Reviewerly/exHarmony huggingface dataset:
MATLAB-Based Algorithm and Software for Analysis of Wavy Collagen Fibers

Knowledge of soft tissue fiber structure is necessary for accurate characterization and modeling of their mechanical response. Fiber configuration and structure informs both our understanding of healthy tissue physiology and of pathological processes resulting from diseased states. This study develops an automatic algorithm to simultaneously estimate fiber global orientation, abundance, and waviness in an investigated image. To our best knowledge, this is the first validated algorithm which can reliably separate fiber waviness from its global orientation for considerably wavy fibers. This is much needed feature for biological tissue characterization. The algorithm is based on incremental movement of local regions of interest (ROI) and analyzes two-dimensional images. Pixels belonging to the fiber are identified in the ROI, and ROI movement is determined according to local orientation of fiber within the ROI. The algorithm is validated with artificial images and ten images of porcine trachea containing wavy fibers. In each image, 80-120 fibers were tracked manually to serve as verification. The coefficient of determination R2 between curve lengths and histograms documenting the fiber waviness and global orientation were used as metrics for analysis. Verification-confirmed results were independent of image rotation and degree of fiber waviness, with curve length accuracy demonstrated to be below 1% of fiber curved length. Validation-confirmed median and interquartile range of R2, respectively, were 0.90 and 0.05 for curved length, 0.92 and 0.07 for waviness, and 0.96 and 0.04 for global orientation histograms. Software constructed from the proposed algorithm was able to track one fiber in about 1.1 s using a typical office computer. The proposed algorithm can reliably and accurately estimate fiber waviness, curve length, and global orientation simultaneously, moving beyond the limitations of prior methods.
Breast Cancer Detection Based on Simplified Deep Learning Technique With Histopathological Image Using BreaKHis Database

Abstract Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)‐based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext‐50, ResNext‐101, DPN131, DenseNet‐169 and NASNet‐A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies.
Simple and Tighter Derivation of Achievability for Classical Communication Over Quantum Channels

A judicious application of the so-called pretty-good measurement in quantum coding considerably simplifies the derivation of one-shot achievability, sharpening many existing quantum information-processing protocols.
Visual Ensemble Analysis of Fluid Flow in Porous Media Across Simulation Codes and Experiment

Abstract We study the question of how visual analysis can support the comparison of spatio-temporal ensemble data of liquid and gas flow in porous media. To this end, we focus on a case study, in which nine different research groups concurrently simulated the process of injecting CO $$_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> into the subsurface. We explore different data aggregation and interactive visualization approaches to compare and analyze these nine simulations. In terms of data aggregation, one key component is the choice of similarity metrics that define the relationship between different simulations. We test different metrics and find that using the machine-learning model “S4” (tailored to the present study) as metric provides the best visualization results. Based on that, we propose different visualization methods. For overviewing the data, we use dimensionality reduction methods that allow us to plot and compare the different simulations in a scatterplot. To show details about the spatio-temporal data of each individual simulation, we employ a space-time cube volume rendering. All views support linking and brushing interaction to allow users to select and highlight subsets of the data simultaneously across multiple views. We use the resulting interactive, multi-view visual analysis tool to explore the nine simulations and also to compare them to data from experimental setups. Our main findings include new insights into ranking of simulation results with respect to experimental data, and the development of gravity fingers in simulations.
Diversity Subsampling: Custom Subsamples from Large Data Sets

Subsampling from a large unlabeled (i.e., no response values are available yet) data set is useful in many supervised learning contexts to provide a global view of the data based on only a fraction of the observations. In this paper, we borrow concepts from the well-known sampling/importance resampling technique, which samples from a specified probability distribution, to develop a diversity subsampling approach that selects a subsample from the original data with no prior knowledge of its underlying probability distribution. The goal is to produce a subsample that is independently and uniformly distributed over the support of distribution from which the data are drawn, to the maximum extent possible. We give an asymptotic performance guarantee of the proposed method and provide experimental results to show that the proposed method performs well for typical finite-size data. We also compare the proposed method with competing diversity subsampling algorithms and demonstrate numerically that subsamples selected by the proposed method are closer to a uniform sample than subsamples selected by other methods. The proposed diversity subsampling (DS) algorithm is more efficient than known methods. It takes only a few minutes to select tens of thousands of subsample points from a data set of size one million. Our DS algorithm easily generalizes to select subsamples following distributions other than uniform. We provide a Python package (FADS) that implements the proposed method. History: Kwok-Leung Tsui served as the senior editor for this article. Funding: This work was supported by the National Science Foundation [Grant CMMI-1436574], Northwestern University, the Advanced Research Projects Agency-Energy, and the U.S. Department of Energy [Award DE-AR0001209]. Data Ethics &amp; Reproducibility Note: No data ethics considerations are foreseen related to this article. The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.8309237.v3 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.00017 ).
Fragile watermarking for medical image authentication based on DCT technique

