Communicative Agents for Software Development

Software engineering is a domain characterized by intricate decision-making processes, often relying on nuanced intuition and consultation. Recent advancements in deep learning have started to revolutionize software engineering practices through elaborate designs implemented at various stages of software development. In this paper, we present an innovative paradigm that leverages large language models (LLMs) throughout the entire software development process, streamlining and unifying key processes through natural language communication, thereby eliminating the need for specialized models at each phase. At the core of this paradigm lies ChatDev, a virtual chat-powered software development company that mirrors the established waterfall model, meticulously dividing the development process into four distinct chronological stages: designing, coding, testing, and documenting. Each stage engages a team of agents, such as programmers, code reviewers, and test engineers, fostering collaborative dialogue and facilitating a seamless workflow. The chat chain acts as a facilitator, breaking down each stage into atomic subtasks. This enables dual roles, allowing for proposing and validating solutions through context-aware communication, leading to efficient resolution of specific subtasks. The instrumental analysis of ChatDev highlights its remarkable efficacy in software generation, enabling the completion of the entire software development process in under seven minutes at a cost of less than one dollar. It not only identifies and alleviates potential vulnerabilities but also rectifies potential hallucinations while maintaining commendable efficiency and cost-effectiveness. The potential of ChatDev unveils fresh possibilities for integrating LLMs into the realm of software development.Comment: 25 pages, 9 figures, 2 table

Exploring Format Consistency for Instruction Tuning

Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of instructions in the training data can consistently enhance generalization performance, which facilitates a recent endeavor to collect various instructions and integrate existing instruction tuning datasets into larger collections. However, different users have their unique ways of expressing instructions, and there often exist variations across different datasets in the instruction styles and formats, i.e., format inconsistency. In this work, we study how format inconsistency may impact the performance of instruction tuning. We propose a framework called "Unified Instruction Tuning" (UIT), which calls OpenAI APIs for automatic format transfer among different instruction tuning datasets. We show that UIT successfully improves the generalization performance on unseen instructions, which highlights the importance of format consistency for instruction tuning. To make the UIT framework more practical, we further propose a novel perplexity-based denoising method to reduce the noise of automatic format transfer. We also train a smaller offline model that achieves comparable format transfer capability than OpenAI APIs to reduce costs in practice

MAPK1 promotes the metastasis and invasion of gastric cancer as a bidirectional transcription factor

Background: The Mitogen-activated protein kinase 1 (MAPK1) has both independent functions of phosphorylating histones as a kinase and directly binding the promoter regions of genes to regulate gene expression as a transcription factor. Previous studies have identified elevated expression of MAPK1 in human gastric cancer, which is associated with its role as a kinase, facilitating the migration and invasion of gastric cancer cells. However, how MAPK1 binds to its target genes as a transcription factor and whether it modulates related gene expressions in gastric cancer remains unclear. Results: Here, we integrated biochemical assays (protein interactions and chromatin immunoprecipitation (ChIP)), cellular analysis assays (cell proliferation and migration), RNA sequencing, ChIP sequencing, and clinical analysis to investigate the potential genomic recognition patterns of MAPK1 in a human gastric adenocarcinoma cell-line (AGS) and to uncover its regulatory effect on gastric cancer progression. We confirmed that MAPK1 promotes AGS cells invasion and migration by regulating the target genes in different directions, up-regulating seven target genes (KRT13, KRT6A, KRT81, MYH15, STARD4, SYTL4, and TMEM267) and down-regulating one gene (FGG). Among them, five genes (FGG, MYH15, STARD4, SYTL4, and TMEM267) were first associated with cancer procession, while the other three (KRT81, KRT6A, and KRT13) have previously been confirmed to be related to cancer metastasis and migration. Conclusion: Our data showed that MAPK1 can bind to the promoter regions of these target genes to control their transcription as a bidirectional transcription factor, promoting AGS cell motility and invasion. Our research has expanded the understanding of the regulatory roles of MAPK1, enriched our knowledge of transcription factors, and provided novel candidates for cancer therapeutics

