Text Alignment Is An Efficient Unified Model for Massive NLP Tasks

Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is often not an efficient formulation for many of the tasks, demanding an extreme scale of model parameters (10s or 100s of billions) and sometimes yielding suboptimal performance. In practice, it is often desirable to build more efficient models -- despite being less versatile, they still apply to a substantial subset of problems, delivering on par or even superior performance with much smaller model sizes. In this paper, we propose text alignment as an efficient unified model for a wide range of crucial tasks involving text entailment, similarity, question answering (and answerability), factual consistency, and so forth. Given a pair of texts, the model measures the degree of alignment between their information. We instantiate an alignment model (Align) through lightweight finetuning of RoBERTa (355M parameters) using 5.9M examples from 28 datasets. Despite its compact size, extensive experiments show the model's efficiency and strong performance: (1) On over 20 datasets of aforementioned diverse tasks, the model matches or surpasses FLAN-T5 models that have around 2x or 10x more parameters; the single unified model also outperforms task-specific models finetuned on individual datasets; (2) When applied to evaluate factual consistency of language generation on 23 datasets, our model improves over various baselines, including the much larger GPT-3.5 (ChatGPT) and sometimes even GPT-4; (3) The lightweight model can also serve as an add-on component for LLMs such as GPT-3.5 in question answering tasks, improving the average exact match (EM) score by 17.94 and F1 score by 15.05 through identifying unanswerable questions.Comment: NeurIPS 2023 Camera Ready, Code available at https://github.com/yuh-zha/Alig

Dataset for predicting cybersickness from a virtual navigation task

This work presents a dataset collected to predict cybersickness in virtual reality environments. The data was collected from navigation tasks in a virtual environment designed to induce cybersickness. The dataset consists of many data points collected from diverse participants, including physiological responses (EDA and Heart Rate) and self-reported cybersickness symptoms. The paper will provide a detailed description of the dataset, including the arranged navigation task, the data collection procedures, and the data format. The dataset will serve as a valuable resource for researchers to develop and evaluate predictive models for cybersickness and will facilitate more research in cybersickness mitigation

DOF: Accelerating High-order Differential Operators with Forward Propagation

Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems. Recent advancements in leveraging deep learning for solving PDE have shown significant promise. However, machine learning methods, such as Physics-Informed Neural Networks (PINN), face challenges in handling high-order derivatives of neural network-parameterized functions. Inspired by Forward Laplacian, a recent method of accelerating Laplacian computation, we propose an efficient computational framework, Differential Operator with Forward-propagation (DOF), for calculating general second-order differential operators without losing any precision. We provide rigorous proof of the advantages of our method over existing methods, demonstrating two times improvement in efficiency and reduced memory consumption on any architectures. Empirical results illustrate that our method surpasses traditional automatic differentiation (AutoDiff) techniques, achieving 2x improvement on the MLP structure and nearly 20x improvement on the MLP with Jacobian sparsity

Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

Landslide is a natural disaster that can easily threaten local ecology, people's lives and property. In this paper, we conduct modelling research on real unidirectional surface displacement data of recent landslides in the research area and propose a time series prediction framework named VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode decomposition, which can predict the landslide surface displacement more accurately. The model performs well on the test set. Except for the random item subsequence that is hard to fit, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the trend item subsequence and the periodic item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for the periodic item prediction module based on XGBoost\footnote{Accepted in ICANN2023}

Recombinase Polymerase Amplification for Rapid Detection of Zoonotic Pathogens: An Overview

With the advent of molecular technology, several isothermal techniques for rapid detection of zoonotic pathogens have been developed. Among them, recombinase polymerase amplification (RPA) is becoming an important technology for rapid, sensitive, and economical detection of zoonotic pathogens. RPA technology has the advantage of being able to be implemented in field settings, because the method requires minimal sample preparation and is performed at a constant low temperature (37–42°C). RPA is rapidly becoming a promising tool for the rapid detection, prevention, and control of zoonotic diseases. This article discusses the principles of RPA technology and its derivatives, including RPA coupled with lateral flow testing (RPA-LF), real-time fluorescence RPA, electrochemical RPA, and flocculation RPA, and their applications in the detection of zoonotic pathogens

Recent advances and prospect in immune microenvironment and its mechanisms of function in head and neck squamous cell carcinoma

Head and neck cancer (HNC) remains a significant cause of morbidity and mortality. The most prevalent pathology among HNC is head and neck squamous cell carcinoma (HNSCC). The tumor microenvironment (TME) encompasses the components surrounding tumor cells, including immune cells, stromal cells, extracellular matrix (ECM), blood and lymph vessels. Strategies targeting the TME have yielded significant outcomes. Thus, further exploration of the interactions between TME components is crucial. This review discussed recent advances in cytotoxic T lymphocytes (CTL), CD4+ T lymphocytes, regulatory T cells (Treg), myeloid-derived suppressor cells (MDSC), natural killer (NK) cells and tumor-associated macrophages (TAM) in HNSCC TME. The article summarized herein primarily focused on restoring the activity of anti-tumor cells and eliminating the immunosuppressive effects of Treg and so on, to provide new insights for more effective HNSCC therapy