Reviewerly/exHarmony huggingface dataset:
On conditional probabilities and their canonical extensions to Boolean algebras of compound conditionals

In this paper we investigate canonical extensions of conditional probabilities to Boolean algebras of conditionals. Before entering into the probabilistic setting, we first prove that the lattice order relation of every Boolean algebra of conditionals can be characterized in terms of the well-known order relation given by Goodman and Nguyen. Then, as an interesting methodological tool, we show that canonical extensions behave well with respect to conditional subalgebras. As a consequence, we prove that a canonical extension and its original conditional probability agree on basic conditionals. Moreover, we verify that the probability of conjunctions and disjunctions of conditionals in a recently introduced framework of Boolean algebras of conditionals are in full agreement with the corresponding operations of conditionals as defined in the approach developed by two of the authors to conditionals as three-valued objects, with betting-based semantics, and specified as suitable random quantities. Finally we discuss relations of our approach with nonmonotonic reasoning based on an entailment relation among conditionals.
Generic Nonadditivity of Quantum Capacity in Simple Channels

Determining capacities of quantum channels is a fundamental question in quantum information theory. Despite having rigorous coding theorems quantifying the flow of information across quantum channels, their capacities are poorly understood due to superadditivity effects. Studying these phenomena is important for deepening our understanding of quantum information, yet simple and clean examples of superadditive channels are scarce. Here we study a family of channels called platypus channels. Its simplest member, a qutrit channel, is shown to display superadditivity of coherent information when used jointly with a variety of qubit channels. Higher-dimensional family members display superadditivity of quantum capacity together with an erasure channel. Subject to the "spin-alignment conjecture" introduced in our companion paper [F. Leditzky, D. Leung, V. Siddhu, G. Smith, and J. A. Smolin, The platypus of the quantum channel zoo, IEEE Transactions on Information Theory (IEEE, 2023), 10.1109/TIT.2023.3245985], our results on superadditivity of quantum capacity extend to lower-dimensional channels as well as larger parameter ranges. In particular, superadditivity occurs between two weakly additive channels each with large capacity on their own, in stark contrast to previous results. Remarkably, a single, novel transmission strategy achieves superadditivity in all examples. Our results show that superadditivity is much more prevalent than previously thought. It can occur across a wide variety of channels, even when both participating channels have large quantum capacity.
A survey on automatic generation of medical imaging reports based on deep learning

Abstract Recent advances in deep learning have shown great potential for the automatic generation of medical imaging reports. Deep learning techniques, inspired by image captioning, have made significant progress in the field of diagnostic report generation. This paper provides a comprehensive overview of recent research efforts in deep learning-based medical imaging report generation and proposes future directions in this field. First, we summarize and analyze the data set, architecture, application, and evaluation of deep learning-based medical imaging report generation. Specially, we survey the deep learning architectures used in diagnostic report generation, including hierarchical RNN-based frameworks, attention-based frameworks, and reinforcement learning-based frameworks. In addition, we identify potential challenges and suggest future research directions to support clinical applications and decision-making using medical imaging report generation systems.
Towards Accelerating Data Intensive Application's Shuffle Process Using SmartNICs

The wide adoption of the emerging SmartNIC technology creates new opportunities to offload application-level computation into the networking layer, which frees the burden of host CPUs, leading to performance improvement. Shuffle, the all-to-all data exchange process, is a critical building block for network communication in distributed data-intensive applications and can potentially benefit from SmartNICs. In this paper, we develop SmartShuffle, which accelerates the data-intensive application's shuffle process by offloading various computation tasks into the SmartNIC devices. SmartShuffle supports offloading both low-level network functions, including data partitioning and network transport, and high-level computation tasks, including filtering, aggregation, and sorting. SmartShuffle adopts a coordinated offload architecture to make sender-side and receiver-side SmartNICs jointly contribute to the benefits of shuffle computation offload. SmartShuffle carefully manages the tight and time-varying computation and memory constraints on the device. We propose a liquid offloading approach, which dynamically migrates operators between the host CPU and the SmartNIC at runtime such that resources in both devices are fully utilized. We prototype SmartShuffle on the Stingray SoC SmartNICs and plug it into Spark. Our evaluation shows that SmartShuffle improves host CPU efficiency and I/O efficiency with lower job completion time. SmartShuffle outperforms Spark, and Spark RDMA by up to 40% on TPC-H.
Quantum key distribution rates from semidefinite programming

