Reviewerly/exHarmony huggingface dataset:
EQCCT: A Production-Ready Earthquake Detection and Phase-Picking Method Using the Compact Convolutional Transformer

We propose to implement a compact convolutional transformer (CCT) for picking the earthquake phase arrivals (EQCCT). The proposed method consists of two branches, with each of them responsible for picking the arrival times of the P- or S-wave phases. We use the STEAD dataset to train and validate the proposed EQCCT algorithm. We split the STEAD dataset into 85% for training, 5% for validation, and 10% for testing To facilitate the training process, we implement several data augmentation strategies to the training set by adding Gaussian noise, randomly shifting the waveforms, adding a second earthquake to the input window, and dropping one or two channels from the seismogram in the STEAD dataset. As a result, the EQCCT model outperforms both EQTransformer and PhaseNet, the two most popular deep-learning-based phase-picking methods. Considering the true positive criterion as the picked phases arriving within 0.5 s of the reference times, the EQCCT achieves the lowest mean absolute error (MAE) compared to the EQTransformer and PhaseNet methods for the STEAD, Japanese, Instance and Texas datasets. Our EQCCT network also demonstrates superior performance in other metrics such as precision, recall, and F1 score. We apply the pre-trained model to three independent datasets (not included in the training set), i.e., the Japanese, Texas, and Instance datasets, and achieve higher picking accuracy than the EQTransformer and the PhaseNet in terms of various statistical metrics, demonstrating a stronger robustness and generalization ability of the EQCCT. The real-time application of EQCCT in the Texas Seismological Network (TexNet) further demonstrates its production-ready performance in terms of detection and phase-picking accuracy.
MedNeXt: Transformer-Driven Scaling of ConvNets for Medical Image Segmentation

There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and consequently, are easily trainable to high performance. Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet by mirroring Transformer blocks. In this work, we improve upon this to design a modernized and scalable convolutional architecture customized to challenges of data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired large kernel segmentation network which introduces – 1) A fully ConvNeXt 3D Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up and downsampling blocks to preserve semantic richness across scales, 3) A novel technique to iteratively increase kernel sizes by upsampling small kernel networks, to prevent performance saturation on limited medical data, 4) Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt. This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation. Our code is made publicly available at: https://github.com/MIC-DKFZ/MedNeXt .
Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review

Solar energy has been rapidly utilized in urban environments owing to its significant potential to fulfill the energy demand. The precise forecasting of solar energy, including solar radiation and photovoltaic power forecasting, is crucial for effective energy utilization in cities. Currently, artificial intelligence algorithms, including machine learning (ML) and deep learning (DL) algorithms, have been widely utilized in the research of solar energy forecasting and have shown excellent performance. The different research scales lead to variations in the influencing factors and forecasting approaches used in solar energy prediction. Therefore, this paper provides a comprehensive review of solar radiation and photovoltaic power forecasting research using ML and DL algorithms from a multi-scale perspective, including the meso-scale, micro-scale, and building-scale forecasting in urban environments. The aim is to summarize the state-of-the-art progress and evaluate the solar forecasting effectiveness in diverse research scenarios. The characteristic analyses of ML- and DL-based solar forecasting approaches, prediction models, forecasting horizons, inputs and outputs were conducted for different research scales. This paper discusses the existing issues and future research directions for multiscale solar energy forecasting in cities. The analyses can help researchers and engineers in identifying suitable prediction algorithms and approaches. Consequently, this exploration can contribute to the efficient utilization of solar radiation and photovoltaic power generation in multi-scale urban environments.
One-time universal hashing quantum digital signatures without perfect keys

