Advertisement, media bias, political polarisation

When consumers value cognitive consistency between the news they read and policies they support, politicians are induced to make policies that conform to a polarized media landscape which not only depends on user subscription for revenue but also advertisement receipts. Following Guo et. al. (2018), I develop a model to study how the dependency on advertisement revenue affects media bias, political polarisation, and voter preferences. I show that though the equilibrium prices of the two newspapers fall when they depend on advertising receipts, the difference between the equilibrium prices and thus ideological position of a marginal voter remains unchanged, compared to Guo et. Al. (2018). Moreover, the extents of political and media polarisation do not depend on newspapers\u27 dependence on advertisement receipts. I also find that when political parties are not ideologically driven and newspapers have a stronger preference for editorial neutrality, then increase in the public\u27s trust on the newspapers for news consumption leads to increased media and political polarisation

DALE: Generative Data Augmentation for Low-Resource Legal NLP

We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP. DALE addresses the challenges existing frameworks pose in generating effective data augmentations of legal documents - legal language, with its specialized vocabulary and complex semantics, morphology, and syntax, does not benefit from data augmentations that merely rephrase the source sentence. To address this, DALE, built on an Encoder-Decoder Language Model, is pre-trained on a novel unsupervised text denoising objective based on selective masking - our masking strategy exploits the domain-specific language characteristics of templatized legal documents to mask collocated spans of text. Denoising these spans helps DALE acquire knowledge about legal concepts, principles, and language usage. Consequently, it develops the ability to generate coherent and diverse augmentations with novel contexts. Finally, DALE performs conditional generation to generate synthetic augmentations for low-resource Legal NLP tasks. We demonstrate the effectiveness of DALE on 13 datasets spanning 6 tasks and 4 low-resource settings. DALE outperforms all our baselines, including LLMs, qualitatively and quantitatively, with improvements of 1%-50%.Comment: Accepted to EMNLP 2023 Main Conference. Code: https://github.com/Sreyan88/DAL

ASPIRE: Language-Guided Augmentation for Robust Image Classification

Neural image classifiers can often learn to make predictions by overly relying on non-predictive features that are spuriously correlated with the class labels in the training data. This leads to poor performance in real-world atypical scenarios where such features are absent. Supplementing the training dataset with images without such spurious features can aid robust learning against spurious correlations via better generalization. This paper presents ASPIRE (Language-guided data Augmentation for SPurIous correlation REmoval), a simple yet effective solution for expanding the training dataset with synthetic images without spurious features. ASPIRE, guided by language, generates these images without requiring any form of additional supervision or existing examples. Precisely, we employ LLMs to first extract foreground and background features from textual descriptions of an image, followed by advanced language-guided image editing to discover the features that are spuriously correlated with the class label. Finally, we personalize a text-to-image generation model to generate diverse in-domain images without spurious features. We demonstrate the effectiveness of ASPIRE on 4 datasets, including the very challenging Hard ImageNet dataset, and 9 baselines and show that ASPIRE improves the classification accuracy of prior methods by 1% - 38%. Code soon at: https://github.com/Sreyan88/ASPIRE.Comment: Pre-print Under Revie

CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models

A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representation between audio and language modalities have improved performance in many downstream applications, including zero-shot audio classification, audio retrieval, etc. However, the ability of these models to effectively perform compositional reasoning remains largely unexplored and necessitates additional research. In this paper, we propose CompA, a collection of two expert-annotated benchmarks with a majority of real-world audio samples, to evaluate compositional reasoning in ALMs. Our proposed CompA-order evaluates how well an ALM understands the order or occurrence of acoustic events in audio, and CompA-attribute evaluates attribute binding of acoustic events. An instance from either benchmark consists of two audio-caption pairs, where both audios have the same acoustic events but with different compositions. An ALM is evaluated on how well it matches the right audio to the right caption. Using this benchmark, we first show that current ALMs perform only marginally better than random chance, thereby struggling with compositional reasoning. Next, we propose CompA-CLAP, where we fine-tune CLAP using a novel learning method to improve its compositional reasoning abilities. To train CompA-CLAP, we first propose improvements to contrastive training with composition-aware hard negatives, allowing for more focused training. Next, we propose a novel modular contrastive loss that helps the model learn fine-grained compositional understanding and overcomes the acute scarcity of openly available compositional audios. CompA-CLAP significantly improves over all our baseline models on the CompA benchmark, indicating its superior compositional reasoning capabilities.Comment: Pre-print under revie

Synthesis and Microstructural Studies of Iron Oxypnictide LaO1-xFxFeAs Superconductors

