Research advances on multifocal electroretinogram in primary open angle glaucoma

Primary open angle glaucoma is a chronic and progressive optic neuropathy. It can lead to serious damage of visual impairment, and it is an important eye disease of blindness. Multifocal electroretinogram is a new way to measure visual electrophysiology. It can measure electroretinogram of the whole visual field of many small parts in a relatively short period of time, and it can reflect the function of regional retina. It has an extremely important value for early diagnosis of primary open angle glaucoma. The research advances on multifocal electroretinogram in diagnosing primary open angle glaucoma were summarized in this paper

Interactive Markov Models of Evolutionary Algorithms

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method

Interactive Markov Models of Evolutionary Algorithms

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method

A Causal View of Entity Bias in (Large) Language Models

Entity bias widely affects pretrained (large) language models, causing them to rely on (biased) parametric knowledge to make unfaithful predictions. Although causality-inspired methods have shown great potential to mitigate entity bias, it is hard to precisely estimate the parameters of underlying causal models in practice. The rise of black-box LLMs also makes the situation even worse, because of their inaccessible parameters and uncalibrated logits. To address these problems, we propose a specific structured causal model (SCM) whose parameters are comparatively easier to estimate. Building upon this SCM, we propose causal intervention techniques to mitigate entity bias for both white-box and black-box settings. The proposed causal intervention perturbs the original entity with neighboring entities. This intervention reduces specific biasing information pertaining to the original entity while still preserving sufficient semantic information from similar entities. Under the white-box setting, our training-time intervention improves OOD performance of PLMs on relation extraction (RE) and machine reading comprehension (MRC) by 5.7 points and by 9.1 points, respectively. Under the black-box setting, our in-context intervention effectively reduces the entity-based knowledge conflicts of GPT-3.5, achieving up to 20.5 points of improvement of exact match accuracy on MRC and up to 17.6 points of reduction in memorization ratio on RE. Our code is available at https://github.com/luka-group/Causal-View-of-Entity-Bias.Comment: Findings of EMNLP 202

Synthesis of Flower-Like Cu 2

Flower-like Cu2ZnSnS4 (CZTS) nanoflakes were synthesized by a facile and fast one-pot solution reaction using copper(II) acetate monohydrate, zinc acetate dihydrate, tin(IV) chloride pentahydrate, and thiourea as starting materials. The as-synthesized samples were characterized by X-ray diffraction (XRD), Raman scattering analysis, field emission scanning electron microscopy (FESEM) equipped with an energy dispersion X-ray spectrometer (EDS), transmission electron microscopy (TEM), and UV-Vis absorption spectra. The XRD patterns shown that the as-synthesized particles were kesterite CZTS and Raman scattering analysis and EDS confirmed that kesterite CZTS was the only phase of product. The results of FESEM and TEM show that the as-synthesized particles were flower-like morphology with the average size of 1~2 μm which are composed of 50 nm thick nanoflakes. UV-Vis absorption spectrum revealed CZTS nanoflakes with a direct band gap of 1.52 eV