Reviewerly/exHarmony huggingface dataset:
Gaussian boson sampling validation via detector binning

Gaussian boson sampling (GBS), a computational problem conjectured to be hard to simulate on a classical machine, has been at the forefront of recent years' experimental and theoretical efforts to demonstrate quantum advantage. The classical intractability of the sampling task makes validating these experiments a challenging and essential undertaking. In this paper, we propose binned-detector probability distributions as a suitable quantity to statistically validate GBS experiments employing photon-number-resolving detectors. We show how to compute such distributions by leveraging their connection with their respective characteristic function. The latter may be efficiently and analytically computed for squeezed input states as well as for relevant classical hypothesis like squashed states. Our scheme encompasses other validation methods based on marginal distributions and correlation functions. Additionally, it can accommodate various sources of noise, such as losses and partial distinguishability, a feature that have received limited attention within the GBS framework so far. We also illustrate how binned-detector probability distributions behave when Haar-averaged over all possible interferometric networks, extending known results for Fock boson sampling.
Optimal Single-Shot Decoding of Quantum Codes

We discuss single-shot decoding of quantum Calderbank-Shor-Steane codes with faulty syndrome measurements. We state the problem as a joint source-channel coding problem. By adding redundant rows to the code's parity-check matrix we obtain an additional syndrome error correcting code which addresses faulty syndrome measurements. Thereby, the redundant rows are chosen to obtain good syndrome error correcting capabilities while keeping the stabilizer weights low. Optimal joint decoding rules are derived which, though too complex for general codes, can be evaluated for short quantum codes.
Sui Lutris: A Blockchain Combining Broadcast and Consensus

Sui Lutris is the first smart-contract platform to sustainably achieve sub-second finality. It achieves this significant decrease in latency by employing consensusless agreement not only for simple payments but for a large variety of transactions. Unlike prior work, Sui Lutris neither compromises expressiveness nor throughput and can run perpetually without restarts. Sui Lutris achieves this by safely integrating consensuless agreement with a high-throughput consensus protocol that is invoked out of the critical finality path but makes sure that when a transaction is at risk of inconsistent concurrent accesses its settlement is delayed until the total ordering is resolved. Building such a hybrid architecture is especially delicate during reconfiguration events, where the system needs to preserve the safety of the consensusless path without compromising the long-term liveness of potentially misconfigured clients. We thus develop a novel reconfiguration protocol, the first to show the safe and efficient reconfiguration of a consensusless blockchain. Sui Lutris is currently running in production as part of a major smart-contract platform. Combined with the Move Programming language it enables the safe execution of smart contracts that expose objects as a first-class resource. In our experiments Sui Lutris achieves latency lower than 0.5 seconds for throughput up to 5,000 certificates per second (150k ops/s with bundling), compared to the state-of-the-art real-world consensus latencies of 3 seconds. Furthermore, it gracefully handles validators crash-recovery and does not suffer visible performance degradation during reconfiguration.
Transductive conformal inference with adaptive scores

Conformal inference is a fundamental and versatile tool that provides distribution-free guarantees for many machine learning tasks. We consider the transductive setting, where decisions are made on a test sample of $m$ new points, giving rise to $m$ conformal $p$-values. While classical results only concern their marginal distribution, we show that their joint distribution follows a P\'olya urn model, and establish a concentration inequality for their empirical distribution function. The results hold for arbitrary exchangeable scores, including adaptive ones that can use the covariates of the test+calibration samples at training stage for increased accuracy. We demonstrate the usefulness of these theoretical results through uniform, in-probability guarantees for two machine learning tasks of current interest: interval prediction for transductive transfer learning and novelty detection based on two-class classification.
Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models

Generalist robot manipulators need to learn a wide variety of manipulation skills across diverse environments. Current robot training pipelines rely on humans to provide kinesthetic demonstrations or to program simulation environments and to code up reward functions for reinforcement learning. Such human involvement is an important bottleneck towards scaling up robot learning across diverse tasks and environments. We propose Generation to Simulation (Gen2Sim), a method for scaling up robot skill learning in simulation by automating generation of 3D assets, task descriptions, task decompositions and reward functions using large pre-trained generative models of language and vision. We generate 3D assets for simulation by lifting open-world 2D object-centric images to 3D using image diffusion models and querying LLMs to determine plausible physics parameters. Given URDF files of generated and human-developed assets, we chain-of-thought prompt LLMs to map these to relevant task descriptions, temporal decompositions, and corresponding python reward functions for reinforcement learning. We show Gen2Sim succeeds in learning policies for diverse long horizon tasks, where reinforcement learning with non temporally decomposed reward functions fails. Gen2Sim provides a viable path for scaling up reinforcement learning for robot manipulators in simulation, both by diversifying and expanding task and environment development, and by facilitating the discovery of reinforcement-learned behaviors through temporal task decomposition in RL. Our work contributes hundreds of simulated assets, tasks and demonstrations, taking a step towards fully autonomous robotic manipulation skill acquisition in simulation.
Boosting Data Analytics With Synthetic Volume Expansion

