Search

This paper introduces the Raw Natural Image Noise Dataset (RawNIND), a diverse collection of paired raw images designed to support the development of denoising models that generalize across sensors, image development workflows, and styles. Two denoising methods are proposed: one operates directly on raw Bayer data, leveraging computational efficiency, while the other processes linear RGB images for improved generalization to different sensors, with both preserving flexibility for subsequent development. Both methods outperform traditional approaches which rely on developed images. Additionally, the integration of denoising and compression at the raw data level significantly enhances rate-distortion performance and computational efficiency. These findings suggest a paradigm shift toward raw data workflows for efficient and flexible image processing.

Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements in face restoration but often fall short of capturing fine details. To address this, we introduce a visual style prompt learning framework that utilizes diffusion probabilistic models to explicitly generate visual prompts within the latent space of pre-trained generative models. These prompts are designed to guide the restoration process. To fully utilize the visual prompts and enhance the extraction of informative and rich patterns, we introduce a style-modulated aggregation transformation layer. Extensive experiments and applications demonstrate the superiority of our method in achieving high-quality blind face restoration. The source code is available at \href{https://github.com/LonglongaaaGo/VSPBFR}{https://github.com/LonglongaaaGo/VSPBFR}., Comment: Published at Pattern Recognition; 13 pages, 11 figures

Existing facial editing methods have achieved remarkable results, yet they often fall short in supporting multimodal conditional local facial editing. One of the significant evidences is that their output image quality degrades dramatically after several iterations of incremental editing, as they do not support local editing. In this paper, we present a novel multimodal generative and fusion framework for globally-consistent local facial editing (FACEMUG) that can handle a wide range of input modalities and enable fine-grained and semantic manipulation while remaining unedited parts unchanged. Different modalities, including sketches, semantic maps, color maps, exemplar images, text, and attribute labels, are adept at conveying diverse conditioning details, and their combined synergy can provide more explicit guidance for the editing process. We thus integrate all modalities into a unified generative latent space to enable multimodal local facial edits. Specifically, a novel multimodal feature fusion mechanism is proposed by utilizing multimodal aggregation and style fusion blocks to fuse facial priors and multimodalities in both latent and feature spaces. We further introduce a novel self-supervised latent warping algorithm to rectify misaligned facial features, efficiently transferring the pose of the edited image to the given latent codes. We evaluate our FACEMUG through extensive experiments and comparisons to state-of-the-art (SOTA) methods. The results demonstrate the superiority of FACEMUG in terms of editing quality, flexibility, and semantic control, making it a promising solution for a wide range of local facial editing tasks., Comment: Published at IEEE Transactions on Visualization and Computer Graphics; 21 pages, 26 figures

Computer-aided diagnosis (CAD) is today considered a vital tool in the field of biological image categorization, segmentation, and other related tasks. The current breakthrough in computer vision algorithms and deep learning approaches has substantially enhanced the effectiveness and precision of apps built to recognize and locate regions of interest inside medical photographs. Among the different disciplines of medical image analysis, bone fracture detection, and classification have exhibited exceptional potential. Although numerous imaging modalities are applied in medical diagnostics, X-rays are particularly significant in this sector due to their broad availability, ease of use, and extensive information extraction capabilities. This research studies bone fracture categorization using the FracAtlas dataset, which comprises 4,083 musculoskeletal radiography pictures. Given the transformational development in transfer learning, particularly its efficacy in medical image processing, we deploy an attention-based transfer learning model to detect bone fractures in X-ray scans. Though the popular InceptionV3 and DenseNet121 deep learning models have been widely used, they still have the potential to be employed in crucial jobs. In this research, alongside transfer learning, a separate attention mechanism is also applied to boost the capabilities of transfer learning techniques. Through rigorous optimization, our model achieves a state-of-the-art accuracy of more than 90\% in fracture classification. This work contributes to the expanding corpus of research focused on the application of transfer learning to medical imaging, notably in the context of X-ray processing, and emphasizes the promise for additional exploration in this domain., Comment: 6 pages, 3 tables, 4 figures, submitted to 27th International Conference on Computer and Information Technology (ICCIT) to be held during 20-22 December, 2024

