Reviewerly/exHarmony huggingface dataset:
Shor's Factoring Algorithm and Modular Exponentiation Operators

We provide a pedagogical presentation of Shor's factoring algorithm, which is a quantum algorithm for factoring very large numbers (of order of hundreds to thousands of bits) in polynomial time. In contrast, all known classical algorithms for the factoring problem take an exponential time to factor such large numbers. Shor's algorithm therefore has profound implication for public-key encryption such as RSA and Diffie–Hellman key exchange. We assume no prior knowledge of Shor's algorithm beyond a basic familiarity with the circuit model of quantum computing. Shor's algorithm contains a number of moving parts, and can be rather daunting at first. The literature is replete with derivations and expositions of Shor's algorithm, but most of them seem to be lacking in essential details, and none of them provide a pedagogical presentation. They require a thicket of appendices and assume a knowledge of quantum algorithms and classical mathematics with which the reader might not be familiar. We therefore start with first principle derivations of the quantum Fourier transform (QFT) and quantum phase estimation (QPE), which are the essential building blocks of Shor's algorithm. We then go on to develop the theory of modular exponentiation (ME) operators, one of the fundamental components of Shor's algorithm, and the place where most of the quantum resources are deployed. We also delve into the number theory that establishes the link between factorization and the period of the modular exponential function. We then apply the QPE algorithm to obtain Shor's factoring algorithm. We also discuss the post-quantum processing and the method of continued fractions, which is used to extract the exact period of the modular exponential function from the approximately measured phase angles of the ME operator. The manuscript then moves on to a series of examples. We first verify the formalism by factoring N=15, the smallest number accessible to Shor's algorithm. We then proceed to factor larger integers, developing a systematic procedure that will find the ME operators for any semi-prime N=p×q (where q and p are prime). Finally, we factor the composite numbers N=21, 33, 35, 143, 247 using the Qiskit simulator. It is observed that the ME operators are somewhat forgiving, and truncated approximate forms are able to extract factors just as well as the exact operators. This is because the method of continued fractions only requires an approximate phase value for its input, which suggests that implementing Shor's algorithm might not be as difficult as first suspected.Quanta 2023; 12: 41–130.
STUDENT STEM RESEARCH IN INSTRUMENT MANUFACTURE

The article provides a brief overview of research directions and publications regarding the implementation of STEM education and educational research in the Ukrainian education system. We considered the purpose and methodology of setting STEM research tasks for students of public secondary schools to create prerequisites for developing relevant competencies in various spheres of life. The article offers a variant of STEM research for students’ extracurricular work based on the following academic disciplines and areas close to them: physics, computer science, and mathematics. In addition to purely school academic disciplines, this variant of studies contributes to forming competencies in programming, medicine, economics, and finance. It is emphasized that physics and informatics in these educational studies are the major disciplines around which all others are grouped. The presence of the primary disciplines greatly facilitates the organization and increases the effectiveness of the research, as it unites and substantiates all its other directions. In this article, the authors present their vision of the possibilities of organizing STEM research for students of public educational institutions in the field of electronics and instrument engineering. A specific sample of such studies is given.
Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling

This research focuses on sentiment analysis in the marketplace reviews in Google Play Store, a platform for downloading Android applications and providing reviews. Sentiment analysis is essential for understanding user responses to applications, particularly in the app marketplace. In this study, two machine learning algorithms, Naïve Bayes and Logistic Regression, are employed to classify user reviews. The application rating is used as a reference to determine the sentiment of each comment. The dataset is divided into two conditions: using 2 labels (positive &amp; negative) and 3 labels (positive, neutral, &amp; negative). The test results indicate that the highest performance is achieved by classifying with Logistic Regression on the Shopee dataset with 2 labels. The accuracy reaches 84.58%, precision reaches 84.66%, and recall reaches 84.63%. Additionally, the fastest processing time occurs when testing the Lazada 2-label dataset with Naïve Bayes, taking only 0.038 seconds. Overall, the research suggests that datasets with 2 labels tend to yield higher accuracy compared to datasets with 3 labels.
Exploring Focus and Depth-Induced Saliency Detection for Light Field