We report on the synthesis and structural/ microstructural studies of iron based fluorine doped LaOFeAs superconductors. We have successfully synthesized fluorine doped superconducting LaO1-xFxFeAs materials by choosing lower temperature (1150^0C) and longer synthesis duration (60 hours) as compared to the standard values of these employed in the pioneering first contribution [Kamihara et al 2008 J Am Chem Soc 130 3296]. Decrease of lattice parameters as determined by x-ray diffraction confirm the substitution of fluorine. The superconducting transition temperature is 27.5 K which is observed at doping level of x=0.2. This superconducting material LaO1-xFxFeAs exhibits interesting microstructural characteristic. This relates to the existence of another structural phase, besides the standard phase, having c parameters of ~12.67A. This suggests existence of modulated structure, similar to the cuprates, in these new oxypnictides. This phase may have new impacts on this new high-Tc family.Comment: 5 pages, 5 figures. Accepted in Supercod. Sci Technol (2008

Late gadolinium enhancement cardiovascular magnetic resonance predicts clinical worsening in patients with pulmonary hypertension

<p>Abstract</p> <p>Background</p> <p>Late gadolinium enhancement (LGE) occurs at the right ventricular (RV) insertion point (RVIP) in patients with pulmonary hypertension (PH) and has been shown to correlate with cardiovascular magnetic resonance (CMR) derived RV indices. However, the prognostic role of RVIP-LGE and other CMR-derived parameters of RV function are not well established. Our aim was to evaluate the predictive value of contrast-enhanced CMR in patients with PH.</p> <p>Methods</p> <p>RV size, ejection fraction (RVEF), and the presence of RVIP-LGE were determined in 58 patients with PH referred for CMR. All patients underwent right heart catheterization, exercise testing, and N-terminal pro-brain natriuretic peptide (NT-proBNP) evaluation; results of which were included in the final analysis if performed within 4 months of the CMR study. Patients were followed for the primary endpoint of time to clinical worsening (death, decompensated right ventricular heart failure, initiation of prostacyclin, or lung transplantation).</p> <p>Results</p> <p>Overall, 40/58 (69%) of patients had RVIP-LGE. Patients with RVIP- LGE had larger right ventricular volume index, lower RVEF, and higher mean pulmonary artery pressure (mPAP), all p < 0.05. During the follow-up period of 10.2 ± 6.3 months, 19 patients reached the primary endpoint. In a univariate analysis, RVIP-LGE was a predictor for adverse outcomes (p = 0.026). In a multivariate analysis, CMR-derived RVEF was an independent predictor of clinical worsening (p = 0.036) along with well-established prognostic parameters such as exercise capacity (p = 0.010) and mPAP (p = 0.001).</p> <p>Conclusions</p> <p>The presence of RVIP-LGE in patients with PH is a marker for more advanced disease and poor prognosis. In addition, this study reveals for the first time that CMR-derived RVEF is an independent non-invasive imaging predictor of adverse outcomes in this patient population.</p

Digitally strengthened, midwife-led intervention to reach the unreached mothers across ten conflict-prone provinces of Afghanistan during humanitarian crisis

Background: Coronavirus disease 2019 (COVID-19) pandemic had significant negative impact on sexual and reproductive health (SRH) with devastating impact on pregnant women in resource constrain humanitarian settings. This paper provides detailed account of a community midwife-led intervention in ten humanitarian settings of Afghanistan using world health organization (WHO) emergency disaster risk management (EDRM) framework.Objectives: The project is aimed at increasing access to Integrated Package of Essential SRH Services and Minimal Initial Service Package (MISP) with a specific focus on prevention of Postpartum Haemorrhage (PPH) and screening and management of preeclampsia and eclampsia.Methods: The project was implemented through 150 Community outreach midwives (COMs). Each midwife served 300 households; mentored by gynaecologists and supervisors. Midwives were trained through a digitally enabled, simulation based training and equipped with a set of off-the shelf devices and kits.Results: During COVID-19 pandemic and in absence of health care services during crisis, this intervention has played as a lifesaving intervention for the community in Afghanistan. Variable digital literacy, sociocultural barriers, reluctance in adapting to digital platforms, security and uncertainties were some of the challenges faced. Adaptation of outreach methods integrated high impactful digital technologies has been the most appropriate strategy "to reach the unreached".Conclusion: Through this model, national and global stakeholders were engaged even during the crisis in Afghanistan. It also provided vital inputs for the donors, governments, civil society organizations and other stakeholders for sustaining and advancing the delivery of quality SRH services in humanitarian settings