Reviewerly/exHarmony huggingface dataset:
Color Matching Images With Unknown Non-Linear Encodings

We present a color matching method that deals with different non-linear encodings. In particular, given two different views of the same scene taken by two cameras with unknown settings and internal parameters, and encoded with unknown non-linear curves, our method is able to correct the colors of one of the images making it look as if it was captured under the other camera's settings. Our method is based on treating the in-camera color processing pipeline as a concatenation of a matrix multiplication on the linear image followed by a non-linearity. This allows us to model a color stabilization transformation among the two shots by estimating a single matrix -that will contain information from both of the original images- and an extra parameter that complies with the non-linearity. The method is fast and the results have no spurious colors. It outperforms the state-of-the-art both visually and according to several metrics, and can handle HDR encodings and very challenging real-life examples.
Multipartite entanglement transfer in spin chains

We investigate the transfer of genuine multipartite entanglement across a spin-12 chain with nearest-neighbour XX-type interaction. We focus on the perturbative regime, where a block of spins is weakly coupled at each edge of a quantum wire, embodying the role of a multiqubit sender and receiver, respectively. We find that high-quality multipartite entanglement transfer is achieved at the same time that three excitations are transferred to the opposite edge of the chain. Moreover, we find that both a finite concurrence and tripartite negativity is attained at much shorter time, making GHZ-distillation protocols feasible. Finally, we investigate the robustness of our protocol with respect to non-perturbative couplings and increasing lengths of the quantum wire.
Target Detection Aided by Quantum Temporal Correlations: Theoretical Analysis and Experimental Validation

The detection of objects in the presence of significant background noise is a problem of fundamental interest in sensing. In this work, we theoretically analyze a prototype target detection protocol, the quantum temporal correlation (QTC) detection protocol, which is implemented in this work utilizing spontaneous parametric down-converted photon-pair sources. The QTC detection protocol only requires time-resolved photon-counting detection, which is phase-insensitive and therefore suitable for optical target detection. As a comparison to the QTC detection protocol, we also consider a classical phase-insensitive target detection protocol based on intensity detection that is practical in the optical regime. We formulated the target detection problem as a total probe photon transmission estimation problem and obtain an analytical expression of the receiver operating characteristic (ROC) curves. We carry out experiments using a semiconductor waveguide source, which we developed and previously reported. The experimental results agree very well with the theoretical prediction. In particular, we find that in a high-level environment noise and loss, the QTC detection protocol can achieve performance comparable to that of the classical protocol (that is practical in the optical regime) but with \(\simeq 57\) times lower detection time in terms of ROC curve metric. The performance of the QTC detection protocol experiment setup could be further improved with a higher transmission of the reference photon and better detector time uncertainty. Furthermore, the probe photons in the QTC detection protocol are completely indistinguishable from the background noise and therefore useful for covert ranging applications. Finally, our technological platform is highly scalable as well as tunable and thus amenable to large scale integration, which is necessary for practical applications.
A broad learning-driven network traffic analysis system based on fog computing paradigm

The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types. Current traffic analysis methods are executed on the cloud, which needs to upload the traffic data. Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes. However, traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model, which are not suitable for fog computing due to the poor computing power. In this study, we design a novel fog computing based traffic analysis system using broad learning. For one thing, fog computing can provide a distributed architecture for saving the bandwidth resources. For another, we use the broad learning to incrementally train the traffic data, which is more suitable for fog computing because it can support incremental updates of models without retraining all data. We implement our system on the Raspberry Pi, and experimental results show that we have a 98% probability to accurately identify these traffic data. Moreover, our method has a faster training speed compared with Convolutional Neural Network (CNN).
Limits of photon-mediated interactions in one-dimensional photonic baths

The exchange of off-resonant propagating photons between distant quantum emitters induces coherent interactions among them. The range of such interactions, and whether they are accompanied by dissipation, depends on the photonic energy dispersion, its dimensionality, and/or the light-matter couplings. We characterize the limits of photon-mediated interactions for the case of generic one-dimensional photonic baths under the typical assumptions, i.e., finite range hoppings for the photonic bath plus local and rotating-wave light-matter couplings. In that case, we show how, irrespective of the system's parameter, the coherent photon-mediated interactions can always be written as a finite sum of exponentials and thus cannot display a power-law asymptotic scaling. As an outlook, we show how by relaxing some of these conditions, e.g., going beyond local light-matter couplings (e.g., giant atoms) or with longer-range photon hopping models, power-law interactions can be obtained within certain distance windows or even in the asymptotic regime for the latter case.
Variational Inference With Parameter Learning Applied to Vehicle Trajectory Estimation

