Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-based Contextual Multi-View Graph Convolutional Networks for Short-term Population Prediction

Short-term future population prediction is a crucial problem in urban computing. Accurate future population prediction can provide rich insights for urban planners or developers. However, predicting the future population is a challenging task due to its complex spatiotemporal dependencies. Many existing works have attempted to capture spatial correlations by partitioning a city into grids and using Convolutional Neural Networks (CNN). However, CNN merely captures spatial correlations by using a rectangle filter; it ignores urban environmental information such as distribution of railroads and location of POI. Moreover, the importance of those kinds of information for population prediction differs in each region and is affected by contextual situations such as weather conditions and day of the week. To tackle this problem, we propose a novel deep learning model called Attention-based Contextual Multi-View Graph Convolutional Networks (ACMV-GCNs). We first construct multiple graphs based on urban environmental information, and then ACMV-GCNs captures spatial correlations from various views with graph convolutional networks. Further, we add an attention module to consider the contextual situations when leveraging urban environmental information for future population prediction. Using statistics population count data collected through mobile phones, we demonstrate that our proposed model outperforms baseline methods. In addition, by visualizing weights calculated by an attention module, we show that our model learns an efficient way to utilize urban environment information without any prior knowledge.
A descriptive approach to higher derived limits

We present a new aspect of the study of higher derived limits. More precisely, we introduce a complexity measure for the elements of higher derived limits over the directed set $\Omega$ of functions from $\mathbb{N}$ to $\mathbb{N}$ and prove that cocycles of this complexity are images of cochains of the roughly the same complexity. In the course of this work, we isolate a partition principle for powers of directed sets and show that whenever this principle holds, the corresponding derived limit $\mathrm{lim}^n$ is additive; vanishing results for this limit are the typical corollary. The formulation of this partition hypothesis synthesizes and clarifies several recent advances in this area.
Local and Global GANs with Semantic-Aware Upsampling for Image Generation

In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is difficulty in generating small objects and detailed local textures. To tackle this issue, in this work we consider generating images using local context. As such, we design a local class-specific generative network using semantic maps as guidance, which separately constructs and learns subgenerators for different classes, enabling it to capture finer details. To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module. To combine the advantages of both global image-level and local class-specific generation, a joint generation network is designed with an attention fusion module and a dual-discriminator structure embedded. Lastly, we propose a novel semantic-aware upsampling method, which has a larger receptive field and can take far-away pixels that are semantically related for feature upsampling, enabling it to better preserve semantic consistency for instances with the same semantic labels. Extensive experiments on two image generation tasks show the superior performance of the proposed method. State-of-the-art results are established by large margins on both tasks and on nine challenging public benchmarks.
BlazeNeo: Blazing Fast Polyp Segmentation and Neoplasm Detection

In recent years, computer-aided automatic polyp segmentation and neoplasm detection have been an emerging topic in medical image analysis, providing valuable support to colonoscopy procedures. Attentions have been paid to improving the accuracy of polyp detection and segmentation. However, not much focus has been given to latency and throughput for performing these tasks on dedicated devices, which can be crucial for practical applications. This paper introduces a novel deep neural network architecture called BlazeNeo, for the task of polyp segmentation and neoplasm detection with an emphasis on compactness and speed while maintaining high accuracy. The model leverages the highly efficient HarDNet backbone alongside lightweight Receptive Field Blocks and a feature aggregation mechanism for computational efficiency. An auxiliary training strategy is proposed to take full advantage of the training data for the segmentation quality. Our experiments on a challenging dataset show that BlazeNeo achieves improvements in latency and model size while maintaining comparable accuracy against state-of-the-art methods. We obtain over 155 fps while outperforming all compared models in terms of accuracy in INT8 precision when deploying on a dedicated edge device with a conventional configuration.
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?