With the advancement of internet and computer technology in the last decade, the ease of editing and altering digital images has significantly increased. Therefore, it is now more crucial than ever for sensitive industries, such as the medical field, to employ image authentication techniques in order to ensure that the images have not been altered. This paper presents a non-blind fragile watermarking technique for medical image authentication. The watermark is generated by downsampling the cover image and embedded into the high-frequency coefficients after applying DCT to the host image. The experimental results demonstrate that the presented technique produces a high watermarked image quality and offers a high sensitivity to various attacks like JPEG compression, collage, and vector quantization. Furthermore, the model localizes with high accuracy the tampered areas within the attacked image.
Quantum Information Decoding Using Dissipative Dynamics

In this paper, we deal with quantum information decoding. Quantum codes are a known technique for correcting errors of quantum information caused by noise or operational errors and one of the most popular types of codes is the so-called stabilizer code. Various implementation methods have been proposed to encode information into a stabilizer code, but there have been few studies on decoding to extract the original information from the code words. In this paper, we propose a decoding method for stabilizer codes using dissipation dynamics and show that the original information can be extracted from the code words.
Learning Flow Functions from Data with Applications to Nonlinear Oscillators

We describe a recurrent neural network (RNN) based architecture to learn the flow function of a causal, time-invariant and continuous-time control system from trajectory data. By restricting the class of control inputs to piecewise constant functions, we show that learning the flow function is equivalent to learning the input-to-state map of a discrete-time dynamical system. This motivates the use of an RNN together with encoder and decoder networks which map the state of the system to the hidden state of the RNN and back. We show that the proposed architecture is able to approximate the flow function by exploiting the system's causality and time-invariance. The output of the learned flow function model can be queried at any time instant. We experimentally validate the proposed method using models of the Van der Pol and FitzHugh-Nagumo oscillators. In both cases, the results demonstrate that the architecture is able to closely reproduce the trajectories of these two systems. For the Van der Pol oscillator, we further show that the trained model generalises to the system's response with a prolonged prediction time horizon as well as control inputs outside the training distribution. For the FitzHugh-Nagumo oscillator, we show that the model accurately captures the input-dependent phenomena of excitability.
Joint Angle-Based Activity Recognition System for PARO Therapy Observation

PARO social robot has been introduced as an alternative therapy for dementia people. However, actual therapy implementation should be evaluated to improve the effectiveness of therapy. We, therefore, propose a system that able to observe and analyze the activity interaction between caregiver and patient in PARO therapy. In this paper, we focus on developing Efficient human activity recognition. The system uses features of the multi-camera-based human skeleton as the input information. We classify the activity recognition process into lower-body and upper-body recognition, which use 11 and 12 joint angles input, respectively. To extract the input data, the system uses a spatiotemporal method of multiclass Support Vector Machine (SVM) algorithm. We use the ADASYN approach for resampling imbalanced datasets. The proposed model has been tested in a sensor room, which utilizes 6 Kinect Azure cameras, to demonstrate its effectiveness. The result shows the proposed method succeeded in recognizing seven basic therapy activities. Then, the proposed method improved the accuracy of the conventional method based on therapeutic scenarios. Furthermore, the therapy's observation method of activity interaction will be conducted.
Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs*

