Einstein-Podolsky-Rosen correlations and Bell correlations in the simplest scenario

Einstein-Podolsky-Rosen (EPR) steering is an intermediate type of quantum nonlocality which sits between entanglement and Bell nonlocality. A set of correlations is Bell nonlocal if it does not admit a local hidden variable (LHV) model, while it is EPR nonlocal if it does not admit a local hidden variable-local hidden state (LHV-LHS) model. It is interesting to know what states can generate EPR-nonlocal correlations in the simplest nontrivial scenario, that is, two projective measurements for each party sharing a two-qubit state. Here we show that a two-qubit state can generate EPR-nonlocal full correlations (excluding marginal statistics) in this scenario if and only if it can generate Bell-nonlocal correlations. If full statistics (including marginal statistics) is taken into account, surprisingly, the same scenario can manifest the simplest one-way steering and the strongest hierarchy between steering and Bell nonlocality. To illustrate these intriguing phenomena in simple setups, several concrete examples are discussed in detail, which facilitates experimental demonstration. In the course of study, we introduce the concept of restricted LHS models and thereby derive a necessary and sufficient semidefinite-programming criterion to determine the steerability of any bipartite state under given measurements. Analytical criteria are further derived in several scenarios of strong theoretical and experimental interest.Comment: New results added, 13 pages, 3 figures; published in Phys. Rev.

Improving Implicit Sentiment Learning via Local Sentiment Aggregation

Recent well-known works demonstrate encouraging progress in aspect-based sentiment classification (ABSC), while implicit aspect sentiment modeling is still a problem that has to be solved. Our preliminary study shows that implicit aspect sentiments usually depend on adjacent aspects' sentiments, which indicates we can extract implicit sentiment via local sentiment dependency modeling. We formulate a local sentiment aggregation paradigm (LSA) based on empirical sentiment patterns (SP) to address sentiment dependency modeling. Compared to existing methods, LSA is an efficient approach that learns the implicit sentiments in a local sentiment aggregation window, which tackles the efficiency problem and avoids the token-node alignment problem of syntax-based methods. Furthermore, we refine a differential weighting method based on gradient descent that guides the construction of the sentiment aggregation window. According to experimental results, LSA is effective for all objective ABSC models, attaining state-of-the-art performance on three public datasets. LSA is an adaptive paradigm and is ready to be adapted to existing models, and we release the code to offer insight to improve existing ABSC models.Comment: Source Code: https://github.com/yangheng95/PyABS

Estimation of Multiple Petrophysical Parameters for Hydrocarbon Reservoirs with the Ensemble-Based Technique

The ensemble-based history matching technique has been successfully applied to simultaneously estimate multiple petrophysical parameters for hydrocarbon reservoirs. The tuning petrophysical properties include horizontal and vertical permeability, porosity and three-phase relative permeability curves. Four scenarios with different combination of the tuning parameters have been evaluated. The ensemble-based history matching technique is found to be capable of estimating multiple petrophysical parameters by conditioning the reservoir geological models to production history. The uncertainty range of production data generated from the updated models is reduced compared to that of initial models. However, the history-matched models may not always provide good production prediction results, especially when absolute permeability and relative permeability are tuned simultaneously. This further illustrates the non-uniqueness of the history matching solutions. In addition, three-phase relative permeability curves are found to be estimated with good accuracy when absolute permeability fields are known.Key words: Petrophysical parameters; Assisted history matching; Ensemble kalman filter (EnKF); PUNQ-S3 mode

InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators

Instruction-based language modeling has received significant attention in pretrained language models. However, the efficiency of instruction engineering remains low and hinders the development of instruction studies. Recent studies have focused on automating instruction generation, but they primarily aim to improve performance without considering other crucial objectives that impact instruction quality, such as instruction length and perplexity. Therefore, we propose a novel approach (i.e., InstOptima) that treats instruction generation as an evolutionary multi-objective optimization problem. In contrast to text edition-based methods, our approach utilizes a large language model (LLM) to simulate instruction operators, including mutation and crossover. Furthermore, we introduce an objective-guided mechanism for these operators, allowing the LLM to comprehend the objectives and enhance the quality of the generated instructions. Experimental results demonstrate improved fine-tuning performance and the generation of a diverse set of high-quality instructions.Comment: Accepted by EMNLP Finding