Reviewerly/exHarmony huggingface dataset:
Fast and robust Bayesian inference using Gaussian processes with GPry

Abstract We present the GPry algorithm for fast Bayesian inference of general (non-Gaussian) posteriors with a moderate number of parameters. GPry does not need any pre-training, special hardware such as GPUs, and is intended as a drop-in replacement for traditional Monte Carlo methods for Bayesian inference. Our algorithm is based on generating a Gaussian Process surrogate model of the log-posterior, aided by a Support Vector Machine classifier that excludes extreme or non-finite values. An active learning scheme allows us to reduce the number of required posterior evaluations by two orders of magnitude compared to traditional Monte Carlo inference. Our algorithm allows for parallel evaluations of the posterior at optimal locations, further reducing wall-clock times. We significantly improve performance using properties of the posterior in our active learning scheme and for the definition of the GP prior. In particular we account for the expected dynamical range of the posterior in different dimensionalities. We test our model against a number of synthetic and cosmological examples. GPry outperforms traditional Monte Carlo methods when the evaluation time of the likelihood (or the calculation of theoretical observables) is of the order of seconds; for evaluation times of over a minute it can perform inference in days that would take months using traditional methods. GPry is distributed as an open source Python package ( pip install gpry ) and can also be found at https://github.com/jonaselgammal/GPry .
Quantum speed limit for complex dynamics

Abstract Quantum speed limit focuses on the minimum time scale for a fixed mission and hence is important in quantum information where fast dynamics is usually beneficial. Most existing tools for the depiction of quantum speed limit are the lower-bound-type tools, which are in fact difficult to reveal the true minimum time, especially for many-body systems or complex dynamics. Therefore, the evaluation of this true minimum time in these scenarios is still an unsolved problem. Hereby we provide the operational definition of quantum speed limit for a general target and propose a three-step (classification-regression-calibration) methodology based on machine learning to evaluate the true minimum time in complex dynamics. Moreover, the analytical expression of the true minimum time is also provided for the time-dependent Hamiltonians with time-independent eigenstates.
A practitioner’s guide to quantum algorithms for optimisation problems

Abstract Quantum computing is gaining popularity across a wide range of scientific disciplines due to its potential to solve long-standing computational problems that are considered intractable with classical computers. One promising area where quantum computing has potential is in the speed-up of NP -hard optimisation problems that are common in industrial areas such as logistics and finance. Newcomers to the field of quantum computing who are interested in using this technology to solve optimisation problems do not have an easily accessible source of information on the current capabilities of quantum computers and algorithms. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques and their practical application, focusing on their near-term potential for noisy intermediate scale quantum devices. The paper starts by drawing parallels between classical and quantum optimisation problems, highlighting their conceptual similarities and differences. Two main paradigms for quantum hardware are then discussed: analogue and gate-based quantum computers. While analog devices such as quantum annealers are effective for some optimisation problems, they have limitations and cannot be used for universal quantum computation. In contrast, gate-based quantum computers offer the potential for universal quantum computation, but they face challenges with hardware limitations and accurate gate implementation. The paper provides a detailed mathematical discussion with references to key works in the field, as well as a more practical discussion with relevant examples. The most popular techniques for quantum optimisation on gate-based quantum computers, the quantum approximate optimisation algorithm and the quantum alternating operator ansatz framework, are discussed in detail. However, it is still unclear whether these techniques will yield quantum advantage, even with advancements in hardware and noise reduction. The paper concludes with a discussion of the challenges facing quantum optimisation techniques and the need for further research and development to identify new, effective methods for achieving quantum advantage.
Digital Twin &amp; Blockchain: Technology Enablers for Metaverse Computing

The Metaverse has been recently envisioned as the next technological revolution with the idea of building fully immersive virtual worlds where users can interact with each other and with mixed-reality environments and objects. The relationship between the digital and the physical worlds represents a challenging opportunity to foster the idea that virtual experiences and interactions can complement and enhance reality enabling the definition of new use cases and services. In this context, Metaverse’s technologies need to rely on a connected, trustworthy, and highly detailed digital representation of users and objects to build an effective virtual world based on trusted and fresh information from the physical world. The need to decouple responsibilities through a multi-layered vision allows Metaverse’s applications to focus only on their business goal by relying on a cyber-physical layer enabling bi-directional communication with users and devices, accessing trusted information, invoking available actions, and retrieving immutable historical data. In this paper, we analyze and propose the combination of Digital Twins and Blockchain technologies as strategic pillars for bridging the gap between the virtual and the physical worlds and supporting the creation of the Metaverse.
Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications

The Metaverse is a new paradigm that aims to create a virtual environment consisting of numerous worlds, each of which will offer a different set of services. To deal with such a dynamic and complex scenario, considering the stringent quality of service requirements aimed at the 6th generation of communication systems (6G), one potential approach is to adopt self-sustaining strategies, which can be realized by employing Adaptive Artificial Intelligence (Adaptive AI) where models are continually re-trained with new data and conditions. One aspect of self-sustainability is the management of multiple access to the frequency spectrum. Although several innovative methods have been proposed to address this challenge, mostly using Deep Reinforcement Learning (DRL), the problem of adapting agents to a non-stationary environment has not yet been precisely addressed. This paper fills in the gap in the current literature by investigating the problem of multiple access in multi-channel environments to maximize the throughput of the intelligent agent when the number of active User Equipments (UEs) may fluctuate over time. To solve the problem, a Double Deep Q-Learning (DDQL) technique empowered by Continual Learning (CL) is proposed to overcome the non-stationary situation, while the environment is unknown. Numerical simulations demonstrate that, compared to other well-known methods, the CL-DDQL algorithm achieves significantly higher throughputs with a considerably shorter convergence time in highly dynamic scenarios.
Stereo Matching: Fundamentals, State-of-the-Art, and Existing Challenges

Stereo matching is the process of generating dense correspondences in stereo images in order to create a disparity map for depth perception. Stereo matching is different from flow estimation task due to stereo rectification, which ensures that correspondences are always co-linear in a pair of stereo images. Stereo vision has become increasingly popular in mobile devices, such as autonomous cars and unmanned aerial vehicles, thanks to recent advances in full-feature embedded microcomputers. However, due to limited computing resources, there is a growing need for stereo matching algorithms that strike a balance between disparity estimation accuracy and efficiency. Challenges in this field include the lack of disparity ground truth, domain adaptation, and intractable areas such as occlusions. This chapter covers the fundamentals of stereopsis, including the perspective camera model and epipolar geometry, and reviews the most advanced stereo matching algorithms. It also explores disparity confidence measures, disparity estimation evaluation metrics, and publicly available datasets and benchmarks, before summarizing the outstanding challenges in this field.
A Survey (NLP) Natural Language Processing and Transactions on (NNL) Neural Networks and learning Systems

Natural Language Processing NLP, or Natural Language Processing, is an area of artificial intelligence (AI) that concentrates on the interaction between computers and human language. Its goal is to develop algorithms and models that enable computers to understand, interpret, and generate human language in a meaningful manner. NLP aims to bridge the gap between human language and computer language, enabling computers to process and comprehend natural language data effectively. This field encompasses a range of tasks, including speech recognition, language translation, sentiment analysis, text summarization, question answering, and many others. These tasks rely on machine learning models that are trained using large amounts of annotated data. media monitoring, information retrieval, customer support chatbots, and many other areas where understanding human language is crucial. As research and development in NLP progress, we can anticipate the emergence of more advanced and sophisticated models that will further enhance the capabilities of language processing and understanding by computers. This advancement holds exciting possibilities for human-computer interaction. NLP research has seamless communication and empowering us to extract valuable knowledge from textual data on a large scale. technique employed in the process of multi-criteria decision-making and prioritizing alternatives. Its purpose is to provide a structured method for assessing and ranking different options in the presence of multiple criteria that may conflict with one another. MOORA is widely utilized across various domains, including operations research, engineering, and management. Alternative: Programming language, Linguistic theory, Hardware platform, Knowledge Representation
Enhancing virtual instruction: leveraging AI applications for success

Purpose This paper aims to describe several artificial intelligence (AI)-based applications that librarians can use to serve and design virtual library instruction, so it will be more effective and efficient. Design/methodology/approach The approach involves a comprehensive review of AI-based applications that bring benefits to librarian to enhance the virtual instructional services (AI). This study explores the existing papers to reveal the potential use of AI for research consultation, designing the instructional services and conducting evaluation of the program. Findings There are some AI-based applications that are available for free that will help instructional librarian jobs. Librarians use the AI to increase effectiveness of the services. The AI-based applications that can be used to support instructional services on research inquiries include virtual assistance, knowledge mapping and note making, and to support designing virtual instruction, librarians can use design apps, image generators, voice generator, grammar checker and paraphrasing. Originality/value There are many studies on AI at the library; however, it’s still rare a paper studied AI-based application that potentially will bring benefit for virtual instructional services. This paper will give overview of AI application that will help instructional librarian on transactions with users and help librarians to create innovative instructional media.
CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control

