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Intentional inhibition, the ability to voluntarily inhibit or suspend an action preparation, is closely related to self-control. It is widely believed that subliminal stimuli can also activate action preparation, but whether intentional inhibition is enhanced or disrupted with greater subliminal action preparation remains unclear. In this study, participants voluntarily decided whether or not to perform the action in the scenario with subliminal action preparation, and the strength of the action preparation was manipulated by a precueing procedure. The results, based on behavioral measures and drift-diffusion models, showed that intentional inhibition enhanced with increasing subliminal action preparation, suggesting that as subliminal action preparation increases, people are more inclined to make inhibitory decisions. This study provides evidence for a framework in which strong subliminal action preparation induces enhanced cognitive monitoring.

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BACKGROUND AND HYPOTHESIS: Individuals with schizophrenia (SZ) and bipolar disorder (BD) show disruptions in self-referential gaze perception-a social perceptual process related to symptoms and functioning. However, our current mechanistic understanding of these dysfunctions and relationships is imprecise. STUDY DESIGN: The present study used mathematical modeling to uncover cognitive processes driving gaze perception abnormalities in SZ and BD, and how they relate to cognition, symptoms, and social functioning. We modeled the behavior of 28 SZ, 38 BD, and 34 controls (HC) in a self-referential gaze perception task using drift-diffusion models parameterized to index key cognitive components: drift rate (evidence accumulation efficiency), drift bias (perceptual bias), start point (expectation bias), threshold separation (response caution), and nondecision time (encoding/motor processes). STUDY RESULTS: Results revealed that aberrant gaze perception in SZ and BD was driven by less efficient evidence accumulation, perceptual biases predisposing self-referential responses, and greater caution (SZ only). Across SZ and HC, poorer social functioning was related to greater expectation biases. Within SZ, perceptual and expectancy biases were associated with hallucination and delusion severity, respectively. CONCLUSIONS: These findings indicate that diminished evidence accumulation and perceptual biases may underlie altered gaze perception in patients and that SZ may engage in compensatory cautiousness, sacrificing response speed to preserve accuracy. Moreover, biases at the belief and perceptual levels may relate to symptoms and functioning. Computational modeling can, therefore, be used to achieve a more nuanced, cognitive process-level understanding of the mechanisms of social cognitive difficulties, including gaze perception, in individuals with SZ and BD.

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Purpose: Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model. Approach: We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies. Results: The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols. Conclusion: This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.

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Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

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Supercritical carbon dioxide (SCCO2) is a non-toxic and environmentally friendly fluid and has been used in polymerization reactions, processing, foaming, and plasticizing of polymers. Exploring the behavior and data of SCCO2 sorption and dissolution in polymers provides essential information for polymer applications. This study investigated the sorption and diffusion of SCCO2 into polyetherimide (PEI). The sorption and desorption processes of SCCO2 in PEI samples were measured in the temperature range from 40 to 60 °C, the pressure range from 20 to 40 MPa, and the sorption time from 0.25 to 52 h. This study used the ex situ gravimetric method under different operating conditions and applied the Fickian diffusion model to determine the mass diffusivity of SCCO2 during sorption and desorption processes into and out of PEI. The equilibrium mass gain fraction of SCCO2 into PEI was reported from 9.0 wt% (at 60 °C and 20 MPa) to 12.8 wt% (at 40 °C and 40 MPa). The sorption amount increased with the increasing SCCO2 pressure and decreased with the increasing SCCO2 temperature. This study showed the crossover phenomenon of equilibrium mass gain fraction isotherms with respect to SCCO2 density. Changes in the sorption mechanism in PEI were observed when the SCCO2 density was at approximately 840 kg/m3. This study qualitatively performed FTIR analysis during the SCCO2 desorption process. A CO2 antisymmetric stretching mode was observed near a wavenumber of 2340 cm-1. A comparison of loss modulus measurements of pure and SCCO2-treated PEI specimens showed the shifting of loss maxima. This result showed that the plasticization of PEI was achieved through the sorption process of SCCO2.

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Previous investigations on the causal neural mechanisms underlying intertemporal decision making focused on the dorsolateral prefrontal cortex as neural substrate of cognitive control. However, little is known, about the causal contributions of further parts of the frontoparietal control network to delaying gratification, including the pre-supplementary motor area (pre-SMA) and posterior parietal cortex (PPC). Conflicting previous evidence related pre-SMA and PPC either to evidence accumulation processes, choice biases, or response caution. To disentangle between these alternatives, we combined drift diffusion models of decision making with online transcranial magnetic stimulation (TMS) over pre-SMA and PPC during an intertemporal decision task. While we observed no robust effects of PPC TMS, perturbation of pre-SMA activity reduced preferences for larger over smaller rewards. A drift diffusion model of decision making suggests that pre-SMA increases the weight assigned to reward magnitudes during the evidence accumulation process without affecting choice biases or response caution. Taken together, the current findings reveal the computational role of the pre-SMA in value-based decision making, showing that pre-SMA promotes choices of larger, costly rewards by strengthening the sensitivity to reward magnitudes.

