Truth Values Research Articles

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach

Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in ∼ 3 min) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values for LV contractility indexed by the end-systolic elastance Ees with a 1% error, which suggests that this parameter is unique. The PINN inverse modeling approach is then applied to estimate Ees using waveforms acquired from 11 swine models, including waveforms acquired before and after administration of dobutamine (an inotropic agent) in 3 animals. The estimated Ees is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements.

Refining Cellular Data for Accurate Trip Chain Identification: A Novel Approach for Urban Travel Analysis

Cellular data play a crucial role in supporting travel demand assessment and urban traffic planning because of their cost-effectiveness and extensive coverage. However, inherent inaccuracies, such as imprecise positioning, data duplication, and abnormal communication frequencies, hinder their ability to depict travelers’ trips accurately. In this paper, we present a novel approach to mitigate these issues by employing base station mapping to refine positioning data, eliminating duplicates and abnormal frequency records. This refinement enables more effective utilization of cellular data for trip chain identification. We address the ping-pong handover effect by employing a finite automaton machine and an approximate nearest neighbor searching method with carefully selected seeds to identify activities. Our method’s accuracy is validated through ground truth value analysis, focusing on permanent resident population estimation and metro passenger flow estimation. Furthermore, we demonstrate the practical effectiveness and value of our proposed method through a series of representative applications in a real-world case study in the city of Nanning, Guangxi Province, China.

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

The persistence of long facts: truth and consequence in Costa Rica’s aquifers

ABSTRACT At the center of decades-long water conflicts in northwest Costa Rica, a particular fact has had a contentious status: whether the aquifers supplying the area hold sufficient water for collective life. I refer to this as the sufficiency fact, which has been established, debunked, and reestablished multiple times. To understand this peculiar dynamic, I zoom into discussions among community and business representatives about the future of these aquifers. There, in the density of social life, I show how as place-specific formations, facts are “long” entities that remain tied to their interpretations and, crucially, to their potential consequences. As opposed to the ideal of short facts that characterizes modern science, long facts reveal the rich social life that facticity takes in the twenty-first century. Long facts are always contested, explicitly political, and unable to be separated from their potential consequences. In contrast to scholarship that diagnoses the loss of the value of truth within the contemporary moment, I suggest it is critical to understand the abundance of regimes of facticity that surround us and what they make possible leaving behind assumptions of deficit and lack.

Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification.

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 μL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification chemistry and time-lapse imaging. It also achieved high precision with a coefficient of variation of <7% and a very large dynamic range, covering cTnI concentrations over 6 orders of magnitude, up to 100 ng/mL, satisfying clinical requirements. In blinded testing, this computational hs-VFA platform accurately quantified cTnI levels in patient samples and showed a strong correlation with the ground truth values obtained by a benchtop clinical analyzer. This nanoparticle amplification-based computational hs-VFA platform can democratize access to high-sensitivity point-of-care diagnostics and provide a cost-effective alternative to laboratory-based biomarker testing.

MG+: Towards Efficient Context Inconsistency Detection by Minimized Link Generation

ABSTRACTSelf‐adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location‐aware navigation or resource‐sensitive suggestions. However, due to inherent noises in the process of sensing and interpreting environmental information, there is a growing demand for guarding the consistency of collected contexts to avoid application misbehaviour and, at the same time, minimize extra costs. Existing work attempted to achieve this by speeding up the kernel constraint checking module inside the consistency guarding process. Most of these efforts were spent on reusing previous checking results or parallelizing the checking process, but they all leave one central step of constraint checking, that is, link generation, untouched. In this step, the checking engine provides reasons to explain the violation of constraints under check. It occupies a substantial part of the total time cost. Focusing on this key link generation step, we proposed MG, which deploys a rigourous analysis to automatically identify and avoid redundancy in the link generation without harming any correctness of the checking results. MG has been proven sound (always guaranteeing correctness) and complete (entirely removing redundancy). Moreover, based on our observation that MG's redundancy elimination also assists another core step of constraint checking to reduce unnecessary computation further, we additionally enhance MG with an escape‐condition optimization to escape unnecessary evaluation of truth values to further improve the efficiency of constraint checking in an aspect other than link generation. We call it MG+ for distinguishing. Our experiments with synthesized and real‐world consistency constraints reported that, compared with existing work, MG eliminates all link redundancy (83% to 0%), and based on it, MG+ further reduces significant truth value calculations (e.g., 49.74% reduction when combined with ECC and Con‐C). Generally, MG brought 14–500 speed‐ups in link generation, and MG+ further made 1.2–1.9 speed‐ups in truth value evaluation. Altogether, MG reduced the total constraint checking time up to 45.4%, and MG+ reduced it up to 61.0%.

