Unsuspected axillary lymph node metastasis of nasopharyngeal and cervical cancer on 18FDG PET/CT: a case report

Axillary lymph node metastasis (ALNM) from cancer except breast cancer is rare. Whole body 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) that simultaneously offers anatomic and metabolic information is widely used and has become an effective modality in many clinical fields, especially oncology, and also may detect an unexpected metastasis. We report two cases of ALMN of nasopharyngeal and cervical cancer that was detected on whole body PET/CT

Towards Objective-Tailored Genetic Improvement Through Large Language Models

While Genetic Improvement (GI) is a useful paradigm to improve functional and nonfunctional aspects of software, existing techniques tended to use the same set of mutation operators for differing objectives, due to the difficulty of writing custom mutation operators. In this work, we suggest that Large Language Models (LLMs) can be used to generate objective-tailored mutants, expanding the possibilities of software optimizations that GI can perform. We further argue that LLMs and the GI process can benefit from the strengths of one another, and present a simple example demonstrating that LLMs can both improve the effectiveness of the GI optimization process, while also benefiting from the evaluation steps of GI. As a result, we believe that the combination of LLMs and GI has the capability to significantly aid developers in optimizing their software.Comment: Accepted to the 12th International Workshop on Genetic Improvemen

Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction

Many automated test generation techniques have been developed to aid developers with writing tests. To facilitate full automation, most existing techniques aim to either increase coverage, or generate exploratory inputs. However, existing test generation techniques largely fall short of achieving more semantic objectives, such as generating tests to reproduce a given bug report. Reproducing bugs is nonetheless important, as our empirical study shows that the number of tests added in open source repositories due to issues was about 28% of the corresponding project test suite size. Meanwhile, due to the difficulties of transforming the expected program semantics in bug reports into test oracles, existing failure reproduction techniques tend to deal exclusively with program crashes, a small subset of all bug reports. To automate test generation from general bug reports, we propose LIBRO, a framework that uses Large Language Models (LLMs), which have been shown to be capable of performing code-related tasks. Since LLMs themselves cannot execute the target buggy code, we focus on post-processing steps that help us discern when LLMs are effective, and rank the produced tests according to their validity. Our evaluation of LIBRO shows that, on the widely studied Defects4J benchmark, LIBRO can generate failure reproducing test cases for 33% of all studied cases (251 out of 750), while suggesting a bug reproducing test in first place for 149 bugs. To mitigate data contamination, we also evaluate LIBRO against 31 bug reports submitted after the collection of the LLM training data terminated: LIBRO produces bug reproducing tests for 32% of the studied bug reports. Overall, our results show LIBRO has the potential to significantly enhance developer efficiency by automatically generating tests from bug reports.Comment: Accepted to IEEE/ACM International Conference on Software Engineering 2023 (ICSE 2023

Search Based Repair of Deep Neural Networks

Deep Neural Networks (DNNs) are being adopted in various domains, including safety critical ones. The wide-spread adoption also calls for ways to guide the testing of their accuracy and robustness, for which various test adequacy criteria and input generation methods have been recently introduced. In this paper, we explore the natural subsequent step: given an input that reveals unexpected behaviour in a trained DNN, we propose to repair the DNN using input-output pairs as a specification. This paper introduces Arachne, a novel program repair technique for DNNs. Arachne first performs sensitivity based fault localisation to limit the number of neural weights it has to modify. Subsequently, Arachne uses Particle Swarm Optimisation (PSO) to directly optimise the localised neural weights until the behaviour is corrected. An empirical study using three different benchmark datasets shows that Arachne can reduce the instances of the most frequent misclassification type committed by a pre-trained CIFAR-10 classifier by 27.5%, without any need for additional training data. Patches generated by Arachne tend to be more focused on the targeted misbehaviour than DNN retraining, which is more disruptive to non-targeted behaviour. The overall results suggest the feasibility of patching DNNs using Arachne until they can be retrained properly

Towards Autonomous Testing Agents via Conversational Large Language Models

Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. The recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized hallucination of LLMs can be beneficial while testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss some potential limitations

Influence Of Choice Context On Consumer Decision Making In Global Electronic Commerce

The objective of this paper is to understand how choice context will influence the decision making process of consumers when shopping at electronic shopping malls. This paper provides a framework for exploring context effects on consumer judgment and choice processes in the special case of electronic commerce. First an outline of the judgment process is presented which is used to identify the stages where the context effects may occur. Theliterature in experimental and social psychology, behavioral decision theory and consumer research are selectively reviewed for evidence regarding context effects on judgment. The approach adopted in this paper borrows directly from at least two converging sources : the cross-functional research in judgment and decision making in consumer behavior and cognitive psychology and the research in marketing issues in electronic commerce. The managerial implications of this research are answers to questions such as how best can the firm exploit this new form of transacting business to maximize its leverage in the market place and increase its market share. The academic contribution of this research on context effects is that it helps to reconcile two diverging research streams on judgment and choice (the economic perspective and the behavioral perspective)

Concurrent recording of co-localised electroencephalography and local field potential in rodent

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the neurogenesis of EEG is still very limited. Local field potentials (LFPs) recorded via a multi-laminar micro-electrode can provide a more detailed account of simultaneous neural activity across different cortical layers in the neocortex, but the technique is invasive. Combining EEG and LFP measurements in a pre-clinical model can greatly enhance our understanding of the neural mechanisms involved in the generation of EEG signals, and facilitate the derivation of a more realistic and biologically accurate mathematical model of EEG. Here we present a simple procedure for acquiring concurrent and co-localised EEG and multi-laminar LFP signals in the anaesthetised rodent. We also investigated whether EEG signals were significantly affected by a burr hole drilled in the skull for the insertion of a micro-electrode. Our results suggest that the burr hole has a negligible impact on EEG recordings