Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning

Previous research on face restoration often focused on repairing a specific type of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the real world, both the above-mentioned forms of image degradation often coexist. Therefore, it is important to design a model that can repair LR occluded images simultaneously. This paper proposes a multi-scale feature graph generative adversarial network (MFG-GAN) to implement the face restoration of images in which both degradation modes coexist, and also to repair images with a single type of degradation. Based on the GAN, the MFG-GAN integrates the graph convolution and feature pyramid network to restore occluded low-resolution face images to non-occluded high-resolution face images. The MFG-GAN uses a set of customized losses to ensure that high-quality images are generated. In addition, we designed the network in an end-to-end format. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms state-of-the-art methods in performing face super-resolution (up to 4x or 8x) and face completion simultaneously. Cross-database testing also revealed that the proposed approach has good generalizability.
On the Decycling Number of $4$-regular Random Graphs

The decycling number $\phi(G)$ of a graph $G$ is the smallest number of vertices which can be removed from $G$ so that the resulting graph has no cycles. Bau, Wormald and Zhou conjectured that with probability tending to one the decycling number of the random $4$-regular graph $G_4(n)$ on $n$ vertices is equal to $\lceil (n+1)/3\rceil$. In this paper we show that this conjecture holds asymptotically, i.e. asymptotically almost surely $\lim_{n \to \infty}\phi(G_4(n))/n = 1/3$.
Stable behaviour of infinitely wide deep neural networks

We consider fully connected feed-forward deep neural networks (NNs) where weights and biases are independent and identically distributed as symmetric centered stable distributions. Then, we show that the infinite wide limit of the NN, under suitable scaling on the weights, is a stochastic process whose finite-dimensional distributions are multivariate stable distributions. The limiting process is referred to as the stable process, and it generalizes the class of Gaussian processes recently obtained as infinite wide limits of NNs (Matthews at al., 2018b). Parameters of the stable process can be computed via an explicit recursion over the layers of the network. Our result contributes to the theory of fully connected feed-forward deep NNs, and it paves the way to expand recent lines of research that rely on Gaussian infinite wide limits.
Optimal Placement of Limited PMUs for Transmission Line Outage Detection and Identification

Phasor Measurement Unit (PMU) technology is increasingly used for real-time monitoring applications, especially line outage detection and identification (D&I) in the power system. Current outage D&I schemes either assume a full PMU deployment or a partial deployment with fixed PMU placement. However, the placement of the PMUs has a fundamental impact on the effectiveness of the D&I scheme. Building on a dynamic relationship between the substation voltage phase angle and active power, we formulated the optimal PMU placement problem for outage D&I as an optimization problem readily solvable by any heuristic algorithm. We tested the formulation using a genetic algorithm and simulated outages of IEEE 39 bus system. The optimal placement found produces a better D&I result of single-line outages than a randomly scattered, tree-like, and degree-based placements.
AoI and Energy Consumption Oriented Dynamic Status Updating in Caching Enabled IoT Networks

Caching has been regarded as a promising technique to alleviate energy consumption of sensors in Internet of Things (IoT) networks by responding to users' requests with the data packets stored in the edge caching node (ECN). For real-time applications in caching enabled IoT networks, it is essential to develop dynamic status update strategies to strike a balance between the information freshness experienced by users and energy consumed by the sensor, which, however, is not well addressed. In this paper, we first depict the evolution of information freshness, in terms of age of information (AoI), at each user. Then, we formulate a dynamic status update optimization problem to minimize the expectation of a long-term accumulative cost, which jointly considers the users' AoI and sensor's energy consumption. To solve this problem, a Markov Decision Process (MDP) is formulated to cast the status updating procedure, and a model-free reinforcement learning algorithm is proposed, with which the challenge brought by the unknown of the formulated MDP's dynamics can be addressed. Finally, simulations are conducted to validate the convergence of our proposed algorithm and its effectiveness compared with the zero-wait baseline policy.
Empirical Photometric Control of Mars Context Camera Images

