Miracle-dz/UniFAR-FTUs huggingface dataset:
ARPM-net: A novel CNN-based adversarial method with Markov Random Field enhancement for prostate and organs at risk segmentation in pelvic CT images

PURPOSE The research is to develop a novel CNN-based adversarial deep learning method to improve and expedite the multi-organ semantic segmentation of CT images and to generate accurate contours on pelvic CT images. METHODS Planning CT and structure datasets for 120 patients with intact prostate cancer were retrospectively selected and divided for 10-fold cross-validation. The proposed adversarial multi-residual multi-scale pooling Markov Random Field (MRF) enhanced network (ARPM-net) implements an adversarial training scheme. A segmentation network and a discriminator network were trained jointly, and only the segmentation network was used for prediction. The segmentation network integrates a newly designed MRF block into a variation of multi-residual U-net. The discriminator takes the product of the original CT and the prediction/ground-truth as input and classifies the input into fake/real. The segmentation network and discriminator network can be trained jointly as a whole, or the discriminator can be used for fine-tuning after the segmentation network is coarsely trained. Multi-scale pooling layers were introduced to preserve spatial resolution during pooling using less memory compared to atrous convolution layers. An adaptive loss function was proposed to enhance the training on small or low contrast organs. The accuracy of modeled contours was measured with the Dice similarity coefficient (DSC), Average Hausdorff Distance (AHD), Average Surface Hausdorff Distance (ASHD), and relative Volume Difference (VD) using clinical contours as references to the ground-truth. The proposed ARPM-net method was compared to several state-of-the-art deep learning methods. RESULTS ARPM-net outperformed several existing deep learning approaches and MRF methods and achieved state-of-the-art performance on a testing dataset. On the test set with 20 cases, the average DSC on the prostate, bladder, rectum, left-femur, and right-femur were 0.88( ± 0.11), 0.97( ± 0.07), 0.86( ± 0.12), 0.97( ± 0.01), and 0.97( ± 0.01), respectively. The Average HD (mm) on these organs were 1.58( ± 1.77), 1.91( ± 1.29), 3.14( ± 2.39), 1.76( ± 1.57), and 1.92( ± 1.01). The Average Surface HD (mm) on these organs are 2.11( ± 2.03), 2.36( ± 2.43), 3.05( ± 2.11), 1.99( ± 1.66), and 2.00( ± 2.07). CONCLUSION ARPM-net was designed for the automatic segmentation of pelvic CT images. With adversarial fine-tuning, ARPM-net produces state-of-the-art accurate contouring of multiple organs on CT images and has the potential to facilitate routine pelvic cancer radiation therapy planning process.
Acoustic effects of medical, cloth, and transparent face masks on speech signals

Face masks muffle speech and make communication more difficult, especially for people with hearing loss. This study examines the acoustic attenuation caused by different face masks, including medical, cloth, and transparent masks, using a head-shaped loudspeaker and a live human talker. The results suggest that all masks attenuate frequencies above 1 kHz, that attenuation is greatest in front of the talker, and that there is substantial variation between mask types, especially cloth masks with different materials and weaves. Transparent masks have poor acoustic performance compared to both medical and cloth masks. Most masks have little effect on lapel microphones, suggesting that existing sound reinforcement and assistive listening systems may be effective for verbal communication with masks.
Bipartite Graph Reasoning GANs for Person Image Generation

We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task. The proposed graph generator mainly consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block aims to reason the crossing long-range relations between the source pose and the target pose in a bipartite graph, which mitigates some challenges caused by pose deformation. Moreover, we propose a new Interaction-and-Aggregation (IA) block to effectively update and enhance the feature representation capability of both person's shape and appearance in an interactive way. Experiments on two challenging and public datasets, i.e., Market-1501 and DeepFashion, show the effectiveness of the proposed BiGraphGAN in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at this https URL.
Social distancing beliefs and human mobility: Evidence from Twitter

We construct a novel database containing hundreds of thousands geotagged messages related to the COVID-19 pandemic sent on Twitter. We create a daily index of social distancing—at the state level—to capture social distancing beliefs by analyzing the number of tweets containing keywords such as “stay home”, “stay safe”, “wear mask”, “wash hands” and “social distancing”. We find that an increase in the Twitter index of social distancing on day t-1 is associated with a decrease in mobility on day t. We also find that state orders, an increase in the number of COVID-19 cases, precipitation and temperature contribute to reducing human mobility. Republican states are also less likely to enforce social distancing. Beliefs shared on social networks could both reveal the behavior of individuals and influence the behavior of others. Our findings suggest that policy makers can use geotagged Twitter data—in conjunction with mobility data—to better understand individual voluntary social distancing actions.
Deep Network with Approximation Error Being Reciprocal of Width to Power of Square Root of Depth

