XPS Characterization of Friedel-Crafts Cross-Linked Polystyrene

The combination of a difunctional alkylating agent, either hydroxymethylbenzyl chloride or α,α′-dichloroxylene with polystyrene or high-impact polystyrene together with a Friedel-Crafts catalyst, 2-ethylhexyldiphenylphosphate, and an amine to react with hydrogen chloride has been studied by X-ray photoelectron spectroscopy. The results confirm what had been suggested from previous investigations using thermogravimetric analysis; cross-linking of the polymer occurs as the temperature is raised and the alcohol-containing alkylating agent gives a greater amount of cross-linking than does the dichloro compound

Multimodality Imaging Assessment of Ocular Ischemic Syndrome

Objectives. To assess the underlying mechanisms of OIS and confirm the haemodynamic and retinal structure changes of early OIS. Methods. An observational cross-sectional study was conducted of 60 internal carotid artery (ICA) stenosis patients, and they were divided into OIS and control group. Colour doppler imaging, optical coherence tomography, and fundus fluorescein angiography were performed. Results. The middle cerebral artery (MCA) stenosis differs significantly between the two groups. More OIS patients had new collateral patency of posterior communicating artery (PCoA) and retrograde flow via the ophthalmic artery (OA) (p<0.001). The peak systolic velocity (PSV) in central retinal artery (CRA) and choroidal thickness (CT) was significantly reduced in OIS patients (p=0.001 and p<0.001). The arm-retina time (ART) and the retinal arteriovenous passage time (AVP) were prolonged in OIS patients (p<0.001 and p=0.001). CT, ART, and PSV of the CRA showed high sensitivity, while ART and ICA stenosis grade showed high specificity for the diagnosis of OIS according to ROC curve. Conclusions. Patients who suffered from severe ipsilateral ICA stenosis, new collateral patency of PCoAs, and MCA stenosis may be more susceptible to OIS. The most sensitive sign is PSV of CRA and CT, and the most specific sign is ART

FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets

In the swiftly expanding domain of Natural Language Processing (NLP), the potential of GPT-based models for the financial sector is increasingly evident. However, the integration of these models with financial datasets presents challenges, notably in determining their adeptness and relevance. This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models, specifically adapted for financial contexts. Through this methodology, we capitalize on the interoperability of open-source models, ensuring a seamless and transparent integration. We begin by explaining the Instruction Tuning paradigm, highlighting its effectiveness for immediate integration. The paper presents a benchmarking scheme designed for end-to-end training and testing, employing a cost-effective progression. Firstly, we assess basic competencies and fundamental tasks, such as Named Entity Recognition (NER) and sentiment analysis to enhance specialization. Next, we delve into a comprehensive model, executing multi-task operations by amalgamating all instructional tunings to examine versatility. Finally, we explore the zero-shot capabilities by earmarking unseen tasks and incorporating novel datasets to understand adaptability in uncharted terrains. Such a paradigm fortifies the principles of openness and reproducibility, laying a robust foundation for future investigations in open-source financial large language models (FinLLMs).Comment: Workshop on Instruction Tuning and Instruction Following at NeurIPS 202

Machine Learning for Cancer Drug Combination

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154605/1/cpt1773_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154605/2/cpt1773.pd