We present CaptAinGlove, a textile-based, low-power (≤ 1.15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (≤ 2MB), designed to recognize hand gestures used for drone control. We employ lightweight convolutional neural networks as the backbone models and a hierarchical multimodal fusion to reduce power consumption and improve accuracy. The system yields an F1-score of 80% for the offline evaluation of nine classes; eight hand gesture commands and null activity. For the RTE, we obtained an F1-score of 67% (one user).
Bridging Multimedia Modalities: Enhanced Multimodal AI Understanding and Intelligent Agents

With the increasing availability of multimodal data, especially in the sports and medical domains, there is growing interest in developing Artificial Intelligence (AI) models capable of comprehending the world in a more holistic manner. Nevertheless, various challenges exist in multimodal understanding, including the integration of multiple modalities and the resolution of semantic gaps between them. The proposed research aims to leverage multiple input modalities for the multimodal understanding of AI models, enhancing their reasoning, generation, and intelligent behavior. The research objectives focus on developing novel methods for multimodal AI, integrating them into conversational agents with optimizations for domain-specific requirements. The research methodology encompasses literature review, data curation, model development and implementation, evaluation and performance analysis, domain-specific applications, and documentation and reporting. Ethical considerations will be thoroughly addressed, and a comprehensive research plan is outlined to provide guidance. The research contributes to the field of multimodal AI understanding and the advancement of sophisticated AI systems by experimenting with multimodal data to enhance the performance of state-of-the-art neural networks.
Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection and classification over aerial image sequences. The proposed model consists of five stages. All of the images are preprocessed in the first stage to reduce noise and raise the brightness level. The foreground items are then extracted from these images using segmentation. The segmented images are then passed onto the YOLOv8 algorithm to detect and locate vehicles in each image. The feature extraction phase is then applied to the detected vehicles. The extracted feature involves Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and KAZE features. For classification, we used the Deep Belief Network (DBN) classifier. Based on classification, the experimental results across the three datasets produced better outcomes; the proposed model attained an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI) and 94.6% over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively. To compare our model with the other standard techniques, we have also drawn a comparative analysis with the latest techniques in the research.
Blockchain for Combating Pharmaceutical Drug Counterfeiting and Cold Chain Distribution

In recent years, pharmaceutical drug traceability systems have been developed as critical tools for improving the digital supply chain's transparency and visibility. Blockchain-based drug traceability proposes a promising solution to create a distributed shared data platform for an irreversible, trustworthy, reliable, and transparent system. This article provides a thorough analysis and summary of the current state of drug traceability distribution research on the blockchain platform by using Hyperledger febric and Hyperledger Besu methodologies. Blockchain based platform, Hyperledger Fabric and Besu meets essential needs for drug traceability such as privacy, dependability, transparency, security, authorization, authentication, and scalability. The Hyperledger Fabric blockchain platform executes drug transactions proficiently and securely in the supply chains within a distributed network of stakeholders. This fabric-enabled private, permissioned distributed network comprising various pharmaceutical stakeholder groups aids in the efficient and secure execution of medication in supply chain transactions. This study also examines the impact of blockchain technology in the healthcare system while exhibiting how some features of this disruptive technology have the potential to transform existing cold chain and drug traceability processes. Blockchain technology embraces significant benefits in the processes of pharmaceutical drug serialization, protecting IoT devices, and ensuring temper-proof transaction sharing. Blockchain is also a potential solution to make use of IoT-enabled vehicles and warehouses for cold chain transportation by using smart sensors to capture critical temperature data. Blockchain-enabled IoT sensors in the cold chain ensure the secure transportation of drugs to pharmaceutical stakeholders in the supply chain network.
JADE OWL: JPEG 2000 Forensics by Wavelet Offset Consistency Analysis