YOLOrot2.0: A novel algorithm for high-precision rice seed size measurement with real-time processing

We present YOLOrot2.0, an automated algorithm for measuring the dimensions of rice seeds, which holds significant importance in evaluating grain quality and genetic breeding research. The current methods for rice seed measurement exhibit various limitations, such as the need for seed preprocessing and selection, specialized equipment for measurement, and prolonged measurement time. To address these issues, YOLOrot2.0 introduces the following improvements: Firstly, it utilizes an anchor-free detection algorithm to optimize the detection of rotated targets. Secondly, the utilization of the Kalman Filter IoU loss function, which combines the horizontal bounding box and smooth L1 loss function, accelerates the convergence speed of the network. Moreover, the YOLOv8 architecture was adjusted by modifying certain convolutional layers and the Context to Fusion module to enhance the detection capabilities for smaller targets. Finally, YOLOrot2.0 enables direct processing of the entire image, eliminating the need for image partitioning and resulting in substantial time and processing savings. Experimental results demonstrate that YOLOrot2.0 achieves an mAP (mean average precision) score of 0.95, surpassing other comparative algorithms. Additionally, YOLOrot2.0 provides highly accurate measurements of seed length and width, closely aligned with ground truth values. Notably, YOLOrot2.0 achieves real-time detection by achieving an average detection time of only 11.5 ms for images sized 3000×4000 pixels.

Road Traffic Noise Levels Estimations by means of Fully Dynamic and Microscopic Approach Compared to CNOSSOS-EU Model

In the last decades, research has been dedicated to refining models for assessing traffic noise levels, supporting national authorities in addressing associated problems. Progress has shifted from basic statistical models to dynamic ones, typically composed of a Noise Emission Model (NEM) for estimating source sound power levels and a sound propagation model for assessing equivalent noise levels at specific receiver points. These models commonly integrate the average speed of traffic flow as an input variable, placing them in the macroscopic or mesoscopic realm. The CNOSSOS-EU model (reference in Europe) exemplifies this macroscopic approach. However, due to the modular structure of road traffic noise models, a NEM can be coupled with different sound propagation models, facilitating the transition from macroscopic to microscopic assessments. This paper demonstrates the coupling of a NEM with two distinct simplified sound propagation models: CNOSSOS-EU and the sum of Sound Exposure Levels (SEL) models. The latter approach showcases the potential of using individual on-road vehicle speeds as inputs, enabling microscopic noise assessments. Vehicle speed data is extracted from video records, while the goodness of the proposed approaches is tested by comparing the models' estimations with ground truth values recorded on a National Road in Portugal.

Vision-guided robot calibration using photogrammetric methods

We propose novel photogrammetry-based robot calibration methods for industrial robots that are guided by cameras or 3D sensors. Compared to state-of-the-art methods, our methods are capable of calibrating the robot kinematics, the hand–eye transformations, and, for camera-guided robots, the interior orientation of the camera simultaneously. Our approach uses a minimal parameterization of the robot kinematics and hand–eye transformations. Furthermore, it uses a camera model that is capable of handling a large range of complex lens distortions that can occur in cameras that are typically used in machine vision applications. To determine the model parameters, geometrically meaningful photogrammetric error measures are used. They are independent of the parameterization of the model and typically result in a higher accuracy. We apply a stochastic model for all parameters (observations and unknowns), which allows us to assess the precision and significance of the calibrated model parameters. To evaluate our methods, we propose novel procedures that are relevant in real-world applications and do not require ground truth values. Experiments on synthetic and real data show that our approach improves the absolute positioning accuracy of industrial robots significantly. By applying our approach to two different uncalibrated UR3e robots, one guided by a camera and one by a 3D sensor, we were able to reduce the RMS evaluation error by approximately 85% for each robot.

