Reviewerly/exHarmony huggingface dataset:
SR-MACL: Session-Based Recommendation with Multi-layer Aggregation Augmentation in Contrastive Learning

Authors:
raw_author_name: Shiwei Gao
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
raw_author_name: Yufeng Zeng
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
raw_author_name: Xiaochao Dang
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
raw_author_name: Xiaohui Dong
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

Abstract: Abstract Session-based recommendation (SBR) aims to predict the next item of interest in chronological order based on a given sequence of short-term behaviour of anonymous users. Due to the limited data available for short-term user interactions, its performance is more susceptible to data sparsity problems than traditional recommendation methods. Contrastive learning is often used to solve the data sparsity problem due to its ability to extract general features from the raw data. Existing session-based recommendation methods based on graph contrastive learning typically build graph contrastive learning by using information from other sessions to generate augmented views. While this avoids the problem that the use of dropout in traditional contrast learning methods can cause damage to the session context, it inevitably introduces irrelevant item information, which interferes with accurately modelling user interests and leads to sub-optimal model performance. To address these issues, we propose a new session recommendation method based on multi-layer aggregation augmentation contrastive learning, namely SR-MACL. In SR-MACL we construct a contrastive view by adding noise to the embedding representation and forming a contrastive embedding representation by multi-layer aggregation, which not only effectively solves the problem that traditional graph enhancement methods can destroy the context of the whole session, but also avoids the interference of irrelevant items. Experimental results on three real datasets have shown that SR-MACL can improve the accuracy of recommendation results and predict the user's next interaction more effectively.
Fast high-fidelity quantum nondemolition readout of a superconducting qubit with tunable transverse couplings

Authors:
raw_author_name: Bryan T. Gard
raw_affiliation_string: Computational and Information Sciences Directorate, U.S. Army Research Laboratory, Adelphi, Maryland 20783, USA; Hearne Institute for Theoretical Physics, Louisiana State University, Baton Rouge, Louisiana 70803, USA
raw_author_name: Kurt Jacobs
raw_affiliation_string: Computational and Information Sciences Directorate, U.S. Army Research Laboratory, Adelphi, Maryland 20783, USA; Department of Physics, University of Massachusetts at Boston, Boston, Massachusetts 02125, USA; Hearne Institute for Theoretical Physics, Louisiana State University, Baton Rouge, Louisiana 70803, USA
raw_author_name: José Aumentado
raw_affiliation_string: National Institute of Standards and Technology, 325 Broadway, Boulder, Colorado 80305, USA
raw_author_name: Raymond W. Simmonds
raw_affiliation_string: National Institute of Standards and Technology, 325 Broadway, Boulder, Colorado 80305, USA

Abstract: Dispersive readout, the current standard for measuring superconducting qubits, relies on relatively easy to engineer, static transverse coupling between a qubit and a detuned cavity mode. However, this method requires circuit design trade-offs between qubit-cavity coupling, cavity-feedline coupling, detuning, and the resultant decoherence introduced by the cavity mode. Additionally, the criteria for performing quantum nondemolition measurements, including reduced cavity photon numbers, further limits speed and fidelity. In this work, we introduce a circuit with pulse-tunable qubit-cavity and cavity-feedline couplings so that the entire qubit-measurement process can be fully optimized. Here, the core measurement process resembles a quantum gate operation between the qubit and the cavity, with the cavity acting like an ancilla, storing the qubit-state information. To fully explore time-dependent effects of the full qubit-cavity dynamics, we go beyond the rotating-wave, Jaynes-Cummings, or dispersive approximations, to develop a pulsed-measurement protocol. In addition, we propose a realistic circuit architecture that can provide a very fast, high-fidelity, QND measurement with near-unit efficiency.
VF-Mask-Net: A Visual Field Noise Reduction Method Using Neural Networks

Authors:
raw_author_name: Haogang Zhu
raw_affiliation_string: State Key Laboratory of Software Development Environment Lab, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100194, China
raw_author_name: Lei Li
raw_affiliation_string: School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China

Abstract: Visual Field (VF) measurements, crucial for diagnosing and treating glaucoma, often contain noise originating from both the instrument and subjects during the response process. This study proposes a neural network-based denoising method for VF data, obviating the need for ground truth labels or paired measurements. Using a mask-imposed VF as an input for the neural network, while the original VF serves as a training label, we evaluated performance metrics such as the accuracy, precision, and sensitivity of denoised VFs. Orthogonal experiments were also employed to assess the impact of mask number, mask structure, and replacement strategy on model accuracy. This study reveals that mask number, replacement strategy, and their interaction significantly affect the accuracy of the denoising model. Under recommended parameters, VF-Mask-Net effectively enhances the accuracy and precision of VF measurements. Furthermore, in deterioration detection tasks, denoised VFs display heightened sensitivity compared to their pre-denoising counterparts.
Research on the development of principles for designing elementary English speaking lessons using artificial intelligence chatbots