We propose depth from coupled optical differentiation, a low-computation passive-lighting 3D sensing mechanism. It is based on our discovery that per-pixel object distance can be rigorously determined by a coupled pair of optical derivatives of a defocused image using a simple, closed-form relationship. Unlike previous depth-from-defocus (DfD) methods that leverage spatial derivatives of the image to estimate scene depths, the proposed mechanism's use of only optical derivatives makes it significantly more robust to noise. Furthermore, unlike many previous DfD algorithms with requirements on aperture code, this relationship is proved to be universal to a broad range of aperture codes. We build the first 3D sensor based on depth from coupled optical differentiation. Its optical assembly includes a deformable lens and a motorized iris, which enables dynamic adjustments to the optical power and aperture radius. The sensor captures two pairs of images: one pair with a differential change of optical power and the other with a differential change of aperture scale. From the four images, a depth and confidence map can be generated with only 36 floating point operations per output pixel (FLOPOP), more than ten times lower than the previous lowest passive-lighting depth sensing solution to our knowledge. Additionally, the depth map generated by the proposed sensor demonstrates more than twice the working range of previous DfD methods while using significantly lower computation.

Recently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references. Most existing methods cluster around two classes: i) Stein's Unbiased Risk Estimate (SURE) and similar approaches that assume full knowledge of the noise distribution, and ii) Noise2Self and similar cross-validation methods that require very mild knowledge about the noise distribution. The first class of methods tends to be impractical, as the noise level is often unknown in real-world applications, and the second class is often suboptimal compared to supervised learning. In this paper, we provide a theoretical framework that characterizes this expressivity-robustness trade-off and propose a new approach based on SURE, but unlike the standard SURE, does not require knowledge about the noise level. Throughout a series of experiments, we show that the proposed estimator outperforms other existing self-supervised methods on various imaging inverse problems.

This study proposes a neural disparity field (NDF) that establishes an implicit, continuous representation of scene disparity based on a neural field and an iterative approach to address the inverse problem of NDF reconstruction from light-field data. NDF enables seamless and precise characterization of disparity variations in three-dimensional scenes and can discretize disparity at any arbitrary resolution, overcoming the limitations of traditional disparity maps that are prone to sampling errors and interpolation inaccuracies. The proposed NDF network architecture utilizes hash encoding combined with multilayer perceptrons to capture detailed disparities in texture levels, thereby enhancing its ability to represent the geometric information of complex scenes. By leveraging the spatial-angular consistency inherent in light-field data, a differentiable forward model to generate a central view image from the light-field data is developed. Based on the forward model, an optimization scheme for the inverse problem of NDF reconstruction using differentiable propagation operators is established. Furthermore, an iterative solution method is adopted to reconstruct the NDF in the optimization scheme, which does not require training datasets and applies to light-field data captured by various acquisition methods. Experimental results demonstrate that high-quality NDF can be reconstructed from light-field data using the proposed method. High-resolution disparity can be effectively recovered by NDF, demonstrating its capability for the implicit, continuous representation of scene disparities., Comment: 12 pages, 7 figures

This work introduces two new distance metrics for comparing labeled arrays, which are common outputs of image segmentation algorithms. Each pixel in an image is assigned a label, with binary segmentation providing only two labels ('foreground' and 'background'). These can be represented by a simple binary matrix and compared using pixel differences. However, many segmentation algorithms output multiple regions in a labeled array. We propose two distance metrics, named LAD and MADLAD, that calculate the distance between two labeled images. By doing so, the accuracy of different image segmentation algorithms can be evaluated by measuring their outputs against a 'ground truth' labeling. Both proposed metrics, operating with a complexity of $O(N)$ for images with $N$ pixels, are designed to quickly identify similar labeled arrays, even when different labeling methods are used. Comparisons are made between images labeled manually and those labeled by segmentation algorithms. This evaluation is crucial when searching through a space of segmentation algorithms and their hyperparameters via a genetic algorithm to identify the optimal solution for automated segmentation, which is the goal in our lab, SEE-Insight. By measuring the distance from the ground truth, these metrics help determine which algorithm provides the most accurate segmentation., Comment: Submitted to: Electronic Letters on Computer Vision and Image Analysis

Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In this study, we introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parameter-free and can be adopted in existing computational pathology pipelines with no significant changes. We evaluate the effectiveness of our method through experiments using a deep-learning pipeline for automatic nuclei segmentation on colorectal images. Our results show that by leveraging multiple reference images, better results can be achieved when generalizing to external data, where the staining can widely differ from the training set.

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches simultaneously. However, previous multi-view approaches could not typically calculate uncertainty by nature, and they generally integrate features from different views in a black-box fashion, hence compromising reliability as well as interpretability of the resulting models. In this work, we propose a new multi-view method based on evidential learning, referred to as MERIT, which tackles the two challenges in a unified framework. MERIT enables uncertainty quantification of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability. Specifically, MERIT models the prediction from each sub-view as an opinion with quantified uncertainty under the guidance of the subjective logic theory. Furthermore, a distribution-aware base rate is introduced to enhance performance, particularly in scenarios involving class distribution shifts. Finally, MERIT adopts a feature-specific combination rule to explicitly fuse multi-view predictions, thereby enhancing interpretability. Results have showcased the effectiveness of the proposed MERIT, highlighting the reliability and offering both ad-hoc and post-hoc interpretability. They also illustrate that MERIT can elucidate the significance of each view in the decision-making process for liver fibrosis staging., Comment: Submitted to Medical Image Analysis

A positive margin may result in an increased risk of local recurrences after breast retention surgery for any malignant tumour. In order to reduce the number of positive margins would offer surgeon real-time intra-operative information on the presence of positive resection margins. This study aims to design an intra-operative tumour margin evaluation scheme by using specimen mammography in breast-conserving surgery. Total of 30 cases were evaluated and compared with the manually determined contours by experienced physicians and pathology report. The proposed method utilizes image thresholding to extract regions of interest and then performs a deep learning model, i.e. SegNet, to segment tumour tissue. The margin width of normal tissues surrounding it is evaluated as the result. The desired size of margin around the tumor was set for 10 mm. The smallest average difference to manual sketched margin (6.53 mm +- 5.84). In the all case, the SegNet architecture was utilized to obtain tissue specimen boundary and tumor contour, respectively. The simulation results indicated that this technology is helpful in discriminating positive from negative margins in the intra-operative setting. The aim of proposed scheme was a potential procedure in the intra-operative measurement system. The experimental results reveal that deep learning techniques can draw results that are consistent with pathology reports., Comment: 1 pages, 6 figures and 2 tables

Self-supervised contrastive learning strategy has attracted remarkable attention due to its exceptional ability in representation learning. However, current contrastive learning tends to learn global coarse-grained representations of the image that benefit generic object recognition, whereas such coarse-grained features are insufficient for fine-grained visual recognition. In this paper, we present to incorporate the subtle local fine-grained feature learning into global self-supervised contrastive learning through a pure self-supervised global-local fine-grained contrastive learning framework. Specifically, a novel pretext task called Local Discrimination (LoDisc) is proposed to explicitly supervise self-supervised model's focus towards local pivotal regions which are captured by a simple-but-effective location-wise mask sampling strategy. We show that Local Discrimination pretext task can effectively enhance fine-grained clues in important local regions, and the global-local framework further refines the fine-grained feature representations of images. Extensive experimental results on different fine-grained object recognition tasks demonstrate that the proposed method can lead to a decent improvement in different evaluation settings. Meanwhile, the proposed method is also effective in general object recognition tasks., Comment: 11 pages, submitted

