Reviewerly/exHarmony huggingface dataset:
Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships

In the pursuit of optimal quantitative structure–activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed filter-induced genetic programming 2 (FIGP2), as an extension of our previously proposed SR method. FIGP2 is designed to improve the generalizability of SR models and to be applicable to data sets in which cost-intensive descriptors are employed. The FIGP2 method incorporates two major improvements: a modified domain filter to eradicate diverging expressions based on optimal calculation and the introduction of a stability metric to penalize expressions that would lead to overfitting. Our retrospective comparative analysis using 12 structure–activity relationship data sets revealed that FIGP2 surpassed the previously proposed SR method and conventional modeling methods, such as support vector regression and multivariate linear regression in terms of predictive performance. Generated mathematical expressions by FIGP2 were relatively simple and not divergent in the domain of function. Taken together, FIGP2 can be used for making interpretable regression models with predictive ability.
Novel neurodigital interface reduces motion sickness in virtual reality

Virtual reality (VR) is a computer-created 3D environment with a focus on realistic scenes and pictures created for entertainment, medical and/or educational and training purposes. One of the major side effects of VR immersion reported in the scientific literature, media and social media is Visually Induced Motion Sickness (VIMS), with clinical symptoms such as disorientation, nausea, and oculomotor discomfort. VIMS is mostly caused by the discrepancy between the visual and vestibular systems and can lead to dizziness, nausea, and disorientation. In this study, we present one potential novel solution to combat motion sickness in VR, showcasing a significant reduction of nausea in VR users employing the META Quest 2 headsets in conjunction with a whole-body controller. Using a neurodigital approach, we facilitate a more immersive and comfortable VR experience. Our findings indicate a marked reduction in VR-induced nausea, paving the way to promote VR technology for broader applications across various fields.
Searching near and far: The attentional template incorporates viewing distance.

According to theories of visual search, observers generate a visual representation of the search target (the "attentional template") that guides spatial attention toward target-like visual input. In real-world vision, however, objects produce vastly different visual input depending on their location: your car produces a retinal image that is 10 times smaller when it is parked 50 compared to 5 m away. Across four experiments, we investigated whether the attentional template incorporates viewing distance when observers search for familiar object categories. On each trial, participants were precued to search for a car or person in the near or far plane of an outdoor scene. In "search trials," the scene reappeared and participants had to indicate whether the search target was present or absent. In intermixed "catch-trials," two silhouettes were briefly presented on either side of fixation (matching the shape and/or predicted size of the search target), one of which was followed by a probe-stimulus. We found that participants were more accurate at reporting the location (Experiments 1 and 2) and orientation (Experiment 3) of probe stimuli when they were presented at the location of size-matching silhouettes. Thus, attentional templates incorporate the predicted size of an object based on the current viewing distance. This was only the case, however, when silhouettes also matched the shape of the search target (Experiment 2). We conclude that attentional templates for finding objects in scenes are shaped by a combination of category-specific attributes (shape) and context-dependent expectations about the likely appearance (size) of these objects at the current viewing location. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Optimizing freeform lenses for extended sourceswith algorithmic differentiable ray tracing andtruncated hierarchical B-splines

We propose a method for optimizing the geometry of a freeform lens to redirect the light emitted from an extended source into a desired irradiance distribution. We utilize a gradient-based optimization approach with MITSUBA 3, an algorithmic differentiable non-sequential ray tracer that allows us to obtain the gradients of the freeform surface parameters with respect to the produced irradiance distribution. To prevent the optimizer from getting trapped in local minima, we gradually increase the number of degrees of freedom of the surface by using Truncated Hierarchical B-splines (THB-splines) during optimization. The refinement locations are determined by analyzing the gradients of the surface vertices. We first design a freeform using a collimated beam (zero-etendue source) for a complex target distribution to demonstrate the method's effectiveness. Then, we demonstrate the ability of this approach to create a freeform that can project the light of an extended Lambertian source into a prescribed target distribution.
A machine learning-based hybrid recommender framework for smart medical systems