Authors:
raw_author_name: Jihee Han
raw_affiliation_string: Nammyeong Elementary School, Namhae, Republic of Korea

Abstract: Abstract The present study was conducted with the aim of developing principles for designing elementary English speaking lessons using artificial intelligence chatbots. To achieve this, design and development research methods were applied, and initial design principles and detailed guidelines were developed through a review of relevant literature. Subsequently, the design principles were modified and refined through two rounds of expert validation and usability evaluation. The research results yielded a total of 10 principles for designing elementary English speaking lessons using artificial intelligence chatbots, including: 1) principle of media selection, 2) principle of creating a learning environment, 3) principle of content restructuring, 4) principle of stimulating and sustaining interest and motivation, 5) principle of providing guidance, 6) principle of scaffolded learning support, 7) principle of individualized feedback provision, 8) principle of fostering a learning environment that supports growth and development, 9) principle of communication and collaboration, and 10) principle of learning management. Additionally, a set of 24 detailed guidelines necessary for implementing each lesson design principle was developed. Based on the research findings, the principles for designing elementary English speaking lessons using artificial intelligence chatbots, as well as the theoretical and practical implications of the study, were discussed. Finally, the limitations of the research were identified, and suggestions for future research were proposed.
DRR: Global Context-Aware Neural Network Using Disease Relationship Reasoning and Attention-Based Feature Fusion

Authors:
raw_author_name: Zhengqiang Li
raw_affiliation_string: The School of Information Science and Engineering, Yunnan University, Kunming 650504, China
raw_author_name: Xi Li
raw_affiliation_string: The School of Information Science and Engineering, Yunnan University, Kunming 650504, China
raw_author_name: Hao Li
raw_affiliation_string: The School of Information Science and Engineering, Yunnan University, Kunming 650504, China

Abstract: The prediction of future disease development based on past diagnosis records has gained significant attention due to the growing health awareness among individuals. Recent deep learning-based methods have successfully predicted disease development by establishing relationships for each diagnosis record and extracting features from a patient’s past diagnoses in chronological order. However, most of these models have ignored the connections between identified diseases and low-risk diseases, leading to bottlenecks and limitations. In addition, the extraction of temporal characteristics is also hindered by the problem of global feature forgetting. To address these issues, we propose a global context-aware net using disease relationship reasoning and attention-based feature fusion, abbreviated as DRR. Our model incorporates a disease relationship reasoning module that enhances the model’s attention to the relationship between confirmed diseases and low-risk diseases, thereby alleviating the current model’s bottlenecks. Moreover, we have established a global graph-based feature fusion module that integrates global graph-based features with temporal features, mitigating the issue of global feature forgetting. Extensive experiments were conducted on two publicly available datasets, and the experiments show that our method achieves advanced performance.
Data-driven criteria for quantum correlations

Authors:
raw_author_name: Katarzyna Roszak
raw_affiliation_string: Institute of Physics (FZU), Czech Academy of Sciences, Na Slovance 2, 182 00 Prague, Czech Republic

Abstract: We build a machine learning model to detect correlations in a three-qubit system using a neural network trained in an unsupervised manner on randomly generated states. The network is forced to recognize separable states, and correlated states are detected as anomalies. Quite surprisingly, we find that the proposed detector performs much better at distinguishing a weaker form of quantum correlations, namely, the quantum discord, than entanglement. In fact, it has a tendency to grossly overestimate the set of entangled states even at the optimal threshold for entanglement detection, while it underestimates the set of discordant states to a much lesser extent. In order to illustrate the nature of states classified as quantum correlated, we construct a diagram containing various types of states---entangled, as well as separable, both discordant and nondiscordant. We find that the near-zero value of the recognition loss reproduces the shape of the nondiscordant separable states with high accuracy, especially considering the nontrivial shape of this set on the diagram. The network architecture is designed carefully: it preserves separability, and its output is equivariant with respect to qubit permutations. We show that the choice of architecture is important to get the highest detection accuracy, much better than for a baseline model that just utilizes a partial trace operation.
Verifiably exact solution of the electronic Schrödinger equation on quantum devices