An abundance of features in the light field has been demonstrated to be useful for saliency detection in complex scenes. However, bottom-up saliency detection models are limited in their ability to explore light field features. In this paper, we propose a light field saliency detection method that focuses on depth-induced saliency, which can more deeply explore the interactions between different cues. First, we localize a rough saliency region based on the compactness of color and depth. Then, the relationships among depth, focus, and salient objects are carefully investigated, and the focus cue of the focal stack is used to highlight the foreground objects. Meanwhile, the depth cue is utilized to refine the coarse salient objects. Furthermore, considering the consistency of color smoothing and depth space, an optimization model referred to as color and depth-induced cellular automata is improved to increase the accuracy of saliency maps. Finally, to avoid interference of redundant information, the mean absolute error is chosen as the indicator of the filter to obtain the best results. The experimental results on three public light field datasets show that the proposed method performs favorably against the state-of-the-art conventional light field saliency detection approaches and even light field saliency detection approaches based on deep learning.
Connecting Everyday Objects with the Metaverse: A Unified Recognition Framework

The recent Facebook rebranding to Meta has drawn renewed attention to the metaverse. Technology giants, amongst others, are increasingly embracing the vision and opportunities of a hybrid social experience that mixes physical and virtual interactions. As the metaverse gains in traction, it is expected that everyday objects may soon connect more closely with virtual elements. However, discovering this "hidden" virtual world will be a crucial first step to interacting with it in this new augmented world. In this paper, we address the problem of connecting physical objects with their virtual counterparts, especially through connections built upon visual markers. We propose a unified recognition framework that guides approaches to the metaverse access points. We illustrate the use of the framework through experimental studies under different conditions, in which an interactive and visually attractive decoration pattern, an Artcode, is used as the approach to enable the connection. This paper will be of interest to, amongst others, researchers working in Interaction Design or Augmented Reality who are seeking techniques or guidelines for augmenting physical objects in an unobtrusive, complementary manner.
Harnessing AI to Power Constructivist Learning: An Evolution in Educational Methodologies

This article navigates the confluence of the age-old constructivist philosophy of education and modern Artificial Intelligence (AI) tools as a means of reconceptualizing teaching and learning methods. While constructivism champions active learning derived from personal experiences and prior knowledge, AI’s adaptive capacities seamlessly align with these principles, offering personalized, dynamic, and enriching learning avenues. By leveraging AI platforms such as ChatGPT, BARD, and Microsoft Bing, educators can elevate constructivist pedagogy, fostering enhanced student engagement, self-reflective metacognition, profound conceptual change, and an enriched learning experience. The article further emphasizes the preservation of humanistic values in the integration of AI, ensuring a balanced, ethical, and inclusive educational environment. This exploration sheds light on the transformative potential of inter-twining traditional educational philosophies with technological advancements, paving the way for a more responsive and effective learning paradigm.
Bi-tuning: Efficient Transfer from Pre-trained Models

It is a de facto practice in the deep learning community to first pre-train a deep neural network from a large-scale dataset and then fine-tune the pre-trained model to a specific downstream task. Recently, both supervised and unsupervised pre-training approaches to learning representations have achieved remarkable advances, which exploit the discriminative knowledge of labels and the intrinsic structure of data, respectively. It follows the natural intuition that both the discriminative knowledge and the intrinsic structure of the downstream task can be useful for fine-tuning. However, existing fine-tuning methods mainly leverage the former and discard the latter. A natural question arises: How to fully explore the intrinsic structure of data for boosting fine-tuning? In this paper, we propose Bi-tuning, a general learning approach that is capable of fine-tuning both supervised and unsupervised pre-trained representations to downstream tasks. Bi-tuning generalizes the vanilla fine-tuning by integrating two heads upon the backbone of pre-trained representations: a classifier head with an improved contrastive cross-entropy loss to better leverage the label information in an instance-contrast way, and a projector head with a newly-designed categorical contrastive learning loss to fully exploit the intrinsic structure of data in a category-consistent way. Comprehensive experiments confirm that Bi-tuning achieves state-of-the-art results for fine-tuning tasks of both supervised and unsupervised pre-trained models by large margins.
UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach

