Sensitivity Analysis and Uncertainty Analysis in a Large-scale Agent-based Simulation Model of Infectious Diseases

The purpose of this study is to develop appropriate statistical methods and procedures for dealing with parameter uncertainty and for improving the computational efficiency of sensitivity analysis in a large-scale agent-based model of infectious disease. An agent-based model is a rule-based computational simulation model that can keep track of the dynamical activities of all agents and their interactions within an environment and analyze the course of a disease through the population and evaluate interventions. Sensitivity analysis is a method for quantifying uncertainty in a complex model by systematically changing inputs (parameters and initial conditions) of the model and quantifying the consequences for the output of the model. Sensitivity analysis and uncertainty analysis are used for agent-based model to analyze the uncertainty in the model. The specific aims of the study are to (1) develop specific procedures and criteria to determine important input parameters in the FRED agent-based influenza model; (2) develop specific procedures and criteria to determine high sensitivity parameters in the FRED agent-based influenza model via local sensitivity analysis; (3) improve the computational efficiency of sensitivity analysis by comparing two sampling procedures for probabilistic sensitivity analysis in agent-based models: simple random sampling and Latin Hypercube sampling; and (4) apply uncertainty analysis procedures to evaluate the cost-effectiveness for different school closure intervention strategies as well as the reliability of the uncertainty analysis in the FRED agent-based influenza model. This study emphasizes the important role of sensitivity analysis, uncertainty analysis and statistical analysis in making better use of simulation results for decision-making in the control of infectious disease. In this study, the FRED (Framework for Replicating Epidemic Dynamics) influenza model is used to produce all the simulation results from sensitivity analysis. The methods and procedures that are developed in this study can be generalized to all kinds of disease models under the FRED framework. In public health practice, this study will help to provide timely responses for decision-making when there is a public health crisis. It also provides important information for public health policy makers about how certainly the FRED framework can provide reliable intervention comparison results for decision-making

Emergent Modularity in Pre-trained Transformers

This work examines the presence of modularity in pre-trained Transformers, a feature commonly found in human brains and thought to be vital for general intelligence. In analogy to human brains, we consider two main characteristics of modularity: (1) functional specialization of neurons: we evaluate whether each neuron is mainly specialized in a certain function, and find that the answer is yes. (2) function-based neuron grouping: we explore finding a structure that groups neurons into modules by function, and each module works for its corresponding function. Given the enormous amount of possible structures, we focus on Mixture-of-Experts as a promising candidate, which partitions neurons into experts and usually activates different experts for different inputs. Experimental results show that there are functional experts, where clustered are the neurons specialized in a certain function. Moreover, perturbing the activations of functional experts significantly affects the corresponding function. Finally, we study how modularity emerges during pre-training, and find that the modular structure is stabilized at the early stage, which is faster than neuron stabilization. It suggests that Transformers first construct the modular structure and then learn fine-grained neuron functions. Our code and data are available at https://github.com/THUNLP/modularity-analysis.Comment: Findings of ACL 202

Impact of salinity of fracturing fluid on the migration of coal fines in propped fractures and cleats

During the hydraulic fracturing of coalbed methane (CBM) well, the deposition of coal fines in propped fractures and the migration of coal fines in cleats will damage the permeabilities of propped fractures and cleats, consequently affecting the hydraulic fracturing and the subsequent drainage of CBM well. For the purpose of dredging propped fractures and avoiding the clogging of cleats effectively, a novel method for the control of coal fines in propped fractures and cleats during hydraulic fracturing was proposed by optimizing the salinity of fracturing fluid. With the salinity decreasing stepwise, the experiments on the migration of coal fines in propped fractures and cleats were conducted on quartz sand-packed columns and anthracite coal plugs, respectively, to investigate the response characteristics of the migration of coal fines to the change of salinity. Additionally, the migration of coal fines was simulated by using the extended DLVO method, to elucidate the influence mechanisms of salinity on the migration of coal fines. On this basis, the optimal salinity range that takes into account the control of coal fines in propped fractures and cleats was explored. The results indicated that there existed a critical salt concentration (CSC) for the migration of coal fines in both propped fractures and cleats. When the salinity was lower than the CSC, the permeability of propped fractures abruptly increased while that of cleats decreased sharply, accompanied by a large amount of coal fines produced. The value of the CSC for the migration of coal fines in propped fractures was higher than that in cleats, which can be attributed to the fact that the surface electronegativity of proppants was stronger than that of cleats, while the hydrophobicity was weaker than that of cleats. With the gradual decrease of salinity, the electric double layer (EDL) repulsive force between coal fines and channel increased continuously. When the salinity decreased to the CSC, the EDL repulsion started to be greater than the sum of Lifshitz-van der Waals attraction and Lewis acid-base attraction, resulting in the migration of coal fines. Both the values of the predicted CSCs for the migration of coal fines in propped fractures and cleats were consistent with experimental data, indicating the effectiveness of the model. During hydraulic fracturing, the salinity of fracturing fluid can be designed between the CSCs for the migration of coal fines in propped fractures and cleats. In that case, the production of coal fines in propped fractures is promoted while the migration of coal fines is inhibited in cleats, so as to achieve the dual purposes of coal fines control in propped fractures and cleats

MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and our code can be obtained from https://github.com/THU-KEG/MAVEN-Argument.Comment: Working in progres

Exploring Universal Intrinsic Task Subspace via Prompt Tuning

Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find evidence indicating that the adaptations of PLMs to various few-shot tasks can be reparameterized as optimizing only a few free parameters in a unified low-dimensional intrinsic task subspace, which may help us understand why PLMs could easily adapt to various NLP tasks with small-scale data. To find such a subspace and examine its universality, we propose an analysis pipeline called intrinsic prompt tuning (IPT). Specifically, we resort to the recent success of prompt tuning and decompose the soft prompts of multiple NLP tasks into the same low-dimensional nonlinear subspace, then we learn to adapt the PLM to unseen data or tasks by only tuning parameters in this subspace. In the experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 250-dimensional subspace found with 100 tasks, by only tuning 250 free parameters, we can recover 97% and 83% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace. Besides being an analysis tool, IPT could further bring practical benefits, such as improving the prompt tuning stability.Comment: Withdrawn from Findings of ACL 202

A novel fusion protein consisting of anti-ANGPTL3 antibody and interleukin-22 ameliorates diabetic nephropathy in mice

IntroductionThe pathogenic mechanisms of diabetic nephropathy (DN) include podocyte injury, inflammatory responses and metabolic disorders. Although the antagonism of Angiopoietin-like protein 3 (ANGPTL3) can alleviate proteinuria symptoms by inhibiting the activation of integrin αvβ3 on the surface of podocytes, it can not impede other pathological processes, such as inflammatory responses and metabolic dysfunction of glucolipid. Interleukin-22 (IL-22) is considered to be a pivotal molecule involved in suppressing inflammatory responses, initiating regenerative repair, and regulating glucolipid metabolism.MethodsGenes encoding the mIL22IgG2aFc and two chains of anti-ANGPTL3 antibody and bifunctional protein were synthesized. Then, the DN mice were treated with intraperitoneal injection of normal saline, anti-ANGPTL3 (20 mg/kg), mIL22Fc (12 mg/kg) or anti-ANGPTL3 /IL22 (25.3 mg/kg) and irrigation of positive drug losartan (20mg/kg/d) twice a week for 8 weeks.ResultsIn this research, a novel bifunctional fusion protein (anti-ANGPTL3/IL22) formed by the fusion of IL-22 with the C-terminus of anti-ANGPTL3 antibody exhibited favorable stability and maintained the biological activity of anti-ANGPTL3 and IL-22, respectively. The fusion protein showed a more pronounced attenuation of proteinuria and improved dysfunction of glucolipid metabolism compared with mIL22Fc or anti-ANGPTL3. Our results also indicated that anti-ANGPTL3/IL22 intervention significantly alleviated renal fibrosis via inhibiting the expression of the inflammatory response-related protein nuclear factor kappa light-chain enhancer of activated B cells (NF-κB) p65 and NOD-like receptor family pyrin domain-containing protein 3 (NLRP3) inflammasome. Moreover, transcriptome analysis revealed the downregulation of signaling pathways associated with injury and dysfunction of the renal parenchymal cell indicating the possible protective mechanisms of anti-ANGPTL3/IL22 in DN.ConclusionCollectively, anti-ANGPTL3/IL22 bifunctional fusion protein can be a promising novel therapeutic strategy for DN by reducing podocyte injury, ameliorating inflammatory response, and enhancing renal tissue recovery