Boosting API Recommendation with Implicit Feedback

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of users' feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting users' feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.Comment: 15 pages, 4 figure

Large Language Models Cannot Self-Correct Reasoning Yet

Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues. Building upon this premise, this paper critically examines the role and efficacy of self-correction within LLMs, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities, without the crutch of external feedback. In the context of reasoning, our research indicates that LLMs struggle to self-correct their responses without external feedback, and at times, their performance even degrades after self-correction. Drawing from these insights, we offer suggestions for future research and practical applications in this field.Comment: ICLR 202

Compositional Semantic Parsing with Large Language Models

Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN. In this work, we identify additional challenges in more realistic semantic parsing tasks with larger vocabulary and refine these prompting techniques to address them. Our best method is based on least-to-most prompting: it decomposes the problem using prompting-based syntactic parsing, then uses this decomposition to select appropriate exemplars and to sequentially generate the semantic parse. This method allows us to set a new state of the art for CFQ while requiring only 1% of the training data used by traditional approaches. Due to the general nature of our approach, we expect similar efforts will lead to new results in other tasks and domains, especially for knowledge-intensive applications.Comment: Fixed metadata. No other change

Reap-2: An Interactive Quantitative Tool for Robust and Efficient Dose-Response Curve Estimation

REAP-2 is an interactive dose-response curve estimation tool for Robust and Efficient Assessment of drug Potency. It provides user-friendly dose-response curve estimation for in vitro studies and conducts statistical testing for model comparisons with a redesigned user interface. We also make a major update of the underlying estimation method with penalized beta regression, which demonstrates great reliability and accuracy in dose estimation and uncertainty quantification. In this note, we describe the method and implementation of REAP-2 with a highlight on potency estimation and drug comparison

In-situ aligning magnetic nanoparticles in thermoplastic adhesives for contactless rapid joining of composite structures

Magnetic nanoparticles of high magnetic susceptibility, such as magnetite (Fe3O4), have been used for wireless heating of adhesives and composites through the magnetic hysteresis loss mechanism, but the high concentrations of nanoparticles needed to meet heating performance targets can degrade mechanical properties. Herein, we present an in-situ aligning method to enhance the heating efficiency of magnetite nanoparticles in a nylon thermoplastic matrix without adversely affecting its mechanical strength. A composite adhesive was made by dispersing Fe3O4 nanoparticles in a nylon matrix followed by hot melting. Experimental results show that by subjecting the adhesive to an alternating magnetic field during the hot-melt process, its heating rate can be improved by 200% compared to that without applying the magnetic field. The improvement in the heating performance has been identified to stem from the alignment of the ease axis of the magnetic nanoparticles. This in-situ aligning technique enables better induction heating performance with the same amount of Fe3O4 nanoparticles, avoiding the agglomeration problem of high nanoparticle concentrations. Moreover, this technique makes it possible to develop high-performance self-heating thermoplastic adhesive for reversible bonding and self-healing solution with a wide range of applications, such as bonding and debonding of composites, temporary attachment of systems, and recyclable bonded structures

Simultaneous improvement of heating efficiency and mechanical strength of a self-healing thermoplastic polymer by hybridizing magnetic particles with conductive fibres

Radio-Frequency (RF) induction heating is a versatile in-situ method for contactless heating of structures by utilizing either magnetic hysteresis loss or eddy-current loss mechanism. Achieving high heating efficiency without degrading mechanical properties is a major challenge. Herein, a RF induction compatible self-healing composite was developed by hybridizing iron oxides (Fe3O4) nanoparticles with carbon fibre veils (CFVs) in poly(ethylene-co-methacrylic acid) (EMAA), which could possess both high magnetic and electrical properties. Owing to the multiscale conductive networks built by Fe3O4 nanoparticles and CFVs, the electrical conductivity of the nanocomposite was found to be higher than the linear combination of the individual contributions, thus creating a synergistic improvement in electrical conductivity and heating efficiency. Furthermore, single lap shear test results demonstrated that the combination of Fe3O4 nanoparticles and CFVs could significantly improve the bonding strength of EMAA polymer. Therefore, the hybridization of magnetic particles with conductive fibres offers a promising technology for a wide range of applications, such as self-healing, reversable bonding, and multiple use bonded composites

A Journey to the West: The Ancient Dispersal of Rice Out of East Asia.

Funder: Max Planck Institute for the Science of Human HistoryRice is one of the most culturally valued and widely grown crops in the world today, and extensive research over the past decade has clarified much of the narrative of its domestication and early spread across East and South Asia. However, the timing and routes of its dispersal into West Asia and Europe, through which rice eventually became an important ingredient in global cuisines, has remained less clear. In this article, we discuss the piecemeal, but growing, archaeobotanical data for rice in West Asia. We also integrate written sources, linguistic data, and ethnohistoric analogies, in order to better understand the adoption of rice outside its regions of origin. The human-mediated westward spread of rice proceeded gradually, while its social standing and culinary uses repeatedly changing over time and place. Rice was present in West Asia and Europe by the tail end of the first millennium BC, but did not become a significant crop in West Asia until the past few centuries. Complementary historical, linguistic, and archaeobotanical data illustrate two separate and roughly contemporaneous routes of westward dispersal, one along the South Asian coast and the other through Silk Road trade. By better understanding the adoption of this water-demanding crop in the arid regions of West Asia, we explore an important chapter in human adaptation and agricultural decision making