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Selecting a set of initial users from a social network in order to maximize the envisaged number of influenced users is known as influence maximization (IM). Researchers have achieved significant advancements in the theoretical design and performance gain of several classical approaches, but these advances are almost reaching their pinnacle. Learning-based IM approaches have emerged recently with a higher generalization to unknown graphs than conventional methods. The development of learning-based IM methods is still constrained by a number of fundamental hardships, including (1) solving the objective function efficiently, (2) struggling to characterize the diverse underlying diffusion patterns, and (3) adapting the solution to different node-centrality-constrained IM variants. To address the aforementioned issues, we design a novel framework DeepIM for generatively characterizing the latent representation of seed sets, as well as learning the diversified information diffusion pattern in a data-driven and end-to-end way. Subsequently, we design a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints. Extensive analyses are conducted over both synthetic and real-world datasets to demonstrate the overall performance of DeepIM.

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BACKGROUND: Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. PURPOSE: This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. METHODS: A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. RESULTS: Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. CONCLUSION: DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

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BACKGROUND: Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high-contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image-guided radiation therapy (IGRT) process. PURPOSE: While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L-DM) framework. METHODS: Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to synthesize lung CT images from dCBCT images and benefit CBCT-based radiotherapy. dCBCT images were used as the constraint for the L-DM. The image quality and Hounsfield unit (HU) values of the synthetic CTs (sCT) images generated by the proposed L-DM were compared to three selected mainstream generation models. RESULTS: We verified our model in both an institutional lung cancer dataset and a selected public dataset. Our L-DM showed significant improvement in the four metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index measure (SSIM). In our institutional dataset, our proposed L-DM decreased the MAE from 101.47 to 37.87 HU and increased the PSNR from 24.97 to 29.89 dB, the NCC from 0.81 to 0.97, and the SSIM from 0.80 to 0.93. In the public dataset, our proposed L-DM decreased the MAE from 173.65 to 58.95 HU, while increasing the PSNR, NCC, and SSIM from 13.07 to 24.05 dB, 0.68 to 0.94, and 0.41 to 0.88, respectively. CONCLUSIONS: The proposed L-DM significantly improved sCT image quality compared to the pre-correction CBCT and three mainstream generative models. Our model can benefit CBCT-based IGRT and other potential clinical applications as it increases the HU accuracy and decreases the artifacts from input CBCT images.

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In this work, we focus on solving the problem of timbre transfer in audio samples. The goal is to transfer the source audio's timbre from one instrument to another while retaining as much of the other musical elements as possible, including loudness, pitch, and melody. While image-to-image style transfer has been used for timbre and style transfer in music recording, the current state of the findings is unsatisfactory. Current timbre transfer models frequently contain samples with unrelated waveforms that affect the quality of the generated audio. The diffusion model has excellent performance in the field of image generation and can generate high-quality images. Inspired by it, we propose a kind of timbre transfer technology based on the diffusion model. To be specific, we first convert the original audio waveform into the constant-Q transform (CQT) spectrogram and adopt image-to-image conversion technology to achieve timbre transfer. Lastly, we reconstruct the produced CQT spectrogram into an audio waveform using the DiffWave model. In both many-to-many and one-to-one timbre transfer tasks, we assessed our model. The experimental results show that compared with the baseline model, the proposed model has good performance in one-to-one and many-to-many timbre transfer tasks, which is an interesting technical progress.

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Sound is one of the important environmental factors that influence individuals' decision-making. However, it is still unclear whether and how natural sounds nudge green product purchases. This study proposes an extension of the Stimulus-Organism-Response (S-O-R) framework, suggesting that natural sounds increase early attentional congruency associated with green products, thereby promoting individuals' green product purchases. To test our theory, we conducted an experiment employing a hierarchical drift-diffusion model (HDDM) and utilized an event-related potentials (ERP) method. Results showed that natural sounds not only increased the purchase rate for green products but also enhanced drift rate in favor of purchasing green products. Additionally, consumers also exhibited a reduced frontal early P2 wave (150-230 ms) in response to green products under natural sounds, indicating that natural sounds increased the early attentional congruency associated with green products. More importantly, neural correlates of early attentional congruency meditated the nudge effect of natural sounds on purchase rate and drift rate for green products. This study contributes to the neural understanding of how natural sounds influence green product purchases and provides actionable implications for market managers to design the green products sales environments.