Synthetic data generation, a cornerstone of Generative Artificial Intelligence, promotes a paradigm shift in data science by addressing data scarcity and privacy while enabling unprecedented performance. As synthetic data becomes more prevalent, concerns emerge regarding the accuracy of statistical methods when applied to synthetic data in contrast to raw data. This article explores the effectiveness of statistical methods on synthetic data and the privacy risks of synthetic data. Regarding effectiveness, we present the Synthetic Data Generation for Analytics framework. This framework applies statistical approaches to high-quality synthetic data produced by generative models like tabular diffusion models, which, initially trained on raw data, benefit from insights from pertinent studies through transfer learning. A key finding within this framework is the generational effect, which reveals that the error rate of statistical methods on synthetic data decreases with the addition of more synthetic data but may eventually rise or stabilize. This phenomenon, stemming from the challenge of accurately mirroring raw data distributions, highlights a "reflection point"-an ideal volume of synthetic data defined by specific error metrics. Through three case studies, sentiment analysis, predictive modeling of structured data, and inference in tabular data, we validate the superior performance of this framework compared to conventional approaches. On privacy, synthetic data imposes lower risks while supporting the differential privacy standard. These studies underscore synthetic data's untapped potential in redefining data science's landscape.
DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

Controversy is a reflection of our zeitgeist, and an important aspect to any discourse. The rise of large language models (LLMs) as conversational systems has increased public reliance on these systems for answers to their various questions. Consequently, it is crucial to systematically examine how these models respond to questions that pertaining to ongoing debates. However, few such datasets exist in providing human-annotated labels reflecting the contemporary discussions. To foster research in this area, we propose a novel construction of a controversial questions dataset, expanding upon the publicly released Quora Question Pairs Dataset. This dataset presents challenges concerning knowledge recency, safety, fairness, and bias. We evaluate different LLMs using a subset of this dataset, illuminating how they handle controversial issues and the stances they adopt. This research ultimately contributes to our understanding of LLMs' interaction with controversial issues, paving the way for improvements in their comprehension and handling of complex societal debates.
Integrated Web-based Palu City Blood Donor Service Application Model Using ReactJS and ExpressJS

Blood donation is an important activity in the medical world that requires efficient coordination between donors and recipients of blood donations so that blood needs are met. However, the blood donation process still needs to be improved because of the lack of donors and the difficulty of blood recipients to find donors. Using this problem, researchers designed and developed a web application that utilizes ReactJS as a frontend Library and ExpressJS as a backend framework. This application will help donors to register, make donor appointments, and access information related to blood donor activities. Admins can use this application to manage information and send notifications to donors. This research system development method needs analysis, system design, application implementation, and testing and evaluation. This research uses two tests, namely Blackbox and a questionnaire. The result of this research is an application system named Badonordarah that can improve the efficiency and effectiveness of the blood donor service process in Palu City and make it easier for donors and blood recipients to interact
TRANSFORMASI SISTEM INFORMASI AKUNTANSI ARUS KAS DENGAN MENGGUNAKAN VISUAL STUDIO DAN MYSQL PADA CV. MUTIARA SEJAHTERA

This research focuses on the importance of accounting information systems in the retail business, with a case study on CV. Mutiara Sejahtera. We use qualitative methods and development cycle systems to understand the needs and overcome the constraints in managing a financial company. With business growth, managing data receipts, transactions, and expenses becomes complicated. A more integrated accounting information system is implemented to record and process data more efficiently. This helps reduce human error and provides better financial reports. The results of the implementation of this new system provide benefits for CV. Mutiara Sejahtera. Finances are managed more efficiently, and more accurate reports help decision-making. Thus, the implementation of an integrated accounting information system has the potential to support business growth and build operational transparency.
Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

