children with cerebral palsy

Objective To investigate the changes of motor function in children with cerebral palsy undergoing ankle-foot strengthening therapy. Methods A total of 80 children with cerebral palsy were enrolled in this study，and divided into the control group（n = 40）and observation group（n = 40）. In the control group，children received regular trunk training based on motor control theory combined with neurodevelopmental therapy，and those in the observation group received ankle-foot strengthening therapy in addition to those interventions in the control group. Pre-and post-treatment assessments including the Gross Motor Function Measure（GMFM-88）scores for Sections D and E，the 6-minute walk test（6-MWT），the Timed Up and Go Test（TUGT），and the Pediatric Balance Scale（PBS）were recorded to compare clinical efficacy between two groups. Results Following corresponding interventions，significant improvement was observed in all observed indexes in two groups（all P < 0.05）. Notably，children in the observation group exhibited better post-treatment scores in GMFM Section E，6-MWT，TUGT，and PBS compared to their counterparts in the control group，with statistically significant differences（all P < 0.05）. Conclusion Ankle-foot strengthening therapy effectively promotes the recovery of motor function in children with cerebral palsy