Reviewerly/exHarmony huggingface dataset:
Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge

Background Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. Methods and performance Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients’ real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models’ inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as ‘ready for discharge’ after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). Conclusion We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.
Evolutionary Algorithm-Based Images, Humanly Indistinguishable and Adversarial Against Convolutional Neural Networks: Efficiency and Filter Robustness

Convolutional neural networks (CNNs) have become one of the most important tools for image classification. However, many models are susceptible to adversarial attacks, and CNNs can perform misclassifications. In previous works, we successfully developed an EA-based black-box attack that creates adversarial images for the target scenario that fulfils two criteria. The CNN should classify the adversarial image in the target category with a confidence ≥ 0.95, and a human should not notice any difference between the adversarial and original images. Thanks to extensive experiments performed with the CNN C = VGG-16 trained on the CIFAR-10 dataset to classify images according to 10 categories, this paper, which substantially enhances most aspects of [1], addresses four issues. (1) From a pure EA point of view, we highlight the conceptual originality of our algorithm EAdtarget,C, versus the classical EA approach. The competitive advantage obtained was assessed experimentally during image classification. (2) We then measured the intrinsic performance of the EA-based attack for an extensive series of ancestor images. (3) We challenged the filter resistance of the adversarial images created by the EA for five well-known filters. (4) We proceed to the creation of natively filter-resistant adversarial images that can fool humans, CNNs, and CNNs composed with filters.
Visual management of medical things with an advanced color-change RFID tag

Abstract This paper proposes a visual management scheme of medical things with a color-change radio frequency identification (RFID) tag. The color-change RFID tag employs a specific RFID tag integrated circuit (IC) and a laminated pH-indicating paper. The IC has energy harvesting and switched ground functions, which enable it to generate electricity to the laminated pH-indicating paper. This phenomenon causes electrolysis of NaCl solution absorbed in the laminated pH-indicating paper. Electrolysis generates alkaline matter to change the color of the pH-indicating paper. This paper gives a new and sensitive structure of the laminated pH-indicating paper. The proposed advanced color-change RFID tag with new laminated pH-indicating paper succeeds in changing its color noticeably at a 1 m distance using an RFID reader radiating 1 W radio waves. The color change was observed 3–5 s after starting radio wave irradiation. The results of this experiment also confirm that the changed color can be held for over 24 h. Furthermore, two demonstrations of the visual management system of medical things (patient clothes and sanitizers) are presented.
Quantum statistics of Schrödinger cat states prepared by logical gate with non-Gaussian resource state

A measurement-induced continuous-variable logical gate is able to prepare Schrödinger cat states if the gate uses a non-Gaussian resource state, such as cubic phase state (I.V. Sokolov (2020) [21]). Our scheme provides an alternative to hybrid circuits which use photon subtraction and (or) Fock resource states and photon number detectors. We reveal the conditions under which the gate conditionally prepares quantum superposition of two undistorted "copies" of an arbitrary input state that occupies a finite area in phase space. A detailed analysis of the fidelity between the gate output state and high-quality Schrödinger cat state is performed. A clear interpretation of the output state quantum statistics in terms of Wigner function in dependence on the gate parameters and measurement outcome is presented for a representative set of input Fock states.
A Full Dichotomy for $\hol^{c}$, Inspired by Quantum Computation

Holant problems are a family of counting problems parameterised by sets of algebraiccomplex valued constraint functions, and defined on graphs.They arise from the theory of holographic algorithms, which was originally inspired by concepts from quantum computation.Here, we employ quantum information theory to explain existing results about holant problems in a concise way and to derive two new dichotomies: one for a new family of problems, which we call Holant + , and, building on this, a full dichotomy for Holant c .These two families of holant problems assume the availability of certain unary constraint functions -the two pinning functions in the case of Holant c , and four functions in the case of Holant + -and allow arbitrary sets of algebraic-complex valued constraint functions otherwise.The dichotomy for Holant + also applies when inputs are restricted to instances defined on planar graphs.In proving these complexity classifications, we derive an original result about entangled quantum states.* This paper combines extended versions of the conference publications [1] and [3] with some new results.
Hierarchy of correlation quantifiers comparable to negativity

Quantum systems generally exhibit different kinds of correlations. In order to compare them on equal footing, one uses the so-called distance-based approach where different types of correlations are captured by the distance to different sets of states. However, these quantifiers are usually hard to compute as their definition involves optimization aiming to find the closest states within the set. On the other hand, negativity is one of the few computable entanglement monotones, but its comparison with other correlations required further justification. Here we place negativity as part of a family of correlation measures that has a distance-based construction. We introduce a suitable distance, discuss the emerging measures and their applications, and compare them to relative entropy-based correlation quantifiers. This work is a step towards correlation measures that are simultaneously comparable and computable.
Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model

