New Luminescent Organic and Organomatallic Materials for OLED Applications

The design, synthesis and photophysics is presented for a new series of fluorescent carbazole-2,5-diphenyl-1,3,4-oxadiazole dyad molecules and in which the topology and electronic properties are systematically varied by chemical modification. Cyclic voltammetric data, HOMO-LUMO calculations, and X-ray crystallographic analyses are also presented. Our study sheds new light on designing ambipolar molecules and we demonstrate a strategy for precisely tuning the singlet and triplet levels in charge transfer molecules. A family of new 2,5-diphenyl-1,3,4-oxadiazole (OXDs) derivatives bearing ortho-alkyl substituents on one of the phenyl rings is reported. The reactions of these OXDs with IrCl3 under standard cyclometalating conditions did not give the usual μ-dichloro bridged diiridium OXDs complexes. Instead, novel diiridium complexes and monoiridium complexes were isolated and characterised by X-ray crystallography. It is proposed that the unusual structures arise due to the ortho-alkyl substituents leading to a substantial twisting of part of the OXDs system which, for steric reasons, changes the normal course of the metal-ligand coordination reactions. A new high triplet host polymer was synthesised and characterised. Photophysical studies and device data are presented. The triplet energy of this polymer is 2.73 eV. Also, the unoptimised device efficiency (device architecture: ITO/PEDOT:PSS/8% 145 and 40 wt% OXD-7 doped in polymer 133/Ba:Al) is 4.5 cd/A. Our study sheds new light on designing high triplet polymers and we demonstrate a strategy for possessing a high triplet level in a polymer by interrupting the conjugation on the polymer backbone

ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair

With the growing interest on Large Language Models (LLMs) for fault localization and program repair, ensuring the integrity and generalizability of the LLM-based methods becomes paramount. The code in existing widely-adopted benchmarks for these tasks was written before the the bloom of LLMs and may be included in the training data of existing popular LLMs, thereby suffering from the threat of data leakage, leading to misleadingly optimistic performance metrics. To address this issue, we introduce "ConDefects", a novel dataset of real faults meticulously curated to eliminate such overlap. ConDefects contains 1,254 Java faulty programs and 1,625 Python faulty programs. All these programs are sourced from the online competition platform AtCoder and were produced between October 2021 and September 2023. We pair each fault with fault locations and the corresponding repaired code versions, making it tailored for in fault localization and program repair related research. We also provide interfaces for selecting subsets based on different time windows and coding task difficulties. While inspired by LLM-based tasks, ConDefects can be adopted for benchmarking ALL types of fault localization and program repair methods. The dataset is publicly available, and a demo video can be found at https://www.youtube.com/watch?v=22j15Hj5ONk.Comment: 5pages, 3 figure

SeTransformer: A Transformer-Based Code Semantic Parser for Code Comment Generation

Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the long-term dependence problem and extract both the text and structure information from the program code, we propose a novel improved-Transformer-based comment generation method, named SeTransformer. Specifically, the SeTransformer utilizes the code tokens and an abstract syntax tree (AST) of programs to extract information as the inputs, and then, it leverages the self-attention mechanism to analyze the text and structural features of code simultaneously. Experimental results based on public corpus gathered from large-scale open-source projects show that our method can significantly outperform five state-of-the-art baselines (such as Hybrid-DeepCom and AST-attendgru). Furthermore, we also conduct a questionnaire survey for developers, and the results show that the SeTransformer can generate higher quality comments than those of other baselines

Large Language Models in Fault Localisation

Large Language Models (LLMs) have shown promise in multiple software engineering tasks including code generation, code summarisation, test generation and code repair. Fault localisation is essential for facilitating automatic program debugging and repair, and is demonstrated as a highlight at ChatGPT-4's launch event. Nevertheless, there has been little work understanding LLMs' capabilities for fault localisation in large-scale open-source programs. To fill this gap, this paper presents an in-depth investigation into the capability of ChatGPT-3.5 and ChatGPT-4, the two state-of-the-art LLMs, on fault localisation. Using the widely-adopted Defects4J dataset, we compare the two LLMs with the existing fault localisation techniques. We also investigate the stability and explanation of LLMs in fault localisation, as well as how prompt engineering and the length of code context affect the fault localisation effectiveness. Our findings demonstrate that within a limited code context, ChatGPT-4 outperforms all the existing fault localisation methods. Additional error logs can further improve ChatGPT models' localisation accuracy and stability, with an average 46.9% higher accuracy over the state-of-the-art baseline SmartFL in terms of TOP-1 metric. However, performance declines dramatically when the code context expands to the class-level, with ChatGPT models' effectiveness becoming inferior to the existing methods overall. Additionally, we observe that ChatGPT's explainability is unsatisfactory, with an accuracy rate of only approximately 30%. These observations demonstrate that while ChatGPT can achieve effective fault localisation performance under certain conditions, evident limitations exist. Further research is imperative to fully harness the potential of LLMs like ChatGPT for practical fault localisation applications