In a world teeming with digital images, the credibility of visual data has become of paramount importance. While it is now simpler than ever to manipulate an image for malicious purposes such as misinformation, the tools for detecting such alterations have predominantly been developed for either uncompressed or JPEG-compressed natural images. However, medical and satellite imagery, domains where the potential for fraud is high, often use a different compression format - JPEG 2000. We present a JPEG 2000 Anomaly Detection Estimator via Offset of Wavelet Localization - the Jade Owl -, a novel method for detecting forgeries in JPEG 2000 images by analyzing the consistency of traces left by its compression. Our technique hinges on the premise that the wavelet coefficients of a JPEG 2000 image are lower when the same offset is applied during the wavelet transform than they are when the offset is different. By employing this principle locally, we're able to detect regions with significantly different offsets, indicating potential forgeries such as copy-move. An accompanying a contrario model further refines this detection to make automatic detections while controlling false positives. To evaluate the method, we've created a unique dataset of JPEG 2000 forgeries. This novel approach significantly paves the way for JPEG 2000 image forensics, introducing a sensitive and efficient tool for authenticity verification in critical sectors such as healthcare and satellite imagery.
When Filters Escape the Smartphone: Exploring Acceptance and Concerns Regarding Augmented Expression of Social Identity for Everyday AR

Mass adoption of Everyday Augmented Reality (AR) glasses will enable pervasive augmentation of our expression of social identity through AR filters, transforming our perception of self and others. However, despite filters’ prominent and often problematic usage in social media, research has yet to reflect on the potential impact AR filters might have when brought into everyday life. Informed by our survey of 300 existing popular AR filters used on Snapchat, Instagram and Tiktok, we conducted an AR-in-VR user study where participants (N=24) were exposed to 18 filters across six categories. We evaluated the social acceptability of these augmentations around others and attitudes towards an individual’s augmented self.Our findings highlight 1) how users broadly respected another individual’s augmented self; 2) positive use cases, such as supporting the presentation of gender identity; and 3) tensions around applying AR filters to others (e.g. censorship, changing protected characteristics) and their impact on self-perception (e.g. perpetuating unrealistic beauty standards). We raise questions regarding the rights of individuals to augment and be augmented that provoke the need for further consideration of AR augmentations in society.
Re-investigating the Effect of the Vergence-Accommodation Conflict on 3D Pointing

The vergence-accommodation conflict (VAC) limits user performance in current Virtual Reality (VR) systems. In this paper, we investigate the effects of the VAC in a single-focal VR system using three experimental conditions: with no VAC, with a constant VAC, and with a varying VAC. Previous work in this area had yielded conflicting results, so we decided to re-investigate this issue. Eighteen participants performed an ISO 9241:411 task in a study that closely replicates previous work, except that the angle of the task space was rotated 20 degrees downward, to make the task less fatiguing to perform, which addresses a potential confound in previous work. We found that the varying VAC condition had worse performance than the other conditions, which indicates that the contrasting results in previous work were very likely due to biomechanical factors. We hope that our work contributes to the understanding of the influence of the VAC in VR systems and potential strategies for improving user experience and performance in immersive virtual environments.
Optimal Protocols for Quantum Metrology with Noisy Measurements

Measurement noise is a major source of noise in quantum metrology. Here, we explore preprocessing protocols that apply quantum controls to the quantum sensor state prior to the final noisy measurement (but after the unknown parameter has been imparted), aiming to maximize the estimation precision. We define the quantum preprocessing-optimized Fisher information, which determines the ultimate precision limit for quantum sensors under measurement noise, and conduct a thorough investigation into optimal preprocessing protocols. First, we formulate the preprocessing optimization problem as a biconvex optimization using the error observable formalism, based on which we prove that unitary controls are optimal for pure states and derive analytical solutions of the optimal controls in several practically relevant cases. Then we prove that for classically mixed states (whose eigenvalues encode the unknown parameter) under commuting-operator measurements, coarse-graining controls are optimal, while unitary controls are suboptimal in certain cases. Finally, we demonstrate that in multiprobe systems where noisy measurements act independently on each probe, the noiseless precision limit can be asymptotically recovered using global controls for a wide range of quantum states and measurements. Applications to noisy Ramsey interferometry and thermometry are presented, as well as explicit circuit constructions of optimal controls.Received 20 March 2023Revised 9 August 2023Accepted 8 September 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040305Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum Fisher informationQuantum fluctuations & noiseQuantum information theoryQuantum metrologyQuantum sensingQuantum Information, Science & Technology