The Mars Reconnaissance Orbiter (MRO) spacecraft has been in orbit around Mars since March 2006. The Context Camera (CTX) on MRO has returned over 100,000 images of the planet at approximately 5-6 meters per pixel, providing nearly global coverage. During that time, Mars has gone through nearly 7 of its own years, changing solar distance from 1.38 to 1.67 AU and the corresponding solar flux by 45% due to its orbital eccentricity. Seasonal effects and transient phenomena affect atmospheric transparency. Combined with an aging detector, CTX images are difficult to mosaic seamlessly, for all of these changes prevent equalizing images to create a visually smoothly illuminated product. We have developed a method, based on previous work by other researchers for other datasets, to mitigate almost all photometric variations between images in order to create the appearance of an evenly illuminated, practically seamless mosaic. We describe how the process works, which uses a reference source to tie brightness values, and demonstrate its effects across Mars' surface. While the workflow developed for this product is applicable to other planetary bodies, it requires a reference source, which may not yet exist.
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extracting visual and/or audio features and training classifiers. In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classification loss, i.e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https://github.com/maysonma/VAANet.
Robust 6D Object Pose Estimation by Learning RGB-D Features

Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping. Previous methods follow a local optimization approach which minimizes the distance between closest point pairs to handle the rotation ambiguity of symmetric objects. In this work, we propose a novel discrete- continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Additionally, the object location is detected by aggregating point-wise vectors pointing to the 3D center. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches. Our code is available at https://github.com/mentian/object-posenet.
Estimating Multiple Precision Matrices With Cluster Fusion Regularization

Abstract We propose a penalized likelihood framework for estimating multiple precision matrices from different classes. Most existing methods either incorporate no information on relationships between the precision matrices or require this information be known a priori. The framework proposed in this article allows for simultaneous estimation of the precision matrices and relationships between the precision matrices. Sparse and nonsparse estimators are proposed, both of which require solving a nonconvex optimization problem. To compute our proposed estimators, we use an iterative algorithm which alternates between a convex optimization problem solved by blockwise coordinate descent and a k-means clustering problem. Blockwise updates for the sparse estimator require computing an elastic net penalized precision matrix estimation problem, which we solve using a proximal gradient descent algorithm. We prove that this subalgorithm has a linear rate of convergence. In simulation studies and two real data applications, we show that our method can outperform competitors that ignore relevant relationships between precision matrices and performs similarly to methods which use prior information often unknown in practice. Supplementary materials for this article are available online.
Subadditivity of Probability Divergences on Bayes-Nets with Applications to Time Series GANs

GANs for time series data often use sliding windows or self-attention to capture underlying time dependencies. While these techniques have no clear theoretical justification, they are successful in significantly reducing the discriminator size, speeding up the training process, and improving the generation quality. In this paper, we provide both theoretical foundations and a practical framework of GANs for high-dimensional distributions with conditional independence structure captured by a Bayesian network, such as time series data. We prove that several probability divergences satisfy subadditivity properties with respect to the neighborhoods of the Bayes-net graph, providing an upper bound on the distance between two Bayes-nets by the sum of (local) distances between their marginals on every neighborhood of the graph. This leads to our proposed Subadditive GAN framework that uses a set of simple discriminators on the neighborhoods of the Bayes-net, rather than a giant discriminator on the entire network, providing significant statistical and computational benefits. We show that several probability distances including Jensen-Shannon, Total Variation, and Wasserstein, have subadditivity or generalized subadditivity. Moreover, we prove that Integral Probability Metrics (IPMs), which encompass commonly-used loss functions in GANs, also enjoy a notion of subadditivity under some mild conditions. Furthermore, we prove that nearly all f-divergences satisfy local subadditivity in which subadditivity holds when the distributions are relatively close. Our experiments on synthetic as well as real-world datasets verify the proposed theory and the benefits of subadditive GANs.
Causal Learning by a Robot with Semantic-Episodic Memory in an Aesop's Fable Experiment

Corvids, apes, and children solve The Crow and The Pitcher task (from Aesop's Fables) indicating a causal understanding of the task. By cumulatively interacting with different objects, how can cognitive agents abstract the underlying cause-effect relations to predict affordances of novel objects? We address this question by re-enacting the Aesop's Fable task on a robot and present a) a brain-guided neural model of semantic-episodic memory; with b) four task-agnostic learning rules that compare expectations from recalled past episodes with the current scenario to progressively extract the hidden causal relations. The ensuing robot behaviours illustrate causal learning; and predictions for novel objects converge to Archimedes' principle, independent of both the objects explored during learning and the order of their cumulative exploration.
Large highly connected subgraphs in graphs with linear average degree

