Miracle-dz/UniFAR-FTUs huggingface dataset:
There Is No Impact of Diabetes on the Endothelial Function of Chronic Kidney Disease Patients

Background Patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (DM) have increased risk of endothelial dysfunction, cardiovascular disease, and mortality. Several studies have separately analyzed endothelial function in these populations. However, data of patients with both CKD and DM are scarce. The aim of this study was to evaluate whether the presence of DM has any additional effect on the endothelial dysfunction of CKD patients. Methods We measured endothelial progenitor cells (EPCs), stromal-derived factor 1 alpha (SDF-1α), serum and urinary nitric oxide (NO), flow-mediated dilation (FMD), and pulse wave velocity (PWV) in 37 CKD patients with DM (CKD-DM group) and in 37 without DM (CKD group). Results CKD-DM group had a higher prevalence of obesity (P < 0.01), previous myocardial infarction (P = 0.02), myocardial revascularization (P = 0.04), and a trend for more peripheral artery disease (P = 0.07). Additionally, CKD-DM group had higher EPC (P = 0.001) and PWV (P < 0.001) values. On the other hand, no difference in SDF-1α and serum or urinary NO and FMD was observed between the groups. Conclusions Endothelial dysfunction is frequent in CKD patients, and an additive effect of diabetes cannot be implicated, suggesting the predominant role of uremia in this condition.
Posterior Reversible Encephalopathy Syndrome Presenting with Atypical Findings: Report of Two Cases

Background Posterior reversible encephalopathy syndrome (PRES) is characterized by a variable association of symptoms including headache, consciousness impairment, visual disturbances, seizures, and focal neurological signs. Treating the underlying cause usually leads to partial or complete resolution of symptoms within days or weeks. Brain MRI findings include hyperintensities on T2-weighted sequences and their reversibility on follow-up exams. We describe two patients, one with an atypical clinical presentation characterized by a severe and prolonged impairment of consciousness and the other with atypical neuroimaging findings. Case Presentation The first patient was a 42-year-old woman, with a negative medical history, presenting with seizures, lethargy, and left hemiparesis, 60 hours after uncomplicated delivery. Brain MRI showed an atypical pattern of alterations, with patchy asymmetric distribution in all lobes. Symptoms completely resolved after twelve days. The second patient was a 59-year-old woman with a history of hypertension, presenting with severe impairment of consciousness, vision loss, and seizures. Symptoms partially resolved after three weeks. Conclusion PRES is characterized by reversible symptoms and radiological findings. Brain MRI usually shows widespread oedema in white matter with typical patterns. The cases we described suggest that PRES may presents with atypical symptoms and radiological manifestations, mimicking other neurological conditions.
Evaluating the Red Belly Blockchain

In this paper, we present the most extensive evaluation of blockchain system to date. To achieve scalability across servers in more than 10 countries located on 4 different continents, we drastically revisited Byzantine fault tolerant blockchains and verification of signatures. The resulting blockchain, called the Red Belly Blockchain (RBBC), commits more than a hundred thousand transactions issued by permissionless nodes. These transactions are grouped into blocks within few seconds through a partially synchronous consensus run by permissioned nodes. It prevents double spending by guaranteeing that a unique block is decided at any given index of the chain in a deterministic way by all participants. We compared the performance of RBBC against traditional Byzantine fault tolerant alternatives and more recent randomized solutions. In the same geo-distributed environment with low-end machines, we noticed two interesting comparisons: (i) the RBBC throughput scales to hundreds of machines whereas the classic 3-step leader-based BFT state machine used by consortium blockchains cannot scale to 40 identically configured nodes; (ii) RBBC guarantees transaction finality in 3 seconds and experiences a third of the latency that randomized-based solutions like HoneyBadgerBFT can offer. This empirical evaluation demonstrates that blockchain scalability can be achieved without sacrificing security.
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation

This paper addresses the challenge of 6DoF pose estimation from a single RGB image under severe occlusion or truncation. Many recent works have shown that a two-stage approach, which first detects keypoints and then solves a Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable performance. However, most of these methods only localize a set of sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise Voting Network (PVNet) to regress pixel-wise vectors pointing to the keypoints and use these vectors to vote for keypoint locations. This creates a flexible representation for localizing occluded or truncated keypoints. Another important feature of this representation is that it provides uncertainties of keypoint locations that can be further leveraged by the PnP solver. Experiments show that the proposed approach outperforms the state of the art on the LINEMOD, Occlusion LINEMOD and YCB-Video datasets by a large margin, while being efficient for real-time pose estimation. We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation. The code is available at https://zju3dv.github. io/pvnet/.
High Quality Monocular Depth Estimation via Transfer Learning

Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction. Existing solutions for depth estimation often produce blurry approximations of low resolution. This paper presents a convolutional neural network for computing a high-resolution depth map given a single RGB image with the help of transfer learning. Following a standard encoder-decoder architecture, we leverage features extracted using high performing pre-trained networks when initializing our encoder along with augmentation and training strategies that lead to more accurate results. We show how, even for a very simple decoder, our method is able to achieve detailed high-resolution depth maps. Our network, with fewer parameters and training iterations, outperforms state-of-the-art on two datasets and also produces qualitatively better results that capture object boundaries more faithfully. Code and corresponding pre-trained weights are made publicly available.
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks

One of the ultimate promises of computer vision is to help robotic agents perform active tasks, like delivering packages or doing household chores. However, the conventional approach to solving "vision" is to define a set of offline recognition problems (e.g. object detection) and solve those first. This approach faces a challenge from the recent rise of Deep Reinforcement Learning frameworks that learn active tasks from scratch using images as input. This poses a set of fundamental questions: what is the role of computer vision if everything can be learned from scratch? Could intermediate vision tasks actually be useful for performing arbitrary downstream active tasks? We show that proper use of mid-level perception confers significant advantages over training from scratch. We implement a perception module as a set of mid-level visual representations and demonstrate that learning active tasks with mid-level features is significantly more sample-efficient than scratch and able to generalize in situations where the from-scratch approach fails. However, we show that realizing these gains requires careful selection of the particular mid-level features for each downstream task. Finally, we put forth a simple and efficient perception module based on the results of our study, which can be adopted as a rather generic perception module for active frameworks.
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github. com/lehaifeng/T-GCN.
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This article addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multiagent deep RL (MADRL) is presented, including nonstationarity, partial observability, continuous state and action spaces, multiagent training schemes, and multiagent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to the future development of more robust and highly useful multiagent learning methods for solving real-world problems.
Federated Learning via Over-the-Air Computation

The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.
Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis

For recovering 3D object poses from 2D images, a prevalent method is to pre-train an over-complete dictionary $\mathcal D=\{B_i\}_i^D$ of 3D basis poses. During testing, the detected 2D pose $Y$ is matched to dictionary by $Y \approx \sum_i M_i B_i$ where $\{M_i\}_i^D=\{c_i \Pi R_i\}$, by estimating the rotation $R_i$, projection $\Pi$ and sparse combination coefficients $c \in \mathbb R_{+}^D$. In this paper, we propose non-convex regularization $H(c)$ to learn coefficients $c$, including novel leaky capped $\ell_1$-norm regularization (LCNR), \begin{align*} H(c)=\alpha \sum_{i } \min(|c_i|,\tau)+ \beta \sum_{i } \max(| c_i|,\tau), \end{align*} where $0\leq \beta \leq \alpha$ and $0<\tau$ is a certain threshold, so the invalid components smaller than $\tau$ are composed with larger regularization and other valid components with smaller regularization. We propose a multi-stage optimizer with convex relaxation and ADMM. We prove that the estimation error $\mathcal L(l)$ decays w.r. t. the stages $l$, \begin{align*} Pr\left(\mathcal L(l) < \rho^{l-1} \mathcal L(0) + \delta \right) \geq 1- \epsilon, \end{align*} where $0< \rho <1, 0<\delta, 0<\epsilon \ll 1$. Experiments on large 3D human datasets like H36M are conducted to support our improvement upon previous approaches. To the best of our knowledge, this is the first theoretical analysis in this line of research, to understand how the recovery error is affected by fundamental factors, e.g. dictionary size, observation noises, optimization times. We characterize the trade-off between speed and accuracy towards real-time inference in applications.
Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach

Reliable 4D aircraft trajectory prediction, whether in a real-time setting or for analysis of counterfactuals, is important to the efficiency of the aviation system. Toward this end, we first propose a highly generalizable efficient tree-based matching algorithm to construct image-like feature maps from high-fidelity meteorological datasets - wind, temperature and convective weather. We then model the track points on trajectories as conditional Gaussian mixtures with parameters to be learned from our proposed deep generative model, which is an end-to-end convolutional recurrent neural network that consists of a long short-term memory (LSTM) encoder network and a mixture density LSTM decoder network. The encoder network embeds last-filed flight plan information into fixed-size hidden state variables and feeds the decoder network, which further learns the spatiotemporal correlations from the historical flight tracks and outputs the parameters of Gaussian mixtures. Convolutional layers are integrated into the pipeline to learn representations from the high-dimension weather features. During the inference process, beam search, adaptive Kalman filter, and Rauch-Tung-Striebel smoother algorithms are used to prune the variance of generated trajectories.
Graph Reduction with Spectral and Cut Guarantees