Although many machine learning methods, especially from the field of deep learning, have been instrumental in addressing challenges within robotic applications, we cannot take full advantage of such methods before these can provide performance and safety guarantees. The lack of trust that impedes the use of these methods mainly stems from a lack of human understanding of what exactly machine learning models have learned, and how robust their behaviour is. This is the problem the field of explainable artificial intelligence aims to solve. Based on insights from the social sciences, we know that humans prefer contrastive explanations, i.e. explanations answering the hypothetical question "what if?". In this paper, we show that linear model trees are capable of producing answers to such questions, so-called counterfactual explanations, for robotic systems, including in the case of multiple, continuous inputs and outputs. We demonstrate the use of this method to produce counterfactual explanations for two robotic applications. Additionally, we explore the issue of infeasibility, which is of particular interest in systems governed by the laws of physics.
Formally Verified Neural Network Control Barrier Certificates for Unknown Systems

This paper is concerned with the controller synthesis problem for discrete-time unknown systems against safety specifications via control barrier certificates. Typically, control barrier certificates provide sufficient conditions for the satisfaction of safety specifications by separating the safe and unsafe regions of the system. By synthesizing these certificates in conjunction with control policies, one is able to keep the system safe. In our work, we parameterize the control barrier certificates and corresponding control policies as neural networks and learn them simultaneously by utilizing finitely many data samples obtained from the unknown system. We derive a so-called validity condition to formally verify the obtained certificates and integrate this condition within the training framework to achieve provably correct guarantees at the end of training time. In particular, we exploit Lipschitz continuity properties of the neural networks and utilize robust training techniques to ensure that the trained networks not only satisfy the required control barrier certificate conditions across the finitely many training data samples but over the entire state set. We then demonstrate the effectiveness of our approach with the help of a case study.
Commentary on “Jaws 30”, by W. B. Langdon

Abstract While genetic programming has had a huge impact on the research community, it is fair to say that its impact on industry and practitioners has been much smaller. In this commentary we elaborate on this claim and suggest some broad research goals aimed at greatly increasing such impact.
Classical Actor-Critic Applied to the Control of a Self – Regulatory Process

The industrial adoption of Reinforcement Learning (RL) is currently hindered by an RL agent's potentially erratic learning behaviour, black-box decision-making, and the need for problem-specific tuning. Considering these shortcomings, an Actor-Critic RL agent is applied within the context of behavioural cloning where an initial policy estimate (the warm-starting policy) is provided before RL adaptation. A PI controller is equivalent to a deterministic policy acting on the state space defined by the error and integral error; thus, a PI controller may be used as the warm-starting policy. Two policy types are proposed: the additive- and the emulating policy type. The additive policy was generated by adding the output from a neural network to the plane defined by a PI controller in the state-action space. The emulating policy was constructed by fitting a neural network to emulate the same PI controller before policy adaptation through RL. The results illustrate inverting gradients to be potentially useful as a safeguard against policy divergence resulting from valve saturation combined with inaccurate state-value estimates. It is further shown that performance improvements can be obtained while maintaining desired properties of the original policy (i.e., PI controller) through application of the Actor-Critic algorithm. Finally, RL is contextualised as an inherent connection between classical and optimal control. By constructing a PI control policy describing the selection of the control input as a function of feedback error and the integral thereof, a graphical representation of the subsequent RL adaptations made is obtained, thus enabling interpretability of the resulting RL controller.
Automated Planning for Inspection and Maintenance operations using Unmanned Ground Vehicles

Offshore oil and gas industry has a strong incentive to improve its traditional operations and move towards more remote controlled and automated installations. This allows for improved efficiency, reduced cost and improved quality, and safety by removing personnel out of harm's way. The use of Unmanned Ground Vehicles (UGVs) in these upcoming platforms, is relevant for Inspection and Maintenance (I&M) operations. Traditionally, UGVs are used only for pre-defined tasks and have no capabilities for replanning, if a new task is required or any unexpected event occurs. This paper presents a novel concept for I&M operations using automated planning for UGVs. The automated planner is based on a temporal planning algorithm, and considers actions related to, for example, visiting a specific waypoint, inspect a sensor or manipulate an actuator. Also, the proposed system allows to perform replanning in case of any specific location needs to be revisited or a path is blocked. In addition, we couple the mission planner with a UGV guidance, navigation and control system, which has path planning, path following and control capabilities. To assess the performance of the proposed system, an use case for I&M operations on board of an oil and gas platform was simulated and promising results were obtained.
Measurement-Based Control for Minimizing Energy Functions in Quantum Systems

