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Publications in Information & Communication Technologies by NOMIS researchers

AI-based approach to dissect the variability of mouse stem cell-derived embryo models

NOMIS Researcher(s)

Published in

February 19, 2025

Recent advances in stem cell-derived embryo models have transformed developmental biology, offering insights into embryogenesis without the constraints of natural embryos. However, variability in their development challenges research standardization. To address this, we use deep learning to enhance the reproducibility of selecting stem cell-derived embryo models. Through live imaging and AI-based models, we classify 900 mouse post-implantation stem cell-derived embryo-like structures (ETiX-embryos) into normal and abnormal categories. Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. Our analysis reveals that normally developed ETiX-embryos have higher cell counts and distinct morphological features such as larger size and more compact shape. Perturbation experiments increasing initial cell numbers further supported this finding by improving normal development outcomes. This study demonstrates deep learning’s utility in improving embryo model selection and reveals critical features of ETiX-embryo self-organization, advancing consistency in this evolving field.

Research field(s)
Bioinformatics, Artificial Intelligence & Image Processing, Biophysics, Developmental Biology

DOI
10.1038/s41467-025-56908-5

New approaches to epidemic modeling on networks

NOMIS Researcher(s)

Published in

January 10, 2023

In this article, we develop two independent and new approaches to model epidemic spread in a network. Contrary to the most studied models, those developed here allow for contacts with different probabilities of transmitting the disease (transmissibilities). We then examine each of these models using some mean field type approximations. The first model looks at the late-stage effects of an epidemic outbreak and allows for the computation of the probability that a given vertex was infected. This computation is based on a mean field approximation and only depends on the number of contacts and their transmissibilities. This approach shares many similarities with percolation models in networks. The second model we develop is a dynamic model which we analyze using a mean field approximation which highly reduces the dimensionality of the system. In particular, the original system which individually analyses each vertex of the network is reduced to one with as many equations as different transmissibilities. Perhaps the greatest contribution of this article is the observation that, in both these models, the existence and size of an epidemic outbreak are linked to the properties of a matrix which we call the R-matrix. This is a generalization of the basic reproduction number which more precisely characterizes the main routes of infection. © 2023, The Author(s).

Research field(s)
Applied Sciences, Information & Communication Technologies, Artificial Intelligence & Image Processing

DOI
10.1038/s41598-022-19827-9

Social Metacognition Drives Willingness to Commit

NOMIS Researcher(s)

Published in

January 1, 2023

Showing or telling others that we are committed to cooperate with them can boost social cooperation. But what makes us willing to signal our cooperativeness, when it is costly to do so? In two experiments,we tested the hypothesis that agents engage in social commitments if their subjective confidence in predicting the interaction partner’s behavior is low. In Experiment 1 (preregistered), 48 participants played a prisoner’s dilemma game where they could signal their intentions to their co-player by enduring a monetary cost. As hypothesized, low confidence in one’s prediction of the co-player’s intentions was associated with a higher willingness to engage in costly commitment. In Experiment 2 (31 participants), we replicate these findings and moreover provide causal evidence that experimentally lowering the predictability of others’ actions (and thereby confidence in these predictions) motivates commitment decisions. Finally, across both experiments, we show that participants possess and demonstrate metacognitive access to the accuracy of their mentalizing processes. Taken together, our findings shed light on the importance of confidence representations and metacognitive processes in social interactions © 2023 American Psychological Association

Research field(s)
Applied Sciences, Information & Communication Technologies, Artificial Intelligence & Image Processing

DOI
10.1037/xge0001419

Microwave quantum illumination with a digital phase-conjugated receiver

NOMIS Researcher(s)

Published in

September 21, 2020

Quantum illumination is a sensing technique that employs entangled signal-idler beams to improve the detection efficiency of low-reflectivity objects in environments with large thermal noise. The advantage over classical strategies is evident at low signal brightness, a feature which could make the protocol an ideal prototype for non-invasive scanning or low-power short-range radar. Here we experimentally investigate the concept of quantum illumination at microwave frequencies, by generating entangled fields using a Josephson parametric converter which are then amplified to illuminate a room-temperature object at a distance of 1 meter. Starting from experimental data, we simulate the case of perfect idler photon number detection, which results in a quantum advantage compared to the relative classical benchmark. Our results highlight the opportunities and challenges on the way towards a first room-temperature application of microwave quantum circuits.

Research field(s)
Applied Sciences, Information & Communication Technologies, Networking & Telecommunications

DOI
10.1109/RadarConf2043947.2020.9266397

Self-serving incentives impair collective decisions by increasing conformity

NOMIS Researcher(s)

Published in

November 1, 2019

The average judgment of large numbers of people has been found to be consistently better than the best individual response. But what motivates individuals when they make collective decisions? While it is a popular belief that individual incentives promote out-of-the-box thinking and diverse solutions, the exact role of motivation and reward in collective intelligence remains unclear. Here we examined collective intelligence in an interactive group estimation task where participants were rewarded for their individual or group’s performance. In addition to examining individual versus collective incentive structures, we controlled whether participants could see social information about the others’ responses. We found that knowledge about others’ responses reduced the wisdom of the crowd and, crucially, this effect depended on how people were rewarded. When rewarded for the accuracy of their individual responses, participants converged to the group mean, increasing social conformity, reducing diversity and thereby diminishing their group wisdom. When rewarded for their collective performance, diversity of opinions and the group wisdom increased. We conclude that the intuitive association between individual incentives and individualist opinion needs revising.

Research field(s)
Applied Sciences, Information & Communication Technologies, Artificial Intelligence & Image Processing

DOI
10.1371/journal.pone.0224725

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