Reviewerly/exHarmony huggingface dataset:
Deterministic Quantum Phase Estimation beyond N00N States

The modern scientific method is critically dependent on precision measurements of physical parameters. A classic example is the measurement of the optical phase enabled by optical interferometry, where the error on the measured phase is conventionally bounded by the so-called Heisenberg limit. To achieve phase estimation at the Heisenberg limit, it has been common to consider protocols based on highly complex N00N states of light. However, despite decades of research and several experimental explorations, there has been no demonstration of deterministic phase estimation with N00N states reaching the Heisenberg limit or even surpassing the shot noise limit. Here we use a deterministic phase estimation scheme based on a source of Gaussian squeezed vacuum states and high-efficiency homodyne detection to obtain phase estimates with an extreme sensitivity that significantly surpasses the shot noise limit and even beats the conventional Heisenberg limit as well as the performance of a pure N00N state protocol. Using a high-efficiency setup with a total loss of about 11%, we achieve a Fisher information of 15.8(6) rad^{-2} per photon-a significant increase in performance compared to state of the art and beyond an ideal six photon N00N state scheme. This work represents an important achievement in quantum metrology, and it opens the door to future quantum sensing technologies for the interrogation of light-sensitive biological systems.
Switching from exploration to exploitation in gravitational search algorithm based on diversity with Chaos

The gravitational search algorithm (GSA) is one of the most promising algorithm in the physics-based metaheuristics category. However, GSA suffers from premature convergence due to rapid reduction in diversity, whereas a chaotic gravitational search algorithm (CGSA) can degrade the convergence speed and exploitation power. To address these issues, the current study proposes an algorithm that enhances the exploration capability of GSA using a disruption strategy with chaotic dynamics. If no significant change is observed in diversity values during the initial stages of the search process, disruption is performed using a sigmoid function. Then, the search process executes gradual exploitation using a sigmoid function without chaotic dynamics. The proposed algorithm is tested with GSA, CGSA, and PSO (particle swarm optimization) on 28 benchmark functions. It is observed that the algorithm outperforms GSA and PSO in 19 cases and CGSA in 20 cases. Diversity analysis shows that the algorithm generates superior exploration versus exploitation percentage with improved mean diversity values. To determine its robustness, the algorithm is applied to four unconstrained engineering problems. The results suggest that the algorithm can solve practical engineering problems in a reasonable number of iterations.
AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models

News media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.
TextCNN-based ensemble learning model for Japanese Text Multi-classification

In this paper, we aim at improving Japanese text classification using TextCNN-based ensemble learning model. Specifically, we first construct three different sub-classifiers, combining ALBERT, RoBERTa, DistilBERT with TextCNN, respectively; and then explore the effectiveness of ensemble learning model to leverage complementary information from different sub-classifiers for better text classification. We also conduct a series of experiments with the dataset collected from Japanese Wikipedia pages, which was divided into 31 categories. The experimental results show that the proposed approach achieves a good performance. The accuracy, precision, recall and F1 scores reach 0.881, 0.884, 0.880 and 0.881, respectively, which shows that the TextCNN-based ensemble learning model can be used for Japanese Text Multi-Classification effectively.
Revolutionizing Supply Chains: Unveiling the Power of Blockchain Technology for Enhanced Transparency and Performance

In recent years, the significance and prevalence of distributed ledger technology have grown. However, there is a lack of empirical studies examining the managerial and technological aspects essential for implementing it in the distribution network. The objective of this research is to develop a comprehensive framework for the adoption of blockchain technology in the entire distribution chain. This will be achieved by identifying the factors that facilitate adoption and experimentally evaluating their interdependencies and effects on adoption through empirical evidence collection. A quantitative approach was used to assess the model's main objective. The study focused on the medicine manufacturing industry to gather responses regarding the intention to use blockchain technology and its impact on supply chain performance. The findings revealed a significant positive relationship in the hypothetical model. Additionally, secondary data analysis, which considered previous research and online surveys, was conducted to assess the impact of blockchain technology on supply chain performance and the mediating role of supply chain transparency.
CoFHEE: A Co-processor for Fully Homomorphic Encryption Execution