Quantum digital signatures (QDSs), generating correlated bit strings among three remote parties for signatures through quantum law, can guarantee nonrepudiation, authenticity, and integrity of messages. Recently, a one-time universal-hashing QDS framework, exploiting the quantum asymmetric encryption and universal hash functions, has been proposed to significantly improve the signature rate and ensure unconditional security by directly signing the hash value of long messages. However, similar to quantum key distribution, this framework utilizes keys with perfect secrecy by performing privacy amplification that introduces cumbersome matrix operations, thereby consuming large computational resources, causing delays, and increasing failure probability. Here, we prove that, different from private communication, imperfect quantum keys with partial information leakage can be used for digital signatures and authentication without compromising the security while having eight orders of magnitude improvement on signature rate for signing a megabit message compared with conventional single-bit schemes. This study significantly reduces the delay for data postprocessing and is compatible with any quantum key generation protocols. In our simulation, taking two-photon twin-field key generation protocol as an example, QDS can be practically implemented over a fiber distance of 650 km between the signer and receiver. For the first time, this study offers a cryptographic application of quantum keys with imperfect secrecy and paves a way for the practical and agile implementation of digital signatures in a future quantum network.
Advances in teaching–learning-based optimization algorithm: A comprehensive survey(ICIC2022)

Teaching-learning-based optimization (TLBO) algorithm which imitates the teaching–learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of teaching–learning-based optimization is divided into two phases and each phase executes iterative learning operation. In this paper, we present a comprehensive survey on the recent advances in TLBO. A review of the current literature reveals intriguing challenges and suggests potential future research directions.
Pauli topological subsystem codes from Abelian anyon theories

We construct Pauli topological subsystem codes characterized by arbitrary two-dimensional Abelian anyon theories–this includes anyon theories with degenerate braiding relations and those without a gapped boundary to the vacuum. Our work both extends the classification of two-dimensional Pauli topological subsystem codes to systems of composite-dimensional qudits and establishes that the classification is at least as rich as that of Abelian anyon theories. We exemplify the construction with topological subsystem codes defined on four-dimensional qudits based on the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mn>4</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:msubsup></mml:math> anyon theory with degenerate braiding relations and the chiral semion theory–both of which cannot be captured by topological stabilizer codes. The construction proceeds by "gauging out" certain anyon types of a topological stabilizer code. This amounts to defining a gauge group generated by the stabilizer group of the topological stabilizer code and a set of anyonic string operators for the anyon types that are gauged out. The resulting topological subsystem code is characterized by an anyon theory containing a proper subset of the anyons of the topological stabilizer code. We thereby show that every Abelian anyon theory is a subtheory of a stack of toric codes and a certain family of twisted quantum doubles that generalize the double semion anyon theory. We further prove a number of general statements about the logical operators of translation invariant topological subsystem codes and define their associated anyon theories in terms of higher-form symmetries.
Exploring the potential of using ChatGPT in physics education

Abstract The pretrained large language models have been widely tested for their performance on some challenging tasks including arithmetic, commonsense, and symbolic reasoning. Recently how to combine LLMs with prompting techniques has attracted lots of researchers to propose their models to automatically solve math word problems. However, most research works focus on solving math problems at the elementary school level and few works aim to solve problems in science disciplines, e.g., Physics. In this exploratory study, we discussed the potential pedagogical benefits of using ChatGPT in physics and demonstrated how to prompt ChatGPT in solving physics problems. The results suggest that ChatGPT is able to solve some physics calculation problems, explain solutions, and generate new exercises at a human level.
Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data, there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data. Data security and data pricing, however, are still widely regarded as major challenges in this respect, which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms. In this context, data recording and trading are conducted separately within two separate blockchains: the data blockchain (DChain) and the value blockchain (VChain). This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market. Moreover, pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users. Specifically, in regular data trading on VChain-S2D, two auction models are employed according to the demand scale, for dealing with users' strategic bidding. The incentive-compatible Vickrey-Clarke-Groves (VCG) model is deployed to the low-demand trading scenario, while the nearly incentive-compatible monopolistic price (MP) model is utilized for the high-demand trading scenario. With temporary data trading on VChain-D2S, a reverse auction mechanism namely two-stage obscure selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Furthermore, experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.
Deep learning of causal structures in high dimensions under data limitations