Authors:
raw_author_name: Scott E. Smart
raw_affiliation_string: Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
raw_author_name: David A. Mazziotti
raw_affiliation_string: Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA

Abstract: Quantum computers have the potential for a significant speedup of molecular computations. However, existing algorithms have limitations; quantum phase estimation (QPE) is intractable on current hardware while variational quantum eigensolvers (VQE) are dependent upon approximate wave functions without guaranteed convergence. In this paper we present an algorithm that yields verifiably exact solutions of the many-electron Schr\"odinger equation. Rather than solve the Schr\"odinger equation directly, we solve its contraction over all electrons except two, known as the contracted Schr\"odinger equation (CSE). The CSE generates a wave-function Ansatz, constructed from an iterative product of nonunitary two-body transformations, whose energy gradient with respect to the two-body operator of the current iteration vanishes if and only if the CSE is satisfied. Because the CSE implies the Schr\"odinger equation, the two-electron Ansatz provides a verifiably exact Ansatz for solving the many-electron Schr\"odinger equation. The exactness property contrasts with that of Ans\"atze built from the product of unitary two-body transformations where the gradient---the residual of the anti-Hermitian part of the CSE (ACSE)---can vanish without implying a solution of the Schr\"odinger equation. We demonstrate the algorithm on both simulators and noisy quantum computers with ${\mathrm{H}}_{2}$ dissociation and the rectangle-to-square transition in ${\mathrm{H}}_{4}$.
Optimized convergence of stochastic gradient descent by weighted averaging

Authors:
raw_author_name: Melinda Hagedorn
raw_affiliation_string: Math.-Nat. Fakultät, Heinrich Heine Univ, Düsseldorf, Germany
raw_author_name: Florian Jarre
raw_affiliation_string: Math.-Nat. Fakultät, Heinrich Heine Univ, Düsseldorf, Germany

Abstract: Under mild assumptions stochastic gradient methods asymptotically achieve an optimal rate of convergence if the arithmetic mean of all iterates is returned as an approximate optimal solution. However, in the absence of stochastic noise, the arithmetic mean of all iterates converges considerably slower to the optimal solution than the iterates themselves. And also in the presence of noise, when a termination of the stochastic gradient method after a finite number of steps is considered, the arithmetic mean is not necessarily the best possible approximation to the unknown optimal solution. This paper aims at identifying optimal strategies in a particularly simple case, the minimization of a strongly convex function with i. i. d. noise terms and termination after a finite number of steps. Explicit formulas for the stochastic error and the optimality error are derived in dependence of certain parameters of the SGD method. The aim was to choose parameters such that both stochastic error and optimality error are reduced compared to arithmetic averaging. This aim could not be achieved; however, by allowing a slight increase of the stochastic error it was possible to select the parameters such that a significant reduction of the optimality error could be achieved. This reduction of the optimality error has a strong effect on the approximate solution generated by the stochastic gradient method in case that only a moderate number of iterations is used or when the initial error is large. The numerical examples confirm the theoretical results and suggest that a generalization to non-quadratic objective functions may be possible.
Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

Authors:
raw_author_name: Dalibor Dobrilovic
raw_affiliation_string: Technical Faculty “Mihajlo Pupin” Zrenjanin, University of Novi Sad, 23000 Zrenjanin, Serbia
raw_author_name: Jasmina Pekez
raw_affiliation_string: Technical Faculty “Mihajlo Pupin” Zrenjanin, University of Novi Sad, 23000 Zrenjanin, Serbia
raw_author_name: Eleonora Desnica
raw_affiliation_string: Technical Faculty “Mihajlo Pupin” Zrenjanin, University of Novi Sad, 23000 Zrenjanin, Serbia

Abstract: The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.
Entity Management Using RDA and Wikibase: A Case Study at the National Library of Greece

Authors:
raw_author_name: Sofia Zapounidou
raw_affiliation_string: Head of the Cataloguing Department, National Library of Greece, Athens, Greece
raw_author_name: Charalampos Bratsas
raw_affiliation_string: Department of Information and Electronic Engineering, International Hellenic University, Thermi, Greece; Open Knowledge Greece, Thessaloniki, Greece

