Reviewerly/exHarmony huggingface dataset:
Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation

Learning the structure of Bayesian networks from data is known to be a computationally challenging, NP-hard problem. The literature has long investigated how to perform structure learning from data containing large numbers of variables, following a general interest in high-dimensional applications (“small n, large p”) in systems biology and genetics. More recently, data sets with large numbers of observations (the so-called “big data”) have become increasingly common; and these data sets are not necessarily high-dimensional, sometimes having only a few tens of variables depending on the application. We revisit the computational complexity of Bayesian network structure learning in this setting, showing that the common choice of measuring it with the number of estimated local distributions leads to unrealistic time complexity estimates for the most common class of score-based algorithms, greedy search. We then derive more accurate expressions under common distributional assumptions. These expressions suggest that the speed of Bayesian network learning can be improved by taking advantage of the availability of closed-form estimators for local distributions with few parents. Furthermore, we find that using predictive instead of in-sample goodness-of-fit scores improves speed; and we confirm that it improves the accuracy of network reconstruction as well, as previously observed by Chickering and Heckerman (Stat Comput 10: 55–62, 2000). We demonstrate these results on large real-world environmental and epidemiological data; and on reference data sets available from public repositories.
Quantum-enhanced sensing of a single-ion mechanical oscillator

The use of special quantum states to achieve sensitivities below the limits established by classically behaving states has enjoyed immense success since its inception. In bosonic interferometers, squeezed states, number states and cat states have been implemented on various platforms and have demonstrated improved measurement precision over interferometers based on coherent states. Another metrologically useful state is an equal superposition of two eigenstates with maximally different energies; this state ideally reaches the full interferometric sensitivity allowed by quantum mechanics. By leveraging improvements to our apparatus made primarily to reach higher operation fidelities in quantum information processing, we extend a technique to create number states up to $n=100$ and to generate superpositions of a harmonic oscillator ground state and a number state of the form $\textstyle{\frac{1}{\sqrt{2}}}(\lvert 0\rangle+\lvert n\rangle)$ with $n$ up to 18 in the motion of a single trapped ion. While experimental imperfections prevent us from reaching the ideal Heisenberg limit, we observe enhanced sensitivity to changes in the oscillator frequency that initially increases linearly with $n$, with maximal value at $n=12$ where we observe 3.2(2) dB higher sensitivity compared to an ideal measurement on a coherent state with the same average occupation number. The quantum advantage from using number-state superpositions can be leveraged towards precision measurements on any harmonic oscillator system; here it enables us to track the average fractional frequency of oscillation of a single trapped ion to approximately 2.6 $\times$ 10$^{-6}$ in 5 s. Such measurements should provide improved characterization of imperfections and noise on trapping potentials, which can lead to motional decoherence, a leading source of error in quantum information processing with trapped ions.
Finding Faster Configurations Using FLASH

Finding good configurations of a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the better configuration, this article introduces Flash, a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. Flash scales up to software systems that defeat the prior state-of-the-art model-based methods in this area. Flash runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this article is to use the prior knowledge of the configuration space (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the better configuration. We evaluate Flash using 30 scenarios based on 7 software systems to demonstrate that Flash saves effort in 100 and 80 percent of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to state-of-the-art techniques.
The Pros and Cons: Rank-Aware Temporal Attention for Skill Determination in Long Videos

We present a new model to determine relative skill from long videos, through learnable temporal attention modules.Skill determination is formulated as a ranking problem, making it suitable for common and generic tasks.However, for long videos, parts of the video are irrelevant for assessing skill, and there may be variability in the skill exhibited throughout a video.We therefore propose a method which assesses the relative overall level of skill in a long video by attending to its skill-relevant parts.Our approach trains temporal attention modules, learned with only video-level supervision, using a novel rank-aware loss function.In addition to attending to taskrelevant video parts, our proposed loss jointly trains two attention modules to separately attend to video parts which are indicative of higher (pros) and lower (cons) skill.We evaluate our approach on the EPIC-Skills dataset and additionally annotate a larger dataset from YouTube videos for skill determination with five previously unexplored tasks.Our method outperforms previous approaches and classic softmax attention on both datasets by over 4% pairwise accuracy, and as much as 12% on individual tasks.We also demonstrate our model's ability to attend to rank-aware parts of the video.
Compiling SU(4) quantum circuits to IBM QX architectures