Computing the key rate in quantum key distribution (QKD) protocols is a long standing challenge. Analytical methods are limited to a handful of protocols with highly symmetric measurement bases. Numerical methods can handle arbitrary measurement bases, but either use the min-entropy, which gives a loose lower bound to the von Neumann entropy, or rely on cumbersome dedicated algorithms. Based on a recently discovered semidefinite programming (SDP) hierarchy converging to the conditional von Neumann entropy, used for computing the asymptotic key rates in the device independent case, we introduce an SDP hierarchy that converges to the asymptotic secret key rate in the case of characterised devices. The resulting algorithm is efficient, easy to implement and easy to use. We illustrate its performance by recovering known bounds on the key rate and extending high-dimensional QKD protocols to previously intractable cases. We also use it to reanalyse experimental data to demonstrate how higher key rates can be achieved when the full statistics are taken into account.
RedTell: an AI tool for interpretable analysis of red blood cell morphology

Introduction: Hematologists analyze microscopic images of red blood cells to study their morphology and functionality, detect disorders and search for drugs. However, accurate analysis of a large number of red blood cells needs automated computational approaches that rely on annotated datasets, expensive computational resources, and computer science expertise. We introduce RedTell, an AI tool for the interpretable analysis of red blood cell morphology comprising four single-cell modules: segmentation, feature extraction, assistance in data annotation, and classification. Methods: Cell segmentation is performed by a trained Mask R-CNN working robustly on a wide range of datasets requiring no or minimum fine-tuning. Over 130 features that are regularly used in research are extracted for every detected red blood cell. If required, users can train task-specific, highly accurate decision tree-based classifiers to categorize cells, requiring a minimal number of annotations and providing interpretable feature importance. Results: We demonstrate RedTell’s applicability and power in three case studies. In the first case study we analyze the difference of the extracted features between the cells coming from patients suffering from different diseases, in the second study we use RedTell to analyze the control samples and use the extracted features to classify cells into echinocytes, discocytes and stomatocytes and finally in the last use case we distinguish sickle cells in sickle cell disease patients. Discussion: We believe that RedTell can accelerate and standardize red blood cell research and help gain new insights into mechanisms, diagnosis, and treatment of red blood cell associated disorders.
Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.
A quantum computing implementation of nuclearelectronic orbital (NEO) theory: Toward an exact pre-Born–Oppenheimer formulation of molecular quantum systems

Nuclear quantum phenomena beyond the Born-Oppenheimer approximation are known to play an important role in a growing number of chemical and biological processes. While there exists no unique consensus on a rigorous and efficient implementation of coupled electron-nuclear quantum dynamics, it is recognized that these problems scale exponentially with system size on classical processors and, therefore, may benefit from quantum computing implementations. Here, we introduce a methodology for the efficient quantum treatment of the electron-nuclear problem on near-term quantum computers, based upon the Nuclear-Electronic Orbital (NEO) approach. We generalize the electronic two-qubit tapering scheme to include nuclei by exploiting symmetries inherent in the NEO framework, thereby reducing the Hamiltonian dimension, number of qubits, gates, and measurements needed for calculations. We also develop parameter transfer and initialization techniques, which improve convergence behavior relative to conventional initialization. These techniques are applied to H2 and malonaldehyde for which results agree with NEO full configuration interaction and NEO complete active space configuration interaction benchmarks for ground state energy to within 10-6 hartree and entanglement entropy to within 10-4. These implementations therefore significantly reduce resource requirements for full quantum simulations of molecules on near-term quantum devices while maintaining high accuracy.
Ethically Governed Artificial Intelligence Based Innovative Business Research in Finance and Marketing System