Can one reduce the size of a graph without significantly altering its basic properties? The graph reduction problem is hereby approached from the perspective of restricted spectral approximation, a modification of the spectral similarity measure used for graph sparsification. This choice is motivated by the observation that restricted approximation carries strong spectral and cut guarantees, and that it implies approximation results for unsupervised learning problems relying on spectral embeddings. The paper then focuses on coarsening---the most common type of graph reduction. Sufficient conditions are derived for a small graph to approximate a larger one in the sense of restricted similarity. These findings give rise to nearly-linear algorithms that, compared to both standard and advanced graph reduction methods, find coarse graphs of improved quality, often by a large margin, without sacrificing speed.
Game Semantics and Linear Logic in the Cognition Process

A description of the environment cognition process by intelligent systems with a fixed set of system goals is suggested. Such a system is represented by the set of its goals only without any models of the system elements or the environment. The set has a lattice structure and a monoid structure; thus, the structure of linear logic is defined on the set. The cognition process of some environment by the system is described on this basis. The environment is represented as a configuration space of possible system positions which are estimated by an information amount (by corresponding sets). This information is supplied to the system by the environment. Thus, it is possible to define the category of Conway games with a payoff on the configuration space and to choose an optimal system's play (i.e., a trajectory). The choice is determined by the requirement of maximal information increasing and takes into account the structure of the system goal set: the linear logic on the set is used to determine the priority of possible different parallel processes. The survey may be useful to describe the behavior of robots and simple biological systems, e.g., ants.
Support Vector Guided Softmax Loss for Face Recognition

Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination. To address it, one group tries to exploit mining-based strategies (\textit{e.g.}, hard example mining and focal loss) to focus on the informative examples. The other group devotes to designing margin-based loss functions (\textit{e.g.}, angular, additive and additive angular margins) to increase the feature margin from the perspective of ground truth class. Both of them have been well-verified to learn discriminative features. However, they suffer from either the ambiguity of hard examples or the lack of discriminative power of other classes. In this paper, we design a novel loss function, namely support vector guided softmax loss (SV-Softmax), which adaptively emphasizes the mis-classified points (support vectors) to guide the discriminative features learning. So the developed SV-Softmax loss is able to eliminate the ambiguity of hard examples as well as absorb the discriminative power of other classes, and thus results in more discrimiantive features. To the best of our knowledge, this is the first attempt to inherit the advantages of mining-based and margin-based losses into one framework. Experimental results on several benchmarks have demonstrated the effectiveness of our approach over state-of-the-arts.
VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, efficiently. To this end, we formulate a queuing-based model and use it at the network orchestrator to optimally match the vertical’s requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out an accounting for multiple scenarios representing the real-world services, shows that MaxZ performs substantially better than the state-of-the-art alternatives and consistently close to the optimum.
Convex Polygons in Cartesian Products

We study several problems concerning convex polygons whose vertices lie in a Cartesian product of two sets of n real numbers (for short, grid). First, we prove that every such grid contains a convex polygon with Ω(log n) vertices and that this bound is tight up to a constant factor. We generalize this result to d dimensions (for a fixed d ∈ ℕ), and obtain a tight lower bound of Ω(logd-1 n) for the maximum number of points in convex position in a d-dimensional grid. Second, we present polynomial-time algorithms for computing the longest convex polygonal chain in a grid that contains no two points with the same x- or y-coordinate. We show that the maximum size of such a convex polygon can be efficiently approximated up to a factor of 2. Finally, we present exponential bounds on the maximum number of convex polygons in these grids, and for some restricted variants. These bounds are tight up to polynomial factors.
Greedy Layerwise Learning Can Scale to ImageNet

Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on image classification tasks using the large-scale ImageNet dataset and the CIFAR-10 dataset. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problems lead to a CNN that exceeds AlexNet performance on ImageNet. Extending this training methodology to construct individual layers by solving 2-and-3-hidden layer auxiliary problems, we obtain an 11-layer network that exceeds several members of the VGG model family on ImageNet, and can train a VGG-11 model to the same accuracy as end-to-end learning. To our knowledge, this is the first competitive alternative to end-to-end training of CNNs that can scale to ImageNet. We illustrate several interesting properties of these models theoretically and conduct a range of experiments to study the properties this training induces on the intermediate layers.
Approximately Optimal Mechanism Design

The field of optimal mechanism design enjoys a beautiful and well-developed theory, as well as several killer applications. Rules of thumb produced by the field influence everything from how governments sell wireless spectrum licenses to how the major search engines auction off online advertising. There are, however, some basic problems for which the traditional optimal mechanism design approach is ill suited—either because it makes overly strong assumptions or because it advocates overly complex designs. This article reviews several common issues with optimal mechanisms, including exorbitant communication, computation, and informational requirements; it also presents several examples demonstrating that relaxing the goal to designing an approximately optimal mechanism allows us to reason about fundamental questions that seem out of reach of the traditional theory.