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1,315 results on '"Ho, Johnny"'

1. The observer effect in quantum: the case of classification

Author

Hoorn, Johan F. and Ho, Johnny K. W.

Subjects
Quantum Physics
Abstract

The observer effect in quantum physics states that observation inevitably influences the system being observed. Our proposed epistemic framework treats the observer as an integral part of sensory information processing within entangled quantum systems, highlighting the subjective and probabilistic aspects of observation and inference. Our study introduces a hierarchical model for fuzzy instance classification, which aligns sensory input with an observer's pre-existing beliefs and associated quantum probability-based truth values. Sensory data evolves via interaction with observer states, as described by the Lindblad master equation, and is then classified adaptively using positive operator-valued measures (POVM). Our parametrization employs measures of concurrent similarity and dissimilarity, facilitating perceptual associations and asymmetric cognition. The observer's position on a skeptic-believer spectrum modulates ambiguous matching of noisy perceptions. We show that sensory information becomes intricately entangled with observer states, yielding a wide array of probabilistic classification results. This framework lays the groundwork for a quantum-probability-based understanding of the observer effect, encouraging further exploration of quantum correlations and properties in cognitive processes., Comment: 28 pages

Published
2024

5. A method for computing hadron-nucleus cross sections in fully active sampling calorimetric targets

Author

Ho, Johnny and Schmitz, David W.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Nuclear Experiment
Abstract

Interaction cross section measurements in fixed-target scattering experiments are typically performed by measuring the attenuation of a beam of particles that is incident upon a target slab of material. A fully active sampling calorimeter, such as the time projection chamber, is a more voluminous target that can be treated as a series of smaller target slabs. We present a method of computing hadron-nucleus interaction cross sections for data taken with fully active sampling calorimeters that is robust even if the target is comprised of numerous slabs of various thicknesses., Comment: 13 pages, 4 figures

Published
2022

14. Van der Waals nanomesh electronics on arbitrary surfaces

Author

Meng, You, Li, Xiaocui, Kang, Xiaolin, Li, Wanpeng, Wang, Wei, Lai, Zhengxun, Wang, Weijun, Quan, Quan, Bu, Xiuming, Yip, SenPo, Xie, Pengshan, Chen, Dong, Li, Dengji, Wang, Fei, Yeung, Chi-Fung, Lan, Changyong, Liu, Chuntai, Shen, Lifan, Lu, Yang, Chen, Furong, Wong, Chun-Yuen, and Ho, Johnny C.

Published
2023
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19. Robot Affect: the Amygdala as Bloch Sphere

Author

Hoorn, Johan F. and Ho, Johnny K. W.

Subjects
Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition, Quantum Physics
Abstract

In the design of artificially sentient robots, an obstacle always has been that conventional computers cannot really process information in parallel, whereas the human affective system is capable of producing experiences of emotional concurrency (e.g., happy and sad). Another schism that has been in the way is the persistent Cartesian divide between cognition and affect, whereas people easily can reflect on their emotions or have feelings about a thought. As an essentially theoretical exercise, we posit that quantum physics at the basis of neurology explains observations in cognitive emotion psychology from the belief that the construct of reality is partially imagined (Im) in the complex coordinate space C^3. We propose a quantum computational account to mixed states of reflection and affect, while transforming known psychological dimensions into the actual quantum dynamics of electromotive forces. As a precursor to actual simulations, we show examples of possible robot behaviors, using Einstein-Podolsky-Rosen circuits. Keywords: emotion, reflection, modelling, quantum computing

Published
2019

44. Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images.

Author

Rahman, Md Fashiar, Tseng, Tzu-Liang, Pokojovy, Michael, McCaffrey, Peter, Walser, Eric, Moen, Scott, Vo, Alex, and Ho, Johnny C.

Subjects
CONVOLUTIONAL neural networks, DEEP learning, DATA augmentation, EVIDENCE gaps, X-ray imaging
Abstract

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network's determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm. [ABSTRACT FROM AUTHOR]

Published
2024
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