Phase transition of the El Niño–Southern Oscillation: A stationary SST mode

ABSTRACT A stationary SST mode is proposed to understand the physical mechanisms responsible for the phase transition of the El Niño-Southern Oscillation. This stationary SST mode differs from the original delayed oscillator mode and the slow SST mode in the sense that it considers both balanced and unbalanced thermocline depth variations and does not take into account the zonal propagation of SST. Within this mode, the Walker circulation acts as a positive feedback mechanism to amplify and maintain an existing interannual SST anomaly, whereas the Hadley circulation acts as a negative feedback mechanism that dismisses the original anomaly and causes the phase shift from a warm (cold) to a cold (warm) episode. The key to the cause of interannual oscillations in the stationary SST mode lies in the zonal-mean thermocline depth variation that is not in equilibrium with the winds. Because of the nonequilibrium, this part of the thermocline depth anomaly tends to have a phase lag with the wind (or SST) anomaly and therefore holds a key for the interannual oscillation. The zonally asymmetric part of the thermocline depth anomaly, on the other hand, is always in Sverdrup balance with the winds. Such a phase relationship agrees well with observations and with GCM simulations. The stationary SST mode strongly depends on the basin width, on the air-sea coupling strength, and on the seasonal-cycle basic state. For a reasonable parameter regime, it depicts an interannual oscillation with a period of 2-7 years. This stationary SST mode is also season dependent: it has a maximum growth rate during the later part of the year and a negative growth rate during the northern spring, which may explain the occurrence of the mature phases of the El Niño in the northern winter and a rapid drop of the lagged correlation of the Southern Oscillation index in the boreal spring

Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education

Developing models to automatically score students' written responses to science problems is critical for science education. However, collecting and labeling sufficient student responses for training models is time and cost-consuming. Recent studies suggest that pre-trained language models (PLMs) can be adapted to downstream tasks without fine-tuning with prompts. However, no research has employed such a prompt approach in science education. As student responses are presented with natural language, aligning the scoring procedure as the next sentence prediction task using prompts can skip the costly fine-tuning stage. In this study, we developed a zero-shot approach to automatically score student responses via Matching Exemplars as Next Sentence Prediction (MeNSP). This approach employs no training samples. We first apply MeNSP in scoring three assessment tasks of scientific argumentation and found machine-human scoring agreements, Cohen's Kappa ranges from 0.30 to 0.57, and F1 score ranges from 0.54 to 0.81. To improve the performance, we extend our research to the few-shots setting, either randomly selecting labeled student responses or manually constructing responses to fine-tune the models. We find that one task's performance is improved with more samples, Cohen's Kappa from 0.30 to 0.38, and F1 score from 0.54 to 0.59; for the two others, scoring performance is not improved. We also find that randomly selected few-shots perform better than the human expert-crafted approach. This study suggests that MeNSP can yield referable automatic scoring for student responses while significantly reducing the cost of model training. This method can benefit low-stakes classroom assessment practices in science education. Future research should further explore the applicability of the MeNSP in different types of assessment tasks in science education and improve the model performance.Comment: 10+3 page

Electromagnetic Nondestructive Evaluation of Wire Insulation and Models of Insulation Material Properties

Polymers have been widely used as wiring electrical insulation materials in space/air-craft. The dielectric properties of insulation polymers can change over time, however, due to various aging processes such as exposure to heat, humidity and mechanical stress. Therefore, the study of polymers used in electrical insulation of wiring is important to the aerospace industry due to potential loss of life and aircraft in the event of an electrical fire caused by breakdown of wiring insulation. Part of this research is focused on studying the mechanisms of various environmental aging process of the polymers used in electrical wiring insulation and the ways in which their dielectric properties change as the material is subject to the aging processes. The other part of the project is to determine the feasibility of a new capacitive nondestructive testing method to indicate degradation in the wiring insulation, by measuring its permittivity

Analysis of Linear Receivers in a Target SINR Game for Wireless Cognitive Networks

Abstract-Signal to Interference plus Noise Ratio (SINR) is a key parameter for every user in a wireless network. Different users with heterogeneous QoS requirements have different target SINR requirements. In cognitive radio (CR) networks, secondary users try to access the available spectrum in order to make successful transmissions. However, without proper regulation, they may transmit at their maximum power to achieve the highest possible SINR, which can be even worse than the current wasteful static spectrum utilization. A target SINR game (TSG) is a powerful tool to regulate each secondary user's behavior, provide them with decent SINRs (i.e. close to their target SINRs) and simultaneously limit the interference they cause to primary users and other secondary users. The goal of this paper is to analyze the performance of the Matched Filter (MF) receiver and the linear MMSE receiver (LMMSE) in a TSG. As expected, the LMMSE shows several advantages in performance over the MF

MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases

Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks like medical question answering (QA). Moreover, they tend to function as "black-boxes," making it challenging to modify their behavior. Addressing this, our study delves into model editing utilizing in-context learning, aiming to improve LLM responses without the need for fine-tuning or retraining. Specifically, we propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then we incorporate them into the query prompt for the LLM. Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM. Notably, our edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%. This work underscores the potential of model editing to enhance LLM performance, offering a practical approach to mitigate the challenges of black-box LLMs.Comment: 6 page

Unidirectional Photonic Reflector Using a Defective Atomic Lattice

Based on the broken spatial symmetry, we propose a novel scheme for engineering a unidirectional photonic reflector using a one-dimensional atomic lattice with defective cells that have been specifically designed to be vacant. By trapping three-level atoms and driving them into the regime of electromagnetically induced transparency, and through the skillful design of the number and position of vacant cells in the lattice, numerical simulations demonstrate that a broad and high unidirectional reflection region can be realized within EIT window. This proposed unidirectional reflector scheme provides a new platform for achieving optical nonreciprocity and has potential applications for designing optical circuits and devices of nonreciprocity at extremely low energy levels

Exploring the Influence of Information Entropy Change in Learning Systems

In this work, we explore the influence of entropy change in deep learning systems by adding noise to the inputs/latent features. The applications in this paper focus on deep learning tasks within computer vision, but the proposed theory can be further applied to other fields. Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this paper aims to rethink whether the conventional proposition always holds. We demonstrate that specific noise can boost the performance of various deep architectures under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the complexity of the task. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we achieved an unprecedented top 1 accuracy of over 95% on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm for improving model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change.Comment: Information Entropy, CNN, Transforme