The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree. The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU).
Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

Purpose The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver. Design/methodology/approach After collecting the ultrasound images, contrast-limited adaptive histogram equalization approach (CLAHE) is applied as preprocessing, in order to enhance the visual quality of the images that helps in better segmentation. Then, adaptively regularized kernel-based fuzzy C means (ARKFCM) is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches. Findings The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost. The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient, dice coefficient, precision, Matthews correlation coefficient, f -score and accuracy. The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f -score value, which is better than the existing algorithms. Practical implications From the experimental analysis, the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm. However, the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm. Originality/value The image preprocessing is carried out using CLAHE algorithm. The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm. In this research, the proposed algorithm has advantages such as independence of clustering parameters, robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.
Towards the Categorization of Brazilian Financial Market Headlines

Financial market news portals are valuable sources of information as they hold great power over investors' decision-making processes. Due to the vast amount of text data produced by news portals, several studies have been conducted to comprehend the behavioral variations of texts and automate the categorization of short texts. However, extracting useful information that influences investors' decision-making process is not a trivial task, given that news portals use a heterogeneous and specific language for each content produced, making it challenging to generate a standard document format. This work proposes GOOSE, a solution for the cateGOrizatiOn of Short texts derived from multiple sources of information, to portray the financial market's current situation. To this end, GOOSE is based on Bidirectional Long Short-Term Memory (Bi-LSTM) and GloVe Embeddings to increase reliability in the short texts classification process. That way, GOOSE obtains data from news portals, which, once combined with a word embedding mechanism, are used as input for the Bi-LSTM to classify financial market news texts. The results obtained showed that GOOSE's efficiency in categorizing texts had an accuracy of 84% but also demonstrated the feasibility of its use in the extraction of information from financial market news portals.
The Short-Term Retention of Depth

We review research on the visual working memory for information portrayed by items arranged in depth (i.e., distance to the observer) within peri-personal space. Most items lose their metric depths within half a second, even though their identities and spatial positions are retained. The paradoxical loss of depth information may arise because visual working memory retains the depth of a single object for the purpose of actions such as pointing or grasping which usually apply to only one thing at a time.
Nonlocality of a type of multi-star-shaped quantum networks

Abstract The correlations in quantum networks have attracted strong interest due to the fact that linear Bell inequalities derived from one source are useless for characterizing multipartite correlations of general quantum networks. In this paper, a type of multi-star-shaped quantum networks are introduced and discussed. Such a network consists of three-grade nodes: the first grade is named party (node) A , the second one consists of m nodes marked B 1 , B 2 , …, B m , which are stars of A and the third one consists of m 2 nodes <?CDATA ${C}_{k}^{j}(j,k=1,2,\dots ,m)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>j</mml:mi> </mml:mrow> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>j</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> , where <?CDATA ${C}_{k}^{j}(k=1,2,\dots ,m)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>j</mml:mi> </mml:mrow> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> are stars of B j . We call such a network a three-grade m -star quantum network and denoted by SQN (3, m ), being as a natural extension of bilocal networks and star-shaped networks. We introduce and discussed the locality and strong locality of a SQN (3, m ) and derive the related nonlinear Bell inequalities, called (3, m )-locality inequalities and (3, m )-strong locality inequalities. To compare with the bipartite locality of quantum states, we define the separability of SQN (3, m ) that imply the locality and then locality of SQN (3, m ). When all of the shared states of the network are pure ones, we prove that SQN (3, m ) is nonlocal if and only if it is entangled.
Random phase retrieval approach based on difference map using VU factorization

In this paper, we proposed a random phase retrieval approach based on two differential interferograms and VU factorization. Firstly, three interferograms with arbitrary phase shifts are subtracted, and then two differential interferograms are obtained. On this basis, the phase related to the matrix is solved via VU factorization and the measured phase is obtained. Compared with the commonly used high-accuracy phase-shifting algorithms, this method does not require long-time iteration and approximate conditions, so there is no requirement for the shape and number of fringes of the interferogram. More importantly, it is an extremely stable and high-accuracy phase-shifting algorithm, which has great advantages for practical applications. Experiments and simulation analysis show that the proposed method is a random phase retrieval approach with good anti-noise and strong applicability.
Information extraction from scanned invoice images using text analysis and layout features

While storing invoice content as metadata to avoid paper document processing may be the future trend, almost all of daily issued invoices are still printed on paper or generated in digital formats such as PDFs. In this paper, we introduce the OCRMiner system for information extraction from scanned document images which is based on text analysis techniques in combination with layout features to extract indexing metadata of (semi-)structured documents. The system is designed to process the document in a similar way a human reader uses, i.e. to employ different layout and text attributes in a coordinated decision. The system consists of a set of interconnected modules that start with (possibly erroneous) character-based output from a standard OCR system and allow to apply different techniques and to expand the extracted knowledge at each step. Using an open source OCR, the system is able to recover the invoice data in 90% for English and in 88% for the Czech set.
Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms

