Recent Advances of ZnO-Based Perovskite Solar Cell

Perovskite solar cells (PSCs) have developed rapidly over the past few years, and the power conversion efficiency (PCE) of PSCs has exceeded 25%. It has the characteristics of low cost, high efficiency, simple process and so on, and hence has a good development prospect. Due to the difference in electrons and holes diffusion lengths, electron transporting materials (ETMs) play a crucial role in the performance of PSCs. ZnO electron transport layer (ETL) has the advantages of high electron mobility, high transmittance, suitable energy level matching with neighbor layer in PSCs, low temperature preparation and environmental friendliness, so it has become the main application material of electron transport layer in perovskite solar cells. In this review, the application of ZnO-ETMs in PSCs in recent years is reviewed, and the effect of ZnO-ETMs on the performance of PSCs is also introduced. Finally, the limitations of ZnO-ETMs based PSCs and the methods to solve these problems are discussed, and the development prospect of PSCs is prospected

Wave breaking for the generalized Fornberg-Whitham equation

This paper aims to show that the Cauchy problem of the Burgers equation with a weakly dispersive perturbation involving the Bessel potential (generalization of the Fornberg-Whitham equation) can exhibit wave breaking for initial data with large slope. We also comment on the dispersive properties of the equation

DePT: Decoupled Prompt Tuning

This work breaks through the Base-New Tradeoff (BNT)dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to new tasks, and vice versa. Specifically, through an in-depth analysis of the learned features of the base and new tasks, we observe that the BNT stems from a channel bias issue, i.e., the vast majority of feature channels are occupied by base-specific knowledge, resulting in the collapse of taskshared knowledge important to new tasks. To address this, we propose the Decoupled Prompt Tuning (DePT) framework, which decouples base-specific knowledge from feature channels into an isolated feature space during prompt tuning, so as to maximally preserve task-shared knowledge in the original feature space for achieving better zero-shot generalization on new tasks. Importantly, our DePT is orthogonal to existing prompt tuning methods, hence it can improve all of them. Extensive experiments on 11 datasets show the strong flexibility and effectiveness of DePT. Our code and pretrained models are available at https://github.com/Koorye/DePT.Comment: 13 page

The Role of Occludin in Vascular Endothelial Protection

Endothelial tight junction proteins play an important role in maintaining the integrity of vascular endothelial structure and physiological function. In recent years, studies have found that alterations in the expression, distribution, and structure of endothelial tight junction proteins may lead to many related vascular diseases and pathologies (such as diabetes, atherosclerosis, neurodegenerative diseases, and hypertension). Therefore, related strategies to prevent and/or tight junction proteins dysfunction may be an important therapeutic target. Occludin, as the most representative one among tight junction proteins, is mainly responsible for sealing intercellular junctions, maintaining cell permeability and the integrity of vascular endothelium. Here, we review the published biological information of occludin. We highlight the relationship between occludin and vascular endothelial injury-related disease. At the same time, we show our current knowledge of how vascular endothelial occludin exerts the protective effect and possible clinical applications in the future

On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection

Detecting adversarial samples that are carefully crafted to fool the model is a critical step to socially-secure applications. However, existing adversarial detection methods require access to sufficient training data, which brings noteworthy concerns regarding privacy leakage and generalizability. In this work, we validate that the adversarial sample generated by attack algorithms is strongly related to a specific vector in the high-dimensional inputs. Such vectors, namely UAPs (Universal Adversarial Perturbations), can be calculated without original training data. Based on this discovery, we propose a data-agnostic adversarial detection framework, which induces different responses between normal and adversarial samples to UAPs. Experimental results show that our method achieves competitive detection performance on various text classification tasks, and maintains an equivalent time consumption to normal inference.Comment: Accepted by ACL2023 (Short Paper

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these flows. This paper instead introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach. Code will be made available.Comment: re-organize the presentatio

DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization

Adversarial training is one of the best-performing methods in improving the robustness of deep language models. However, robust models come at the cost of high time consumption, as they require multi-step gradient ascents or word substitutions to obtain adversarial samples. In addition, these generated samples are deficient in grammatical quality and semantic consistency, which impairs the effectiveness of adversarial training. To address these problems, we introduce a novel, effective procedure for instead adversarial training with only clean data. Our procedure, distribution shift risk minimization (DSRM), estimates the adversarial loss by perturbing the input data's probability distribution rather than their embeddings. This formulation results in a robust model that minimizes the expected global loss under adversarial attacks. Our approach requires zero adversarial samples for training and reduces time consumption by up to 70\% compared to current best-performing adversarial training methods. Experiments demonstrate that DSRM considerably improves BERT's resistance to textual adversarial attacks and achieves state-of-the-art robust accuracy on various benchmarks.Comment: Accepted by ACL202

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values. This alignment requires a vast corpus of human feedback to learn a reward model, which is subsequently used to finetune language models. However, we have identified that the reward model often finds shortcuts to bypass its intended objectives, misleadingly assuming that humans prefer longer responses. The emergence of length bias often induces the model to favor longer outputs, yet it doesn't equate to an increase in helpful information within these outputs. In this paper, we propose an innovative solution, applying the Product-of-Experts (PoE) technique to separate reward modeling from the influence of sequence length. In our framework, the main expert concentrates on understanding human intents, while the biased expert targets the identification and capture of length bias. To further enhance the learning of bias, we introduce perturbations into the bias-focused expert, disrupting the flow of semantic information. Experimental results validate the effectiveness of our approach, indicating that language model performance is improved, irrespective of sequence length.Comment: EMNLP 2023 findings, Length Bias in RLHF, Mitigate bias in reward modelin

MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities