Reviewerly/exHarmony huggingface dataset:
clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization

Alternating least squares (ALS) has been proved to be an effective solver for matrix factorization in recommender systems. To speed up factorizing performance, various parallel ALS solvers have been proposed to leverage modern multi-cores and many-cores. Existing implementations are limited in either speed or portability. In this paper, we present an efficient and portable ALS solver (clMF) for recommender systems. On one hand, we diagnose the baseline implementation and observe that it lacks of the awareness of the hierarchical thread organization on modern hardware. To achieve high performance, we apply the thread batching technique, the fine-grained tiling technique and three architecture-specific optimizations. On the other hand, we implement the ALS solver in OpenCL so that it can run on various platforms (CPUs, GPUs and MICs). Based on the architectural specifics, we select a suitable code variant for each platform to efficiently map it to the underlying hardware. The experimental results show that our implementation performs 2.8×–15.7× faster on an Intel 16-core CPU, 23.9×–87.9× faster on an NVIDIA K20C GPU and 34.6×–97.1× faster on an AMD Fury X GPU than the baseline implementation. On the K20C GPU, our implementation also outperforms cuMF over different latent features ranging from 10 to 100 with various real-world recommendation datasets.
FC-RCCN: Fully convolutional residual continuous CRF network for semantic segmentation

Enlarging the spatial resolution of features generated by fully convolutional networks (FCNs) can improve the performance of semantic segmentation. To achieve this goal, deeper network with deconvolutional structure can be applied. However, when the network architecture becomes more complex, the training efficiency may degrade. To address the joint optimization problem of improving spatial resolution through deeper networks and training deeper networks more effectively, we propose a Fully Convolutional Residual Continuous CRF Network (FC-RCCN) for semantic segmentation. FC-RCCN is composed of three subnetworks: a unary network, a pairwise network, and a superpixel based continuous conditional random filed (C-CRF) network. In order to generate full spatial resolution predictions with high-quality, a residual block based unary network with multi-scale features fusion is proposed. Even though the unary network is a deeper network, the whole framework can be trained effectively in an end-to-end way using the joint pixel-level and superpixel-level supervised learning strategy which is optimized by a pixel-level softmax cross entropy loss and a superpixel-level log-likelihood loss. Besides, C-CRF inference is fused with pixel-level prediction during the test procedure, which guarantees the method's robustness to the superpxiel errors. In the experiments, we evaluatee the power of the three subnetworks and the learning strategy comprehensively. Experiments on three benchmark datasets demonstrate that the proposed FC-RCCN outperforms previous segmentation methods and obtains the state-of-the-art performance.
Toward Precise Gaze Estimation for Mobile Head-Mounted Gaze Tracking Systems

The gaze estimation in the mobile scenario often suffers from the extrapolation and parallax errors. In this paper, we propose a novel calibration framework to achieve the precise gaze estimation for head-mounted gaze trackers. Our proposed framework consists of two steps to learn a point-to-point and a point-to-line relations, respectively. The aim of step I is to infer the relation between pupil centers and spatially constrained points of regard. By adopting the “CalibMe” gaze data acquisition method, a sparse Gaussian Process using pseudo-inputs is used to capture the smooth residual field unmodeled by the polynomial function. Meanwhile, a distraction detection criterion is introduced to identify the moment when user's attention is taken away from the calibration point thereby removing outliers. By combining with the point-to-point relation inferred in step I, the observed parallax errors are leveraged in step II to obtain a point-to-line relation, i.e., each pupil center will correspond to an epipolar line. Thus, the real image gaze point projected from different depths is predicted as the intersection of two epipolar lines inferred from binocular data. The simulation and experimental results show the effectiveness of our proposed calibration framework for head-mounted gaze trackers.
Hierarchical extreme learning machine based image denoising network for visual Internet of Things

In the visual Internet of Things (VIoT), imaging sensors must achieve a balance between limited bandwidth and useful information when images contain heavy noise. In this paper, we address the problem of removing heavy noise and propose a novel hierarchical extreme learning machine-based image denoising network, which comprises a sparse auto-encoder and a supervised regression. Due to the fast training of a hierarchical extreme learning machine, an effective image denoising system that is robust for various noise levels can be trained more efficiently than other denoising methods, using a deep neural network. Our proposed framework also contains a non-local aggregation procedure that aims to fine-tune noise reduction according to structural similarity. Compared to the compression ratio in noisy images, the compression ratio of denoised images can be dramatically improved. Therefore, the method can achieve a low communication cost for data interactions in the VIoT. Experimental studies on images, including both hand-written digits and natural scenes, have demonstrated that the proposed technique achieves excellent performance in suppressing heavy noise. Further, it greatly reduces the training time, and outperforms other state-of-the-art approaches in terms of denoising indexes for the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM).
Quantum interferometry via a coherent state mixed with a photon-added squeezed vacuum state