Abstract Causal learning is a key challenge in scientific artificial intelligence as it allows researchers to go beyond purely correlative or predictive analyses towards learning underlying cause-and-effect relationships, which are important for scientific understanding as well as for a wide range of downstream tasks. Here, motivated by emerging biomedical questions, we propose a deep neural architecture for learning causal relationships between variables from a combination of high-dimensional data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide an approach that is demonstrably effective under the conditions of high dimensionality, noise and data limitations that are characteristic of many applications, including in large-scale biology. In experiments, we find that the proposed learners can effectively identify novel causal relationships across thousands of variables. Results include extensive (linear and nonlinear) simulations (where the ground truth is known and can be directly compared against), as well as real biological examples where the models are applied to high-dimensional molecular data and their outputs compared against entirely unseen validation experiments. These results support the notion that deep learning approaches can be used to learn causal networks at large scale.
Unveiling the role of supply chain parameters approved by blockchain technology towards firm performance through trust: The moderating role of government support

This research study aims to reveal the role of supply chain parameters approved by blockchain technology toward firm performance through trust. This study has also examined the moderating role of government support between trust and firm performance. The underlying theories reinforce the usefulness and flexibility of a supply chain in regulating daily fluctuations and uncertainties in supply and demand. Blockchain technology adoption with supply chain tactics provides a more suitable environment for better firm performance. Governments address legal and security concerns related to blockchain technology and provide clear guidelines and standards for its use in supply chains, which build trust among firms and stakeholders. A conceptual model has been developed with the assistance of past empirical research studies and associated theories. This research study has examined the following relationships from a sample of 465 employees from textile industrial firms listed on the Pakistan Stock Exchange. The current research study assesses these parameters using the partial least squares structural equation modeling (PLS-SEM) method. The analysis showed that supply chain parameters (alignment, agility, adaptability) approved by blockchain technology positively correlate with firm performance. Trust positively mediated the relationship between supply chain parameters approved by blockchain technology and firm performance. Furthermore, government support positively moderated the relationship between trust and firm performance. The study would provide directions for further research. These findings will give the global supply chain industry valuable insights into blockchain technology for firm performance. In theory, this research study would contribute to the scientific literature by answering how trust and government support affect the overall firm performance.
Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

Large Language Models (LLMs) are increasingly being integrated into applications, with versatile functionalities that can be easily modulated via natural language prompts. So far, it was assumed that the user is directly prompting the LLM. But, what if it is not the user prompting? We show that LLM-Integrated Applications blur the line between data and instructions and reveal several new attack vectors, using Indirect Prompt Injection, that enable adversaries to remotely (i.e., without a direct interface) exploit LLM-integrated applications by strategically injecting prompts into data likely to be retrieved at inference time. We derive a comprehensive taxonomy from a computer security perspective to broadly investigate impacts and vulnerabilities, including data theft, worming, information ecosystem contamination, and other novel security risks. We then demonstrate the practical viability of our attacks against both real-world systems, such as Bing Chat and code-completion engines, and GPT-4 synthetic applications. We show how processing retrieved prompts can act as arbitrary code execution, manipulate the application's functionality, and control how and if other APIs are called. Despite the increasing reliance on LLMs, effective mitigations of these emerging threats are lacking. By raising awareness of these vulnerabilities, we aim to promote the safe and responsible deployment of these powerful models and the development of robust defenses that protect users from potential attacks.
Universal Sampling Lower Bounds for Quantum Error Mitigation

Numerous quantum error-mitigation protocols have been proposed, motivated by the critical need to suppress noise effects on intermediate-scale quantum devices. Yet, their general potential and limitations remain elusive. In particular, to understand the ultimate feasibility of quantum error mitigation, it is crucial to characterize the fundamental sampling cost-how many times an arbitrary mitigation protocol must run a noisy quantum device. Here, we establish universal lower bounds on the sampling cost for quantum error mitigation to achieve the desired accuracy with high probability. Our bounds apply to general mitigation protocols, including the ones involving nonlinear postprocessing and those yet to be discovered. The results imply that the sampling cost required for a wide class of protocols to mitigate errors must grow exponentially with the circuit depth for various noise models, revealing the fundamental obstacles in the scalability of useful noisy near-term quantum devices.
Advancing Cancer Document Classification with R andom Forest