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Rhythm, as a prominent characteristic of auditory experiences such as speech and music, is known to facilitate attention, yet its contribution to working memory (WM) remains unclear. Here, human participants temporarily retained a 12-tone sequence presented rhythmically or arrhythmically in WM and performed a pitch change-detection task. Behaviorally, while having comparable accuracy, rhythmic tone sequences showed a faster response time and lower response boundaries in decision-making. Electroencephalographic recordings revealed that rhythmic sequences elicited enhanced non-phase-locked beta-band (16 Hz-33 Hz) and theta-band (3 Hz-5 Hz) neural oscillations during sensory encoding and WM retention periods, respectively. Importantly, the two-stage neural signatures were correlated with each other and contributed to behavior. As beta-band and theta-band oscillations denote the engagement of motor systems and WM maintenance, respectively, our findings imply that rhythm facilitates auditory WM through intricate oscillation-based interactions between the motor and auditory systems that facilitate predictive attention to auditory sequences.

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Increased aggression due to gaming addiction is a widespread and highly publicized problem. The underlying processes by which verbal aggression, a more harmful and persistent subcategory of aggression, is affected by gaming addiction may differ from other types of aggression. In this study, data came from 252 randomly recruited current university students (50.79% male, mean age 19.60 years, SD: 1.44 years, range 17 to 29 years). Participants reported gaming addiction and different types of aggression through questionnaires. In addition, two important explanatory processes, inhibitory control, and risk preference, were measured through behavioral experiments. A Bayesian hierarchical drift-diffusion model was employed to interpret the data from the risk preference task. In contrast to previous work, the study found that inhibitory control did not significantly correlate with either gaming addiction or any form of aggression However, the drift rate, a measure of decision-making inclination under risk, partially mediates the relationship between gaming addiction and verbal aggression (but not other forms of aggression). The findings illuminate risk preference under adverse conditions as a key predictor of verbal aggression, offering avenues for early intervention and suggesting game design modifications to mitigate verbal aggression by adjusting reward mechanisms.

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Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce signals with an inadequate signal-to-noise ratio (SNR) for reliable vital sign measurement due to artifacts like head motion and measurement noise. Another pivotal factor is the overlooking of the inherent properties of signals generated by rPPG (rPPG-signals). To address these limitations, we introduce DiffPhys, a novel deep generative model particularly designed to enhance the SNR of rPPG-signals. DiffPhys leverages the conditional diffusion model to learn the distribution of rPPG-signals and uses a refined reverse process to generate rPPG-signals with a higher SNR. Experimental results demonstrate that DiffPhys elevates the SNR of rPPG-signals across within-database and cross-database scenarios, facilitating the extraction of cardiovascular metrics such as HR and HRV with greater precision. This enhancement allows for more accurate monitoring of health conditions in non-clinical settings.

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Background: During the initial phase of the COVID-19 pandemic, the Government of India implemented a nationwide lockdown, sealing borders across states and districts. The northeastern region of India, surrounded by three international borders and connected to mainland India by a narrow passage, faced particular isolation. This isolation resulted in these states forming a relatively closed population. Consequently, the availability of population-based data from Indian Council of Medical Research, tracked through national identification cards, offered a distinctive opportunity to understand the spread of the virus among non-vaccinated and non-exposed populations. This research leverages this dataset to comprehend the repercussions within isolated populations. Methods: The inter-district variability was visualized using geospatial analysis. The patterns do not follow any established grounded theories on disease spread. Out of 7.1 million total data weekly 0.35 million COVID-19-positive northeast data was taken from April 2020 to February 2021 including "date, test result, population density, area, latitude, longitude, district, and state" to identify the spread pattern using a modified reaction-diffusion model (MRD-Model) and Geographic Information System. Findings: The analysis of the closed population group revealed an initial uneven yet rapidly expanding geographical spread characterized by a high diffusion rate α approximately 0.4503 and a lower reaction rate ß approximately 0.0256, which indicated a slower growth trajectory of case numbers rather than exponential escalation. In the latter stages, COVID-19 incidence reached zero in numerous districts, while in others, the reported cases did not exceed 100. Interpretation: The MRD-Model effectively captured the disease transmission dynamics in the abovementioned setting. This enhanced understanding of COVID-19 spread in remote, isolated regions provided by the MRD modelling framework can guide targeted public health strategies for similar isolated areas. Funding: This study is Funded by Indian Council of Medical Research (ICMR).

