RLTF: Reinforcement Learning from Unit Test Feedback

The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing reinforcement learning (RL) to improve the performance of large language models (LLMs) for code. However, these RL methods have only used offline frameworks, limiting their exploration of new sample spaces. Additionally, current approaches that utilize unit test signals are rather simple, not accounting for specific error locations within the code. To address these issues, we proposed RLTF, i.e., Reinforcement Learning from Unit Test Feedback, a novel online RL framework with unit test feedback of multi-granularity for refining code LLMs. Our approach generates data in real-time during training and simultaneously utilizes fine-grained feedback signals to guide the model towards producing higher-quality code. Extensive experiments show that RLTF achieves state-of-the-art performance on the APPS and the MBPP benchmarks. Our code can be found at: https://github.com/Zyq-scut/RLTF

Antibacterial characterization of Bacillus velezensis LG37 and mining of genes related to biosynthesis of antibacterial substances

Bacillus velezensis LG37 secretes various antibacterial substances and inhibits the growth of other bacteria. Here, we analyzed the antibacterial characteristics and the screening and verification of genes related to the synthesis of the antibacterial substance of LG37 by antibacterial activities experiment, Local BLAST+, and RT-PCR. LG37 was isolated from aquaculture water and preserved in our laboratory. The phylogenetic tree was used to analyze the genetic relationship between LG37 and the bacteriostatic test indicator strain. LG37 had a more substantial inhibitory effect on closely related strains, while the inhibitory effect on the more distantly related strains was weak. Combined with the results of genome sequencing, the ribosomal peptide (RP) bacteriocin gene and non-ribosomal peptide synthetase (NRPSs) related gene clusters were screened and analyzed. A total of six gene-coding RP bacteriocins and two genes coding surfactins and fengycin A NRPSs gene cluster were screened. Local BLAST+ analysis revealed a total of 11 NRPSs gene clusters. The active expression of the NRPSs and RP encoding genes was further validated by RT-PCR. The findings revealed various genes and gene clusters encoding RP bacteriocins and NRPSs in B. velezensis LG37. The bacterium is potentially valuable in diverse applications in aquaculture

Identification of new antibacterial targets in RNA polymerase of Mycobacterium tuberculosis by detecting positive selection sites

Bacterial RNA polymerase (RNAP) is an effective target for antibacterial treatment. In order to search new potential targets in RNAP of Mycobacterium, we detected adaptive selections of RNAP related genes in 13 strains of Mycobacterium by phylogenetic analysis. We first collected sequences of 17 genes including rpoA, rpoB, rpoC, rpoZ, and sigma factor A-M. Then maximum likelihood trees were constructed, followed by positive selection detection. We found that sigG shows positive selection along the clade (M. tuberculosis, M. bovis), suggesting its important evolutionary role and its potential to be a new antibacterial target. Moreover, the regions near 933Cys and 935His on the rpoB subunit of M. tuberculosis showed significant positive selection, which could also be a new attractive target for anti-tuberculosis drugs

Minimiser la consommation d’énergie pour des tâches en temps réel sur des plateformes hétérogènes avec contraintes de délais et de fiabilité

Low energy consumption and high reliability are widely identified as increasingly relevant issues in real-time systems on heterogeneous platforms. In this paper, we propose a multi-criteria optimization strategy to minimize the expected energy consumption while enforcing the reliability threshold and meeting all task deadlines. The tasks arrive periodically. Each instance of a task is replicated to ensure a prescribed reliability threshold. The platform is composed of processors with different (and possibly unrelated) characteristics, including speed profile, energy cost and failure rate. We provide several mapping and scheduling heuristics to solve this challenging optimization problem. Specifically, a novel approach is designed to control (i) how many replicas to use for each task, (ii) on which processor to map each replica and (iii) when to schedule each replica for eachtask instance on its assigned processor. Different mappings achieve different levels of reliability and consume different amounts of energy. Scheduling matters because once a task replica is successful, the other replicas of that task instance are canceled, which calls for minimizing the amount of temporal overlap between any replica pair. The experiments are conducted for a comprehensive set of execution scenarios, with a wide range of processor speed profiles and failure rates. The comparison results reveal that our strategies perform better than the random baseline, with a gain in energy consumption of more than 40% for nearly all cases. The absolute performance of the heuristics is assessed by a comparison with a lower-bound; the best heuristics achieve an excellent performance. It saves only 2% less energy than the lower-bound.La faible consommation d’énergie et la haute fiabilité sont identifiées comme des problèmes de plus en plus pertinents dans les systèmes en temps réel sur des plateformes hétérogènes. Dans ce rapport, nous proposons une stratégie d’optimisation multi-critère pour minimiser l’espérance de laconsommation d’énergie tout en respectant le seuil de fiabilité et toutes les échéances des tâches. Les tâches arrivent périodiquement. Chaque instance d’une tâche est répliquée pour garantir un seuil de fiabilité prescrit. La plateforme est composée de processeurs avec des caractéristiques différentes (et éventuellement sans corrélation), y compris la vitesse, le coût énergétique et le taux de panne. Nous fournissons plusieurs heuristiques de placement et d’ordonnancement pour ce problème d’optimisation difficile. Plus précisément, une nouvelle solution est conçue pour contrôler (i) le nombre de répliques à utiliser pour chaque tâche, (ii) sur quel processeur doit-on placer chaque réplique et (iii) comment ordonnancer chaque réplique de chaque instance de tâche sur le processeur qui lui est affecté. Différents placements atteignent différents niveaux de fiabilité et consomment différentes quantités d’énergie. L’ordonnancement est important car une fois qu’une réplique de tâche réussit, les autres répliques de cette instance sont annulées, ce qui demande de minimiser le recouvrement en temps entre toute paire de répliques. Les expériences sont exécutées pour un grand ensemble de scénarios, avec une large gamme de vitesses et de taux d’échec pour les processeurs. Les résultats montrent que nos stratégies fonctionnent mieux que la référence de base aléatoire, avec un gain de 40 % en consommation d’énergie, dans presque tous les cas. La performance absolue de l’heuristique est évaluée en la comparant avec une borne inférieure. La meilleure heuristique atteint une excellente performance, avec une valeur moyenne supérieure de seulement 2% à la borne inférieure