In this paper, we present the blueprint of a specialized co-processor for Fully Homomorphic Encryption, dubbed CoFHEE. With a small design area of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$12mm^{2}$</tex> , CoFHEE incorporates ASIC implementations of fundamental polynomial operations, such as polynomial addition and subtraction, Hadamard product, and Number Theoretic Transform, which are underneath all higher-level FHE primitives. CoFHEE has native support of polynomial degrees of up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=2^{14}$</tex> with a coefficient size of 128 bits. We evaluate our chip with performance and power experiments and compare it against state-of-the-art software implementations and other ASIC designs. A more elaborate description of the CoFHEE design can be found in [1].
Tree-level entanglement in quantum electrodynamics

We report on a systematic study on the entanglement between helicity degrees of freedom generated at tree level in quantum electrodynamics two-particle scattering processes. We determine the necessary and sufficient dynamical conditions for outgoing particles to be entangled with one another, and expose the hitherto unknown generation of maximal or nearly maximal entanglement through Bhabha and Compton scattering. Our work is an early step in revisiting quantum field theory and high-energy physics in the light of quantum information theory.
Chained-Drones: Blockchain-based privacy-preserving framework for secure and intelligent service provisioning in Internet of Drone Things

We leverage blockchain technology for malicious node detection in Internet of Drone Things (IoDTs). IoDTs additionally provide a secure mechanism of service delivery based on cascade encryption and feature assessment. By making localization and service delivery more challenging, malicious nodes deter new users. The identification and removal of such nodes is necessary for establishing trust between entities. Using federated learning, the proposed system identifies malware. Using SVM and RF classifiers, federated learning is capable of identifying malicious nodes. For the purpose of identifying malicious nodes, we examine their transparency and latency. Nodes that provide services to others provide one another incentives. During service delivery in IoDTs, the challenges that must be addressed are provider and consumer mistrust. These concerns are addressed via feature assessment and cascade encryption. The digital signature used in cascade encryption renders the service provider irrefutable. Due to feature evaluation, consumers get their requested services without fail. Functional testing ensures that a service fulfills industry requirements. Our simulation results demonstrate that our non-spurning model works as designed. Accuracy, recall, and overall F1 score are compared between SVM and RF classifiers. The accuracy of SVM is 79%, its F1 score is 0.88, and its recall is 0.78. The RF classifier has an accuracy rate of 95%, a precision rate of 0.92, an F1 score of 0.96, and a recall rate of 1. Utilizing RF significantly improves the precision of identifying malicious nodes.
Teaching Prolog with Active Logic Documents

Teaching materials for programming languages, and Prolog in particular, classically include textbooks, slides, notes, and exercise sheets, together with some Prolog programming environment. However, modern web technology offers many opportunities for embedding interactive components within such teaching materials. We report on our experiences in developing and applying our approach and the corresponding tools to facilitating this task, that we call Active Logic Documents (ALD). ALD offers both a very easy way to add click-to-run capabilities to any kind of teaching materials, independently of the tool used to generate them, as well as a tool-set for generating web-based materials with embedded examples and exercises. Both leverage on (components of) the Ciao Prolog Playground. Fundamental principles of our approach are that active parts run locally on the student’s browser, with no need for a central infrastructure, and that output is generated from a single, easy to use source that can be developed with any editor. We argue that this has multiple advantages from the point of view of scalability, low maintenance cost, security, ease of packaging and distribution, etc. over other approaches.
All-Microwave Leakage Reduction Units for Quantum Error Correction with Superconducting Transmon Qubits

Minimizing leakage from computational states is a challenge when using many-level systems like superconducting quantum circuits as qubits. We realize and extend the quantum-hardware-efficient, all-microwave leakage reduction unit (LRU) for transmons in a circuit QED architecture proposed by Battistel et al. This LRU effectively reduces leakage in the second- and third-excited transmon states with up to 99% efficacy in 220 ns, with minimum impact on the qubit subspace. As a first application in the context of quantum error correction, we show how multiple simultaneous LRUs can reduce the error detection rate and suppress leakage buildup within 1% in data and ancilla qubits over 50 cycles of a weight-2 stabilizer measurement.
Unveiling the Enigma: Exploring Risk Factors and Mechanisms for Psychotic Symptoms in Alzheimer’s Disease through Electronic Medical Records with Deep Learning Models