The Noisy Intermediate-Scale Quantum (NISQ) technology is currently investigated by major players in the field to build the first practically useful quantum computer. IBM QX architectures are the first ones which are already publicly available today. However, in order to use them, the respective quantum circuits have to be compiled for the respectively used target architecture. While first approaches have been proposed for this purpose, they are infeasible for a certain set of SU(4) quantum circuits which have recently been introduced to benchmark corresponding compilers. In this work, we analyze the bottlenecks of existing compilers and provide a dedicated method for compiling this kind of circuits to IBM QX architectures. Our experimental evaluation (using tools provided by IBM) shows that the proposed approach significantly outperforms IBM's own solution regarding fidelity of the compiled circuit as well as runtime. Moreover, the solution proposed in this work has been declared winner of the IBM QISKit Developer Challenge. An implementation of the proposed methodology is publicly available at http://iic.jku.at/eda/research/ibm_qx_mapping.
Bayesian fractional posteriors

We consider the fractional posterior distribution that is obtained by updating a prior distribution via Bayes theorem with a fractional likelihood function, a usual likelihood function raised to a fractional power.First, we analyze the contraction property of the fractional posterior in a general misspecified framework.Our contraction results only require a prior mass condition on certain Kullback-Leibler (KL) neighborhood of the true parameter (or the KL divergence minimizer in the misspecified case), and obviate constructions of test functions and sieves commonly used in the literature for analyzing the contraction property of a regular posterior.We show through a counterexample that some condition controlling the complexity of the parameter space is necessary for the regular posterior to contract, rendering additional flexibility on the choice of the prior for the fractional posterior.Second, we derive a novel Bayesian oracle inequality based on a PAC-Bayes inequality in misspecified models.Our derivation reveals several advantages of averaging based Bayesian procedures over optimization based frequentist procedures.As an application of the Bayesian oracle inequality, we derive a sharp oracle inequality in multivariate convex regression problems.We also illustrate the theory in Gaussian process regression and density estimation problems.
Quantum Security Proofs Using Semi-classical Oracles

We present an improved version of the one-way to hiding (O2H) Theorem by Unruh, J ACM 2015. Our new O2H Theorem gives higher flexibility (arbitrary joint distributions of oracles and inputs, multiple reprogrammed points) as well as tighter bounds (removing square-root factors, taking parallelism into account). The improved O2H Theorem makes use of a new variant of quantum oracles, semi-classical oracles, where queries are partially measured. The new O2H Theorem allows us to get better security bounds in several public-key encryption schemes.
Performance of an artificial intelligence algorithm for reporting urine cytopathology

Unlike Papanicolaou tests, there are no commercially available computer-assisted automated screening systems for urine specimens. Despite The Paris System for Reporting Urinary Cytology, there still is poor interobserver agreement with urine cytology and many cases in which a definitive diagnosis cannot be made. In the current study, the authors have reported on the development of an image algorithm that applies computational methods to digitized liquid-based urine cytology slides.A total of 2405 archival ThinPrep glass slides, including voided and instrumented urine cytology cases, were digitized. A deep learning computational pipeline with multiple tiers of convolutional neural network models was developed for processing whole slide images (WSIs) and predicting diagnoses. The algorithm was validated using a separate test data set comprised of consecutive cases encountered in routine clinical practice.There were 1.9 million urothelial cells analyzed. An average of 5400 urothelial cells were identified in each WSI. The algorithm achieved an area under the curve of 0.88 (95% CI, 0.83-0.93). Using the optimal operating point, the algorithm's sensitivity was 79.5% (95% CI, 64.7%-90.2%) and the specificity was 84.5% (95% CI, 81.6%-87.1%) for high-grade urothelial carcinoma.The authors successfully developed a computational algorithm capable of accurately analyzing WSIs of urine cytology cases. Compared with prior studies, this effort used a much larger data set, exploited whole slide-level and not just cell-level features, and used a cell gallery to display the algorithm's output for easy end-user review. This algorithm provides computer-assisted interpretation of urine cytology cases, akin to the machine learning technology currently used for automated Papanicolaou test screening.
A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.
A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes

How to build a generic deep one-class (DeepOC) model to solve one-class classification problems for anomaly detection, such as anomalous event detection in complex scenes? The characteristics of existing one-class labels lead to a dilemma: it is hard to directly use a multiple classifier based on deep neural networks to solve one-class classification problems. Therefore, in this article, we propose a novel DeepOC neural network, termed as DeepOC, which can simultaneously learn compact feature representations and train a DeepOC classifier. Only with the given normal samples, we use the stacked convolutional encoder to generate their low-dimensional high-level features and train a one-class classifier to make these features as compact as possible. Meanwhile, for the sake of the correct mapping relation and the feature representations' diversity, we utilize a decoder in order to reconstruct raw samples from these low-dimensional feature representations. This structure is gradually established using an adversarial mechanism during the training stage. This mechanism is the key to our model. It organically combines two seemingly contradictory components and allows them to take advantage of each other, thus making the model robust and effective. Unlike methods that use handcrafted features or those that are separated into two stages (extracting features and training classifiers), DeepOC is a one-stage model using reliable features that are automatically extracted by neural networks. Experiments on various benchmark data sets show that DeepOC is feasible and achieves the state-of-the-art anomaly detection results compared with a dozen existing methods.
Blockchain adoption is inevitable—Barriers and risks remain

Abstract Blockchain technology possesses a vast potential to transform traditional business models. Savvy business executives are already exploring how blockchain might impact their industries and their competitive positions within those industries. Yet, there are significant barriers and risks associated with blockchain technology. In this article, we highlight some of the major obstacles and risks associated with this transformative technology. The topics discussed here are neither definitive nor comprehensive; however, they provide accounting and finance professionals with a timely introduction to the barriers and perils related to blockchain technology. Although blockchain adoption is inevitable for business enterprises, careful consideration of the risks and challenges before, during, and after blockchain implementation will help ensure long‐term success.
Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation

Despite the recent improvements in overall accuracy, deep learning systems still exhibit low levels of robustness. Detecting possible failures is critical for a successful clinical integration of these systems, where each data point corresponds to an individual patient. Uncertainty measures are a promising direction to improve failure detection since they provide a measure of a system’s confidence. Although many uncertainty estimation methods have been proposed for deep learning, little is known on their benefits and current challenges for medical image segmentation. Therefore, we report results of evaluating common voxel-wise uncertainty measures with respect to their reliability, and limitations on two medical image segmentation datasets. Results show that current uncertainty methods perform similarly and although they are well-calibrated at the dataset level, they tend to be miscalibrated at subject-level. Therefore, the reliability of uncertainty estimates is compromised, highlighting the importance of developing subject-wise uncertainty estimations. Additionally, among the benchmarked methods, we found auxiliary networks to be a valid alternative to common uncertainty methods since they can be applied to any previously trained segmentation model.
A powerful variant of symbiotic organisms search algorithm for global optimization

This paper suggests a new variation to the existing symbiotic organisms search (SOS) algorithm developed by simulating three symbiotic strategies of mutualism, commensalism and parasitism used by the organisms. In the revised version called improved SOS (ISOS), the theory of quasi-oppositional based learning is employed during generation of initial population and in the parasitism phase to raise the possibility of getting closer to high-quality solutions. An efficient alternative for parasitism phase is also presented. The two upgraded parasitism strategies avoid the over exploration issue of original parasitism phase that causes unwanted long-time search in the inferior search space as the solution is already refined. To guide the algorithm perform an exhaustive search around the best solution in attempting to further improve the search model of ISOS, a chaotic local search based on the piecewise linear chaotic map is coupled into the proposed algorithm. Twenty-six benchmark functions and three engineering design problems are tested and a contrast with other popular metaheuristics is widely established. Comparative results substantiate the great contribution of proposed ISOS algorithm in solving various optimization problems with superior global search capability and convergence characteristics which render it useful in handling global optimization problems.
ACE: Adapting to Changing Environments for Semantic Segmentation