The development of machine learning and artificial intelligence algorithms, as well as the progress of unmanned aerial vehicle swarm technology, has significantly enhanced the intelligence and autonomy of unmanned aerial vehicles in search missions, resulting in greater efficiency when searching unknown areas. However, as search scenarios become more complex, the existing unmanned aerial vehicle swarm search method lacks scalability and efficient cooperation. Furthermore, due to the increasing scale of search scenarios, the accuracy and real-time performance of global information are difficult to ensure, necessitating the provision of local information. This paper focuses on the large-scale search scenario and split it to provide both local and global information for running unmanned aerial vehicle swarm search algorithms. Since the search environment is often unknown, dynamic, and complex, it requires adaptive decision-making in a constantly changing environment, which is suitable for modeling as a Markov decision process. Considering the sequential-based scenario, we propose a distributed collaborative search method based on a multi-agent reinforcement learning algorithm, which can operate efficiently in complex and large-scale scenarios. Additionally, the proposed method can utilize a convolutional neural network to process high-dimensional map data with almost no loss of the structure information. Experimental results demonstrate that the proposed method can collaboratively search unknown areas, avoid collisions and repetitions, and find all targets faster compared with the benchmarks.
Digital Simulation of Single Qubit Markovian Open Quantum Systems: A Tutorial

One of the first proposals for the use of quantum computers was the simulation of quantum systems. Over the past three decades, great strides have been made in the development of algorithms for simulating closed quantum systems and the more complex open quantum systems. In this tutorial, we introduce the methods used in the simulation of single qubit Markovian open quantum systems. It combines various existing notations into a common framework that can be extended to more complex open system simulation problems. The only currently available algorithm for the digital simulation of single qubit open quantum systems is discussed in detail. A modification to the implementation of the simpler channels is made that removes the need for classical random sampling, thus making the modified algorithm a strictly quantum algorithm. The modified algorithm makes use of quantum forking to implement the simpler channels that approximate the total channel. This circumvents the need for quantum circuits with a large number of CNOT gates.Quanta 2023; 12: 131–163.
Exploring applications of blockchain in healthcare: road map and future directions

Blockchain technology includes numerous elements such as distributed ledgers, decentralization, authenticity, privacy, and immutability. It has progressed past the hype to find actual use cases in industries like healthcare. Blockchain is an emerging area that relies on a consensus algorithm and the idea of a digitally distributed ledger to eliminate any intermediary risks. By enabling them to trace data provenance and any changes made, blockchain technology can enable different healthcare stakeholders to share access to their networks without violating data security and integrity. The healthcare industry faces challenges like fragmented data, security and privacy concerns, and interoperability issues. Blockchain technology offers potential solutions by ensuring secure, tamper-proof storage across multiple network nodes, improving interoperability and patient privacy. Encrypting patient data further enhances security and reduces unauthorized access concerns. Blockchain technology, deployed over the Internet, can potentially use the current healthcare data by using a patient-centric approach and removing the intermediaries. This paper discusses the effective utilization of blockchain technology in the healthcare industry. In contrast to other applications, the exoteric evaluation in this paper shows that the innovative technology called blockchain technology has a major role to play in the existing and future applications of the healthcare industry and has significant benefits.
Multimixer-128: Universal Keyed Hashing Based on Integer Multiplication

In this paper we introduce a new keyed hash function based on 32-bit integer multiplication that we call Multimixer-128. In our approach, we follow the key-then-hash parallel paradigm. So, we first add a variable length input message to a secret key and split the result into blocks. A fixed length public function based on integer multiplication is then applied on each block and their results are added to form the digest. We prove an upper bound of 2−127 for the universality of Multimixer-128 by means of the differential probability and image probability of the underlying public function.There are vector instructions for fast 32-bit integer multiplication on many CPUs and in such platforms, Multimixer-128 is very efficient. We compare our implementation of Multimixer-128 with NH hash function family that offers similar levels of security and with two fastest NIST LWC candidates. To the best of our knowledge, NH hash function is the fastest keyed hash function on software and Multimixer-128 outperforms NH while providing same levels of security.
Roles of customer and employee satisfaction on corporate performance: An empirical investigation

Employee satisfaction must be improved since it is essential to any organization's ability to succeed financially. This research aims to investigate the link between customer satisfaction and employee satisfaction as well as how each affects organizational success. The impact of several organizational elements on employee satisfaction is examined in this research. In this cohort study, qualitative research techniques were used. A self-administrated questionnaire with multiple choice and release ruined questions were used to gather the data. The results of the principal component analysis (PCA) based on the correlation matrix showed that among the cohorts examined, where consumers had also indicated satisfaction with the current services, there was a high level of employee (hotel staff) satisfaction. Customers' comfort and subsequent satisfaction have mostly been influenced by ambient cleanliness, wholesome meals, and hotel services. From the viewpoint of the workers, a positive work environment combined with rewards like pay and regular training motivated the staff to work devotedly to improve the organization, which is reflected in the customers' satisfaction levels. Our research supports the existence of an indirect relationship mediated by customers between organizational performance and employee happiness. In conclusion, it is plausible to assume that awareness of employee roles is crucial since it seems to be a crucial component of the success of contemporary organizations.
A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits

Variational quantum computing schemes have received considerable attention due to their high versatility and potential to make practical use of near-term quantum devices. At their core, these models train a loss function by sending an initial state through a parametrized quantum circuit, and evaluating the expectation value of some operator at the circuit's output. Despite their promise, the trainablity of these algorithms is hindered by barren plateaus induced by the expressiveness of the parametrized quantum circuit, the entanglement of the input data, the locality of the observable, or the presence of hardware noise. Up to this point, these sources of barren plateaus have been regarded as independent and have been studied only for specific circuit architectures. In this work, we present a general Lie algebraic theory that provides an exact expression for the variance of the loss function of sufficiently deep parametrized quantum circuits, even in the presence of certain noise models. Our results unify under one single framework all aforementioned sources of barren plateaus by leveraging generalized (and subsystem independent) notions of entanglement and operator locality, as well as generalized notions of algebraic decoherence due to noise. This theoretical leap resolves a standing conjecture about a connection between loss concentration and the dimension of the Lie algebra of the generators of the parametrized circuit.
Adapting Large Language Models via Reading Comprehension

We explore how continued pre-training on domain-specific corpora influences large language models, revealing that training on the raw corpora endows the model with domain knowledge, but drastically hurts its prompting ability for question answering. Taken inspiration from human learning via reading comprehension--practice after reading improves the ability to answer questions based on the learned knowledge--we propose a simple method for transforming raw corpora into reading comprehension texts. Each raw text is enriched with a series of tasks related to its content. Our method, highly scalable and applicable to any pre-training corpora, consistently enhances performance across various tasks in three different domains: biomedicine, finance, and law. Notably, our 7B language model achieves competitive performance with domain-specific models of much larger scales, such as BloombergGPT-50B. Furthermore, we demonstrate that domain-specific reading comprehension texts can improve the model's performance even on general benchmarks, showing the potential to develop a general model across even more domains. Our model, code, and data are available at https://github.com/microsoft/LMOps.
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition

Real-world data often exhibits a long-tailed distribution, in which head classes occupy most of the data, while tail classes only have very few samples. Models trained on long-tailed datasets have poor adaptability to tail classes and the decision boundaries are ambiguous. Therefore, in this paper, we propose a simple yet effective model, named Dual-Branch Long-Tailed Recognition (DB-LTR), which includes an imbalanced learning branch and a Contrastive Learning Branch (CoLB). The imbalanced learning branch, which consists of a shared backbone and a linear classifier, leverages common imbalanced learning approaches to tackle the data imbalance issue. In CoLB, we learn a prototype for each tail class, and calculate an inter-branch contrastive loss, an intra-branch contrastive loss and a metric loss. CoLB can improve the capability of the model in adapting to tail classes and assist the imbalanced learning branch to learn a well-represented feature space and discriminative decision boundary. Extensive experiments on three long-tailed benchmark datasets, i.e., CIFAR100-LT, ImageNet-LT and Places-LT, show that our DB-LTR is competitive and superior to the comparative methods.
Modular Cluster Circuits for the Variational Quantum Eigensolver

The variational quantum eigensolver algorithm recently became a popular method to compute the quantum chemical properties of molecules on noisy intermediate scale quantum (NISQ) devices. In order to avoid noise accumulation from the NISQ device in the quantum circuit, it is important to keep the so-called quantum depth of the circuit at a minimum, defined as the minimum number of quantum gates that must be operated sequentially. In the present work, we introduce a modular 2-qubit cluster circuit that allows for the design of a shallow-depth quantum circuit compared to previously proposed architectures without loss of chemical accuracy. Moreover, by virtue of the simplicity of the cluster circuit, it is possible to assign a valence bond chemical interpretation to the cluster circuit. The design was tested on the H2, (H2)2, and LiH molecules, as well as the finite-size transverse-field Ising model, as the latter provides additional insights into the construction of the circuit in a resonating valence bond picture.