Around 50% of patients with Alzheimer’s disease (AD) may experience psychotic symptoms after onset, resulting in a subtype of AD known as psychosis in AD (AD + P). This subtype is characterized by more rapid cognitive decline compared to AD patients without psychosis. Therefore, there is a great need to identify risk factors for the development of AD + P and explore potential treatment options. In this study, we enhanced our deep learning model, DeepBiomarker, to predict the onset of psychosis in AD utilizing data from electronic medical records (EMRs). The model demonstrated superior predictive capacity with an AUC (area under curve) of 0.907, significantly surpassing conventional risk prediction models. Utilizing a perturbation-based method, we identified key features from multiple medications, comorbidities, and abnormal laboratory tests, which notably influenced the prediction outcomes. Our findings demonstrated substantial agreement with existing studies, underscoring the vital role of metabolic syndrome, inflammation, and liver function pathways in AD + P. Importantly, the DeepBiomarker model not only offers a precise prediction of AD + P onset but also provides mechanistic understanding, potentially informing the development of innovative treatments. With additional validation, this approach could significantly contribute to early detection and prevention strategies for AD + P, thereby improving patient outcomes and quality of life.
Synergetic and innovative approaches to education

A significant number of research results in different fields of knowledge are correlated by researchers with synergetics. The problems, content, research methods and results related to synergetics are characterized by ambiguous assessments and uncertainty. At the same time, synergetics as a scientific area of ​​research is in demand by society. The organic need of modern science for generalizing interdisciplinary research continues to stimulate the further development of synergetics and its emergence to a new level of integration interactions. Synergetics operates today as a category of scientific knowledge. The context of synergetics makes it possible for scientists of different specializations to interact fruitfully in the language of system understanding and the search for new solutions. The education system is one of the social subsystems of society.The modern socio-cultural space in which the education system functions is unusually rich in diverse content. Numerous social systems, traditionally existing and newly emerged, interact with each other, enter into certain relationships, and are in a state of constant activity. The combination of traditions and innovations is the main socio-cultural norm of relations between the old and the new, excluding their opposition and affirming their complementarity.
Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language

ChatGPT (Generative Pre-Trained Transformer) is a chatbot that is being widely used by the public. This technology is based on Artificial Intelligence and is capable of having conversational interactions with its users just like humans, but in the form of automated text. Because of this capability, online forums such as Brainly and the like can be overtaken by these smart chatbots. Therefore, this study was conducted to determine the positive and negative sentiments towards ChatGPT using Naive Bayes Classification algorithm on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had a positive sentiment of 57.6% towards ChatGPT, while the negative sentiment reached 42.4%. The resulting classification model had an accuracy of 80%, indicating a good classification model in determining sentiment probabilities. These findings provide a basis for the development of better AI chatbot technology that can meet user needs. The results of this study provide insights into user sentiment towards ChatGPT and can be used as a reference for future AI chatbot development.
Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images

Abstract Selecting regions of interest (ROI) is a common step in medical image analysis across all imaging modalities. An ROI is a subset of an image appropriate for the intended analysis and identified manually by experts. In modern pathology, the analysis involves processing multidimensional and high resolution whole slide image (WSI) tiles automatically with an overwhelming quantity of structural and functional information. Despite recent improvements in computing capacity, analyzing such a plethora of data is challenging but vital to accurate analysis. Automatic ROI detection can significantly reduce the number of pixels to be processed, speed the analysis, improve accuracy and reduce dependency on pathologists. In this paper, we present an ROI detection method for WSI and demonstrated it for human epidermal growth factor receptor 2 (HER2) grading for breast cancer patients. Existing HER2 grading relies on manual ROI selection, which is tedious, time-consuming and suffers from inter-observer and intra-observer variability. This study found that the HER2 grade changes with ROI selection. We proposed an ROI detection method using Vision Transformer and investigated the role of image magnification for ROI detection. This method yielded an accuracy of 99% using 20 × WSI and 97% using 10 × WSI for the ROI detection. In the demonstration, the proposed method increased the diagnostic agreement to 99.3% with the clinical scores and reduced the time to 15 seconds for automated HER2 grading.
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory

We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization.CLIP-Fields learns a mapping from spatial locations to semantic embedding vectors.Importantly, we show that this mapping can be trained with supervision coming only from webimage and web-text trained models such as CLIP, Detic, and Sentence-BERT; and thus uses no direct human supervision.When compared to baselines like Mask-RCNN, our method outperforms on few-shot instance identification or semantic segmentation on the HM3D dataset with only a fraction of the examples.Finally, we show that using CLIP-Fields as a scene memory, robots can perform semantic navigation in real-world environments.Our code and demonstration videos are available here: https://mahis.life/clip-fields
Prime: Privacy-preserving video anomaly detection via Motion Exemplar guidance

Video anomaly detection (VAD) involves identifying events or behaviours in video sequences that deviate from expected patterns. Most VAD models to date focus on seeking continuous improvement by directly learning identifiable visual cues from information-rich appearance data, regardless of the critical issue of privacy and data security in public places. This paper explores the possibility of addressing privacy-preserving VAD by privacy-independent data, such as human body skeleton and optical flow. However, due to the imbalanced nature of normality and anomaly, direct learning of the consistency of heterogeneous data may result in normality bias. To address the issues, we propose a novel motion exemplar-guided approach (a.k.a. Prime) that explicitly incorporates the support set of human skeleton poses into the VAD framework for breaking through the usefulness-versus-privacy dilemma. The support set containing diverse motion exemplars from the large-scale human skeleton-based action database enables our model to disentangle the coarsely defined anomalies. To learn the abnormal consistency between poses and optical flow, we introduce a Non-Minimum Suppression (NMS) strategy that adaptively highlights the correlation of anomalous pairs. The proposed architecture allows us to train our model with both fully and weakly-supervised paradigms in an end-to-end manner. We conducted performance evaluations of our method on three well-established datasets for VAD tasks: UCSD Ped2, Avenue, and ShanghaiTech. These evaluations were carried out in both privacy and non-privacy settings to assess the effectiveness of our approach. The results demonstrate that our approach surpasses the performance of most state-of-the-art (SOTA) methods, both in fully-supervised and weakly-supervised paradigms.
Enhancing quantum teleportation efficacy with noiseless linear amplification

Abstract Quantum teleportation constitutes a fundamental tool for various applications in quantum communication and computation. However, state-of-the-art continuous-variable quantum teleportation is restricted to moderate fidelities and short-distance configurations. This is due to unavoidable experimental imperfections resulting in thermal decoherence during the teleportation process. Here we present a heralded quantum teleporter able to overcome these limitations through noiseless linear amplification. As a result, we report a high fidelity of 92% for teleporting coherent states using a modest level of quantum entanglement. Our teleporter in principle allows nearly complete removal of loss induced onto the input states being transmitted through imperfect quantum channels. We further demonstrate the purification of a displaced thermal state, impossible via conventional deterministic amplification or teleportation approaches. The combination of high-fidelity coherent state teleportation alongside the purification of thermalized input states permits the transmission of quantum states over significantly long distances. These results are of both practical and fundamental significance; overcoming long-standing hurdles en route to highly-efficient continuous-variable quantum teleportation, while also shining new light on applying teleportation to purify quantum systems from thermal noise.
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning

Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-modal learning. In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity. Concretely, we propose Hierarchical Banzhaf Interaction (HBI) to value possible correspondence between video frames and text words for sensitive and explainable cross-modal contrast. To efficiently realize the cooperative game of multiple video frames and multiple text words, the proposed method clusters the original video frames (text words) and computes the Banzhaf Interaction between the merged tokens. By stacking token merge modules, we achieve cooperative games at different semantic levels. Extensive experiments on commonly used text-video retrieval and video-question answering bench-marks with superior performances justify the efficacy of our HBI. More encouragingly, it can also serve as a visualization tool to promote the understanding of cross-modal interaction, which have a far-reaching impact on the community. Project page is available at https://jpthu17.github.io/HBI/.