Feature diffusion reconstruction mechanism network for crop spike head detection.

Monitoring crop spike growth using low-altitude remote sensing images is essential for precision agriculture, as it enables accurate crop health assessment and yield estimation. Despite the advancements in deep learning-based visual recognition, existing crop spike detection methods struggle to balance computational efficiency with accuracy in complex multi-scale environments, particularly on resource-constrained low-altitude remote sensing platforms. To address this gap, we propose FDRMNet, a novel feature diffusion reconstruction mechanism network designed to accurately detect crop spikes in challenging scenarios. The core innovation of FDRMNet lies in its multi-scale feature focus reconstruction and lightweight parameter-sharing detection head, which can effectively improve the computational efficiency of the model while enhancing the model's ability to perceive spike shape and texture.FDRMNet introduces a Multi-Scale Feature Focus Reconstruction module that integrates feature information across different scales and employs various convolutional kernels to capture global context effectively. Additionally, an Attention-Enhanced Feature Fusion Module is developed to improve the interaction between different feature map positions, leveraging adaptive average pooling and convolution operations to enhance the model's focus on critical features. To ensure suitability for low-altitude platforms with limited computational resources, we incorporate a Lightweight Parameter Sharing Detection Head, which reduces the model's parameter count by sharing weights across convolutional layers. According to the evaluation experiments on the global wheat head detection dataset and diverse rice panicle detection dataset, FDRMNet outperforms other state-of-the-art methods with mAP@.5 of 94.23%, 75.13% and R 2 value of 0.969, 0.963 between predicted values and ground truth values. In addition, the model's frames per second and parameters in the two datasets are 227.27,288 and 6.8M, respectively, which maintains the top three position among all the compared algorithms. Extensive qualitative and quantitative experiments demonstrate that FDRMNet significantly outperforms existing methods in spike detection and counting tasks, achieving higher detection accuracy with lower computational complexity.The results underscore the model's superior practicality and generalization capability in real-world applications. This research contributes a highly efficient and computationally effective solution for crop spike detection, offering substantial benefits to precision agriculture practices.

Analisis Nilai-Nilai Spiritual dalam Tradisi Posuo Masyarakat Buton

This research examines spiritual values in the posuo tradition of the Butonese people. The posuo tradition is a traditional ceremony for girls that marks the transition from adolescence to adulthood, carried out once in a lifetime. The approach and type of research used is a qualitative approach with library research. Data was obtained through relevant literature, including books, journals, articles, and research reports. The findings show that the posuo tradition contains four main spiritual values based on Notonegoro's theory, namely the values of beauty, truth, goodness, and religiosity. The value of beauty can be seen in traditional clothing and ceremonies, The truth value of implementing customs is by the teachings passed down from generation to generation and still refers to a combination of Islamic teachings and established customary rules., the value of goodness from ethical and moral education, and the value of religiosity from spiritual practices during ceremonies. This tradition not only functions as a means of self-cleaning but also as a medium for education and character formation which is important for Butonese women in living their adult lives.

Quantity implicature interpretations in bilingual population: the case of Imbabura Kichwa.

