Reviewerly/exHarmony huggingface dataset:
Fast Bayesian inference in large Gaussian graphical models

Abstract Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and conditional independence structures between variables by multiple testing, which bypasses the exploration of the model space. Specifically, we introduce closed-form Bayes factors under the Gaussian conjugate model to evaluate the null hypotheses of marginal and conditional independence between variables. Their computation for all pairs of variables is shown to be extremely efficient, thereby allowing us to address large problems with thousands of nodes as required by modern applications. Moreover, we derive exact tail probabilities from the null distributions of the Bayes factors. These allow the use of any multiplicity correction procedure to control error rates for incorrect edge inclusion. We demonstrate the proposed approach on various simulated examples as well as on a large gene expression data set from The Cancer Genome Atlas.
DiffQue

Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies that addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properties of CQA services such as metadata of users and posts, temporal information, and textual content. In this article, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem. DiffQue starts by constructing a novel network structure that captures different notions of difficulties among a pair of questions. It then measures the relative difficulty of two questions by predicting the direction of a (virtual) edge connecting these two questions in the network. It leverages features extracted from the network structure, metadata of users/posts, and textual description of questions and answers. Experiments on datasets obtained from two CQA sites (further divided into four datasets) with human annotated ground-truth show that DiffQue outperforms four state-of-the-art methods by a significant margin (28.77% higher F 1 score and 28.72% higher AUC than the best baseline). As opposed to the other baselines, (i) DiffQue appropriately responds to the training noise, (ii) DiffQue is capable of adapting multiple domains (CQA datasets), and (iii) DiffQue can efficiently handle the “cold start” problem that may arise due to the lack of information for newly posted questions or newly arrived users.
Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection

Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this article, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection.
Generative Cooperative Net for Image Generation and Data Augmentation

How to build a good model for image generation given an abstract concept is one of fundamental problems in computer vision. In this paper, we explore a generative model for the task of generating fictitious images with desired features. We propose the Generative Cooperative Net (GCN) for image generation. The idea is similar to generative adversarial networks (GANs) except that the generators and discriminators are trained to work accordingly. We conducted experiments on hand-written digit generation and facial expression generation. In experimental studies, we found that GCN's two cooperative counterparts (the generator and the classifier) can work together nicely and achieve promising results. Such generative model can be used as a data-augmentation tool. Our experiment of applying this method to an emotion classification task shows that the synthesis images can help to improve classification accuracy. It is easy to set up and generate a very large synthesized dataset.
Low-degree planar polynomials over finite fields of characteristic two

Planar functions are mappings from a finite field Fq to itself with an extremal differential property. Such functions give rise to finite projective planes and other combinatorial objects. There is a subtle difference between the definitions of these functions depending on the parity of q and we consider the case that q is even. We classify polynomials of degree at most q1/4 that induce planar functions on Fq, by showing that such polynomials are precisely those in which the degree of every monomial is a power of two. As a corollary we obtain a complete classification of exceptional planar polynomials, namely polynomials over Fq that induce planar functions on infinitely many extensions of Fq. The proof strategy is to study the number of Fq-rational points of an algebraic curve attached to a putative planar function. Our methods also give a simple proof of a new partial result for the classification of almost perfect nonlinear functions.
Adversarial Contract Design for Private Data Commercialization

The proliferation of data collection and machine learning techniques has created an opportunity for commercialization of private data by data aggregators. In this paper, we study this data monetization problem as a mechanism design problem, specifically using a contract-theoretic approach. Our proposed adversarial contract design framework provides a fundamental extension to the classic contract theory set-up in order to account for the heterogeneity in honest buyers' demands for data, as well as the presence of adversarial buyers who may purchase data to compromise its privacy. We propose the notion of Price of Adversary $(PoAdv)$ to quantify the effects of adversarial users on the data seller's revenue, and provide bounds on the $PoAdv$ for various classes of adversary utility. We also provide a fast approximate technique to compute contracts in the presence of adversaries.
Learning A Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution

Adversarially trained deep neural networks have significantly improved performance of single image super resolution, by hallucinating photorealistic local textures, thereby greatly reducing the perception difference between a real high resolution image and its super resolved (SR) counterpart. However, application to medical imaging requires preservation of diagnostically relevant features while refraining from introducing any diagnostically confusing artifacts. We propose using a deep convolutional super resolution network (SRNet) trained for (i) minimising reconstruction loss between the real and SR images, and (ii) maximally confusing learned relativistic visual Turing test (rVTT) networks to discriminate between (a) pair of real and SR images (T1) and (b) pair of patches in real and SR selected from region of interest (T2). The adversarial loss of T1 and T2 while backpropagated through SRNet helps it learn to reconstruct pathorealism in the regions of interest such as white blood cells (WBC) in peripheral blood smears or epithelial cells in histopathology of cancerous biopsy tissues, which are experimentally demonstrated here. Experiments performed for measuring signal distortion loss using peak signal to noise ratio (pSNR) and structural similarity (SSIM) with variation of SR scale factors, impact of rVTT adversarial losses, and impact on reporting using SR on a commercially available artificial intelligence (AI) digital pathology system substantiate our claims.
WSIA: Web Ontological Search Engine Based on Smart Agents Applied to Scientific Articles

The Semantic Web proposed by the W3C (Word Wide Web Consortium), aims to make the automation of the information contained in the current web through semantic processing based on ontologies that define what must be the rules used for the representation knowledge. This article resulting from the research project “Model for the representation of knowledge based on Web ontologies and intelligent search agents, if required: Scientific articles WSIA” proposes an architecture for finding information through intelligent agents and ontologies Web of scientific articles. This paper shows the architecture, implementation and comparing these with traditional applications.
Memory-sample tradeoffs for linear regression with small error

We consider the problem of performing linear regression over a stream of d-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples (a1,b1), (a2,b2)…, with ai drawn independently from a d-dimensional isotropic Gaussian, and where bi = ⟨ ai, x⟩ + ηi, for a fixed x ∈ ℝd with ||x||2 = 1 and with independent noise ηi drawn uniformly from the interval [−2−d/5,2−d/5]. We show that any algorithm with at most d2/4 bits of memory requires at least Ω(d loglog1/є) samples to approximate x to ℓ2 error є with probability of success at least 2/3, for є sufficiently small as a function of d. In contrast, for such є, x can be recovered to error є with probability 1−o(1) with memory O(d2 log(1/є)) using d examples. This represents the first nontrivial lower bounds for regression with super-linear memory, and may open the door for strong memory/sample tradeoffs for continuous optimization.
A Blockchain based access control for IoT

Traditional access control models rely on a central entity. This may cause single point of failure, ethical and privacy problems when applied in the Internet of Things (IoT). A distributed access control can overcome the single point failure problem of centralized access models. In this paper, we propose a distributed and trustworthy access control solution for the IoT by applying the smart contract-enabled blockchain.
Density Peaks Clustering Based on Local Minimal Spanning Tree

The fake center is a common problem of density-based clustering algorithms, especially for datasets with clusters of different shapes and densities. Clustering by fast search and find of density peaks (DPC) and its improved versions often ignore the effect of fake centers on clustering quality. They usually have a poor performance even the actual number of centers are used. To solve this problem, we propose a density peaks clustering based on local minimal spanning tree (DPC-LMST), which generates initial clusters for each potential centers first and then introduce a sub-cluster merging factor (SCMF) to aggregate similar sub-clusters. Meanwhile, we introduce a new strategy of representative points to reduce the size of data and redefine local density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{i}$ </tex-math></inline-formula> and distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{i}$ </tex-math></inline-formula> of each representative point. Furthermore, the hint of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> is redesigned to highlight true centers for datasets with clusters of different densities. The proposed algorithm is benchmarked on both synthetic and real-world datasets, and we compare the results with K-means, DPC, and the three state-of-the-art improved DPC algorithms.
Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models

