Reviewerly/exHarmony huggingface dataset:
Direct Characterization of Quantum Measurements Using Weak Values

The time-symmetric formalism endows the weak measurement and its outcome, the weak value, with many unique features. In particular, it allows a direct tomography of quantum states without resorting to complicated reconstruction algorithms and provides an operational meaning to wave functions and density matrices. Here, we propose and experimentally demonstrate the direct tomography of a measurement apparatus by taking the backward direction of weak measurement formalism. Our protocol works rigorously with the arbitrary measurement strength, which offers improved accuracy and precision. The precision can be further improved by taking into account the completeness condition of the measurement operators, which also ensures the feasibility of our protocol for the characterization of the arbitrary quantum measurement. Our work provides new insight on the symmetry between quantum states and measurements, as well as an efficient method to characterize a measurement apparatus.
Complete Classification and Efficient Determination of Arrangements Formed by Two Ellipsoids

Arrangements of geometric objects refer to the spatial partitions formed by the objects, and they serve as an underlining structure of motion design, analysis, and planning in CAD/CAM, robotics, molecular modeling, manufacturing, and computer-assisted radio-surgery. Arrangements are especially useful to collision detection, which is a key task in various applications such as computer animation, virtual reality, computer games, robotics, CAD/CAM, and computational physics. Ellipsoids are commonly used as bounding volumes in approximating complex geometric objects in collision detection. In this article, we present an in-depth study on the arrangements formed by two ellipsoids. Specifically, we present a classification of these arrangements and propose an efficient algorithm for determining the arrangement formed by any particular pair of ellipsoids. A stratification diagram is also established to show the connections among all the arrangements formed by two ellipsoids. Our results, for the first time, elucidate all possible relative positions between two arbitrary ellipsoids and provide an efficient and robust algorithm for determining the relative position of any two given ellipsoids, therefore providing the necessary foundation for developing practical and trustworthy methods for processing ellipsoids for collision analysis or simulation in various applications.
Spectral characterization of Jordan--Segal isomorphisms of quantum observables

We present characterizations of isomorphisms of Jordan algebras of quantum observables using only certain spectrum-preserving properties without assuming any kind of linearity. Similar problems involving the norm in the place of the spectrum are also studied.
A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems

Hybridization of metaheuristic algorithms with local search has been investigated in many studies. This paper proposes a hybrid pathfinder algorithm (HPFA), which incorporates the mutation operator in differential evolution (DE) into the pathfinder algorithm (PFA). The proposed algorithm combines the searching ability of both PFA and DE. With a test on a set of twenty-four unconstrained benchmark functions including both unimodal continuous functions, multimodal continuous functions, and composition functions, HPFA is proved to have significant improvement over the pathfinder algorithm and the other comparison algorithms. Then HPFA is used for data clustering, constrained problems, and engineering design problems. The experimental results show that the proposed HPFA got better results than the other comparison algorithms and is a competitive approach for solving partitioning clustering, constrained problems, and engineering design problems.
Quantum walks of interacting Mott-insulator defects with three-body interactions

Quantum walks of interacting particles may display nontrivial features due to the interplay between the statistical nature and the many-body interactions associated with them. We analyze the quantum walk of interacting defects on top of a uniform bosonic Mott insulator at unit filling in a one-dimensional graph. While the quantum walk of a single-particle defect shows trivial features, the case of two particles exhibits the interesting phenomenon of quantum walk reversal as a function of additional on-site three-body attractive interactions. In the absence of a three-body interaction a quantum walk of pairs of particles is obtained, and as the strength of the three-body interaction becomes more and more attractive, independent-particle behavior appears in the quantum walk. Interestingly, further increase in the three-body interaction leads to the reappearance of the quantum walk associated with a pair of particles. This quantum walk reversal phenomenon is studied using the real-space density evolution, Bloch oscillation, and two-particle correlation functions.
An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors

Abstract In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates dispro-portions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.
A Freights Status Management System Based on Dart and Flutter Programming Language

Abstract In this research, we present freight status app as mobile application in android and iOS devices. We used Dart language and Flutter framework for developing the management system. Flutter is Google’s portable user interface structure for making top notch local interfaces on Android and iOS in specific time. Flutter operates with existing code, is utilized by engineers and associations around the globe, and it is release and not closed source. Superior and efficiency in Flutter are accomplished by utilizing a few strategies. Dart is a coding language that we will use to build up our application in Flutter. The system has been designed by IntelliJ IDE. Clients are able to browse Freights Status mobile application, check relegation of merchandise committal cost standard, glance through enterprises news, and freight data and industry patterns by means of application. Through the framework behind the stage the board, charging staff can build up and alter dispatch notes; charging individual and accepting representatives can check transfer data. Flutter applications are achieved in the Dart coding language and aggregate to local code, so the exhibition is outrageously extraordinary. Dart is a customer enhanced language for quick applications on any stage made by Google.
Kaldi recipe in Hindi for word level recognition and phoneme level transcription

