Reviewerly/exHarmony huggingface dataset:
An efficient algorithm for detection of white blood cell nuclei using adaptive three stage PCA-based fusion

Morphological analysis of white blood cells under a microscope is a crucial laboratory procedure for the diagnosis of several diseases including leukemia. This process is however, tedious and subjective, hence the need for innovative, efficient, and automatic analytical techniques. From shortcomings of algorithms in the literature such as inability to segment images with poor brightness and low contrast or deterioration of morphological structure of nucleus during segmentation, this paper sets forth an automatic method for segmentation of white blood cell nuclei in peripheral blood smear images in an attempt to cover these defects. In the proposed method, an adaptive fusion based on principal components analysis is used to fuse color components derived from HSV and CMYK color spaces. The proposed method enables the extraction of the nuclei without the application of morphological operations and presents the advantage of accentuated contrast thanks to PCA-based fusion and independence to color and brightness variation in the peripheral images. The developed algorithm was tested on three completely different public image datasets. The comparison of the results of the proposed algorithm with several other existing methods on the same datasets demonstrated its greater efficacy. A dice similarity coefficient of 97.06% was obtained for CellaVision, 94.75% for BloodSeg base, and 90.79% for JTSC databases.
Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality

Fixation identification is an essential task in the extraction of relevant information from gaze patterns; various algorithms are used in the identification process. However, the thresholds used in the algorithms greatly affect their sensitivity. Moreover, the application of these algorithm to eye-tracking technologies integrated into head-mounted displays, where the subject’s head position is unrestricted, is still an open issue. Therefore, the adaptation of eye-tracking algorithms and their thresholds to immersive virtual reality frameworks needs to be validated. This study presents the development of a dispersion-threshold identification algorithm applied to data obtained from an eye-tracking system integrated into a head-mounted display. Rules-based criteria are proposed to calibrate the thresholds of the algorithm through different features, such as number of fixations and the percentage of points which belong to a fixation. The results show that distance-dispersion thresholds between 1–1.6° and time windows between 0.25–0.4 s are the acceptable range parameters, with 1° and 0.25 s being the optimum. The work presents a calibrated algorithm to be applied in future experiments with eye-tracking integrated into head-mounted displays and guidelines for calibrating fixation identification algorithms
A laser emitting contact lens for eye tracking

In this paper, we present the first realisation and experimentation of a new eye tracking system using an infrared (iR) laser pointer embedded into a wireless smart contact lens. We denote this contact lens prototype as the cyclops lens, in reference to the famous hero of the X-Men comics. The full eye tracker device combines the smart contact lens and its eyewear, which provides a primary source of energy and the beam detection system. We detail the assembling and encapsulation process of the main functionalities into the contact lens and present how a gaze tracking system is achieved, compared to existing conventional eye-tracking ones. Finally, we discuss future technical improvements.
Complete complementarity relations for multipartite pure states

Complementarity relations for wave-particle duality are saturated only for pure, single-quanton, quantum states. For a completely incoherent state, it is known that wave and particle quantifiers can reach zero, and hence no information about the wave and particle aspects of the system can be obtained. This means that the information is being shared with another systems, and quantum correlations can be seen as responsible for the loss of purity of the quanton, provided that the quanton is part of a multipartite pure quantum system. In this paper, by exploring the purity of bi- and tri-partite pure quantum states, we show that it is possible to obtain complete complementarity relations. This procedure allows us to create a general framework for obtaining complete complementarity relations for a subsystem that belongs to an arbitrary multi-partite quantum system in a pure state. Besides, by some simple examples, we show that if the predictability measure is changed then the correlation measure must also be changed in order to obtain complete complementarity relations for pure cases.
Physics-Informed Echo State Networks for Chaotic Systems Forecasting

We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional loss function during the training of the ESNs, which penalizes non-physical predictions without the need of any additional training data. This approach is demonstrated on a chaotic Lorenz system, where the physics-informed ESNs improve the predictability horizon by about two Lyapunov times as compared to conventional ESNs. The proposed framework shows the potential of using machine learning combined with prior physical knowledge to improve the time-accurate prediction of chaotic dynamical systems.
Dark Web Marketplaces and COVID-19: before the vaccine

The COVID-19 pandemic has reshaped the demand for goods and services worldwide. The combination of a public health emergency, economic distress, and misinformation-driven panic have pushed customers and vendors towards the shadow economy. In particular, dark web marketplaces (DWMs), commercial websites accessible via free software, have gained significant popularity. Here, we analyse 851,199 listings extracted from 30 DWMs between January 1, 2020 and November 16, 2020. We identify 788 listings directly related to COVID-19 products and monitor the temporal evolution of product categories including Personal Protective Equipment (PPE), medicines (e.g., hydroxyclorochine), and medical frauds. Finally, we compare trends in their temporal evolution with variations in public attention, as measured by Twitter posts and Wikipedia page visits. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs, especially now that real vaccines are available and in short supply. We anticipate our analysis will be of interest both to researchers and public agencies focused on the protection of public health.
Resource theory of imaginarity: Quantification and state conversion