Mean-Field Control (MFC) is a powerful tool to solve Multi-Agent Reinforcement Learning (MARL) problems. Recent studies have shown that MFC can well-approximate MARL when the population size is large and the agents are exchangeable. Unfortunately, the presumption of exchangeability implies that all agents uniformly interact with one another which is not true in many practical scenarios. In this article, we relax the assumption of exchangeability and model the interaction between agents via an arbitrary doubly stochastic matrix. As a result, in our framework, the mean-field `seen' by different agents are different. We prove that, if the reward of each agent is an affine function of the mean-field seen by that agent, then one can approximate such a non-uniform MARL problem via its associated MFC problem within an error of $e=\mathcal{O}(\frac{1}{\sqrt{N}}[\sqrt{|\mathcal{X}|} + \sqrt{|\mathcal{U}|}])$ where $N$ is the population size and $|\mathcal{X}|$, $|\mathcal{U}|$ are the sizes of state and action spaces respectively. Finally, we develop a Natural Policy Gradient (NPG) algorithm that can provide a solution to the non-uniform MARL with an error $\mathcal{O}(\max\{e,\epsilon\})$ and a sample complexity of $\mathcal{O}(\epsilon^{-3})$ for any $\epsilon>0$.
Correlation of powers of H\"usler-Reiss vectors and Brown-Resnick fields, and application to insured wind losses

Hüsler–Reiss vectors and Brown–Resnick fields are popular models in multivariate and spatial extreme-value theory, respectively, and are widely used in applications. We provide analytical formulas for the correlation between powers of the components of the bivariate Hüsler–Reiss vector, extend these to the case of the Brown–Resnick field, and thoroughly study the properties of the resulting dependence measure. The use of correlation is justified by spatial risk theory, while power transforms are insightful when taking correlation as dependence measure, and are moreover very suited damage functions for weather events such as wind extremes or floods. This makes our theoretical results worthwhile for, e.g., actuarial applications. We finally perform a case study involving insured losses from extreme wind speeds in Germany, and obtain valuable conclusions for the insurance industry.
Clustering drives cooperation on reputation networks, all else fixed

Reputation-based cooperation on social networks offers a causal mechanism between graph properties and social trust. Using a simple model, this paper demonstrates the underlying mechanism in a way that is accessible to scientists not specializing in networks or mathematics. The paper shows that when the size and degree of the network is fixed (i.e. all graphs have the same number of agents, who all have the same number of connections), it is the clustering coefficient that drives differences in how cooperative social networks are.
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks

Collaborative machine learning settings such as federated learning can be susceptible to adversarial interference and attacks. One class of such attacks is termed model inversion attacks, characterised by the adversary reverse-engineering the model into disclosing the training data. Previous implementations of this attack typically only rely on the shared data representations, ignoring the adversarial priors, or require that specific layers are present in the target model, reducing the potential attack surface. In this work, we propose a novel context-agnostic model inversion framework that builds on the foundations of gradient-based inversion attacks, but additionally exploits the features and the style of the data controlled by an in-the-network adversary. Our technique outperforms existing gradient-based approaches both qualitatively and quantitatively across all training settings, showing particular effectiveness against the collaborative medical imaging tasks. Finally, we demonstrate that our method achieves significant success on two downstream tasks: sensitive feature inference and facial recognition spoofing.
DMF-Net: A decoupling-style multi-band fusion model for real-time full-band speech enhancement

—Full-band speech enhancement based on deep neural networks is still challenging for the difﬁculty of modeling more frequency bands and real-time implementation. Previous studies usually adopt compressed full-band speech features in Bark and ERB scale with relatively low frequency resolution, leading to degraded performance, especially in the high-frequency region. In this paper, we propose a decoupling-style multi-band fusion model to perform full-band speech denoising and dereverberation. Instead of optimizing the full-band speech by a single network structure, we decompose the full-band target into multi sub bands and then employ a multi-stage chain optimization strategy to estimate clean spectrum stage by stage. Speciﬁcally, the low-(0-8 kHz), middle-(8-16 kHz), and high-frequency (16-24 kHz) regions are mapped by three separate sub-networks and are then fused to obtain the full-band clean target STFT spectrum. Comprehensive experiments on two public datasets demonstrate that the proposed method outperforms previous advanced systems and yields promising performance in terms of speech quality and intelligibility in real complex scenarios.
FIRL: Fast Imitation and Policy Reuse Learning