The challenging problem of conducting fully Bayesian inference for the reaction rate constants governing stochastic kinetic models (SKMs) is considered. Given the challenges underlying this problem, the Markov jump process representation is routinely replaced by an approximation based on a suitable time-discretisation of the system of interest. Improving the accuracy of these schemes amounts to using an ever finer discretisation level, which in the context of the inference problem, requires integrating over the uncertainty in the process at a predetermined number of intermediate times between observations. Pseudo-marginal Metropolis–Hastings schemes are increasingly used, since for a given discretisation level, the observed data likelihood can be unbiasedly estimated using a particle filter. When observations are particularly informative, an auxiliary particle filter can be implemented, by propagating particles conditional on the next observation. Recent work in state-space settings has shown how the pseudo-marginal approach can be made much more efficient by correlating the underlying pseudo-random numbers used to form the estimate of likelihood at the current and proposed values of the unknown parameters. This approach is extended to the time-discretised SKM framework by correlating the innovations that drive the auxiliary particle filter. The resulting approach is found to offer substantial gains in efficiency over a standard implementation.
Finite Memory Structure Filtering and Smoothing for Target Tracking in Wireless Network Environments

In this paper, a state estimation problem is considered for a target tracking scheme in wireless network environments. Firstly, a unified algorithm of finite memory structure (FMS) filtering and smoothing is proposed for a discrete-time state-space model. As shown in the terminology unified, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint. Hence, the unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate using only finite measurements and inputs on the most recent window. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness, which cannot be obtained by the recursive infinite memory structure (IMS) filtering such as Kalman filter. The on-line computational complexity of the proposed unified algorithm is discussed. Secondly, as an application of the proposed unified algorithm, a target tracking scheme in wireless network environments is considered via computer simulations for moving target’s accelerations of various shapes. The proposed unified algorithm-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Due to intrinsic robustness and deadbeat properties, the proposed unified algorithm-based scheme can outperform the existing IMS filtering-based scheme when acceleration suddenly changes.
Variational Object-Aware 3-D Hand Pose From a Single RGB Image

We propose an approach to estimate the 3D pose of a human hand while grasping objects from a single RGB image. Our approach is based on a probabilistic model implemented with deep architectures, which is used for regressing, respectively, the 2D hand joints heat maps and the 3D hand joints coordinates. We train our networks so to make our approach robust to large objectand self-occlusions, as commonly occurring with the task at hand. Using specialized latent variables, the deep architecture internally infers the category of the grasped object so to enhance the 3D reconstruction, based on the underlying assumption that objects of a similar category, i.e., with similar shape and size, are grasped in a similar way. Moreover, given the scarcity of 3D hand-object manipulation benchmarks with joint annotations, we propose a new annotated synthetic dataset with realistic images, hand masks, joint masks and 3D joints coordinates. Our approach is flexible as it does not require depth information, sensor calibration, data gloves, or finger markers. We quantitatively evaluate it on synthetic datasets achieving state-of-the-art accuracy, as well as qualitatively on real sequences.
Fast and strong convergence of online learning algorithms

In this paper, we study the online learning algorithm without explicit regularization terms. This algorithm is essentially a stochastic gradient descent scheme in a reproducing kernel Hilbert space (RKHS). The polynomially decaying step size in each iteration can play a role of regularization to ensure the generalization ability of online learning algorithm. We develop a novel capacity dependent analysis on the performance of the last iterate of online learning algorithm. This answers an open problem in learning theory. The contribution of this paper is twofold. First, our novel capacity dependent analysis can lead to sharp convergence rate in the standard mean square distance which improves the results in the literature. Second, we establish, for the first time, the strong convergence of the last iterate with polynomially decaying step sizes in the RKHS norm. We demonstrate that the theoretical analysis established in this paper fully exploits the fine structure of the underlying RKHS, and thus can lead to sharp error estimates of online learning algorithm.
Fast robust methods for singular state-space models

