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News
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.
September 21, 2026
Researchers Develop Methodology to Assess the Quality of Legal Representation in Criminal Proceedings
Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.

 

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?

Filter-Based Preprocessing Neural Network Model for Microorganism Detection Improvement

P. 308–320.
Aleksei Samarin, Aleksei Toropov, Alexander Savelev, Dozortseva A., Motyko A., Kotenko E., Nazarenko A., Dzestelova A., Elena Mikhailova, Valentin Malykh

This research explores an innovative approach to enhancing the accuracy of detecting small microorganisms in complex microscopic environments. Our study introduces a streamlined, hybrid image pre-processing model specifically designed to address the challenges of identifying diplococci in live microscopy of dynamic samples. By integrating pre-defined filtering techniques with predictive adjustments for optimal applicability, our method effectively reduces artifacts—such as blurred boundaries and unclear edges—that commonly hinder precise detection in live, unstained samples. The results demonstrate a marked improvement in detection quality over conventional approaches, highlighting the potential of our model to refine and elevate microorganism detection standards in both biomedical and industrial applications.

Language: English
DOI
Text on another site
Keywords: neural networkscomputer visionobject detection image preprocessingmicroorganism detectionmicroscopy image analysis

In book

Pattern Recognition. ICPR 2024 International Workshops and Challenges
Springer, Cham, 2025.
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