We present parameter learning in a Gaussian variational inference setting using only noisy measurements (i.e., no groundtruth). This is demonstrated in the context of vehicle trajectory estimation, although the method we propose is general. The letter extends the Exactly Sparse Gaussian Variational Inference (ESGVI) framework, which has previously been used for large-scale nonlinear batch state estimation. Our contribution is to additionally learn parameters of our system models (which may be difficult to choose in practice) within the ESGVI framework. In this letter, we learn the covariances for the motion and sensor models used within vehicle trajectory estimation. Specifically, we learn the parameters of a white-noise-on-acceleration motion model and the parameters of an Inverse-Wishart prior over measurement covariances for our sensor model. We demonstrate our technique using a 36 km dataset consisting of a car using lidar to localize against a high-definition map; we learn the parameters on a training section of the data and then show that we achieve high-quality state estimates on a test section, even in the presence of outliers. Lastly, we show that our framework can be used to solve pose graph optimization even with many false loop closures.
Fast Optical Flow Extraction From Compressed Video

We propose the fast optical flow extractor, a filtering method that recovers artifact-free optical flow fields from HEVCcompressed video. To extract accurate optical flow fields, we form a regularized optimization problem that considers the smoothness of the solution and the pixelwise confidence weights of an artifactridden HEVC motion field. Solving such an optimization problem is slow, so we first convert the problem into a confidence-weighted filtering task. By leveraging the already-available HEVC motion parameters, we achieve a 100-fold speed-up in the running times compared to similar methods, while producing subpixel-accurate flow estimates. Je fast optical flow extractor is useful when video frames are already available in coded formats. Our method is not specific to a coder, and works with motion fields from video coders such as H.264/AVC and HEVC.
A Universal Approximation Theorem of Deep Neural Networks for Expressing Distributions.

This paper studies the universal approximation property of deep neural networks for representing probability distributions. Given a target distribution $\pi$ and a source distribution $p_z$ both defined on $\mathbb{R}^d$, we prove under some assumptions that there exists a deep neural network $g:\mathbb{R}^d\rightarrow \mathbb{R}$ with ReLU activation such that the push-forward measure $(\nabla g)_\# p_z$ of $p_z$ under the map $\nabla g$ is arbitrarily close to the target measure $\pi$. The closeness are measured by three classes of integral probability metrics between probability distributions: $1$-Wasserstein distance, maximum mean distance (MMD) and kernelized Stein discrepancy (KSD). We prove upper bounds for the size (width and depth) of the deep neural network in terms of the dimension $d$ and the approximation error $\varepsilon$ with respect to the three discrepancies. In particular, the size of neural network can grow exponentially in $d$ when $1$-Wasserstein distance is used as the discrepancy, whereas for both MMD and KSD the size of neural network only depends on $d$ at most polynomially. Our proof relies on convergence estimates of empirical measures under aforementioned discrepancies and semi-discrete optimal transport.
The fuzzy logic controllers synthesis method in the vector control system of the wind turbine doubly-fed induction generator

The article is devoted to the improvement of control systems for wind turbines by developing fuzzy controllers with higher transient characteristics and low computational costs of identification in comparison with the applied PI controllers. Based on the self-organization method, a fuzzy speed controller of the doubly-fed induction generator (DFIG) of a wind turbine was synthesized, which uses a zero-order Sugeno fuzzy inference system and is made in the form of a block-oriented Wiener model. This regulator is an element of the vector control system of the transistor converter on the rotor side. The results of simulation modeling of the fuzzy controller showed that it provides a lower transition time compared to the PI controller, by 53.59% during acceleration and by 79.76% during braking, and 23.81% less error speed deviations from the reference signal. Such indicators can minimize losses while maintaining the maximum output power point of the power plant. The implementation of the developed system on wind turbines contributes to increasing the efficiency of wind farms, reducing the cost of electricity production, reducing the payback period of equipment, and the sustainable development of alternative energy in general.
Effect of the Latent Structure on Clustering With GANs