This study aims to evaluate the functional status of bilateral neurovascular bundles (NVBs) using pulsed wave Doppler ultrasound in patients undergoing prostate radiotherapy (RT). Sixty-two patients (mean age: 66.1 +/- 7.2 years) underwent transrectal ultrasound scan using a conventional ultrasound scanner, a 7.5 MHz bi-plane probe and a mechanical stepper. The ultrasound protocol comprised 3 steps: 1) 3D B-mode scans of the entire prostate, 2) localization of NVBs using color flow Doppler imaging, and 3) measurement of NVB function using pulsed wave Doppler. Five pulsed Doppler waveform features were extracted: peak systolic velocity (PSV), end-diastolic velocity (EDV), mean velocity (Vm), resistive index (RI), and pulsatile index (PI). In summary, this study presents a Doppler evaluation of NVBs in patients undergoing prostate RT. It highlights substantial differences in Doppler ultrasound waveform features between bilateral NVBs. The proposed ultrasound method may prove valuable as clinicians strive to deliver NVB-sparing RT to preserve sexual function effectively and enhance patients' overall well-being., Comment: 14 pages, 4 figures

Normalized cross-correlation is the reference approach to carry out template matching on images. When it is computed in Fourier space, it can handle efficiently template translations but it cannot do so with template rotations. Including rotations requires sampling the whole space of rotations, repeating the computation of the correlation each time. This article develops an alternative mathematical theory to handle efficiently, at the same time, rotations and translations. Our proposal has a reduced computational complexity because it does not require to repeatedly sample the space of rotations. To do so, we integrate the information relative to all rotated versions of the template into a unique symmetric tensor template -which is computed only once per template-. Afterward, we demonstrate that the correlation between the image to be processed with the independent tensor components of the tensorial template contains enough information to recover template instance positions and rotations. Our proposed method has the potential to speed up conventional template matching computations by a factor of several magnitude orders for the case of 3D images.

Audio-Visual Segmentation (AVS) aims to extract the sounding object from a video frame, which is represented by a pixel-wise segmentation mask for application scenarios such as multi-modal video editing, augmented reality, and intelligent robot systems. The pioneering work conducts this task through dense feature-level audio-visual interaction, which ignores the dimension gap between different modalities. More specifically, the audio clip could only provide a Global semantic label in each sequence, but the video frame covers multiple semantic objects across different Local regions, which leads to mislocalization of the representationally similar but semantically different object. In this paper, we propose a Cross-modal Cognitive Consensus guided Network (C3N) to align the audio-visual semantics from the global dimension and progressively inject them into the local regions via an attention mechanism. Firstly, a Cross-modal Cognitive Consensus Inference Module (C3IM) is developed to extract a unified-modal label by integrating audio/visual classification confidence and similarities of modality-agnostic label embeddings. Then, we feed the unified-modal label back to the visual backbone as the explicit semantic-level guidance via a Cognitive Consensus guided Attention Module (CCAM), which highlights the local features corresponding to the interested object. Extensive experiments on the Single Sound Source Segmentation (S4) setting and Multiple Sound Source Segmentation (MS3) setting of the AVSBench dataset demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. Code is available at https://github.com/ZhaofengSHI/AVS-C3N., Comment: Accepted by IEEE Transactions on Multimedia; 16 pages

Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the generative adversarial network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model., Comment: 6 pages, 3 figures

