One of the most popular approaches to unconscious cognition is the technique of “post hoc selection”: Priming effects and visibility ratings are measured in multitasks on the same trial, and only trials with the lowest visibility ratings are selected for analysis of (presumably unconscious) priming effects. In the past, the technique has been criticized for creating statistical artifacts and capitalizing on chance. Here, we argue that post hoc selection constitutes a sampling fallacy, confusing sensitivity and response bias, wrongly ascribing unconscious processing to stimulus conditions that may be far from indiscriminable. In response to a high-profile “best practice” paper by Stockart et al. (2025) that condones the technique, we use standard signal detection theory to show that post hoc selection only isolates trials with neutral response bias, irrespective of actual sensitivity, and thus fails to isolate trials where the critical stimulus is “unconscious”. Our own data demonstrate that zero-visibility ratings are consistent with uncomfortably high levels of sensitivity. As an alternative to post hoc selection, we advocate the study of functional dissociations, where direct (D) and indirect (I) measures are conceptualized as spanning a two-dimensional D-I space wherein simple, sensitivity, and double dissociations appear as distinct curve patterns. While Stockart et al.’s recommendations cover only a single line of that space where D is close to zero, functional dissociations can utilize the entire space. This circumvents requirements like null visibility and exhaustive reliability, allows for dissociations among different measures of awareness, and supports the planful measurement of functional relationships between direct and indirect measures.
Introduction: In adults, olfactory loss is one of the earliest and most frequent acute clinical manifestations of SARS-CoV-2 infection. The number of children infected with SARS-CoV-2 is relatively small, perhaps due to the lower expression of Angiotensin Converting Enzyme 2 (ACE2) in children compared to adults. Little is known about foetal impairment in mothers infected with SARS-CoV-2. Objective: The goal of the present study is to develop and validate a behavioural evaluative scale of olfactory perception in healthy new-borns and to apply this scale to new-born children of women infected with COVID-19 during pregnancy comparing to new-born children of women without COVID-19 infection history. Methods: This is a retrospective comparative analytical cohort study of 300 new-borns exposed and unexposed to COVID-19 during pregnancy. The data collection will follow the experimental procedure in a previous study that explored odours of the maternal breastmilk, vanilla (sweet) and distilled water (neutral). A coffee smell was implemented as an addition to this previous study in order to include the acid/bitterness category to the categories of stimuli. Discussion: It is feasible to argue the hypothesis of the involvement of the foetus' olfactory bulb during intrauterine life as one of the indelible pathophysiological manifestations to the clinical diagnosis of COVID-19 with neurosensory olfactory deficit in foetuses and new-borns affected by intrauterine infection. This study aims to investigate if new-born children of women infected with COVID-19 during pregnancy have olfactory sensory changes. The clinical trial was registered in the Brazilian Registry of Clinical Trials (ReBEC- RBR-65qxs2).
True random number generators (TRNGs) underpin modern cryptography, yet existing implementations face fundamental trade-offs between speed, scalability, and entropy quality. Here, we demonstrate that stochastic switching in the bistable regime of spin-wave dynamics provides a physical entropy source for high-quality random number generation. Our magnonic random number generator (mRNG), based on a lithography-patterned microstrip on yttrium iron garnet (YIG), exploits thermal fluctuations near the nonlinear bistable regime to generate random bitstreams that pass all 15 NIST SP 800-22 statistical tests at rates with 20 Mb/s. We implement a random-bit multiplier using synchronized mRNG units and demonstrate scalability to 200-nm-wide nanoscale waveguides, establishing spin-wave bistability as a viable physical entropy source for integrated random number generation.
Pinching antenna systems (PASS) employing dielectric waveguides have recently emerged as a promising flexible antenna architecture for high-frequency wireless communications. While prior work has focused primarily on millimeter-wave regimes, extending PASS to the terahertz (THz) band introduces distinct electromagnetic phenomena that invalidate conventional modeling assumptions. This paper develops the first analytical framework for THz-PASS that integrates in-waveguide propagation attenuation, evanescent coupling via coupled-mode theory, and THz-specific free-space effects including molecular absorption and its re-radiation noise. Using this model, we benchmark THz-PASS against conventional phased arrays under identical propagation scenarios. Our comparative evaluation reveals that THz-PASS achieves effective gains in spectral efficiency through proximity exploitation, making it particularly well-suited for confined and linear deployment topologies.
Accurate instance segmentation of yeast cells in microstructured environments remains a challenging problem due to the visual similarity between cells and the surrounding trap structures. This complexity is further amplified under varying imaging conditions and limited annotated data. In this study, a robust transfer-based instance segmentation framework is presented that jointly segments both yeast cells and traps. Our approach integrates an attention-enhanced backbone to better capture fine-grained features crucial for precise segmentation. To evaluate the model’s robustness, an extensive evaluation is performed using test-time image degradation scenarios, including Gaussian noise, blur, and contrast variations. Additionally, the model’s generalization capability is assessed across complex spatial configurations involving multiple trap types and dense cell populations. The proposed method achieves state-of-the-art performance and demonstrates strong label efficiency, maintaining high segmentation accuracy even with limited training data. These results highlight the model’s potential for deployment in real-world bioengineering applications.
