Rationalization for explainable NLP: a survey.

Recent advances in deep learning have improved the performance of many Natural Language Processing (NLP) tasks such as translation, question-answering, and text classification. However, this improvement comes at the expense of model explainability. Black-box models make it difficult to understand the internals of a system and the process it takes to arrive at an output. Numerical (LIME, Shapley) and visualization (saliency heatmap) explainability techniques are helpful; however, they are insufficient because they require specialized knowledge. These factors led rationalization to emerge as a more accessible explainable technique in NLP. Rationalization justifies a model's output by providing a natural language explanation (rationale). Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users. Since rationalization is a relatively new field, it is disorganized. As the first survey, rationalization literature in NLP from 2007 to 2022 is analyzed. This survey presents available methods, explainable evaluations, code, and datasets used across various NLP tasks that use rationalization. Further, a new subfield in Explainable AI (XAI), namely, Rational AI (RAI), is introduced to advance the current state of rationalization. A discussion on observed insights, challenges, and future directions is provided to point to promising research opportunities.

Toward sustainable culture media: Using artificial intelligence to optimize reduced-serum formulations for cultivated meat.

When considering options for future foods, cell culture approaches are at the fore, however, culture media to support the process has been identified as a significant contributor to the overall global warming potential (GWP) and cost of cultivated meat production. To address this issue, an artificial intelligence-based approach was applied to simultaneously optimize the GWP, cost, and cell growth rate of a reduced-serum culture media formulation for a zebrafish (ZEM2S cell line) cultivated meat production system. Response surface methodology (RSM) was used to design the experiments, with seven components - IGF, FGF, TGF, PDGF, selenium, ascorbic acid, and serum - selected as independent variables, given their influence on culture media performance. Radial basis function (RBF) neural networks and genetic algorithm (GA) were applied for prediction of dependent variables, and optimization of the culture media formulation, respectively. The results indicated that the developed RBF could accurately predict the GWP, cost and growth rate, with a model efficiency of 0.98. Subsequently, the three developed RBF neural networks predictive models were used as the inputs for a multi-objective genetic algorithm, and the optimal quantities of the independent variables were determined using a multi-objective optimization algorithm. The suggested RSM + RBF + GA framework in this study could be applied to sustainably optimize serum-free media development, identifying the combination of media ingredients that balances yield, environmental impact, and cost for various cultivated meat cell lines.

Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management.

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e., Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision-making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial) lead to shifts in planning and budgeting, but most importantly, reduce confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This paper presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.

Study of smart antibacterial PCL-xFe3 O4 thin films using mouse NIH-3T3 fibroblast cells in vitro.

Surface energy plays a major role in prokaryotic and eukaryotic cell interactions with biomedical devices. In the present study, poly(ε-caprolactone)-xFe3 O4 nanoparticles (PCL-xFO NPs; x = 0, 10, 20, 30, 40, 60 wt% FO concentration in PCL) composite thin films were developed for skin tissue regeneration. The surface properties in terms of roughness, surface energy, wettability of the thin films were altered with the incorporation of Fe3 O4 NPs. These thin films show antimicrobial properties and cyto-compatibility with NIH 3T3 mouse embryonic fibroblast cells. The porosity and thickness of the films were controlled by varying RPM of the spin coater. Interestingly, at 1000 RPM the roughness of the film decreased with increasing concentrations of FO NPs in PCL, whereas the surface energy increased with increasing FO NPs concentrations. Furthermore, the spreading of NIH-3T3 cells grown on PCL-xFO thin films was less as compared to control (TCPS), however cells overcame this effect after 48 h of seeding and cells spread similarly to those grown on TCPS after 48 h. Also, the incorporation of FO NPs in thin films induced inner membrane permeabilization in E. coli bacteria leading to bacterial cell death. The viability of E. coli bacteria decreased with increasing concentration of FO NPs in PCL. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 795-804, 2017.