Most studies on the pragmatic interpretation of existential quantifiers have been conducted in major Indo-European languages like English, Spanish, French, and Greek, focusing mainly on monolingual participants. However, in indigenous linguistic research, especially experimental research, it is crucial to consider several linguistic and extra-linguistic factors for successful implementation. Our research centered on the experimental investigation of the pragmatic interpretation of the quantifier wakin, meaning some in Kichwa, with Kichwa-Spanish bilingual adults from the province of Imbabura Ecuador. We employed the Truth Value Judgement Task (TVJT) for our experiments and incorporated the explicit Question Under Discussion (QUD) paradigm to facilitate pragmatic interpretations among our participants. Our initial experiment revealed a 78% acceptance level for the pragmatic interpretation of "some, but not all" in Kichwa, significantly lower than the 95% acceptance range observed in other languages. We hypothesized that access to technology and formal education might influence these results, leading us to simplify our experiment by eliminating the technological components of the research. In our subsequent experiment, adult speakers of Imbabura Kichwa achieved a 97% accuracy level, comparable to speakers of other languages. To benchmark our results against speakers of other languages under similar conditions, we evaluated whether Spanish speakers from two varieties in Ecuador (Quito and Guayaquil) could generate the "some, but not all" scalar implicature with the Spanish quantifier algunos. Our findings indicated that speakers achieved 95.5 and 97.4% accuracy for both varieties, respectively. Therefore, this study infers that under optimal conditions, speakers of indigenous languages in rural communities demonstrate commendable performance in experimental linguistic studies. Nonetheless, it underscores the necessity for meticulous planning and distinct handling in experimental studies involving speakers of these languages residing in rural areas without access to technological elements. We propose that such research broadens our comprehension of language utilization in minority communities and positively influences language restoration efforts by expanding experimental linguistics studies to indigenous languages.

PPAT: An effective scheme ensuring privacy-preserving, accuracy, and trust for worker selection in mobile crowdsensing networks

The data content privacy protection and data accuracy are two important research issues in Mobile Crowdsensing (MCS). However, current researches have rarely been able to satisfy privacy protection as well as data accuracy at the same time, thus hindering the development of MCS. To solve the above issues, for the first time, we have proposed a Privacy Preserving, Accuracy, and Trust data collection scheme (PPAT) for MCS, which can protect the privacy of data content and maintain high accuracy at low-cost style. In PPAT scheme, First, we proposed a scrambled data privacy protection framework which can protect the data of each worker from being known to any third party, which can protect the data privacy of workers. The second, more importantly, we propose a truth value estimation method based on trust computing, which can obtain the truth value more accurately compared to the classic methods under privacy-preserving. In the proposed trust-based truth value calculation, the worker's trust is determined by comparing it with the weight of the trusted worker. Then, the truth value is calculated by the trust of the workers, so that the truth value obtained is more accurate. Through theoretical analysis, it is proved that the proposed PPAT scheme has good worker data content, worker trust, and truth value content privacy protection. Through a large number of simulation experiments, the strategy proposed in this paper has a good ability to protect data content privacy compared to the previous strategy, while improving data quality by 0.5%∼5.7%, and reducing data collection costs by 35.6%∼54.9%.

Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer.

Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Automating Information Retrieval from Biodiversity Literature Using Large Language Models: A Case Study