Generative adversarial networks (GANs) have shown remarkable success in the generation of data from natural data manifolds such as images. In several scenarios, it is desirable that generated data is well-clustered, especially when there is severe class imbalance. In this paper, we focus on the problem of clustering in the generated space of GANs and uncover its relationship with the characteristics of the latent space. We derive from first principles, the necessary and sufficient conditions needed to achieve faithful clustering in the GAN framework: (i) presence of a multimodal latent space with adjustable priors, (ii) existence of a latent space inversion mechanism and, (iii) imposition of the desired cluster priors on the latent space. We also identify the GAN models in the literature that partially satisfy these conditions and demonstrate the importance of all the components required, through ablative studies on multiple real-world image datasets. Additionally, we describe a procedure to construct a multimodal latent space which facilitates learning of cluster priors with sparse supervision. Codes for our implementation is available at https://github.com/NEMGAN/NEMGAN-P.
On the Performance of Differential Evolution Variants in Constrained Structural Optimization

Constrained optimization is a highly important field of engineering as most real-world optimization problems are associated with one or several constraints. Such problems are often challenging to solve due to their complexity and high nonlinearity. Differential evolution (DE) is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems and has shown to be very efficient when solving constrained global optimization problems. In this paper we investigate the performance of several DE variants existing in the literature such as the traditional DE, the composite DE (CoDE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (jDE and SaDE), for handling constrained structural optimization problems. The performance of each DE variant is quantified by using three well-known benchmark structures in 2D and 3D. It is shown that JADE, which updates control parameters in an adaptive way, truly exhibits superior performance and outperforms the other DE variants in all the cases examined.
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction.

Clinical outcome prediction based on Electronic Health Record (EHR) helps enable early interventions for high-risk patients, and is thus a central task for smart healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the long and irregular clinical event sequences in EHR. We make the observation that clinical events at a long time scale exhibit strong temporal patterns, while events within a short time period tend to be disordered co-occurrence. We thus propose differentiated mechanisms to model clinical events at different time scales. Our model learns hierarchical representations of event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture their core temporal dependencies. Experimental results on real clinical data show that our model greatly improves over previous state-of-the-art models, achieving AUC scores of 0.94 and 0.90 for predicting death and ICU admission, respectively. Our model also successfully identifies important events for different clinical outcome prediction tasks.
Decision Trees and Applications

In many cases, the meaning of information is wrongly related to either the sense of data or the notion of knowledge. There is a crucial sequence of steps before information becomes knowledge and the value of data depends in the existence of information so as to produce knowledge. The most common method for producing knowledge through data is based on data analysis and primarily in the interpretation of results. This is the way humans make decisions, based on their existing knowledge, and thus using this method, they try to simulate several artificial decision tools. Decision trees (DTs) are such a tool. Their goal is consisted of automatic or semiautomatic big data analysis as well as creating new patterns. DT can be applied in various scientific fields such as bioinformatics. The most commonly used applications of decision trees are data mining and data classification. This study reviews these applications in bioinformatics.
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks With Path Planning

We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios. Functional decomposition between planning and low-level control is achieved by explicitly separating the state-action spaces across the hierarchy, which allows the integration of task-relevant knowledge per layer. We propose an RL-based planner to efficiently leverage the information in the planning layer of the hierarchy, while the control layer learns a goal-conditioned control policy. The hierarchy is trained jointly but allows for the modular transfer of policy layers across hierarchies of different agents. We experimentally show that our method generalizes across unseen test environments and can scale to 3x horizon length compared to both learning and non-learning based methods. We evaluate on complex continuous control tasks with sparse rewards, including navigation and robot manipulation.
A Novel Hybrid Secure Image Encryption Based on the Shuffle Algorithm and the Hidden Attractor Chaos System

Aiming at the problems of small key space, low security of encryption structure, and easy to crack existing image encryption algorithms combining chaotic system and DNA sequence, this paper proposes an image encryption algorithm based on a hidden attractor chaotic system and shuffling algorithm. Firstly, the chaotic sequence generated by the hidden attractor chaotic system is used to encrypt the image. The shuffling algorithm is used to scramble the image, and finally, the DNA sequence operation is used to diffuse the pixel value of the image. Experimental results show that the key space of the scheme reaches 2327 and is very sensitive to keys. The histogram of encrypted images is evenly distributed. The correlation coefficient of adjacent pixels is close to 0. The entropy values of encrypted images are all close to eight and the unified average change intensity (UACI) value and number of pixel changing rate (NPCR) value are close to ideal values. All-white and all-black image experiments meet the requirements. Experimental results show that the encryption scheme in this paper can effectively resist exhaustive attacks, statistical attacks, differential cryptanalysis, known plaintext and selected plaintext attacks, and noise attacks. The above research results show that the system has better encryption performance, and the proposed scheme is useful and practical in communication and can be applied to the field of image encryption.
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error

Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. The concept of model compression is also important for analyzing the generalization error of deep learning, known as the compression-based error bound. However, there is still huge gap between a practically effective compression method and its rigorous background of statistical learning theory. To resolve this issue, we develop a new theoretical framework for model compression and propose a new pruning method called {\it spectral pruning} based on this framework. We define the ``degrees of freedom'' to quantify the intrinsic dimensionality of a model by using the eigenvalue distribution of the covariance matrix across the internal nodes and show that the compression ability is essentially controlled by this quantity. Moreover, we present a sharp generalization error bound of the compressed model and characterize the bias--variance tradeoff induced by the compression procedure. We apply our method to several datasets to justify our theoretical analyses and show the superiority of the the proposed method.
Extreme data compression while searching for new physics

Bringing a high-dimensional dataset into science-ready shape is a formidable challenge that often necessitates data compression. Compression has accordingly become a key consideration for contemporary cosmology, affecting public data releases, and reanalyses searching for new physics. However, data compression optimized for a particular model can suppress signs of new physics, or even remove them altogether. We therefore provide a solution for exploring new physics \emph{during} data compression. In particular, we store additional agnostic compressed data points, selected to enable precise constraints of non-standard physics at a later date. Our procedure is based on the maximal compression of the MOPED algorithm, which optimally filters the data with respect to a baseline model. We select additional filters, based on a generalised principal component analysis, which are carefully constructed to scout for new physics at high precision and speed. We refer to the augmented set of filters as MOPED-PC. They enable an analytic computation of Bayesian evidences that may indicate the presence of new physics, and fast analytic estimates of best-fitting parameters when adopting a specific non-standard theory, without further expensive MCMC analysis. As there may be large numbers of non-standard theories, the speed of the method becomes essential. Should no new physics be found, then our approach preserves the precision of the standard parameters. As a result, we achieve very rapid and maximally precise constraints of standard and non-standard physics, with a technique that scales well to large dimensional datasets.
DIGITALIZATION IN INDUSTRY 4.0: THE ROLE OF MOBILE DEVICES

We live in the era of the Fourth Industrial Revolution (I4.0),where physical development has slowed down, and digital development takes most of its place.If we look at our own lives, we see that we are using digital technologies almost every part of our life already.If we use its potential, we can highly increase our performance and efficiency.Nowadays, every part of a modern business is controlled by an Enterprise Resource Planning (ERP) system, which needs huge variety of digital devices.Most of the companies use their system access points as stationary machines or some mobile devices for higher ranking employees.In this paper we give an example what will happen if every company worker has access to the system through mobile devices, where we consider the Return of Investment based on standard data, that can be collected in every company.
Benchmarking a $$(\mu +\lambda )$$ Genetic Algorithm with Configurable Crossover Probability

We investigate a family of $$(\mu +\lambda )$$ Genetic Algorithms (GAs) which creates offspring either from mutation or by recombining two randomly chosen parents. By scaling the crossover probability, we can thus interpolate from a fully mutation-only algorithm towards a fully crossover-based GA. We analyze, by empirical means, how the performance depends on the interplay of population size and the crossover probability. Our comparison on 25 pseudo-Boolean optimization problems reveals an advantage of crossover-based configurations on several easy optimization tasks, whereas the picture for more complex optimization problems is rather mixed. Moreover, we observe that the “fast” mutation scheme with its are power-law distributed mutation strengths outperforms standard bit mutation on complex optimization tasks when it is combined with crossover, but performs worse in the absence of crossover. We then take a closer look at the surprisingly good performance of the crossover-based $$(\mu +\lambda )$$ GAs on the well-known LeadingOnes benchmark problem. We observe that the optimal crossover probability increases with increasing population size $$\mu $$ . At the same time, it decreases with increasing problem dimension, indicating that the advantages of the crossover are not visible in the asymptotic view classically applied in runtime analysis. We therefore argue that a mathematical investigation for fixed dimensions might help us observe effects which are not visible when focusing exclusively on asymptotic performance bounds.