Psychological trait estimation from external factors such as movement and appearance is a challenging and long-standing problem in psychology, and is principally based on the psychological theory of embodiment. To date, attempts to tackle this problem have utilized private small-scale datasets with intrusive body-attached sensors. Potential applications of an automated system for psychological trait estimation include estimation of occupational fatigue and psychology, and marketing and advertisement. In this work, we propose PsyMo (Psychological traits from Motion), a novel, multi-purpose and multi-modal dataset for exploring psychological cues manifested in walking patterns. We gathered walking sequences from 312 subjects in 7 different walking variations and 6 camera angles. In conjunction with walking sequences, participants filled in 6 psychological questionnaires, totalling 17 psychometric attributes related to personality, self-esteem, fatigue, aggressiveness and mental health. We propose two evaluation protocols for psychological trait estimation. Alongside the estimation of self-reported psychological traits from gait, the dataset can be used as a drop-in replacement to benchmark methods for gait recognition. We anonymize all cues related to the identity of the subjects and publicly release only silhouettes, 2D / 3D human skeletons and 3D SMPL human meshes., Comment: Accepted at 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

The analysis of patterns of walking is an important area of research that has numerous applications in security, healthcare, sports and human-computer interaction. Lately, walking patterns have been regarded as a unique fingerprinting method for automatic person identification at a distance. In this work, we propose a novel gait recognition architecture called Gait Pyramid Transformer (GaitPT) that leverages pose estimation skeletons to capture unique walking patterns, without relying on appearance information. GaitPT adopts a hierarchical transformer architecture that effectively extracts both spatial and temporal features of movement in an anatomically consistent manner, guided by the structure of the human skeleton. Our results show that GaitPT achieves state-of-the-art performance compared to other skeleton-based gait recognition works, in both controlled and in-the-wild scenarios. GaitPT obtains 82.6% average accuracy on CASIA-B, surpassing other works by a margin of 6%. Moreover, it obtains 52.16% Rank-1 accuracy on GREW, outperforming both skeleton-based and appearance-based approaches.

Gait, the manner of walking, has been proven to be a reliable biometric with uses in surveillance, marketing and security. A promising new direction for the field is training gait recognition systems without explicit human annotations, through self-supervised learning approaches. Such methods are heavily reliant on strong augmentations for the same walking sequence to induce more data variability and to simulate additional walking variations. Current data augmentation schemes are heuristic and cannot provide the necessary data variation as they are only able to provide simple temporal and spatial distortions. In this work, we propose GaitMorph, a novel method to modify the walking variation for an input gait sequence. Our method entails the training of a high-compression model for gait skeleton sequences that leverages unlabelled data to construct a discrete and interpretable latent space, which preserves identity-related features. Furthermore, we propose a method based on optimal transport theory to learn latent transport maps on the discrete codebook that morph gait sequences between variations. We perform extensive experiments and show that our method is suitable to synthesize additional views for an input sequence., Comment: 4 Tables, 6 Figures, 1 Algorithm

The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in nature. Federated learning, which enables cooperative model training without direct data exchange, presents a promising solution. Nevertheless, the inherent vulnerabilities of federated learning necessitate further privacy safeguards. This study addresses this need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification. We introduce a novel differentially private federated learning model and meticulously examine its impacts on privacy preservation and model performance. Our research confirms the existence of a trade-off between model accuracy and privacy settings. However, we demonstrate that strategic calibration of the privacy budget in differential privacy can uphold robust image classification performance while providing substantial privacy protection., Comment: 18 pages, 3 figures, 1 table, 1 algorithm

The mobile cloud gaming industry has been rapidly growing over the last decade. When streaming gaming videos are transmitted to customers' client devices from cloud servers, algorithms that can monitor distorted video quality without having any reference video available are desirable tools. However, creating No-Reference Video Quality Assessment (NR VQA) models that can accurately predict the quality of streaming gaming videos rendered by computer graphics engines is a challenging problem, since gaming content generally differs statistically from naturalistic videos, often lacks detail, and contains many smooth regions. Until recently, the problem has been further complicated by the lack of adequate subjective quality databases of mobile gaming content. We have created a new gaming-specific NR VQA model called the Gaming Video Quality Evaluator (GAMIVAL), which combines and leverages the advantages of spatial and temporal gaming distorted scene statistics models, a neural noise model, and deep semantic features. Using a support vector regression (SVR) as a regressor, GAMIVAL achieves superior performance on the new LIVE-Meta Mobile Cloud Gaming (LIVE-Meta MCG) video quality database., Comment: Accepted to IEEE SPL 2023. The implementation of GAMIVAL has been made available online: https://github.com/lskdream/GAMIVAL

