Reviewerly/exHarmony huggingface dataset:
Geometrical Structure of Bifurcations during Spatial Decision-Making

Authors:
raw_author_name: Iain D. Couzin
raw_affiliation_string: Department of Biology, University of Konstanz, 78464 Konstanz, Germany; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany

Abstract: In the study, authors reveal new geometric principles of bifurcations during spatiotemporal decision-making and demonstrate that a non-Euclidean neural representation of space simplifies decision-making in multi-target environments.
Using quantum annealing to design lattice proteins

Authors:
raw_author_name: Anders Irbäck
raw_affiliation_string: Computational Biology & Biological Physics, Centre for Environmental and Climate Science (CEC), Lund University, 223 62 Lund, Sweden
raw_author_name: Lucas Knuthson
raw_affiliation_string: Computational Biology & Biological Physics, Centre for Environmental and Climate Science (CEC), Lund University, 223 62 Lund, Sweden
raw_author_name: Sandipan Mohanty
raw_affiliation_string: Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, D-52425 Jülich, Germany
raw_author_name: Carsten Peterson
raw_affiliation_string: Computational Biology & Biological Physics, Centre for Environmental and Climate Science (CEC), Lund University, 223 62 Lund, Sweden

Abstract: Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups: hydrophobic (H) and polar (P). Using a set of 22 HP sequences with up to 64 amino acids, we demonstrated the fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure, implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30-64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the QPU, which confines us to sizes $\le$20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schr\"odinger equation. We find that the simulated success rates in the presence of control noise semi-quantitatively reproduce the modest pure QPU results for larger chains.
Bayesian estimation of the Kullback-Leibler divergence for categorical systems using mixtures of Dirichlet priors

Authors:
raw_author_name: Francesco Camaglia
raw_affiliation_string: Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université and Université de Paris, 75005 Paris, France
raw_author_name: Ilya Nemenman
raw_affiliation_string: Department of Physics, Department of Biology, and Initiative for Theory and Modeling of Living Systems, Emory University, Atlanta, Georgia 30322, USA
raw_author_name: Thierry Mora
raw_affiliation_string: Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université and Université de Paris, 75005 Paris, France
raw_author_name: Aleksandra M. Walczak
raw_affiliation_string: Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université and Université de Paris, 75005 Paris, France

Abstract: In many applications in biology, engineering, and economics, identifying similarities and differences between distributions of data from complex processes requires comparing finite categorical samples of discrete counts. Statistical divergences quantify the difference between two distributions. However, their estimation is very difficult and empirical methods often fail, especially when the samples are small. We develop a Bayesian estimator of the Kullback-Leibler divergence between two probability distributions that makes use of a mixture of Dirichlet priors on the distributions being compared. We study the properties of the estimator on two examples: probabilities drawn from Dirichlet distributions and random strings of letters drawn from Markov chains. We extend the approach to the squared Hellinger divergence. Both estimators outperform other estimation techniques, with better results for data with a large number of categories and for higher values of divergences.
Multi-source adaptive thresholding adaboost with application to virtual metrology

Authors:
raw_author_name: Yifan Xie
raw_affiliation_string: Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA
raw_author_name: Tianhui Wang
raw_affiliation_string: Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA
raw_author_name: Myong K. Jeong
raw_affiliation_string: Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA
raw_author_name: Gyeong Taek Lee
raw_affiliation_string: Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA; Department of Mechanical, Smart, and Industrial Engineering, Gachon University, Seongnam, Republic of Korea

Abstract: To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks.
Real-time adaptive estimation of decoherence timescales for a single qubit

Authors:
raw_author_name: Muhammad Junaid Arshad
raw_affiliation_string: SUPA, Institute of Photonics and Quantum Sciences, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom
raw_author_name: Ben Haylock
raw_affiliation_string: SUPA, Institute of Photonics and Quantum Sciences, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom
raw_author_name: Daniel White
raw_affiliation_string: SUPA, Institute of Photonics and Quantum Sciences, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom
raw_author_name: Benjamin Griffiths
raw_affiliation_string: Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom
raw_author_name: Jason Smith
raw_affiliation_string: Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom
raw_author_name: Yoann Altmann
raw_affiliation_string: Institute of Signals, Sensors and Systems, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom
raw_author_name: Erik M. Gauger
raw_affiliation_string: SUPA, Institute of Photonics and Quantum Sciences, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom

Abstract: Characterizing the time over which quantum coherence survives is critical for any implementation of quantum bits, memories, and sensors. The usual method for determining a quantum system's decoherence rate involves a suite of experiments probing the entire expected range of this parameter, and extracting the resulting estimation in postprocessing. Here we present an adaptive multiparameter Bayesian approach, based on a simple analytical update rule, to estimate the key decoherence timescales (${T}_{1}$, ${T}_{2}^{\ensuremath{\ast}}$, and ${T}_{2}$) and the corresponding decay exponent of a quantum system in real time, using information gained in preceding experiments. This approach reduces the time required to reach a given uncertainty by a factor up to an order of magnitude, depending on the specific experiment, compared to the standard protocol of curve fitting. A further speedup of a factor approximately $2$ can be realized by performing our optimization with respect to sensitivity as opposed to variance.
Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic

Authors:
raw_author_name: Maëlick Claes
raw_affiliation_string: Epiroc AB, Luleå, Sweden
raw_author_name: Umar Farooq
raw_affiliation_string: Mashal, Helsinki, Finland
raw_author_name: Iflaah Salman
raw_affiliation_string: School of Engineering Science, Lappeenranta-Lahti University of Technology, Mukkulankatu 19 (Lahti Campus), Lahti, Finland
raw_author_name: Raija Halonen
raw_affiliation_string: M3S Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

Abstract: Abstract With the outbreak of the COVID-19 pandemic, researchers have studied how people reacted on social media during the pandemic. Sentiment analysis has been leveraged to gain insight. However, much of the research conducted on both sentiment analysis and social media analysis of COVID-19 often focuses on widespread languages, such as English and Chinese. This is partly due to the scarcity of resources for natural language processing and sentiment analysis for morphologically complex and less prevalent languages such as Finnish. This paper aims to analyze sentiments on Twitter in the Finnish language during the COVID-19 pandemic. We manually annotate with sentiments a random sample of 1943 tweets about COVID-19 in Finnish. We use it to build binomial and multinomial logistic regression models with Lasso penalty by exploiting ngrams and two existing sentiment lexicons. We also build two similar models using an existing (pre-COVID-19) Twitter dataset for comparison. The best-performing model for the Finnish language is then used to determine the trends of positive, negative, and neutral opinions on a collection of tweets in Finnish extracted between April 21 and June 18, 2020. The best sentiment polarity prediction model for the Finnish language attain 0.785 AUC, 0.710 balanced accuracy, and 0.723 macro-averaged F1 for predicting positive and negative polarity (binomial classification), and 0.667 AUC, 0.607 balanced accuracy, and 0.475 F1 when adding neutral tweets (multinomial classification). On the other hand, the pre-COVID-19 model trained on the same number of tweets exhibits higher accuracy for the multinomial model (0.687 balanced accuracy, and 0.588 F1). We hypothesize that this loss of performance is due to the COVID-19 context that makes sentiment analysis of neutral tweets more difficult for the machine learning algorithm to predict. Running the model on all the extracted Finnish tweets, we observe a decrease in negativity and an increase in positivity over the observed time as the Finnish government lifts restrictions. Our results show that applying an existing general-purpose sentiment analyzer on tweets that are domain-specific, such as COVID-19, provides lower accuracy. More effort in the future needs to be invested in using and developing sentiment analysis tools tailored to their application domain when conducting large-scale social media analysis of specific medical issues, such as a global pandemic.
A machine learning approach for early identification of patients with severe imported malaria

Authors:
raw_author_name: Francesco Rinaldi
raw_affiliation_string: Department of Mathematics “Tullio Levi-Civita”, University of Padova, Via Trieste, 63, 35131, Padua, Italy