The ascent of a few insightful labor and products throughout the course of recent years, as well as their business feasibility and financial impacts, have driven some to contemplate whether the ongoing coming of computer based intelligence is just marketing publicity or really can possibly change society. The review investigates the few impacts of artificial intelligence (artificial intelligence), and digs further into both good and troublesome consequences for legislatures, networks, organizations, and individuals. The entire impacts of simulated intelligence, from exploration and advancement to execution, are analyzed in this paper. With the advancement of computer based intelligence innovations, the marketing business is developing rapidly. Artificial intelligence offers numerous open doors, including the capacity to acquire data, hyper-customize administrations, further develop consumer loyalty, save working expenses, support efficiency, and so forth. For both monetary administrations organizations and advertisers, artificial intelligence has changed the game.
Hashtags as signals of political identity: #BlackLivesMatter and #AllLivesMatter

We investigate perceptions of tweets marked with the #BlackLivesMatter and #AllLivesMatter hashtags, as well as how the presence or absence of those hashtags changed the meaning and subsequent interpretation of tweets in U.S. participants. We found a strong effect of partisanship on perceptions of the tweets, such that participants on the political left were more likely to view #AllLivesMatter tweets as racist and offensive, while participants on the political right were more likely to view #BlackLivesMatter tweets as racist and offensive. Moreover, we found that political identity explained evaluation results far better than other measured demographics. Additionally, to assess the influence of hashtags themselves, we removed them from tweets in which they originally appeared and added them to selected neutral tweets. Our results have implications for our understanding of how social identity, and particularly political identity, shapes how individuals perceive and engage with the world.
Augmented Reality Application Development using Unity and Vuforia

This article presents a method to develop an application based on Augmented Reality technology in a Unity development environment.The article presents two test scenarios that give an insight into the possibilities of augmented reality applications.The first scenario is a presentation of a simple game based on a graphic marker and its generated content in the form of a maze.By physically moving the marker in front of the camera, the maze and the ball inside is moved.The second scenario includes a simulation environment of a collaborative robot in the form of an augmented reality application that offers the possibility of planning the robot's trajectories.The possibilities of this technology are endless and cover a large segment of application development from educational to industrial applications.Marker based Augmented Reality using predefined visual markers embedded within the system, physical world objects are detected for superimposition of virtual elements.The test application was designed using a combination of Vuforia, Unity 3D platforms, and the mobile phone used was an Android based Xiaomi Redmi 10 pro phone.This article focuses on the implementation challenges faced whilst designing Augmented Reality applications on mobile platforms.
Comparisons between fast algorithms for the continuous wavelet transform and applications in cosmology: the 1D case

Abstract The continuous wavelet transform (CWT) is very useful for processing signals with intricate and irregular structures in astrophysics and cosmology. It is crucial to propose precise and fast algorithms for the CWT. In this work, we review and compare four different fast CWT algorithms for the 1D signals, including the FFTCWT, the V97CWT, the M02CWT, and the A19CWT. The FFTCWT algorithm implements the CWT using the Fast Fourier Transform (FFT) with a computational complexity of $\mathcal {O}(N\log _2N)$ per scale. The rest algorithms achieve the complexity of $\mathcal {O}(N)$ per scale by simplifying the CWT into some smaller convolutions. We illustrate explicitly how to set the parameters as well as the boundary conditions for them. To examine the actual performance of these algorithms, we use them to perform the CWT of signals with different wavelets. From the aspect of accuracy, we find that the FFTCWT is the most accurate algorithm, though its accuracy degrades a lot when processing the non-periodic signal with zero boundaries. The accuracy of $\mathcal {O}(N)$ algorithms is robust to signals with different boundaries, and the M02CWT is more accurate than the V97CWT and A19CWT. From the aspect of speed, the $\mathcal {O}(N)$ algorithms do not show an overall speed superiority over the FFTCWT at sampling numbers of N ≲ 106, which is due to their large leading constants. Only the speed of the V97CWT with real wavelets is comparable to that of the FFTCWT. However, both the FFTCWT and V97CWT are substantially less efficient in processing the non-periodic signal because of zero padding. Finally, we conduct wavelet analysis of the 1D density fields, which demonstrate the convenience and power of techniques based on the CWT. We publicly release our CWT codes as resources for the community.
Emergency evacuation risk assessment method for educational buildings based on improved extreme learning machine

