Stochastic Explicit Calibration Algorithm for Survival Models by Jeongho Park, Sangwook Kang, Gwangsu Kim

“…Although extensive research has focused on calibration in classification and regression tasks using deep neural networks, survival analysis remains relatively underexplored, resulting in the lack of improved calibration methods. …”
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Speech Stream Composition Affects Statistical Learning: Behavioral and Neural Evidence by Ana Paula Soares, Dario Paiva, Alberto Lema, Diana R. Pereira, Ana Cláudia Rodrigues, Helena Mendes Oliveira

“…Here, we tested if SL is affected by the composition of the speech streams by expositing participants to auditory streams containing either four nonsense words presenting a transitional probability (TP) of 1 (unmixed high-TP condition), four nonsense words presenting TPs of 0.33 (unmixed low-TP condition) or two nonsense words presenting a TP of 1, and two of a TP of 0.33 (mixed condition); first under incidental (implicit), and, subsequently, under intentional (explicit) conditions to further ascertain how prior knowledge modulates the results. …”
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Analysis of Four-Species Diffusive and Non-Diffusive Food Chains Using Artificial Neural Networking by Muhammad Shoaib Arif, Ateeq Ur Rehman, Asad Ejaz

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Aquifer vulnerability assessment in data-scarce areas: a spatially explicit assessment by Changhyun Jun, Dongkyun Kim, Sayed M. Bateni, Meghdad Biyari, Ely Salwana, Farzaneh Sajedi Hosseini, Amir Mosavi, Hao-Ting Pai, Bahram Choubin

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Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen by Saad Alatefi, Okorie Ekwe Agwu, Ahmad Alkouh

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Leveraging Neural Trojan Side-Channels for Output Exfiltration by Vincent Meyers, Michael Hefenbrock, Dennis Gnad, Mehdi Tahoori

“…Additionally, we explore countermeasures and discuss their implications for the design of secure neural network accelerators. To the best of our knowledge, this work is the first to present a passive output recovery attack on neural network accelerators, without explicit trigger mechanisms. …”
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Benchmarking Spiking Neural Network Learning Methods With Varying Locality by Jiaqi Lin, Sen Lu, Malyaban Bal, Abhronil Sengupta

“…Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have been shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machine learning tasks. …”
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Neural correlates reveal separate stages of spontaneous face perception by Amanda K. Robinson, Greta Stuart, Sophia M. Shatek, Adrian Herbert, Jessica Taubert

“…While this illusion reveals the automaticity of face detection, it also presents a paradox: how does the brain process stimuli that are simultaneously faces and objects? …”
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SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation by Aravind R. Voggu, Kanish R, Nishith Akula, Lohitaksh Maruvada, Takanori Shimizu, Madhav Rao

“…This paper presents a novel approach to Quadrature Amplitude Modulation (QAM) demodulation using neural networks, addressing the limitations of traditional demodulation techniques in complex channel conditions. …”
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Predefined-Time Antisynchronization of Two Different Chaotic Neural Networks by Lixiong Lin

“…The designed controller presents the practical advantage that the least upper bound for the settling time can be explicitly defined during the control design. …”
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Common neural recruitment across diverse sustained attention tasks. by Jessica A Grahn, Tom Manly

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Neural network distillation of orbital dependent density functional theory by Matija Medvidović, Jaylyn C. Umana, Iman Ahmadabadi, Domenico Di Sante, Johannes Flick, Angel Rubio

“…These goals are achieved by using a recently developed class of robust neural network models capable of modeling functionals, as opposed to functions, with explicitly enforced spatial symmetries. …”
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Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels by Antonino Laudani, Gabriele Maria Lozito, Francesco Riganti Fulginei, Alessandro Salvini

“…A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. …”
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General and stable emulation of finite state machines with spiking neural networks by Ziyang Sun, Zhong Zheng, Binying Zhang, Hanle Zheng, Zikai Wang, Hao Guo, Lei Deng

“…However, they face challenges in modeling complex especially black-box systems without explicit state descriptions. Neural networks, conversely, excel at modeling implicit and continuous systems but struggling with the temporally stable and precise tasks which FSMs can handle effectively. …”
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Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…In MACE prediction, our model outperformed the traditional score-based models, with 8.2% higher AUC than Systematic COronary Risk Evaluation (SCORE), 3.5% for SCORE 2 and 7.1% for the Framingham Risk Score (with p value<0.05 for all three comparisons).Conclusions Our algorithm estimates the 5-year risk of MACE based on retinal images, while explicitly presenting which risk factors should be checked and intervened. …”
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Improving collective interpretation by extended potentiality assimilation for multi-layered neural networks by Ryotaro Kamimura, Haruhiko Takeuchi

“…The present paper aims to extend the potential learning method to overcome the problem of collective interpretation, which aims to interpret multi-layered neural networks by compressing them into the simplest ones. …”
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Optical classification of images at different wavelengths using spectral diffractive neural networks by G.A. Motz, D.V. Soshikov, L.L. Doskolovich, E.V. Byzo, E.A. Bezus, D.A. Bykov

“…A solution of several different problems of image classification at several different wavelengths using a diffractive neural network (DNN) consisting of sequentially located phase diffractive optical elements (DOEs) is considered. …”
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NeRFOrtho: Orthographic Projection Images Generation based on Neural Radiance Fields by Dongdong Yue, Xinyi Liu, Yi Wan, Yongjun Zhang, Maoteng Zheng, Weiwei Fan, Jiachen Zhong

“…However, the former suffers from projection differences and stitching lines, while the latter is plagued by poor model quality and high costs. This paper presents NeRFOrtho, a new method for generating orthographic projection images from neural radiance fields at arbitrary angles. …”
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Exploring the suitability of piecewise-linear dynamical system models for cognitive neural dynamics by Jiemin Wu, Boateng Asamoah, Zhaodan Kong, Zhaodan Kong, Jochen Ditterich, Jochen Ditterich, Jochen Ditterich

“…Dynamical system models have proven useful for decoding the current brain state from neural activity. So far, neuroscience has largely relied on either linear models or non-linear models based on artificial neural networks (ANNs). …”
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A machine learning neural network architecture for the accelerating universe based modified gravity by Zulqurnain Sabir, Basma Souayeh, Zahraa Zaiour, Alyn Nazal, Mir Waqas Alam, Huda Alfannakh

“…The current investigations present the numerical outputs of the mathematical accelerating universe based modified gravity model (MAUMGM) by designing a computational stochastic structure using the Bayesian regularization neural network. …”
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