Private-Library-Oriented Code Generation with Large Language Models

Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. This paper will delve into utilizing LLMs for code generation in private libraries, as they are widely employed in everyday programming. Despite their remarkable capabilities, generating such private APIs poses a formidable conundrum for LLMs, as they inherently lack exposure to these private libraries during pre-training. To address this challenge, we propose a novel framework that emulates the process of programmers writing private code. This framework comprises two modules: APIFinder first retrieves potentially useful APIs from API documentation; and APICoder then leverages these retrieved APIs to generate private code. Specifically, APIFinder employs vector retrieval techniques and allows user involvement in the retrieval process. For APICoder, it can directly utilize off-the-shelf code generation models. To further cultivate explicit proficiency in invoking APIs from prompts, we continuously pre-train a reinforced version of APICoder, named CodeGenAPI. Our goal is to train the above two modules on vast public libraries, enabling generalization to private ones. Meanwhile, we create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval, and meticulously handcraft test cases for each benchmark to support comprehensive evaluations. Numerous experiments on the four benchmarks consistently affirm the effectiveness of our approach. Furthermore, deeper analysis is also conducted to glean additional insights

The neural correlates of apathy in the context of aging and brain disorders: a meta-analysis of neuroimaging studies

IntroductionApathy is a prevalent mood disturbance that occurs in a wide range of populations, including those with normal cognitive aging, mental disorders, neurodegenerative disorders and traumatic brain injuries. Recently, neuroimaging technologies have been employed to elucidate the neural substrates underlying brain disorders accompanying apathy. However, the consistent neural correlates of apathy across normal aging and brain disorders are still unclear.MethodsThis paper first provides a brief review of the neural mechanism of apathy in healthy elderly individuals, those with mental disorders, neurodegenerative disorders, and traumatic brain injuries. Further, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, the structural and functional neuroimaging meta-analysis using activation likelihood estimation method is performed on the apathy group with brain disorders and the healthy elderly, aiming at exploring the neural correlates of apathy.ResultsThe structural neuroimaging meta-analysis showed that gray matter atrophy is associated with apathy in the bilateral precentral gyrus (BA 13/6), bilateral insula (BA 47), bilateral medial frontal gyrus (BA 11), bilateral inferior frontal gyrus, left caudate (putamen) and right anterior cingulate, while the functional neuroimaging meta-analysis suggested that the functional connectivity in putamen and lateral globus pallidus is correlated with apathy.DiscussionThrough the neuroimaging meta-analysis, this study has identified the potential neural locations of apathy in terms of brain structure and function, which may offer valuable pathophysiological insights for developing more effective therapeutic interventions for affected patients

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given

Influence of radiation on Hemarthria compressa's genetic variations

Using the material of Hemarthria compressa (L.F.) R.Br. cv. YA’AN, we carried out this research to study the influence of radiation on the genetic variation of plants. Genetic difference was analyzed with expressed sequence tag-simple sequence repeat (EST-SSR) molecular marker through the comparison of 60Co-γ radiation on H. compressa seed stems and original variety. By using 20 primer pairs, 176 polymerase chain reaction (PCR)-amplifications with clear and consistent bands were obtained. The results showed that 155 of 176 bands were polymorphic, which indicating an 88.07% polymorphism rate, and each pair of primers had 8.8 amplified bands on average; the amplitude of polymorphism information content was 0.4709–0.6952 with an average value 0.6081. The genetic similarity coefficient of H. compressa and its mutants ranged from 0.4318 to 0.8239 with an average of 0.6671. As a consequence, existence of genetic differences between the mutants and the basic material was proved.We gratefully acknowledge financial support from the Modern Agro-industry Technology Research System (CARS-34) and the Sichuan Province Breeding Research grant (2016NZ0098-11).Peer reviewe

Large Language Models Meet NL2Code: A Survey

The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence. Thanks to the rapid development of pre-training techniques, surging large language models are being proposed for code, sparking the advances in NL2Code. To facilitate further research and applications in this field, in this paper, we present a comprehensive survey of 27 existing large language models for NL2Code, and also review benchmarks and metrics. We provide an intuitive comparison of all existing models on the HumanEval benchmark. Through in-depth observation and analysis, we provide some insights and conclude that the key factors contributing to the success of large language models for NL2Code are "Large Size, Premium Data, Expert Tuning". In addition, we discuss challenges and opportunities regarding the gap between models and humans. We also create a website https://nl2code.github.io to track the latest progress through crowd-sourcing. To the best of our knowledge, this is the first survey of large language models for NL2Code, and we believe it will contribute to the ongoing development of the field.Comment: Accepted to the main conference of ACL 2023 (long paper

Focus on blood pressure levels and variability in the early phase of acute ischemic stroke with hypertension and carotid stenosis

Abstract To investigate the optimal blood pressure (BP) levels and relative importance of BP and BP variability in the early phase of acute ischemic stroke (AIS) for hypertensive patients with carotid artery stenosis (CAS). A single‐center cohort study included 750 AIS patients with hypertension and tests were performed for CAS. Participants were categorized to Group 1 (SBP < 140 mm Hg and DBP < 90 mm Hg), Group 2: (SBP: 140–159 mm Hg and or DBP: 90–99 mm Hg), and Group 3: (SBP ≥160 mm Hg and/or DBP ≥100 mm Hg) according to the guidelines. The associations of mean BP levels and variability with outcomes (recurrent stroke, all‐cause death and the composite cardiovascular events) at 6 months were analyzed by Cox proportional hazard models. The associations of BP variability with BP levels and cerebral blood flow (CBF) were analyzed by linear regression and generalized additive models. Both for primary and secondary outcome, more events occurred in Group 1 compared with Group 2, while no significant difference was found in Group 3 with higher BP levels. Lower systolic BP variability showed better prognosis and higher CBF. The associations were more significant in patients with CAS ≥50%. BP variability exhibited a linear negative relationship with BP levels. In the early phase of AIS with hypertension and CAS, maintaining low blood pressure variability may be important to improve outcomes while low BP levels (SBP/DBP < 140/90 mm Hg) were harmful, especially in those patients with CAS ≥ 50%