Abstract: The force driving the implementation of Linked Data (LD) technologies in libraries is the aspiration for widespread interoperability of library data and for advanced information services. Toward these goals, libraries change their data models, update their communication formats, and develop prototype applications that use new models and formats and enable entity-based cataloguing. The National Library of Greece (NLG) has been actively adapting its policies, workflows, and tools to make its data LD-ready. Within this context, NLG has developed -in cooperation with Open Knowledge Greece- an entity management prototype using RDA/RDF in a Wikibase instance. The vision is to build a shared entity management system used by all memory institutions in Greece to describe entities of Greek interest. Data is to be stored as a knowledge graph that can be exported in RDF serializations, and in legacy MARC format. This paper first examines related efforts from other National Libraries, and then outlines the steps involved in the publication of the NLG Authority File as linked data; namely dataset selection, modeling, data curation, mapping, and publication. The development of the NLG entity management prototype is also presented. The paper concludes with challenges, lessons learned, and next steps.
An attack on free speech? Examining content moderation, (de-), and (re-) platforming on American right-wing alternative social media

Authors:
raw_author_name: Brittany Shaughnessy
raw_affiliation_string: University of Florida, USA

Abstract: Contemporary research on social media looks different than it did in the late 2010s, with users facing a high-choice social media environment as new platforms emerge. Subsequently, alt-right sites have experienced a rise in users—sometimes those who have experienced content moderation by traditional social media sites. As such, scholars have investigated the impact of this content moderation (e.g. de-platforming) on users and the content posted on new alt-right platforms. This work seeks to expand extant research through analyzing a survey of Gab, Parler (now defunct), Truth Social, and Rumble users ( N = 427) who have experienced content moderation on other social media sites. While we find that those temporarily or permanently banned from traditional sites are unlikely to leave the platform altogether for a right-wing alternative social media (RWASM) site, there are active users on these sites worth studying.
Gold and Arsenic in Pyrite and Marcasite: Hydrothermal Experiment and Implications to Natural Ore-Stage Sulfides

Authors:
raw_author_name: Elena V. Kovalchuk
raw_affiliation_string: Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry (IGEM RAS), 35, Staromonetnyi per., 119017 Moscow, Russia
raw_author_name: Boris R. Tagirov
raw_affiliation_string: Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry (IGEM RAS), 35, Staromonetnyi per., 119017 Moscow, Russia
raw_author_name: Sergei E. Borisovsky
raw_affiliation_string: Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry (IGEM RAS), 35, Staromonetnyi per., 119017 Moscow, Russia
raw_author_name: Nina V. Sidorova
raw_affiliation_string: Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry (IGEM RAS), 35, Staromonetnyi per., 119017 Moscow, Russia
raw_author_name: Vsevolod Yu. Prokofiev
raw_affiliation_string: Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry (IGEM RAS), 35, Staromonetnyi per., 119017 Moscow, Russia
raw_author_name: Ilya V. Vikentyev
raw_affiliation_string: Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry (IGEM RAS), 35, Staromonetnyi per., 119017 Moscow, Russia

Abstract: Hydrothermal synthesis experiments were performed in order to quantify the states of Au and As in pyrite and marcasite. The experiments were performed at 350 °C/500 bar and 490 °C/1000 bar (pyrite–pyrrhotite buffer, C(NaCl) = 15 and 35 wt.%). The synthesis products were studied by EPMA, LA-ICP-MS, and EBSD. The EPMA was applied for simultaneous determinations of Au, As, Fe, and S, with a Au detection limit of 45–48 ppm (3σ). The analyses were performed along profiles across zonal grains. The concentrations of As and Au up to 5 wt.% and 8000 ppm, respectively, were determined in pyrite and up to 6 wt.% and 1300 ppm in marcasite. In pyrite, the Au concentration decreases with fluid salinity and temperature increases. Strong positive Au–As correlation and strong negative Au–Fe and As–S correlation were identified in pyrite. Comparison of the correlations with theoretical lines implies Au–As clustering. The cluster stoichiometry is inferred to be [AuAs10]. Most probably, As in pyrite presents in the form of clusters and in the As→S solid solution. Incorporation of Au in As-rich pyrite can be controlled by the reductive deposition mechanism. In marcasite, the concentrations of Au are not correlated with the As content. The [AuAs10] clusters enrich the {210}, {113}, and {111} pyrite faces, where the former exhibits the highest affinity to Au and As. The affinity of {110} and {100} forms to Au and As is lower. Implication of the experimental results to data for natural auriferous pyrite shows that the increase of Au content at C(As) &gt; 0.5–1 wt.% is caused by the incorporation of the Au-As clusters, but not because of the formation of Au→Fe solid solution. Therefore, the concentration of “invisible” gold in pyrite is dictated solely by the hydrothermal fluid chemistry and subsequent ore transformations.