Reviewerly/exHarmony huggingface dataset:
Quantum Control Landscapes for Generation of H and T Gates in an Open Qubit with Both Coherent and Environmental Drive

An important problem in quantum computation is the generation of single-qubit quantum gates such as Hadamard (H) and π/8 (T) gates, which are components of a universal set of gates. Qubits in experimental realizations of quantum computing devices are interacting with their environment. While the environment is often considered as an obstacle leading to a decrease in the gate fidelity, in some cases, it can be used as a resource. Here, we consider the problem of the optimal generation of H and T gates using coherent control and the environment as a resource acting on the qubit via incoherent control. For this problem, we studied the quantum control landscape, which represents the behavior of the infidelity as a functional of the controls. We considered three landscapes, with infidelities defined by steering between two, three (via Goerz–Reich–Koch approach), and four matrices in the qubit Hilbert space. We observed that, for the H gate, which is a Clifford gate, for all three infidelities, the distributions of minimal values obtained with a gradient search have a simple form with just one peak. However, for the T gate, which is a non-Clifford gate, the situation is surprisingly different—this distribution for the infidelity defined by two matrices also has one peak, whereas distributions for the infidelities defined by three and four matrices have two peaks, which might indicate the possible existence of two isolated minima in the control landscape. It is important that, among these three infidelities, only those defined with three and four matrices guarantee the closeness of the generated gate to a target and can be used as a good measure of closeness. We studied sets of optimized solutions for the most general and previously unexplored case of coherent and incoherent controls acting together and discovered that they form sub-manifolds in the control space, and unexpectedly, in some cases, two isolated sub-manifolds.
Mixed integer linear programming formulation for K-means clustering problem

Abstract The minimum sum-of-squares clusering is the most widely used clustering method. The minimum sum-of-squares clustering is usually solved by the heuristic KMEANS algorithm, which converges to a local optimum. A lot of effort has been made to solve such kind of problems, but a mixed integer linear programming formulation (MILP) is still missing. In this paper, we formulate MILP models. The advantage of MILP formulation is that users can extend the original problem with arbitrary linear constraints. We also present numerical results, we solve these models up to sample size of 150.
Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques

Handling missing values is a critical component of the data processing in hydrological modeling. The key objective of this research is to assess statistical techniques (STs) and artificial intelligence-based techniques (AITs) for imputing missing daily rainfall values and recommend a methodology applicable to the mountainous terrain of northern Thailand. In this study, 30 years of daily rainfall data was collected from 20 rainfall stations in northern Thailand and randomly 25-35 % of data was deleted from four target stations based on Spearman correlation coefficient between the target and neighboring stations. Imputation models were developed on training and testing datasets and statistically evaluated by mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and correlation coefficient (r). This study used STs, including arithmetic averaging (AA), multiple linear regression (MLR), normal-ratio (NR), nonlinear iterative partial least squares (NIPALS) algorithm, and linear interpolation was used.•STs results were compared with AITs, including long-short-term-memory recurrent neural network (LSTM-RNN), M5 model tree (M5-MT), multilayer perceptron neural networks (MLPNN), support vector regression with polynomial and radial basis function SVR-poly and SVR-RBF.•The findings revealed that MLR imputation model achieved an average MAE of 0.98, RMSE of 4.52, and R2 was about 79.6 % at all target stations. On the other hand, for the M5-MT model, the average MAE was 0.91, RMSE was about 4.52, and R2 was around 79.8 % compared to other STs and AITs. M5-MT was most prominent among AITs. Notably, the MLR technique stood out as a recommended approach due to its ability to deliver good estimation results while offering a transparent mechanism and not necessitating prior knowledge for model creation.
Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

This paper proposes an adaptive fuzzy-neural inference system (ANFIS)-based control approach for a six degrees of freedom (6-DoF) robotic manipulator. Its main objective is to guarantee the error convergence of the controlled system in the presence of uncertainties and unknown disturbances. The suggested controller is a parallel combination of an ANFIS network with a proportional-integral-derivative (PID) controller. The ANFIS system is used as an estimator to approximate a part of the system and then applied as the feedback linearization in the suggested control structure. The convergence of system errors to zero was proven using Barbalat's lemma. The suggested control law combines the simplicity and ease of implementation of PID control with the estimation properties of ANFIS networks. The suggested approach was evaluated using a simulation study and further validated experimentally using the 6-DoF IRB-120 robotic manipulator (IRB-120-RM). The obtained results confirmed its superior performance and suitability for practical implementation to industrial actuators.
FOURIER NEURAL OPERATOR SURROGATE MODEL TO PREDICT 3D SEISMIC WAVES PROPAGATION