This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy. The second method implements the term frequency and inverse document frequency (TF-IDF) algorithm to construct a model based on the patient’s symptom vector space, incorporating score weighting, modified cosine similarity, and K-means clustering. Then, the alternating least squares (ALS) matrix decomposition and user collaborative filtering algorithm are applied to calculate patients’ predicted scores for doctors and recommend top-performing doctors. Experimental results show significant improvements in metrics called precision and recall rates compared to conventional methods, making the proposed approach a practical solution for department triage and doctor recommendation in medical appointment platforms.
Architectural Design for Data Security in Cloud-based Big Data Systems

Architecture design of system is creative process after all. Non-functional requirements and software architecture have a strong relationship. Security of system is one of great concern and it is a non-functional need and requirement. The architectural style and structure chosen for a system should be based on the non-functional system requirements. Big Data technologies are used to store handles huge amounts of operational data in the public domain. Structure and behavior diagrams of the system is proposed that use a secure architectural design for a system that works over the cloud. Different components of Big Data have been investigated in this research work due to the seriousness of security issues focus on privacy and confidentiality. The security of data over the cloud being outsourced is a growing concern. There are outsider and insider malicious database administrators who can gain access and modify private and sensitive data therefore database service provider can be deceitful. Security mechanisms are necessarily required to protect sensitive data residing on public clouds. At the application level, the proposed solution ensures privacy and confidentiality of data at rest over the cloud. Extensive tests have been carried out, and the results of this research work are as follows that the recorded results revealed statistically significant variations in plaintext and multimedia contents (i.e., audio and video) with time measured in milliseconds for small, medium, and large datasets..
Diffractive optical computing in free space

Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.
Tracking hand movements with a smart glove

Dynamic hand movements could be detected in real time using machine learning and a smart textile glove.
The Role of Artificial Intelligence in Shaping the Future of Education at Higher Secondary Level

This study examines the implications of Artificial Intelligence (AI) in enhancing educational methodologies at the higher secondary level, focusing on its impact on student engagement and learning outcomes, the experiences and challenges faced by educators in integrating AI tools, and the broader ethical and privacy considerations. Utilizing a qualitative methodology based on interviews with educators and students, the research aims to explore the nuanced perspectives on the role of AI in education, identifying both the potential benefits and drawbacks. Initial findings highlight the positive effects of AI on personalized learning experiences alongside significant challenges related to resource limitations and the need for comprehensive support for educators. Moreover, concerns about ethical implications and data privacy emerge as critical issues requiring careful management. The study concludes that while AI offers transformative potential for educational practices, ensuring effective integration, addressing ethical concerns, and preparing students for future technological challenges is essential for leveraging AI's full benefits in the educational sector.
MCMC algorithm based on Markov random field in image segmentation

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.
Evaluation of an artificial intelligence-based clinical trial matching system in Chinese patients with hepatocellular carcinoma: a retrospective study

Abstract Background Artificial intelligence (AI)-assisted clinical trial screening is a promising prospect, although previous matching systems were developed in English, and relevant studies have only been conducted in Western countries. Therefore, we evaluated an AI-based clinical trial matching system (CTMS) that extracts medical data from the electronic health record system and matches them to clinical trials automatically. Methods This study included 1,053 consecutive inpatients primarily diagnosed with hepatocellular carcinoma who were referred to the liver tumor center of an academic medical center in China between January and December 2019. The eligibility criteria extracted from two clinical trials, patient attributes, and gold standard were decided manually. We evaluated the performance of the CTMS against the established gold standard by measuring the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and run time required. Results The manual reviewers demonstrated acceptable interrater reliability (Cohen’s kappa 0.65–0.88). The performance results for the CTMS were as follows: accuracy, 92.9–98.0%; sensitivity, 51.9–83.5%; specificity, 99.0–99.1%; PPV, 75.7–85.1%; and NPV, 97.4–98.9%. The time required for eligibility determination by the CTMS and manual reviewers was 2 and 150 h, respectively. Conclusions We found that the CTMS is particularly reliable in excluding ineligible patients in a significantly reduced amount of time. The CTMS excluded ineligible patients for clinical trials with good performance, reducing 98.7% of the work time. Thus, such AI-based systems with natural language processing and machine learning have potential utility in Chinese clinical trials.
Laboratory experiments of model-based reinforcement learning for adaptive optics control

Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, making their detection difficult. Consequently, the adaptive optics (AO) system’s control algorithm must be carefully designed to distinguish the exoplanet from the residual light produced by the host star. A promising avenue of research to improve AO control builds on data-driven control methods, such as reinforcement learning (RL). RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment. Thus, RL can be seen as an automated approach to AO control, where its usage is entirely a turnkey operation. In particular, model-based RL has been shown to cope with temporal and misregistration errors. Similarly, it has been demonstrated to adapt to nonlinear wavefront sensing while being efficient in training and execution. In this work, we implement and adapt an RL method called policy optimization for AO (PO4AO) to the GPU-based high-order adaptive optics testbench (GHOST) test bench at ESO headquarters, where we demonstrate a strong performance of the method in a laboratory environment. Our implementation allows the training to be performed parallel to inference, which is crucial for on-sky operation. In particular, we study the predictive and self-calibrating aspects of the method. The new implementation on GHOST running PyTorch introduces only around 700 μs of in addition to hardware, pipeline, and Python interface latency. We open-source well-documented code for the implementation and specify the requirements for the RTC pipeline. We also discuss the important hyperparameters of the method and how they affect the method. Further, the paper discusses the source of the latency and the possible paths for a lower latency implementation.
Breast Cancer Image Classification based on Adaptive Interpolation Approach Using Clinical Dataset

In the healthcare and bioinformatics disciplines, the categorization of breast cancer has become an emerging paradigm due to the second most common cause of cancer-related mortality in women. A biopsy is a procedure in which tissue has been examined to determine whether or not it is breast cancer through histopathologists that may lead to a mistaken diagnosis. The research is mainly focused on the patient data (884 case reports of patients) is acquired from the American Oncology Institute implemented with preprocessing techniques consists of missing values and those values are recovered with Novel Modified Interpolation (MI) Method. Deep learning networks effectively detect and assess the pattern for annotating histological data based on the labelling, which preserves time, system cost and enhances the system accuracy. This framework addressed feature acquisition and missing analysis strategies based on entropy confidence weight factor. First, the iterative patterns have been treated as a potential diagnostic rule, and the attention-based rule combination formulates the classification issue based on integrating convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches. Second, the key part of label construction is carried out with an entropy confidence-weight factor assessment which detects and predict different patterns to construct the rule for classification. Third, optimization of clustering data by assessing missing parameters based on mean square error and the concept of interpolation to reduce data loss by around 20% and enhance the system accuracy. Simulation results show that the proposed system achieves 91.3% accuracy to state-of-art approaches, potentially allied in clinical applications. Modified Interpolation (MI) method recovered missing values with least mean square error and less data loss of 0.0123 and 1.38% respectively. This method also compared with existing Linear Interpolation (LI) method which is able to recover least men square error and data loss of 3.295 and 18.925% respectively. Comparatively the modified interpolation method recovered the missing values with less mean square error and less data loss.
Fourier domain filtering analysis for color polarization camera demosaicking

We review Fourier-domain methods for demosaicking Bayer-filter color cameras and monochrome polarization cameras, and then generalize the approach for the quad-Bayer-filter mosaic and for color-polarization cameras. For each of these four mosaic filter types, we provide theoretical expressions for the sampling functions, the Fourier-domain channels, and the linear combination of reconstructed channels (the demosaicking algorithm) needed to estimate the input (presampled) image. A useful advantage of the Fourier-domain approach is that it provides a direct means of visualizing and quantifying when aliasing is likely or unlikely to be present. For the Bayer and quad-Bayer-filter types, we provide simulated images, while for the polarization camera types we provide experimental images and videos to illustrate the algorithm and analyze crosstalk error.
Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.
Hourglass, a compass navigating global and regional heterogeneity of pancreatic cancer<sup>†</sup>

Advances in the digital pathology field have facilitated the characterization of histology samples for both clinical and preclinical research. However, uncovering subtle correlations between bioimaging, clinical and molecular parameters requires extensive statistical analysis. As a user-friendly software, Hourglass, simplifies multiparametric dataset analysis through intuitive data visualization and statistical tools. Systemic analysis of interleukin-6 (IL-6)/pStat3 signaling pathway through Hourglass revealed differences in regional immune cell composition within tumors. Moreover, these regional disparities were partially mediated by sex. Overall, Hourglass simplifies information extraction from complex datasets, resolving overlooked regional and global spatial tumor differences. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.