&#x0D; In this study, we address the challenging task of biomedical text document classification of Cancer Doc Classification, specifically focusing on lengthy research papers related to cancer. Unlike previous research that often deals with shorter abstracts and concise summaries, we curated a unique dataset comprising documents with more extensive content, each exceeding 6 pages in length. To tackle this classification challenge, we employed the Random Forest Tree method. Random Forest is a powerful ensemble learning technique that combines multiple decision trees to enhance classification accuracy and robustness. It has been widely adopted in the field of machine learning and data science for its effectiveness in handling complex classification tasks.
Metaverse

In recent years, the concept of the metaverse has garnered substantial attention as an emerging digital realm that combines virtual reality, augmented reality, and various interactive technologies to create immersive and interconnected digital spaces. As traditional fitness routines and sports activities transform due to technological advancements, virtual gyms and sports have emerged as innovative solutions to engage individuals in physical activities within the metaverse. Dive into the dynamic realm of the metaverse with this chapter on virtual gyms and sports. The metaverse's business models, user experience design, and scaling strategies are explored, as are its applications in healthcare, therapy, and sports training. As the curtain falls, the authors delve into virtual fan engagement, community building, and future trends. The dynamic landscape of the metaverse awaits your exploration within these pages. Join the researchers in navigating the boundless possibilities of virtual gyms and sports, unraveling their impact on society, industry, and beyond.
MEGA: Multilingual Evaluation of Generative AI

Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Quantum repeaters: From quantum networks to the quantum internet

Quantum technology is now at a point where practical work can begin on creating the quantum internet. However, numerous challenges must be overcome before this vision becomes a reality. A global-scale quantum internet requires the development of the quantum repeater, a device that stores and manipulates qubits while interacting with or emitting entangled photons. This review examines different approaches to quantum repeaters and networks, covering their conceptual frameworks, architectures, and current progress in experimental implementation.
Blockchain technology and its applications in digital accounting systems: insights from Jordanian context

Purpose The recent progress of digital accounting has significantly affected businesses’ sustainable production process. Businesses generally use digital accounting applications to automate their operational procedures and increase their corporate efficiencies through improved output quality and sustainability. Consequently, the purpose of this study is to look into the antecedent factors that directly and indirectly influence blockchain technology adoption in the context of digital accounting systems. Design/methodology/approach The data of the current study were obtained from 346 accountants working in information technology companies. Partial least squares structural equation modeling was used to test the research proposal model. Findings The empirical results confirmed that the adoption of blockchain technology is most considerably impacted by perceived usefulness, whereby it was also revealed that perceived ease of use has a direct and indirect effect on blockchain technology adoption. Originality/value According to the researchers’ knowledge, this study addresses a vital research gap in the literature by suggesting a comprehensive research model that can help garner enhanced usage of blockchain technology and its implications in digital accounting systems in the Jordanian context.
Foreign language teacher competences as perceived by English language and literature students

This paper considers competences of the foreign language teacher in the Republic of Cro-atia. Despite the clear classifi cation of foreign language teacher competences on a primary level, which was developed within the TEMPUS project, no reliable instrument has yet been constructed to account for those competences. The purpose of my research was to investigate the metric characteristics of an instrument which could be useful for determining foreign language teacher competences in the Republic of Croatia. A questionnaire was applied to 126 study participants (bachelor and master English language and literature students). The fi nal version of the questionnaire consisted of eighteen items and four factors, which were extrac-ted using exploratory factor analysis. The results have shown that the developed instrument is not fully applicable to the Croatian foreign language learning context. Still, some of its aspects may be used in future instrument construction.