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

DeepSum: A Deep Learning Framework for Summarizing Animal Behavior

The burgeoning field of ethology necessitates efficient tools for analyzing extensive video recordings of animal behavior, as manually sifting through hours of footage is both time-consuming and susceptible to observer bias. Here we present an innovative deep learning framework tailored for summarizing animal behavior videos, aiming to distill lengthy recordings into concise, informative segments. Leveraging the latest advancements in hierarchical video summarization, our approach employs a combination of Convolutional Neural Networks (CNNs) and Transformer models to extract and understand complex spatial-temporal patterns inherent in animal movements and interactions. The model is designed to recognize and prioritize key behavioral events, ensuring the retention of critical moments in the summarized output. Additionally, an attention mechanism is incorporated to adaptively focus on salient features, enhancing the model’s capability to discern subtle yet significant behavioral nuances. We assess our framework on a range of datasets containing different species and behavioral situations, and find that it outperforms current state-of-the-art techniques in terms of accuracy, coherence, and informativeness of the generated summaries. In addition to providing a consistent, objective method of analyzing animal behavior, DeepSum dramatically reduces the amount of manual labor needed for behavioral analysis, opening the door for advancements in ethological research and wildlife conservation

COMPARISON OF DENOISING FILTERS ON COLOUR TEM IMAGE FOR DIFFERENT NOISE

TEM (Transmission Electron Microscopy) is an important morphological characterization tool for Nanomaterials. Quite often a microscopy image gets corrupted by noise, which may arise in the process of acquiring the image, or during its transmission, or even during reproduction of the image. Removal of noise from an image is one of the most important tasks in image processing. Denoising techniques aim at reducing the statistical perturbations and recovering as well as possible the true underlying signal. Depending on the nature of the noise, such as additive or multiplicative type of noise, there are several approaches towards removing noise from an image. Image De-noising improves the quality of images acquired by optical, electro-optical or electronic microscopy. This paper compares five filters on the measures of mean of image, signal to noise ratio, peak signal to noise ratio & mean square error. In this paper four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by various filters (WFDWT, BF, HMDF, FDE, DVROFT). Further results have been compared for all noises. It is observed that for Gaussian Noise WFDWT & for other noises HMDF has shown the better performance results

Comparison of single dose transdermal patches of diclofenac and ketoprofen for postoperative analgesia in lower limb orthopaedic surgery

Background: Transdermal patch is a very simple and painless method for providing postoperative analgesia. The aim of the study was to compare the  efficacy and safety of transdermal patch of ketoprofen in comparison to diclofenac patch for postoperative analgesia. It is a randomized single blind study.Methods: Sixty patients were randomly allocated to receive either ketoprofen or diclofenac patch at the end of surgery under spinal anaesthesia. Statistical analyses used, data were analyzed using statistical package for social sciences version 15.0.Results: In diclofenac group the post-operative VAS was 2.4±0.72 and in ketoprofen group, post-operative VAS was 1.4±0.3 which was significantly low when compared to group D (p<0.05 value). 11 patients in group D and 3 patients in group K required rescue analgesia (Inj. tramadol) in the first 24 hours which was statically significant (p<0.05).Conclusions: Both ketoprofen and diclofenac transdermal patch are effective for postoperative analgesia but less number of patients required rescue analgesic in ketoprofen group

Review of the selection Criteria for energy auditor to identify the energy efficient projects

this study indicated the role of energy auditor to identify the energy efficient projects. Three main types of audits are: Preliminary, Single Purpose, and Comprehensive. Selecting the appropriate type of audit for your facility saves you time and money. Each type is distinguished by the level of detail and analysis required to complete the audit. The less detailed the audit, the less accurate the estimates of project costs and energy savings. Depending on your organization’s contracting requirements, the consultant who will conduct the energy audit and prepare the technical report can be selected either by sole source or competitive bid. The cost of an audit can be determined through price negotiations or competitive bidding. In either case, you must inform the bidders of the scope of the audit and its minimum reporting and analytical requirements, such as those contained in the Energy Commission’s feasibility study guide. This is to ensure that you are getting audit costs for comparable work

FinRED: A Dataset for Relation Extraction in Financial Domain

Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets. In the current literature, there is no extensive open-source relation extraction dataset specific to the finance domain. In this paper, we release FinRED, a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. FinRED has been created by mapping Wikidata triplets using distance supervision method. We manually annotate the test data to ensure proper evaluation. We also experiment with various state-of-the-art relation extraction models on this dataset to create the benchmark. We see a significant drop in their performance on FinRED compared to the general relation extraction datasets which tells that we need better models for financial relation extraction.Comment: Accepted at FinWeb at WWW'2