Complex numbers are widely used in both classical and quantum physics and are indispensable components for describing quantum systems and their dynamical behavior. Recently, the resource theory of imaginarity has been introduced, allowing for a systematic study of complex numbers in quantum mechanics and quantum information theory. In this work we develop theoretical methods for the resource theory of imaginarity, motivated by recent progress within theories of entanglement and coherence. We investigate imaginarity quantification, focusing on the geometric imaginarity and the robustness of imaginarity, and apply these tools to the state conversion problem in imaginarity theory. Moreover, we analyze the complexity of real and general operations in optical experiments, focusing on the number of unfixed wave plates for their implementation. We also discuss the role of imaginarity for local state discrimination, proving that any pair of real orthogonal pure states can be discriminated via local real operations and classical communication. Our study reveals the significance of complex numbers in quantum physics and proves that imaginarity is a resource in optical experiments.
Blockchain, trust, and trust accounting: can blockchain technology substitute trust created by intermediaries in trust accounting A theoretical examination

Blockchain technology features such as distributed ledger, decentralisation, smart contracts, and consensus mechanisms promise to trust in transactions. It could be beneficial to trust accounting processes. This paper, using theoretical analysis, reviews these features and examines if they could lead to the substitution or complement the role of trust agents such as legal firms, lawyers, accountants or auditors, and even regulatory authorities. The investigation shows that while blockchain presents novel features that are key to enhancing trust, the technology can only complement that trust accounting process, due to the technical skills required. At the same time, the paper discusses the relationship between trust and intermediaries. Future investigations could analyse the relationship between faith and technology, considering practitioners' view.
Adaptive-Optics-Enabled Quantum Communication: A Technique for Daytime Space-To-Earth Links

Previous demonstrations of free-space quantum communication in daylight have been touted as significant for the development of global-scale quantum networks. Until now, no one has carefully tuned their atmospheric channel to reproduce the daytime sky radiance and slant-path turbulence conditions as they exist between space and Earth. In this article we report a quantum communication field experiment under conditions representative of daytime downlinks from space. Higher-order adaptive optics increased quantum channel efficiencies far beyond those possible with tip/tilt correction alone while spatial filtering at the diffraction limit rejected optical noise without the need for an ultra-narrow spectral filter. High signal-to-noise probabilities and low quantum-bit-error rates were demonstrated over a wide range of channel radiances and turbulence conditions associated with slant-path propagation in daytime. The benefits to satellite-based quantum key distribution are quantified and discussed.
Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.
Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study

Background: Ex vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time. Material and Methods: Tissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy. Results: The model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study. Conclusion: In this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.
Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining

Text mining aims at constructing classification models and finding interesting patterns in large text collections. This paper investigates the utility of applying these techniques to media analysis, more specifically to support discourse analysis of news reports about the 2007 Kenyan elections and post-election crisis in local (Kenyan) and Western (British and US) newspapers. It illustrates how text mining methods can assist discourse analysis by finding contrast patterns which provide evidence for ideological differences between local and international press coverage. Our experiments indicate that most significant differences pertain to the interpretive frame of the news events: whereas the newspapers from the UK and the US focus on ethnicity in their coverage, the Kenyan press concentrates on sociopolitical aspects.
Macrocoding the Lexicon with Co-occurrence Knowledge

Neither syntax nor semantics can justify the use of a certain class of English word combinations. This class contains word pairs that often appear together in a given context of meaning. Such pairs are called co-occurrence relations or idiosyncratic collocations. To correctly understand or produce natural language, such lexical relations need to be specifically encoded in the lexicon. In this work we focus on language generation, co-occurrence knowledge is particularly important for this task because without it, awkward or incorrect sentences could be produced. Most previous work either deferred the issue or handled it by hand encoding the knowledge. In this chapter, we show how word-based lexicons can be enriched with automatically acquired lexical relations. We call this process macrocoding the lexicon, because it corresponds to the pre-encoding of lexical associations in a regular lexicon. We describe Xtract, a co-occurrence compiler that retrieves lexical relations from the analysis of a large corpus and we show how this acquired knowledge can be used in the framework of language generation.
A Cloud Brokerage Architecture for Efficient Cloud Service Selection

The expanding cloud computing services offer great opportunities for consumers to find the best service and best pricing. Meanwhile, it also raises new challenges for consumers who need to select the best service out of such a huge pool since it will be time-consuming for consumers to collect the necessary information and analyze all service providers to make the decision. Therefore, in this paper, we propose a novel brokerage-based architecture in the cloud, where the cloud brokers is responsible for the service selection. We also design an efficient indexing structure, called B <inline-formula><tex-math notation="LaTeX">$^{cloud}$</tex-math></inline-formula> -tree, for managing the information of a large number of cloud service providers. We then develop the service selection algorithm that recommends most suitable cloud services to the cloud consumers. We carry out extensive experimental studies on real and synthetic cloud data, and demonstrate a significant performance improvement over previous approaches.
The Spiral Optimization Algorithm: Convergence Conditions and Settings

