Statistics of polymer adsorption under shear flow

Using non-equilibrium Brownian dynamics computer simulations, we have investigated the steady state statistics of a polymer chain under three different shear environments: i) linear shear flow in the bulk (no walls), ii) shear vorticity normal to the adsorbing wall, iii) shear gradient normal to the adsorbing wall. The statistical distribution of the chain end-to-end distance and its orientational angles are calculated within our monomer-resolved computer simulations. Over a wide range of shear rates, this distribution can be mapped onto a simple theoretical finite-extensible-nonlinear-elastic dumbbell model with fitted anisotropic effective spring constants. The tails of the angular distribution functions are consistent with scaling predictions borrowed from the bulk dumbbell model. Finally, the frequency of the characteristic periodic tumbling motion has been investigated by simulation as well and was found to be sublinear with the shear rate for the three set-ups, which extends earlier results done in experiments and simulations for free and tethered polymer molecules without adsorption.Comment: 10 figure

Experimental Study on the Characteristics of Three-Dimensional Flow Structures with Changing Condition in Bifurcation Area

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

OverPrompt: Enhancing ChatGPT Capabilities through an Efficient In-Context Learning Approach

The exceptional performance of pre-trained large language models has revolutionised various applications, but their adoption in production environments is hindered by prohibitive costs and inefficiencies, particularly when utilising long prompts. This paper proposes OverPrompt, an in-context learning method aimed at improving LLM efficiency and performance by processing multiple inputs in parallel. Evaluated across diverse datasets, OverPrompt enhances task efficiency and integrates a diverse range of examples for improved performance. Particularly, it amplifies fact-checking and sentiment analysis tasks when supplemented with contextual information. Synthetic data grouping further enhances performance, suggesting a viable approach for data augmentation

CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models

Text classifiers built on Pre-trained Language Models (PLMs) have achieved remarkable progress in various tasks including sentiment analysis, natural language inference, and question-answering. However, the occurrence of uncertain predictions by these classifiers poses a challenge to their reliability when deployed in practical applications. Much effort has been devoted to designing various probes in order to understand what PLMs capture. But few studies have delved into factors influencing PLM-based classifiers' predictive uncertainty. In this paper, we propose a novel framework, called CUE, which aims to interpret uncertainties inherent in the predictions of PLM-based models. In particular, we first map PLM-encoded representations to a latent space via a variational auto-encoder. We then generate text representations by perturbing the latent space which causes fluctuation in predictive uncertainty. By comparing the difference in predictive uncertainty between the perturbed and the original text representations, we are able to identify the latent dimensions responsible for uncertainty and subsequently trace back to the input features that contribute to such uncertainty. Our extensive experiments on four benchmark datasets encompassing linguistic acceptability classification, emotion classification, and natural language inference show the feasibility of our proposed framework. Our source code is available at: https://github.com/lijiazheng99/CUE.Comment: Accepted to UAI 202

Could protein tertiary structure influence mammary transgene expression more than tissue specific codon usage?

Animal mammary glands have been successfully employed to produce therapeutic recombinant human proteins. However, considerable variation in animal mammary transgene expression efficiency has been reported. We now consider whether aspects of codon usage and/or protein tertiary structure underlie this variation in mammary transgene expression

Mid-frequency prediction of transmission loss using a novel hybrid deterministic and statistical method

A novel hybrid deterministic-statistical approach named ES-FE-SEA method specially used to predict the sound Transmission loss of panels in mid-frequency is proposed in this paper. The proposed hybrid methods takes the best advantages of edged-based smoothing FEM (ES-FEM) and statistical energy analysis (SEA) to further improve the accuracy of mid-frequency transmission loss predictions. The application of ES-FEM will “soften” the well-known “overly-stiff” behavior in the standard FEM solution and reduce the inherent numerical dispersion error. While the SEA approach will deal with the physical uncertainty in the relatively higher frequency range. Two different types of subsystems will be coupled based on “reciprocity relationship” theorem. The proposed was firstly applied to a standard simple numerical example, and excellent agreement with reference results was achieved. Thus the method is then applied to a more complicated model-a 2D dash panel in a car. The proposed ES-FE-SEA is verified by various numerical examples

CHIME : Cross-passage hierarchical memory network for generative review question answering

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module

Distilling ChatGPT for Explainable Automated Student Answer Assessment

Providing explainable and faithful feedback is crucial for automated student answer assessment. In this paper, we introduce a novel framework that explores using ChatGPT, a cutting-edge large language model, for the concurrent tasks of student answer scoring and rationale generation. We identify the appropriate instructions by prompting ChatGPT with different templates to collect the rationales, where inconsistent rationales are refined to align with marking standards. The refined ChatGPT outputs enable us to fine-tune a smaller language model that simultaneously assesses student answers and provides rationales. Extensive experiments on the benchmark dataset show that the proposed method improves the overall QWK score by 11% compared to ChatGPT. Furthermore, our thorough analysis and human evaluation demonstrate that the rationales generated by our proposed method are comparable to those of ChatGPT. Our approach provides a viable solution to achieve explainable automated assessment in education. Code available at https://github.com/lijiazheng99/aera.Comment: Accepted EMNLP 202