Reviewerly/exHarmony huggingface dataset:
Learned Harmonic Mean Estimation of the Marginal Likelihood with Normalizing Flows

Computing the marginal likelihood (also called the Bayesian model evidence) is an important task in Bayesian model selection, providing a principled quantitative way to compare models. The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood. The learned harmonic mean estimator learns an importance sampling target distribution that approximates the optimal distribution. While the approximation need not be highly accurate, it is critical that the probability mass of the learned distribution is contained within the posterior in order to avoid the exploding variance problem. In previous work, a bespoke optimization problem is introduced when training models in order to ensure this property is satisfied. In the current article, we introduce the use of normalizing flows to represent the importance sampling target distribution. A flow-based model is trained on samples from the posterior by maximum likelihood estimation. Then, the probability density of the flow is concentrated by lowering the variance of the base distribution, i.e., by lowering its “temperature”, ensuring that its probability mass is contained within the posterior. This approach avoids the need for a bespoke optimization problem and careful fine tuning of parameters, resulting in a more robust method. Moreover, the use of normalizing flows has the potential to scale to high dimensional settings. We present preliminary experiments demonstrating the effectiveness of the use of flows for the learned harmonic mean estimator. The harmonic code implementing the learned harmonic mean, which is publicly available, has been updated to now support normalizing flows.
Redox-based ion-gating reservoir consisting of (104) oriented LiCoO2 film, assisted by physical masking

Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO3 as the channel are not sufficient due to irreversible Li+ trapping in the WO3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO2 ⟺ Li1-xCoO2 + xLi+ + xe-) with the insertion and desertion of Li+. The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO3, which changes are due to the nonlinear and reversible electrical response of LiCoO2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.
Qudit Entanglers Using Quantum Optimal Control

We study the generation of two-qudit entangling quantum logic gates using two techniques in quantum optimal control. We take advantage of both continuous, Lie algebraic control and digital, Lie group control. In both cases, the key is access to a time-dependent Hamiltonian, which can generate an arbitrary unitary matrix in the group SU(d2). We find efficient protocols for creating high-fidelity entangling gates. As a test of our theory, we study the case of qudits robustly encoded in nuclear spins of alkaline earth atoms and manipulated with magnetic and optical fields, with entangling interactions arising from the well-known Rydberg blockade. We applied this in a case study based on a d=10 dimensional qudit encoded in the I=9/2 nuclear spin in 87Sr, controlled through a combination of nuclear spin resonance, a tensor ac-Stark shift, and Rydberg dressing, which allows us to generate an arbitrary symmetric entangling two-qudit gate, such as CPhase. Our techniques can be used to implement qudit entangling gates for any 2≤d≤10 encoded in the nuclear spin. We also studied how decoherence due to the finite lifetime of the Rydberg states affects the creation of the CPhase gate and found, through numerical optimization, a fidelity of 0.9985, 0.9980, 0.9942, and 0.9800 for d=2, d=3, d=5, and d=7, respectively. This provides a powerful platform to explore the various applications of quantum information processing of qudits, including metrological enhancement with qudits, quantum simulation, universal quantum computation, and quantum error correction.3 MoreReceived 22 December 2022Accepted 30 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040333Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum information processingQuantum information theoryQuantum Information, Science & Technology
Quantifying measurement-induced quantum-to-classical crossover using an open-system entanglement measure

The impact of measurements on the entanglement of mixed states and stochastic pure-state trajectories is compared. Whereas at long times the response is different, at intermediate times the two descriptions respond similarly in terms of a coherence length whose dependence on the measurement strength is explained by a cascade of underdamped-to-overdamped transitions.
Lightweight Privacy-Preserving Remote User Authentication and Key Agreement Protocol for Next-Generation IoT-Based Smart Healthcare

The advancement and innovations in wireless communication technologies including the Internet of Things have massively changed the paradigms of health-based services. In particular, during the COVID-19 pandemic, the trends of working from home have been promoted. Wireless body area network technology frameworks help sufferers in remotely obtaining scientific remedies from physicians through the Internet without paying a visit to the clinics. IoT sensor nodes are incorporated into the clinical device to allow health workers to consult the patients’ fitness conditions in real time. Insecure wireless communication channels make unauthorized access to fitness-related records and manipulation of IoT sensor nodes attached to the patient’s bodies possible, as a result of security flaws. As a result, IoT-enabled devices are threatened by a number of well-known attacks, including impersonation, replay, man-in-the-middle, and denial-of-service assaults. Modern authentication schemes do solve these issues, but they frequently involve challenging mathematical concepts that raise processing and transmission costs. In this paper, we propose a lightweight, secure, and efficient symmetric key exchange algorithm and remote user authentication scheme. Our research proposal presents a successful privacy-protecting method for remote users and provides protection against known attacks. When compared to conventional options, this technique significantly reduces calculation costs by up to 37.68% and transmission costs by up to 32.55%.
The Cost of Nonconvexity in Deterministic Nonsmooth Optimization