Abstract: The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting.This is a single-centre cross-sectional study, patients with confirmed malaria, consecutively hospitalized to the Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy from January 2007 to December 2020, were recruited. Different ML approaches were used to perform the analysis of this dataset: support vector machines, random forests, feature selection approaches and clustering analysis.A total of 259 patients with malaria were enrolled, 89.5% patients were male with a median age of 39 y/o. In 78.3% cases, Plasmodium falciparum was found. The patients were classified as severe malaria in 111 cases. From ML analyses, four parameters, AST, platelet count, total bilirubin and parasitaemia, are associated to a negative outcome. Interestingly, two of them, aminotransferase and platelet are not included in the current list of World Health Organization (WHO) criteria for defining severe malaria.In conclusion, the application of ML algorithms as a decision support tool could enable the clinicians to predict the clinical outcome of patients with malaria and consequently to optimize and personalize clinical allocation and treatment.
Learning to Detect Attended Objects in Cultural Sites with Gaze Signals and Weak Object Supervision

Authors:
raw_author_name: Michele Mazzamuto
raw_affiliation_string: University of Catania, Catania, Italy and Next Vision S.R.L, Catania, Italy
raw_author_name: Francesco Ragusa
raw_affiliation_string: University of Catania, Catania, Italy and Next Vision S.R.L, Catania, Italy
raw_author_name: Antonino Furnari
raw_affiliation_string: University of Catania, Catania, Italy and Next Vision S.R.L, Catania, Italy
raw_author_name: Giovanni Maria Farinella
raw_affiliation_string: University of Catania, Catania, Italy and Next Vision S.R.L, Catania, Italy

Abstract: Cultural sites such as museums and monuments are popular tourist destinations worldwide. Visitors come to these places to learn about the cultures, histories and arts of a particular region or country. However, for many cultural sites, traditional visiting approaches are limited and may fail to engage visitors. To enhance visitors’ experiences, previous works have explored how wearable devices can be exploited in this context. Among the many functions that these devices can offer, understanding which artwork or detail the user is attending to is fundamental to provide additional information on the observed artworks, understand the visitor’s tastes and provide recommendations. This motivates the development of algorithms for understanding visitor attention from egocentric images. We considered the attended object detection task, which involves detecting and recognizing the object observed by the camera wearer, from an input RGB image and gaze signals. To study the problem, we collect a dataset of egocentric images collected by subjects visiting a museum. Since collecting and labeling data in cultural sites for real applications is a time-consuming problem, we present a study comparing unsupervised, weakly supervised, and fully supervised approaches for attended object detection. We evaluate the considered approaches on the collected dataset, assessing also the impact of training models on external datasets such as COCO and EGO-CH. The experiments show that weakly supervised approaches requiring only a 2D point label related to the gaze can be an effective alternative to fully supervised approaches for attended object detection. To encourage research on the topic, we publicly release the code and the dataset at the following url: https://iplab.dmi.unict.it/EGO-CH-Gaze/.
Simulation-based mathematical learning for higher education students from heterogeneous backgrounds

Authors:
raw_author_name: Angel A. Juan
raw_affiliation_string: Department of Applied Statistics and Operational Research, Universitat Politecnica de Valencia, Alcoy, Spain; Department of Computer Science, Universitat Oberta de Catalunya, Barcelona, Spain

Abstract: This paper explores the use of simulation-based training for mathematical learning in undergraduate and graduate mathematics, science, and engineering courses. Simulation-based training offers the advantages of active learning and inquiry-based learning techniques. Furthermore, it provides extensive flexibility, ranging from user-level usage of simulations to the modification or creation of new possibilities by the student, thus engaging different cognitive levels to achieve the learning objectives. This is particularly interesting in groups consisting of students from diverse backgrounds and levels, due to factors such as their international origin or varying prior education, especially in interdisciplinary Master's degree programmes. Additionally, in online or blended environments (which have become widespread during the last years), simulation-based learning has the advantage of granting students a certain degree of autonomy, which can, to some extent, compensate for the absence of the instructor's physical presence.
Multi-channels Prototype Contrastive Learning with Condition Adversarial Attacks for Few-shot Event Detection

Authors:
raw_author_name: Shengwei Tian
raw_affiliation_string: College of Software Engineering, Xinjiang University, Urumqi, 830008, Xinjiang, China
raw_author_name: Long Yu
raw_affiliation_string: Internet Information Center, Xinjiang University, Urumqi, 830049, Xinjiang, China
raw_author_name: Qimeng Yang
raw_affiliation_string: College of Software Engineering, Xinjiang University, Urumqi, 830008, Xinjiang, China

