Influence of inversion on Mg mobility and electrochemistry in spinels

Magnesium oxide and sulfide spinels have recently attracted interest as cathode and electrolyte materials for energy-dense Mg batteries, but their observed electrochemical performance depends strongly on synthesis conditions. Using first principles calculations and percolation theory, we explore the extent to which spinel inversion influences Mg$^{2+}$ ionic mobility in MgMn$_2$O$_4$ as a prototypical cathode, and MgIn$_2$S$_4$ as a potential solid electrolyte. We find that spinel inversion and the resulting changes of the local cation ordering give rise to both increased and decreased Mg$^{2+}$ migration barriers, along specific migration pathways, in the oxide as well as the sulfide. To quantify the impact of spinel inversion on macroscopic Mg$^{2+}$ transport, we determine the percolation thresholds in both MgMn$_2$O$_4$ and MgIn$_2$S$_4$. Furthermore, we analyze the impact of inversion on the electrochemical properties of the MgMn$_2$O$_4$ cathode via changes in the phase behavior, average Mg insertion voltages and extractable capacities, at varying degrees of inversion. Our results confirm that inversion is a major performance limiting factor of Mg spinels and that synthesis techniques or compositions that stabilize the well-ordered spinel structure are crucial for the success of Mg spinels in multivalent batteries

Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles

We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Biased NLP models can perpetuate stereotypes and lead to algorithmic discrimination, posing a significant challenge to the fairness and justice of AI systems. Our study employs a two-step mixed-methods approach that includes both quantitative and qualitative analysis to identify and understand the impact of nationality bias in a text generation model. Through our human-centered quantitative analysis, we measure the extent of nationality bias in articles generated by AI sources. We then conduct open-ended interviews with participants, performing qualitative coding and thematic analysis to understand the implications of these biases on human readers. Our findings reveal that biased NLP models tend to replicate and amplify existing societal biases, which can translate to harm if used in a sociotechnical setting. The qualitative analysis from our interviews offers insights into the experience readers have when encountering such articles, highlighting the potential to shift a reader's perception of a country. These findings emphasize the critical role of public perception in shaping AI's impact on society and the need to correct biases in AI systems

Nationality Bias in Text Generation

Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models. This paper examines how a text generation model, GPT-2, accentuates pre-existing societal biases about country-based demonyms. We generate stories using GPT-2 for various nationalities and use sensitivity analysis to explore how the number of internet users and the country's economic status impacts the sentiment of the stories. To reduce the propagation of biases through large language models (LLM), we explore the debiasing method of adversarial triggering. Our results show that GPT-2 demonstrates significant bias against countries with lower internet users, and adversarial triggering effectively reduces the same.Comment: Paper accepted in the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL2023

A Bibliometric Perspective Survey of Astronomical Object Tracking System

Advancement in the techniques in the field of Astronomical Object Tracking has been evolved over the years for more accurate results in prediction. Upgradation in Kepler’s algorithm aids in the detection of periodic transits of small planets. The tracking of the celestial bodies by NASA shows the trend followed over the years It has been noted that Machine Learning algorithms and the help of Artificial Intelligence have opted for several techniques allied with motion and positioning of the Celestial bodies and yields more accuracy and robustness. The paper discusses the survey and bibliometric analysis of Astronomical Object Tracking from the Scopus database in analyzing the research by area, influential authors, institutions, countries, and funding agency. The 93 research documents are extracted from the research started in this research area till 6th February 2021 from the database. Bibliometric analysis is the statistical analysis of the research published as articles, conference papers, and reviews, which helps in understanding the impact of publication in the research domain globally. The visualization analysis is done with open-source tools namely GPS Visualizer, Gephi, VOS viewer, and ScienceScape. The visualization aids in a quick and clear understanding of the different perspective as mentioned above in a particular research domain search

The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis

We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets. Our investigation stems from the recognition that SA has become an integral component of diverse sociotechnical systems, exerting influence on both social and technical users. By delving into sociological and technological literature on sentiment, we unveil distinct conceptualizations of this term in domains such as finance, government, and medicine. Our study exposes a lack of explicit definitions and frameworks for characterizing sentiment, resulting in potential challenges and biases. To tackle this issue, we propose an ethics sheet encompassing critical inquiries to guide practitioners in ensuring equitable utilization of SA. Our findings underscore the significance of adopting an interdisciplinary approach to defining sentiment in SA and offer a pragmatic solution for its implementation.Comment: This paper has been accepted and will appear at the EMNLP 2023 Main Conferenc

Identification of essential genes for cancer immunotherapy

Somatic gene mutations can alter the vulnerability of cancer cells to T-cell-based immunotherapies. Here we perturbed genes in human melanoma cells to mimic loss-of-function mutations involved in resistance to these therapies, by using a genome-scale CRISPR–Cas9 library that consisted of around 123,000 single-guide RNAs, and profiled genes whose loss in tumour cells impaired the effector function of CD8+ T cells. The genes that were most enriched in the screen have key roles in antigen presentation and interferon-γ signalling, and correlate with cytolytic activity in patient tumours from The Cancer Genome Atlas. Among the genes validated using different cancer cell lines and antigens, we identified multiple loss-of-function mutations in APLNR, encoding the apelin receptor, in patient tumours that were refractory to immunotherapy. We show that APLNR interacts with JAK1, modulating interferon-γ responses in tumours, and that its functional loss reduces the efficacy of adoptive cell transfer and checkpoint blockade immunotherapies in mouse models. Our results link the loss of essential genes for the effector function of CD8⁺ T cells with the resistance or non-responsiveness of cancer to immunotherapies