In 1972 Mader proved that every graph with average degree at least $4k$ has a $(k+1)$-connected subgraph with more than $2k$ vertices. We improve this bound by showing that the constant $4$ can be replaced by $3+\frac{1}{3}$; this bound is sharp.
Exploring Backdoor Poisoning Attacks Against Malware Classifiers

Current training pipelines for machine learning (ML) based malware classification rely on crowdsourced threat feeds, exposing a natural attack injection point. We study for the first time the susceptibility of ML malware classifiers to backdoor poisoning attacks, specifically focusing on challenging "clean label" attacks where attackers do not control the sample labeling process. We propose the use of techniques from explainable machine learning to guide the selection of relevant features and their values to create a watermark in a model-agnostic fashion. Using a dataset of 800,000 Windows binaries, we demonstrate effective attacks against gradient boosting decision trees and a neural network model for malware classification under various constraints imposed on the attacker. For example, an attacker injecting just 1% poison samples in the training process can achieve a success rate greater than 97% by crafting a watermark of 8 features out of more than 2,300 available features. To demonstrate the feasibility of our backdoor attacks in practice, we create a watermarking utility for Windows PE files that preserves the binary's functionality. Finally, we experiment with potential defensive strategies and show the difficulties of completely defending against these powerful attacks, especially when the attacks blend in with the legitimate sample distribution.
Landscape of coexisting excitonic states in the insulating single-layer cuprates and nickelates

We present an $\textit{ab initio}$ study of the excitonic states of a prototypical high-temperature superconductor La$_2$CuO$_4$ and compare them to the isostructural single-layer nickelate La$_2$NiO$_4$. Key difference in the low-energy electronic structure leads to very different excitonic behavior. Excitons in La$_2$CuO$_4$ are delocalized and can freely move in the CuO$_2$ plane without disturbing the antiferromagnetic order. In contrast, in La$_2$NiO$_4$ we find the low-lying excitonic states to be extremely localized, producing a nearly flat dispersion. The theoretically obtained excitonic dispersion and behavior are in excellent agreement with RIXS observations. To classify the excitons we project the electron-hole coupling onto each atomic site including the full manifold of atomic orbitals. We find the excitons to be composed of a linear combination of exciton classes, including Mott-Hubbard, $d-d$, and charge-transfer. The implication of these excitations to the high-$T_c$ pairing mechanism is also discussed.
Experimental evidence for transparency, band gaps and Anderson localization in two-dimensional hyperuniform disordered photonic materials

The recent prediction of full photonic bandgaps in amorphous dielectric materials is surprising since it defies the picture of bandgap formation by reflections and interferences from Bragg planes in a periodically repeating environment. Numerical work on stealthy hyperuniform solids also suggested that disordered photonic materials can display additional transport regimes: the same material can show transparency, photon diffusion, Anderson localization, or a full bandgap, depending on the frequency $\nu$ of the electromagnetic wave. Here we demonstrate experimentally, using microwaves, that the density of states and the different transport properties can be observed and quantified in a two-dimensional hyperuniform disordered array of cylinders with high dielectric permittivity. Interestingly, we find a second weaker bandgap at higher frequencies, which can be linked to higher-order spatial correlations and the second peak in the structure factor. Our results emphasize the importance of spatial correlations for the formation of photonic band gaps.
Review—The Development of Wearable Polymer-Based Sensors: Perspectives

The development of smart polymer materials is reviewed and illustrated. Important examples of these polymers include conducting polymers, ionic gels, stimulus-response be used polymers, liquid crystalline polymers and piezoelectric materials, which have desirable properties for use in wearable sensors. This review outlines the mode of action in these types of smart polymers systems for utilisation as wearable sensors. Categories of wearable sensors are considered as tattoo-like designs, patch-like, textile-based, and contact lens-based sensors. The advantages and disadvantages of each sensor types are considered together with information on the typical performance. The research gap linking smart polymer materials to wearable sensors with integrated power systems is highlighted. Smart polymer systems may be used as part of a holistic approach to improve wearable devices and accelerate the integration of wearable sensors and power systems, particularly in health care.
Preventing Clean Label Poisoning using Gaussian Mixture Loss