Abstract: Abstract Few-shot Event Detection (FSED) is a sub-task of Event Detection that aims to accurately identify event types with limited training instances and enable smooth transfer to newly-emerged event types. Recently, the dominant works have used the prototypical network to accomplish this task and employ contrastive learning to alleviate the issue of semantically-close categories. Nevertheless, these methods still suffer from two serious problems: (1) inadequate learning of prototype representations resulting from limited training data; (2) hard-easy sample imbalance and categories imbalance caused by the large number of non-trigger word("O" tags) in the token-level classification task. To address the problems, this paper proposes the Multi-channels Prototype and Contrastive learning method with Conditional Adversarial attack, which introduces the improved multi-channels prototype and contrastive networks to alleviate the categories and hard-easy samples imbalance. Moreover, we devise a constrained adversarial attack to improve the problem of limited training data. Extensive experimental results show that our model performs better than other FSED methods. All the code and data will be available for online public access.
Chaos based Medical Image Encryption Techniques: A Comprehensive Review and Analysis

Authors:
raw_author_name: Mohit Dua
raw_affiliation_string: Department of Computer Engineering, National Institute of Technology, Kurukshetra, India

Abstract: In the past two decades, we have seen the proliferation of Internet and its applications. The information exchange happens via images, videos, texts, audio clips, etc. Medical experts use images as the most common form of information exchange such as X-rays, MRI scans, CT scans, etc. These scanned images contain confidential information about a patient. With images being the most attractive mode for medical information exchange, it becomes the target of many security attacks. Many researchers have put out their best efforts to protect the images from these attacks by proposing several techniques. Despite all efforts, many cryptosystems still compromise with one or more key attributes like efficiency or time required for them to be top-notch. Along with an appreciation of work, careful inspection is also required that can influence the future work and make them better. Hence, this survey aims to summarize and analyze the existing chaos-based medical image encryption techniques introduced in the last five years. The paper starts with a brief introduction about the basic chaos maps, security parameters, and then, progresses to analysis of the encryption algorithms based on categories followed by comparative study and open security issues.
Digital competence and students’ engagement: a comprehensive analysis of smartphone utilization, perceived autonomy and formal digital learning as mediators

Authors:
raw_author_name: Imdadullah Hidayat-Ur-Rehman
raw_affiliation_string: MIS Department, Faculty of Business Administration, University of Tabuk, Tabuk, Saudi Arabia

Abstract: Purpose The integration of digital technologies into education has brought about a profound transformation, fundamentally reshaping the learning landscape. The purpose of this study is to underscore the importance of investigating the factors influencing students’ engagement (SE) in this evolving digital era, particularly within formal digital learning environments. To address this need, the study is grounded in self-determination theory (SDT) and presents a comprehensive model comprising interconnected elements: digital competence (DC), smartphone use (SPU), perceived autonomy (PA), digital formal learning (DFL) and SE. Design/methodology/approach The research conducted an investigation within Saudi Arabian universities, collecting a robust data set of 392 cases. This data set underwent rigorous analysis to validate the proposed model. To untangle the intricate relationships within the framework, the study used partial least squares structural equation modelling. Given the distinct dimensions of the two constructs under study, the researcher used a disjoint two-stage approach to establish reflective-formative higher-order constructs (HOC). Findings The findings revealed that digital literacy and digital skills (DS) constitute the foundational constituents of DC. Simultaneously, the study identified facilitation, distraction and connectedness as integral components of SPU. Importantly, the study established that DC, SPU, PA and DFL significantly influence SE. Furthermore, the research illuminated the mediating roles played by SPU, PA and DFL in the complex relationship between DC and SE. Originality/value This study advances the literature by delineating the dynamic interplay between DC, SPU and SE in digital learning. It extends SDT within educational contexts, emphasizing the role of internal motivations and DS. Methodologically, it innovates through reflective-formative HOCs, deepening the analysis of complex educational constructs. Managerially, it guides institutions in enhancing DC and integrating smartphones effectively into learning, advocating for tailored strategies to foster engaging and autonomous digital learning environments, thereby enriching both theoretical understanding and practical application in education.