Differentiable rendering is a growing field that is at the heart of many recent advances in solving inverse graphics problems, such as the reconstruction of 3D scenes from 2D images. By making the rendering process differentiable, one can compute gradients of the output image with respect to the different scene parameters efficiently using automatic differentiation. Interested in the potential of such methods for the analysis of fluorescence microscopy images, we introduce deltaMic, a microscopy renderer that can generate a 3D fluorescence microscopy image from a 3D scene in a fully differentiable manner. By convolving the meshes in the scene with the point spread function (PSF) of the microscope, that characterizes the response of its imaging system to a point source, we emulate the 3D image creation process of fluorescence microscopy. This is achieved by computing the Fourier transform (FT) of the mesh and performing the convolution in the Fourier domain. Naive implementation of such mesh FT is however slow, inefficient, and sensitive to numerical precision. We solve these difficulties by providing a memory and computationally efficient fully differentiable GPU implementation of the 3D mesh FT. We demonstrate the potential of our method by reconstructing complex shapes from artificial microscopy images. Eventually, we apply our renderer to real confocal fluorescence microscopy images of embryos to accurately reconstruct the multicellular shapes of these cell aggregates., Comment: 11 pages, 8 figures

Recent advances in unsupervised learning have highlighted the possibility of learning to reconstruct signals from noisy and incomplete linear measurements alone. These methods play a key role in medical and scientific imaging and sensing, where ground truth data is often scarce or difficult to obtain. However, in practice, measurements are not only noisy and incomplete but also quantized. Here we explore the extreme case of learning from binary observations and provide necessary and sufficient conditions on the number of measurements required for identifying a set of signals from incomplete binary data. Our results are complementary to existing bounds on signal recovery from binary measurements. Furthermore, we introduce a novel self-supervised learning approach, which we name SSBM, that only requires binary data for training. We demonstrate in a series of experiments with real datasets that SSBM performs on par with supervised learning and outperforms sparse reconstruction methods with a fixed wavelet basis by a large margin., Comment: https://openreview.net/forum?id=ioFIAQOBOS

Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the benefits of extracting domain-invariant representations on domain generalization. However, the interpretability of domain-invariant features remains a great challenge. To address this problem, we propose an interpretable Bayesian framework (BayeSeg) through Bayesian modeling of image and label statistics to enhance model generalizability for medical image segmentation. Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively. Then, we model the segmentation as a locally smooth variable only related to the shape. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these explainable variables. The framework is implemented with neural networks, and thus is referred to as deep Bayesian segmentation. Quantitative and qualitative experimental results on prostate segmentation and cardiac segmentation tasks have shown the effectiveness of our proposed method. Moreover, we investigated the interpretability of BayeSeg by explaining the posteriors and analyzed certain factors that affect the generalization ability through further ablation studies. Our code will be released via https://zmiclab.github.io/projects.html, once the manuscript is accepted for publication., Comment: Submitted to Medical Image Analysis

Craniofacial Superimposition involves the superimposition of an image of a skull with a number of ante-mortem face images of an individual and the analysis of their morphological correspondence. Despite being used for one century, it is not yet a mature and fully accepted technique due to the absence of solid scientific approaches, significant reliability studies, and international standards. In this paper we present a comprehensive experimentation on the limitations of Craniofacial Superimposition as a forensic identification technique. The study involves different experiments over more than 1 Million comparisons performed by a landmark-based automatic 3D/2D superimposition method. The total sample analyzed consists of 320 subjects and 29 craniofacial landmarks., Comment: 7 pages, 4 figures. To be submitted to Scientific Reports