Understanding the Impact of Early Citers on Long-Term Scientific Impact

This paper explores an interesting new dimension to the challenging problem of predicting long-term scientific impact (LTSI) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years after publication) acquired by a paper positively affects its LTSI. However, there is no work that investigates if the set of authors who bring in these early citations to a paper also affect its LTSI. In this paper, we demonstrate for the first time, the impact of these authors whom we call early citers (EC) on the LTSI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC - we call those authors who have high overall publication/citation count in the dataset as influential and the rest of the authors as non-influential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LTSI in terms of these properties. In contrast to popular perception, we find that influential EC negatively affects LTSI possibly owing to attention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing effect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper

Surgeon’s perspective on rare yet potentially fatal complication of GI perforation following endoscopy

Gastrointestinal endoscopy plays an essential role in the diagnosis, staging, and treatment of pathologies of the GI tract. New-generation endoscopes, advanced imaging technologies, the introduction of new therapeutic devices into clinical practice, and modification of old techniques have expanded both the diagnostic and therapeutic armamentarium of the endoscopist. complications are rare with a rate of less than 1 per 5000 cases. perforations are either due to therapeutic dilatation,  coagulation or passage of side viewing instrument  into the duodenum. Here we present a case of 56 yr old male who underwent diagnostic endoscopy for peptic ulcer. I t lead to endoscope induced large duodenal perforation of about 10 cms in its long axis recognized at laparotomy 10 days after the intervention.it is important to mention the perforation was repaired surgically and patient developed no post operative complications. Undesired complications though rare, are potentially fatal and risks need to be evaluated before performing all endoscopic procedure

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.Comment: NeurIPS 202

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffirm prior conclusions that CLIP models can inadvertently absorb societal stereotypes. To counter this, we present a novel algorithm, called Multi-Modal Moment Matching (M4), designed to reduce both representation and association biases (i.e. in first- and second-order statistics) in multimodal data. We use M4 to conduct an in-depth analysis taking into account various factors, such as the model, representation, and data size. Our study also explores the dynamic nature of how CLIP learns and unlearns biases. In particular, we find that fine-tuning is effective in countering representation biases, though its impact diminishes for association biases. Also, data balancing has a mixed impact on quality: it tends to improve classification but can hurt retrieval. Interestingly, data and architectural improvements seem to mitigate the negative impact of data balancing on performance; e.g. applying M4 to SigLIP-B/16 with data quality filters improves COCO image-to-text retrieval @5 from 86% (without data balancing) to 87% and ImageNet 0-shot classification from 77% to 77.5%! Finally, we conclude with recommendations for improving the efficacy of data balancing in multimodal systems.Comment: 32 pages, 20 figures, 7 table

Extended-spectrum β-lactamase and AmpC β-lactamase Production among Gram-negative Bacilli Isolates Obtained from Urinary Tract Infections and Wound Infections

Extended-spectrum β-lactamases (ESBLs) and AmpC β-lactamases continue to be a major problem in healthcare settings. Due to the scarcity of information regarding the antibiotic susceptibility patterns particularly from urinary tract infection (UTI) and wound infections, the current study was carried out to assist the clinicians to prescribe appropriate antibiotics against Gram-negative clinical isolates. In the current study, urine (n = 620) and pus (n = 228) samples were collected from different sites (at various clinical departments) and subjected to direct microscopic examination, culture and antibiotic susceptibility testing (AST). In the AST testings, the isolates that exhibited reduced zone of inhibition to one or more of the antibiotics such as cefotaxime (≤27 mm), ceftriaxone (≤25 mm), ceftazidime (≤22 mm), cefpodoxime (≤17 mm) and aztreonam (≤27 mm) were considered as potential ESBL producers and the ESBL production was confirmed using phenotypic screening test (double-disk synergy test) and phenotypic confirmatory test (combined-disk test). However, isolates showing resistance or decreased sensitivity to cefoxitin, cefotaxime, ceftriaxone, ceftazidime, cefpodoxime or aztreonam and sensitive to cefepime were considered as a screen positive AmpC producer and subjected to AmpC disk tests. The current study concluded that 72.41% and 21.76% of ESBL and AmpC producers were detected, respectively in our hospital. It was also observed that the double-disk synergy and combined-disk tests were equally effective for ESBL detection. Further, AmpC disk test is simple, easy to perform and interpret, requiring less expertise for the rapid detection of AmpC isolates