We theoretically investigate the phase sensitivity with parity detection on a Mach–Zehnder interferometer with a coherent state combined with a photon-added squeezed vacuum state. Our results show that when the phase shift to be estimated approaches zero, the squeezed vacuum state is indeed the optimal state within a constraint on the average photon number. However, when the phase shift slightly deviates from zero, in terms of parity detection, the photon-added squeezed vacuum state can give the better phase sensitivity than both the squeezed vacuum state and the photon-subtracted squeezed vacuum state when these interferometer states carry many photons. In addition, we also show that the quantum Cramér–Rao bound can be reached by parity detection when the phase shift approaches zero.
Visual Tracking and Depth Estimation of Mobile Robots Without Desired Velocity Information

In this paper, a visual servoing approach is developed for the trajectory tracking control and depth estimation problem of a mobile robot without a priori knowledge about desired velocities. By exploiting the multiple images captured by the on-board camera, the current and desired poses (i.e., scaled translation and orientation) of the mobile robot are reconstructed to define system errors. Then, an adaptive time-varying controller is proposed to achieve the trajectory tracking task in the presence of nonholonomic constraint and unknown depth parameters. Most of previous works require the measurement of the desired velocity information to facilitate the controller design, leading to tedious offline computation. In this paper, to eliminate this requirement, the desired velocities are estimated in real-time based on a reduced order observer. Moreover, an augmented update law is designed to compensate for the unknown depth parameters and identify the inverse depth constant. The Lyapunov-based method is employed to prove that the proposed controller achieves asymptotic tracking, and the inverse depth estimate converges to its actual value provided that a persistent excitation condition is satisfied. Subsequently, a robust data-driven algorithm is introduced to ensure the convergence of the inverse depth estimate under a relaxed finite excitation condition. Simulation and experimental results are provided to demonstrate the effectiveness of the proposed approach.
Security of quantum key distribution with detection-efficiency mismatch in the single-photon case: Tight bounds

One of the challenges in practical quantum key distribution is dealing with efficiency mismatch between different threshold single-photon detectors. There are known bounds for the secret key rate for the BB84 protocol with detection-efficiency mismatch provided that the eavesdropper sends no more than one photon to the legitimate receiver. Here we improve these bounds and give tight bounds for the secret key rate with a constant detection-efficiency mismatch under the same single-photon assumption. We propose a method based on the analytical minimization of the relative entropy of coherence, which can be used in other problems in quantum key distribution. Also we propose an adaptation of the decoy state method to prove the security in the case of weak coherent pulses on the source side.
Model-based co-clustering for mixed type data

The importance of clustering for creating groups of observations is well known. The emergence of high-dimensional data sets with a huge number of features leads to co-clustering techniques, and several methods have been developed for simultaneously producing groups of observations and features. By grouping the data set into blocks (the crossing of a row-cluster and a column-cluster), these techniques can sometimes better summarize the data set and its inherent structure. The Latent Block Model (LBM) is a well-known method for performing co-clustering. However, recently, contexts with features of different types (here called mixed type data sets) are becoming more common. The LBM is not directly applicable to this kind of data set. Here a natural extension of the usual LBM to the "Multiple Latent Block Model" (MLBM) is proposed in order to handle mixed type data sets. Inference is performed using a Stochastic EM-algorithm that embeds a Gibbs sampler, and allows for missing data situations. A model selection criterion is defined to choose the number of row and column clusters. The method is then applied to both simulated and real data sets.
Implementation of a generalized controlled-NOT gate between fixed-frequency transmons

We have embedded two fixed-frequency ${\mathrm{Al}/\mathrm{AlO}}_{x}$/Al transmons, with ground-to-excited transition frequencies at 6.0714 and 6.7543 GHz, in a single three-dimensional Al cavity with a fundamental mode at 7.7463 GHz. Strong coupling between the cavity and each transmon results in an effective qubit-qubit coupling strength of 26 MHz and a $\ensuremath{-}1$ MHz dispersive shift in each qubit's transition frequency, depending on the state of the other qubit. Using the all-microwave SWIPHT (speeding up wave forms by inducing phases to harmful transitions) technique [Economou and Barnes, Phys. Rev. B 91, 161405 (2015)], we demonstrate the operation of a generalized controlled-not gate between the two qubits, with a gate time of ${\ensuremath{\tau}}_{g}=907$ ns optimized for this device. Using quantum process tomography we find that the gate fidelity is 83--84%, somewhat less than the 87% fidelity expected from relaxation and dephasing in the transmons during the gate time.
Multi-level context-adaptive correlation tracking