With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations.Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configuration.With neural operators, one can learn the general solution of Partial Differential Equations, such as the elastic wave equation, with varying parameters.There have been very few applications of neural operators in seismology.All of them were limited to two-dimensional settings, although the importance of three-dimensional (3D) effects is well known.In this work, we apply the Fourier Neural Operator (FNO) to predict ground motion time series from a 3D geological description.We used a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies.With this database, we show that the FNO can produce accurate ground motion even when the underlying geology exhibits large heterogeneities.Intensity measures at moderate and large periods are especially well reproduced.We present the first seismological application of Fourier Neural Operators in 3D.Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features such as sedimentary basins on ground motion, which is paramount to evaluating site effects.
Independence and Unity: Unseen Domain Segmentation Based on Federated Learning

The distinct attributes of Internet of Things (IoTs) devices, including the disparity between training and testing data distributions and limited availability of training data, pose challenges for deep learning models in effectively addressing unseen domain segmentation tasks. Federated Learning (FL) can increase the participation of various data contributors, thus has great potential to develop a unified framework to shed light on the relationship between unseen domains and generalized domains. In this paper, we proposed an FL-based unseen domain segmentation model. The architecture includes (1) an external memory module as an object feature guide to reduce the feature ambiguity of unseen domain objects. (2) A re-attention activation mechanism for better completing localization of unseen domain objects, enhancing the features of potential targets and suppressing interference features. (3) A self-supervised learning paradigm for achieving specific object feature exploration. Based on flexible splitting and combining, our model is able to capture both personalization and generalization capabilities, the client side retains a strong personalization ability, while the server side has a strong generalization ability. Moreover, taking into account the inherent limitations in computing and storage resources commonly associated with IoT devices, the introduced model leverages the concept of optional dependencies to enable efficient inference within resource-constrained client environments. Our proposed model is validated through extensive experiments. The approach proposed in this paper outperforms the generalization capabilities of state-of-the-art work on several benchmarks.
Humboldt Squid Optimization Algorithm (HSOA): A Novel Nature-Inspired Technique for Solving Optimization Problems

This study presents a new natural-based algorithm called the Humboldt Squid Optimization Algorithm (HSOA). HSOA is inspired by Humboldt squids hunting, moving, and mating behavior. The HSOA search procedure involves an attack on fish schools, a fish’s escape, a successful attack, an attack of bigger squids on smaller ones, and mating, which is the inspiration for creating an algorithm to address existing issues. In HSOA, half of the best populations are Humboldt squid, and the rest are school fish. Individuals connect with each other and cooperate to achieve the optimal response. HSOA is versatile and applicable to mathematical and engineering problems. Solving eighty-four benchmark function problems (twenty-three classic functions, twenty-nine CEC-BC-2017 with 10, 30, 50, and 100 dimensions, ten CEC-C06 2019, ten CEC2020 with 5, 10, 15, and 20 dimensions, and twelve CEC2022 with 10 and 20 dimensions) and twenty-four engineering problems (six CEC2006 and eighteen CEC2011) shows that our proposed algorithm provides proper and acceptable answers to nine algorithms, including well-known (PSO, DE, WOA), recent (AVOA, RW_GWO, HHO and GBO), and state-of-the-art algorithms (LSHADE and EBOwithCMAR). Friedman’s rank from HSOA for one hundred and eight problems was 16.45% and 7.45% lower than LSHADE and EBOwithCMAR. Thus, HSOA has the potential to solve various complex problems in the sciences and engineering fields.
A New Approach for Mineral Mapping Using Drill-Core Hyperspectral Image