T3P: Demystifying Low-Earth Orbit Satellite Broadband

The Internet is going through a massive infrastructural revolution with the advent of low-flying satellite networks, 5/6G, WiFi7, and hollow-core fiber deployments. While these networks could unleash enhanced connectivity and new capabilities, it is critical to understand the performance characteristics to efficiently drive applications over them. Low-Earth orbit (LEO) satellite mega-constellations like SpaceX Starlink aim to offer broad coverage and low latencies at the expense of high orbital dynamics leading to continuous latency changes and frequent satellite hand-offs. This paper aims to quantify Starlink's latency and its variations and components using a real testbed spanning multiple latitudes from the North to the South of Europe. We identify tail latencies as a problem. We develop predictors for latency and throughput and show their utility in improving application performance by up to 25%. We also explore how transport protocols can be optimized for LEO networks and show that this can improve throughput by up to 115% (with only a 5% increase in latency). Also, our measurement testbed with a footprint across multiple locations offers unique trigger-based scheduling capabilities that are necessary to quantify the impact of LEO dynamics.Comment: 16 page

Copy-move forgery detection using combined features and transitive matching

Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks

Twentieth-century warming preserved in a Geladaindong mountain ice core, central Tibetan Plateau

High-resolution δ18O records from a Geladaindong mountain ice core spanning the period 1477-1982 were used to investigate past temperature variations in the Yangtze River source region of the central Tibetan Plateau (TP). Annual ice-core δ18O records were positively correlated with temperature data from nearby meteorological stations, suggesting that the δ18O record represented the air temperature in the region. A generally increasing temperature trend over the past 500 years was identified, with amplified warming during the 20th century. A colder stage, spanning before the 1850s, was found to represent the Little Ice Age with colder periods occurring during the 1470s–1500s, 1580s–1660s, 1700s–20s and 1770s–1840s. Compared with other temperature records from the TP and the Northern Hemisphere, the Geladaindong ice-core record suggested that the regional climate of the central TP experienced a stronger warming trend during the 20th century than other regions. In addition, a positive relationship between the Geladaindong δ18O values and the North Atlantic Oscillation index, combined with a wavelet analysis of δ18O records, indicated that there was a potential atmospheric teleconnection between the North Atlantic and the central TP

10.13% Efficiency All-Polymer Solar Cells Enabled by Improving the Optical Absorption of Polymer Acceptors

The limited light absorption capacity for most polymer acceptors hinders the improvement of the power conversion efficiency (PCE) of all-polymer solar cells (all-PSCs). Herein, by simultaneously increasing the conjugation of the acceptor unit and enhancing the electron-donating ability of the donor unit, a novel narrow-bandgap polymer acceptor PF3-DTCO based on an A–D–A-structured acceptor unit ITIC16 and a carbon–oxygen (C–O)-bridged donor unit DTCO is developed. The extended conjugation of the acceptor units from IDIC16 to ITIC16 results in a red-shifted absorption spectrum and improved absorption coefficient without significant reduction of the lowest unoccupied molecular orbital energy level. Moreover, in addition to further broadening the absorption spectrum by the enhanced intramolecular charge transfer effect, the introduction of C–O bridges into the donor unit improves the absorption coefficient and electron mobility, as well as optimizes the morphology and molecular order of active layers. As a result, the PF3-DTCO achieves a higher PCE of 10.13% with a higher short-circuit current density (Jsc) of 15.75 mA cm−2 in all-PSCs compared with its original polymer acceptor PF2-DTC (PCE = 8.95% and Jsc = 13.82 mA cm−2). Herein, a promising method is provided to construct high-performance polymer acceptors with excellent optical absorption for efficient all-PSCs

Hsf transcription factor gene family in peanut (Arachis hypogaea L.): genome-wide characterization and expression analysis under drought and salt stresses

Heat shock transcription factors (Hsfs) play important roles in plant developmental regulations and various stress responses. In present study, 46 Hsf genes in peanut (AhHsf) were identified and analyzed. The 46 AhHsf genes were classed into three groups (A, B, and C) and 14 subgroups (A1-A9, B1-B4, and C1) together with their Arabidopsis homologs according to phylogenetic analyses, and 46 AhHsf genes unequally located on 17 chromosomes. Gene structure and protein motif analysis revealed that members from the same subgroup possessed similar exon/intron and motif organization, further supporting the results of phylogenetic analyses. Gene duplication events were found in peanut Hsf gene family via syntenic analysis, which were important in Hsf gene family expansion in peanut. The expression of AhHsf genes were detected in different tissues using published data, implying that AhHsf genes may differ in function. In addition, several AhHsf genes (AhHsf5, AhHsf11, AhHsf20, AhHsf24, AhHsf30, AhHsf35) were induced by drought and salt stresses. Furthermore, the stress-induced member AhHsf20 was found to be located in nucleus. Notably, overexpression of AhHsf20 was able to enhance salt tolerance. These results from this study may provide valuable information for further functional analysis of peanut Hsf genes