Deep neural networks exhibit exceptional accuracy when they are trained and tested on the same data distributions. However, neural classifiers are often extremely brittle when confronted with domain shift---changes in the input distribution that occur over time. We present ACE, a framework for semantic segmentation that dynamically adapts to changing environments over time. By aligning the distribution of labeled training data from the original source domain with the distribution of incoming data in a shifted domain, ACE synthesizes labeled training data for environments as it sees them. This stylized data is then used to update a segmentation model so that it performs well in new environments. To avoid forgetting knowledge from past environments, we introduce a memory that stores feature statistics from previously seen domains. These statistics can be used to replay images in any of the previously observed domains, thus preventing catastrophic forgetting. In addition to standard batch training using stochastic gradient decent (SGD), we also experiment with fast adaptation methods based on adaptive meta-learning. Extensive experiments are conducted on two datasets from SYNTHIA, the results demonstrate the effectiveness of the proposed approach when adapting to a number of tasks.
Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global spectral correlation. Existing nonlocal low-rank tensor approximation methods were mainly based on two common decomposition [Tucker or CANDECOMP/PARAFAC (CP)] methods and achieved the state-of-the-art results, but they are subject to certain issues and do not produce the best approximation for a tensor. For example, the number of parameters for Tucker decomposition increases exponentially according to its dimensions, and CP decomposition cannot better preserve the intrinsic correlation of the HSI. In this article, a novel nonlocal tensor-ring (TR) approximation is proposed for HSI denoising by using TR decomposition to explore the nonlocal self-similarity and global spectral correlation simultaneously. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted third-order tensors, which has strong ability to explore these two intrinsic priors and to improve the HSI denoising results. Moreover, an efficient proximal alternating minimization algorithm is developed to optimize the proposed TR decomposition model efficiently. Extensive experiments on three simulated data sets under several noise levels and two real data sets verify that the proposed TR model provides better HSI denoising results than several state-of-the-art methods in terms of quantitative and visual performance evaluations.
Editing Text in the Wild

In this paper, we are interested in editing text in natural images, which aims to replace or modify a word in the source image with another one while maintaining its realistic look. This task is challenging, as the styles of both background and text need to be preserved so that the edited image is visually indistinguishable from the source image. Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module. The text conversion module changes the text content of the source image into the target text while keeping the original text style. The background inpainting module erases the original text, and fills the text region with appropriate texture. The fusion module combines the information from the two former modules, and generates the edited text images. To our knowledge, this work is the first attempt to edit text in natural images at the word level. Both visual effects and quantitative results on synthetic and real-world dataset (ICDAR 2013) fully confirm the importance and necessity of modular decomposition. We also conduct extensive experiments to validate the usefulness of our method in various real-world applications such as text image synthesis, augmented reality (AR) translation, information hiding, etc.
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes

Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothness constraint to train the networks. Their performance degrades at regions where repetitive textures or occlusions occur. In this paper, we propose Deep Epipolar Flow, an unsupervised optical flow method which incorporates global geometric constraints into network learning. In particular, we investigate multiple ways of enforcing the epipolar constraint in flow estimation. To alleviate a ``chicken-and-egg'' type of problem encountered in dynamic scenes where multiple motions may be present, we propose a low-rank constraint as well as a union-of-subspaces constraint for training. Experimental results on various benchmarking datasets show that our method achieves competitive performance compared with supervised methods and outperforms state-of-the-art unsupervised deep-learning methods.
A blockchain-based attribute-based signcryption scheme to secure data sharing in the cloud

Traditional cloud data sharing schemes have relied on the architecture of the network and large storage providers. However, these storage providers work as trusted third parties to transfer and store data. This kind of cloud storage model has some weak points, such as data availability, centralized data storage, high operational cost, and security concern. In this paper, we combine the concept of blockchain with attribute-based signcryption to provide a secure data sharing in the cloud environment. The proposed scheme satisfies the security requirements of the cloud computing such as confidentiality and unforgeability. Further, the smart contract solves the problem of cloud storage such as returning wrong results as in the traditional cloud server. Finally, the performance comparisons and simulation results show that our proposed scheme is more efficient than others, and it is practical.