CIP2A facilitates the G1/S cell cycle transition via B-Myb in human papillomavirus 16 oncoprotein E6-expressing cells

Infection with high-risk human papillomaviruses (HR-HPVs, including HPV-16, HPV-18, HPV-31) plays a central aetiologic role in the development of cervical carcinoma. The transforming properties of HR-HPVs mainly reside in viral oncoproteins E6 and E7. E6 protein degrades the tumour suppressor p53 and abrogates cell cycle checkpoints. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein that is involved in the carcinogenesis of many human malignancies. Our previous data showed that CIP2A was overexpressed in cervical cancer. However, the regulation of CIP2A by HPV-16E6 remains to be elucidated. In this study, we demonstrated that HPV-16E6 significantly up-regulated CIP2A mRNA and protein expression in a p53-degradation-dependent manner. Knockdown of CIP2A by siRNA inhibited viability and DNA synthesis and caused G1 cell cycle arrest of 16E6-expressing cells. Knockdown of CIP2A resulted in a significant reduction in the expression of cyclin-dependent kinase 1 (Cdk1) and Cdk2. Although CIP2A has been reported to stabilize c-Myc by inhibiting PP2A-mediated dephosphorylation of c-Myc, we have presented evidence that the regulation of Cdk1 and Cdk2 by CIP2A is dependent on transcription factor B-Myb rather than c-Myc. Taken together, our study reveals the role of CIP2A in abrogating the G1 checkpoint in HPV-16E6-expressing cells and helps in understanding the molecular basis of HPV-induced oncogenesis

Continuum Foam: A Material Point Method for Shear-Dependent Flows

© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Yue, Y., Smith, B., Batty, C., Zheng, C., & Grinspun, E. (2015). Continuum Foam: A Material Point Method for Shear-Dependent Flows. Acm Transactions on Graphics, 34(5), 160. https://doi.org/10.1145/2751541We consider the simulation of dense foams composed of microscopic bubbles, such as shaving cream and whipped cream. We represent foam not as a collection of discrete bubbles, but instead as a continuum. We employ the material point method (MPM) to discretize a hyperelastic constitutive relation augmented with the Herschel-Bulkleymodel of non-Newtonian viscoplastic flow, which is known to closely approximate foam behavior. Since large shearing flows in foam can produce poor distributions of material points, a typical MPM implementation can produce non-physical internal holes in the continuum. To address these artifacts, we introduce a particle resampling method for MPM. In addition, we introduce an explicit tearing model to prevent regions from shearing into artificially thin, honey-like threads. We evaluate our method's efficacy by simulating a number of dense foams, and we validate our method by comparing to real-world footage of foam.This work was supported in part by the JSPS Postdoctoral Fellowshipsfor Research Abroad, NSF (Grants IIS-13-19483, CMMI-11-29917, CAREER-1453101), NSERC (Grant RGPIN-04360-2014), Intel, The Walt Disney Company, Autodesk, Side Effects, NVIDIA,Adobe, and The Foundry

A versatile hybrid polyphenylsilane host for highly efficient solution-processed blue and deep blue electrophosphorescence

A universal hybrid polymeric host (PCzSiPh) for blue and deep blue phosphors has been designed and synthesized by incorporating electron-donating carbazole as pendants on a polytetraphenylsilane main chain. The polymer PCzSiPh (4) has a wide bandgap and high triplet energy (ET) because of the tetrahedral geometry of the silicon atom in the tetraphenylsilane backbone. The distinct physical properties of good solubility, combined with high thermal and morphological stability give amorphous and homogenous PCzSiPh films by solution processing. As a result, using PCzSiPh as host with the guest iridium complex TMP-FIrpic gives blue phosphorescent organic light-emitting diodes (PhOLEDs) with overall performance which far exceeds that of a control device with poly(vinylcarbazole) (PVK) host. Notably, FIrpic-based devices exhibit a maximum external quantum efficiency (EQE) of 14.3% (29.3 cd A−1, 10.4 lm W−1) which are comparable to state-of-the-art literature data using polymer hosts for a blue dopant emitter. Moreover, the versatility of PCzSiPh extends to deep blue PhOLEDs using FIr6 and FCNIrpic as dopants, with high efficiencies of 11.3 cd A−1 and 8.6 cd A−1, respectively