This paper discusses an automatic speech recognition (ASR) system in Hindi. The language models and acoustic models are built using the open source toolkit Kaldi. A significant portion of the corpus built for this work pertains to the medical domain, as our primary emphasis lies in the application of speech processing for medical transcription. The various acoustic models used for the comparison of word error rates (WER) in Kaldi include HMM-GMM (Hidden Markov Model-Gaussian Mixture Model) based Monophone, Triphone (tri1,tri2, tri3) and SGMM(Sub Space Gaussian Mixture Model). Comparing the WER for various acoustic models used, it was observed that tri3 model has the least WER over the other acoustic models. Also, the possible mappings of phonemes detected have been shown
EgoMap: Projective Mapping and Structured Egocentric Memory for Deep RL

Tasks involving localization, memorization and planning in partially observable 3D environments are an ongoing challenge in Deep Reinforcement Learning. We present EgoMap, a spatially structured neural memory architecture. EgoMap augments a deep reinforcement learning agent's performance in 3D environments on challenging tasks with multi-step objectives. The EgoMap architecture incorporates several inductive biases including a differentiable inverse projection of CNN feature vectors onto a top-down spatially structured map. The map is updated with ego-motion measurements through a differentiable affine transform. We show this architecture outperforms both standard recurrent agents and state of the art agents with structured memory. We demonstrate that incorporating these inductive biases into an agent's architecture allows for stable training with reward alone, circumventing the expense of acquiring and labelling expert trajectories. A detailed ablation study demonstrates the impact of key aspects of the architecture and through extensive qualitative analysis, we show how the agent exploits its structured internal memory to achieve higher performance.
Gaussian Processes on Graphs Via Spectral Kernel Learning

We propose a graph spectrum-based Gaussian process for prediction of signals defined on nodes of the graph. The model is designed to capture various graph signal structures through a highly adaptive kernel that incorporates a flexible polynomial function in the graph spectral domain. Unlike most existing approaches, we propose to learn such a spectral kernel, where the polynomial setup enables learning without the need for eigen-decomposition of the graph Laplacian. In addition, this kernel has the interpretability of graph filtering achieved by a bespoke maximum likelihood learning algorithm that enforces the positivity of the spectrum. We demonstrate the interpretability of the model in synthetic experiments from which we show the various ground truth spectral filters can be accurately recovered, and the adaptability translates to superior performances in the prediction of real-world graph data of various characteristics.
Gaussian processes for surrogate modeling of discharged fuel nuclide compositions

Several applications such as nuclear forensics, nuclear fuel cycle simulations and sensitivity analysis require methods to quickly compute spent fuel nuclide compositions for various irradiation histories. Traditionally, this has been done by interpolating between one-group cross-sections that have been pre-computed from nuclear reactor simulations for a grid of input parameters, using fits such as Cubic Spline. We propose the use of Gaussian Processes (GP) to create surrogate models, which not only provide nuclide compositions, but also the gradient and estimates of their prediction uncertainty. The former is useful for applications such as forward and inverse optimization problems, the latter for uncertainty quantification applications. For this purpose, we compare GP-based surrogate model performance with Cubic- Spline-based interpolators based on infinite lattice simulations of a CANDU 6 nuclear reactor using the SERPENT 2 code, considering burnup and temperature as input parameters. Additionally, we compare the performance of various grid sampling schemes to quasirandom sampling based on the Sobol sequence. We find that GP-based models perform significantly better in predicting spent fuel compositions than Cubic-Spline-based models, though requiring longer computational runtime. Furthermore, we show that the predicted nuclide uncertainties are reasonably accurate. While in the studied two-dimensional case, grid- and quasirandom sampling provide similar results, quasirandom sampling will be a more effective strategy in higher dimensional cases.
Social creation networks for designing low income interfaces in programmable thermostats