Since 2014 when Szegedy et al. showed that carefully designed perturbations of the input can lead Deep Neural Networks (DNNs) to wrongly classify its label, there has been an ongoing research to make DNNs more robust to such malicious perturbations. In this work, we consider a poisoning attack called Clean Labeling poisoning attack (CLPA). The goal of CLPA is to inject seemingly benign instances which can drastically change decision boundary of the DNNs due to which subsequent queries at test time can be mis-classified. We argue that a strong defense against CLPA can be embedded into the model during the training by imposing features of the network to follow a Large Margin Gaussian Mixture distribution in the penultimate layer. By having such a prior knowledge, we can systematically evaluate how unusual the example is, given the label it is claiming to be. We demonstrate our builtin defense via experiments on MNIST and CIFAR datasets. We train two models on each dataset: one trained via softmax, another via LGM. We show that using LGM can substantially reduce the effectiveness of CLPA while having no additional overhead of data sanitization. The code to reproduce our results is available online.
BitcoinF: Achieving Fairness for Bitcoin in Transaction-Fee-Only Model

A blockchain, such as Bitcoin, is an append-only, secure, transparent, distributed ledger. A fair blockchain is expected to have healthy metrics; high honest mining power, low processing latency, i.e., low wait times for transactions and stable price of consumption, i.e., the minimum transaction fee required to have a transaction processed. As Bitcoin matures, the influx of transactions increases and the block rewards become insignificant. We show that under these conditions, it becomes hard to maintain the health of the blockchain. In Bitcoin, under these mature operating conditions (MOC), the miners would find it challenging to cover their mining costs as there would be no more revenue from merely mining a block. It may cause miners not to continue mining, threatening the blockchain's security. Further, as we show in this paper using simulations, the cost of acting in favor of the health of the blockchain, under MOC, is very high in Bitcoin, causing all miners to process transactions greedily. It leads to stranded transactions, i.e., transactions offering low transaction fees, experiencing unreasonably high processing latency. To make matters worse, a compounding effect of these stranded transactions is the rising price of consumption. Such phenomena not only induce unfairness as experienced by the miners and the users but also deteriorate the health of the blockchain. We propose BitcoinF transaction processing protocol, a simple, yet highly effective modification to the existing Bitcoin protocol to fix these issues of unfairness. BitcoinF resolves these issues of unfairness while preserving the ability of the users to express urgency and have their transactions prioritized.
UFTR: A Unified Framework for Ticket Routing

Corporations today face increasing demands for the timely and effective delivery of customer service. This creates the need for a robust and accurate automated solution to what is formally known as the ticket routing problem. This task is to match each unresolved service incident, or "ticket", to the right group of service experts. Existing studies divide the task into two independent subproblems - initial group assignment and inter-group transfer. However, our study addresses both subproblems jointly using an end-to-end modeling approach. We first performed a preliminary analysis of half a million archived tickets to uncover relevant features. Then, we devised the UFTR, a Unified Framework for Ticket Routing using four types of features (derived from tickets, groups, and their interactions). In our experiments, we implemented two ranking models with the UFTR. Our models outperform baselines on three routing metrics. Furthermore, a post-hoc analysis reveals that this superior performance can largely be attributed to the features that capture the associations between ticket assignment and group assignment. In short, our results demonstrate that the UFTR is a superior solution to the ticket routing problem because it takes into account previously unexploited interrelationships between the group assignment and group transfer problems.
Stein Variational Inference for Discrete Distributions

Gradient-based approximate inference methods, such as Stein variational gradient descent (SVGD), provide simple and general-purpose inference engines for differentiable continuous distributions. However, existing forms of SVGD cannot be directly applied to discrete distributions. In this work, we fill this gap by proposing a simple yet general framework that transforms discrete distributions to equivalent piecewise continuous distributions, on which the gradient-free SVGD is applied to perform efficient approximate inference. The empirical results show that our method outperforms traditional algorithms such as Gibbs sampling and discontinuous Hamiltonian Monte Carlo on various challenging benchmarks of discrete graphical models. We demonstrate that our method provides a promising tool for learning ensembles of binarized neural network (BNN), outperforming other widely used ensemble methods on learning binarized AlexNet on CIFAR-10 dataset. In addition, such transform can be straightforwardly employed in gradient-free kernelized Stein discrepancy to perform goodness-of-fit (GOF) test on discrete distributions. Our proposed method outperforms existing GOF test methods for intractable discrete distributions.