Understanding Code Semantics: An Evaluation of Transformer Models in Summarization

This paper delves into the intricacies of code summarization using advanced transformer-based language models. Through empirical studies, we evaluate the efficacy of code summarization by altering function and variable names to explore whether models truly understand code semantics or merely rely on textual cues. We have also introduced adversaries like dead code and commented code across three programming languages (Python, Javascript, and Java) to further scrutinize the model's understanding. Ultimately, our research aims to offer valuable insights into the inner workings of transformer-based LMs, enhancing their ability to understand code and contributing to more efficient software development practices and maintenance workflows.Comment: Accepted at GenBench, EMNLP 2023. All authors are co-first authors and have equal contribution

PHOTOCATALYTICDEGRADATION OF RHODAMIN-B USING METAL COMPLEXES AND HYDROGEN PEROXIDE

ABSTRACT The photocatalytic degradation has been considered to be an efficient process for degradation of organic pollutants, which are present in the effluents released by industries. Thephotocatalytic bleaching was carried out on rhodamin-B (cationic dye) in the presence of iron (III) complex, hydrogen peroxide and was observed spectrophotometrically. The effect of various operating variables like pH, concentration of complex and dye, amount of HzOzand light intensity etc. was also observed on the efficiency of the reaction. A tentative mechanism has also been proposed for this photocatalytic degradation of rhodamin-B. Keywords: Photocatalytic degradation, rhodamin-B, metal complexes, hydrogen peroxide

On Surgical Fine-tuning for Language Encoders

Fine-tuning all the layers of a pre-trained neural language encoder (either using all the parameters or using parameter-efficient methods) is often the de-facto way of adapting it to a new task. We show evidence that for different downstream language tasks, fine-tuning only a subset of layers is sufficient to obtain performance that is close to and often better than fine-tuning all the layers in the language encoder. We propose an efficient metric based on the diagonal of the Fisher information matrix (FIM score), to select the candidate layers for selective fine-tuning. We show, empirically on GLUE and SuperGLUE tasks and across distinct language encoders, that this metric can effectively select layers leading to a strong downstream performance. Our work highlights that task-specific information corresponding to a given downstream task is often localized within a few layers, and tuning only those is sufficient for strong performance. Additionally, we demonstrate the robustness of the FIM score to rank layers in a manner that remains constant during the optimization process.Comment: Accepted to EMNLP 202

WHO global research priorities for antimicrobial resistance in human health

The WHO research agenda for antimicrobial resistance (AMR) in human health has identified 40 research priorities to be addressed by the year 2030. These priorities focus on bacterial and fungal pathogens of crucial importance in addressing AMR, including drug-resistant pathogens causing tuberculosis. These research priorities encompass the entire people-centred journey, covering prevention, diagnosis, and treatment of antimicrobial-resistant infections, in addition to addressing the overarching knowledge gaps in AMR epidemiology, burden and drivers, policies and regulations, and awareness and education. The research priorities were identified through a multistage process, starting with a comprehensive scoping review of knowledge gaps, with expert inputs gathered through a survey and open call. The priority setting involved a rigorous modified Child Health and Nutrition Research Initiative approach, ensuring global representation and applicability of the findings. The ultimate goal of this research agenda is to encourage research and investment in the generation of evidence to better understand AMR dynamics and facilitate policy translation for reducing the burden and consequences of AMR

PHOTOCATALYTIC DEGRADATION OF RHODAMIN-B USING METAL COMPLEXES AND HYDROGEN PEROXIDE

The photocatalytic degradation has been considered to be an efficient process for degradation of organic pollutants, which are present in the effluents released by industries. The photocatalytic bleaching was carried out on rhodamin-B (cationic dye) in the presence of iron (III) complex, hydrogen peroxide and was observed spectrophotometrically. The effect of various operating variables like pH, concentration of complex and dye, amount of H2O2 and light intensity etc. was also observed on the efficiency of the reaction. A tentative mechanism has also been proposed for this photocatalytic degradation of rhodamin-B.   Keywords: Photocatalytic degradation, rhodamin-B, metal complexes, hydrogen peroxid

Photocatalytic degradation of sunset yellow FCF in presence of some transition metal complexes and hydrogen peroxide

397-400The photocatalytic degradation of sunset yellow FCF by thiocyanate complexes of iron, copper and cobalt and hydrogen peroxide is reported here. The effect of different parameters, such as the pH, concentration of the complexes and dye, amount of H₂O₂ and light intensity on the rate of photocatalytic degradation has also been studied. The mechanism for degradation of the dye is also proposed