Hyperspectral remote sensing technology has been successfully applied to geological fields. Drill-core hyperspectral imagery has the characteristics of segmented processing and large data volume. Due to simple principle and high accuracy, the Spectral Angle Mapping (SAM) has become the most commonly used method for mineral mapping using drill-core hyperspectral images. However, SAM analyzes the entire spectral form of minerals, and is not sensitive enough to small differences in drill-core mineral spectra. Compared with traditional machine learning methods, deep learning has more powerful feature learning and feature expression capabilities. In order to improve the mineral mapping accuracy, this paper proposes a new approach called Graph Convolutional Neural Networks-Spectral Angle Mapping (GCNNSAM), which integrates the advantages of deep learning and spectral matching to extract mineral information from drill-core hyperspectral images. Taking the drill-core hyperspectral data near the depth of 240m as an example, this paper compares the performances of SAM, GCNN and GCNNSAM mapping methods. The results show that the overall accuracy of the GCNNSAM mapping is 89.23%, and the overall accuracies of SAM and GCNN mapping methods are 80.25%, 83.58%, respectively. Comparing the mineral mapping statistical results of GCNNSAM with the measured geological statistical results, the maximum statistical error of mineral relative content is 1.4%, and the errors are all less than 2%, which verifies the reliability of the proposed method in this study. The method provides a new idea for mineral information acquisition in geological research.
What variables are connected with system usability and satisfaction? Results from an educational virtual reality field trip

Exploring the usability of educational virtual reality (VR) is essential. Research in this area can explain the adoption of VR as a new technology, contribute to examining paths toward effective learning, and provide recommendations for effective educational design. This study investigated usability in a sample of N = 64 university students learning about simplified construction engineering topics from an interactive VR field trip. Two research objectives were pursued. First, the level of achieved usability was examined using a mixed-methods approach, including data from semi-structured interviews and a survey on system usability and satisfaction. Second, the relationships between several connected variables (ease of use, usefulness, presence, and cognitive load) and system usability and satisfaction were investigated quantitatively. With respect to the first research objective, system usability and satisfaction were evaluated positively in the interviews and rated highly in the surveys. These results indicate that VR field trips, which are increasingly replacing excursions, are regarded as an appropriate instructional method. Concerning the second research objective, ease of use, usefulness, presence, and extraneous load were found to predict system usability. Further, the factors of usefulness, presence, and intrinsic cognitive load helped explain the variance in satisfaction. These findings highlight that the variables connected to system usability and satisfaction in VR learning vary. In addition to these and other theoretical implications, practical implications and recommendations for educational design are discussed.
Transformer-based hierarchical dynamic decoders for salient object detection

Global context and global contrast are crucial clues for Salient Object Detection (SOD) in images. Most advanced SOD methods exploit CNN-based architectures, achieving impressive results. However, these methods have intrinsic limitations in capturing long-range global information since a CNN extracts feature in local sliding windows. In contrast, transformers exploit a self-attention mechanism to extract features, gaining a powerful capability of learning global cues. Nonetheless, a pure transformer-based network consumes a large computational overhead and easily suffers from attention collapse, as it goes deeper. To address this issue, in this paper, we propose a Transformer-based Hierarchical Dynamic Decoder (T-HDDNet) for image salient object detection. Specifically, our T-HDDNet employs the transformer to encode each image patch into multi-level and multi-resolution features based on the long-range dependencies among pixels. To obtain an accurate saliency map of high resolution, we develop a dynamic dual upsampling mechanism to enlarge feature spatial size in a data-driven manner, together with a dynamic feature fusion unit. Ultimately, the hierarchical dynamic decoders built on the basis of these two units are used to attain the final saliency progressively. Extensive experimental results show that the proposed method achieves the best performance on all benchmarks, in comparison with state-of-the-art technologies.
Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values

Abstract Shapley values have achieved great popularity in explainable artificial intelligence. However, with standard sampling methods, resulting feature attributions are susceptible to adversarial attacks. This originates from target function evaluations at extrapolated data points, which are easily detectable and hence, enable models to behave accordingly. In this paper, we introduce a novel strategy for increased robustness against adversarial attacks of both local and global explanations: Knockoff imputed Shapley values. Our approach builds on the model-X knockoff methodology, which generates synthetic data that preserves statistical properties of the original samples. This enables researchers to flexibly choose an appropriate model to generate on-manifold data for the calculation of Shapley values upfront, instead of having to estimate a large number of conditional densities or make strong parametric assumptions. Through real and simulated data experiments, we demonstrate the effectiveness of knockoff imputation against adversarial attacks.
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring

Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in a secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. In this article, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE. To the best of our knowledge, it is the first scheme to embed the watermark to models under a secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure that the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack.
A Path Planning Algorithm of Inspection Robots for Solar Power Plants Based on Improved RRT*

In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is proposed. Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants.
Maximising mobile user experience through self-adaptive content- and ambient-aware display brightness scaling

Display subsystems have become the predominant user interface on mobile devices, serving as both input and output interfaces. For a better quality of user experience (QoE), the display subsystem is expected to provide appropriate resolution and brightness despite its impact on battery life. Existing display brightness approaches either consider content- and ambient-light in isolation or do not account for the user's expected battery life, thereby failing to maximise the QoE. This paper proposes aCADS, a self-Adaptive Content- and Ambient-aware Display brightness Scaling in mobile devices that maximises QoE while meeting battery life expectations. The approach employs a content- and ambient lighting-aware profiler that learns and classifies each sample into predefined clusters at runtime by leveraging insights on user perceptions of content and ambient luminances variations. We maximise QoE through adaptive scaling of the display's brightness using an energy model that determines appropriate brightness levels while meeting expected battery life. The evaluation on a commercial smartphone shows that aCADS improves QoE by up to 32.5 % compared to state-of-the-art.
Postdigital Epistemic Violence

Postdigital epistemic violence encompasses a large variety of harms that occur through speech acts about the lived experiences of knowers from marginalised or minoritised groups in online environments using digital and smart technology. Knowers may be silenced, gaslit, trolled, or doxed because of structural prejudicial stereotypes about their sex, gender, ethnicity, and so on. These epistemic practices result in the deflation of their testimony, and the inflation of the credibility of other speakers to produce dysfunctions in the knowledge economy. The knowledge of the powerful and advantaged prevail over other kinds of knowing, resulting in epistemic violence.
Building Human-Like Artificial Agents: A General Cognitive Algorithm for Emulating Human Decision-Making in Dynamic Environments

One of the early goals of artificial intelligence (AI) was to create algorithms that exhibited behavior indistinguishable from human behavior (i.e., human-like behavior). Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human cogntion and action. In this paper, I explore the overarching question of whether computational algorithms have achieved this initial goal of AI. I focus on dynamic decision-making, approaching the question from the perspective of computational cognitive science. I present a general cognitive algorithm that intends to emulate human decision-making in dynamic environments, as defined in instance-based learning theory (IBLT). I use the cognitive steps proposed in IBLT to organize and discuss current evidence that supports some of the human-likeness of the decision-making mechanisms. I also highlight the significant gaps in research that are required to improve current models and to create higher fidelity in computational algorithms to represent human decision processes. I conclude with concrete steps toward advancing the construction of algorithms that exhibit human-like behavior with the ultimate goal of supporting human dynamic decision-making.
Contrastive Transformer Hashing for Compact Video Representation

Video hashing learns compact representation by mapping video into low-dimensional Hamming space and has achieved promising performance in large-scale video retrieval. It is challenging to effectively exploit temporal and spatial structure in an unsupervised setting. To fulfill this gap, this paper proposes Contrastive Transformer Hashing (CTH) for effective video retrieval. Specifically, CTH develops a bidirectional transformer autoencoder, based on which visual reconstruction loss is proposed. CTH is more powerful to capture bidirectional correlations among frames than conventional unidirectional models. In addition, CTH devises multi-modality contrastive loss to reveal intrinsic structure among videos. CTH constructs inter-modality and intra-modality triplet sets and proposes multi-modality contrastive loss to exploit inter-modality and intra-modality similarities simultaneously. We perform video retrieval tasks on four benchmark datasets, i.e., UCF101, HMDB51, SVW30, FCVID using the learned compact hash representation, and extensive empirical results demonstrate the proposed CTH outperforms several state-of-the-art video hashing methods.