The discriminative correlation filter (DCF) has shown impressive performance in visual tracking. Context has two functions in DCF: addressing the disturbance in target locating, and supplying cues for locating the target within the context. To improve the context utilization, we introduce a multi-level context-adaptive tracking (MCAT) approach for DCF tracking. Firstly, a multi-level context representation—called a context pyramid—is proposed to exploit the relationship between the target and its context for better visual tracking. Secondly, for each level of the context pyramid, we control the effect of context in DCF learning and tracking using context-adaptive spatial windows. An accurate target model can thereby be learned, even when the background clutter is severe. Moreover, the target can be more easily tracked when the background is weakened by the spatial window. Thirdly, a robust prediction of the target position is obtained with the multi-level structure of the context pyramid. Experimental results showed that, with conventional hand-crafted features, our tracker provided state-of-the-art performance on OTB100 comparable to those of deep-learning-based trackers.
Quantum Synchronizable Codes From the Cyclotomy of Order Four

Quantum synchronizable codes (QSCs) are special quantum error-correcting codes (QECCs) that correct the effects of both quantum noise on qubits and misalignment in block synchronization. In this letter, we construct two classes of QSCs from the cyclic codes which are obtained by the cyclotomic classes of order four. The QSCs constructed are Calderbank-Shor-Steane QECCs which can tolerate maximum number of misalignment errors. Moreover, the QSCs usually possess good error-correcting capability towards bit error and phase error, due to many cyclic codes employed to construct QSCs are optimal or almost optimal.
Adaptive neural networks-based visual servoing control for manipulator with visibility constraint and dead-zone input

This paper proposed an online image-based visual servoing (IBVS) controller for manipulator systems with dead-zone input. The adaptive neural networks (NNs) are used to approximate the unknown nonlinear dynamics. The Barrier Lyapunov Function (BLF) is constructed to overcome the visibility constraint problem, in which both the constant symmetric barriers and time-varying asymmetric barriers are considered. With the proposed control method, it is proved that all the signals in the closed-loop system are semi-globally uniformly bounded and the image error is remained in a bounded compact set. Finally, simulation examples are given to illustrate the effectiveness of the proposed control method.
Nonconvex TGV regularization model for multiplicative noise removal with spatially varying parameters

In this article, we introduce a novel variational model for the restoration of images corrupted by multiplicative Gamma noise. The model incorporates a convex data-fidelity term with a nonconvex version of the total generalized variation (TGV). In addition, we adopt a spatially adaptive regularization parameter (SARP) approach. The nonconvex TGV regularization enables the efficient denoising of smooth regions, without staircasing artifacts that appear on total variation regularization-based models, and edges and details to be conserved. Moreover, the SARP approach further helps preserve fine structures and textures. To deal with the nonconvex regularization, we utilize an iteratively reweighted $\ell_1$ algorithm, and the alternating direction method of multipliers is employed to solve a convex subproblem. This leads to a fast and efficient iterative algorithm for solving the proposed model. Numerical experiments show that the proposed model produces better denoising results than the state-of-the-art models.
Separability of completely symmetric states in a multipartite system

Symmetry plays an important role in the field of quantum mechanics. In this paper, we consider a subclass of symmetric quantum states in the multipartite system $N^{\otimes d}$, namely, the completely symmetric states, which are invariant under any index permutation. It was conjectured by L. Qian and D. Chu [arXiv:1810.03125 [quant-ph]] that the completely symmetric states are separable if and only if it is a convex combination of symmetric pure product states. In this paper, we proved that this conjecture is true for both bipartite and multipartite cases. And we proved the completely symmetric state $\rho$ is separable if its rank is at most $5$ or $N+1$. For the states of rank $6$ or $N+2$, they are separable if and only if their range contains a product vector. We apply our results to a few widely useful states in quantum information, such as symmetric states, edge states, extreme states, and nonnegative states. We also study the relation of CS states to Hankel and Toeplitz matrices.
Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method

The transformed domain fearures of images show effectiveness in distinguishing salient and non-salient regions. In this paper, we propose a novel deep complex neural network, named SalDCNN, to predict image saliency by learning features in both pixel and transformed domains. Before proposing Sal-DCNN, we analyze the saliency cues encoded in discrete Fourier transform (DFT) domain. Consequently, we have the following findings: 1) the phase spectrum encodes most saliency cues; 2) a certain pattern of the amplitude spectrum is important for saliency prediction; 3) the transformed domain spectrum is robust to noise and down-sampling for saliency prediction. According to these findings, we develop the structure of SalDCNN, including two main stages: the complex dense encoder and three-stream multi-domain decoder. Given the new SalDCNN structure, the saliency maps can be predicted under the supervision of ground-truth fixation maps in both pixel and transformed domains. Finally, the experimental results show that our Sal-DCNN method outperforms other 8 state-of-theart methods for image saliency prediction on 3 databases.
Generating Live Soccer-Match Commentary from Play Data