In variational quantum algorithms (VQAs), the most common objective is to find the minimum energy eigenstate of a given energy Hamiltonian. In this paper, we consider the general problem of finding a sufficient control Hamiltonian structure that, under a given feedback control law, ensures convergence to the minimum energy eigenstate of a given energy function. By including quantum non-demolition (QND) measurements in the loop, convergence to a pure state can be ensured from an arbitrary mixed initial state. Based on existing results on strict control Lyapunov functions, we formulate a semidefinite optimization problem, whose solution defines a non-unique control Hamiltonian, which is sufficient to ensure almost sure convergence to the minimum energy eigenstate under the given feedback law and the action of QND measurements. A numerical example is provided to showcase the proposed methodology.
Exploring Symmetry in Digital Image Forensics Using a Lightweight Deep-Learning Hybrid Model for Multiple Smoothing Operators

Digital images are widely used for informal information sharing, but the rise of fake photos spreading misinformation has raised concerns. To address this challenge, image forensics is employed to verify the authenticity and trustworthiness of these images. In this paper, an efficient scheme for detecting commonly used image smoothing operators is presented while maintaining symmetry. A new lightweight deep-learning network is proposed, which is trained with three different optimizers to avoid downsizing to retain critical information. Features are extracted from the activation function of the global average pooling layer in three trained deep networks. These extracted features are then used to train a classification model with an SVM classifier, resulting in significant performance improvements. The proposed scheme is applied to identify averaging, Gaussian, and median filtering with various kernel sizes in small-size images. Experimental analysis is conducted on both uncompressed and JPEG-compressed images, showing superior performance compared to existing methods. Notably, there are substantial improvements in detection accuracy, particularly by 6.50% and 8.20% for 32 × 32 and 64 × 64 images when subjected to JPEG compression at a quality factor of 70.
Automatic Pricing and Replenishment Decision-Making for Vegetable Commodities Based on Bi-directional Long Short-Term Memory Recurrent Neural Networks and Markov Prediction Models

Reasonable pricing and replenishment strategies are crucial for vegetable superstores to maximise profitability. In this paper, we firstly analyse the distribution pattern and interrelationship of each category of vegetables through Pearson correlation coefficient, and then use MATLAB tools to analyse and predict the data through the time series analysis of Long Short-Term Memory Recurrent Neural Networks (LSTM), and solve the optimal daily replenishment and pricing of each category in the coming week; lastly, we firstly use the small-period sample prediction method, i.e., the Markov prediction model, to analyse the data, and then construct a multi-objective planning model to further determine the optimal pricing strategy. Finally, we use the small-period sample prediction method, i.e., Markov prediction model, to analyse the data and then construct a multi-objective planning model to further determine the optimal pricing strategy.
Jaws 30

Abstract It is 30 years since John R. Koza published “Jaws”, the first book on genetic programming [Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)]. I recount and expand the celebration at GECCO 2022, very briefly summarise some of what the rest of us have done and make suggestions for the next thirty years of GP research.
A Pragmatic Theory of Computational Artefacts

Abstract Some computational phenomena rely essentially on pragmatic considerations, and seem to undermine the independence of the specification from the implementation. These include software development, deviant uses, esoteric languages and recent data-driven applications. To account for them, the interaction between pragmatics, epistemology and ontology in computational artefacts seems essential, indicating the need to recover the role of the language metaphor. We propose a User Levels (ULs) structure as a pragmatic complement to the Levels of Abstraction (LoAs)-based structure defining the ontology and epistemology of computational artefacts. ULs identify a flexible hierarchy in which users bear their own semantic and normative requirements, possibly competing with the logical specification. We formulate a notion of computational act intended in its pragmatic sense, alongside pragmatic versions of implementation and correctness.
Tomography of quantum detectors using neural networks

Quantum detector tomography is a fundamental technique for calibrating quantum devices and thus lay foundations for quantum information processing tasks. In this work, we propose a quantum detector tomography method that employs deep neural networks to reconstruct quantum detectors from a set of probe states with high efficiency. Numerical results demonstrate that the proposed method exhibits a significant potential to estimate phase-insensitive detectors.
A Duality-Based Approach to Inverse Kalman Filtering

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.