The spiral optimization (SPO) algorithm proposed by Tamura and Yasuda is a relatively novel and simple search concept inspired by natural spiral phenomena. This algorithm searches continuous space using no gradient and only spiral trajectories composed of spiral vectors generated by deterministic spiral models. The primary purpose of this paper is to propose conditions and settings that mathematically ensure the convergence of the SPO algorithm to a stationary point. The conditions relating to the sizes and directions of the spiral vectors and the initial search points are based on direct search theory and recent SPO algorithm theories. The presented convergence was numerically verified using test functions with different properties.
Synergy

Convolutional Neural Networks (CNN) have been widely deployed in diverse application domains. There has been significant progress in accelerating both their training and inference using high-performance GPUs, FPGAs, and custom ASICs for datacenter-scale environments. The recent proliferation of mobile and Internet of Things (IoT) devices have necessitated real-time, energy-efficient deep neural network inference on embedded-class, resource-constrained platforms. In this context, we present Synergy , an automated, hardware-software co-designed, pipelined, high-throughput CNN inference framework on embedded heterogeneous system-on-chip (SoC) architectures (Xilinx Zynq). Synergy leverages, through multi-threading, all the available on-chip resources, which includes the dual-core ARM processor along with the FPGA and the NEON Single-Instruction Multiple-Data (SIMD) engines as accelerators. Moreover, Synergy provides a unified abstraction of the heterogeneous accelerators (FPGA and NEON) and can adapt to different network configurations at runtime without changing the underlying hardware accelerator architecture by balancing workload across accelerators through work-stealing. Synergy achieves 7.3X speedup, averaged across seven CNN models, over a well-optimized software-only solution. Synergy demonstrates substantially better throughput and energy-efficiency compared to the contemporary CNN implementations on the same SoC architecture.
Low-cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem

Abstract We develop circuit implementations for digital-level quantum Hamiltonian dynamics simulation algorithms suitable for implementation on a reconfigurable quantum computer, such as trapped ions. Our focus is on the codesign of a problem, its solution, and quantum hardware capable of executing the solution at the minimal cost expressed in terms of the quantum computing resources used, while demonstrating the solution of an instance of a scientifically interesting problem that is intractable classically. The choice for Hamiltonian dynamics simulation is due to the combination of its usefulness in the study of equilibrium in closed quantum mechanical systems, a low cost in the implementation by quantum algorithms, and the difficulty of classical simulation. By targeting a specific type of quantum computer and tailoring the problem instance and solution to suit physical constraints imposed by the hardware, we are able to reduce the resource counts by a factor of 10 in a physical-level implementation and a factor of 30–60 in a fault-tolerant implementation over state-of-the-art.
Superactivation of monogamy relations for nonadditive quantum correlation measures

We investigate the general monogamy and polygamy relations satisfied by quantum correlation measures. We show that there exist two real numbers $\ensuremath{\alpha}$ and $\ensuremath{\beta}$ such that for any quantum correlation measure $Q,{Q}^{x}$ is monogamous if $x\ensuremath{\ge}\ensuremath{\alpha}$ and polygamous if $0\ensuremath{\le}x\ensuremath{\le}\ensuremath{\beta}$ for a given multipartite state $\ensuremath{\rho}$. For $\ensuremath{\beta}<x<\ensuremath{\alpha}$, we show that the monogamy relation can be superactivated by finite $m$ copies ${\ensuremath{\rho}}^{\ensuremath{\bigotimes}m}$ of $\ensuremath{\rho}$ for nonadditive correlation measures. As a detailed example, we use the negativity as the quantum correlation measure to illustrate such superactivation of monogamy properties. A tighter monogamy relation is presented at last.
The evolution of argumentation mining: From models to social media and emerging tools

Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.
Rapid Classification of Quantum Sources Enabled by Machine Learning

Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by the means of optical characterization. Second-order autocorrelation is a cornerstone measurement that is particularly time-consuming to realize on a large scale. We have implemented supervised machine learning-based classification of quantum emitters as "single" or "not-single" based on their sparse autocorrelation data. Our method yields a classification accuracy of over 90% within an integration time of less than a second, realizing roughly a hundredfold speedup compared to the conventional, Levenberg-Marquardt approach. We anticipate that machine learning-based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices and can be directly extended to other quantum optical measurements.
Scalar q-subresultants and Dickson matrices

Following the ideas of Ore and Li we study $q$-analogues of scalar subresultants and show how these results can be applied to determine the rank of an $\mathbb{F}_q$-linear transformation $f$ of $\mathbb{F}_{q^n}$. As an application we show how certain minors of the Dickson matrix $D(f)$, associated with $f$, determine the rank of $D(f)$ and hence the rank of $f$.