We address the task of generating live soccer-match commentaries from play event data. This task has characteristics that (i) each commentary is only partially aligned with events, (ii) play event data contains many types of categorical and numerical attributes, (iii) live commentaries often mention player names and team names. For these reasons, we propose an encoder for play event data, which is enhanced with a gate mechanism. We also introduce an attention mechanism on events. In addition, we introduced placeholders and their reconstruction mechanism to enable the model to copy appropriate player names and team names from the input data. We conduct experiments on the play data of the English Premier League, provide a discussion on the result including generated commentaries.
Fine-Grained Search Space Classification for Hard Enumeration Variants of Subset Problems

We propose a simple, powerful, and flexible machine learning framework for (i) reducing the search space of computationally difficult enumeration variants of subset problems and (ii) augmenting existing state-of-the-art solvers with informative cues arising from the input distribution. We instantiate our framework for the problem of listing all maximum cliques in a graph, a central problem in network analysis, data mining, and computational biology. We demonstrate the practicality of our approach on real-world networks with millions of vertices and edges by not only retaining all optimal solutions, but also aggressively pruning the input instance size resulting in several fold speedups of state-of-the-art algorithms. Finally, we explore the limits of scalability and robustness of our proposed framework, suggesting that supervised learning is viable for tackling NP-hard problems in practice.
Evolving Action Abstractions for Real-Time Planning in Extensive-Form Games

A key challenge for planning systems in real-time multiagent domains is to search in large action spaces to decide an agent’s next action. Previous works showed that handcrafted action abstractions allow planning systems to focus their search on a subset of promising actions. In this paper we show that the problem of generating action abstractions can be cast as a problem of selecting a subset of pure strategies from a pool of options. We model the selection of a subset of pure strategies as a two-player game in which the strategy set of the players is the powerset of the pool of options— we call this game the subset selection game. We then present an evolutionary algorithm for solving such a game. Empirical results on small matches of µRTS show that our evolutionary approach is able to converge to a Nash equilibrium for the subset selection game. Also, results on larger matches show that search algorithms using action abstractions derived by our evolutionary approach are able to substantially outperform all state-of-the-art planning systems tested.
Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience

We propose an actor-critic algorithm that uses past planning experience to improve the efficiency of solving robot task-and-motion planning (TAMP) problems. TAMP planners search for goal-achieving sequences of high-level operator instances specified by both discrete and continuous parameters. Our algorithm learns a policy for selecting the continuous parameters during search, using a small training set generated from the search trees of previously solved instances. We also introduce a novel fixed-length vector representation for world states with varying numbers of objects with different shapes, based on a set of key robot configurations. We demonstrate experimentally that our method learns more efficiently from less data than standard reinforcementlearning approaches and that using a learned policy to guide a planner results in the improvement of planning efficiency.
The Challenges of Existence, Status, and Value for Improving Blockchain

Blockchain is defined as a distributed ledger technology that can implement financial models. An improved blockchain provides a democratic virtual economic system (DVES) that can verify payments, reach consensus, and store encrypted data in virtual economic systems. In this paper, we review the latest progress and possibilities in improved blockchain with respect to openness, data security, and scalability. This paper outlines the challenges of value, existence, and status (VES) and the state-of-the-art solutions for improved blockchain. Then, this paper discusses the VES in terms of distributed energy, ownership certification, infrastructure, and other fields. More importantly, it analyzed the importance of scale out, which can be a key enabler to solve the main practical problems in constructing DVES.
HoloLens and VIVE Pro: Virtual Reality Headsets

Virtual reality headsets are poised to become the next must-have tool for education, training, and research. Though a wide variety of devices are available in the virtual reality market, there is not yet a recognized leader for educational, research, or training-specific applications. Two headsets that are currently available, the HTC VIVE Pro and the Microsoft HoloLens, are recognized as robust and powerful tools; however, both devices have requirements for space and support that libraries will need to consider carefully when planning access and programming for virtual reality.
DeepFlash: Turning a flash selfie into a studio portrait

We present a method for turning a flash selfie taken with a smartphone into a photograph as if it was taken in a studio setting with uniform lighting. Our method uses a convolutional neural network trained on a set of pairs of photographs acquired in an ad-hoc acquisition campaign. Each pair consists of one photograph of a subject’s face taken with the camera flash enabled and another one of the same subject in the same pose illuminated using a photographic studio-lighting setup. We show how our method can amend defects introduced by a close-up camera flash, such as specular highlights, shadows, skin shine, and flattened images.