Seeking out non-public information : sell-side analysts and the freedom of information act

A number of sell-side healthcare analysts gain access to information outside the purview of management through Freedom of Information Act requests to the Food and Drug Administration for records on factory inspections, complaints, and drug and medical device applications. Using a difference-in-differences methodology, we find that buy (sell) recommendations and upgrades (downgrades) earn higher (lower) stock returns over the year following the receipt of FDA records. We also examine the type of information revealed in FDA factory inspection reports, and find that analysts are less likely to downgrade and are less pessimistic in their recommendations than the consensus recommendation when the information contained in the FDA report is not particularly severe. Our findings are consistent with a subset of analysts utilizing non-public information channels independent of management to gain value-relevant information about their covered firms

Predicting adsorbed gas capacity of deep shales under high temperature and pressure: Experiments and modeling

Temperature and pressure conditions of deep shale are beyond experiment range, and the amount of adsorbed gas is difficult to determine. To predict the adsorbed gas content of deep shales under formation conditions, isothermal adsorption experiments and model building were conducted on shale samples from Longmaxi Formation in China. A temperature-dependent adsorption model based on the Langmuir equation is proposed, which can be well-fitted by observed isotherms with a high correlation coefficient. Based on the fitted parameters at 303.15 K, the isothermal adsorption curves at 333.15 K, 363.15 K, and 393.15 K are predicted, showing a good agreement with experimental curves available. Compared with previous prediction methods, the biggest advantage of the proposed method is that it can be carried out only based on one-time isothermal adsorption experiment. Based on the predictions, the downward trend of the excess adsorption curves will slow down under high temperature and pressure conditions, and when the pressure reaches a certain level (> 80 MPa), the temperature has little effect on the excess adsorption capacity. While for absolute adsorption, the gas adsorption reaches saturation much slowly at high temperature, it can also reach saturation under formation pressure. Under the burial depth of marine shale, temperature plays a major role in controlling the adsorbed gas, resulting in the decrease of adsorbed gas content in deep shale, and its ratio will further decrease as the depth increases.Cited as: Zhou, S., Wang, H., Li, B., Li, S., Sepehrnoori, K., Cai, J. Predicting adsorbed gas capacity of deep shales under high temperature and pressure: Experiments and modeling. Advances in Geo-Energy Research, 2022, 6(6): 482-491. https://doi.org/10.46690/ager.2022.06.0

DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs

Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset DialogRE^C+, introducing coreference resolution into the DRE scenario. With the aid of high-quality coreference knowledge, the reasoning of argument relations is expected to be enhanced. In DialogRE^C+ dataset, we manually annotate total 5,068 coreference chains over 36,369 argument mentions based on the existing DialogRE data, where four different coreference chain types namely speaker chain, person chain, location chain and organization chain are explicitly marked. We further develop 4 coreference-enhanced graph-based DRE models, which learn effective coreference representations for improving the DRE task. We also train a coreference resolution model based on our annotations and evaluate the effect of automatically extracted coreference chains demonstrating the practicality of our dataset and its potential to other domains and tasks.Comment: Accepted by NLPCC 202

LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model

Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM. Syntactic structure information, a type of effective feature which has been extensively utilized in IE community, should also be beneficial to UIE. In this work, we propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. A heterogeneous structure inductor is explored to unsupervisedly induce rich heterogeneous structural representations by post-training an existing GLM. In particular, a structural broadcaster is devised to compact various latent trees into explicit high-order forests, helping to guide a better generation during decoding. We finally introduce a task-oriented structure fine-tuning mechanism, further adjusting the learned structures to most coincide with the end-task's need. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system. Further in-depth analyses show that our GLM learns rich task-adaptive structural bias that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying. Source codes are open at https://github.com/ChocoWu/LasUIE.Comment: NeurIPS2022 conference pape

An analytical investigation on the dynamic stability of a rotor filled with liquid

This paper deals with the dynamic stability of a rigid rotor arbitrarily filled with liquid. On the basis of the established coupled-field equations of the rotor system, the general whirling eigenequation, which is a quartic complex coefficients equation, is derived. In order to obtain the solutions of the general whirling eigenequation, a mathematical method is proposed. To illustrate the precision of calculating results, a comparison is carried out between the present analysis and the numerical results. The results show that two calculation results are in good agreement. Then the stability of the rotor system is analyzed. It is shown that the dynamic instability occurs at a particular bound of the spinning speed. Moreover, the effects of system parameters, such as fluid-fill ratio and mass ratio, on the unstable regions are discussed

Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition

It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in recent years, where a diverse range of methods has been proposed for securing better task performance. Most existing works treat MM-ERC as a standard multimodal classification problem and perform multimodal feature disentanglement and fusion for maximizing feature utility. Yet after revisiting the characteristic of MM-ERC, we argue that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement and fusion steps. In this work, we target further pushing the task performance by taking full consideration of the above insights. On the one hand, during feature disentanglement, based on the contrastive learning technique, we devise a Dual-level Disentanglement Mechanism (DDM) to decouple the features into both the modality space and utterance space. On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively. They together schedule the proper integrations of multimodal and context features. Specifically, CFM explicitly manages the multimodal feature contributions dynamically, while CRM flexibly coordinates the introduction of dialogue contexts. On two public MM-ERC datasets, our system achieves new state-of-the-art performance consistently. Further analyses demonstrate that all our proposed mechanisms greatly facilitate the MM-ERC task by making full use of the multimodal and context features adaptively. Note that our proposed methods have the great potential to facilitate a broader range of other conversational multimodal tasks.Comment: Accepted by ACM MM 202