A new network with super-approximation power is introduced. This network is built with Floor (⌊x⌋) or ReLU (max{0,x}) activation function in each neuron; hence, we call such networks Floor-ReLU networks. For any hyperparameters N∈N+ and L∈N+, we show that Floor-ReLU networks with width max{d,5N+13} and depth 64dL+3 can uniformly approximate a Hölder function f on [0,1]d with an approximation error 3λdα/2N-αL, where α∈(0,1] and λ are the Hölder order and constant, respectively. More generally for an arbitrary continuous function f on [0,1]d with a modulus of continuity ωf(·), the constructive approximation rate is ωf(dN-L)+2ωf(d)N-L. As a consequence, this new class of networks overcomes the curse of dimensionality in approximation power when the variation of ωf(r) as r→0 is moderate (e.g., ωf(r)≲rα for Hölder continuous functions), since the major term to be considered in our approximation rate is essentially d times a function of N and L independent of d within the modulus of continuity.
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their “maximum motion displacement” plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low.
Unified Representation Learning for Cross Model Compatibility

We propose a unified representation learning framework to address the Cross Model Compatibility (CMC) problem in the context of visual search applications. Cross compatibility between different embedding models enables the visual search systems to correctly recognize and retrieve identities without re-encoding user images, which are usually not available due to privacy concerns. While there are existing approaches to address CMC in face identification, they fail to work in a more challenging setting where the distributions of embedding models shift drastically. The proposed solution improves CMC performance by introducing a light-weight Residual Bottleneck Transformation (RBT) module and a new training scheme to optimize the embedding spaces. Extensive experiments demonstrate that our proposed solution outperforms previous approaches by a large margin for various challenging visual search scenarios of face recognition and person re-identification.
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions

Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different sources e. g. on-board sensors like radar, camera, ultrasound and lidar, offline map data or backend information. The second challenge comprises in finding an abstract representation of this aggregated information with suitable interfaces for different driving functions and traffic situations. To overcome these challenges, an extended environment model is a reasonable choice.In this paper, we show that role-based motion predictions in combination with v2x-extended environment models are able to contribute to increased traffic safety and driving comfort. Thus, we combine the mentioned research areas and show possible improvements, using the example of a threading process at a motorway access road. Furthermore, it is shown that already an average v2x equipment penetration of 80 % can lead to a significant improvement of 0.33 m/s2 of the total acceleration and 12 m more safety distance compared to non v2x-equipped vehicles during the threading process.
Navarro-Frenk-White dark matter profile and the dark halos around disk systems

Context. The Λ cold dark matter (ΛCDM) scenario is able to describe the Universe at large scales, but clearly shows some serious difficulties at small scales. The core-cusp question is one of these difficulties: the inner dark matter (DM) density profiles of spiral galaxies generally appear to be cored, without the r−1 profile that is predicted by N-body simulations in the above scenario. Aims. It is well known that in a more physical context, the baryons in the galaxy might backreact and erase the original cusp through supernova explosions. Before the efficiency and the presence of this effect is investigated, it is important to determine how wide and frequent the discrepancy between observed and N-body-predicted profiles is and what its features are. Methods. We used more than 3200 quite extended rotation curves (RCs) of good quality and high resolution of disk systems that included normal and dwarf spirals as well as low surface brightness galaxies. The curves cover all magnitude ranges. All these RCs were condensed into 26 coadded RCs, each of them built with individual RCs of galaxies of similar luminosity and morphology. We performed mass models of these 26 RCs using the Navarro-Frenk-White (NFW) profile for the contribution of the DM halo to the circular velocity and the exponential Freeman disk for the contribution of the stellar disk. Results. The fits are generally poor in all the 26 cases: in several cases, we find χred2 > 2. Moreover, the best-fitting values of three parameters of the model (c, MD, and Mvir) combined with those of their 1σ uncertainty clearly contradict well-known expectations of the ΛCDM scenario. We also tested the scaling relations that exist in spirals with the outcome of the current mass modeling: the modeling does not account for these scaling relations. Conclusions. The results of testing the NFW profile in disk systems indicate that this DM halo density law cannot account for the kinematics of the whole family of disk galaxies. It is therefore mandatory for the success of the ΛCDM scenario in any disk galaxy of any luminosity or maximum rotational velocity to transform initial cusps into the observed cores.
Persistence behaviour of heat and momentum fluxes in convective surface layer turbulence