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BACKGROUND AND OBJECTIVE: Medicine image classification are important methods of traditional medical image analysis, but the trainable data in medical image classification is highly imbalanced and the accuracy of medical image classification models is low. In view of the above two common problems in medical image classification. This study aims to: (i) effectively solve the problem of poor training effect caused by the imbalance of class imbalanced data sets. (ii) propose a network framework suitable for improving medical image classification results, which needs to be superior to existing methods. METHODS: In this paper, we put in the diffusion model multi-scale feature fusion network (DMSFF), which mainly uses the diffusion generation model to overcome imbalanced classes (DMOIC) on highly imbalanced medical image datasets. At the same time, it is processed according to the cropped image augmentation strategy through cropping (IASTC). Based on this, we use the new dataset to design a multi-scale feature fusion network (MSFF) that can fully utilize multiple hierarchical features. The DMSFF network can effectively solve the problems of small and imbalanced samples and low accuracy in medical image classification. RESULTS: We evaluated the performance of the DMSFF network on highly imbalanced medical image classification datasets APTOS2019 and ISIC2018. Compared with other classification models, our proposed DMSFF network achieved significant improvements in classification accuracy and F1 score on two datasets, reaching 0.872, 0.731, and 0.906, 0.836, respectively. CONCLUSIONS: Our newly proposed DMSFF architecture outperforms existing methods on two datasets, and verifies the effectiveness of generative model inverse balance for imbalance class datasets and feature enhancement by multi-scale feature fusion. Further, the method can be applied to other class imbalanced data sets where the results will be improved.

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The classic metaphyseal lesion (CML) is a unique fracture highly specific for infant abuse. This fracture is often subtle in radiographic appearance and commonly occurs in the distal tibia. The development of an automated model that can accurately identify distal tibial radiographs with CMLs is important to assist radiologists in detecting these fractures. However, building such a model typically requires a large and diverse training dataset. To address this problem, we propose a novel diffusion model for data augmentation called masked conditional diffusion model (MaC-DM). In contrast to previous generative models, our approach produces a wide range of realistic-appearing synthetic images of distal tibial radiographs along with their associated segmentation masks. MaC-DM achieves this by incorporating weighted segmentation masks of the distal tibias and CML fracture sites as image conditions for guidance. The augmented images produced by MaC-DM significantly enhance the performance of various commonly used classification models, accurately distinguishing normal distal tibial radiographs from those with CMLs. Additionally, it substantially improves the performance of different segmentation models, accurately labeling areas of the CMLs on distal tibial radiographs. Furthermore, MaC-DM can control the size of the CML fracture in the augmented images.

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Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for training is to adopt a semi-supervised approach. In this regard, generative deep learning models, concretely Generative Adversarial Networks (GANs), have been adapted to semi-supervised training of segmentation tasks. This work proposes MaskGDM, a deep learning architecture combining some ideas from EditGAN, a GAN that jointly models images and their segmentations, together with a generative diffusion model. With careful integration, we find that using a generative diffusion model can improve EditGAN performance results in multiple segmentation datasets, both multi-class and with binary labels. According to the quantitative results obtained, the proposed model improves multi-class image segmentation when compared to the EditGAN and DatasetGAN models, respectively, by [Formula: see text] and [Formula: see text]. Moreover, using the ISIC dataset, our proposal improves the results from other models by up to [Formula: see text] for the binary image segmentation approach.

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Passive sampling is a crucial method for evaluating concentrations of hydrophilic organic compounds in the aquatic environment, but it is insufficiently understood to what extent passive samplers capture the intermittent emissions that frequently occur for this group of compounds. In the present study, silicone sheets and styrene-divinyl benzene-reversed phase sulfonated extraction disks with and without a polyethersulfone membrane were exposed under semi-field conditions in a 31 m3 flume at three different flow velocities. Natural processes and spiking/dilution measures caused aqueous concentrations to vary strongly with time. The data were analyzed using two analytical models that account for these time-variable concentrations: a sampling rate model and a diffusion model. The diffusion model generally gave a better fit of the data than the sampling rate model, but the difference in residual errors was quite small (median errors of 19 vs. 25% for silicone and 22 vs. 25% for SDB-RPS samplers). The sampling rate model was therefore adequate enough to evaluate the time-integrative capabilities of the samplers. Sampler performance was best for SDB-RPS samplers with a polyethersulfone membrane, despite the occurrence of lag times for some compounds (0.1 to 0.4 days). Sampling rates for this design also spanned a narrower range (80 to 110 mL/day) than SDB-RPS samplers without a membrane (100 to 660 mL/day). The effect of biofouling was similar for all compounds and was consistent with a biofouling layer thickness of 150 µm.

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