—Intelligent robotics policies have been widely re-searched for challenging applications such as opening doors, washing dishes, and table organization. We refer to a ”Policy Pool”, containing skills that be easily accessed and reused. There are researches to leverage the pool, such as policy reuse, modular learning, assembly learning, transfer learning, hierarchical reinforcement learning (HRL), etc. However, most methods generally do not perform well in learning efﬁciency and require large datasets for training. This work focuses on enabling fast learning based on the policy pool. It should learn fast enough in one-shot or few-shot by avoiding learning from scratch. We also allow it to interact and learn from humans, but the training period should be within minutes. We propose FIRL, Fast (one-shot) Imitation, and Policy Reuse Learning. Instead of learning a new skill from scratch, it performs the one-shot imitation learning on the higher layer under a 2-layer hierarchical mechanism. Our method reduces a complex task learning to a simple regression problem that it could solve in a few ofﬂine iterations. The agent could have a good command of a new task given a one-shot demonstration. We demonstrate this method on the OpenDoors mini-grid environment, and the code is available on http://www.github. com/yiwc/ﬁrl.
Parameter estimation for WMTI‐Watson model of white matter using encoder–decoder recurrent neural network

Biophysical modeling of the diffusion MRI (dMRI) signal provides estimates of specific microstructural tissue properties. Although non‐linear least squares (NLLS) is the most widespread fitting method, it suffers from local minima and high computational cost. Deep learning approaches are steadily replacing NLLS, but come with the limitation that the model needs to be retrained for each acquisition protocol and noise level. In this study, a novel fitting approach was proposed based on the encoder–decoder recurrent neural network (RNN) to accelerate model estimation with good generalization to various datasets.
Interpreting Video Engagement Prediction: A Deep Learning Framework

: Predicting video engagement is a top priority for content creators and video-sharing platforms. Content creators feed on such predictions to maximize influences and minimize video budgets. Video-sharing platforms rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions video engagement prediction, it lacks interpretability, which is fundamental to increase adoption of these prediction models and prescribe actionable advice to improve video engagement. Following the design science paradigm, we propose a novel interpretable deep learning framework, Precise Wide and Deep Learning (Prec-WD), to interpret video engagement prediction. Improving upon state-of-the-art interpretable frameworks, Prec-WD designs an unstructured component and offers precise total effects. Prec-WD’s prediction outperforms strong benchmarks on two case studies: health video engagement prediction and misinformation viewership prediction. A user study confirms the superior interpretability of Prec-WD. This study contributes to IS knowledge base with a novel and generalizable interpretable deep learning framework. Our findings provide implications to improve viewer engagement and video credibility.
FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis

Utilizing multi-modal neuroimaging data has been proved to be effective to investigate human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination cost, long acquisition time, and image corruption. In addition, these data are dispersed into different medical institutions and thus cannot be aggregated for centralized training considering the privacy issues. There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions. In this paper, we propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) to bridge the gap between federated learning and medical GAN. FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators, and is widely applied to different proportions of unpaired and paired data with variation adaptation property. We treat the gradient penalties by federally averaging algorithm and then leveraging differential privacy gradient descent to regularize the training dynamics. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods, which shows the new state-of-the-art performance by our FedMed-GAN. Our code has been released on the website: https://github.com/M-3LAB/FedMed-GAN
First-Order General-Relativistic Viscous Fluid Dynamics

We present the first generalization of Navier-Stokes theory to relativity that satisfies all of the following properties: (a) the system coupled to Einstein’s equations is causal and strongly hyperbolic; (b) equilibrium states are stable; (c) all leading dissipative contributions are present, i.e., shear viscosity, bulk viscosity, and thermal conductivity; (d) non-zero baryon number is included; (e) entropy production is non-negative in the regime of validity of the theory; (f) all of the above holds in the nonlinear regime without any simplifying symmetry assumptions. These properties are accomplished using a generalization of Eckart’s theory containing only the hydrodynamic variables, so that no new extended degrees of freedom are needed as in Müller-Israel-Stewart theories. Property (b), in particular, follows from a more general result that we also establish, namely, sufficient conditions that when added to stability in the fluid’s rest frame imply stability in any reference frame obtained via a Lorentz transformation. All our results are mathematically rigorously established. The framework presented here provides the starting point for systematic investigations of generalrelativistic viscous phenomena in neutron star mergers.
A Statistical Inference Framework for the Minimal Clinically Important Difference