Recently, Large Language Models (LLMs) have transformed information retrieval, becoming widely adopted across various domains due to their ability to process extensive textual data and generate diverse insights. Biodiversity literature, with its broad range of topics, is no exception to this trend (Boyko et al. 2023, Castro et al. 2024). LLMs can help in information extraction and synthesis, text annotation and classification, and many other natural language processing tasks. We leverage LLMs to automate the information retrieval task from biodiversity publications, building upon data sourced from our previous work (Ahmed et al. 2024). In our previous work (Ahmed et al. 2023, Ahmed et al. 2024), we assessed the reproducibility of deep learning (DL) methods used in biodiversity research. We developed a manual pipeline to extract key information on DL pipelines—dataset, source code, open-source frameworks, model architecture, hyperparameters, software and hardware specs, randomness, averaging result and evaluation metrics from 61 publications (Ahmed et al. 2024). While this allowed analysis, it required extensive manual effort by domain experts, limiting scalability. To address this, we propose an automatic information extraction pipeline using LLMs with the Retrieval Augmented Generation (RAG) technique. RAG combines the retrieval of relevant documents with the generative capabilities of LLMs to enhance the quality and relevance of the extracted information. We employed an open-source LLM, Hugging Face implementation of Mixtral 8x7B (Jiang et al. 2024), a mixture of expert models in our pipeline (Fig. 1) and adapted the RAG pipeline from earlier work (Kommineni et al. 2024). The pipeline was run on a single NVIDIA A100 40GB graphics processing unit with 4-bit quantization. To evaluate our pipeline, we compared the expert-assisted manual approach with the LLM-assisted automatic approach. We measured their consistency using the inter-annotator agreement (IAA) and quantified it with the Cohen Kappa score (Pedregosa et al. 2011), where a higher score indicates more reliable and aligned outputs (1: maximum agreement, -1: no agreement). The Kappa score among human experts (annotators 1 and 2) was 0.54 (moderate agreement), while the scores comparing human experts with the LLM were 0.16 and 0.12 (slight agreement). The difference is partly due to human annotators having access to more information (including code, dataset, figures, tables and supplementary materials) than the LLM, which was restricted to the text itself. Given these restrictions, the results are promising but also show the potential to improve them by adding further modalities to the LLM inputs. Future work will involve several key improvements to our LLM-assisted information retrieval pipeline: Incorporating multimodal data (e.g., figures, tables, code, etc.) as input to the LLM, alongside text, to enhance the accuracy and comprehensiveness of the information retrieved from publications. Optimizing the retrieval component of the RAG framework with advanced techniques like semantic search, hybrid search or relevance feedback can improve the quality of outputs. Expanding the evaluation to a larger corpus of biodiversity literature could provide a more comprehensive understanding of pipeline capabilities, and this paves the way for pipeline optimization. A human-in-the-loop approach for evaluating the LLM-generated outputs by matching the ground truth values from the respective publications, will increase the quality of the overall pipeline. Employing more metrics for the evaluation beyond the Cohen Kappa score to better understand the LLM-assisted outputs. Incorporating multimodal data (e.g., figures, tables, code, etc.) as input to the LLM, alongside text, to enhance the accuracy and comprehensiveness of the information retrieved from publications. Optimizing the retrieval component of the RAG framework with advanced techniques like semantic search, hybrid search or relevance feedback can improve the quality of outputs. Expanding the evaluation to a larger corpus of biodiversity literature could provide a more comprehensive understanding of pipeline capabilities, and this paves the way for pipeline optimization. A human-in-the-loop approach for evaluating the LLM-generated outputs by matching the ground truth values from the respective publications, will increase the quality of the overall pipeline. Employing more metrics for the evaluation beyond the Cohen Kappa score to better understand the LLM-assisted outputs. Leveraging LLMs to automate information retrieval from biodiversity publications signifies a notable advancement in the scalable and efficient analysis of biodiversity literature. Initial results show promise, yet there is substantial potential for enhancement through the integration of multimodal data, optimized retrieval mechanisms, and comprehensive evaluation. By addressing these areas, we aim to improve the accuracy and utility of our pipeline, ultimately enabling broader and more in-depth analysis of biodiversity literature.

A Theory of Inaccessible Information

What would be the consequences if there were fundamental limits to our ability to experimentally explore the world? In this work we seriously consider this question. We assume the existence of statements whose truth value is not experimentally accessible. That is, there is no way, not even in theory, to directly test if these statements are true or false. We further develop a theory in which experimentally accessible statements are a union of a fixed minimum number of inaccessible statements. For example, the value of truth of the statements "a" and "b" is not accessible, but the value of truth of the statement "a or b" is accessible. We do not directly assume probability theory, we exclusively define experimentally accessible and inaccessible statements and build on these notions using the rules of classical logic. We find that an interesting structure emerges. Developing this theory, we relax the logical structure to a probabilistic one, obtaining a theory rich in structure that we call "theory of inaccessible information". Surprisingly, the simplest model of theory of inaccessible information is the qubit in quantum mechanics. Along the path for the construction of this theory, we characterise and study a family of multiplicative information measures that we call "inaccessibility measures".