&lt;p&gt;With the complexity of the robot operating environment increases, there becoming higher demands on the optimal path planning for robots. Most of the path planning is performed in known environments and static models. However, there are still challenges for robots to perform path planning in complex unknown or dynamic environments, which will suffer from deadlock problems and obstacle avoidance failures. Reinforcement learning (RL) can help fuzzy algorithm to optimize the strategy. However, the difficulty of designing the rewards in RL makes the algorithm require a large number of samples to learn the strategy, resulting in computational complexity. To solve these problems, a new local path planning based on the improved fuzzy and Q(&amp;lambda;)-learning algorithms is proposed, aiming to plan the shortest path and avoid obstacles. For solving the problems of breaking through and avoiding obstacles, a fuzzy controller is designed. The distance of nearest obstacle in front of the mobile robot and the distance between the obstacles in the two breakout directions are regarded as the two inputs for this controller. And the two fuzzy quantities of the mobile robot&amp;rsquo;s running angle and the safe step length are outputted. In the path planning, the Q(&amp;lambda;)-learning algorithm are used to optimize the weights of the running angle and the safe step, obtaining a more accurate robot position and speeding up path planning efficiency. Furthermore, to solve the overlap problems among the starting point, end point, and obstacles, a safer running environment is designed considering radiuses of these objects. Besides, the mobile robot breakout scheme and sustainable obstacle avoidance scheme are designed to solve the deadlock problem and &amp;ldquo;large obstacle&amp;rdquo; avoidance problem, respectively. Simulation results in the sparse and complex operating environment show that our proposed algorithm can plan a relatively optimal and safe path, improving the success rate of path planning.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Thermal states of two identical two-level atoms interacting with the single-mode fields of the Arik–Coon and Biedenharn–Macfarlane q-oscillators

We consider the Jaynes–Cummings models (JCMs) of two identical two-level atoms interacting with a single mode of the Arik–Coon and Biedenharn–Macfarlane [Formula: see text]-boson fields in a cavity. It is shown that there is a constant of motion, so-called the total number of excitation, which leads to decomposition of the total Hilbert space of [Formula: see text]-deformed system into three invariant subspaces. So, we have the block diagonal structure with three blocks as the Hamiltonian matrices. The exact energy spectrum and eigenstates of the [Formula: see text]-deformed models are obtained by diagonalizing each block. Assuming that the cavity fields and atoms are in thermal equilibrium, the partition function and thermal density matrix of the models are calculated. Finally, the influence of deformation parameters of the Arik–Coon and Biedenharn–Macfarlane [Formula: see text]-oscillator light fields at low-temperatures on the entropy and internal energy of the Jaynes–Cummings models as well as on the atom–atom entanglement via the concurrence measure is analyzed.
Section 3: Libraries quality control (QC) v1

This protocol details quality control of libraries and should be performed after Section 2: NGS library preparation for sequencing.
Relative Risk Regression for Clustered Data with Application to Oral Health Research

Introduction: Cluster-weighted generalized estimating equations (CWGEE) and doubly-weighted GEE (DWGEE) are used to produce unbiased estimates when informative cluster size (ICS) exists. However, their performance in estimating the relative risk (RR) from a Poisson regression is unknown. Methods: Using the dental data from the San Juan Overweight Adults Longitudinal Study (SOALS), we estimated the association between patient-level (sex, education level, smoking status, age) and tooth-level (bleeding upon probing) predictors and two types of outcomes with high and low prevalence each. We compared the odds ratio (OR) and RR estimates from logistic and Poisson CWGEE/DWGEE, respectively. Results: For patient-level covariates, the ORs estimated from logistic CWGEE/DWGEE and the RRs estimated from Poisson CWGEE/DWGEE were similar to the low-prevalence outcome (tooth loss). With the high-prevalence outcome (tooth loss or increase in attachment loss or pocket depth), the ORs were further from the null compared to the RRs. For example, within CWGEE, the OR of smoking was 1.301 (95% CI: 1.063-1.592), whereas the RR of smoking was 1.235 (95% CI: 1.052-1.450). For the tooth-level covariate (bleeding), there was a considerable difference between OR/RR on tooth loss estimated from CWGEE vs DWGEE. For example, the RR estimated from CWGEE was 1.692 (95% CI: 1.386-2.067), whereas the RR estimated from DWGEE was 1.354 (95% CI: 1.072-1.710). Discussion: CWGEE and DWGEE may produce different estimates for tooth-level (sub-cluster) covariates, especially when the prevalence of the outcome is low. In general, RRs estimated from Poisson CWGEE/DWGEE are closer to the null compared to ORs estimated from logistic CWGEE/DWGEE.
TEACHERS' AND STUDENTS' PERCEPTIONS AND THE VARIABLES THAT IMPACT THE PERCEPTIONS OF USING THE ENGLISH CONVERSATION PRACTICE APPLICATION IN SPEAKING CLASS

