Reviewerly/exHarmony huggingface dataset:
Real-time adaptive estimation of decoherence timescales for a single qubit

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.
Understanding New Machine Learning Architectures: Practical Generative Artificial Intelligence for Anesthesiologists

Recent advances in neural networks have given rise to generative artificial intelligence, systems able to produce fluent responses to natural questions or attractive and even photorealistic images from text prompts. These systems were developed through new network architectures that permit massive computational resources to be applied efficiently to enormous data sets. First, this review examines autoencoder architecture and its derivatives the variational autoencoder and the U-Net in annotating and manipulating images and extracting salience. This architecture will be important for applications like automated x-ray interpretation or real-time highlighting of anatomy in ultrasound images. Second, this article examines the transformer architecture in the interpretation and generation of natural language, as it will be useful in producing automated summarization of medical records or performing initial patient screening. The author also applies the GPT-3.5 algorithm to example questions from the American Board of Anesthesiologists Basic Examination and find that, under surprisingly reasonable conditions, it correctly answers more than half the questions.
Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic

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

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

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

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

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

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

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.
INFORMATE: When Are the Data?

In a recent Upstream blog post we explored where data connected to papers funded by several U.S. Federal Agencies are published. Different data sharing practices across these agencies led to very different distributions of datasets across various repositories. We used CHORUS reports that combine linked article and dataset metadata as input for that work.
Is the Tamil language Conjugation or Grafting

Language is the main tool that helps people to communicate their thoughts, emotions, likes and dislikes to others. Research about the origin of the language are often biased. Hence the origin of the language cannot be determined. In order to understand the language, linguists classify it in various ways based on its nature, character, usage, number of users etc. Among these, the languages of the world are divided into independent, conjunctive, and so on. Although many languages have appeared in the world, only a few languages have survived. Among the languages that have survived, some have lost its value as spoken language and living only as literary languages. Among the languages that appear, grow and disappear, Tamil is the most important language that appeared in ancient times and has grown with influence and is still alive today. This Tamil language, which has an ancient grammatical and literary richness and the unique feature of adapting itself according to the times, has two different linguistic elements in itself, grafting and conjugation. Pointing to the interdisciplinary nature of Tamil, which is often labeled as a hybrid language, this article is to analyse whether Tamil is a conjugation or grafting.
A design team’s perspective-mixing for shared empathic understandings

Empathic design involves two social practices – collaboration with users and intra team collaboration forshared empathic understandings. This study is focused on the latter phenomenon, aiming to characterise a social mechanism underlying it. To achieve this objective, we used a framework of perspective-mixing in empathic design, including three distinct perspectives: first, second, and third-person perspectives. With this framework, we traced how team members share and integrate individuals' user knowledge and insights to develop and apply their shared empathic understandings. We conducted conversation analysis to examine one design team's conversations over a design journey from need-finding to initial ideation to prototyping and testing. To characterise this team's perspective-mixing during their conversations, we analysed how team members took and combined the three perspectives within and between their utterances (perspective transitions). Our analysis revealed that the team used the third-person perspective as an underlying mechanism for perspective-mixing. In addition, the nature of perspective-mixing was distinct for specific design activities – the transitions involving the first and second-person perspectives were prominent during need-finding and initial ideation, while the third-person perspective was predominant during prototyping and testing. Based on these findings, we discuss what and how perspective-mixing can support intrateam collaboration for shared empathic understandings.
Physics-informed differentiable method for piano modeling

Numerical emulations of the piano have been a subject of study since the early days of sound synthesis. High-accuracy sound synthesis of acoustic instruments employs physical modeling techniques which aim to describe the system’s internal mechanism using mathematical formulations. Such physical approaches are system-specific and present significant challenges for tuning the system’s parameters. In addition, acoustic instruments such as the piano present nonlinear mechanisms that present significant computational challenges for solving associated partial differential equations required to generate synthetic sound. In a nonlinear context, the stability and efficiency of the numerical schemes when performing numerical simulations are not trivial, and models generally adopt simplifying assumptions and linearizations. Artificial neural networks can learn a complex system’s behaviors from data, and their application can be beneficial for modeling acoustic instruments. Artificial neural networks typically offer less flexibility regarding the variation of internal parameters for interactive applications, such as real-time sound synthesis. However, their integration with traditional signal processing frameworks can overcome this limitation. This article presents a method for piano sound synthesis informed by the physics of the instrument, combining deep learning with traditional digital signal processing techniques. The proposed model learns to synthesize the quasi-harmonic content of individual piano notes using physics-based formulas whose parameters are automatically estimated from real audio recordings. The model thus emulates the inharmonicity of the piano and the amplitude envelopes of the partials. It is capable of generalizing with good accuracy across different keys and velocities. Challenges persist in the high-frequency part of the spectrum, where the generation of partials is less accurate, especially at high-velocity values. The architecture of the proposed model permits low-latency implementation and has low computational complexity, paving the way for a novel approach to sound synthesis in interactive digital pianos that emulates specific acoustic instruments.
The Use of Pronoun Interpretation Biases in L1 Spanish: The Role of Language Proficiency, Exposure, and Use in Heritage Speakers

Abstract In an offline sentence comprehension task, we test the interpretation biases for null and overt pronouns and anaphoric and cataphoric pronouns in heritage speakers ( HS s) whose first language (L1) is Spanish (L2 English). Previous studies have shown that Spanish HS s can interpret overt anaphoric pronouns differently than monolingual speakers. A question that remains unexplored is how HS s interpret cataphoric pronouns and how L1 experience affects pronoun resolution biases in these individuals. We recruited 48 HS s and 48 monolingual Spanish speakers. The results show that pronoun interpretation in HS s differs from monolingual biases, with HS s demonstrating a higher subject antecedent preference for anaphoric and cataphoric pronouns. The results further show that a continuous measure of language proficiency, exposure, and use may contribute to explaining pronoun interpretation biases in heritage Spanish. We discuss the findings in light of recent research demonstrating that language experience can contribute to shape discourse patterns.
End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Abstract Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.
Wansing's bi-intuitionistic logic: semantics, extension and unilateralisation

The well-known algebraic semantics and topological semantics for intuitionistic logic (Int) is here extended to Wansing's bi-intuitionistic logic (2Int). The logic 2Int is also characterised by a quasi-twist structure semantics, which leads to an alternative topological characterisation of 2Int. Later, notions of Fregean negation and of unilateralisation are proposed. The logic 2Int is extended with a ‘Fregean negation’ connective ∼, obtaining 2Int∼, and it is showed that the logic N4⋆ (an extension of Nelson's paraconsistent logic) results to be the unilateralisation of 2Int∼ via ∼. The logic 2Int∼ is also characterised by a Kripke-style semantics, a twist structure semantics and a topological semantics.