State-space models are used in a wide range of time series analysis applications. Kalman filtering and smoothing are work-horse algorithms in these settings. While classic algorithms assume Gaussian errors to simplify estimation, recent advances use a broad range of optimization formulations to allow outlier-robust estimation, as well as constraints to capture prior information. Here we develop methods on state-space models where either transition or error covariances may be singular. These models frequently arise in navigation (e.g. for ‘colored noise’ models or deterministic integrals) and are ubiquitous in auto-correlated time series models such as ARMA. We reformulate all state-space models (singular as well as nonsingluar) as constrained convex optimization problems, and develop an efficient algorithm for this reformulation. The convergence rate is locally linear, with constants that do not depend on the conditioning of the problem. Numerical comparisons show that the new approach outperforms competing approaches for nonsingular models, including state of the art interior point (IP) methods. IP methods converge at superlinear rates; we expect them to dominate. However, the steep rate of the proposed approach (independent of problem conditioning) combined with cheap iterations wins against IP in a run-time comparison. This suggests that the proposed approach can be a default choice for estimating state space models outside of the Gaussian context for singular and nonsingular models. To highlight the capabilities of the new framework, we focus on navigation applications that use singular process covariance models. The methods have been implemented in an open source Python code.
Stochastic Artificial Potentials for Online Safe Navigation

Consider a convex set of which we remove an arbitrary number of disjoints convex sets-the obstacles-and a convex function whose minimum is the agent's goal. We consider a local and stochastic approximation of the gradient of a Rimon-Koditschek navigation function where the attractive potential is the convex function that the agent is minimizing. In particular, we show that if the estimate available to the agent is unbiased, convergence to the desired location while avoiding the obstacles is guaranteed with probability one under the same geometrical conditions as in the deterministic case. Qualitatively these conditions are that the ratio between the maximum and minimum eigenvalue of the Hessian of the objective function is not too large and that the obstacles are not too flat or too close to the desired destination. Moreover, we show that for biased estimates convergence to a point arbitrarily close to the goal is achieved with probability one. The assumptions on the bias for the result to hold are motivated by the study of the estimate of the gradient of a Rimon-Koditschek navigation function for sensor models that fit circles around the obstacles. Numerical examples explore the practical value of these theoretical results.
Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences

Two novel methods for fully unsupervised human action retrieval using 3D mesh sequences are presented. The first achieves high accuracy but is suitable for sequences consisting of clean meshes, such as artificial sequences or highly post-processed real sequences, while the second one is robust and suitable for noisy meshes, such as those that often result from unprocessed scanning or 3D surface reconstruction errors. The first method uses a spatio-temporal descriptor based on the trajectories of 6 salient points of the human body (i.e. the centroid, the top of the head and the ends of the two upper and two lower limbs) from which a set of kinematic features are extracted. The resulting features are transformed using the wavelet transformation in different scales and a set of statistics are used to obtain the descriptor. An important characteristic of this descriptor is that its length is constant independent of the number of frames in the sequence. The second descriptor consists of two complementary sub-descriptors, one based on the trajectory of the centroid of the human body across frames and the other based on the Hybrid static shape descriptor adapted for mesh sequences. The robustness of the second descriptor derives from the robustness involved in extracting the centroid and the Hybrid sub-descriptors. Performance figures on publicly available real and artificial datasets demonstrate our accuracy and robustness claims and in most cases the results outperform the state-of-the-art.
RePair in Compressed Space and Time

Given a string T of length N, the goal of grammar compression is to construct a small context-free grammar generating only T. Among existing grammar compression methods, RePair (recursive paring) [Larsson and Moffat, 1999] is notable for achieving good compression ratios in practice. In this paper, we propose the first RePair algorithm working in compressed space, i.e., potentially o(N) space for highly compressible texts. The key idea is to give a new way to restructure an arbitrary (context-free) grammar S for T into RePair(T) in compressed space and time. We propose an algorithm for RePair(T) running in O(min(N, nm log N)) space and expected O(min(N, nm log N) m) time or O(min(N, nm log N) log log N) time, where n is the size of S and m is the number of variables in RePair(T). We implemented our O(min(N, nm log N) m)-time algorithm and show it can actually run in compressed space. We also present a new approach to reduce the peak memory usage of existing RePair algorithms combining with our algorithms, and show that the new approach outperforms, both in computation time and space, the most space efficient linear-time RePair implementation to date.