The advancement of technology has positive impacts on education. This study aims to identify teachers' and students' perceptions and the variables that impact EFL teachers' and learners' perceptions of using the English Conversation Practice application in speaking class. This study involved thirty-three pupils and three English teachers from SMK Negeri 1 Gianyar, who were chosen using the purposive sampling technique. The data were collected through interviews and observation and analyzed using the interactive data analysis model. The study results show that the students and teachers positively perceive implementing the 'English Conversation Practice' application in speaking class. Besides, the variables that impact EFL teachers' and learners' perceptions of the use of the English Conversation Practice application in speaking class were that the application gave them a new experience in learning to speak, it is simple to use, it does not require a large internet quota, and it allows for the flexibility of learning at any time and in any location. Thus, considering the study's results, it is recommended that EFL teachers use the Conversation Practice application in their speaking class.
On the possibility of heavy duty conceptual engineering

Conceptual engineering is the process of assessing and improving our conceptual repertoire. Some authors have claimed that introducing or revising concepts through conceptual engineering can go as far as expanding the realm of thinkable thoughts and thus enable us to form beliefs, hypotheses, wishes, or desires that we are currently unable to form. We call this kind of conceptual engineering heavy-duty conceptual engineering. As exciting as the idea of heavy-duty conceptual engineering sounds, it has never been developed or defended. In this paper, we pursue a two-fold goal. First, to offer a theory of heavy-duty conceptual engineering that distinguishes it from other kinds of conceptual engineering; second, to show that heavy-duty conceptual engineering is possible, both in theory and in practice, and to explain how it can be pursued. The central idea is that conceptual engineering can enhance the semantic expressive power of a conceptual system by the use of bootstrapping processes.
STUDENTS’ PERCEPTION TOWARD THE ENGLISH TEACHER’S TEACHING STYLE ON THE EIGHTH-GRADE STUDENTS AT SMP NEGERI 7 BAUBAU

Problem statement that is answered in this research is what is the teaching style the English teacher use in teaching English at the eighth-grade students of SMP Negeri 7 Baubau. The objective of this research was to find out the teaching style the English teacher use in teaching English at the eighth-grade students of SMP Negeri 7 Baubau. The research used a quantitative approach with applying the descriptive method. The population of this research was the eighth-grade students of SMP Negeri 7 Baubau who registered in the school year of 2022/2023 in odd semester. In taking sample of the population, the researcher used cluster sampling technique by taking 60 students to be the sample of this research. Instrument that was used in this research will be a teacher style questionnaire. Technique of data analysis that was applied in this research is a descriptive statistics. Based on the finding of the research and the data analysis, it was found that the mean score of expert style was 2.9 which achieves high category, the mean score of formal authority style was 2.7 which achieves moderate category, the mean score of personal model style was 2.5 which achieves moderate category, the mean score of facilitator style was 2.9 which achieves high category, and the mean score of delegator style was 2.8 which achieves moderate category. From those results, it could be concluded that the most teaching styles used by the English teacher at grade eight at SMP Negeri 7 Baubau were the expert style and the facilitator style.
PERANCANGAN APLIKASI BANK SAMPAH “SAMPAHQU” BERBASIS MOBILE DI TANGERANG SELATAN MENGGUNAKAN RAPID APPLICATION DEVELOPMENT

Currently, household waste management has been managed by a waste bank so that the existing daily waste is collected and sorted to be recycled to make the environment better and less pollution-free. In carrying out its activities, the Garbage Bank in South Tangerang City experienced problems starting from the process of depositing waste from customers, the process of recording transactions and reporting transactions that occurred, modifying so that customer data collection, types of waste, and existing reports were still not well organized and there were no media. Communication can be used between the waste bank and waste bank customers. This research will design a waste bank information system for waste management in South Tangerang from the existing problems. The Rapid Application Development (RAD) methodology with the prototyping method is used in designing a mobile-based integrated waste bank application. The proposed system uses UML (Unified Modeling Language) modeling. The results of this study are an integrated waste bank application that can make a good contribution, namely helping the community to deposit their household waste and assisting waste bank managers in making effective and efficient recording and reporting