The most common form of cancer among women in both developed and developing countries is breast cancer. The early detection and diagnosis of this disease is significant because it may reduce the number of deaths caused by breast cancer and improve the quality of life of those effected. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods have shown promise in recent years for aiding in the human expert reading analysis and improving the accuracy and reproducibility of pathology results. One significant application of CADe and CADx is for breast cancer screening using mammograms. In image processing and machine learning research, relevant results have been produced by sparse analysis methods to represent and recognize imaging patterns. However, application of sparse analysis techniques to the biomedical field is challenging, as the objects of interest may be obscured because of contrast limitations or background tissues, and their appearance may change because of anatomical variability. We introduce methods for label-specific and label-consistent dictionary learning to improve the separation of benign breast masses from malignant breast masses in mammograms. We integrated these approaches into our Spatially Localized Ensemble Sparse Analysis (SLESA) methodology. We performed 10- and 30-fold cross validation (CV) experiments on multiple mammography datasets to measure the classification performance of our methodology and compared it to deep learning models and conventional sparse representation. Results from these experiments show the potential of this methodology for separation of malignant from benign masses as a part of a breast cancer screening workflow.
Two Guarded Recursive Powerdomains for Applicative Simulation

Clocked Cubical Type Theory is a new type theory combining the power of guarded recursion with univalence and higher inductive types (HITs).This type theory can be used as a metalanguage for synthetic guarded domain theory in which one can solve guarded recursive type equations, also with negative variable occurrences, and use these to construct models for reasoning about programming languages.Combining this with HITs allows for the use of type constructors familiar from set-theory based approaches to semantics, such as quotients and finite powersets in these models.In this paper we show how to reason about the combination of finite non-determinism and recursion in this type theory.Unlike traditional domain theory which takes an ordering of programs as primitive, synthetic guarded domain theory takes the notion of computation step as primitive in the form of a modal operator.We use this extra intensional information to define two guarded recursive (finite) powerdomain constructions differing in the way non-determinism interacts with the computation steps.As an example application of these we show how to prove applicative similarity a congruence in the cases of may-and must-convergence for the untyped lambda calculus with finite non-determinism.Such results are usually proved using operational reasoning and Howe's method.Here we use an adaptation of a denotational method developed by Pitts in the context of domain theory.
Piloting a full-year, optics-based high school course on quantum computing

Quantum computing was once regarded as a mere theoretical possibility, but recent advances in engineering and materials science have brought practical quantum computers closer to reality. Currently, representatives from industry, academia, and governments across the world are working to build the educational structures needed to produce the quantum workforce of the future. Less attention has been paid to growing quantum computing capacity at the high school level. This article details work at The University of Texas at Austin to develop and pilot the first full-year high school quantum computing class. Over the course of two years, researchers and practitioners involved with the project learned several pedagogical and practical lessons that can be helpful for quantum computing course design and implementation at the secondary level. In particular, we find that the use of classical optics provides a clear and accessible avenue for representing quantum states and gate operators and facilitates both learning and the transfer of knowledge to other Science, Technology, and Engineering (STEM) skills. Furthermore, students found that exploring quantum optical phenomena prior to the introduction of mathematical models helped in the understanding and mastery of the material.
Perceived Service Quality in HRI: Applying the SERVBOT Framework

Services are intangible in nature and as a result, it is often difficult to measure the quality of the service. In the service literature, the service is usually delivered by a human to a human customer and the quality of the service is often evaluated using the SERVQUAL dimensions. An extensive review of the literature shows there is a lack of an empirical model to assess the perceived service quality provided by a social robot. Furthermore, the social robot literature highlights key differences between human service and social robots. For example, scholars have highlighted the importance of entertainment value and engagement in the adoption of social robots in the service industry. However, it is unclear whether the SERVQUAL dimensions are appropriate to measure social robot’s service quality. The paper proposes the SERVBOT model to assess a social robot’s service quality. It identifies, reliability, responsiveness, assurance, empathy, and entertainment as the five dimensions of SERVBOT. Further, the research will investigate how these five factors influence emotional engagement and future intentions to use the social robot in a concierge service setting. The model was tested using student sampling, and a total of 94 responses were collected for the study. The findings indicate empathy and entertainment value as key predictors of emotional engagement. Further, emotional engagement is a strong predictor of future intention to use a social robot in a service setting. This study is the first to propose the SERVBOT model to measure social robot’s service quality. The model provides a theoretical underpinning on the key service quality dimensions of a social robot and gives scholars and managers a method to track the service quality of a social robot. The study also extends on the literature by exploring the key factors that influence the use of social robots (i.e. emotional engagement).