We study the impact of nonconvexity on the complexity of nonsmooth optimization, emphasizing objectives such as piecewise linear functions, which may not be weakly convex. We focus on a dimension-independent analysis, slightly modifying a 2020 black-box algorithm of Zhang-Lin-Jegelka-Sra-Jadbabaie that approximates an ϵ-stationary point of any directionally differentiable Lipschitz objective using [Formula: see text] calls to a specialized subgradient oracle and a randomized line search. Seeking by contrast a deterministic method, we present a simple black-box version that achieves [Formula: see text] for any difference-of-convex objective and [Formula: see text] for the weakly convex case. Our complexity bound depends on a natural nonconvexity modulus that is related, intriguingly, to the negative part of directional second derivatives of the objective, understood in the distributional sense. Funding: This work was supported by the National Science Foundation [Grant DMS-2006990].
Parametric Testing of EQTransformer’s Performance against a High-Quality, Manually Picked Catalog for Reliable and Accurate Seismic Phase Picking

Abstract This study evaluates EQTransformer, a deep learning model, for earthquake detection and phase picking using seismic data from the Southern Alps, New Zealand. Using a robust, independent dataset containing more than 85,000 manual picks from 13 stations spanning almost nine years, we assess EQTransformer’s performance and limitations in a practical application scenario. We investigate key parameters such as overlap and probability threshold and their influences on detection consistency and false positives, respectively. EQTransformer’s probability outputs show a limited correlation with pick accuracy, emphasizing the need for careful interpretation. Our analysis of illustrative signals from three seismic networks highlights challenges of consistently picking first arrivals when reflected or refracted phases are present. We find that an overlap length of 55 s balances detection consistency and computational efficiency, and that a probability threshold of 0.1 balances detection rate and false positives. Our study thus offers insights into EQTransformer’s capabilities and limitations, highlighting the importance of parameter selection for optimal results.
Global Seismic Networks Operated by the U.S. Geological Survey

Abstract The U.S. Geological Survey (USGS) Global Seismographic Network (GSN) Program operates two thirds of the GSN, a network of state-of-the-art, digital seismological and geophysical sensors with digital telecommunications. This network serves as a multiuse scientific facility and a valuable resource for research, education, and monitoring. The other one third of the GSN is funded by the National Science Foundation (NSF), and the operations of this component are overseen by EarthScope. This collaboration between the USGS, EarthScope, and NSF has allowed for the development and operations of the GSN to be a truly multiuse network that provides near real-time open access data, facilitating fundamental discoveries by the Earth science community, supporting the earthquake hazards mission of the USGS, benefitting tsunami monitoring by the National Oceanic and Atmospheric Administration, and contributing to nuclear test monitoring and treaty verification. In this article, we describe the installation and evolution of the seismic networks operated by the USGS that ultimately led to the USGS portion of the GSN (100 stations under network codes IU, IC, and CU) as they are today and envision technological advances and opportunities to further improve the utility of the network in the future. This article focuses on the USGS-operated component of the GSN; a companion article on the GSN stations funded by the NSF and operated by the Cecil and Ida Green Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California at San Diego by Davis et al. (2023) appears in this volume.
Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques

Due to the importance of social networking in our lives today, online impersonations and fake accounts have become very common. Machine-learning-based techniques have been proposed to combat fake users in several studies. Detecting fake users is a crucial problem. Various machine learning algorithms, such as Random Forests, Support Vector Machines, and Neural Networks, are applied to detect these accounts. To determine which model produces the most accurate results and which algorithm yields the best results, the accuracy and performance of all three models were tested and compared. RF model was observed to be the most accurate at predicting fake accounts in the study.
Data Augmentation for Sample Efficient and Robust Document Ranking

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this paper, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.
Focused Issue on Digital Library Challenges to Support the Open Science Process

Open Science is the broad term that involves several aspects aiming to remove the barriers for sharing any kind of output, resources, methods or tools, at any stage of the research process ( https://book.fosteropenscience.eu/en/ ). The Open Science process is a set of transparent research practices that help to improve the quality of scientific knowledge and are crucial to the most basic aspects of the scientific process by means of the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Thanks to research transparency and accessibility, we can evaluate the credibility of scientific claims and make the research process reproducible and the obtained results replicable. In this context, digital libraries play a pivotal role in supporting the Open Science process by facilitating the storage, organization, and dissemination of research outputs, including open access publications and open data. In this focused issue, we invited researchers to discuss innovative solutions, also related to technical challenges, about the identifiability of digital objects as well as the use of metadata and ontologies in order to support replicable and reusable research, the adoption of standards and semantic technologies to link information, and the evaluation of the application of the FAIR principles.
A Black Box Comparison of Machine Learning Reverse Image Search for Cybersecurity OSINT Applications