The user interface serves as a critical contact point between electronic devices and end-users. They aim to increase usability and optimize communicative relations between humans and machines. In this regard, theories related to human-computer interaction (HCI) stress the importance of interface design methods that consider needs, expectations, and requirements of end-users. In this paper, we propose a design methodology of a programmable interface for low-income household thermostats based on product social creation through “personas,” scenarios, and usability heuristics. As traditional market research methods used to design interfaces are inherently expensive and time-consuming, we propose a web-based design model based on the extraction and analysis of Facebook public profiles. We consider that by using Facebook profiles, the most essential interface-related needs, such as simplicity and interactivity, can be addressed in a more efficient, complete, and realistic manner comparing with the traditional product design methods. Finally, we present a mock interface and a 3D model as a way to validate the proposed design methodology based on the usability analysis.
Solving two-dimensional linear partial differential equations based on Chebyshev neural network with extreme learning machine algorithm

Purpose This paper aims to develop a novel algorithm and apply it to solve two-dimensional linear partial differential equations (PDEs). The proposed method is based on Chebyshev neural network and extreme learning machine (ELM) called Chebyshev extreme learning machine (Ch-ELM) method. Design/methodology/approach The network used in the proposed method is a single hidden layer feedforward neural network. The Kronecker product of two Chebyshev polynomials is used as basis function. The weights from the input layer to the hidden layer are fixed value 1. The weights from the hidden layer to the output layer can be obtained by using ELM algorithm to solve the linear equations established by PDEs and its definite conditions. Findings To verify the effectiveness of the proposed method, two-dimensional linear PDEs are selected and its numerical solutions are obtained by using the proposed method. The effectiveness of the proposed method is illustrated by comparing with the analytical solutions, and its superiority is illustrated by comparing with other existing algorithms. Originality/value Ch-ELM algorithm for solving two-dimensional linear PDEs is proposed. The algorithm has fast execution speed and high numerical accuracy.
Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space.
A Brief Review of Database Solutions Used within Blockchain Platforms

The rise of blockchain technology has inspired changes in the architecture of modern applications in various domains (e.g., finances, transport, etc.). Moving from a centralized data storage paradigm to the distributed ledger technology without central data storage raises important challenges, mostly in terms of scalability, integrity, and privacy. Even though decentralization, each node within the blockchain network implements a storage mechanism to keep track of the blockchain states. In this paper, we perform a detailed study of selected blockchain platforms in order to discover which underlying database solutions they use to store blockchain states. Our results show that 13 out of 20 platforms use key-value stores (LevelDB or RocksDB), write-optimized storage solutions able to perform fast lookups due to their underlying indexing structure used. Moreover, there are also platforms, which make use of the benefits of relational and document-oriented databases.
A Real-Time Infrared Stereo Matching Algorithm for RGB-D Cameras’ Indoor 3D Perception

Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper.
Machine Learning Modelling and Feature Engineering in Seismology Experiment

This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.
Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry

This article presents a five-year collaboration situated at the intersection of Art practice and Scientific research in Human-Computer Interaction (HCI). At the core of our collaborative work is a hybrid, Art and Science methodology that combines computational learning technology -- Machine Learning (ML) and Artificial Intelligence (AI) -- with interactive music performance and choreography. This article first exposes our thoughts on combining art, science, movement and sound research. We then describe two of our artistic works \textit{Corpus Nil} and \textit{Humane Methods} -- created five years apart from each other -- that crystallize our collaborative research process. We present the scientific and artistic motivations, framed through our research interests and cultural environment of the time. We conclude by reflecting on the methodology we developed during the collaboration and on the conceptual shift of computational learning technologies, from ML to AI, and its impact on Music performance.
FAIR, safe and high-quality data: The data infrastructure and accessibility of the YOUth cohort study

The YOUth cohort study aims to be a trailblazer for open science. Being a large-scale, longitudinal cohort following children in their development from gestation until early adulthood, YOUth collects a vast amount of data through a variety of research techniques. Data are collected through multiple platforms, including facilities managed by Utrecht University and the University Medical Center Utrecht. In order to facilitate appropriate use of its data by research organizations and researchers, YOUth aims to produce high-quality, FAIR data while safeguarding the privacy of participants. This requires an extensive data infrastructure, set up by collaborative efforts of researchers, data managers, IT departments, and the Utrecht University Library. In the spirit of open science, YOUth will share its experience and expertise in setting up a high-quality research data infrastructure for sensitive cohort data. This paper describes the technical aspects of our data and data infrastructure, and the steps taken throughout the study to produce and safely store FAIR and high-quality data. Finally, we will reflect on the organizational aspects that are conducive to the success of setting up such an enterprise, and we consider the financial challenges posed by individual studies investing in sustainable science.