The characterization of heat and momentum fluxes in wall-bounded turbulence is of paramount importance for a plethora of applications, ranging from engineering to Earth sciences. However, how the turbulent structures associated with velocity and temperature fluctuations interact to produce the emergent flux signatures, is not evident till date. In this work, we investigate this fundamental issue by studying the switching patterns of intermittently occurring turbulent fluctuations from one state to another, a phenomenon called persistence. We discover that the persistence patterns for heat and momentum fluxes are widely different. Moreover, we uncover power-law scaling and length scales of turbulent motions that cause this behavior. Furthermore, by separating the phases and amplitudes of flux events, we explain the origin and differences between heat and momentum transfer efficiencies in convective turbulence. Our findings provide new understanding on the connection between flow organization and flux generation mechanisms, two cornerstones of turbulence research.
Gas Exchange, Metabolism, and "Ventilation" in Gelatinous Frog Egg Masses

Earlier studies of gelatinous egg masses laid by many amphibians suggested that the egg mass center was extremely hypoxic and that embryos located there suffered delayed growth and high mortality. The present study has reassessed conditions for respiration within the spherical egg masses of the pickerel frog, Rana palustris. Partial pressures of O₂ and CO₂, and the pH of egg jelly, as well as lactate concentration of embryonic tissue, were measured at 1-cm depth increments from surface to center of the egg mass. Oxygen uptake of intact egg masses and eggs separated from the mass was determined as a function of temperature and ambient O₂ levels. Although significant gradients for O₂, CO₂, and pH existed from surface to center, the magnitude of these gradients was too small to directly affect O₂ uptake of the egg mass or individual eggs under a variety of experimental conditions likely to occur in nature. Exchange of gases between the egg mass surface and center occurs primarily by diffusion through the egg jelly, which has a Krogh's O₂ diffusion constant 75% that of distilled water. However, intense convection of fluid within each egg, generated by the embryo's ciliated epithelium, functionally reduces by 17% the diffusion distance between egg mass surface and center by generating a convective "ventilation. " These data collectively refute earlier findings by demonstrating that the center of ranid egg masses is not severely hypoxic and not limiting to aerobic metabolism.
Sharp Multiple Instance Learning for DeepFake Video Detection

With the rapid development of facial manipulation techniques, face forgery has received considerable attention in multimedia and computer vision community due to security concerns. Existing methods are mostly designed for single-frame detection trained with precise image-level labels or for video-level prediction by only modeling the inter-frame inconsistency, leaving potential high risks for DeepFake attackers. In this paper, we introduce a new problem of partial face attack in DeepFake video, where only video-level labels are provided but not all the faces in the fake videos are manipulated. We address this problem by multiple instance learning framework, treating faces and input video as instances and bag respectively. A sharp MIL (S-MIL) is proposed which builds direct mapping from instance embeddings to bag prediction, rather than from instance embeddings to instance prediction and then to bag prediction in traditional MIL. Theoretical analysis proves that the gradient vanishing in traditional MIL is relieved in S-MIL. To generate instances that can accurately incorporate the partially manipulated faces, spatial-temporal encoded instance is designed to fully model the intra-frame and inter-frame inconsistency, which further helps to promote the detection performance. We also construct a new dataset FFPMS for partially attacked DeepFake video detection, which can benefit the evaluation of different methods at both frame and video levels. Experiments on FFPMS and the widely used DFDC dataset verify that S-MIL is superior to other counterparts for partially attacked DeepFake video detection. In addition, S-MIL can also be adapted to traditional DeepFake image detection tasks and achieve state-of-the-art performance on single-frame datasets.
Lifted Multiplicity Codes

Lifted Reed-Solomon codes and multiplicity codes are two classes of evaluation codes that allow for the design of high-rate codes that can recover every codeword or information symbol from many disjoint sets. Recently, the underlying approaches have been combined to construct lifted bi-variate multiplicity codes, that can further improve on the rate. We continue the study of these codes by providing lower bounds on the rate and distance for lifted multiplicity codes obtained from polynomials in an arbitrary number of variables. Specifically, we investigate a subcode of a lifted multiplicity code formed by the linear span of $m$-variate monomials whose restriction to an arbitrary line in $\mathbb{F}_q^m$ is equivalent to a low-degree uni-variate polynomial. We find the tight asymptotic behavior of the fraction of such monomials when the number of variables $m$ is fixed and the alphabet size $q=2^\ell$ is large. For some parameter regimes, lifted multiplicity codes are then shown to have a better trade-off between redundancy and the number of disjoint recovering sets for every codeword or information symbol than previously known constructions. Additionally, we present a local self-correction algorithm for lifted multiplicity codes.
Modeling Unresolved Binaries of Open Clusters in the Color–Magnitude Diagram. I. Method and Application of NGC 3532