Machine learning algorithms for reverse image search (a subset of open source intelligence or OSINT) provide a free, useful tool for determining the content of an image, where and when it was captured, and, in some cases, whether it has been digitally modified. Using a test data set of 24 images, we compared the performance of reverse image search for Google, Bing, and Yandex. Our black box experimental results are presented for three different categories of images (uncluttered images, images with significant background clutter, and facial recognition). The total number of correct images was highest for Google (65%), while Bing (55%) and Yandex (50%) yielded different results. Google was also the best at identifying cluttered, uncluttered, and facial images. We compare these results with previous studies and review how relative performance has changed over time. Accurate recognition rates for all reverse search platforms tested were higher for original images not previously uploaded, as opposed to images used in earlier studies. We validate our results using exchangeable image file format (EXIF) data and error level analysis (ELA) of selected images. Based on these results, best practices for OSINT image investigation are proposed.
Kernel-Based Independence Tests for Causal Structure Learning on Functional Data

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert-Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.
Anomaly Detection with Sub-Extreme Values: Health Provider Billing

Abstract Anomaly detection within the context of healthcare billing requires a method or algorithm which is flexible to the practicalities and requirements of manual case review, the volumes and associated effort of which can determine whether anomalous output is ultimately actioned or not. In this paper, we apply a modified version of a previously introduced anomaly detection algorithm to address this very issue by enacting refined targeting capability based on the identification of sub-extreme anomalies. By balancing the anomaly identification process with appropriate threshold setting tailored to each sample health provider discipline, it is shown that final candidate volumes are controlled with greater accuracy and sensitivity. A comparison with standard local outlier factor (LOF) scores is included for benchmark purposes.
LLMs Performance on Vietnamese High School Biology Examination

Large Language Models (LLMs) have received significant attention due to their potential to transform the field of education and assessment through the provision of automated responses to a diverse range of inquiries. The objective of this research is to examine the efficacy of three LLMs - ChatGPT, BingChat, and Bard - in relation to their performance on the Vietnamese High School Biology Examination dataset. This dataset consists of a wide range of biology questions that vary in difficulty and context. By conducting a thorough analysis, we are able to reveal the merits and drawbacks of each LLM, thereby providing valuable insights for their successful incorporation into educational platforms. This study examines the proficiency of LLMs in various levels of questioning, namely Knowledge, Comprehension, Application, and High Application. The findings of the study reveal complex and subtle patterns in performance. The versatility of ChatGPT is evident as it showcases potential across multiple levels. Nevertheless, it encounters difficulties in maintaining consistency and effectively addressing complex application queries. BingChat and Bard demonstrate strong performance in tasks related to factual recall, comprehension, and interpretation, indicating their effectiveness in facilitating fundamental learning. Additional investigation encompasses educational environments. The analysis indicates that the utilization of BingChat and Bard has the potential to augment factual and comprehension learning experiences. However, it is crucial to acknowledge the indispensable significance of human expertise in tackling complex application inquiries. The research conducted emphasizes the importance of adopting a well-rounded approach to the integration of LLMs, taking into account their capabilities while also recognizing their limitations. The refinement of LLM capabilities and the resolution of challenges in addressing advanced application scenarios can be achieved through collaboration among educators, developers, and AI researchers.
Moderating manipulation: Demystifying extremist tactics for gaming the (regulatory) system

Abstract Due to its ease of scalability and broad applicability, the use of artificial intelligence (AI) and machine learning in platform management has gained prominence. This has led to widespread debates about the use of deplatforming as the default tool for repeated or severe violations of terms or service. But technologically deterministic approaches are not infallible and can be predictable based on their actions. This opens the door for manipulation of media content and technological affordances to become tactical options for actors seeking to subvert regulation. Existing discussions often neglect the topic of manipulation of content, algorithms, or platform affordances as a primary aspect of the strategies used by extremists in relation to the difficulties of moderation from a policy perspective. This study argues that it is essential to understand how extremists and conspiracy theorists use manipulation tactics to ‘game’ the current policy, regulatory and legislative systems of content moderation. Developing approaches that attend to manipulation as a strategy and focus on platform and context‐specific tactics will generate more effective policies, platform rules, AI developments and moderation procedures. This study analyses and demystifies three primary tactics, which the authors categorize as numerology, borderlands and merchandising, regularly used by extremists online in their strategies to ‘game’ content moderation. We provide case examples from a variety of ideologies including far‐right, QAnon and male supremacism to highlight the tactics rather than ideological nature of such manipulation. We conclude with a discussion of how demystification processes could be incorporated into content moderation settings. This study contributes new insights about evasion tactics to the content moderation discussion and expands current understanding of how platforms can develop sociotechnical remedial measures.
Leveraging blockchain for energy transition in urban contexts

This article explores the potential of leveraging blockchain technology to facilitate energy transition within urban settings. It explores three innovative market models—peer-to-peer, community self-consumption, and transactive energy—which hold promise for a shift in (local) electricity trading due to decentralized and digital transactional characteristics. Utilizing a scenario building framework, this research scrutinizes these market models, their corresponding trading mechanisms, and the advantages and disadvantages of implementing blockchain technology. The results provide valuable insights into investment necessities, market democratization, service quality and reliability, urban governance, civic engagement and citizenry welfare. Consequently, this study offers a novel conceptualization of market models, laying the groundwork for a systematic understanding of blockchain’s potentiality in ecosystem governance in the context of energy transition.