In educational facility interiors, the risk of congestion and trampling among occupants during the evacuation process presents a significant safety concern. Therefore, assessing the risk of the evacuation process is of great practical and academic importance. To meet the requirements of rapid and timely risk assessment, this article proposes an emergency evacuation risk assessment model based on the Improved Extreme Learning Machine (ELM). The ELM with fast learning speed and good generalization performance is improved to form the Deep Extreme Learning Machine (DELM) and Kernel Based Extreme Learning Machine (KELM) models, and the Improved Seagull Optimization Algorithm (ISOA) was used to constitute the ISOA-DELM and ISOA-KELM models for training. Taking a university library as an example, the evaluation process of model data acquisition, training, and testing is analyzed and compared. The prediction accuracy of the ISOA-DELM and ISOA-KELM models proposed in this paper reached more than 92%. The results show that improved extreme learning machine models can enable an efficient and fast risk assessment.
Fast quantum transfer mediated by topological domain walls

The duration of bidirectional transfer protocols in 1D topological models usually scales exponentially with distance. In this work, we propose transfer protocols in multidomain SSH chains and Creutz ladders that lose the exponential dependence, greatly speeding up the process with respect to their single-domain counterparts, reducing the accumulation of errors and drastically increasing their performance, even in the presence of symmetry-breaking disorder. We also investigate how to harness the localization properties of the Creutz ladder---with two localized modes per domain wall---to choose the two states along the ladder that will be swapped during the transfer protocol, without disturbing the states located in the intermediate walls between them. This provides a 1D network with all-to-all connectivity that can be helpful for quantum information purposes.
SCIENTIFIC REFERENCE MODEL – DEFINING STANDARDS, METHODOLOGY AND IMPLEMENTATION OF SERIOUS 3D MODELS IN ARCHAEOLOGY, ART AND ARCHITECTURAL HISTORY

Abstract. In object-oriented historical research the need to combine hypotheses and textual arguments with the critical analysis based on sources – such as floor plans, sections, perspectives, and photographs – has considerably benefited from the developments in Digital Humanities (Münster, 2022). The use of digital 3D models has overcome many limitations inherent to two-dimensional records. Since the early 1990s hypothetical 3D reconstructions have therefore increasingly become routine research tools and essential means of representation capable of offering new methods of investigation, enabling new insights into the object-related research. In terms of a holistic approach to the analysis and case studies, i.e. the enhanced ability to examine and explore (Favro, 2012) serious challenges remain regarding documentation, interoperability and long-term access to 3D-based research outputs.In this context, numerous initiatives and research projects have emerged with the common objective of systematising and rationalising the various problems identified by scholars. Such projects still tend to remain isolated, lacking a significant impact on the community of potential users. 3D research outputs are not widely applicable, due to the complex prototypes of the software architecture, difficult to apply in a broad sense. Furthermore, the ‘old’ problems still exist, i.e. the traditional approaches - which do not consider a 3D model as a scholarly result, but only an investigative tool - and the reluctance to share these results and the associated procedures. Therefore, an attempt is being made to define the development and evaluation of an applicable methodology for the hypothetical 3D historical reconstruction, based on a shared theoretical approach.The working method presented here reflects many years of engagement with source-based hypothetical 3D reconstruction of no longer extant or unrealised architecture for teaching and research. Our focus is therefore on a low-threshold, application-oriented method of the Scientific Reference Model (SRM) as a documented and published basic model. The structured SRM represents an important working and knowledge state, which clarifies the essential information about the object, its components, its credibility or extent of hypothesis and copyright. Such SRM is made available for further research, edits and refinement, as well as further derivatives (special applications). Thus SRM represents a findable referential result of a scholarly investigation of a material object that physically no longer exists.
The School of the Future

Technology is developing at a rapid pace, affecting the socioeconomic situation of the planet through innovation and the evolution of applications for easier and faster access to goods and services. The development of new technologies has also affected education. In this paper, the school of the future is presented with regard to emerging or exponential disruptive technologies and the impact of emotional intelligence on those involved in education. The politics of globalization, global perspectives, perceptions, and contemporary social values lead to the education of individuals with accessibility for everyone, from everyone, and from everywhere and at any time in an inclusive world. The school is changing. Research showed that the school of the future reshapes its learning environment to meet the increasing demands of the 21st century which are positively correlated with dynamic, flexible, interactive, creative, and self-directed learning technologies.