In clinical research, the effect of a treatment or intervention is widely assessed through clinical importance, instead of statistical significance. In this paper, we propose a principled statistical inference framework to learn the minimal clinically important difference (MCID), a vital concept in assessing clinical importance. We formulate the scientific question into a statistical learning problem, develop an efficient algorithm for parameter estimation, and establish the asymptotic theory for the proposed estimator. We conduct comprehensive simulation studies to examine the finite sample performance of the proposed method. We also re-analyze the ChAMP (Chondral Lesions And Meniscus Procedures) trial, where the primary outcome is the patient-reported pain score change and the ultimate goal is to determine whether there exists a significant difference in post-operative knee pain between patients undergoing debridement versus observation of chondral lesions during the surgery. Some previous analysis of this trial exhibited that the effect of debriding the chondral lesions does not reach a statistical significance. Our analysis reinforces this conclusion in that the effect of debriding the chondral lesions is not only statistically non-significant, but also clinically un-important.
Proline-based solution maintains cell viability and stemness of canine adipose-derived mesenchymal stem cells after hypothermic storage

Transportation of mesenchymal stem cells (MSCs) under hypothermic conditions in 0.9% normal saline solution (NSS) might increase cell death and alter the stemness of MSCs. The present study aimed to evaluate the effect of proline-based solution (PL-BS) on cell viability and the stemness of newly established canine adipose-derived mesenchymal stem cells (cAD-MSCs) under hypothermic conditions. Characterized cAD-MSCs were stored in 1, 10, and 100 mM PL-BS or NSS at 4°C for 6, 9, and 12 hours prior to an evaluation. The results demonstrated that storage in 1 mM PL-BS for 6 hours decreased cell apoptosis and proliferation ability, but improved cell viability and mitochondrial membrane potential. cAD-MSCs maintained their high expression of CD44 and CD90, but had a low expression of CD34 and MHC class II. Trilineage differentiation ability of cAD-MSCs was not affected by storage in 1 mM PL-BS. Gene expression analysis demonstrated that immunomodulatory genes, including IDO, HGF, PGE-2, and IL-6, were upregulated in cAD-MSCs stored in 1 mM PL-BS. In conclusion, PL-BS can be effectively applied for storing cAD-MSCs under hypothermic conditions. These findings provide a new solution for effective handling of cAD-MSCs which might be promising for clinical applications.
Secondary postpartum hemorrhage: Incidence, etiologies, and clinical courses in the setting of a high cesarean delivery rate

Objectives To evaluate the incidence, etiologies, and clinical outcomes of secondary postpartum hemorrhage in a hospital with a high cesarean section rate and to compare the etiologies of secondary postpartum hemorrhage following cesarean delivery versus vaginal delivery. Materials and methods This retrospective study included 123 women with secondary postpartum hemorrhage who were treated at a tertiary-level hospital between January 2004 and June 2018. Descriptive statistics and the chi-square test were used for data analysis. Results The incidence of secondary postpartum hemorrhage was 0.21%. The median onset of bleeding was 12 days after delivery. Fifty-two percent of the deliveries were by cesarean section. The most common etiology of secondary postpartum hemorrhage was endometritis (67.5%), followed by retained placental tissue (21.1%). Women who delivered by cesarean section had a higher rate of endometritis (80.0% vs 53.4%) and a lower rate of retained placental tissue (10.8% vs. 32.8%) than those who delivered vaginally. Surgical intervention included uterine evacuation in 29.3% and hysterectomy in 8.1% of the patients. Five percent of women were treated by embolization. Conclusions Endometritis was the most common cause of secondary postpartum hemorrhage. Women who delivered by cesarean section were less likely to have retained placental tissue but were at higher risk for endometritis and uterine pseudoaneurysm than those who delivered vaginally.
Searching for genetic modulators of the phenotypic heterogeneity in Brugada syndrome

In Brugada syndrome, even within the same family where all affected individuals share the same mutation, phenotypic variation is prominent, with variable penetrance and expressivity, presenting different degrees of involvement. It is difficult to establish a direct correlation between genotype and phenotype to predict prognosis in complications and risk of sudden death. The factors that modulate this inter- and intra-familial phenotypic variability remain to be determined. With the intention of testing whether other genetic factors, in addition to the causal mutation in SCN5A, may have a modulating effect on the Brugada phenotype and the risk of sudden death, we have studied 8 families with a causal variant in SCN5A with at least two affected individuals, one of whom has suffered cardiac arrest or sudden death. Whole exome sequencing was performed looking for additional variants that modify the phenotype and allow us to predict a better or worse prognosis for the evolution of the disease. The results did not show any clear genetic modifier; nevertheless, highlight the possible implication of the cholesterol and fibrosis pathways, as well as the circadian rhythm, as possible modulators of Brugada syndrome phenotype.