Microwave‐Assisted Pyrolysis of Biomass for Bio‐Oil Production

Microwave‐assisted pyrolysis (MAP) is a new thermochemical process that converts biomass to bio‐oil. Compared with the conventional electrical heating pyrolysis, MAP is more rapid, efficient, selective, controllable, and flexible. This chapter provides an up‐to‐date knowledge of bio‐oil production from microwave‐assisted pyrolysis of biomass. The chemical, physical, and energy properties of bio‐oils obtained from microwave‐assisted pyrolysis of biomass are described in comparison with those from conventional pyrolysis, the characteristics of microwave‐assisted pyrolysis as affected by biomass feedstock properties, microwave heating operations, use of exogenous microwave absorbents, and catalysts are discussed. With the advantages it offers and the further research and development recommended, microwave‐assisted pyrolysis has a bright future in production of bio‐oils that can effectively narrow the energy gap and reduce negative environmental impacts of our energy production and application practice

Advantages of GaN Based Light-Emitting Diodes with a P-InGaN Hole Reservoir Layer

A p-type InGaN hole reservoir layer (HRL) was designed and incorporated in GaN based light-emitting diodes (LEDs) to enhance hole injection efficiency and alleviate efficiency droop. The fabricated LEDs with p-type HRL exhibited higher light output power, smaller emission energy shift and broadening as compared to its counterpart. Based on electrical and optical characteristics analysis and numerical simulation, these improvements are mainly attributed to the alleviated band bending in the last couple of quantum well and electron blocking layer, and thus better hole injection efficiency. Meanwhile, the efficiency droop can be effectively mitigated when the p-InGaN HRL was used

Alkali extraction and physicochemical characterization of hemicelluloses from young bamboo (Phyllostachys pubescens Mazel)

Two hemicellulose fractions were obtained by extraction of one-month- old young bamboo (Phyllostachys pubescens Mazel). The fractionation procedure employed 2% NaOH as extractant, followed by filtration, acidification, precipitation, and washing with 70% ethanol solution. The total yield was 26.2%, based on the pentosan content in bamboo. The physicochemical properties were determined and sugar composition analysis showed that the hemicelluloses consisted mainly of xylose, arabinose, galactose, and a small amount of uronic acid. Furthermore, based on FT-IR and NMR spectra analyses, the structure of hemicelluloses was determined to be mainly arabinoxylans linked via (1→4)-β-glycosidic bonds with branches of arabinose and 4-O-methyl-D-glucuronic acid. The molecular weights were 6387 Da and 4076 Da, corresponding to the hemicelluloses HA and HB. Finally, the thermal stability was elucidated using the TG-DTG method. The obtained results can provide important information for understanding young bamboo and the hemicelluloses in it

Establishing a screening strategy for non-discriminatory reactive blood donors by constructing a predictive model of hepatitis B virus infection status from a single blood center in China

BackgroundWhen employing the transcription-mediated amplification method for screening blood donors, there are some non-discriminatory reactive results which are screening assay reactive but HBV-DNA discriminatory assay negative. This raises concerns regarding the possibility of false positives among donors, which may lead to permanent deferral of blood donors and affect blood supply. This study aimed to elucidate the infection status of these non-discriminatory reactive blood donors and develop and validate a model to predict individualized hepatitis B status to establish an optimal screening strategy.MethodsSupplementary tests were conducted on initial non-discriminating reactive donations to determine their HBV infection status, including repeat testing, viral load, serological marker detection, and follow-up. Primary clinical variables of the donors were recorded. Based on the Akaike information criterion, a stepwise forward algorithm was used to identify the predictive factors for information and construct a predictive model. The optimal screening strategy was determined through cost-effectiveness analysis.ResultsAt the Blood Center of Zhejiang Province, 435 cases of initial non-discriminatory reactive donations were collected over two successive periods and sub-categorized through repeated testing into the following three groups: non-repeated positive group, non-discriminated positive group, and non-repeated HBV-DNA positive group. The HBV discriminatory rate increased after repeated testing (110/435, 25.29%). According to supplementary tests, the HBV-DNA positivity rate was 65.52% (285/435), and occult HBV infection was a significantly different among groups (χ2 = 93.22, p < 0.01). The HBV serological markers and viral load in the non-repeated positive group differed from those in the other two groups, with a lower viral load and a higher proportion of false positives. The predictive model constructed using a stepwise forward algorithm exhibited high discrimination, good fit, high calibration, and effectiveness. A cost-effectiveness analysis indicated that utilizing repeated discriminatory testing and the predictive model is an extremely beneficial screening approach for non-discriminatory reactive blood donors.ConclusionNearly two-third (65.52%) of the non-discriminatory reactive blood donors were HBV-DNA positive. Our innovative approach of constructing a predictive model as a supplementary screening strategy, combined with repeated discriminatory experiments, can effectively identify the infection status of non-discriminatory reactive blood donors, thereby increasing the safety of blood transfusions