The binary properties of open clusters place crucial constraints on star formation theory and clusters' dynamical evolution. We develop a comprehensive approach that models the color-magnitude diagram (CMD) of the cluster members as the mixture of single stars and photometric unresolved binaries. This method enables us to infer the binary properties, including the binary fraction $f_\mathrm{b}$ and binary mass-ratio distribution index $\gamma_q$ when a power-law is assumed, with high accuracy and precision, which were unfeasible in conventional methods. We employ a modified Gaussian process to determine the main sequence ridge line and its scatter from the observed CMD as model input. As a first example, we apply the method to the open cluster NGC3532 with the Gaia DR2 photometry. For the cluster members within a magnitude range corresponding to FGK dwarfs, we obtain $f_\mathrm{b} = 0.267\pm0.019$ and $\gamma_q = - 0.10\pm0.22$ for binaries with mass ratio $q > 0.2$. The $f_\mathrm{b}$ value is consistent with the previous work on NGC3532 and smaller than that of field stars. The close to zero $\gamma_q$ indicates that the mass ratios of binaries follow a nearly uniform distribution. For the first time, we unveil that the stars with smaller mass or in the inner region tend to have lower $f_\mathrm{b}$ and more positive value of $\gamma_q$ due to the lack of low mass-ratio binaries. The clear dependences of binary properties on mass and radius are most likely caused by the internal dynamics.
NonUniqueness in Law for Two-Dimensional Navier-Stokes Equations with Diffusion Weaker than a Full Laplacian

We study the two-dimensional Navier-Stokes equations forced by random noise with a diffusive term generalized via a fractional Laplacian that has a positive exponent strictly less than one. Because intermittent jets are inherently three-dimensional, we instead adapt the theory of intermittent form of the two-dimensional stationary flows to the stochastic approach presented by Hofmanov$\acute{\mathrm{a}}$, Zhu $\&$ Zhu (2019, arXiv:1912.11841 [math.PR]) and prove its non-uniqueness in law.
Estimation of Static Community Memberships from Temporal Network Data

This article studies the estimation of static community memberships from temporally correlated pair interactions represented by an $N$-by-$N$-by-$T$ tensor where $N$ is the number of nodes and $T$ is the length of the time horizon. We present several estimation algorithms, both offline and online, which fully utilise the temporal nature of the observed data. As an information-theoretic benchmark, we study data sets generated by a dynamic stochastic block model, and derive fundamental information criteria for the recoverability of the community memberships as $N \to \infty$ both for bounded and diverging $T$. These results show that (i) even a small increase in $T$ may have a big impact on the recoverability of community memberships, (ii) consistent recovery is possible even for very sparse data (e.g. bounded average degree) when $T$ is large enough. We analyse the accuracy of the proposed estimation algorithms under various assumptions on data sparsity and identifiability, and prove that an efficient online algorithm is strongly consistent up to the information-theoretic threshold under suitable initialisation. Numerical experiments show that even a poor initial estimate (e.g., blind random guess) of the community assignment leads to high accuracy after a small number of iterations, and remarkably so also in very sparse regimes.
The Role of miR-375-3p and miR-200b-3p in Gastrointestinal Stromal Tumors

Deregulated microRNA (miRNA) expression profiles and their contribution to carcinogenesis have been observed in virtually all types of human cancer. However, their role in the pathogenesis of rare mesenchymal gastrointestinal stromal tumors (GISTs) is not well defined, yet. In this study, we aimed to investigate the role of two miRNAs strongly downregulated in GIST—miR-375-3p and miR-200b-3p—in the pathogenesis of GIST. To achieve this, miRNA mimics were transfected into GIST-T1 cells and changes in the potential target gene mRNA and protein expression, as well as alterations in cell viability, migration, apoptotic cell counts and direct miRNA–target interaction, were evaluated. Results revealed that overexpression of miR-375-3p downregulated the expression of KIT mRNA and protein by direct binding to KIT 3′UTR, reduced GIST cell viability and migration rates. MiR-200b-3p lowered expression of ETV1 protein, directly targeted and lowered expression of EGFR mRNA and protein, and negatively affected cell migration rates. To conclude, the present study identified that miR-375-3p and miR-200b-3p have a tumor-suppressive role in GIST.