High-precision and non-invasive measurement of crestal bone level by optical coherence tomography

Background/purposeIdentifying crestal bone level (CBL) on the buccal and lingual aspects poses challenges in conventional dental radiographs. Given that optical coherence tomography (OCT) has the capability to non-invasively provide in-depth information about the periodontium, this in vitro study aimed to assess whether OCT can effectively identify periodontal landmarks and measure CBL in the presence of gingiva. Materials and methodsAn in-house handheld scanning probe connected to a 1310-nm swept-source OCT (SS-OCT) system, along with self-developed algorithms were employed to measure the CBL in dental models with artificial gingiva. Markers were positioned 0.5 mm above the artificial gingival margin (SG) and the crestal bone (SC) on both the mid-buccal and mid-lingual sides of 28 plastic teeth. The distances between the paired SG and SC were measured in the OCT images after correcting for the optical path through the covering artificial gingiva. These measurements were subsequently compared to the ground truth values obtained using a 2.5D inspection system. ResultsThe mean difference in CBL measured by SS-OCT and 2.5D was 0.008 mm (95 % CI: −0.092 to 0.108 mm). Statistical analysis using a three-way ANOVA indicated that the measurement differences were not significant across maxillary/mandible, anterior/posterior, and buccal/lingual dimensions. Furthermore, these differences were not associated with gingival thickness (α = 0.05). ConclusionThe proposed SS-OCT system demonstrated its capability to accurately and non-invasively assess CBL through artificial gingiva. Moreover, it facilitated the semi-automatic delineation of critical periodontal landmarks on OCT en face images, highlighting its potential for clinical applications.

Deep Spatio-Temporal Network for Low-SNR Cryo-EM Movie Frame Enhancement.

Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused by beam striking make the task very challenging. Video enhancement algorithms in computer vision shed new light on tackling such tasks by utilizing deep neural networks. However, they are designed for natural images with high SNR. Meanwhile, the lack of ground truth in cryo-EM movie seems to be one major limiting factor of the progress. Hence, we present a synthetic cryo-EM movie generation pipeline, which can produce realistic diverse cryo-EM movie datasets with low-SNR movie frames and multiple ground truth values. Then we propose a deep spatio-temporal network (DST-Net) for cryo-EM movie frame enhancement trained on our synthetic data. Spatial and temporal features are first extracted from each frame. Spatio-temporal fusion and high-resolution re-constructor are designed to obtain the enhanced output. For evaluation, we train our model on seven synthetic cryo-EM movie datasets and infer on real cryo-EM data. The experimental results show that DST-Net can achieve better enhancement performance both quantitatively and qualitatively compared with others.

Broad feature extraction and multi-directional imbalanced weighted broad learning system for the unsupervised stereo matching method

The practical application of supervised stereo matching methods is often limited by the requirement of obtaining ground-truth disparity maps in advance. To address this issue, we introduce a novel unsupervised stereo matching method based on the broad feature extraction and multi-directional imbalanced weighted broad learning system. In this study, an end-to-end unsupervised stereo matching method based on broad feature extraction was developed to generate an initial disparity map. Subsequently, a multi-directional imbalanced weighted broad learning system that considers the uneven distribution of disparity values was designed to effectively remove and redefine outlier disparities and enhance the accuracy of generating multiple candidate disparity maps. Multi-direction consistency verification was integrated into the system to further eliminate anomalous disparity values. This verification relies on uniqueness, which guarantees the consistency of the disparity truth values at the same location in various directions. Finally, the local gravity weight method based on a broad feature space was introduced to select a suitable disparity value from neighboring pixels to replace invalid pixel positions. Experiments were conducted under both indoor and outdoor scenarios to demonstrate the effectiveness and versatility of our approach, confirming that it outperforms state-of-the-art techniques, highlighting its superiority.

The higher dimensional propositional calculus

Abstract In recent research, some of the present authors introduced the concept of an $n$-dimensional Boolean algebra and its corresponding propositional logic $n\textrm{CL}$, generalizing the Boolean propositional calculus to $n\geq 2$ perfectly symmetric truth values. This paper presents a sound and complete sequent calculus for $n\textrm{CL}$, named $n\textrm{LK}$. We provide two proofs of completeness: one syntactic and one semantic. The former implies as a corollary that $n\textrm{LK}$ enjoys the cut admissibility property. The latter relies on the generalization to the $n$-ary case of the classical proof based on the Lindenbaum algebra of formulas and Boolean ultrafilters.