A distance measure of interval-valued belief structures

Interval-valued belief structures are generalized from belief function theory, in terms of basic belief assignments from crisp to interval numbers. The distance measure has long been an essential tool in belief function theory, such as conflict evidence combinations, clustering analysis, belief function and approximation. Researchers have paid much attention and proposed many kinds of distance measures. However, few works have addressed distance measures of interval-valued belief structures up. In this paper, we propose a method to measure the distance of interval belief functions. The method is based on an interval-valued one-dimensional Hausdorff distance and Jaccard similarity coefficient. We show and prove its properties of non-negativity, non-degeneracy, symmetry and triangle inequality. Numerical examples illustrate the validity of the proposed distance

A novel prestack sparse azimuthal AVO inversion

In this paper we demonstrate a new algorithm for sparse prestack azimuthal AVO inversion. A novel Euclidean prior model is developed to at once respect sparseness in the layered earth and smoothness in the model of reflectivity. Recognizing that methods of artificial intelligence and Bayesian computation are finding an every increasing role in augmenting the process of interpretation and analysis of geophysical data, we derive a generalized matrix-variate model of reflectivity in terms of orthogonal basis functions, subject to sparse constraints. This supports a direct application of machine learning methods, in a way that can be mapped back onto the physical principles known to govern reflection seismology. As a demonstration we present an application of these methods to the Marcellus shale. Attributes extracted using the azimuthal inversion are clustered using an unsupervised learning algorithm. Interpretation of the clusters is performed in the context of the Ruger model of azimuthal AVO

Development of an activatable far-red fluorescent probe for rapid visualization of hypochlorous acid in live cells and mice with neuroinflammation

Recent investigations have suggested that abnormally elevated levels of HOCl may be tightly related to the severity of neuroinflammation. Although some successes have been achieved, fluorescent probes with far-red fluorescence emission and capable of detecting HOCl with high specificity in pure aqueous solution are still urgently needed. Herein, a responsive far-red fluorescent probe, DCI-H, has been constructed to monitor HOCl activity in vivo and in vitro. DCI-H could rapidly respond to HOCl within 120 s and had a low detection limit for HOCl of 1.5 nM. Importantly, physiologically common interfering species, except for HOCl, did not cause a change in the fluorescence intensity of DCI-HOCl at 655 nm. The results of confocal imaging demonstrated the ability of DCI-H to visualize endogenous HOCl produced by MPO-catalyzed H2O2/Cl− and LPS stimulation. With the assistance of DCI-H, upregulation of HOCl levels was observed in the mice model of LPS-induced neuroinflammation. Thus, we believed that DCI-H provided a valuable tool for HOCl detection and diagnosis of inflammation-related diseases

Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

With increasing trend of polypharmacy, drug-drug interaction (DDI)-induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score-adjusted three-component mixture model (PS-3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug-drug-ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS-3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS-3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS-3CMM prioritizes DDI signals differently. PS-3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS-3CMM is a new method that is complement to the existing DDI signal detection methods

ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation

In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first class-level code generation benchmark ClassEval of 100 class-level Python code generation tasks with approximately 500 person-hours. Based on it, we then perform the first study of 11 state-of-the-art LLMs on class-level code generation. Based on our results, we have the following main findings. First, we find that all existing LLMs show much worse performance on class-level code generation compared to on standalone method-level code generation benchmarks like HumanEval; and the method-level coding ability cannot equivalently reflect the class-level coding ability among LLMs. Second, we find that GPT-4 and GPT-3.5 still exhibit dominate superior than other LLMs on class-level code generation, and the second-tier models includes Instruct-Starcoder, Instruct-Codegen, and Wizardcoder with very similar performance. Third, we find that generating the entire class all at once (i.e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3.5, while method-by-method generation (i.e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information. Lastly, we find the limited model ability of generating method-dependent code and discuss the frequent error types in generated classes. Our benchmark is available at https://github.com/FudanSELab/ClassEval

Leptin regulates the reward value of nutrient

We developed an assay for quantifying the reward value of nutrient and used it to analyze the effects of metabolic state and leptin. In this assay, mice chose between two sippers, one of which dispensed water and was coupled to optogenetic activation of dopaminergic (DA) neurons and the other of which dispensed natural or artificial sweeteners. This assay measured the reward value of sweeteners relative to lick-induced optogenetic activation of DA neurons. Mice preferred optogenetic stimulation of DA neurons to sucralose, but not to sucrose. However, the mice preferred sucralose plus optogenetic stimulation versus sucrose. We found that food restriction increased the value of sucrose relative to sucralose plus optogenetic stimulation, and that leptin decreased it. Our data suggest that leptin suppresses the ability of sucrose to drive taste-independent DA neuronal activation and provide new insights into the mechanism of leptin's effects on food intake